Jervis's Insects as Natural Enemies: Practical Perspectives 3031238796, 9783031238796

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Ian C. W. Hardy Eric Wajnberg Editors

Jervis’s Insects as Natural Enemies: Practical Perspectives

Jervis’s Insects as Natural Enemies: Practical Perspectives

Ian C. W. Hardy  Eric Wajnberg Editors

Jervis’s Insects as Natural Enemies: Practical Perspectives Formerly Edited by Mark A. Jervis

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Editors Ian C. W. Hardy Department of Agricultural Sciences University of Helsinki Helsinki, Finland

Eric Wajnberg INRAE and INRIA Sophia Antipolis Cedex, France

ISBN 978-3-031-23879-6 ISBN 978-3-031-23880-2 https://doi.org/10.1007/978-3-031-23880-2

(eBook)

© Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Illustration: Robber fly (Diptera: Asilidae) feeding on a phorid (Diptera: Phoridae). Photographer: Bernard D Roitberg, Professor Emeritus, Behavioral Ecology Research Group and Centre For Pest Management Department of Biosciences Simon Fraser University Burnaby BC Canada V5A 1S6 [email protected] This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To the memory of Mark Jervis and to all other natural enemy biologists, past, present and future

Mark A. Jervis during the 2006 Behavioural Ecology of Insect Parasitoids (BEPAR) meeting in Antibes, France (Photograph by George E. Heimpel).

Preface to the Third Edition

This book is not entirely as it should be. In a more ideal world Mark A. Jervis would have been an active contributor to this edition of his book, having co-edited the first edition (Jervis and Kidd, 1996) and sole-edited the second edition (Jervis, 2005). Mark died unexpectedly in March 2014. In his honour and memory, we edited a special issue of the journal Entomologia Experimentalis et Applicata (Hardy and Wajnberg, 2016a). That endeavour led to the idea that it might be due time for another edition of Mark’s respected and valuable instructional volume on insects as natural enemies. We duly proceeded, with the full consent of Mark’s family, and here it is. This book is not, primarily, another memorial to Mark. The special issue serves that purpose more directly and our personal recollections of our association with Mark can be found within it (Hardy and Wajnberg, 2016b). What this book is, we hope, is a vibrant, continuation of Mark’s vision for the type of resource that is needed by researchers studying insects as natural enemies, both in terms of fundamental research and of agro-ecological applications, as outlined in the prefaces to the previous editions. In the preface to the first edition it was mentioned that ‘Statistical aspects of sampling and experimental design are hardly gone into’: we have addressed that particular lacuna by adding a new chapter on statistical approaches to studying the biology and ecology of natural enemies. We hope that this will be useful to natural enemy biologists and, as it is written quite generally, also to researchers beyond our focal field. We would like to thank all the contributors to this book for their sterling efforts in helping to bring this to fruition. Every previous chapter has been actively revised and updated since the second edition and has acquired at least one new co-author. It has been a privilege to work with both the ‘old guard’ and the relative newcomers, and with everyone else in between. They have all proven to be excellent natural enemy scientists. We also need to mention, with sadness, that Simon Leather, co-author of the substantial chapter on Populations and Communities, passed away before this book could be submitted for publication, although we are pleased to note that his own chapter was fully revised and completed before his demise. The final phase of the preparation of this book was greatly assisted by us being in the same place at the same time for five months during the spring of 2022. We were among a group of Fellows at the Israel Institute for Advanced Studies (IIAS) studying ‘Mathematical modeling of biological control interactions to support agriculture and conservation’, as were a number of other chapter authors. We are deeply grateful to the IIAS for supporting this vii

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research group programme and we also thank our co-Fellows for their interest in, and support of, our work on this edition. We thank Bernie Roitberg for the cover photograph and George Heimpel for the photograph of Mark. We would, of course, also like to thank our colleagues at Springer, especially Zuzana Bernhart, for all their help during the (long) gestation period of the current edition. We have tried to input approximately equally into the editorial process: we refer to this as the “Hardy‒Wajnberg Equilibrium”, and, indeed, we have waited many years for an opportunity to make that comment in print. We thank each other for our friendly but robust discussions and the associated détente. Having completed this third edition, we doubt that we ourselves will be the editors of a fourth, but we hope that in due course there will be further editions: this book has plenty of potential to assist parasitoid and predator researchers for years to come, especially if periodically updated to reflect new concepts, new techniques and new results. Our notion is that Jervis’s Natural Enemies will serve as something akin to Gray’s Anatomy (Standring, 2020) or, closer to our entomological homes, The Insects (Chapman, 2013). These books roll on through time, absorbing and disseminating the current state of the art. We therefore suggest that some of the younger researchers reading this book might, in due course, consider offering to construct a further edition. All in all, we hope to have borrowed and continued Mark’s legacy in a manner, and to a standard, that would have pleased him and, as in the words of Scott Cook (see below), we hope now also to pass it along. We hope that Jervis’s Insects as Natural Enemies: Practical Perspectives will successfully both encourage and assist research into the fascinating biology of insect predation and parasitism. Pass it along (excerpt) Oh, and everywhere there are teachers, though some fell along the way. The words they said still reach us, just like you’re reaching me here today. And you may not speak it loud, but it’s clear in what you do. And I hope to make you proud, ‘cause I borrowed it from you’ Pass it along, pass it along. May it land in careful hands when we’re gone. You carry it for a moment but time won’t loan it to you for long. You don’t own it, pass it along. Scott Cook, One more time around (2013) Helsinki, Finland Sophia Antipolis, France October 2022

Ian C. W. Hardy Eric Wajnberg

References Chapman, R. F. (2013). The Insects: Structure and Function (5th edn) (eds S. J. Simpson & A. E. Douglas). Cambridge University Press, Cambridge. Hardy, I. C. W., & Wajnberg, E. (2016a) (eds) Special issue: Mark Jervis memorial. Entomologia Experimentalis et Applicata, 159, 117‒285.

Preface to the Third Edition

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Hardy, I. C. W., & Wajnberg, E. (2016b) In memory of Mark Jervis. Entomologia Experimentalis et Applicata, 159, 117‒118. Jervis, M. A. (2005) (ed) Insects as Natural Enemies: Practical Approaches to Their Study and Evaluation. Springer, Dordrecht, pp. 748. Jervis, M., & Kidd, N. (1996) (eds) Insect Natural Enemies: Practical Approaches to Their Study and Evaluation. Chapman and Hall, London, pp. 491. Standring, S. (2020) (ed) Gray’s Anatomy: The Anatomical Basis of Clinical Practice (42nd edn). Elsevier, Amsterdam.

Preface to the Second Edition

The past three decades have seen a dramatic increase in practical and theoretical studies on insect natural enemies. The importance and appeal of insect predators, and of parasitoids in particular, as research animals derives from the relative ease with which many species can be cultured and experimented with in the laboratory, the simple life-cycles of most parasitoids, and the increasing demand for biological control of insect pests. This book – an updated and considerably expanded version of Jervis and Kidd (1996) – is intended to guide enquiring students and research workers to those approaches and techniques that are most appropriate to the study and evaluation of predators and parasitoids. It is neither a practical manual nor a ‘recipe book’ – most chapters are accounts of major aspects of the biology of natural enemies, punctuated with advice on which experiments or observations to conduct and how, in broad terms, to carry them out. Detailed protocols are usually not given, but guidance is provided, where necessary, on literature that may need to be consulted on particular topics. I hope that Insects as Natural Enemies: A Practical Perspective will successfully both encourage and assist research into the fascinating biology of insect predation and parasitism. Cardiff, 2005

Mark A. Jervis

Reference Jervis, M., & Kidd, N. (1996) (eds) Insect Natural Enemies: Practical Approaches to Their Study and Evaluation. Chapman and Hall, London, pp. 491. Dedication: For Julia, George and William, and in memory of my parents.

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Preface to the First Edition

The past two decades have seen a dramatic increase in practical and theoretical studies on insect natural enemies. The importance and appeal of insect predators, and of parasitoids in particular, as research animals derives from the relative ease with which many species can be cultured and experimented with in the laboratory, the simple life cycles of most parasitoids, and the increasing demand for biological control. Unfortunately, despite a burgeoning of the literature on insect natural enemies, there has as yet been no general text available to guide enquiring students or research workers to those approaches and techniques that are most appropriate to the study and evaluation of such insects. Guidance on experimental design is particularly sought by newcomers to the subject together with some idea of the pitfalls associated with various approaches. The need for such a book as this was realized as a result of our experiences in supervising students in the large entomology post-graduate school at the University of Wales, Cardiff. We also took our inspiration from Aphid Technology, edited by H.F. van Emden (1972), which satisfied an important need among entomologists and ecologists by providing practical advice on how to study a particular group of insects. Our book is aimed at any student or professional interested in investigating the biology of predators and parasitoids, but will, we hope, be especially useful to post-graduates. Our book is neither a practical manual nor a recipe book. Most chapters are accounts of major aspects of the biology of natural enemies, punctuated by practical information and advice on which experiments or observations to conduct and how, in broad terms, to carry them out. Detailed protocols are usually not given. Guidance is also provided, where necessary, on literature that may need to be consulted on particular topics. The coverage of the book is far from being exhaustive; some readers may be surprised at the omission of important topics such as dormancy, and others may be appalled that there is negligible treatment of systematics! For this we apologise, but space was at a premium. Statistical aspects of sampling and experimental design are hardly gone into. This may be seen by some as an unforgivable lapse, but a wealth of study is already contained in texts such as Southwood (1978), McDonald et al. (1989), Mead (1989), Hairston (1989) and Crawley (1993) (the latter text is concerned with a particularly valuable software tool, GLIM). Ecologically minded students of natural enemies will also find valuable advice in the special feature on statistics published in the September 1993 issue of the journal Ecology. xiii

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Preface to the First Edition

We hope that Insect Natural Enemies will successfully both encourage and assist the reader in his or her research into insect predators and parasitoids. Cardiff, 1996

Mark A. Jervis Neil A.C. Kidd

References Crawley, M. J. (1993) GLIM for Ecologists, Blackwell Scientific Publications, Oxford. van Emden, H. F. (1972) Aphid Technology (with special reference to the study of aphids in the field). Academic Press, London. Hairston, N. G. (19899 Ecological Experiments: Purpose Design and Execution, Cambridge University Press, Cambridge. McDonald, L. L., Manly, B., Lockwood, J., & Logan, J. (1989) (eds) Estimation and Analysis of Insect Populations, Lecture Notes in Statistics 55, Springer Verlag, Berlin. Mead, R. (1989) The Design of Experiments, Cambridge University Press, Cambridge. Southwood, T. R. E. (1978) Ecological Methods (2nd edn), Chapman and Hall, London.

Contents

1 Foraging Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mark D. E. Fellowes, Jacques J. M. van Alphen, K. S. Shameer, Ian C. W. Hardy, Eric Wajnberg, and Mark A. Jervis

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2 The Life-Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Mark A. Jervis, Michael J. W. Copland, K. S. Shameer, and Jeffrey A. Harvey 3 Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Leo W. Beukeboom, Bas J. Zwaan, Sean Mayes, , and Tamsin M. O. Majerus 4 Mating Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Rebecca A. Boulton, Ian C. W. Hardy, Michael T. Siva-Jothy, and Paul J. Ode 5 Mating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Ian C. W. Hardy, Rebecca A. Boulton, Michael T. Siva-Jothy, and Paul J. Ode 6 Populations and Communities . . . . . . . . . . . . . . . . . . . . . . . . . . 415 Keith D. Sunderland, Wilf Powell, William O. C. Symondson, Simon R. Leather, Steve J. Perlman, and Paul K. Abram 7 Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 Mark A. Jervis, Neil A. C. Kidd, Nicholas J. Mills, Saskya van Nouhuys, Abhyudai Singh, and Maryam Yazdani 8 Phytophagy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Mark A. Jervis, Alejandro Tena, and George E. Heimpel 9 Statistical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Ian C. W. Hardy and Daniel R. Smith Genus and Species Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751

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Contributors

Paul K. Abram Agriculture and Agri-Food Canada, Agassiz Research and Development Centre, Agassiz, British Colombia, Canada Leo W. Beukeboom Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands Rebecca A. Boulton University of Stirling, Stirling, Scotland, UK Michael J. W. Copland WyeBugs, Wye, Ashford, UK Mark D. E. Fellowes The University of Reading, Whiteknights, Reading, UK Ian C. W. Hardy Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland Jeffrey A. Harvey Department of Terrestrial Ecology, Netherlands Institute of Ecology, Wageningen, The Netherlands; Department of Ecological Sciences, Section Animal Ecology, Vrije Universiteit, Amsterdam, The Netherlands George E. Heimpel University of Minnesota, Department of Entomology, St. Paul MN, USA Mark A. Jervis Cardiff School of Biosciences, Cardiff University, Cardiff, Wales, UK Neil A. C. Kidd Cardiff School of Biosciences, Cardiff University, Cardiff, Wales, UK Simon R. Leather Department of Agriculture and Environment, Harper Adams University, Newport, Shropshire, UK Tamsin M. O. Majerus School of Life Sciences, University of Nottingham, Nottingham, UK Sean Mayes School of Biosciences, University of Nottingham, Loughborough, UK Nicholas J. Mills Department of Environmental Science, Policy and Management, University of California, Berkeley CA, USA

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Paul J. Ode Graduate Degree Program in Ecology, Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA Steve J. Perlman Department of Biology, University of Victoria, Victoria, British Colombia, Canada Wilf Powell Division of Plant and Invertebrate Ecology, Rothamsted Research, Harpenden, UK K. S. Shameer Insect Ecology and Ethology Laboratory, Department of Zoology, University of Calicut, Malappuram, Kerala, India Abhyudai Singh Departments of Electrical and Computer Engineering, University of Delaware, Delaware, Newark, USA Michael T. Siva-Jothy Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK Daniel R. Smith Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden Keith D. Sunderland Department of Entomological Sciences, Horticulture Research International, Wellesbourne, UK William O. C. Symondson Cardiff School of Biosciences, Cardiff University, Cardiff, Wales, UK Alejandro Tena Center of Plant Protection and Biotechnology, IVIA, Valencian Institute for Agricultural Research, València, Spain Jacques J. M. van Alphen Naturalis Biodiversity Center, Leiden, The Netherlands Saskya van Nouhuys Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India Eric Wajnberg INRAE, Sophia Antipolis, Cedex, France; INRIA Sophia Antipolis, Sophia Antipolis, Cedex, France Maryam Yazdani Health and Biosecurity, CSIRO, Brisbane, Queensland, Australia Bas J. Zwaan Laboratory of Genetics, Department of Plant Sciences, Wageningen, The Netherlands

Contributors

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Foraging Behaviour Mark D. E. Fellowes, Jacques J. M. van Alphen, K. S. Shameer, Ian C. W. Hardy, Eric Wajnberg, and Mark A. Jervis

1.1

Behaviour of Insect Parasitoids and Predators

In this chapter, we consider practical aspects of the foraging behaviour of insect natural enemies in its widest sense (so wide that we even include a few examples concerning non-insect arthopods, such as mites). Initially, most insect natural enemies must locate the habitat where potential victims may be found. Within that habitat, the victims themselves must be discovered. Once a patch of potential targets is identified, the predator or female parasitoid must choose its victim. Furthermore, in judging host quality, a female parasitoid must decide whether to feed from the host, to oviposit, or to do both. If she does decide to oviposit, then there are questions of sex allocation and offspring number that need

M. D. E. Fellowes The University of Reading, 3.19 Whiteknights House, Whiteknights, Reading RG6 6AJ, UK e-mail: [email protected] J. J. M. van Alphen Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, The Netherlands e-mail: [email protected] K. S. Shameer Insect Ecology and Ethology Laboratory, Department of Zoology, University of Calicut, Calicut University P.O., Malappuram, Kerala 673635, India e-mail: [email protected]

to be addressed (Fig. 1.1). All of these activities fall under the aegis of ‘foraging behaviour’. Studies of the foraging behaviour of insect natural enemies lie at the heart of much of modern ecology. These studies have taken two broadly defined pathways, where the emphasis is determined by the interest of the researcher (see below). Irrespective of the motivation of the researcher, it is clear that any attempt to understand the foraging behaviour of a predator or a parasitoid will greatly benefit from knowledge gleaned from both approaches. This crossfertilisation of ideas is something we try to emphasise in this chapter. In addition, we provide a review of the foraging behaviour of insect natural enemies. This is meant to be illustrative, with stress placed on the experiments used to study the behaviour itself. For greater detail on the behaviour of parasitoids one should refer to Godfray (1994), Quicke I. C. W. Hardy (&) Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland e-mail: ian.hardy@helsinki.fi E. Wajnberg INRAE, 400 Route des Chappes, BP 167, 06903 Sophia Antipolis, Cedex, France e-mail: [email protected] INRIA Sophia Antipolis, Project Hephaistos, 2004 Route des Lucioles, BP 93, 06902 Sophia Antipolis, Cedex, France M. A. Jervis Cardiff School of Biosciences, Cardiff University, P.O. Box 915, Cardiff CF10 3TL, Wales, UK

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2_1

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M. D. E. Fellowes et al.

sections (e.g., sex allocation, Sect. 1.11) where the examples come exclusively from the parasitoid literature, and other sections (e.g., superparasitism/cannibalism, Sect. 1.9) where both are dealt with, but separately. Nevertheless, many of the approaches to studying foraging behaviour, and the theory underpinning it, are similar for both predators and parasitoids. In this chapter, we first describe the methodological approaches that underpin studies of the foraging behaviour of insect natural enemies. Second, we discuss how the predators and parasitoids find the habitat patches where potential prey or hosts may be encountered. Third, we reflect on what occurs after the prey or host is found, dealing with issues such as clutch size and sex allocation decisions, and patch defence behaviour. We also deal with considerations such as the cost of reproduction to natural enemies and the resistance of their hosts or prey to being exploited. Finally, we touch briefly upon some of the wider population and ecological consequences of insect natural enemy foraging behaviour.

1.2

Methodology

1.2.1 The Causal Approach Fig. 1.1 Foraging decisions. In adopting either the functional or the causal approach to studying predator and parasitoid behaviour, it is useful to consider that foraging insect natural enemies are faced with a number of consecutive or simultaneous decisions. Listed in this figure are some of the questions that may need to be addressed by a gregarious host-feeding parasitoid

(1997) and Wajnberg et al. (2008) and, for a shorter overview, to Hardy and Godfray (2023). The literature on insect predators is much more diffuse, but New (1991) provides a good introduction to the behaviour of predators in general, while Dixon (2000) reviews the behaviour of ladybird beetles (Coccinellidae), and Davies et al. (2012) provide an excellent introduction to many aspects of animal behaviour in general. Where possible, we deal with insect predators and parasitoids together, although there are some

Until the late 1970s, parasitoid foraging behaviour was mostly studied from a proximate (i.e., causal or mechanistic) standpoint, with a strong emphasis on identifying which stimuli parasitoids respond to both in finding and in recognising their hosts. Through this approach, fascinating insights into parasitoid foraging behaviour have been gained, and it has been demonstrated that often an intricate tritrophic relationship exists between phytophagous insects, their host plants and parasitoids. We now know the identities of some of the chemical compounds eliciting certain behaviours in parasitoids. Some of the research in this field has been devoted to the application to crops of chemical substances, as a mean of manipulating parasitoid behaviour in such a way that parasitism of crop pests is increased. Many parasitoid

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Foraging Behaviour

species display individual plasticity in their responses to different cues. Associative learning (Sect. 1.6.2) of odours, colours or shapes related to the host’s environment has been described for many parasitoid species (e.g., Dugatkin & Alfieri, 2003; Meiners et al., 2003a). Often, causal questions do not involve elaborate theories. Questions of whether an organism responds to a particular chemical stimulus or not, or whether it reacts more strongly to one stimulus than to another, lead to straightforward experimental designs. It is in the technical aspects of the experiment rather than the underlying theory that the experimenter needs to be creative. However, the study of causation can be extended to ask how information is processed by the central nervous system. One can ask how a sequence of different stimuli influences the behavioural response of the animal, or how responses to the same cue may vary depending on previous experience and the internal state of the animal (Putters & van den Assem, 1988; Morris & Fellowes, 2002). Two different causal approaches have been adopted in the study of the integrated action of a series of different stimuli on the behaviour of a foraging animal: 1. The formalisation of a hypothesis into a model of how both external information and the internal state of the animal result in behaviour, and the testing, through experiments, of the predictions of the model. Waage (1979) pioneered this approach for parasitoids. Artificial neural network models have also been used to analyse sex allocation behaviour in parasitoids (Putters & Vonk, 1991; Vonk et al., 1991). 2. The statistical analysis of time-series of behaviour to assess how the timing and sequence of events influences the behaviour of the organism. An example of this approach is the analysis of the factors influencing patch time allocation of a parasitoid, using the proportional hazards model (Haccou et al., 1991; Wajnberg et al., 1999; Tenhumberg et al., 2001a; Wajnberg, 2003, 2004, 2006 Burger et al., 2006; Parent et al., 2017).

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1.2.2 The Functional Approach The functional approach to the study of parasitoid behaviour is based on Darwinian ideas initially formalised by MacArthur and Pianka (1966) and Emlen (1966). Termed ‘natural selection thinking’ by Charnov (1982), it asks how natural selection may have moulded the behaviour under study. Because foraging decisions (Fig. 1.1) determine the number of offspring produced, foraging behaviour must be under strong selection pressures. Assuming that natural selection has shaped parasitoid searching and oviposition behaviour in such a way that it maximises the probability of leaving as many healthy offspring as possible, thus maximising the ability to contribute genetically to the next generations, it is possible to predict the optimised behaviour under given circumstances. In the real world, no ‘Darwinian monsters’ exist that can produce limitless numbers of offspring at zero cost. Because resources are often limited and because reproduction incurs a cost (e.g., in materials and energy and foraging time, Chap. 2) to an individual, increasing investment in reproduction must always be traded off against other factors decreasing fitness (e.g., more offspring often means smaller individual offspring with shorter lifespans or lower competitive abilities). Thus, producing the maximum possible number of offspring may not be the optimal strategy. We refer to natural selection thinking as the functional approach, because its aim is to define the function of a particular behaviour. To achieve this goal, it is necessary to show that the behaviour contributes more to the animal’s fitness than alternative behaviours in the same situations. The foraging behaviour of female parasitoids has a direct influence on both the number and the quality of their offspring, so it is particularly suited for testing optimisation hypotheses. The functional approach can be applied not only to theoretical problems but also to problems such as the selection, the evaluation and the mass rearing of natural enemies and their efficacy in

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biological control (van Lenteren, 2003; Plouvier & Wajnberg, 2018; Chap. 7). There are several (related) ways of investigating functional problems in behavioural ecology, all using quantitative optimality models. One is to predict the optimised behaviour under given and relatively fixed environmental conditions, considering that the state of the foraging animal (e.g., its egg load, age, energy reserve, etc.) remains fixed throughout its lifetime. These models may be referred to as ‘static’. Other approaches explicitly take into account that the state of the animal can change, essentially due to its foraging activity and its success in finding and exploiting resources. This class of models may be referred to as ‘dynamic’. Usually, for these two types of model, the environment does not contain other competing decision-makers. In this chapter, these two first classes of model will be referred to as ‘classical models’. A third class of models takes into account the possibility that the optimal behavioural strategy will be dependent on what other individuals, attacking the same host or prey population (i.e., competing decision-makers), are doing, since these competitors are also trying to optimise their own foraging decisions. This third class of model is based on game theory. All approaches can be used to inform practical studies of insect natural enemies, and in a similar fashion, the results of practical studies can be used to construct more realistic models. Classical Optimality Models Optimality models are used to predict how an animal should behave so as to maximise its fitness in the long term. Classical optimality models do not explicitly take into account the foraging decisions taken by competing decisionmakers. They can be designed by determining: 1. What decision assumptions apply, i.e., which of the forager’s choices (problems) are to be analysed. Some of the decisions faced by foraging natural enemies are shown in Fig. 1.1. Sexually reproducing gregarious parasitoids need to make the simultaneous

M. D. E. Fellowes et al.

decision not only of what size of clutch to lay but also of what sex ratio of progeny to produce. The progeny and sex allocation of such parasitoids may be easier to model if the two components are assessed independently; i.e., it is assumed that the female need make only one decision. In a formal model, the decision studied must be expressed as one or more algebraic decision variables (see, e.g., Wajnberg, 2012). In some models of progeny (clutch size) allocation, the decision variable is the number of eggs laid per host, while in most models of patch exploitation the decision variable is patch residence time. 2. What currency assumptions or optimality criteria apply, i.e., how the various choices are to be evaluated. A model’s currency is the criterion used to compare alternative values of the decision variable (in other words, it is what is taken to be maximised by the animal in the short term for long-term fitness gain). For example, some foraging models maximise the net rate of energy gain per time unit while foraging, whereas others maximise the fitness of offspring per host attacked. 3. What constraint assumptions apply, i.e., what factors limit the animal’s choices, and what limits the ‘payoff’ that may be obtained. There may be various types of constraint upon foragers. These range from the phylogenetic, through the developmental, physiological and behavioural, to the animal’s time budget. Taking as an example clutch size in parasitoids, and the constraints there may be on a female’s behavioural options, an obvious constraint is the female’s lifetime pattern of egg production. In a species that develops eggs continuously throughout its life, the optimal clutch size may be larger than the number of eggs a female can possibly produce at any one time. An example of both a behavioural and a time-budget constraint upon the behavioural options of both parasitoids and predators is the inability of the forager to handle and search for prey simultaneously. Here, time spent handling the prey

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Foraging Behaviour

is at the cost of searching for further prey. For a detailed discussion of the elements of foraging models, see Stephens and Krebs (1986) or Cézilly and Benhamou (1996). Sometimes the investigator knows, either from the existing literature or from personal experience, the best choices of decision assumption, currency assumption or constraint assumption. If it is impossible to decide on these based on existing knowledge, one can build models for each alternative and compare the predictions of each model with the observed behaviour of the parasitoid or predator. In this way, it is possible to gain insight into the nature of the selective forces working on the insect under study (Waage & Godfray, 1985; Mangel, 1989; Cézilly & Benhamou, 1996). Classical optimality models assume a static world in which individual parasitoids search for hosts. While these models are still useful research tools, they ignored the possibility that for a forager, today’s decision may affect tomorrow’s internal state, which may in turn affect tomorrow’s decision, and so on. The internal state of a searching parasitoid changes during adult life: its egg load (the number of mature eggs in the ovaries) and its energy reserves may decrease, and the probability that it will survive to another day decreases. The optimal behavioural strategy will depend on these changes. Likewise, the environment is not static. Bad weather or the start of an unfavourable season can also influence the optimal strategy. Dynamic foraging models have been subsequently designed to take into account internal physiological changes and changes in the environment (Mangel & Clark, 1988; Chan & Godfray, 1993; Weisser & Houston, 1993, Tenhumberg et al., 2001b). Implicit in some optimality models is the assumption that the forager is omniscient or capable of calculation, e.g., that a parasitoid wasp has some knowledge of the relative profitability of different patches without actually visiting them (Cook & Hubbard, 1977).

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Behavioural studies on parasitoids have shown, however, that insects can behave optimally by employing very simple quick ‘rule’ mechanisms such as the mechanism determining patch time allocation in Venturia canescens described in Sect. 1.5 and the males-first mechanism used by some species in progeny sex allocation (Sect. 1.11.5 and Fig. 1.19). These mechanisms approximate well the optimal solution in each case. Evolutionarily Stable Strategies Almost all parasitoids leave the host in situ. Thus, there is always the possibility that other parasitoids may find the same host and also oviposit in it. The optimal behaviour of the first female thus depends on what other parasitoids may do (i.e., the environment of a focal individual contains other competing individuals), especially since other individuals are also expected to to adopt their own optimal reproductive behaviours. Likewise, the best time allocation strategy for a parasitoid leaving a patch in which it has parasitised a number of hosts depends both on the probability that other wasps will visit that patch and on the probability that other parasitoids may have already exploited the patches it visits next. For this reason, problems concerning the allocation of patch time, progeny and sex require models in which the evolutionarily stable strategy (ESS; Maynard Smith, 1974; Mesterton-Gibbons, 2019) is calculated. The ESS approach asks what will happen in a population of individuals that play all possible alternative strategies, and is based on game theory. The fact that individuals lack control over all decision variables affecting their rewards is what makes such situations a game, and what distinguishes them from classical optimisation problems (Mesterton-Gibbons, 2019). A strategy is an ESS if, when adopted by most members of a population, it cannot be invaded by the spread of any rare alternative strategy (Maynard Smith, 1972). In seeking an ESS, theoreticians are looking for a strategy that is robust against mutants playing alternative

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strategies. The ESS, like the optimum in models for single individuals, is calculated using a costbenefit analysis. We refer the reader to Maynard Smith (1982), Parker (1984) and MestertonGibbons (2019) for descriptions of wellexplored ESS models, details of how to calculate the ESS and their use in behavioural ecology, and to Hardy and Mesterton-Gibbons (2023) for a recent discussion of game theory in relation to natural enemy behaviour. Why Use Classical Optimality and ESS Models? Sometimes, experimental tests of optimality and ESS models will produce results not predicted by the models. At other times, only some of the predictions of the theoretical model are confirmed by empirical tests. Rarely is a perfect quantitative fit between model predictions and empirical test results obtained. Irrespective of whether a good fit is obtained, valuable insights are likely to be gained into the behaviour of the insect. Construction of models helps in the precise formulation of hypotheses and quantitative predictions and allows us to formulate new hypotheses when the predictions of our model are not met. Thus, classical optimality and ESS models are nothing more or less than research tools. Ideally, both causal and functional questions should be asked when studying the foraging behaviour of insect parasitoids and predators. In the sections on superparasitism (Sect. 1.9.4) and patch time allocation (Sect. 1.5), we will show how, by ignoring functional questions, one may hamper the interpretation of data gathered to establish that a certain mechanism is responsible for some type of behaviour. Ignoring causal questions can likewise hamper research aimed at elucidating the function of a behavioural pattern; e.g., research into causal factors can demonstrate the existence of a constraint, not accounted for in a functional model, upon the behaviour of the parasitoid. Both causal and functional approaches are required for a thorough understanding of parasitoid behaviour.

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1.2.3 The Comparative Method Introduction Perhaps the approach with the longest pedigree in studying animal behaviour is the comparative method. With this method, data are collated across species, and a search is made for statistical patterns (Harvey & Pagel, 1991). One advantage of this method is that data are often already available (although often widely scattered) in the literature (e.g., see analyses by Blackburn, 1991a, 1991b; Mayhew & Blackburn, 1999; Jervis et al., 2001, 2003). Until recently, sets of species average data were usually analysed in much the same way as within-species data. However, the fundamental assumption of most early statistical analyses—that speciescomparative data are independent observations (i.e., are independent of each other)—may not hold. Also, we want to know whether observed interspecific similarities have evolutionary meanings in the present time or whether they derive from common ancestors in the phylogenetic tree. Cross-species data may actually be non-independent, because the species are related through phylogeny (i.e., they share an evolutionary history). Comparative biologists have developed methods which use phylogenetic information in conjunction with species data sets to generate independent values for statistical analysis and to enable a more accurate evolutionary interpretation of interspecific observed similarities or differences (Felsenstein, 1985; Harvey & Pagel, 1991; Harvey & Nee, 1997; Freckleton et al., 2002; Wajnberg et al., 2003). The Method of Independent Contrasts Probably the most commonly employed method involves ‘independent comparisons’, also known as ‘independent contrasts’ (originally developed by Felsenstein, 1985: simple examples are given in Harvey & Pagel, 1991; Purvis & Rambaut, 1995; Harvey, 1996; Mayhew & Pen, 2002). The approach assumes that the branches of a phylogeny can be modelled by a Brownian motion

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process, such that successive changes are independent of one another and that the expected total change summed over many independent changes is zero. The original method for contrast analysis assumes that the lengths of branches in the phylogeny are known, but often they are not, in which case they can be assumed to be equal (e.g., Jervis et al., 2001, 2003). Branch lengths are estimated by genetic distances (divergence times, in relation to the present, estimated from the fossil record or from molecular clocks) or by the number of character changes, determined from a cladistic analysis (Harvey & Pagel, 1991). An independent contrast is obtained from each node in the phylogeny for each measured variable. Imagine that you are studying five species (Fig. 1.2a): it is clear that A and B, and X and Y are more closely related to each other than to members of the other clade, and, by comparing Fig. 1.2 Independent contrasts: For n species, there are (n – 1) independent contrasts which can be calculated for a fully phylogeny that is fully resolved at each node (a). Where the phylogeny is less well resolved, resulting in polytomies at some nodes, the number of possible independent contrasts is diminished (b), reducing the potential power of the analysis.Contrasts are shown by double headed arrows, nodes by dots and the species considered are A, B, X, Y and Z

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their values, we then only include independent evolutionary trajectories. By calculating an ancestral trait value at the node below A-B and X-Y, we gain another contrast. By comparing this ancestral node value with the value for species Z, we gain another independent contrast. Therefore, we gain four contrasts from five species. If two traits are of interest in the comparative analysis and if they are continuous variables, then typically the data are analysed using a linear regression, constrained to pass through the origin (i.e., there should be no intercept in the regression model, Garland et al., 1992). As we have seen, with a perfectly resolved phylogeny of n species, there are n − 1 possible contrasts available. This may result in statistical difficulties when data sets are small. While this problem may be alleviated by the addition of

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extra species to the analysis, a more invidious difficulty is introduced when a phylogeny is poorly resolved. While phylogenies usually consist of bifurcating lineages, for some taxa the phylogeny may not be well resolved, and so it will contain polytomies (trifurcations, etc.) and hence fewer nodes for a given number of species. Thus, the number of contrasts obtained is less than with a fully resolved (bifurcating) tree, reducing the size of the data set and also the statistical power of subsequent analyses. Reconsider the figure with species A, B, X, Y and Z (Fig. 1.2a). If we do not know the evolutionary relationships among X, Y and Z, we must assume that they all originated from the same common ancestor (a polytomy; Fig. 1.2b). We now have fewer contrasts and hence lower statistical power in the analysis. Such problems can be overcome by obtaining a sufficiently wellresolved phylogeny. Unfortunately, such phylogenies are not always available. Garland and Díaz-Uriate (1999) provide further discussion. Although a well-resolved, published taxonomy (more often than not, based exclusively on morphology) can be used to approximate the true phylogenetic tree (e.g., Abram et al., 2023), it is important to be aware that some currently accepted taxonomic groupings may not be monophyletic, i.e., they may not contain all the descendants of a common ancestor. Phylogenybased comparative methods assume groupings to be monophyletic, so using an incorrect phylogeny will seriously undermine the value of the analyses undertaken. In the absence of molecular-based (i.e., DNA) phylogenies, cladistically-based taxonomies are the most suitable taxonomies for comparative studies as they are intended to closely reflect phylogeny. There are now a number of software packages available that allow users to perform rigorous comparative analyses, many of which are freely available over the internet (http://evolution. genetics.washington.edu/phylip/software.html provides access to many of these packages, and more). Several R packages (R Core Team, 2020) are also available for that, e.g., caper (Orme, 2012), ape (Paradis et al., 2004), geiger (Harmon, 2009), etc.

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The comparative method can also be used to make predictions concerning the ecology of a species. Hardy et al. (1992a), used a phylogenetic tree, based solely on morphological characters (now published incorporating molecular data; Schilthuizen et al., 1998), of the six Drosophila parasitoid species of Leptopilina occurring in Europe, to predict where in the environment L. longipes, a species whose hosts and host habitat were unknown, would be found (Fig. 1.3). The five other species are all parasitoids of Drosophila. The tree divides initially into two branches. When examining how the character ‘host habitat choice’ is distributed over the tree (i.e., the character is ‘mapped’ onto the tree), it appears that the upper branch of the tree contains the species finding its hosts in fermenting fruits (L. heterotoma), while the other branch contains species finding their hosts in fungi and/or decaying plant matter (L. clavipes, L. australis and L. fimbriata). Because L. longipes is most closely related to L. fimbriata, it was predicted that it is attracted, like its close relative, to decaying plant material. Subsequently, L. longipes was trapped with baits comprising rotting cucumber containing Drosophila larvae, and during fieldwork it was also found on decaying stalks of the umbellifer Heracleum and on fungi.

Fig. 1.3 Cladogram, based on adult morphology, of the Leptopilina species (Hymenoptera: Eucoilidae, parasitoids of Drosophila) occurring in northwestern Europe. Microhabitat use is ‘mapped’ onto the ends of the tree branches. X = principal microhabitat, (X) = microhabitat from which a species has occasionally been recovered. PM = decaying plant material; FI = fungi; FT = fermenting fruit; SF = sap fluxes. Microhabitat use by L. longipes was predicted from that species’ position on the cladogram

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The Treatment of Parasitoids Prior to Their Use in Experiments

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underestimate of the variation in natural populations, and, at worst, provides a skewed view of the true variation present.

1.3.1 Rearing 1.3.2 Experience The species and quality of the host a parasitoid is reared on can have a marked influence on its subsequent behaviour, for example through its effect on egg load and life expectancy (Sects. 2.7.3 and 2.8.3). While the influence of the host-related phenotypic variation in natural enemy traits on the results of laboratory trials may generally be mimimised or even avoided by altering the rearing regime, more insidious problems arise when insect populations are reared in mass culture. The first problem is ubiquitous and unavoidable. Natural selection will operate in the controlled environment room as much as anywhere else, changing the genetic composition of the population and, as a result, potentially influencing the behaviour of the population of interest (e.g., Matos et al., 2000; Simoes et al., 2007; Burke & Rose, 2009; Diamantidis et al., 2011; Hoffmann & Ross, 2018 ). There are two approaches to avoiding the complications of such adaptation. The first is to simply measure the traits of interest before significant selection occurs, so ideally few generations will have passed between capture and experiment. Second, if one is interested in using selection experiments to probe the nature of the trait, then we recommended that outbred populations of the species of interest be maintained for at least ten generations in the laboratory. This allows adaptation to laboratory conditions to occur and should avoid complications from any inadvertent selection pressures during the experiment. Further, maintenance of separate lines from an initially common population can also be used as a tool to generate and then test the effects of genetic differences (Mathiron et al., 2019). A more serious problem results from small effective population sizes, leading to genetic drift and inbreeding depression. Testing the variation in an inbred population, at best, results in an

It has been shown for several parasitoid species that an individual’s previous experience can modify its behaviour (Sect. 1.6.2). This phenomenon has been observed in all phases of the foraging process and often involves responses to chemical stimuli (Vet & Dicke, 1992). For instance, females Goniozus nephantidis, a gregarious ectoparasitoid naturally associated with Opisina arenosella, that have developed on the factitious host Corcyra cephalonica, prefer C. cephalonica when offered a choice between the two host species, but prefer O. arenosella after having been exposed to their odour (Subaharan et al., 2005). Previous ovipositions in hosts of a certain species can also influence host species selection in choice experiments (van Alphen & Vet, 1986), while the decision to oviposit into an already parasitised host (i.e., superparasitism) also depends on previous experience with unparasitised or parasitised hosts (Visser et al., 1992b; Hubbard et al., 1999; Chen et al., 2020; Ayala et al., 2021). Thus, when designing experiments, one should always be aware that the previous history of an individual may influence its behaviour (as may the ecological history of the population the individual is drawn from, Vyas et al., 2019). Such history can affect the results of experiments on patch time allocation, superparasitism and also the results of experiments in which interactions between adult parasitoids are studied. Storing parasitoids in the absence of either hosts or host-related cues can have an effect. Visser et al. (1990) showed that it matters whether wasps are stored in a vial singly or with other females prior to conducting an experiment. Such effects have also caused problems when parasitoids and hosts are mass reared for biological control purposes. Rearing the apple pest Cydia pomonella on an artificial diet reduced the ability of the parasitoid Hyssopus

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pallidus to respond to host location cues, as it changed the composition of the kairomones normally found in the host’s frass (Gandolfi et al., 2003). Conditioning parasitoids, by allowing them to search and oviposit for some time before an experiment, can nonetheless be a sensible practice. Inexperienced parasitoids often show lower encounter rates and are less successful in handling their hosts (Samson-Boshuizen et al., 1974). By allowing parasitoids access to hosts before they are actually used in an experiment, one can often save many hours that would otherwise be wasted in observing parasitoids that are ‘unwilling’ to search. Often, however, it is advisable to use freshly emerged, inexperienced females, for example in choice experiments, either where different host plants, host instars or host species are offered or where the olfactory responses of parasitoids to different chemicals are studied. Often, one is interested in the performance of natural populations. These comprise individuals with different experiences and/or different degrees of experience, so using only inexperienced females in the laboratory gives a distorted view of what happens in nature. One approach is to collect adults from the field for study in the laboratory. A large enough sample should give a reasonable idea of how individuals in the population behave on average. However, one should be aware of the problems of genotype-byenvironment interactions, where not all genotypes respond to changes in environment in the same way. Ideally, the laboratory conditions will reflect what is likely to be encountered in the field, especially in terms of temperature. Because experience can influence subsequent behaviour, the results of experiments in which an insect encounters two situations in succession can depend on which situation is encountered first. In such cases, one should take care that in half of the replicates one situation is encountered first, while in the other half the sequence is reversed.

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1.3.3 Sex Ratio While such effects of experience can have a great influence on the behaviour of insects, more subtle problems ought to be borne in mind. An often overlooked problem in sex ratio studies is the possible presence of Wolbachia and other male-killing bacteria in the study organism (Ode & Hardy, 2008; Chaps. 3, 5 and 6). For example, Majerus et al. (1998) found that almost 50% of females from a Japanese population of the coccinellid Harmonia axyridis were attacked by a male-killing bacterium, resulting in a heavily female-biased sex ratio. Those ladybirds from a Mongolian and a Russian population had low (1000 individuals) when mating is random and neither natural selection nor genetic drift are operating (e.g., Ridley, 1993a). Deviations from Hardy‒Weinberg expectation imply that mating is non-random throughout the population, that the population is finite and/or that natural selection is operating. A major reason for non-random mating is that populations consist of isolated or partially isolated sub-populations (or demes). Various techniques can be used to assess variation in DNA or its products (Sect. 3.2; an entomological introduction is provided by Cook, 1996, and see Kazmer, 1990; Hadrys & SivaJothy, 1994; Hoy, 1994; Scott & Williams, 1994; Behura, 2006). An established, economical, and powerful method involves screening the electrophoretic mobility of enzymes (DNA products) (Sects. 3.2.2 and 6.3.11). Although enzyme markers can be used to study population structure and the parentage or relatedness of individuals, they are often relatively invariant compared to DNA and, since they can be expressed at different points in the life history of an insect, they can be unreliable if used to compare adults and early instars, especially in holometabolous insects. Furthermore, variation in allozyme frequencies may themselves reflect selection on traits other than those reflecting population mating structure. The range of molecular and associated analytical techniques for the direct assessment of DNA variation has, since the publication of the first edition of this book, expanded dramatically. Examples include random amplified polymorphic DNA [RAPDs], DNA fingerprints [using RFLP;

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restriction length polymorphism], DNA microsatellites and amplified fragment length polymorphisms [AFLPs] (Sect. 3.2; and further reviews in Queller et al., 1993; Avise, 1994; Cook, 1996; Mueller & LaReesa Wolfenberger, 1999). RAPD and AFLP amplify certain regions in the DNA and allow testing of relatedness and population structure based on the number and size of the fragments that are cleaved and amplified. These techniques do not require time-consuming primer design and so can be done rapidly using ‘off-the-shelf’ kits. AFLP in particular requires no prior knowledge of the organism’s genome. Despite some impressive successes resulting from the application of RAPD to insect mating systems (e.g., Hadrys et al., 1992, 1993), the technique has largely fallen out of use. This is perhaps due to the limited scope of information available (nucleotide sequences are unknown and it is generally not possible to assign individuals as hetero- or homozygous; Allan & Max, 2010) as well as problems with cross-contamination from other genomes compared to recently developed techniques. The most commonly used techniques for assessing genetic variation involve amplification of DNA microsatellites (MSATs) which are repetitive dinucleotide sequences in which the number of repeats at a locus is variable. MSATs are routinely used to identify individuals, assign kinship and assess differentiation between populations and sub-populations (Allan & Max, 2010). Microsatellites have advantages over other genetic markers in that they are highly polymorphic single loci, thereby providing much information (examples of the application of microsatellites to mating system research are provided by Estoup et al., 1994; Molbo et al., 2002, see also Abe et al., 2021; Liu et al., 2023). The technique is PCR based, and so one can use minute quantities of DNA, and the results can be rapidly and inexpensively visualized using fluorescent emission of labelled primers (rather than more time- and resource-consuming gel electrophoresis). Multiple variable loci occur but each locus can be studied independently, which aids interpretation and allows the use of existing computer programs for data analysis (e.g.,

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Goodnight & Queller, 1999; Wang, 2009; Jones & Wang, 2010). The technique has one timeconsuming step: the development of primers for individual repetitive loci for the species concerned. However, more and more primers are becoming available and expertise in primer development is now common. Perhaps the most important advances have been in the development of next-generation sequencing techniques (SOLiD, Illumina and Pyro sequencing), which has allowed whole genomes to be sequenced rapidly at very low cost (around $0.10 for 1 million base pairs, compared to $1000 for Sanger sequencing). Nextgeneration sequencing has meant genomes for non-model species can be assembled costeffectively and that single nucleotide polymorphisms (SNPs) can be easily identified and used to investigate population structure (see Helyar et al., 2011, for a review). Although MSATs tend to have higher allelic diversity per locus and have the advantage of being selectively neutral, SNPs can be highly informative for population structuring. When combined with the relative ease and speed with which SNP markers are developed and screened, their value for studying the genetic population structure of small and understudied non-model species, such as many natural enemies, is clear (de Boer et al., 2015). The influence of genetics on mating systems There are several ways in which genetic systems possessed by insect natural enemies can influence the evolution of their mating systems. Here we discuss the ability of females to reproduce without mating and the genetic disadvantages of mating between close relatives. Individuals of one sex may also adopt an alternative mating tactic, which may have a genetic basis (e.g., Gross, 1996; Neff & Svensson, 2013; Sect. 5.3.1). Virgin reproduction Many insect species have a chromosomal, or heterogametic, sex determination mechanism (both sexes are diploid) (e.g., Cook, 2002; Blackmon et al., 2017). In such species, the sex chromosomes inherited at the time of fertilisation dictate whether an individual develops as a male

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or as a female; one sex is heterogametic and the other homogametic. Aside from possibly constraining the production of biased sex ratios (Hamilton, 1979; Bull & Charnov, 1988; Hardy, 2002b; West & Sheldon, 2002), heterogamety requires that eggs must be fertilised before they can develop and thus females must mate (or selffertilise) to reproduce. In contrast, other insect species, including many parasitoid wasps, are arrhenotokous (e.g., Cook, 2002; Blackmon et al., 2017). Under arrhenotoky, fertilised (diploid) eggs generally develop into female offspring and unfertilised (haploid) eggs develop into males (Sect. 3.3). Once mated, females store sperm (in the spermatheca) and potentially control the sex of offspring by regulating the fertilisation of eggs (hence the enormous interest in parasitoid sex ratios). Virgin females are also able to reproduce but are constrained to produce male offspring only. Empirical observations indicate that, with some exceptions, virgin parasitoids make ready use of this ability (e.g., Godfray & Hardy, 1993; Ode et al., 1997). Virginity has been studied in relation to mating systems using an ESS model (Godfray, 1990; Sect. 5.3.1) and in relation to clutch size and developmental mortality under strict local mating (Sect. 5.4.2) using a static optimality model (Heimpel, 1994; Sect. 5.4.7; see also Kokko & Mappes, 2005, for a more general theoretical perspective). Tests of model predictions require estimates of the prevalence of virginity and there are several methods available (Godfray, 1988, 1990; Hardy & Godfray, 1990; Godfray & Hardy, 1993; Hardy & Cook, 1995; Ode et al., 1997; Hardy et al., 1998; West et al., 1998). A direct method is to dissect females that are foraging for oviposition opportunities, or those that have dispersed from the natal site in species with strict local mating, and examine the contents of their spermathecae: females without sperm are either virgin or have been mated but become sperm depleted. Alternatively, females can be provided with hosts suitable for daughter production, and the sex of their progeny examined. Molecular methods can be integrally used to estimate actual versus realized paternity, as well as patterns of sib-mating, by genotyping

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spermathecal contents and offspring respectively (Fernández-Escudero et al., 2002). Indirect methods can be used for gregarious species in which sex ratio optima are likely to be female biased or unbiased. Broods containing only males are likely to have been produced by virgin mothers and the proportion of all-male broods serves as an estimate of the proportion of virginity. Similarly, in species with strict local mating, females that develop in all-female broods (e.g., due to developmental mortality of males) will remain virgin, and the proportion of such females observed can be used as an estimator of the proportion of virginity in the population. Empirical work by King (2002) found that female Spalangia endius, a parasitoid with a local mating population structure, produced a greater proportion of sons among their offspring in response to the presence of another mated female conspecific, but not when the conspecific was a virgin, a finding consistent with predictions of local mate competition theory (Sect. 5.4.2). Some parasitoid species are thelytokous: males are not produced and females arise from unfertilised eggs that become diploid by fusion of meiotic products (e.g., Luck et al., 1993; Cook, 1993a; Schneider et al., 2002; Belshaw & Quicke, 2003; see Sect. 3.3.2 for more detail). Obviously, thelytokous species do not have mating systems as mating does not occur. However, some species have thelytokous and sexual members (e.g., Schneider et al., 2002; Sandrock & Vorburger, 2011). Thelytoky is sometimes caused by microbial (Wolbachia) infection in some parasitoids and can be cured by antibiotic or high-temperature treatment (Stouthamer et al., 1992a, 2002; Arakaki et al., 2000; Pannebakker et al., 2004). In Trichogramma spp. (egg parasitoids), most infected populations consist of infected and uninfected individuals (Gonçalves et al., 2006) but in all other documented cases infections have gone to fixation in the population (Stouthamer, 1997). In some parasitoid species, the males produced by cured females have often lost the full capacity to perform mating behaviour (Stouthamer, 1997). Further, thelytoky may be more likely to arise in species with high levels of

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inbreeding, as it circumvents problems associated with producing highly female-biased and precise sex ratios (Cornell, 1988; Hardy, 1992; Cook, 1993a; Hardy et al., 1998; Khidr et al., 2013; Wilkinson et al., 2016, Sect. 5.4.5); this constitutes a further potential influence of mating systems on genetics. Inbreeding Depression and Sex Determination in the Hymenoptera Unlike diplo-diploid species, sex in the Hymenoptera is not determined by sex chromosomes or the ratio of sex chromosomes to autosomes. Instead, Hymenoptera are haplodiploid, with males developing from unfertilised eggs and females developing from fertilised eggs (e.g., Cook, 2002). The discovery of diploid males in Habrobracon hebetor nearly 100 years ago (Whiting & Whiting, 1925) prompted four models of sex determination in the Hymenoptera: single-locus complementary sex determination (CSD), multiple-locus CSD, genic balance, and genomic imprinting. Of these, single-locus CSD is thought to have the biggest impact on mating systems because its effects are exacerbated during inbreeding (sustained inbreeding of species with multiple-locus CSD is also expected to generate diploid males). Diploid males have been found across the Hymenoptera (Cook, 1993a; Duchateau et al., 1994; Carvalho et al., 1995; Holloway et al., 1999), in the Apoidea (bees), Vespoidea (‘social’ wasps), Tenthredinoidea (sawflies), Ichneumonoidea, Chalcidoidea, and the Cynipoidea (van Wilgenburg et al., 2006, and Heimpel & de Boer, 2008, provide more comprehensive reviews). Various techniques have been used to detect diploid males, including karyotyping, flow cytometry, visible genetic markers, allozymes and molecular markers, and morphological characters (Cook, 1993a; van Wilgenburg et al., 2006; Asplen et al., 2009; Weis et al., 2017). All of these techniques are dependent on diploid males developing to the point at which they can be sexed (typically as pupae or adults). In many of the species in which diploid males are known, their viability is similar to that of haploid males and diploid females. Yet, diploid male

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production may be much more prevalent than the number of known cases suggests; if diploid males are all inviable, diploid male production can go unnoticed. In this case, even if ‘ploidy’ can be established for eggs or larvae it may be very difficult, if not impossible, to assign sex to a diploid individual. One possible exception would be the identification of a sex-specific protein or imaginal disk tissue present early in development. Statistical comparison of survivorship of eggs from inbred and outbred females can provide support for the presence or absence of CSD as well as the number of loci involved (Weis et al., 2017). Many of the species known to produce diploid males appear to follow the single-locus CSD model (reviewed in Cook, 1993a; van Wilgenburg et al., 2006), where sex is determined at a single locus (Whiting, 1939, 1943). Under single-locus CSD, individuals that are heterozygous (diploid) at the sex locus develop into females, whereas individuals that are either hemizygous (haploid) or homozygous (diploid) at the sex locus develop into males. An important prediction of the single-locus CSD model is that diploid male production should increase with inbreeding (inbreeding increases levels of homozygosity at all loci including the sex locus). The impact that single-locus CSD has on a population mating system depends on the interaction between the amount of inbreeding, the number of sex alleles in the population, the number of times a female will mate, and whether diploid males are viable, able to disperse and can transfer sperm to females (Cook & Crozier, 1995; Winkert et al., 2019). Species possessing single-locus CSD may avoid high rates of diploid male production by outbreeding; such appears to be the case with Habrobracon (= Bracon) hebetor (Antolin & Strand, 1992; Ode et al., 1995; Antolin et al., 2003; see also Garba et al., 2019). Outbreeding coupled with high sex allele diversity within populations can reduce the genetic load associated with single-locus CSD. Allelic diversity is expected to be maintained by strong frequency-dependent selection. The number of sex alleles in a population has been estimated using matched matings (e.g., Whiting, 1943;

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Heimpel et al., 1999; Zhou et al., 2006; Weis et al., 2017) as well as maximum-likelihood approaches (e.g., Owen & Packer, 1994) and, most recently, a whole genome approach combined with flow cytometry to extrapolate from population levels of kin mating and diploid male production (de Boer et al., 2015). Estimates in natural populations appear to be high enough to make the production of diploid males rare. Finally, there may be selection for avoidance of mating with close relatives (e.g., Ode et al., 1995; Metzger et al., 2010; Collet et al., 2020) or for polyandry (e.g., Page, 1980). In species where females mate only once, females that mate with viable diploid males are constrained to produce only sons (e.g., Heimpel et al., 1999); multiple mating can increase the chance that females can produce daughters, assuming that multiple mating increases the likelihood of mating with a haploid male (see Boulton et al., 2015, for a review). Such considerations may be important when mass-rearing parasitoids for biological control programmes. Initial sampling procedures should involve obtaining naturally high allelic diversity in the founder culture of species with CSD, and measures such as maintaining large cultures or parallel smaller subcultures are recommended to prevent loss of allelic diversity during culturing (Stouthamer et al., 1992b; Cook, 1993b). The phylogenetic distribution of CSD suggests it is ancestral: it is found in the Ichneumonoidea, Apoidea, Vespoidea, Formicoidea, and Tenthredinoidea (Cook & Crozier, 1995). There are, however, insufficient data to establish whether multiple-locus or single-locus CSD is ancestral (Asplen et al., 2009). It is tempting to suggest that the mating systems of these groups should be confined to outbreeding. However, the prevalence of single-locus CSD as well as the type of mating systems found within each of these superfamilies is largely unknown. In at least some species of Ichneumonoidea (e.g., Cotesia glomerata; Kitano, 1976; Tagawa & Kitano, 1981; Gu & Dorn, 2003; and C. vestalis; de Boer et al., 2015) there is evidence suggesting a limited amount of local mating. Whether these

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species represent exceptions or whether local mate competition (LMC, Sect. 1.11.2) exists without inbreeding is unknown. However, many parasitoid species inbreed with no apparent production of diploid males. In these species, sex appears to be determined by a mechanism other than single-locus CSD (Cook, 1993a). Single-locus CSD has been ruled out for nine species of Chalcidoidea, Cynipoidea, and Chrysidoidea (see references in Cook, 1993a). It is possible that the presumed absence of singlelocus CSD has allowed members of these superfamilies to evolve mating systems incorporating inbreeding and LMC. Again, it is important to note that differences in the mating systems between the Ichneumonoidea and the Chalcidoidea (for instance) are largely unknown. Multiple-locus CSD may explain sex determination in some of these cases, although to date there is no strong evidence for this mechanism. Multiple-locus CSD is similar to single-locus CSD except that more than one locus is involved in determining sex. Individuals homozygous at all of the sex loci are diploid males. Individuals heterozygous even at only one of the loci develop into diploid females. Inbreeding under multiple-locus CSD leads to increased diploid male production, but at a much slower rate than single-locus CSD. Consequently, detection of multiple-locus CSD requires several generations of inbreeding. Multiple-locus CSD has been suggested for Cotesia vestalis and C. rubecula (de Boer et al., 2008, 2012; Weis et al., 2017), Diachasmimorpha longicaudata (Carabajal Paladino et al., 2015; see also Asplen et al., 2009) and tested for, but not detected, in Nasonia vitripennis (Skinner & Werren, 1980) and Goniozus nephantidis (Cook, 1993c). Finally, a more recently developed model of sex determination in the Hymenoptera is based on genomic imprinting (Poirié et al., 1993, cited in Beukeboom, 1995). Under this model, one or more loci are imprinted paternally or maternally. Fertilised eggs have a paternally imprinted copy of the genome which initiates female development (for details see van der Zande & Verhulst, 2014). This model has been confirmed and the

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mechanism described for Nasonia vitripennis (Verhulst et al., 2010; see also Dobson & Tanouye, 1998, Sect. 3.4). Although sex is determined by allelic diversity in probably the minority of haplodiploid species, it is thought to be the ancestral sex determination mechanism in the Hymenoptera and is better understood, both in terms of theory and in empirical work, than its alternatives: nevertheless, much remains to be discovered about the underlying mechanisms and the consequences of sex determination for mating systems.

5.3.4 Comparative Studies While some studies look for relationships between variables within species, others look for relationships across species: these are ‘comparative studies’ (introduced in Sect. 1.2.3). Comparative studies may make use of the results of many behavioural and/or genetic studies and thus offer a means of assessing generality or testing otherwise untestable predictions, but often they sacrifice experimental rigour. The data of interest from the literature are often collected for different reasons unrelated to comparative study purposes. As a result, the quality of data is often variable, sometimes seriously weakening the validity of the conclusions that can be drawn from comparative studies. Furthermore, information on within-species variation will be ignored when species averages are used. The necessity of controlling for phylogeny in such studies is now well established (Garamszegi, 2014). First of all, phylogenetic controls are necessary to take into account statistical nonindependence of species that are closely related. Furthermore, controlling for phylogeny enables the distinction between interspecific similarities having a real evolutionary meaning and being simply the result of common ancestry. Methods such as phylogenetically independent contrasts (PIC) and phylogenetic least squares (PGLS) consider relatedness between species when looking at relationships between variables (asking, for instance, whether parasitoid oviposition strategy is associated with host specialization;

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Boulton & Heimpel, 2018). Recent developments in phylogenetic comparative methods include techniques such as ancestral state reconstructions and phylogenetic path analysis, which can help when disentangling evolutionary correlations between complex sets of traits (Gonzalez-Voyer & von Hardenberg, 2014). Advanced phylogenetic comparative methods have become increasingly easy for a nonspecialist to implement, using R packages such as ape (Paradis & Schliep, 2018) and phytools (Revell, 2012) and the computer program BayesTraits (Meade & Pagel, 2022). Despite these advances, few studies have sought to investigate evolutionary correlations between mating systems and associated traits in parasitoids. The few phylogenetically controlled studies so far have considered evolutionary correlations between behavioural reproductive strategies (Wajnberg et al., 2003), between clutch size and sex ratio (Smart & Mayhew, 2009), and clutch size and the female mating rate (Ridley, 1993b), with the former and the latter showing a significant association. Ridley (1993b) showed that in gregarious (multiple eggs per host) parasitoids females tend to mate multiply (polyandry) while solitary (single egg per host) species are monandrous (females mate only once). Godfray (1994) proposed that the likelihood and costs of becoming sperm depleted resulted in the evolution of polyandry in the parasitoids, as gregarious species have greater sperm requirements as they lay more fertilised eggs (daughters) over their lifetime. As more, better-quality, data are accumulated on the parasitoids, phylogenetic comparative studies will offer ever greater potential for trying to disentangle complex correlations of traits that drive, and are driven by, the evolution of parasitoid mating systems.

5.4

Parasitoid Mating Systems

5.4.1 Introduction Natural enemies are generally small and, although desirable, it may be very difficult to study a mating system in its entirety. An overall

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idea of how common local or non-local mating may be obtained by fitting a predictive model for partial local mating to sex ratio data (Debout et al., 2002; Gu & Dorn, 2003; see also Read et al., 1992, 2002, for related discussions of the selfing rates of Protozoa). Mating systems can be divided into several component parts and investigation of one of these can prove useful. Investigation of more than one component is, of course, even more useful. Good examples of studies studying several component parts of mating systems and employing a variety of techniques are Myint and Walter (1990) and Fauvergue et al. (1999). Ware and Compton (1994a, b) used sticky traps to investigate dispersal of female fig wasps in the field: similar techniques can be employed to gain insight into parasitoid mating systems (e.g., Asplen et al., 2016). Combining these with molecular techniques to assay genetic structure of populations is a particularly powerful way to understand natural enemy mating systems in more detail (Antolin et al., 2003; Gu & Dorn, 2003).

5.4.2 Emergence and Mating at the Natal Site Temporal and spatial patterns in the emergence of adult natural enemies are important aspects of mating systems. Offspring that develop in a group, whether sharing the same host (gregarious) or on separate hosts clustered together (quasi-gregarious), may have the opportunity of mating with other group members on maturity. Offspring developing singly in isolated hosts (solitary) do not. Emergence The sexes may emerge simultaneously or males (protandry) or females (protogyny) may emerge first within an offspring group or population. There are few reports of protogyny in parasitoid wasps (Hennessey & West, 2018), but protandry is widespread (Boulton et al., 2015). Males generally develop more rapidly than female conspecifics and are also generally smaller (Hurlbutt, 1987; Hirose et al., 1988; Ramadan

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et al., 1991; Godfray, 1994; Hardy & Mayhew, 1998a, Sect. 2.9.2). Small male size may reflect selection for protandry, or protandry may be an incidental consequence of maternal sexallocation strategies that lead to males developing in smaller hosts (King, 1993; Moynihan & Shuker, 2011). Protandry in gregarious and quasi-gregarious species is often, but not always, associated with a high degree of mating at the emergence site, as males generally remain and mate with females as these emerge (see Bourdais & Hance, 2019, for a notable exception). Protandry can be studied by simple field or laboratory observations of developing broods or ‘populations’ (Kitano, 1976; Tagawa & Kitano, 1981; Nadel & Luck, 1985, 1992; Hirose et al., 1988; van Dijken et al., 1989; Kajita, 1989; Myint & Walter, 1990; Ramadan et al., 1991; Pompanon et al., 1995; Ode et al., 1996; Fauvergue et al., 1999; Hardy et al., 1999, 2000; Loch & Walter, 1999, 2002; Gu & Dorn, 2003; Moynihan & Shuker, 2011; Bourdais, 2012; Kapranas et al., 2013; Asplen et al., 2016; Bourdais & Hance, 2019), and recent studies have employed video tracking software to more accurately track emergence and dispersal (Kapranas et al., 2013). However, in some parasitoids mating may take place within the confines of the host or the host’s covering (e.g., puparium; within-host-mating, WHM), which may obscure some details of mating behaviour (Suzuki & Hiehata, 1985; Tepidino, 1988; Drapeau & Werren, 1999; Leonard & Boake, 2006; Trienens et al., 2021). This complication does not occur with either quasi-gregarious endoparasitoids (e.g., Nadel & Luck, 1985, 1992; Myint & Walter, 1990) or ectoparasitoids, although ectoparasitoids mating within pupal cocoons may be difficult to observe (Tagawa & Kitano, 1981; Dijkstra, 1986; Hardy et al., 1999). Protandrous male bethylids (ectoparasitoids of many Lepidoptera and Coleoptera of economic importance) often inseminate unemerged females by chewing an entrance hole in the female’s cocoon (Griffiths & Godfray, 1988; Hardy & Mayhew, 1998a) and the behaviour, and the resulting holes, can be readily observed before female emergence. However, female bethylids may also mate post

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eclosion, which is exclusively the case in Goniozus triangulifer (Legaspi et al., 1987). Male and female emergence patterns have been studied in Habrobracon hebetor, a species noted above to exhibit severe inbreeding depression. In a laboratory study, Antolin and Strand (1992) found that males and females emerged and dispersed from a brood throughout the day; no difference was found in the emergence patterns of males and females. Females tended to disperse from the emergence site before males and most females left the natal area unmated, suggesting dispersal acts as a mechanism to curtail sibmating and reduce costly inbreeding under singlelocus CSD (Ode et al., 1998). Fauvergue et al. (1999) assessed emergence patterns of Leptopilina heterotoma and L. boulardi (Hymenoptera: Eucoilidae), parasitoids of frugivorous Drosophila larvae, by rearing out wasps from bananas exposed for two weeks in fruit orchards and then taken to the laboratory. Across all patches (bananas) males and females emerged simultaneously over about a ten-day period. Although in each species both sexes emerged from virtually every patch during this period, individual parasitoids frequently emerged without any conspecifics of the opposite sex emerging from the same patch on the same day. These observations were supplemented by a laboratory experiment in which offspring laid within a 24-h period were reared out and individually observed until emergence using an automated image analysis system. Adults emerged over a five-day period and both species were highly protandrous. Similar studies on other parasitoids of flies have documented protandry as well as prolonged female emergence over several days (Legner, 1968; Nadel & Luck, 1985). Bourdais and Hance (2019) studied emergence patterns in the quasi-gregarious aphid parasitoid Aphidius matricariae. In this species, males emerged only slightly before females in general, but protandry was extreme within broods. Even when a female laid a batch of eggs within a short period, brothers and sisters rarely emerged at the same time, which likely constitutes a mechanism to reduce inbreeding risk under single-locus CSD.

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Local mating Some models developed to predict sex allocation optima assume that when the offspring of several females (foundresses) develop in spatial aggregation, mating occurs exclusively between group members (strict local mating) and randomly, with respect to genetic relatedness (e.g., Hamilton, 1967; Table 5.1). Under these conditions, sex ratios are predicted to increase, from very low proportions of males towards approximate sexratio equality, as the number of foundresses increases. There have been many tests of this relationship using individual parasitoid species (e.g., Werren, 1983; Waage & Lane, 1984; Strand, 1988; Burton-Chellew et al., 2008; Abdi et al., 2020; Abe et al., 2021; Liu et al., 2023) as well as extensions of the basic theory (Hardy, 1994a; West & Herre, 1998a; Shuker et al., 2005, 2006; West, 2009; Gardner & Hardy, 2020; Lehtonen et al., 2023); in general, sex ratios become less female biased as foundress number increases, as predicted (further discussed by Orzack, 1993; Godfray, 1994; West, 2009; Boulton et al., 2015). This is because when multiple females contribute offspring to a patch the reproductive value of sons increases as mate competition among brothers over females is reduced (sons can secure matings with non-sibling females and so increase their mother’s evolutionary fitness more than if only sisters are available as mates). Several comparative studies have examined cross-species relationships between foundress number and offspring sex ratio in species with strict local mating. The best-known of these have employed natural variation in foundress number in fig-pollinating wasps (Agaonidae), which are not insect natural enemies, but share much biology with parasitoid wasps (e.g., Herre, 1985, 1987; Herre et al., 1997; West & Herre, 1998b). In these species (winged) females enter a fig, lay eggs and die (their corpses can be counted to estimate foundress number) while males are wingless and unable to disperse from the natal fig (providing what has been thought to be good evidence for strict local mating. However wingless males in some non-pollinating fig wasps do disperse; Cook et al., 1999; Greeff & Ferguson,

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1999; Bean & Cook, 2001; and see Greeff et al., 2003 for evidence of male dispersal in pollinating fig wasps). Their sex ratios support the predicted relationship across species (and within species, Frank, 1985a; Herre, 1985; but see Kathuria et al., 1999; Chung et al., 2019). Despite the enormous interest in parasitoid sex ratios, there have been relatively few comparative studies and the evidence for strict local mating in other species is less clear than for figpollinator wasps where males cannot leave the natal fig (see Martel et al., 2016, for more discussion). Griffiths and Godfray (1988) examined a predicted relationship between sex ratio and clutch size in bethylid wasps, assuming both strict local mating and that offspring groups are produced by single foundresses. We discuss this more fully below (Sect. 5.4.5). Random local mating The assumption of random within-group mating will be violated if mating with non-relatives is avoided and/or if non-siblings are less likely to encounter one another (due to asynchronous maturity resulting from asynchronous offspring production by mothers; Bourdais & Hance, 2019). Sex ratios more female biased than under random mating are predicted (Stubblefield & Seger, 1990; Nunney & Luck, 1988; Table 5.1, Fig. 5.3; see also Kinoshita et al., 2002). Conversely, if individuals prefer non-siblings as mates, sex ratios are predicted to be less female biased (Stubblefield & Seger, 1990; Table 5.1; see Boulton et al., 2015, for a review of the evidence for kin discrimination in parasitoids). Of course, these predictions only apply to offspring groups produced by more than one foundress. There have been few empirical investigations of within-group mating patterns. One of these examined mating patterns in fig-pollinating wasps. Mating is difficult to observe directly as it occurs within the fig. Frank (1985b) cut figs into halves, marked one male from each half and then joined two halves from different figs together. Males tended to remain in the half-fig of their origin, suggesting sibling-biased mating

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Fig. 5.3 Predicted sex ratio optima in response to foundress number, maturation asynchrony and non-local mating. The model assumes haplodiploid genetics and that, after all females in the natal patch are mated, males disperse to seek virgin females in other patches. The degree of non-local mating is represented by M, the number of patches found by a dispersing male. Solid lines show the prediction assuming offspring maturation is synchronous and dashed lines are for asynchrony. Increasing foundress numbers, increasing non-local mating and increasing synchrony each lead to less femalebiased sex ratios. Source Nunney and Luck (1988). Reproduced by permission of Elsevier Science

within figs and a possible explanation for observations of fig wasp sex ratios more biased than predicted by models assuming random within-group mating (Frank, 1985b). Molbo and Parker (1996) and Burton-Chellew et al. (2008) used genetic techniques (allozyme polymorphism and MSATs, Sect. 5.3.3) to estimate foundress numbers in patches of hosts in natural populations of Nasonia vitripennis, a pteromalid wasp-parasitising fly pupae. In this species, males are protandrous and mate with females on emergence (e.g., King et al., 1969; Leonard & Boake, 2006; Trienens et al., 2021). Males also have reduced wings and are probably unable to disperse significantly from the emergence site so mating is thought to be mostly local (references in Hardy, 1994a). Molbo and Parker (1996) found variation in the timing of emergence of adult wasps, and that this was correlated with the sex ratio. In some patches of hosts,

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emergence of all adults was highly synchronous and occurred in one day, while in other patches emergence was highly asynchronous, with adult wasps emerging over periods of up to 8 days (although most wasps emerged within four days). This variation across patches is most likely a result of different foundresses ovipositing on patches at different times. As predicted by theory (Fig. 5.3), sex ratios in patches with longer, asynchronous emergence were more female biased than in patches with short, synchronous emergence (Molbo & Parker, 1996; Fig. 5.4). This is because if early emerging males do not disperse, they will remain on the patch as competitors for later emerging males. A female that oviposits on a patch later than other foundresses will maximise her fitness by producing a higher proportion of daughters (since her sons will face greater competition from non-dispersing males). The importance of asynchronous oviposition has since been integrated into LMC theory (asymmetric LMC; Shuker et al., 2005, 2006), and empirical studies have shown that N. vitripennis females take into account the age of other broods on a patch when allocating sex (Shuker et al., 2005, 2006).

Fig. 5.4 Sex ratios in patches of Nasonia vitripennis in the field in relation to foundress number and synchrony of emergence within each patch (1–8 days). Curves show the predicted optimal sex ratio with inbreeding coefficients of 0 and 1 (dashed lines) and observed inbreeding (F = 0.31) (solid line). Source Molbo and Parker (1996). Reproduced by permission of The Royal Society of London

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5.4.3 Dispersal from the Natal Site While properties such as protandry and mating at the natal site (local mating) can be demonstrated within the confines of a glass vial, such observations deny individuals the opportunity to disperse prior to mating, and thus may overestimate the importance of local mating. Modelling studies predict that dispersal and non-local mating can strongly influence parasitoid sex ratios (Table 5.1, Fig. 5.3), but empirical investigations are relatively scarce (reviewed by Hardy, 1994a). Field observations and indirect monitoring are possible in some species (e.g., Tagawa & Kitano, 1981; Kajita, 1989; Loch & Walter, 1999; Asplen et al., 2016; ). In the laboratory, the propensity for, and timing of, dispersal can be investigated by simply allowing emerging individuals to leave the vial, or similar methodology (Dijkstra, 1986; Myint & Walter, 1990; Forsse et al., 1992; Fauvergue et al., 1999; Hardy et al., 1999, 2000; Loch & Walter, 2002; Gu & Dorn, 2003; Moynihan & Shuker, 2011; Bourdais, 2012; Kapranas et al., 2013; Bourdais & Hance, 2019; Böttinger & Stökl, 2020). Dijkstra (1986), studying Colpoclypeus florus (Hymenoptera: Eulophidae), a gregarious

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parasitoid of tortricid leaf-roller larvae, Hardy et al. (1999) studying Goniozus nephantidis (Hymenoptera: Bethylidae), a gregarious larval parasitoid of lepidopteran coconut pests, Hardy et al. (2000) studying Goniozus legneri, a parasitoid of lepidopteran almond pests, and Bourdais and Hance (2019) studying the aphid parasitoid Aphidius matricariae all found that virtually all males and females dispersed from the natal site (Figs. 5.5 and 5.6). Females collected post dispersal can be provided with hosts (e.g., Hardy et al., 1999, 2000) or the contents of their spermathecae can be examined following dissection (Nadel & Luck, 1985, 1992; Dijkstra, 1986; Ode et al., 1998) to assess whether or not they have mated. Populations of parasitoids may have several levels of organisation ranging from individual broods to sub-populations to the entire population. Populations of Habrobracon hebetor are frequently subdivided and approximately half of the females disperse from sub-populations before they have mated (Ode et al., 1998).

5.4.4 Post-Dispersal Mating: Natal Site Immigrants and Emigrants Although the above methods (Sect. 5.4.3) can demonstrate dispersal and the prevalence of local mating, they do not assess post-dispersal mating; i.e., it is unknown whether dispersing males obtain matings elsewhere or, similarly, whether females mate or re-mate after dispersal. They also do not allow the possibility of mating by immigrants at the natal patch. Several studies have been carried out in which a fuller range of behaviours is possible, ranging from laboratory cages, through much larger enclosed spaces, to semi-natural and natural field populations. Comparative methods can also be used to explore relationships between the expected degree of local mating and the sex ratio (Sect. 5.4.6). Laboratory cages Vos et al. (1993) observed groups of Leptomastix dactylopii, a parasitoid wasp that develops quasigregariously in isolated patches of hosts (citrus

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mealybug) emerging in a laboratory cage. Almost all parasitoids left the natal patch before mating and at least some subsequent (off-patch) mating was observed. Both males and females were attracted to small traps containing members of the opposite sex. Cotesia glomerata also show partial off-patch mating (50% of females disperse without mating). Gu and Dorn (2003) filmed C. glomerata emergence in laboratory cages and scored emergence and mating in the natal patch using this method. Later work has shown (again using laboratory observation cages) that male C. glomerata do not disperse, but female dispersal is motivated by interactions with kin (Ruf et al., 2011). This study also found that dispersing females were more likely to mate than philopatric females, and they tended to disperse to patches with mating opportunities rather than control patches where no males were present. Fauvergue et al. (1999) observed emergence and dispersal of the congeners Leptopilina heterotoma and L. boulardi from patches within laboratory cages. They found that the sex ratios produced by these species aligned with their emergence patterns: the sex ratio was not strongly female biased as predicted by LMC, likely because males emerged before females and dispersed soon after emergence, suggesting nonlocal mating. More recently Böttinger and Stökl (2020; see also Quicray et al., 2023) have extended this study, showing that across four species of Leptopilina, dispersal is a strong predictor of the volatility of sex pheromones. Species that disperse from the natal patch to mate must find mates at other locations (Sect. 5.4.5) and so greater investment in signalling and reception of cues is expected. Species that disperse from the natal patch soon after emergence produce more volatile sex pheromones which are detectable over a longer range compared to congeners which mate on the natal patch. Similarly, pilot studies of Goniozus nephantidis in table-top sized cages indicate that males developing in all-male broods disperse and immigrate into broods of females. Non-sibling mating readily occurs, both in and around the females’ natal area (S. Barnard and I.C.W. Hardy, unpublished).

374 Fig. 5.5 Eclosion, local mating, and dispersal from mixed-sex broods in Colpoclypeus florus, a gregarious eulophid wasp in which broods develop within the web of the lepidopteran host (tortricid leaf-roller larvae). Upper panel: Laboratory apparatus which allows dispersal from the natal site but makes return unlikely. Central panel: Timing of eclosion and dispersal. Males eclose shortly before females and copulation takes place within the host’s web. Both sexes disperse around two days after eclosion. Lower panel: Insemination capacity of males. Male body weight (estimated by pupal weight) is positively correlated with the capacity to inseminate females (males were in contact with 15 females for a total of either 21 or 45 h, although, to simulate natural emergence patterns, not all 15 females were presented simultaneously). Recently mated females do not re-mate, but it is not known whether they remain unreceptive. Although the mean sex ratio is female biased, these investigations suggest that the mating system is unlikely to conform absolutely to ‘strict local mating’. Source Dijkstra (1986). Reproduced by permission of Brill Uitgeverij

I. C. W. Hardy et al.

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Fig. 5.6 Eclosion, local mating and dispersal in Goniozus nephantidis, a gregarious bethylid wasp in which broods of siblings develop within narrow feeding galleries constructed by the lepidopteran host (oecophorid larvae which feed on coconut leaves). Broods may contain both sexes or, due to male mortality, and subsequent female virginity, only one sex of offspring. Individual broods were allowed to mature in the central of three chambers in a 150 mm-long opaque plastic block decked with Plexiglass (upper left panel). A 1 mm-wide slot connected chambers and provided access to a larger transparent box from which dispersed individuals (those outside the block) were collected. A block with one chamber was also present to provide structure for dispersed adults. Observations were made every 3 h. Dispersed females from mixed-sex broods were allowed to produce offspring to assess their mating status. In mixed-sex broods, almost all females mated before dispersal (lower left panel). Males were generally protandrous, males dispersed from both mixed-sex and all-male broods, but males in all-male broods disperse more slowly (right hand panels). Virgin females dispersed from allfemale broods (lower right panel). Although the mean sex

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ratio is female biased, these investigations suggest that the mating system of G. nephantidis is unlikely to conform absolutely to ‘strict local mating’, although field observations and/or genetic studies are required. Lower left panel: the probability that all females are inseminated in relation to the number of females eclosing from 13 broods containing only one male. The data in the lower left panel are binary (1 = all females inseminated, 0 = some females uninseminated). and the fitted curve is the probability of complete insemination, estimated using logistic regression. Although the two broods with incomplete insemination contained relatively large numbers of females the regression falls just short of significance, and in each of these only one female was uninseminated. Thus, a single male was sufficient to inseminate virtually all females, even when brood sizes were large. Similar results were obtained from Goniozus legneri using the same protocol (Hardy et al., 2000) and a recent study found that G. legneri females produced less biased offspring sex ratios when mated with males from a nonlocal strain (Du et al., 2021). Source Hardy et al. (1999). Reproduced by permission of Blackwell WissenschaftsVerlag

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Fig. 5.7 Plan of the experimental greenhouse used to observe mating and dispersal of P. vindemmiae. Four groups of three single-foundress broods were arranged on the floor on slightly raised ‘brood stations’ (white plastic discs). Each group contained a small brood (S) of 5 hosts, a large brood (L) of 12 hosts and a large late brood (LL) of 12 hosts parasitised 2 days later than S and L. Wasps could only move between broods by flying (both

sexes can fly). Wasps were not marked. Brood stations were checked every 2 hduring a 12-h period for 6–7 days (wasps were found to be quiescent outside this period). Several oviposition site ‘traps’ were used to catch dispersed females, which were dissected to determine whether their spermathecae contained sperm. Honey was provided but wasps were not attracted to this. Source Nadel and Luck (1992)

Greenhouse populations

the opportunity of pre-dispersal mating with same-patch or immigrant males, although some virgin females were collected at oviposition sites. Males also migrated to oviposition sites, where some virgin females mated. Overall, there were 26‒37% extra-brood matings. Although these observations demonstrate a complex mating system, it is difficult to assign relative importance to the observed mating system components with confidence, because of unnatural elements of the experiment (further discussed by Nadel & Luck, 1992; Hardy, 1994a).

Using greenhouse populations, Nadel and Luck (1992) investigated dispersal, immigration and mating on the natal patch and elsewhere in Pachycrepoideus vindemmiae (a quasigregarious pteromalid which parasitises Drosophila pupae) (Fig. 5.7). Males were protandrous and often waited for females to emerge and mate (female P. vindemmiae mate only once, Nadel & Luck, 1985). Males also emigrated from broods and arrived at non-natal broods, before and after female dispersal in both cases. Most females had

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Semi-natural and natural populations Several studies of parasitoid mating systems have been carried out in ‘agricultural’ environments. It might be argued that such unnatural settings could influence aspects of the mating system under investigation, because: (1) population densities are likely to be unnaturally high; (2) parasitoids are relatively poorly co-evolved with their hosts; (3) host distributions within an agricultural setting may be distorted relative to ‘natural’ environments. However, some parasitoids have inhabited agricultural settings for thousands of years. For instance, Habrobracon hebetor has been associated with pests of stored grain for at least 3,500 years (Chaddick & Leek, 1972). Myint and Walter (1990) examined aspects of the mating system of Spalangia cameroni (Hymenoptera: Pteromalidae), a quasi-gregarious parasitoid of housefly pupae, in ‘field’ investigations carried out in a poultry shed. How closely this represents natural conditions, and thus the quantitative importance of the results, remains unclear. Males generally emerged before females developing in the same batch of hosts, and most wasps left the natal site without mating (mating was observed when the emergence of males and females happened to coincide). Olfactometer (odour choice) experiments in the laboratory (Sect. 1.6.2) showed that males were attracted to odours emanating from the host habitat. Males were also trapped in the field at patches of hosts suitable for oviposition by females (in both the presence and the absence of females) and at batches of parasitized hosts from which females were close to emerging. Sibmating and local mating are probably uncommon in natural populations. Loch and Walter (1999) observed the eclosion and mating behaviour of Trissolcus basalis (Scelionidae), a quasi-gregarious parasitoid of homopteran eggs, emerging from host egg batches placed in the field. Males are protandrous and engage in aggressive contests for egg masses. Nearly 20% of females were not mated by the dominant male on emergence, 25% of females mated multiply, sometimes with multiple males, and both virgin and mated females moved

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slowly from the emergence site to its surroundings before dispersing. Males also dispersed from the natal site, both during and after the period of female emergence (Loch & Walter, 2002). Both local and non-local mating probably occur in nature, but the extent of outbreeding remains unknown. Kitano (1976) and Tagawa and Kitano (1981) observed the eclosion and mating behaviour of Cotesia (Apanteles) glomerata, a gregarious parasitoid of lepidopteran larvae. Observations were carried out in an agricultural system (cabbage fields). The sex ratios of most broods were female biased, although there were some all-male broods. Males tended to remain at the natal site after emergence from both brood types. In mixed-sex broods, males usually emerged first and began to search for females to court. Immigrant males arrived at both mixed-sex and allmale broods. Recently emerged females usually remained inactive near the natal site, mated within a few minutes and then dispersed. Local mating occurred but many females (likely more than 40%) mated with immigrant males, some dispersed before mating and it is unknown whether these mated elsewhere, although other field studies (references in Ikawa et al., 1993) have estimated that almost all females mate. Van Dijken et al. (1989) observed emergence and mating of Apoanagyrus lopezi, a quasigregarious parasitoid of the cassava mealybug, in fields of cassava. Hosts occur in isolated batches. Males and females emerged from each batch over protracted periods and in random sequence. Experiments using cages of A. lopezi placed out in the field showed that males do not attract females or males, males are attracted to virgin females and less attracted to mated females. Males were also attracted to unparasitised hosts. A. lopezi probably has a panmictic mating system. Similarly, Powell and King (1984) placed cages containing adult Microplitis croceipes (Braconidae), a parasitoid of moth larvae infesting cotton, in cotton crops. Cages containing mated males and mated females were not attractive to free-living M. croceipes, but cages containing virgin females attracted males. Fauvergue et al. (1999) also used individuals in

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cages to study attraction between male and female L. heterotoma and L. boulardi in the field. Cages contained virgin females, mated females, males or no individuals (control). Virgin females were by far the most attractive to free-living conspecific males. Adams and Morse (2014) took advantage of the large size of Alabagrus texanus, a solitary larval parasitoid of fern moths, to study their mating behaviour in the field. They were able to mark and release males, and ‘tether’ virgin females to fern fronds from which they naturally emerge. They showed that males readily find and court tethered females, and that similar proportions of these females (1) mated with the first male they encountered, (2) rejected the first male but mated a subsequent male, or (3) did not mate at all. They also used this technique to assess male and female mate choice, which were confirmed under laboratory conditions. Kazmer and Luck (1991; see also Antolin, 1999) used electrophoretic techniques (Kazmer, 1990; Sects. 3.2.2 and 5.3.3) to determine allozyme variation in populations of Trichogramma pretiosum and Trichogramma sp., parasitoids of lepidopteran eggs, and to assess the degree of non-sibling mating. Trichogramma sp. occurred as a natural population while T. pretiosum was collected from an agricultural population (tomato plots). Both species were sampled by rearing wasps from host eggs collected in the field. Although both species develop gregariously in isolated hosts and have female-biased sex ratios, only moderate frequencies (55–64%) of sibmating were estimated. About one-third of the total mating was non-local in T. sp. and 2–13% in T. pretiosum. Further evidence for male dispersal and non-local mating is that cages of virgin females placed out in the field attracted large numbers of males (cages containing mated females did not, Kazmer & Luck, 1991). Patterns of sib-mating have been inferred using similar methods in field studies of two species with single-locus CSD, Venturia canescens (Collet et al., 2020; random mating with respect to sibship) and Cotesia vestalis (de Boer et al., 2015; non-random mating, suggesting sib avoidance).

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Eggleton (1990) observed the emergence of a female Lytarnis maculipennis (Ichneumonidae) (a parasitoid of wood-inhabiting insects) in the field. Well before the female bored an exit to the surface of the wood, 4–6 patrolling males gathered to await her emergence. There were aggressive interactions between the males and one, which was larger than the others, was dominant and appeared to mate with the female while the other males apparently did not.

5.4.5 Post-Dispersal Mating: NonNatal Mating Localities The aforementioned studies stress the importance of the natal site as a mating locality for both emergent and immigrant individuals. However, many species normally mate in other localities, such as oviposition sites, feeding sites and even arbitrary sites. It is important to obtain information on these to fully understand the mating system. Mating at oviposition sites Several studies have investigated the possibility that males seek mates at oviposition sites. Some of these are summarised above (van Dijken et al., 1989; Myint & Walter, 1990; Nadel & Luck, 1992; Dias et al., 2014; see also Boulton et al., 2015; Sect. 5.4.4) and indicate that mating can occur at oviposition sites. However, Hardy and Godfray (1990) caught three species of parasitoids of Drosophila larvae at oviposition sites in the field and while more than a hundred females of each species were caught during the season, no males of two species and only one male of the third were caught. It is unlikely that oviposition sites are mating sites in these species. Males in at least one of these species do, however, disperse from the natal site and are attracted by virgin females (Fauvergue et al., 1999). Mating at feeding sites Feeding during the adult stage is common among parasitoids. In many cases hosts suitable for oviposition may serve as food for adults (e.g., Jervis & Kidd, 1986; Heimpel & Rosenheim,

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1995; Heimpel & Collier, 1996; Chap. 8) and thus feeding and oviposition sites are spatially coincident. However, they may not be spatially coincident and feeding sites may serve as a mating site. Many parasitoid wasp species have been observed to visit flowers, in many cases to feed from nectar. Jervis et al. (1993) observed 249 parasitoid species visiting and feeding at flowers: 18.5% of these were represented by both males and females and thus some may use flowers as mating sites, as has been observed in nectar-feeding species of Agathis (Braconidae) (Belokobylskij & Jervis, 1998). Males of the desert bee-fly Lordotus miscellus (Bombyliidae) defend rabbitbrush plants (Chrysothamnus nauseosus) which the females of this parasitoid visit for feeding (Toft, 1984). Mating at arbitrary sites: Swarming behaviour (‘lekking’) The males of several parasitoid species swarm or form leks on the substrates that are not resources, e.g., Blacus species, Diachasmimorpha longicaudata (Braconidae) (Donisthorpe, 1936; Syrjämäki, 1976; van Achterberg, 1977; Sivinski & Webb, 1989), Napo townsendi (Braconide) (Robinson et al., 2015), Diplazon pectoratorius (Ichneumonidae) (Rotheray, 1981), Aphelopus melaleucus (Dryinidae) (Jervis, 1979) and some Encyrtidae, Pteromalidae and Eulophidae (Nadel, 1987; Graham, 1993). Pajunen (1990) suggests that swarming could have evolved from territorial behaviour when territorial systems broke down due to high population densities; however, this would not apply to all cases of swarming, e. g., A. melaleucus (M.A. Jervis, personal communication). Nadel (1987) investigated mixed swarms of two species of encyrtid wasp (Bothriothorax nigripes and Copidosoma bakerii) and a species of pteromalid wasp (Pachyneuron sp.). Males were observed either to fly or to crawl over boulders. The swarms appeared at the same sites over a period of several weeks and contained several thousands of individuals of each species. In each species the vast majority were males. Swarms formed in the early morning and broke up during the night. Nadel (1987) established

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that females arrived for mating purposes: she dissected newly arriving females and found their spermathecae to be empty. Courtship and copulation occurred on boulders. Females were receptive upon arrival, were mated soon after, and became unreceptive. Graham (1993) described the swarming behaviour of eulophids (in particular Chrysocharis gemma) and torymids (mostly Torymus spp.). Swarms comprising many thousands of individuals, the overwhelming majority being female, were observed year after year upon and around the same clump of trees, and they were present for much of the reproductive season (such site fidelity is also reported by Svensson and Petersson (2000), who studied predatory dance flies (Empididae)). The hosts of the observed species were not present locally. The ovaries of Chrysocharis females contained no mature eggs but it was not possible to ascertain whether their spermathecae contained sperm which would indicate whether they were virgins (van den Assem, 1996). The function, if any, of these swarms remains unknown: they may not be mating swarms at all. Syrjämäki (1976) observed swarms of Blacus ruficornis and found these never to contain females (>8000 individuals were examined). Similarly, Quimio and Walter (2000) found that swarms of the braconid wasp Fopius arisanus occurring in tree canopies were comprised of sexually immature adult males, while mature males were found in loose aggregations in lower-storey vegetation. More recently Robinson et al. (2015) described facultative lek formation in the braconid Napo townsendi. Males did display on leaf tops singly, but also appeared in groups (leaves provide no resources to females and so this fits the definition of a lek). Both solitary and aggregated males were successful in mating, but males in leks appear to gain some protection from predators. It may be that males calling in groups are more protected from predation (Hamilton, 1971), but that this tendency to aggregate has been coopted for female mate choice. Males in a genuine mating swarm may release chemical signals in concert and thus amplify the attractive effects. In mixed-species swarms, the

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signals of the different species may be very similar but interspecific communication is considered unlikely. Most probably, swarms are not formed in response to active long-distance signalling by the participants, rather, certain peculiarities of the site itself attract both sexes when the insects are receptive to mating. Nadel (1987) mentions that the sites she observed were on the only low, yet abrupt, peak within a 7.5 km radius of a mountain ridge. The sites were no more profitable in terms of food or oviposition than the surrounding areas, but they offered a distinct landmark and a certain degree of shelter from strong winds. This ‘hilltopping’ behaviour is known in many insects (Thornhill & Alcock, 1982). Likewise, many matings in Habrobracon hebetor, which attacks stored product pyralid moths, occur after dispersal from the natal site. Both males and females exhibit a prolonged period of unwillingness to mate during which time individuals disperse from the natal site (Ode et al., 1995). Males aggregate on mounds on the surface of the grain and most matings appear to occur on these (Antolin & Strand, 1992). Sometimes, these aggregation sites are coincident with higher concentrations of hosts that will attract females. As a final point regarding dispersal behaviour, we would like to stress that, while several studies have documented the dispersal of males and females from a natal site or even a population, it is equally important to determine the fate of dispersing individuals. The one study that we are aware of concerns dispersal of the mymarid egg parasitoid, Anagrus delicatus (Antolin & Strong, 1987). By marking individuals, these authors were able to document female-biased dispersal to small islands more than 1 km from the nearest potential source population. The number, sex, and mating status of immigrants founding a new population have all been modelled as important variables in determining whether a new population will become established or extinct (Hopper & Roush, 1993) (see Sect. 7.4.3 in relation to biological control introductions).

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5.4.6 Comparative Studies of Local and Non-local Mating Some studies have examined cross-species relationships between sex ratio and one important aspect of mating systems: the degree of local mate competition (LMC; mating at the natal site). A recent cross-species study has also addressed how the level of LMC influences the evolution of insemination capacity across the parasitoids (Sect. 5.4.7), showing that males in species that experience high LMC can generally inseminate more females than they are expected to encounter (perhaps due to low levels of off-patch mating, asynchronous emergence and sperm competition; Martel et al., 2016). Nyabuga et al. (2012) investigated the role of resource exploitation and ant attendance on local mating in three species of aphid parasitoid (Lysiphlebus hirticornis, Pausia pini, and Aphidius ervi). They found that local mating scaled with the proportion of aphids parasitized within a patch (and that this was related to ant attendance, which may be used as a cue for patch quality). All three species have been reported to produce a female-biased sex ratio, but quantitative comparisons according to the predicted levels of LMC are yet to be made. Other studies have considered the sex ratio and mating system across congeneric species which experience different levels of LMC due to ecological factors, such as differences in within-host mating or male dispersal ability (Suzuki & Hiehata, 1985; Kazmer & Luck, 1991; King & Skinner, 1991; B. Stille & E. D. Parker, unpublished). A study by Stille and Parker (on gall wasps; despite being herbivores, they share much biology with parasitoids) used both allozyme techniques and morphological differences to test the relationship between the sex ratio and the degree of LMC. Suzuki and Hiehata (1985) assessed mating systems by direct observations, Kazmer and Luck (1991) used genetic (allozyme) techniques (Sects. 5.3.3 and 5.4.4), and King and Skinner (1991) used interspecific differences in wing morphology of Nasonia

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vitripennis and N. giraulti to infer male dispersal ability. Differences in estimated levels of nonlocal mating and sex ratio were in qualitative agreement with theoretical predictions in three studies, but King and Skinner (1991) found differences in the opposite direction (the species in which males are flightless had less biased sex ratios than the species with winged males). The interpretation of all four studies suffers from complications (discussed in the original papers, and by Hardy, 1994a) and from the low statistical power obtained from comparing only two species (i.e., the high likelihood of interspecific correlations between sex ratio and mating structure occurring by chance). However, an advantage of the aforementioned studies is that they were specifically designed to be comparative studies of mating systems. Some larger-scale comparative studies (see below) have been forced to use data from many sources, which often were not gathered with respect to research on mating systems. Several recent studies have compared more than two species, thus improving statistical power, and have also employed phylogeny-based analyses (Sect. 5.3.4). Drapeau (1999) and Drapeau and Werren (1999) compared mating behaviour and sex ratio in 17 strains of three species of Nasonia: vitripennis, longicornis and giraulti. They documented that within-host mating occurs in all three species, with the mean percentages of females mating within the host being 1.0%, 9.1% and 64.4% respectively. Higher levels of within-host mating are very likely to be correlated with higher levels of local mate competition, and thus more female-biased sex ratios. Drapeau and Werren (1999) examined the sex ratios produced by single foundresses and found that N. giraulti had more biased sex ratios than N. longicornis and those of N. vitripennis were even less biased. More direct examination of the mating system thus accounted for the apparently anomalous result (King & Skinner, 1991, see above) that the species whose males had the longest wings (suggestive of a greater degree of male dispersal capability and thus less intense local mate competition) had the more female-biased sex ratio. Further comparative studies of these three

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species (Leonard & Boake, 2006, 2008) plus the relatively recently described Nasonia oneida (Trienens et al., 2021) have examined patterns of within-host mating, WHM, male behaviour and re-mating by females. For instance, inter-male aggression in defence of the emergence site of females correlates with their opportunities to mate with emerging females: in N. giraulti, females almost always mated within the host and males were not aggressive and dispersed from the emergence site; in N. vitripennis females almost always emerged unmated and males were highly aggressive and did not disperse; and N. longicornis was intermediate for both characteristics (Leonard & Boake, 2006). Exit holes from the host are usually chewed by the males in these species and whether these are chewed before or after mating determines the natural frequency of WHM. The artificial creation of exit holes reduced WHM in N. giraulti (Trienens et al., 2021). Hardy and Mayhew (1998a) extended Griffiths and Godfray’s (1988) comparative study of the relationship between sex ratio and clutch size in bethylids to include more species and further consideration of bethylid mating systems. Based on the general biology of the Bethylidae, which indicates that most offspring develop in singlefoundress broods and that mating is predominantly local, Griffiths and Godfray (1988) made a working assumption of strict local mating. Using cross-species comparisons, they tested the prediction that average sex ratio will equal the reciprocal of average clutch size (i.e., one male per clutch, based on the assumption that one male adequately mates all of his sisters). Sex ratio and clutch size data were obtained from the published literature. Griffiths and Godfray (1988) found qualitative support for the prediction but treated species data as statistically independent (with the justification that sex ratio is likely to be an evolutionarily labile character in bethylids; indeed there is much evidence for within-species adaptive variation). Although phylogenetic constraints upon sex allocation decisions are unlikely, confounding correlations with an unknown variable may have generated spurious significance (Ridley, 1989).

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While local mating almost certainly predominates in the Bethylidae, males and unmated females in some species disperse and some nonlocal mating seems likely (Hardy & Mayhew, 1998a). Hardy and Mayhew (1998a) reasoned that as bethylid offspring groups are usually produced by single foundresses (due to aggressive host and post-oviposition brood defence; although see Abdi et al., 2020; Gardner & Hardy, 2020), the influence of non-local mating on sex ratio should be marked (Nunney & Luck, 1988; Fig. 5.3). and potentially detectable in addition to the relationship with clutch size (Fig. 5.8). In the absence of direct evidence, sexual dimorphism in body size was used as an estimator of male dispersal ability relative to females (which must disperse from the natal site) as this potentially correlates with non-local mating. Dimorphism estimates were obtained by measuring specimens from field collections, cultures and, mainly, entomological museums. As predicted, sex ratios across species were less female biased in species with larger males (large relative to expected male size across bethylids; males were usually smaller or similar in size to conspecific females). This result was due mostly to differences between two bethylid subfamilies: Epyrinae have larger males and less female-biased sex ratios, and Bethylinae have smaller males and more female-biased sex ratios. While the species values are consistent with the adaptive hypothesis, differences between the two subfamilies not considered may also account for the cross-species trend. To carry out a phylogeny-based analysis (Sect. 5.3.4), an estimate of phylogeny was obtained from the taxonomic literature (Fig. 5.8). This was poorly resolved within genera and consequently few independent contrasts were obtained (Fig. 5.8). Analysis of contrasts found no significant relationship between sex ratio and dimorphism. Furthermore, there was no significant relationship with clutch size in the most restrictive analysis, although it was confirmed in a phylogenetic analysis using data from further species. These results are probably attributable to the low statistical power obtained from a poorly resolved phylogeny. The relationships between sexual dimorphism, non-local mating, and sex ratio are

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tantalisingly, but inconclusively, suggested. These comparative studies have the potential to be revisited and improved by the use of newer, and likely better, estimates of the phylogenetic relationships between the species included and addition of data from more species (i.e., Carr et al., 2010). Further, some species that were included in both studies, and that had some of the largest brood sizes and most female-biased sex ratios, would likely best be excluded: species in the genus Sclerodermus commonly exhibit multifoundress broods and their sex ratios may be influenced by factors other than, or additional to, local mate competition (Tang et al., 2014; Kapranas et al., 2016; Iritani et al., 2021; Guo et al., 2022, 2023; Malabusini et al., 2022; Lehtonen et al., 2023). A similar approach was taken in a field study testing the influences of foundress number and non-local mating on the sex ratios of nonpollinating fig wasps (West & Herre, 1998a; see also Hardy & Mayhew, 1998b; Fellowes et al., 1999; Mayhew & Pen, 2002). Many nonpollinating species are parasitoids of pollinating fig wasps. Unlike pollinator wasps (Sect. 5.4.2), female non-pollinating fig wasps lay eggs into a number of different figs from the outside. Consequently the number of foundresses contributing offspring to a particular fig is much less apparent than in pollinator species. West and Herre (1998a) predicted foundress number using a model considering three possible distributions (even, random and aggregated) of foundresses foraging for oviposition opportunities among figs. For all three scenarios, the proportion of figs in which a wasp species occurs is positively related to the average number of females laying eggs into each fig. They then used this proportion as an indirect estimate of foundress number in a test of the relationship between foundress number and sex ratio across 17 Panamanian species. Field samples found a positive relationship as predicted and as already found among pollinator species (Sect. 5.4.2). However, males in 10 species are wingless (as in pollinators) while in the other 7 males have wings. Both types mate within the fig, but winged males disperse concurrently with the females, presumably to forage

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Fig. 5.8 A comparative investigation of mating structure and sex ratio in bethylid wasps. Treating species as statistically independent samples (i.e., ignoring phylogeny constraints) found a negative relationship between sex ratio (proportion of offspring that were males) and clutch size (upper left panel) and that degree of sexual dimorphism (used as a proxy for male dispersal ability and thus an indirect estimate of mating structure) was also related to sex ratio, suggesting that non-local mating influences sex ratio optima. Since cross-species analyses may be flawed, the analysis was repeated using the phylogenybased method of independent contrasts (Sect. 5.4.4) generated using species data in conjunction with an

estimate of bethylid phylogeny (upper right panel, the letters at the nodes refer to the contrasts plotted in the lower panels). Because the phylogeny estimate is poorly resolved, the number of independent contrasts for analysis is much reduced compared to the species values data. Phylogeny-based analysis confirmed the relationship between sex ratio and clutch size (lower left panel). The effect of sexual dimorphism was no longer significant, although sex ratio contrasts were generally higher when males were relatively larger (lower right panel). Source Hardy and Mayhew (1998a), in which full analytical details are given. Reproduced by permission of Springer Verlag

for non-local mating opportunities (however, see Greeff & Ferguson, 1999; Bean & Cook, 2001, for evidence of dispersal of wingless male nonpollinating fig wasps, and Greeff et al., 2003, for pollinators). For a given estimated foundress number, the sex ratios of species with winged males were less female biased than those of wingless-male species, a result consistent with

predictions of theory considering non-local mating (Nunney & Luck, 1988; Fig. 5.3). West and Herre’s (1998a) cross-species analyses were supplemented with phylogeny-based methods (Sect. 5.3.4). The phylogeny of the species is partially known from molecular studies (Herre et al., 1996) but incomplete resolution resulted in only 9 contrasts. The effect of

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foundress number survived the loss in statistical power but no significant effect of male morphology was shown. As with the Bethylidae (Hardy & Mayhew, 1998a), this difference makes interpreting the data difficult because the cross-species result could be due to other differences between taxa that happen also to differ in male morphology, and reduced power from unresolved phylogenies emerges as a common problem. In a closely related study, Fellowes et al. (1999) examined sex ratios of 44 species of Old World non-pollinating fig wasps, which included eight species in which all males are winged, 32 in which all males are wingless, and four species in which males are dimorphic. The mean sex ratio of wingless-male species was lower than the mean sex ratio of male dimorphic species, which was in turn lower than that of the winged-male species (Fig. 5.9). The result that sex ratio is positively related to ‘wingedness’ was also found

Fig. 5.9 Mean sex ratios (± standard error) of 44 species of Old World non-pollinating fig wasps. In 32 species all males are wingless and unable to disperse from the natal fig, so mating is strictly local. In 8 species all males are winged and non-local mating is likely to be the norm. In 4 species both wingless and winged males are found, and a mixture of local and non-local mating is expected. In accordance with theoretical predictions, sex ratios are positively related to the expected degree of non-local mating. Source Fellowes et al. (1999). Reproduced by permission of Springer Verlag

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using phylogenetically based analysis, despite an incompletely resolved estimate of phylogeny (Fellowes et al., 1999, see also Hardy & Mayhew, 1999). The aforementioned studies were aimed primarily at understanding relationships between sex ratio and mating system, and employed sexual dimorphism as an indirect estimator of non-local mating. Related comparative investigations have focused more directly on sexual dimorphism itself and found relationships between wing dimorphism and the probable mating opportunities of winged and wingless males in fig wasps (Hamilton, 1979; Cook et al., 1997). Female fig wasps have wings. In pollinator species all males are wingless. All males of a given non-pollinator species may have wings or be wingless (as in West & Herre’s, 1998a, study, see above). Some non-pollinator species have both types of males (Hamilton, 1979; Cook et al., 1997). With few known exceptions, winged males mate outside the fig and wingless males mate inside (Cook et al., 1997; West & Herre, 1998a, provide evidence for winged males mating within figs; Greeff & Ferguson, 1999; Greeff et al., 2003, provide evidence of dispersal of wingless males). Since wings make movement inside the fig awkward and their manufacture requires resources, winglessness is thought to represent an adaptation to within-fig mating. In species with small broods (here ‘brood size’ describes the number of conspecifics developing in a fig, but these may be the progeny of a number of mothers), most females develop without any conspecific males sharing their natal fig and thus most mating must be non-local. Among these, species with only winged males predominate. Conversely, winged males are rare in species with larger broods: males are usually present in the natal fig and thus within-fig mating is more common. In male dimorphic (parasitic) species, winged males are more common in species with larger proportions of females developing in broods without males (Cook et al., 1997). Hamilton (1979) developed a model to explore more formally the observed relationship between male dimorphism and mating

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opportunities within each species. Assuming that each member of a brood is the progeny of a different mother and that females mate with either wingless males (before dispersal) or winged males (after dispersal), the model predicts that if the male morphs (alternative tactics, Sect. 5.3.1) have equal fitness the proportion of winged males should be equal to the proportion of females which develop in figs without wingless males. As the assumption that each offspring in a fig is laid by a different mother is likely to be incorrect in the vast majority of cases, Greeff (1995, 1998) extended the model to incorporate multiple-egg oviposition. When a female lays several eggs into a fig, her wingless sons may compete with each other for mating opportunities (Hamilton, 1967), while winged sons do not. Thus, individual wingless sons generate less fitness for mothers than do winged sons, and Greeff (1995) consequently predicted that the proportion of winged males may exceed the proportion of females developing in broods without wingless males. In eight out of nine male dimorphic species examined by Cook et al. (1997) the proportion of winged males equalled or exceeded the proportion of females developing in figs without wingless males. It is thus likely that the ratios of winged and wingless male morphs in these species have indeed been selected by the relative mating opportunities of the different morphs. The link between male wing polymorphism and mating systems has also been considered by Yashiro et al. (2012) across three recently reported species of Trichogramma. The solitary species, T. kurosae, produces a greater proportion of winged males than the two quasigregarious species (T. tajimaense and T. semblidis), which do not need to disperse to secure matings. These data are yet to be considered quantitatively, in the light of Hamilton’s (1979) model, nor has the sex ratio (which is expected to be more female biased in the quasi-gregarious species with fewer winged males, e.g., Nunney & Luck, 1988) been formally assayed. Finally, Eggleton (1991) used a morphometric analysis to predict the type of mating system found in the Rhyssini, a monophyletic tribe of

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ichneumonid wasps. He measured the shape of the male gaster of specimens from nine species of Rhyssini that have been shown to exhibit one of three mating systems: males aggregate around emergence sites of females, males guard female emergence sites, and males gather around a recently emerged female. Males mate with females before female emergence in the first two systems. Eggleton (1991) found that the male gaster showed an allometric change in shape with an increase in body size in species exhibiting a mating system where males competed for mates before females emerged; no correlation was found with the other two mating systems. Eggleton (1991) argued that morphometric analyses can be used to predict the mating systems of species where no direct observations are currently available. How applicable this technique is to other taxa remains to be seen.

5.4.7 Female Receptivity and Male Ability Females of haplodiploid species do not have to mate to be able to reproduce, but virgin, as well as sperm-depleted females, are constrained to produce only male offspring. Selection may also favour females that mate multiply. Multiple mating will almost certainly be favoured in males, but males may be constrained by limited mating ability. In this section we examine these issues in relation to mating systems. Constrained oviposition Whether constrained oviposition is advantageous, neutral, or disadvantageous depends on both the mating system and the sex ratios produced by other females (Godfray, 1990, 1994; Hardy & Godfray, 1990; Godfray & Hardy, 1993; Heimpel, 1994; Godfray & Cook, 1997; Hardy & Maalouf, 2017; Lehtonen & Schwanz, 2018). In panmictic populations with unbiased sex ratios, male and female offspring are of equal value and fitness costs to constrained reproduction are thus expected to be negligible. Virgin reproduction might even be advantageous if there are energetic, time or mortality costs associated

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with mating, or with the production of female offspring (Nishimura, 1997; Rautiala et al., 2018; see also Chevrier & Bressac, 2002). However, if constrained oviposition becomes common, the population sex ratio may become male biased and male offspring will have less fitness than daughters due to frequency-dependent selection (Fisher, 1930; Bull & Charnov, 1988; Gardner, 2023). This may select for increased mating by eclosing females and/or for mated females to produce a higher proportion of daughters among their progeny (Godfray, 1990). Further, females might monitor the time between eclosion and mating and use this as an estimate of the level of virgin reproduction in the population. If mating status does not affect oviposition rate, an assumption that has been met in at least some species (e.g., Ode et al., 1997; Boulton & Shuker, 2015), virgin reproduction becomes more common as the time between eclosion and mating increases and females, once mated, are predicted to produce more female-biased progeny sex ratios. The most thorough examination of the role of constrained oviposition in parasitoid mating systems has been a series of laboratory and field studies of the stored products pest natural enemy, Habrobracon hebetor (Guertin et al., 1996; Ode et al., 1997). As noted earlier, individual H. hebetor females produce female-biased sex ratios, yet several mechanisms reduce the likelihood of inbreeding (Ode et al., 1995; Antolin et al., 2003; Garba et al., 2019). Field studies following two populations over three years were conducted to examine whether the proportion of constrained (virgin or sperm depleted) females was consistently high enough to select for female-biased sex allocation and if mated (unconstrained) females were able to track fluctuations in the proportion of constrained females in the population (Ode et al., 1997). Males and females were counted weekly at eight locations at each field site. Females were brought into the laboratory where they were given several hosts each day until they died. Examination of the progeny allowed determination of whether females had stores of sperm at the time of collection as well as what sex allocation decisions they were making. At any given time, 20–30% of

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the females collected from the field were found to be constrained (either sperm depleted or virgin). Guertin et al. (1996) suggested that newly emerged females may experience a trade-off between remaining on the surface of the grain and finding a mate or going beneath the grain surface in search of hosts. This may explain the consistently high levels of virginity found in field populations. For each site and sample date, the observed proportion of constrained females was used to calculate a predicted sex ratio value (see Godfray, 1990) that was compared to the observed sex ratio decisions made by mated females from the collection date/site. While the proportion of constrained females appeared to explain much of the observed female bias in individual sex allocation decisions, mated females did not appear to be able to track fluctuations in the proportion of constrained females over the course of the season (Ode et al., 1997). Likewise, mated females held overnight with either virgin or mated females produced similar sex ratios, suggesting that females were not able to track levels of constrained oviposition and instead may have been responding to a long-term average. The relationship between the time between eclosion and mating and the sex ratio has been tested in a laboratory study using Aphelinus asychis (Hymenoptera: Aphelinidae), a solitary parasitoid of aphids, in which mating is probably panmictic and the age of females at mating varies (Fauvergue et al., 1995, 1998). Virgin females were presented with mating opportunities at 1, 8 or 15 days after emergence, or were denied mating opportunities altogether (laboratory longevity is about 38 days). All females were provided with 100 hosts each day until death, and their progeny reared out and sexed. In accordance with the assumption that mating status does not affect oviposition rate, age at mating did not affect fecundity. As predicted, post-mating progeny sex ratio became more female biased with increasing age at mating (Fauvergue et al., 1998; Fig. 5.10). Similar patterns are reported in other parasitoids (Hoelscher & Vinson, 1971; Rotary & Gerling, 1973) but the interpretation of these results is hampered by uncontrolled variables (discussed in Fauvergue et al., 1998).

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Fig. 5.10 Progeny sex ratio (mean proportion males ± standard error, with sample sizes) as a function of time from emergence (age in weeks) of female Aphelinus asychis mated at one day old (filled circles), at 8 days (squares) and at 15 days (triangles). Generally, mated females produce lower sex ratios when mated at an older age. Source Fauvergue et al. (1998). Reproduced by permission of Birkhäuser Verlag AG

When populations experience high levels of local mate competition (LMC; Hamilton, 1967, Sects. 1.11.2 and 5.4.2), optimal progeny sex ratios are female biased, virginity is thus expected to be extremely disadvantageous to individual females and mated females are predicted to make no, or very minor, adjustments to their progeny sex ratios in response to virgin reproduction in the population (Godfray, 1990). Godfray and Hardy (1993; see also Hardy & Godfray, 1990) explored the prevalence of virginity in relation to mating system across 24 parasitoid wasp species, using gregarious development as an estimator of local mating (gregarious species are generally expected to experience LMC while solitary species generally will not). Contrary to expectation, virginity was more prevalent among gregarious species (these comparisons were not phylogeny based, Sect. 5.3.4; and there are examples of gregarious species that do not experience LMC). However, it was implicitly assumed that developmental mortality was absent. Under LMC, developmental mortality can lead to considerable levels of virginity

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in gregarious species, because if no males survive to maturity the resulting brood of females will be virgins. Extra ‘insurance’ males can be produced, but at the cost of daughters (Green et al., 1982; Heimpel, 1994; Nagelkerke & Hardy, 1994; West et al., 1997; Hardy et al., 1998; see also Khidr et al., 2013; Martel et al., 2016). Under optimal sex allocation, high levels of virginity are expected when clutch sizes are small and developmental mortality is common (Heimpel, 1994; West et al., 1997). These predictions are generally supported by tests within gregarious parasitoids with a range of clutch sizes and developmental mortality rates (Morgan & Cook, 1994; Hardy & Cook, 1995; Hardy et al., 1999), but comparative analyses of the correlation between mortality and virginity using the mean values from these species gives equivocal results (dependent upon whether crossspecies or phylogenetically based methods are used, Sect. 5.3.4; Hardy et al., 1998). Virginity levels across 53 species of fig wasps are negatively correlated with brood size, using both cross-species and phylogeny-based comparative methods (West et al., 1997). Female sperm depletion and polyandry Sperm-depleted females have mated but have used up sperm stored in their spermathecae. Like virgin females, they are unable to fertilise their eggs and are thus constrained to produce only male offspring. Depending on the mating system, this may or may not be disadvantageous (Godfray, 1990; Sect. 5.4.5; see also Cook & Crozier, 1995, for the influence of sex determination mechanisms on selection for polyandry). When constrained oviposition is disadvantageous, sperm-depleted females are expected to re-mate (Leatemia et al., 1995), and females with stored sperm may also mate again to reduce the probability of future sperm depletion (Chevrier & Bressac, 2002). However, although re-mating has been shown to replenish and increase sperm stores in many parasitoid species (i.e., Spalangia endius, King, 2018; Cephalonomia hyalinipennis, Pérez-Lachaud, 2010; Dinarmus basalis, Chevrier & Bressac, 2002), facultative re-mating does not appear to be widespread (Boulton et al.,

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2015; Abe, 2019). In a study of field-collected females, nearly 75% of mated Habrobracon hebetor females became sperm depleted at some point before the end of their reproductive lifespan, as evidenced by the production of only sons (Ode et al., 1997). Fewer than 5% of these females were willing to re-mate after sperm depletion. Therefore, once they become sperm depleted, females are likely to remain constrained to produce only sons for the rest of their lives. Similarly in Lariophagus distinguendus, females that mated with sperm-depleted males experienced reduced daughter production but did not re-mate (Steiner et al., 2008). A phylogeny-based comparative study, using data from 97 species of parasitoid wasps, found a significant association between polyandry (mating with multiple males) and gregarious development (Ridley, 1993b). Ridley (1993b) suggested that females of gregarious species are polyandrous to increase within-brood genetic variation among their progeny which, he argued, would decrease resource competition between siblings feeding on the same host (e.g., by resource partitioning). However, genetic diversity is also expected to promote competition as brood members have divergent evolutionary interests (Hardy, 1994b). An alternative explanation for Ridley’s (1993b) result is that females are polyandrous because the extra sperm obtained reduce the risk of subsequent sperm depletion and that sperm depletion is a greater disadvantage for gregarious species than solitary species since gregarious species often inbreed while solitary mainly outbreed (Godfray, 1994; Hardy, 1994b; Boulton et al., 2015). A key test to distinguish between these explanations would be to establish whether females that mate multiple times subsequently store more sperm than do monandrous females. Possible sperm depletion appears to select for polyandry in at least one parasitoid species (Dinarmus basalis, Chevrier & Bressac, 2002; see Fjerdingstad & Boomsma, 1998, for an ant example). Furthermore, due to inbreeding and local mate competition, sex ratio optima of gregarious species will often be female biased, and thus require more sperm (a greater proportion of eggs must be fertilised) than

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solitary species with similar fecundity. Males of gregarious species may mate many times in rapid succession and themselves become sperm depleted (Sect. 5.4.5), a further reason for gregarious polyandry if Godfray’s explanation applies. Most parasitoid biologists would probably favour Godfray’s (1990) explanation over Ridley’s (1993b), since hosts are often entirely consumed by gregarious broods and significant resource partitioning seems unlikely. Further, polyandrous females would be expected to seek genetically diverse mates if Ridley’s (1993b) explanation were correct, and this may influence the mating systems of gregarious species by encouraging outbreeding (further discussed by Hardy, 1994b). One way of distinguishing between the alternative explanations would be to re-categorise the solitary species as ‘truly solitary’ (developing singly in hosts, hosts isolated) or ‘quasigregarious’ (developing singly in hosts, hosts in clumps). Interaction of developing siblings cannot occur in quasi-gregarious species (classified as solitary by Ridley, 1993b) but in their mating systems they are likely to be similar to gregarious species: Godfray’s (1990) hypothesis predicts polyandry while Ridley’s (1993b) hypothesis predicts monandry (Hardy, 1994b). Since only two of the 68 ‘solitary’ species in Ridley’s (1993b) study are reported as polyandrous, if many of these 68 species were quasi-gregarious, the support would be for Ridley’s (1993b) hypothesis. Ridley’s (1993b) study was hampered by the generally poor knowledge of parasitoid mating behaviour and a low-resolution phylogeny. Although better supported molecular phylogenies are now available for many parasitoid families (i.e., Heraty et al., 2013), extensions of his work are likely to suffer from similar issues when compiling trait data. This again illustrates that, despite the vast knowledge of parasitoid biology in general, there is a great need for basic data on mating systems and mating behaviour. Godfray’s (1990) hypothesis could only explain the incidence of polyandry if female parasitoids do indeed become sperm depleted. There is evidence that females can be very

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Fig. 5.11 Sperm depletion of female Cephalonomia hyalinipennis in relation to the number of adult daughters produced (hosts were provided in batches of 10 hosts [4 prepupae plus 6 pupae] every 3–4 days). Data are binary (0 = female died with no evidence of sperm depletion, 1 = female was sperm depleted before death [only males emerged from a batch of hosts, and no further daughters

were produced]). Where multiple data points overlap, some are displaced from their binary positions to show sample sizes. The curve illustrates the probability of becoming sperm depleted, estimated using logistic regression. Source Pérez-Lachaud and Hardy (1999). Reproduced by permission of Elsevier Science

economical with their stored sperm, with as few as one spermatozoid released per fertilised egg in Habrobracon hebetor (H. juglandis) (Braconidae), a parasitoid of pyralid moths and Dahlbominus fuscipennis (Eulophidae), a parasitoid of sawflies (Speicher, 1936; Wilkes, 1965). However, sperm depletion of females is reported from many laboratory studies (e.g., Gordh et al., 1983; Hardy & Cook, 1995; Luft, 1996; Fauvergue et al., 1998; Pérez-Lachaud & Hardy, 1999; Chevrier & Bressac, 2002; King & Bressac, 2010; Pérez-Lachaud, 2010). For example, Pérez-Lachaud and Hardy (1999) presented female Cephalonomia hyalinipennis (Bethylidae), a parasitoid of the coffee berry borer, Hypothenemus hampei (Coleoptera: Scolytidae), with batches of hosts at regular intervals until the female died: 77% of females ran out of sperm before the end of their reproductive lives (females then produced broods containing only adult males, and no further daughters). The probability of sperm depletion was positively

correlated with the number of daughters produced (Fig. 5.11). An estimate of the minimum number of spermatozoids initially stored can be obtained by assuming that only one spermatozoid is used per fertilisation and dividing the number of daughters produced prior to sperm depletion by the probability of egg-to-adult survival (Pérez-Lachaud & Hardy, 1999). While laboratory evidence is useful, it must be borne in mind that under laboratory conditions females may be presented with an unnaturally large number of reproductive opportunities, sperm depletion may thus be overestimated, and estimates may also be affected by the re-mating opportunities presented in the laboratory. Laboratory studies will generally be more useful for assessing the numbers of spermatozoids that can be stored than for estimating the natural occurrence of sperm-depleted females. The one example of estimations of sperm depletion in the field comes from Habrobracon hebetor (Ode et al., 1997, see above for discussion).

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Laboratory studies can also shed light on how processes other than sperm depletion can contribute to the costs and benefits of polyandry in the parasitoids. For instance, direct or material benefits appear to provide fecundity and longevity benefits to parasitoid females in some species (Trichogramma evanescens, Jacob & Boivin, 2005; Nasonia vitripennis, Boulton & Shuker, 2015). Laboratory studies can be designed in a way that allow investigators to assess and/or model the ecological salience of these processes, as well as their importance in an applied context. For instance, multiple mating in some cases appears to temporarily disrupt sperm use by female parasitoids (Flanders, 1956; Anaphes nitens, Santolamazza-Carbone & Pestaña, 2010; Nasonia vitripennis, Boulton et al., 2018a, b), resulting in an overproduction of males. Under laboratory culture conditions, where females have more opportunity to re-mate, this can be detrimental to mass rearing for biological control (Fuester et al., 2003); understanding why and how this occurs can help practitioners to design mass rearing conditions which circumvent this issue (i.e., by permitting female dispersal). By varying the context in which mating occurs, investigators can also extrapolate about the ecological importance of processes such as this. For instance, Boulton et al. (2019) found that in Nasonia vitripennis overproduction of sons immediately after mating was reduced under multi-foundress (low LMC) compared to singlefoundress (high LMC) conditions. We may then expect that the costs of re-mating will vary according to the mating system (i.e., inbreeding and high LMC versus outbreeding and low LMC), resulting in different levels of female receptivity to re-mating across populations. Male sperm depletion and polygyny While selection may favour female virginity, monandry or polyandry, males will virtually always be selected to mate with many females (polygyny). A literature survey of parasitoid mating behaviour by Gordh and DeBach (1978) found that in all examined species (n = 48) males were polygynous (only 5/34 species of females were polyandrous). There have been several

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studies showing that body size has a positive influence on the number of females a male can successfully mate (e.g., Jones, 1982; King, 1987; van den Assem et al., 1989; Heinz, 1991; Godfray, 1994; Ode & Strand, 1995; Lacoume et al., 2006). Patterns of spermatogenesis in male parasitoids are variable across species: in many species spermatogenesis ceases before eclosion (‘pro-spermatogenic’ species; e.g., Gerling & Legner, 1968; Martel et al., 2008a; Martel et al., 2016), while in others it may continue during adult life (e.g., Wilkes, 1963; Laing & Caltagirone, 1969; Gerling & Rotary, 1974; Gordh & DeBach, 1976; Nadel & Luck, 1985; He & Wang, 2008; Martel et al., 2016) and, unusually, there may be a premating period during which adult males do not have sperm ready to ejaculate (Quimio & Walter, 2000). In social Hymenoptera, the testes normally degenerate upon male migration, the lifetime supply of sperm being stored in the seminal vesicles, but a few species have wingless males that continue spermatogenesis throughout life, and do not disperse and compete aggressively with other wingless males for within-nest mating opportunities (Heinze et al., 1998). Male parasitoids that mate many times in rapid succession may run out of sperm, either temporarily or permanently, and females receive no sperm or a reduced quantity (King and Schlinger & Hall, 1960; Wilkes, 1965; Laing & Caltagirone, 1969; Gordh & DeBach, 1976; Nadel & Luck, 1985; van den Assem, 1986; Dijkstra, 1986; Ramadan et al., 1991; Ode et al., 1996; Bressac, 2010; Kant et al., 2012; King, 2018). Nadel and Luck (1985) assessed the insemination capacity of male Pachycrepoideus vindemmiae (Hymenoptera: Pteromalidae), a quasigregarious pupal parasitoid of Drosophila and other flies. One-day-old males were presented with a succession of ten 18-h-old virgin females (each female singly). Females were replaced with virgins immediately after mating. It took about one hour for a male to mate with all ten females. Mated females were either offered hosts and the sexes of their progeny recorded or dissected and the contents of their spermathecae examined. The amount of sperm transferred on mating decreased

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as the number of females mated increased. In a similar experiment, males were presented with a rapid succession of mating opportunities, allowed to rest for 24 h and then presented with further virgin females. A full complement of sperm was transferred to the latter class of female, showing that males replenish their sperm supplies (spermatogenesis). The duration of the rest period was then varied: sperm replenishment following one mating was found to require about 30 min. Time between matings is thus an important influence on male insemination capacity. Nadel and Luck (1985) investigated the timing of adult emergence from batches of hosts (Sect. 5.4.2). Males usually emerged one day before females. Females tended to emerge in the mornings and the mean interval between female emergence during daytime was 125 min. In 24% of cases, females emerged less than 30 min apart, but the majority of intervals were >30 min. Thus, in nature, males are probably able to fully inseminate the majority of females that emerge in their natal patch. Similar methods have been used to examine sperm replenishment patterns in Habrobracon lineatellae (Laing & Caltagirone, 1969) and H. hebetor (Ode et al., 1996), Aphidius ervi (He & Wang, 2008) and Anisopteromalus calandrae (Abe, 2019). Ode et al. (1996) presented males with either a high encounter rate of virgin females (until males did not mate with an additional female; about twenty females per day) or a low encounter rate (one female per day). Body sizes of the males were measured (all females were of a standard size) and all mated females were given four hosts per day until death. By rearing the progeny, the timing of sperm depletion and replenishment patterns were determined, as well as the number of daughters each male had sired. Furthermore, when mating opportunities were plentiful (10 matings per day), large males were able to deliver sperm to more females than small males, both because they were able to mate with more females and because they were able to deliver more sperm to the females that they did mate. When mating opportunities were rare (1 per day), male size had no effect on the rate of sperm depletion (Ode et al., 1996).

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Rather than presenting males with virgin females individually (Nadel & Luck, 1985; Ode et al., 1996), females can be presented simultaneously, as was done by Dijkstra (1986) to assess the insemination capacity of Colpoclypeus florus. Individual males, of known weight, were presented with five females simultaneously for four hours, then five more virgin females were added, then after a further four hours five more females were added. Males were in contact with 5 rising to 15 females for a total of 45 h. The number, timing and duration of female presentation was chosen to reflect the emergence patterns and sex ratios that this species would naturally exhibit. In a further experiment, males were presented with females as above, but males and females were only kept together for a total of 21 h. Males in both experiments were offered another batch of five females after the main experiment to determine whether their sperm stores were replenished. Insemination was assessed by examining the contents of the females’ spermathecae. The proportion of females inseminated did not depend on whether males were in contact with females for 21 or 45 h. Females that mated with a small male or a male that had mated many times were less likely to receive a full complement of sperm, suggesting that males, particularly small males, do become sperm depleted. Dijkstra (1986) concluded that, under normal circumstances, a single male is sufficient to inseminate fifteen or more females. Limited male insemination capacity is unlikely to account for the observation that an increasing number of male eggs is laid in larger clutches; the high developmental mortality in C. florus broods is a more likely explanation (Hardy et al., 1998). Ramadan et al. (1991) used two methods to study male mating ability in four species of Opiinae (Braconidae), solitary parasitoids of tephritid fruit-fly larvae. In the first method, which was similar to Nadel and Luck’s (1985) and Dijkstra’s (1986), individual males were presented with ten virgin females for 24 h and then the females were dissected for spermathecal examination. Replicates were carried out using males of different ages and sizes. Male ability peaked at around four days and, in two species,

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larger males were able to inseminate more females than smaller males. In the second method, 200 males and 200 females were placed together in cages (the studied species are solitary and may mate in swarms as well as immediately after eclosion). Every 24 h, fifteen females from each cage were dissected (fifteen males were also removed and discarded to maintain an even sex ratio). The experiment was stopped when all fifteen females in a sample were inseminated. Most females mated during the first 24 h (but some did not mate until more than 7 days old) and were observed to be polyandrous. Some researchers have thus assessed male capacity at maximum rates of presentation of virgin females and then related this to the natural timing of female emergence (Nadel & Luck, 1985) while others first assessed emergence and then presented virgin females so as to mimic these patterns (Dijkstra, 1986). Note that these approaches are not applicable to species such as Habrobracon hebetor (Ode et al., 1996), where mating occurs at sites other than the site of emergence. An alternative, though closely related, approach was used by Hardy et al., (1999, 2000) and Loch and Walter (2002): eclosion and dispersal in broods of Goniozus nephantidis was monitored and dispersed females were provided with hosts to assess whether they were mated (Sect. 5.4.2). Brood size varied naturally and many of the broods contained only one male. Virtually all females were inseminated (Fig. 5.6). This method thus estimates the capacity of males to inseminate females as individuals mature and disperse naturally. No decisions about the timing and duration of presentation, or the number and age of individuals used, need be made by the investigators. Such decisions may influence the results if, for example, females have short periods of receptivity (Sect. 4.3.6) or if male mating ability is age dependent during the first few days of adult life (Hirose et al., 1988; Ramadan et al., 1991). However, the efficiency of this method is reduced by natural variation that may make some replicates less valuable (e.g., broods from which two males eclose), and important factors such as brood size (number of virgin females per male) are not under direct experimental control. A similar approach

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was taken by West et al. (1998), who examined insemination capacity in five species of fig wasp. Females that had emerged naturally in figs containing only one male were dissected. The maximum number of inseminated females in a fig was much greater than the mean number of females per fig. Males were also dissected to check whether their seminal vesicles contained sperm: none had empty vesicles. It is thus unlikely that males of these species become sperm depleted. Male and female mate choice Given the fitness consequences of mating with a sperm-depleted male, and the relative rarity of polyandry (Ridley, 1993b; Boulton et al., 2015), it is unsurprising that in several species females preferentially mate with males with sufficient sperm stores (He & Wang, 2008; Ruther et al., 2009; Wittman & King, 2019). These cues may be indirect and may not necessarily reflect active female choice (i.e., younger or larger males have more sperm and are more successful in securing matings), but in Nasonia vitripennis virgin females exhibit a preference for males which excreted greater amount of a pheromone (Ruther et al., 2009). In this species, small males and sperm-depleted males produce less pheromone and are less attractive to females (Ruther et al., 2009; Blaul & Ruther, 2012). Low levels of polyandry across the parasitoids can also render male mate choice favourable because it reduces wasted effort courting unreceptive females. Moreover, sperm precedence is often biased towards first males when polyandry does occur in parasitoids (Holmes, 1974; Damiens & Boivin, 2006; Martel et al., 2008a), and so investing limited sperm stores in already mated females is likely to be costly. Mate choice by males for virgin females and a male aversion to mated females (or their pheromones) has been demonstrated in several parasitoid species (Aphelinus asychis, Fauvergue et al., 1995; Spalangia endius, King et al., 2005, Fischer & King, 2012, Trichogramma euproctidis, Martel et al., 2008b), likely as a result of these investment costs. The parasitic Hymenoptera can contribute much to understanding of the evolution of mate choice under systems of inbreeding, both in

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terms of discriminating sperm levels or kinship (i.e., Wittman et al., 2016; Wittman & King, 2019; Collet et al., 2020) as well as understanding the mechanisms underlying choice (Ruther et al., 2009; Blaul & Ruther, 2012; Xu et al., 2019). The methods used to study mate choice typically involve no-choice or choice laboratory-based trials. Selecting the appropriate methodology is important as preferences are typically reduced under ‘no-choice trials’ (in which only one stimulus is presented and the response, or the speed of the response, to it is recorded) compared to ‘choice trials’ in which two or more mate stimuli are presented (Dougherty & Shuker, 2015; Dougherty, 2020). Although choice tests are more likely to show whether individuals can discriminate between different mates and show a preference, it is important that ecological relevance is considered during study design. For instance, a no-choice scenario might be ecologically relevant for a solitary species, but not a gregarious or quasigregarious one. Wittman and King (2019) used a series of two-choice tests in the quasi-gregarious pteromalid Urolepis rufipes. In three separate experiments they presented females with males that were either (1) once mated or multiply mated, (2) one day old or 10 days old (and virgin) or (3) infected or uninfected with a pathogenic bacterium (and virgin). They used a Petri dish divided with a silicone insert as a choice arena and tested female choice, as opposed to male propensity to mate, by allowing each test male to mark one side of the arena. The males and the silicone insert were then removed and a female was introduced into the arena and the time that she spent in each side of the arena was recorded. They found that females spent more time in the side of the arena marked by oncemated males, compared to those which had mated multiply (and so were less likely to be sperm depleted). They also found that this preference was advantageous in terms of increasing daughter production. Female U. rufipes also preferred young males to old males, although this did not provide any fitness benefit when males were virgin; it may be that male age is an additional proxy for likely sperm depletion. Finally,

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females preferred males that were uninfected, but only when the choice was between males that were uninfected or infected with a low dose of bacteria (which killed only 10% of infected males). When stimulus males were infected with a high dose (which killed 50%), females switched their preference to infected males. The authors suggest that this is because at high infection levels males may increase their investment in reproduction if death is imminent, whereas low-dose males invest in immune function to overcome the infection. This may mean that pheromonal marks produced by highly infected males are more potent than those produced by uninfected males, or of males that were exposed to only low doses of bacteria. Techniques such as those used by Ruther et al. (2009), Blaul and Ruther (2011, 2012) and Xu et al. (2019) would no doubt elucidate the mechanisms underlying these patterns of choice. These studies employed dichloromethane extractions, gas chromatography‒mass spectrometry (GC-MS) and gas chromatography preparative fraction collect (GC-PFC) to identify and measure volatile compounds produced by Nasonia vitripennis (Ruther et al., 2009) and Cotesia glomerata (Xu et al., 2019). Responses to these volatile cues were then measured using Y-tube or four-arm olfactometers. Different volatile stimuli are allowed to diffuse into different arms of the olfactometer, an individual is released into the apparatus and their preference recorded based on their movement towards the stimuli. An additional method employed by Xu et al. (2019) was gas chromatography electro-antennographic detection (GC-EAD), whereby the electrical output produced by an antenna is recorded in response to different olfactory stimuli. Studying the composition and volume of volatiles from signalling individuals will provide information not only on the mechanism of choice but may shed light on how choice has evolved and to what extent it represents sexual conflict (i.e., whether males mark females with antiaphrodisiac pheromones to repel competitor males, or if female pheromonal composition changes after mating such that they are no longer attractive to males).

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5.5

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Predator (and Other NonParasitoid) Mating Systems

5.5.1 Introduction The study of non-parasitoid insect mating systems has largely adopted the non-genetic, individual-based approach to defining and understanding an insect’s mating system (Sect. 5.3.1). As outlined earlier, there are four basic types of mating system: monogamy, polygyny, polyandry and polygamy (Alcock, 1998). Monogamy is a relatively rare mating system requiring special ecological constraints. One of the best examples comes from nonpredators: there are termite species in which winged sexuals establish a new colony after their nuptial flight. The colony is founded by a single male and female, both of whom dig a new nest after removing their wings. The opportunity for further nuptial flights is therefore lost, and monogamy ensues in these primary reproductives: the male and female invest huge amounts of resource into their gonads and soon become immobile, reinforcing the reliance on their subterranean partner (Wilson, 1974). Polyandry is widespread in the insects and direct or material benefits that increase female longevity and fecundity are common (Arnqvist & Nilsson, 2000). Perhaps the most interesting examples come from social insects (e.g., Wilson, 1971) where monogamy is ancestral to taxa that have evolved obligate eusociality and currently observed polyandry is a derived state (Hughes et al., 2008a; Boomsma, 2009, 2013; Quiñones & Pen, 2017; Boomsma & Gawne, 2018; Smith et al., 2018). Newly emerged honeybee queens restrict their nuptial flights to a few days after emergence when they rendezvous with the drones from other colonies in mating swarms that aggregate high above landmarks. Females need to mate several times in order to receive enough sperm to generate a long-lived, viable colony. The combination of queen rarity at mating swarms (i.e., the OSR is highly male biased) and the need for multiple mating has led to the evolution of the peculiar copulatory mechanism in

this species. Drones autotomise their genitalia once they have inserted their intromittent organ and released semen into the queen. This means that drones only get one opportunity at mating (they die soon afterwards) whilst females can mate with several males during their relatively short visits to mating swarms (Wilson, 1971). Since males only mate once, and females mate multiply, this is a polyandrous mating system. Perhaps the best example of insect polygyny occurs in a predator, a protandrous eumenid wasp whose males defend clusters of female brood (Smith & Alcock, 1980). Males defend their cluster vigorously, and often violently, because it offers them the opportunity to mate with several females without incurring large search costs. This type of polygynous mating system (termed ‘female defence polygyny’) is associated with female monogamy, the ability of females to mate soon after emergence and predictable clustering of receptive females. Polygamy is, by and large, typical in insect mating systems (Thornhill & Alcock, 1982), with males and females mating multiply throughout their lives (and thereby providing the preconditions for sperm competition, Sect. 4.5.2). There is an ever-expanding list of variants on the polygamous theme. For example, when fertilisable females are plentiful and predictable in space and time, and are defendable, we usually observe a mating system where males defend a resource that is required by females and exchange access to it for copulations. A good example of this type of mating system comes from the predatory yellow dung-fly (Scathophaga stercoraria), where males defend spatially restricted oviposition sites (fresh dung) which females require to lay eggs. Males fight for areas of the dung pat that maximise their opportunity to encounter sexually receptive, gravid females as they arrive to oviposit (Fig. 5.12). This polygamous mating system is termed resource defence polygamy and is relatively common (see below). Demonstrating resource defence polygamy first requires careful observations of the mating system, which should include measures of male size and mating success, as well as

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Fig. 5.12 A pair of yellow dung-flies (Scathophaga stercoraria) copulate on a fresh dung pat. Males which are able to displace rivals from the areas around the fresh dung pat that maximise the probability of encountering a receptive, gravid female achieve relatively high reproductive success. By mounting, and hence guarding,

females when they arrive, and copulating directly before these guarded females oviposit onto the dung, these males ensure they fertilise most of the eggs the female lays. The dung-fly mating system can be broadly described as ‘resource defence polygamy’. Photograph M. Siva-Jothy

measures of those aspects of the putative resource that the observer feels are related to the mating success of the resource-holding male. In the majority of cases, defended resources are usually a spatially and/or temporally restricted food and/or oviposition resource, so measures of size and age are usually relevant. Once correlations have been established between resource variables and mating success it is a relatively straightforward matter to manipulate the key resource variable and predict mating outcomes. This general approach has been used to identify quite obscure resource variables that play a central role in determining mating outcomes (e.g., Gibbons & Pain, 1992; Siva-Jothy et al., 1995). The non-genetic, individual-based approach adopted by students of non-parasitic insect

mating systems considers three major selective forces that underpin mating system evolution. First, and perhaps ultimately, ecological factors impose constraints upon the availability of receptive females. Second, the degree to which males compete for access to females as a consequence of female availability and third, the extent to which female behaviour (choosiness) influences male reproductive success. The first of these forces is a sufficiently intuitive, large, and well-reviewed area that we will not consider further here (Thornhill & Alcock, 1982; Kokko et al., 2014, provide reviews). We will, however, briefly consider the evolutionary forces that result in male and female traits (and the conflict that sometimes arises between males and females) that subsequently refine an insect species’ mating system.

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5.5.2 Competition for Females The most overt manifestation of Bateman’s principle (Sect. 5.2) is the enormous range of male sexual traits devoted to securing matings and ensuring fertilisation. In the first instance this phenomenal array of male traits is manifest as ‘attractiveness’ traits such as acoustic (BennetClarke, 1970), visual (Lloyd, 1966) and/or chemical signals (Breed et al., 1980). However, when ecological factors dictate, males supplement, or forego, signalling by actively searching out females (Smith & Alcock, 1980). In more extreme instances, males are also selected to wait in the vicinity of resources that are themselves attractive to females. In general these resources tend to be feeding sites (Severinghaus et al., 1981), oviposition sites (Siva-Jothy et al., 1995), emergence sites (e.g., Gilbert, 1976) or conspicuous aspects of the habitat (landmarks) (Downes, 1970). As the OSR becomes more male biased at these sites, selection will favour the evolution of agonistic behaviour in males directed at defending the resource that attracts females and/or the females themselves (Fig. 5.12). Males that best defend the resource will be favoured since they will have a higher encounter rate with receptive females: the stage is now set for the evolution of additional morphological and behavioural traits that enhance a male’s fighting ability. However, if the OSR is too male biased the pay-offs of defence may be too low, or even negative (Alcock et al., 1978). At such times, a malebiased OSR, a mating system known as ‘scramble competition’, generally evolves: males are selected to get to a receptive female as quickly as possible and not waste time or energy in fighting (Smith & Alcock, 1980). The ecological idiosyncrasies of particular species can also favour the evolution of alternative male mating behaviours within a mating system. Such mating systems support two (sometimes more) male tactics for securing mates (Johnson, 1982). These alternative tactics can be underpinned by different alleles; they can have a genetic basis which triggers a shift from one tactic to the other as conditions dictate; or they can be condition dependent, in which case an individual is not

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obliged to express both tactics but uses the one most appropriate for its condition (reviewed by Cade, 1980). Variation within a mating system and the evolutionary conditions underpinning it have been particularly attractive to game theory modelling (e.g., Dawkins, 1980; MestertonGibbons, 1999; Neff & Svensson, 2013; Abe & Kamimura, 2015; Parker, 2020). Competition between males does not stop once a mate is secured. Because polyandry is the norm in insects, most insect mating systems generate sperm competition (Sect. 4.5.2), a selective force which favours the evolution of two suites of antagonistic male traits (Parker, 1970): one set reduces the likelihood of the female re-mating (Fig. 5.2) whilst the other increases the competitiveness of a male’s ejaculate (Simmons, 2001). Both of these traits operate to reduce levels of sperm competition and can have direct effects on the nature of mating systems. For example, if males are selected to reduce the chances of female re-mating to reduce the likelihood of future sperm competition, then the OSR will become more male biased, and the foundation may be laid for an evolutionary arms race with females (since females may benefit from re-mating). Much of the variation in mating systems is generated by selection on males to enhance their ability to secure matings and fertilisations, but it is clear from this last scenario that females also play a role in determining the characteristics of mating systems: there has been a noticeable shift towards understanding female roles in shaping mating systems, particularly in insects (Rowe et al., 1994; Arnqvist & Nilsson, 2000; Pizzari & Wedell, 2013; Boulton et al., 2018a, b).

5.5.3 Choosy Females In any mating system where females gain a net benefit from being choosy about their mating partners, males will be subjected to additional selection pressures. For example, females might be choosy because they benefit from gaining access to better nutritional (Thornhill, 1980a) or ovipositional (Siva-Jothy et al., 1995) resources or, because they obtain ‘good’ male genes

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(Andersson, 1994). These genes may be ‘good’ because they have utility via parasite resistance (Zuk, 1988; Siva-Jothy & Skarstein, 1998) or developmental stability (manifested as ‘fluctuating asymmetry’, Thornhill, 1992; Møller & Swaddle, 1997). Alternatively, ‘good genes’ may provide a benefit to the choosy female via the attractiveness they confer on her sons (Jones et al., 1998). The challenge for evolutionary ecologists is to disentangle the relative importance of direct (i.e., resource-based) and indirect (i.e., gene-based) benefits to females (Arnqvist & Nilsson, 2000; Slatyer et al., 2012). A classic example of the pivotal role of female ‘choice’ in determining the nature of a mating system comes from studies of scorpion flies (Mecoptera) (e.g., Thornhill, 1980a). Male fertilisation success is determined by the duration of copulation, which in turn is determined by the size of the prey item

Fig. 5.13 A male panorpid scorpion fly copulates with a female whilst she feeds on the cluster of dead insects he has captured, enveloped and suspended from a leaf. Males attract females by utilising pheromones. and males with larger gifts obtain higher levels of reproductive success because females prolong copulation in order to continue feeding (Thornhill, 1980b). Photograph M. SivaJothy

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that a male presents to a female (Fig. 5.13). Males that present larger nuptial gifts secure longer copulations, transfer more sperm, and transfer receptivity-reducing compounds that delay female re-mating (Thornhill, 1980b). Female-based behaviour (nuptial feeding) results in selection favouring males that can secure and defend the largest nuptial gifts. Nuptial gift giving also results in selection on polyandry, if females can acquire more resources by mating with multiple males (Arnqvist & Nilsson, 2000) and this can also depend on female condition (in many species females in poor condition re-mate at higher rates to gain greater material benefits, Toft & Albo, 2015). Insect predators are particularly useful models for studies investigating direct benefits of polyandry and mate choice as the energetic benefits of nuptial gifts combined with the high costs of hunting mean that male gift

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giving (and the evolutionary arms races that can follow from it) is particularly likely (Ghislandi et al., 2017). Further to this overt form of female-driven preference for male nuptial gifts, Eberhard (1996) proposed that females may choose males cryptically by biasing fertilisation events (i.e., the outcome of sperm competition, Sect. 5.2) in a particular male’s favour. As well as influencing mating system parameters, cryptic female choice has been suggested as operating on the ability of a male to stimulate the female during courtship, and that this is a driving force for speciation via the rapid and divergent evolution of male genitalia (Eberhard, 1985; Hosken & Stockley, 2004). The selective forces generated by sperm competition produce a number of male traits that do not always function in the female’s interest. In many cases females can control some sperm precedence patterns (Wilson et al., 1997; Snook & Hosken, 2004; Peretti & Eberhard, 2010). By examining the variation in sperm precedence patterns in the bean weevil, Callosobruchus maculatus, using identical sets of paired matings between and within female sib-groups, Wilson et al. (1997) showed that some of the variance in sperm precedence (i.e., male fertilisation success) was explained by female genotype. Females therefore appear to be wresting control of fertility away from males. But male C. maculatus have their own agenda: males deliberately damage the female’s genital tract during mating (Crudgington & Siva-Jothy, 2000). This male trait is believed to increase female reliance on her last mate’s sperm by inducing reluctance to re-mate and increasing the re-mating interval. This is a particularly good example of sexual conflict since not only is the male trait easy to document and examine, but also the female shows a behavioural response (kicking), the experimental elimination of which results in increased costs for females. Examination of anatomical traits that may be involved in determining fitness consequences of mating have been dealt with in Chap. 4 (Sect. 4.5.2). A recent example, again from C. maculatus, shows that female genital morphology and immune function have co-

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evolved with male traits that inflict damage (Dougherty et al., 2017). Another spectacular example of a reproductive conflict comes from studies of the ejaculate of Drosophila melanogaster. Male products transferred to the female in the ejaculate incapacitate rival sperm in storage in the female, and so have clear benefit to the copulating male (Harshman & Prout, 1994; but see Holman, 2009). However, they also reduce the longevity of the female (Chapman et al., 1995; but see Holman, 2009). These substances are therefore the basis of a reproductive conflict of interest between male and female D. melanogaster that has led to an evolutionary arms race as intense as in any host‒parasite system (Rice, 1996). Sexual conflict is now a well-developed field which has been studied in a wide range of taxa (Chapman et al., 2003; Pizzari & Snook, 2004; Arnqvist & Rowe, 2005; Fricke et al., 2009) and has been expanded to consider genomic as well as physical conflict between males and females (Bonduriansky & Chenoweth, 2009). In the case of physical sexual conflict, the first step is always to identify a system that is likely to be under sexual conflict. To reveal whether sexual conflict is operating, studies must start by demonstrating that a reduction in fitness for one sex is co-dependent on the behaviour or physiology of the trait that benefits the opposite sex. The mating rate is a commonly cited example of sexual conflict, as females usually have a lower optimum mating rate than males. Stutt and Siva-Jothy (2001) demonstrated this experimentally using bed bugs. They adjusted the male copulation rate to maintain maximum fertility with a minimum of copulations and showed that male-controlled copulation rates resulted in a 25% reduction in female survivorship. Similarly sexual conflict over female re-mating is ubiquitous as it reduces male fertilisation success. Male tactics such as male mate guarding are the most obvious manifestation but physiological adaptations such as ‘mating plugs’ are also commonplace (Stockley et al., 2020). In these cases, carefully designed studies are needed to assess the nature and strength of the conflict. For

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instance, mate guarding might reflect conflict if it serves only to safeguard male fertilisation success (i.e., mate guarding in absentia, Sect. 5.2) but not necessarily, as it can also benefit females (i.e., protecting them from harassment or predation during oviposition or foraging; Rowe et al., 1994; Boulton et al., 2018a, b). Other techniques that have been employed more recently involve investigating species- or population-level associations between the harmful and beneficial traits (Dougherty et al., 2017) or using experimental evolution to track evolutionary arms races (Edward et al., 2010).

5.6

Conclusions

With recent advances in evolutionary thinking, and the integrated use of recently available investigational and analytical techniques, the study of natural enemy mating systems continues to provide us with: (1) insight into the evolutionary mechanisms that generate diversity in reproductive traits, (2) an understanding of the utility of those traits, and (3) an understanding of the determinants of reproductive success and optimal reproductive decisions in natural populations. Descriptions and studies of mating systems have largely been of interest to those seeking empirical support for evolutionary theory. Via effects on sex ratio and fecundity, mating systems influence natural enemy population dynamics (Sect. 7.4.3) as such, their potential for informing pest control strategies is probably under-utilised (Hardy & Ode, 2007; Ode & Hardy, 2008). Acknowledgements We dedicate this chapter to the late Mark Jervis who commissioned the original version of this chapter (Hardy et al., 2005). For comments and assistance on that version we thank Mark Jervis, George Heimpel, Paul Rugman-Jones, Xavier Fauvergue, Dave Parker, David Nash, James Cook, Mark Fellowes, Andrew Loch and Gimme Walter. We thank Eric Wajnberg for comments on the current revision. Author contribution statement: Ian Hardy, Paul Ode and Michael Siva-Jothy wrote the original chapter and Ian Hardy, Rebecca Boulton and Paul Ode revised and updated the text for the current edition. Michael Siva-Jothy approved the updated text. Rebecca Boulton was supported by a postdoctoral talent fellowship from Wageningen Graduate School. Paul Ode was partially funded by USDA NIFA AFRI Foundational

399 Award number 2019-67013-29368. Ian Hardy and Paul Ode acknowledge support from the Israel Institute for Advanced Studies for the research group programme ‘Mathematical modeling of biological control interactions to support agriculture and conservation’.

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Populations and Communities Keith D. Sunderland, Wilf Powell, William O. C. Symondson, Simon R. Leather, Steve J. Perlman, and Paul K. Abram

6.1

Introduction

In nature, any particular terrestrial habitat contains animal, plant, fungi, and microbe species that exist together in both time and space. Many of these species will interact with each other, for example when one species feeds on another or when two species compete for the same food or other resource. A group of species having a high degree of spatial and temporal concordance, and in which member species mutually interact to a

K. D. Sunderland Department of Entomological Sciences, Horticulture Research International, Wellesbourne CV35 9EF, UK W. Powell Division of Plant and Invertebrate Ecology, Rothamsted Research, Harpenden AL5 2JQ, UK W. O. C. Symondson Cardiff School of Biosciences, Cardiff University, P.O. Box 915, Cardiff, Wales CF10 3TL, UK e-mail: [email protected] S. R. Leather Department of Agriculture and Environment, Harper Adams University, Newport TF10 8NB, Shropshire, UK S. J. Perlman Department of Biology, University of Victoria, 3800 Finnerty Rd, Victoria, British Colombia V8P 5C2, Canada e-mail: [email protected] P. K. Abram (&) Agriculture and Agri-Food Canada, Agassiz Research and Development Centre, Agassiz, British Colombia, Canada e-mail: [email protected]

greater or lesser extent, constitute a community (Askew & Shaw, 1986). The size and complexity of a community will depend upon how broadly that community is defined. For example, we could consider as a community the organisms which interact with each other within a particular area of woodland, the herbivore species which compete for a particular food plant or the complex of natural enemies associated with a particular prey or host species. Here, we are especially interested in communities of natural enemies, which are often surprisingly species rich (Carroll & Risch, 1990; Hoffmeister & Vidal, 1994; Settle et al., 1996; Sunderland et al., 1997; Memmott et al., 2000; Peralta et al., 2014; Wirta et al., 2014; Frost et al., 2016; Shameer et al., 2018). The animal species of a community obtain their food directly or indirectly from plants which are the primary producers of the community. Herbivores feed directly on plants whilst predators and parasitoids are either primary carnivores, feeding on herbivores, or secondary or tertiary carnivores, feeding on other predators or parasitoids. The successive positions in this feeding hierarchy are termed trophic levels. Thus, traditionally, green plants occupy the first trophic level, herbivores the second level, carnivores which eat herbivores the third level, secondary carnivores the fourth level, and so on, although a species may occupy more than one level. For example, some insect parasitoids are facultative hyperparasitoids, thus having the potential to occupy two trophic levels. Similarly, some carabid beetles eat both insect prey and plant

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2_6

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seeds, and because they are polyphagous predators, the insect prey consumed may consist of herbivores, detritivores or even other carnivores. In fact, since omnivory (feeding on more than one trophic level) is very common (Polis & Strong, 1996; Coll & Guershon, 2002) it is often more helpful to think in terms of food webs (trophic webs, or trophic networks) (Sect. 6.3.12) than trophic levels. Empirical studies show food webs to be much more complex than was previously thought (Williams & Martinez, 2000; Wirta et al., 2014). Many generalist predators are known to kill and consume other predators (Rosenheim et al., 1995) and parasitoids (Brodeur & Rosenheim, 2000). When the predators concerned also belong to the same guild, the interaction is termed intraguild predation (IGP) and the top predator benefits by obtaining a meal from the victim predator or parasitoid and by simultaneously reducing competition for the shared herbivore food resource (Polis et al., 1989). There is now a consensus that IGP amongst predators (Fagan et al., 1998; Janssen et al., 1998; Eubanks, 2001; Lang, 2003; Gagnon et al., 2011) and between predators and parasitoids (Sunderland et al., 1997; Colfer & Rosenheim, 2001; Meyhöfer, 2001; Meyhöfer & Klug, 2002; Snyder & Ives, 2003; Frago, 2016) is very common in agricultural systems, but also occurs in diverse natural systems (Denno et al., 2002; Woodward & Hildrew, 2002). Some parasitoid species can probably reduce the incidence of predation on their populations by avoiding habitat patches emanating olfactory cues associated with predators (Moran & Hurd, 1998; Taylor et al., 1998; Raymond et al., 2000; Frago & Godfray, 2014). A given predator or parasitoid taxon can vary in its degree of impact on herbivore prey populations, and thereby on plant yield (i.e., vary in its propensity to initiate a significant trophic cascade; Polis, 1999), even within a year in one type of crop (Snyder & Wise, 2001). The definition of trophic cascades has recently been updated and refined to “indirect species interactions that originate with predators and spread downward through food webs” (Ripple et al., 2016). This definition includes parasitoids as a

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type of predator, in line with previous efforts to unifynatural enemy roles in food webs (Raffel et al., 2008; Buck & Ripple, 2017). The incidence of trophic cascades is quite variable in natural systems, as cascades depend on a number of interacting factors, including the relative colonisation rates of herbivores, predators, and parasitoids in relation to the spatial distribution pattern of host plants (Thomas, 1989), the relative strengths of interactions in the upper trophic levels (Spiller & Schoener, 1990), and the productivity level of the system (Müller & Brodeur, 2002). These patterns are also likely to vary strongly over time (reviewed in Piovia-Scott et al., 2017). Natural enemies in the higher trophic levels can exert a positive influence not only on plant productivity, but also on plant biodiversity (Moran & Scheidler, 2002; Schmitz, 2003). Trophic cascades are very common (Morin & Lawler, 1995; Halaj & Wise, 2001; Symondson et al., 2002; Ripple et al., 2016), and occurred in 45 out of the 60 independent cases (from 41 studies) analysed by Schmitz et al. (2000). Moran and Scheidler (2002) point out that trophic cascades operate in many systems ranging from simple (e.g., arctic) to very diverse (e.g., tropical rainforests), suggesting that diversity has little effect on the strength of top-down trophic interactions. Buck and Ripple (2017) reviewed the role of parasites in trophic cascades, finding that parasitoids were the group of parasitic organisms most commonly implicated in initiating trophic cascades. Strong trophic interactions can exist in diverse systems that also encompass an abundance of weaker interactions. Available evidence suggests that intraguild predation and omnivory do not, in general, prevent generalist predators from initiating strong trophic cascades (Halaj & Wise, 2001; Costamagna et al., 2007). In addition, both direct and indirect effects of natural enemies on their prey and resulting trophic cascades can be mediated by interactions ranging along a continuum from (1) consumptive effects, due to a natural enemy consuming a host, to (2) non-consumptive or ‘trait-mediated’ effects, where a natural enemy does not kill hosts or prey but rather causes them

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to adopt costly defensive strategies (Werner & Peacor, 2003; Preisser et al., 2005; Preisser & Bolnick, 2008; Abram et al., 2019). In this chapter, we will focus on the elucidation of consumptive effects, for which most methodology, analytical techniques, and theory have been developed. Amongst the many methods described in this chapter there are likely to be at least a few that are appropriate for studying trophic interactions in any given agricultural or natural system. In the realm of food webs, theory abounds, and whereas empirical evidence was once scarce (Morin & Lawler, 1995), new methods are increasingly improving identification of processes influencing food web structure (Boyer et al., 2016; Frost et al., 2016; GonzálezChang et al., 2016). The range of methods described here will help to guide researchers in the collection of data vital for continuing to develop and test theory. Techniques useful for discovering who eats whom are outlined in Sect. 6.3. In addition, since symbiotic microbes can also be transferred from one host species to another through the links in a food web (e.g., effects of Wolbachia spp. propagating through herbivore–parasitoid food webs) (Werren et al., 2008; McLean et al., 2016), we describe, in Sect. 6.5, methods for the detection and identification of Wolbachia and other symbionts in food webs. Phytophagy by natural enemies is discussed in Chap. 8. Species within a community exist as populations. In its broadest sense the term population can be applied to any group of individuals of the same species occupying a particular space. This space may vary greatly in size, for example from a single tree to a wide geographical area, depending upon how the population is defined. It is important to define the spatial scale over which the population is to be studied at the start of an ecological investigation, since the principal factors influencing the population dynamics of a species may vary depending upon spatial scale. For example, immigration and emigration may have a much greater influence on population persistence at small spatial scales than they do at larger ones (Dempster, 1989). The term ‘population’ is sometimes incorrectly used to refer to

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the combined numbers of a range of related species occupying a discrete area, for example the ‘carabid population’ of a field. Great care must be taken in the interpretation of changes in the abundance of such a ‘population’ since it will comprise a mixture of species with differing ecologies, and different species will be affected in different ways by the same environmental factors. The concept of a metapopulation as a collection of sub-populations of a species, each occupying a discrete habitat patch but with some level of genetic interchange, has received much attention in the study of species population dynamics (Gilpin & Hanski, 1991; van Nouhuys & Lei, 2004). This concept is discussed further in Sect. 6.4. In order to study a natural enemy population or community it is usual to select at random a representative group, a sample, of individuals on which to make the appropriate observations or measurements so that valid generalisations concerning the population or community as a whole can be made. Often, but not always, the sampled individuals need to be removed from their natural habitat using an appropriate collection technique. Most sampling methods are destructive, involving the physical removal of organisms from the study area. It is however, sometimes possible to sample by counting organisms in situ (Sect. 6.2.6). It is essential that before starting a sampling programme, sampling techniques are chosen that are appropriate for both the type of ecological problem being investigated and the particular natural enemy species being investigated. Section 6.2 is devoted to a description of the most commonly used sampling techniques and their limitations, with reference to examples from the literature on natural enemies. Table 6.1 lists some applications of these techniques. Basset et al. (1997) provide a key to assist ecologists in the choice of sampling methods suitable for one specific habitat – the tree canopy. Ausden (1996) gives a table which summarises the relative applicability of various sampling techniques for a range of invertebrate groups (but not exclusively natural enemies). In summary, this chapter is concerned with the sampling and monitoring, in time and space,

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Table 6.1 Applications of different field sampling methods Data required

Sampling technique

Comments

Absolute abundance

Pitfall traps

Do not provide data on absolute abundance

Vacuum net

When used to sample a defined area or unit of habitat, calibration is necessary. Large, active insects may flee before sample taken. Multiple resampling or combination with ground search will increase accuracy

Sweep net

Estimates of absolute abundance difficult to obtain

Knock-down

For chemical knock-down, unit of habitat (e.g., whole plant) needs to be enclosed; calibration necessary; cannot be done when windy; ineffective for species enclosed within vegetation (e.g., galls, leaf-mines and silken retreats)

Visual count

Labour-intensive; inects need to be conspicuous if census-walk method used; efficiency varies with insect activity and observer; insects may be absent (e.g., underground) during daytime

Mark-release-recapture

Important to satisfy a number of assumptions; choose appropriate calculation methods

Pitfall traps

Factors affecting locomotor activity, catchability and escape rate by different species, need to be taken into account

Vacuum net

Efficiency can change with height and density of vegetation

Sweep net

A wide range of factors cause sampling variability; significant variability between operators

Knock-down

Except for chemical knock-down, may not sample all species with the same efficiency

Visual count

Labour-intensive; insects need to be conspicuous if census-walk method used; efficiency varies with insect activity and observer

Attraction

Difficult to define area of influence; insect responses may change with time

Pitfall traps

Trap spacing important in minimising inter-trap interference; vegetation around individual traps can affect capture rates; some carabids aggregate in traps

Vacuum net

Vegetation type can affect efficiency

Sweep net

Disturbance can change dispersion pattern during sampling

Knock-down

Chemical knock-down needs to be confined to defined sampling areas

Visual count

Detectability needs to be constant over study area

Examination of hosts for parasitism

Identification of immature stages may prove problematic (continued)

Relative abundance

Dispersion pattern

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Table 6.1 (continued) Data required

Sampling technique

Comments

Phenology

Pitfall traps

Cannot detect immobile insects (e.g., during cold weather)

Sweep net

Changes in vertical distribrution within the vegetation may result in non-detection

Malaise trap

Provides useful information on flight periods

Visual count

Changes in behaviour may affect ease of detection

Examination of hosts for parasitism

Rearing provides information on diapause characteristics

Attraction

Responses to visual or chemical stimuli may be restricted to certain periods or behavioural states

Sticky traps, window traps

Only valid during active flight periods

Pitfall traps

May not sample all species with the same efficiency; provide useful data on presence; lack of catch does not prove absence

Vacuum net

Night samples need to be taken for nocturnal insects

Sweep net

Only efficient for groups active in the vegetation canopy

Knock-down

Very activeflyers may escape, but, otherwise, thecatch from chemical knock-down is not dependent on the activity of insects and is not influenced by ‘trap behaviour’

Visual count

Most efficient for very conspicuous groups

Attraction

Different species may not respond to the same visual or chemical stimuli

Sticky/window traps

Species without active flying stage will not be recorded

Relative abundance of species

Pitfall traps, vacuum net, sweep net, Malaise trap, knock-down, attraction, visual count

Except for chemical knock-down, may not sample all species with the same efficiency

Locomotor activity

Pitfall traps

Linear pitfall traps provide useful information on population movements, especially direction of movements; best to combine with a marking technique

Visual count

Can provide useful information, especially if combined with a marking technique

Attraction

Attractant properties of trap can interfere with insect behaviour

Mark-release-recapture

Provides useful information such as minimum distance travelled

Sticky and window traps

Provide useful information on height of flight, as well as on direction

Species composition

420

of natural enemy populations and communities, and describes a number of techniques that can be used for measuring or estimating the abundance of natural enemies, determining the structure and composition of communities and examining the spatial distribution of natural enemies in relation to their host or prey populations. Most of the techniques discussed can be used to obtain qualitative data relating to the predator or the parasitoid species present in a community or the prey/host species attacked by a particular natural enemy. Some of the techniques can also be used in obtaining quantitative estimates of natural enemy abundance or predation and parasitism, an aspect of natural enemy biology taken further in Chap. 7. Estimates of animal numbers may be expressed in terms of either density per unit area or unit of habitat, and the unit of habitat can be an area of ground or a unit of vegetation such as a leaf or a whole plant. Estimates of this type are termed absolute estimates of abundance and must be distinguished from relative estimates of abundance which are not related to any defined habitat unit (Southwood & Henderson, 2000). Relative estimates are expressed in terms of trapping units or catch per unit effort and are influenced by other factors (e.g., climatic conditions) besides the abundance of the insect being sampled. When considering absolute estimates of insect abundance, the term population density may be applied to numbers per unit area of habitat and the term population intensity applied to numbers per leaf or shoot or host (Southwood & Henderson, 2000). This chapter is concerned with practical techniques and we say little about the statistical analysis and interpretation of sampling data. Information on these topics can be found either elsewhere in this book (Chap. 9) or in the following publications: Cochran (1983), McDonald et al. (1988), Perry (1989, 1998), Eberhardt and Thomas (1991), Crawley (1993, 2002), Sutherland (1996), Krebs (1999), Southwood and Henderson (2000), McGarigal et al. (2013), Young and Young (2013).

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6.2

Field Sampling Techniques

Our discussion of techniques is mainly confined to the sampling of insects from terrestrial habitats and from air. Sunderland et al. (1995a) discuss sampling options for determining the density of predators in agroecosystems, and Williams and Feltmate (1992), Ausden (1996), Southwood and Henderson (2000) give fuller accounts of sampling methods for use in aquatic habitats.

6.2.1 Pitfall Trapping A pitfall trap is a simple interception device consisting of a smooth-sided container which is sunk into the ground so that its open top lies flush with the ground surface (Fig. 6.1a). Invertebrates moving across the soil surface are caught when they fall into the container. Pitfall traps (Fig. 6.1b) are the most commonly used method of sampling ground-dwelling predators such as carabid and staphylinid beetles, spiders and predatory mites, but have occasionally been used to sample other groups of predators, such as dolichopodid flies (Pollet & Grootaert, 1987), pompilid wasps (Field, 1992), ants (Cherix & Bourne, 1980; Samways, 1983; Melbourne, 1999; Morrison, 2002), heteropteran bugs (Rácz, 1983; Basedow, 1996) and harvestmen (Cherix & Bourne, 1980; Jennings et al., 1984; Newton & Yeargan, 2002). Abundance data for fire ants (Solenopsis spp.) obtained by pitfall trapping may need to be interpreted with caution in cases where these ants are attracted to build nests under the pitfall traps (Penagos et al., 2003). The containers that can be used as pitfall traps are many and varied, but round plastic pots and glass jars with a diameter of 6‒10 cm are the most popular. It is important to remember, however, that both trap size and trap material are known to influence trap catches, sometimes very strongly (Luff, 1975; Adis, 1979; Scheller, 1984). That said, there are ways in which bias can be minimised and pitfall trap used to give

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Fig. 6.1 a A simple pitfall trap (with rain cover) for sampling predators moving across the soil surface, e.g., carabid and staphylinid beetles and spiders; b carabid beetles, staphylinid beetles and spiders caught in a single pitfall trap over a 7-day period in a cereal crop in southern England

meaningful measures of abundance (e.g., Gist & Crossley, 1973; Woodcock, 2005). A liquid preservative is often placed in the trap, both to kill and to preserve the catch, thereby reducing the risk of escape and preventing predation within the trap, particularly of small individuals by larger ones (conspecifics or heterospecifics). Amongst the preservatives that have been used are formaldehyde, alcohol, ethylene glycol, propylene glycol, acetic acid, picric acid, sodium benzoate and concentrated salt solutions, but there is evidence that some preservatives have an attractant or repellent effect on some insects and that such effects can differ between the two sexes of the same species (Luff,

1968; Skuhravý, 1970; Adis & Kramer, 1975; Adis, 1979; Scheller, 1984; Pekár, 2002). Lemieux and Lindgren (1999) showed that, although the efficiency of trapping carabid beetles did not differ significantly between concentrated brine (a super-saturated solution of sodium chloride) and antifreeze (ethylene glycol), the former was a poor preservative of predators. Addition of detergent can greatly increase the catch of small predators (such as linyphiid spiders), which may otherwise fail to break the surface film and thus be able to escape (Topping & Luff, 1995). Detergent may, however, reduce the catch of some species of carabid and staphylinid beetle (Pekár, 2002). Pitfall traps can be used to catch natural enemies

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whose DNA is to be analysed for determination of diet (Sect. 6.3.12), or in a genetics study. In such cases, it is recommended to test the effect of the trapping fluid/preservative on the DNA of the target natural enemy. Gurdebeke and Maelfait (2002) found that DNA of the spider Coelotes terrestris was adequately preserved when 96% ethanol was used as trapping fluid in a funnel pitfall trap (the funnel reduced the evaporation rate of ethanol). RAPD profiles could not be generated from spiders collected in formaldehyde. If pitfall trapping is used as a source of predators for stable isotope analysis (Sect. 6.3.1) traps should be emptied at least every 48 h (Ponsard & Arditi, 2000) because isotopic content changes during decomposition. Skvarla et al. (2014) give an extensive review of the relative merits of different preservatives for pitfall trapping. It is sometimes necessary, for example when carrying out mark-release-recapture studies (Sect. 6.2.10), to keep specimens alive within the trap. In such cases, it is advisable to place some kind of material in the bottom of the trap to provide a refuge for smaller individuals. Compost, small stones, leaf litter, moss or even polystyrene granules may be used for this purpose. Small pieces of meat may also be added to the traps to reduce cannibalism and interspecific predation (Markgraf & Basedow, 2000), but this may introduce the complication of differential species-specific olfactory attraction (Sect. 6.2.8) which will make the results more difficult to interpret in some types of study. Live-trapping may yield different relative species compositions of predators such as spiders and carabid beetles compared with kill-trapping, and so the two methods are not necessarily comparable (Weeks & McIntyre, 1997). At the end of a trapping period, it is advisable to replace traps with fresh ones so that the catch can be taken en masse back to the laboratory. Plastic pots with snap-on lids are readily available commercially, and are convenient when large numbers of traps need to be transported. To prevent the sides of the hole from collapsing during trap replacement, a rigid liner is useful, and liners can be readily made from sections of plastic drainpipe of an appropriate diameter or by

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using another plastic cup. In open habitats it is also advisable to place covers over traps in order both to prevent birds from preying on the catch, and to minimise flooding during wet weather. Covers for simple, round traps can be made from inverted plastic plant pot trays, supported by wire. Such covers may increase the catch of carabid and staphylinid beetles that are behaviourally adapted to finding refuge under stones and logs. Soil-coring tools, including bulb planters, can be used to make the initial holes when setting pitfall traps, but it is essential to ensure that the lip of the trap is flush with, or slightly below the soil surface. When traps are operated over a prolonged period of time, some maintenance work is often necessary. For example, in hot, dry weather some soils crack and shrink, creating gaps around the edge of traps, thus reducing their efficiency. A number of variations on the conventional pitfall trap have been developed in attempts to improve their efficiency or to tailor them for particular habitats and target taxa (reviewed in Skvarla et al., 2014). Lemieux and Lindgren (1999) tested a trap (modified from a design by Nordlander (1987)) with fifteen 1.3 cm-diameter holes around the trap circumference at 2 cm below the upper rim. This trap incorporated a tight-fitting lid and was buried so that the lower 3 mm portion of the holes was below ground level, thus predators could enter the trap through the holes only. The overall catch of carabid beetles was no different from that of conventional pitfalls, but the Nordlander trap had the advantage of excluding vertebrates and reducing the dilution of trapping fluid caused by rainfall. Several workers have used linear traps made from lengths of plastic or metal guttering to increase catches and to obtain directional information on insect movements (Sect. 6.2.11). Sigsgaard et al. (1999) separated two plastic cup pitfalls (positioned back to back) by a barrier of semitransparent hard plastic sheet to obtain data on directional movement of spiders, carabids and ants entering and leaving a rice field, and a similar design was used by Hossain et al. (2002) to record spiders and carabid beetles moving from harvested to unharvested plots of alfalfa.

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Fig. 6.2 A litter-covered pitfall trap. Source Edwards (1991). Reproduced by permission of Elsevier Science

Two-cup pitfalls may be placed a short distance apart and joined by a solid barrier which diverts walking insects into the traps at either end (Wallin, 1985; Jensen et al., 1989; French et al., 2001), or the trap can be placed at the apex of Vshaped fences (Culin & Yeargan, 1983; Markgraf & Basedow, 2000). Winder et al. (2001) found that the use of five traps arranged in a cross formation, and connected by plastic guide barriers (0.5 m lengths of lawn edging sunk 5 cm into the ground), was a more efficient design than single traps for catching some species of carabid and staphylinid beetle, and lycosid spiders (but not linyphiid spiders). Mechanical devices have been used to allow automatic, time-based sorting of trap catches (Williams, 1958; Ayre & Trueman, 1974; Barndt, 1976; Alderweireldt & Desender, 1992; Chapman & Armstrong, 1997; see also Sect. 6.2.11), whilst Heap (1988) suspended UV-emitting, fluorescent light tubes above his traps to increase the catch rate. A funnel trap can be used for sampling in litter, where litter is placed on a gauze mesh across the mouth of the funnel, and predators fall through the mesh and are directed by the funnel into a pitfall trap (Edwards, 1991) (Fig. 6.2). Lund and Turpin (1977) used a funnel pitfall containing liquid

nitrogen to freeze carabid beetles as soon as they entered the trap. Immediate freezing prevented predation within the trap and arrested decomposition and enzymatic breakdown of the gut contents of the beetles (which were to be tested serologically (Sect. 6.3.9) for evidence of predation on cutworm larvae). One litre of liquid nitrogen per pitfall lasted about 24 h. A similar funnel pitfall trap (modified to kill entering predators with carbon tetrachloride vapour) was used by Sunderland and Sutton (1980) to collect predators of woodlice in dune grassland. The funnel pitfall of French et al. (2001) contained insecticidal cattle ear tags to kill trapped carabid beetles. Hengeveld (1980) used a funnel trap to direct carabids into a tube containing 4% formalin solution. Luff (1996) considered funnels to be valuable as a way of removing the lip of the pitfall. A funnel was found to prevent carabid beetles hanging onto the trap edge, and it also reduced the likelihood of trapped beetles escaping. Newton and Yeargan (2002) added a varnished hardboard apron surrounding the funnel, and this design was successful for monitoring harvestmen (Opiliones) in soybean, grass and alfalfa crops (see also the experiments of Epstein & Kulman, 1984). Funnel traps were found to be

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up to three times more efficient per centimetre of trap diameter, compared to standard cup pitfalls, for catching carabid and staphylinid beetles and lycosid spiders, but no significant improvement in efficiency was reported for ants and linyphiid spiders (Obrist & Duelli, 1996). ‘Hanging desk traps’ are pitfalls for collecting from rock faces. They have flat collars attached that are hung at a slight angle to the rock face (to prevent rain water flowing in) with the inner edge of the collar taped to the rock and covered with small stones (Růžička & Antušs, 1997). So far, they have been used to assess spider diversity in mountainous areas (Růžička, 2000). Weseloh (1986a) sampled the ground beetle Calosomasycophanta on tree trunks using traps that consisted of plastic tree bands that directed climbing beetles into waxed papercups attached to the tree. A similar method, reported by Hanula and Franzreb (1998), utilised an inverted metal funnel attached to the tree trunk. Arthropods, including spiders and harvestmen (Phalangida), crawling up the tree trunk, pass through the funnel and enter a collection container attached to its upturned spout. A ‘stalk trap’, employing the same principle but on a smaller scale, was used by Lövei and Szentkirályi (1984) to trap carabid beetles ascending and descending the stalks of maize plants. Bostanian et al. (1983) used a ‘ramp pitfall’ that has two metal ramps leading to a rectangular pitfall, and a metal roof. Several of these traps were run in an untreated carrot field with traditional circular plastic pitfalls running concurrently. The latter caught more carabids plus a range of other invertebrates, but the ramp trap caught 99.5% carabids and was selective, catching the larger species (virtually none [aut]Xie, F. spectra of parasitised and unparasitised pupae may have been due to differences in moisture, chitin or lipid compositions (Dowell et al., 2000). For researchers who have access to gas chromatography equipment, hydrocarbon profiles can provide a reliable method for identifying parasitoids, before or after leaving the host. Geden et al. (1998) used cuticular hydrocarbon profiles to identify three species of Muscidifurax (Pteromalidae, parasitoids of house fly and stable fly pests) which are difficult to separate morphologically. Furthermore, they showed that specimens of M. raptor from five countries and three continents could all be identified by the method. The method can be successful with adult parasitoids and with pupal exuviae (Carlson et al., 1999). Techniques are described in Phillips et al. (1988) and other applications of the method are listed in Symondson and Hemingway (1997). Electrophoretic, immunological and DNAbased techniques have been developed for detecting parasitoid immature stages within their hosts (Powell & Walton, 1989; Stuart & Greenstone, 1996; Demichelis & Manino, 1998; Greenstone, 2006; Gariepy et al., 2007) (Sects. 6.3.9, 6.3.11 and 6.3.12 provide detailed discussion). Tilmon et al. (2000) used a two-stage molecular approach for determining the rate at which three species of Peristenus (Hymenoptera: Braconidae) were parasitising Lygus lineolaris (Heteroptera: Miridae) in an alfalfa field. They used PCR (polymerase chain reaction) to target part of the mitochondrial cytochrome oxidase I

K. D. Sunderland et al.

(COI) gene. Using mitochondrial genes ensured that there was a high copy number per cell while the part of the COI gene chosen has low intraspecific variability. Primers were designed that amplified DNA from all three Persistenus species and these primers were used to detect parasitoids within L. lineolaris nymphs. Restriction digests of the PCR product were then carried out to determine which of the three parasitoid species had been present. This procedure took days (compared with months for the rearing-out method, Sect. 6.3.6) and also gave the researchers the flexibility to store samples until convenient for processing (Tilmon et al., 2000). Gariepy et al. (2005) further refined the detection and identification of Lygus parasitoids within their hosts, developing the first single-step multiplex PCR assay with species-specific primers that could detect parasitism within three days of the host being parasitised. Traugott and Symondson (2008) found that it was possible to detect parasitoids (Lysiphlebus testaceipes) within their aphid host (Aphis fabae) as little as 5 min. after parasitism using PCR. Section 6.3.11 provides more detailed discussion of the molecular analysis of parasitoids within hosts. DNA-based methods have also been used for the rapid identification of taxonomically difficult parasitoids, such as Trichogramma species (Menken & Raijmann, 1996; van Kan et al., 1996; Stouthamer et al., 1999; Chang et al., 2001 Sumer et al., 2009) and Muscidifurax species (Taylor & Szalanski, 1999), and, similarly, for predators such as phytoseiid mites (Navajas et al., 1999; Okassa et al., 2010). Sumer et al. (2009) present an identification key for ten common species of Trichogramma in the Mediterranean based on PCR and subsequent restriction fragment-length polymorphisms of ITS-2 fragments. RAPD-PCR assays of the encyrtid parasitoid Ageniaspis citricola suggested that it may be two species, one occurring in Thailand and the other in Taiwan (Hoy et al., 2000): such information can be crucial to the success of biological control programmes.

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6.2.10 Mark-Release-Recapture Methods for Estimating Population Density Many of the sampling methods discussed above provide only relative estimates of predator and parasitoid population densities, and of those that provide absolute estimates, some only work well for conspicuous or less active species. Inconspicuous or very active natural enemies, such as carabid beetles and lycosid spiders, are more difficult to count, and an alternative method of estimating population levels is to estimate densities using mark-release-recapture data. These data are obtained by live-trapping a sample of individuals from the population, marking the insects so that they can be distinguished from uncaptured individuals, releasing them back into the population, and then re-sampling the population. An absolute estimate of population density can be calculated from the proportion of recaptured, marked individuals in the second sample, provided that a number of assumptions are satisfied. The key assumptions are: 1. That marking neither hinders the movement of an individual nor makes it more susceptible to predation or any other mortality factor. 2. That marks are retained throughout the trapping period. 3. That marked and unmarked individuals have an equal chance of being captured. 4. That, following release after marking, marked individuals become completely and randomly mixed into the population before the next sample is taken. The original method of estimating total population size from mark-release-recapture data was devised by Lincoln (1930) for the study of waterfowl populations. The standard Lincoln Index formula is: N¼

an r

ð6:1Þ

where N is the population estimate, a is the number of marked individuals released, n is the total number of individuals captured in the

sample, and r is the number of marked individuals captured. The Lincoln Index relies upon the sample of marked individuals that is released back into the population becoming diluted in a random way, so that the proportion of marked individuals in a subsequent sample is the same as the proportion of marked individuals originally released within the total population. The original Lincoln Index method assumes, unrealistically, that the population is closed, with no losses or gains either from emigration and immigration or from deaths and births, during the period between the taking of consecutive samples. Since the Lincoln Index was devised, a number of modifications and alternative methods of calculating population density have been developed. The best known are those of Fisher and Ford (1947), Bailey (1951, 1952), Craig (1953), Seber (1965), Jolly (1965), Parr (1965), Manly and Parr (1968), Fletcher et al. (1981). These methods, together with the formulae used to estimate population sizes, are described and discussed in detail by Seber (1973), Begon (1979, 1983), Blower et al. (1981), Pollock et al. (1990), Sutherland (1996), Krebs (1999), Southwood and Henderson (2000). Begon (1983) reviews the use of Jolly’s method, based on 100 published studies, and discusses potential alternatives. The computer programme ‘Capture’ can be used to select the most appropriate model for a specific set of circumstances (Otis et al., 1978; White et al., 1982; Samu & Kiss, 1997; Kiss & Samu, 2000). A practical problem that is often encountered with carabids (Ericson, 1977; Kromp & Nitzlader, 1995; Samu & Sárospataki, 1995b) and lycosids (Samu & Kiss, 1997) is that, in spite of a large expenditure of effort devoted to marking individuals, the recapture rate of some species can be less than 10%. Begon (1979) provides tables of minimum sample sizes of marked and recaptured individuals for stated levels of accuracy of the population estimate. Among natural enemies, carabid beetles are the group most often subjected to mark-releaserecapture studies, but mainly for the purpose of investigating activity and dispersal (Sect. 6.2.11).

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There have also been several attempts at estimating carabid (Ericson, 1977, 1978; den Boer, 1979; Brunsting et al., 1986, Nelemans et al., 1989; Hamon et al., 1990; Thomas et al., 1998), staphylinid (Frank, 1968) and lycosid (Greenstone, 1979; Kiss & Samu, 2000) population densities using mark-release-recapture data. In a ten-year study of the carabid Nebria brevicollis, 11,521 beetles were individually marked using a branding technique (Nelemans et al., 1989) (marking methods are discussed more fully in Sect. 6.2.11). The beetle population was sampled continuously using pitfall traps, and two methods were used to calculate population sizes: Jolly’s method, as modified by Seber (1973), and Craig’s method (Craig, 1953). Of the two, Craig’s method gave the better estimates, those obtained using Jolly’s (1965) method being much too low. All the methods used to estimate population sizes from mark-release-recapture data assume constant catchability over the trapping period, but Nelemans et al. (1989) found significant between-year differences in recapture probabilities. Also, the frequency distribution of recaptures deviated significantly from that predicted by a Poisson distribution, more beetles than expected failing to be recaptured and more than expected being recaptured three or more times. Consequently, there is a danger of significant errors occurring in estimates of carabid numbers based on mark-release-recapture data from pitfall trapping, Jolly’s method in particular tending to underestimate populations (den Boer, 1979; Nelemans et al., 1989). The size of errors arising from variability in the chances of recapture will depend to some extent on the species being studied. Since the behaviour of individuals affects catchability in pitfall traps, it is sometimes advisable to treat the sexes separately (Ericson, 1977; Samu & Sárospataki, 1995b; Samu & Kiss, 1997). Furthermore, the handling procedure during marking and release will affect the activity of beetles following release. This can be mitigated, to some extent, by leaving an interval of a few days between successive trapping periods to allow marked beetles time to redistribute

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themselves within the population before being recaptured (Ericson, 1977). Because carabid beetles are highly mobile, some marked individuals are likely to leave and re-enter the trapping area, thereby biasing population estimates (Ericson, 1977). To avoid this problem, enclosures have been used during some carabid mark-release-recapture studies (Loreau, 1984a; Brunsting et al., 1986; Nelemans et al., 1989) (Sect. 6.2.1). In a similar way, a field cage was used to assess the accuracy of Jolly’s method for estimating populations of the ladybird beetles Coccinella californica and Coccinella trifasciata in alfalfa and oat crops (Ives, 1981). Beetles were captured in experimental plots using a visual searching method; these were marked at the site of capture with spots of enamel paint, and were then released immediately after marking. Estimates of population density were calculated using Jolly’s method and, in the caged plots, these proved to be very accurate, although estimates of populations in open plots were likely to be less precise. When there was no limitation on flight, it was considered necessary that aphid densities in the study area should be high enough to provide adequate food for the ladybirds, thus preventing dispersal. A limitation noted in Ives’ study was the amount of time spent marking captured beetles, which restricted the numbers that could be caught in a sampling period, thereby reducing the accuracy of the markrelease-recapture method. To alleviate this problem, visual counts were made whilst walking through the plots, in addition to the counts made whilst marking. A similar method was used to estimate wolf spider population densities in an estuarine salt marsh (Greenstone, 1979). As an alternative to enclosure, records of the distance moved by marked predators within a trapping grid (e.g., a grid of pitfalls) can be used to calculate the ‘area of influence’ of traps or grid (Kuschka et al., 1987; Franke et al., 1988). Area of influence can also be estimated by defining concentric rectangles within the grid and noting at which size of rectangle the density is stabilised (Loreau & Nolf, 1993). Population size (from

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mark-release-recapture) divided by area of influence provides an estimate of population density. Some mark-release-recapture studies have been carried out on hover fly populations, but on phytophagous and saprophagous rather than predatory species (Neilsen, 1969; Conn, 1976). Adult narcissus bulb flies, Merodon equestris, were marked on the tibiae with cellulose paint applied with a fine blade of grass (Conn, 1976), a method that could also be used with aphidophagous syrphids. In this study, population size was estimated using Jolly’s method and the Fisher‒Ford method. Despite recapture rates of 30–50%, both methods gave large errors in daily estimates of population size because samples were small, and the total amount of variation in estimates using Jolly’s method was usually two to three times higher than that obtained by the Fisher‒Ford estimates. Multiple regression analyses revealed that 35–40% of the variation in the Fisher‒Ford estimates and 35–45% of the variation in the Jolly estimates was attributable to the effect of variation in temperature. Adult parasitoids are much more difficult to mark for mark-release-recapture studies because many of them are small-bodied and difficult to handle. Some success has been achieved by labelling braconids, mymarids and aphelinids with fluorescent dust or trace elements (Jackson et al., 1988; Jackson & Debolt, 1990; Hopper & Woolson, 1991; Corbett et al., 1996; Bellamy & Byrne, 2001; Wanner et al., 2006). In some cases, the trace elements were added to artificial diets on which the hosts of the parasitoids were reared. Fleischer et al. (1986) studied patterns of uptake and elimination of rubidium by the mirid bug Lygus lineolaris by spraying mustard plants with varying rates of Rb. A concentration of 200 ppm RbCl added to the diet of its Lygus spp. hosts provided Rb-labelled Leiophron uniformis which could be distinguished from wasps collected from field populations for 6–8 days (Jackson & Debolt, 1990). Similarly, Microplitis croceipes adults labelled with rubidium or strontium via the diet of their hosts, Helicoverpa spp., at a concentration of 1000–2000 ppm, were distinguishable from field-collected, unlabelled

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wasps for up to 20 days (Hopper & Woolson, 1991). There is a danger, however, that high levels of these elements could affect the biology of the labelled insects. Jackson et al. (1988) noted that labelling Anaphes ovijentatus (Mymaridae) with high doses of rubidium (1000 ppm RbCl), via the eggs of its Lygus spp. hosts, tended to reduce longevity and fecundity slightly. Strontium was found to be superior to rubidium for labelling parasitic Hymenoptera because: (a) background levels are lower, (b) elimination rates are less, (c) it does not affect parasitoid development and behaviour, and (d) it is less expensive (Gu et al., 2001). The radioisotope 32P was used to label Trichogramma dendrolimi, a parasitoid of lepidopteran eggs, in order to evaluate the impact of mass releases against a tortricid moth (Feng et al., 1988). In this case, no adverse effects of the radioisotope labelling on longevity, reproduction or sex ratio of the parasitoid were detected. Similarly, Goubault and Hardy (2007) found no effect of heavy water (deuterium oxide) marking (via development on hosts injected with the marker) on the lifetime reproductive success of Goniozus legneri, making it a promising candidate marker for mark-release-recapture studies as well as laboratory studies of realtime chemical volatile emissions associated with competition. This method was also shown to be cost-effective, and more sensitive than other labelling methods. It is also possible to label parasitoids indirectly by applying stable isotopes to host plants. For example, Wanner et al. (2006) showed that applying aqueous drenches containing the rare stable calcium isotope 44Ca to cabbage plants resulted in transfer of the isotope to parasitoids (Cotesia glomerata) developing in caterpillars (Pieris brassicae) that had been reared on the isotope-drenched plants, without effects on host or parasitoid development or survivorship. Greenhouse experiments showed that the isotope could then be transferred to hosts by labelled parasitoids, providing a method to measure dispersal and foraging behaviours of parasitoids without the need to recapture released individuals. A drawback of this method,

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however, is that analysis of 44Ca-labelled samples using stable isotope analysis requires a large amount of biomass (often necessitating pooling of samples), which can be problematic for smaller parasitoid species. By applying unique marks to each individual insect in a mark-release-recapture study, additional information on longevity and survival rates can be obtained. Conrad and Herman (1990) used data from a mark-release-recapture study of dragonflies to calculate daily survival estimates using the Manly-Parr method (Manly, 1971; Southwood & Henderson, 2000), which were then converted to daily expected life span estimates using the formula of Cook et al. (1967): Expected life span = -1=loge ðsurvivalÞ. Conn (1976) calculated the longevity of phytophagous hover flies from mark-release-recapture data.

6.2.11 Methods Used in Investigating Natural Enemy Movements Predators need to locate their prey in order to feed, and parasitoids must find their hosts in order to lay eggs on/in them. Biocontrol practitioners need to determine how far parasitoids can move between host areas and areas of non-host foods to decide, for example, where to site plants for the supply of pollen and nectar (Chap. 8). Prey/host location generally requires spatial displacement on the part of the predator or parasitoid, and this may be either directed (e.g., movement towards the source of a stimulus) or random. Such locomotor activity is a major factor influencing the population dynamics of natural enemies because it affects the rate of encounters with prey and host patches and so influences the amount of predation or parasitism. It occurs as a result of behavioural responses to stimuli which may be internal, physiological stimuli, e.g., hunger (Mols, 1987), or external, environmental stimuli, e.g., infochemicals (Sect. 1.6). In the main, host- and prey-location behaviour involving spatial dispacement constitutes trivial movement, i.e., it is restricted to the

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habitat normally occupied by the insects, whereas migratory movements take the insect away from its original habitat (Southwood, 1962), although there are notable exceptions such as some mymarid wasps. The techniques described here can be applied to either type of movement, although so far as trivial movements are concerned, foraging behaviour at a low level of host and prey patchiness is not dealt with (Chap. 1 discusses this aspect of behaviour). The study of insect movement in the field is often difficult; in general, it is easier to measure the consequences of movement than it is to examine the process itself. For example, it is easier to record that an individual has shifted from one location to another during the period between two sampling occasions than it is to record the path taken by that individual, or the speed at which it travelled. Some measure of the level of locomotor activity within a population can be obtained by intercepting moving individuals. For a given population, the higher the level of activity, the more individuals will be intercepted within a given time. Pitfall traps, for example (Sect. 6.2.1), can be used to study the activity of ground-dwelling predators, such as carabid beetles. In order to study the diurnal activity patterns displayed by carabid beetles and other surfaceliving predators, automatic, time-sorting pitfall traps have been developed and used in a variety of habitats (Williams, 1958; Ayre & Trueman, 1974; Barndt, 1976; Luff, 1978; Desender et al., 1984; De Keer & Maelfait, 1987; Alderweireldt & Desender, 1990; Kegel, 1990; Bayram, 1996). Individuals falling into the trap are channelled into fresh collecting tubes or compartments after predetermined time intervals, which may be as short as two hours (Ayre & Trueman, 1974; Kegel, 1990). Funke et al. (1995) used timesorting pitfall traps at the tops of two-metre-high artificial tree trunks to investigate the diel periodicity of flying predators in deciduous woodland. These authors also devised ring-shaped pitfall traps, and deployed them, with and without artificial tree trunks in the middle, to determine which species of predator (carabid and

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staphylinid beetles, spiders, harvestmen and centipedes) use trunk silhouettes for orientation when moving on the ground. Population survival can depend on the rate of exchange of individuals between subpopulations. For non-flyers this exchange is by walking. To measure relative rates of dispersal by walking, five species of carabid were individually marked and released in the centre of 7 m-radius fenced areas, then caught in pitfalls at the edge. Circles were set up in areas of highand low-density vegetation (Klazenga & De Vries, 1994). Similarly, a 10 m-diameter circle of pitfalls was placed such that half the circle was in an oat field and half in adjacent grassland. Carabids were captured and marked with coloured paint (a different colour for each habitat), then released. 11% were recaptured and about 4% were recaptured in a different habitat from the one where they were originally caught (Kajak & Lukasiewicz, 1994). The same approach, using 10 m-diameter enclosures, was employed to investigate the effects of insecticides on the movement of several carabid species (Kennedy et al., 1996; Kennedy & Randall, 1997). Circles of 10 m–20 m radius were used by Ellers et al. (1998) who released marked braconid parasitoids (Asobara tabida) in the centre of the circle and investigated dispersal ability in relation to the body size of individual wasps. Chapman and Armstrong (1996) employed pitfalls, together with time-specific enclosure of some plots with plastic barriers, and deduced that some carabid beetles moved from intercropped to monocropped plots at night. This methodology showed that, at night, the carabid Pterostichus melanarius will move a short distance out from dense vegetation into adjacent open crops, which could justify the use of grass strips and weed strips for enhancing biocontrol within fields (Chapman et al., 1999). Linear pitfall traps can also be used to gain information on direction of movement (Duelli et al., 1990). If such traps are positioned along the boundary between two distinct habitats, they will detect major population movements across the boundary, and this could be related to changing conditions, such as levels of prey availability, within habitats. Linear traps

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placed at increasing distances from a habitat boundary will give some indication of rates of immigration into that habitat, e.g., rates of colonisation of arable fields from overwintering refuges (Pausch et al., 1979). Petersen (1999) sited a vertical polyethylene barrier in an arable field parallel to the field edge; the barrier was set in a zig-zag pattern and pitfalls were sunk in corners of the barrier on the edge side to record spring emigration of the carabid, Bembidion lampros, from overwintering sites in a grass bank. Fagan (1997) studied the movement of two species of mantid out of experimental plots. The plots were surrounded by sticky plastic sheets which acted as absorbing boundaries. The pattern of emigration for Mantis religiosa was stepped and a ‘boundary-flux’ diffusion model was inappropriate, but cumulative dispersal of Tenodera sinensis was sigmoidal and the diffusion model provided a good fit. The model assumes that all individuals are identical, exhibit Brownian random movement at a constant rate, and that the area is homogeneous (Fagan, 1997). Diffusion models have been used to predict the effectiveness of agroecosystem diversification for enhancing natural enemies. The spatial scale at which enhancement occurs is influenced by mobility (represented by the diffusion coefficient) of the natural enemy (Corbett & Plant, 1993). Coloured water traps placed, for example, at different distances from a field edge, may enable the distribution of flying predators and parasitoids to be mapped (Hradetzky & Kromp, 1997). Interception traps, such as window traps and sticky traps, can be used to monitor the activity and movements, including the direction of movement and height above ground, of flying insects. A window trap consists of a sheet of transparent material, such as glass or plastic, supported in a vertical position, at an appropriate height above the ground, by means of a rigid frame (Fig. 6.17a). Flying insects collide with the window and fall into a water-filled tray fixed to its lower edge. Lengths of plastic guttering, fitted with end stops, make convenient trays. A drainage hole bored into the base of the tray, and closed by means of a rubber or plastic bung,

468 Fig. 6.17 Traps for interception of flying insects: a window trap; b sticky trap. The window trap consists of a sheet of transparent plastic; the insects are caught in sections of guttering containing water with a small amount of detergent

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allows it to be emptied at the end of each sampling period. Catches can then be sorted in the laboratory. The ‘vane-style’ window trap (made from two intersecting pieces of clear PVC plastic arranged over a funnel and collecting vessel) can be set on the ground or hung in the canopy of a tree (Juvonen-Lettington & Pullen, 2001). Separate trays are normally fitted to each side of standard window traps to provide information on flight direction. Directional information can be increased by using two traps positioned at right angles to one another. Data from window traps have been used: (a) to relate the flight activity of carabid beetles to wind direction and to the reproductive state of females (van Huizen, 1990), (b) to monitor the immigration of carabids into newly-formed polders in the Netherlands, and thereby detect the establishment of new populations (Haeck, 1971), (c) to monitor levels of predator activity in cereal fields (StorckWeyhermüller, 1988), and (d) to detect periods of aerial dispersal by spiders (De Keer & Maelfait, 1987), carabid beetles (Markgraf & Basedow, 2000) and staphylinid beetles (Markgraf & Basedow, 2002). Hance (1995) used directional window traps to show that more ladybirds, Coccinella 7-punctata, were entering a maize field from an adjacent orchard than from an adjacent fallow field. He also noted that some carabid species (such as Demetrias atricapillus and Bradycellus verbasci) were well represented in window traps but were very rarely caught in pitfall traps in the same area. Thus, window traps are an important supplement to pitfalls for documenting the carabid fauna of a region. Window traps can also be hung from the trunks of trees to intercept strong-flying arthropods, such as wasps and predatory beetles that are flying towards the tree (Hanula & Franzreb, 1998). Stenbacka et al. (2010) compared the use of trunk window traps to trunk emergence traps in estimating species richness, abundance, and community composition of ichneumonid parasitoids in forest stands. They found that window traps gave better measures of species composition in different forest stand types, but that trunk emergence traps gave more information about phenology, substrate requirements, and host species use. Another type

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of flight-interception trap, used to monitor the movement of staphylinid beetles in a Canadian raspberry plantation, comprised 1.8 m2 window screen mesh (1.5 mm) held over galvanised metal pans containing 2% formalin solution (Levesque & Levesque, 1996). Boiteau et al. (1999) constructed a 15 m-high sampling tower holding 40 interception traps (10 per side), placed at regular intervals above ground. Each trap was a plywood board, painted yellow, with an antifreeze-filled gutter below. The tower was sited in a pasture in an agricultural area and showed that, for thirteen species of ladybird, more than half the flights were at or below 3.8 m. The tower was also used to monitor aerial dispersal and vertical flight profiles of other predators, including Carabidae, Staphylinidae and Neuroptera (Boiteau et al., 2000a, b). Sticky traps work on a similar principle to window traps but consist of a sticky surface to which the intercepted insects adhere upon impact (Fig. 6.17b). Any suitable surface, either flat or curved, can be coated with a weatherproof adhesive, several types of which are commercially available. Sticky traps supported on poles at various heights were used to monitor the activity of predatory anthocorid bugs in pear orchards (Hodgson & Mustafa, 1984). Very few anthocorids were caught on traps placed within the orchard, but those placed at its edges successfully detected two peaks of flight activity during the year, and the peaks are believed to represent movement to and from hibernation sites. The airborne dispersal of the predatory mite Phytoseiuliis persimilis and its prey Tetranychus urticae was investigated using sticky traps made from microscope slides coated with silicon grease (Charles & White, 1988). These were clipped in a vertical position onto pieces of wooden dowelling, which were arranged in the form of a horizontal cross at the top of a supporting pole. Asplen et al. (2016) used sticky traps (rectangular pieces of cut transparency film coated with spray adhesive) placed at different heights with respect to the canopy of soybean plants to measure the dispersal of the parasitoid Binodoxys communis. Using this method, they were able to show sex-specific differences in

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dispersal characteristics; for example, female parasitoids were more likely than males to climb into higher air columns above the canopy and engage in wind-assisted dispersal. A network of 29 yellow sticky traps was used to study dispersal of coccinellid beetles on a 330 ha farm. The study showed that Propylea 14punctata and Adalia bipunctata appeared first on traps placed near grass banks and hedgerows, but Coccinella 7-punctata was more evenly distributed (Zhou et al., 1994). Greenstone et al. (1985) compared horizontal sticky wires with vertical sticky panels for monitoring ballooning spiders above a soybean field. Wire traps underestimated numbers of small money spiders (Linyphiidae). Traps subsequently used (Greenstone, 1990) comprised 16 sheets of 25 cm x 50 cm, 12.7 mm mesh, galvanised hardware cloth, coated with adhesive, and stapled to a wooden frame. They were hung by clamps from parallel horizontal pairs of clothes lines attached to vertical posts set in the ground. This design enabled rapid replacement of traps, which was done weekly. In some habitats sticky traps can also be useful for monitoring natural enemies that are walking or running, rather than flying. Docherty and Leather (1997) attached two polythene bands, covered with sticky Oecotak™, to the trunks of pine trees to estimate the numbers of spiders and harvestmen (Opiliones) ascending and descending the tree trunks. Van Laerhoven and Stephen (2002) suspended a series of stickycoated wire-mesh traps from a rope at 4 m intervals to a height of 16 m on pine tree trunks to catch foraging adult parasitoids of the Southern Pine Beetle Dendroctonus frontalis. They noted that, for maximum efficiency, traps should be in contact with the trunk because parasitoids disperse by walking as well as flying. Braun et al., (1990) applied 10 cm bands of sticky Tanglefoot® directly to the trunks of trees to monitor movement of the natural enemies of fruittree leafroller, Archips argyrospila. Directional arboreal photoeclectors (Sect. 6.2.7) can also be used to study upward or downward migration of arthropods on tree trunks (Basset et al., 1997).

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Sticky trap catches of parasitoids and predators can sometimes be increased by using colour as an attractant (Neuenschwander, 1982; Moreno et al., 1984; Trimble & Brach, 1985; Samways, 1986; Antolin & Strong, 1987) (Sect. 6.2.8), but it is questionable whether meaningful information on natural patterns of movement can be obtained using coloured sticky traps, because of the likelihood of the traps’ attractant properties interfering with the natural behaviour of insects. The material comprising interception traps, even colourless, translucent ones, should be tested for its degree of UV reflectance, if such traps are to be used for investigations of insect movements. The direction and height of flight of green lacewings in alfalfa fields was recorded using wire-mesh sticky traps attached to poles, as were data on both diel and seasonal patterns of flight activity (Duelli, 1980). Similarly, wire-mesh traps, measuring 1 m2 and placed at a range of heights, were used to monitor insect flight activity in cereal crops (Kokuba & Duelli, 1980). The main predators caught were adult ladybirds, hover flies and lacewings, but there was evidence that different species preferred to fly at different heights, so that the choice of trap height can be very important. On a larger scale, sticky nets measuring 2.5 m high by 1.5 m wide were used to monitor the flight activity of two ladybird species around experimental plots of alfalfa and oats (Ives, 1981). Captures of marked beetles in these nets helped to demonstrate that reductions in ladybird numbers recorded in certain plots were due to the emigration of beetles, rather than to beetle mortality. Sticky material (e.g., Tanglefoot®) can also be used in a different way to study small-scale movements of walking natural enemies. Rosenheim and Brodeur (2002) used Tanglefoot® to create funnel shapes on papaya petioles. Ladybird larvae (Stethorus siphonulus) detected and avoided the sticky walls of the funnel and were channelled into a holding area (delimited by a solid barrier of Tanglefoot®) on the petiole, because it was easy for them to enter by the wide end of the funnel but very difficult to exit by the narrow end. These funnel traps required only

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Fig. 6.18 a Insect net deployed in flight showing details of harness, reducing pulley, and main line weak link. b Frame for mounting an insect net atop an automobile. Source Greenstone et al., (1991). Reproduced by permission of The Entomological Society of America

minor repair over a ten-day period, including light rainfall, and enabled the authors to determine the stage during larval development at which S. siphonulus exits from papaya leaves. Nets are more efficient than solid interception traps for catching weak flyers, because they interfere much less with wind flow. It was estimated that the wire-mesh sticky traps used to catch green lacewings reduced wind speeds by only 10% (Duelli, 1980). Therefore, Malaise traps constructed of netting, and the MasnerGoulet trap (Sect. 6.2.4), might be useful for monitoring the flight activity and movement

patterns of insect parasitoids. The vertical distribution of aeronautic spiders and mites at heights up to 100 m was established by attaching nets to three tall radio masts (Freeman, 1946). A different approach to netting natural enemies is to employ mobile nets, which provides great versatility in the choice of horizontal and vertical sample zones. Greenstone et al. (1991) carried 0.62 m-diameter nets attached to an automobile and a light aircraft (Fig. 6.18). A slow-flying (72 km h–1) fixed-wing aircraft did not destroy small or delicate natural enemies, and the pair of nets attached to such an aircraft sampled at a rate of

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38,910 m3 h–1 (compared with automobile sampling at 21,420 m3 h–1). Natural enemies sampled by this method included spiders, predatory flies (Dolichopodidae), wasps, ants and parasitoid wasps, staphylinid and coccinellid beetles and anthocorid bugs. Many of these were still alive when nets were emptied (Greenstone et al., 1991). Other options for flying aerial nets, for which methods have been devised, include helium-filled balloons, parafoil kites, radiocontrolled model aircraft and helicopters (Reynolds et al., 1997). Currently, the use and application of unmanned ‘drones’ is developing rapidly (Kim et al., 2018). Water-filled tray traps (deposition traps) were used to monitor aerial dispersal of spiders (Topping & Sunderland, 1995; Weyman et al., 1995; Thomas & Jepson, 1997, 1999). Each trap was a 10 cm deep, 1 m2, fibreglass tray, filled with water and ethylene glycol (20:1) plus 1% detergent, fitting inside a 1.6 m2 metal tray containing the same fluid (Fig. 6.19). The inner tray sloped towards a central hole to which a muslin tube was attached and tied. The outer tray acted as a barrier (moat) to prevent spiders

Fig. 6.19 Plan of a deposition trap. Source Topping and Sunderland (1995). Reproduced by permission of Aarhus University Press

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walking from the crop to the inner tray, which therefore received only aerial immigrants (Topping & Sunderland, 1995). These deposition traps caught more spiders than sticky sheets of the same area laid flat on the ground (deposition:sticky was 1.9:1). Spiders in the vegetation may have snagged their ballooning lines on the edge of the deposition trap and then been able to run across the moat into the inner tray. Deposition traps have the advantage that they can be left unattended for long periods, but they are more labour-intensive to operate than rotor and suction traps (below) (Topping & Sunderland, 1995). Suction traps (see Southwood & Henderson, 2000, for general design) have been used to determine the phenology and vertical location of ballooning spiders (Salmon & Horner, 1977; Dean & Sterling, 1990; Sunderland, 1991; Blandenier & Fürst, 1998; Topping & Sunderland, 1998; Thorbek et al., 2002), carabid and staphylinid beetles (Sunderland, 1992) and the green lacewing, Chrysoperla carnea (Perry & Bowden, 1983). Traps were located variously in fields, at the edge of fields, and on the tops of buildings. Holzapfel and Perkins (1969) caught spiders,

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ants, parasitoid wasps, staphylinid beetles and predatory Heteroptera in an electric suction trap on board ship during nine cruises across the Pacific. The traps used in these studies were the Johnson‒Taylor suction trap (Johnson & Taylor, 1955), the 46 cm-diameter Enclosed-cone Propeller Suction Trap (Taylor, 1955) and the Rothamsted Insect Survey 12.2 m suction trap (Taylor, 1962). These traps have motor-driven fans that draw air down a tube, and the arthropods are segregated from the air by a filter and then deposited in a container filled with preservative. Miniature fan traps have been used to monitor the movement of parasitoids. Bellamy and Byrne (2001) deployed 102 such fan traps to study dispersal of the aphelinid Eretmocerus eremicus. Each trap had a 12 V DC 0.12 A fan which drew air into a 7.6 cm-long PVC pipe containing a plastic collecting vial with an organdy base; wasps were drawn into the trap and held against the base of the vial by suction. The cost of materials for such small battery-operated fan traps is about US$10, and they are ideally suited for parasitoid studies in that they capture insects intact and alive and are insufficiently powerful to catch larger insects (Hagler et al., 2002), which results in more efficient processing of the catch.

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Collecting vials can be quickly and easily capped, removed and replaced. Zhang et al. (1997) caught flying carabids (Harpalus rufipes) in blacklight traps (Sect. 6.2.8) and found that 97% were sexually immature, suggesting a pre-reproductive dispersal phase in this species. It is likely that these beetles disperse by flight over great distances since flights of tethered beetles in the laboratory typically lasted for about two hours (Zhang et al., 1997). Vertical-looking radar (VLR), which can automatically monitor the horizontal speed, direction, orientation, body mass and shape of arthropods intercepting the beam (Smith & Riley, 1996) could, potentially, provide useful information on the aerial migration of natural enemies and other benefical insects (Chapman et al., 2004). This technique has recently been used to investigate the dispersal of two ladybird species in the UK (Jeffries et al., 2015). Techniques such as blacklight and Malaise traps rely mainly on the behaviour of the natural enemy. Suction traps and sticky traps tend to be sensitive to weather conditions and the size of the organism. To circumvent some of these problems, a large rotary interception trap was devised (Fig. 6.20). It had a high efficiency and had the

Fig. 6.20 Diagrammatic representation of a rotary sampler. Source Topping et al. (1992). Reproduced by permission of The Association of Applied Biologists

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advantage that the sampling area was precisely defined. The rotary trap was more efficient than a co-located suction trap at catching Syrphidae, Staphylinidae and parasitic Hymenoptera (Topping et al., 1992). If the vertical aerial distribution of natural enemies is being investigated, then it is important to bear in mind that the number of insects trapped at a particular trap elevation will be a function of both the spatial density of the insects at that elevation and the downwind component of their ground speed (Duelli, 1980). The latter depends upon the wind velocity (Vw) and the flight velocity (air speed) (VL) of the insects. If the flight course coincides with the wind direction, the two velocities sum, but if the insects fly on a course at an angle a to the wind direction, the downwind component of the ground speed is VW + VL.cos a (Duelli, 1980). If, as is likely, wind velocity increases with height from the ground (Fig. 6.21a), the numbers of trapped

Fig. 6.21 Correcting the results of sticky trapping (traps set at varying heights above the ground) for the effects of wind velocity, in the green lacewing, Chrysoperla carnea, in alfalfa fields. This figure shows: a wind velocity (Vw, see text) as a function of height in an alfalfa field. In a night with average wind speed, the boundary layer (the layer within which the insect’s air speed exceeds wind speed) is at 50 cm. Above 1 m the ground speed is mainly a function of the wind speed; b the relative numbers (NF,

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insects (NT) need to be corrected for each trap elevation, to obtain the relative densities (NF) per unit volume of air, as follows (see Fig. 6.21b): N F ¼ N T =ðV W þ V L cosðaÞÞ

ð6:2Þ

Interception traps can also be used to study the movement of aquatic predators which are carried along in flowing water (Elliott, 1970; Williams & Feltmate, 1992; Southwood & Henderson, 2000). These devices generally take the form of tapering nets, with or without a collecting vessel attached, which are either positioned on the stream bed (Waters, 1962), or are designed to float (Elliott, 1967). Valuable information on natural enemy activity in the field can be gained by monitoring the movements of marked individuals (Scott, 1973). The commonest of the marking techniques used for this purpose is the application of spots of paint (usually enamel paint because

see text) of insects flying at a particular elevation. The numbers can be estimated from the vertical distribution of trapped insects by correcting for wind speed, air speed of the lacewing, and the angle between wind direction and flight course (see text for details). The corrected vertical distribution of females in the oviposition phase and of males, are treated here as linear regressions. Source Duelli (1980). Reproduced by permission of Springer Verlag

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some other types of paint may have toxic effects), which has been used to mark carabid beetle adults (Jones, 1979; Perfecto et al., 1986; French et al., 2001) and larvae (Nelemans, 1988), cantharid beetle larvae (Traugott, 2002), lycosid spiders (Hackman, 1957; Dondale et al., 1970; Samu & Sárospataki, 1995b; Samu & Kiss, 1997), linyphiid spiders (Samu et al., 1996), araneid spiders (Olive, 1982) and adult ladybirds (Ives, 1981). Tiny protonymphs of predatory phytoseiid mites have even been marked using watercolours (Schausberger, 1999). Acrylic paint was used to mark Episyrphus balteatus (Syrphidae) for short-duration dispersal studies. Individuals did not appear to be adversely affected and were seen foraging normally for 90 min after marking (MacLeod, 1999). Alternative fluid markers are inks (Kromp & Nitzlader, 1995) and typewriter correction fluid (Holland & Smith, 1999; Raworth & Choi, 2001). These marks may, however, be lost, particularly by carabid species which burrow in the soil or squeeze themselves into cracks or under stones. It is advisable, therefore, to test the durability of any marking system in preliminary trials. Raworth and Choi (2001) advocate painting identical marks on each elytron, since the probability of losing both marks (at least for short-duration studies of epigeal beetles) is very low. More robust marking systems have been used with (or are suitable for) carabid beetles, including branding with a surgical cautery needle (Fig. 6.22a, b), or a finepointed electric soldering iron (Manga, 1972; Ericson, 1977; Nelemans et al., 1989; Mikheev & Kreslavskii, 2000), etching the elytral surface with a high-speed drill (Best et al., 1981; Wallin, 1986; Thomas et al., 1998; Fournier & Loreau, 2002), cutting notches in the edges of the elytra and thorax with a small medical saw (Benest, 1989; Loreau & Nolf, 1993), cutting the tips off the elytra (Wallin, 1986) and etching the dorsal surface of the elytra with an engineering laser (Griffiths et al., 2001). It is important, especially when using a technique which involves physical mutilation, to ensure that marked individuals have the same survival rate as unmarked individuals and that their subsequent behaviour is unaffected.

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Fig. 6.22 a Surgical cautery needle used for marking the elytra of carabid beetles; b carabid beetle (Pterostichus melanarius) marked using the needle

A system of mark positions can be devised that will allow large numbers of a natural enemy species to be individually marked (Samu et al., 1996; Thomas et al., 1998; Holland & Smith, 1999; Southwood & Henderson, 2000). For example, Kiss and Samu (2000) marked the dorsum of the cephalothorax and abdomen of lycosid spiders with dots of enamel paint in such a way that the trap in which the spider was collected was coded by position of the dots, the date of trapping was coded by colour, and a supplementary dot was added if the spider was recaptured. Information on minimum distances travelled and speed and direction of movement can be obtained from the recapture data of marked individuals (Evans et al., 1973; Ericson, 1978; Greenstone, 1979; Best et al., 1981; Nelemans,

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1988; Mader et al., 1990; Lys & Nentwig, 1991; Zhang et al., 1997; Thomas et al., 1998). Evans et al. (1973) studied the movements of adult mutillid wasps in an open sandy habitat. The area was divided into a grid using wooden stakes and patrolled daily by an observer. Adult wasps were caught, marked with spots of coloured paint (of the sort used for model aircraft) using the head of an insect pin, and then released. Individuals could be recognised by the colour and positions of paint spots. Similarly, valuable data about the dispersal behaviour of parasitoid wasps attacking a tephritid fly on thistles were obtained by paintmarking (Jones et al., 1996). Jones et al. marked adult parasitoids with acrylic paint, and the location of individuals on thistles within a study site was recorded daily. Laboratory tests showed that marking reduced longevity of one species slightly, but the others were unaffected. The average recapture rate was 22%. The activity of wolf spiders in an estuarine salt marsh was investigated in a similar way; the spiders were caught using an aspirator and individually marked with enamel paint (Greenstone, 1979). During an investigation of the population dynamics of the dragonfly Calopteryx aequabilis, Conrad and Herman (1990) studied movement patterns in relation to adult territoriality by marking insect wings with a drawing pen so that individuals could be recognised. The study was carried out along a 635 m section of a stream, which was divided into 5 m sectors, delineated with small numbered flags. Observers patrolling the stream noted the sex, age category and sectorlocation of all individuals sighted. Population movements were analysed using a modification of Scott’s (1973) method, which allows separation of the velocity and distance components of movements. Marking can be dispensed with in cases where the species to be studied is new to an area. For example, Coll and Bottrell (1996) studied the eulophid Pediobius foveolatus, which parasitises Mexican bean beetle (Epilachna varivestis, Coccinellidae), and does not overwinter in Maryland where the studies were conducted. The authors released parasitoids at the edges of field plots, and, after various periods of time

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(depending on the experiment), they vacuumed all plants in the plots to record the movement of the parasitoids (Coll & Bottrell, 1996). In an analogous study, Lysiphlebus cardui (Braconidae, Aphidiinae), adults were released in a mountain area of Germany before the natural populations were active. Potted thistles containing colonies of the host aphid (Aphis fabae cirsiiacanthoidis) were distributed at up to 470 m from the release point, but female L. cardui were found to move a maximum of 20 m to a new host patch (Weisser & Völkl, 1997). In the context of the release of new species of parasitoid, hosts as ‘bait’ (Sect. 6.2.8) can be used to monitor the progress of dispersal. ‘Sentinel’ egg masses of the spruce budworm (Choristoneura fumiferana) were placed out at designated locations in a Canadian forest and later retrieved and taken to the laboratory to rear the parasitoids out. These egg masses showed that the released parasitoids Trichogramma minutum dispersed 19 m in 5 days (Smith, 1988). The braconid parasitoid Cotesia glomerata was introduced into North America to control the caterpillar pest Pieris rapae in brassica fields, but there were fears that it might disperse into woodland and reduce populations of the native butterfly Pieris virginiensis. Benson et al. (2003) put out sentinel larvae of Pieris rapae and Pieris napi in woodland sites, and subsequent dissection revealed no Cotesia eggs or larvae in the sentinel hosts: they concluded that C. glomerata (even though present in adjacent meadows) do not forage in forested habitats and so are not a threat to P. virginiensis. To study dispersal of the parasitoid Venturia canescens in relation to environmental heterogeneity, Desouhant et al. (2003) released wasps (marked on the thorax with a dot of acrylic paint) from a central point and monitored their speed and pattern of spread out to 97 traps (moth larvae in food medium on 1 m high poles set 10 m apart). This design allowed the authors to relate dispersal to various factors such as sunlight intensity, amount and type of vegetation (Desouhant et al., 2003). Monitoring dispersal of mass-reared natural enemies of the same species as occur naturally in the area of release would be facilitated if the reared

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individuals carry a genetic marker, such as an eye or body colour mutation (Akey, 1991), which is noticeable visually, or which can be detected unambiguously by biochemical means (Symondson & Liddell, 1996). Some attempts have been made to obtain information on the movement of individuals without interrupting that movement. Baars (1979b) labelled adult carabids with an iridium isotope (192Ir) by mixing it with quick-drying enamel paint. Using a portable scintillation detector, it was possible to track the movements of up to ten individuals over the same period of time in the field, although the beetles frequently lost their paint marks and there was some evidence from laboratory tests that the radiation affected beetle mortality rates. The chemical composition of natural enemies (chemoprinting) might, potentially, be used to identify their area of origin, and thus be used to determine, indirectly, the minimum distance dispersed. This might be successful if they were to disperse as adults after pre-adult development on hosts/prey living on soils and vegetation with a characteristic chemical fingerprint. Wavelength dispersive X-ray fluorescence spectrometry can be used to make quantitative measurements of the elemental composition of individuals (Bowden et al., 1979). Dempster et al. (1986) used this method to measure the relative composition of nine chemical elements in adult Brimstone butterflies (Gonepteryx rhamni), and they found differences between sexes, sites, seasons and years. For this method to be of use, the chemical pattern obtained by pre-adult stages should be stable in the adult, and inter-site variation should be greater than intra-site variation. Unfortunately, in the Dempster et al. (1986) study, the differences gained in pre-adult stages were quickly masked as the adults aged and fed. Sherlock et al. (1986) also failed to demonstrate clear sourcerelated chemoprints in the cereal aphid Rhopalosiphum padi. The proportions and composition of stable isotopes, (e.g., deuterium), in the bodies of migrating animals may also reflect their area of origin (Hobson, 1999). Hobson et al. (1999) described the first application of the stable

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isotope technique to infer migration of an invertebrate. They showed that the stable hydrogen isotopic composition of wing keratin (metabolically inert after eclosion) of monarch butterflies (Danaus plexippus) was highly correlated with that in their milkweed host plants, which, in turn, was related to stable geographical patterns of deuterium in rainfall. Using these techniques it was possible to determine, approximately, the natal latitude in eastern USA of monarchs collected after a period of migration (e.g., when overwintering in Mexico) (Hobson et al., 1999). This technique has more recently been applied to studying hover fly (Episyrphus balteatus) migration in Western Europe (Raymond et al., 2014). Using a combination of wing morphometrics and hydrogen isotopic ratios, the authors concluded that hover fly populations in France were mostly made up of local individuals, with a limited amount of migration from more southerly populations. The chemical elements C, N, S, H and O all have more than one isotope and isotopic compositions can be measured with great precision using a mass spectrometer (Peterson & Fry, 1987). Stable C and N isotopes have been used in entomological studies. Carbon, for example, is fixed in C3 plants (such as cotton) by the Calvin photosynthetic pathway and in C4 plants (such as sorghum) by the Hatch‒Slack pathway. C3 and C4 plants contain different ratios of isotopes 13C and 12C, and these ratios are maintained at higher trophic levels. Using a stable isotope mass spectrometer, Ostrom et al. (1997) showed that isotope values (isotopic ratios for C and N) were sensitive to change of laboratory diet by the ladybird Harmonia variegata, within a period of three weeks. These authors then documented diet shifts (alfalfa-, wheat- and maize-based feeding) in fieldcollected ladybirds (Coleomegilla maculata), which implied local dispersal of the beetles between habitats. Similar methods were developed by Ouyang et al. (2014) to study prey origins of the predatory beetle Propylea japonica. Hurd et al. (2015) used 13C and 15N ratios to demonstrate ontogenetic changes in the prey use of praying mantids, Tenodera aridifolia sinensis, by comparing stable isotope levels of field-collected

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individuals to individuals fed known diets in the laboratory. Their results suggested that mantids do not feed on prey in the field in a frequencydependent fashion, and that they prey upon species from higher trophic levels more commonly as they progress in their development. Instead of relying on natural labelling (e.g., chemical fingerprint or stable isotope ratio), natural enemies can be labelled artificially with one or more specific trace elements (e.g., rubidium and strontium). This technique of elemental marking is defined as the establishment of an elemental concentration in an individual, significantly higher than that carried by the indigenous population (Akey, 1991). This method can be applied without having to handle the insect (as is necessary with paints, dyes, powders etc.), and it does not entail environmental contamination (as for radioactive isotopes). An atomic absorption spectrophotometer and graphite furnace permits analysis of micrograms of sample. Parasitoids and predators labelled with rubidium can be distinguished from controls for 4 to 20 days, depending on species. The method is useful for a wide range of parasitoids and predators (Graham et al., 1978; Prasifka et al., 2001). After plots of cotton and sorghum were treated with foliar solutions of rubidium chloride, a range of ladybirds, spiders, predatory beetles and predatory bugs became labelled and were found to have moved between fields. Recapture rates were three to four times higher than for the use of fluorescent dust markers in a comparable system (Prasifka et al., 1999, 2001). Elemental marking usually does not affect the biology of the labelled animal. One exception to this was a reduction in longevity of male parasitoids, Anaphes iole (Mymaridae) labelled with 1000 ppm rubidium. There were no adverse effects, however, on the parasitoid Microplitis croceipes, triple-labelled with caesium, rubidium and strontium at 1000 ppm (Jackson, 1991). Corbett et al. (1996) treated prune trees with rubidium in the autumn. Adult prune leafhoppers, Edwardsiana prunicola, became labelled and their mymarid parasitoid, Anagrus epos, carried a label 3.9 times greater than the background level of rubidium. Emerging parasitoids retained the label

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throughout their lifetime. Such labelling could, potentially, be used to quantify dispersal of the parasitoid from prune to grapevines, but, unfortunately, there was overlap in rubidium level between treated and natural populations of the parasitoid, and probabilistic statistical techniques will be needed to quantify dispersal in this context. Detection of marked eggs from marked mothers is possible, and so this technique can provide information on survival, dispersal and oviposition (Akey, 1991). Small natural enemies may not be ideal candidates for marking with paints and tags, and detection of trace elements requires specialised equipment. An alternative is to mark by immersing the insects in rabbit immunoglobulin (IgG) and then assay (after dispersal) by ELISA (enzyme-linked immunosorbent assay) (Sect. 6.3.9) with goat anti-rabbit IgG. This marker can remain detectable for at least eight days. This is, however, an external marker, which will be lost on moulting or during development to a different life stage (Hagler et al., 1992a). Internal protein markers can be used for marking fluid-feeding natural enemies (Hagler & Jackson, 2001). Whitefly parasitoids (Eretmocerus eremicus) marked both internally (fed on honey solution containing rabbit IgG) and externally (exposed to a mist of rabbit IgG solution from a medical nebuliser), were released into a cotton field and 40% of recaptured individuals (during 32 h after release) were carrying the label. This study provided valuable information about gender-related dispersal, diel activity pattern, and distance moved by this parasitoid (Hagler et al., 2002). Radio telemetry, a standard method for studying vertebrate dispersal, can also be used for studies of large invertebrate predators. Riecken and Raths (1996) fixed 0.7 g transmitters (with an aerial length of 5 cm and a battery life of 28 days) to the elytra of Carabus coriaceus (the largest species of carabid beetle in West Germany) using silicone glue. Heavy rain sometimes caused transmitter malfunction, but, despite such problems, the authors were able to detect beetles at a maximum range of 400 m. This method, unlike harmonic radar (below),

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allows transmission at different frequencies, and, therefore, the individual tracking (without capture and disturbance) of more than one specimen in the same area (Riecken & Raths, 1996). This property of radio telemetry was exploited by Janowski-Bell and Horner (1999) to track individual tarantula spiders (Aphonopelma hentzi) in a Texas scrub habitat. Transmitters weighing 0.6–0.8 g were attached to adult male spiders weighing 2.5–7.5 g, and half of these spiders retained their transmitters for three or more days. The signal was received at a distance of several hundred metres and spiders could be detected in burrows and under rocks. Males were observed to move up to 1300 m during 18 days when searching for females. Although the possibility that the transmitter and aerial affected behaviour to some extent could not be excluded, tagged spiders were, nevertheless, observed to enter burrows and crawl past resident females, as normal (Janowski-Bell & Horner, 1999). Remote sensing techniques, such as harmonic radar, can also be used to track the movement of natural enemies. The natural enemy target of harmonic radar tracking must carry a transponder (antenna plus diode), which detects radio waves transmitted from a hand-held device, and reradiates some of the received energy at a harmonic frequency. This method does not require a battery to be attached to the target, which means that extremely lightweight tags can be developed (Kutsch, 1999). An harmonic radar system has been used to track the movements of large carabids over the ground (Lövei et al., 1997; Mascanzoni & Wallin, 1986; Wallin & Ekbom, 1988, 1994), and the foraging flights of bumblebees, Bombus terrestris (Osborne et al., 1999). Improved tags are now available for reflecting harmonic radar. The 0.4 mg tags are an order of magnitude lighter than those previously used on bees, and two orders of magnitude lighter than those used on carabids. Detection is possible from a distance of 50 m. The tag increased the weight of a tachinid fly by 0.1–0.9% and, 48 h after release, the fly had moved 100 m (Roland et al., 1996). In general, tags weighing up to 1.5% of insect body weight should have no deleterious effects on flight performance (Riley

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et al., 1996; Roland et al., 1996; Boiteau & Colpitts, 2001). Even lighter tags have now been developed for the study of colony collapse disorder in bees (Tsai et al., 2013). Metal detection equipment, employing a pulse field induction loop, is many times less expensive than harmonic radar. It is most suitable for detecting microhabitat preferences and local movement (e.g., to find pupation sites) of relatively sedentary cryptic invertebrates. Piper and Compton (2002) tagged beetle larvae with tiny steel tags (1 x 3 mm; 0.35 mg) and released them in leaf litter. Using a hand-held metal detector powered by a 12 V battery, they were able to detect 90% of the released larvae after five months in the field. The detection range is only 3–7 cm, but tagged animals can be detected through litter and soil, and laboratory experiments showed no significant effect of tagging on movement of larvae through leaf litter (Piper & Compton, 2002). Other marking techniques which have been used to monitor the movements of natural enemies include: (a) numbered plastic discs glued to the dorsum of adult or larval carabids (Nelemans, 1986, 1988; Lys & Nentwig, 1991), (b) coloured and numbered bee tags attached to adult Tenodera aridifolia mantids (Eisenberg et al., 1992) and adult paper wasps (Schenk & Bacher, 2002), (c) spots of typewriter correction fluid applied to carabids (Wallin, 1986; Hamon et al., 1990; Holland & Smith, 1997), (d) spots of acrylic paint applied to the pronotum of adult eucoilid and braconid parasitoids with a fine brush (Driessen & Hemerik, 1992; Ellers et al., 1998), (e) numbers written on the wings of damselflies with a drawing pen (Conrad & Herman, 1990), (f) fluorescent powder applied to green lacewings (Duelli, 1980), and (g) stable isotopes fed to coccinellid adults in drinking water (Iperti & Buscarlet, 1986). Driessen and Hemerik (1992) attempted to mark adult Leptopilina clavipes, a figitid parasitoid of Drosophila flies, with micronised fluorescent dust. Although the dust worked well with the drosophilids, it was unsuccessful as a means of marking the parasitoids because it was rapidly lost from the smooth surface of the exoskeleton and elicited prolonged preening behaviour in the wasps. A potential problem with the use of dusts is that

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they may become accidentally transferred to unmarked individuals of the same species during the recapture process. Fluorescent dusts have also been used to detect visits by parasitoids to specific plants by applying the dusts to the leaves of the plant and then searching for traces of the dust on parasitoids under UV illumination (Ledieu, 1977). Corbett and Rosenheim (1996) used parasitoid mark-recapture data to estimate the ‘diffusion’ parameter D, which is the random component of movement, independent of forces such as wind, attraction to resources and repulsion from conspecifics. Grape leaves containing leafhopper (Erythroneura elegantula) eggs, parasitised by the mymarid Anagrus epos, were put in a bag and gently rolled for 1 min in a mixture of 100 ml Day-GloR fluorescent powder and 1 kg walnut husks (as a carrier). As they emerged from the host egg, 85% of parasitoid adults marked themselves with a few minute particles of powder, which lasted at least 48 h, and permitted markrecapture studies in vineyards (recaptures on yellow sticky traps). Marks were too small for detection in UV light, and a microscope was needed. Since only a few particles of marker adhered, minimal effects on survival and behaviour were expected (Corbett & Rosenheim, 1996). Self-marking with fluorescent powder was also used for opiine braconid parasitoids of tephritid fruit flies in Hawaii in a study of dispersal in the field (Messing et al., 1993), but, unfortunately, this marker was found to greatly increase adult parasitoid mortality, compared to controls. Garcia-Salazar and Landis (1997) studied the effects of wet and dry Day-GloR fluorescent marker, at three doses, on the survival and flight behaviour of Trichogramma brassicae. In the dry method, powder was added to glass beads in a small plastic cup and shaken to build up a deposit on the walls. The beads and excess marker were removed and wasps were added and allowed to self-mark through contact with the walls. In the wet method, the marker was suspended in ethyl alcohol and swirled to coat the walls. Marking did not affect wasp survival, but the flight response was reduced at the highest dose for the dry method. More individuals, however, retained the

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mark for 24 h in the dry method. Adults of Spalangia cameroni, a hymenopteran parasitoid of the house fly Musca domestica, were allowed to self-mark in sawdust containing Day-Glo® fluorescent powder. Particles of fluorescent powder could be detected on the parasitoids with the naked eye for about four days, and marking reduced their survival only slightly, from eleven days to ten days (Skovgård, 2002). Prasifka et al. (1999) marked predators with fluorescent dust in a sorghum field and made predator collections one day later in an adjacent cotton field. Examination of predators (mainly Orius bugs), under a dissecting microscope, revealed particles of dust, and demonstrated that predators had dispersed from the senescing sorghum crop into cotton. Six colours of fluorescent dust were used, with each colour being applied to a crop zone at a specific distance from the sorghum‒cotton interface, so that the distance moved by predators could be determined. Dust was applied at 4.8 g per m of row at 3.5 kg cm–2 of pressure, through a compressed-air sandblast gun, with a spray nozzle internal diameter of 4.4 mm. Perry et al. (2017) developed a method to study the dispersal distances of a wide variety of forest-floor invertebrate species involving the deposition of three concentric rings of differentcoloured fluorescent powder, and subsequent collection of invertebrates to record how many bands they had crossed (and thus the minimum distance they had travelled). Thousands of individuals of the staphylinid beetle Aleochara bilineata were marked with DayGlo pigment and released weekly from 24 Canadian gardens. Three percent of marked beetles were recaptured with barrier pitfalls, water traps and interception traps in the gardens. Recaptured beetles had dye particles adhering to the intersegmental membranes of the abdomen, and at the bases of wings, head and coxae. Capture of marked beetles in control gardens (where no releases had been made) showed that they were capable of flying at least 5 km (Tomlin et al., 1992). Judging by experience with marking grasshoppers (Narisu et al., 1999), large natural enemies, such as carabid beetles, marked with fluorescent powder, should be detectable under

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UV light at night, without the need for recapture, which would enable dispersal of the subject to be monitored without disturbance. Narisu et al. (1999) reported that 64% of marked grasshoppers were relocated with UV light at night, compared with only 28% by visual searching during the daytime. Schmitz and Suttle (2001) marked individuals of a large lycosid spider, Hogna rabida, with fluorescent powder and recorded (from a distance of 2.5 m using binoculars) horizontal and vertical distances moved every five minutes until the spider noticed the presence of an observer (which could take up to 6 h). Natural enemies with pubescent cuticles could be marked with pollen of a species that does not occur in the study area. Many pollens can be identified to species with a microscope, they are durable and some have evolved to adhere tenaciously to the insect exoskeleton. In some contexts, ingested pollen can also yield valuable information about natural enemy dispersal. Wratten et al. (2003) dissected the guts of adult hover flies, stained the contents with saffranin (White et al., 1995), and were able to detect pollen of Phacelia tanacetifolia which the hover flies had taken from strips of this flower planted at the edge of fields. This method was exploited to quantify the degree of inhibition of hover fly dispersal attributable to various types of hedges and fences in the farmed landscape. Phacelia pollen was detectable in the hover fly gut for less than 8 h, which enabled calculation of a crude estimate of rate of dispersal (Wratten et al., 2003). Vital dyes could prove useful in dispersal studies. Braconids were labelled with the vital dye acridine orange, through their food. The alimentary canal and eggs of these parasitoids became labelled within 24 h, and could be seen fluorescing under an epifluorescent microscope. Tissues continued to fluoresce for 6 weeks after death. Parasitoid longevity was unaffected, and no behavioural effects were observed. Oviposited eggs of some species were labelled, and the label persisted for about 2 days. Stained eggs hatched normally and eventually produced adult offspring (Strand et al., 1990). Topham and Beardsley (1975) labelled tachinid parasitoids of sugarcane weevils with a radioactive marker, in an attempt to monitor the

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distance travelled by the adult flies between oviposition sites within crops and nectar food sources at field margins (Sect. 6.5). Adults of the eulophid wasp Colpoclypeus florus, a parasitoid of leafrollers, were fed on honey-water containing the radioisotope 65Zn. Labelling did not affect fecundity, but it significantly reduced longevity and the viability of oviposited eggs (Soenjaro, 1979). Emerging adults of the egg parasitoid Trichogramma semifumatum were fed on 32P honey-water solution. Three-and-a-half million wasps were released in Californian alfalfa and sampled with sweep nets and vacuum insect nets. Only a few hundred were recaptured, due to rapid dispersal and dilution of marked wasps in the area, but tagged wasps lived at least 15 days after release, and the study provided useful data on rates of dispersal (Stern et al., 1965). Trichogramma ostriniae (an egg parasitoid of the Asian corn borer, Ostrinia furnacalis) were dipped or sprayed with 32P, because less than 25% became marked if allowed to feed on radioactive honey-water. The label did not affect longevity or reproduction, but 63–83% of the label was lost over five days in the field, probably due to selfgrooming. The method has potential for shortterm ecological studies in situations where radioactive markers are permitted (Chen & Chang, 1991). In some countries there are environmental concerns and regulations concerning the use of radioactive markers in field studies (Greenstone, 1999). Southwood and Henderson (2000) provide further information on marking techniques and the use of codes, which allow the recognition of large numbers of individuals. In the context of commercial rear-and-release biocontrol programmes, quantitative assessments of factors that affect searching efficiency (such as flight initiation and walking activity) of parasitoids is an important aspect of quality control (Bigler et al., 1997). A simple laboratory test was devised and showed that strains of Trichogramma brassicae varied considerably in flight initiation (percentage flyers and non-flyers in a three day post-emergence period at 25 °C). Results from field cage studies, using cages with clear plastic walls and sticky strips, confirmed the laboratory results (Dutton & Bigler, 1995). The laboratory

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flight-test apparatus used by Ilovai and van Lenteren (1997) to monitor quality of mass-reared parasitoids (Aphidius colemani) consisted of a glass cylinder with the internal wall coated with repellent, and the exterior wall shaded to exclude light. Mummies were placed at the bottom of the cylinder and emerging parasitoids flew up to the light at the top of the cylinder, where they were caught on a glass plate, coated with adhesive. Only 40–65% of emerging adults were found to be capable of flight. The technology is available to study patterns of diel activity efficiently in the laboratory, with a combination of robotics and video image analysis, in real time. A video camera can be moved, automatically, to partition its time between a number of subjects. A system such as this was used to analyse diel cycles of locomotor activity by Trichogramma brassicae, measuring the percentage of time spent moving, linear speed, angular speed, and sinuosity of trajectory. Forty individuals were studied, with one 5 s recording every 5 min (Allemand et al., 1994). For most parasitoids, the last stage of host-finding is by walking, and parasitisation rate is heavily influenced by rate of walking and searching. With this in mind, Wajnberg and Colazza (1998) videorecorded the walking path of T. brassicae, and used an automatic tracking computer device to transform the path to X–Y coordinates (with an accuracy of 25 points s–1). The track width was set at two reactive distances (this species can perceive host eggs at a mean reactive distance of 3.7 mm), and the area searched per unit time was computed with each surface unit counted once, even if the wasp crossed its own path. T. brassicae was found to search, on average, at 28 mm2 s–1. The method can be used to guide genetic selection of mass-reared Trichogramma species. Using similar technology, Van Hezewijk et al. (2000) found a significant amount of variation between strains of Trichogramma minutum, and within strains over time. A wide variety of automated video-tracking systems are now available to record insect behaviour, some of which are low-cost and open source (reviewed in Dell et al., 2014). In addition, automated locomotor activity monitors that record insect activity by

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recording numbers of infrared beam breaks inside replicated, isolated units (e.g., glass tubes) are commercially available and easy to use (Pfeiffenberger et al., 2010). To date, these setups have mostly been applied to neurological and genetic studies of Drosophila, but they could also be usefully applied to studying circadian rhythms and relative activity levels of different species or intraspecific variants of natural enemies in the laboratory. Rasmussen et al. (2018) conducted one of the first such studies applied to a biological control agent. DNA-based techniques (Sect. 6.3.12) may enable dispersal of natural enemies released in biocontrol programmes to be distinguished from that of native natural enemies of the same species, and also permit tracking of the genetic marker through successive generations (which would not be possible with other marker systems) (Greenstone, 2006). Gozlan et al. (1997) attempted to use RAPD-PCR (Sect. 6.3.12) to distinguish between strains of three species of Orius (predatory heteropteran bugs) used in augmentative release programmes. Species were readily distinguished, but a high degree of polymorphism prevented discrimination between strains. This method was used successfully, however, by Edwards and Hoy (1995), to monitor insecticide-resistant biotypes of Trioxys pallidus (a parasitoid of the walnut aphid, Chromaphis juglandicola) released into orchards, and to distinguish them from wild populations. The resistant biotype disappeared from one orchard by a year after release, but it was detected for three years in two other orchards. It might be possible to use PCR techniques to monitor dispersal of virus-labelled natural enemies. De Moraes et al. (1998) successfully used PCR to detect nucleopolyhedroviruses in homogenates of hemipteran predators up to 48 days after application of these viruses in a soybean field. Although peak levels of virus detection occurred at 10–45 days after application, the virus was found in predator homogenates five days before it was found in the host larvae (velvetbean caterpillar, Anticarsia gemmatalis), suggesting that the foraging predators became externally contaminated with virus particles.

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Similarly, Smith et al. (2000) used PCR to detect genetically engineered baculoviruses in predators (spiders, ladybirds and heteropterans) five days after a cotton field was sprayed with these viruses. If natural enemies, before release, were deliberately surface-contaminated with a unique (e.g., genetically manipulated) virus, this could be useful for short-term dispersal studies (baculoviruses are usually inactivated by UV light after two or three days).

6.3

Determining Trophic Relationships

6.3.1 Introduction In nature, the trophic interrelationships of invertebrates within a community rarely, if ever, consist of simple food chains (Sect. 6.1). They often comprise an extensive feeding web composed of several trophic levels. The comparative ease with which parasitoids of endophytic hosts can be reared from, and their remains (egg chorions, larval exuviae) located within, structures such as leaf mines, stem borings and galls, together with the consequent certainty with which parasitoid‒host relationships can be discerned, has meant that some of the most detailed studies of food webs involving insects have been on gall-formers, leafminers and stem borers (Askew & Shaw, 1986; Redfern & Askew, 1992; Claridge & Dawah, 1993; Memmott et al., 1994; Dawah et al., 1995; Martinez et al, 1999; Maunsell et al., 2015). Food webs associated with endophytic insects are also attractive to researchers because of their greater complexity. Askew (1984) has documented more than 50 species of cynipid gall wasps, and their associated parasitoids, on oak and rose galls in Britain, and Redfern and Askew (1992), Claridge and Dawah (1993), Valladares and Salvo (1999), Memmott et al. (1994), Dawah et al. (1995) Lewis et al. (2002), Maunsell et al. (2015) give diagrammatic representations of a range of food webs based on gall-formers, stem borers and leafminers. Complex webs have also been constructed for non-endophytic pests such as aphids,

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their parasitoids and secondary parasitoids (Müller et al., 1999; Lohaus et al., 2013) as well as for lepidopterans (Henneman & Memmott, 2002; Timms et al., 2012; Peralta et al., 2014; Wirta et al., 2014; Rocca & Greco, 2015; Frost et al., 2016; Shameer et al., 2018; ). They have also been used to answer specific ecological questions, such as the degree to which parasitoid biocontrol agents are penetrating natural communities (Henneman & Memmott, 2002), the impact of the establishment of introduced herbivores on native food webs (Timms et al., 2012), the importance of functional complimentarity and redundancy of parasitoids as determinants of parasitism rates (Peralta et al., 2014), and to quantify the importance of apparent competition in trophic webs (Frost et al., 2016). Further considerations on food webs, their construction and use can be found in Sect. 6.3.12. Described below are several methods used (Table 6.3 summarises their relative merits) for elucidating the trophic relationships between invertebrates and their natural enemies from the standpoint of: (a) the host or prey, i.e., the species composition of its natural enemy complex and (b) the natural enemy, i.e., its host or prey range. Some of these methods may also be used when the trophic relationships are already known, either to confirm that a relationship exists in a particular locality, and/or to record the amount of predation and parasitism: their use in quantitative studies is largely dealt with in Chap. 7. In cases where a predator is likely to be feeding in more than one ecosystem during a relatively short period of time, analyses using stable isotopes of carbon and nitrogen (Fantle et al., 1999; Post et al., 2000; Post, 2002) might be applicable for determining the relative contribution of prey from each ecosystem to the overall diet of the predator (but without any precision concerning exactly which species have been consumed). Stable isotope analyses applied to body tissues can summarise some general aspects of the diet of an individual over a long period of time, as compared with the various methods of gut contents analysis (Sects. 6.3.7– 6.3.12) which describe foods consumed during one or a few meals. Stable isotope ratios

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Table 6.3 Comparison of methods for investigating qualitative aspects of trophic relationships in natural enemy complexes and communities. Advantages and disadvantages of each method are discussed in more detail in the text (Sect. 6.3) Method

Advantages

Disadvantages

Field observation

Immediate and usually unequivocal results obtained with minimal equipment

Time-consuming and labour-intensive; small, hidden, nocturnal, subterranean or fast-moving invertebrates difficult to observe; possibility of disturbance to invertebrates during observation; high cost of video equipment; unlikely to observe predation by highly mobile predators at natural (low) densities

Collection of prey and remains from within and around burrows and webs

Prey items conveniently located in or around nest/burrow/web

Often involves disturbing predators; applicable to few natural enemies; invertebrates caught in webs may not be prey

Tests of prey/host acceptability

Results usually unequivocal Defines prey/host that a predator/parasitoid is physically and behaviourally capable of attacking

Identification of parasitoid immatures to species level often not possible due to lack of descriptions and keys; false negatives a problem; may not apply to prey choices in the field (false positives)

Rearing parasitoids from hosts

Results usually unequivocal

False negatives a problem, if sampling is not sufficiently extensive and intensive; hosts of koinobionts need to be maintained until parasitoid larval development is complete

Gut dissection and faecal analysis

Simple and quick to perform with minimal equipment; samples can be stored either frozen or in preservative for long periods prior to testing; direct information on prey consumption in the field

Can only be used if predator or parasitoid ingests solid, identifiable parts of prey or hosts that possess solid parts; identification of prey remains not always possible; possibility of misleading results due to scavenging and secondary predation

Immunological analyses of gut contents

Accurate, highly sensitive and reproducible techniques available; cell lines generating monoclonal antibodies can be retained for future use, giving reproducibility to assays over time; sample can be stored frozen for long periods prior to testing; large numbers of predators can be analysed rapidly

Time-consuming to develop and calibrate, requires specialist equipment and expertise; risk of false positives through cross-reactions, scavenging (and, possibly, secondary predation); has been superseded by DNA-based techniques

Prey labelling

Simpler to perform than either electrophoretic or immunological tests; dyes easily detected; samples can be stored frozen for long periods prior to testing

Expensive equipment required to detect some labels; radioactive labels a hazard to operator and environment; usually involves severe disturbance to the system under study; labels can be lost rapidly; label passes through trophic levels by a variety of routes (e.g., coprophagy) (continued)

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Table 6.3 (continued) Method

Advantages

Disadvantages

Electrophoresis

Can be accurate and sensitive once standardised; large numbers of samples can be analysed rapidly

Expensive, time-consuming to calibrate, and requires specialist equipment and expertise; risk of false positives through scavenging and secondary predation; inappropriate for highly polyphagous species (coincidence of diagnostic bands); has been superseded by DNA-based techniques

DNA-based techniques

Can provide highly accurate data identifying predation on different species, biotypes and populations; all prey in the predators can be identified simultaneously using high-throughput sequencing

Requires molecular biology facilities; risk of false positives through scavenging, secondary predation and (where PCR is involved) cross-contamination; some species detected by high-throughput sequencing may not be on sequence databases (may need to create your own sequence database)

(Sect. 6.2.11) are useful natural tracers of nutrient flows in ecosystems (Lajtha & Michener, 1994; Ponsard & Arditi, 2000) and could be used to provide a broad summary of how organic matter flows through a food web (Sect. 6.3.12), including cryptic food webs in soil (Eggers & Jones, 2000; Scheu & Falca, 2000). 15N/14N ratios, for example, tend to increase with each successive link in a food chain (e.g., plant–herbivore–predator) because deamination and transamination enzymes preferentially remove light (14N) amine groups and the 15N/14N ratio in organisms becomes progressively heavier than that of atmospheric nitrogen. Excreted nitrogen is, therefore, lighter than body tissue nitrogen. Interpreting the isotopic signature of a consumer in terms of its trophic position needs to be in relation to an appropriate isotopic baseline. Post (2002) discusses methods for generating an isotopic baseline and evaluates assumptions underlying estimation of trophic position. In the northern temperate lake ecosystems studied by Post (2002), snails were a suitable baseline for the littoral food web and mussels for the pelagic food web. Lycosid spiders, Pardosa lugubris, were shown to have higher 15N/14N ratios than their prey, suggesting that stable isotope analysis may be appropriate for assessing the trophic position of the organisms tested (Oelbermann & Scheu, 2002). 15N content in predators, however, is affected by food quality and starvation

(Oelbermann & Scheu, 2002). Such effects need to be taken into account in determining trophic position. In addition, scavenger species often have 15N levels within the range of values found for predators (Ponsard & Arditi, 2000), which needs to be acknowledged when interpreting results. Stable isotope ratios have been used to elucidate features of rocky littoral food webs that were not detected by analysis of gut contents (Pinnegar & Polunin, 2000). Using stable isotope analysis of carbon and nitrogen, McNabb et al. (2001) found that linyphiid and lycosid spiders in experimental cucurbit gardens treated with straw and manure mulches appeared to be preying largely on detritivores (such as Collembola). They caution, however, that 13C values of Collembola were not significantly different from those for spotted cucumber beetle (Diabrotica undecimpunctata howardi), which was a major herbivore in the system under study. Thus, in some cases, stable isotope analysis may lack the precision to discriminate between consumption of detritivores and herbivores. A very useful review of the topic summarising the methodology involved, and highlighting the usefulness of the method for understanding predator‒prey dynamics in the field, is available elsewhere (Hood-Nowotny & Knols, 2007). Unbiased sampling of predators that are to be assayed by post-mortem methods (e.g., gut dissection, immunoassays, electrophoresis, DNA

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techniques) should, ideally, employ at least one sampling method relying on predator activity (e.g., sticky trap, Malaise trap, pitfall), run for at least 24 h (to accommodate the predator’s diel activity cycle), and one that will include inactive predators (e.g., ground search). The latter is not, however, always feasible; for example, with large carabids that are frequently found at densities well below one per square metre, in which case data should be interpreted with caution. Violent methods (e.g., sweeping, beating, suction sampling) are not advisable in circumstances where damaged target prey could contaminate the predators and produce false positives (Crook & Sunderland, 1984). Dry pitfall traps should be avoided if there is a significant degree of entry of the traps by the target prey (i.e., if the density of prey in the traps is greater than that found in the same area of open ground), because artificial confinement of prey with hungry predators may produce an erroneous assessment of prey consumption in the field. This can sometimes be overcome by using dry traps with a mesh insert, so that larger predators are separated physically from smaller prey. There is evidence that this works well for negatively phototactic prey, such as earthworms, which remain in the bottom of the trap, but less well for positively phototactic prey, such as aphids, which can climb up the sides of the trap, and thus come within reach of the predators once more (W. O. C. Symondson, D. M. Glen, M. L. Erickson, J. E. Liddell and C. Langdon, unpublished data). An additional problem can be predation by one predator upon another within the trap, where the predator consumed has previously fed on the target prey. Hengeveld (1980), for example, found the incidence of spider remains in the guts of carabid beetles to be much greater when the beetles were collected in live-trapping pitfalls than in funnel traps where the beetles were killed instantly in 4% formalin solution. The inclusion of stones, soil or mesh can help to minimise this problem in dry traps. If the predator of interest is, for example, a large carabid, then holes in the bottom of the trap can allow small predators, and other target or nontarget prey, to escape. Wet traps can also lead to biased results. Water can attract prey, such as

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slugs (and possibly many insects during dry weather), to the local area of the trap leading to increased consumption by predators in that area (Symondson et al., 1996). It has also been shown that when slugs drown in a trap, carabid beetles drowning in that same water may ingest proteins released by the dying or dead prey, again leading to false positives (W. O. C. Symondson, unpublished data). Trapping reagents that cause protein denaturation (e.g., formalin, alcohol) should be avoided. Regular emptying of traps, and transport of predators on ice, will reduce the rate of microbial degradation of the gut contents of dead predators (Sunderland, 1988), and slow down digestion in live predators caught in dry traps (Symondson et al., 1996). With most of the post-mortem methods described below, it is advisable to carry out some initial laboratory studies with all predator species that will be collected from the field for testing. Predators that have just eaten a large meal of the target prey should be tested. This initial screening will identify any species that have an extreme degree of extra-oral digestion (Cohen, 1995), and which will therefore often be negative in the test, even though they may be important consumers of the target prey in the field. The rates at which prey remains decay within predators are discussed in Sect. 6.3.9. False negatives are a problem common to all the methods; that is, due to undersampling, some parasitoid or predator species comprising an insect’s natural enemy complex may be overlooked, as may certain insect species comprising a natural enemy’s prey/host range. More serious, however, is the problem of false positives with several of the methods discussed here (e.g., tests of prey and host acceptability, immunoassays, prey/host labelling, electrophoresis). Positives in post-mortem tests indicate consumption of the target prey, but not necessarily predation, because the label can enter the predator by other routes, such as scavenging and secondary predation (Sunderland, 1996), or by consumption of autotomised parts of the prey (e.g., the slug Deroceras reticulatum sheds the posterior part of its foot and escapes when attacked by a large generalist carabid beetle, which then eats the autotomised

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organ—Pakarinen, 1994), or by trophallaxis (Morris et al., 2002). An investigation of an aphid‒spider‒carabid system suggested that, at least in this system, secondary predation will cause little error in predation estimates; detection of secondary predation using a sensitive antiaphid monoclonal antibody (Sect. 6.3.9) only occurred where carabids were killed immediately after consuming at least two spiders, which were, in turn, eaten immediately after consuming aphids (Harwood et al., 2001b). Under certain circumstances there may be no requirement to separate predation from scavenging. In the study by Symondson et al. (2000), monoclonal antibodies were used to assess the importance of earthworms simply as an alternative source of non-pest food that could help maintain populations of the carabid Pterostichus melanarius when pest density was low. In this scenario, dead earthworms were as useful as live ones.

6.3.2 Field Observations on Foraging Natural Enemies Watching natural enemies in action is the simplest and most unequivocal for gathering evidence on the trophic relationships within both predator‒prey and parasitoid‒host systems. Generally, it falls into two areas. First, direct observation with the naked eye and binoculars in the field (Holmes, 1984; Heimpel & Casas, 2008), and second, remote observation using video-recording apparatus. There are, however, some practical drawbacks to these techniques: they are often very labour-intensive, data often accumulate very slowly, and data may be of limited value if the trophic relationship itself is disturbed during observations. In addition, many natural enemies are nocturnal, live concealed in the soil, or are very small, creating difficulties for observation. Sometimes, predator‒prey interactions under a closed crop canopy within arable ecosystems are the focus of study, making observation without disturbance particularly difficult. Such drawbacks are especially important when studying minute, but often very fastmoving, parasitoids. An added complication

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when attempting to record oviposition by endoparasitoids is that it is sometimes difficult to assess whether or not an ovipositor insertion has in fact resulted in an egg being laid (Chap. 1). Despite the drawbacks outlined above, direct field observation has been used in a number of studies of predation and parasitism. Workers have either used prey that has deliberately been placed out in a habitat (sentinel prey), or they have used naturally occurring populations of prey. Greenstone (1999) tabulates studies in which direct observation has been used to determine the spectrum of spider species attacking pests. Predation of the eggs of cotton bollworms, Helicoverpa zea, was investigated by attaching batches of eggs to the upper surface of cotton leaves, using fresh egg white and a small paint brush (Whitcomb & Bell, 1964). The eggs were observed directly for 12 h periods during daylight and all acts of predation were recorded. Predators were identified whenever possible, and the type of feeding damage inflicted was noted. Ants tended to remove eggs completely, explaining the disappearance of eggs from cotton plants recorded in earlier experiments. Using a similar approach, Buschman et al. (1977) allowed adults of the velvetbean caterpillar, Anticarsia gemmatalis, to lay eggs on soybean plants in laboratory cages, and then placed the egg-laden plants out in soybean fields. The plants were continuously observed through the daylight hours by a team of observers working in 2 h shifts. A critique of the use of sentinel prey as a technique has been published recently (Lövei & Ferrante, 2017). Weseloh (1989) identified potential ant predators of spongy moth larvae by placing moth larvae on the forest floor in an area where a particular ant species was seen to be foraging, and then noting the ants’ behavioural reaction to the larvae. Morris et al. (1998), in their study of predation by ants (Tapionoma nigerriumus) on the olive moth, Prays oleae, recorded the number and proportion of ants carrying this prey, as they approached the nest along one of their many trails, during 10 min intervals throughout the day. Interestingly, far fewer ants were seen carrying P. oleae than might have been suggested

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by analyses using ELISA (Morris et al., 1999b, 2002). This was explained by the fact that ants participate in trophallaxis, the exchange of food between individuals, and illustrates very well the need to complement biochemical approaches with direct observation and/or behavioural studies. Riddick and Mills (1994) used binoculars, and a red flashlight, to observe carabid predation of tethered codling moth (Cydia pomonella) larvae in an apple orchard. Red-light illumination is used to minimise disturbance to the predator and prey but allow observation. Binoculars enabled the researchers to monitor the larvae from several metres away and allowed identification of the carabids, but without disturbing their behaviour. Similarly, Villemant and Ramzi (1995) used binoculars to observe predation on egg masses of Lymantria dispar on cork oak. A large wolf spider, Hogna rabida, was the focus of observation by Schmitz and Suttle (2001), but this species was found to retreat into the litter if researchers approached within 1.5 m. To circumvent this problem, individual spiders were dusted with fluorescent powder and observed from 2.5 m using binoculars. Diurnal predation of the cotton fleahopper, Pseudatomoscelis seriatus (Heteroptera, Miridae), was observed by walking through cotton crops (Nyffeler et al., 1992) (Sect. 6.2.6). During 1 h observation periods, the number of predators without prey, the number of predators with fleahopper prey, and the number of predators with alternative prey were all counted. Predators carrying prey were collected, and both the predator and its prey were identified later in the laboratory. Nyffeler (1999) collated data from 31 similar published observational studies of spiders attacking pests in the field. Lavigne (1992) observed the predatory behaviour of Australian robber flies (Diptera, Asilidae) in pastures, using two main approaches: continuous observation of single flies over extended periods, and transect walks to record as much behaviour as possible. Feeding flies were collected, identified, and finally released, once the prey had been removed for identification. Similarly, Halaj and Cady (2000) made 30‒60 min walks from 0800 to 1100 and 2130 to 0030 (using headlamps), and

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recorded feeding activity by harvestmen (Opiliones) in a soybean field and an adjacent hedgerow. When feeding was observed, the harvestman and its prey were preserved in alcohol for later identification in the laboratory. Van den Berg et al. (1997) made daytime visual observations of predator feeding rate on soybean aphids during 10 min periods. Their data supported calculation of functional responses for a ladybird, Harmonia arcuata, and a rove beetle, Paederus fuscipes, but the foraging of spiders, ants and crickets was found to be too cryptic for application of this method. Holmes (1984) examined individual colonies of the cereal aphid Sitobion avenae on selected ears of wheat, identified by a plastic label, and reexamined them at regular intervals, to observe the searching behaviour of staphylinid beetles. The data not only confirmed that aphids were consumed, but also provided quantitative information on the rate of prey consumption by predators. Griffiths et al. (1985) studied nocturnal carabids in cereals, using arenas placed out in the field at night, and illuminated with red light, to which the carabids do not respond. It is, however, important to note that not all arthropods are insensitive to red light (Sunderland, 1988): harvestmen (Opiliones) and springtails (Collembola) are known exceptions. Many such studies do not yield quantitative data, since the prey has to be removed to be identified, and the predator’s normal behaviour is disturbed (Chap. 7 discusses quantitative aspects of this technique). Diel activity patterns of a squash bug egg parasitoid, the scelionid Gryon pennsylvanicum, were recorded at 3 h intervals in a squash field. A range of behaviours, which included ‘probingovipositing’, was recorded by direct observation. A red flashlight was used at night (Vogt & Nechols, 1991). Landis and van der Werf (1997) observed sugar beet plants during the daytime for 200 min per day, and took care not to touch the plants, or cast shadows on them. Aphids were observed being eaten on six occasions, including by cantharid and anthicid beetles. Rosenheim et al. (1993), using field enclosures, showed that predation by heteropteran bugs on lacewing larvae (intraguild predation)

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can release cotton aphids from effective biological control. Because of the possibility that enclosures might distort the normal behaviour of the predators in this system, Rosenheim et al. (1999) later made 450 h of direct visual observations in cotton fields, and demonstrated that lacewing larvae (Chrysoperla carnea) were, indeed, killed frequently by predatory Heteroptera (various species of Orius, Geocoris, Nabis and Zelus). A few studies have managed to directly observe behaviour of several species of parasitoids in the field, even very minute species (Heimpel & Casas, 2008). For example, Heimpel et al. (1996) were able to confirm an association between the likelihood for Aphytis spp. parasitoids to host feed versus oviposit and their egg load by directly following individuals in the field and observing their behaviour. Similarly, one of the only studies to directly quantify predation on parasitoids was conducted by Heimpel et al. (1997), who conducted almost 90 h of direct observation of foraging or resting parasitoids and determined that spiders, ants, and assassin bugs preyed on Aphytis spp. on almond trees. With sufficient ingenuity, even predation occurring below the soil surface can be monitored directly. Brust (1991) observed underground predation of larvae of southern corn rootworm (Diabrotica undecimpunctata) on maize roots. He positioned a Plexiglass plate close to the roots, and had a bag of soil that could be removed to allow root observation at 2 h intervals for 24 h. This method showed that carabid larvae (in the genera Harpalus and Pterostichus) were significant predators of firstto third-instar rootworm larvae. A similar method was used to investigate predation of eggs of southern masked chafer (Cyclocephala lurida) and Japanese beetle (Popillia japonica) by ants in turfgrass (Zenger & Gibb, 2001). Such approaches are excellent for observation of predatory behaviour and for confirming trophic links but provide limited quantitative information. Time-lapse video-recording techniques, which reduce the arduous and time-consuming nature of direct observation, are available (Wratten, 1994).

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These techniques are very useful for observing attacks on relatively sedentary prey, but are not appropriate, in most cases, for recording attacks on small active prey, or for monitoring the foraging behaviour of highly mobile natural enemies in complex three-dimensional habitats, such as plant canopies. Recording is possible at very low light intensities, or under infrared light, thus reducing disturbance (Howling & Port, 1989; Howling, 1991; Schenk & Bacher, 2002). Amalin et al. (2001), using a video time-lapse cassette recorder and red-light illumination (5.0 lx) in the laboratory, discovered that nocturnal clubionid spiders are able to detect leafminers through the leaf epidermis and then either puncture the mine and eat the larva in situ, or cut a slit in the mine and remove the prey. Laboratory studies of the foraging behaviour of some slug species have been carried out by several workers (Bailey, 1989; Howling & Port, 1989; Howling, 1991) and the methods could be adapted for studying the foraging behaviour of predators. Halsall and Wratten (1988b) used time-lapse video equipment to compare the responses of polyphagous predators to plots of high and low aphid density in a winter wheat field. Cameras were focussed on a 9 cm x 11 cm patch of moistened silver sand within each plot, and night illumination was provided by 100 W IR lights, powered from a portable generator. The nocturnal carabid, Anchomenus dorsalis (= Agonum dorsale), was found to make a greater number of entries into the high than the low aphid density plot. Similar methods were used in a field of winter wheat by Lys (1995), who focussed the camera on a 10 cm x 10 cm area of ground where Drosophila puparia stuck to cardboard had been placed. In addition to securing data on relative predation rates by diurnal and nocturnal predators, this study provided fascinating insights into the agonistic behaviour of carabids under field conditions (e.g., Poecilus (= Pterostichus) cupreus attacked and repelled conspecifics as well as smaller predators). The simultaneous use of multiple video cameras in the field can enable a more efficient collection of quantitative data on predation and parasitism in some systems. Meyhöfer (2001) used a system of 16 cameras

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equipped with infrared diodes for night vision, a video multiplexer and a time-lapse video recorder to study predation of aphid mummies in a sugar beet field. Camera input routed via the multiplexer was stored on a single videotape (720 h of observation on a 3 h tape). Schenk and Bacher (2002) employed a similar range of equipment to study predation on sedentary lateinstar larvae of the shield beetle, Cassida rubiginosa, on creeping thistle. Equipment was checked several times daily, and, when necessary, cameras were re-focussed or missing larvae replaced. Examination of the video tapes showed that 99% of predation events were due to the paper wasp, Polistes dominulus. Technological advances now allow the input of up to 72 cameras to be stored directly to computer hard disc (Meyhöfer, 2001). Security and protection of video equipment is an important consideration in field studies. Practical advice concerning equipment, arenas and automatic analysis can be found in Varley et al. (1993) and Dell et al. (2014). The advent of digital recording has greatly improved the ability of researchers to remotely observe predators in action. Digital technology is cheaper, has higher video resolution and is easier to deploy. The results obtained using these techniques can often be surprising and at odds with the predators caught using more traditional methods such as pitfall traps. For example, Grieshop et al. (2012) found that, in three different agroecosystems, ants were the most important predators and beetles were much less effective, despite their abundance in pitfall traps. In addition to being less time-consuming, digital surveillance of predators can be used to compare different sampling methods. For example, a study comparing predators of Aphis glycines, using vacuum sampling, direct human observation and digital surveillance, showed that the former method was better at assessing abundance, but that video surveillance, although missing some of the smaller predators, gave a better estimate of time spent foraging (Wolz & Landis, 2014). As mentioned earlier, sentinel prey are often used to estimate predator and parasitoid abundance and effectiveness. Whilst this may be suitable for normally immobile prey items such as eggs and

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pupae, the use of immobilised, normally active prey items may skew results. Digital video surveillance of live and sentinel Brown Plant Hopper in the field showed that frogs were the major predators of live individuals, whereas sentinel leaf hoppers were mainly taken by spiders and water bugs (Zou et al., 2017). Because an individual act of predation is often completed in a relatively short period of time, and predators spend only part of their time foraging for food, the number of records of predatory acts occurring during an observation period can be very low (Sterling, 1989). Consequently, the likelihood of observing any acts of predation is often low for many predator species. It is sometimes possible to increase the rate of field data accumulation by artificially increasing prey densities. In an investigation of spider predation on the rice green leafhopper, Nephotettix cincticeps, Kiritani et al. (1972) placed, in paddies, rice plants that had been artificially infested with unnaturally high numbers of leafhopper eggs. Observers patrolled the rice plots after the eggs had begun to hatch, and recorded occurrences of spiders feeding on leafhoppers, noting the species of both the predator and the prey involved. Similarly, Way et al. (2002) planted potted rice plants infested with the brown planthopper, Nilaparvata lugens, into tropical upland rice fields and observed predation by ants both on the plants and on the ground below. Direct observation can be used to estimate predation rates (Greenstone, 1999), if data are collected, not only on the percentage of predators seen with prey, but also on the handling time (the period from attack to cessation of feeding), and on the time available for prey capture in the field (Nyffeler & Benz, 1988b).

6.3.3 Collection or Examination of Prey and Prey Remains If the predator does not consume all of its prey, the remains of the cadaver may display distinctive marks that enable the perpetrator to be identified. For example, Neuroptera leave a pair of small holes, Heteroptera a single hole, and

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491

Fig. 6.23 Damage caused to egg masses of Ostrinia nubilalis (Lepidoptera: Pyralidae) by various predators: a predation by the staphylinid beetle Stenus flavicornis, b predation by a lacewing larva (Chrysopa sp.), arrow marks a feeding hole, c predation by the heteropteran bug

Orius insidiosus, d damage from predation by O. insidiosus, arrow marks fungal growth at feeding hole, e damage from mite predation, arrow marks damage. Source Andow (1990). Reproduced by permission of The Entomological Society of America

ants a peppering of small holes (Mills, 1997). Andow (1990) studied, in the laboratory, the nature of the damage caused to egg masses of Ostrinia nubilalis (Lepidoptera, Pyralidae) by various predators (Fig. 6.23). Adults of the coccinellid Coleomegilla maculata consumed the entire egg mass, leaving only the basal and lateral parts of the egg chorion. The staphylinid beetle Stenus flavicornis chewed around the edges of an egg mass and killed many more eggs than were actually consumed. Larvae of the lacewing Chrysopa sp. left distinctive feeding holes in the egg chorion. The heteropteran bug Orius insidiosus did not completely consume the egg, and extensive melanisation occurred near the site of

attack. The phytoseiid mite Amblyseius sp. also caused melanisation, but of more restricted extent. Andow (1992) used this information to interpret damage to sentinel egg masses in maize crops, and showed that C. maculata predation was greatest in plots established after ploughing, compared with Chrysopa predation, which was greatest in no-till plots. Hossain et al. (2002) used sentinel moth eggs of Helicoverpa spp. (live eggs attached to sections of stiff paper which were stapled to lucerne plants) to provide an index of egg predation intensity in plots of harvested and unharvested lucerne. To avoid egg hatch in the field, eggs less than 24 h old were used, and were transported to the field at 9 °C in

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a portable refigerator. Egg cards were then retrieved from the field after 24 h’ exposure to predation (Hossain et al., 2002). When prey are extremely small, scanning electron microscopy (SEM) can be used successfully to categorise characteristic predator-specific feeding traces in the laboratory, and then screen prey taken from the field to determine the incidence of predation by different species of predator. Using SEM in this way, Kishimoto and Takagi (2001) showed that staphylinid beetles of the genus Oligota were the main predators of spider mite eggs on pear trees in Japan. Feeding traces have also been used to identify the carabid, staphylinid and mammalian predators of winter moth (Operophtera brumata) pupae in oak woodland (East, 1974). Caterpillars of fruittree leafroller, Archips argyrospila, have a characteristic blackened and shrunken appearance after being fed on by carabid beetles, and this was used to estimate predation rates after examination of infested foliage (Braun et al., 1990). Similarly, spongy moth (Lymantria dispar) pupae with characteristic large, jagged wounds, were known to have been attacked by the carabid beetle Calosoma sycophanta (Fuester & Taylor, 1996). Examination of damaged egg masses of Lymantria dispar on cork oak showed cork dust and faecal pellets characteristic of oöphagous beetle larvae, Tenebroides maroccanus (Trogossitidae) (Villemant & Ramzi, 1995). Morrison et al. (2016) conducted laboratory feeding trials using fieldcollected predators and developed a classification system for different damage syndromes (complete chewing, incomplete chewing, stylet sucking, and punctured sucking). They then used this classification system to identify probable predators of field-deployed sentinel egg masses of the brown marmorated stink bug (Halyomorpha halys). Mummies of Aphis gossypii containing large ragged-edged holes were considered to have been attacked by coccinellid beetles (Colfer & Rosenheim, 2001). Predation marks on collected mines were used to identify which predators had attacked citrus leafminer Phyllocnistis citrella (Amalin et al., 2002). Ants, for example, slit open the mine and pull out the prey but lacewing larvae pierce the mine and suck out

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the fluid contents of the prey. For a few species, visual inspection reveals a clear difference between parasitised and unparasitised hosts that can be assigned to a particular species of parasitoid. For example, healthy pupae of the small white buttefly, Pieris rapae, are green, but those attacked by Pteromalus puparum are brown (Ashby & Pottinger, 1974). Pupae of the glasshouse whitefly, Trialeurodes vaporariorum, when attacked by the parasitoid Encarsiaformosa, turn from white to black after several weeks when the parasitoid pupates (Sunderland et al., 1992). With the workers of some predatory ant species it is possible, using forceps, to gently remove food items (Ibarra-Núñez et al., 2001) from the jaws of the ants as they return to the nest (Stradling, 1987). Rosengren et al. (1979), in a detailed study of wood ants (Formica rufa group) in Finland, used this method to investigate the diet of Formica polyctena (Table 6.4). Vogt et al. (2001) exposed the underground foraging trails of fire ants (Solenopsis invicta) to collect the ants together with the material they were carrying. Unfortunately, the manual method of food item collection disturbs the ants, and some prey items may be missed. Skinner (1980) overcame these problems when studying the feeding habits of the wood ant Formica rufa (Formicidae), by using a semi-automatic sampling device which collected the ant ‘booty’ as it was carried back to the nest. With this method, an ant nest is surrounded by a barrier which forces the ants to use the exit and entry ramps provided. The incoming ants are then treated in either of two ways: 1. They are periodically directed through a wooden box with an exit-hole sufficiently large to allow the ant, but not its booty, to pass through. Prey left in the box may then be retrieved and identified. 2. They are allowed to fall off the end of the entry ramp into a solution of 70% alcohol. As the latter treatment kills the ants it can be used for short periods only. The remains of the prey of larval ant lions (Neuroptera: Myrmeleontidae) can be located, either buried in the predator’s pit, or on the sand

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Table 6.4 Pooled data from four nests showing number of prey items (n) expressed as a percentage of total items (%) carried to the nest of Formica polyctena during two periods in 1978

493 30 June–3 July

Homoptera (mostly aphids)

27 July–31 July

n

%

n

135

20.4

15

% 2.4

Adult Diptera (mostly Nematocera)

122

18.4

147

23.4

Sawfly larvae (Tenthredinidae)

73

11.0

50

7.9

Lepidoptera larvae

74

11.2

43

6.8

Unidentified carrion

53

8.0

78

12.4

Adult Coleoptera (mostly Cantharidae)

51

7.7

34

5.4

Ant workers

33

5.0

52

8.3

Ant reproductives

2

0.3

38

6.0

Lumbricidae (number of pieces)

23

3.5

21

3.3

Homoptera Auchenorrhyncha

19

2.9

22

3.5

All other groups

76

11.5

129

20.5

Total number of items

661

99.9

629

99.9

Source Rosengren et al. (1979). Reproduced by permission of the International Organisation for Biological and Integrated Control

surface close to the pit, and can be identified, as shown by Matsura (1986). The diet of first-instar larvae of Myrmeleon bore was far less varied than that of second- and third-instar larvae. Food items may be either observed in situ on, or manually collected, from the webs of spiders. This method was used by Middleton (1984) in a study of the feeding ecology of web-spinning spiders in the canopy of Scots Pine (Pinus sylvestris), and by Sunderland et al. (1986b) when measuring predation rates of aphids in cereals by money spiders (Linyphiidae). Alderweireldt (1994) collected linyphiid spiders that were carrying prey in their chelicerae, and prey remains were collected from their webs, in Belgian maize and ryegrass fields. Jmhasly and Nentwig (1995) made regular daytime surveys of spider webs in a field of winter wheat. Each web was visited at 45-min intervals in an attempt to record prey cadavers in the webs before spiders had time to discard the remains. The timing of web observations can be optimised in relation to environmental conditions and spider biology. Miyashita (1999), for example, visited webs of Cyclosa spp. in hedgerows between 1100 and 1300, because about half of the webs were destroyed by 1600 (due to the action of wind and damage by prey) and webs were rebuilt only at night. Pekár

(2000) collected entire webs of the theridiid spider Theridion impressum, and captured prey were picked out and identified later in the laboratory. In all these studies, examination of webs was confined to the daylight hours, because small prey items in delicate webs on or near the ground cannot be recorded reliably at night. The prey spectrum at night could be different from that which has been recorded during the daytime, and so techniques need to be developed to investigate nocturnal predation. Some ant species make refuse piles which could be examined to determine their diet. Caution is needed here, however, since Vogt et al. (2001) found that the refuse piles of Solenopsis invicta, which were dominated by remains of Coleoptera, were entirely different from material seen to be carried into the nest, which was dominated by larvae of Lepidoptera. Prey and hosts of fossorial wasps, such as Sphecidae and Pompilidae, may be analysed by examining the prey remains within the nest. Larval provisioning by the wasp Ectemnius cavifrons, a predator of hover flies, was investigated by Pickard (1975), who removed prey remains from the terminal cell of a number of burrows in a nest. Other fossorial wasps may be investigated using artificial nests: i.e., already hollow plant stems, or pieces of

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dowelling or bamboo that have been hollowed out by drilling (Cooper, 1945; Fye, 1965; Bohart, 1966; Krombein, 1967; Parker & Danks, 1971; Southwood & Henderson, 2000). The stem or dowelling is split, and then bound together again, with, for example, string or elastic bands. The binding may subsequently be removed to allow examination of the nest contents. Details of one of the aforementioned methods, together with photographs of artificial nests and individual cells, are contained in Krombein (1967). Thiede (1981) describes a trap-nesting method involving the use of transparent acrylic tubes. An entrance trap that separates yellowjacket wasps (Vespula spp.) entering and leaving the nest has been devised. Those entering can be diverted into a gassing chamber and anaesthetised with carbon dioxide to enable their prey load to be removed and identified. The wasps treated in this way eventually recover, and 30–60 wasps can be sampled per hour (Harris, 1989). The large tube of an entrance trap can be fitted to the nest entrance at night, using black polythene and soil. Then, an inner collecting container (with integral funnel) can be inserted into the large tube during daytime to trap returning foragers (Harris & Oliver, 1993).

6.3.4 Tests of Prey and Host Acceptability Information on host or prey range in a natural enemy species can be obtained by presenting the predators or parasitoids, in the laboratory, with potential prey or hosts, and observing whether the latter are accepted (e.g., Goldson & McNeill, 1992; Schaupp & Kulman, 1992, for studies on parasitoids). For visually hunting predators, it may be possible to use video-imagery techniques that enable the prey stimulus to be controlled with precision, and standardised. Clark and Uetz (1990) showed that the jumping spider Maevia inclemens did not discriminate between live prey and a simultaneous video image of prey displayed on Sony Watchman micro-television units. These spiders stalked and attacked the televised prey. Arachnids have flicker fusion

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frequency values in a similar range to that of humans, and so images are percieved as moving objects, rather than as a series of static frames. Some insects, however, have critical flicker fusion frequency values greatly in excess of 60 Hz (the human range is 16‒55 Hz and arachnids span the range 10‒37 Hz) and would not be good subjects for this methodology. Simple laboratory assessments of prey and host acceptability should always be treated with caution, because of the artificial conditions under which they are conducted (Greenstone, 1999). They can only indicate potential trophic relationships, and other factors need to be taken into consideration when extrapolating to the field situation. For example, it is necessary to establish that the natural enemy being investigated actually comes into contact with the potential prey or host species under natural conditions. The predator or parasitoid may only be active within the habitat for a limited period during the year, and this may not coincide with the presence of vulnerable stages of the prey, or host, in that habitat. In order to avoid testing inappropriate predator/prey or parasitoid/host combinations it is thus advisable to establish whether there is both spatial and temporal synchrony before carrying out laboratory acceptability tests (Arnoldi et al., 1991). The placing of insects in arenas, such as Petri dishes and small cages, also raises serious questions regarding the value of the method. Confining potential prey or hosts in such arenas alters prey and host dispersion patterns, and, in particular, it is likely to reduce the opportunities for prey to escape from the natural enemy, so increasing the risk of false positives being recorded. Some insect species may rarely, if ever, be attacked in the field by a particular natural enemy species, because they are too active to allow capture by the latter. For some types of predator, such as web-building spiders, prey acceptability tests can be carried out in the field. The same strictures about testing appropriate prey types still apply to field testing, but at least the behaviour of the predator is natural and unconstrained. Henaut et al. (2001), for example, used an aspirator to gently blow live potential prey organisms into the orb webs of spiders in a coffee plantation.

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They used prey types that were abundant in the plantation and recorded the extent to which the web retained the prey, as well as capture and consumption rates by the spiders. Some of the problems of testing prey preferences in the laboratory can be overcome by providing more architecturally complex arenas e.g., by adding stepped levels or obstacles (Carter & Dixon, 1982) or even using whole plants (e.g., Carter et al., 1984). Laboratory studies are particularly useful in determining prey size choice, helping to determine an upper limit to the size of prey that the predator is capable of tackling (Greene, 1975; Loreau, 1984b; Digweed, 1993; McKemey et al., 2001), and, sometimes, a lower limit below which single, unaggregated prey are ignored (Greene, 1975). Finch (1996) showed that carabids (Fig. 6.24) with mandibles above a certain size were incapable of manipulating, and therefore of consuming, the eggs of the cabbage root

495

fly, Delia radicum. These limits on prey size can be particularly relevant in cases where size determines the species range or life stage that will be attacked by a polyphagous predator. McKemey et al. (2001) found that the carabid Pterostichus melanarius had a preference for smaller slugs within laboratory arenas, but this result was not supported by choice experiments within a crop. Within the structurally complex habitat of a crop of wheat no slug size preferences were found (McKemey et al., 2003), apparently because the smallest slugs had greater access to refugia from the beetles within cut wheat stems and clods of soil. Again, therefore, laboratory studies can provide misleading data; they can show which sizes of prey the predators are capable of killing, but not necessarily the preferences the predators have in the field. Predators are often also scavengers. A predator that is known to consume a particular prey type may be (with respect to that prey type) a

Fig. 6.24 Size range of carabid beetles in relation to predation of the eggs of cabbage root fly, Delia radicum. Source Finch (1996). Reproduced by permission of The Royal Entomological Society

496

scavenger, rather than a true predator. This is an important distinction if the impact of predation on the prey species is being inferred from the postmortem examination of field-collected predators. Laboratory studies are appropriate for determining whether a predator has the physical and behavioural capacity to kill a particular potential prey species (Greenstone, 1999; Sunderland, 2002). Sunderland and Sutton (1980) showed that eight species of predator that regularly consumed woodlice (Isopoda) in the field (as determined from serological investigations), were, in fact, unable to kill even tiny woodlice in laboratory tests, and were therefore unlikely to have been predators of woodlice in the field. Halaj and Cady (2000) frequently observed harvestmen (Opiliones) eating earthworms (Lumbricidae) in the field, but laboratory tests showed that even the largest species of harvestman was incapable of killing earthworms of a size that were regularly consumed in the field. Therefore, harvestmen in the field were probably scavenging. Some species of ant that were shown (by ELISA) to contain remains of the olive moth, Prays oleae, were thought to have obtained these remains primarily by scavenging (Morris et al., 2002). Even when a parasitoid stabs the host with its ovipositor, the host may still be rejected for oviposition (McNeill et al., 2000; Sect. 1.9). It is therefore important that egg release is confirmed during host acceptability tests involving parasitoids. In a few cases this can be done by observing the behaviour of the parasitoid; for example, in egg parasitoids that conspicuously rub their ovipositor over the surface of the host when they deposit a marking pheromone (Rabb & Bradley, 1970). Otherwise, it can be done either by dissecting hosts shortly after exposure to parasitoids, to locate eggs within the host’s body (Chap. 2), or by rearing hosts until parasitism becomes detectable. Furthermore, just because an insect species is accepted for oviposition, does not mean that the species is suitable for successful parasitoid development. Under laboratory conditions many parasitoids will, if given no alternative, oviposit in or on unsuitable ‘hosts’, but subsequent monitoring of the progeny of endoparasitoids will show that they have

K. D. Sunderland et al.

been killed by the host’s physiological defences (Sect. 2.10.2), while for ectoparasitoids, eggs may hatch but fail to develop successfully due to the host being biochemically unsuitable (I. C. W. Hardy, personal communication). On the other hand, oviposition in unsuitable hosts is not always a laboratory artefact; it does occur in some host‒parasitoid associations (Heimpel et al., 2003; Condon et al., 2014; Abram et al., 2016, 2019), including in new interactions between native and exotic species (Heimpel et al., 2003; Abram et al., 2014; Gariepy et al., 2019). In these situations, it is critical to directly observe the parasitoid’s behaviour (i.e., whether it oviposits in the host or not) to avoid misinterpretations that can arise from ‘black-box’ tests. If parasitoid behaviour is not observed whilst in contact with the host, resultant lack of parasitoid offspring emergence could be due to either lack of acceptance by the maternal parasitoid, or poor host suitability for offspring development once accepted for oviposition. For example, Abram et al. (2014) showed, first in the laboratory using filming and rearing, that the native North American parasitoid Telenomus podisi readily oviposited in eggs of the invasive stink bug Halyomorpha halys but parasitoid offspring did not successfully develop (other studies have since shown that very low levels of successful development of native parasitoids does occur in some locations; e.g., Dieckhoff et al., 2017). Similar results were found with egg parasitoids native to Europe (Haye et al., 2015). Two follow-up studies used molecular tools and found that 20‒55% of field-collected H. halys egg massed in invaded regions contained the DNA of native parasitoids (Konopka et al., 2018; Gariepy et al., 2019), but parasitoid emergence was negligible, indicating that unsuccessful parasitism of H. halys eggs by native egg parasitoids is common in nature. Thus, laboratory results accurately predicted parasitoid host acceptance behaviour in the field, and conducting direct observations of parasitoid behaviour in laboratory suitability tests was necessary to make this prediction. Because such unsuccessful parasitism may contribute to egg limitation of the maternal

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Populations and Communities

female parasitoid, may lead to the death of the parasitoid offspring, and may also kill the host in some cases, its ecological relevance and contribution to biological control cannot be discounted (Condon et al., 2014; Abram et al., 2016, 2019; Kaser et al., 2018). In host‒parasitoid associations where this is a possibility, laboratory trials should always include both direct observations of parasitoid behaviour and unattacked control hosts run in parallel with hosts exposed to parasitoids to test the possibility that hosts are being killed by unsuccessful parasitoid attack (with the possibility of mortality from host feeding also in mind, depending on the species) (Abram et al., 2016). Abram et al. (2019) review the ways that parasitoids can reduce the fitness of their hosts without producing offspring themselves (i.e., ‘nonreproductive effects’), provide methods of measurement, and discuss their relevance to biological control. When the introduction of an exotic natural enemy species into a new geographical area is being contemplated (in a classical biological control programme), it is important to determine whether the natural enemy is potentially capable of parasitising or preying on members of the indigenous insect fauna (Sect. 7.4.4). This can only be done safely through laboratory acceptability tests. The braconid parasitoid Microctonus hyperodae was collected from South America and screened for introduction into New Zealand as a biological control agent of the weevil Listronotus bonariensis, a pest of pasture (Goldson & McNeill, 1992). Whilst in quarantine, the parasitoid was exposed to as many indigenous weevil species as possible, giving priority to those of a similar size to the target host. In the tests, 25–30 weevils of each species were exposed to a single parasitoid for 48 h in small cages, after which the weevils were held in a larger cage to await the emergence of parasitoids. In a second series of trials, the parasitoids were given a choice of target hosts (L. bonariensis) and test weevils in the same cages. It is important to carry out such a choice test, since in no-choice laboratory trials natural enemies may attack insect species which they would normally ignore in field situations, thereby giving a false

497

impression of the natural enemy’s potential behaviour following field release. However, choice tests are not always relevant, depending on whether the species being tested would be expected to encounter both host species in the same area in nature. Suggested methodology for such non-target host range testing is discussed at length elsewhere (Bigler et al., 2006; van Lenteren et al., 2006; Sect. 7.4.4).

6.3.5 Examination of Hosts for Parasitoid Immatures The principle behind this technique is that by examining (or dissecting, in the case of endoparasitoids) field-collected hosts, parasitoid immatures, or their remains, located upon or within hosts, they can be identified to species, thus providing information on the host range of parasitoids, and the species composition of parasitoid complexes. Unfortunately, the requirement that the parasitoid taxa involved be identified can rarely be satisfied. Few genera, and even fewer higher taxa, have a published taxonomy (i.e., keys and descriptions to the species developed for their immature stages, Sect. 6.2.9), and, where such taxonomies are available, they are rarely complete. Nevertheless, informal taxonomies can be developed in conjunction with the rearing method described in the next section (Sect. 6.3.6), by associating immature stages, or their remains, with reared adult parasitoids. Progress has, however, been made using PCR to identify parasitoid remains within hosts for up to three weeks after parasitoids have emerged (Gariepy et al., 2014; Varennes et al., 2014).

6.3.6 Rearing Parasitoids from Hosts One of the most obvious ways of establishing host‒parasitoid trophic associations is to rear the parasitoids from field-collected hosts. Smith (1974), Gauld and Bolton (1988), Shaw (1997) give general advice, and Jervis (1978), Starý (1970) describe methods of rearing parasitoids of leafhoppers and aphids, respectively. The ease

498

with which this can be done varies with parasitoid life-history strategy. It is usually far easier to rear idiobionts from eggs, pupae or paralysed larvae, than it is to rear koinobionts (Sect. 2.9), since the hosts of koinobionts usually require feeding. When supplying plant material to phytophagous hosts of koinobionts during rearing, it is very important to ensure that the material does not contain individuals of other insect species from which parasitoids could also emerge, as this can lead to erroneous host‒parasitoid records. The risk of associating parasitoids with the wrong hosts is particularly acute when parasitoids are reared from hosts in fruits, seed heads and galls, as these may contain more than one herbivore, inquiline or the host species. Such material should be dissected after the emergence of any parasitoids, so that the host remains can be located and identified, and the parasitoid‒host association determined correctly. Whenever possible, hosts should be reared individually in containers, so that emerging adult parasitoids can be associated with particular host individuals. This allows the recording of accurate data on any parasitoid preferences for particular host developmental stages or sexes, and it also avoids potential problems arising from the failure of the entomologist to distinguish between closely related host species. The choice of suitable containers will depend upon the host involved, but very often simple boxes, tubes or plastic bags will suffice (Starý, 1970; Smith, 1974; Jervis, 1978; Grissell & Schauff, 1990; Godfray et al., 1995; Shaw, 1997). The addition of materials such as vermiculite, plaster of Paris, or wads of absorbent paper, to rearing containers is recommended to avoid problems such as growth of moulds associated with the accumulation of excessive moisture. Some parasitoids, including several Braconidae (Shaw & Huddleston, 1991), cause changes in host behaviour that often result in the movement of parasitised individuals from their normal feeding sites prior to death, as was recorded, for example, for the aphid parasitoid, Toxares deltiger (Powell, 1980). A significant proportion of the mummies formed by several aphid parasitoid species occur some distance

K. D. Sunderland et al.

away from the aphid colony, and even away from the food plant. They tend to be missed during collection, leading to inaccuracies in measurements of parasitism, and underestimation of the size of the parasitoid complex (Powell, 1980). As an insurance against the possible complicating effects of parasitoid-induced changes in host behaviour, it is advisable to collect and rear a random sample of apparently healthy hosts, in addition to obviously parasitised individuals. With aphids, it is also advisable to rear parasitoids from both live aphids and mummies, because some hyperparasitoids oviposit into the mummy stage, whereas others oviposit into the larval parasitoid prior to host mummification (Dean et al., 1981). Parasitoids may also be collected from the field after they have killed, and emerged from, the host. The larvae of some species leave the body of their host and pupate, either upon the host remains, or on surrounding vegetation. The host remains (for subsequent identification) and the parasitoid pupae (which may be in cocoons or the exuvium of the last larval instar) may be collected, and parasitoids reared from the latter in individual containers. Where gregarious species are concerned, the pupae from a particular host individual should be kept together. The importance of keeping detailed records of all parasitoid rearings cannot be overemphasised. At the time of collection from the field, the identity and developmental stage of the host should be recorded, along with habitat and food plant data, location and date (Gauld & Bolton, 1988). Where possible, the date of host death, together with that of parasitoid emergence, should be recorded. When adult parasitoids are retained as mounted specimens, the remains of the host and parasitoid pupal cases and cocoons should also be retained, to aid identification. Such remains are best kept in a gelatin capsule, which can be impaled on the pin of the mounted parasitoid. If a previously unrecorded host‒parasitoid association is recorded during rearing, it is very important to provide ‘voucher’ specimens for deposition in a museum collection. In order to ensure, as far as possible, that all the species in the parasitoid complex of a host are

6

Populations and Communities

reared, samples of parasitised hosts need to be taken from a wide area, from both high- and lowdensity host populations, from a variety of host habitats, and over the full timespan of the host life cycle, or at least the timespan of the host stage that one is interested in (for example, the pupal stage, if only pupal parasitoids are the subject of interest). A parasitoid species may be present in some local host populations, but not in others. Its absence may be due to climatic unsuitability, or because of an inability of the parasitoid to locate some populations. Ecologists refer to the constancy of a parasitoid species (Zwölfer, 1971): the probability with which a species may be expected to occur in a host sample. Constancy is expressed as the percentage, or the proportion, of samples taken that include the species, e.g., see Völkl and Starý (1988). Host species with a wide geographical distribution require more extensive sampling to reveal the total diversity of their parasitoid complex than do host species with a restricted distribution (Hawkins, 1994). One can reasonably assume that the relationship between the extent of the host’s distribution and parasitoid species richness is linear (Tylianakis et al., 2006).

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Major temporal changes may occur in the species composition of a parasitoid complex. Askew and Shaw (1986) refer to the example of the lepidopteran Xestia xanthographa. Samples of larvae of this host taken 10 weeks apart yielded very different species of parasitoid (Table 6.5). Another factor to consider is sample size. The probability of a parasitoid species being reared from a host will depend on the percentage parasitism inflicted, and also on the size of the host sample taken. The larger the sample of hosts from a locality, the greater the probability that all the parasitoid species in the local complex will be reared. This relationship is probably asymptotic, and sample sizes of 1000 hosts, or larger, provide good estimates of parasitoid species richness that are not so strongly dependent on sample size (B. A. Hawkins, personal communication). Martinez et al. (1999) discuss the amount of sampling effort required to reveal the structural properties of parasitoid food webs. As well as providing information on host range, parasitoid complex, and community structure, rearing of parasitoids can also yield valuable data on parasitoid phenologies. For example, by noting the times of emergence of

Table 6.5 Parasitism of Xestia xanthographa collected near Reading, UK, on two dates during spring 1979. The figures take no account of larval-pupal parasitoids. Numbers of unparasitised larvae

Sampling dates

Numbers of larvae parasitised by

2 March

12 May

188

50

24

0

Tachinidae

Periscepsia spathulata (Fallén) Pales pavida (Meigen)

1

0

Braconidae

Glyptapanteles fulvipes (Haliday)

8

0

Cotesia hyphantriae Riley

1

0

Meteorus gyrator (Thunberg)

1

0

Aleiodes sp. A

46

0

Aleiodes sp. B

10

0

Hyposoter sp

1

0

Ophion scutellaris Thomson

0

6

Total larvae sampled

280

56

Percentage parasitism

33

11

Ichneumonidae

Source Askew and Shaw (1986). Reproduced by permission of Academic Press Ltd.

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larval parasitoids from the hosts, and the time of adult emergence, Waloff (1975), Jervis (1980b) obtained valuable information on the timing and duration of adult flight period, and on the incidence of diapause, in several species of Dryinidae and Pipunculidae. Rearing can also be a valuable source of material for taxonomic study. If a larval taxonomy of the parasitoids is to be developed (Sect. 6.2.9), association of parasitoid adults with larvae can usually only be achieved reliably through rearing. By associating reared adults with their puparial remains, Benton (1975), Jervis (1992), developed a larval taxonomy for a number of Pipunculidae. Some parasitoids are more easily identified through examination of their larval/puparial remains than they are through examination of the adult insects (Jervis, 1992). The molecular analysis of hosts to detect immature parasitoids is discussed in Sect. 6.3.11.

6.3.7 Faecal Analysis For predators that have faeces which contain identifiable prey remains, faecal analysis can be used to estimate dietary range. This method has been applied to aquatic insects (Lawton, 1970; Cloarec, 1977; Thompson, 1978; Folsom & Collins, 1984). Folsom and Collins (1984) collected larvae of the dragonfly Anax junius and kept them until their faecal pellets were egested. The pellets were subsequently placed in a drop of glycerin-water solution on a microscope slide and teased apart. Prey remains were identified by reference to faecal pellets obtained through feeding Anax larvae on single species of prey, by examination of whole prey items and by examination of previously published illustrations of prey parts. The data obtained were used to compare the proportion of certain prey types in the diet, with the proportions found in the aquatic environment. Carabid beetles often consume solid fragments of their prey (Sect. 6.3.8), and may retain these in the gut for days or weeks before defaecation of the meal is completed (Young & Hamm, 1986; Sunderland et al., 1987a; Pollet & Desender, 1990). This makes

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field-collected carabids suitable candidates for faecal analysis in the laboratory. Unless there is some reason for the beetles to be retained alive (e.g., they may be of an endangered species), gut dissection (Sect. 6.3.8) is more convenient than faecal analysis as a means of assessing carabid diet. Other types of predator, however, may be very difficult to dissect, in which case faecal analysis can be very useful. Phillipson (1960) examined 1367 faecal pellets from the harvestman Mitopus morio (Opiliones), and was able to determine the relative contribution of plant and animal material to its diet. Burgess and Hinks (1987) checked the faeces of several hundred field-collected crickets (Gryllus pennsylvanicus) and found that 0–28% (depending on collection site and season) had been feeding on adult flea beetles (Phyllotreta cruciferae), judging by characteristic remains of elytra, antennae and metathoracic legs. These authors had earlier determined, from laboratory experiments (Sect. 6.3.4), that G. pennsylvanicus is a voracious predator of P. cruciferae. Rate of production of faeces has also been used in an attempt to estimate predation rates by ladybirds (Honěk, 1986), and to estimate the time of the most recent meal in the field by carabids (Young & Hamm, 1986).

6.3.8 Gut Dissection If a predator is of the type that ingests the hard, indigestible, parts of its prey, simple dissection of the gut might easily disclose the prey’s identity, as is indeed the case for a number of predators. The main advantages of this method for investigating trophic relationships are the simplicity of equipment required and the immediacy of results. Dissection is usually performed using entomological micropins or fine watchmaker’s forceps, which are used to remove and transfer the digestive tract (the crop, proventriculus, mid gut and hind gut, Chap. 2) to a microscope slide where it can be teased apart and the prey fragments identified. As with faecal analysis (Sect. 6.3.7), the prey remains found in fieldcollected predators can be compared with those

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on reference slides prepared by feeding a predator with a single, known type of prey. Predators need to be killed as soon as possible after collection, otherwise the gut contents may be lost due to the predator defecating or regurgitating. Sialis fuliginosa loses a proportion of its gut contents by regurgitation when placed in preservative (Hildrew & Townsend, 1982) and many carabids will regurgitate if handled roughly. Not all invertebrates lose gut contents in this manner, but this possibility must be borne in mind when carrying out gut dissection. Some aquatic predators collected in nets continue feeding in the net, so need to be narcotised immediately upon capture, e.g., Chaoborus larvae (Pastorok, 1981). Similar precautions are needed for samples taken from terrestrial pitfall traps. Gut dissection has been used to study the diet of a range of predators: (a) aquatic insects (Bay, 1974; Fedorenko, 1975; Hildrew & Townsend, 1976, 1982; Pastorok, 1981; Woodward & Hildrew, 2001, 2002), (b) carabid beetles (Hengeveld, 1980; Sunderland & Vickerman, 1980; Dennison & Hodkinson, 1983; Chiverton, 1984; Pollet & Desender, 1986; Luff, 1987; Sunderland et al., 1987a; Dixon & McKinlay, 1992; Holopäinen & Helenius, 1992; Holland et al., 1996), (c) staphylinid beetles (Kennedy et al., 1986; Chiverton, 1987), (d) coccinellid beetles (Eastop & Pope, 1969; Ricci, 1986; Triltsch, 1997), (e) earwigs (Dermaptera) (Crumb et al., 1941; Skuhravý, 1960), (f) harvestmen (Opiliones) (Dixon & McKinlay, 1989), and (g) centipedes (Chilopoda) (Poser, 1988). When dissecting carabid beetles, it can be worthwhile to examine not only the crop, but also the proventriculus and rectum, because characteristic prey fragments (such as the S-shaped chaetae of lumbricid earthworms) can lodge here for longer periods than in the crop (Pollet & Desender, 1990). Sunderland (1975) examined the crop, proventriculus and rectum of a variety of predatory beetles (Carabidae, Staphylinidae). In a similar study, the crop and the hindgut of two New Zealand carabid species were placed separately on microscope slides and examined in 50% glycerol at up to 400 x magnification. Food

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remains were identified, as far as possible, using microscopical preparations from reference collections (grass and weed seeds, other plant material, invertebrates from a range of sampling methods) for the areas where the carabids were caught (Sunderland et al., 1995b). These methods were also used to investigate diet of the carabid Pterostichus versicolor from heathland. Beetles were caught in pitfall traps and dissected. Slides were prepared of the fore- and the hindgut. Reference slides were made by feeding beetles with known prey, and also by macerating known arthropods (Bruinink, 1990). Gut contents of freshwater aquatic predatory invertebrates can also be identified from reference slides (Woodward & Hildrew, 2002). Although, in studies such as the above, many fragments found in the gut could be placed in general prey categories, identification of prey remains to species has often only been possible if distinctive pieces of prey cuticle (e.g., aphid siphunculi) remained intact. Recognisable fragments generally found in the guts of carabid and staphylinid beetles include: chaetae and skin of earthworms; cephalothoraces of spiders; claws, heads and/or antennae from Collembola; aphid siphunculi and claws; sclerotised cuticle, mandibles and legs from beetles and the head and tarsal claws of some Diptera (Fig. 6.25). Hengeveld (1980), Chiverton (1984), in their studies of Carabidae, also had to group most dietary components, assigning only a few prey remains to species. Pollet and Desender (1988) were able to identify, to species, the remains of some Collembola in the guts of carabid beetles. The efficiency of detecting large Collembola was 67% (based on recovery of Collembola mandibles), and a semi-quantitative analysis of consumption was therefore possible. Similarly, Dixon and McKinlay (1989) identified, to species, some of the aphid remains from the guts of harvestmen, and were able to estimate that some individual Phalangium opilio contained the remains of at least five aphids. An obvious difficulty with the gut dissection method is the digestion of prey prior to dissection. The abilities of the different investigators to recognise the prey remains will influence

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Fig. 6.25 Examples of prey fragments found in dissections of carabid beetle guts: a lycosid spider, (i) bristle, (ii) claw, (iii) chelicera; b carabid or staphylinid beetle larva, (i) legs, (ii) mandible; c lepidopterous (?) larva, (i) leg, (ii) mandibles, (iii) unidentified part; d fragment of exoskeleton of (i) lepidopterous (?) larva, (ii) heteropteran bug; e components of ant’s mouthparts (i) and tarsus (ii); f ant’s (i) antenna, (ii) leg, (iii) exoskeleton (fragment),

(iv) ocellus, g components of aphid’s (i, iii) mouthparts, (ii) leg, (iv) antenna; h bibionid fly, i antennal segment, (ii) antenna, (iii, iv) parts of leg; j Acalypta parvula (Hemiptera: Tingidae), (i, ii) parts of leg, (iii) part of antenna, (iv) fragment of forewing. Source Hengeveld (1980). Reproduced by permission of E. J. Brill (Publishers) Ltd

comparative studies, as will the ability to distinguish between fragments from the same or different individuals within a prey species or group. Although the gut contents of predator species probably derive mainly from predation, carrionfeeding (scavenging) is also known to take place in some species. Therefore, a knowledge of carrion availability may also be useful in predicting the diet of a species. While scavenging is clearly problematic in a study of the effects of predation on pest population dynamics, it is not a problem for studies that set out to assess the role of

alternative animal foods in sustaining predator populations (Symondson et al., 2000). Alternative foods may help to sustain generalist predators in a crop when pest numbers are low, allowing the predators to survive, ready to feed on the pest when it becomes available (Murdoch et al., 1985). The acquisition by a predator of prey materials through secondary predation (i.e., feeding upon another predator species which itself contains prey items) can also produce misleading results. Although countable remains of certain

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ingested structures (e.g., the mandibles of beetles or the shells of slugs) may provide some quantitative information, these parts of the prey may well be avoided by the predators. Whether they are eaten, even by a predator that is not a fluidfeeder and which consumes more solid remains, may depend upon the size of the indigestible parts (biasing the data towards small prey) or the state of hunger of the predator. An awareness of these problems is also required with the faecal analysis method described earlier. The majority of predators are obligate fluidfeeders, and other predators are facultative fluidfeeders, with an unknown proportion of their ingestion confined to prey fluids. The following techniques (antibody, electrophoretic and DNAbased analyses) are suitable for investigating food consumption by fluid-feeders (Symondson, 2002b).

6.3.9 Antibody and Electrophoretic Techniques Most predators are solely or partly fluid-feeders, and so their diet cannot be investigated using the aforementioned faecal analysis and gut dissection methods. Ecologists have, in the past (Loughton et al., 1963), used a variety of antibody and electrophoretic methods to study predation and parasitism. Since the publication of the first edition of this volume, however, these methods have been largely superseded by DNA-based techniques in recent years (Sect. 6.3.11). Thus, in this reduced section, we refer readers elsewhere for information on these techniques and their applications. Standard methodologies for production and use of monoclonal antibody are described in Blann (1984), Goding (1986), Liddell and Cryer (1991), Liddell and Weeks (1995). Examples of the application of antibody techniques to determining natural enemy trophic relationships can be found in Symondson et al. (1996, 2000). Variations on ELISA techniques are reported and described by Voller et al. (1979), and references dealing with their entomological applications are cited in Sunderland (1988), Greenstone

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(1996), Symondson and Hemingway (1997), Symondson (2002a). Examples of applications of ELISA in predation studies can be found in Fichter and Stephen (1979), Ragsdale et al. (1981), Crook and Sunderland (1984), Lövei et al., (1985), Service et al. (1986), Sunderland et al. (1987a), Greenstone and Morgan (1989), DuDevoir and Reeves (1990), Cameron and Reeves (1990), Hagler et al., (1992b), Hagler and Naranjo, (1994), Symondson et al. (1996), Symondson et al. (2000), Bohan et al. (2000). ELISA has also been applied to the investigation of parasitoids (Stuart & Burkholder, 1991; Allen et al., 1992; Stuart & Greenstone, 1996; Keen et al., 2001). Many texts are available which deal with the general methods and techniques used in electrophoresis, e.g., Chrambach and Rodbard (1971), Gordon (1975), Sargent and George (1975), Hames and Rickwood (1981), Richardson et al. (1986), Pasteur et al. (1988), Hillis and Moritz (1990), Quicke (1993). The reader is referred to these texts for detailed descriptions and experimental protocols and to Menken and Ulenberg (1987), Loxdale and den Hollander (1989), Loxdale (1994), Symondson and Liddell (1996), Symondson and Hemingway (1997) for reviews of the application of electrophoretic methods in agricultural entomology.

6.3.10 Labelling of Prey and Hosts Potential prey can be labelled with a chemical that remains detectable in the predator or parasitoid (Southwood & Henderson, 2000). By screening different predators within the prey’s habitat for the presence of the label, the species composition of the predator complex can be determined. Various labels are available for studying predation and parasitism. These include radioactive isotopes, stable isotopes, rare elements and dyes (usually fluorescent), which are introduced into the food chain, where their progress is monitored. The label is injected directly into the prey, or it is put into the prey’s food source. Parasitoid eggs can be labelled by adding a marker to the

504

food of female parasitoids. Appropriate field sampling can then reveal whether suspected predators have eaten labelled prey, or whether suspected hosts have been oviposited in by a particular parasitoid species. Collembola have been labelled with radioactive 32P through their food. This did not affect Collembola survival rate, or the probability of predation by carabids. In a laboratory experiment, predation rates estimated by radiotracers were similar to those estimated from a comparison of Collembola survival in containers with and without predators (Ernsting & Joosse, 1974). Helicoverpa armigera and H. punctigera eggs were radiolabelled by injecting adult females with 32P, and the fate of these eggs was then monitored in the food chain (Room, 1977). Both adults and larvae of the bean weevil, Sitona lineatus, were labelled with 32P by allowing them to feed on broad bean plants which had their roots immersed in distilled water containing the radiolabel (Hamon et al., 1990). The labelled weevils were then exposed to predation by carabid beetles within muslin field cages, and the levels of radioactivity shown by the carabids subsequently measured using a scintillation counter. Greenstone (1999) lists six other studies in which radionuclides were used to detect consumption of pests by spiders. Breene et al. (1988) labelled mosquito larvae with radioactive 32P and released them into simulated ponds where spiders were present. After 48 h spiders were removed to assess levels of radioactivity: 30‒ 77% had eaten the labelled prey. Spiders were observed preying on the mosquito larvae by grasping them from beneath the surface of the water, pulling their bodies through the surface tension and then consuming them. Labelling may also be used to investigate intraspecific trophic relationships, such as maternal care. Adult female lycosid spiders (Pardosa hortensis) were labelled for a month with radioactive tritium and leucine through their food (Drosophila flies). When they produced cocoons (which are carried attached to the spinnerets), the cocoons were removed and replaced with unlabelled cocoons, which the mothers

K. D. Sunderland et al.

adopted readily. Twenty days later, the spiderlings inside the cocoon were found to be labelled with radioactive tritium, and parts of the cocoon were labelled with radioactive leucine (an amino acid found in cocoon silk). This finding suggests that the mother periodically opened the cocoon, regurgitated fluid into it for the spiderlings, and then repaired the opening with fresh silk (Vannini et al., 1993). Monitoring of radioactive labels may be done using a Geiger counter or a scintillation counter, to measure a- and/or bemissions (Hagstrum & Smittle, 1977, 1978), or by autoradiography (McCarty et al, 1980). In autoradiography, the sample containing the potentially labelled individuals is brought into contact with X-ray sensitive film, and dark spots on the developed film indicate the position of labelled individuals. However, although simpler to perform than either serological or electrophoretic methods, hazards to both the environment and the operator posed by radiolabelling mean that the method needs to be confined to laboratory studies or is used in the field such a way that the labelled insects can be safely and reliably recovered at the end of the experiment. The cereal aphid Sitobion avenae was marked through its food with the stable isotope 15N, samples being analysed with an elemental analyser coupled to a mass spectrometer (Nienstedt & Poehling, 2000) (Sect. 6.2.11). The label was detected successfully in a linyphiid spider (Erigone atra) and a carabid beetle, Anchomenus dorsalis (= Agonum dorsale), in laboratory tests. The isotope had no effect on the environment or on the behaviour of the labelled animals. Predators that had eaten two aphids had a significantly higher level of marker than those that had eaten one aphid, and the marker was detectable in predators for at least eleven days (Nienstedt & Poehling, 1995). 15N can also be used to mark parasitoids (by rearing them on hosts that have been raised on a diet incorporating 15N). Parasitoid adults are expected to retain the label throughout their life because nitrogen is a major constituent of fibrin and chitin (Steffan et al., 2001). Utilisation of stable isotopes of carbon and

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Populations and Communities

nitrogen as natural tracers of nutrient flows in ecosystems is discussed in Sect. 6.3.1. Rare elements such as rubidium or strontium may be used as markers, in a similar way to radioactive elements (Shepard & Waddill, 1976; Graham et al., 1978). For example, spongy moth larvae were successfully labelled with rubidium, and their phenology and survival were not affected. The label was retained for five days in these larvae, and adult carabids (Carabus nemoralis) eating larvae in laboratory trials acquired the tag. Rubidium concentration in beetles was positively correlated with number of larvae eaten, and negatively with the number of days since feeding (Johnson & Reeves, 1995). In such studies the path of the rare element through the food chain is monitored with the aid of an atomic absorption spectrophotometer. Unfortunately, while such markers may be retained for life, and self-marking is possible using labelled food sources, the equipment necessary for detection is expensive, both to purchase and to run, as well as requiring trained operators. Prey can be labelled with rabbit immunoglobulin (IgG), then predators that feed on the labelled prey can be detected with a goat anti-rabbit IgG. The method is only appropriate for predators with chewing mouthparts (that ingest the exoskeleton of their prey), as only 30% of sucking predators were positive 1 h after feeding (Hagler & Durand, 1994). Fluorescent dyes offer another means of marking prey. Hawkes (1972) marked lepidopteran eggs with such dyes during studies of predation by the European earwig, Forficula auricularia. An alcoholic suspension of dye was sprayed onto eggs, which were then eaten by the earwigs. Thereafter, the earwig guts were dissected out and examined under UV light. Although such dyes are useful, being both simple and inexpensive to use, they are relatively shortlived, and possible repellant effects, due to either the dye or the carrier, must be taken into account. The dye is usually voided from the predator gut within a few days. Hawkes (1972) found no evidence of dye retention in any of the internal structures of earwigs dissected. In a few specific cases, naturally coloured prey can be seen inside

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the gut of a living predator. For example, remains of citrus red mite (Panonychus citri) imparted a red colouration to the gut of the phytoseiid predator, Euseius tularensis, that did not fade until more than 8 h after ingestion (Jones & Morse, 1995). These authors devised a ten-point gut colouration rating scale to score the rate of decline of this natural label, after a meal, at a range of temperatures. Pigments from the mites, Panonychus ulmi and Bryobia arborea, can be seen in the guts of predators, such as lacewing larvae (Chrysopidae), that accumulate digestive wastes from large numbers of prey (Putman, 1965). Some of these ingested mite pigments fluoresce brilliantly in UV light. Pigments from very small numbers of mites can be detected by chromatography in small predators, such as anthocorid bugs, predatory thrips, and theridiid spiders, but, unfortunately, consumption of other prey can obscure the pigments (Putman, 1965). Fluorscent dye was also used to successfully track the movement and behaviour of the diamondback moth Plutella xylostella and its parasitoid Diadegma semiclausum in non-crop and crop areas (Schellhorn et al., 2008). A potential alternative approach might be to live-stain host or prey organisms. Lessard et al. (1996) marked protists with a fluorescent DNAspecific stain (DAPI—2,4-diamadino-6phenylindole; Sect. 3.4.1) which had no negative effects on their swimming behaviour or growth rate, and which was visualised easily in guts of their fish predators (larval walleye pollock, Theragra chalcogramma) using epifluorescence microscopy. It is not yet known whether this method could be used easily to study predation or parasitism (or, indeed, dispersal; Sect. 6.2.11) by arthropod natural enemies. Major potential problems with the use of labels are secondary predation (Putman, 1965; Sunderland et al., 1997), scavenging and trophallaxis. The label may be recorded in several predatory and scavenger species within the prey’s habitat, but only some of these may be directly responsible for prey mortality. Other food chains may also become contaminated (e.g., in the Johnson and Reeves (1995) study,

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rubidium ‘leaked’ from both larvae and beetles in their frass), further adding to the confusion. An additional disadvantage is that, in most cases, prey must be handled, to some extent, to achieve the labelling, and this may disturb the system under investigation. Released prey may not have time to re-establish themselves, either in terms of spatial separation, or in their favoured microhabitats, possibly making them more vulnerable to predation and other biotic mortality factors (such as parasitism and disease).

6.3.11 DNA-Based Techniques Molecular technology has rapidly permeated all areas of biology, and insect ecology is no exception. DNA techniques have almost entirely replaced electrophoresis, and other protein-based methodologies, in insect taxonomy for distinguishing between morphologically similar species, for constructing phylogenetic relationships, and for examining the stucture of populations and communities (Hrček & Godfray, 2014). This is mainly because DNA can provide, reliably, far more information, at all levels, than electrophoresis or any other previous approach (Symondson & Hemingway, 1997; Symondson, 2002b). DNA can be extracted from minute samples of insects (Hemingway et al., 1996), and the techniques are sufficiently fast for use in relation to pest management programmes (Loxdale et al., 1996). Useful manuals providing details of basic techniques can readily be found (e.g., Berger & Kimmel, 1987; Sambrook et al., 1989). Much is now known about how different regions of the genome evolve at different rates (Avise, 1994), and, hence, which target genes are most appropriate for examining differences at the level of the species, population or individual (e.g., Moritz et al., 1987; Liu & Beckenbach, 1992; Brower & DeSalle, 1994; Simon et al., 1994; Lunt et al., 1996; Caterino et al., 2000). A range of techniques has been developed, that are appropriate for use in entomological studies (Loxdale et al., 1996; Loxdale & Lushai, 1998). While the application of DNA techniques to the study of trophic interactions is still relatively

K. D. Sunderland et al.

new, it is growing exponenially. Here, we concentrate on techniques that have proved successful in studies of predation and parasitism, confining ourselves to studies which seek to detect and quantify interactions between organisms, rather than focusing on the genetics of the predators and prey or parasites and hosts. In general, methods for extracting DNA to detect parasitism and predation are the same as those in the many studies dealing with the molecular taxonomy of insects. A recent assessment of different methods for diet tracing, comparing molecular diagnostics with other techniques, can be found in Nielsen et al. (2018). Detection of parasitoids within hosts The rapid detection, identification and quantification of parasitoid early stages within their hosts is a major concern for practitioners of IPM who wish to avoid pesticide intervention wherever possible, and who also wish to monitor the spread of newly introduced biological control agents. Simple dissection is slow, labourintensive and innacurate, and hence molecular techniques have been developed. One of the simplest, fastest and least expensive molecular approaches is to use RAPD-PCR (Random Amplified Polymorphic DNA—Polymerase Chain Reaction) (Micheli et al., 1994; Rollinson & Stothard, 1994). The great advantage of this approach is that short random primers are selected initially, without the need for prior knowledge of target sequences. Although PCR conditions must be optimised, primers can be screened rapidly to find those that are capable of revealing reproducible, species-specific banding patterns on a gel for parasitoids within their hosts. Black et al. (1992) detected parasitoids in aphids (Diaeretiella rapae in Diuraphis noxia and Lysiphlebus testaceipes in Schizaphis graminum), and the parasitoid patterns were species specific. Parasitoid DNA was detectable by six days after parasitism. PCR and arbitrary primers were used to distinguish between eight strains of D. rapae, three strains of Aphidius matricariae, and three other parasitoid species. Most primers give distinctly different patterns with different

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Populations and Communities

species, and this should allow determination of the species involved in parasitism in field collections. Concerns regarding reproducibility of the banding profiles have meant that RAPD-PCR is now rarely used for trophic analysis and many journals will no longer publish papers using this approach. A more precise approach is to use primers for a target sequence within a specific gene. Much attention has been directed at genomic ribosomal gene clusters (rDNA), of which there are hundreds of tandemly repeated copies per cell. The level of conservation of these regions is such that universal primers are now available for amplifying sequences from a broad range of insects. Within these clusters, internal transcriber spacer regions (ITS1, ITS2) have been shown to reveal useful differences in length and sequence between closely related species (e.g., Black et al., 1989; Chen et al., 1999; Proft et al., 1999; Taylor & Szalanski, 1999). Amornsak et al. (1998) used PCR to amplify the ITS2 regions of the moth pests Helicoverpa armigera and H. punctigera, and a parasitoid of their eggs, Trichogramma australicum. The DNA sequence data obtained from the wasp were then used to create primers that permitted the specific amplification of T. australicum DNA from parasitised moth eggs. It was possible to detect parasitism only 12 h after the eggs were parasitised, and the authors considered that the method is likely to prove useful for evaluating the potential of T. australicum for biocontrol of Helicoverpa species in Australia. Zhu and Greenstone (1999) and Zhu et al. (2000) also used ITS2 regions to distinguish between Aphelinus albipodus, A. varipes, A. hordei, A. asychis and Aphidius colemani. DNA of A. asychis, A. hordei and Aphidius colemani was detectable in the Russian wheat aphid, Diuraphis noxia, as soon as one day after parasitism. Techniques have improved to the point where DNA from parasitoid eggs within host can be detected immediately after parasitism has taken place (Traugott & Symondson, 2008). Other potential targets for molecular detection of parasitism are the 12 and 16S ribosomal RNA genes (rRNA) within mitochondria. With many

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hundreds of copies per cell, mitochondrial genes, again, potentially provide a large target for PCR. The rRNA genes are known to expose relatively recent evolutionary events (Hillis & Dixon, 1991), and have been shown to be effective at revealing differences between closely related species of Hymenoptera (Whitfield & Cameron, 1998). Most primers used for detecting parasitoids and other invertebrates are now based upon parts of the barcoding region of the mitochondrial COI gene (Folmer et al., 1994; Hebert et al., 2003; Hrček & Godfray, 2014; Frost et al., 2016). This region is conveniently conserved at, or close to, the species level. Although a PCR stage increases the chance of detection by amplifying the target sequence, other approaches have been used successfully. Clearly, if PCR can be omitted, this could potentially increase the rate at which hosts might be screened. Greenstone and Edwards (1998) developed a species-specific hybridisation probe to a multiple-copy genomic DNA sequence from the braconid wasp Microplitis croceipes, and used it to detect parasitism of Helicoverpa zea. Despite the lack of a PCR stage, it could detect first-and second-instar larvae as efficiently as host dissection but was not effective for detecting parasitoid eggs. The probe reacted with the congener M. demolitor, but not with a representative growth stage of another parasitoid of this host (Cotesia marginiventris), or with H. zea itself. The authors reported that the whole procedure from host collection to evaluation of results is possible in around 24 h. DNA-based approaches have now been developed that can be used to screen whole parasitoid communities attacking a target host. This can include both primary parasitoids and hyperparasitoids, as well as multiparasitism by different primary parasitoids species within the same host individuals (Traugott et al., 2008). This was achieved in a study of the parasitoid community attacking the aphid Sitobion avenae in wheat. In some cases, it was even possible to detect DNA from the remains of primary parasitoids attacked by the larvae of hyperparasitoids within a host. Such molecular approaches can rapidly screen large numbers of hosts and reveal

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a lot about the ecology of the interactions involved. However, only rearing can tell us which parasitoid species might eventually emerge where multiparasitism is involved. In the Traugott et al. (2008) study, multiparasitism by primary hosts was found in 1.6% of the 1061 aphids screened. An additional element facilitated by DNAbased approaches is that they may allow plant‒ herbivore‒parasitoid‒hyperparasitoid food webs to be extended to intraguild competitiors, specifically predators (Moreno-Ripoll et al., 2012; Traugott et al., 2012). When predatory beetles or spiders attack aphids, for example, they are also consuming parasitoids within some of those hosts. The question then arises: do predators have a preference for parasitised over unparasitised hosts? There may be a nutritional advantage to doing so, or the predators may gain from eliminating intraguild competitors. Traugott et al. (2012) showed high rates of direct and indirect intraguild predation on parasitoids in wheat fields by carabid beetles and linyphiid spiders using PCR. Direct predation was attributed to predators that contained parasitoid, but no aphid, DNA in their guts. ‘Coincidental’ was where aphid and parasitoid DNA were both detected in the same individual predator. However, as the latter may be the result of either predation on parasitised hosts, or consumption of both adult parasitoids and aphids in quick succession, interpretation is difficult. In another recent study, Ye et al. (2017) were able to extend the food web to include herbivore (aphid)‒parasitoid‒hyperparasitoid‒endosymbiont interactions. Derocles et al. (2014), using molecular methods reported in Derocles et al. (2012), provide a good example of how host‒parasitoid webs can now be constructed using molecular data. DNA extracted from aphids was targeted with groupspecific primers designed to amplify the Aphidiinae. Fragments of two genes, the mitochondrial 16S and the nuclear LWRh, were amplified to refine parasitoid identification, in most cases to species level. When positive detection was found, the DNA was amplified and Sanger sequenced. Aphids from several different crops and from field

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margins were screened. Only three of the 32 parasitoid species observed were found in both the crops and the margins, indicating very little sharing of parasitoids and strong compartmentalisation in the food webs. The conclusion was that the field margins did not provide a source of parasitoids sufficient to suppress aphids within the crops and effect biocontrol. There is continuing interest in the food webs that establish around invasive species. Again, molecular diagnostics can be used to detect and identify parasitoids within invasive hosts, providing rapid evidence regarding the species of parasitoid most likely to be capable of exercising some level of control. Recent examples include (Kitson et al., 2019) (parasitoids attacking the Oak Processionary Moth, Thaumetopoea processionea) and Gariepy et al. (2019) (parasitoids attacking the brown marmorated stink bug, Halyomorpha halys). Detection of prey remains within predators. DNA methods are also starting to be used in predation studies. There is an important role for such methods, which are, potentially, an efficient means of determining predator diet both qualitatively and quantitatively. The use of speciesspecific primer design, polymerase chain reaction (PCR) and most recently next-generation sequencing has greatly enhanced the use of molecular techniques to determine the role of predators in food webs (Symondson, 2002a, b; Sheppard & Harwood, 2005; Pompanon et al., 2012; Greenstone et al., 2014). The sources of blood meals by haematophagous insects, such as mosquitoes, ticks and crab lice, have been detected using such techniques (Coulson et al., 1990; Tobolewski et al., 1992; Gokool et al., 1993; Lord et al., 1998; Gariepy et al., 2012). Prey DNA in the guts of predators and host-feeding parasitoids is likely to be in a highly degraded state, and so markers based on PCR techniques offer the most promise. Zaidi et al. (1999) demonstrated that multiplecopy genomic DNA sequences that confer insecticide resistence on mosquitoes, Culex quinquefasciatus, could be detected for at least 28 h after the mosquitoes were fed to carabid

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beetles, Poecilus (= Pterostichus) cupreus. Detection periods were shown to depend upon the length of DNA sequence, with successful amplifications of 146 and 263 bp sequences. Longer sequences could not be amplified reliably from gut samples, suggesting that short sequences survive intact for a longer period during the digestion process. The prey were equally detectable whether the beetles had eaten one mosquito or six, digested for zero or 28 h. DNA was extracted from the whole predator in this experiment, yet PCR was able to amplify the target sequences from the much smaller prey within the digestive systems of the carabid. Having demonstrated that the 40–50-fold replication of genomic multiple-copy genes allowed clear detection of semi-digested prey DNA within predators, Zaidi et al. (1999) speculated that the far greater copy number of mitochondrial genes per cell would make these attractive targets. This was confirmed by Chen et al. (2000), who used PCR primers to amplify mitochondrial COII sequences (77 to 386 bp) from six species of cereal aphid, and were able to detect aphid remains in the guts of predators. After feeding on a single Rhopalosiphum maidis aphid, then on five aphids of the related R. padi, a 198 bp fragment, specific for R. maidis, could be amplified from 50% of coccinellid predators after 4 h, and 50% of chrysopid predators after 9 h. The authors claim this technique to be superior to the use of monoclonal antibodies, in being more certain of success, faster, and less expensive to develop, yet of similar sensitivity and specificity. The overall costs of developing probes and assaying predators from the field appear to be comparable with those for ELISA. Agustí et al. (1999) also found that detection periods were strongly affected by sequence length. Primers were designed to amplify sequence-characterised amplified regions (SCARs) derived from a randomly amplified polymorphic DNA (RAPD) band. Immediately after feeding ten eggs of the target prey, Helicoverpa armigera, to the predator, Dicyphus tamaninii (Heteroptera: Miridae), SCAR primers could amplify successfully 600 and 254 bp fragments, but not a larger 1100 bp sequence

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using a third set of primers. After four hours’ digestion in D. tamaninii, only the 254 bp sequence could be detected in 45% of fed predators. In specificity tests, the primers failed to amplify a 254 band from any of the other species tested (five lepidopterans, two whiteflies and two predators), but, in two cases, did amplify sequences of different sizes. SCAR primers were also prepared from DNA of the glasshouse whitefly (Trialeurodes vaporariorum), and a short fragment (310 bp) was used to detect whitefly DNA in the gut of Dicyphus tamaninii (Agustí et al., 2000). At 25 °C, 60% of predators were positive four hours after eating ten T. vaporariorum nymphs, and the primer detected eggs and adults of this species, but also gave faint bands with other species of whitefly and aphid (Agustí et al., 2000). Hoogendoorn and Heimpel (2001) prepared four primer pairs, of different lengths, that were specific to the European corn borer, Ostrinia nubilalis. Using the shortest primer, a meal of corn borer eggs was detectable in the ladybird Coleomegilla maculata for up to 12 h, and longer primers gave shorter detection periods. The authors suggested that such a set of primers could be used to determine the range of times since feeding by predators in the field. Attempts by Johanowicz and Hoy (1999) to detect 16S mitochondrial genes from Wolbachia, eaten by the mite predator Amblyseius reductus together with their host, the prey mite Tetranychus urticae, failed, probably because the chosen primers were designed to amplify a relatively long, 900 bp sequence. Greatorex (1996) prepared primers to detect segments of DNA of the prey mite Orthotydeus caudatus in its predator (the phytoseiid mite Typhlodromus pyri) using RAPD-PCR. Some primers amplified a range of mite species, but one pair were specific to O. caudatus, which it could detect in samples containing a 100,000th of a mite. The rate of digestion of prey DNA by the predator was not determined. Future approaches could, potentially, be borrowed from studies of predation in vertebrate systems. PCR amplification of microsatellite markers (i.e., tandemly repeated short nucleotide

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sequences) have been used successfully to detect predation in vertebrate systems. Scribner and Bowman (1998) used this method to identify species of waterfowl preyed upon by glaucous gulls. Some remnants of DNA from consumed goslings were estimated to be detectable 8–16 h after ingestion. Asahida et al. (1997) used PCR and restriction analysis of mitochondrial DNA to identify stone flounder (Kareius bicoloratus) DNA in the stomach of the sand shrimp (Crangon affinis), but for only up to five hours after ingestion. Their method can only detect the presence or absence of target DNA, but it has the potential for use in the simultaneous detection of multiple prey species. Other examples of detection of vertebrate prey remains in predator guts or faeces have been reviewed in Symondson (2002b), Sheppard and Harwood (2005), Pompanon et al. (2012). There are now many reports of the detection of predation on target invertebrate species (usually a pest) in agricultural crops using one or more prey-specific primer pairs. This is a quick and simple approach, depending for its success mainly on the specificity of the primers. Recent examples include primers specific for pests such as the squash bug Anasa tristis (Schmidt et al., 2014), corn rootworm Diabrotica vergifera (Lundgren & Fergen, 2014), cassava whitefly Aleurotrachelus socialis (Lungren et al., 2014), coffee berry borer Hypothenemus hampei (Jaramillo et al., 2010), aphids (Chen et al., 2000) and leafhoppers (Virant-Doberlet et al., 2011). If a high proportion of the gut samples of predator contain DNA from the target species it is assumed that the predator might be capable of controlling pest numbers. However, this approach is being replaced to some extent by the advent of high-throughput (next-generation) sequencing (NGS). What a predator eats depends upon what is available. Alternative prey may divert the predators from eating the pests (reducing their pest controlling effect) or help to balance the nutritional requirements of the predator, leading to increases in the population density of that predator. There may be intraguild predation taking place, reducing the effectiveness of the predator community to control the pests.

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The advantage of NGS is that it can detect the whole range of invertebrate prey consumed. Food web analyses (Sect. 6.3.12) can then be used to determine the strengths of the interactions taking place. Software has been developed (e.g., the ‘econullnetr’ package in R statistical software) that allows the relative densities of different prey in the environment (determined by a survey) to be compared with the frequency with which different prey are consumed (measured by NGS detecting numbers of predators testing positive for each prey) (Vaughan et al., 2018). Essentially the approach tests for deviations from random feeding, which can show either preference or avoidance. Pearson et al. (2018) provide an example of using this approach in an aquatic system. In this study, feeding by two competing predator species (the Caddisfly Rhyacophila dorsalis and the Stonefly Dinocras cephalotes) was analysed in a community of 30 prey species. The presence of key species within rivers is widely used to measure its health, but to date there has been little regard paid to the trophic relationships that may affect species abundance, especially in relation to agricultural intensification gradients. Few studies have used NGS to analyse predation by invertebrates in other applied contexts, such as agriculture, horticulture and forestry. In more ecological contexts, NGS has been used to analyse the diets of spiders in a range of habitats, including high arctic, sea shore, forest and desert environments (e.g., Petráková et al., 2015; Hambäck et al., 2016; Toju & Baba, 2018; Eitzinger et al., 2019). However, linyphiid spiders have been analysed in a study of their diets in arable fields (Pinol et al., 2014). When general invertebrate primers are used to analyse gut samples from invertebrates, the expectation would be that most of the sequences detected derive from the predator, rather than from the degraded prey in its gut. While it is possible to partially prevent this by using blocking probes (Vestheim & Jarman, 2008), such probes may block the detection of some prey (e.g., a spider predator eating other spider species). By not using a blocking probe, Pinol et al. (2014) found that although most of the sequences obtained were from the spider predator (Oedothorax fuscus),

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there was sufficient depth to the sequencing to show that this cereal crop spider had been eating members of the Diptera, Lepidoptera, Collembola and Nematoda, as well as other spider species. The numbers of prey sequences that can be amplified from spiders can be maximised, while minimising amplification of the predator sequences, by selectively removing longer amplicons of DNA and only extracting from DNA from the mid and hind gut (Krehenwinkel et al., 2017): longamplicon DNA is more likely to derive from the predator while short-amplicon DNA will predominantly derive from semi-digested prey remains. Pearson et al. (2018) found that if DNA was extracted from the gut contents by dissection first, rather than extracting DNA from the whole homogenised predator or segments thereof, minimal amplification of the predator resulted when no blocking probe was applied. It is also possible to combine the use of species-specific primers with NGS. For example, Gomez-Polo et al. (2016) used specific primers to analyse predation by Orius majusculus on aphids (Nasonovia ribisnigri) and thrips (Frankliniella occidentalis) in a lettuce crop, then applied NGS with general primers to detect predation on other invertebrates, including intraguild competitors: Collembola proved to be a major alternative prey. Similar methods were used to analyse the diets of hover flies (Syrphidae) in the same crop (Gomez-Polo et al., 2015). A different approach used in some studies has been to design separate primers for each target prey species and develop multiplexes allowing many species to be analysed at the same time. This was applied to analyse predation on aphids, alternative prey and intraguild predators in cereal fields (Staudacher et al., 2016, 2017; Roubinet et al., 2017). The advantage of this approach is that it can precisely target species known to be present within the crop. The advantage of NGS is that it should, in principle, amplify all prey species, without prior knowledge of what is present. In practice, no general primers have yet been developed that amplify every invertebrate species and therefore more than one set of general primers, with abilities to amplify different ranges of taxa, may need to be applied, adding to costs.

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Many vertebrate predators eat invertebrates and NGS has been used to detect predation on crop pest species as well as alternative prey. For example, Taylor et al. (2017) demonstrated that a third of faecal samples from bats contained the DNA of the stink bug Nezara viridula, a major pest of macademia. Other bats have been shown, for example, to consume the pine pest Bupalus pinaria (Krüger et al., 2014), pests of oil palm plantations (Brandon-Mong et al., 2018), mosquitoes (Culex pipens), black flies (Simulum spp.) and paper wasps (Polistes spp.) (Clare et al., 2014). Similarly, NGS has been used to show that birds, such as Western Bluebird, (Sialia mexicana), are potentially valuable predators of pests in vinyards, consuming herbivorous Hemiptera and Lepidoptera, as well as mosquitoes (Jedlicka et al., 2017).

6.3.12 Food Web Construction and Analysis The methods described above can help both to determine and to quantify predator‒prey and parasitoid‒host relationships. However, to appreciate fully the significance of these relationships for the organisation and functioning of a community, techniques for integrating and analysing these data are required. This is a nontrival task because understanding ecological communities is among the most complex challenges in any scientific discipline (Godfray & May, 2014). This is due to the large number of organisms and inter-organism interactions involved, each with their own evolutionary and co-evolutionary histories (Peralta et al., 2014; Levine et al., 2017). It is also a desirable task because it allows assessment and even prediction of how communities respond to natural and anthropomorphic impacts (Montoya et al., 2015; Rocca & Greco, 2015; Sanders et al., 2018). Food webs (trophic webs) can be used to map all the trophic interrelationships of interest in a particular system (Bukovinszky et al., 2008; Peralta et al., 2014; Montoya et al., 2015; Henri & van Veen, 2016; Sanders et al., 2018), in principle considering both species diversity and

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the transfer of metabolites between species through their interactions (Montoya et al., 2015). Mapping trophic interrelationships of interest in a particular system gives a more or less static representation of a community (Hall & Raffaelli, 1993), and manipulation experiments may be preferable for investigating the dynamics of community organisation (Memmott et al., 1994; Frost et al., 2016; Barbosa et al., 2017). Unfortunately, for many communities, it is not feasible to manipulate component species on the scale required, and, in these cases, the detection of patterns in food webs is the next best means of gaining insights into community structure and function. Methods to describe and analyse the more complex and dynamic aspects of food webs, including the many categories of potential indirect effects, are still being developed and debated (Abrams et al., 1996; Polis et al., 1996; Blüthgen, 2010; Poulin, 2010; Fontaine et al., 2011; Allesina & Tang, 2012). Host‒parasitoid trophic relationships are more easily quantified than is the case for predators and their prey (Godfray & Müller, 1999; Lewis et al., 2002) and construction of trophic webs has been successful in the study of communities of insect herbivores and their parasitoids (Memmott & Godfray, 1994; van Veen et al., 2006a, 2006b, 2008; Bukovinszky et al., 2008; Hrček & Godfray, 2014; Peralta et al., 2014; Maunsell et al., 2015; Rocca & Greco, 2015; Henri & van Veen, 2016; Sanders et al., 2018). In contast, dietary information for predators is usually incomplete, and often reflects the amount of observational effort expended (e.g., the number of prey species of the scorpion Paruroctonus mesaensis reached 100 on the 181st survey night, and never reached an asymptote after 2000 person-hours of observation over five years, Polis, 1991). DNA-based molecular methods have recently revolutionised the elucidation of food webs, revealing trophic links that otherwise would not be detectable with standard rearing methodologies (e.g., Condon et al., 2014; Hrček & Godfray, 2014; Wirta et al., 2014; Frost et al., 2016).

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There are a number of different types of food web (Memmott & Godfray, 1994). Some show trophic linkages only, while others also describe the strengths of these interactions, but note that webs typically include little or no information on the factors that determine the linkages themselves: 1. ‘Connectance web’—only the existence of interactions between host/prey and natural enemy species is recorded (i.e., a topological food web; Shameer et al., 2018; Woodward & Hildrew, 2001; Yodzis & Winemiller, 1999) (Fig. 6.26). 2. ‘Semi-quantitative web’—the relative numbers of (or % parasitism by) each natural enemy species per host/prey species are recorded (e.g., Dawah et al., 1995). 3. ‘Quantitative web’—the densities of host/prey, natural enemies and the strengths (i.e., % parasitism) of host/prey‒natural enemy interactions are recorded (e.g., Müller et al., 1999; Schönrogge & Crawley, 2000; Lewis et al., 2002; Hirao & Murakami, 2008; Peralta et al., 2014; Wirta et al., 2014; Maunsell et al., 2015; Rocca & Greco, 2015; Barbosa et al., 2017; Sanders et al., 2018) (Fig. 6.26). 4. ‘Source web’—is centred on a plant or herbivore and extends to all higher trophic levels (e.g., Hövemeyer, 1995; Memmott et al., 2000). 5. ‘Sink web’—is centred on a natural enemy and includes all relevant lower trophic levels (e.g., Schoenly et al., 1991). 6. ‘Community web’—is non-centred and includes all species (e.g., Schoenly et al., 1991). Webs can also be site specific, time specific (e.g., for a single crop growth stage), or cumulative (gathered over many occasions within a discrete area). The IRRI cumulative rice web, for example, contains 687 taxa and more than 10,000 trophic links (Schoenly et al., 1996a). Some published webs combine data from localities, seasons or years, (‘composite webs’, e.g.,

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Fig. 6.26 Comparison of a connectance web and a quantified web for a plant‒ hispine‒parasitoid food web. a Connectance web displaying all interactions, b quantified web showing the absolute abundance of leafminers, parasitoids and interaction strength. The first rectangle (*) in the leafminer level represents 30 minersparasitoids reared per 1000 m2. Numbers and letters each refer to different species of plant, leafminer and parasitoid. Source Memmott and Godfray (1993). Reproduced by permission of CAB Publishing

Dawah et al., 1995; Shameer et al., 2018, or ‘metawebs’, Frost et al., 2016), but there is a danger that this may obscure subtle patterns in web structure. Constructing webs for the overall community and separately for each site and season allows assessment of the potential for spatial and seasonal variability in the distribution of feeding links to affect community stability (Henri & van Veen, 2016). For instance, studying both pest infestation and non-infestation periods can help to assess the importance of intercrops for the maintenance of lepidopteran and parasitoid populations in cropping systems (Saeed et al., 2015; Shameer et al., 2018), and

comparing food webs constructed within each period would likely be informative. Web studies vary in the degree of their trophic resolution (e.g., in some studies facultative hyperparasitoids are not distinguished from other parasitioids, Lewis et al., 2002). Webs may be constructed from either taxonomic species or trophic species (i.e., functional groups that contain organisms that eat and are eaten by the same species within a food web, Cohen & Briand, 1984), and less sampling effort is required to characterise webs accurately in the latter case (Martinez et al., 1999). Trophic species (= trophospecies) are currently delimited

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subjectively, and it would be better to use objective quantitative methods, but the identification of optimal methods to do so is proving to be elusive (Yodzis & Winemiller, 1999). Web data are useful in a variety of contexts (Memmott & Godfray, 1994), such as: (a) comparison between hosts in different feeding locations, (b) comparison between early- and latesuccessional habitats, (c) comparison between tropical and temperate regions, (d) comparsion between low and high elevation (Maunsell et al., 2015), (e) study of the determinants of parasitoid host range, (f) as a guide to the probability of apparent competition (Holt & Hochberg, 2001; van Veen et al., 2006b, Sect. 7.3.7) between hosts (Rott & Godfray, 2000; Lewis et al., 2002; Frost et al., 2016; Shameer et al., 2018), (g) to probe the intricacies of community structure and function (e.g., degree of compartmentation, Montoya et al., 2015; influence of keystone species, role of the frequency distribution of different body sizes, Woodward & Hildrew, 2002), (h) to assess the effect of alien hosts on native parasitoids and native hosts (Schönrogge & Crawley, 2000), (i) to quantify prenetration of food webs in native habitats by introduced alien parasitoids (Henneman & Memmott, 2002), (j) to assesss host‒parasitoid communities establishing in introduced crops (Rocca & Greco, 2015), and (k) to evaluate community stability and vulnerability to extinction (Peralta et al., 2014; Barbosa et al., 2017; Sanders et al., 2018). Comparison of collections of food webs may uncover patterns in the structure of natural communities (Rott & Godfray, 2000). Study of food webs is also likely to guide the development of models into profitable and realistic directions (Cohen et al., 1994; Memmott & Godfray, 1994). Food web data can also be valuable in underpinning other studies. West et al. (1998), for example, used knowledge of leafminer‒parasitoid and aphid‒parasitoid‒hyperparasitoid food webs in an investigation of the potential mechanisms of horizontal transfer of various strains of Wolbachia bacteria, which can cause parthenogenesis in parasitoids (Sect. 6.5). Valladares and Salvo (1999) compared plant—leafminer (Diptera: Agromyzidae)—parasitoid webs in a natural

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area (woodland and grassland), and an agricultural area (containing various crops, including potatoes, brassicas and beans). They considered that exercises of this sort could be useful in identifying parasitoids with potential for the biological control of pests. There are now many, often closely interrelated, metrics developed for studying food webs (Memmott & Godfray, 1993; Peralta et al., 2014; Maunsell et al., 2015; Montoya et al., 2015; Frost et al., 2016; Barbosa et al., 2017). These can facilitate comparisons between webs (SchmidAraya et al., 2002) and can indicate community productivity (Montoya et al., 2015). Further, descriptive metrics can be fitted as response or exaplanatory variables (Chap. 9) within formal null hypothesis testing statistical analyses of food web properties (Peralta et al., 2014; Maunsell et al., 2015; Montoya et al., 2015; Frost et al., 2016; Barbosa et al., 2017). Such metrics include: (a) number of species [S] and trophic levels, number of links (trophic interactions) [L], (b) linkage density or ‘average degree’ [L/S], (c) connectance, a proportional measure of community complexity, usually the number of trophic interactions divided by the number of possible interactions [L/S2] (e.g., Warren, 1994; Maunsell et al., 2015; Montoya et al., 2015; Rocca & Greco, 2015, reviews various definitions of connectance), (d) average interaction strength between species (Laska & Wootton, 1998, identify four different theoretical concepts of interaction strength), (e) ratios of number of species at different trophic levels, (f) degree of compartmentation (i.e., where species interactions are arranged in blocks and within-block interactions are strong but betweenblock interactions are weaker, Raffaelli & Hall, 1992; Montoya et al., 2015), (g) number of species feeding at more than one trophic level (omnivory), (h) incidence of trophic loops (e.g., A attacks B attacks A, Polis et al., 1989), (i) overlap, the number of pairs of species at one trophic level that share at least one natural enemy at the next highest trophic level, divided by the total possible number of such links (van Veen et al., 2008; Frost et al., 2016), (j) redundancy, the average number of natural enemies attacking

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each host species weighted by the frequency of the trophic interactions (Peralta et al., 2014), redundancy is high when many species feed on the same resource in the same way, and (k) trophic complimentarity, a measure related to overlap and nestedness, describing how connected a food web is (Peralta et al., 2014; Montoya et al., 2015): species specialising by feeding on different resources leads to high resource complimentarity and species having different natural enemies leads to high predation complimentarity, with trophic complimentarity representing the interaction between these. Some of these metrics are sensitive to the degree of taxonomic and trophic resolution of the web, but the majority are stable over a wide resolution range (Sugihara et al., 1997). Various types of robustness analysis can be used to quantify the extent to which sampling effort may bias these web metrics. Lewis et al. (2002), for example, investigated the effects of omitting the least frequent linkages from the dataset (84 parasitoid species attacking 93 species of leafmining insect in tropical forest in Belize). They concluded that their sampling programme had uncovered the majority of host and parasitoid species in the community under study, but not all the interactions among species. Quantitative webs can give strong indications about which species are likely to be keystone species (e.g., species whose interactions dominate the system numerically), and may also be useful for testing the current hypotheses of food web theory (Müller et al., 1999). Not all web types are suitable for estimating the general properties of community webs. Hawkins and Marino (1997), for example, showed that webs based on single, or few, sources underestimated the number of links per species, compared with full webs, and produced inconsistent estimates of the ratios of number of species at the different trophic levels. Practical problems to be aware of when constructing a web include: (a) the difficulty of making large collections of uncommon hosts for rearing (Schönrogge & Crawley, 2000), a limitation for a connectance web, but less serious for a quantitative web (where interactions between rare prey/hosts and their natural enemies do not

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have a strong numerical influence); (b) sample bias in relation to parasitised versus unparasitised hosts, and between parasitoid species that take very different times to develop in the host, or bias against collecting parasitoids that attack very young or very small hosts; (c) bias due to differential ease of rearing-out (Rott & Godfray, 2000; Lewis et al., 2002); and (d) failure to sample unexpected host locations (e.g., leafminers pupating on the ground) (Memmott & Godfray, 1994). Some of these problems can be overcome by following the suggestions given in Sects. 6.3.5 and 6.3.6. Müller et al. (1999) stress the importance of obtaining quantitative information specifically for food web construction, rather than adapting data that was previously collected for some other purpose. This directed approach also means that data collection can employ common methodologies, shared between different communities, which will then facilitate more meaningful comparisons between these communities. Some progress may nethertheless be made, in the absence of such quality information, at least to provide a tentative estimate of community structure and to suggest directions for future, more directed, studies. For instance, Shameer et al. (2018) constructed continentalscale connectance webs of the ‘plant‒lepidopteran herbivore–parasitoid’ community in coconut agro-ecosystems using literature records for the Indian sub-continent. They found that that crop and intercrop plant species shared few herbivore pest species, suggesting that feeding herbivores are unlikely to experience direct interspecific competition, a promoter of competitive exclusion. They also found that pest herbivores share large proportions of their parasitoids, suggesting that indirect ecological interactions (apparent competition, Holt & Lawton, 1994; Sect. 7.3.7) between herbivore species are likely to be important determinants of herbivore and parasitoid population dynamics (Shameer et al., 2018). It should be noted that detection of some of the rarer interactions in a web will only become apparent after very intensive and extensive surveying. For example, Dawah et al. (1995)

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demonstrated the existence of tertiary parasitism in grassland galler-parasitoid communities in the UK, but parasitoids of hyperparasitoids were recorded for only 63 out of 64,781 observations. Woodward and Hildrew (2001) showed that, in food web studies involving predation, a large number of gut contents samples are needed to achieve an acceptable degree of accuracy. They constructed yield-effort curves for the dominant predators in their study system (an acid stream flowing through a lowland heath) to examine how the cumulative percentage of feeding links detected increased with cumulative number of guts examined. Only species that had reached an asymptote for their feeding links were included in the calculation of food web statistics, and, in most cases, this required several hundred gut contents samples per species. There are many methodological variants that have been employed in the construction of different types of food web, for various natural enemies and communities. The study of Müller

et al. (1999), however, will be described as an example of some procedures for sampling and analysis that can be very useful. These authors constructed monthly quantitative parasitoid webs for aphid‒parasitoid relationships (based on 71 species of aphid and parasitoid) in an abandoned damp field in England over a period of two years (Godfray & Müller, 1999). Random plant units (usually a whole plant or a grass ramet) in each square of a 20 x 20 m grid were taken twice each month, and the numbers of aphids and mummies on these were counted. Aphid and mummy densities were calculated after measuring the number of plant units per m2. Mummies were placed in gelatin capsules until the parasitoids emerged. Monthly webs were visualised in diagrams (Fig. 6.27) showing the relative abundance (indicated by bar width) of different aphid species in the centre of the diagram, with the relative abundance of primary parasitoids below, and the relative abundance of secondary parasitoids above that of the aphids, and with wedges

Fig. 6.27 Example of an aphid‒parasitoid web in an abandoned field in southern England, July 1994. Relative aphid abundances are shown in the centre with primary parasitoids below and secondary parasitoids above

(hyperparasitoids in grey and mummy parasitoids in black). Each number refers to a different species. Species densities are shown to scale. Source Müller et al., (1999). Reproduced by permission of Blackwell Publishing

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indicating the relationships between species. These illustrations were produced with low-level graphics routines in the computer package Mathematica (more recent and freely available software [for Microsoft Windows platforms] for visualising interaction networks is provided by Sint & Traugott, 2016). The Dirichlet distribution (Goodhardt et al., 1984) was found to be an effective means of summarising species compositions in the different webs, and showed that there were many rare, and few common, species of both aphids and parasitoids. Parasitoid overlap graphs (Fig. 6.28) were constructed to summarise the patterns of shared parasitism. In these graphs, vertices, representing different aphid species, are linked by lines if both are attacked by a species of the same category of parasitoid (i.e., primary parasitoids, hyperparasitoids that attack the primary parasitoid within the living aphid, and mummy parasitoids that attack the mummy irrespective of whether it contains a primary or a secondary parasitoid). These graphs illustrate the potential for aphid species on different host plants to interact indirectly through shared parasitoids (apparent competition, Holt & Lawton, 1994; Frost et al., 2016; Chap. 7), so they provide a simple visual indication of the potential cohesiveness of the community. In speciose communities, it may be more practictable to illustrate the same information in tabular form (Shameer et al., 2018). Polyphagous parasitoids may exert more influence on a given host species if alternative host species are present in the habitat (Lawton, 1986; Settle & Wilson, 1990), and parasitoid overlap graphs summarise the extent to which diverse host reservoirs are available to this category of parasitoid. Müller et al. (1999) developed a new type of graph (the quantitative parasitoid overlap diagram) to show the relative importance of any one species of aphid as a source of parasitoids to attack other species of aphid (Fig. 6.29). In these diagrams, which are modifications of the traditional parasitoid overlap graph, the size of each vertex circle (again representing an aphid species) is proportional to aphid abundance, and the degree of vertex shading and the width of the links are related to

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the degree to which each aphid species is a source of parasitoids attacking itself or other aphid species. These diagrams were useful in the Müller et al. (1999) study, in showing that aphidprimary parasitoid dynamics of the common aphid species were not strongly linked, whereas mummy parasitoids attacking the common aphid species were not dominated by individuals that had developed in those common species (i.e., few aphid species acted as the main source of their own mummy parasitoids). Mummy parasitoids were thus shown to be important agents of linkage in the community between distinct aphid‒parasitoid‒hyperparasitoid sets. This method was extended to examine apparent competition in food webs determined with DNAbased methods in Frost et al. (2016). Hrček and Godfray (2014) provide an overview of the potential of molecular genetic methods in food web research, and a detailed analysis of how molecular data may be turned into ecological networks can be found in Clare et al. (2019). Construction of food webs based upon molecular detection of parasitoids within hosts and prey within predators is discussed in Sect. 6.3.11. Quantitative parasitoid overlap diagrams have also been used to display the extent to which leafminer hosts (twelve species of Phyllonorycter attacking alder, willow, oak and beech) are a source of their own parasitoids, or a source of parasitoids attacking congeners (Rott & Godfray, 2000), and to show the potential for apparent competition between lepidopterans in native and planation forests (Frost et al., 2016). As with non-quantative overlap graphs, quantitative parasitoid overlap can be presented in tabular form and it may further be informative to calculate overlap separately for differnet classes of natural enemy, such as egg, larval and pupal parasitoids (Shameer et al., 2018). Graphs of predator overlap (links joining predators that share prey) and prey overlap (links joining prey that share predators) can also be constructed. In a study of changes in a stream food web after invasion by a new top predator (the dragonfly Cordulegaster boltonii), Woodward and Hildrew (2001) found that pre- and post-invasion predator overlap graphs could be

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Fig. 6.28 Parasitoid overlap graphs for an aphid‒parasitoid web in an abandoned field in southern England. The vertices represent the aphids from which at least one species of parasitoid were obtained. Vertices are joined by

edges when the aphids share at least one species of parasitoid. Each number refers to a different species. Source Müller et al., (1999). Reproduced by permission of Blackwell Publishing

represented in one dimension (termed ‘interval graphs’), but that the prey overlap graphs were non-interval because several species could not be placed in one dimension with other species in the graph. Interval graphs denote a high degree of interconnectedness (in this case a high degree of generalist feeding), which non-linear modelling suggests may stabilise food webs (McCann et al., 1998). In the acid fish-free stream studied by Woodward and Hildrew (2002), all six invertebrate predators ate nearly every invertebrate taxon smaller than themselves. To summarise this system, and its dynamic changes, they

presented a series of monthly intraguild food webs and niche overlap webs (the latter based on Pianka’s Niche Overlap Index, Pianka, 1973). In these web diagrams, circles (representing predator species) varied in area according to predator density, and vertical location of circles was used to indicate the relative body size of each predator species. Intraguild predation in this system was shown to be strongly asymmetric (with larger predators dominant) and niche overlap decreased as discrepancy in body size increased. In spite of the importance of host‒parasitoid web analyses in themselves, it remains desirable

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Fig. 6.29 Quantitative parasitoid overlap diagrams for an aphid‒parasitoid web in an abandoned field in southern England. The vertices represent aphids and they are linked if the two species share a parasitoid. The size of each vertex is proportional to aphid abundance. The extent to which the vertices are coloured black, and the strength and symmetry of the links, measure the importance of

each aphid species as a source of parasitoids attacking other species of aphids and itself. Each number refers to a different species: a primary parasitoids, b hyperparasitoids, and c mummy parasitoids. Source Müller et al., (1999). Reproduced by permission of Blackwell Publishing

to integrate further information about predators and pathogens that attack the same species of host (Godfray & Müller, 1999). Van Veen et al. (2008) conducted one of the first of these studies, creating a food web including 29 aphid, 24 parasitoid, and 13 predator species. Interactions between these natural enemy groups (Sunderland et al., 1997) could also influence community dynamics. In general, over-simplification of food web descriptions in the past may have misled theorists into making erroneous assumptions and

generalisations (Polis, 1991; Hall & Raffaelli, 1993). Quantifying the degree of compartmentation of food webs can be a useful step towards understanding their dynamics. A keystone predator, for example, can maintain a diversity of prey species by preventing a single dominant prey species from out-competing a range of other prey species, which form the base of a whole compartment of prey, predator and hyperpredator species. Thus, knowledge of compartmentation

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enables prediction of cascade effects arising from removal or inhibition of keystone predator species (Raffaelli & Hall, 1992). Raffaelli and Hall calculated the trophic similarity between all species in a web using the Jaccard coefficient, Sij = a/a + b + c (where a is the number of species that interact with i and j, b is the number of species interacting with i only, and c is the number of species interacting with j only), and then graphed the frequency distribution of these coefficients. Webs with no compartments exhibit more or less unimodal distributions of trophic similarity, whereas for compartmented webs the distributions will be U-shaped, bi- or polymodal. They then carried out an ordination analysis (Hill, 1973) on the similarity matrix, to identify which species comprised the compartment. Compartmentalisation (or modularity) can be informative in a biocontrol context, in which it may demonstrate the degree to which natural enemies are shared between crop and non-crop habitats. For example, both Macfadyen et al. (2011), Derocles et al. (2014) showed little sharing of parasitoids, suggesting that non-crop areas may not be as useful as is usually assumed as sources for at least some groups of natural enemies. Food web methodology can be profitably applied in ecotoxicological investigations, as a powerful summary of system changes following pesticide applications. Schoenly et al. (1996b) constructed plot-specific food webs for a plot of irrigated rice in the Philippines, sprayed with the synthetic pyrethroid deltamethrin, compared with an unsprayed control plot. Species composition and abundance were determined from vacuum netting (Sect. 6.2.2), and trophic relationships were taken from the larger ‘Philippines cumulative rice web’, i.e., 546 taxa and 9319 consumerresource links at 23 sites (Cohen et al., 1994). Schoenly et al. (1996b) calculated the magnitude and direction of differences in percentage herbivores, and natural enemies of herbivores, on fourteen sampling dates, and the mean food chain length (mean number of links of maximal food chains from basal species to top predator), of sprayed and unsprayed webs for each date. The insecticide was found to cause a dramatic

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increase in percentage herbivores, and a reduction in mean food chain length. These changes were associated with pest outbreaks.

6.4

Genetic Variability in Field Populations of Natural Enemies

Individuals within and among populations of the same natural enemy species vary genetically (Hopper et al., 1993; Wajnberg, 2004, 2010; Chap. 3). Patterns of genetic variation within and among populations, usually assayed using molecular markers, reflect historical and contemporary processes such as mutation, selection, drift, and gene flow. These patterns of genetic variation can thus be used to infer information about dispersal (gene flow) over relatively small geographic areas, and geographic origins of natural enemies over larger distances (Lombaert et al., 2014; van Nouhuys, 2016). In addition, within-species genetic variation is often expressed as marked differences in phenotypic attributes of natural enemies such as morphology, physiology, and behaviour (Wajnberg, 2004, 2010; Lommen et al., 2017). A variety of molecular markers can be used to quantify patterns of genetic variation within and among populations. The frequency of use of various molecular markers has changed dramatically over time (Schlötterer, 2004; Seeb et al., 2011). In general, molecular markers used to study within-species genetic variation can be divided into three conceptual classes (Schlötterer, 2004): (1) protein variants (allozymes), (2) DNA sequence polymorphisms (e.g., amplified fragment-length polymorphisms—AFLPs, restriction fragment-length polymorphisms— RFLPs, single nucleotide polymorphisms— SNPs, next-generation sequencing—NGS), and (3) DNA repeat variation (e.g., microsatellites). Allozymes, AFLPs, and RFLPs were commonly used in the past but have now been superseded by microsatellites, SNPs, and NGS (Schlötterer, 2004; Seeb et al., 2011). Single-locus, lowvariability markers such as mitochondrial DNA, while useful for species-level identification, have also fallen out of favour as stand-alone tools for

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studying within-species genetic variation (Allendorf, 2017). Advantages and disadvantages of different molecular markers, details of the analysis of molecular marker data, and the best applications for each type of marker are described elsewhere (Morin et al., 2004; Schlötterer, 2004; Ekblom & Galindo, 2011; Guichoux et al., 2011; Seeb et al., 2011; Putman & Carbone, 2014; Allendorf, 2017). In general, as the cost of generating DNA sequence data continues to decrease and the ease of sequence data processing increases, there is a progressive move towards the use of SNPs (Seeb et al., 2011), including the use of NGS techniques to generate genome-wide SNP data for natural enemies (e.g., Zhang et al., 2018a, b). However, the generation of large amounts of DNA sequence data is not necessary to answer all questions about genetic variation in natural enemy populations. For example, microsatellite markers are still widely used and are particularly useful for applications such as parentage analysis (Seeb et al., 2011). Van Nouhuys (2016) gives a concise overview of research using different molecular markers used to study genetic variation and population structure of parasitoids. Most natural enemy populations are patchily distributed across landscapes, due to discontinuities in the distribution of hosts or prey, or other essential resources. The movement of individuals within and among local populations (within which there is regular movement of individuals between habitat patches), a series of which forms a metapopulation (a collection of local populations with relatively little movement of individuals among them), is reflected in the genetic structure of a given population; i.e., the degree of differentiation of local populations within a metapopulation (Couchoux et al., 2016). The amount of genetic structure within and among local populations is influenced by the dynamics of local populations, the structure of the landscape (e.g., barriers to or facilitators of dispersal), and the inherent dispersal ability of the natural enemy species in question (Kankare et al., 2005). Couchoux et al. (2016) used microsatellite markers to study the metapopulation structure of the parasitoid Hyposoter horticola in a fragmented island landscape. In contrast to its host butterfly, which

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has strong spatial population structure, the parasitoid had low spatial genetic structure due to a high dispersal ability. Estimated as the maximum geographic distance between full-sibling parasitoids (using microsatellite markers), the maximum dispersal range for the parasitoid was estimated to be 7.5 km. The reverse situation was described by Wei et al. (2017), again using microsatellite markers. They showed that, in China, the parasitoid Cotesia vestalis has higher levels of genetic differentiation (indicating less gene flow) among populations compared to its migratory host, Plutella xylostella, indicating that parasitoid populations do not engage in annual migrations with their host, but rather form resident local populations. Nair et al. (2016) used microsatellite markers to study the metapopulation dynamics of the fourth trophic level (a hyperparasitoid, Mesochorus stigmaticus). They found that the population genetic structure of the hyperparasitoid was weaker (indicating more gene flow across the landscape) than that of the primary parasitoid or the host. It was concluded that although resources may be more fragmented for higher trophic levels, they are sometimes able to overcome this fragmentation to some degree with high dispersal ability. Studying the structure of genetic variation can also help to understand gene flow at larger spatial scales; for example, to reconstruct the history of the introduction of organisms to new continents. While these techniques have mostly been applied to invasive pest species to date (Estoup & Guillemaud, 2010), they are likely to be applied to natural enemies more in the future due to the large number of recent adventive (unintentional) introductions of natural enemies to new areas outside of their native range (Mason et al., 2017). A notable example of using genetic variation to reconstruct introduction pathways of a natural enemy comes from studies of the harlequin ladybird beetle, Harmonia axyridis. Analyses using microsatellite marker data suggested multiple separate introductions followed by population admixture in several invaded areas (Lombaert et al., 2014). The analysis also allowed the detection of ‘bridgehead populations’, invasive H. axyridis populations that served as the

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source for additional invasions in other areas of the world. Lawson Handley et al. (2011) provide a useful introduction to methods used to study the ecological genetics of invasive species that could be applied to natural enemies that have been introduced—whether intentionally or unintentionally—to new geographic areas. The molecular markers described above are useful to assay genetic variation and genetic population structure, but usually do not provide any direct indication of how the genetic variation being quantified relates to within-species variation in phenotypic traits (i.e., physiology, behaviour, life history) that determine the effectiveness of natural enemies as biological control agents (Wajnberg, 2004, 2010 provides reviews). Roush (1989, 1990), Hopper et al. (1993), Lommen et al. (2017) discuss genetic considerations in the use of entomophagous insects as biological control agents. These authors point out that most research on withinspecies genetic variation in insect natural enemies to date has focussed on quantifying phenotypic variation in important traits, rather than exploiting the variation to improve natural enemy performance or investigating the genetic basis of target traits. Examples of parasitoid behavioural traits that have been shown to be genetically variable include sex allocation for Nasonia vitripennis (Parker & Orzack, 1985; Orzack & Parker, 1990), the ability to evade encapsulation for Leptopilina boulardi (Carton et al., 1989), odour-conditioned ovipositor probing behaviour for L. boulardi (Pérez-Maluf et al., 1998), the circadian rhythm of locomotor activity for Leptopilina heterotoma (Fleury et al., 1995), the area searched per unit time for Trichogramma brassicae (Wajnberg & Colazza, 1998), patch time allocation for Telenomus busseolae (Wajnberg et al., 1999), the influence of mutual interference on patch exploitation behaviour in Trissolcus basalis (Wajnberg et al., 2004), and the temporal pattern of egg maturation in Trichogramma brassicae (Wajnberg et al., 2012) (Chap. 3 provides further examples). Lommen et al. (2017) provide a discussion of what traits can be assayed for variation, the the

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potential for selective breeding to optimise particular traits, and methods to determine the genetic architecture underlying trait variation. Chap. 3 discusses methods for studying the genetic basis of phenotypic traits in natural enemies, including the use of iso-female lines and heritability experiments.

6.5

(Mostly) Facultative Inherited Insect Symbionts

Intimate associations with beneficial infectious microorganisms have had an enormous influence on the ecology and evolution of multicellular organisms. Insects are no exception, and the past two decades have seen an explosion in the study of symbioses between insects and microbes, especially those that are passed from mothers to their offspring, often in the egg cytoplasm; this is the focus of this section. We will not cover extracellular gut symbionts here, but instead refer to some excellent reviews on that topic (Dillon & Dillon, 2004; Engel & Moran, 2013). As we will emphasise inherited symbionts that pertain most to insects as natural enemies, we will focus mainly on facultative symbioses. There are many excellent books and reviews on aspects of this topic; for readers who would like to examine this topic in more detail these include: O’Neill et al. (1997), Stouthamer et al. (1999), Moran et al. (2008), Werren et al. (2008), Engelstaedter and Hurst (2009), Oliver et al. (2010), Feldhaar (2011), Zchori-Fein and Bourtzis (2011). Obligate symbionts It has been estimated that at least half of all insect species host maternally inherited intracellular symbionts. These symbionts fall into two main types—obligate and facultative (Moran et al., 2008). Most obligate symbioses are nutritional and tend to be found in insects that feed on nutrient-poor diets. For example, virtually all insect lineages that feed exclusively on plant sap or animal blood host obligate nutritional symbionts that supplement their host with essential amino acids or vitamins that are missing from the

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diet. Other lineages with obligate symbionts include cockroaches (their obligate Blattabacterium symbionts contribute to nitrogen recycling; Sabree et al., 2009), and granivorous beetles (symbionts provide additional tyrosine that is essential for cuticle thickness; Anbutsu et al., 2017). Obligate nutritional symbionts are often housed in specialised cells and organs, called bacteriocytes and bacteriomes, respectively. They commonly exhibit patterns of ancient cospeciation with their hosts, and severely reduced genomes. Due to relaxed selection and very small effective population sizes associated with host bottlenecks, they also show patterns of genome decay (McCutcheon & Moran, 2012). As a result, there have been many examples where obligate symbionts are lost and replaced with, or propped up by, another ‘notyet-as-broken’ symbiont (Bennett & Moran, 2015; Sudakaran et al., 2017). Most obligate nutritional symbionts of insects are bacteria, primarily in the Gammaproteobacteria and Bacteroidetes, although there are a few examples of obligate Betaproteobacteria (e.g., Portiera in whiteflies, Zinderia/Vidania/Nasuia in the Fulgoroidea), Alphaproteobacteria (e.g., Hodgkinia in cicadas), and fungal symbionts (e.g., Ophiocordyceps in cicadas; Matsuura et al., 2018). There are hardly any examples of obligate inherited symbionts of insect parasitoids or predators, and we are not aware of any that involve nutrition. Philanthine beewolves harbour obligate symbiotic Streptomyces bacteria in glands in their antennae (Kaltenpoth et al., 2005). The bacteria protect wasp larvae against pathogenic soil fungi using a complex cocktail of antibiotics. The association between beewolves and Streptomyces symbionts is at least 60 million years old but has not followed a pattern of strict cospeciation, as some wasp species have exchanged symbionts (Kaltenpoth et al., 2014). As far as we are aware, no obligate insect symbionts have been directly implicated in protection against parasitoids or predators. This is perhaps not so surprising, as their tiny genomes mostly retain genes involved in nutrient production, although one interesting exception is Profftella armatura, an obligate Betaproteobacterial

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symbiont of the Asian citrus psyllid, Diaphorina citri. Although it has a tiny genome, about onesixth of its genome is composed of genes involved in the biosynthesis of polyketide toxins showing potent toxicity against human HeLa and rat neuroblastoma cells (Nakabachi et al., 2013). It is not yet known whether these toxins are deployed in the wild and against what enemy. Facultative symbionts Facultative symbionts, i.e., symbionts that are not essential for the reproduction and survival of their insect host, are more common than obligate symbionts (O’Neill et al., 1997; Stouthamer et al., 1999; Moran et al., 2008; Werren et al., 2008; Engelstadter & Hurst, 2009) and are the focus of the rest of the chapter. Unlike obligate symbionts, which appear to be restricted to certain lineages depending on host diet or metabolism, facultative inherited symbionts occur in every order of insect. Although they are primarily vertically transmitted over ecological timescales, their long-term evolutionary patterns differ from obligate inherited symbionts, as they do not exhibit ancient cospeciation. Instead there is little concordance between host and symbiont phylogenies, so that unrelated hosts often harbour closely related symbionts, and related hosts often differ in their symbiont status. As we will discuss below, one of the major unresolved questions in the field is how facultative inherited symbionts infect new hosts, and what determines their suitability and successful establishment in a new host. Also, while it is clear that there is much horizontal transmission over evolutionary timescales, it is not at all clear how important or common horizontal transmission is over ecological timescales; this is likely to be very much dependent on the specific symbiont. Vertical transmission has very important consequences for the ecology and evolution of symbiosis. The fitness of a vertically transmitted symbiont is directly tied to the fitness of its host, and so this bears directly on the strategies that symbionts have evolved, from increasing the fitness of their hosts under certain conditions, to manipulating host reproduction in order to increase the frequency of infected females. So a

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symbiont’s effect on its host can give us important clues as to how it is transmitted, and vice versa. The prevalence and persistence of a symbiont that is primarily maternally transmitted will largely depend on two main factors: its effect on host fitness, and how efficiently it is transmitted. Facultative inherited symbionts vary widely in prevalence in their hosts, and there can often be major differences in prevalence between different populations, for example between native versus invasive ranges (e.g., Nguyen et al., 2016). Fitness effects can be incredibly hard to measure, especially in the field. Fitness differences can be subtle, and in the laboratory, might only be uncovered in population cages or under stressful conditions (e.g., Oliver et al., 2008). Vertical transmission efficiency is often high in the laboratory, but can be much lower in the wild, where all sorts of abiotic and biotic factors, such as varying temperature or the presence of other symbionts could interfere with transmission (e.g., Turelli & Hoffmann, 1995; Rock et al., 2018). In general, mechanisms regulating transmission efficiency and symbiont titre within a host are not well understood. Facultative symbionts have evolved a number of different strategies to ensure efficient germline transmission (Russell et al., 2009). For example, Spiroplasma that infect Drosophila target yolk protein to enter oocytes (Herren et al., 2013), and Wolbachia have an affinity for stem cells (Frydman et al., 2006). Symbiont titres can vary widely within individuals (Unckless et al., 2009), but the consequences of titre variation on either transmission or fitness are not well understood (but see Martinez et al., 2014). Symbiont dynamics will be completely altered when there is horizontal transmission. Indeed, many infectious microorganisms have mixed modes of transmission (Ebert, 2013), and we might expect that those with mixed transmission have less efficient vertical transmission. Once the link between host and symbiont fitness is weakened, strong fitness benefits are no longer required to explain persistence, and we begin to see and expect stronger costs of infection, and possibly pathogenicity. So it is very important to

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consider the extent to which horizontal transmission occurs on ecological timescales, and how important is horizontal transmission in explaining symbiont persistence. Non-maternal routes of transmission that have been experimentally confirmed in facultative inherited symbionts include sexual transmission (Moran & Dunbar, 2006), transmission via plants (CaspiFluger et al., 2012), via blood of shared hosts (Hirunkanokpun et al., 2011), and from parasitoid to parasitoid in a shared host (Huigens et al., 2000). Parasitoids and parasitic mites have also been shown to transfer symbionts between hosts (Jaenike et al., 2007; Gehrer & Vorburger, 2012).

6.5.1 Reproductive Parasitism Facultative inherited symbionts are very well known for their ability to manipulate reproduction to either increase the frequency of infected females (i.e., the transmitting sex) or to decrease the frequency of uninfected females. There are four common types of reproductive manipulation: cytoplasmic incompatibility, and three different types of sex-ratio distortion—male-killing, feminisation, and parthenogenesis-induction. For more detailed information about reproductive manipulation, there are a number of excellent reviews, including Werren and O’Neill (1997), Stouthamer et al. (1999), Werren et al. (2008), Engelstaedter and Hurst (2009), Drew et al. (2019). It should be pointed out that maternally inherited symbionts are predicted to bias their transmission in many interesting ways that remain to be discovered (Hurst, 1993; Werren & O’Neill, 1997). For example, a recent study demonstrated for the first time that Wolbachia can interfere with the proper inheritance of sex chromosomes in butterflies (Kageyama et al., 2017). Reproductive manipulators can also benefit their hosts in ways that are independent of reproductive manipulation (Drew et al., 2019). For example, male-killing Wolbachia and Spiroplasma have been shown to protect their hosts against natural enemies in the laboratory (Unckless & Jaenike, 2012; Xie et al., 2014).

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A long-term challenge is to understand the relative importance of reproductive manipulation versus other fitness effects in nature. Cytoplasmic incompatibility Cytoplasmic incompatibility, or CI, is the reproductive manipulation that is the most widespread and that has received by far the most attention. Cytoplasmic incompatibility results from mating incompatibilities between infected males and uninfected females, fitting a toxinantitoxin model, with the inherited microbe producing a toxic factor that damages or modifies male sperm, and an antidote in females (Weisser & Völkl, 1997; Beckmann et al., 2019). Uninfected females are therefore at a severe disadvantage. Without the antidote, their offspring die early in development and, as a result, uninfected females can be quickly replaced by infected ones, and suffer a growing disadvantage every generation as they encounter fewer and fewer compatible and uninfected males to mate with (Turelli, 1994). Indeed, a number of studies have documented the very rapid spread and replacement of insect populations with a CI-microbe infected strain (Turelli & Hoffmann, 1991; Turelli et al., 2018). A variant of CI has also been documented in some haplodiploid insects, such as the parasitoid wasp Nasonia vitripennis, whereby uninfected females that mate with infected males produce a very heavily malebiased offspring sex ratio (Ryan & Saul, 1968; Breeuwer & Werren, 1990). This is because the paternal ‘poisoned’ chromosomes die, but the eggs develop as haploid males from chromosomes they inherited from their mother. Despite affecting a wide range of arthropods, only a handful of bacteria are known to cause cytoplasmic incompatibility (Yen & Barr, 1971, 1973; Hunter et al., 2003; Takano et al., 2017). By far the most common is Wolbachia, and it has been shown to be able to do this across an incredibly wide range of species and orders, suggesting a general mode of action of poisoning sperm and rescuing eggs. A huge, and recent, development in the field has been the longawaited discovery of the toxin-antitoxin factors that cause CI, called cifA and cifB (or cidA and

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cidB) (Lepage et al., 2017; Shropshire et al., 2018; Beckmann et al., 2017). Cif genes are encoded in Wolbachia prophages that are highly mobile across Wolbachia genomes, and are also frequently degraded or lost (Lindsey et al., 2018), explaining why closely related Wolbachia differ in the ability to induce CI. A next major step will be to determine how these factors interact with the host. This will help address the as yet unresolved mystery of why some hosts appear to be more resistant to CI than others, and why some recently invaded strains do not appear to cause CI, despite having apparently functional cif genes (Turelli et al., 2018). More recently, the symbiont Cardinium was shown to also cause cytoplasmic incompatibility in four arthropod orders, Hymenoptera (Hunter et al., 2003), Acari (Gotoh et al., 2007), Hemiptera (Nakamura et al., 2012) and Thysanoptera (Nguyen et al., 2017). The mechanism is completely different, as its genome does not contain cif genes (Penz et al., 2012). Finally, an unnamed alphaproteobacterium has been implicated in CI in a beetle pest of coconut, Brontispa longissima (Takano et al., 2017), but only a single paper about this has so far been published. CI microbes are relevant for researchers studying insect natural enemies for a number of reasons. They have the potential to quickly transform a species, and may explain strange patterns of incompatibility and egg hatch failure when working with insects that differ in infection status. They can also contribute to interesting mate discrimination patterns that may lead to reproductive isolation. This has perhaps best been demonstrated in a species pair of woodland flies, Drosophila recens and D. subquinaria (Jaenike et al., 2006), with the former infected with a strain of Wolbachia that causes strong CI. The two species have a broad range of overlap, and when female D. subquinaria mate with male D. recens, few viable offspring are produced. As a result, where they overlap, D. subquinaria shows strong mating avoidance with D. recens, and a striking by-product of this discrimination is that allopatric mating is also avoided. Even before its causal agent was known, cytoplasmic incompatibility was used for pest

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control: Hannes Laven (1967) released incompatible Culex pipiens male mosquitoes in Rangoon (Yangon), where they mated with uninfected females in a manner analogous to sterile insect male release. Incompatible males can also be generated in the laboratory by embryonic injection (Zabalou et al., 2004; O’Connor et al., 2012; Cattel et al., 2018). In another control approach, Aedes aegypti mosquitoes have been transfected with a strain of Wolbachia from Drosophila that causes CI and also suppresses RNA viruses, including dengue (Hoffmann et al., 2011; Walker et al., 2011). Female mosquitoes were released in the wild, where they quickly spread through the population as a result of CI, bringing virus suppression ability along for the ride. Male-killing Male-killing by maternally transmitted bacteria or viruses has evolved independently at least six times, including Wolbachia, Rickettsia, Spiroplasma, Arsenophonus, and Flavobacterium, with killing typically occurring early in embryonic development (Hurst & Jiggins, 2000). Most male-killers are found at low prevalence, often due to incomplete vertical transmission and reduced fitness of infected females. But there are exceptions, such as the nymphalid butterfly Acraea encedon, with over 95% of females infected with a male-killing Wolbachia in the wild (Jiggins et al., 2002). A number of studies have looked for fitness benefits of male-killing infections, for example, testing whether infected females are larger, possibly as a result of reduced competition over resources, but fitness benefits have proven elusive (but see Koop et al., 2009). Male-killing is especially widespread in ladybird beetles. Ladybirds lay their eggs in clutches and sibling cannibalism is common. Newly hatched infected females thus gain a direct fitness benefit from feeding on their dead brothers (Osawa, 1992; Nakamura et al., 2006). Alternatively, male-killers may offer another fitness benefit that is not directly related to malekilling, and this may be important in explaining their persistence in the wild.

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A few studies have documented rapid evolution by insects to suppress male-killing, such as in the predatory green lacewing, Mallada desjardini (Hayashi et al., 2018). If suppression is common, then male-killers may be more widespread than appreciated, but hidden. As an example, when a strain of Wolbachia that causes CI in Drosophila recens was introgressed into D. subquinaria, it caused male-killling in some but not all lines (Jaenike, 1997), demonstrating that there is segregating genetic variation in D. subquinaria to suppress male-killing (and that this trait is fixed in D. recens). Researchers demonstrated rapid evolution of host suppression of male-killing by Wolbachia in the butterfly Hypolimnas bolimna (Hornett et al., 2006). Suppression, however, did not result in elimination of the Wolbachia, because it also causes cytoplasmic incompatibility (Hornett et al., 2008), further demonstrating a link between CI and male-killing in Wolbachia. Little else is known about the mechanism of male-killing in general, and it is striking that symbionts have evolved the ability to kill males in arthropod lineages with incredibly diverse modes of sex determination. A recent breakthrough identified the factor that causes male-killing in the Spiroplasma symbiont of Drosophila melanogaster, a toxin called spaid (Harumoto & Lemaitre, 2018). Sex ratio distortion via induction and feminisation

parthenogenesis-

A number of symbionts cause an increase in the prevalence of females by transforming males into females. This has been most studied and is widespread in isopods and amphipods (Bouchon et al., 1998; Terry et al., 2004), where microsporidia and Wolbachia feminise males by suppressing the development of the maledetermining androgenic gland. Feminising Wolbachia have also been documented in butterflies and moths (Hiroki et al., 2002; Kageyama et al., 2002) and leafhoppers (Negri et al., 2006). Cardinium feminises the false spider mite Brevipalpus phoenicis, resulting in the only documented case of haploid females in animals (Weeks et al., 2001).

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Three symbionts have independently evolved the ability to induce parthenogenesis in their hosts—Wolbachia, Cardinium, and Rickettsia. Parthenogenesis-induction (or PI) is especially common in haplodiploids (Ma & Schwander, 2017), having been conclusively demonstrated in Hymenoptera (Stouthamer et al., 1990), Acari (Weeks & Breeuwer, 2001), and Thysanoptera (Arakaki et al., 2001). The tight link between PI and haplodiploidy stems from two major reasons. First, some types of haplodiploid sex determination are particularly vulnerable to PI microbes, requiring the conversion of unfertilised eggs from haploid to diploid. This has been shown to result via gamete duplication, where meiosis proceeds normally but chromosomes fail to separate following an aborted mitosis (e.g., Stouthamer & Kazmer, 1994), or by elimination of meiosis altogether (Adachi-Hagimori et al., 2008). Second, PI may be easier to demonstrate in haplodiploids because antibiotic treatment (often, but not always) results in viable haploid males. A number of diploid parthenogenetic insects are fixed for inherited symbiont infection but antibiotic treatment results in sterility (e.g., Yusuf & Turner, 2004; Pike & Kingcombe, 2009), and so it is more challenging to confirm that the symbiont is the causal agent. In the whitefly parasitoid Encarsia hispida, which is infected with Cardinium, antibiotic treatment results in diploid males, demonstrating that the lines between PI and feminisation can sometimes be blurred (Giorgini et al., 2009). With few exceptions, PI infections are rapidly fixed because the short-term benefit of asexual reproduction is so great. In addition, as PI infections in haplodiploids spread, they are predicted to select for refractoriness to mating (Stouthamer, 1997). This is because haplodiploid females that do not mate will produce only sons, which will be at a reproductive advantage as PI initially spreads through the population. Curing insects of their PI infection will cause major problems—often resulting in refractory females or sterile males that have acquired deleterious mutations in genes that are important in sexual reproduction and/or male function (Gottlieb & Zchori-Fein, 2001; Jeong & Stouthamer, 2005;

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Pannebakker et al., 2005). Thus there is reduced gene flow between PI-infected lineages and their sexual relatives. Interestingly, polymorphic lineages containing parthenogenetic (and symbiontinfected) and sexual uninfected members are very common, and these often show differences in their geographic distribution (Ma & Schwander, 2017).

6.5.2 Conditional Mutualism, with a Focus on Phenotypes Affecting Insect Natural Enemies Many facultative inherited symbionts increase the fitness of their hosts under certain conditions, for example increasing a herbivore’s plant host range (Tsuchida et al., 2004; Wagner et al., 2015) or helping a host tolerate various abiotic stresses, such as high temperature (Montllor et al., 2002). In the following section, we will focus on phenotypes affecting insect natural enemies, either with respect to protecting hosts against parasitoids or predators, or with respect to symbionts that affect a parasitoid or predator’s host use. Defensive symbionts A number of inherited symbionts have been shown to protect insects against a wide range of natural enemies, from microbial pathogens, such as fungi (Scarborough et al., 2005) and viruses (Hedges et al., 2008; Teixeira et al., 2008), to parasitic nematodes (Jaenike et al., 2010), parasitic wasps (Oliver et al., 2003) and predators (Kellner, 2001). Protection can occur in diverse ways (Haine, 2008; Florez et al., 2015; Vorburger & Perlman, 2018). First, symbionts may produce toxins that directly harm the natural enemy. This is a very common type of protection, and one that is likely to promote specificity and coevolutionary interactions. Second, symbionts may trigger a host immune response that targets the natural enemy, such as in recently established strains of Wolbachia in mosquitoes (Moreira et al., 2009). Finally, symbionts may negatively affect a natural enemy by depleting it of a limiting essential nutrient, such as

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Wolbachia that compete with viruses over cholesterol (Caragata et al., 2013). There are also a few examples of symbionts that prevent infection or attack. For example, infection with a strain of Rickettsiella changes the colour of their pea aphid hosts (Tsuchida et al., 2010), resulting in less predation by ladybird beetles (Polin et al., 2015). Finally, plants fed on by facultative symbiont-infected aphids release fewer volatiles, attracting fewer parasitoids than plants fed on by symbiont-free aphids (Frago et al., 2017). We are only aware of one example of an inherited insect symbiont that provides protection against predators. This was the first documented insect defensive symbiosis (Kellner, 2001). A number of rove beetles in the subfamily Paederinae are well known for being highly toxic; handling them can cause terrible blistering, due to toxic polyketides called pederins that are produced by endosymbiotic Pseudomonas bacteria (Kellner, 2002a; Piel, 2002). Pseudomonas symbionts acquired pederin synthesis genes via horizontal transfer; symbionts of sponges (Piel et al., 2004) and psyllids (Nakabachi et al., 2013) have also acquired related poisons. Pederins protect rove beetle larvae from spiders (Kellner & Dettner, 1996). Beetles are often aposematically coloured, and some individuals in the wild appear to be symbiont free (Kellner & Dettner, 1995), presumably benefiting from the protection conferred by their infected conspecifics. Mothers secrete bacteria over the surface of eggs. Uninfected larvae can also acquire symbionts by feeding on infected eggs, and although this does not appear to protect them, they can then pass symbionts on to their offspring (Kellner, 1999). Horizontal transmission has been shown to occur both intra- and inter-specifically (Kellner, 2002b). Symbiont-mediated protection against parasitoids has been studied in much greater detail, particularly in aphids (Oliver et al., 2014). By far the most work has been done on the aphid facultative symbiont Hamiltonella defensa. Protection is due to diverse toxins that are encoded by phages (Oliver et al., 2009; Brandt et al., 2017), and related phages and toxins have been found in Arsenophonus symbionts (Duron, 2014),

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including ones that have been correlated with parasitoid presence (Hansen et al., 2007), suggesting a role in protection for this symbiont as well. Protection by Hamiltonella confers a powerful benefit, and in both experimental population cages and field experiments (Oliver et al., 2008; Rothacher et al., 2016), has been shown to rapidly alter population dynamics, decreasing susceptible wasp abundance. However, in the absence of wasp infection, the presence of Hamiltonella (or Regiella, a related aphid defensive symbiont) imposes a strong fitness to the aphid host (Oliver et al., 2008; Vorburger & Gouskov, 2011). Researchers have also demonstrated a high level of specificity in protection and in wasp counter-resistance that has shaped coevolutionary interactions between host, symbiont and wasp. This results in dynamic spatial and temporal variation in symbiont infection status and strain diversity, and in wasp community composition (e.g., Vorburger et al., 2009; Oliver et al., 2012; Smith et al., 2015; Mclean & Godfray, 2017). Spiroplasma symbionts, including malekilling strains, have also been shown to protect Drosophila flies against parasitic wasps in the laboratory (Xie et al., 2010, 2014), although it is not yet known whether this is important in nature. Two mechanisms have been implicated in protection: resource competition over host lipids (Paredes et al., 2016) and toxins. Spiroplasma produce a wide range of toxins called ribosomeinactivating proteins (RIPs) that are homologous to well-known plant and bacterial poisons such as ricin and Shiga toxin, respectively (Hamilton et al., 2016). These poisons target a highly conserved adenine in eukaryotic 28S ribosomal RNA, and, in Spiroplasma-infected hosts, wasp ribosomes shown a characteristic signature of RIP attack (Ballinger & Perlman, 2017). It is not yet known how RIPs enter wasp cells, or why host ribosomes are relatively unaffected. A recent study documented wasp species that are resistant to Spiroplasma protection, suggesting that there may be coevolutionary interactions between wasps and Spiroplasma (Mateos et al., 2016). We are not aware of any biological control programmes that have been shown to be

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impacted by defensive symbionts, but given how strong the benefit to the pest can be, this should certainly be considered, especially where biological control failure and/or rapid evolution of resistance have been reported. For example, a recent study documented rapid decline in the success of an introduced Microctonus wasp against an exotic weevil in New Zealand (Tomasetto et al., 2017). It is not known if this biocontrol failure is due to a cryptic endosymbiont, although an earlier survey found diverse facultative symbionts in pest weevils (White et al., 2015). In an experimental test of the effect of defensive symbionts on the efficacy of biological control, control by Lysiphlebus wasps against black bean aphids collapsed in the span of months, because of strong selection for Hamiltonella-infected aphid clones (Kach et al., 2018). Symbionts of natural enemies that affect host use or host range There are few examples of inherited symbionts of natural enemies that affect host usage or host range, and this would be interesting to look into in more detail. For example, we are not aware of any cases of insect ‘offensive’ bacterial symbionts that aid in attacking a host, similar to Photorhabdus and Xenorhabdus symbionts of entomopathogenic nematodes that kill and then protect prey items from other competitors. Perhaps the best example of a symbiont affecting host behaviour is a strain of Cardinium that infects the whitefly parasitoid Encarsia tabacivora (formerly Encarsia pergandiella). Most Encarsia have an unusual host-use behaviour, called autoparasitism, whereby (diploid) female eggs are laid in whitefly or scale insect hosts, and (haploid) male eggs are laid inside developing wasps (i.e., males are hyperparasitoids). Interestingly, infection with a parthenogenesisinducing Cardinium affects female oviposition behaviour. Parthenogenetic females oviposit equally in whitefly and wasp hosts, but upon antibiotic treatment, strongly prefer laying eggs in wasps (Zchori-Fein et al., 2001; Kenyon & Hunter, 2007).

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An especially promising place to look for host manipulation is in inherited viruses of parasitoids (e.g., Renault et al., 2005), such as the wellknown obligate symbiotic polydnaviruses of braconids and ichneumonids (Strand & Burke, 2013). This is an exciting area of research, with genomics helping to discover diverse and interesting infections. For example, an intriguing RNA virus was recently discovered in Dinocampus coccinellae, a braconid wasp that parasitises ladybird beetles (Dheilly et al., 2015). The virus is injected into hosts along with wasp eggs, replicates in beetle nervous tissue, and is associated with beetle paralysis. In another interesting example of manipulation, Varaldi and colleagues discovered an inherited DNA virus of Leptopilina wasps that modifies wasp behaviour, such that infected females are more likely to superparasitise hosts (Varaldi et al., 2003), increasing the likelihood of horizontal transmission.

6.5.3 Taxonomic Distribution of Facultative Inherited Symbionts In the following section, we will briefly introduce the main lineages of facultative inherited symbionts of insects. Most facultative inherited symbionts are bacteria, with five extraordinarily widespread lineages, Wolbachia, Cardinium, Rickettsia, Arsenophonus, and Spiroplasma (Duron et al., 2008a, b), and a number that are less studied and/or less widespread. Although bacterial symbionts are probably more common than other types of microbial symbionts, there has also been a strong bias towards their discovery, due to ‘universal’ primers that target bacterial 16S rRNA, and because of the availability of antibiotics to remove them. However, one should be mindful that there may be bacterial symbionts that have been missed because of unusual 16S rRNA sequences (Brown et al., 2015a, b), or that are difficult to cure with antibiotics. Inherited viruses, or microbial eukaryotes, such as yeasts or fungi (Gibson &

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Hunter, 2010), have been much less studied, and this is an emerging frontier. Wolbachia The alphaproteobacterium Wolbachia is by far the most important and most studied facultative symbiont, for two good reasons. First, the sheer number of terrestrial arthropods that it infects is astounding. It has been estimated to infect *40‒ 50% of all terrestrial arthropods, making it perhaps the most abundant microbe on the planet (Zug & Hammerstein, 2012; Weinert et al., 2015). In addition to terrestrial arthropods, about 40% of filarial nematode species harbour Wolbachia (Bandi et al., 1998; Ferri et al., 2011), and it was recently discovered in the plant-parasitic nematodes Radopholus similis (Haegeman et al., 2009) and Pratylenchus penetrans (Brown et al., 2016). Second, no other symbiont comes close in terms of the range of effects that Wolbachia strains exert on their hosts. Wolbachia is best known as a reproductive manipulator, with various strains causing cytoplasmic incompatibility, male-killing, parthenogenesis-induction and feminisation across a wide range of arthropod orders. Kageyama et al. (2017) recently showed that Wolbachia interferes with sex chromosome inheritance in the butterfly Eurema mandarina, the first demonstration of a symbiont that causes meiotic drive. This should serve to highlight the fact that not only are there new types of reproductive manipulation to discover, but that we still do not understand how the vast majority of Wolbachia infections persist in their hosts. Many strains either do not show reproductive manipulation or are weak reproductive manipulators. For example, the strain that infects D. melanogaster causes weak cytoplasmic incompatibility that is not strong enough on its own to explain its persistence, and this has motivated much research into identifying other host effects, for example, increasing fecundity under periods of iron stress (Brownlie et al., 2009). Indeed, many reproductive manipulators can increase host fitness in various, often conditional, contexts (Drew et al., 2019), although a long-term challenge will be to

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determine whether these fitness benefits occur in the wild (Zug & Hammerstein, 2015). A major development since the second edition of this book has been the discovery that some strains of Wolbachia protect their hosts against positive-sense RNA viruses (Hedges et al., 2008; Teixeira et al., 2008; Moreira et al., 2009). This was first discovered in Drosophila and has spurred a global initiative to control dengue, Chikungunya and Zika virus, by transfecting Aedes aegypti mosquitoes with Wolbachia strains that cause both cytoplasmic incompatibility and virus suppression, and releasing them into the wild (Hoffmann et al., 2011; Walker et al., 2011; O'Neill, 2018). Since the initial releases in northern Queensland, Australia, there have not been any reported cases of dengue there (O'Neill, 2018), and a randomised control trial to determine whether Wolbachia releases have reduced dengue is underway in Indonesia (Anders et al., 2018). How Wolbachia suppresses positive-sense RNA viruses is not currently known, and is an active area of research (Caragata et al., 2013; Bhattacharya et al., 2017; Geoghegan et al., 2017; Lindsey et al., 2018; Schultz et al., 2018; Thomas et al., 2018). While protection has been shown in the laboratory and in transfected mosquitoes in the wild, as far as we are aware, there is not any evidence demonstrating protection in a natural system in the wild (Shi et al., 2018). Protection in insects other than Drosophila or mosquitoes is largely unstudied but Wolbachia has been shown to cause increased susceptibility to nucleopolydrovirus in the African armyworm Spodoptera exempta (Graham et al., 2012). Not all Wolbachia are facultative. A divergent Wolbachia has evolved to become the obligate nutritional symbiont of bedbugs, where it is housed in bacteriomes and provides essential B vitamins (Hosokawa et al., 2010). Wolbachia is required for oogenesis in the braconid parasitoid wasp, Asobara tabida, as its removal causes sterility (Dedeine et al., 2001). This strain has been implicated in interfering with the progression of programmed cell death that is required for the proper development of eggs (Pannebakker et al., 2007). Wolbachia is also essential in

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filarial nematodes (Bandi et al., 1999; Hoerauf et al., 1999), but the reason for this is not well understood, particularly because many filarial nematode species do not harbour Wolbachia (Ferri et al., 2011), nor have they acquired other symbionts or novel metabolic genes (Desjardins et al., 2013). Instead, Wolbachia appears to be required for proper germline development (Foray et al., 2018), in a manner that is reminiscent of what is seen in Asobara. Thus, Wolbachia appears to have exploited its ability to manipulate germline to become essential in Asobara wasps and filarial nematodes. A major unresolved pair of questions in the study of Wolbachia concern how and how much it colonises new species. From phylogenetic analyses, it is clear that there is a great deal of horizontal transmission, with closely related strains found in unrelated hosts (Werren et al., 2008). Species that share a food resource, parasitoids and their hosts, and predators and their prey, have all been shown to harbour related Wolbachia (e.g., Vavre et al., 1999; Stahlhut et al., 2010), suggesting ecological routes of transmission. Despite the many examples of distant host switches, there is also a great deal of phylogenetic signal in Wolbachia infection, with related strains often clustering in closely related hosts (Russell et al., 2009). This may be due to cospeciation, introgression, or horizontal transmission, and without extensive sequencing it may be difficult to distinguish between these alternatives (Turelli et al., 2018). Also, new infections are more likely to establish if they occur in closely related hosts. Finally, although Wolbachia has such a wide host range with extensive horizontal transmission over evolutionary timescales, experimental examples of ecologically realistic horizontal transmission are exceedingly rare. In the best example of ecological transmission of Wolbachia, Huigens et al., (2000, 2004) showed that uninfected Trichogramma parasitic wasps can acquire parthenogenesis-inducing Wolbachia from infected wasps developing in the same moth egg. It has been reported that Bemisia tabaci whiteflies can acquire Wolbachia from both parasitoids and plants (Ahmed et al., 2015;

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Li et al., 2017), but the donor and recipient in both studies was the same species with the same mitochondrial haplotype, so it is difficult to rule out laboratory artefacts. It is important to note that establishing novel Wolbachia infections typically requires laborious embryonic microinjection, with very low success rates. Cardinium Cardinium was the last of the five major facultative symbionts to be discovered (Weeks et al., 2001; Zchori-Fein et al., 2001, 2004). It is also the only one in the phylum Bacteroidetes, which is a source of many obligate symbionts in insects (e.g., Bandi et al., 1994; Moran et al., 2005; Chong & Moran, 2018; Engl et al., 2018). The closest known relative of Cardinium is a facultative symbiont of amoeba, Amoebophilus (Horn et al., 2001). Cardinium is estimated to infect *5‒10% of terrestrial arthropods (Weeks et al., 2003; Zchori-Fein & Perlman, 2004; Duron et al., 2008a; Weinert et al., 2015); infections are especially common in some arthropod lineages, such as chalcidoid wasps (Zchori-Fein et al., 2001), biting midges (Nakamura et al., 2009), mites (Gotoh et al., 2007), and spiders (Duron et al., 2008b). It has also been reported in the plant-parasitic nematodes Pratylenchus penetrans, Globodera pallida, and Heterodera glycines (Shepherd et al., 1973; Noel & Atibalentja, 2006; Brown et al., 2018). Cardinium strains exhibit a distinct morphology that is discernible with electron microscopy (ZchoriFein et al., 2001), with microfilament-like structures attached to membrane. The function of these structures is not known in Cardinium, but in Amoebophilus they were recently shown to form part of a secretion system that injects effectors into host cells (Bock et al., 2017). How Cardinium affects its hosts has only been demonstrated in a handful of cases, but it has received the most attention for causing diverse reproductive manipulations, including parthenogenesis and feminisation in parasitic wasps (Zchori-Fein et al., 2004; Giorgini et al., 2009) and mites (Weeks et al., 2001). Cardinium also causes cytoplasmic incompatibility in parasitic wasps (Hunter et al., 2003; Gebiola et al., 2016),

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mites (Gotoh et al., 2007), planthoppers (Nakamura et al., 2012) and thrips (Nguyen et al., 2017), and this represents an interesting counterpoint and comparison to cytoplasmic incompatibility induced by Wolbachia. The genome and transcriptome of a CI-inducing Cardinium strain were recently sequenced, and it does not appear to share genes with Wolbachia, suggesting that the mechanisms of cytoplasmic incompatibility are completely different (Penz et al., 2012; Mann et al., 2017). Rickettsia The alphaproteobacterial symbiont Rickettsia is another extremely widespread facultative symbiont, estimated to infect * 5‒20% of terrestrial arthropods from a wide range of orders (Perlman et al., 2006; Duron et al., 2008a, b; Weinert et al., 2009, 2015); it also occurs in glossiphoniid leeches (Kikuchi et al., 2002). Although the bestknown Rickettsia cause diseases in humans and are vectored by blood-feeding arthropods, such as lice, fleas, and ticks, these represent just a few branches on the Rickettsia phylogenetic tree. Most Rickettsia, however, are primarily maternally transmitted symbionts of insects that do not blood-feed, with host effects that are largely unknown. Rickettsia are male-killers in coccinellid and buprestid beetles (Werren et al., 1994; Lawson et al., 2001), and cause parthenogenesis in eulophid parasitic wasps (Hagimori et al., 2006; Giorgini et al., 2010). Rickettsia was also recently implicated in feminisation in the linyphiid spider, Mermessus fradeorum (Curry et al., 2015). An inherited Rickettsia in the sweet potato whitefly Bemisia tabaci was recently found to rapidly spread across the southwestern United States, with the prevalence of Rickettsia-infected whiteflies increasing from 1 to 97% in just six years (Himler et al., 2011). Rickettsia resulted in higher whitefly fecundity and female-biased sex ratios, but what caused the fitness increase and sex ratio distortion is not known. The fitness benefit appeared to be conditional, and was affected by host genotype (Cass et al., 2016; Hunter et al., 2017) and environment (Cass et al., 2015). This strain was also found to protect

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whiteflies against entomopathogenic bacteria (Hendry et al., 2014). Variable effects of Rickettsia have also been reported in pea aphids. Infected aphids weigh less and produce fewer offspring (Sakurai et al., 2005) but are protected against pathogenic fungi (Lukasik et al., 2013). Since the best-known Rickettsia can be horizontally transmitted from blood-feeding arthropods to vertebrates, it should perhaps not be surprising that ecological horizontal transmission has been demonstrated in a number of Rickettsia. Uninfected cat fleas can acquire R. felis by cofeeding on the same vertebrate host as infected conspecifics (Brown et al., 2015a, b; Hirunkanokpun et al., 2011), and the authors suggest that horizontal transmission is required for persistence of the infection as vertical transmission is quite inefficient (Wedinkamp and Foil, 2002). Co-feeding with uninfected rat fleas resulted in interspecific transmission. Bemisia tabaci whiteflies have been shown to acquire Rickettsia through phloem (Caspi-Fluger et al., 2012), although this mode of transmission was ruled out in the case of rapid spread of Rickettsia (Himler et al., 2011). Finally, in the leafhopper Nephotettix cincticeps, Rickettsia shows both maternal and paternal transmission, and not only does it reside in sperm cytoplasm but it also invades nuclei (Watanabe et al., 2014). Arsenophonus Arsenophonus, in the Enterobacteriaceae (Gammaproteobacteria), is also widespread and relatively understudied, infecting *5% of insects across a broad range of orders (Duron et al., 2008a, b; Novakova et al., 2009). A few Arsenophonus lineages have evolved into obligate nutritional symbionts of blood-feeding insects, such as human lice and their relatives (where they have also been called Riesia), and louse flies and bat flies. Only a handful of facultative inherited Arsenophonus have been implicated in any host phenotypes. In the invasive psyllid Glycaspis brimblecombei, the presence of an Arsenophonus symbiont is highly correlated with the prevalence of parasitism by encyrtid wasps (Hansen et al., 2007). This symbiont harbours a phage that is related to the

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APSE phage found in Hamiltonella defensive symbionts of aphids and that provide protection against parasitic wasps. Many Arsenophonus strains harbour APSE phages, suggesting that defence by Arsenophonus may be common (Duron, 2014). A number of Arsenophonus have interesting associations with plants. In the cowpea aphid Aphis craccivora, Arsenophonus increases plant host range, permitting development on locust plants (Wagner et al., 2015). Two unrelated strains of Arsenophonus cause diseases in strawberries (called marginal chlorosis) and beets (called SBR, or syndrome basses richesses) and are vectored by planthoppers (Danet et al., 2003; Semetey et al., 2007; Salar et al., 2010; Bressan et al., 2012). These plant-pathogenic Arsenophonus are transmitted both vertically and horizontally (Bressan et al., 2009). The only Arsenophonus known to be a reproductive manipulator is worth special mention because it is so unusual. Arsenophonus nasoniae, also known as ‘son-killer’, kills male Nasonia vitripennis and other chalcidoid parasitoids that attack fly pupae (Werren et al., 1986; Duron et al., 2010). But A. nasoniae is neither intracellular nor transmitted in eggs. Instead, adult female wasps inject the bacteria into fly pupae when they oviposit. Wasp larvae then ingest the bacteria, which kill *80% of developing male larvae (Huger et al., 1985; Skinner, 1985). Bacteria can also therefore be ingested by and transmitted to uninfected broods, as well as different wasp species (Duron et al., 2010) developing in the same fly pupa. Indeed, it was recently shown that son-killer cannot persist in population cages without ample horizontal transfer via superparasitism (Parratt et al., 2016). Arsenophonus nasoniae is also unusual in that it is relatively easy to culture (Gherna et al., 1991).

Mollicutes lack a cell wall and are therefore missing the typical bacterial elicitors of animal and plant immune systems, which helps explain why they are such successful infectious agents. It is difficult to estimate the prevalence of inherited Spiroplasma for a few reasons, although it has been estimated at *5% (Duron et al., 2008a, b). First, because Spiroplasma is old, diverse, and paraphyletic, diagnostic primers may be more appropriate for specific species groups. Second, many Spiroplasma reside in the gut, as commensals or pathogens. Well-studied pathogenic Spiroplasma include S. melliferum in bees, S. eriocheiris in crayfish, and S. citri and S. kunkelii, which cause citrus stubborn and corn stunt disease, respectively, in multiple crops, and are vectored by leafhoppers. Vertical transmission has evolved independently in Spiroplasma numerous times (Haselkorn et al., 2009), and in a wide range of arthropod orders. Two distantly related lineages, the ixodetis and poulsonii groups, have been studied the most, and have been shown to kill males in beetles (Hurst et al., 1999), flies (Poulson & Sakaguchi, 1961), lacewings (Hayashi et al., 2018), and butterflies (Jiggins et al., 2000), and to protect Drosophila flies from parasitic nematodes and wasps (Jaenike et al., 2010; Xie et al., 2010), and pea aphids from pathogenic fungi (Lukasik et al., 2013). In addition to ovarian tissue, vertically transmitted Spiroplasma are abundant in hemolymph, where they are extracellular. It is therefore easier to establish novel infections of many inherited Spiroplasma, as hemolymph can be transferred from infected to uninfected hosts (Sakaguchi & Poulson, 1963). By feeding on infected hemolymph, ectoparasitic mites were shown to transfer Spiroplasma between species, acting like a dirty syringe (Jaenike et al., 2007).

Spiroplasma

Other symbionts

Spiroplasma is a member of the Mollicutes, a class of gram-positive bacteria that consists entirely of host-associated microbes, including Mycoplasma and Phytoplasma, common animal and plant parasites, respectively (Anbutsu & Fukatsu, 2011; Ballinger & Perlman, 2019).

Beyond the five major lineages of facultative inherited symbionts of insects, there are many other inherited bacteria. For example, the alphaproteobacterium Lariskella is widespread in stinkbugs and ticks, but its biology is completely unknown (Matsuura et al., 2012). Three

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gammaproteobacterial lineages likely infect a wide range of insects but have been little studied as symbionts. While members of the genus Rickettsiella are well-known pathogens of insects and crustaceans (Duron et al., 2018), a number are either confirmed or likely vertically transmitted symbionts in aphids (Tsuchida et al., 2010), leafhoppers (Iasur-Kruh et al., 2013), and spiders (Zhang et al., 2018a, b), and this is likely to be just the tip of the iceberg (Duron et al., 2018). Primers are available for multi-locus sequence typing of Rickettsiella (Leclerque et al., 2011), so more widespread screens and phylogenetic characterisation should be relatively straightforward. The only inherited Rickettsiella symbiont that has been studied in any detail infects pea aphids, causing them to change in colour from red to green (hence its name R. viridis) (Tsuchida et al., 2010), resulting in reduced predation by ladybird beetles (Polin et al., 2015). Rickettsiella infection also protects aphids against pathogenic fungi (Lukasik et al., 2013). Within the Gammaproteobacteria, the Enterobacteriaceae has repeatedly given rise to many insect symbionts, and they can be difficult to discriminate because they are often closely related to common host-associated bacteria. At least two lineages of enteric bacteria are likely widespread symbionts, although it will be challenging to discriminate between gut symbionts and intracellular inherited ones. Facultative Sodalis infections have been found in parasitic and social Hymenoptera (Betelman et al., 2017; Rubin et al., 2018), lacewings (Sontowski et al., 2020), and in tsetse flies. Interestingly, many Sodalis strains have repeatedly evolved into obligate symbionts, in many Hemiptera, weevils, lice, and louse flies (Santos-Garcia et al., 2017). A clade allied with Pectobacterium includes facultative symbionts of bedbugs (Hypsa & Aksoy, 1997) and leafhoppers (Campbell & Purcell, 1993), and obligate symbionts of mealybugs (Husnik & McCutcheon, 2016) and lygaeids (Kuechler et al., 2011). More host-restricted enteric symbionts include the well-studied sister clades Hamiltonella and Regiella that defend aphids against parasitic wasps and pathogenic fungi (Oliver et al., 2003; Scarborough et al., 2005;

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Vorburger et al., 2010); Hamiltonella is also an obligate symbiont in some Bemisia tabaci whiteflies (Rao et al., 2015). Work on inherited bacterial symbionts of insects has eclipsed that on eukaryotic and viral symbionts. While a number of insects have evolved obligate inherited nutritional symbioses with fungi (e.g., Fukatsu & Ishikawa, 1992; Gomez-Polo et al., 2017; Matsuura et al., 2018), facultative fungal symbionts have been largely overlooked (Gibson & Hunter, 2010). As one example, the cockroach parasitoid Comperia merceti hosts a vertically transmitted ascomycete that reduces host fitness in the laboratory and is thought to persist only due to horizontal transmission (Gibson & Hunter, 2009). Microsporidia are common intracellular parasites of insects and are also a likely source of inherited symbioses. Indeed, feminising microsporidia have evolved independently numerous times in amphipods (Terry et al., 2004) but have not yet been documented in insects. Finally, there are a few documented cases of inherited viruses that are malekillers, in the tea tortrix moth Homona magnanima (Nakanishi et al., 2008) and in the fruit fly Drosophila biauraria (Kageyama et al., 2017). Viruses with at least some vertical transmission in their life cycle are ubiquitous (e.g., O’Neill et al., 1995; Longdon & Jiggins, 2012; Xu et al., 2014), and uncovering their host effects without a striking phenotype such as male-killing will be very challenging.

6.5.4 Suggestions and Potential Pitfalls for Entomologists Studying Inherited Symbionts for the First Time In this final section, we briefly provide some suggestions for researchers who are studying insect parasitoids and predators but who have not previously worked on insect symbionts, on some first steps and potential pitfalls in beginning to characterise inherited symbionts. The first step in characterising the inherited symbionts in one’s favourite insect study

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organism will involve screening samples from the wild and/or laboratory colonies, using both targeted and untargeted approaches. Targeted approaches involve amplifying symbiont DNA using polymerase chain reaction (PCR) with symbiont-specific primers; a wide range of primers are available that target the main lineages of facultative symbionts (e.g., Duron et al., 2008a, b; Russell et al., 2013), as well as others. It is essential to confirm that there has not been spurious DNA amplification, by Sanger sequencing some of the PCR products. Untargeted amplicon sequencing, using conserved ‘universal’ bacterial 16S ribosomal RNA primers and PCR, is also very useful for identifying potentially interesting bacterial symbionts. However, this approach will amplify (many) bacteria that are not intimate insect associates, as well as gut and inherited symbionts, and it is recommended to sample different parts of the insect’s body (for example, removing the gut), and to include negative controls and sufficient numbers of replicates. Bacteria with divergent 16S rRNA sequences, that are found at relatively low titres, or that have restricted distributions in the body (for example, in bacteriomes), have the potential to be missed in these surveys. To screen for fungal symbionts, one can use untargeted amplicon sequencing of fungal 18S rRNA, or fungal-specific PCR primers (e.g., Fukatsu and Ishikawa, 1995), although ribosomal and mitochondrial genes of many fungal insect symbionts contain large insertions of selfish genetic elements, such as mobile introns, and can be therefore challenging to amplify. Putative symbiotic viruses will be the most challenging to identify, as they evolve so rapidly and are so diverse that there are not conserved primers to amplify them, and metatranscriptome or metagenome sequencing is necessary (e.g., Webster et al., 2015). Once a potentially interesting symbiont has been identified, a straightforward next step is to determine its prevalence in the field, using PCR with symbiont-specific primers. This will give clues as to the role the symbiont may be playing. For example, sex-ratio distorters will be absent or rare in males, and symbionts that induce strong cytoplasmic incompatibility are expected to

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occur at high prevalence. At the same time, it is also very useful to obtain insect mitochondrial sequence, as insect mitochondria are inherited maternally. Symbionts or symbiont haplotypes that are perfectly associated with mitochondrial haplotypes provide strong evidence that the symbiont is vertically transmitted (Hurst & Jiggins, 2005). If a symbiont infection is found to be only associated with one mitochondrial haplotype and with little sequence variation, then it might suggest that the symbiont has recently spread (e.g., Jaenike et al., 2010; Himler et al., 2011), for example in association with cytoplasmic incompatibility (Turelli et al., 1992). Sequencing multiple genes will help determine the symbiont’s phylogenetic affinities. 16S rRNA sequence is not sufficient as it (usually) evolves too slowly and is not variable enough, and the commonly used Wolbachia wsp gene experiences extensive recombination (Werren & Bartos, 2001); sequencing multiple genes is now standard and there are multiple primers and multi-locus sequence typing approaches available for many symbiont lineages (Baldo et al., 2006; Haselkorn et al., 2009; Weinert et al., 2009; Duron et al., 2010). Sequencing multiple genes will also help identify symbionts that may lie on an interesting branch of the phylogenetic tree, and that should be targeted for wholegenome sequencing. In fact, the costs of sequencing have dropped such that it is feasible to generate complete or partial genomes from an interesting symbiont using next-generation sequencing approaches, and we recommend reaching out and establishing collaborations with researchers with expertise on the genomics of specific symbionts. There are two major sources of false positives when screening for symbionts. First, there is the possibility of amplifying DNA from symbionts of prey, parasites, or endoparasitoids. There are a number of ways to get around this, including sampling different parts of the body (Goodacre et al., 2006), such as the head, legs, or ovaries, or rearing insects in the laboratory, or bringing them into the laboratory and only extracting DNA after they have not had any food for a few days. Another possible source of contamination is

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symbiont DNA that has become incorporated in the host genome via lateral gene transfer. There are many examples of this, especially involving Wolbachia (Kondo et al., 2002; Hotopp et al., 2007), and this is perhaps not surprising since facultative inherited symbionts are so abundant in germline, and since so many insects harbour them. Sometimes entire or almost entire symbiont chromosomes are integrated, and these integrations can be fixed or polymorphic. For example, a recent genomic survey of 200 wild D. melanogaster lines found that four had large germline insertions of Wolbachia (Huang et al., 2014). Integrated symbiont DNA is often nonfunctional and pseudogenised, although a recent study found that * 80% of the genome of a feminising strain of Wolbachia has integrated into a pillbug genome, where it now acts as a female-determining sex chromosome (Leclercq et al., 2016). There are a number of ways to potentially rule out horizontal transfer, including sequencing multiple genes and confirming that they have not become pseudogenised (e.g., Morrow et al., 2015). Also, inserted sequences will not be eliminated following antibiotic treatment. As well as false positives, one should also be mindful of false negatives. These can occur for a number of reasons, including primer mismatches and difficulties in extracting DNA, for example due to thick cell or spore walls. Some symbionts have been reported to be found at very low titres in their hosts (e.g., Arthofer et al., 2009), which makes it more challenging to successfully amplify their DNA. Low-titre infections may still have important impacts on their hosts. For example, a beautiful (and cautionary) study recently reported an interesting low-titre strain of Wolbachia in Drosophila pseudotakahashii (Richardson et al., 2019). This Wolbachia is often absent or found at low titre in the adult sons of infected females, yet still causes strong CI (i.e., when the sons of infected females mate with uninfected females). Finally, one should be mindful about multiple infections. Many insects host multiple inherited symbionts (e.g., Gueguen et al., 2010; Toju & Fukatsu, 2011; Russell et al., 2012) and/or

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multiple strains of the same symbiont; the latter is especially common in CI Wolbachia, with coinfecting strains that are often mutually incompatible (Perrot-Minnot et al., 1996; Duron et al., 2006). So one should be careful not to ascribe an effect due to a symbiont without ruling out the presence of others. We wonder whether some phenotypes have been inadvertently attributed to Wolbachia, because it is so common and has been studied in more depth and often before other symbionts were known or appreciated, and because antibiotic treatment may remove all symbionts. Finally, the presence of multiple symbionts can affect host fitness and/or reproductive manipulation. For example, Sogatella furcifera planthoppers infected with both Wolbachia and Cardinium induce much stronger CI than insects infected with either symbiont alone (Nakamura et al., 2012). Of course, once one has identified a symbiont infection, the next steps are to understand how it affects its host and how it is transmitted, in the laboratory and in the field. This means that it is important to establish symbiont-free insect lines as controls, either by antibiotic or heat treatment (Li et al., 2014), or by isolating a naturally uninfected line. Care should also be taken to ensure that the lines have similar nuclear genetic backgrounds and gut microbiota, and that there are not residual effects of antibiotic treatment (e.g., Ballard & Melvin, 2007).

6.5.5 Conclusion It is a very exciting time to study facultative inherited symbionts, and the field is changing rapidly, with new approaches and developments, from both applied and basic research perspectives. Genomics has revolutionised the study of insect symbionts. Other recent breakthroughs have included the ability to culture symbionts (e.g., Brandt et al., 2017; Masson et al., 2018), and to express symbiont genes in flies and yeast, in order to understand their function (Sheehan et al., 2016; Lepage et al., 2017; Harumoto & Lemaitre, 2018). Even though so many exciting discoveries have been made, many new symbiont

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lineages almost certainly await discovery. Further, we do not understand how most of the symbionts we already know about persist in their hosts. Indeed, many of the bottlenecks in terms of understanding insect‒symbiont interactions are ecological, and this is especially relevant in terms of symbionts that affect insect natural enemies. How do they move through food webs? How important are they in affecting host defence, host choice, host range, host and prey suitability? Entomologists and insect ecologists ignore symbionts at their peril. Acknowledgements We dedicate this chapter to the memory of our co-author Simon Leather, who passed away after the writing of this updated chapter for the third edition was completed. We wish to thank: Mark Walton for contributions to several sections, Brad Hawkins for sharing his thoughts concerning parasitoid communities, Mike Solomon for some mite technique references, Ian Hardy for editorial work and contributions to the food webs section, Eric Wajnberg for editorial suggestions, Hugh Loxdale, Nigel Stork and Anja Steenkiste for providing photographic material, Jan Cawley and Vyv Williams for photographic assistance, and the HRI Library staff for tracing publications. KDS and WP gratefully acknowledge funding by the UK Department for Environment, Food and Rural Affairs. SP acknowledges funding support from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant Program), and the Swiss National Science Foundation (Sinergia program). PKA acknowledges funding support from Agriculture and AgriFood Canada (A-BASE #2955).

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589 Zou, Y., de Kraker, J., Bianchi, F. J. J. A., van Telgen, M. D., Xiao, H., & van der Werf, W. (2017). Video monitoring of brown planthopper predation in rice shows flaws of sentinel methods. Scientific Reports, 7, 42210. Zug, R., & Hammerstein, P. (2012). Still a host of hosts for Wolbachia: Analysis of recent data suggests that 40% of terrestrial arthropod species are infected. PLoS ONE, 7, e38544. Zug, R., & Hammerstein, P. (2015). Bad guys turned nice? A critical assessment of Wolbachia mutualisms in arthropod hosts. Biological Reviews, 90, 89–111. Zwölfer, H. (1971). The structure and effect of parasite complexes attacking phytophagous host insects. In P. J. den Boer, & G. R. Gradwell (Eds.), Dynamics of populations. Proceedings of the Advanced Study Institute on ‘Dynamics of Numbers in Populations’, Oosterbeck (pp. 405–418). Centre for Agricultural Publishing and Documentation.

7

Population Dynamics Mark A. Jervis, Neil A. C. Kidd, Nicholas J. Mills, Saskya van Nouhuys, Abhyudai Singh, and Maryam Yazdani

7.1

Introduction

Predators and parasitoids are important components of all insect communities and are therefore of central interest to ecologists studying the complex factors driving the dynamics of species interactions and community structure. Knowledge gained from studies of predator and parasitoid populations is also of immense practical value in insect pest management (Hassell &

M. A. Jervis . N. A. C. Kidd Cardiff School of Biosciences, Cardiff University, P.O. Box 915, Cardiff, Wales CF10 3TL, UK N. J. Mills Department of Environmental Science, Policy and Management, University of California, Mulford Hall, California 94720 Berkeley, USA e-mail: [email protected] S. van Nouhuys (&) Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India e-mail: [email protected] A. Singh Departments of Electrical and Computer Engineering, University of Delaware, Delaware 19716 Newark, USA e-mail: [email protected] M. Yazdani Health and Biosecurity, CSIRO, P.O. Box 2583, Brisbane 4001, Queensland, Australia e-mail: [email protected]

Waage, 1984; Murdoch et al., 1985; DeBach and Rosen, 1991; Van Driesche et al., 2010; Heimpel & Mills, 2017; Hajek & Eilenberg, 2018; McEvoy, 2018; Segoli et al., 2023). In this chapter, we aim to demonstrate how ecologists and biological pest control researchers can assess the role of natural enemies in insect population dynamics, and how the information obtained can be put to use in biological control. We begin by reviewing methods for demonstrating and quantifying predation and parasitism (Sect. 7.2). We then examine the different techniques for determining the effects of natural enemies on insect population dynamics empirically and through mathematical modeling (Sect. 7.3). Finally, we examine ways in which this and other information can be used in choosing appropriate biological control agents for introduction (Sect. 7.4).

7.2

Demonstrating and Quantifying Predation and Parasitism

7.2.1 Introduction Most studies of pest control by predators and parasitoids examine pest and natural enemy presence and/or abundance and then qualitatively infer their impact. While this provides useful data to address a range of ecological questions, a quantitative measure of impact is critical for guiding pest management decision-making. For

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2_7

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example, Mace and Mills (2017) argued that to encourage adoption of conservation biological control, metrics need to be developed that can predict current activity and future potential of biological control. They evaluated natural enemy metrics to explore how well they performed in predicting current and future biological control of the walnut aphid, Chromaphis juglandicola, in California walnut orchards. Some metrics based on direct measures of natural enemy activity, such as percent parasitism and predator–prey ratio, were effective indicators of current biological control activity. However, Mace and Mills (2017) highlighted that predicting future control through the season using natural enemy metrics can be misleading due to the confounding effect of within-year density dependence in the pest population. Furlong and Zalucki (2010) reported that less than half the studies of lepidopteran pests and their natural enemies used methodologies that would allow measurement and objective assessment of the impact of natural enemies. Similarly, a meta-analysis of the response of pests and natural enemies to landscape complexity found only 13 of 46 studies included a measure of natural enemy impact (Chaplin-Kramer et al., 2011). Merely examining species presence and/or abundance and inferring impact means it is difficult to make informed pest control decisions incorporating natural enemy activity, as there is no quantitative evidence of impact. Direct estimation of natural enemy impact can provide a tangible metric for determining the point at which the impact no longer maintains populations of pests below economic damage thresholds (Macfadyen et al., 2015). In this section we present techniques that can be applied to both field and laboratory populations of natural enemies and their prey (1) to demonstrate that natural enemies can have a significant impact upon host and prey populations, and (2) to quantify rates of predation and parasitism to provide indices of the impact of biological control of value to pest management.

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7.2.2 Exclusion of Natural Enemies Natural Enemy Exclusion Exclusion methods, in which pest abundances are monitored in the absence and presence of natural enemies, are widely used to estimate the impact of predation and parasitism in the field. Suitably designed exclusion barriers coupled with careful non-destructive population sampling has been used, in combination with life-table construction, to effectively demonstrate the impact of predator and parasitoid complexes on pest populations under a range of conditions (Furlong et al., 2004b, 2008). The principle behind their use is to quantify natural enemy impact by comparing the growth in prey population in plots (any habitat unit, from part of a plant to a whole plant or a group of plants) from which natural enemies have been excluded with that in control plots to which natural enemies have free access. In the context of predation, although it is commonly assumed that prey missing in the field have been eaten by predators, this may not always be the case and Castellanos et al. (2015) have documented the bias that can result from non-consumptive effects of predation in exclusion experiments (Sect. 7.2.5). Various exclusion techniques have been employed, including mesh cages placed over individual plants or groups of plants, mesh sleeve cages placed over branches or leaves, clip cages attached to leaves, greased plastic bands tied around tree branches and trunks, and vertical barriers, constructed of plastic or wood, around plants. The most appropriate technique will depend upon the natural enemies being investigated, and whether the aim is to exclude all natural enemies or to exclude particular species or groups of species. For example, a terylene mesh/gauze cage placed over a plant ought, if the mesh size is sufficiently small, to exclude all aerial and surface-dwelling insect natural enemies. By increasing the mesh size slightly, small parasitoid wasps may be allowed in, while increasing the mesh size further will allow larger

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types of natural enemy to enter as well. Using a cage with its sides raised slightly above the ground allows ground-dwelling predators such as carabid beetles and ants to have access to aphids on cereals, while excluding adult hoverflies and flying parasitoids. Conversely, a trench or a barrier can prevent access to ground-dwelling predators but allow access to aerial predators and parasitoids. Barriers that exclude only a sub-set of the natural enemies that attack a pest can be used to illustrate the importance of specific enemy groups to biological control (Bográn et al., 1998; Medina and Barbosa, 2002; Gardiner and Landis, 2007; Xiao and Fadamiro, 2010; Martin et al., 2013; Rusch et al., 2013). When paired with population models, exclusion methods can provide valuable insight into the direct economic impact of certain biological control agents (Östman et al., 2003; Landis et al., 2008). Exclusion barriers can be used to enclose already existing populations of prey, in which case the density of the prey at the start of the experiment will need to be estimated and recorded. Alternatively, exclusion barriers can be used to enclose plants or plant parts that were free of, or have been cleared of, prey and that can then be loaded with a fixed number of prey. The latter approach has the advantage that equivalent starting densities of prey/hosts can be used in both exclusion and control plots, and that the potential for immature stages of parasitoids being present within pre-existing hosts can be eliminated from the experimental plots. It may be necessary to use a systemic insecticide when eradicating prey such as leafhoppers or planthoppers from a plot, in order that any prey eggs present within plant tissues are killed. Of course, loading with prey cannot take place until one can be sure that the plant is free of the insecticide. Exclusion studies need to account for both the effectiveness of the barrier and its effect on the survivorship or population growth of the focal herbivore. For example, mesh cages can alter microclimatic factors such as light intensity, humidity, and temperature. To account for this, exclusion cages that prevent all predators are sometimes paired with sham cages with a larger mesh size or cutouts allowing predator access

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(e.g., Costamagna et al., 2007). This approach has been used in several studies (Costamagna et al., 2008; Costamagna & Landis, 2011; Samaranayake & Costamagna, 2018). Medina and Barbosa (2002) used cages with varied mesh size along with sticky barriers to examine predation of large and small tussock moth (Orgyia leucostigma) larvae by flying invertebrates, crawling invertebrates, and birds. Large larvae were more frequently removed from cages allowing access by birds. However, the results for small larvae illustrated the importance of using adequate controls, as just as many small larvae disappeared from the treatment that excluded all predators as from the control treatment allowing access to all predators. In order to separate the effects of microclimate and natural enemy exclusion upon prey populations, it is necessary to use exclusion techniques that are either: (1) as similar as possible in construction, or (2) very different in construction, but which nevertheless provide similar microclimatic conditions in their interiors. Kaser and Heimpel (2018) conducted an exclusion-cage experiment designed to isolate the impact of an accidentally introduced parasitoid of the soybean aphid (Aphis glycines) in North America from the other resident natural enemies of the soybean aphid. They designed five exclusion cages, including a sham cage. The sham cages were intended to simulate the microclimatic conditions of predator and total exclusion cages, but to allow natural enemies to enter in a manner similar to open cages. They found no significant differences between sham cages and open cages in aphid densities or aphid population growth rates; therefore, cage microclimate did not differentially affect birth and death rates of the aphids between treatments. If the prey or hosts are mobile, both immigration and emigration may differ between exclusion and inclusion treatments, which can be a problem (Kindlmann et al., 2015). In order to rule out the possibility that aphid densities in fully caged cereal plots were augmented as a result of prevention of emigration of alatae, Chambers et al. (1983) removed all alate aphids that settled on the insides of some of the

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experimental cages whilst allowing them to services index (BSI) to quantify the extent of remain in other experimental cages. Removal of natural enemy control in crop fields, where: alatae was found to not alter the pattern of popPx Ac;p-Ao;p ulation change in the cages. Therefore, rep¼1 Ac;p BSI ¼ ð7:1Þ colonisation of shoots inside experimental cages x was unlikely to have been a cause of the difference observed in aphid densities between cagedAc is the number or density of prey in each exclusion plot p a given number of days following the and open plots. If prey densities increase in the exclusion initiation of the experiment, Ao is the correplots, they may do so to such an extent that sponding number or density of prey in each open predator species (e.g., coccinellids, hoverflies) plot p with access to natural enemies, and x is the other than the ones that are excluded (e.g., number of replicate plots. Gardiner et al. (2009) carabid beetles) are preferentially attracted to the found that BSI values for an invasive aphid, exclusion plots through their aggregative Aphis glycines, in soybean fields in north-central responses (Sect. 1.15.2). The impact of the USA increased with landscape diversity. Simiexcluded natural enemy species may thus be larly, Woltz et al. (2012) found BSI values to be underestimated. This limitation applies particu- high in soybean fields in Michigan, USA larly to the use of barriers and trenches, where regardless of local habitat management or the the enclosed plants remain exposed to invasion diversity of the surrounding landscape. by a variety of aerial predators. In addition, total Natural Enemy Interference exclusion of natural enemies is difficult to achieve and, consequently, it is important to Although physical removal is considered a check for the presence of natural enemies and to method of predator exclusion it is, as mentioned count them in the exclusion plots either during or above, rarely completely effective and is thus at the end of an exclusion experiment. Exclusion better described as ‘interference’. For large, relamethods employing barriers that are far from tively slow-moving predators it involves removal completely effective in excluding natural ene- by hand while small, active predators and paramies are, strictly speaking, interference methods sitoids can be removed using an aspirator. This method has advantages in that confounding (see below). Whilst exclusion methods can reveal that nat- microclimatic effects can be ruled out (since cages ural enemies have a significant impact upon prey are not used), and the contribution of particular populations, other methods generally need to be natural enemy species to parasitism and predation applied before the predator‒prey interaction can can be relatively easily assessed. However, the be quantified. The results need to be related to the method also has the disadvantage that removal of density of predators present in the habitat if natural enemies is very labour intensive. For the realistic estimates of predation rates are to be method to provide more than just a crude measure obtained. Additionally, exclusion experiments of natural enemy effectiveness, a 24-h per day provide minimal information, if any, on the watch needs to be kept on plants, and several dynamics of the predator‒prey or parasitoid‒host observers need to be involved in removing interaction, a limitation that applies also to several insects. Additionally, removal of natural enemies of the other methodologies described below. This may disturb prey and thereby increase prey emiproblem can be at least partly overcome by the gration, and predators and parasitoids may have construction of paired life tables for the insects in the opportunity to kill or parasitise hosts before exclusion and control plots (Van Driesche & they are detected and removed. A related ‘biological check’ method of interBellows, 1996; Itioka et al., 1997) (Sect 7.3.4). Gardiner et al. (2009) used data from exclu- ference exploits the fact that honeydew-feeding sion cages to develop a biological control ant species, when foraging for honeydew sources

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Fig. 7.1 The average proportion of taxa observed visiting sentinel Diaphorina citri cohorts under no and sticky barrier treatments at three localities. a Biocontrol Grove, b Lochmoor, and c Jurupa sites in Riverside County, California, USA. The “Other” category is comprised of uncommon predatory taxa including Coccinellidae, Anthocoridae, Mantidae, Forficulidae, and the brown garden snail, Cornu aspersum (from Kistner et al., 2017, with permission)

and tending homopteran prey, interfere with nonant predators and parasitoids (Fig. 7.1), either causing them to disperse or killing them. In one set of plots, ants are allowed to forage over

plants, whereas they are excluded from the other set. Natural enemies have access to both types of plot, but they are subject to interference by ants in the former. The method can be used with prey

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that do not produce honeydew, provided either natural or artificial honeydew is made available to the ants. This method has several of the disadvantages of other interference and exclusion methods. For the insecticidal interference method, test plots are treated with an insecticide, so as to eliminate the natural enemies, and the control plots are untreated. The insecticide used is either a selective one, or a broad-spectrum one that is applied in such a way as to be selective (e.g., reduced concentrations), affecting only the natural enemies. The main advantages of the method are that potential confounding effects of microclimate can be ruled out, and very large experimental plots can be used. As an alternative to blanket spraying of test plots, an insecticide trap method can be used. Ropes of plaited straw treated with insecticide, trenches dug in the soil and containing formalin solution or insecticidesoaked straw, or some other insecticideimpregnated barrier, can severely reduce the numbers of natural enemies entering test plots. Asiimwe et al. (2016) used this method to reduce natural enemies in treatment plots compared with unsprayed controls through applications of broad-spectrum insecticides, and to show that natural enemies exert a greater influence than plant quality on the seasonal dynamics of the whitefly pest Bemisia tabaci in cotton fields in Arizona, USA. A limitation of insecticidal interference is that the numbers of prey may be inadvertently reduced due to the toxic effects of the insecticide (i.e., either the insecticide turns out not to be selective in action, or drift of a broad-spectrum insecticide has occurred) or they may be inadvertently increased due to some stimulatory, sublethal effect of the insecticide upon prey reproduction (e.g., prey fecundity may be increased). Insecticides can be tested in the laboratory for their possible sublethal effects upon prey reproduction. As for other interference methods (see above), total elimination of natural enemies from the test plots may not be achieved and so the full potential of natural enemies to reduce prey numbers is often underestimated. Additionally, limited information is provided on

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the dynamics of the predator‒prey interaction, even where densities of natural enemies are known. Combining Exclusion with Inclusion One solution to the problem of achieving similar conditions in the different exclusion treatments is to carry out an exclusion/inclusion experiment. This involves the use of identical cages for the two inclusion treatments, an experimental treatment in which a known number of prey/hosts are added to the cage, and a control treatment in which a known number of predators and/or parasitoids as well as a known number of prey/hosts are added to the cage (e.g., Lingren et al., 1968; Rusch et al., 2016). This type of experiment has the added advantage that the densities of natural enemies are more precisely known and that per capita predation and parasitism rates can be calculated provided the densities used reflect those normally recorded in the field (taking aggregative responses of the enemies into account; Dennis and Wratten, 1991). A major disadvantage of inclusion experiments is that the cages can severely restrict or prevent the dispersal of natural enemies. The long-distance searching behaviour of foraging predators and parasitoids, in response to kairomones, may also be interfered with.

7.2.3 Sentinel Prey and Hosts One method of examining predation and parasitism by invertebrates is to actively manipulate prey/host availability by establishing patches of sentinel prey/hosts and recording the rate of prey disappearance or accumulation of detectable traces of predation, or the rate of parasitism after a set period of exposure in the field. While the use of sentinel prey/hosts often includes ‘nonnatural’ elements, such as inflated densities, nonnatural distributions, and immobilisation of prey/hosts, which may distort the natural enemy‒ host interaction, it is suitable for comparative purposes. Real, live or dead, sentinel prey/hosts in field experiments were initially used to measure

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parasitism (Ôtake, 1967) or predation (Speight & Lawton, 1976) more than 50 years ago, and have been used productively since (e.g., Wratten & Pearson, 1982; Perez-Alvarez et al., 2019). However, several studies have documented important differences in predation between live and dead, and mobile and immobile prey. For example, the probability of removal of immobilised prey may be higher than for unmanipulated prey that are able to escape or defend themselves (Zou et al., 2017). Natural enemies may also have a preference for either mobile or immobile prey (Nagy et al., 2020). For example, Brooks et al. (2009) found higher predation of live, mobile prey than of dead, immobilised prey in a freshwater macroinvertebrate system, while Steward et al. (1988) reported that predatory wasps (Vespidae) preferred pinned to unpinned caterpillar prey. Hence, the method of prey manipulation can affect the estimation of predation rates. A further important aspect of determining the validity of the sentinel method is to assess whether the predator of immobilised prey also consumes the prey in unmanipulated settings. Direct observation (Sect. 7.2.4) can provide first-hand information about the predators involved in pest suppression (Pfannenstiel & Yeargan, 2002; Westerman et al., 2003), but this method is laborious and less practical at night or under adverse weather conditions. Video recording of exposed prey can resolve these limitations as it allows continuous monitoring for extended periods under a wide range of environmental conditions (Frank et al., 2007; Grieshop et al., 2012; Nurdiansyah et al., 2016; Zou et al., 2017; PerezAlvarez et al., 2019). Predation can also be assessed using artificial prey. A rather superficial similarity to real prey is often sufficient to attract predators, though such artificial prey cannot move, defend themselves, or behave as true prey would, and the absence of chemical cues may conceal prey identity (Howe et al., 2009; Lövei & Ferrante, 2017). Artificial sentinel prey was first used by Edmunds and Dewhirst (1994) and have since been used successfully to quantify predator impacts against caterpillars in the field (e.g., Seifert et al., 2015,

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2016; Clayborn & Koptur, 2017). Although artificial sentinel prey is less natural, traces of predation left by different predators are sometimes identifiable, making them suitable for comparative studies and the partitioning of total predation pressure by predator types (Lövei & Ferrante, 2017). Artificial sentinel prey is also cheaper to use than live prey, do not require rearing, can be simple to produce (as in the case of caterpillar models; Howe et al., 2009), can be standardised across sites (Roslin et al., 2017), and their density and distribution can be easy manipulated. For these reasons, the artificial sentinel method has been recommended for obtaining quantitative estimates of predation as an ecosystem service under field conditions (Meyer et al., 2015). However, it can only be used for generalist predators and not for specialist predators or for parasitoids. While sentinel prey is often used to quantify predation, hosts can also be placed in the field for a set exposure period to estimate the impact of parasitoids (e.g., Letourneau et al., 2012; Thomson & Hoffmann, 2013). This is most commonly used for studies of egg (e.g., Keller & Lewis, 1985; Glenn & Hoffmann, 1997) and pupal parasitoids (Geden et al., 2020; Nieto et al., 2021), but can also be used for larval parasitoids (Todd et al., 2018; Rutledge et al., 2021). This allows comparisons of standardised parasitism rates across different crop types, at different times throughout the season, and across multiple habitats in agricultural landscapes (e.g., Thomson & Hoffmann, 2013; Macfadyen et al., 2015).

7.2.4 Direct Field Observation Predation and parasitism can be observed directly in the field, which is valuable to identify relevant species interactions (Rosenheim et al., 1999), and to understand and quantify searching behaviour (Waage, 1983; Schenk and Bacher, 2002; Brechbuhl et al., 2010) and prey defence (Nelson, 2007). When sufficient observations are made it can also be used to quantify rates of predation or parasitism (van Nouhuys & Ehrnsten, 2004; Costamagna and Landis, 2007;

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Latham and Mills, 2010; Naranjo & Ellsworth, 2017). Increasingly, video is being used for observation of relatively sedentary prey. This is efficient because multiple videos can be viewed by researchers at high speed, allowing systematic data collection and observation of even infrequent events. Additionally, infrared cameras can be used to record activities at night. Video cameras are generally less disruptive than human observation of natural interactions, though the installation of cameras can still be disruptive (Grieshop et al., 2012; Hemerik et al., 2018). The Asian citrus psyllid, Diaphorina citri, is an economic pest of citrus because it vectors a bacterium that causes the lethal citrus disease huanglongbing. Kistner et al. (2017) studied predation and parasitism of D. citri colonies with and without access for ant mutualists in urban citrus. Based on a total of 19,200 h of video they were able to identify the natural enemy community and to show that when ants were excluded by a sticky barrier, visitation by syrphids, which are key predators, and the imported psyllid parasitoid, Tamarixia radiata, increased (Fig. 7.1). Relatively few observational field studies include data that is sufficiently extensive and systematic to be used quantitatively. Those that do quantify the rates of predation or parasitism use many different approaches. As a classic example, Kiritani et al. (1972) estimated that, depending on season and leafhopper instar, between 10 and 63% of rice leafhoppers in a field are eaten by spiders, by estimating the number (n) of rice leafhoppers killed by spiders per rice hill per day as follows: n ¼ FC=P

ð7:2Þ

where F is the number of predators seen feeding per rice hill during the observation period, C is the number of hours in a day that predators are actively feeding, and P is the average time, in hours, taken to eat a prey individual. Also studying spiders, Sunderland et al. (1986)

quantified predation of aphids by web-spinning spiders based on: n ¼ prk

ð7:3Þ

where n is the number of aphids killed/m2/day, p is the proportion of ground covered by webs, r is the number of aphids falling from plants per m2/day, and k is the proportion of aphids entering webs that are killed (determined from field observations and laboratory experiments). Using this approach, Sunderland et al. (1986) estimated that aphid populations could be reduced by up to 40% by spider predation.

7.2.5 Non-consumptive Effects of Predators and Parasitoids Although the direct effects of predation and parasitism on prey abundance are critical to understanding population and community dynamics, indirect effects through prey responses that reduce the risk of predation and parasitism can also play an important role (Sih, 1986). Responses of insect prey to the threat of predation and parasitism include changes in behaviour (Ballantyne and Willmer, 2012; Siepielski et al., 2014) , life-history (Elliott et al., 2015; Sitvarin et al., 2015; Xiong et al., 2015), and physiology (Thaler et al., 2012; Rendon et al., 2016). Natural enemy-mediated changes in prey traits that do not involve direct consumption are termed nonconsumptive effects (NCEs), risk effects or traitmediated interactions (Hermann & Landis, 2017). The majority of studies to date link NCEs to changes in behaviour, including changes in feeding (Rypstra & Buddle, 2013; Thaler et al., 2014), oviposition (Wasserberg et al., 2013; Sendoya et al., 2015), colonisation or dispersal (Ninkovic et al., 2013; Bucher et al., 2015; Kersch-Becker and Thaler, 2015), host-plant preference or habitat use (Wilson & Leather,

7

Population Dynamics

2012; Sidhu & Wilson Rankin, 2016) and increased predator avoidance (Hoefler et al., 2012; Lee et al., 2014). In general, prey tend to respond to natural enemies behaviourally to become less apparent and reduce encounters, which can often lead to a reduction in fitness due to reduced food intake and reproductive success. The non-consumptive effects on prey from natural enemies can be quantified by manipulation of predator mouthparts (physical removal or gluing them shut), physical isolation of natural enemies from hosts using a barrier, or by isolation of individual natural enemy cues (such as visual or chemical cues). Such approaches allow different mechanisms of natural enemy detection to be studied and their impacts on prey fitness quantified (Hermann & Landis, 2017). NCEs of natural enemies can be stronger than their consumptive effects and can have indirect effects that act at the ecosystem level (Preisser et al., 2005; Creel & Christianson, 2008; Buck et al., 2018). For example, Fill et al. (2012) studied the NCEs of an aphid parasitoid Aphidius colemani on both Myzus persicae, a host aphid, and Acyrthosiphon pisum, a non-host aphid. They found that the parasitoid reduced the population growth rate of the non-host aphid, probably through direct encounters while foraging for the host aphid, which caused the non-host aphid to drop from its host plant in response to the risk of attack. Thus, even specialist natural enemies have the potential to cause non-target impacts on insect herbivores in the broader community via NCEs. Similarly, Ingerslew and Finke (2017) extended this same study system to include a second aphid parasitoid Aphidius ervi that only parasitises A. pisum. The outcome for aphid suppression was influenced by interference in the consumptive effects of the two parasitoids, but also by additive contributions from both parasitoids to their NCEs. This illustrates that NCEs can arise from responses to both enemy and nonenemy species, adding further to the complexity of quantifying the impacts of predation and parasitism.

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7.2.6 Molecular Approches for Determining Species Identities and Trophic Relationships in the Field Electrophoresis was among the first molecular approaches used to quantify predation by fluidfeeding arthropod predators (reviewed by Solomon et al., 1996). However, this technique has been superseded by more sensitive methods of molecular gut content analysis (MGCA) that include both serological and DNA-based techniques (Symondson, 2002). Immunoassays using polyclonal antisera plus enzyme-linked immunosorbent assay (ELISA) have been used extensively for predator gut content analysis. For example, Sunderland et al. (1987) used this approach to compare different polyphagous predator species and the detectability of cereal aphid proteins in their gut contents. They found that antibodies to aphid proteins could be detected in the gut of a predator for relatively long time periods, and that ‘maximum detectability times’ were longer for spiders and staphylinid beetles than for some other predators. Two key problems with use of polyclonal antisera were a lack of reproducibility and a tendency to cross-react with proteins from other prey species (Symondson, 2002). An alternative monoclonal antibody-ELISA approach has been developed to overcome these limitations but has not been extensively used due to the extensive time and high cost associated with production of a suitable clone. The specificity of monoclonal antibodies can extend to detection of different life stages of conspecific prey in predator gut contents. It was used by Sigsgaard et al. (2002) to investigate cannibalism in the corn earworm, Helicoverpa zea. The related technique of immunomarking, in which prey items are marked with a generic immunoglobulin G (IgG) that can be detected in the gut contents of predators using an IgG-specific ELISA, has the distinct advantage that it avoids the need for development of prey-specific monoclonal antibodies (Hagler, 2006; Hagler et al., 2018). Prey items marked in

600

this manner can also be used to identify cannibalism and to determine whether the gut contents of predators represent true predation events or result of scavenging and the consumption of carrion. Following the demonstration that shorter DNA sequences increased the amount of time that prey are detectable in predator guts, and that detectability was improved by using primers that amplified sequences from multiple copy DNA (Zaidi et al., 1999), drastic advances were made in the development of DNA-based detection techniques (Furlong, 2015). DNA markers have become the most widely used approach for the analysis of trophic interactions, with conventional and multiplex polymerase chain reaction (PCR) approaches based on specific primers being sufficient for detection of parasitism and predation by known natural enemy species, and next-generation sequencing based on universal primers providing an opportunity to investigate more complex species interactions that might include unknown species (Gonzalez-Chang et al., 2016; Schmidt et al., 2021). Polymerase chain reaction primers have not only been used for quantifying predation, but also to provide accurate estimates of parasitism rates and identification of immature parasitoids dissected from hosts (Jones et al., 2005). In addition, as secondary parasitoids can be difficult to identify using morphological characters, Chen et al. (2006) developed specific primers for identification of two secondary parasitoids of Lysiphlebus testaceipes, a common generalist parasitoid of aphids. More recently, DNA metabarcoding (Sect. 3.2.2) has also been used to identify the primary parasitoids of the millet head miner Heliocheilus albipunctella in Senegal, where it was used as a viable alternative to host rearing for estimating rate of parasitism (Sow et al., 2019). Parasitoid richness and parasitism rates at four field sites were consistently higher for DNA metabarcoding than for host rearing, indicating that this technique shows promise for quantifying the importance and complexity of host‒parasitoid interactions in the field. Liang et al. (2018) also developed a reliable and robust molecular technique to characterise

M. A. Jervis et al.

the competitive interaction between two parasitoids Diachasmimorpha longicaudata and Fopius arisanus of the oriental fruit fly Bactrocera dorsalis. Recent developments in the MGCA of predators include the field application of realtime PCR to estimate the number of copies of prey DNA in predator guts (Zhang et al., 2007), quantitative PCR to determine prey consumption indices for different predator species (Lundgren et al., 2009), ligase detection reaction (LDR) PCR (Li et al., 2011), terminal restriction fragment length polymorphism (tRFLP) (Juen et al., 2012) and next-generation sequencing (NGS) techniques (Pinol et al., 2014) for the investigation of predator diet breadth and detection of multiple predation events. One challenge in the application of NGS for MGCA is the excessive amount of predator DNA relative to prey DNA produced using universal primers. A cost-effective method to enrich prey DNA without the need to block predator sequences in PCR amplification can be achieved through DNA extraction from the gut only, coupled with a long lysis time and size selection for low molecular weight DNA (Krehenwinkel et al., 2017, 2019). In parallel with these technological advances, protocols designed to maintain the integrity of field-collected predators for MGCA have been developed following the field testing of different predator collection methods (Greenstone et al., 2011, 2012). While molecular approaches provide several advantages over classic methodologies (e.g., natural enemy exclusion and direct observation) for the identification of trophic linkages involving predators, these tools still lack the quantitative rigour that clearly links prey detection to the number of pests killed (Furlong, 2015). For example, Firlej et al. (2013) used large field cages to measure the impact of predation by Carabidae on soybean aphid populations and found that predators identified to be of importance from MGCA did not provide suppression of aphid populations in field cages. A key advantage of molecular approaches, however, is that they provide a more accurate assessment of the diet breadth of generalist predators.

Population Dynamics

Macfadyen et al. (2015) stated that predators are often described as “generalist” feeders that consume a wide range of prey despite a lack of evidence. Using a PCR approach to determine what generalist predators have recently eaten, it is now clear that some species are not as “generalist” as previously thought (Chapman et al., 2013). Similarly, DNA-based approaches have the advantage that they can effectively reveal interactions within natural enemy communities as well as interactions with a target pest. For example, Traugott et al. (2012) found that more than half of the parasitoid DNA detected in the gut contents of generalist predators stems from direct predation of adult parasitoids. Molecular methods are used to study biological control interactions involving small and sometimes cryptic predators and parasitoids (reviewed by Symondson, 2002; Harwood and Obrycki, 2005; Gariepy et al., 2007; Harwood et al., 2009; Weber and Lundgren, 2009; Furlong, 2015; Schmidt et al., 2021). They have also proved to be a valuable approach for assessing the occurrence of intraguild predation in the field (Aebi et al., 2011; Schoeller et al., 2012; Traugott et al., 2012; Davey et al., 2013; Rondoni et al., 2015). Such techniques not only enable researchers to accurately identify interactions between natural enemies and host or prey insect pests in the field, but also to quantify, at least in the case of parasitoids, their contributions to pest control services. Both are critical elements for building successful and sustainable IPM strategies for crop pests and will undoubtedly benefit from further technological advances (Schmidt et al., 2021).

7.3

The Role of Natural Enemies in Insect Population Dynamics

7.3.1 Introduction Having reviewed some of the methods by which insect mortality due to natural enemies can be quantified, we now turn to the task of assessing the significance of mortality, due to natural enemies, for the dynamics of hosts or prey populations. Mortality factors acting on an insect

601

population can decrease insect population size or induce fluctuations in population sizes, and potentially contribute to the regulation of population size toward a stable equilibrium. Population regulation is directly relevant to the process of the pest control using natural enemies. For a factor, such as parasitism or predation, to regulate, the strength of its action must be dependent on the density of the population affected. That is, it needs to be prey density dependent, so that the effect of the predator or parasitoid is proportionally larger at high prey population densities and smaller at low prey densities (Fig. 7.2). Negative density dependence operates through negative feedback on population size. This is most often considered as due to host or prey mortality, but may also involve decreased reproductive rate, dispersal, and immigration. If the proportion of hosts parasitised varies with changing host density, either temporally or spatially, this can profoundly affect the dynamics of the species. Density-dependent factors can also affect average population sizes (Sects. 7.3.4 and 7.3.7) and can, under certain conditions, induce perturbations (Sect. 7.3.4). We begin by addressing the pros and cons of using percentage parasitism estimates as a metric to assess the impact of parasitoids on host population dynamics (Sect. 7.3.2). We then discuss the simple technique of assessing the impact of natural enemies by assessing the correlation of

DD Percentage mortality

7

DI

Prey population density Fig. 7.2 A density-dependent mortality factor (DD) in which proportional prey mortality increases with population prey density and a density-independent factor (DI) in which proportional prey mortality is unrelated to prey population density

602

their numbers with those of the host populations (Sect. 7.3.3). We then review classical life-table analysis (Sect. 7.3.4) used to parse the contribution of each host stage and mortality factor to the dynamics of the host population. Next, we move to experimental rather than observational approaches to quantifying pest population control by natural enemies using manipulation and factorial experiments (Sect. 7.3.5). Following this, we discuss the influence of landscape fragmentation and metapopulations on natural enemy‒host dynamics (Sect. 7.3.6) before reviewing the structure and stability properties of discrete-time and hybrid parasitoid‒host models (Sect. 7.3.7) and closing the section with how to confront population models with field data (Sect. 7.3.8).

7.3.2 Percent Parasitism The percent parasitism is the fraction of the host population that is observed to be parasitised. A high ‘percent parasitism’ of an insect pest population suggests that a parasitoid has a large impact on the host population size. While a high rate of parasitism will reduce the host population size, it does not necessarily reflect host population regulation, or long-term pest control. First, the percent parasitism reported in a publication is the percent of a sample of the host population. It can either under- or over-estimate the impact of parasitoids on host population dynamics, depending on the size of a sample, the timing of the sample relative to the phenology of the species, and where the sample is taken from, relative to the distributions of both species. A lifetable study of the host population (Sect. 7.3.4) can be used to assess a parasitoid’s contribution to host population mortality locally. However, because it is not generally possible to obtain all of the information needed to make a life table, it is worthwhile to consider percent parasitism of a sample taking into account its limitations. When samples are taken over time, say over a generation of a host, the percent parasitism will vary temporally depending on the phenologies of both species. Specific aspects of phenology that

M. A. Jervis et al.

are relevant to how percent parasitism should be considered, such as whether generations are discrete or continuous, the length of time the host is susceptible, when the parasitoid is active during that time, and the development time of the parasitoid relative to the host (Godfray et al., 1994; van Nouhuys & Lei, 2004). Phenological asynchrony of the host and parasitoid can be accounted for by measuring recruitment to both the host and the parasitoid (parasitised hosts) populations continuously. The ratio of total parasitoid recruitment to total host recruitment provides an unbiased estimate of total losses to parasitism. Another method uses the attack rate from field samples (Bellows et al., 1992). If individuals are collected at frequent intervals, reared under field temperatures, and the proportion dying from each cause recorded from one sample to the next, then the original percentage of the sample that was parasitised can be estimated. Gould et al. (1990) and Buonaccorsi and Elkinton (1990) provide equations for the calculations. The method requires that all hosts have entered the susceptible stage before the first sample and that no host recruitment occurs during the sampling period. Details and examples of these and other techniques for determining rate of parasitism can be found in Van Driesche and Bellows (1988), Furlong et al. (2004a), Toepfer and Kuhlmann (2006), Jenner et al. (2010) and Asiimwe et al. (2016). The sampling method used will introduce a further error into the estimate of parasitism rate as methods are biased towards either parasitised or unparasitised hosts. In the above scenarios, samples are collected over time, generally covering the whole susceptible life stage of a host that has relatively discrete generations. In many instances, samples are taken less frequently or only once. If the sample is taken early, the percent parasitism for the host generation will likely be underestimated. If it is taken late, it may be overestimated. Similarly, rate of parasitism is generally spatially heterogeneous, at many scales (Hassell, 2000a; Segoli, 2016). Parasitism also varies among hosts in a population depending on what plant part they are on, the age of the plant and the plant species or variety (Kaiser et al.,

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2017). For instance, Kishinevsky et al. (2016) found that parasitism of the whitefly Bemisia tabaci by Encarsia parasitoids varied among host plant species within a field, and was low on flowering host plant individuals. Parasitism rate also differs in different parts of a field (Ferguson et al., 2006) due to both local host density (Gunton & Pöyry, 2016) as well as location relative to field edges (Cronin, 2009), different parts of an agricultural landscape (Segoli et al., 2020), and in different types of landscapes (Marino & Landis, 1996; Tscharntke et al., 2007; Grab et al., 2018). Thus, sampling location, distribution of sampling points, and field site can either underor over-estimate the rate of parasitism. Finally, the percent parasitism can underestimate the role of parasitoids for the dynamics of host populations if there are other forms of parasitoidinduced mortality. Host feeding (Jervis & Kidd, 1996; Zang & Liu, 2008; Emerick and Singh, 2016), unsuccessful parasitism and nonconsumptive effects (Abram et al., 2019) contribute to host mortality and sometimes outweigh the effects of parasitism.

7.3.3 Correlation Methods A useful indication of the impact of natural enemies can often be obtained by statistically correlating their numbers against those of their hosts. A high positive correlation may indicate a degree of prey specificity on the part of the natural enemy, that might reflect a rapid numerical response to variations in host density. Blubaugh et al. (2018), for example, found that the density of aphid parasitoids, primarily Diaeretiella rapae, was positively associated with aphid density on broccoli on farms with few lepidopteran pests (Fig. 7.3a). But on farms with many lepidopteran pests there was no association between parasitoid and aphid densities (Fig. 7.3b). They conducted an experiment and concluded that aphid parasitism was reduced by the presence of lepidopteran herbivores, presumably due to indirect effects of lepidopteran feeding influencing host plant cues used by foraging aphid parasitoids.

Fig. 7.3 Regression plots of a mixed-effects model of aphid densities and aphid parasitoid densities from a 2014‒2015 survey of 52 organic farms where a lepidopteran herbivores rarely occurred (n = 54) and b where lepidopteran herbivores co‐occurred with aphids at densities > 0.5 larvae/plant (n = 50). The dashed lines show the expected value of the linear regression, conditional on the random factors in the model, which were sample date and farm. The grey bands are 95% confidence intervals around expected values. The points are partial residuals, showing the association between aphid and parasitoid densities given the other factors in the model, sample date and farm (from Blubaugh et al., 2018, with permission)

Negative correlations, on the other hand, may indicate a slow or lagged numerical response by a predator to changing prey density. These responses are commonly shown by highly polyphagous predators that may ‘switch’ to feeding on a prey type only after it has increased in relative abundance in the environment. Negative correlations are also more likely to be associated with prey species that tend to show rapid changes

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Fig. 7.4 Relationships between predator and prey population numbers which produce either positive or negative correlations: a a positive correlation between predator and prey numbers produced by a slow rate of prey increase coupled with a relatively low predator attack rate, such

that prey numbers are not reduced, while numbers are still rising; b a negative correlation predator and prey numbers caused by predators ing prey numbers, which only increase after numbers have declined

in abundance, or with predators having a high attack rate (Fig. 7.4) (Murdoch, 1969). Negative correlations are often found between aphids and their natural enemies because the aphids colonise a crop early in the season and increase rapidly. Enemies follow at different rates. For example, Bannerman et al. (2018) found a strong negative association between the soybean aphid (Aphis glycines) and its coccinellid predator Harmonia axyridis in soybean in Minnesota, USA. The coccinellid was absent early in the season as the aphid population increased. The population density of the aphid peaked at about seven weeks, and the coccinellid population peaked three weeks later, probably forcing the already declining aphid population to crash. While in many cases natural enemies do cause host densities to decline, correlations can be created just as easily by predator populations tracking changes in prey numbers, rather than by bringing about those changes. Also, absence of a correlation should not be taken to imply that predators do not have any impact (Hassell & Waage, 1984). Therefore, conclusions based on correlation should be drawn with caution, and then only with an appreciation of the biology of the species involved and follow-up experiments.

7.3.4 Life-Table Analysis

predator between depresspredator

Introduction A life table shows, for each age, stage or time period, what the probability is that an individual will die before reaching the next age, stage or time period. It is especially useful in the study of insects, where developmental stages are discrete and mortality rates, and the causes of mortality, may vary widely from one lifecycle stage to another. Life tables are used to analyse the mortality of insect populations and determine key factors responsible for the pattern of change in total generation mortality within a population. They can further be used to determine how specific mortality factors, such as natural enemy species, affect prey or host population dynamics (Bellows et al., 1992). Below we show how types of processes relevant for pest control or population regulation can be identified and quantified using life tables. For instance, key lifestages for explaining population growth or decline under different conditions can be identified (e.g., Malabusini et al., 2022), density-dependent mortality can be distinguished from density-independent mortality, and delayed or over-compensating density dependence can be detected. Because some insect populations (e.g., aphids) tend to

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have generations which overlap in time, while others do not, two quite different approaches have been developed for each category: the agespecific life table and the time-specific life table, respectively. Age-specific Life Tables The life-table approach was pioneered for insect populations in a study of Drosophila melanogaster by Pearl and Parker (1921). Varley and Gradwell (1960) extended the approach to discrete generations of the winter moth (Operophtera brumata) in the UK using key factor analysis, based on Haldane’s (1992, reprinted from his 1949 publication) logarithmic method for comparing the contribution of successive mortality factors to total mortality (K). Varley and Gradwell’s (1960) approach has some shortfalls (Royama, 1996) and in practice has been replaced by more powerful and sophisticated techniques focused on population growth rate rather than total mortality, and accommodating overlapping generations as well as sex (Brown et al., 1993; Sibly & Smith, 1998; Coulson et al., 2005; Chi et al., 2020). However, because the concept of key factor analysis, as presented by Varley and Gradwell (1960) , provides an intuitive way of quantifying and analysing the immediate cause of changes in population size, we illustrate it below. The usefulness of Varley and Gradwell’s (1960) approach depends on the availability of sequential life tables for several generations of a univoltine insect population. In temperate regions, for example, it is common for insect populations to overwinter as eggs and develop through a series of discrete stages in the spring and summer (Fig. 7.5). The adults then mature in the autumn to lay a new generation of overwintering eggs before dying. In this situation, generations remain separate. By obtaining population density estimates for the numbers entering each stage in the life-cycle, it is then possible to construct a composite life table from a sequence of life tables for each generation (Table 7.1). The numbers entering each stage can be estimated by direct assessment of recruitment (for example, by measuring fecundity or

Fig. 7.5 Schematic life-cycle of a typical temperate zone univoltine insect population

fertility), or by indirect calculation from counts of each stage. Several techniques are available which provide an estimate by the second route, and these are reviewed by Southwood and Henderson (2000). Where stage mortalities can be partitioned into a number of definable causes, such as parasitism, predation and desiccation, they can be quantified separately in the table. In this way it may be possible to build similar life tables for particular natural enemies. By converting the numbers entering each stage in Table 7.1 to logarithms (log10), we can calculate for each successive mortality in any generation: k ¼ log10 number before mortality - log10 number after mortality

ð7:4Þ

where k is a logarithmic measure of the proportion dying from the action of the mortality factor. Within each generation, we can thus determine a sequence of k-values, k1, k2, k3, … kn, corresponding to each successive mortality factor up to the adult stage. Mortality during the adult stage can be counted as one or more k-factors acting on the adults, or alternatively as a k-mortality acting on the next generation of eggs (Varley et al., 1973). The final post-reproductive mortality to act on a generation, i.e., that which brings generation numbers to zero, contributes nothing to between-generation variation in numbers and is not included in the analysis. The advantages of using k-values instead of

606 Table 7.1 Composite life tables for six generations of a hypothetical insect population with discrete generations. Each k-value is calculated as k = (log10 before mortality – log10 after mortality). K = k1 + k2 + k3

M. A. Jervis et al. Year

Eggs

k1

k2

Pupae

k3

1

1000

0.824

Larvae 150

0.398

60

1.080

2

800

0.426

300

0.685

62

3

1200

0.681

250

0.455

50

4

700

0.942

50

0.204

5

500

0.553

140

0.301

6

1200

1.000

120

0.150

Adults

K

5

2.302

1.190

4

2.301

0.824

12

1.960

50

0.699

10

1.845

70

0.766

12

1.620

85

1.230

5

2.380

Note while such life tables have traditionally been presented in columns, putting them in rows, as is done here, makes spreadsheet regression calculations easier) (see also Fig. 7.6)

Fig. 7.6 Key factor analysis of the mortalities acting on a hypothetical insect population (see Table 7.1 for data)

population change (Fig. 7.6). Here, variations in k3 between generations most closely follow variations in overall mortality (K), indicating that k3, is the key factor. Note that the key factor is not necessarily the factor causing greatest total mortality (k1 in this case). Detecting Density Dependence

percentage mortalities lie in the ease of calculation and the fact that k-values can be added together to give a measure for total generation mortality (K) (adding percentages would have no meaning because they would sum to well over 100%). Plotting the k-values against generation may be enough to reveal the key factor(s) causing

Assessing which factors contribute to regulation of the population again involves plotting each kvalue, this time against the log10 density on which it acts (i.e., before the mortality). In our example (Fig. 7.7) the plot of k1 against log density of eggs contains six data points, corresponding to each generation. Similarly, k2 is plotted against log10 density of new larvae, again with six data points, and so on. Positive relationships for any of these plots indicate that mortality is acting in a densitydependent fashion. A horizontal slope indicates density independence, while a negative slope indicates inverse density dependence. Regression analysis is generally used to calculate the significance of the slopes. Here, the only significant density dependence is found in k2. However, the problem of statistical validity arises because as kvalues are calculated in the first place from log10 densities, the two axes are not independent. Moreover, the independent variable (log10 density), estimated from population samples, is not error-free. If density dependence is accepted, then the slope of the regression, b, can be taken as a measure of the strength of the density dependence. The closer b is to 1, the greater the stabilising effect of the mortality. A slope of b = 1

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Fig. 7.7 The identification of density-dependent factors from life-table data. k-values for the different mortalities are plotted against the population densities on which they acted. In this case, only k2 is significantly densitydependent (k2 = 0.86L–1.52; R2 = 0.84; k1 = 0.74; k3, = 0.96). k4 is the last mortality to act, bringing

numbers down to 0 (or in this case 1, which was used to make the log calculations workable). This remaining mortality is, by its nature, always density dependent but is not included in the analysis, as it contributes nothing to population variation or regulation

will compensate perfectly for any changes in density at this stage, while a slope of b < 1 will be unable to compensate completely for any changes (undercompensation). Slopes of b > 1 suggest overcompensation.

bud burst. The caterpillars feed on the foliage until fully grown, whereupon they descend to the forest floor on lines of silk and pupate in the soil. Adults emerge in November and December and females ascend the trees to mate and oviposit in crevices on the bark. There is, therefore, one generation each year (univoltinism). Data collected between 1950 and 1962 reveal that ‘winter disappearance’ (k1), during the period between the egg stage and that of the fully grown larvae, is the key factor explaining population variation between years. Parasitism, disease, and predation (k2–k6) are relatively insignificant in this respect (Fig. 7.8). The only significant regulating factor to be detected, however, was predation on pupae (k5, Fig. 7.9), subsequently shown to be caused mainly by shrews and ground beetles (Frank, 1967; East, 1974; Kowalski, 1977). Parasitism showed no sign of being density dependent, either at the larval stage (k2) or at the pupal stage (k6), leading the authors to suggest that the wide variations in

A Case Study: The Winter Moth Varley and Gradwell’s (1968, 1970) own study of the winter moth (Operophtera brumata), together with the various follow-up studies in England and Canada, are the best understood and most widely quoted examples of the use of agespecific life tables. We will briefly review some of the features of this study and use it to illustrate some of the potential problems in using key factor analysis. The winter moth feeds on a wide range of mainly deciduous trees, and occasionally defoliates oaks. The life-cycle at Wytham Wood, near Oxford, UK, where the study was carried out, is as follows: eggs are laid in early winter in the tree canopy and hatch in spring to coincide with

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Fig. 7.8 Key factor analysis of the mortalities acting on the winter moth (from Varley et al., 1973, reproduced by permission of Blackwell Publishing)

densities from year to year, caused by the key factor ‘winter disappearance’, may be obscuring a possible delayed density-dependent relationship. The lack of any detectable regulating potential by the larval parasitoid Cyzenis albicans (k2) was particularly surprising as this tachinid fly had been introduced in 1955 as a very effective biological control agent against winter moth in Nova Scotia, Canada (Roland & Embree, 1995). This difference could perhaps be explained by higher levels of C. albicans mortality in the UK. The parasitoid, although attacking the moth in the larval stage, continues to develop within the moth pupae throughout the summer and early winter and is therefore exposed to the same mortality factors as the moth pupae. Varley and Gradwell (1968, 1970) recorded as much as 98% mortality of C. albicans puparia. This is higher than that for winter moth pupae, but understandable as C. albicans spends 4 to 5 months longer in the soil, emerging in the spring.

Disadvantages of the Approach The difficulty of obtaining sufficient field data highlights the single biggest problem of the approach, namely that of securing a long enough sequence of data to perform the analysis with a reasonable likelihood of detecting statistically significant relationships (Hassell et al., 1987). For insect populations having one generation a year, there is no guarantee of success with even a decade of data. This is especially a problem in the face of environmental change that alter processes affecting a species over the duration of the study. Moreover, the approach depends heavily on knowing all of the important factors to include in a study at the outset. There is not much scope for incorporation of new components at a later stage. There are several additional problems: (1) Several agents may act at the same time on a life-cycle stage, which can be accounted for using the marginal death rates which represent the proportion dying due to a factor in the absence of other independent factors that may act

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Fig. 7.9 K-values of the winter moth mortalities plotted against the population densities on which they acted. k1, k2, k4 and k6 are density independent; k3 is weakly inversely density dependent; k5 is strongly density dependent (from Varley et al., 1973, reproduced by permission of Blackwell Publishing)

at the same time (Elkinton et al., 1992). (2) Some of the mortality categories in the life table may contain, or mask, a number of others which could be important key or regulating factors. This is particularly likely to be the case with poorly understood, broad categories, such as ‘winter disappearance’ in the winter moth example. (3) Life-table analysis methods are based on correlation, so do not provide an unambiguous estimation of cause-and-effect relationships. (4) It is possible for strongly regulated populations to show little variation from equilibrium, and this may make statistical detection of the processes of

regulation difficult using traditional life-table methods. Time-Specific Life Tables Time-specific (or vertical) life tables are suitable for use with populations in which the generations overlap, due to a short development time of the immature stages relative to the reproductive period of the adults (Kidd, 2010). At equilibrium, such species (such as humans and aphids) achieve a stable age distribution (Lotka, 1922) in which the proportion of the population in each age group or stage remains constant. In this

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situation, all the ecological processes affecting the population are, at least in theory, operating concurrently. This means that the relative numbers in each age group at any instant in time provide an indication of the proportional mortality from one age group to the next. We cannot deduce from this what mortality factors are operating, or whether any regulation is occurring, so the value of a time-specific life table is limited in this respect. Estimating mortality from parasitism is difficult when generations overlap. Van Driesche and Bellows (1988) provide an analytical method for doing this. Hughes (1962) developed a technique based on the time-specific life-table approach, which could be used for analysing aphid populations with a stable age (i.e., instar) distribution. Using a graphical method to compare population profiles at successive physiological time intervals, Hughes (1963) was able to partition the mortalities acting on the different instars, for example, parasitism, fungal disease and ‘emigration’. As Hughes (1972) pointed out, there is, however, no easy way of estimating errors in the construction of these life-table diagrams. In fact, the technique is dependent on the assumption of a stable age distribution. Whilst Hughes’ (1972) method is now considered to be of limited applicability, his work did lead to the development of the earliest simulation models for analysing insect populations with relatively complex population processes, such as density dependence (Knape and de Valpine, 2012; Andow & Kiritani, 2016). For field populations with overlapping or partially overlapping generations, the use of such models is now the only sensible way forward. These techniques are discussed in detail below (Sect. 7.3.8).

7.3.5 Manipulation and Factorial Experiments The problems of detecting density dependence from life-table data have already been discussed (Sect. 7.3.4). One way of testing directly whether density-dependent mechanisms are operating is

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to carry out a ‘convergence experiment’ (Nicholson, 1957), in which subpopulations are manipulated to achieve artificially high or low densities and are then monitored through time (Harrison & Cappuccino, 1995). Convergence to a common density is then taken as evidence for density-dependent regulation. Practical difficulty in manipulating the density of some species, and knowledge of what densities are high or low, may limit the usefulness of this technique. Among successful studies, Brunsting and Heessen (1984) manipulated densities of the carabid predator Pterostichus oblongopunctatus in field enclosures and found evidence for convergence within two years. Criticisms can be levelled at this technique in that enclosures may prevent emigration. Gould et al. (1990) manipulated densities of gypsy moth by artificially loading eight forest areas with different densities of egg masses to achieve a wide range of infestation levels. This method revealed previously undetected density-dependent mortality in the larval stage, primarily due to two parasitoid species. Factorial experiments can be used to determine whether factors potentially capable of limiting population numbers combine in a simple additive way or show more complex patterns (synergistic or antagonistic interactions; Chap. 9). A fully factorial experiment is designed to include all possible combinations of two or more factors, and each of the levels within the factors. To be useful, the factorial experiment must be replicated and last long enough to produce time-series data sufficient to assess equilibrium population levels around which numbers fluctuate (Rosenheim, 1998; Sih et al., 1998). Such experiments are used to examine the emergent effects of multiple enemies on prey populations primarily due to intraguild predation (e.g., Mitchell et al., 1992; Costamagna and Landis, 2007; Straub and Snyder, 2008; Frago and Godfray, 2014; Wu et al., 2016; Chailleux et al., 2017; Alhadidi et al., 2019), and how multiple prey species may indirectly impact the role of natural enemies for one another. For example, using field enclosures (Sect. 7.2.3) in an alfalfa field, Cardinale et al. (2003) manipulated the presence of two important

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predators, the coccinellid beetle Harmonia axyridis and the damsel bug Nabis sp., and one parasitoid, Aphidius ervi, each individually and in all combinations. They followed the population dynamics of the natural enemies and the prey species, pea aphids Acyrthosiphon pisum, and found that the pea aphid was suppressed most in the treatment with all three natural enemies, suggesting a synergistic (greater than additive) effect (Fig. 7.10). However, closer inspection of the data from all treatments and the field revealed that this suppression was mediated by a second prey species, the cowpea aphid Aphis craccivora. The cowpea aphid inhibited parasitism of the pea aphid by A. ervi. So, when the cowpea aphid was suppressed by the two predators, the parasitoid population increased and suppressed the pea aphid population (Fig. 7.10). These same factorial experimental methods are also used to explore the effects of abiotic factors (Miller et al., 2017) or agricultural manipulations such as mulching (Schmidt et al., 2004), in conjunction with natural enemies.

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7.3.6 Landscape Scale Patterns, and Metapopulation and Metacommunity Dynamics An insect herbivore usually occurs on a crop or natural plant species that is present at multiple sites in a region, with variable conditions spatially and temporally. In order for a natural enemy to have an impact on the pest, and persist in the long term, it must do so on a landscape scale. The structure of a landscape, such as its configuration, complexity or the fraction of the area cultivated, can determine the regional long-term persistence of an individual natural enemy species, or the composition of the community of natural enemies, and their effectiveness against insect pests. The dynamics of populations in agricultural or other heterogeneous or fragmented landscapes can be modelled as metapopulations. By extension, a community of natural enemies and their prey can be seen as a metacommunity. In this section we first present aspects of landscape ecology that have been used to make predictions about the impact of landscape structure on biological control. Then we present the existing and potential application of metapopulation and metacommunity ecology for predicting the dynamics of pests and their natural enemies. Landscape Ecology

Fig. 7.10 Natural enemies of the pea aphid (Acyrthosiphon pisum) treatments on the final date of the study. The densities of predators Harmonia axyridis and Nabis sp. are based on the final numbers of individuals captured per cage. Parasitism by Aphidius ervi is compared among treatments with the ratio [(mummies stem−1)/(mummies + Acyrthosiphon pisum stem−1)]. Bars are the mean ± SE of n = 3 cages for treatments 1–4, and n = 4 cages for treatment 5. For comparison, data points give the naturally occurring densities of enemies in the alfalfa field (from Cardinale et al., 2003, with permission)

Agricultural intensification leads to a simplified landscape. This simplification is associated with decreased biodiversity and increases in economically important pests on cultivated crops. In some cases, the increase in pests is due to decreased effectiveness of natural enemies (Tscharntke et al., 2007; Cohen & Crowder, 2017; Perez-Alvarez et al., 2019). Both conservation and importation biological control may be influenced by the landscape context of the crops. Grab et al. (2018) investigated the relationship between landscape simplification and the importation and conservation biological control of Lygus bugs, Lygus lineolaris, in cultivated strawberry. They found increased pest density and reduced parasitism rates of crop pests in landscapes with more intensive agriculture

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Fig. 7.11 a Parasitism rates of Lygus lineolaris nymphs by Peristenus digoneutis and b the average number of L. lineolaris nymphs collected from strawberry fields, as a

function of the proportion of open semi-natural habitat at 750 m surrounding the sampling location within each strawberry field (from Grab et al., 2018, with permission)

(Fig. 7.11), probably because there were few host and food resources for the parasitoids in simplified landscape (Jonsson et al., 2015). Grab et al. (2018) also found, irrespective of landscape simplification, only introduced parasitoids and no native parasitoids of Lygus bugs in strawberry fields. In this case there was no conservation biological control being provided by parasitoids,

even where non-crop habitat was abundant. In contrast, Winqvist et al. (2011) found decreased conservation biological control of aphids on cereal crops by generalist predators in simplified landscapes. However, it is notable that this held for organic farms but not conventional farms (Fig. 7.12), suggesting that landscape at multiple spatial scales is relevant for the maintenance of

Fig. 7.12 Percentage of aphids eaten (mortality) as a function of percent arable crops in the landscape. The figure was created by plotting residual values for each sampling point (agricultural field) along the landscape gradient. The residual values come from the statistical analysis of variance model, accounting for the variation associated with the random effects of farm nested in

region, which leads to apparent aphid mortality of greater than 100% in some fields. Organic fields: open circles and dotted regression line. Conventional fields: filled circles and solid regression line. The interaction is significant, i.e., the significant effect of landscape in the organic fields differs from the insignificant effect in conventional fields (from Winqvist et al. 2011, with permission)

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natural enemy populations. These examples illustrate that while biodiversity increases with landscape complexity, increased non-crop habitat in the surrounding landscape does not consistently improve pest management as natural enemy responses can vary from positive to negative (Karp et al., 2018). Metapopulation and Metacommunity Ecology The above associations of pests, damage and natural enemies with landscape complexity indicate that the landscape is important for natural enemy populations, and illustrate important ecological patterns, but they do not quantify the dynamics of populations. To do that, we must measure or model change in population sizes over time and/or space. Since most insect pests are distributed in a dynamic patchwork landscape, it is intuitive to place them in a metapopulation context. A metapopulation is a spatially structured population that persists over time as a set of local populations with limited dispersal between them. Local population processes (reproduction, predation, etc.) occur mostly within the local populations. Between-patch variation in parasitism and predation influence dynamics at the local population level, and dispersal between local populations may account for the persistence of regional populations, despite unstable fluctuations or extinctions at the local level (for reviews see Taylor, 1990, and Hanski, 1998). While the concept existed earlier, the term “metapopulation” was first used by Levins (1969) to describe his model of the potential for insect pest control, including biological control and insecticides, at a regional level. Levins (1969) formulated the rate of change of the fraction of habitat patches occupied by a species in a landscape (p). He used the same logistic differential equation that is used in classical population models (Sect. 7.3.8), but with the number of individuals replaced by the fraction of occupied patches p, dp=dt ¼ cpð1 - pÞep

ð7:5Þ

Here, c is the rate constant for colonisation of empty patches and e is the rate constant for

extinction of local populations. Levins’ (1969) model includes an intrinsic exponential growth rate cp for colonisation as well as a term that inhibits growth once the metapopulation is large (−cp2), at which point there are few available sites left to colonise. Colonisation is the result of immigration from neighbouring populations. The rate of extinction is proportional to the fraction of occupied patches with the probability of extinction of each patch (e) being independent. Since Levins’ (1969) model, more realistic deterministic models have been developed (Adler & Nuernberger, 1994) as well as probabilistic patch occupancy models, such as the incidence function model that accounts for spatial variation of colonisation and extinction probability (Hanski, 1994), and Bayesian models to address spatial and temporal variability of conditions (Smith et al., 2014), as well as individual agentbased models in which the behaviour of each individual animal is accounted for (Uchmański, 2016). These models consistently show that persistence at the regional level can be enhanced by dispersal between local populations, provided that: (1) local populations fluctuate asynchronously between habitat patches, (2) predator rates of colonisation are not too rapid relative to those of the prey, and (3) some local density dependence is present. While the degree of density dependence may be quite low, resulting in frequent local extinctions, the metapopulation may persist for a long time (Kean & Barlow, 2000; Hanski et al., 2017). For the most part, these models describe the dynamics of a single predator or parasitoid species reliant on a prey or host resource with independent dynamics. However, they are also applied to systems in which predator‒prey dynamics are interdependent (Taylor, 1990; Holt, 1997; Fernandes et al 2022). This includes a number of laboratory studies exploring the effects of spatial structure and dispersal on persistence of predator‒prey systems (Huffaker, 1958; Pimentel et al., 1963; Holyoak & Lawler, 1996; Bonsall et al., 2002). Pimentel et al. (1963), for example, examined the interaction between a parasitoid wasp and its fly host in artificial environments consisting of small

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boxes connected by tubes. The interaction persisted longer with more boxes and with reduced parasitoid dispersal. Bonsall et al. (2002) developed a similar system of interconnecting boxes to study a bruchid beetle‒parasitoid metapopulation interaction, with comparable results. While agreement with theory may be encouraging, the small scale on which these experiments, by necessity, have been carried out, may not reflect processes at the regional metapopulation level. Metapopulation processes have been detected in some large-scale field predator‒prey systems, but joint metapopulation dynamics of the prey with the predator or parasitoid have rarely, if ever, been identified (Walde, 1995; Harrison and Taylor, 1997; Weisser, 2000; Cronin, 2004; Cosentino et al., 2011). One of the best-studied examples involving an arthropod predator‒prey system is provided by the Glanville Fritillary butterfly, Melitaea cinxia, and its specialist braconid parasitoid, Cotesia melitaearum, in the Åland Islands, Finland. In Åland there are around 3,200 suitable dry meadows, of which several hundred are occupied by the butterfly in any one year, and the parasitoid is present in about 10% of the local butterfly populations (Lei & Hanski, 1997; van Nouhuys and Hanski, 2002; Hanski et al., 2017). The dynamics of the butterfly are predicted by metapopulation theory for the observed areas and isolation of habitat patches. The parasitoid also exists as a metapopulation, greatly influenced by the spatial dynamics of the host. However, due to density-dependent hyperparasitism, parasitoid dispersal limitation, and an overarching dependency of the butterfly on host plant quality, the dynamics of the host do not depend on the parasitoid (van Nouhuys & Hanski, 2002; Øpedal et al., 2020). Clearly, more detailed empirical studies of this nature, especially in biological control systems, are required to provide a ‘reality check’ to the theoretical literature (Cronin & Reeve, 2005). Insect pests and their natural enemies are part of a community of species in a landscape that is often a patchwork of cultivated and uncultivated land. Thus, the system can be thought of as a metacommunity, which is a community of interacting species made up of local communities

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linked by dispersal (Leibold et al., 2004). There is a robust literature of metacommunity theory (Logue et al., 2011), but it has not yet been applied quantitatively to questions of efficiency of importation or conservation biological control. Nonetheless, some metacommunity processes are broadly relevant for predicting persistence or dynamics in biological control. One example of this is the consequence of hyperparasitoids for the effectiveness of parasitoids as biological control agents. The patch occupancy model of metacommunity theory (Leibold et al., 2004) predicts that with increasing habitat fragmentation, higher trophic level species such as hyperparasitoids fail to persist because the resources become sparser and more unpredictable at increasing trophic levels (Holt, 2002; Wang et al., 2021). Thus, we predict hyperparasitoids to be present where a plant and insect pest are common in a landscape. This could either stabilise or destabilise effectiveness of biological control (Rosenheim, 1998). A second example of the potential application of metacommunity theory to biological control is the concept of spillover between cultivated and uncultivated parts of the landscape in conservation biological control (Tscharntke et al., 2007; Blitzer et al., 2012). The mass effects model of metacommunity dynamics predicts the movement of individuals within a landscape based on changing resource availability (Leibold et al., 2004). Based on this, a community of natural enemies may persist in a dynamic landscape, such as an agricultural system, in which local populations fluctuate due to changes in resource availability caused by harvest, crop rotation, insecticide application and seasonality.

7.3.7 Analytical Models of Population Dynamics A continuous-time framework is generally used to model populations with overlapping generations and all-year-round reproduction (Hassell, 2000a, 2000b; Murdoch et al., 2003). In contrast, discrete-time models are more suited for populations with non-overlapping generations that

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reproduce in a discrete pulse determined by season. We review simple models of host‒parasitoid interactions in discrete-time formalism and describe tools for elucidating their dynamical behaviours. One advantage of simple models is that they are often analytically tractable, i.e., their analysis using mathematical tools can provide generic insights into regulatory mechanisms across parameter regions that lead to stable, unstable, or oscillatory population dynamics. While reviewing classical models introduced decades ago, we also highlight new modelling frameworks and results from recent literature. We emphasise that while we primarily focus on discrete-time models in this chapter, many host‒ parasitoid systems are more appropriately modelled using the continuous-time framework of Lotka‒Volterra predator‒prey models (Murdoch et al., 1987, 2003; Ives, 1992; Gurney & Nisbet, 1998; Sanchez et al., 2018; Singh, 2021a). Simple Discrete-Time Models Discrete-time models have been a tradition in arthropod host‒parasitoid systems; their usage is primarily motivated by the univoltine lifehistories of insects residing in the temperate regions of the world. A typical life-cycle consists of adult hosts emerging during spring, laying eggs that hatch into larvae. Hosts then overwinter in the pupal stage and emerge as adults the following year. The host becomes vulnerable to parasitoid attacks at one stage of its life-cycle (typically the larval stage). Adult female parasitoids search and attack hosts during this time window of vulnerability. While adult parasitoids die after this time window, the parasitised hosts support juvenile parasitoids, which pupate, overwinter, and emerge as adult parasitoids the following year. Synchronised life-cycles, with no overlap of generations in both the host and the parasitoid makes discrete-time models highly appropriate for these systems. A model describing host‒parasitoid dynamics in discrete time is given by

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H t þ 1 ¼ RH t f ðRH t ; Pt Þ Pt þ 1 ¼ kRH t ½1 - f ðRH t ; Pt Þ]

ð7:6Þ

where H t and Pt are the adult host and the adult parasitoid densities, respectively, at the start of year t, and R [ 1 denotes the host’s reproductive rate. Note that R [ 1 is needed to avoid population extinction of the host. If the host is vulnerable to the parasitoid at its larval stage, then RH t is the host larval density exposed to parasitoid attacks. Parasitoids attack host larvae during the vulnerable period leading to two categories of hosts within the population: parasitised and unparasitised larvae. The function f ðRH t ; Pt Þ is the fraction of host larvae escaping parasitism (sometimes referred to as the escape response). In the absence of the parasitoid f ðRH t ; 0Þ ¼ 1 and the host population grows unboundedly as H t þ 1 ¼ RH t : Finally, RH t ½1 - f ðRH t ; Pt Þ] is the net density of parasitised larvae, with each larva giving rise to k adult female parasitoids in the next generation. Renaming variables, where now H t denotes the host larval density, results in H t þ 1 ¼ RH t f ðH t ; Pt Þ Pt þ 1 ¼ kH t ½1 - f ðH t ; Pt Þ] and both forms of the model have been used in the literature. The simplest formulation of Eq. 7.6 is the classical Nicholson‒Bailey model H t þ 1 ¼ RH t expð-cTPt Þ Pt þ 1 ¼ kRH t ½1 - expð-cTPt Þ]

ð7:7Þ

where c represents the rate at which parasitoids locate/parasitise hosts, and T is the duration of the host vulnerable stage (Nicholson & Bailey, 1935). The model assumes that parasitoids search for hosts randomly, are never egg limited, and have rapid handling times. Given the random

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General Stability Analysis Given a model of the form represented by Eq. 7.6, one is typically interested in knowing if the model can support a stable host‒parasitoid interaction, and if so, then for what parameter values. Simulating the model for a few test parameters (this can be done, for example, in a spreadsheet package, such as Microsoft Excel, by starting with an initial condition and iterating forward in time) can provide the first answers but a systematic quantification of stability regions proceeds along the following steps: Step 1: Determining the Equilibrium Point

Fig. 7.13 a A typical host‒parasitoid population time series for the Nicholson‒Bailey model (Eq. 7.7), b for a model with either a weak host refuge (l ¼ 0:05 in Eq. 7.12) or c) a moderate host refuge (l = 0.20 in Eq. 7.12) (Singh & Emerick, 2021, with permission). Host reproductive rate is assumed to be R = 2

host‒parasitoid interaction, the number of parasitoid attacks per host follows a Poisson distribution, with mean cTPt , then the escape response expð-cTPt Þ is the probability of zero attacks in the Poisson distribution. A typical time series of the Nicholson‒Bailey model is shown in Fig. 7.13a. Both populations grow at low densities, but at large host densities, the parasitoid begins to overexploit the host. This leads to a crash in the host population, followed by a crash of the parasitoids. These cycles of overexploitation and crashes result in an unstable interaction, with both populations exhibiting diverging oscillations. Before discussing generalisations to the Nicholson‒Bailey model, we briefly review mathematical approaches used for dissecting dynamical behaviours.

The model's equilibrium or fixed points are the population densities that remain constant across years. Let H * and P* denote the host and parasitoid densities at equilibrium, respectively. These equilibrium densities are obtained by first substituting Pt þ 1 ¼ Pt ¼ P* and H t þ 1 ¼ H t ¼ H * in Eq. 7.6 and then solving the resulting equations. Note that the model has a trivial equilibrium P* ¼ H * ¼ 0 where both populations are extinct, but we are primarily interested in the non-trivial equilibrium that represents the coexistence of species. For model 7.6 this nontrivial equilibrium is the solution to the following two equations 1 ¼ Rf ðRH * ; P* Þ P* ¼ kðR - 1ÞH * Solving these equations for the Nicholson‒Bailey (1935) model yields a single non-trivial equilibrium point H* ¼

lnR ðR - 1ÞkcT

P* ¼

lnR ; cT

where the host equilibrium levels decrease, and the parasitoid equilibrium levels increase, with increasing host growth rate R:

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Step 2: Linearising Around the Equilibrium Real populations are never in equilibrium as environmental fluctuations in model parameters and unmodeled interactions constantly perturb the system out of any equilibrium it may have momentarily reached. If these perturbations transiently decay and the populations return to equilibrium, then the equilibrium point is said to be stable. Alternatively, if perturbations amplify, and the populations increasingly deviate from the equilibrium, then the equilibrium point is said to be unstable. Considering small perturbations pt ¼ Pt - P* , ht ¼ H t - H * and linearising model nonlinearities in Eq. 7.6 around the equilibrium, results in the following linear discretetime system "

ht þ 1 pt þ 1

#

"

ht ¼A pt

# ð7:8Þ

where A represents a 2 x 2 matrix whose entries are related to the slope, or sensitivity, of the escape response to population densities (detailed formulas describing each entry of A are provided in the Appendix to Singh & Emerick, 2021). The matrix A is mathematically referred to as the Jacobian matrix and its eigenvalues are intricately linked to the stability of the equilibrium. Step 3: Checking for Stability The necessary and sufficient condition for stability of a linear discrete-time dynamical system, such as Eq. 7.8, is that all the eigenvalues of A have an absolute value of less than one (Elaydi, 1996). For a 2 x 2 matrix, this corresponds to the equilibrium H * , P* being stable if the following three conditions all hold

dH * [0 dR

ð7:10Þ

which leads to a simple, yet powerful stability condition: the model’s equilibrium is stable, if and only if, the adult host equilibrium density increases with increasing host growth rate R (Singh et al., 2009). Recall that H * in the Nicholson‒Bailey model is decreasing with R, and its instability is reflective of this simplified stability criterion. Using the fact that P* ¼ kðR - 1ÞH * ; the above condition on H * can also be written in terms of the parasitoid equilibrium density dP* P* [ dR R-1

1 - Tr ð AÞ þ DetðAÞ [ 0 1 þ Trð AÞ þ DetðAÞ [ 0 1 - DetðAÞ [ 0

equilibrium point is unstable if any one of the inequalities does not hold (we refer the reader to Elaydi, 1996, for details on the mathematical terminology used here). In many cases these stability conditions can be further simplified. For example, if the escape response f ðPt Þ only depends on parasitoid density, then the first two conditions always hold, and stability is completely determined by the third inequality 1 - DetðAÞ [ 0. If 1 Detð AÞ ¼ 0 then the equilibrium point is said to be neutrally stable (on the edge of stability and instability), and both host and parasitoid populations cycle with a period of 6 or higher (Singh & Nisbet, 2007). If 1 - Detð AÞ\0 then the equilibrium is unstable, and populations either show diverging oscillations that grow unboundedly (as in the Nicholson‒Bailey, 1935, model; Fig. 7.13a), or they settle into bounded population cycles (i.e., a stable limit cycle; Fig. 7.13b). Interestingly, for a parasitoid-dependent escape response f ðPt Þ the inequality 1 DetðAÞ [ 0 can be rewritten in a different form

ð7:9Þ

where Tr and Det refer to the trace and the determinant of the matrix, respectively. The

revealing that stability requires parasitoid densities to increase sufficiently rapidly with increasing host growth rate. It is important to emphasise that these simplified conditions are only to be used for a host-independent escape response. When f ðRH t ; Pt Þ depends on both populations,

Log sensitivity of host density to host growth rate

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Expanding the Nicholson‒Bailey Model

1.0

Host refuge Variation in host risk Parasitoid aggregation Parasitoid interference

We next discuss expansions of the Nicholson‒ Bailey model, emphasising the stabilising and destabilising effects of different mechanisms.

0.5 STABILITY

INSTABILITY

0.0 Type lll functional response Nicholson-Bailey Model

-0.5

Type ll functional response

-1.0 -1.0

-0.5

0.0

0.5

1.0

Log sensitivity of escape response to host density

Fig. 7.14 Stability regions for the host‒parasitoid model (Eq. 7.6) in terms of the sensitivity of the host density to * the host growth rate dH dR , and the sensitivity of the escape df response to the host density dH * (Singh & Emerick, 2021). * The Nicholson‒Bailey model corresponds to dH dR \0 and df dH * ¼ 0 and is unstable. Stability arises in two orthogonal * ways: (1) an increase in dH dR to make it positive, which occurs with parasitoid interference and aggregation or df host refuge; (2) a decrease in dH * which occurs with a Type III functional response in the parasitoid attack rate (Sect. 1.14). In this figure, the host growth rate is assumed to be R = 2 with the axes being dimensionless log * df R dH * sensitivities Hf dH * and H * dR (modified from Singh & Emerick, 2021, with permission)

the stability conditions (Eq. 7.9) can be graphically represented in terms of two relevant quantities: *

df dH and dR dH * where the latter denotes the sensitivity of the escape response to the host density. Figure 7.14 shows that stability region with respect to both these quantities for a general escape response f ðRH t ; Pt Þ, and stability is more likely to occur when the escape response is a decreasing function of the host density, rather than an increasing function (Singh & Emerick, 2021).

Host and Parasitoid-Dependent Attack Rates. The Nicholson‒Bailey model assumes that parasitoids search and attack hosts with a constant rate c implying a Type I functional response (Sect. 1.14). This assumption is relaxed by considering a Type II functional response (Sect. 1.14 ). Prior studies have implemented it by modifying the attack rate in Eq. 7.7 to c¼

c1 1 þ c1 T h RH t

where c1 is the attack rate at low host densities, and T h is the handling time (Rogers, 1972). The net attack rate per parasitoid cRH t increases with H t and saturates at 1/T h at high host densities. With this change, the escape response is now an increasing function of H t and it makes the model even more unstable if we move the Nicholson‒ Bailey point further to the right, away from the stability boundary in Fig. 7.14. Similarly, a Type III functional response (Sect. 1.14) is incorporated by setting c¼

c1 ðRH t Þq 1 þ c1 T h ðRH t Þq þ 1

ð7:11Þ

with q [ 0 capturing the acceleration of attack rate with increasing H t at low host densities, and analysis shows that such responses fail to stabilise the population dynamics irrespective of the value of q (Hassell & Comins, 1978). The fact that a Type III response is not stabilising in the Nicholson‒Bailey framework is surprising, since it is known to have a stabilising effect in the continuous-time framework of Lotka‒Volterra models (Murdoch & Oaten, 1975). As discussed below, this discrepancy arises from how the Type III response is phenomenologically

7

Population Dynamics

introduced. Indeed, modelling host mortality during the vulnerable period as a continuoustime process confirms the stabilising properties of a Type III response, removing the discrepancy between the two frameworks. In contrast to a host-dependent attack rate, a parasitoid-dependent attack rate is stabilising. Consider the scenario where the attack rate is proportional to c / P-m and decreases with t increasing Pt due to interference between parasitoids with 0\m\1 quantifying the degree of interference. Since the escape response only depends on Pt ; stability can be discerned by simply testing for host equilibrium density increasing with growth rate. Solving for the host equilibrium and applying Eq. 7.10 reveals the system to be stable for RlnR þ 1 - R \m\1 RlnR which corresponds to 0:28\m\1 for R ¼ 2, and 0:5\m\1 for R ¼ 5 (Hassell & Varley, 1969). In the context of Fig. 7.14, stability arises by moving the Nicholson‒Bailey model ‘up* wards’ via a change in the sign of dH dR : Host Refuge. Some hosts may be in some form of a refuge that protects them from parasitoids. There may be physical refuges in which hosts are protected from parasitism or some hosts may be physiologically immune to parasitoid attack or there may be a seasonal mismatch between host and parasitoid, for instance early developing hosts may avoid searching parasitoids. Refuges may also be statistical: hosts may escape parasitism by chance more often than if parasitoid search was truly random. Host refuges can be incorporated into the Nicholson‒Bailey model in two different ways: a constant host density protected from parasitism, or a constant fraction of hosts in the refuge. Both forms of the refuge stabilise host‒ parasitoid dynamics (Hassell, 2000a, 2000b). For example, a constant host fraction l in the refuge leads to the following model

619

H t þ 1 ¼ RH t ðl þ ð1 - lÞexpð-cTPt ÞÞ Pt þ 1 ¼ kð1 - lÞRH t ½1 - expð-cTPt Þ] ð7:12Þ While a weak refuge results in bounded oscillations, a moderate refuge stabilises the population dynamics (Fig. 7.13b,c). However, stability is again lost for a strong refuge with both hosts and parasitoids growing unboundedly. As before, the stability regime in terms of l can be obtained by simply checking the criterion given by Eq. (7.10). Variation in Risk The stability arising from a host refuge can be generalised under the concept of variation in risk. The Nicholson‒Bailey model assumes that all hosts are identical in terms of their vulnerability to parasitism. Perhaps a more realistic scenario is individual hosts differing in their risk of parasitism due to genetic factors, spatial heterogeneities, or the duration or timing of their exposure to parasitism (Bailey et al., 1962). In essence, the product cT in Eq. (7.7) represents the attack rate integrated over time, and by transforming it into a random variable x we obtain Z1 H t þ 1 ¼ RH t

pðxÞexpð-xPt Þdx x¼0

Z1 Pt þ 1 ¼ kRH t ½1 -

pðxÞexpð-xPt Þdx] x¼0

where pðxÞ is the distribution of parasitism risk across hosts (Singh et al., 2009). A key assumption in this formulation is that risk is independent of the local host density if hosts are non-uniformly distributed in space. Assuming pðxÞ follows a Gamma distribution (a versatile distribution commonly used for capturing skewed population behaviours) with mean c and coefficient of variation CV, we obtain the following escape response

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Z1 pðxÞexpð-xPt Þdx ¼ ( x¼0

1 1 þ cCV 2 Pt

)

1 CV 2

;

that yields the model Ht þ 1 ¼ ( 0

RH t 1 þ cCV 2 Pt

Pt þ 1 ¼ kRH t @1 - (

)

1 CV 2

1 1 þ cCV 2 Pt

1 )

1 CV 2

A ð7:13Þ

Stability analysis of this model leads to a classical result: CV [ 1stabilises the population dynamics, irrespective of all other model parameters (R, k, c) (May, 1978; Chesson and Murdoch, 1986; Murdoch and Stewart-Oaten, 1989; Hassell et al., 1991; Taylor, 1993). The stabilising risk distribution is shown in Fig. 7.15 where most hosts are at low risk, and stability arises from parasitoid attacks being skewed or aggregated towards a small fraction of high-risk individuals, with 1=CV 2 representing the degree of aggregation. This stability criterion motivated several studies investigating spatial pattern of parasitism in the field, and many data sets were found to be consistent with CV [ 1 (Pacala & Hassell, 1991).

Fig. 7.15 The distribution of risk as obtained from host parasitism data across patches from Reeve et al. (1994) (see Singh et al., 2009, for details on obtaining the distribution of risk). The estimated value of CV for this distribution is 1.31 and the dashed line corresponds to an inverse Gaussian distribution with same mean and CV as the distribution of risk (Singh et al., 2009, with permission)

Recent work in this direction has relaxed the assumption of a Gamma-distributed risk. It turns out that if the host reproduction R ~ 1 then CV [ 1 is the necessary and sufficient condition for stability irrespective of what form pð xÞ takes (Singh et al., 2009; Singh, 2021b). However, if R >> 1, stability requires a skewed risk distribution with the modal risk being zero (as in the Gamma distribution for CV [ 1). We illustrate this point with the data presented in Fig. 7.15 where pð xÞ is approximated by an inverse Gaussian distribution that has a non-zero mode. Despite having a CV ~ 1:3, this risk distribution is stabilising only for 1\R\2 (Singh et al., 2009). Interestingly, if the host risk follows an inverse Gaussian distribution and R [ 5, then the host‒parasitoid equilibrium can never be stabilised irrespective of how high CV is. In summary, for host growth rates close to one, sufficient variation in host risk (CV [ 1) is stabilising. In contrast, at high growth rates, the shape of the distribution for low-risk individuals is crucial in determining stability (Singh et al., 2009). Semi-Discrete Hybrid Models As illustrated above, for most host‒parasitoid models the escape response is phenomenologically chosen or designed to recapitulate field observations. While these models have tremendously improved our understanding of stabilising processes, mechanistic modelling frameworks are needed to translate insect life-histories and behaviours into discrete-time models. For example, consider a scenario where the parasitoids have a density-dependent mortality from predation or food limitation. It is not obvious how to modify the Nicholson‒Bailey model to reflect this density dependence. For this purpose, semi-discrete or hybrid frameworks have been proposed; these use ordinary differential equations to track population densities within the host’s vulnerable period during a given year (Rohani et al., 1994; Bonsall and Hassell, 1999; Geritz & Kisdi, 2004; Pachepsky et al., 2008). The solution of the differential equations at the end of the vulnerable period predicts the population densities for the next year. We discuss this semi-discrete formulation in further detail below.

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Population Dynamics

621

Let s denote the time within the host vulnerable stage that varies from 0 to T corresponding to the start and end of the vulnerable stage. The density of parasitoids (P), unparasitised (L) and parasitised host larvae (I) at time s within the vulnerable stage of year t, follows the differential equations. dPðs; tÞ ¼ -cPðs; tÞLðs; tÞ - cP Pðs; tÞ ds dLðs; tÞ ¼ -cPðs; tÞLðs; tÞ - cL Pðs; tÞ ds dI ðs; tÞ ¼ cPðs; tÞLðs; tÞ - cI Pðs; tÞ: ds

ð7:14Þ

Here c represents the parasitoid's attack rate, and cP , cL ; cI are the mortality rates. Solving the differential equations with initial conditions at the start of the vulnerable period s ¼ 0 Lð0; T Þ ¼ RH t ; Pð0; tÞ ¼ Pt ; I ð0; tÞ ¼ 0 predicts the parasitised and unparasitised larval population at the end of the season s ¼ T: This leads to a more general discrete-time model. H t þ 1 ¼ FðH t ; Pt Þ Pt þ 1 ¼ GðH t ; Pt Þ where update functions are obtained by setting F ðH t ; Pt Þ ¼ LðT; tÞ and GðH t ; Pt Þ ¼ kI ðT; tÞ: As expected, a constant attack rate c with no mortalities (cP ¼ cL ¼ cI ¼ 0) results in the Nicholson‒Bailey model. Allowing for non-zero density-independent mortalities leads to models very similar in structure to the Nicholson‒Bailey models with unstable population dynamics (Singh & Emerick, 2021). Revisiting Functional Responses A functional response can be incorporated in the semi-discrete framework by having the attack rate take the form c¼

c1 Lðs; tÞ

Density-Dependent Host Mortality A key mechanism known to have a stabilising effect on the host‒parasitoid populations is the density-dependent self-limitation in the host (May et al., 1981; Neubert and Kot, 1992; Marcinko & Kot, 2020). Density-dependent host mortality can be modelled by assuming that the death rate cL ¼ ch Lðs; tÞ is proportional to the host density. Solving the continuous dynamics (Eq. 7.14) for a constant parasitoid attack rate yields the following discrete-time model (Singh & Nisbet, 2007). H t þ 1 ¼ LðT; tÞ ¼

q

1 þ c1 T h Lðs; tÞq þ 1

where q ¼ 0 corresponds to a Type II functional response, and q [ 0 a sigmoidal Type III response (Sect. 1.14). Singh and Nisbet (2007) show that the resulting discrete-time model based on the hybrid framework is stable for q [ 1, assuming that the handling time is significantly shorter than the vulnerable stage duration (T h 1 Rule.’ Trends in Ecology and Evolution, 8, 400–405. Thacker, J. R. M. (2002). An introduction to arthropod pest control. Cambridge University Press. Thaler, J. S., Contreras, H., & Davidowitz, G. (2014). Effects of predation risk and plant resistance on Manduca sexta caterpillar feeding behaviour and physiology. Ecological Entomology, 39, 210–216. Thaler, J. S., McArt, S. H., & Kaplan, I. (2012). Compensatory mechanisms for ameliorating the fundamental trade-off between predator avoidance and foraging. Proceedings of the National Academy of Sciences USA, 109, 12075–12080. Thomson, L. J., & Hoffmann, A. A. (2013). Spatial scale of benefits from adjacent woody vegetation on natural enemies within vineyards. Biological Control, 64, 57–65. Thomson, L. J., Macfadyen, S., & Hoffmann, A. A. (2010). Predicting the effects of climate change on natural enemies of agricultural pests. Biological Control, 52, 296–306. Todd, J. H., Pearce, B. M., & Barratt, B. I. P. (2021). Using qualitative food webs to predict species at risk of indirect effects from a proposed biological control agent. BioControl, 66, 45–58. Todd, J. H., Poulton, J., Richards, K., & Malone, L. A. (2018). Effect of orchard management, neighboring land use and shelterbelt tree composition on the parasitism of pest leafroller (Lepidoptera: Tortricidae) larvae in kiwifruit orchard shelterbelts. Agriculture, Ecosystems and Environment, 260, 27–35. Toepfer, S., & Kuhlmann, U. (2006). Constructing lifetables for the invasive maize pest Diabrotica virgifera virgifera (Col.; Chrysomelidae) in Europe. Journal of Applied Entomology, 130, 193–205. Tomasetto, F., Casanovas, P., Brandt, S. N., & Goldson, S. L. (2018). Biological control success of a pasture pest: Has its parasitoid lost its functional mojo? Frontiers in Ecology and Evolution, 6, 00215. Tougeron, K., & Tena, A. (2019). Hyperparasitoids as new targets in biological control in a global change context. Biological Control, 130, 164–171. Traugott, M., Bell, J. R., Raso, L., Sint, D., & Symondson, W. O. C. (2012). Generalist predators disrupt parasitoid aphid control by direct and coincidental intraguild predation. Bulletin of Entomological Research, 102, 239–247. Tscharntke, T., Bommarco, R., Clough, Y., Crist, T. O., Kleijn, D., Rand, T. A., Tylianakis, J. M., van

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8

Phytophagy Mark A. Jervis, Alejandro Tena, and George E. Heimpel

8.1

Introduction

This chapter considers ways in which phytophagy by parasitoids and predators may be studied, particularly with respect to conservation biological control programmes that involve the manipulation of insect natural enemies by means of supplemental food provision. Many insect naturalenemy species, at least at some stage during their life-cycle, exploit plant-derived food in addition to insects (i.e., are omnivorous). They feed: (1) Directly, upon plants, consuming floral and extrafloral nectar, pollen, seeds (either whole seeds or specific tissues), and, less commonly, materials such as plant sap (including the juices of fruits), epidermis, trichomes and plant tissue; and/or

M. A. Jervis Cardiff School of Biosciences, Cardiff University, P.O. Box 915, Cardiff CF10 3TL, Wales, UK A. Tena Center of Plant Protection and Biotechnology, IVIA, Valencian Institute for Agricultural Research, València Carretera CV315, Km 10.7 46113 Moncada, Spain e-mail: [email protected] G. E. Heimpel (&) Department of Entomology, University of Minnesota, Minneapolis 1980 Folwell Avenue St. Paul MN 55108, USA e-mail: [email protected]

(2) Indirectly, consuming honeydew (modified plant sap) produced by hemipterans, e.g., aphids, mealybugs, soft scale insects and whiteflies, among others, that feed on plant sap. In many species of predatory insects, only the adults are phytophagous. This is the case, for example, in aphidophagous hoverflies, predatory ants and some lacewings where the adults feed exclusively on plant materials. In others, the larvae as well as the predatory adults feed on plants, e.g., the ladybird Coccinella septempunctata. Among parasitoids, generally only the adults consume plant-derived foods (part of the definition of the term ‘parasitoid’ is that the developing offspring feed exclusively on one host); exceptions include certain Eurytomidae which as larvae are zoophytophagous, developing initially as parasitoids and completing development by feeding upon plant tissues (Henneicke et al., 1992). For many years, most investigations of predator or parasitoid foraging behaviour and population dynamics were concerned only with a natural enemy’s interaction with its prey or host species, and either ignored or overlooked its interaction with non-prey/non-host food sources. However, since the 1990s there has been growing interest in the importance of plant-derived foods in the behaviour and ecology of parasitoids

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2_8

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and predators (e.g., Evans, 1993; Jervis et al., 1993; Cisneros & Rosenheim, 1998; Gilbert & Jervis, 1998; Heimpel & Jervis, 2005; Wäckers et al., 2005, 2008; Bernstein & Jervis, 2008; Lundgren, 2009; Tena et al., 2016; Benelli et al., 2017; Gurr et al., 2017; Perovic et al., 2018; Heimpel, 2019). Information on the range of prey or host types attacked by natural enemies remains far more comprehensive and detailed compared with information on the types of plant material that many consume. This difference in emphasis is to be expected, since researchers have tended to regard the consumption of plant materials as somewhat peripheral to what is generally considered to be the most important aspect of natural-enemy biology, namely entomophagy. Another reason is that the methods used to measure host/prey range in the field are far more developed than those used to determine the plant-derived sources of insect natural enemies, especially when the plant-derived food is honeydew. In this chapter, we suggest how the source and identity of plant-derived food comprising the diet of natural enemies might be determined and discuss some ways in which this information might be used to gain key insights into the behaviour, physiology, population dynamics and pest control potential of parasitoids and predators. The chapter reflects our research bias: nectar-, pollen- and honeydew-feeding are emphasised over other types of feeding behaviour. For information on plant sap-feeding by predatory heteropteran bugs, see Naranjo Naranjo and Gibson (1996), Coll (1998), Eubanks and Strisky (2005), Castañé et al. (2011) and Pérez-Hedo et al. (2021), for information on seed-feeding by carabids and ants see Lundgren (2009) and Honek et al. (2003) and for information on plant feeding by predatory mites see van Rijn and Tanigoshi (1999), Nomikou et al. (2003), Adar et al. (2012), McMurtry et al. (2013) and Cruz‐Miralles et al. (2019).

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8.2

What Plant Materials Do Natural Enemies Feed upon and from What Sources?

8.2.1 Introduction While it is well known that natural enemies feed on nectar, pollen and honeydew, the range of flowers and honeydews exploited as food under field conditions is poorly known for most species of predators and parasitoids. Knowing the food source used by natural enemies is especially important for biological control researchers when there are food sources with different accessibility and/or nutritional value. For example, in citrus, there are numerous species of hemipterans that excrete honeydew with different nutritional value for the natural enemies that feed on them (Tena et al., 2013a, 2013b, 2016). Similarly, in aphidinfested crops with floral resources, natural enemies have access to both nectar and honeydew with different accessibility and value for natural enemy fitness (e.g., Lee et al., 2006). Here, we review direct and indirect methods that researchers have used to ascertain the food sources used by natural enemies in the field.

8.2.2 Direct Observations on Insects Floral Materials Generally, insects, including even very small parasitoid species (e.g., most Chalcidoidea, Cynipoidea and Proctotrupoidea) can be easily observed visiting flowers and extrafloral nectar. Numerous direct observations of predators and parasitoids visiting flowers have been published over the past two centuries, many of these listed in works such as: Müller (1883), Willis and Burkill (1895), Knuth (1906, 1908, 1909) and Robertson’s (1928) which deal with various flower-visiting insects; Drabble and Drabble (1927) with various Diptera; Allen (1929) with

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Tachinidae in particular; Drabble and Drabble (1917) and Hamm (1934) with Syrphidae (hoverflies), including a few aphidophagous species. More recent records are in Parmenter (1956, 1961), Herting (1960), Karczewski (1967), Judd (1970), Kevan (1973), Sawoniewicz (1973), Barendregt (1975), Maier and Waldbauer (1979), Primack (1983), Toft (1983), Haslett (1989a), de Buck (1990), Maingay et al. (1991), Cowgill et al. (1993), Jervis et al. (1993), Al-Doghairi and Cranshaw (1999) and Colley and Luna (2000). Tooker and Hanks (2000) also analysed Robertson’s (1928) data. Direct observations of natural enemies feeding at extrafloral nectaries have also been recorded (Nishida, 1958; Putman, 1963; Keeler, 1978; Beckmann & Stucky, 1981; Hespenheide, 1985; Bugg et al., 1987). The mere presence of natural enemies on or in flowers does not in itself indicate that they are feeding from them. This and other key points should be considered when natural enemies are observed visiting flowers: (1) Observers should record what materials (whether nectar, pollen or both) the insects are feeding upon, if they are feeding at all (natural enemies may visit flowers either accidentally or solely for purposes other than feeding, i.e., sheltering, meeting mates and locating prey and hosts, although it is likely that where flowers are visited for mating purposes, feeding also occurs (e.g., Toft, 1989a, 1989b; Belokobilskij & Jervis, 1998). If the insects can be seen (either with the naked eye or with magnification) to apply their mouthparts to a nectar or pollen source, then it is reasonable to infer that feeding is taking place. The same inference can be drawn from observations of parasitoids, ants and other natural enemies visiting extrafloral nectaries. In plants with concealed nectaries, it is more difficult to determine whether feeding is taking place and/or what materials are being fed upon, although in some cases it may be reasonably inferred from the insect’s behaviour that nectar-feeding rather than pollen-

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feeding is taking place. For example, in the creeping buttercup, Ranunculus repens, and its close relatives, the nectaries are situated near the bases of the petals, and are each concealed by a flap or scale (Percival, 1965). The adults of a variety of small parasitoid wasps may be observed with their heads either at the flap edge or beneath the flap, suggesting nectar-feeding (Jervis et al., 1993). The activities of small parasitoids visiting some other flower types may be interpreted as nectar-seeking behaviour. For example, in the flowers of Convolvulus repens and Calystegia sepium (Convolvulaceae), wasps and ants crawl down the narrow passages at the base of the corolla that lead to the nectaries (see also Haber et al., 1981, on ants). (2) With many plants that have concealed floral nectaries, particularly species with narrow corollas (e.g., members of the daisy family [Asteraceae]), it is sometimes possible to ascertain directly what materials the insect visitors are feeding on, although this is not the case with most parasitoid hymenopterans (Jervis et al., 1993). Even in the absence of close observations, it may be reasonable either to infer from the structure of the insects’ mouthparts or from the insects’ behaviour that particular plant materials are being sought. For example, among flies (nemestrinids, phasiine tachinids and some conopids) and among wasps (some chrysidids, leucospids and a few braconids and ichneumonids) some species have long, slender proboscides that are unlikely to be used for removing any materials other than floral nectar (Gilbert & Jervis, 1998; Jervis, 1998). Aphidophagous syrphids, however, use their proboscides for obtaining both pollen and nectar (Gilbert, 1981). Identification can sometimes be problematic for small parasitoids and this should be taken into consideration when reviewing the older literature on parasitoids; early publications probably contain a high proportion of misidentifications but parasitoid, especially wasp, taxonomy has since advanced greatly.

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(3) When considering a particular natural enemy, one should not attach too much significance to those plant species for which no record of visits has been obtained. If the insect species being investigated has a preference for the flowers of certain plant species (Sect. 8.3), then the probability of the investigator recording that natural enemy on those species will be significantly greater than on others. Because of this, a superficial field survey could give the misleading impression that the least preferred plant species are not exploited at all. This is an important point to consider when dealing with almost all the published records of visits by insects to flowers. (4) Finally, recording the sex of the insects involved greatly increases the scientific value of the information obtained. Examination of plants in the field for natural enemies carrying out nectar- and pollen-feeding may prove extremely difficult and timeconsuming (aphidophagous Syrphidae being an obvious exception). Flowers may therefore be presented to the insects in the laboratory, and observations on behaviour carried out. This has been done for many parasitoid species (e.g., Györfi, 1945; Leius, 1960; Shahjahan, 1974; Syme, 1975; Idris & Grafius, 1995; Patt et al., 1997; Drumtra & Stephen, 1999). However, the results of such tests need to be viewed with caution, because under field conditions the insects may not visit the same plant species as those with which they are presented in the laboratory. Even greater caution needs to be applied to the results of laboratory tests that involve presenting nectar extracted from different flowers to insects, as has been done for ants (Feinsinger & Swarm, 1978; Haber et al., 1981), because parasitoids might not be able to access the nectary under field conditions. Honeydew Hemipteran honeydew is hypothesised to be widely exploited as food by insect predators, including ants, and parasitoids in agroecosystems

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because honeydew is often the main sugar source available (see and Lundgren, 2009, for a broadranging review; Majerus, 1994, on Coccinellidae ; New, 2002, on Chrysopidae ; Gilbert & Jervis, 1998, and Jervis, 1998, on parasitoid flies; Tena et al., 2016, on hymenopteran parasitoids; Evans, 1993, on various natural enemies of aphids). The list of parasitoids and predators observed in the field feeding on, or searching for, honeydew is, however, much shorter than that for nectarfeeding. Honeydew feeding has received much less attention, mostly both because honeydew was considered a carbohydrate source of poorer quality compared to nectar (Wäckers et al., 2008) and because it is difficult to detect its presence in the field compared to finding nectar in flowers. Some parasitoids of honeydew-producing species take honeydew directly from the host’s anus, as ants do (Rasekh et al., 2010). Honeydew can solidify very rapidly after deposition, becoming a crystalline sugar film, which many Diptera such as tachinids and syrphids can readily exploit using their fleshy labella (Allen, 1929; Downes & Dahlem, 1987; Gilbert & Jervis, 1998). Feeding on wet honeydew is likely to be practised by many parasitoid wasp species, particularly in arid habitats, but feeding on dried honeydew has been observed in only a few parasitoid wasp species (Bartlett, 1962). Obtaining direct evidence of feeding by parasitoids on honeydew deposits can be problematical because: (1) the deposits can be difficult for the researcher to locate, despite being often highly abundant; (2) owing to their often great abundance within a habitat, a given-sized population of natural enemies is likely to be more highly dispersed over honeydew patches than over flowers; and (3) as mentioned above, some parasitoid wasps apply their mouthparts to honeydew films, but do so solely for the purpose of detecting hostfinding kairomones (Budenberg, 1990). Finally, when several hemipteran species share the same patch in a plant (e.g., several aphid and psyllid species are generally observed in citrus flush) it might not be possible to determine the species that has excreted the honeydew from which the natural enemy is feeding.

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Plant leachates Plant leachates, which can contain high levels of carbohydrates, have been found on the surfaces of all plant species examined (Tukey, 1971). Sisterton and Averill (2002) observed the braconid wasp Phanerotoma franklini to apply its mouthparts to the surface of cranberry leaves in the field, a behaviour that suggested it was feeding on leachates. Biochemical analysis techniques (Sect. 8.2.3) would need to be used to confirm this. Given the ubiquity of leachates, and the knowledge that some non-parasitoid species feed on them (Stoffolano, 1995), their potential as a food source for natural enemies should be borne in mind when undertaking investigations of parasitoid feeding ecology (Sisterton & Averill, 2002). Tukey (1971) considered plant guttation a category of leaching. Plant guttation is the fluid from xylem and phloem sap secreted at the margins and tips of leaves through hydathodes (Singh, 2016). Guttation drops generally contain low levels of carbohydrates and proteins and they have been considered a source of water for beneficial insects. However, Urbaneja-Bernat et al. (2020) found that guttation drops secreted by highbush blueberries contain high levels of carbohydrates, are present on leaves during the entire growing season, and are visited by numerous predators and parasitoids of different orders that increase their survival when feeding on it.

8.2.3 Indirect Methods Where direct evidence of plant-derived food consumption is unavailable or when researchers are interested in quantifying the percentage of a population that is feeding on plant-derived sources, other evidence needs to be sought. Here we consider indirect methods for documenting and quantifying consumption of various plant-derived food sources by natural enemies. Pollen The body surface, including the mouthparts, of flower-visitors may be examined for the presence of pollen grains irrespective of whether the

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insects are collected at flowers (e.g., Holloway, 1976; Stelleman & Meeuse, 1976; Gilbert, 1981; Dafni, 1992) or in other circumstances. The plant species source of such grains can be identified either: (1) by using identification works (e.g., Reitsma, 1966; Erdtman, 1969; Sawyer, 1981, 1988; Faegri & Iversen, 1989; Moore et al., 1991); and/or (2) by comparing the grains with those collected by the investigator from plants in the parasitoid’s field habitat (flowering plant species, in some cases even closely related ones, differ with respect to pollen surface sculpturing). However, the presence of pollen grains on the body surface, even on the mouthparts, does not necessarily constitute proof that pollen is ingested, since the insects may become contaminated with grains whilst seeking nectar. For example, Lysenkov and Galinskaya (2017) did not find a correlation between the pollen compositions in the guts and on the bodies of three genera of hoverflies. When collecting insects for the purpose of examining their body surface for pollen grains, it is essential that insects are individually isolated to prevent cross-contamination with (sticky type) grains. The body surface of insects can also be examined for the presence of fungal spores, which insects may passively accumulate as they brush against fruiting bodies. If spores are present, it does not necessarily indicate that the insects feed on fungal materials, although some parasitoids do feed on the sugar-rich spermatial fluid of fungi (Rathay, 1883). Gut dissections may reveal the presence of pollen grains. This technique has been used with hoverflies (e.g., Holloway, 1976; Leereveld, 1982; Haslett, 1989a; Hickman et al., 2001; Villa et al., 2016), parasitoid wasps (Györfi, 1945; Leius, 1963; Hocking, 1967; Jervis et al., 1993), coccinellid beetles (Hemptinne & Desprets, 1986; Hoogendoorn & Heimpel, 2004; Lundgren et al., 2004; Ricci et al., 2005), and green lacewings (Sheldon & MacLeod, 1971; Villaneve et al., 2005). This method has been applied to immature and adult specimens that were dried, preserved in ethanol, and deep-frozen (Holloway, 1976; Leereveld, 1982; Haslett, 1989a). Because the exines (outer coverings) of pollen grains are

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refractory structures (i.e., they are resistant to either decay or chemical treatment), they retain much of their original structure (hoverflies, at least, do not have to grind pollen in order to extract nutrients; Gilbert, 1981; Haslett, 1983), and the original plant source can thus often be identified, as indicated above. Hunt et al. (1991) devised a pollen exine detection method in which the abdomens (or gasters in the case of wasps) of dried, preserved insects are cleansed, crushed, heated in a mixture of acetic anhydride and concentrated sulphuric acid, the mixture centrifuged, and the pollen exines subsequently isolated and identified as above (for further details, see Lewis et al., 1983; Hunt et al., 1991). Golding and Edmunds (2003) proposed and tested the use of frass pellets of different species of aphidophagous lady beetles to detect the presence of pollen. Beetles were held for 48 h in the laboratory and the largest faecal pellet produced by each individual during this 48-h holding period was selected for dissection and analysis. However, since so many types of pollen and fungal spores could be found in faecal pellets, they combined and scored them as a single category. Electrophoresis of the gut contents or similar methods (Sect. 6.2.9) also have potential as a method for detecting the presence of pollen in the diets of arthropods and can be particularly useful for the identification of pollen to the species level (van der Geest & Overmeer, 1985; Corey et al., 1998). Indeed, the presence of pollen in hoverflies, as well as other natural enemies, can be identified using DNA metabarcoding (Lucas et al., 2018). This technique does not require a high level of experience in the taxonomic identification of pollen. The presence of pollen exines within the gut or faeces of a predator or parasitoid does not necessarily indicate that the insect has been feeding directly at pollen sources (i.e., anthers). Pollen may fall or be blown from anthers and subsequently become trapped in nectar, honeydew or dew (Todd & Vansell, 1942; Townes, 1958; Hassan, 1967; Sheldon & MacLeod, 1971). It is possible that, for some species, the consumption of nectar, honeydew or dew is the sole means of obtaining pollen. Also, with

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predators such as carabid beetles, pollen grains may enter the gut via the prey (Dawson, 1965). Lastly, pollen grains that land upon the surfaces of leaves are deliberately taken by some insects, e.g., adults of the non-aphidophagous hoverfly genus Xylota (Gilbert, 1991), and it is possible that some parasitoid flies do the same (Gilbert & Jervis, 1998). Orb-weaving spiders (Araneidae) are commonly regarded as generalist predators but their webs can also trap pollen. Eggs and Sanders (2013) found, using stable isotope ratios, that pollen was a substantial component of the diet of two species of orb-weaving spiders. They suggested that pollen ingestion was not accidental because the pollen grains in their study were rather large and had first to be digested extra-orally by enzymes in an active act of consumption. Sugars (nectar and honeydew) Plant-derived sugars in the guts of parasitoids and predators can be detected using various biochemical techniques that were originally designed for biting (mammal-blood feeding) flies of various kinds (Jervis et al., 1992; Heimpel et al., 2004; Heimpel & Jervis, 2005; Lee, 2019). Van Handel (1972, 1984) developed a series of simple biochemical colorimetric assays based on anthrone (9(10H)-Anthracenone; C14H10O). Solutions of anthrone and sulphuric acid change from yellow to blue-green upon contact with most sugars (Morris, 1948; Seifter et al., 1950; Scott & Melvin, 1953), and the specific sugars that induce the colour change vary with temperature. Fructose (either by itself or as a moeity of other sugars including sucrose) reacts with anthrone at room temperature within an hour (the ‘cold anthrone test’), whereas various other sugars require short incubation at 90ºC or a much longer incubation of at least 12 h at room temperature. Given the absence of fructose and sucrose from insect haemolymph, a positive reaction from an insect by means of the cold anthrone test signifies the presence of either or both of these plant-derived sugars in the insect’s gut. The cold anthrone test has since been used on laboratory and field-caught parasitoids (e.g., Olson et al., 2000; Fadamiro & Heimpel, 2001;

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Lee & Heimpel, 2003; Lee et al., 2004; Fadamiro & Chen, 2005; Heimpel & Jervis, 2005; Lee et al., 2006; Wyckhuys et al., 2008; Rand & Lundgren, 2018; Tena et al., 2018). The incidence of anthrone test-positive individuals in field populations of parasitoids ranges considerably, from 20% for Macrocentrus grandiii, Trichogramma ostriniae and Aphytis aonidiae to > 70% for Cotesia glomerata and C. rubecula. This method has been also used for predators such as coccinelids (Lundgren & Seagraves, 2011; Seagraves et al., 2011). Heimpel et al. (2004) developed a cold anthrone technique for small-bodied parasitoid wasps, involving the placing of individuals into a small droplet of anthrone on a microscope slide, squashing the insect with a cover slip, and recording the presence (sugar positive) or absence (negative) of a green halo around the parasitoid’s body. This method was later used by Segoli and Rosenheim (1998) and Kishinevsky et al. (2018) to evaluate a large number of parasitoids, identifying those that had recently fed on carbohydrates and others that had not fed. The cold anthrone test cannot distinguish between sugars from nectar (floral or extrafloral), honeydew, or other much less common sources such as plant exudates, fruit juices or artificial sugar sprays. Chromatographic techniques (HPLC‒high performance liquid chromatography, GC–gas chromatography, and TLC–thin layer chromatography) can tell us much more about the specific kinds of sugar present in insect guts. Honeydews have signature sugars (most notably erlose, melezitose, trehalulose and stachyose, Zoebelein, 1956; Wäckers, 2001; Wyckhuys et al., 2008; Tena et al., 2013b), whereas nectars generally do not. These signature sugars can be used to determine honeydew feeding by natural enemies. However, precaution should be taken because some parasitoids are able to synthesise these sugars (Wäckers et al., 2006a, 2006b). Even in these cases, however, the proportion of signature sugars can be used to determine honeydew feeding (Hogervorst et al., 2007; Tena et al., 2013b; Calabuig et al., 2015). It can be more difficult to determine the hemipteran species that has excreted the honeydew on which

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the natural enemy has fed when several hemipteran coexist in the field (Tena et al., 2016). The melezitose component of the honeydew produced by some aphid species can vary based upon the plant species the aphids have fed upon (Fischer et al., 2005) and can also be influenced by ant tending (Fischer & Shingleton, 2001). A new enzymatic method for measuring insect sugar concentrations has been developed that could be used to determine nectar-feeding in natural enemies (Phillips et al., 2018). This method has been tested for use in measuring glucose, fructose and trehalose in the parasitoids Microctonus aethiopoides and M. hyperodae under controlled conditions. Compared to the previous tests (cold anthrone and HPLC), this enzymatic method is quicker and less expensive than HPLC, and is safer, faster and more sensitive than the anthrone test. If the method allows the measurement of other sugars, it could in principle also be used to investigate honeydew feeding. Another indirect method of determining whether natural enemies consume sugar-rich foods is to mark potential food sources with dyes or other markers such as rare elements (e.g., rubidium) or radioactive elements (e.g., H332PO4), and examine the guts of the parasitoids or predators for the presence of the marker (e.g., Freeman-Long et al., 1998). However, it is important to be confident that the label truly has been obtained via the presumed food type: it might mark pollen or it could be passed onto herbivores feeding on the labelled plants and subsequently onto predators and parasitoids feeding on these herbivores (Payne & Wood, 1984; Jackson et al., 1988; Hopper, 1991; Hopper & Woolson, 1991; Jackson, 1991; Corbett & Rosenheim, 1996). Acquisition of the marker via pollen can be ruled out if gut dissections do not reveal the presence of pollen exines in any individuals from among a reasonable-sized sample. A simpler method could be colouring the food source, as has been proposed by Joschinski and Krauss (2017) for aphid honeydew. The problem of using markers is to ensure adequate uptake of the marker by the plants, and it may be necessary to apply the marker to the nectar of a large number of flower species and to a large number of honeydew

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production sites. Furthermore, it is vital to ensure that the marker is not repellent or toxic. Finally, several studies have employed a combined NMR and liquid chromatography–mass spectrometry (LC–MS) metabolomics approach to simultaneously assess differences in individual lipids and complementary information on polar metabolite concentrations, including haemolymph sugars, amino acids, and organic acids (Kapranas et al., 2016; Snart et al., 2018). This approach was used to compare the nutritional profile of wasps with different diets and ages under controlled conditions and could also be used to identify plant‒ insect interactions in the field (Snart et al., 2015). A common problem of all of these methods is that, for natural enemies that feed on nectar and honeydew but also on plant sap, it may be difficult to determine whether they have fed on plantderived materials or sap. These include mirid bugs and phytoseiid mites. Seed predation by ants, crickets, some carabids and other insects can be important in limiting the spread of weedy species (e.g., Lundgren, 2009). Seed predation by ants in particular can be established by examining the ‘booty’ being carried in ant columns (Sect. 6.3.3). The columns can also be followed to determine which plant species are being visited, although an important point to bear in mind is that the ants may be taking seeds from the ground instead of from the plants themselves.

8.3

Do Natural Enemies Show Specificity in the Plant Food Sources They Exploit?

Only in a relatively small number of cases can we be confident that a predator or parasitoid species is discriminating in the food sources it visits under field conditions. Nevertheless, we can reasonably expect some degree of behavioural specificity (i.e., specificity beyond that attributable to spatio-temporal synchrony between insects and food sources) to be shown generally among natural enemies. The range of different food types exploited will depend on insect morphology to some extent.

In tachinid flies, possession of a greatly elongated proboscis is linked to exploitation of floral nectar only, while a moderately long or a short proboscis is linked to feeding on a broader range of sugarrich food types (floral nectar, honeydew, extrafloral nectaries) (Gilbert & Jervis, 1998, using field observation data of Allen, 1929). Among flower-visitors, the range of plant species exploited will depend largely also on flower anatomy, reflecting a correlation between proboscis length and diet (van Rijn & Wäckers, 2016). Within the Syrphidae (hoverflies), as proboscis length increases, the flies are more often found to be associated with flowers having deep corollae, and the proportion of nectar in the diet increases (pollen being the alternative food, see below) (Gilbert, 1981). In the case of Episyrphus balteatus (the ‘marmalade hoverfly’), feeding is related to effective flower depth and the critical flower depth is 1.6 mm, which is less than the proboscis size of this hoverfly. The critical floret depth, however, may vary among plant species and it is less than 1.0 mm for Asteraceae (van Rijn & Wäckers, 2016). In parasitoid wasps also, proboscis type and length appear to be correlated with the degree of nectar concealment (Gilbert & Jervis, 1998; Jervis, 1998). Corbet (2000) has shown that, in butterflies, body mass and wing loading, in addition to proboscis length, determine which flower type is visited (flower type being defined in terms of corolla depth and the degree of flower clustering); the same may apply to predators and parasitoids. Among flower-visitors, the range of plant species exploited will also depend upon colour and odour (Shahjahan, 1974; Gilbert, 1981; Haslett, 1989b; Jervis et al., 1993; Wäckers, 1994; Idris & Grafius, 1995; Orr & Pleasants, 1996; Wäckers et al., 1996; Baggen et al., 1999; Patt et al., 1999; Sutherland et al., 1999; Wäckers & van Rijn, 2012; Géneau et al., 2013; Foti et al., 2017). Taste and nectar toxicity undoubtedly also play a role (Wäckers, 1999; Gardener & Gillman, 2002). Microorganisms such as bacteria can affect nectar chemistry by altering its acidity, sugar and amino acid composition/concentration and by adding synthesised compounds. Lenaerts et al. (2017) changed the composition of nectar by

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adding different groups of bacteria: these inoculations did not affect nectar consumption but did affect the longevity of parasitoids that fed on the nectar. In parasitoids, the compatibility of morphology (body size, proboscis length) with flower anatomy can sometimes be easily inferred from size measurements (Jervis et al., 1993), although observations of parasitoid behaviour can provide more useful insights into constraints imposed by floral anatomy (e.g., Plepys et al., 2002). When conducting such assays, the nutritional status of the parasitoid and the stage of the plant should be taken into consideration because flower odours and the response of parasitoids might change with the stage of the plant and the status of parasitoid (Takasu & Lewis, 1993; Jönsson et al., 2005; Bianchi & Wäckers, 2008). Moreover, as in the case with host searching, the selectivity of parasitoids can also change with their previous experience. Thus, parasitoids can become attracted by a flower after feeding on its nectar (Takasu & Lewis, 1993; Wäckers et al., 2006a, 2006b; Fataar et al., 2019). Selectivity by hoverflies (Gilbert, 1981; Haslett, 1989a; Cowgill et al., 1993; Inouye et al., 2015), and also by bee-flies (Toft, 1983), varies among species, and the same undoubtedly applies to parasitoids. Some species exploit the flowers of only a few plant species, in some cases only one (Inouye et al., 2015). Toft (1983), for example, found several bee-fly species to be restricted to the flowers of one plant species. At the other extreme are species such as the aphidophagous hoverfly Episyrphus balteatus, which exploits a much larger number of plant species. The terms ‘specialist’ and ‘generalist’ are used to distinguish between the two types (Toft, 1983; Haslett, 1989a). Generalist flower-visitors exploiting the flowers of a range of concurrently blooming plant species are very likely to behave as butterflies and some other insects do, visiting some flower types more frequently than would be expected on the basis of their respective abundances, thereby displaying a preference (defined in Sect. 1.6.7). Preferences are unlikely to be fixed and might change:

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(1) As individual insects respond to the changing profitability of any of the plant species within its food plant range, for example due to: (1) exploitation by competitors for the resource (as shown by Toft, 1984, for bombyliid flies; Sect. 8.3); (2) phenological changes in flower abundance and dispersion through the season; (3) changes in nectar secretion rates through the day; and/or; (2) As the nutritional requirements of the insects themselves change, either through their lifetimes or through the day. Adult syrphids display flower constancy in the manner of bees (Gilbert & Owen, 1990; Goulson & Wright, 1998; Goulson, 2000). That is, individuals specialise temporarily (e.g., for the duration of a foraging bout, or over several successive bouts) on one flower species. Flower constancy in syrphids is due to a combination of preferences for flower height, colour, and type preference in combination with local flowering phenology (Ssymank, 2003, in Inouye et al., 2015). Protocols for the measurement of flower constancy are given by Dafni (1992) and Goulson and Wright (1998). For a discussion of the adaptive significance of flower constancy, see Dafni (1992), Goulson (2000) and Inouye et al. (2015). In flower-visiting nectarivores, food plant ranges and preferences are likely to be based upon the following floral characteristics that could affect the insect’s foraging energetics: flower abundance and dispersion, nectar volume, concentration and degree of accessibility. Since nectar is a source not only of sugars (energy) but also of other nutrient classes (amino acids occur in a wide range of nectars, Baker and Baker, 1983), a preference could also be based upon the net rate of acquisition of these metabolites. The relationship between attractiveness of flowers to parasitoids and enhancement of parasitoid fitness has received little attention (for exceptions see Géneau et al., 2013; Foti et al., 2017). The

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preference could have a more complex basis, such as the relationship between nectar sugar concentration and amino acid concentration. Toxins or the composition of microorganisms in the nectar could also play a role (Adler, 2000; Lenaerts et al., 2017). Clearly, any investigation that attempts to relate flower preference to the above-mentioned floral characteristics could prove very complex if combined nectar and pollen-feeding by flower-visitors is considered. The standard method of assessing (i.e., detecting and measuring) flower preferences in insects in the field is to record the relative frequency with which an insect species visits the flowers of each of a range of available plant species measured in terms of the number of sightings of individual foragers during a census walk (Sect. 6.2.6), and relating this to the abundances of the different flower types (Toft, 1983, 1984; Cowgill et al., 1993). The number and species of natural enemies present per flower can be also measured by collecting the flowers in bags and extracting the arthropods in the laboratory (Wäckers & van Rijn, 2012 give more details). As an example of these studies, a 50 x 1 m sampling area containing a range of flowering plants was traversed at a constant speed on each observation day, and all sightings of the hoverfly Episyrphus balteatus were recorded (Cowgill et al., 1993). The behaviour of the insects at the time of first sighting was noted. Data gathered in this way can be analysed to determine whether or not the different flower species are visited in proportion to their abundances (i.e., detection of preference; Sect. 1.6.7) and to determine to what degree each species is preferred. In such studies, the following challenges need to be addressed: (1) Can the flowers of different plant species, particularly those of different structural types, be considered as equivalent foraging units? Observations of the insect’s foraging behaviour upon particular flower types, made prior to the preference study, could be useful in resolving this question; such an approach is better than making assumptions

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as to what the insect perceives as a patch (Chap. 1). (2) Can the flowers of different plant species be considered as equivalent resource units? Published studies on flower preferences of hoverflies have considered preferences in terms of floral abundance and visitation rates, instead of more realistically in terms of the availability and quality of food materials and the consumption rates of the insects. Plant species may differ both in the rate at which they produce pollen or nectar, and in their corresponding biochemical content. Also, for the forager there may be differences in handling times among plant species (Haslett, 1989a). Handling time (defined in Sect. 1.14) will depend upon the rate of nectar secretion and on nectar viscosity (Harder, 1983). A number of interacting variables may thus be involved in flower preference, making an objective assessment of the factors determining flower selectivity difficult, especially if the insects utilise both pollen and nectar (Haslett, 1989a). To overcome the drawbacks associated with methods involving behavioural observation, Haslett (1989a) examined the gut contents of hoverflies in his study of flower selectivity, counting the number of pollen grains found and identifying their source. He measured pollen availability in terms of a ‘floral area’ index, which was calculated partly on the basis of flower diameter, an index that is unlikely to provide a realistic measure of pollen availability, thus compromising the value of his preference analyses. Toft (1983), however, argued that corolla diameter can be used as a reasonable relative measure of the nectar content of different flower types, an assertion that still requires verification. Toft (1983) expressed resource availability in terms of flower number, both unweighted and weighted by corolla diameter, and found that weighting altered preference estimates only slightly. Nowadays, pollen identification can be simplified and facilitated using DNA metabarcoding (Lucas et al., 2018).

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While the range of flowers used by natural enemies, mainly hoverflies, has been studied, their preference for different honeydews needs to be understood as well. It is expected that natural enemies of honeydew producers feed on the honeydew excreted by their host or prey species. This assumption has never been tested, but Lenaerts et al. (2017) have demonstrated that the aphid parasitoid Aphidius ervi prefers and consumes more sugars that occur in honeydew (i.e., sucrose, fructose or melezitose) than those that do not (glucose and rhamnose). This situation is different for natural enemies of non-honeydew producers, which have to search in different patches for carbohydrate sources and for hosts or prey. In some ecosystems, these natural enemies can select among different honeydews during their search for carbohydrates. For example, many hemipteran species feed on citrus and excrete honeydew with different nutritional values for parasitoids of non-honeydew producers (Tena et al., 2013a, 2013b). The parasitoid Aphytis melinus increases its longevity up to ten days when it feeds on honeydew excreted by the mealybug Planococcus citri but not by the whitefly Aleurothrixus floccosus. A similar phenomenon occurs in olive orchards, where the black scale Saissetia oleae and the olive psyllid Euphyllura olivina (both in the order Hemiptera) each excrete honeydew of different nutritional value in spring and in the summer (Villa et al., 2016). Whether natural enemies show a preference for any of these honeydews remains to be investigated. Studies on parasitoids and predators that combine realistic measurements of floral food availability and consumption have yet to be carried out. Hoverflies are perhaps not ideal subjects for such research, since they consume both pollen and nectar, while parasitoids, which rarely feed directly upon pollen (Jervis et al., 1993; Gilbert & Jervis, 1998; Jervis, 1998), are less easy to observe in the field. Nevertheless, there is a pressing need for information on the flower preferences of natural enemies, due to an increasing awareness among biological control researchers of the potential importance of nonprey and non-host foods in the population

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dynamics of natural enemies (Powell, 1986; Kidd & Jervis, 1989; Jervis & Kidd, 1999; Heimpel & Jervis, 2005; Heimpel, 2019; Lee, 2019) (Sect. 8.6) and the need to establish the role of natural enemies as pollinators (Sect. 8.7).

8.4

Interpreting Patterns of Resource Utilisation

Coexisting hoverfly and bee-fly species divide up the available floral resources in the following (not necessarily exclusive) ways: (1) by being active at different times of the day (Toft, 1984; Gilbert, 1985a; D’Amen et al., 2013; Inouye et al., 2015); (2) by exploiting a different range of flower species (Gilbert, 1980, 1981; Toft, 1983; Haslett, 1989a; Ionuye et al., 2015); and (3) by exploiting nectar and pollen in different proportions (Gilbert, 1981, 1985b). Are species differences in resource exploitation the result of competition for limited resources? Gilbert (1985a) investigated the diurnal activity patterns of several hoverfly species in the field. He carried out census walks during which he noted what types of behaviour individual flies were performing and used the data to construct activity budgets for each species. With each observation of a fly, he measured ambient light intensity, temperature and humidity. From his observations and measurements, Gilbert (1985a) concluded that the differences in diurnal activity patterns are partly due to: thermal constraints (flies need to maintain a ‘thermal balance’; Willmer (1983) discusses this concept, and Unwin and Corbet (1991) give details of techniques involved in the measurement of microclimate), and the need to synchronise their visits with the pollen and nectar production times of flowers. He did not invoke interspecific competition as a possible cause of the observed patterns of activity. Toft (1984) constructed diurnal activity budgets for two coexisting species of bombyliids (Lordotus) that feed almost exclusively upon the flowers of Chrysothamnus nauseosus (Asteraceae). She found that one species, L. pulchrissimus, engaged in aggressive interactions, and

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visited flowers mainly in the morning, while L. miscellus performed these activities over a much longer period of the day. Toft (1984) argued that competition is the cause of the interspecific differences recorded, concluding that these results corroborate the conclusion drawn in her study on bee-fly communities (Toft, 1983, 1984). As for floral food selectivity, several investigations aimed at testing whether competition is responsible for patterns of resource partitioning among taxonomic groups of flower-visitors have been carried out, but few have dealt with a group of insect natural enemies. Toft (1983, 1984) adopted an observational approach by assessing preferences for flower species and measuring niche breadths at two study sites, concluding that patterns of resource utilisation by bee-flies resulted from interspecific competition. Interference competition was thought not to be a significant mechanism due to a lack of convincing evidence for interspecific aggression. Exploitation competition was thought more likely on the evidence that members of two genera (Pthiria and Oligodranes) tend to have longer mouthparts than a third (Geron), suggesting they may reduce nectar levels in flower corollas to such a level that they cannot be exploited by Geron. Gilbert and Owen (1990) used another observational approach to investigate determinants of resource partitioning among hoverflies: they looked for evidence of interspecific competition as indicated by population fluctuations and morphological relationships between species. They tested the hypothesis that competition will be stronger between species belonging to the same guild (i.e., species having similar ecological requirements) and that morphologically similar species will compete more strongly. If this hypothesis is correct, strong interspecific competition ought to be evident as reciprocal fluctuations in density among the species. Gilbert (1985b) had already established that morphological similarity (measured in terms of distance apart in multivariate space) and ecological similarity (foraging niche overlap) are correlated: species with similar size and shape feed on similar types of flowers and take similar floral food types. Gilbert and Owen (1990) found no

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convincing evidence that members of the same adult feeding guild compete: reciprocal fluctuations in population density did not occur, and thus species appeared to be ‘tracking’ resources independently of one another. An alternative to the above observational approaches to studying competition for food resources among parasitoids and predators would be to manipulate the insect populations, for instance, by removing individuals of one or more species and seeking evidence of competitive release in the remaining members of the community. Inouye (1978) and Bowers (1985) have shown that this approach can be successfully applied to bumblebees. Gilbert and Owen (1990), however, expressed the view that manipulation experiments on hoverflies represent an unrealistic goal because of the great mobility of adult flies, and suggested a way of circumventing the problem, at least in the case of woodland species (F.S. Gilbert, personal communication): manipulating species densities by continually adding laboratory-reared hoverflies to, rather than by removing flies from, a site. The daily pattern of the utilisation of carbohydrate resources has also been studied in several species of parasitoids by measuring the amounts of sugars present with HPLC (Tena et al., 2013b; Dieckhoff et al., 2014) or by determining the presence of fructose with cold anthrone (Segoli & Rosenheim, 2013). The percentage of mymarids of the genus Anagrus feeding on sugars tended to increase throughout the day and feeding did not occur at night (Segoli & Rosenheim, 2013). Segoli and Rosenheim (2013) suggested that floral nectar was likely to be the main sugar resource for these mymarids. In contrast, the percentage of braconid Binodoxys communis and the aphelinid Aphytis melinus that had recently fed on sugar sources tended to decrease. Both species of parasitoids feed commonly on honeydew. Therefore, this pattern suggests that they feed on honeydew either at the end or the beginning of the day (Tena et al., 2013b; Dieckhoff et al., 2014). Competition with ants can also affect the pattern of honeydew utilisation as a sugar source: Calabuig et al. (2015) demonstrated that the sugar-feeding incidence of

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both the parasitoid Aphytis chrysomphali and the predator Chrysoperla carnea on citrus trees in summer was negatively affected by the activity of the ants Lasius grandis, Pheidole pallidula and Plagiolepis schmitzii.

8.5

Insect Preferences and Foraging Energetics

The relationship of insect predators and parasitoids to plant-derived foods has hardly been considered from the point of view of foraging models (for discussions of general models, see Pyke, 1983, Stephens & Krebs, 1986, and Pleasants, 1989, and for parasitoid‒host models see Sirot & Bernstein, 1996, and Tenhumberg et al., 2006). We are concerned here with the characteristics of nectar sources (extrafloral nectaries, as well as flowers) and their insect visitors, that may need to be quantified in experimental or observational tests of foraging models. As far as nectar-feeding is concerned, the literature on butterflies (e.g., Boggs, 1987; May, 1988; Corbet, 2000) and bees (Heinrich, 1975; Hodges, 1985a, 1985b; Waddington, 1987; Pleasants, 1989; Corbet et al., 1995; Cresswell et al., 2000) provides useful information on approaches and techniques to adopt when planning investigations into the foraging behaviour, including flower preferences, of natural enemies (see also the textbooks of Dafni, 1992, and Kearns & Inouye, 1995). Such investigations, if carried out on nectarivores, may involve quantifying, or simply recording, some or all the following characteristics of the nectar sources. A: The mean energy content of individual flowers or florets of different flower types. Energy content is determined primarily by volume and sugar concentration. The latter variables need to be measured during the periods of the day when the insects feed most frequently. For information on methodologies relating to the sampling of nectar, measurement of nectar volume and concentration, and the chemical analysis of nectar constituents, see Corbet (1978), Bolten et al. (1979), Corbet et al. (1979), McKenna and

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Thomson (1988), May (1988), Dafni (1992) and Kearns and Inouye (1995). The concentration of nectars that are mixtures of glucose, fructose and sucrose can be expressed as the equivalent amount of sucrose. Given the concentration and volume of the nectar, milligrammes of sugar per flower or per floret can be calculated and this quantity converted to energy per flower or per floret, assuming 16.8 J/mg of sucrose (Dafni, 1992). Note that care must be exercised when converting from refractometer readings of nectar concentration to milligrams of sucrose equivalents (Bolten et al., 1979). In studies of nectarivory, nectar has either been sampled from flowers or florets that have been protected against visits or it has been taken from unprotected flowers or florets. With the former method, problems of interpretation can arise because the nectar can: (1) accumulate to abnormally high levels when flowers are protected (Lee & Heimpel, 2003); (2) decline in quantity due to nectar resorption by the plant (Burquez & Corbet, 1991); and (3) remain at around the original level (in some plant species removal of nectar increases net production of nectar, Pyke, 1991). Thus, we recommend that data be obtained from unprotected flowers only. Of course, diel variation in nectar availability and concentration also ought to be measured (for further discussion of the practical aspects of nectar sampling and analysis, see Dafni, 1992, Kearns & Inouye, 1995, and Lee & Heimpel, 2003). B: The relative accessibility of nectar in different flower types, e.g., distance from corolla opening to the nectar. Insects lacking an elongated proboscis, when extracting nectar from flowers or florets with long, tubular corollas, are likely to incur a larger handling cost, in terms of both time and energy, than when extracting nectar from flowers with either short corollas or completely exposed nectaries (see also C). Some parasitoids have an elongated proboscis (modified labiomaxillary complex, Gilbert and Jervis (1998) and Jervis (1998)). Using artificial flowers (capillary tubes), Harder (1983) showed that in bees the advantage of such an elongated

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proboscis depends on body size: large-bodied, long-tongued species (the tongue being the key functional part of the proboscis mouthparts in terms of nectar extraction) ingested nectar (sugar solution of fixed concentration) more rapidly than short-tongued species of equivalent size. Small-bodied, long-tongued bees ingested it at the same rate as small-bodied short-tongued bees. Note, however, that a long tongue can constitute a handling time constraint when a series of corollas is exploited on a flower (Plowright & Plowright, 1997). C: The dispersion pattern of flowers or florets in each plant species. The degree of clumping of these plant parts will have an important bearing upon the insect’s foraging behaviour, determining the amount of time spent travelling between the parts and the number of parts visited per unit of time (Corbet, 2000). D: The abundances (density per unit area) of the different flower types. In one example of floral density assessment, Baldock et al. (2015) sampled flowers at intervals along transects in different ecosystems. All flowering plant species in a defined quadrat were identified and counted for each species. The following characteristics of the insect’s foraging behaviour, in relation to certain flower types, may need to be quantified: E: The number of inflorescences, flowers or florets visited per foraging bout, and the number of foraging bouts performed per observation period. These features allow the flower visitation rate (number of inflorescences, flowers or florets visited per unit time) to be calculated (see Goulson, 2000, for a protocol). A ‘bout’ may be difficult to define. May (1988) took it to be the time period between a butterfly’s entrance and exit either from a flower patch (it is unclear whether he meant an inflorescence or a group of inflorescences) or from his field of view. The number of nectar sources probed per unit time will depend on the time spent travelling between them (F, below) and on handling time (G, below). Goulson (2000) addressed the question of why empirical studies have shown flower-visitors

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to visit a larger number, but a smaller proportion, of flowers in larger patches compared with smaller patches. He concluded that this behaviour is an optimal strategy, as searching for the remaining unvisited inflorescences is easier in a small patch. Goulson (2000) also showed that a departure rule, based on two successive encounters with empty inflorescences, closely predicts the observed behaviour. F: The mean time spent travelling (either walking or flying) between flower patches, between inflorescences, flowers or florets of a particular plant species during a foraging bout. A protocol for measuring between-patch time allocation can be found in Goulson (2000). It is important to observe the foraging behaviour of individual insects closely, as this will reveal, for example, whether the insects fly between flowers but walk between the florets comprising an individual flower (this has important implications in terms of energy expenditure), and whether one or a series of flowers is visited during a foraging bout. The foraging movements of a nonaphidophagous hoverfly (Eristalis tenax) upon flower capitulae of Aster novae-angliae (family Asteraceae) were elucidated by Gilbert (1983). The flies systematically probe the nectarproducing disc florets which form a narrow ring, leaving the capitulum when they have circled it once. They probe a variable number of florets on each capitulum, a behaviour that is thought to result from decisions made by the insect after each floret is visited about whether to probe another floret on the same capitulum or move to another capitulum (Pyke, 1984; Hodges, 1985a, 1985b). The ‘rule’ nectarivores use in making these decisions may be a threshold departure rule, i.e., if the reward (nectar) received from the current floret is less than a certain threshold amount, then the insect should leave the capitulum and locate a different one. Pleasants (1989) provides a discussion of how one might test whether: (1) insects that probe a variable number of florets per capitulum use a threshold departure rule to decide whether to continue foraging on a capitulum or to leave it

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and locate another one (e.g., hoverflies, see above), or (2) the threshold used is optimal, i.e., maximises the energy intake rate. G: The mean time spent dealing with each flower or floret of a flower type (probing it, perching on it, climbing into it, feeding, climbing out of it), i.e., the handling time. See B, above. This will depend in part on ‘tongue’ length, corolla depth and on the nectar volume per flower/floret. H: The gross energy intake during foraging of the insect for a given flower type. This can be calculated, either on a per-bout or perobservation period basis, as follows: Ein ¼ Rv Ex

ð8:1Þ

where Ein is energy intake, Rv is number of flowers visited per bout, and Ex is the mean quantity of energy (joules) extracted per flower/floret as measured in unprotected flowers (see above) at the same time of day. The gross energy intake per second can be calculated by dividing by the mean length, in seconds, of the foraging bouts. For insects that are able to consume relatively large amounts of nectar during a foraging bout, the volume of nectar removed from each flower or floret can be measured and the mean quantity of energy extracted per flower or floret calculated by multiplying this amount by the nectar concentration, with suitable corrections (Bolten et al., 1979). The amount of nectar removed can be measured perhaps most easily either by comparing the nectar volume in flowers or florets visited once by an individual insect with the volume in non-visited flowers or florets or any weight gain in the insect immediately after it has finished feeding at the flower or floret. Note that the quantity of nectar extracted may alter with any changes in nectar concentration that may occur, and may also alter as successive flowers/florets are visited, due to satiation or gut limitation. The mean quantity of energy extracted per flower may also vary with factors such as flower or floret density, the amount of nectar removed per flower decreasing with increasing

flower or floret (and therefore nectar) availability (Heinrich, 1975). Average and maximum crop volumes can also be measured quite easily in insects such as hoverflies (Gilbert, 1983). Insects such as small parasitoids will remove very small amounts of nectar from a flower or floret, so estimating the volume of nectar extracted by comparing visited and non-visited flowers is likely to be difficult. Therefore, measuring either crop volume or weight gain in insects that have recently fed may be better alternatives. Again, measurements need to be taken at several stages during a foraging bout. I: The energy expenditure of insects in relation to a given flower type. Following May (1988), this can be calculated, on a per-bout or per-observation period basis, as follows: ( ) Ecost ¼ Tfl Efl þ ðTh Eh Þ

ð8:2Þ

where Ecost is energy expenditure, Tfl is the period of time spent in flight per foraging bout, Th is the period of time spent handling each flower or floret of flower type per foraging bout, Efl is the metabolic cost per second of travelling between nectar sources, and Eh is the metabolic cost per second of handling those sources. The energy expenditure per second can be calculated by dividing the right side of the equation by the mean length, in seconds, of the foraging bouts. Efl and Eh can be estimated in the laboratory using methods for measuring metabolic rates of insects when flying and stationary, respectively, at the range of temperatures experienced by the insects in the field (e.g., Acar et al., 2001). To measure Efl insects can be allowed to fly, either tethered or free (e.g., Dudley & Ellington, 1990), within a respirometer, and their rate of oxygen consumption measured. The respirometer used by Gilbert (1983) to measure the oxygen consumption rate of loose-tethered Eristalis tenax was a paramagnetic oxygen analyser (other types of respirometer can be used, Acar et al., 2001). Alternatively, a ‘flight mill’ can be used and the amount of flight fuel consumed calculated. With flight mills, insects are tight-tethered to a balanced, lightweight arm, and are made to fly

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continuously until they become exhausted (e.g., Akbulut & Linit, 1999). Gilbert (1983) used such a device to measure the rate of energy consumption in flying E. tenax, simply recording the amount of weight loss during flight, and, from the figure obtained, estimated the amount of carbohydrate (presumed to be the flight fuel) utilised and, thus, the amount of energy expended over the period of flight. Hocking (1953), by contrast, provided insects that had become exhausted on the flight mill with a known quantity of carbohydrate (glucose). The duration of the next ‘flight’ by the insect could then be measured and the rate of fuel consumption calculated. Tight-tethering in flight mills, because the insect does not have to support its own weight, may cause flight duration to be overestimated and thereby cause energy consumption to be underestimated. Even loose-tethering, as employed by Gilbert (1983) in respirometry, is likely to influence average flight performance, so affecting energy consumption. A better system may involve flight chambers that do not involve tethering at all (e.g., Asplen et al., 2009). Measuring Eh could prove difficult. Heinrich (1975) suggested that the resting metabolic rates of nectarivorous insects approximate to the metabolic costs of feeding and also the costs of walking, so the practical problems associated with estimating the metabolic costs of feeding and walking may be conveniently avoided. However, this assumption needs to be tested for the insect species being studied. The metabolic rates of resting insects can easily be measured using respirometers (diurnal insects can be kept stationary in the dark). If the nature of the metabolic fuel utilised by the insects under study is known, the energy required to sustain the observed metabolic rate at particular activities can be calculated. Such fuels may be carbohydrates, lipids and amino acids (e.g., proline) and the fuel used may vary according to the type of activity, and even within an activity type, such as during flight (Beenakers et al., 1984; but see Amat et al., 2012, for an example of invariant glycogen use by a parasitoid).

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J: The net amount of energy that can be obtained from different flower types visited over a certain period of the day. Net energy, Enet, is the difference between gross energy intake (Ein) and energy expenditure (Ecost): Enet ¼ Ein - Ecost

ð8:3Þ

May (1988) observed flower preferences in terms of Enet, which was used as the measure of ‘profitability’ of flower types. Enet is in fact the net energy value of a flower type and not its profitability, which is Enet/(Tfl + Tp) (Stephens & Krebs, 1986). May (1988) asked whether the Enet of a flower type was largely a function of: (1) mean nectar volume; (2) mean nectar concentration, accessibility of the nectar (e.g., corolla length); (3) flower density; (4) flower dispersion; and (5) the density of florets per inflorescence, noting that (1) and (2) may vary with the time of day). May (1988) used multivariate statistical analyses to establish the main ways in which flower species differed with respect to these variables. As nectar volume explained nearly all of the variation in energy content among different nectar sources, it was concluded that nectar volume was the best single predictor of Enet, while nectar concentration and flower density were poor predictors. Multivariate analyses could also be carried out to determine what factors mainly determine the profitability sensu stricto of a flower type; handling time and flight time should be among the variables considered. As noted earlier, relating flower selectivity to foraging profitability in pollen- and pollen-plusnectar-feeding insects will prove more difficult than with nectarivores. One of the few possible advantages of studying pollen-feeders is that the amount of pollen consumed may be easier to quantify compared with the amount of nectar. In considering the exploitation of non-prey and non-host food sources by natural enemies, it is also important to bear in mind that, as well as having to decide which types of food source (e.g., flower species) to visit and what materials to consume, foragers need to decide how to

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allocate their time and energy between foraging for non-prey or non-host food versus foraging for prey or hosts on the other. Among many species of parasitoids and aphidophagous hoverflies, females forage for hosts or prey and for food in distinctly different parts of a habitat, and as a consequence must be incurring significant opportunity (from an oviposition standpoint) and energy costs compared with insects that can forage for non-host and non-prey foods in the same areas as their hosts or prey. The problem of choosing between ovipositing or feeding in patches of differing profitability (in terms of net energy and net fitness gain) is of importance from an ‘applied’ (conservation biological control, Sect. 8.6) as well as a ‘pure’ perspective. State-dependence of behaviour (Sect. 1.8) is a key consideration. The state-dependent optimal strategy, based on the amount of energy reserves, for a parasitoid faced with a choice of visiting host or food patches has been examined from a theoretical standpoint by Sirot and Bernstein (1996) using dynamic programming (Sect. 1.2.2 ). The Sirot and Bernstein (1996) model is based on pro-ovigenic parasitoids (i.e., energy reserves determine only life-span, not egg supply), a lifehistory extreme which appears to be exceedingly rare (Jervis et al., 2001). However, modifying a dynamic programming model so that it is applicable to synovigenic parasitoids (which mature eggs during their adult lives, dependant on energy reserves) is possible as well (Heimpel et al., 1998).

8.6

Pest Management Through the Provision of Plant-Derived Foods: A Directed Approach

There have been numerous cases where the incorporation of plant diversity within an agricultural system has led to a decrease in insect pest densities (reviewed by Risch et al., 1983; Russell, 1989; Andow, 1991; Bugg & Waddington, 1994; Coll, 1998; Gurr et al., 2000, 2016; Landis et al., 2000; Bommarco & Banks, 2003; Fiedler et al., 2008; Isaacs et al., 2009; Jonsson et al., 2010; ; Wyckhuys et al., 2013; Heimpel & Mills,

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2017; Penalver-Cruz et al., 2019). One explanation for pest decrease is that increased plant diversity enhances the action of natural enemies of pests ‒this is the ‘enemies hypothesis’ of Root (1973) (reviewed by Russell, 1989, and Heimpel & Mills, 2017). The basis of this hypothesis is that increased plant diversity provides natural enemies with resources that are in limited supply in monocultures; these resources include a favourable microclimate, alternative hosts or prey, and plant-derived foods (Jervis et al., 1992, 2004; Landis et al., 2000). During the last two decades, this topic has been reviewed in detail by Landis et al. (2000), Heimpel and Jervis (2005), Wäckers and van Rijn (2012), Gurr et al. (2017), and Perovic et al. (2018). Most of the studies have analysed the improvement of the effectiveness of parasitoids and predators through the provision of ‘supplemental foods’. The latter take the form of nectar- and/or pollen-providing plants and artificial substitutes (e.g., sprayed solutions of sucrose, often incorporating nitrogenous materials, so-called ‘artificial honeydews’, reviewed by Wade et al., 2008; Tena et al., 2016). Biological control practitioners intend that, by providing either natural or substitute foods, the local natural enemy population will be positively affected in one or, preferably, more ways: (1) the natural enemies will be attracted into the crop (i.e., food provision encourages immigration into pest-containing areas); (2) the natural enemies are retained within the crop (i.e., food provision discourages emigration from pest-containing areas); (3) changes occur in natural enemy lifehistory variables with the result that per capita searching efficiency and the predator’s or parasitoid’s numerical response are enhanced, such that, within the crop, the rate of parasitism or predation is increased, and that pest numbers are thus ultimately reduced to a desirable level. In cases where plants are used to provide the supplemental or substitute foods (floral nectar, pollen, extrafloral nectar), they are: (1) Deliberately sown. In most cases, the sown plants are not harvested (although they can be a species of crop plant), but in some

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cases they can be harvested as part of an intercropping system (e.g., Gallego et al., 1983; Letourneau, 1987). Intercropping, as employed in this context, takes various forms (e.g., Schoonhoven et al., 1998; Lopes et al., 2016; Gontijo, 2019; PenalverCruz et al., 2019; Iuliano & Gratton, 2020): (1) mixed cropping‒the growing of the two crops (pest-bearer and food-provider) with no distinct row arrangement; (2) row intercropping–the growing of the different crops in distinct, interdigitating rows; and (3) strip intercropping −the growing of the different crops in strips sufficiently wide to allow independent cultivation but narrow enough for the crops to interact (and therefore enable manipulation of natural enemies on the pest-bearing crop), or: (2) Present naturally as ‘weeds’, which are tolerated or encouraged (e.g., Zandstra & Motooka, 1978; Altieri & Whitcomb, 1979; Andow, 1988; Bugg & Waddington, 1994; Bugg & Pickett, 1998; Nentwig, 1998; van Mele & van Lenteren, 2002; Gunton, 2011; Seguni et al., 2011; Araj & Wratten, 2015; Araj et al., 2019; Gontijo, 2019; Möller et al., 2021). In some cases, they are intended to serve as both a natural-enemy food source and as trap plants for diverting pests away from the crop (e.g., Leeper, 1974). To increase the likelihood of success of a habitat manipulation programme, fundamental research should be conducted into natural-enemy resource requirements: it should provide information that will at best increase the chances of success of the programme and will, at the very least, provide valuable insights into natural-enemy biology (Jervis et al., 2004). Investigating the effects manipulation will have on the component members of a pest’s natural enemy complex, as opposed to entire guilds or source subwebs, is advocated (Jervis et al., 2004), that is, in the terminology of Gurr et al. (2003), using a ‘directed’ as opposed to a ‘shotgun’ approach (see also Heimpel & Mills, 2017). This, it is argued, will not only increase the chances of obtaining a

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positive outcome of manipulation but will also provide a deeper understanding of the mechanisms responsible for failures as well as successes (see also the ‘active adaptive management’ approach advocated by Shea et al., 2002, for various problems in pest management). Through a directed approach, the gathering of even the most basic biological information (e.g., on whether a parasitoid needs to feed as an adult), can enable candidate species from among a pest’s enemy complex to be compared and ranked with respect to pest control potential under a manipulation programme. The directed approach ought, ideally, to involve the research on several fronts in the following order: 1. Establishing that the candidate natural enemies actually require, and will seek out, supplemental or substitute foods to a significant degree. Species whose adults have vestigial mouthparts (Gilbert & Jervis, 1998; Jervis, 1998) will have no need for external nutrient inputs and are very unlikely to respond behaviourally or reproductively to food sources, so can be excluded from the list of candidate agents. Dissection of very recently emerged females, and measurement of the ovigeny index using the total number of oöcytes as the estimate of lifetime potential fecundity can provide a useful clue to the dependency of a parasitoid species on foods (see Sect. 2.3.4 for caveats). Females of proovigenic species ought, theoretically, to have less need for food than synovigenic species. There are, however, exceptions; for example, while mymarids appear in general to be proovigenic (Jervis et al., 2001), and are only occasionally observed feeding in the field (Jervis et al., 1993), a high proportion of field-caught individuals of one species contained sugars in the gut (Heimpel & Jervis, 2005). Similarly, the sugar content of two species of Anagrus, also pro-ovigenic mymarids, vary among seasons and time of the day (Segoli & Rosenheim, 2013). Although synovigenic insects will typically be more dependent on foods than will proovigenic species, some can obtain nutrients

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via feeding on host or prey haemolymph and/or tissues (i.e., ‘host feeding’, Jervis & Kidd, 1986; Heimpel & Collier, 1996), as has been demonstrated for the eulophid Eupelmus vuilletti (Giron et al., 2002, 2004). The latter physiological type of insects ought to be less sensitive to manipulation via the provision of sugar-rich non-host foods. Nonetheless, most parasitoids cannot achieve maximal fecundity by host feeding alone: females need to feed on non-host sugars in order to fully benefit from host meals (Heimpel et al., 1997a; Kapranas & Luck, 2008; Benelli et al., 2017) as the amounts of carbohydrates obtained by host feeding is very limited (Tena et al., 2013b). 2. Demonstrating that food resources are truly limited in supply, both within and in the vicinity of the crop and thus that the natural enemy is sugar limited. Demonstrating that natural enemies are sugar limited can be accomplished by the use of biochemical techniques (Sect. 8.2.3). While it is a valid generalisation that nectar is in short supply in monocultures, natural enemies can still feed on honeydew. For example, olive orchards in southern Spain are devoid of flowering plants (Jervis et al., 1992; Jervis & Kidd, 1993), but the hemipteran Saissetia oleae, the black scale, which is present in the crop throughout the year, excretes honeydew of high nutritional value for natural enemies and other insects (Wang et al., 2011; Villa et al., 2016). Other crops, such as alfalfa and citrus, also support populations of hemipterans (which may or may not be the target pest) that produce honeydew on which natural enemies feed (e.g., Evans & England, 1996; Tena et al., 2013b, 2016; Rand & Lundgren, 2018). However, the value of such honeydew may be limited if it is nutritionally poor or if it occurs in insufficient quantities (if the population density of the producer is low), or if natural enemies compete with ants for access to it (Tena et al., 2013b, 2016; Calabuig et al., 2015). The crop may also produce extrafloral nectar that parasitoids can feed on (Heimpel & Jervis, 2005; Röse et al.,

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2006). In fact, parasitoids are, after ants, the second most commonly reported insects feeding on extrafloral nectar (Heil, 2015). 3. Determining the plant-based diet breadth and feeding preferences of the natural enemy. Once it has been demonstrated that natural enemies need food sources, and that they are in short supply, it is necessary to select an appropriate food source for the selected natural enemy. As already mentioned (Sect. 8.3), mouthpart morphology can be very useful in this respect. Some species of flower-visiting natural enemies exploit a very wide range of flower species (e.g., Tooker & Hanks, 2000); a high degree of specificity is shown in others, particularly some bee-flies (Sect. 8.3). To assess preferences, field counts can be made of foragers (e.g., on different resources, Cowgill et al., 1993; Colley & Luna, 2000); ideally, these should be related to the abundance of the resource (Sect. 8.4). Research into the preferences that natural enemies have for certain food types, including nectar constituents, can create opportunities for the selection of food supplements (whether natural or artificial) specifically targeted at the natural enemy (e.g., Potter & Bertin, 1988; Lanza, 1991; Gurr et al., 1999). Such work, particularly when combined with investigations that examine the effect particular constituents, or combinations thereof, have on key life-history variables of both natural enemies and pests (e.g., longevity; Wäckers, 2001), can identify those food sources that should be excluded due to their positive effect on the pests (see below). 4. Evaluating the attraction of supplementary foods to natural enemies. Olfactometry and other behavioural assay techniques (Sect. 1 .5.2) can be used to establish and compare the attractiveness of flowers. They can also help in maximising the attractiveness of artificial honeydew mixtures (for protocols, see van Emden & Hagen, 1976; Dean & Satasook, 1983; McEwen et al., 1993). It is important that the state-dependence of behaviour be considered in the experimental

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design (e.g., Lewis & Takasu, 1990; Wäckers, 1994; Hickman et al., 2001, sect. 1.3). For example, recently fed natural enemies are not likely to be strongly attracted to food sources, nor are females that have a high egg load (Jervis et al., 1996). Knowledge of the attractiveness of a food, of the nature of the stimuli involved, and of the dispersal capabilities of natural enemies, can shed useful light on: (1) whether immigration can be significantly enhanced by food provision; a principal aim of manipulation is that attraction, either acting alone or in combination with arrestment will result in withincrop aggregation at the population level (Sect. 1.15); (2) the potentially most effective spatial arrangements (e.g., different intercropping regimes, see above) of the supplemental/substitute food resources relative to crop plantings. Synthetic herbivoreinduced plant volatiles (HIPVs) combined with floral resources have been tested to increase the recruitment of natural enemies and feed them. This combined application, named ‘attract and reward’, has shown variable results because, although both of these approaches offer potential to enhance biological control separately, their synergistic effect its unclear (Simpson et al., 2011; Kaplan, 2012; Gordon et al., 2013). 5. Evaluating the effect of supplementary foods on the life-history variables in such a way as to increase the host or prey death rate and, if a consideration, the natural enemy’s rate of increase. This includes measuring the effects of supplemental feeding on reproduction, growth, development, survival and fecundity, and also sex ratio. Protocols, some of which can also involve measurement of functional responses (see 10, below) can be found in Chap. 2. Ideally, both lifetime realised fecundity and longevity should be measured (see 7, below), involving at least the range of host or prey densities that characterise field conditions, and with nonhost or non-prey foods present and absent. If the use of a range of densities is not possible, predators and female parasitoids should be

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given a superabundance of hosts (this also applies to measurements of maximum attack rates). Researchers concerned only with longevity should consider the likelihood that for some, if not most, parasitoids (even including pro-ovigenic species) oviposition incurs a life-span cost (Jervis et al., 2001), and that host-deprived females of some synovigenic species may obtain nutrients for somatic maintenance by resorbing eggs. Longevity estimates made for host-deprived females may therefore be strongly biased and poorly indicative of the field situation. Lastly, life-span needs also to be considered in relation to mortality factors unrelated to nutrition. For example, if predation of adult parasitoids is so strong that life-span is reduced to starvation levels in the field (Heimpel et al., 1997b; Rosenheim, 1998), food provision will have little impact on lifespan in the field (Heimpel & Jervis, 2005; Heimpel & Casas, 2008). Increased longevity of natural enemies can be important during periods when host or prey resources are absent, for example, during the period following harvesting and prior to reseeding or the planting of the crop. For some predator and parasitoid species, supplemental foods may serve only as ‘stop-gap’ resources (Gurr et al., 2003), enabling survival (and a high search rate) over a short time period, whereas for others they may permit a substantial amount of development, reproduction and survival to occur in the absence or scarcity of prey (e.g., Smith, 1961, 1965; Kiman & Yeargan, 1985; Cottrell & Yeargan, 1998; Crum et al., 1998; van Rijn & Tanigoshi, 1999; Wäckers, 2005; Beltrá et al., 2014). 6. Demonstrating that the natural enemies feed on the selected materials under field conditions. Whilst a supplemental food source may have been shown to be attractive in the laboratory, additional experiments will need to be conducted to establish that food location is not seriously constrained, under field conditions, as a result of interference from factors associated with other vegetation,

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including the pest-bearing crop. Presence of odours may disrupt olfactory responses (e.g., Shahjahan & Streams, 1973). Moreover, the natural enemy may not be amenable to manipulation if individuals are incapable of, or are otherwise poor at, travelling to the food source and in moving from the food source to the pests within the crop. This may apply particularly to small-bodied, apterous or brachypterous species. The ability of natural enemies to feed on the provided foods can be determined by direct observation (Sect. 8.2.2) and/or by the use of dissection, biochemical techniques and food labelling (Sect. 8.2.3). In the case of biochemical techniques, it can be difficult to determine whether natural enemies are feeding on the provided alternative food source or other food sources available in the field (Lee et al., 2006). Laboratory behavioural tests alone are insufficient also because of the presence of competitors in the field (Lee & Heimpel, 2003; Lee & Calabuig et al., 2015), as well as extremes of temperature and humidity that may affect the suitability of the food source (Winkler et al., 2009). For example, honeydew and artificial sugars can crystalise under warm and dry conditions (Wäckers, 2000; Wade et al., 2008). Researchers should also take into consideration that the requirements may vary among seasons, as has been demonstrated for mymarid and aphelinid parasitoids (Segoli & Rosenheim, 2013; Tena et al., 2013b). Therefore, the nutritional status should be measured at different periods, especially during those when nectar and honeydew may be scarce or not accessible (Tena et al., 2016). 7. Demonstrating that parasitoid fitness increases in the field. Even if a supplemental food source is shown to be effective in promoting development, reproduction and survival in the laboratory, and it is shown that it is used in the field, it does not necessarily follow that parasitoid fitness will be increased in the field. Confounding factors include rainfall, extremes of temperature and humidity, and bacterial or fungal

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contamination (e.g., Sikorowski et al., 1992). In pro-ovigenic parasitoids, an increase in realised fecundity can be measured by counting the number of eggs in females collected in the field soon after they died naturally and comparing with the number of eggs of newly emerged females (Segoli & Rosenheim, 2013). In synovigenic parasitoids, two numbers are needed: the realised fecundity per day, which can be measured by counting the number of eggs inside parasitoids that were caught at the beginning or at the end of the day, and the number of eggs that they mature per day, which can be measured by maintaining female parasitoids under controlled conditions (Casas et al., 2000; Lee & Heimpel, 2008; Dieckhoff et al., 2014; Tena et al., 2015). Measuring longevity in the field is likely to be more challenging. For parasitoids, wing wear indices using counts of broken setae on the fringe of the forewing as an estimation of longevity can used for some species (Heimpel et al., 1996; Lee & Heimpel, 2008; Miksanek & Heimpel, 2020). 8. Demonstrating that, within the current pest generation, the supplemental food does in fact increase the densities of natural enemy within the crop, whether by attraction, by arrestment, or by both processes (see Chap. 6 for protocols on estimating parasitoid and predator densities). Note that a high degree of attractiveness of a food source does not necessarily translate into increased natural enemy densities (e.g., Bugg et al., 1987) and that an increase of natural enemy densities can also be the result of an increase of their longevity (Tylianakis et al., 2004; Tena et al., 2015). Arrestment will occur by virtue of the natural enemies visiting, probing, and feeding at food sources. However, there may be additional arrestment effects, acting through searching movements. Artificial honeydews are likely to act as arrestants in the same way as natural ones, resulting in alterations in search paths (and increasing the rate of egg deposition) (Carter & Dixon, 1984; Ayal,

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pollen-feeding: the negative effect of a 1987; Budenberg, 1990; van den Meiracker reduction in the predator’s functional et al., 1990; McEwen et al., 1996). Thus, response, and ‘positive’ effect of an accelwhen using artificial honeydews, consideraerated pest population growth rate. tion needs to be given to the possibility that a For supplementation to have a useful effect powerful formulation, in arrestment terms, or a weak spatial association between spray in the vicinity of the floral strip or other deposits and host patches, may confound the manipulation, natural-enemy numerical responses or fitness increases must be reamanipulation by constraining natural enemy searching efficiency. Using paint marking, lised locally. However, some parasitoids may coupled with intra-strip counts of adults and engage in medium- or long-range dispersal measurements of dispersal rates from strips, upon obtaining a sugar meal. Under the MacLeod (1999) was able to assess the sugar-fuelled dispersal hypothesis, paraeffectiveness of field-margin vegetational sitoids use sugar meals to enable strategies in strips of different floral richness in retaining which offspring are dispersed among widely adults of a beneficial hoverfly. separated host patches. Potential selective 9. Establishing the potential for supplemental forces that would favour sugar-fuelled disfood to increase the natural enemy’s persal include the avoidance of selfnumerical response. Recruitment into the superparasitism, density-dependent hypersubsequent generation of the natural-enemy parasitism or inbreeding, among other factors population may be a key consideration in (Heimpel, 2019). While such strategies may those manipulation programmes in which the reduce the local effects of sugar supplemenpest is present in the crop for longer than the tation, they presumably still increase the fitduration of one parasitoid or predator ness of parasitoids and therefore parasitism generation. rates at a broader spatial scale. The numerical response depends largely on 10. Demonstrating that food provision has the potential to affect the prey or host death rate. three components: development rate, larval A parasitoid’s functional response to host survival, and the realised fecundity of density will vary with female fecundity and females, all of which can be positively egg maturation rate (Chap. 1). Non-host food influenced by food provision (see 7, above). provision may alter the shape of the funcIn anautogenous natural enemies (adult females must feed on a host or prey before tional response curve in parasitoids, raising maturing and laying eggs) the numerical parasitism in cases where age-specific response can also be positively affected due potential fecundity (egg load) is limited by to the lowering of the threshold prey density external nutrient availability. The parasitism at which eggs can be laid. Glasshouse and inflicted by each individual female parasitoid cage experiments can provide valuable on a host population over her lifetime will be information on the effects of supplemental influenced by variation in age-specific fefood on the numerical response, as the concundity and by life-span; both of these lifefounding effects of immigration and emihistory variables will alter with the availgration can be ruled out. Using a glasshouse ability and the quality of energy-rich foods. experimental set-up, van Rijn (2002) showed Lifetime functional response experiments are that pollen provision can greatly improve the therefore recommended (for protocols, see control of thrips by predatory mites (in this Bellows, 1985; Sahragard et al., 1991, and case, despite the fact that the pollen was fed Chap. 1). In predators, supplemental food upon by the prey as well as the predator). provision might result in a decreased per Improved control results because the predacapita predation rate because the suppletors’ numerical response to pollen and thrips mental food enables insects to meet their density outweighs the two other effects of nutrient requirements for growth and

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development, egg production and survival. Such reduction of predation has been observed in different families of predators under laboratory conditions (Lundgren, 2009, provides examples on lacewings, phytoseiid mites, anthocorids and coccinellids). This potential negative effect may be offset by higher levels of fitness among predators that eat a diversified diet (Rijn, 2002). 11. Demonstrating that food provision brings about a significant reduction in pest densities, e.g., to a level below an economic spray threshold (Tena et al., 2015). 12. Finally, when undertaking the ranking of potential food sources, consideration needs to be given to ease of cultivation and the potential for undesirable side-effects. The latter include an unacceptable level of invasiveness (i.e., weed status needs to be considered), positive effects on the pest (adults of lepidopteran pests, for example, may feed at, and benefit reproductively from the flowers; Baggen & Gurr, 1998; Baggen et al., 1999; Romeis & Wäckers, 2000; Wäckers et al., 2007), and positive effects on hyperparasitoids or other higher-level predators associated with the natural enemy that is to be manipulated (Stephens et al., 1998; Goelen et al., 2018; Tougeron & Tena, 2019). An additional consideration in ranking food-providing plants is the potential for interactions with other visitors (such as bees and ants) arising from interference or resource exploitation (Lee & Heimpel, 2003; Calabuig et al., 2015).

8.7

Natural Enemies as Pollinators and the Use of Flower Visitation Webs

When visiting flowers to feed, aphidophagous hoverflies become contaminated with pollen over their body surfaces (Stelleman & Meeuse, 1976; Doyle et al., 2020). Although the amount of pollen carried by a fly in this way is probably small compared with that carried by non-

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predatory and hairier relatives such as Eristalis tenax, ‘the common drone fly’ (a species of hoverfly) (Holloway, 1976), it has been demonstrated that hoverflies can increase pollination and fruit production in sweet pepper (Pekas et al., 2020; Moerkens et al., 2021). Due to the flower constancy of individuals, even generalist hoverfly species may be effective pollinators (McGuire & Armbruster, 1991; Inouye et al., 2015; Rader et al., 2016). The same can be said for parasitoid flies, particularly tachinids (Al-Dobai et al., 2012; Wiesenborn, 2015; Glinos et al., 2019; Skaldina, 2020; Martel et al., 2021). Parasitoid wasps, particularly those visiting flowers with narrow tubular corollas, are likely to come into accidental contact with the anthers (if not actually feeding on the pollen, Jervis et al., 1993; Gilbert & Jervis, 1998; Jervis, 1998) and so pick up pollen grains. Whether parasitoid wasps are significant pollinators will depend on (1) them visiting more than one individual plant during a foraging bout; (2) whether they show some degree of flower constancy, and (3) whether the pollen-bearing part of the body surface comes into contact with stigmas. These are aspects of parasitoid wasp behaviour about which we know very little. Most knowledge pertains to wasps having an association with orchids, involving pseudo-copulation by the males: certain Tiphiidae, Scoliidae and pimpline lchneumonidae visit the orchid inflorescence solely for the purpose of mating, the orchids providing no food (e.g., van der Pijl & Dodson, 1966; Stoutamire, 1974; Kullenberg & Bergstrom, 1975). It is interesting to note that the book British Flora, published by Clapham et al. (1989), does not mention parasitoids specifically as flower-visitors. The role of parasitoids in pollination is an area that clearly has been neglected, perhaps due to: 1. The very small size and relative inconspicuousness of many species; 2. The tendency of some of the larger parasitoids not to linger at inflorescences in the manner of some bees, butterflies, beetles and hoverflies (Hassan, 1967; Jervis et al., 1993); 3. The often immense difficulties associated with their identification.

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It is possible to demonstrate that an insect is capable of cross-pollinating, by depositing dyed, or otherwise labelled, pollen grains on inflorescences in anthesis, and determining whether these identifiable grains appear on the stigmas of other inflorescences. Stelleman and Meeuse (1976) used this approach and established that the aphidophagous hoverflies Melanostoma and Platycheirus do transfer pollen between spikes of Plantago lanceolata. Pollination efficiency of insects can be measured according to the protocols given in Dafni (1992) and Kearns and Inouye (1995). Memmott (1999), a pioneer in the construction of quantitative parasitoid‒host food webs (Sect. 6.3.12), applied similar methodology to flower-visitors to construct a quantitative flower visitation web for 26 species of flowering plant and 79 species of insects, among which were several aphidophagous hoverflies. By incorporating data on pollination efficiency, visitation webs can be developed into pollinator webs (e.g., Traveset et al., 2015). The latter could be used to identify which insect natural enemies are useful as pollinators as well as biological control agents. The information gained may be useful both in agriculture (e.g., maximising seed and fruit yields) and in plant conservation (e.g., habitat restoration) (Memmott, 1999). Acknowledgements We are most grateful for the constructive comments and advice of Francis Gilbert, Rob Paxton, Robert Belshaw, Charlie Ellington, Janice Hickman, Flick Rothery and Steve Wratten on the previous edition of this chapter. We thank Ian Hardy and Eric Wajnberg for comments on the current version. Jonathan Dregni provided assistance with the references.

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9

Statistical Approaches Ian C. W. Hardy and Daniel R. Smith

9.1

Introduction

Entomologists often want to test hypotheses (Popper, 1968) or quantify the magnitude of effects, and this can be accomplished using statistical models. Statistics is the study of variation. It is not surprising that entomologists employ statistical methods to analyse their data, since in biological systems variation is the rule. This chapter uses words, diagrams and just a few very basic mathematical formulae to explain how statistical models work. We first provide an overview of foundation material that underpins the statistical framework that most scientists use (known as the frequentist approach; the alternative being the Bayesian approach which we do not discuss here; see Wilkinson et al., 2016, and Aguirre et al., 2021, for natural-enemy examples and Fornacon-Wood et al. (2022) for an entry into the fundamental differences between

I. C. W. Hardy (&) Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland e-mail: ian.hardy@helsinki.fi D. R. Smith Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden e-mail: [email protected]

these paradigms). We focus on parametric statistical models. The most important message is that these models form a family of closely interrelated models which all work conceptually in the same way. The family is called generalised linear models (GLMs, sometimes also called GLZs, to distinguish them from the sub-family of general linear models) and can be used to test hypotheses, quantify the magnitude, direction and precision of effects, and make predictions. We note that it would take a large book, rather than a single chapter, to cover in depth and detail the range of statistical approaches that are available for, and relevant to, research on insects as natural enemies, and we have not attempted that here. This chapter is pitched at an introductory level, assuming the reader to have little prior statistical knowledge, but also with the aim of equipping the reader to be sufficiently conversant with relatively advanced analytical approaches to be able to then go on to acquire further statistical skills. We also use schematic figures as illustrations that are intended to assist understanding rather than to represent accurately the formats of figures that would appear in research reports. Further, we have not written this chapter only for those studying natural-enemy biology: our text is largely ‘taxon free’ and the key illustrations use imaginary, non-entomological, examples. Nonetheless, the background to this chapter is firmly rooted in analyses of data on parasitoids and other natural enemies and the approaches and examples we discuss should be readily applicable

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2_9

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to these study systems. We do give references to examples from natural-enemy studies but without attempting to be exhaustive.

9.2

Foundations of Statistics

9.2.1 Descriptive and Inferential Statistics The foundations of modern statistical theory were built in the twentieth century, largely from the works of the English statistician Sir Ronald A. Fisher (Hald, 1998). Though diverse in its scope, statistics can perhaps be summarised by just three words: ‘descriptions’, ‘differences’ and ‘associations’. Descriptive statistics, e.g., the mean (a measure of central tendency, or location) and standard deviation (a measure of spread), are estimates of population parameters (fixed and usually unknown quantities) important for summarising raw data. Raw data are a collection of numbers (or possibly non-numerical responses) from an experiment or observational study that have not been transformed in any way. Describing properties of data sets is very useful and it is these descriptive statistics that are commonly displayed in tables and figures in scientific publications. Often, however, we want to increase our understanding of patterns in our sample data by asking questions such as: ‘is A different from B?’; or ‘are changes in C associated with changes in D?’ Assessing whether there are differences or associations is performed using inferential methods, which form the focus of this chapter. By using probability theory, we can make inferences about the population using samples at our disposal.

9.2.2 Hypothesis Testing Statistical tests, either directly or indirectly, involve the formulation of hypotheses, which are tested and either retained or rejected. Under the null hypothesis (H0), we initially regard any observed difference or association as being due to chance variation. For instance, in an experiment exploring the potential effect of different diet

treatments on the subsequent adult size of developing insects, H0 might be stated as: There is no underlying effect of treatment on adult size. In contrast, the alternative hypothesis (H1) implies any difference or association is due to a real effect, i.e., there is an effect of treatment on the size of insects. Hypothesis testing is fundamentally about finding the most parsimonious (simplest adequate) description of our observations. The decision is usually made by comparing the value of a test statistic with some pre-determined critical value for a given significance level (Sect. 9.2.3) and degrees of freedom (df). The df of a statistical test is usually the sample size (n) minus the number of parameters estimated from the data. If the test statistic is larger than the critical value, then we can conclude the difference or association is ‘statistically significant’. Conversely, if the test statistic is smaller than, or equal to the critical value, we conclude there is no statistical significance (NS). Not too long ago, critical values had to be looked up manually in published tables (a whole book of such tables is provided by Rohlf & Sokal, 1995), and P-values would be reported as less than a given value, e.g., 0.05), we would not add this interaction term back to our model and would focus next on the second-order interactions. We would select one of the second-order interactions (e.g., the one which looks the least likely to be significant on the basis of the parameter estimates of the current model), and formally test its significance by dropping it from the model. If P ≤ 0.05, we would add it back into the model or, if P > 0.05, we would leave it out of the model. After all the second-order interactions have been dropped and tested, we can then go to the first-order interactions and repeat the process until we reach the main effects. Main effects are tested in the same way as above, i.e., dropped one by one from the model

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Fig. 9.20 Main effects and interactions (denoted by *) of the first, second and third order for four explanatory variables x1, x2, x3 and x4. The most complex possible maximal model is the model containing all variables and all possible interactions, but note that we may choose to adopt a simpler maximal model, such as one containing only main effects and firstorder interactions

x1*x2*x3*x4 Third order interactions x1*x2*x3 x1*x2*x4 Second order interactions x2*x3*x4 x3*x4*x1 x1*x2 x1*x3 x1*x4 First order interactions x2*x3 x2*x4 x3*x4 x1 x2 Main effects x3 x4

Table 9.4 Model simplification: terminology and steps involved in top-down analyses (after Crawley, 1993). A ‘saturated (full) model’ has perfect fit; zero deviance, zero degrees of freedom and one parameter for each observation (in some model types, deviance, Sect. 9.7.1, is the same as sums of squares, Sect. 9.3.1). A ‘maximal model’ contains all factors, interaction terms and covariates under consideration. A ‘current model’ contains a number of parameters less than or equal to the maximal and greater than or equal to the minimal model. A ‘minimal model’ contains the minimal number of terms to adequately describe patterns in the data, in which all parameters are significantly different from zero, and no important terms have been omitted. A ‘null model’ contains only one parameter, the grand mean, and the deviance is equal to the total sum of squares in Gaussian models. The following sequence of steps in stepwise model simplification provides a general guide Step

Procedure

Explanation

1

Fit the maximal model

Fit all the factors, interactions and covariates of interest. Note the residual deviance (or SS) If you are using Poisson or binomial errors, check for overdispersion and rescale if necessary

2

Begin model simplification

Inspect the parameter estimates Remove the least significant terms first, starting with the highestorder interactions, progressing on to lower-order interaction terms and then main effects

3

If the deletion causes an insignificant increase in deviance

Leave that term out of the model Inspect the parameter values again Remove the least significant term remaining

4

If the deletion causes a significant increase in deviance

Put the term back in the model These are the statistically significant terms as assessed by deletion from the maximal model

5

Keep removing terms from the model

Repeat steps 3 or 4 until the model contains nothing but significant terms This is the minimal adequate model If none of the parameters are significant, then the minimal adequate model is the null model

9

Statistical Approaches

to assess the significance of the change, and not replaced in the model if P > 0.05. At the end of this process we are then left with the minimal adequate model, which is a parsimonious description of relationships in the data and the major goal of the analysis (Table 9.4). An alternative approach is to build the model in a forwards bottom-up manner. As one might expect, this approach starts by testing the significance of adding a series of individual main effects into the model (immediately removing them if they are not significant) and then proceeding to add first-, then second-, then higherorder interactions. One drawback of the bottomup approach is that a variable may not be significant on its own but might contribute significantly via higher-order interactions. A further drawback is that there is no formal means of deciding which order to enter terms into the model (in contrast to the top-down analysis where the decision can be based on the parameter estimates in a current model). While we advise using the top-down rather than bottom-up approach where possible, we do note that there is concern in the statistical literature that backwards elimination procedures are not always able to select the most influential variables from an initial set. For instance, dropping a term during a top-down procedure might result in a previously dropped and excluded factor becoming significant, or a previously dropped and re-included term becoming nonsignificant. One procedure to deal with this would be to re-restart the analysis each time a term is dropped or added in a combined topdown and bottom-up manner (Wajnberg et al., 1999).

9.6.2 Beyond Forwards or Backwards Procedures The decision to include or exclude terms in backwards or forwards stepwise procedures is based on some measure of the additional change in residual variance accounted for by the relevant

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variable (Roff, 2006), and the procedures outlined above effectively involve a series of nullhypothesis significance tests (e.g., F ratio tests). Stepwise methods should be used with caution. In particular, treating confidence intervals and Pvalues at the end of an automated selection algorithm in the same way as those derived from a pre-specified model is invalid. Some software packages allow estimates from such stepwise methods to be ‘corrected’ using bootstrap routines (e.g., in the R package ‘rms’). An alternative approach is to use Akaike’s information criterion (AIC) which adjusts (penalises) the deviance (Sect. 9.7.1) for a given model by the number of predictor variables modelled (Quinn & Keough, 2002; Holmes et al., 2023). A low AIC implies better model fit and if a number of models have similar AIC values the model with the fewest terms should be selected (Quinn & Keough, 2002). Note, however, that employing AIC to choose a parsimonious model does not in itself involve tests of null hypotheses. While this may take some getting used to for many biologists, there are compelling arguments for reducing the current emphasis on null-hypothesis significance testing (Nakagawa & Cuthill, 2007; Halsey, 2019, Sect. 9.8.1).

9.6.3 Differences Between Factor Levels We have so far looked at detecting significant differences in means between multiple groups or factor levels (ANOVA, ANCOVA, factorial ANOVA), but this alone will not inform us of where the significance actually lies. Supposing we perform ANOVA for three levels A, B and C of a single categorical explanatory variable (i.e., a factor). If we obtain P < 0.05, we know there is a significant difference between means of at least two levels of the factor, but we do not know which ones. In fact, the model is assessing whether each sample originated from a population with the same mean. It could be because A is different from B and/or C, or B is different from

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C or because all three levels are different from each other. One possibility would simply be to perform three separate ANOVA tests, though this is unwise. Recall that every time we carry out a statistical hypothesis test we are accepting a 5% probability of Type I error (Sect. 9.2.4), so the more tests we perform the greater the likelihood of obtaining a falsely significant result (McDonald, 2014, provides clear guidance on multiple hypothesis testing and how to correct for it). One approach is to aggregate factor levels. Thus, in our above example we could aggregate A and B into a new, common, level ‘AB’ and test the significance of the change using an F test. Note that C is present in both versions of the model so the test using aggregation is not quite the same as a separate test of the difference between A and B. If P > 0.05 we would retain the aggregation; this is essentially saying that there is no significant difference between A and B and we are better served by using the mean of A and B combined. Conversely, if P ≤ 0.05, this implies a significant change in the model and so we would discard the aggregation and return to using our original groups A and B (i.e., there is a significant difference between A and B). Aggregation of factor levels can also be used in situations where other continuous (ANCOVA) or categorical (factorial ANOVA) explanatory variables have a significant interaction with the categorical variable for which factor levels are being aggregated. An appealing aspect of aggregating factor levels is that the procedure remains within the framework of model simplification we have already seen (Sect. 9.6.1). However, there are other approaches to comparing factor levels that are commonly used. One simple way to test for significant differences between means of different factor levels is, for example, to use Tukey’s honestly significant difference (HSD) test, which compares each group mean with every other group mean in a pair-wise manner and controls the family-wise Type I error rate to no more than the nominal level (e.g., 0.05) (Quinn & Keough, 2002).

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9.7

Generalised Linear Models

We have now built up an understanding of standard parametric methods, i.e., regression, multiple regression, ANOVA, factorial ANOVA and ANCOVA: these are collectively known as general linear models (e.g., Grafen & Hails, 2002) which all use the same set of assumptions, including that the errors are normally distributed (Box 9.1). However, the error distribution for some types of data is not likely to be normally distributed and thus the application of standard parametric methods for normally distributed data is no longer appropriate and likely to give misleading (incorrect) answers. The traditional approach to non-normality is to transform the sample data prior to standard analysis in an attempt to ‘normalise’ it (i.e., make the data conform to the assumption). Rather than forcing the data to fit the assumption of normality, the modern approach is to adopt initial assumptions that are more likely to fit the data. The assumption of a normal distribution can be replaced by an assumption of another exponential-family distribution, i.e., the Poisson, binomial, negative binomial, Weibull, or gamma distributions (Sect. 9.2.5). This wider family of methods, which includes the sub-family of general linear models, is known as generalised linear models, or GLMs (sometimes called GLZs). Responses are modelled by inclusion of a ‘link function’ to connect the mean of the response variable to the linear combination of parameters estimated. Here we discuss GLMs that adopt the initial assumption that errors conform to either a Poisson distribution (e.g., log-linear models) or a binomial distribution (e.g., logistic regression). Other assumptions are the same as for general linear models (Box 9.1).

9.7.1 Log-Linear Models: Count Data The response variable in many studies of naturalenemy biology will be gathered as counts, i.e., positive integers, with a lower bound of zero and

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Statistical Approaches

no upper bound. For instance, the number of eggs in a stink bug egg mass (Abram et al., 2023) or in a parasitoid’s clutch (Goubault et al., 2007) or a parasitoid’s lifetime fecundity (Zaviezo & Mills, 2000). When data consist of counts it is a reasonable starting point to assume that the errors are Poisson distributed. With counts of ‘largevalued’ numbers (e.g., 50, 82, 670) the mean will be large and the Poisson distribution tends to a normal distribution, but with counts of small value (e.g., between 1 and 6, and also with counts of zero), the Poisson distribution is quite different in form (Fig. 9.1). To deal with non-normality the data could be, as mentioned above, transformed, e.g., by logtransformation prior to employing an analysis that assumes normally distributed errors, although such transformation does not necessarily achieve normality, especially when there are many (say, 20‒30) observations of zero in the data set, and thus this approach is no longer generally advised (O’Hara & Kotze, 2010). A better alternative is to use an analysis that initially assumes that the errors are Poisson distributed (we revisit this initial assumption in Sect. 9.7.3). Such analyses are known as Poisson or log-linear models and there are log-linear versions of each of the general linear models we have previously discussed. Log-linear analyses work in essentially the same way as those standard analyses but, when assuming Poissondistributed errors, estimates of parameters are performed using maximum likelihood (see below). Of the possible test statistics, so-called likelihood ratio tests are v2-distributed under the H0 (see below). We illustrate the approach using another nonentomological and imaginary example: a set of data on the numbers of fish caught (response variable) in locations with different amounts of available food (explanatory variable). Figure 9.21a depicts a standard regression analysis of such data, along with the naïve assumption of a normal distribution of errors at various points along the regression line. It can easily be seen that this approach can predict the possibility of negative value observations, which of course is nonsense: we cannot catch a negative number of

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Fig. 9.21 Why the assumption of Poisson-distributed errors is appropriate for the analysis of count data. a A standard regression of Poisson-distributed data assumes impossible negative values and, incorrectly, constant variance. b These problems are solved using a log-linear regression: impossible negative data values are not assumed and the variance is correctly assumed to increase with the mean

fish. Explicitly, note that the tail of the normal distribution extends below zero within the range of food abundances and also that extrapolation of the regression line to the left of the data (which we might do if we wanted to know how many fish we might catch when food was even less abundant than in the most spartan of our sampling localities) would predict negative count values. (We, however, in passing, generally advise against extrapolation due to its likelihood of inaccuracy.) Standard methods also assume that

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the variance does not increase with the mean, while with count data it does. A potential solution to these problems is to use a log-linear regression that assumes Poisson-distributed errors (Fig. 9.21b). The Poisson distribution is a oneparameter distribution, specified by the population mean. In fact, in the Poisson distribution, the population mean equals the variance, therefore: variance ¼1 mean

ð9:4Þ

The regression equation for log-linear models is a simple modification of that for the straight line (Eq. 9.2): log(y) = mx + c, such that the y value is now predicted by the antilogarithm of mx + c: y ¼ antilogðmx þ cÞ

ð9:5Þ

which can also be expressed as: y ¼ eðmx þ cÞ

ð9:6Þ

Because the Poisson distribution lacks one tail at low mean values, impossible negative counts are never assumed. Neither are they predicted from extrapolation, as the regression line can never fall below zero (as the antilog of x is always positive or zero). Unlike general linear models that use the least-squares (LS) technique for finding the best fit line, log-linear (and logistic models, Sect. 9.7.2) use maximum likelihood estimation (MLE). In non-technical language, this means that given the sample data and a particular model, the estimates of the parameters (i.e., slopes and the intercepts) are those that would make the observed data most likely. (Note that with normally distributed data, LS and ML are the same.) When carrying out log-linear analyses assuming Poisson-distributed errors, significance is assessed using change in deviance based on MLE. Deviance has different mathematical definitions under different error structures: for normally distributed data, SS can also be called deviance but, with Poisson errors, deviances are not the same as sums of squares. The significance of a change in deviance ratio, G, is assessed

using the v2 distribution at the appropriate degrees of freedom (rather than F ratio tests, Sect. 9.4.3, but note that G is asymptotically v2distributed).

9.7.2 Logistic Analyses: Binary Data The raw data in many studies of natural-enemy biology will be binary. Such data may give rise to proportions that range from zero to one. For instance, the proportion of eggs in a stink bug egg mass that were parasitised (Tillman et al., 2020; Cornelius et al., 2021) or the proportion of male parasitoids (i.e., the sex ratio) among the emerging progeny (Stahl et al., 2018; Holmes et al., 2023; Liu et al. 2023). For clarity, a proportion is calculated as the number of times something of interest did happen divided by the total number of times if might have happened, with the former (the numerator) thus never exceeding the latter (the denominator). When binary events give rise to proportions, it is a reasonable starting point to assume that errors will follow a binomial distribution (we revisit this initial assumption in Sect. 9.7.3). A traditional method to deal with binomially distributed errors is to transform the data prior to analysis by angular transformation (also known as arcsine square root transformation) but such transformation does not necessarily achieve normality, especially when many observations in the data set are of extreme proportions. Another problem is that this transformation discards information on the size of each data sample: an observation of two eggs parasitised out of a clutch of 10 stinkbug eggs will be given the same weight in the analysis as an observation of 20 eggs parasitised in a clutch of 100: although the two estimates of egg parasitism in this instance give the same value the latter is by definition a better estimate of egg parasitism rates in the underlying population because it is based on more information. A better alternative is to use an analysis that initially assumes that the errors are binomially, rather than normally, distributed (Crawley, 1993; Wilson & Hardy, 2002; Briffa et al., 2013). Such

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analyses are known as logistic regressions and there are logistic versions of the general linear models we have met so far. These work in essentially the same way as we have seen and, as with log-linear models (Sect. 9.7.1), the test of significance uses G, which is asymptotically v2distributed under H0. The logistic regression can be described by: y¼

1 1þ

1 eðmx þ cÞ

ð9:7Þ

Again, this is called a logistic regression, because the link function is the logit function logð p=ð1 - pÞÞ, where p is the probability that y=1. At first glance this equation may seem rather complicated but is in fact just a modification of our original equation describing a straight regression line (Eq. 9.2). As with Eqs. 9.5 and 9.6 we have simply added a statement that the y values (the proportional response we are interested in understanding variation in) are predicted by a back-transformation of mx þ c. In the logistic case this transformation is the antilogit, the ‘1/[1 + {1/(exp(x))}]’ part, rather than the antilog which was used in Eq. 9.5. When the resulting regression line is plotted it can never fall below zero (just like with log-linear models) but can also not rise above 1. This makes sense because proportions must always take values between 0.0 and 1.0. Binary data can be of two types: ungrouped or grouped. Ungrouped indicates that each output value, i.e., 0 or 1, comes from a separate single event (often called a ‘trial’). Because there is just one event (denominator = 1) the resulting proportion must be either 1.0 (1/1) or 0.0 (0/1). Grouped binary data refers to cases where a number of events can happen simultaneously (denominator >1) and therefore output can take a proportion between 0 and 1. Ungrouped data is simply a special case of grouped data and is technically called a Bernoulli trial. We, again, use a non-entomological and imaginary example to illustrate the approach for ungrouped binary data. Suppose we wanted to know whether the probability of individual

school children being able to clear a 1.5 m highjump is dependent on their leg length. For each child attempting the jump, we have measures of their leg length (a continuous variable) and whether or not they managed the jump, which is the binary response variable (0 = no, 1 = yes). Each child only had one attempt (Bernoulli trial) and there were 16 children. (We are using a relatively small imaginary sample size to ease illustration but note that the minimum recommended sample size for estimating an intercept in binary logistic regression is 96; Harrell, 2015.) We can carry out a logistic regression and obtain a graph like Fig. 9.22a. This shows that children with longer legs have a higher probability of successfully clearing the jump; the probability of success for the shortest-legged children is effectively zero whilst the longestlegged children almost always jump successfully, and there is a fairly steep transition in success as leg length increases beyond intermediate measures. We could imagine dozens of other common scenarios to which we could apply such logistic analyses. For instance, whether the probability of individual parasitoids winning fights is dependent on their weight (dis)advantage compared to their opponents (Hardy & Field, 1998; Briffa et al., 2013; Hardy et al., 2013; Guerra-Grenier et al., 2020; Chap. 1), whether the probability of an emerging solitary parasitoid being male (or female) is associated with the quality (e.g., size) of its host (King, 1993; Wilson & Hardy, 2002; Karsai et al., 2006; Chap. 1), whether a male parasitoid manages to mate with females (Hardy et al., 2000; Mowles et al., 2013; Chap. 4), whether female parasitoids release volatile chemicals in response to contest intensity (Goubault et al., 2006), whether parasitoid species are pro-ovigenic (Ellers & Jervis, 2004) or whether the probability of a caught fish having an empty stomach is dependent on its geographic locality and/or its trophic level (Warton & Hui, 2011). These are all examples of binary responses that can be analysed by logistic regressions (because the explanatory variables are all continuous, such as leg length, contestant weight difference, host size).

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Probability of success

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b Fig. 9.22 Illustrations of outcomes of logistic regression.

a Simple logistic regression of ungrouped binary data. Note how individual outcomes are constrained to be either 0.0 or 1.0 but the estimated probability of success can take any value between and including these extremes. b Logistic one-way ANOVA-type: the height of each bar represents the overall (mean) success of boys or girls. c Logistic ANCOVA-type: combining (a) and (b): note that there is no (significant) interaction term as the two regression lines are shown parallel (they have the same shape and will never cross, even if used to predict success at more extreme values of leg length). d Simple logistic regression of grouped binary data. Here each data point represents the outcome of ten jumps (each by a different female of a given age) and thus the outcomes can take values intermediate to 0.0 or 1.0. We have shown a fitted regression line with the same slope as in other panels of this figure, but of course in a real example the lines would likely be different

We could similarly think of examples of binary responses to categorical explanatory variables which could be explored using the ANOVA-type approach in a logistic model, such as whether schoolgirls with a given leg length are better at clearing the high-jump than schoolboys, or whether fed or starved parasitoids tend to win contests for hosts (Snart et al., 2018). Figure 9.22b depicts the (imaginary) result that girls are a little (but significantly) more successful at the high-jump than are boys. We could, of course, explore simultaneously the effects of both leg length and sex on high-jump success using logistic ‘ANCOVA-type’ modelling: Fig. 9.22c depicts both the effect of leg length on high-jump success (the slope of the fitted regression lines) and the greater success of girls compared to boys of the equivalent leg length (the regression lines have different intercepts, which is most obvious at intermediate values of leg length). Now let us imagine a similar set of grouped binary data. Suppose we took groups of 10 schoolgirls and female university students belonging to a range of age classes (i.e., 10 eightyear-olds, 10 nine-year-olds, etc., with twelve age classes overall, and thus a total sample of 120 females, aged 8–20 years) and asked each participant to attempt the 1.5 m high-jump once. As above we would obtain a binary response (0 or 1) for each individual but this time the individuals belong to age groups and for each age group, as

Statistical Approaches

(a) Probability of success

the denominator = 10 individuals per group, the possible proportions of successful jumpers are 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0. (If we had trialled only 4 jumpers per age group then our range of possible proportional responses would have been 0.0. 0.25, 0.50, 0.75 and 1.00). These data can be analysed using logistic regression in much the same way as the ungrouped data, but now each data point represents the combined performance of 10 individuals. Of course, trialling ten jumpers rather than, say, four, will likely give us a better estimate of jumping success, because it is based on more information. In logistic regressions, estimates from larger groups contribute more information than estimates from smaller groups, thus avoiding the disadvantage of equal weighting of information after angular transformation (see above). Given that success in jumping a fixed-height bar is likely to increase with age (older jumpers will generally have longer legs, among possible other differences) we might expect the resulting graph to look something like Fig. 9.22d: most (but not quite all) young children (left side of the figure) cannot successfully clear the jump and success generally improves with age, such that university students (the age classes to the right of the figure) nearly always jump successfully. As with the ungrouped data, we could carry out more detailed explorations of factors that potentially influence success by gathering data on males as well as females, or on Chinese and Chilean jumpers etc., and thus run logistic versions of ANOVAs, ANCOVAs or multiple regressions, depending on the combination of continuous and discrete explanatory variables included. To conclude this section we explicitly illustrate why standard regression analysis of binary proportional data is inappropriate. Figure 9.23a depicts a standard regression of grouped binary data, along with the assumed normal (Gaussian) distribution of errors at various points along the regression line. Notice that the graph has been drawn inside a box to emphasise the fact that the response variable is strictly bounded (proportions outside the range 0.0–1.0 are impossible). It can easily be seen that the standard regression

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Fig. 9.23 Why the assumption of binomially distributed outcomes is appropriate for the analysis of proportional data. a A standard regression of grouped binary data (from Fig. 9.22d) assumes and predicts impossible proportions: i.e., the distribution of outcomes (bell-shaped histograms) and the fitted regression line (respectively) include proportions of less than 0.0 and greater than 1.0 (note that assuming a normal distribution is most reasonable for data on proportions close to 0.5). b These problems are resolved using a logistic regression as assumed distribution of outcomes, which changes shape according to probability (histograms) and the predicted proportions (regression line) remain within strict bounds

approach assumes that ‘nonsense’ proportions greater than 1 and less than 0 are possible. One tail of the normal distribution protrudes 1 when the proportion of successes is high. The assumption of normality is tolerable when proportions take intermediate values, around 0.5. Additionally, extrapolation of the relationship, which we might do if we wondered what would happen if we trialled girls of even older or even younger ages, also predicts

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impossible proportions. These problems are solved by using a logistic regression that assumes binomially distributed errors (Fig. 9.23b). Because the binomial distribution lacks a lefthand tail at low mean values, impossibly low (1) proportions are not assumed when the mean is high. Extrapolation also brings no such problems since the regression line can never take values 1.

9.7.3 Logisitic and Log-Linear Model Checking As with general linear models, it is common practice to perform model checking (Crawley, 1993) following logistic and log-linear analyses. This is because we have assumed that the errors are perfectly binomially or Poisson distributed (Sects. 9.7.1 and 9.7.2), but they may in fact not be. When analysing ungrouped binary data we are constrained to assume a perfect match to a binomial distribution, but for analyses of grouped binary data and of count data we can probe our initial assumptions and make subsequent adjustments. Most statistical packages output the so-called ‘heterogeneity factor’ (HF), also called the ‘scale parameter’, ‘scaling parameter’ or ‘dispersion parameter’, estimated from the data to assess how well the data conform to a perfect Poisson or binomial distribution. If HF = 1 this indicates that the variance in the data is equal to what you would expect under the assumption of Poisson or binomial errors. HF < 1 indicates underdispersion, whereas HF > 1 indicates overdispersion. Although there are no fixed rules, HF > 1.5 is generally a cause for concern. Overdispersion can indicate that data are somehow more clumped than random. In the case of count data it could be, for instance, that the fish species we have studied (Sect. 9.7.1) swim in shoals rather than as individuals (such that catches tend to being either zero or large numbers) and in the case of proportional data it could be that some groups of schoolgirls have received more high-jump training than others,

I. C. W. Hardy and D. R. Smith

making the binary responses of the girls within a group (Sect. 9.7.2) non-independent of each other. Overdispersion tends to be more common than underdispersion (Bourguignon et al., 2022), but underdispersion could arise in fish-count data if individuals maintained exclusive territories, leading to similar numbers of fish caught by each standard sweep of the net (Sect. 9.7.1). For proportional data we could imagine that the 10 schoolgirls and university students in each age group were chosen non-randomly to represent the full spectrum of abilities within each age group by an egalitarian teacher, leading to lower than binomial variation in abilities across groups. Among natural enemies, evolutionary natural selection favours low variation (underdispersion) in the sexual composition of offspring groups for parasitoids with local mating systems, while developmental mortality of some offspring within those groups can lead to overdispersion (Krackow et al., 2002; Kapranas et al., 2011; Wilkinson et al., 2016). We need to be aware of these issues because overdispersion can lead to Type I errors whereas underdispersion may cause Type II errors (Table 9.1). Both over- and under- dispersion can be dealt with by replacing the assumed value of HF (this will be 1 by default in most statistical software) with the actual value (estimated by the ratio of the residual deviance to the residual df), and re-fitting the model based on the assumption of quasi-Poisson or quasi-binomially (i.e., a Poisson or a binomial distribution with a dispersion parameter added) distributed errors, then testing significance using F-tests) (e.g., Hardy & Cook, 1995; Cusumano et al., 2022; Liu et al., 2023). Note that this process does not change the estimates of the slope and the intercept but their standard errors are increased (for overdispersion) or decreased (for underdispersion), which in turn affects the calculated P-value. Adjusting for overdispersion thus has the effect of making it more difficult to obtain a significant result from the data (and thus reduces Type I error rate) while adjusting for underdispersion makes significance more likely (fewer Type II errors). We can make the analytical process more straightforward by simply using empirically estimated distributions

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Statistical Approaches

straight away, rather than first assuming perfect Poisson or binomial distributions and then adjusting them (e.g., Kapranas et al., 2011; Holmes et al., 2023). This is easily done in statistical packages (e.g., GenStat, GLIM, R) and we recommend doing this before attempting to normalise variables by transformation or resorting to non-parametric analyses (see Bourguignon et al., 2022, for further possibilities). Note, however, that we cannot adjust for under- or overdispersion when analysing ungrouped binary data (Crawley, 1993; Wilson & Hardy, 2002): as mentioned above, we just have to assume that the data are truly binomially distributed. On a somewhat philosophical note, we regard estimating the scale parameter empirically as the

Fig. 9.24 Schematic overview of the generalised linear model (GLM) family of techniques using icons to denote the typical form of graphs resulting from these analyses. The sub-family of ‘standard’ techniques assuming Gaussian (normally distributed) errors, general linear models, forms the left-hand column with the other columns

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next level of analytical sophistication above that of assuming non-normality. This is because it is another step towards making the statistical test fit the way that the data were originally collected rather than making the data fit the assumptions of a test.

9.8

Beyond This Chapter

We have now covered a range of parametric techniques, for which the collective terminology is summarised in Fig. 9.24. You may have skipped ahead to look at this when reading Sect. 9.3: whether or not you did, it will probably repay some contemplation at this stage. We have

representing the sister techniques which adopt different assumptions about the distribution of errors for situations in which these are likely to be non-Gaussian. Note that there are further members of the GLM family that are not included in this representation

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found this visualisation extremely useful, and we have not seen it explicitly given elsewhere. An understanding of the generalised linear model (GLM) family provides a platform for expanding one’s range of statistical abilities to include yet further techniques, and thus the end of this chapter can also be the beginning of much more. These are at least three interrelated reasons for this. Firstly, many of the concepts we have met during the chapter are generic rather than particular to GLMs and this pre-adapts us to learn further statistical methodologies (many of which are to be found in, for example, Harvey & Pagel, 1991, Sokal & Rohlf, 1995, Quinn & Keough, 2002, and Roff, 2006) or understand, at least in essence, analyses presented in papers and reports we need to read, write and evaluate. Secondly, and more directly, there are many additional techniques that can be used within the GLM framework but that we have not covered in any detail here: examples include the use of polynomial terms and splines to model nonlinearities, the range of variations of the ANOVA approach, for instance those that include blocking (e.g., Quinn & Keough, 2002), analysis of data with gamma-distributed errors (e.g., parasitoid development times: Holmes et al., 2023 parasitoid patch residence times: Rohlfs & Hoffmeister, 2004; parasitoid time to mating and mating duration:Charrat et al., 2023), negative binomial errors (e.g., egg parasitism rates: Cornelius et al., 2021) or cohort survival analysis (Crawley, 1993; Zhang, 2016; Chap. 2). We have found the latter to be particularly useful in studies of parasitoid reproductive performance (recent examples include, Amante et al., 2017, Lupi et al., 2017, Snart et al., 2018, Jucker et al., 2020, Abdi et al., 2021, and Malabusini et al., 2022). Thirdly, some new techniques we may meet are further generalisations of GLMs themselves. For instance, the GLMs we have covered allow us to test the effect of one or more explanatory variables on a single response variable. Sometimes, however, we may want to test the effects of one or more explanatory variables on multiple response variables as well as interactions between response variables. Techniques that do this are called multivariate analyses, such as

I. C. W. Hardy and D. R. Smith

multivariate analysis of variance (MANOVA) (Quinn & Keough, 2002). Many statistical packages allow multivariate analyses and the results can be interpreted in much the same way as those for GLMs. Examples of natural-enemy studies that have employed MANOVAs include Khidr et al. (2013), Morrison et al. (2018), Bui et al. (2020), Aspin et al. (2021), and VelascoHernandez et al. (2021). Another technique that is being increasingly used, and which builds on GLMs, is generalised linear mixed modelling (GLMM, e.g., Bolker et al., 2009): this can explore both fixed effects and also random effects on a single response variable. Fixed effects are the type of explanatory variables, and their interactions, that we have covered in this chapter so far. Random effects are variables (e.g., the identity of each organism or population contributing to a set of data) that are not experimentally replicable or fully controllable, due to inter-individual variation or the differing environments in which populations are found (e.g., Krackow & Tkadlec, 2001; Bolker et al., 2009). They can also consider the fact that we sometimes have to handle a set of different levels of a factor that are just a random choice of an infinity of possible levels. For example, we may wish to consider the ‘species effects’ on a binary outcome or on an integer response, but we will likely only have a random sample of species drawn from all possible species that could be studied. For many scientists the most familiar example of a random effect will be blocking, as used to reduce nuisance environmental variation by traditional agricultural experimental designs that employ variants of ANOVA. GLMMs provide an alternative method for dealing with overdispersion due to between-group variation (Sect. 9.7.3, Krackow & Tkadlec, 2001; Wilson & Hardy, 2002; Briffa et al., 2013) as well as modelling non-independent observations. We have used GLMMs in several recent studies of natural-enemy biology (e.g., Abdi et al., 2020; Guo et al., 2022), as have others (e.g., Vanbergen et al., 2007; Do Thi Khanh et al., 2012; Villacañas de Castro & Thiel, 2017; Liu & Hao, 2019; Guerra-Grenier et al., 2020; Cornelius

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Statistical Approaches

et al., 2021; Charrat et al., 2023; Liu et al., 2023) and, philosophically, regard them as the next level of analytical sophistication above GLMs. This is because they neither ignore random effects nor treat them as if they were fixed effects. GLMMS are conditional models that lead to inferences about how individuals respond to a given set of circumstances, taking their nonindependence (random effects) into account. An alternative is to use the generalised estimating equation (GEE) (Liang & Zeger, 1986; Pekár & Brabec, 2018). GEEs are marginal models that take into account non-independent data and lead to inferences on how population averages respond, with no emphasis on the random effects. Natural-enemy studies that have used the GEE approach include Fauvergue et al. (2007), Le Lann et al. (2011), Alvarez-Baca et al. (2020), and Volter et al. (2022).

9.8.1 Hypothesis Testing Revisited We began this chapter with the concept of hypothesis testing, the traditional and still most commonly used statistical approach (Sect. 9.2.2). Let us finish by noting that there is a view that, when considering a body of evidence in the published literature, less attention should be given to the results of null-hypothesis significance testing, which leads to noting that study A concluded X and study B concluded Y (e.g., ‘vote counting’), especially when sample sizes within each study are small. Rather, the focus should be more on what the general trend has become as studies accumulate, which can be achieved by meta-analysis. Meta-analysis is a systematic review of the literature which incorporates the effect sizes and confidence intervals of individual studies to yield an overall summary effect (e.g., Gates, 2002; Stewart, 2010; Yang et al., 2023) and a measure of heterogeneity of effects. The effect size represents the strength of the relationship (or ‘the degree to which the phenomenon is present in the population’; Cohen, 1988; see also Nakagawa & Cuthill, 2007) and can be simply thought of as the ‘steepness of the slope’ (regression) or the ‘extent of the difference’

737

(ANOVA). In meta-analysis, the P-value for the summary effect is substantially more compelling than that of any single study (Borenstein et al., 2009). Meta-analysis is becoming more common and is widely seen as more informative than traditional narrative or vote-counting reviews. Traditional null-hypothesis testing does not include reporting of either the effect size or the precision of the estimate of effect size, and, as such, both are now encouraged when reporting results within individual studies in order to facilitate future meta-analyses (Nakagawa & Cuthill, 2007; Halsey, 2019; Charrat et al., 2023) that will provide overview evaluations of important scientific questions.

9.9

Conclusions

Scientific research, including studies of insects as natural enemies, is fundamentally about testing hypotheses, and, to do that, we need statistical analyses. Inferential statistics allow us to assess sample data sets for significant differences or associations among observed measures and the principle of parsimony often plays a central role. Parametric approaches are powerful and flexible and are very commonly used. General linear models include regression, ANOVA, ANCOVA, factorial ANOVA and multiple regression, all of which assume a normal error distribution. General linear models are a sub-family within the wider family of statistical methods known as generalised linear models (GLMs, sometimes called GLZs) which all work, at least conceptually, in the same way. GLMs include log-linear and logistic alternatives for each of the general linear models. These can be appropriate when errors are non-normally distributed. An understanding of the GLM family provides a solid platform for expanding one’s statistical repertoire yet further to incorporate techniques such as GLMMs and many others. Acknowledgements Statistics is a continually developing field: we managed to reach some sort of agreement for most of the aspects on which we initially seemed to differ with those who helped us develop this chapter: any remaining inaccuracies or misunderstandings may well be

738 ours. We recognise that, in parts, the terminology we use might aggravate some practising statisticians: our aim has been to provide a non-technical introduction. If you happen to find it overly simplistic, bear in mind that colleagues and students who had struggled with statistics have greatly appreciated having access to early drafts: this is for them and for others like them.We are very grateful for the constructively critical comments, on various drafts, of Eric Wajnberg, Richard Wilkinson and Nick Galway. We are also grateful to several further colleagues for numerous interactions over statistical issues: Peter Alderson, Jim Craigon, Martin Gammell, Charlie Hodgman, Chungui Lu and Debbie Sparkes spring particularly to mind as regards the history behind this chapter, which was begun at the Sutton Bonington Campus of the University of Nottingham, UK. The chapter has even deeper roots, as it follows the approach of GLIM for Ecologists by M. J. Crawley (1993). In his book M. J. Crawley thanked his colleague’s neighbour for introducing his colleague to GLIM and, in turn, his colleague for passing on the information. ICWH now thanks M. J. Crawley for his role in the further dissemination of the GLM approach and DRS in turn thanks ICWH. We hope that you will, in the course of time, thank DRS. As it says in the Preface, pass it along.

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Genus and Species Index

A Acalyptera parvula (Tinidae), 502 Aceratoneuromyia granularis (Eulophidae), 312 Acerophagus coccois (Encyrtidae), 39 Achrysocharoides (Eulophidae), 312 Acrolepiopsis assectella (Yponomeutidae), 33 Acyrthosiphon pisum (Aphididae), 24, 26, 31, 33, 79, 135, 162, 193, 275, 599, 611, 630 Adalia (Coccinellidae), 175, 179 bipunctata, 53, 134, 135, 140, 152–154, 156, 157, 174, 175, 178, 240, 241, 250, 262, 268, 453, 470 decempunctata, 133, 179, 250 Aenasius vexans (Encyrtidae), 39 Aeschynanthus ellipticus (Gesneriaceae), 190 Agathis (Braconidae), 379 Ageniaspis citricola (Encyrtidae), 462 Agonum dorsale, see Anchomerus dorsalis Agrotis (Noctuidae), 166 Aleiodes (Braconidae), 499 Aleochara (Staphylinidae) bilineata, 49, 480 bipustulata, 49 Alloxysta victrix (Figitidae), 34 Amblyseius (Phytoseiidae), 491 reductus, 509 Amitus vesuvianus (Platygastridae), 311 Anagasta, see Ephestia Anagrus (Mymaridae), 309, 459, 460, 480 columbi, 73 delicatus, 380 epos, 460, 478, 480 mutans, 165 silwoodensis, 165 sophiae, 12, 73 Anagyrus (Encyrtidae), 640 kamali, 60, 141, 148 mangicola, 52 pseudococci, 147, 190, 312 Anaphes (Mymaridae) iole, 478 ovijentatus, 465 victus, 51 Anastatus semiflavidus (Eupelmidae), 196 Anax junius (Aeshnidae), 500

Anchomerus dorsalis (Carabidae), 489, 504 Anisopteromalus calandrae (Pteromalidae), 309, 310, 314, 324, 391 Anthocoris (Anthocoridae) confuses, 133, 136 nemoralis, 160 nemorum, 200 Anticarsia gemmatalis (Noctuidae), 482, 487 Aonidiella aurantii (Diaspididae), 638 Apanteles (Braconidae) carpatus, 165 glomeratus, see Cotesia glomerata, 377 Aphaereta (Braconidae), 201 pallipes, 182 Aphelinus (Aphelinidae), 125 abdominalis, 60 albipodus, 507 asychis, 307, 308, 340, 386, 387, 392, 507 hordei, 507 semiflavus, 140, 196 varipes, 507 Aphelopus melaleucus (Dryinidae), 379 Aphidius (Braconidae), 190 colemani, 253, 482, 507, 599 eadyi, 29 ervi, 24, 26, 27, 29, 31, 33, 34, 39, 63, 65, 71, 118, 137, 162–165, 169, 273, 304, 380, 391, 452, 453, 599, 611, 679 matricariae, 130, 196, 370, 373, 506 nigripes, 196 rhopalosiphi, 446 rosae, 142 smithi, 131, 183, 192–194 sonchi, 192–195 uzbeckistanicus, 69 Aphis (Aphididae) fabae, 27, 29, 77, 135, 275, 462 fabae cirsiiacanthoidis, 476 gossypii, 492, 630 jacobaeae, 80 Aphonopelma hentzi (Theraphosidae), 479 Aphytis (Aphelinidae), 489 aonidiae, 675 lingnanensis, 46, 140, 300

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2

743

744 melinus, 45, 120, 135, 146, 307, 325, 453, 626, 627, 679, 680 mytilaspidis, 252 Apis mellifera mellifera (Apidae), 281 Apoanagyrus (=Epidinocarsis) (Encyrtidae) diversicornis, 190, 253 lopezi, 26, 30, 43, 196, 255, 256, 377, 639 Aprostocetus (Eulophidae), 114, 124 ceroplastae, 190 hagenowii, 309 Archenomus longiclava (Aphelinidae), 311 Archips argyrospila (Tortricidae), 459, 470, 492 Armadillidium vulgare (Isopoda), 264 Arsenophonus (Enterobacteriaceae), 262, 526, 528, 529, 532, 533 Asaphes vulgaris (Pteromalidae), 312 Ascogaster reticulatus (Braconidae), 32, 307 Asellus (Isopoda), 65 Asobara (Braconidae) citri, 66 rufescens, 78 tabida, 36, 37, 40–42, 47, 49, 51, 63, 78, 145, 146, 186, 190, 200, 271–275, 458, 467, 530 Asparagus (Liliaceae), 17, 78 Aster novae-angliae (Asteraceae), 682 B Bactrocera (Tephritidae) dorsalis, 453, 600 oleae, 454 Baryscapus daira (Eulophidae), 314 Bathyplectes curculionis (Ichneumonidae), 148 Bembidion lampros (Carabidae), 467 Bemisia tabaci (Aleyrodidae), 531, 532, 534, 596, 603, 650 Biosteres fletcheri (Braconidae), 182 Blacus (Braconidae), 379 ruficornis, 379 Blepharidopterus angulatus (Miridae), 149, 154, 155, 157, 177, 179 Bombus (Apidae) terrestris, 479 Bothriothorax nigripes (Encyrtidae), 379 Brachinus lateralis (Carabidae), 115 Brachycaudus cardui (Aphididae), 80 Brachymeria (Chalcididae) intermedia, 311, 312, 341 ovata, 461 Bracon (Braconidae) hebetor, see Habrobracon hebetor, 116 hylobii, 449 Bradycellus verbasci (Carabidae), 469 Brassica (Cruciferae), 446 oleracea, 30 Brevicoryne brassicae (Aphididae), 33 Bryobia arborea (Tetranychidae), 505 Buchnera (Enterobacteriaceae), 275

Genus and Species Index C Callosobruchus (Bruchidae) maculates, 398 Calopteryx aequabilis (Calaeopterygidae), 476 Calosoma (Carabidae) calidum, 427 sycophanta, 424, 492 Calystegia sepium (Convolvulaceae), 671 Carabus (Carabidae) coriaceus, 478 nemoralis, 505 Cardiochiles nigriceps (Braconidae), 28, 168 Cassida rubiginosa (Chrysomelidae), 490 Catolaccus hunteri (Pteromalidae), 196 Cephalonomia (Bethylidae) hyalinipennis, 387, 389, 644 stephanoderis, 146 Ceraeochrysa cubana (Chrysopidae), 78 Ceriagrion tenellum (Agrionidae), 362 Chalarus (Pipunculidae), 460 Chaoborus (Chaoboridae), 501 Chelonus (Braconidae), 115 sp. nr curvimaculatus, 142, 148 Chilocorus nigritus (Coccinellidae), 17 Chironomus plumosus (Chironomidae), 70 Choetospila elegans (Pteromalidae), 312 Choristoneura (Tortricidae) fumiferana, 476 Chromaphis juglandicola (Aphididae), 482, 592 Chrysocharis (Eulophidae) gemma, 379 Chrysopa (Neuroptera: Chrysopidae), 165, 181, 491 septempunctata, 113 Chrysoperla (Chrysopidae), 165, 181 carnea, 52, 118, 132, 138, 154–157, 198, 200, 453, 472, 474, 489, 681 Chrysothamnus nauseosus (Asteraceae), 379, 679 Cicadella viridis (Cicadellidae), 165 Cirrospilus (Eulophidae), 191, 196 sp. nr lyncus, 191 Cloeon (Baetidae), 65 Coccinella (Coccinellidae) californica, 464 septempunctata, 36, 69, 157, 198, 199, 669 trifasciata, 464 undecimpunctata, 145, 158 undecimpunctata aegyptiaca, 134 Coccophagus (Aphelinidae) atratus, 110, 115, 122, 123 semicircularis, 58 Coelotes terrestris (Amaurobiidae), 422 Coenagrion puella (Coenagrionidae), 115, 175, 176 Colastes (Braconidae), 126 Colcondamyia auditrix (Tachinidae), 17 Coleomegilla (Coccinellidae) maculata, 157, 180, 453, 477, 491, 509 Colpoclypeus florus (Eulophidae), 372, 374, 391, 481 Comperiella bifasciata (Encyrtidae), 186 Convolulus repens (Convolvulaceae), 671

Genus and Species Index Copidosoma (Encyrtidae) bakeri, 379 koehleri, 174 Cordulegaster boltonii (Cordulegasteridae), 517 Cotesia (Braconidae), 111, 114, 116, 127, 309 flavipes, 51, 327 glomerata, 28, 32, 42, 56, 119, 125, 161, 170, 309, 310, 338, 367, 373, 377, 393, 465, 476, 675 hyphantriae, 499 kariyai, 28, 171 marginiventris, 28, 31, 327, 507 melitaearum, 614, 645 rubecula, 144, 162, 166, 296, 297, 305, 313, 322, 325, 326, 328, 367, 451, 675 sesamiae, 29 Crangon affinis (Crangonidae), 510 Crioceris (Chrysomelidae) asparagi, 17, 78 duodecimpunctata, 78 Cryptolaemus montrouzieri (Coccinellidae), 36, 141 Culex (Culicidae) quinquefasciatus, 508 Cyclocephala lurida (Scarabaeidae), 489 Cycloneda sanguinea (Coccinellidae), 180 Cyclosa (Araneidae), 493 Cydia pomonella (Tortricidae), 9, 488, 643 Cyzenis albicans (Tachinidae), 115, 118, 608 D Dacus, see Bactrocera Dahlbominus (Eulophidae) fuliginosus, 55, 259 fuscipennis, 389 Danaus plexippus (Danaidae), 477 Daphnia (Cladocera) magna, 160 Delia radicum (Anthomyiidae), 74, 495 Demetrias atricapillus (Carabidae), 469 Dendrocerus carpenteri (Megaspilidae), 162 Dendroctonus frontalis (Scolytidae), 449, 470 Deroceras (Limacidae) reticulatum, 486 Diabrotica (Chrysomelidae) undecimpunctata, 489 undecimpunctata howardi, 485 virgifera virgifera, 458 Diachasmimorpha longicaudata (Braconidae), 304, 367, 379, 600 Diadegma (Ichneumonidae) armillata, 188, 189 insulare, 167, 453 Diadromus (Ichneumonidae) pulchellus, 33 subtilicornis, 146 Diaeretiella rapae (Braconidae), 30, 33, 309, 506, 603 Diaparsis truncatus (Ichneumonidae), 17 Dicondylus indianus (Dryinidae), 130, 145 Dicranotropis hamata (Delphacidae), 165

745 Dicyphus tamaninii (Miridae), 509 Diglyphus (Eulophidae) intermedius, 173 isaea, 61, 148 Dinarmus basalis (Pteromalidae), 51, 202, 387, 388 Dinocampus coccinellae (Braconidae), 462, 529 Diomus (Coccinellidae), 36 Diplazon (Ichneumonidae) fissorius, 182 pectoratorius, 379 Diplonychus rusticum (Belostomatidae), 70 Diuraphis noxia (Aphididae), 506, 507 Dorylomorpha (Pipunculidae), 460 Drosophila (Drosophilidae), 8, 12, 24, 26, 37, 41, 58, 186, 190, 240, 244, 246, 252, 261, 272, 274–276, 302, 370, 376, 378, 390, 458, 479, 482, 489, 504, 524–526, 528, 530, 533 melanogaster, 24, 29, 31, 32, 37, 40, 42, 48, 186, 188, 246, 271, 273–276, 280, 333, 398, 526, 530, 536, 605 phalerata, 33 subobscura, 40, 42 Dytiscus verticalis (Dytiscidae), 38 E Ectemnius cavifons (Sphecidae), 493 Edwardsiana prunicola (Cicadellidae), 478 Elachertus cacoeciae (Eulophidae), 161 Enallagma boreale (Coenagrionidae), 159 Encarsia (Aphelinidae), 139, 142, 183 formosa, 51, 80, 112, 120, 139, 142, 180, 184, 261, 492 partenopea, 311 perniciosi, 173, 196 Enicospilus americanus (Ichneumonidae), 110 Ephestia (Pyralidae), 166, 624 kuehniella, 15, 46, 57, 67, 624 Epidinocarsis, see Apoanagyrus Epilachna varivestis (Coccinellidae), 476 Episyrphus balteatus (Syrphidae), 29, 41, 115, 157, 196, 452, 475, 477, 676–678 Epitheca cynosura (Corduliidae), 160, 180 Eretmocerus (Aphelinidae) eremicus, 301, 322, 340, 473, 478 Erigone (Linyphiidae), 458 atra, 504 Eristalis (Syrphidae) arbustorum, 268 tenax, 682, 683, 691 Erythroneura elegantula (Cicadellidae), 460, 480 Eucalyptus marginata (Myrtaceae), 448 Eupelmus (Eupelmidae) spongipartus, 312 urozonus, 125 vuilletti, 45, 116, 119, 146, 201, 687 Euphasiopteryx (Tachinidae) depleta, 457 ochracea, 17

746 Eurytoma (Eurytomidae), 460 Euseius tularensis (Phytoseiidae), 505 Exeristes ruficollis (Ichneumonidae), 29 Exochomus (Coccinellidae), 36 flaviventris, 30 F Fopius arisanus (Braconidae), 379, 600 Forficula auricularia (Forficulidae), 434, 459, 505 Formica (Formicidae) polyctena, 492, 493 rufa, 492 Frankliniella occidentalis (Thripidae), 53, 511 G Galleria melonella (Pyralidae), 52 Gasterocantha fornicata (Araneae), 38 Gelis festinans (Ichneumonidae), 458 Geocoris (Lygaeidae), 489 Geron (Tachinidae), 680 Glyptapanteles (Braconidae) flavicoxis, 29, 308 fulvipes, 499 Gonatocerus (Mymaridae), 111 cinticipitis, 196 Gonepteryx rhamni (Pieridae), 477 Goniozus (Bethylidae) legneri, 66, 255, 373, 375, 465 nephantidis, 9, 54, 66, 116, 120, 134, 136, 144, 146, 161, 167, 258, 367, 373, 375, 392 triangulifer, 370 Gryllus (Gryllidae), 457 integer, 17 pennsylvanicus, 500 Gryon pennsylvanicum (=atriscapus) (Scelionidae), 62, 488 Gyranusoidea tebygi (Encyrtidae), 52, 640 H Habrobracon (Braconidae), 258 hebetor, 121, 161, 171, 181, 185, 186, 246–249, 253, 256, 257, 259, 301, 307, 324, 326, 327, 330, 333, 337, 338, 340, 341, 366, 370, 373, 377, 380, 386, 388, 389, 391, 392 juglandis, see hebetor, 389 lineatellae, 391 Habrolepis rouxi (Encyrtidae), 190 Haematobia irritans (Muscidae), 457, 458 Halticoptera (Pteromalidae) laevigata, 36 rosae, 36 Haplogonatopus atratus (Dryinidae), 51 Harmonia (Coccinellidae) arcuata, 488 axyridis, 10, 37, 53, 148, 158, 160, 325, 521, 522, 604, 611

Genus and Species Index variegata, 477 Harpalus (Carabidae), 489 rufipes, 473 Hedera helix (Araliaceae), 190, 191 Helicoverpa (=Heliothis) (Noctuidae), 340, 465, 491, 507 armigera, 504, 507, 509 punctigera, 504, 507 zea, 28, 487, 507, 599 Heliothis virescens (Noctuidae), 28, 168 Hippodamia (Coccinellidae) convergens, 29 sinuata, 157 Hobbya stenonotata (Pteromalidae), 312 Hockeria rubra (Chalcididae), 259 Hogna rabida (Lycosidae), 481, 488 Homotrixa alleni (Tachinidae), 18 Hylobius abietis (Curculionidae), 449 Hypera postica (Curculionidae), 460 Hyperecteina cinerea (Tachinidae), 110 Hyperomyzus lactucae (Aphididae), 195 Hyposoter (Ichneumonidae), 499 Hypothenemus hampei (Scolytidae), 54, 389, 510, 644 Hyssopus pallidus (Eulophidae), 10 I Inostemma (Platygastridae), 107 Ischnura (Coenagriidae), 63, 153, 160, 178 elegans, 150, 152–155, 160, 175, 178 Itoplectis naranyae (Ichneumonidae), 51, 52 Ixodiphagus hookeri (Encyrtidae), 39 J Junonia coenia (Nymphalidae), 78 K Kareius bicoloratus (Pleuronectidae), 510 Kickxia elatine (Scrophulariaceae), 78 L Lariophagus (Pteromalidae), 328 distinguendus, 31, 137, 173, 296, 326, 338, 388 Leiophron uniformis (Braconidae), 465 Lepisiota (Formicidae), 457 Leptinotarsa decemlineata (Chrysomelidae), 49 Leptomastix dactylopii (Encyrtidae), 30, 180, 373 Leptopilina (Eucoilidae), 8, 272 australis, 8 boulardi, 29, 31, 51, 59, 145, 188, 271–275, 370, 373, 378, 522 clavipes, 8, 15, 34, 248, 252, 479 fimbriata, 8 heterotoma, 8, 14, 24, 33, 34, 47–50, 59, 74, 168, 169, 184, 186, 272, 326, 370, 373, 378, 522 longipes, 8, 37 Leschenaultia exul (Tachinidae), 110

Genus and Species Index

747

Lestes sponsa (Lestidae), 156, 161, 175 Linepithema humile (Phoridae), 30 Linyphia triangularis (Linyphiidae), 179 Listronotus bonariensis (Curculionidae), 497, 637 Lixophaga diatraeae (Tachinidae), 180 Lordotus (Bombyliidae) miscellus, 379, 680 pulchrissimus, 679 Lucanus cervus (Lucanidae), 259 Lygaeus equestris (Lygaeidae), 79 Lygus (Miridae), 465 lineolaris, 462, 465, 611, 612, 634 Lymantria dispar (Lymantriidae), 28, 29, 458, 488, 492 Lysiphlebus (Braconidae) cardui, 77, 476 testaceipes, 25, 462, 506, 600 Lytarnis maculipennis (Ichneumonidae), 378

Mitopus morio (Phalangiidae), 500 Mnais pruinosa pruinosa (Calopterygidae), 332, 334 Monodontomerus (Torymidae), 318 obscurus, 311 Musca (Muscidae), 458 domestica, 31, 339, 480 Muscidifurax (Pteromalidae), 252, 339, 340, 462 raptor, 259, 339, 462 raptorellus, 339 zaraptor, 339 Myiopharus (Tachinidae) aberrans, 49 doryphorae, 49 Myrmeleon (Myrmeleontidae) bore, 493 immaculatus, 38 Myzus persicae (Aphididae), 29, 135, 599

M Macrocentrus (Braconidae) cingulum, 49 grandii, 453, 675 Macroleon quinquemaculatus (Myrmeleontidae), 160 Macroneura vesicularis (Eupelmidae), 114 Macrosiphum (Aphididae) euphorbiae, 60 Maevia inclemens (Salticidae), 494 Mamestra brassicae (Noctuidae), 55 Mantis religios (Mantidae), 467 Medetera (Dolichopodidae), 452 Megacephala fulgida (Cicindelidae), 457 Megaselia scalaris (Phoridae), 254 Melangyna (Syrphidae) novaezelandiae, 452 viridiceps, 452 Melanostoma (Syrphidae), 692 fasciatum, 452 Melitaea cinxia (Nymphalidae), 614, 645 Melittobia (Eulophidae), 307, 318, 319, 328 acasta, 296, 318, 320, 322, 338 Merodon equestris (Syrphidae), 465 Mesopolobus mediterraneus (Pteromalidae), 313 Metaphycus (Encyrtidae), 10, 146 stanleyi, 190, 191 Metapolophium dirhodum (Aphididae), 60 Metasyrphus corollae (Syrphidae), 29 Meteorus gyrator (Braconidae), 499 Microctonus (Braconidae) aethiopoides, 174, 626, 627, 675 hyperodae, 174, 497, 637, 675 vittatae, 168 Microlophium carnosum (Aphididae), 80 Microplitis (Braconidae) croceipes, 28, 30, 31, 36, 327, 377, 445, 465, 478, 507 demolitor, 180, 181, 240, 507 rufiventris, 28 Microterys ferrugineus (Encyrtidae), 312

N Nabis (Nabidae), 611 Nanophya pygmaea (Libellulidae), 334 Nasonia (Pteromalidae) giraulti, 240, 243, 244, 248, 281, 303, 306, 315, 381 longicornis, 240, 381 vitripennis, 10, 63, 107, 108, 112, 120, 121, 124, 136, 137, 146, 201, 240, 243, 244, 246, 248, 258–263, 270, 281, 296, 300, 302, 303, 306, 309–316, 318–320, 322–331, 336, 338, 339, 367, 368, 371, 372, 381, 390, 392, 393, 522, 525, 533 Nebria brevicollis (Carabidae), 464 Neodiprion lecontei (Diprionidae), 55 Neoseiulus cucumeris (Phytoseiidae), 53 Nephaspis oculatus (Coccinellidae), 196 Nephotettix cincticeps (Cicadellidae), 490, 532 Nesolynx (Eulophidae) albiclavatus, 316 glossinae, 317 Nilaparvata lugens (Delphacidae), 457, 459, 490 Notiophilus biguttatus (Carabidae), 137, 148 Notonecta (Notonectidae), 151, 178 glauca, 63–65, 69, 149, 179 hoffmani, 157, 161 undulata, 151, 155 O Oecophylla smaragdina (Formicidae), 445 Oligodranes (Bombyliidae), 680 Oligosita (Trichogrammatidae), 459 Oligota (Staphylinidae), 492 Ooencyrtus kuvanae (Encyrtidae), 141, 148, 361 Operophtera brumata (Geometridae), 492, 605, 607 Ophion scutellaris (Ichneumonidae), 499 Orgilus lepidus (Braconidae), 35 Orius (Anthocoridae), 196, 434, 480, 482, 489 insidiosus, 157, 491 laevigatus, 53, 196

748 sauteri, 452 Ormia (Tachinidae) depleta, 18 lineifrons, 17 ochracea, 18, 457 Orocharis (Gryllidae), 457 Orthotydeus caudatus (Tydeidae), 509 Ostrinia (Pyralidae) furnacalis, 264, 481 nubilalis, 27, 49, 453, 491, 509 P Pachycrepoideus vindemmiae (Pteromalidae), 31, 312, 329, 330, 376, 390 Pachydiplax longipennis (Libellulidae), 158 Pachyneuron (Pteromalidae), 379 Paederus fuscipes (Staphylinidae), 488 Pales pavida (Tachinidae), 499 Panonychus (Tetranychidae) citri, 505 ulmi, 505 Paratenodera angustipennis (Mantidae), 133 Pardosa (Lycosidae) hortensis, 504 lugubris, 485 Paruroctonus mesaensis (Vaejovidae), 512 Pediobius (Eulophidae), 312 foveolatus, 476 Perillus bioculatus (Pentatomidae), 28, 160 Periscepsia spathulata (Tachinidae), 499 Peristenus (Braconidae), 462 Phacelia tanacetifolia (Hydrophyllaceae), 481 Phalangium opilio (Phalangiidae), 501 Phanerotoma franklini (Braconidae), 120, 138, 673 Pheidole (Formicidae), 457 Phenacoccus (Pseudococcidae) herreni, 253 manihoti, 30, 253, 627, 635 Pholetesor ornigis (Braconidae), 453 Phyllocnistis citrella (Phyllocnistidae), 492 Phyllonorycter (Gracillariidae), 517 malella, 37, 38 Phyllotreta cruciferae (Chrysomelidae), 500 Phytoseiulus persimilis (Phytoseiidae), 32, 469 Pieris (Pieridae) brassicae, 28, 32, 42, 56, 166, 170, 465 napi, 476 rapae, 28, 31, 166, 170, 174, 461, 476, 492 virginiensis, 476 Pimpla (Ichneumonidae) luctuosa, 31 nipponica, 45, 51, 52 turionellae, 37, 38, 115 Pinus sylvestris (Pinaceae), 493 Pirata (Lycosidae), 425 Planococcus citri (Pseudococcidae), 30, 679 Plantago lanceolata (Plantaginaceae), 78, 157, 692 Platycheirus (Syrphidae), 692

Genus and Species Index Platythyrea modesta (Formicidae), 37 Pleolophus basizonus (Ichneumonidae), 35 Plodia interpunctella (Pyralidae), 163, 169, 170, 181, 184, 624 Plutella xylostella (Yponomeutidae), 167, 453, 505, 521 Podisus (Pentatomidae) distinctus, 456 maculiventris, 20, 23, 28, 133, 138, 147, 157, 201, 456 Poecilus (Carabidae), 428 cupreus, 489, 509 Polistes (Vespidae) dominulus, 490 Popillia japonica (Scarabaeidae), 477, 489 Praon (Braconidae), 29, 455 palitans, 115, 140, 196 pequodorum, 63, 65, 183 Prays oleae (Yponomeutidae), 487, 496 Prokelisia crocea (Delphacidae), 73 Propylea 14-punctata (Coccinellidae), 470 Protopulvinaria pyriformis (Coccidae), 190 Pseudaletia (Noctuidae) separata, 28 unipuncta, 458 Pseudatomascelis seriatus (Chrysomelidae), 488 Pseudeucoila bochei, see Leptopilina heterotoma Pseudococcus affinis (Pseudococcidae), 190 Pseudomethoca canadensis (Tiphiidae), 259 Psilus silvestri (Diapriidae), 182 Pteromalus (Pteromalidae) puparum, 42, 147, 170, 309, 461, 492 Pterostichus (Carabidae), 489 cupreus, see Poecilus cupreus, 489 melanarius, 426–428, 430, 467, 475, 487, 495 oblongopunctatus, 428, 610 versicolor, 501 Pthiria (Bombyliidae), 680 Q Quadraspidiotus perniciosus (Diaspididae), 455 R Ranunculus repens (Ranunculaceae), 671 Rastrococcus invadens (Pseudococcidae), 52, 640 Rhopalosiphum (Aphididae) maidis, 27, 509 padi, 60, 80, 477, 509 Rhyssa persuasoria (Ichneumonidae), 66, 640 Rhyzobius forestieri (Coccinellidae), 199 Rickettsia (Rickettsiaceae), 262, 526, 527, 529, 532 Rorippa indica (Cruciferae), 28 S Sarcophaga (Sarcophagidae), 458 Scaiarasaga quadrata (Tettigoniidae), 18 Scapteriscus (Gryllotalpidae), 457

Genus and Species Index

749

Scathophaga stercoraria (Scathophagidae), 147, 201, 394, 395 Schefflera arboricola (Araliaceae), 190, 191 Schizaphis graminum (Aphididae), 506 Schizocosa ocreata (Lycosidae), 16 Scolytus multistriatus (Curulionidae), 455 Sesamia nonagroides (Noctuidae), 16, 23 Sialis fuliginosa (Sialidae), 501 Sitobion avenae (Aphididae), 34, 60, 175, 446, 488, 504, 507 Sitona (Curculionidae) discoideus, 626 lineatus, 504 Sitotroga (Gelechiidae), 166 Solenopsis (Formicidae), 420 invicta, 457, 458, 492, 493 Spalangia (Pteromalidae) cameroni, 312, 377, 480 endius, 253, 316, 317, 326, 327, 341, 365, 387, 392 nigra, 341 Spodoptera (Noctuidae) frugiperda, 456 littoralis, 27, 28, 31 Stenus flavicornis (Staphylinidae), 491 Stethorus (Coccinellidae) punctillum, 196 siphonulus, 470, 471 Streptocarpus hybridus (Gesneriaceae), 190 Sympiesis sericeicornis (Eulophidae), 12, 37, 38, 312 Syrphus ribesii (Syrphidae), 41 Systole albipennis (Eurytomidae), 312

Triatoma phyllosoma pallidipennis (Reduviidae), 55 Trichogramma (Trichogrammatidae), 39, 250, 252, 253, 264, 308, 365, 378, 385, 453, 462, 482, 632, 642 australicum, 507 brassicae, 32, 57, 248, 260, 307, 308, 480–482, 522 californicum, 253 dendrolimi, 465 embryophagum, 50 evanescens, 50, 55, 59, 161, 165, 166, 311, 312, 321, 330, 341, 390 fasciatum, 270 kaykai, 261 minutum, 56, 57, 476, 482 ostriniae, 481, 675 perkinsii, 253 pretiosum, 174, 240, 378 semifumatum, 481 turkestanica, 46 Triclistus yponomeutae (Ichneumonidae), 184 Trieces tricarinatus (Ichneumonidae), 184 Trioxys (Braconidae) pallidus, 174 utilis, 140, 196 Trissolcus (Scelionidae) basalis, 54, 66, 181, 377, 452, 522 brochymenae, 23, 39 busseolae, 522 Tropidothorax leucopterus (Lygaeidae), 79 Trybliographa rapae (Eucoilidae), 74, 75 Tryporyza (=Scirpophaga) innotata (Pyralidae), 457 Typhlodromus pyri (Phytoseiidae), 509

T Tachyporus (Staphylinidae), 434 Tapionoma nigerrimus (Formicidae), 487 Telenomus (Scelionidae) farai, 55 heliothidis, 39, 61 lobatus, 165, 181 reynoldsi, 196 Tenebroides maroccanus (Trogossitidae), 492 Tenodera (Mantidae) aridifolia, 479 sinensis, 156, 477 Tetranychus urticae (Tetranychidae), 36, 469, 509 Tetrastichus (Eulophidae), 78 coeruleus (=asparagi), 78 sesamiae, 312, 314 Thanasimus dubius (Cleridae), 145, 449 Theragra chalcogramma (Gadidae), 505 Theridion impressum (Theridiidae), 493 Therioaphis maculata (Aphididae), 196, 644 Tineola bisselliella (Tineidae), 165 Torymus (Torymidae), 379 Toxares deltiger (Braconidae), 498 Trachysphyrus albatorius (Ichneumonidae), 110 Trialeurodes vaporariorum (Aleyrodidae), 492, 509

U Urolepis rufipes (Pteromalidae), 196, 307, 309, 393 Utetheisa ornatrix (Arctiidae), 78 V Venturia canescens (Ichneumonidae), 5, 13, 34, 35, 51, 52, 66, 67, 69, 122, 125, 162, 163, 165, 168–170, 180, 181, 184, 185, 187, 188, 202, 251–253, 272, 378, 476, 624 Vespula (Vespidae), 494 germanica, 456 pensylvanica, 456 Vicia faba (Fabaceae), 31 Vrestovia fidenas (Pteromalidae), 312 W Wolbachia (Anaplasmataceae), 253, 417 X Xestia xanthographa (Noctuidae), 499 Xylota (Syrphidae), 674

750 Y Yponomeuta (Yponomeutidae) cagnagellus, 188, 189 evonymellus, 188, 189 mahalebellus, 189

Genus and Species Index Z Zelus (Reduviidae), 489

Subject Index

A Abundance, estimates, 420 Acari, 525, 527 Accessory glands (=colleterial glands), 39 Acid gland, see Venom gland Acoustic behaviour, 313 recording, 318 signals/stimuli, 17 Activity abundance/density, 424, 426–428, 453, 454 diurnal variation, 141, 437, 439, 466, 679 Additive component (VA) in quantitative genetics, 265, 266 Additive effects, in quantitative genetics, 265 Aedeagus, 329, 338 Aestivation, 626 AFLP, see Amplified Fragment Length Polymorphism Agaonidae, 370 Age at death, 144 Age distribution, 609, 610 Age-dependent foraging decisions, 77 Age-specific fecundity (mx), 123, 129, 130, 139, 192, 193, 646, 690 instantaneous death rate, 144 survival (lx), 130, 192 Age-structure of population, 627 Agglutination, see Serological methods Aggregation behavioural mechanisms, 72 definition, 73 factor levels, 728 incorporation into Nicholson-Bailey model, 618 pest equilibria, 636, 637 spatial scale, 73 stabilising potential, 618, 635 Aggregative response, 72, 370, 379, 594, 596, 638 Aggression parasitoids, 66, 281 predators, 54 Airflow olfactometer(s), 18, 20, 21, 23 Akaike’s Information Criterion (AIC), 727 Alarm pheromone, 30 Aleyrodidae, 340 Alkaline gland, see Dufour’s gland

Allee effect(s), 55, 637, 639 Allelic diversity, 364 Allelochemicals, 18 Allomones, 18, 34, 36 Allozyme(s), 236, 237, 246, 340 Alternative hypothesis (H1), 706, 707 Alternative strategies, 5, 361 Ambush strategy, 38 Amplified Fragment Length Polymorphism (AFLP), 235, 237, 238, 248, 364, 520 Analysis of covariance (ANCOVA), 711, 721–725, 727, 728, 732, 733, 737 Analysis of variance (ANOVA), 709, 710, 713–721, 724, 725, 727, 728, 732, 733, 736, 737 Analytical models, 610, 614, 625, 626, 633 population dynamics, 417, 466, 476, 502, 528 Anaplasmataceae, see Wolbachia Anatomy, 58, 106, 108, 333, 334, 337, 676 Anautogeny, 118, 132 Anhydropy/anhydropic egg production, 119 Antennal search, 37 Anthocorid bugs, 27, 53, 54 Anthrone reaction, 202 Antibiotics, 10 Antibodies monoclonal, 484, 487, 503, 509, 599 polyclonal, 599 production, 503 Antibody techniques, 503 Antisera, 599 Ants, see Formicidae Aphelinidae, 300, 446 Aphid mummies, 370, 386 Apoidea see also Apidae, 366, 367 Apomixis, 251, 252 Apostatic selection (=switching behaviour), 63 Apparent competition, 79, 358, 367, 378, 381, 382, 624, 625 mutualism, 358, 367, 378, 381, 382 Aquatic insects, 149, 439, 449, 454, 459, 500, 501 Araneidae, 674 Arctiid moths, 78 Area of discovery, 366 Arrestment behaviour, 23, 35 Arrhenotoky, 365

© Springer Nature Switzerland AG 2023 I. C. W. Hardy and E. Wajnberg (eds.), Jervis’s Insects as Natural Enemies: Practical Perspectives, https://doi.org/10.1007/978-3-031-23880-2

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752 Artificial foods of natural enemies, 687 neural network models, 3 selection, 244, 268 Asilidae, 437, 488 Assimilation efficiency, 151, 153, 161 Associative learning, 30 Assumptions (of statistical test), 709 Asteraceae (=Compositae), 379 Attack capacity, parasitoid, 357–359, 365, 367, 635, 636, 645 Attraction auditory (phonotaxis), 18 refuges, 459 visual, 451, 152 Augmentative releases, 457, 482 Autogeny, 118 Automaton principle, 319 Automixis, 252 Avoidance behaviour (host), 161 B Baits, trapping, 8, 427, 455, 456, 457, 458 Barometric pressure, 142 Basic reproductive rate (=net reproductive rate, R0), 192 Bateman’s principle, 358, 396 Beating technique, 441 Bees, see Apidae Behavioural analysis, 11 fever, 191 recording, 11 Berlese funnel, 433, 448, 450 Best-fit line, 710–714, 720, 730 Bethylidae, 25, 54, 116, 373, 381, 382, 384, 389 Bi-directional cytoplasmic incompatibility, 263 Binary data, 730, 731 Binary data (grouped), 731–734 Binomial distribution, 709, 712, 728, 730, 734, 735 BIOCAT database, 634–636, 640, 647 Biological ‘check’ method, 594 Biological control aggregative response, 370, 379, 380 California red scale, 638 climatological origin of parasitoids, 639 collecting of agents, 642 conservation, 592, 611, 612, 614, 643, 669, 685 density-dependent mortality, 642 destructive host-feeding, 359, 375, 378, 388, 396, 397 developmental time host and parasitoid, 174 dispersal of agents, 11 ease of handling/culturing of agents, 640 economic threshold, 650 establishment of agents, 357 exploration, 630, 633, 639 evaluation of effects of introduced agent(s), 298, 506, 608, 632, 649–651 evaluation of pest problem, 367, 380, 390, 399

Subject Index foraging behaviour, 365, 382, 399, 459, 465, 466, 489 generalists, 368 historical data, 359 holistic criteria, 633, 634 importation of natural enemies, 358, 359, 361, 363, 364, 368–370 indigenous natural enemies, 129 inoculative release, 195 interactions between agents, 457, 482, 483, 497, 506, 522 intraguild predation, 53, 644 intrinsic rate of natural increase (rm), 129, 130, 192, 196 introductions, 174, 188, 195, 497, 591, 630–635, 637, 639–641, 644, 647, 649 lottery model, 633 mango mealybug, 640 mass-rearing of agents, 32 maximum level of parasitism, 635 metapopulation dynamics, 521 models, 390, 399, 462, 497, 528 mode of reproduction, 358, 385–387, 393 monitoring changes in pest populations, 373, 377, 386, 399 multiple agent introductions, 633, 643, 644 new associations, 368, 388, 399 non-host foods, 146, 466, 679 non-target effects, 630, 647–649 numerical response, 636 pest age-structure, 373, 377, 380, 386, 399 plant resistance, 645 polyphagy, 639 psyllid, 679 quarantine, of agents, 497 rm, 645, 646 ratio-dependent functional responses, 360, 381, 386 reconstruction of enemy complexes/guilds, 361, 368, 376 risks of, 647 searching efficiency, 481 spatial heterogeneity in natural enemy attack, 638 specialist parasitoids, 357, 359, 365, 367–373, 377–380, 386, 388, 390, 392 success rate, 639, 647 temporal heterogeneity in natural enemy attack, 634 test/control plots, 592–594 transient impact, 645 whitefly, 529, 679 winter moth, 608 Biotypes (=strains), 174, 482, 485 Blacklight traps, 454, 473 Blocking, 510, 511, 736 Body size in natural enemies clutch size, 55, 161 egg load, 55, 140, 200 egg size, 115, 116 fecundity, 116, 130, 131, 133, 134, 136, 139, 142, 143, 157 fitness, 54, 169, 170

Subject Index flower-visiting, 687 larval feeding history, 138 longevity, 129, 145, 146 mating, 126, 138 measurement, 150 metabolism, 118, 302 ovariole number, 116 ovigeny index, 117–119 resource allocation, 200 superparasitism, 165, 168, 169, 184, 185 Bombyliidae, 379 Bottom-up/top-down effects, 138, 724, 727 Box trap, 441, 449 Braconidae, 20, 25, 42, 54, 111, 114–116, 118, 126, 168, 169, 296, 297, 301, 305, 326, 338, 377, 379, 389, 391, 445, 446, 453, 455, 462, 476, 498, 499 Bradford assay, 201 Branch lengths, 7 Breeding value, 118 Broad-sense heritability (=h2), 265 Brother-sister (sib-) mating(s)/crosses, 59 Bruchidae, 398 Bt toxin, 143 Bursa copulatrix, 332 Butterflies, see Nymphaloidea C c, see Murdoch’s preference index C3, C4 plants, 477 Calyx, 114, 125, 188 CAMERA, 598 Cannibalism, see Sexual cannibalism Canopy raft, 607 Cantharidae, 493 Capital breeding, 118 Carabidae, 134, 469, 501, 600 Carbohydrate(s), energy value, 202, 684 Carrying capacity of the environment (=K), 645 Carry-over of resources, 118 Cassava mealybug(s), 30, 39, 73, 253, 377, 627, 635, 645 Categorical variable, 714, 716, 721, 725, 728 Causation behaviour, 3 Cautery needle, 475 Censor effect, censoring of data, 71 Census walk method, 444, 445 Central fusion, 251, 252 Ceraphronidae, 452 Chalcididae, 311 Chalcidoidea see also individual families, 125, 128, 649, 670 Chemical signals/stimuli, 9 Chemoprinting, 477 Choice hosts/prey, 41, 42, 363, 537 odours, 17, 18 Choosy females, 396 Chorion, 109, 112, 113, 115, 118–120, 483, 491

753 Chromatographic techniques, 675 Chromosome(s) doubling, 251 numbers, 63, 193 position, 279 Chrysomelidae, 632 Chrysopidae, 132, 436, 444, 505, 672 CI, see Cytoplasmic incompatibility Cladistics, 7 Clam trap, 437 Classical biological control, see Biological control, classical Clerid beetles, 449 Climate, 628, 629, 635, 639, 640, 645 Climatic adaptation, 639 Climatic matching programmes (CLIMEX), 640 Clutch size biological control, 365, 368, 371, 381–383, 387 body size in progeny, 55 decisions, 54–58 host size, 54, 56 mating, 365, 368 manipulation, 55 models/progeny allocation, 54–57 Coccinellidae, 2, 52, 116, 117, 134, 136, 143, 145, 148, 153, 175, 179, 436, 444, 595, 633, 672 Coefficient of determination (r2), 707, 712, 713, 718 Coefficient of variation squared (=CV2), 372 Coexistence, 616, 622, 624, 625, 643 Cohort fecundity schedules, 129 Cohort generation time (=Tc), 195 Cohort survivorship curves, 143 Cold anthrone reaction, 674, 675, 680 Cold tolerance, 148 Coleoptera, see individual families Collembola, 485, 488, 501, 504, 511 Colleterial glands (=accessory glands), 114, 125 Colorado beetle, 28 Community/communities parasitoid behaviour, 79, 80 sampling methods, 417 trophic relationships, 483, 484 web(s), 512, 515 Comparative method/comparative analyses, 6–8, 116, 119, 147, 234 Compartmentation in food webs, 514, 519 Compensation, degree of (density-dependence), 160, 169 Competition asymmetrical, 159 biological control, 358–361, 363, 365, 367, 370–372, 378, 380–383, 386–388, 394, 396, 398 interference, 158, 159, 638, 680 interspecific, 54, 643, 679, 680 intraspecific, 43, 160 larval growth, 626 larval survival, 169 population dynamics, 358–361, 363, 365, 368, 371, 372, 375, 381, 383, 386, 387, 391, 395, 397, 399 survival, 180

754 Complementary Sex Determination (=CSD), 256 Complement fixation, see Serological methods Compositae, see Asteracae Composite interval mapping, in quantitative genetics, 279, 280 mortalities, in population dynamics, 365, 375, 385, 387, 391 web(s), 512 Computer software, see Software packages Conditional sex allocation, 10, 60 Conditional strategy, 361 Conditioning, 10 Confidence Interval (CI), 707, 714, 715, 718, 727 Conflict intersexual, 357 intrasexual, 357 Connectance web(s), 512, 513, 515 Connectance, in food webs, 512, 513, 516 Conservation biological control, see Biological control, conservation Constancy, 499, 677, 691 Constrained oviposition, 385–387 Constraint assumptions in modelling, 4, 5 Consumption rate, 134, 140, 145, 151–156, 175–179, 191, 635, 678, 683 Contact chemicals (kairomones, pheromones), 32, 33, 35, 125 Contest competition, 160, 181 Continuous time analytical models, 614 Conversion efficiency, see Efficiency of conversion of ingested food Convolvulaceae, 671 Copulation and insemination, 327 Correlated evolution, 341 Correlation methods in population dynamics, 368, 381, 395, 601 Cost of reproduction, 77 Count data, 728–730, 734 Courtship acoustic stimuli, 304, 311 age of insect, 299 amount required, 281 automata, 320 chemical stimuli, 310, 314 cyclical displays, 318 duration of displays, 319 efficiency, 312 head-nodding behaviour, 314, 319 intervals between displays, 319 male investment in, 318, 319 mouthpart extrusion, 314–316 nuptial gifts, 398 pheromones, 297, 298, 301, 303, 309, 313–318, 322, 323, 326, 340, 392 position, 310, 312, 342 postures, 305, 310, 311, 341 receptivity in female, 312–315, 318, 331 recording, 319 refractory period, 327

Subject Index sequences, 321 stimulus production, 310 switching off, 325, 326 tactile and visual stimuli, 309 temporal organisation/patterning, 339, 340 visual and tactile stimuli, 316, 317 Cox’s proportional hazards model, 15, 144 CPST, see Centrifugal phylogenetic screening technique Craig’s method, 464 Critical photoperiod, 199 Critical value, 264, 706, 721 Crop of natural enemy gut, 481, 500 Crosses, genetic, 235 Cruciferae, 28, 500 Cryptic female choice, 333, 335, 398 CSD, see Complementary sex determination Culturing of parasitoids, 641 Curculionidae, 632 Currency assumptions in behavioural models, 4 Current model, 725–727 CV2 rule, 620 Cyclical displays, 318, 321, 322 Cynipidae, 254, 326 Cynipoidea, 366, 367, 670 Cytoplasmic Incompatibility (CI), 263, 524-526, 530, 531, 535 D D, diffusion parameter, 480 D, genetic distance, 7, 237, 340 Damping oscillations, 459, 516 Dams, 28, 268, 270 Damselfies, see Odonata Dance-flies, see Empididae Day-degree(s)/models, 172, 174 Decay curve(s), 164 De Lury’s method, 444 Decision variables, 4 foraging, 2–4 Deductive modelling, explained, 74 Defences, of hosts and prey behavioural, 42, 43, 78, 358–362, 368, 396, 398 physiological, 55, 181, 186, 362, 398 Degree of heterozygosity (=H), see Heterozygosity, degree of Degree of polymorphism (=P), see Polymorphism, degree of Degrees of freedom (df), 706, 720, 721, 725, 726, 730 Delta trap, 455 Density-dependence detection of, 367, 393 delayed, 604, 608 exactly compensating, 160 overcompensating, 160 sex ratio, 61, 62, 360, 362, 365, 368, 370–373, 375, 377, 380–387, 391, 399 spatial, 358, 369, 370, 379, 395 strength, 398

Subject Index undercompensating, 180 Density-independence, 377, 601, 604, 621 Deposition traps, see Water-filled tray traps Descriptive statistics, 706 Destructive host-feeding, 378, 388, 396, 397 Detectability/reliability hypothesis, 26, 599, 600 Detection of parasitoids within/upon hosts, 506, 517 Detection period (=tDP), 509 Deterministic models, 613 Deuterotoky, 613 Developmental arrest, 198 Development, of natural enemy immatures development period, 361, 370, 372, 376, 377, 379, 380, 391, 392 idiobionts, 162 koinobionts, 126, 165 physical factors affecting, 171 rate, 141, 151–156, 162–165, 168–173, 176, 183, 196 superparasitism, 168, 170 temperature, 138, 171, 174, 175 Developmental zero (=t), 172 Deviance, 726, 727, 730, 734 change in (G), 730 DGGE, see Denaturing gradient gel electrophoresis Diapause, 142, 197–200, 233, 260, 300, 419, 446, 461, 500 Diapriidae, 182 Diel patterns in activity, 438, 449, 470, 478, 482, 486, 488 Diffusion parameter (=D), 480 Digestive efficiency, 151, 155, 156 Dimer, 236 Diploid male production, 258, 261 Diploidy restoration, 252 Diptera, see individual families Disc equation, 376 Discrete generations, 602, 605, 606, 645 Discrete time models, in population dynamics, 615 Dispersal, 73, 299, 363, 369, 425, 455, 457, 463–465, 467, 469, 470, 472, 473, 475–478, 480–483, 505, 520, 521, 596, 598, 601, 613, 614, 638, 639, 688, 690 Dispersion parameter, 734 Display(s) in courtship, 301 Dissection equipment, 106 hosts, 62, 189, 341 natural enemies, 357–359, 361, 363, 364, 368–370, 399 Distribution, 706, 708, 709, 712, 720, 728, 730, 733–735, 737 Diurnal activity, 191, 439, 466, 679 DNA-based techniques genetics, 482 detecting/identifying parasitoids within hosts, 462, 506, 517 detecting/identifying prey remains within predators, 508, 509, 517 determining trophic relationships, 483

755 natural enemy movements, 237, 482 Dolichopodidae, 439, 451, 472 Dominance component (=VD) in quantitative genetics, 234, 265 Dormancy aestivation, 197, 198 defined, 196 fitness costs, 200 host physiology, 198 moisture, 199 obligate/predictive, 197 photoperiod, 199 population dynamic effects, 199 prey availability/quality, 198 temperature, 196, 199 Doubling time (=DT), 193 Dragonflies, see Odonata Drop trap methods, 432 Dryinidae, 51, 130, 379 DT, see Doubling time Dufour’s gland, 51, 125, 126 Dummies, use of studies of host acceptance, 31, 39, 321, 324 studies of parasitoid mating behaviour, 31, 39, 309, 310, 313, 319, 321–324, 327 Dyes, 236, 299, 675 Dynamic optimisation/programming models, 44, 685 E E, see Environmental effect Earwigs, see Forficulidae ECI, see Efficiency of conversion of ingested food Eclectors, 448 Economic threshold, 363, 369 Ectoparasitoids, 43, 51, 54, 63, 128, 149, 171, 183, 272 Efficiency of Conversion of Ingested food (=ECI), 150 Eggs(s) alecithal, see hydropic anhydropic/anhydropic, 113, 119, 120 chorion, 109, 112, 113, 115, 118, 120, 483, 491 contents, 116, 190 development (=oögenesis =ovigenesis), 108, 121–123, 131, 132, 251, 252 distributions in hosts, 47–49 fertilisation, 58 fertility, 129, 133, 605 hydropic, 115, 118, 119, 128 lecithal, see anhydropic -limitation, 358, 391, 622, 626 load, 9, 45, 46, 56, 76, 105, 117, 119, 121–123, 129, 136–139, 141, 190, 200, 201, 366, 489, 635, 648, 688, 690 locating in hosts, 128 micropyle, 112 movement along ovipositor, 35, 37, 39, 43, 51, 109, 115, 180, 496 resorption, 118, 120–122, 145, 202 sex, 626

756 shape/size, 622 storage, 115, 122, 200 supernumerary, 47–49 yolk, 113, 118 Egress boundaries, 181 Ejaculate/ejaculation, 325, 329, 330–333, 337, 338, 390, 396, 398 Electroantennography, 315 Electrophoresis, 235–238, 364, 485, 486, 503, 506, 599, 674 Elemental marking, 478 ELISA, see Enzyme-linked immunosorbent assay Emergence traps, 425, 449, 450, 469 Empididae, 379, 437 Encapsulation, of endoparasitoids by the host, 43, 180, 181, 185, 186, 188, 189, 191, 369 Encounter rate(s), 40, 43, 44, 66 Encyrtidae, 54, 116, 120, 191, 379 Endomitosis, 252 Endoparasitoids, 43, 118, 119, 122, 128, 149, 162, 170, 171, 180, 181, 183, 184, 186, 188, 190, 198, 199 Enemies hypothesis, 685 Energy content, 202, 681, 684 expenditure, 682–684 intake, 683, 684 reserves, 201, 202, 685 Enterobacteriaceae, 532 Environmental effect (=E) in quantitative genetics, 129, 201 Environmental variance (=VE) in quantitative genetics, 160 Enzyme-linked immunosorbent assay (=ELISA) population dynamics, 478, 488, 496, 503, 509, 599 Epistatic component (=VI) in quantitative genetics, 265, 267, 269, 280, 281 Equilibrium population levels, 363, 610 Errors, 708, 709, 711, 712, 716, 721, 723, 725, 726, 728–730, 733–737 Type I error, 707, 708, 728, 734 Type II error, 708, 734 ESS, see Evolutionarily stable strategy Esterline Angus event recorder, 303 Ethovision, 304, 318 Eucoilidae, 8, 370, 373 Eulophidae, 111, 114, 124, 296, 312, 632, 687 Eupelmidae, 114, 125 Eurytomidae, 107, 124, 126, 669 Event recorder(s), 303, 304 Evolutionarily stable strategy/strategies (=ESS), 5, 6, 75 Exclusion techniques, 592, 593 Experience, effect on natural enemy behaviour, 9 Experimental component analysis, 368 Explanatory variable, 710, 711, 714–716, 720, 721, 723–729, 731–733, 736 Exploitation competition, 158–160, 179, 180, 380, 643, 680, 680 Extraction, sampling methods, 236, 237 Extreme synovigeny, 117

Subject Index F Factorial ANOVA, 711, 721, 723–725, 727, 728, 737 Factor levels, 725, 727, 728 Faecal analysis, 500, 503 False indications from data (false positives/negatives), 484, 486, 494, 536 Fat body/fat reserves, 118, 146, 200 Fecundity biological control, 386, 388, 390, 394, 399 biotic factors affecting, 129, 143, 149, 198 body size, 113, 115–117, 119, 129, 137, 140, 143, 144, 146, 147, 170, 196, 200, 361, 363, 382, 385, 390, 391 cohort fecundity schedules, 129, 192 cumulative, 122, 130, 149, 154 establishment rate, in biological control, 639, 647 female size, 129, 144 fitness, 116, 122, 129, 130, 141, 168, 169, 171 food consumption, 138, 140, 141, 146, 150, 151, 156, 177 host density, 690 host quality, 134, 145 host size, 137, 138, 147, 161–165, 169, 180, 181 humidity, 131, 139, 142, 143, 148, 149, 174, 176, 191, 199 lifetime, 116, 117, 122, 129–132, 137, 139–142, 148, 635, 686, 688, 690, 729 light intensity, 131, 141 mating, 138, 139, 147 measurement of, 127, 686 mutual interference, 136 photoperiod, 131, 139, 141, 142 physical factors affecting, 139 potential, 686, 690 realised, 117, 129, 130, 137, 139, 142, 642, 688–690 schedules, 129–132, 139, 140, 192, 196 temperature, 130, 139, 140, 143, 149, 176, 190, 196 triangular fecundity function, 131 weather, 142, 176 Feeding digestion, 153 effects on life-history parameters, 195 fecundity, 138 food consumption, 138, 673 food conversion, 156 history, 155 ingestion (=consumption), 132–134 non-host and non-prey foods, 684 nutritional indices, 150 nutritional stress, 45 site, in mating, 378 Feminisation, by Wolbachia, 127 Fenced pitfall traps, 425, 427, 429, 433 Fertilisation, 112 Fertility, 129, 133, 605 Field observations foraging on natural enemies, 487 Fig wasps, see Agaonidae Finite capacity for increase (k), 192 Finite displays in courtship, 318, 319

Subject Index FISH, see Fluorescence in situ hybridisation Fisher-Ford method, 465 Fitness body size, 42, 55, 56, 170 clutch size, 54–58 costs, 168, 200 function, 54 host stage, 42, 45 measures, 54–56, 106 Fixed effects, 736, 737 Fixed-time experiments, 70 Flight, 240, 241, 281, 308, 419, 438, 439, 445, 450, 453, 464, 469–471, 473, 474, 479–482, 500, 683, 684 Flowers/flower-visiting, 452, 481, 670, 677, 687 Fluctuating asymmetry, 148, 172 Fluorescence In Situ Hybridisation (=FISH), 63 Fluorescent dusts, 11 Fluorescent marker substances, 11 Fogging with insecticides, 439, 442, 443 Food stress, 159, 177, 179 Food webs compartmentalisation, 508, 520 construction and analysis, 511 reconstruction, in classical biological control, 341, 368 Food, see Feeding Foraging age-dependence, 77 analysing data, 11 approaches to study, 3–7, 680 area of discovery, 365, 399 assumptions in model construction, 4 causation, 3 clutch size, 54–58 comparative aspects, 106 costs, 43, 683 data handling, 11 decisions, 2 distribution of parasitoids over host population, 72, 73 egg-limitation, 76, 622, 626 encounter rate, 64, 65 energetics, 677, 681 equipment required by investigator, 484 experience, 50 field observations, 67, 487 flight, 17, 683 function, 3, 6, 627 functional responses, 67, 69–71 genetic variation, 29, 637 handling time, 490, 678 host-feeding, 45 host discrimination, 46–51 host/prey location behaviour, 17 interference, 73, 75, 594 learning, 30 males-first mechanism, 62 metabolic cost, 683, 684 models, 4, 5, 44, 45, 56, 57, 65, 70, 680, 681 patch defence behaviour, 65, 66

757 patch-leaving mechanism, 16 patch time allocation, 69–71, 682 path(s), 33, 35, 689 reliability/detectability, 26, 651 rules, 61, 682 searching efficiency, 396, 645, 685, 690 sex allocation, 58 state-dependence, 685, 687 taxonomy, 77, 671 threshold departure rule, 682 time-budget, 4 time-limitation, 76, 77, 358, 391 treatment of insects prior to study, 9 Forficulidae, 595 Formicidae, 492 Forwards (bottom-up) analysis, 727 Foundress number, 58 F-ratio, 720, 721, 725, 727, 730 Fratricide, 307 Frequency-dependent selection behaviour, 58 Frequentist statistics, 705, 706 Full-sib design, 267 Functional approach/analysis in behavioural studies, 3, 4, 6 Functional response(s) age-dependent/specific, 77 biological control, 73 classification, 70 curve-fitting routines, 69 defined, 67 estimation of parameters, 68, 70 experimental design, 67, 69–71, 688, 717, 736 fixed-time/variable-time experiments, 70 handling time, 615, 621, 622, 682–684 Holling’s disc equation, 132, 154 lifetime, 359, 368, 390, 635, 677, 690 non-host foods, 679, 684, 687, 690 total, 67 Type 1, 50, 67, 618, 626 Type 2, 67, 69, 70, 131, 133, 154, 618, 627, 635 Type 3, 67, 69, 70, 131, 618, 621, 623, 635 Type 4, 70 Fundamental niche, 80 Fungi, 8, 33, 415, 460, 523, 527, 529, 532–534, 632, 673 G G, see Deviance G, see Genotypic value Game theory, 5 Gamete duplication, 251 Gamma distribution, 709, 728 Gas chromatography, 462, 675 Gaussian distribution, 709 General linear models, 705, 709–712, 724, 728–731, 734, 735, 737 Generalised Estimating Equation (GEE), 737 Generalised linear interactive modelling, see GLIM statistical package

758 Generalised linear mixed modelling (GLMM), 712, 736, 737 Generalised Linear Models (GLMs, GLZs), 705, 709–711, 728, 735–737 Generalists, 28, 368, 416, 487, 502, 518, 597, 600, 601, 612, 625, 650, 674, 677 Generation time, 192, 195, 636 Generation Time Ratio (=GTR), 636, 637 Genetic(s) correlation, 266, 268, 270, 273 distance (=D), 7 drift, 9 maps and mapping, 236, 241, 247 mating systems, 253 model(s), 360 quantitative, 129, 160, 201, 235, 258, 259, 264–266, 273, 275, 276 variability in populations, 30 variance (=VG), 265–268, 270, 271, 273, 275 Genic balance, 366 Genitalia female parasitoids, 12–14, 39, 51, 59, 109, 172, 177, 329, 390, 504, 731 male dragonflies, 109 male parasitoids, 123, 140, 329, 390, 730 see also Ovipositor; Reproductive anatomy Genome size, 246 Genomic imprinting, 258, 270 Genotype-by-environment interactions, 10 Genotype, defined, 167 Genotypic value (=G), 265, 276, 277, 278 Gerridae, 116, 436 GLIM statistical package, 725, 735 Glycogen, 201 Greenhouse populations, 376 Gregarious parasitoids, 47, 54, 161, 168–171, 181, 185, 186, 201, 255, 255, 365, 368, 369, 373–375, 377, 378, 387, 636, 646 Gross Reproductive Rate (=GRR), 192 Growth of immatures consumption/ingestion rate, 151 feeding history, 155 growth rate, 151 growth trajectory, 164 host size, 162 host stage, 165, 196 idiobionts, 161 koinobionts, 162 measurement, 149 physical factors affecting, 171 prey species, 157 rate, 149–151, 154, 160, 163, 174 superparasitism, 168, 185 temperature, 174, 175 threshold prey density, 154 trajectories, 163, 164 GRR, see Gross reproductive rate GTR, see Generation time ratio Gut

Subject Index contents/dissection, 423, 483, 484, 486, 501, 502, 511, 516, 673, 675 fullness/limitation, 41, 683 Gynandromorph (=sex mosaic), 258, 259 Gypsy moth, 29 H H, see Heterozygosity, degree of Habitat manipulation, in biological control, 428 Habituation, to a stimulus, 35 Haemolymph sugars, 676 Haldane’s mapping function, 247 Half-sib design, 268, 269, 273 Hamilton box trap, 449 Handling of insects, 302 Handling time, 41, 44, 66, 69, 70, 490, 615, 618, 621, 622, 678, 682, 684 Haplo-diploidy, 58 Hardy-Weinberg ratio, 363 Harmonic radar, 478, 479 Hazard function, 144 Head-nodding by courting male, 314, 319 Hemiptera, see individual families Heritability (=h2), 266, 269, 270, 273, 274, 280 Hermaphroditism, 234 Heterogamety, 254, 365 Heterogeneity Factor (HF), 734 Heteroptera, see individual families Heterospecific superparasitism (= multiparasitism), 52, 168–171, 181, 184, 186, 191, 507 Heterozygosity, degree of (=H), 237, 251, 252, 256 High performance liquid chromatography, 675 Hill-topping behaviour, 380 Holistic criteria in biological control, 641 Holling’s disc equation, 132, 154 Homogamety, 365 Homoptera, see individual families Honey, 120, 122, 132, 139, 146, 148 Honeydew, 29, 32, 452, 594, 596, 669, 670, 672, 674–676, 679, 680, 685, 687, 689, 690 Horizontal transfer/transmission of endosymbionts, 514, 528, 536 Host age, 181 Host defences, 43, 187 Host diet, 138, 165 Host mutilation (parasitoids), 476 Host size/stage parasitoid foraging behaviour, 42, 43, 61 parasitoid growth and development, 164 parasitoid survival, 181, 186, 190 rm/rc, 196 Host species parasitoid foraging behaviour, 40, 41 parasitoid growth and development, 171 rm/rc, 196 Host/prey selection acceptance, 39, 43, 49, 109, 496, 648 discrimination, 46–48

Subject Index encounter rate(s), 40, 66 host discrimination, 46–49 host habitat location, 16–18 host/prey location, 10, 17, 29, 32, 33, 458, 466, 515 host quality, 40, 41, 69–71, 134, 145 preference, 40–42 recognition, 39, 40 rejection see also host discrimination, 14, 15, 43, 49, 65, 186, 340 size selection, 40 specificity, 188, 189, 298, 509, 510, 527, 528, 599, 603, 634, 639, 647, 648 stage selection, 42, 43 see also Clutch size; Sex allocation, 2, 4, 44, 54, 55, 56, 57, 58, 138, 161, 166, 170, 371, 381, 382, 383, 387 species selection, 40, 41 switching behaviour, 63–65 Host-feeding (parasitoids), 45, 105, 118, 125, 133–135, 145, 146, 180, 184, 359, 375, 378, 379, 388, 396, 397 HT, see Horizontal transfer/transmission of endosymbionts Hughes’ time-specific life-table method, 610 Hydropy/hydropic egg production, 119 Hymenoptera, see individual families Hyperparasitism/hyperparasitoids, 34, 131, 161, 163, 167, 240, 614, 639 Hypobaric olfactometer systems, 22 Hypotheses testing, 705, 706, 708, 721, 727, 737 I Ichneumonidae, 110, 113, 114, 116, 117, 125, 148, 163, 167, 168, 302, 321, 341, 378, 379, 439, 458, 499 Idiobionts/idiobiosis, 43, 112, 116, 118, 126, 161, 162, 165, 180 Immatures (of natural enemies) growth and development, 148, 149 instar number, 150 survival, 106, 176, 177, 180, 185, 190 Immune response(s) of hosts, 42, 43, 78, 187 Immunoassays fluorescence, 63, 477, 505 Immunoglobulin, 478, 505, 599 Inbreeding crosses, 257 depression, 9 Income breeding, 118 Independent comparisons/contrasts method, 6–8 Individual-based approach, in population dynamics, 394, 395 Infochemical(s) allelochemicals, 18 allomones, 18 kairomones, 18, 32, 33, 39 pheromones, 18 synomones, 18, 27 use in collecting insects, 432

759 Initial egg load, 117, 119, 129, 201 Insecticidal interference method, 594, 596 Insecticide(s) resistance, 235, 324 trap method, 369, 373, 376, 377, 596 use in knock-down, 441 use in quantifying predation and parasitism, 591, 650 Insemination, 295, 296, 300, 325, 327, 329, 330, 331, 334, 338, 362, 374, 375, 380, 391, 392 Instantaneous risk of death, 144 Instars duration, 151, 154, 156, 177 distinguishing between, 237, 298, 309, 388 Integer, 709, 728, 736 Interaction, 708, 721, 723–728, 732, 736, 738 components in quantitative genetics, 265 effects in population dynamics, 358, 359, 361, 363, 371, 375, 381, 383, 386, 387, 397, 399 statistical, 721–728, 732 strength, in food webs, 513, 514 Interception devices/traps, 420, 437, 439, 467, 469–471, 473, 474, 480 Intercropping, 686 Interference behaviour, 73–75 Intermediate predator(s), 360, 361, 381 Intersexes, 258 Inter-sexual conflict, 335, 337, 357, 358, 393, 398 Interval mapping, 227, 279, 280 Intragenomic conflict, 261 Intraguild predators/predation, 53, 601, 610, 644 Intra-sexual conflict, 357 Intrinsically superior parasitoids, 183 Intrinsic rate of natural increase (=rm), 191, 192, 194, 196, 645 Inundative release(s), 141 Inverse density-dependence, 131, 606 Invulnerable host stage, 626, 636 Irradiated male technique, 336 Iso-female lines, 244, 259, 267, 268, 273, 274, 522 Isopoda, 496 Isotopes, 422, 465, 466, 477, 478, 479, 481, 483, 485, 503, 504, 674 J Jaccard coefficient, 520 Johnson-Taylor suction trap, 473 Joinmap, 248 Jolly’s method, 464, 465 K K, see Carrying capacity of the environment K, see Thermal constant Kairomones, 12–16, 32, 33, 596, 648 Karyotype(s), 254, 255, 263 Key factor(s)/key factor analysis in studies of population dynamics, 604–610 Keystone species, 515

760 Killing agents, chemical, 106, 432 Knock-down techniques chemical, 441 mechanical, 440 Koinobionts/koinobiosis, 27, 42, 43, 169 Kosambi’s mapping function, 247 k-values, 160, 180, 186, 605–607, 609 L k, see Finite capacity for increase lx, see Survival/survivorship, age-specific/lx Labelling natural enemies, 478, 482 prey and hosts, 486, 503 Lacewings, see Chrysopidae Lack clutch size, 54 Ladybird beetles, see Coccinellidae Larvae/larval combat/fighting, 181, 184, 185 growth and development, 149, 150, 157, 168 instars, 148, 153, 155, 156, 165, 174, 182 survival, 116, 142, 158, 169, 176–178, 180, 181, 189, 191 Laser vibrometry, 37 Leafhoppers, see Cicadellidae Learning, 30, 32 Least-squares (LS), 710, 712, 720, 730 Leguminosae, see Fabaceae Lekking behaviour, 379 Lepidoptera, see individual families, also Moths and Nymphaloidea Life-history trade-offs, 75, 106, 117 Life-span, see Survival/survivorship/life-span/lx/longevity Life-tables/life-table analysis age-specific, 192, 604–608 ‘before and after’, 202, 295, 376, 649 composite, 605 spatial heterogeneity in parasitism, 638 see also Key factor(s) analysis, Intrinsic rate of, 605, 606, 608 natural increase, 191, 192, 194, 196, 645 Light intensity and quality, 131, 141, 142, 199 photoperiod, 131, 141, 142 Light traps, 454, 473 Likelihood analysis, in quantitative genetics, 275 Likelihood ratio test, 729 Limit cycles, 617 Lincoln Index, 463 Linear pitfall traps, 419 Linkage disequilibrium, 276 maps, 236, 246, 247, 248, 249, 264, 276 Link function, 728, 731 Linyphiidae, 135, 179 Lipids, 108, 116, 119, 146, 147, 201, 202, 462, 528, 676, 684 LMC, see Local mate competition

Subject Index Local Mate Competition (=LMC), 58, 59, 360, 365, 367, 380, 381, 387, 388 Local/global stability in population models, 638 Local mating, 58, 59, 308, 360, 365, 367, 369–375, 377, 378, 380–384, 387, 734 Locating eggs in hosts, 127 Logarithm Of Odds (=LOD), 248 Logistic models, 712, 730, 732 Logit, 731 Log-linear models, 712, 728–731, 734 Longevity, see Survival Lotka-Volterra model, 615, 618 Lottery model, 633 LT50, 143 Lycosidae, 436, 447 M mx, see Fecundity, age-specific Main effects, 725–727 Malachiidae, 455 Malaise trap, 419, 437, 438, 439, 486 Male-killing microbes, 10 Males-first mechanism/rule, 5, 61 Manipulation experiments in population dynamics, 610, 611 Mantids, see Mantodea Map-distances, 243, 244, 248 Mapmaker, 248 Marginal hosts, 189 Marker(s) genetic/phenotypic, 12, 336, 360, 640 other, 675 Marking hosts/substratum, by parasitoids, 32, 33, 35, 57 natural enemies see also Labelling, 11, 295–299 techniques, 299, 302 Mark-release-recapture methodology, 56 Masner-Goulet trap, 471 Mass-rearing of natural enemies, 60 Mate choice, 298, 301, 303, 304, 311, 312, 321, 323, 327, 330, 335, 363, 378, 379, 392, 393, 397 competition, 10, 58, 59, 295, 329, 360, 365, 367, 370, 380–382, 387, 388 guarding, 325, 359, 361, 362, 363, 398, 399 recognition, 315 searching, 299, 303, 307 sneaking/sneaky males, 327 Maternal care, 65 Maternal sex ratio factor/element (=MSR), 261 Mating ability, 385, 391, 392 alternative tactics, 361, 385, 396 comparative studies, 59, 326, 338, 341, 342, 380, 381 competition for females, 396 egg production, 301, 338 non-local, 308, 360, 369, 371, 372, 377, 378, 380, 382, 383, 384

Subject Index receptivity, 300, 301, 324, 325, 331, 390, 392, 397 ‘scramble competition’, 396 site, 378, 379 sneaky males, 327 sperm depletion, 250 strategies, 399 success, 139, 301, 302, 312, 394, 395, 637 systems see also Mating systems, 253, 360 virginity, 365, 387 within-host, 303, 306, 311, 369, 380, 381 Mating behaviour acoustic signals, 304, 308 alternative tactics, 361, 385, 396 causation, 297, 298 chemical stimuli, 9, 314, 315, 419 comparative aspects, 106 confinement, effects of, 139, 147 copulation and insemination, 327 culturing/handling insects for study, 300, 640 definition, 295 describing, 304 dummies, 320 duration of copulation, 328, 397 egg production, 338, 428 equipment for recording, 11, 33, 72, 161 evolution, 315, 342 experience, effects of, 10 female receptivity, 318, 321, 325, 331, 385 frequency of mating, 337 function, 304 gregarious parasitoids, 307, 324, 330 investment in courtship, 318, 319 making records, 302, 306 marking behaviour, 299 organisation, 240, 340 orientation during courtship, 309 pair formation and courtship, 306 phylogenetic aspects, 341 posture, 327 readiness to copulate, 327 receptivity, 327 searching for mates, 306 sexual cannibalism, 338 solitary parasitoids, 307 stimulus production by males, 310 swarming behaviour, 311 taxonomy, 338 temperature, 302 territoriality, 476 visual and tactile stimuli, 316 Mating systems choosy females, 396 comparative studies, 338, 380 cryptic female choice, 398 defined, 358 female receptivity and male ability, 385 local mating, 370 parasitoids, 126, 394–399 post-dispersal mating and immigration, 373, 378

761 predators, 394 theory, 358 Maximal model, 725, 726 Maximum consumption method, 635 Maximum Likelihood (ML), 729, 730 Mayflies (Ephemeroptera), 64 McPhail trap, 454–456 Mealybugs, see Pseudococcidae Mean generation time (=T), 192 Mean relative growth rate (=MRGR), 149 Meiosis, 234, 246, 251, 252, 527 MESD, see Minimum effective sterilising dose Meta-analysis, 737 Metabolism/metabolic rate/metabolic costs, 118, 132, 134, 140, 151, 154–156, 158, 161, 175, 178, 523, 683 Metal detection equipment, 479 Metaphase chromosomes, 256 plates, 255 Metapopulation concept, 613 dynamics, 521, 601, 611, 613, 614 models, 602, 611, 613, 614 Microclimate, 449, 593, 596, 679 Micropyle, 112, 115 Microsatellites, 63, 235, 238, 246, 255, 325, 337, 364, 509, 520, 521 Migratory movements, 466 Minimal adequate model, 725–727 Minimum Effective Sterilising Dose (=MESD), 336 Minisatellites, 235, 238 Miridae, 179 Mites, see Acari Mitochondrial DNA, 234, 253, 510, 520 Mitosis, 527 Mixed cropping, 686 Mixed strategy, 272, 361 mlCSD, see Multi-locus complementary sex determination Mode of reproduction, see Reproductive mode Model checking, 734 Model selection, 724 Model simplification, 724–726, 728 Models/modelling age-specific, 605, 607, 646 artificial neural network, 3 biological realism, 148 bottom-up/top-down, 727 cell occupancy, 613, 614 constraint assumptions, 5 continuous time, 364, 366, 370, 372, 380, 385–388, 391, 393, 394, 396 deterministic, 613 development rate, 626 discrete time, 615 dynamic optimality, 50, 56 ESS, 5, 6, 50, 57, 59, 75, 361, 365 experimentation, 370, 376, 377, 391–393

762 generality, 368, 626 global/local stability, 638 growth rate, 613, 617, 621 Holling’s disc equation, 132 host-feeding, 45, 46, 623, 626 intermediate complexity, 360, 361, 381, 626 local/global stability, 638 phenotypic, 360 population, 357, 358–361, 364, 371, 390, 397, 593, 602, 613, 634, 650 predation rate models in serology, 628 preference, 40, 41 prospective, 642 retrospective, 650 sensitivity analysis, 626, 627 sex allocation, 360 simulation, 610, 625–628, 635, 644 stage/age-structured, 636, 643 stochastic, 629, 630, 638 Weibull, 144, 709, 728 Molecular clock, 7 markers, 171 mimicry, 272 Monoclonal antibodies, 599–601 Monogamy, 394 Monomer, 236 Monophagy, 30, 40, 639 Morphometric techniques/morphometry, 385 Mother-son matings/crosses, 256, 257 Moths see also individual families, 378, 380, 389, 632 Motivation to oviposit, 14, 123 Mouthparts, 150, 502, 505, 599, 671–673, 680, 682, 686, 687 Movements (=spatial displacements) of insects, 394 MRGR, see Mean relative growth rate MSR, see Maternal sex ratio factor mtDNA, see Mitochondrial DNA Multiple hypothesis testing, 728 Multiple-locus complementary sex determination (=mlCSD), 258 Multiple regression, 711, 716, 721, 723–725, 728, 733, 737 Multiple regression, in quantitative genetics, 258 Multispecies introductions in classical biological control, 359, 360, 364, 365, 368–371, 373, 375, 377, 379–381, 385, 386, 388, 390, 394, 396, 398, 399 Multitrophic interactions, 140, 167, 181, 644, 645 Multivariate analysis of variance (MANOVA), 736 Mummies, aphid, 611 Murdoch’s preference index (=c), 40 Mutants (phenotypic markers), 171 Mutual interference, 73–75, 135, 136, 357 Mymaridae, 106, 116, 127, 309, 380 Myrmeleontidae, 492

Subject Index N

15

N/14N ratios, 485 Nabidae, 432 Narrow-sense heritability (=h2), 266 Nectar/nectaries, 452, 456, 466, 481, 669–685, 687, 689 Negative binomial distribution, 709, 728 Negative feedback, in population models, 601 Negative switching behaviour, 63 Nests, contents of, 612 Net reproductive rate (=R0), 192 Nets, 431–439, 470–472, 474, 481, 501 Neural network models, 3 New associations, theory of, 359, 368, 388, 399, 633, 634 Niche, 640, 643, 680 Nicholson-Bailey model, 615–621, 623, 625 aggregative response, incorporation of, 13, 72, 596, 636 basic structure, 616 functional responses, incorporation of, 618, 621 stability properties, 602, 625 Nitrogen, 201, 423, 483, 485, 505, 523, 685 Noctuidae, 340 Nocturnal insects, 419 Non-host/non-prey foods, 121, 132, 135, 142, 146, 187, 599, 669, 684, 688 Non-parametric tests, 708, 712, 721 Non-target effects, in classical biological control, 630, 647–649 Non-target hosts/prey, 486, 647 Normal distribution, 709, 728, 729, 733 North Carolina Experiment I, 269, 646 No-switch model, 64 Notonectidae, 179, 635 Null hypothesis (H0), 706, 708, 727, 737 Null model, 726 Numerical response(s), 603, 634, 636–638, 685, 690 Nuptial gifts, 337, 338, 397, 398 Nutritional indices, 150, 151 O Obligate dormancy, 142, 197 Observer, 594 Observer Video-Pro, 671 Odonata see also individual families, 147, 152–156, 159, 176, 334 Odours host, 17, 26, 28, 677 parasitoid, 33–36, 676 plant, 21, 26, 28, 676 prey, 26, 29, 36 Olfactometers/olfactometry air-flow, 18 hypobaric systems, 18, 20, 22 supplemental foods, 688

Subject Index Olfactory attraction, 16–18, 29, 422, 454 Omnivory, 416, 514 One-point cross, 240, 241 Oöcyte, see Egg Oögenesis see also Ovigenesis, 108, 121, 122, 252, 530 Operational Sex Ratio (=OSR), 362 Opiliones, 423, 441, 470, 488, 496, 500, 501 Opportunity time, 43 Optimality criteria, 4 Optimality models, 4, 5, 44, 50 Oscillations, in population dynamics, 358, 359, 363, 368, 371, 375, 381, 383, 385–387, 391, 395, 397, 399, 616, 617, 619, 622 OSR, see Operational sex ratio Ovaries/ovarioles ovariole number, 113 polytrophic ovarioles, 113 telotrophic ovarioles, 113 Overall performance (parasitoids), measure of, 368, 376 Overdispersion, 726, 734–736 Overwintering, 200, 450, 459, 467, 477, 605, 643, 645 Ovicide, 131 Oviduct(s), 113–115, 120, 122, 123, 125, 188, 250, 332 Ovigenesis schedule, 123, 131 Ovigeny/ovigeny index, 686 Oviposition clutch size, 54–58 deterrent substances, 51 functional responses, 67, 69–71 host discrimination, 46, 47, 49 host species selection, 39, 40 host stage selection, 42–44 movement of eggs along ovipositor, 115 sex allocation behaviour, 55, 60 site, 56, 376, 378, 379, 394, 396, 481, 623 switching behaviour, 63–65 Ovipositor function, 109 search, 37 sensilla, 109 structure, 109 P

32

P, 465, 481, 504, 675 P, overall population sex ratio, 194 P, phenotypic value, 265, 269, 276, 277, 279, 280 P, see Polymorphism, degree of P2 value, 332, 333 PAGE, see Polyacryldamide gel electrophoresis Paint-marking, 299, 476, 690 Paired life-tables, 398, 594 Pair formation and courtship female receptivity, 300, 312–315, 318, 320, 321, 325, 331, 385 male investment in courtship, 318, 319 male orientation during courtship, 309 proximate factors and display release, 309

763 searching for mates, 306 stimulus production by males, 310 Panmixis, 58, 59, 360, 363 Parametric tests, 708 Parasitoid(s)/parasitism aggregation/aggregative response, 72, 73, 370, 379, 380, 618, 620, 624, 625 anhydropy/hydropy, 118, 119 arrestment, 33, 35–38, 689 arrhenotokous, 138, 637 associative learning, 30 attraction, 16–18, 689 autogeny/anautogeny, 118 autoparasitoid(s), 529 body size, 42, 116, 677 brood-guarding, 66 clutch size, 54–58, 161, 635 coexistence, 616, 622, 624, 625, 643 competition, adult, 54 competition, larval, 182, 621, 623, 626, 642, 643, 649 conditioning, 10, 37 constancy, 499, 677, 691 CV2, 623 decisions, 2 density-dependence, 360, 362, 367, 369–373, 375, 608 dispersal, 73, 363, 369–376, 378, 380–383, 392, 614, 690 distribution over host population, 72, 73 dormancy, 197 egg(s), 26, 49, 51, 78, 114, 127, 128, 170, 180, 181, 184, 185, 187–190, 256, 272–274, 460, 503, 507 egg-limitation, 75, 76 egg load, 41, 46, 51, 67, 122 egg resorption, 120 egg storage, 115 encapsulation, 180, 185–188 encounter rate(s), 66, 67 experience, 9, 10, 46, 64 flower-visiting, 670, 673, 676, 677, 679, 682, 687, 691, 692 fecundity, 116, 123, 127, 138 food webs, 79, 80, 692 functional responses, 67 genetic variability, 65, 67 growth trajectories, 164 guild(s), 416, 644, 647, 680, 686 habitat choice, 8 handling time, 67 hilltopping behaviour, 380 host acceptance, 39, 43, 54, 648 host discrimination, 46–49 host-feeding behaviour, 45, 46, 68, 105, 118, 125, 133–135, 145, 146, 180, 184, 359, 375, 378, 379, 388, 396, 397 host habitat location, 16–32 host location, 29 host recognition, 39, 40 host size determination, 57

764 host size selection, 43 host species selection, 40–44 host specificity, 639 host stage/size selection, 42–44, 602 hydropy/anhydropy, 119 idiobionts, 43, 118, 122, 161, 162, 198 immature stages, sampling, 459 impact on host populations, 363, 365, 368, 369, 371 intrinsic rate of natural increase (rm), 191, 192 koinobiont, 42, 43, 118, 161, 162 life-history strategies, 498 lekking, see swarming behaviour longevity, 145–147 maximum level, 635 measurement of parasitism, 498 mutual interference, 73–75, 135, 136, 522 niche, 80 non-host foods, 121, 135 numerical response, 603, 636–639 ovaries/ovarioles, 5, 46, 76, 105, 109, 113, 115, 120, 123, 129 overlap diagram, 517, 519 parental care, 358–360 patch defence behaviour, 77 patch marking behaviour, 52 patch time allocation, 72 per cent parasitism, problem of, 592, 602, 603, 630 physiological resource allocation, 200–202 pollination by, 691 polyphagous, 517 preference, 498 progeny allocation, 55 pro-ovigenic, 76, 129, 685 pseudo-interference, 74 quasi-gregarious, 369, 370, 377 rates, 596 recognition of hosts, 39, 40 release strategy, 54 rearing methods, 460, 461, 497, 512 rejection of hosts, 14, 15, 49 search mode, 36–38 searching efficiency, 32, 42, 73–75, 105, 122, 135, 142 self-recognition, 51 sex ratios, 127, 194, 195 superparasitism, 50–52, 65, 168–171 survival (immatures), 106 swarming behaviour, 379 switching behaviour, 63 synchrony with host, 645 synovigenic, 76, 115, 118, 120, 129, 685, 689 taxonomy, 500 time-limitation, 75, 76 treatment of, 9–11 Parental care, 358–360 Parent-offspring regressions, 259, 260, 269 Parsimony, 710, 713, 714, 718, 724, 737 Parthenogenesis induction (PI) by Wolbachia, 532

Subject Index Patch(es) arrestment in, 35 defence, 65, 66 emergence, 370, 372 foundress number, 58 heterogeneity in parasitism, 638 marking, 35, 36 mating system, 360, 370, 376 profitability, 33 residence times, see time allocation see also Aggregative response, Spatial stimuli, 33, 35 time allocation, 35, 67–69, 71 travel time between patches, 66 Paternal Sex Ratio factor/element (=PSR), 261, 262 Pathogens, 393, 632, 633, 643 Pay-off from superparasitism, 184 PCR, see Polymerase chain reaction Penis, see Aedeagus Pentatomidae, 456 Percent(age) parasitism, measurement/problems, 381, 602, 603 Pest management, 342, 373, 377, 386, 399, 441, 506, 591, 592, 630, 632, 633 Pettersson olfactometer (4-way), 20–23 Phalangiidae, 424 Phenological matching, 677 Phenology/phenologies of natural enemies/hosts, 602 Phenotypic markers, 240 value (=P) in quantitative genetics, 265, 269, 276, 277, 279, 280 variance (=VP) in quantitative genetics, 265, 266 Pheromone(s), 18, 125 Pheromone traps, 29, 455, 456 Phonotaxis, 18 Phoridae, 254 Photoeclector, 429, 449, 470 Photoperiod, effects on life-history variables, 131, 139, 141, 142, 148, 149, 176, 191, 197, 199 Phylogenetic screening technique in biological control, 648 Phylogeny/phylogenies/phylogenetics, 6–8, 119, 233, 239, 298, 312, 326, 332, 341, 342, 648 Physical barriers, 425 Physical removal technique, 594, 599 Physiological defences of hosts, 78, 362, 398, 648 Phytophagy, 669–676 PI, see Parthenogenesis, induction by Wolbachia Pipunculidae, 123, 437, 500 Pitfall trapping, 420, 422, 425, 427, 429, 430, 443, 464 Pivotal age, 192, 194 Placing-out of prey, 397 Plant architecture, 71 leachates, as food, 673 materials, as food, 447, 448, 498, 501 resistance, 78, 397, 645 volatiles, 20, 21

Subject Index Planthoppers, see Delphacidae Platygastridae, 54, 107, 452 Poison gland, see Venom gland Poisson distribution, 709, 728–730 Pollen, as food, 452, 466, 481 Pollination by natural enemies, 691, 692 Polyandry, 323–326, 329, 330, 335, 358, 367, 368, 387, 388, 390, 392, 394, 396, 397 Polydnaviruses, 114, 529 Polyethylene barriers, 426 Polygamy, 394, 395 Polygyny, 324, 358, 390, 394 Polymerase Chain Reaction (=PCR)/primers, 235, 508, 535, 600 Polymers, 236 Polymorphism, degree of (=P), 237, 482 Polyphagy, 52, 639 Pompilidae, 148 Pond-skaters, see Gerridae Population(s)/population dynamics absolute/relative estimates of abundance, 449 age-structure, 605 cycles, 617 definition(s), 591 delayed density-dependence, 604, 608 density-dependence/regulation, 606, 607, 610 density-independence, 606 diversity centres, 594 equilibrium/equilibria, 363 extinction, 514, 613, 615, 634, 638 factorial experiments, 610 fluctuations, 386 genetic variability, 32, 41 intensity, 420 life-table analysis, 604 limit cycles, 617 local, 359, 360, 365, 367, 369–374, 377, 380, 381, 384, 387, 388 metapopulation, 521 numerical response(s), 636 per cent parasitism, problem of, 602 persistence, 417 refugia, 634 regional, 613 regulation, see density-dependence suppression, 635 stability/stabilisation, 397 synchrony, 371, 372 time-sequence plots, 383 transient dynamics, 643 Post-dispersal mating, 373, 378 Post-mating events, 331 Postures in courtship and mating, 311 Power (statistical), 707, 708 Precipitation, see Serological Methods, precipitation Predator(s)/predation attraction, 28 adult longevity, 142 aggregation, 72

765 ambush, 38 anatomy, 106 aquatic, 474, 501 arrestment, 36, 37 autogeny/anautogeny, 118 body size, 117, 136, 138, 142 cannibalism, 52, 53, 176, 180 capture efficiency, 397 competition, 360, 361, 365, 367, 368, 370–372, 380–382, 387, 388, 394, 396, 398 consumption rate, 145 dormancy, 196 fecundity, 135, 136, 139, 142 feeding history, 41 foraging behaviour, 32, 52 food webs, 416 functional response(s), 67, 69–71 growth and development, 141, 148–150, 157, 167, 175 guild(s), 644 impact on prey populations, 592, 594 ingestion rate, 133 larva growth and development, 149, 150, 157, 158, 175 measurement, 362 mutual interference, 136 non-prey foods, 134 numerical response(s), 603, 690 ovaries/ovarioles, 113 ovicide, 131 patch-marking behaviour, 33, 35 phytophagy, 669, 670, 672–676, 679–681, 685, 688–691 pollination by, 691 polyphagy, 52 predation indices, 592 preference, 17, 67, 69–71 prey acceptance behaviour, 40 prey habitat location, 16–18 prey location behaviour, 32 prey quality, 146 prey range, 483, 494 prey size/stage selection, 44 prey specificity, 603, 634 rm, 646 rate, 358, 366, 367, 386, 387, 391, 392, 397, 398 ratio-dependent response, 360, 369, 380, 381, 386 release strategy, 54 sexual cannibalism, 338 survival, 145, 159, 167, 176, 178 switching behaviour, 63–65 temperature history during development, 138 top, 416, 517, 520 Predictive dormancy, 197 Preference, 17, 19, 26–32, 40, 41, 52, 451–453, 479, 495, 498, 508, 510, 597, 644, 672, 677–681, 684, 687 Premeiotic doubling, 251, 252 Pre-oviposition period, 105, 135, 137, 141 Preservatives, for sampled insects, 421

766 Prey acceptability tests, 494 location behaviour, 16, 466 quality, 134, 150, 157 selection, see Host/prey selection size, 37, 44, 157, 495 species, effects on life-history variables, 196 Preying mantids, see Mantodea Primary sex ratio, 62 Primers, 237, 238, 249, 262, 364, 462, 506–511, 529, 533–536, 600 Probability, 706–708, 720, 728, 731–733 Probit analysis, 41 Proctotrupoidea, 670 Profitability of non-host foods/food sources, 684 patches, 5 Progeny allocation, see Clutch size, Sex allocation Pro-ovigeny, 76, 117, 131 Proportion, 707, 709, 712, 730, 731, 733, 734 Proportional hazards model, 3, 15 Prospective models in biological control, 649 Protandry, 369, 370, 372 Proximate approach, see Causation Pseudococcidae, 54 Pseudocopulation, 691 Pseudo-interference, 73 PSR, see Paternal sex ratio factor/element Pteromalidae, 42, 61, 107, 112, 121, 124, 137, 173, 296, 307, 312, 316, 323, 329, 339, 377, 379, 390 P-value, 706, 707, 721, 725, 727, 734, 737 Pyralidae, 340, 380, 389 Q QTL loci/mapping, see Quantitative trait/loci, mapping QTs, see Quantitative, traits Quantitative food web(s), 512, 515, 648 parasitoid-overlap diagram, 517, 519 trait mapping (=QTL mapping), 246, 264, 276, 277, 280–282 Quiescence, 197–200 Quip (=quarter-instar period), 363 q-values, see Classical biological control, q-values R R, see Selection response (=R), in quantitative genetics rc, see Innate capacity for natural increase rm, see Intrinsic rate of natural increase Ro, see Net reproductive rate Radar, 473, 478, 479 Radioactive markers, 675 Radiolabelling, 504 Radio telemetry, 478, 479 Radiotracers, see Radioactive markers Random Amplified Polymorphic DNA (=RAPD), 50, 235, 237, 246, 248, 281

Subject Index Random effect, 736, 737 Random predator and parasite equations, 506 RAPD, see Random amplified polymorphic DNA Rare elements, see Trace elements Ratio-dependent functional responses, 360, 363, 369, 380, 381, 386, 627 Realised heritability, 269, 270 niche, 80 Rearing parasitoids, 273 Receptive posture, 322 Receptivity in females, 300, 312–315, 318, 320–326, 331, 340, 385 Recognition time, 77 Recombinant isogenic lines (=RILs), 280 Recombination, genetic, 234, 244, 246 Recording equipment, in behavioural studies, 11 Rectum, 501 Reductionist criteria in biological control, 342, 633, 634 Reference strains, 271, 273 Refractometer, 681 Refractory period, in mating, 327, 527 Refuges for natural enemies, 422, 459, 467, 634, 645 for hosts/prey, 616, 618, 619, 634, 635, 643 Regression, 707, 709–725, 728–734, 737 Regression coefficient, 707, 713 Relative estimates of population abundance, 420 Relative growth rate, 149 Remains, collection/examination, 490–494 Reproductive anatomy female parasitoids, 1, 3, 44, 329, 390 female predators, 138 male parasitoids, 132, 140, 329, 390 incompatibility, 185, 340 Reproductive mode, 357–359, 361–363, 368, 370, 379, 388, 389, 394, 395, 397–399 Resistance, host, 78, 190 Resorption muscles, 202 Resource(s) allocation, 200, 201 defence, 65 depression, 159 dynamics, 201 tracking, 202 Respiratory rate, 151, 153–156 Respirometry/respirometers, 684 Restricted canopy fogging, 443 Restriction Fragment Length Polymorphism (=RFLP), 237, 363, 462, 520 Retrospective models in biological control, 650 RFLP, see Restriction fragment length polymorphism RILs, see Recombinant, isogenic lines Risks of classical biological control introductions, 647 Robber-flies, see Asilidae Robustness analysis, 626 Rotary interception trap, 473

Subject Index Row intercropping, 686 Rubidium, 465, 478, 505, 506, 675 S S, see Selection differential in quantitative genetics Salticidae, 429 Sample, 706–710, 712–716, 718–721, 723, 724, 727, 728, 730, 732, 736, 737 Sampling techniques attraction, 418, 419, 450 chemical knockdown, 418, 419, 440, 442, 443 comparisons, 436 examination of hosts for parasitoid immatures, 418, 419 extraction of insects from plants, leaf-litter and soil, 423, 430, 447–450 knock-down, 418, 419 Malaise trapping, 419 mark-release-recapture, 418, 419 mechanical knockdown, 440–443 olfactory attraction, 455, 456 parasitoid immatures, 255 pitfall trapping, 418, 419 sweep netting, 418, 419 use of hosts and prey as ‘bait’, 457 vacuum netting, 418, 419 visual attraction, 418, 419 visual counting, 418, 419 Sample size (n), 706, 707, 712, 713, 718, 720, 721, 724, 731, 737 Satiation, 41 Saturated (full) model, 726 Scale (scaling) parameter, 734 Scale insects, see Coccidae, Diaspididae SCARS, see Sequence-characterised amplified regions Scavenging, 484–487, 496, 502, 505, 600 Scelionid wasps, 39, 632 Scoliidae, 691 Scolytidae, 389 Scott’s method, 476 Scramble competition, 159, 180, 396 Screening of biological control agents, 357, 367, 380, 390, 399, 630 Search image, 65 Searching efficiency, 396, 645 Searching for mates, 306 Search modes, within patch, 36–38 Secondary predation, 379, 484–487, 502, 505 Secondary sex ratio, 62, 127 Seed predation, 676 Selection criteria in biological control absence of intraguild predatory behaviour, 53, 644 collecting parasitoids from non-outbreak areas in the pest’s native range of the pest, 642–647 destructive host-feeding behaviour, 359, 375, 378, 388, 396–398 ease of handling and culturing, 367 general aspects, 369, 377, 380, 395, 396

767 gregariousness, 636 high degree of climatic adaptation, 639 high degree of host specificity, 639, 648 high degree of seasonal synchrony with the host, 645 high dispersal capability, 638 high intrinsic rate of natural increase (rm), 191–196, 645–647 high maximum level of parasitism in the host’s native range, 635 high parasitoid fecundity, 361, 382, 385, 386, 388, 390, 391, 394, 399 high searching efficiency, 481, 645, 685 holistic criteria, 633, 641 mode of reproduction, 358, 385, 387, 393 non-target effects, 640, 647–649 number of female parasitoids produced per clutch, 381 parasitoid guild, 358, 359, 365, 367–373, 377, 378, 382, 386, 387 plant effects, 190 rapid numerical response, 364, 369, 388, 390, 391, 398 reconstructing natural enemy communities, 357, 361, 363, 369, 370, 372, 376, 377, 380, 392, 399, 417, 420, 484 reductionist criteria, 342, 633, 634 risks to non-target hosts/prey, 639, 640 selection of agents that follow major density-dependent mortalities in the pest life-cycle, 642 short generation time ratio (GTR), 636 Selection differential (=S), in quantitative genetics, 270 Selection experiments, 9 Selection response (=R), in quantitative genetics, 270 Selfish genetic elements, 10 Semiochemicals, see Infochemicals Semi-quantitative web(s), 512 Sensitivity analysis, in simulation modelling, 626 Sentinel hosts/prey, 596, 597, 642 Sequence-Characterised Amplified Regions (=SCARS), 509 Sequencing (DNA), 236, 239 Serological methods antibody production, 503, 599 enzyme-linked immunosorbent assay (=ELISA), 599 labelled antibody techniques, 504 Sex alleles, 256, 260, 366 Sex allocation behaviour, in parasitoids, 10, 44, 55–58, 60 Sex chromosomes, 234, 245, 254, 258, 364, 366, 524, 530, 536 Sex determination, 234, 235, 254, 256–258, 260 Sex locus, 256, 257, 366 Sex mosaic (=gynandromorph), 258 Sex pheromones, 29, 297, 298, 307, 309, 315, 317, 326, 340, 373, 427, 455, 456 Sex ratio arrhenotoky, 250 bias, see skewed deuterotoky, 253, 254 differential mortality of sexes among immatures, 62

768 distorter(s), 10 effects of temperature, 196 general aspects, 483 genetics, 10, 58, 127, 254, 258–260, 360 host quality/size, 63 intrinsic rate of natural increase (rm), 194 mating system, 357 operational (=OSR), 307, 362, 394, 396 optimal, 59 population dynamics, 358–361, 363, 365, 368, 371, 372, 375, 381, 383, 386, 387, 391, 395, 397, 399 skewed, 169 Sexual cannibalism, see Cannibalism, sexual conflict, 335, 337, 357, 358, 393, 398 dimorphism, 382–384 selection, 298–300, 325, 335, 337, 361 Sib-mating, 59 Sigmoid functional response(s), see Functional response (s), Type Signature sugars, 675 Significance, 706–708, 712, 713, 716, 718, 721, 725, 727, 728, 730, 731, 734, 737 Single-locus Sex Determination mechanism (=slCSD), 256–258 Single Strand Conformation Polymorphism (=SSCP), 248 Sink web(s), 512 Sires, 270, 331, 332 Sit-and-wait predation strategy, 38 Sitter/Rover polymorphism, 274 sk, see Son-killer slCSD, see Single-locus complementary sex determination Sneaky males, 327 Soft scale insects, see Coccidae Software packages, 8, 143, 144, 304, 706, 725, 727 Soil flooding, 447, 450 Solitary parasitoids, 52, 57, 62, 69, 162, 168, 170, 181–184, 201, 307, 368, 369, 378, 379, 385–388, 391–393, 646, 731 Sonagram records of acoustic signals, 314 Son-killer (=sk), 262, 533 Sound traps, 457 Source web(s), 512 Spatial density-dependence in parasitism, 474 Spatial displacements, see Movements of insects Spatial heterogeneity in parasitism behavioural mechanisms underlying, 72, 73 biological control, 357, 359, 361, 363–365, 367–369, 373, 377, 380, 382, 386, 388, 390, 395, 396, 399 Specialists/specificity, 28, 457, 484, 485, 509, 510, 527, 528, 599, 648, 676, 687 Sperm competition, displacement, precedence, 109, 295, 296, 302, 319, 325–329, 331–337, 380, 392, 394, 396, 398 depletion, 138 ejaculate/ejaculation, 325, 328–333, 337, 390 layer, 329

Subject Index P2 values, 333 storage, 123, 330, 331, 334 transfer, 326, 340, 390 use, 125 Spermatogenesis, 390, 391 Spermtheca/spermathecal complex, 123 Sphecidae, 299, 456, 493 Spider mites, see Phytoseiidae, Tetranychidae Spiders, see also individual families SSCP, see Single strand conformation polymorphism Stability analysis, explained, 397, 620 Stability boundaries, 397, 618 Stable age distribution, 609, 610 isotopes, 674 limit cycles, 617 Stains/staining techniques, 120 Standard Deviation (SD), 706 Standard Error (SE), 734 Standardisation of insects, 300–302 Staphylinidae, 113, 469, 474, 501 Starvation, 45, 120, 159, 176, 187, 190, 485, 688 State-dependence of behaviour, 687 Static-air olfactometer(s), 23 Static optimality models, 5, 50 Sterile male technique, 336 Sticky traps/trapping, 369, 419, 453–455, 467–471, 473, 474, 486 Stimuli in foraging behaviour, 16 Strains, parasitoid see also Markers, 105 Strip intercropping, 686 Substitute foods, 132, 685, 686, 688 Suction traps, 142, 435, 472–474 Sugars energy content, 681 food see also Honeydew, Nectar/nectaries, 146, 675, 676 host haemolymph, 45 nitrogenous materials, 685 Suitable hosts, 166 Sum of Squares (SS), 710, 712, 718–721, 726, 730 Supernumerary larvae, 168, 181 Superparasitism age of parasitoid, 77 avoidance of, 46, 47, 49–52 body size of progeny, 47, 168–171 conspecific, 46–49, 165, 365, 369, 370, 378, 382, 384 defined, 40, 168, 170 encapsulation, 186, 190 effects on parasitoid growth and development, 165 effects on parasitoid survival, 186 egg distributions, 46, 47, 49 fitness costs, 169 gregarious parasitoids, 168 heterospecific, 52, 168, 169, 181, 184, 191 solitary parasitoids, 181 threshold time interval(s), 184 Supplemental foods, 669, 685, 688–690

Subject Index Survival/survivorship adults, 143, 145, 167, 389 age at death, 144 age-specific, of adults (lx), 130, 192 analysis, 143, 145 biotic factors, 177 body size, 146 cohort survivorship curves, 143, 366, 398, 736 consumption rate, 178, 191 field estimates, 56 foods, 142 host/prey density, 145 host/prey quality, 134, 135 humidity, 191 lx, 130, 192, 194 life-span, 145 longevity due to biotic factors, 177 longevity due to physical factors, 190 Manly-Parr method, 466 photoperiod, 191 physical factors, 190 sex differences, 181, 185 starvation, 190 temperature, 190 Weibull model, 144 Swarming behaviour, 379 Sweep netting, 435–437, 440 Switching behaviour, 63–65, 393 Switching-off process in mating behaviour, 300, 321, 325, 326 Synchrony, of parasitoid and host populations, 365, 368 Synomones, 18, 27 Synovigeny, 76, 117, 131, 686, 688, 689 Syrphidae, 107, 108, 117, 118, 123, 132, 436, 437, 444, 474, 475, 511, 671, 672, 676 T t, see Developmental zero T, see Mean generation time Tc, see Cohort generation time tDP, see Detection period Tachinidae, 110, 116, 117, 123, 437, 441, 460, 499, 640, 649, 671 Tactile stimuli, 316 Taxonomy, 8, 77, 497, 500, 506, 671 Temperature life-history variables, 197 threshold(s), 171, 174, 199 Temporal density-dependence, 358, 369 Tenthredinidae, 493 Tenthredinoidea, 366, 367 Terminal fusion, 252 Territoriality, 476 Test-crosses, 241, 246, 262, 269 Testes, 126, 261, 329, 390 Test statistic, 706, 720, 725, 729 Tethering

769 flying natural enemies, 466 prey, 378 Thelytoky, 365, 637 Thermal balance, 679 constant (=K), 172, 199 preference behaviour, 141 stress, 191 summation technique, 172, 173 Thermocouple, 141 Three-point cross, 241, 243, 245 Threshold departure rule, 682 ingestion rate, 132, 134, 151, 157 Thysanoptera, 234, 250, 525, 527 Time allocation by foragers, 69 -limitation (parasitoids), 76, 358, 391, 648 Time-series, population, 610, 628 Time-specific life-tables, 605, 609, 610 Tingidae, 502 Tiphiidae, 691 Top-down analysis, 726, 727 Top predator, 358, 359, 379, 394, 397 Tortricidae, 308, 373, 374 Torymidae, 114, 125 Toxins/toxicity, 523, 525–528, 644, 676, 678 Trace elements, 465, 478 Trade-off(s), 3, 66, 75, 106, 116, 118, 119, 143, 145, 164, 165, 200, 266, 270, 273, 275, 276, 386, 634, 649 Trait(s)/loci, quantitative (QTLs), 239, 246, 260, 264, 276–282, 710 Transformation, 729–731, 733, 735 Transient dynamics, 643 Trapping methods/devices Malaise trapping, 437, 439 plants, 379 rotary interception, 473 sound traps, 457 sticky traps, 453, 468, 470, 486 suction, 142, 473, 474 water traps, 452 Trap plant method, 379 Travel time between flowers/food sources, 682 between host patches, 66 Triangular fecundity function, 131 Trichogrammatid wasps, 307, 632 Triple Test Crosses (=TTCs), 269 Tritrophic systems, 28 Trivial movements, 466 Trogossitidae, 492 Trophallaxis, 487, 488, 505 Trophic cascade, 416 levels, 614, 626, 627, 731 loops, 514 species, 513

770 Trophic links/relationships within communities DNA-based techniques, 406–409 electrophoresis, 237, 485 faecal analysis, 484 field observations, 484 food web construction, 483 gut dissection, 484 serological techniques, 599 tests of prey and host acceptability, 484, 486, 494 Trophospecies, 513 TTCs, see Triple test crosses T-test, 706, 711, 715 Tukey’s honestly Significant difference (HSD) test, 728 Tullgren funnel, 448 Two-point cross, 241, 242 Tydeidae, 450 U Umbellifiers, see Apiaceae Unbeatable sex ratio, 59 Underdispersion, 734 Unidirectional cytoplasmic incompatibility, 263 Uterus/uteri, 115 Utilisation efficiency, 150, 153 V VA, see Additive component VD, see Dominance component VE, see Environmental variance VG, see Genetic variance VI, see Epistatic component VP, see Phenotypic variance Vacuum nets and related devices, 432 Vagina, 115, 123, 125 Validation, in modelling, 368 Vanillin reaction, 201 Variable-time experiments, 70 Variances, in quantitative genetics, 235, 259, 264, 265, 273, 275, 276 Varley and Gradwell’s method, 605, 607, 608 Venom gland, 125–127 Vertical-looking radar, 473 Vespidae, 597 Vespoidea, 366, 367 Vibrokinesis, 37 Vibrotaxis, 37, 38 Video recording, 11, 33, 35, 72, 303, 304, 306, 369, 597 Virgin reproduction, 364, 386 Virgin(s)/virginity, 241–243, 247, 253, 255, 256 Virus-like particles (VLPs), 272, 275 Visual counting methods, 436, 443–446

Subject Index attraction, in sampling, 450, 452 signals/stimuli, 307, 308, 309 Vital dyes, 481 VLPs, see Virus-like particles Vortis sampling device, 435 Voucher specimens, 498 VT, see Vertical transmission of endosymbionts W Wasteful killing, 70 Waterboatmen, see Notonectidae Water-filled tray traps, 472 Water-striders, see Gerridae Weather foraging behaviour, 5 Web (www) sites, 447, 453 Web types, 415 Weeds, 427, 428, 444, 446, 467, 501, 633, 647, 648, 686, 691 Weibull distribution, 144, 709, 728 Window traps, 419, 439, 467–469 Wind tunnels, 24, 25 Wind velocity, 474 Winkler/Mozarski eclector, 448 Winter disappearance, 607–609 Wolbachia ‘curing’, 281 cytoplasmic incompatibility (=CI), 526, 530 detection and identification, 417 feminisation effect, 264, 526, 530 food webs, 417 genes, 509, 532 -mediated thelytoky, 264 multiple infections, 536 parthenogenesis induction (=PI), 527, 530 sex ratio distortion, 10 transmission, 523, 524, 528, 531, 532 vertical transmission (=VT) defined, 523, 524, 526, 532, 533 wsp, 535 X X-ray fluorescence spectrometry, 477 X-rays, use in detection of insects, 240 Y Y-tube olfactometer, 5, 18–22, 239 Z

65

Zn, 481