Hidden pathways to extinction 9783030867638, 9783030867645, 3030867641

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Fascinating Life Sciences

Giovanni Strona

Hidden Pathways to Extinction

Fascinating Life Sciences

This interdisciplinary series brings together the most essential and captivating topics in the life sciences. They range from the plant sciences to zoology, from the microbiome to macrobiome, and from basic biology to biotechnology. The series not only highlights fascinating research; it also discusses major challenges associated with the life sciences and related disciplines and outlines future research directions. Individual volumes provide in-depth information, are richly illustrated with photographs, illustrations, and maps, and feature suggestions for further reading or glossaries where appropriate. Interested researchers in all areas of the life sciences, as well as biology enthusiasts, will find the series’ interdisciplinary focus and highly readable volumes especially appealing.

More information about this series at https://link.springer.com/bookseries/15408

Giovanni Strona

Hidden Pathways to Extinction

Giovanni Strona Faculty of Biological and Environmental Sciences University of Helsinki Helsinki, Finland

ISSN 2509-6745 ISSN 2509-6753 (electronic) Fascinating Life Sciences ISBN 978-3-030-86763-8 ISBN 978-3-030-86764-5 (eBook) https://doi.org/10.1007/978-3-030-86764-5 © Springer Nature Switzerland AG 2022 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 picture by Gabriele Retez (2021). This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgements

Writing this book has been a long and challenging journey, and the least I can do is say thanks to those who kept me company—and often guided me—along the way. These include, first of all, my family. There is no single relevant achievement in my life for which I do not feel indebted to Marta, my parents and my sisters for their continuous and unconditional support. Then my scientific mentors and the many friends and colleagues who truly understand why I am dedicating so much of my life to parasites, computer simulations and, in general, to a lot of stuff that most human beings deem irrelevant. I cannot name all of them, but Simone Fattorini, Corey Bradshaw, Kevin Lafferty, Pieter Beck, Jesus San-Miguel-Ayanz and Paolo Galli have certainly played a fundamental role in determining my research path. I am deeply grateful to Otso Ovaskainen, Anna-Liisa Laine, Tomas Roslin, Marjo Saastamoinen and Jarno Vanhatalo for welcoming me to the Research Centre for Ecological Change. And, of course, to the University of Helsinki for supporting my research and, more importantly, my personal and professional growth. Here I have met many wonderful and inspiring people, Mar Cabeza, Alf Norkko, Laura Antão, Elina Kaarlejärvi, Maria Hällfors and Benjamin Weigel to name a few. Many reviewers dedicated their time to make this book a better one. Thanks to all of them, but particularly to Alyssa Cirtwill, Skylar Hopkins, John Llewelyn, Tom Matthews, Hanna Susi and Joe Veech. Finally, I would like to thank my Editor, Lars Koerner, without whom this book would not exist.

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Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 9 9

2

We Are Chicxulub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 21 21

3

Everything Is Connected . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25 37 38

4

Ecological Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 53 53

5

Integrating Interaction Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57 70 71

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Modelling Co-extinctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75 98 99

7

Extinction Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

8

The Specialization Paradox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

9

Nestedness and Ecological Network Stability . . . . . . . . . . . . . . . . . . . . . 137 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 vii

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10 Life-Cycle Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 11 Planetary Life Annihilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 12 Higher-Order Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 13 Biological Invasions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 14 Artificial Intelligence and the Future of Biodiversity . . . . . . . . . . . . . . 221 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

About the Author

Giovanni Strona (Italy, 1982) is an associate professor in ecological data sciences at the University of Helsinki. His research lies at the interface between ecology, computer science and physics. He has published tens of influential scientific papers on macro-ecology and biodiversity research, introducing theoretical and methodological novelties. In recent years, he has advanced our understanding of the role of ecological interactions in the ongoing mass extinction, quantifying the relative impact of co-extinctions on global biodiversity loss. His findings identify key ecological mechanisms that might dramatically amplify global change’s detrimental effects and reveal how a planetary ecosystem collapse might be way closer than we expected.

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Chapter 1

Introduction

It has not been a long time since Toughie (Fig. 1.1) died. He was a famous frog. A Rabbs’ fringe-limbed treefrog to be precise, or Ecnomiohyla rabborum. The reason for Toughie’s fame, however, is a sad one. The frog was the last specimen of his species known to be alive. Chytrid fungus disease nearly wiped out Toughie’s population—the species’ last remaining one—in 2006. Conservation biologists tried to save E. rabborum by collecting and sending specimens to various facilities and botanical gardens. They transferred Toughie and a few others of his kind to the Atlanta Botanical Garden. A year later, in 2007, with Toughie in Atlanta, the last male living in the wild was heard singing, to call a female that never arrived. In 2009, Toughie’s partner, with whom he had sired tadpoles that did not survive, died [1]. Toughie waited seven more years for his fate to come. Until this finally came on September 26, 2016. But his sad, lonely song is still echoing, reminding us that we cannot race extinctions. We are losing species at an unprecedented rate. There is debate around the magnitude of this loss, but the picture is dramatic even if we consider the most conservative estimates. Extinction is a fundamental ecological process. However, the long-term increase in diversity observed in the fossil record (Fig. 1.2) [2]) offers strong support to the idea that, in general, the number of new species evolving from existing ones should compensate or exceed the number of species going extinct. The problem is that the estimated current extinction rate might be about one thousand times the “background extinction rate”, that is, the percentage of species expected to go extinct per year during a geological period in between two mass extinction events, which is ≈0.0001%. This means that on average, 0.1% of extant species might go extinct each year [3], while it is improbable that the emergence of new species compensates these losses. But, before we try to understand the true magnitude of this problem, we first need to ask ourselves a fundamental question: How many species are out there in the world? The short answer is many more than we will ever know and definitely more than we know now. Studies suggest that there are at least nine million eukaryotic species © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_1

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1 Introduction

Fig. 1.1 Toughie, the last living specimen of Rabbs’ fringe-limbed treefrog, Ecnomiohyla rabboc Brian Gratwicke (2011), used under a CC BY 2.0 (https://creativecommons.org/licenses/ rum.  by/2.0/)

Fig. 1.2 Theoretical and empirical trajectories of species diversification through geological time. A, B: theoretical models hypothesizing different trajectories of diversification either in absence (A) or in presence (B) of major perturbations (mass extinctions). In both plots, the upper curve is the logistic or equilibrium model, the middle curve is the additive or linear model, and the lower curve is the exponential model. C-F: trajectories of diversification in well-skeletonized marine animals, at different taxonomic levels. Note that the plots for orders (C), families (D) and genera (E) are counts based on empirical data, while the plot for species (F) is based on a combination of real and simulated data, and its values are expressed as a percentage of modern diversity. Adapted from [2], with kind permission from John Wiley and Sons

globally, of which around two million are marine [4]. If we combine this number with the yearly extinction rate, we obtain a back-of-the-envelope estimate of around 9000 species extinctions per year, which means at least one species loss per hour. That sounds like a large number. It is stunning how this information is a complete surprise to most people and that the public perceives extinctions as rare events instead of ordinary happenings of our daily life. I see two main reasons for this misconception. The first one is that, as I will discuss in better detail later, many extinction events

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involve small species that people, including scientists, do not even know have existed. The second one is the general perception we have about dramatic events: self-defence mechanisms encourage us to think that dramatic should be a synonym of exceptional. As I sit in front of my computer after breakfast, even the news that almost ten children die every single minute of malnutrition-related causes [5] does not send to my brain the message of terror it should send. How could we expect people to care for the disappearance of an exotic plant or an obscure, millimetric bug? I wonder why primary schools do not provide children with this kind of information. I am not saying we should try to scare them, but we are not protecting them by saying that everything is going well. It is their right to know that they are living in a world where species are continually disappearing. Toughie’s death and the final loss of Rabbs’ fringe-limbed treefrog are robust evidence of this and an example capable of capturing people’s attention and kids’ imagination. But for one Toughie gaining the cover of National Geographic magazine, there are—and have been—many other lone survivors waiting for a mate that will not arrive, sadly doomed to disappear from this world unnoticed. Too often, we—and I am now talking about the human race, not about ecologists or conservation biologists—are tempted to focus our attention on charismatic species: big animals, cute animals and then, of course, in an attempt of redemption, our victims. Animals chased by poachers or doomed by light-hearted choices we take in our everyday lives, such as primates now endangered by palm oil industry [6]. Although this makes sense in terms of popular propaganda, it might not be the best first step towards a rigorous science-based conservation plan. The problem is that research is not cheap and that human feelings often play an essential role in determining where the money goes. Often, convincing founders that, to save our planet, we have to start from polar bears is the best chance to get some money for research. Thus, scientists are encouraged—or even forced—to take that path. But then, it becomes a bit tricky to use the money to study something different and possibly more relevant. Letting emotions affect research could slow down scientific development and even result in failed conservation attempts, providing dangerous arguments against the practical value of conservation actions in general. There is merit in most conservation efforts, and we need testimonials capable of bringing global change to everybody’s sight. However, the anthropocentric, emotional view on species vulnerability lets us see just a tiny dot in the big natural world’s picture. Taking a few steps back would be necessary to appreciate the whole scene. And, I am afraid, this will not be as peaceful as a Sunday on La Grande Jatte. Toughie was not as cute as a playful panda nor as dramatic as a starving polar bear. But the truthfulness of his story, his final failed attempt to reproduce, and then the loneliness, the absolute tragedy of being the last one of his kind, those make Toughie the very picture of extinction. We are losing species, and we are losing them fast. Most of them are as small as or smaller than Toughie and hard to find, like insects and parasites. But not paying attention to those losses means missing the magnitude of the ongoing mass extinction event completely.

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Part of the guilt is on us scientists and how we have shaped the academic system. For several reasons, we have progressively belittled the importance of taxonomists’ work. To date, we have described a bit more than one million species over the nine million estimated. As argued above, this minimizes the general perception of the ongoing biodiversity crisis, as eight out of nine extinction events involve species we are not even aware of their existence. In a way, losing an unknown species might appear relatively painless. But if we think twice, it becomes clear that such a loss is one we should regret even more, as it leaves us with no clue of what we had lost. This is not just a philosophical matter. We take most of our resources from the living world, not only in terms of food and material but also of drugs and inspiration. The more we get to know about the livings, the more we realize that nature has invented so much—starting from the last common ancestor, 4 billion years ago [7]— that it might be more convenient for us to copy, instead of trying to reinvent the wheel [8]. Unfortunately, we will never know if the insect that is going extinct as you read this page is nothing special or, instead, it is the most beautiful arthropod in the world. Or it can heal people. Or it is gifted with some extraordinary properties that could (but will not) pave the way to exciting discoveries in material science or engineering. Increasing knowledge and technological advances, such as novel molecular analysis techniques, provide us with new, faster ways to identify species [9, 10]. Yet, traditional taxonomists, particularly those spending more time looking through a microscope than staring at a computer screen, belong to a shrinking minority of scientists that, unfortunately, is struggling to survive in the current academic system [11]. Recent achievements in DNA science have generated more questions than answers, revealing that organisms hide in their genetic code much more diversity than Linnaeus had ever imagined. These findings are now challenging the concept of species itself, with implications beyond giving names to biological entities [12]. Even assuming that old school taxonomists will survive their object of study and that new generations of molecular ecologists will compensate for taxonomists’ decline, we can predict that the ongoing mass extinction will be completed before we will have described all existing species (see Fig. 1.3). Looking at the half-full glass, at that point, taxonomic gaps will probably not be on the top list of our problems. I tend to be overly pessimistic when thinking about our planet’s future. But I am in good company in my attitude. In a famous article entitled “Can a collapse of global civilization be avoided?” [14], Paul and Anne Ehrlich have provided a powerful synthesis of why being pessimistic on this topic is reasonable. The current human population is already far beyond the planet’s carrying capacity. To support the extant seven billion people sustainably, under the assumption that nothing will change in terms of local technological development and living standards, we would need half more Earth. If all the Earth people decided to behave like the average US citizen, then the additional resources required for global sustainment will go up to four–five extra Earths. Now, considering that world population will likely exceed nine billion persons by 2050 [15], the magnitude of the problem should be quite evident. The themes that the Ehrlichs cover in the paper—climate disruption, accelerating rates of extinction, land degradation and land-use change, pollution, resource depletion—are all well-known causes of species loss. But there are many more hidden pathways to

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Fig. 1.3 Trends in species description (cyan and dark blue lines) and global diversity loss (green line) based on a simple model assuming that current estimated rates of description (≈8000 species a year) and extinction (≈0.72% per year) will remain constant over time, and with current global diversity assessed at ≈8 M species (as in [13]). Starting year is 2015. The horizontal grey line indicates the approximate survival level in the five previous mass extinction events (≈25% of initial diversity). The cyan trajectory of species description was derived assuming that extinctions do not happen among species that have been already described. By contrast, the dark blue trajectory was derived assuming that extinctions happen with identical probability among described and undescribed species. Under the first assumption (which is the one behind the projections in [13]), the moment all extant species will be known to science (in 2153), global diversity will have dropped to 33% of current diversity. Under the latter assumption, the moment will arrive more than one hundred years later, with global diversity reduced to 15% of the current one, i.e. way below the reference mass extinction level (dashed line)

extinction that could have even harsher effects than the obvious drivers. The Ehrlichs conclude that the chances to solve these planetary issues—and possibly reversing the dramatic trends going on—are at best very slim and strongly depend on how much the humanity of today is willing to engage in a generous effort that would benefit the society of tomorrow. The current climate and environmental change trends obliterate the hope that conservation actions alone could halt global biodiversity loss. Think of what happened in the past five mass extinction events. The whole world was a huge protected area back then, and no humans were exploiting any available resource and boosting global change. But this did not modify the course of history. Do not get me wrong: conservation actions are fundamental, and there are many examples where the establishment of protected areas has brought benefit to wildlife [16]. I do hope that their implementation will grow exponentially in the short term. But they will be useless unless a global change in human lifestyle and attitude towards environmental

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problems occurs in parallel. Unfortunately, this is as much needed as it is hard to see coming. Among those who think that man will not change, there are two categories. The first one is that of pessimistic–pessimistic ones, who believe that human inertia will destroy the planet, and we will follow. The second one is that of pessimistic– optimistic ones, who think that if things continue like this, there will be a major event that will drive society to change, bringing the survived humanity to a brighter future. Such as a mass mortality event due to nuclear war or a global outbreak of an airborne or, following the current zombie fashion, bite-borne deadly disease. Although both scenarios are dramatic and far from being desirable, the second one (some abrupt planetary disaster sparing a handful of survivors) promises at least a glimpse of hope for future generations, even if at a high price. Yet, among the two, it is also the most unrealistic. The demographic momentum is so intense now that even a dramatic global scale catastrophic events killing billions of people will not significantly impact the future world population’s magnitude. This rules out demographic control as a quick fix for environmental problems [17]. In turn, it leaves a profound societal change as our only hope to avoid we destroy the planet before we realize we are screwed. However, while we wait for this to happen and for contagious altruism to spread around like a pandemic, we can—or, better, must—do something. And restrain from doing many more things. This urgent need to take action motivates conservation biologists to get out of bed even if they know that nobody will notice their absence in the laboratory, let alone in a remote jungle. I am not saying that conservation biologists wake up because they are confident they will save the world. On the contrary, most of them know that even the planet itself will hardly notice if they decide to stay in bed. They wake up because they firmly believe it is their responsibility to try hard. Being actively involved in conservation research, and having many friends and colleagues even more involved and active than me, I have developed the idea that this kind of resigned attitude is part of conservation biologists’ DNA. While writing these lines, I had a look over the Internet to see if other people shared my perception in the field. Thus, I naively googled “conservation biologists’ attitude about chances to make a difference”. This search brought me straight to what I was looking for, namely a paper by two wildlife scientists [18], which says that: (1) I am right; and (2) we, ecologists and conservation biologists, are wrong. In the sense that being pessimistic, although tempting, is not bringing benefit to the field. If there is no hope at all, then what is the purpose of even trying? Why giving money to save polar bears if we cannot save them? Why spending underpaid years doing a PhD in conservation genetics instead of trying a better, rewarding career in the biotechnology industry? Why not just giving up and enjoying the present world? It is getting more and more like rotting fruit, but it is still in that phase where at least some parts of the fruit are edible, and the fruit was so good at the beginning that those parts still taste amazing, and sometimes even sweeter than ever. During my postdoc, I have had chances to visit stunning tropical marine localities and be many times in the Maldivian isles. Renown as one of the most beautiful locations in the Indian Ocean, the Maldives has experienced a tourism boom starting

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from the 70s, with the first resort established in 1972. In the 60s, the United Nations, following a visit to the archipelago in the context of a mission to identify potential development targets, declared the islands not suitable to tourism [19]. Yet, in 2016, Maldives was offering to visitors 126 resorts, 16 hotels, 382 guest houses and 144 “safari vessels” (i.e. cruising boats mainly aimed at diving tourism), for a total operational capacity of more than 30,500 beds and a yearly arrival of more than 1.2 million international tourists [20]. This is quite remarkable for a country with a land surface of around 298 km2 and a resident population of about 400,000 inhabitants [21]. Not surprisingly, the cost of tourism, which has soon become the primary source of income for the country, is on the environment, with waste management being the most macroscopic problem. Until it has been possible, the waste issue has been made invisible to tourists. Still, now the amount of rubbish floating in the water or sank at the bottom frustrates the expectation of visitors who landed with the picture of immaculate white beaches in their eyes. Well, Maldivian beaches are still beautiful, but you would better watch your step because there is a non-negligible risk of harming your feet on a rusted tuna can. All of this, paired with the ever-increasing occurrence of large-scale coral mortality related to climate change (following temperature-driven bleaching events or outbreaks of coral diseases and coral predators), makes the experience of visiting and diving in the Maldives a bit odd and causes mixed emotions. It is still a beautiful place, but it is hard not to see that something is just not right even if you do not pay too much attention. And I guess one would have the same impression visiting many other places that we keep calling exotic, even if tourists now swamp them1 . As dangerous as a tuna can might be, this can be considered a marginal issue compared to plastic and all the problems deriving from its management. Plastic bottles and even big floating sacks of garbage are becoming a much more common sight while cruising between atolls than pelagic fish. As throughout most oceans, the visible plastic is just the evident part of a bigger, invisible problem represented by microplastic particles that are rapidly accumulating across food webs [23, 24]. The more we learn about the problem, the more clearly we realize how serious and far reaching it is. The ingestion of microplastics by corals has been reported for the first time in 2015 [25], in corals from the Great Barrier Reef. Researchers also showed through experiments that corals can easily mistake microplastic particles for food and eat as much plastic as plankton. A more recent study conducted in Rhode Island, USA, confirmed those results [26]. Researchers first found a high abundance of microplastic particles (more than 100 particles per polyp) in wild colonies of the coral Astrangia poculata. They also showed through laboratory experiments that corals might prefer tiny plastic beads over real food (brine shrimp eggs of the same diameter of plastic particles). Besides, plastic particles satiated corals so much that they refused shrimp 1

A notable example is that of Mount Everest, where the problem of waste abandoned by expeditions attempting the climb was already harsh 20 years ago [22]. I have been lucky enough to be there (as high as base camp one, at around 5000 m above sea level). Again, the beauty of landscapes makes you almost forget of pollution; and of the fact that your presence, no matter how hard you try to be good, is contributing to killing the place.

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eggs offered afterwards, which demonstrates the actual risk, i.e. the potential death of corals by starvation, similar to the dramatic, better documented process affecting sea birds [27] and mammals [28]. Coming back to conservation biologists’ daily mission and their resigned, pessimistic attitude: What is the point? Instead of wasting time trying to save tropical ecosystems from being poisoned by plastic debris [29], wouldn’t it be better to embark as a guide on a cruising boat and enjoy swimming with the last whale sharks as long as there is any? Well, some do this. By providing tourists with basic notions about biodiversity and the environment, they also contribute to the (lost) cause. However, academic researchers often look at them with critical eyes for giving up with “science”. This is a bad attitude, I have to admit, I am sometimes guilty of myself. But many others do not take this path and keep on collecting samples, taking measurements and staring at microscope slides. The key motivation that permits them to keep on is identifying a realistic target and then being conscious that reaching it would be a great success, even if that, at the planetary scale, would represent only a small achievement. This may sound as they are cheating on themselves, but if you think better, it is what everybody does in everyday life. Whenever you play a game with your office mates, you probably enjoy winning, even if the game is not the Olympics final. As long as we think that conservation biologists should win the Olympics and “halt biodiversity loss by 2020”—as stated in the European Commission’s EU biodiversity strategy to 2020 [30]—we will always consider any conservation action as hopeless and any missed achievement as a failure. The one target I am setting for this book is taking you on a journey through the multitude of paths that can lead to biodiversity loss. Some of those are quite evident and not much different from what would happen to the inhabitants of an aquarium when we set the thermostat to a too high temperature. But many others are much more elusive and often consisting of hardly predictable chain reactions which might eventually collapse entire natural systems. Working in collaboration with scientists from various fields (and particularly physics, mathematics and computer science), many ecologists have been tackling the issue for, at least, the past two decades. Yet, in many regards, it looks like we are still scratching the surface of a colossal iceberg. For my target to be realistic, I am not claiming that the following pages will reveal all of the complexity which hides underwater. My hope, instead, is that you might obtain enough information and evidence from this book to become at least aware that there are natural forces we are severely underestimating in our daily life. And that, despite deniers, the ongoing global planetary crisis might be even worse than it appears. In my attempt to do that, I will provide you with facts, numbers and simulations, and hopefully with enough technical guidance and ideas so that you might take up the challenge of diving into freezing waters and tell the world how the submerged part of the iceberg looks.

References

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Summary The world is collapsing at breakneck speed. The detrimental effects of human presence and activities are so widespread that finding “naturalness” is becoming more challenging every day. This calls for the fundamental question of whether we can halt or, at least, slow down this process. From a pessimistic yet realistic perspective, we have to recognize that averting the ongoing planetary crisis would be, at best, extremely challenging and, at worst, infeasible. Yet, from a more optimistic view, there are potential actions that might cushion the fall. Conservation alone cannot do miracles, so that a global change in human attitude is urgently needed before it is too late. Identifying achievable targets—both to advance scientific knowledge and reduce the impact of our daily lives—is a fundamental step we must take. But before we do that, we need first to get a glimpse of the overarching diversity and complexity of the mechanisms which are dismantling natural systems worldwide. The most transparent processes currently identified as the lead drivers of diversity loss might represent the tip of an enormous extinction iceberg. This book will take readers on a profound journey to see what is hidden below the water surface.

References 1. Mendelson JR (2011) Shifted baselines, forensic taxonomy, and Rabbs’ fringe-limbed treefrog: the changing role of biologists in an era of amphibian declines and extinctions. Herpetolog Rev 42(1):21–25 2. Benton MJ, Emerson BC (2007) How did life become so diverse? The dynamics of diversification according to the fossil record and molecular phylogenetics. Palaeontology 50(1):23–40 3. De Vos JM et al (2015) Estimating the normal background rate of species extinction. Conservat Biol 29(2):452–462 4. Mora C et al (2011) How many species are there on Earth and in the ocean? PLoS Biol 9(8):e1001127 5. Black RE, Morris SS, Bryce J (2003) Where and why are 10 million children dying every year? The Lancet 361(9376):2226–2234 6. Strona G et al (2018) Small room for compromise between oil palm cultivation and primate conservation in Africa. Proc Nat Acad Sci 115(35):8811–8816 7. Becerra A et al (2007) The very early stages of biological evolution and the nature of the last common ancestor of the three major cell domains. Annu Rev Ecol Evol Syst 38:361–379 8. Bar-Cohen Y (2006) Biomimetics—using nature to inspire human innovation. Bioinspiration Biomimetics 1(1):P1 9. Valentini A et al (2016) Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecol 25(4):929–942 10. Taberlet P et al (2012) Towards next-generation biodiversity assessment using DNA metabarcoding. Molecular Ecol 21(8):2045–2050 11. Strona G (2015) Species naming: taxonomic glory easier on eBay? Nature 523(7558):35–35 12. Agapow P-M et al (2004) The impact of species concept on biodiversity studies. Q Rev Biol 79(2):161–179 13. Mora C, Rollo A, Tittensor DP (2013) Comment on can we name earth’s species before they go Extinct? Science 341(6143):237 14. Ehrlich PR, Ehrlich AH (2013) Can a collapse of global civilization be avoided? Proc R Soc B 280(1754): 20122845

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15. Department of Economic and United Nations Social Affairs Population Division. World population prospects 2019: highlights (ST/ESA/SER. A/423). In (2019) 16. Laurance WF et al (2012) Averting biodiversity collapse in tropical forest protected areas. Nature 489(7415):290 17. Bradshaw CJA, Brook BW (2014) Human population reduction is not a quick fix for environmental problems. Proc Nat Acad Sci 111(46):16610–16615 18. Swaisgood RR, Sheppard JK (2010) The culture of conservation biologists: show me the hope! BioScience 60(8):626–630 19. Kundur SK (2012) Development of tourism in Maldives. Int J Sci Res Publ 2(4):1–5 20. Zahir S (2016) Maldives Tourism Industry Review. 2017. http://www.hoteliermaldives.com/ 2016-maldives-tourism-industry-review/ (visited on 06/03/2017) 21. CIA. The CIA World Factbook (2016). http://www.cia.gov/cia/publications/factbook/ 22. Bishop B, Naumann C (1996) Mount everest: reclamation of the world’s highest junk yard. Mount Res Dev 16(3):323–327 23. Saliu F et al (2018) Microplastic and charred microplastic in the Faafu Atoll, Maldives. Marine pollution bulletin 136:464–471 24. Saliu F et al (2019) Microplastics as a threat to coral reef environments: detection of phthalate esters in neuston and scleractinian corals from the Faafu Atoll, Maldives. Marine Pollut Bull 142:234–241 25. Hall NM et al (2015) Microplastic ingestion by scleractinian corals. Marine Biol 162(3):725– 732 26. Rotjan RD et al (2019) Patterns, dynamics and consequences of microplastic ingestion by the temperate coral. Astrangia Poculata. Proc Royal Soc B 286(1905):20190726 27. Pierce KE et al (2004) Obstruction and starvation associated with plastic ingestion in a Northern Gannet Morus bassanus and a Greater Shearwater Puffinus gravis. Marine Ornithol 32:187–189 28. De Stephanis R et al (2013) As main meal for sperm whales: plastics debris. Marine Pollution bull 69(1–2):206–214 29. Imhof HK et al (2017) Spatial and temporal variation of macro-, meso- and microplastic abundance on a remote coral island of the Maldives. Indian Ocean. Marine Pollut Bullet 116(1):340–347 30. EU Commission et al (2011) Our life insurance, our natural capital: an EU biodiversity strategy to 2020. In: COM (2011) 244 (2011). http://eur-lex.europa.eu/legal-content/EN/ ALL/?uri=CELEX:52011DC0244

Chapter 2

We Are Chicxulub

It is hard to speak the words “mass extinction” and not think about a giant asteroid. One day, there were plenty of dinosaurs walking around in the sun, and the day after, it was dark, and it was cold, and they were doomed [1]. Well, as it turned out, this is just a part of the story—the unhappy ending. When the Chicxulub impactor hit the ground some 66 million years ago (in modern-day Mexico, Yucatán peninsula), dinosaurs were already not doing well. A study focusing on speciation and extinction rates in dinosaur lineages has revealed that million years before the catastrophic event, the number of dinosaurs going extinct had surpassed that of new species emerging, making the “big lizards” critically endangered at the moment of the impact [2]. What is behind the decline of dinosaurs is hard to tell. Nor it is obvious which had been the leading causes for the other four mass extinction events that our planet has witnessed to date. Climate change is the most common explanation for all of them. In the case of dinosaurs, researchers have modelled that the amount of sulphur and carbon dioxide instantly exploded into the atmosphere by the Chicxulub asteroid would have started a nuclear winter lasting more than 30 years and characterized by a planetary drop of temperatures up to 20 degrees Celsius [3]. For the current extinction crisis, the scenario is the opposite, with warming temperatures playing a fundamental role in the decline of biodiversity [4]. Later in this book (Chap. 12), I will argue that global warming might be the worst-case scenario for species loss, leading to a much faster collapse of natural systems than a potential planetary cooling [5]. Yet, it would be unfair to consider the rise in temperatures as the only culprit behind the ongoing sixth mass extinction process [6]. We well deserve the blame for this crime against the planet, and not just because we are speeding up the global warming process. We might blindly embrace denial and opt to defend the idea that temperatures are not rising. Or we might wear a hypocritical mask of humbleness and claim ourselves too insignificant to affect climate. Still, we will have a hard time disputing the fact that we humans are responsible for most species losses regardless of rising temperatures. © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_2

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Fig. 2.1 Peanut, a freshwater turtle (Trachemys scripta elegans) that, in the 1980s, became entangled in a plastic ring of a six-pack can holder while swimming in the waters of Missouri. When rescuers found Peanut in 1993, they cut her free from the plastic ring. But it was too late; the ring had already severely deformed her shell. After that, she became an important testimonial in Missouri’s “No More Trash campaign” to sensitize people about the dramatic environmental effects of litter; c John Miller, Missouri Department of Conservation, reproduced with permission 

The direct effects of anthropogenic activity on biodiversity are self-evident. We are emptying oceans by overfishing, polluting lakes and clear-cutting forests. We hunt down large mammals to hang their heads on a wall or make money from their skins, fur and teeth. Less spectacularly, we live our daily lives, even if our socioeconomic system makes us feel more comfortable driving a car, drinking water from plastic or eating a steak than buying a rhino horn or a gorilla hand ashtray. Indirect effects, by contrast, are often so elusive that even scientists struggle to understand them. With those, I am not just referring to a tortoise getting caught in a six-pack ring (Fig. 2.1). I am talking about the chain of ecological reactions following species declines and extinctions. Although mostly unpredictable, it is becoming increasingly evident that this kind of cascading effects could be among the main culprits of the current biodiversity crisis. Scientists started warning us about such processes well before global warming became a world priority [7]. In the 80s, Jared Diamond had already identified four major causes of the ongoing mass extinction that he compared to the Four Horsemen of the Apocalypse: overkill (or over-exploitation), biological invasions, habitat destruction and, last but not least, co-extinctions, the secondary loss of species following the depletion of their resources [8]. The first three causes, which are direct effects of human activity, have been extensively investigated. Still, there are many open questions about the mechanisms behind co-extinction processes [9], even if their potential relevance has been advocated for a long time [10], with different models identifying them as fundamental drivers of species loss [11, 12].

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The curses brought by the Four Horsemen are becoming so widespread around the globe that an additional problem is their synergy [13]. Combined stresses might doom species otherwise capable of coping with them individually. And it is becoming more and more evident that various problems are deeply interconnected. For example, warming water temperatures promote large-scale events of coral mortality [14]. The rise in water temperature is also promoting disease and predator outbreaks. These, in turn, cause further mortality on corals already weakened by environmental stress, and hence unable to defend themselves properly from the multiple aggressors [15]. The daily movement of goods from one end of the planet to the other generates countless opportunities for novel colonization events. Consequently, diseases and predators are often also invasive species whose distribution is rapidly expanding due to environmental change and globalization. Disease and predators can also reinforce one another’s effects. For example, wounds caused by corallivorous predators’ activity can make it easier for pathogens to penetrate coral tissues, while predators, moving from one colony to another, may act as potential pathogen spreaders and promote the emergence of outbreaks [16, 17]. This conservative oversimplification of the actual processes at play suggests that, even in situations where the specific impact of individual threats is somehow limited, their combination may generate a self-reinforcing loop capable of destroying reef ecosystems in the glimpse of an eye. But, let’s take one step further. The effects of coral loss on fish populations are not apparent. Still, empirical and modelling studies have shown how fish abundance and taxonomical and functional diversity can be negatively affected by coral loss [18– 20]. Simultaneously, demand for fish is increasing with global population growth so much that fish populations already stressed by coral loss are also under direct attack by humans.1 Also, fishing activities tend to be rather selective, targeting a few high commercial value species, such as groupers or tunas. This selectivity puts the most targeted species at high risk and has severe environmental consequences. The increasing difficulties in catching species that are becoming less common often translate into longer fishing trips and more destructive techniques. Furthermore, they lead to higher amounts of by-catch (species of lesser interest caught together with the target ones and thrown back to water while dying or when already dead) [22]. As suggested by the previous examples, the mechanisms by which extinction drivers can act in synergy rely upon species’ interactions. These are also the obvious paths for the fourth Horseman of the biodiversity apocalypse, that is, co-extinctions [23–25]. Thus, there is no doubt that focusing on how species interact is critical to improving our understanding of how (and why) species are rapidly disappearing. By no means I am claiming originality for this idea, though. The first to highlight the importance of accounting for ecological interactions was an exceptional scientist 1

Actually, the problem of overfishing could be an even worse threat to reef ecosystems than warming temperatures. An analysis of past trends in various guilds of reef species (fish, corals, suspension feeders and seagrasses) spanning from the pre-human era to modern times showed how reefs were declining well before the recent increase in coral mortality due to extreme water temperature and disease outbreaks. This decline strongly points to human exploitation as the main responsible for reef degradation [21].

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named Alexander von Humbolt—so exceptional to be known as the most famous man on earth of his times after Napoleon. Among the others, he described countless species, discovered the magnetic equator and invented the meridians. Despite the greatness of those achievements, one of his most important contributions to science was probably suggesting that each natural world component is strictly dependent on every other one [26]. The conceptual shift from food chains to trophic webs is something not particularly new in ecology. It dates back to the contributions of Elton, who identified the key to understanding ecosystem functioning in “food cycles”, that is, the integration of all food chains in an ecological system [27, 28]. Yet, Eltonian trophic webs are very far from depicting the whole story. The amount of direct and indirect interactions that connect species besides trophic links is overwhelming. When one takes those into account, patterns and processes that seemed evident in a food web need to be reconsidered, giving room to new ideas, theories and discoveries about the mechanisms behind natural complexity (see, for example, [29, 30]). Let’s go back to coral reefs. I referred to coral mortality induced by warming temperatures,2 but I did not go into details. One cause for coral mortality is the disruption of the intimate symbiosis between corals and tiny unicellular algae called zooxanthellae, which play several roles fundamental to coral survival, such as facilitating them to build their skeleton [32]. When temperatures rise beyond a certain threshold, corals expel the symbiotic algae. Since zooxanthellae’s photosynthetic pigments provide corals with their colour, without them, corals become white, which is why the phenomenon is called “bleaching” [33]. If temperatures roll back soon to acceptable levels, the populations of symbiotic algae can recover (both through recruitment of “new” algae from water by corals, and reproduction of survived individuals within corals). Interestingly, the new populations could become more tolerant to environmental stress than the previous ones by selecting more resistant symbiont types [34–36]. However, corals cannot survive long without algae and hence are doomed to die if temperatures exceed their tolerance limits for a prolonged period. Assessing the actual status of symbiont communities within a bleached coral, that is, understanding if corals have chances to recover, is complex [37]. It is striking how what is considered one of the most critical ecological emergencies, the loss of coral reefs ecosystems, starts from the loss of a non-trophic interaction. What is even more striking is the abundance of examples where other kinds of symbiosis control communities, such as host–parasite interactions (Fig. 2.2) [38–40]. In an ambitious study, and with a massive effort, a team of researchers from the University of California, Santa Barbara, quantified the free-living and parasite biomass in three estuaries [38]. Guess what? Parasite biomass was incredibly abundant, even surpassing that of the local top predators, piscivorous birds. These results promoted substantial arguments to to the idea that, besides being exceptionally abundant (and diverse [41]), parasites might also have strong effects at the ecosystem level, as the 2

Note that temperatures are not the only cause for the disruption of the coral–algae symbiosis; other sources of environmental stress can contribute to bleaching, most notably ocean acidification and eutrophication [31].

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Fig. 2.2 Network representations of the food web of Estero de Punta Banda (Baja California, Mexico) including only free-living species (a), and including also host–parasite interactions (b). Green spheres indicate basal taxa, red ones indicate free-living taxa, and blue ones indicate parasites. Modified from [40], under a CC0 1.0 Universal Public Domain Dedication https://creativecommons. org/publicdomain/zero/1.0/

cumulative and synergistic result of the interactions happening between individual host–parasite pairs. The fact that parasites can promote ecosystem functioning and biodiversity suggests that a healthy ecosystem is rich in parasites [42]. Indeed, parasites can regulate host populations, change competitive interactions among host species and promote evenness through rapid evolutionary adaptation targeting dominant species. Although various experimental studies have tackled specific examples [29, 40], we are probably just scratching the surface of how, and to what extent, parasites control the natural world. Much of the complexity generated by ecological interactions is not in plain sight. Taking this into account would result in adding nodes (that is, species) and links (that is, interactions) to ecological networks (Fig. 2.2). An important question is how these additions possibly affect ecosystem stability, meant here, in a rough simplification, as a system’s overall ability to persist in the face of external disturbances. To answer this question and find out how the balance between diversity and stability is maintained, we should go beyond the plain count of species or ecological interactions. Sir Robert May was the first to investigate the trade-off between complexity and stability in natural systems rigorously. Back in the Seventies, May examined the issue in formal terms, trying to identify how the diversity of species, the richness of interactions and, to some extent, the geometry of these interactions affected the ability of natural systems to cope with small perturbations. Contrary to intuition, he found that the more complex a system is (in terms of diversity of species and interactions), the less it is stable [43]. This result, known as the “May’s paradox” due to its striking contrast with the existence (and apparent stability) of very complex natural systems with a high diversity of species and ecological interactions, has provided food for thoughts to generations of ecologists.

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In his seminal paper [43], May recognized that his conclusions are strongly dependent on the assumption of random interactions between species, while particular configurations of the interaction matrix could lead to different scenarios. For example, he noticed that asymmetry in interaction strengths, a situation where species have either many but weak associations or few but strong ones, might increase community stability. Similarly, he pointed out that “modular” communities, characterized by independent groups of strongly interacting species, might be more stable than their random counterparts. He highlighted how those kinds of situations are not rare in natural systems, suggesting that focusing on structural patterns might be a good starting point to understand complexity in the natural world better. Although several ecologists had followed the advice, there is still no consensus on which structures promote stability. For example, various studies have identified “nestedness”, a pattern where most pairs of species (e.g. two pollinators) in a network tend to share the resources they depend on (e.g. plants), as the key to multi-species co-existence in mutualistic networks [44, 45]. In a nested network, the joint contribution of competitors to the abundance of shared resources might reduce competition (fig. 9.1). However, this mechanism does not necessarily hold for antagonistic interactions. Additionally, a theoretical study closer to May’s original approach [43] came to different conclusions, suggesting that nestedness could be instead detrimental to the stability of mutualistic networks [46]. Looking at this fundamental issue from a slightly different perspective, we might think of ecosystems as high towers, where each brick both sits on top of and supports other bricks. Regardless of which kind of structure—the way bricks are layered— ensures better stability, the tower’s survival depends both on the synergistic and on the individual roles of each building block. Furthermore, not all bricks play an equally important role in maintaining stability and easing the addition of new species to the system. That is, while a few bricks can be taken away or replaced by others with little harm done, taking away too many or a few but crucial ones would eventually lead the tower to collapse. Identifying which bricks are necessary is, therefore, one of the biggest challenges in ecology and conservation. This concept is all but new in ecology, analogous to the classical idea of keystone species. The keystone species concept was formally introduced by Robert T. Paine in the late Sixties [47]. Before getting to the definition, Paine spent much time working on empirical food webs. His most famous experiment was performed on tidal food webs at Mukkaw Bay, in Costa Rica [48]. There, the top predators were starfish of the genus Pisaster, which fed mainly on acorn barnacles Balanus glandula, but also on various other species (Fig. 2.3). To verify the actual role played by Pisaster starfish in the food web, he removed all of them from a typical piece of shoreline. A few months after the removal, acorn barnacles—starfish’s main prey—became dominant, occupying from 60 to 80% of available space. One year after, acorn barnacles were replaced by stronger competitors, and the area became eventually dominated by Mytilus bivalves. The net result of the food web alteration was a dramatic drop in biodiversity (from 17 to 8 species).

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Fig. 2.3 Simplified scheme of the tidal trophic web at Mukkaw Bay dominated by Pisaster starfish. Pisaster was observed feeding on 1049 food items, while the other main predators in the food web, snails of the genus Thais, were observed preying on 287 food items. The two numbers on each arrow indicate the percentage contribution of prey to the predator’s diet in terms of number of consumed individuals (left number) and calories provided (right number). For example, the chitons consumed by Pisaster account for only 3% of the total number of consumed prey items, but they provide 41% of the total calories. Redrawn from [48]

Paine identified a few criteria to qualify a species as keystone [47]. First, the changes caused by the absence of the keystone species should not be reproducible by removing other consumers. Second, the increasing abundance of consumers after removing the keystone species should not bring the system back to its original status. These aspects make a keystone species not too different from a fundamental brick— or node in an ecological network—that would cause the tower to collapse if removed. Thus, again, where is the novelty in here? Well, it possibly lays in the criteria to identify keystone species. The idea that keystone species most often coincide with apex predators is deeply rooted in ecological thinking and supported by notable examples. A striking one is that of wolves in Yellowstone. After an intense debate and a comprehensive environmental impact assessment, wolves were reintroduced in Yellowstone National Park. There is a popular, emotional video with tens of millions of views on YouTube entitled “How wolves change rivers”, which describes the potential top-down effects of wolves on the Yellowstone ecosystem. Those consisted of reducing the number of elks and altering

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their feeding habitats by making them less prone to show up in areas where wolves could have ambushed them. Consequently, riparian vegetation, usually overgrazed by elks (such as aspen), recovered, leading to increased bird diversity. In the video, the narrator explains that together with birds, also beavers arrived because “beavers like to eat the trees”. And by building dams, beavers created habitats and niches for many other animals, such as otters, muskrats, ducks, fish, reptiles and amphibians. All of this stemming indirectly from the return of wolves. There is still a lively debate in the scientific community about many of these aspects, starting from lack of consensus regarding wolves’ actual role in promoting the recovery of aspen populations [49]. Researchers have argued that the changes observed in Yellowstone after wolf reintroduction could have had a bottom-up origin instead of a top-down one. Moreover, several of the observed patterns might correspond to correlations without causation [50]. For example, an interesting, often untold truth about the story is that beavers populations started to grow after a reintroduction campaign in the north of the park [51]. This kind of debate and questions have forced us to move from Paine’s work to a new set of primary ecological challenges. As highlighted by Princeton’s professor Andy Dobson, “All of the work from Yellowstone cries out for the development of next-generation, population-based ecosystem models that focus on interactions between climate, vegetation, and the dominant herbivore and carnivore species in the park” [50]. And, of course, this need extends far beyond the Yellowstone ecosystem, representing a major ecological priority. Without facing this critical challenge, we have little hope to understand the mechanisms behind the complexity of natural systems and slow down their collapse. Although, in recent years, scientists have been providing many novel insights on the mechanisms permitting the emergence of ecological complexity based on both theoretical and empirical evidence [46, 52–55], such advances in scientific knowledge have not proceeded in parallel with conservation actions. A major difficulty reconciling new ideas is the unbalanced emotional link we have with some organisms but not with others. The image of a pack of wolves moving in the wilderness is so powerful that the Yellowstone example will remain popular in public perception even if future science will show that the actual ecosystem effect of wolf reintroduction was not as important as previously thought. There are plenty of examples where conservation efforts are promoted by subjective human feelings more than ecological criteria, while other fundamental ecosystem players are completely ignored. As discussed in the previous paragraphs, there is a growing recognition of parasites’ fundamental role in ecosystems. Parasites can be wolves in their own way and have widespread ecological effects sometimes comparable to those of top predators [38, 42, 56–58]. Yet, they are not—or perhaps not yet? [59]—a target of conservation actions. As biodiversity is collapsing fast, conservation resources are shrinking due to the rise of societal emergencies that are being given priority. For this reason, we cannot afford to waste money. The rain is pouring, and there is not enough space on the ark to save every living form. As upsetting as this sounds, we are not in the position to waste resources on lost causes. This consideration is uncomfortable since it implies

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that we need to make decisions that will possibly save some species while dooming others. Yet, the concept is gaining increasing visibility and promoting much debate in conservation planning, under the self-explanatory name of “conservation triage” [60]. In an optimistic view, the need for such difficult choices seems to clarify the grand challenge, namely identifying which species occupy vital roles in ecological communities. Researchers working in fields other than ecology, such as economic or social sciences, have shown how disrupting a network is not a trivial operation. The naive practice of removing nodes at random is way less effective than using more targeted, often counter-intuitive, criteria [61]. Similarly, minor modifications to the network, like a wise rearrangement of the position of a few links, can sensibly improve its robustness against external disturbances [62]. Applying this knowledge to ecological networks might provide “objective” tools to identify key species in complex natural communities. However, despite the promises offered by highly efficient network tools, their actual applicability to real-world ecology and conservation issues is limited by two factors. First, in most cases, we have limited knowledge of the authentic architecture of ecological networks. Second, natural systems’ dynamic nature might rapidly modify such architecture and undermine the validity of previous inferences. For example, the fast changes in environmental conditions our planet is now facing cannot grant that species that have been fundamental in the past will be of any importance in the future (see Chap. 9). Moreover, not all kinds of disturbances are expected to have the same effect. For instance, one can compare a hypothetical scenario where a pest decimates a dominant plant’s population in a well-structured terrestrial community against a scenario of a pathogen targeting top predator species. In the first case, the effect of plant loss will propagate bottom-up to herbivores, possibly reducing their populations. Such a reduction will result in less prey for carnivores, and so on. In the second scenario, the loss of predators might result in a top-down effect leading to an increase in herbivores, released from predator pressure, and possibly overgrazing. It would be tough to predict which of the two scenarios would produce the worst ecosystem effects. Although an oversimplification, this example conveys that ecosystems’ robustness is not fully embedded into their architecture. We can build a house with the strongest walls, but it will fall sooner or later if we place it on unstable terrain. And even safe terrain, given enough time, can become unstable, making good architecture and materials no longer effective. Natural systems are dynamic entities that are continuously evolving (through speciation and extinction events) to become more “adapted” to conditions that are also dynamic. Just like a rabbit needing to run as fast as it can to stay right where it is [63]. But although adaptation might ensure stability in the mid-term, it might also increase ecosystems’ vulnerability towards epochal change [64]. The more and faster a system is driven away from its natural conditions, the higher its risk of collapsing.

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This issue has been at the centre of my interest for a long time. Researching on this topic has led me to realize that the challenges we are now posing to natural systems are some they are not ready to cope with, not just because of their magnitude but also because of their quality [5, 64]. Looking at how our planet looks now, this finding is not surprising. We have been paving the way for the disaster since the very beginning of our civilization [65]. We have modified the environment so radically that it has even become hard to identify a frame of reference for the concept of naturalness. Recognizing the profound gap between what we perceive as natural and what is indeed natural from an ecological perspective [66] is fundamental to understand how we are driving to extinction thousands of species per year. There is overarching evidence that finding wilderness in our world is becoming more and more difficult every day, and that many of the biological communities we see in the “wild” are, at best, deeply influenced by human activity, and, at worst, a pure product of the Anthropocene [65, 66]. What makes it difficult to see the extent of human impact on the planet is our tendency to look at natural systems from a perspective strongly biased by our ephemeral life span. It does not matter to us if, from the moment we were born to the moment we will die, a scrap piece of polystyrene foam will not have aged a day [67], or that all that time, from a mountain’s perspective, would have lasted less than ten minutes [68]. Our inability to look at natural processes freely from our own time-scale generates the unbalanced perception that natural is everything that had not been deeply affected by human activity in the last few decades. Thus, we often mistake natural with traditional, and we are unable, for example, to see how far from naturalness an olive orchard is [69]. In this regard, it helps thinking about the Greenland shark, Somnius microcephalus. During development, the shark inherits from its mother diet a signature of the current level of atmospheric carbon-14. This signature is stored in the eyes and maintained for all of the shark’s life. Comparing it to the decline of atmospheric carbon-14 since the pulse produced by nuclear tests in the 1950s provides a tool for the accurate dating of sharks’ birth [70]. Using this approach, researchers created growth curves for S. microcephalus based on a sample of 28 female specimens (with a total length varying between 81 and 502 cm) collected in Greenland during 2010– 2013. The analysis reveals that this giant creature can live for centuries, representing the longest-lived vertebrate known [71]. One of the main challenges of the study was that, of the 28 females captured, only three were born after or during the bomb pulse onset, while the remaining ones were considerably older. However, some individuals that are now swimming in the dark and freezing Greenland waters have likely been around since the 17th century. How would you perceive the speed the world has been changing in the last 50 years had you be born in 1600? That is the kind of question we should ask ourselves every day to start perceiving the impending collapse. The ever-growing fracture between the world we live in and the world before us makes ineffective many of the mechanisms that have protected natural communities since (and even before) the last mass extinction event. This time, we will not need an asteroid. We are the asteroid.

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Summary Differently from previous mass extinct events identifying the main culprits of the ongoing diversity loss is relatively straightforward. We kill animals, we destroy habitats, we pollute the environment, we promote biological invasions. And, of course, we fuel global warming. These mechanisms are not the only ones behind the ongoing Sixth Mass Extinction event. It is becoming increasingly clear that cascading effects triggered by primary extinctions (e.g. extinctions caused directly by climate change, habitat destruction or over-harvesting) might play a lead role in the current biodiversity crisis. Yet, interpreting, modelling and possibly predicting how different kinds of perturbations propagate across the multitude of elusive links connecting species in networks of ecological interactions is challenging. However, tackling this challenge is fundamental not only to advance our understanding of how ecosystems respond to global change threats but also to strengthen the efficacy of conservation and restoration actions. Novel theoretical knowledge generated by physicists and mathematicians offers promising tools to bring classical ecological concepts such as that of keystone species into modern days. In particular, formal metrics of ecosystem-wide species importance—where species are considered as nodes in an ecological network—appear as valuable alternatives to identifying conservation priorities based on less objective, often purely emotional criteria. Unfortunately, due to their overarching complexity and dynamic nature, we are still a long way from a magic recipe for choosing where to allocate resources to save natural systems from collapse. Approaching that ideal target requires improving our knowledge of real-world ecological networks’ existing architecture and getting a deeper comprehension of how the quality of disturbances—and not just their magnitude—interacts with natural systems’ structure. The same communities might be, at the same time, extremely robust against some kinds of strong disturbance and very fragile against some other weak stressors. This “relative” vulnerability might be of crucial importance for current ecosystems, as evidence suggests that they might be substantially unprepared to face global change challenges.

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7. Mann ME, Bradley RS, Hughes MK (1998) Global- scale temperature patterns and climate forcing over the past six centuries. Nature 392(6678):779–787 8. Diamond JM (1984) “Normal” extinctions of isolated populations. In: Nitecki MH (Ed) Extinctions. University of Chicago Press, Chicago, pp 191–246 9. Pimm SL, Raven P (2000) Biodiversity: extinction by numbers. Nature 403(6772):843–845 10. Stork NE, Lyal CHC (1993) Extinction or’coextinction’rates? Nature 366:307 11. Koh LP et al (2004) Species coextinctions and the biodiversity crisis. Science 305(5690):1632– 1634 12. Dunne JA, Williams RJ (2009) Cascading extinctions and community collapse in model food webs. Philos Trans Roy Soc Lond B Biol Sci 364(1524):1711–1723 13. Brook BW, Sodhi BW, Bradshaw CJA (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23(8):453–460 14. Hughes TP et al (2017) Global warming and recurrent mass bleaching of corals. Nature 543(7645):373 15. Bellwood DR et al (2004) Confronting the coral reef crisis. Nature 429(6994):827–833 16. Williams DE, Miller MW (2005) Coral disease outbreak: pattern, prevalence and transmission in Acropora cervicornis. Marine Ecology Progress Series, vol 301, pp 119–128 17. Nicolet KJ, Chong-Seng KM, Pratchett MS, Willis BL, Hoogenboom MO (2018) Predation scars may influence host susceptibility to pathogens: evaluating the role of corallivores as vectors of coral disease. Sci report 8(1):1–10 18. Pratchett MS et al (2008) Effects of climate-induced coral bleaching on coral-reef fishes. Ecol Econ Conseq Oceanogr Mar Biol Ann Rev 46:251–296 19. Richardson LE et al (2018) Mass coral bleaching causes biotic homogenization of reef fish assemblages. Glob Change Biol 24:3117–3129 20. Strona G, Lafferty KD, Fattorini S, Beck PS, Guilhaumon F, Arrigoni R, Montano S, Seveso D, Galli P, Planes S, Parravicini V (2021) Global tropical reef fish richness could decline by around half if corals are lost. Proc Royal Society B 288(1953):20210274 21. Pandolfi JM et al (2003) Global trajectories of the long-term decline of coral reef ecosystems. Science 301(5635):955–958 22. Johnson AE (2010) Reducing bycatch in coral reef trap fisheries: escape gaps as a step towards sustainability. Mar Ecol Progress Ser 415:201–209 23. Dunn RR et al (2009) The sixth mass coextinction: are most endangered species parasites and mutualists? Proc Roy Soc B Biol Sci 276(1670):3037–3045 24. Moir ML et al (2010) Current constraints and future directions in estimating coextinction. Conserv Biol 24(3):682–690 25. Colwell RK, Dunn RR, Harris NC (2012) Coextinction and persistence of dependent species in a changing world. Ann Rev Ecol Evol Syst 43:183–203 26. Wulf A (2015) The invention of nature: Alexander von Humboldt’s new world. Knopf 27. Elton CS (1927) Animal ecology. University of Chicago Press 28. Layman CA et al (2015) A primer on the history of food web ecology: fundamental contributions of fourteen researchers. Food Webs 4:14–24 29. Lafferty KD, Dobson AP, Kuris AM (2006) Parasites dominate food web links. Proc Nat Acad Sci 103(30):11211–11216 30. Godoy O et al (2018) Towards the integration of niche and network theories. Trends Ecol & Evol 33(4):287–300 31. van Oppen MJH, Lough JM (2018) Coral bleaching. Springer International Publishing, Cham, p 356 32. Gattuso J-P, Allemand D, Frankignoulle M (1999) Photosynthesis and calcification at cellular, organismal and community levels in coral reefs: a review on interactions and control by carbonate chemistry. Am Zool 39(1):160–183 33. Brown BE (1997) Coral bleaching: causes and consequences. Coral Reefs 16(1):S129–S138 34. Bay LK et al (2016) Recovery from bleaching is mediated by threshold densities of background thermo-tolerant symbiont types in a reef-building coral. Roy Soc Open Sci 3(6):160322

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35. Silverstein RN, Cunning R, Baker AC (2015) Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob Change Biol 21(1):236–249 36. Morikawa MK, Palumbi SR (2019) Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc Nat Acad Sci 116(21):10586–10591 37. Fitt WK et al (2001) Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20(1):51–65 38. Kuris AM et al (2008) Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454(7203):515 39. Lafferty KD et al (2008) Parasites in food webs: the ultimate missing links. Ecol Lett 11(6):533– 546 40. Dunne JA et al (2013) Parasites affect food web structure primarily through increased diversity and complexity. PLoS Biol 11(6):e1001579 41. Strona G, Fattorini S (2014) Parasitic worms: how many really? Int J Parasitol 44(5):269–272 42. Hudson PJ, Dobson AP, Lafferty KD (2006) Is a healthy ecosystem one that is rich in parasites? Trends Ecol Evol 21(7):381–385 43. May RM (1972) Will a large complex system be stable? Nature 238(5364):413–414 44. Rohr RP, Saavedra S, Bascompte J (2014) On the structural stability of mutualistic systems. Science 345(6195):1253497 45. Bastolla U et al (2009) The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458(7241):1018–1020 46. Allesina S, Tang S (2012) Stability criteria for complex ecosystems. Nature 483(7388):205–208 47. Paine RT (1969) A note on trophic complexity and community stability. Am Nat 103(929):91– 93 48. Paine RT (1966) Food web complexity and species diversity. Am Nat 100(910):65–75 49. Mech LD (2012) Is science in danger of sanctifying the wolf?? Biol Conserv 150(1):143–149 50. Dobson AP (2014) Yellowstone wolves and the forces that structure natural systems. PLoS Biol 12(12):e1002025 51. Smith DW, Tyers DB (2008) The beavers of Yellowstone. Yellowstone Sci 16(3):4 52. Shear McCann K (2000) The diversity-stability debate. Nature 405(6783):228–233 53. Neutel A-M et al (2007) Reconciling complexity with stability in naturally assembling food webs. Nature 449(7162):599 54. Allesina S et al (2015) Predicting the stability of large structured food webs. Nature Commun 6:7842 55. Grilli J et al (2017) Higher-order interactions stabilize dynamics in competitive network models. Nature 548(7666):210 56. Poulin R (1999) The functional importance of parasites in animal communities: many roles at many levels? Int J Parasitol 29(6):903–914 57. Lefevre T et al (2009) The ecological significance of manipulative parasites. Trends Ecol Evol 24(1):41–48 58. Vannatta JT, Minchella DJ (2018) Parasites and their impact on ecosystem nutrient cycling. TRENDS Parasitol 34(6):452–455 59. Carlson CJ et al (2020) A global parasite conservation plan. Biol Conserv 250:108596 60. Gerber LR (2016) Conservation triage or injurious neglect in endangered species recovery. Proc Nat Acad Sci 113(13):3563–3566 61. Morone F, Makse HA et al (2015) Influence maximization in complex networks through optimal percolation. Nature 524(7563):65-U122 62. Schneider CM et al (2011) Mitigation of malicious attacks on networks. Proc Nat Acad Sci 108(10):3838–3841 63. Van Valen L (1973) A new evolutionary law. Evol Theor 1:1–30 64. Strona G, Lafferty KD (2016) Environmental change makes robust ecological networks fragile. Nat Commun 7:12462 65. Lyons SK et al (2016) Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529(7584):80–83

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66. Strona G et al (2016) Far from naturalness: how much does spatial ecological structure of European Tree assemblages depart from potential natural vegetation? PloS One 11(12):e0165178 67. Otake Y et al (1995) Biodegradation of low-density polyethylene, polystyrene, polyvinyl chloride, and urea formaldehyde resin buried under soil for over 32 years. J Appl Polym Sci 56(13):1789–1796 68. Egholm DL, Knudsen MF, Sandiford M (2013) Lifespan of mountain ranges scaled by feedbacks between landsliding and erosion by rivers. Nature 498(7455):475–478 69. Strona G, Beck PSA, Carstens CJ (2016) Network analysis shows why Xylella fastidiosa will persist in Europe. Sci Rep 7(71) 70. Lynnerup N et al (2008) Radiocarbon dating of the human eye lens crystallines reveal proteins without carbon turnover throughout life. PLoS One 3(1):e1529 71. Nielsen J et al (2016) Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science 353(6300):702–704

Chapter 3

Everything Is Connected

The intuitive idea that the fate of species on Earth is linked—at a different degree—to all other species by an elusive net of ecological interactions brings us to a radical epiphany. As well as a mechanical clock cannot correctly work without the tiniest of its wheels, every single species could play an essential role in the proper functioning of our biosphere. This concept does not imply that all species have the same importance. There are likely many of them whose absence will not cause visible effects. But with “visible” effects, we are referring to our perspectives. From the omniscient view of Nature, any extinction, be it of the smallest, rarest fly on the planet, will affect to some degree other species. These include the species possibly depending on the extinct one, or those the extinct species depended on, or even species disconnected from the lost one, but linked to it by invisible (or “higher-order”) wires [1]. In a best-case scenario, the disappearance of a species could benefit the other species in the system. In a way, this is a natural process that is at the basis of how ecosystems build up robustness. Further in this book, we will explore how the progressive selection of stable interactions improves systems’ ability to respond to perturbations. Without entering into details, this concept is similar to the classical ecological view of natural successions leading communities from low diverse assemblages of pioneering species—such as encrusting lichens and mosses—to mature, high diverse communities—such as climax forests [2, 3]. Here the subsequent, local extinction of species and their replacement with others, far from being detrimental, is key to the emergence of ecosystem complexity. However, the most common kind of species loss we are witnessing is a very different process than natural species turnover. We cannot exclude that the disappearance of a species might have positive effects at the community or ecosystem levels. Still, most often, the expectation for such loss is that of adverse effects on other species’ population dynamics (e.g. reducing their abundance) and ability to cope with disturbances. In the worst possible scenario, the loss of a single species leads to the cascading extinction of more species and eventually causes the collapse of the entire community. This catastrophic domino © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_14

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effect is considered one of the main (and yet poorly documented) culprits of the ongoing biodiversity crisis, and likely one of the most common ways species are disappearing from Earth [4]. Such extinction mechanism dramatically complicates conservation actions, as it implies that classical conservation strategies focusing on protecting target species could fail due to the lack of consideration of fundamental ecological dependencies. Despite this might seem obvious, identifying which species are essential for others’ survival is often challenging—as it is often challenging to predict what could happen to an ecosystem following the species extinctions. Conservation’s tendency to focus on species instead of on ecological processes combines with the issue (discussed in Chaps. 1 and 2) of unbalanced attention towards species that we perceive as important. If we look at the IUCN Red List of endangered species, we have good evidence of this: only a few invertebrates appear in the list [5] and almost no parasites [6]. Despite being one of the most common lifestyles on Earth and the most common consuming strategy [7], parasitism has been historically neglected by conservation biologists and in the context of major ecological fields such as food web science. I owe much to pathogens and parasites. They have been putting food on my table for many years, after all. Yet, although, from time to time, it happens to me to donate to bears or pandas, I have never given money to save an intestinal worm. I am tempted to argue that protecting a panda could be the first necessary step to help its worms, but I suspect that most living pandas (at least those you can give money to) do take their fair dose of anthelmintics. This thought reminds me of how Colpocephalum californici, a louse parasitizing the critically endangered California condor, had become extinct. After they had transferred the few remaining condors to Los Angeles Zoo and San Diego Zoo’s Wild Animal Park in the mid-eighties, conservationists cleaned them from their louses using pesticide dust. Hence, in their zeal to be helpful, they became paradoxically responsible for one of the few documented parasite extinctions [8, 9]. So, I am probably not helping any panda’s worm out there. In my defence, I have never found an advertisement about a parasite charity organization needing help in my mailbox. I guess neither have you. The truth is that, apart from parasitologists, nobody likes parasites. Common sense would suggest that they are something that not only a species could live without, but also something that it should better get rid of. Actually, it is not just common sense. We are genetically programed to hate parasites in the same way we are programed to stay away from sick people [10]. As unethical as it might sound, the tendency to avoid infective agents, a behavioural response investigated in the context of the so-called disgust hypothesis, is a simple and effective innate form of defence against disease [11]. However, if we try to keep our repulsion under control for one moment, we will see that our negative feelings for parasites disguise the uncomfortable truth that parasites might be lead actors in this world. The effects parasites can have on host health scale up from individuals to communities/food webs. For example, a study conducted in a temperate lake in Germany focusing on the influence of eye flukes on the diet of the European perch (Perca fluviatilis) revealed a positive association between the level of infection within an individual and its dietary preferences towards the benthic macroinvertebrate Dikerogam-

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Fig. 3.1 Eye flukes and their effect on fish eyes. A: Metacercariae of different species of eye flukes, Diplostomum spp., collected from round goby (Neogobius melanostomus) eyes in the Kalmar Sound of the Baltic Sea. B: Different degrees of eye damage from Diplostomum infection in round goby specimens, ranging from less than 50% cataract coverage to 100% coverage. Modified from [15]. c Henrik Flink, Jane W. Behrens, P. Andreas Svensson (2017), used under a Creative Commons  Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/)

marus villosus. Conversely, mildly infected and uninfected fish showed a substantially higher trophic generalism [12]. Eye flukes are diplostomid trematodes with a three-host life cycle. Parasites reach maturity and reproduce sexually in a piscivorous bird, which disperses the parasites’ eggs through its faeces in the water. Eggs hatch into free-swimming larvae (miracidia) that infect freshwater snails. In the snail host, parasites reproduce asexually, and the new larval stages (cercariae), released in the water, actively seek fish hosts. After penetrating a suitable host, the cercariae migrate to the eyes, where they develop, often causing partial or complete blindness in infected hosts [13] (Fig. 3.1). Thus, a likely reason for the observed difference in the diet of fish with varying intensity of infection is that eye flukes, by reducing eyesight efficiency of infected hosts, could impede their ability to detect small prey [14] and force them to consume selectively larger (and hence more visible) invertebrates. Given the worldwide distribution of eye flukes and the broad diversity of fish species they can infect, these results highlight how these parasites could play a significant role in aquatic food webs globally [12]. In addition to alterations in host behaviour caused by direct, detrimental effects of parasites on host health, often parasites induce weird behavioural modifications into their hosts as an adaptive strategy aimed at increasing their chances to develop and reproduce [16]. Let us consider another example in the context of fish–eye fluke interactions. A study found a 90–100% prevalence of eye fluke infections in round gobies Neogobius melanostomus in the Kalmar Sound of the Baltic Sea, and that gobies’ response to simulated avian attacks decreased proportionally with the intensity of the cataract caused by parasites. Researchers interpreted this result as a behavioural change induced by parasites to increase their chances of reaching a final host [15]. Another study drew similar conclusions. There, scientists found that fish harbouring mature eye fluke metacercariae are more active. Furthermore, they have

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a higher tendency to stay close to the water surface and spend less time immobile after simulated attacks of bird predators than the uninfected control fish (with the intensity of these behavioural changes not correlating with that of the infection) [17]. Similarly, various trematodes can alter the swimming behaviour of infected crustacean hosts to make them easier prey to fish (the next host in the parasite life cycle) [18]. In a more spectacular example, a tetradonematid nematode has been observed modifying both the aspect and the behaviour of ants to make them look like berries. This alteration increases the ants’ chances to be eaten by birds, where parasites can complete their life cycle [19] (Fig. 3.2). Understanding how parasites alter the behaviour of their hosts is often challenging. Parasitic manipulation might involve different kinds of physiological and neurological alterations in hosts [20], thus representing a great example of how co-evolution can promote the emergence of fascinating, unexpected and complex natural processes. A consequence of the high specificity of such co-evolutionary adaptations is that they only work as expected when the parasite is in the correct host. By contrast, the effect of a manipulator parasite on the behaviour of a wrong host could be unpredictable. The protozoan Toxoplasma gondii is a parasite capable of infecting most species of warm-blooded animals. In humans, it is responsible for a disease (very often asymptomatic) known as toxoplasmosis. This condition is hazardous for pregnant women due to the parasite’s immunosuppression effect, increasing the chances for the newborn to be a male and be affected by Down’s syndrome. In its typical life cycle, T. gondii is transmitted to a final feline host by a small infected (intermediatehost) mammal, usually a mouse. The presence of the parasite in the prey makes the transmission event not the most stimulating hunting experience for the cat, since T. gondii makes its host less cautious than it should be and even attracted by its predators’ smell. Occasional transmission of the parasite from cats to humans can happen following consumption of food or water contaminated with cat faeces or when humans are exposed to contaminated environmental samples such as, for example, the litter box of a pet cat. In most cases, such events result in a “dead end” for the parasite, meaning that the parasite cannot reproduce there, nor be transmitted to a suitable host (with an exception being the rare—but not impossible—cases where a big cat eats an infected human [21]). Yet, even if it cannot progress in its life cycle, the parasite can survive indefinitely within the human host. The presence of T. gondii in human hosts has been shown responsible for light personality alterations, such as increases in reaction time, in the tendency for schizophrenia and in testosterone (which also has physical effects, increasing average height of infected males and their facial appearance) [22]. Combined with the exceptionally high global prevalence of the parasite, which is estimated around 25–30% of the world’s human population (with significant differences between countries, ranging from 10 to 80%) [23], those minor effects at the level of infected individuals might have non-negligible echoes on society. For example, the decrease in attention can correlate with an increase in car and work accidents [22]. Quantifying the actual effect of T. gondii at the societal level is problematic due to the number of factors,

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Fig. 3.2 a Comparison between an uninfected Cephalotes atratus worker and a worker of the same species infected by a parasitic nematode (Myrmeconema neotropicum). b A mating pair of the parasitic nematode M. neotropicum (male top, female bottom). The nematode can alter both the aspect and the behaviour of ants, making them look like berries which increases their chances of being eaten by a bird (the nematode’s final host) and hence those of the parasite to complete its life cycle. Adapted from [27]. Photos by Stephen P. Yanoviak, used under a Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/)

such as climate, that correlate with parasite prevalence and that can also be fundamental drivers of human behaviour [24]. Yet, the idea that a microscopic organism might influence human culture and society on a global scale is both fascinating and disturbing. Even without going that far and speculating about a global parasitic puppet master [25], there is scientific evidence showing how parasitic manipulation can have substantial, widespread effects at the ecosystem level. An amazing–or creepy, depending on the viewpoint—example is that of freshwater nematomorphs, which are parasitic invertebrates resembling nematodes. They are also known as “Gordian worms” because they reproduce by creating hairballs that cannot be untied, such as the famous namesake knot. Although they breed in water, Gordian worms spend part of their life cycle on the land or, better, within a terrestrial arthropod host, such as a cricket or grasshopper. Once mature, they occupy most of their host body cavity, and they induce the host to wander around, looking for water. When the host eventually finds a stream, a pond or even a dog bowl (Fig. 3.3), it commits suicide by jumping into the water. At that point, the worm leaves its host and starts its one in a lifetime journey looking for a partner. The story would be interesting enough if we stopped here. But there is more to it [26]. The implications of host manipulation by nematomorphs extend far beyond improving the reproductive success of parasites. After releasing the worm, abandoned hosts are often alive and, surprisingly, suffering minor damage from their former tenant. And yet, they are doomed to die by drowning or becoming easy prey for aquatic predators, such as fish, amphibians or predatory invertebrates. This process is part of a complex, much broader ecological mechanism. The additional (and

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Fig. 3.3 Nematomorph worm I have found in my dog’s water bowl, together with its “suicidal” host (a), a juvenile grasshopper. A few hours after having been taken out of the water, the grasshopper was apparently doing well (b). Photos by Marta Baglioni, reproduced from Strona [26] with kind permission from Elsevier

exogenous) food input represented by suicidal, infected arthropods might reduce predatory fish pressure on other available prey. A set of field experiments showed that this generates a “trophic cascade” increasing the abundance of grazers and shredders, resulting, in turn, in a decrease of benthic algae and, even if to a lesser extent, in an increase in leaf breakdown rate [28] (Fig. 3.4). Such results provide solid support to the idea that healthy ecosystems are rich in parasites and stress the importance of taking parasites into account in conservation planning [29]. Scientists are increasingly recognizing the role of parasites both as vital contributors to biodiversity and as lead actors in trophic web structure and dynamics [7, 30, 31]. With the growing interest in co-extinctions as a primary path to biodiversity loss, this recognition has generated momentum for essential changes. The above example of the paradoxical extinction of the California condor lice in the context of a conservation plan is now commonly used by scientists [8] to highlight how conservation biology might benefit from emerging ecological knowledge. To some extent, this is already happening, giving some meaning to the condor louse’s sacrifice, as more and more scientists are calling for actions and conservation plans focusing on parasites [6]. Only a few years ago, it would have been heretic to think about conserving parasites. Eradication was by far a better option. Then, among the others, a courageous researcher named Donald A. Windsor pleaded equal rights for parasites [32]. In the early nineties, a pioneer in parasite conservation, Windsor explicitly addressed the

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Fig. 3.4 How nematomorph parasites affect aquatic ecosystems? By inducing their hosts (crickets) to jump into the water, nematomorphs provide aquatic predators (trouts) with an additional food source. By feeding on suicidal crickets, trouts reduce their predatory pressure on the benthic community, which, in turn, leads to a decreased biomass of benthic algae and a slightly increased leaf breakdown rate (left panel). Preventing infected hosts to jump into the water (ideally the same as removing nematomorphs from the system) results in an increased predatory pressure of trouts on benthic macro-invertebrates, which cascades into a reduction in algae consumption and in leaf breakdown rate. Adapted from Sato et al. [28] with kind permission from John Wiley and Sons

fact that conservation biologists were much worried about saving free-living species but not at all about preserving worms and ticks. As his first piece did not receive much attention (possibly due to the cryptic title, “Heavenly hosts" [32]), Windsor tried to clarify and develop his ideas in several subsequent papers. The closing line of “Heavenly hosts"—Equal rights for parasites!—so intimately convinced him that he made it his trademark. Indeed, he used it as a title for three different articles [33– 35]. However, alongside this rather political-sounding apology of parasites’ rights, Windsor provided important ecological arguments to support the relevance of conserving parasites, including their exceptional diversity (possibly surpassing that of free-living species [36]) and their crucial role in ecosystems [37]. Windsor was not alone in his battle. Nigel Stork and Christopher Lyal in 1992 brought the co-extinction issue to the broad audience in a correspondence piece in Nature proposing that the loss of a keystone species can trigger co-extinctions capable of dooming many other species. Interestingly, their whole letter seems to revolve around parasites, as highlighted by the closing paragraph “There may be conflicts in conservation needs, forcing us to bid farewell to gorilla louse or the lice of the Californian condor while retaining their hosts. If so, we should do so in the full knowledge of what is being lost” [38].

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Eventually, parasites became the main characters in the co-extinction scene. Throughout the last two decades, the concept of co-extinction has been applied to diverse kinds of associations, such as those involving plant and herbivores, prey and predators, plant and pollinators and dung beetles and their dung-dealers [4, 39]. Yet, parasites have continued to play a significant role in the field, also because their strict dependence on hosts contributes to making the mechanisms behind host parasite co-extinctions more intuitive than those behind co-extinctions of other resource– consumer associations [40, 41]. A host is an ark for parasites and, if that ark sinks, parasites drown. Of course, the loss of flowers threatens specialized pollinators as much as that of hosts threaten specialist parasites. Yet, it is easier to think of a parasite on a critically endangered species as a Leonardo DiCaprio disappearing in freezing water. Conversely, an insect flying free in a colourful field will look in total control, even when flying in circles looking for a flower no longer there. When first conceived, this book was making no exception and on its way to talk about co-extinctions from a parasite’s perspective only. But while pondering the pros and cons of taking this direction, I realized that my effort would have provided a more comprehensive view on the mechanisms of biodiversity loss if freed from such a constraint. We are in deep need of a synthetic co-extinction theory capable of reconciling the achievements researchers have reached so far working on specific issues and providing a general framework for future work. It sounds overly ambitious to state after these few pages that I will accomplish all of this. Still, I hope that this book will help the field move at least a few steps in the right direction by bridging different research areas and touching on a broad array of perspectives. Attempting to reduce the focus on parasites has been a tough call for me. I have probably studied parasites more than any other subject in my career, and often colleagues think of me as a parasitologist. Kevin D. Lafferty is living evidence that one can be both a parasitologist and making it look cooler than studying white sharks (incidentally, he also studies white sharks from time to time [42]). I met him at the Fisheries Society of The British Isles’ annual meeting in Cardiff in 2008. I was a PhD student at the time. I was as much impressed by his talk about the ecosystem consequences of fish parasites as I was when, chatting on the way to congress dinner, he revealed that the main criterion he used to choose which scientific conferences to attend was the distance to surfable spots. I tried to get the best out of this lesson, and, one year later, I convinced him to invite me for a month to visit his research group at the University of California, Santa Barbara. Kevin gave me an ocean view desk, office keys and a longboard to practice my surfing skills. Access to the closest surf spot, “Campus Point”, happened to be one minute walk from the desk. The last and possibly the most critical question Kevin asked me before I left Santa Barbara was: “So Giovanni, are you a surfer now?”. I am not sure whether or not I lied to myself, but with a tiny bit of hesitation, I simply answered “Yes”. Once back in Italy, I did my best to be a Californian abroad. I grew my hair and beard long, started commuting on a skateboard (I still do that from time to time) and practised my sloppy surfing ability the few times I got close enough to waves. Yet, during my postdoc years, I have often faced the disappointing truth that Californians do not have to scratch ice from the car window after waking up at 5.30 AM in complete

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darkness and rush to get a two-hour train ride to the foggy Milan. To compensate for that, I started calling myself an “ecologist”, finding comfort in the idea that often this definition makes people think of rubber boats chasing whaling ships over rough, freezing waters. That is a much better picture than that of sleepless nerds looking for worms in dead fish. Which, unfortunately, is what people like me do. And people like Kevin. When the surf is flat, I mean. However, my public image is just a side reason why this book is not a parasitology text. Or at least, why I am trying hard to convince you that it is not a parasitology text. I will be honest: this book does include many examples from parasitology, possibly more than from other fields. But, let me clarify from now that, even when I talk about a parasite infecting a host, I am actually talking about a consumer using a resource. From this general perspective, it does not matter what the resource and the consumer look like. Thus, you should hear me describing a disgusting tongue-eating parasite, please be reassured that this would just be my personal choice, and feel perfectly free to replace the image with that of a bee collecting pollen from a beautiful flower. With this, I am not saying that mutualistic and antagonistic interactions are all the same. They are not and play different roles in maintaining ecosystem stability and diversity [43]. Nevertheless, the core of the co-extinction concept revolves around the idea that a consumer needs resources to survive. This simple mechanism applies to the vast majority of ecological interactions, which prompts the question of whether we can identify general rules [44] describing how the effect of biodiversity loss can propagate through ecosystems at multiple levels. Valuable attempts at such a generalization have been made [8, 40, 45–47] and will be the backbone of this book. However, there is still room and need for additional efforts, in particular, to draw links between ecological networks’ history (i.e. how networks became complex through a long, co-evolutionary path) and how this history relates to their future against the backdrop of global change [48]. In a way, natural communities show similarities to planetary systems. Like orbiting planets, species populations follow trajectories that are not independent one from another. The degree a species influences another one compares to the mutual effect of different masses sparse in the universe. Although lost in the infinitesimal, the gravitational force between any two objects will never be zero, regardless of their distance. Similarly, no species in a natural system can be considered wholly isolated from the others. The problem is that, although challenging from a computational perspective, identifying the effect of one planet on another one is made possible by a well-established science that started developing under an apple tree and based on measurable quantities. By contrast, the paths connecting a species to “distant” ones across the hidden web of interactions keeping together a natural system are often so counterintuitive and chaotic that tracking them seems a task beyond our reach. Like in a planetary system, everything in a natural community is moving, but the dynamic sum of positive and negative forces often generates a seemingly stable equilibrium (in terms of population dynamics). I wrote “seemingly” because the perception of stability depends on the temporal frame of reference. Planetary systems are evolving entities subjected to constant change. Still, they appear immutable from a human perspective. For ecosystems, things are different. Human lifespan is enough

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Fig. 3.5 Bleached corals from reefs in the Red Sea (Al Lith, Saudi Arabia). Pictures were taken in December 2015, at a depth of around 10 m (a). Coral colonies in panels b and c belong, respectively, c Davide Seveso (2015), reproduced with permission. to Stylophora pistillata and Goniopora sp. 

to witness and perceive ecological change. In the previous chapter, I briefly talked about the symbiotic association between corals and zooxanthellae micro-algae. The destruction of this intimate symbiosis due to various sources of environmental stress causes coral bleaching and, in case of prolonged disturbance, coral death [49]. At the time I am writing, coral reefs are experiencing one of the largest, global scale mass mortality events ever recorded [50]. The effects of this on marine species (and human society) associated with reef ecosystems are still to be defined but will be most likely dramatic [51]. When thinking of a dark future, we are often bound to assume that this will be a matter of concern for our children more than for ourselves, driven by the perception that the time frame of change is way slower than our lifetime. In this sense, coral bleaching represents an awakening experience. In a matter of days, a few degrees of increase in water temperature could turn huge areas previously covered by colourful living organisms into ghostly white graveyards of coral bones. They may still recover, but that is not the rule (Fig. 3.5). The hidden paths connecting every life form are so dense, complex and chaotic that a proper understanding of how a given disturbance will propagate to other living things is most often out of our reach. Unless, as it applies to the description of species diversity, we reach a point where the natural world will be depauperated to the end of being fully investigable [52]. Making an effort to think about the connections between organisms and the environment is the only way to convince ourselves that we can see the hardly visible strings connecting the livings to convince ourselves that we are responsible for what is happening and in a position to make a difference. If I had to propose an example for this, one capable of revealing, at least, some of the hidden strings, I would probably leave aside polar bears and choose the humble hermit crab. Hermit crabs need a gastropod shell of the right size at the right moment:

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c Shawn Miller (2014), Fig. 3.6 Hermit crab using plastic litter as a shell in Okinawa, Japan.  reproduced with permission.

without a shell, they are easy prey and will not survive long. Thus, their survival is strongly dependent on how well gastropod populations are doing. Furthermore, other organisms, such as sipunculid worms, compete with the crabs for available shells [53]. When shells are lacking, a desperate crab will use any shelter in its struggle to survive, including plastic bottle caps (Fig. 3.6). But, assuming that plastic litter may solve shell shortage by providing new houses for hermit crabs is a big mistake. The importance of finding a shell of the right size is fundamental to hermit crabs. There has been a long evolutionary history of hermit crabs’ adaptation to shell morphology. Using a shell or a half medicine bottle is not the same: in many cases, the crab cannot hide properly in the plastic shell, making it ineffective against predators. Past work has shown that artificial shells mimicking true ones can help investigate why hermit crabs choose a shell over another. Now, with 3D printing becoming mainstream, some have been promoting the production of low-cost, optimal synthetic shells for hermit crabs, with the aim of “addressing shell shortages in the wild” [54]. Although the idea is well intentioned, it sounds to me a bit like knitting wool jumpers for little penguins affected by oil pollution [55]. It shows that some people care about environmental problems and want to provide concrete help. Still, it is hard to see these actions, which deserve praise, as real solutions to the underlying issues. Sadly, pictures like the one in Fig. 3.6 are becoming more and more common. When looking at the crab in the bottle cap, several questions arise. First, why is the crab using a plastic container instead of a shell? As hinted above, a simple answer could be “shell shortage”, which opens additional questions. Is shell shortage due to a decline in gastropods’ populations? An alternative hypothesis could be that there are not enough shells because there are too many hermit crabs, and bottle caps have something to do with this. In a pristine environment, predators would have rapidly eliminated hermit crabs unable to find a shell in due time. But the increased

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availability of plastic litter could provide hermit crabs with a novel opportunity to find a suboptimal, temporary shell capable of reducing their odds to be preyed upon during their quest for the right house. From this perspective, the abundance of plastic litter may increase the environmental carrying capacity and boost hermit crab populations—as it would happen by dropping large amounts of empty shells in a given area [56]. Now, let us consider the alternative hypothesis that the shell shortage results from a decline in mollusc populations and in the availability of empty shells, due to habitat degradation, pollution, overharvesting by shell collectors and ocean acidification.1 Now, suppose hermit crabs are using plastic litter because there are no shells. In that case, the optimistic picture described above (where plastic litter provided an extra resource to hermit crab populations and not just a low-quality replacement) will need reconsideration. The use of suboptimal shelter will increase hermit crab mortality. Furthermore, it will likely disrupt many of the essential social interactions at play in hermit crab populations, which determine how shells pass from one owner to another [58]. It will reduce both food availabilities for predators and disrupt the ecosystem services provided by hermit crabs as detritivores [59]. From an evolutionary perspective, we might even imagine a situation where shell shortage favours the selection of “nasty” hermit crabs. Hermit crabs are not entirely pacific organisms. They use to fight both for food and an empty shell [60]. Still, they are not belligerent towards their shell providers. Indeed, hermit crabs are unable—or unwilling—to remove alive gastropods from their shell actively and sometimes even occupy shells with dead molluscs without removing them [61]. The same has been observed with live sipunculid worms occupying shells. In those cases, the hermit crab occupied the shell together with the sipunculid, which led to an uncomfortable situation where the crab could not wholly retreat into the shell [53]. On a general note, these examples reveal how hermit crabs’ policy—respecting their primary source of resources and refraining from overexploiting it—is wiser than ours. Now, let us imagine a hypothetical, hermit crab gone wrong scenario where a severe shortage of shells promotes the emergence of more aggressive behaviours. Some crabs—we may call them cheaters—take the initiative and succeed in killing and removing a gastropod from its shell. Since those would experience an immediate advantage over less aggressive crabs, the behaviour may spread rapidly. However, the killing of gastropods by the cheating crabs would exacerbate the decline in gastropod populations and shell availability. This would eventually result in the collapse of the hermit crab population and, hence, the disappearance of cheaters. Yet, the possibility for hermit crabs to survive by using plastic litter could prevent or, at least, slow down the collapse increasing the chances for the cheating strategy to become fixed in the 1

Ocean acidification due to increasing levels of atmospheric CO2 can have strong detrimental effects on shelled molluscs [57]. Shells produced in acidified waters are weak. Such weakness makes predation easier (both on the original owners, i.e. gastropods, and on hermit crabs) and reduces the lifetime of empty shells and their availability (a hermit crab will not enter a shell already damaged by a predator).

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population. In a dystopian evolutionary scenario, we might envision future hermit crabs fully adapted to shelter in plastic litter. Although the previous paragraphs are mere speculations, they exemplify how even a seemingly simple ecological setting can hide multiple levels of complexity. To have a glimpse at them, we need to stop looking at apparent, isolated patterns and search for the invisible strings connecting these to other aspects of reality. Using this investigative attitude in our everyday life could be a valuable exercise to improve our understanding of how the natural world is affected by our actions, even if those effects are far from our sight. While writing these lines, I feel a bit annoyed by tiny flies that have invaded my office for a few days. Those are pretty common animals, called fungus gnats, flies from the Sciaridae family whose maggots feed on decaying organic matter, and whose adults are a nuisance in houses. The infestation source is a pot containing a young avocado tree I have grown from seed. My idea is to get rid of the flies before my office mate gets rid of my avocado tree, but I am postponing the event because the flies are already part of a food webs of at least four trophic levels. A small spider hidden under one of the avocado leaves plays the top predator role. The avocado tree provides degrading organic material for fungi in the pot that the sciarid maggots consume. And, once adults, the flies provide food for the spider. I guess that similar food webs exist in most houses where plants are. If you have a garden, step out and take a moment to track down at least some of the most apparent trophic relationships going on among the tiny creatures you can see. You will be surprised by the amount of stunning complexity out there unnoticed. Take your time to think of what might happen if, at some point, you decide to get rid of the annoying flies. Or to treat the avocado against fungi. Or to not water the pot for a while. Try imagining the same situation on a bigger scale. Think of sharks instead of spiders, of wolves and of starving polar bears. Think of herrings instead of flies. Can you see my point now?

Summary Each species on Earth is linked to all other species by an elusive net of ecological interactions. This means that any extinction might affect to some degree of other species. Yet, conservation tends to focus on species—especially charismatic ones— instead of ecological processes. This attitude is problematic, as it might lead to disregarding critical players in natural communities and missing to consider complex yet fundamental ecological mechanisms that might either accelerate or slow down biodiversity loss. Parasites provide a remarkable example of this issue. Although parasitism is the most common consuming strategy on Earth, conservation actions almost completely ignore parasites. But parasites are not just abundant and diverse. They offer countless evidence of how the ecological effects of biotic interactions can propagate across food web links and have ecosystem-wide impacts. For instance, by manipulating their hosts’ behaviour to increase their chances of reproducing, parasites can indirectly affect trophic interactions in free-living species and drive

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energy fluxes. More in general, the multitude of visible and invisible paths connecting species into complex ecological networks determines how natural systems can cope with and respond to disturbances. This makes it clear that failing to consider them might hinder our already slim chances to tackle the ongoing mass extinction.

References 1. Wootton JT (2002) Indirect effects in complex ecosystems: recent progress and future challenges. J Sea Res 48(2):157–172 2. Clements FE (1916) Plant succession: an analysis of the development of vegetation. Number 242. Carnegie Institution of Washington 3. Prach K, Walker LR (2011) Four opportunities for studies of ecological succession. Trends Ecol Evol 26(3):119–123 4. Brook BW, Sodhi NS, Bradshaw CJA (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23(8):453–460 5. Cardoso P, Borges PAV, Triantis KA, Ferrández MA, Martín JL (2012) The underrepresentation and misrepresentation of invertebrates in the IUCN red list. Biol Conserv 149(1):147–148 6. Carlson CJ, Hopkins S, Bell KC, Doña J, Godfrey SS, Kwak ML, Lafferty KD, Moir ML, Speer KA, Strona G et al (2020) A global parasite conservation plan. Biol Conserv 250:108596 7. Lafferty KD, Dobson AP, Kuris AM (2006) Parasites dominate food web links. Proc Nat Acad Sci 103(30):11211–11216 8. Colwell RK, Dunn RR, Harris NC (2012) Coextinction and persistence of dependent species in a changing world. Ann Rev Ecol Evol Systemat 43:183–203 9. Dunn RR (2009) Coextinction: anecdotes, models, and speculation. Holocene Extinctions, pp 167–180 10. Curtis V, Biran A (2001) Dirt, disgust, and disease: is hygiene in our genes? Perspect Biol Med 44(1):17–31 11. Prokop P, Usak M, Fancovicova J (2010) Health and the avoidance of macroparasites: a preliminary cross-cultural study. J Ethol 28(2):345–351 12. Vivas Muñoz JC, Feld CK, Hilt S et al. (2021) Eye fluke infection changes diet composition in juvenile European perch (Perca fluviatilis). Sci Rep 11:3440 13. Rosser TG, Alberson NR, Khoo LH, Woodyard ET, Pote LM, Griffin MJ (2016) Characterization of the life cycle of a fish eye fluke, Austrodiplostomum ostrowskiae (digenea: Diplostomidae), with notes on two other diplostomids infecting Biomphalaria havanensis (Mollusca: Planorbidae) from catfish aquaculture ponds in Mississippi, USA. J. Parasitol 102(2):260–274 14. Vivas Muñoz JC, Staaks G, Knopf K (2017) The eye fluke Tylodelphys clavata affects prey detection and intraspecific competition of European perch (Perca fluviatilis). Parasitol. Res. 116(9):2561–2567 15. Flink H, Behrens JW, Svensson PA (2017) Consequences of eye fluke infection on anti-predator behaviours in invasive round gobies in Kalmar sound. Parasitol Res 116(6):1653–1663 16. Poulin R (2010) Parasite manipulation of host behavior: an update and frequently asked questions. Adv. Study Behav. 41:151–186 17. Gopko M, Mikheev VN, Taskinen J (2017) Deterioration of basic components of the antipredator behavior in fish harboring eye fluke larvae. Behav Ecol Sociobiol 71(4):68 18. Kunz AK, Pung OJ (2004) Effects of Microphallus turgidus (trematoda: Microphallidae) on the predation, behavior, and swimming stamina of the grass shrimp Palaemonetes pugio. J Parasitol 90(3):441–445 19. Yanoviak SP, Kaspari M, Dudley R, Poinar G Jr (2008) Parasite-induced fruit mimicry in a tropical canopy ant. Am Natural 171(4):536–544 20. Thompson SN, Kavaliers M (1994) Physiological bases for parasite-induced alterations of host behaviour. Parasitology 109(S1):S119–S138

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21. Shepherd SM, Mills A, Shoff WH (2014) Human attacks by large felid carnivores in captivity and in the wild. Wilderness and Environ Med 25(2):220–230 22. Flegr J (2013) Influence of latent Toxoplasma infection on human personality, physiology and morphology: pros and cons of the Toxoplasma-human model in studying the manipulation hypothesis. J Exper Biol 216(1):127–133 23. Robert-Gangneux F, Darde M-L (2021) Epidemiology of and diagnostic strategies for toxoplasmosis. Clin Microbiol Rev 25(2):264–296 24. Lafferty KD (2006) Can the common brain parasite, Toxoplasma gondii, influence human culture? Proc Royal Soc Lond B: Biol Sci 273(1602):2749–2755 25. Adamo SA (2012) The strings of the puppet master: how parasites change host behavior. Host Manipulation by Parasites 36:51 26. Strona G (2017) Why do nematomorphs leave their hosts? Int J Parasitol: Paras Wildlife 6(3):226 27. Laciny A (2021) Among the shapeshifters: parasite-induced morphologies in ants (Hymenoptera, Formicidae) and their relevance within the ecoevodevo framework. EvoDevo 12(1):1–21 28. Sato T, Egusa T, Fukushima K, Oda T, Ohte N, Tokuchi N, Watanabe K, Kanaiwa M, Murakami I, Lafferty KD (2012) Nematomorph parasites indirectly alter the food web and ecosystem function of streams through behavioural manipulation of their cricket hosts. Ecol Lett 15(8):786–793 29. Dougherty ER, Carlson CJ, Bueno VM, Burgio KR, Cizauskas CA, Clements CF, Seidel DP, Harris NC (2016) Paradigms for parasite conservation Biol 30(4):724–733 30. Kuris AM, Hechinger RF, Shaw JC, Whitney KL, Aguirre-Macedo L, Boch CA, Dobson AP, Dunham EJ, Fredensborg BL, Huspeni TC et al (2008) Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454(7203):515 31. Lafferty KD, Allesina S, Arim M, Briggs CJ, De Leo G, Dobson AP, Dunne JA, Johnson PTJ, Kuris AM, Marcogliese DJ et al (2008) Parasites in food webs: the ultimate missing links. Ecol Lett 11(6):533–546 32. Windsor DA (1990) Heavenly hosts. Nature 348:104 33. Windsor DA (1995) Equal rights for parasites. Conservat Biol 9(1):1–2 34. Windsor DA (1997) Equal rights for parasites. Perspect Biol Med 40(2):222–229 35. Windsor DA (1998) Equal rights for parasites. Bioscience 48(4):244–244 36. Windsor DA (1998) Controversies in parasitology, most of the species on earth are parasites. Int J Parasitol 28(12):1939–1941 37. Windsor DA (1997) Stand up for parasites. Trends Ecol Evol 12(1):32 38. Stork NE, Lyal CHC (1993) Extinction of co-extinction rates? Nature 366:307 39. Carpaneto GM, Mazziotta A, Pittino R, Luiselli L (2011) Exploring co-extinction correlates: the effects of habitat, biogeography and anthropogenic factors on ground squirrels-dung beetles associations. Biodivers Conservat 20(13):3059–3076 40. Moir ML, Vesk PA, Brennan KEC, Keith DA, Hughes L, Mccarthy MA (2010) Current constraints and future directions in estimating coextinction. Conserv Biol 24(3):682–690 41. Strona G (2015) Past, present and future of host-parasite co-extinctions. Int J Parasitol: Paras Wildlife 4(3):431–441 42. Lafferty KD, Benesh KC, Mahon AR, Jerde CL, Lowe CG (2018) Detecting southern California-white sharks with environmental DNA. Front Mar Sci 5:1–6 43. Allesina S, Tang S (2012) Stability criteria for complex ecosystems. Nature 483(7388):205–208 44. Lafferty KD, DeLeo G, Briggs CJ, Dobson AP, Gross T, Kuris AM (2015) A general consumerresource population model. Science 349(6250):854–857 45. Brodie JF, Aslan CE, Rogers HS, Redford KH, Maron JL, Bronstein JL, Groves CR (2014) Secondary extinctions of biodiversity. Trends Ecol Evol 29(12):664–672 46. Dunne JA, Williams RJ (2009) Cascading extinctions and community collapse in model food webs. Philosophical Trans Royal Soc London B: Biol Sci 364(1524):1711–1723 47. Eklöf A, Ebenman BO (2006) Species loss and secondary extinctions in simple and complex model communities. J Anim Ecol 75(1):239–246

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48. Strona G, Lafferty KD (2016) Environmental change makes robust ecological networks fragile. Nat commun 7:12462 49. van Oppen MJH, Lough JM (2018) Coral bleaching. Springer 50. Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, Babcock RC, Beger M, Bellwood DR, Berkelmans R et al (2017) Global warming and recurrent mass bleaching of corals. Nature 543(7645):373 51. Strona G, Lafferty KD, Fattorini S, Beck PSA, Guilhaumon F, Arrigoni R, Montano S, Seveso D, Galli P, Planes S et al (2021) Global tropical reef fish richness could decline by around half if corals are lost. Proc Royal Soc B 288(1953):20210274 52. Mora C, Rollo A, Tittensor DP (2013) Can we name Earth’s species before they go extinct? Science 341(6143):237–237 53. Kellogg CW (1976) Gastropod shells: a potentially limiting resource for hermit crabs. J Exp Mar Biol Ecol 22(1):101–111 54. MakerBot Industries and TeamTeamUSA (2017) Project shelter 55. Penguin Foundation (2017) Wildlife rehabilitation 56. Vance RR (1972) Competition and mechanism of coexistence in three sympatric of intertidal hermit crabs. Ecology 53(6):1062–1074 57. Gazeau F, Parker LM, Comeau S, Gattuso J-P, O’Connor WA, Martin S, Pörtner H-O, Ross PM (2013) Impacts of ocean acidification on marine shelled molluscs. Marine Biol 160(8):2207 58. Chase ID, Weissburg M, Dewitt TH (1988) The vacancy chain process: a new mechanism of resource distribution in animals with application to hermit crabs. Animal Behav 36(5):1265– 1274 59. Hazlett BA (1981) The behavioral ecology of hermit crabs. Ann Rev Ecol Systemat 12(1):1–22 60. Greggor AL, Laidre ME (2016) Food fights: aggregations of marine hermit crabs (Pagurus samuelis) compete equally for food-and shell-related carrion. Bullet Marine Sci 92(3):293– 303 61. Laidre ME (2011) Ecological relations between hermit crabs and their shell-supplying gastropods: constrained consumers. J Exper Mar Biol Ecol 397(1):65–70

Chapter 4

Ecological Networks

Try thinking of whatever aspect of reality, and it will be most likely representable in the form of a network. Relationships of various kind kinds between people [1], including scientific collaborations [2]. Roads or transportation and shipping routes connecting different locations [3–5]. Physical links between computers, such as the Internet [6]. Metabolic pathways [7]. Gene mutations [8]. Protein structure [9]. And, of course, the World Wide Web [10]. Despite their obvious differences, all of these systems can be described in a deeply informative way using a basic alphabet, having only two characters: nodes, the items to be connected, and edges, the connections between nodes. If we want to complicate the issue a bit more, we can add direction and weight to edges, depending on the interactions we are trying to represent. The existence of a “universal” network language has profound implications for scientific research. It permits scientists from different disciplines, such as (but not limited to) mathematics, physics, social sciences, computer science, economics, biology and, what we are primarily interested in here, ecology, to use (and contribute to the development of) the same tools. Most of the momentum in the theoretical and formal advance of the network field comes from physicists and mathematicians. Still, scientists from other areas contribute as well, especially in showing how one can apply the findings obtained in theoretical studies to real-world situations. As for ecology, network theory is becoming central to the study of natural systems. Networks offer a very convenient model to synthesize different kinds of species interactions [11], as well as a suitable framework to investigate how the effects of the loss of one species can propagate to others, possibly generating cascades of coextinctions. Thus, a general introduction of how networks try to represent the natural world is a fundamental premise to the topics treated later in this book. This chapter will introduce a few fundamental concepts of network theory and terminology, representing the minimum amount of information you will need to follow my reasoning. Still, I recommend interested readers to refer to more specific work [12] to get a more complete and detailed overview of the subject. Here, I will mainly focus on how we currently represent natural systems using networks, the © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_3

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limitations of this representation, and what steps one could take to increase network realism. In this last regard, I have to anticipate that the chapter will sound a bit discouraging. You will soon realize that there is a long way to make ecological networks truthful. In turn, the oversimplification of the natural world’s complexity which is often behind available networks could question some of the paradigms scientists have elaborated on to date. Nevertheless, my aim here is not that of being disruptive but, instead, of introducing a few ideas I deem important to move the field further. By no means I am suggesting that current limitations should prevent us from exploring ecological patterns and processes through the lens of network analysis. At the same time, however, recognizing that the depth and breadth of information we have at hands constrains the range of questions we can tackle is a fundamental premise to the healthy use of networks in ecology. Inferring general ecological mechanisms using networks that contain limited information could lead to biased conclusions, while the same networks could be fit to investigate some more contained issues. In ecological networks, nodes most often correspond to taxonomic units (e.g. species), while links indicate several possible interactions. Those can be consuming strategies, such as predation, foraging, parasitism or other kinds of ecological interactions, such as cleaning activity, seed dispersal or pollination [11]. Ecological networks involving clear resource–consumer relationships are usually directed; that is, they permit identifying who consumes whom. Typically, the direction goes from resources to consumers [11]. For example, in a host–parasite network, links go from hosts to parasites [13]. Thus, in its most straightforward representation, an ecological network appears as a list of node pairs, where the first node in each couple indicates a resource, while the second node indicates a consumer using that resource. Sometimes, identifying the direction of links is not straightforward. This difficulty applies most commonly to mutualistic networks, where both interacting partners get benefits from their symbiotic association, and it is not possible to make a distinction between resources and consumers. However, even in cases where a network aims at representing non-consuming interactions, there might be some resource–consumer interaction involved. For example, in cleaning symbioses, clients (e.g. large fish or sea turtles) are not consumed by a cleaner (e.g. a small fish or a shrimp). Still, the cleaner feeds on the clients food leftovers or parasites and sometimes also on the clients’ mucus or tissues, hence acting as a microparasite [14]. Similarly, a pollinator usually also forages on a plant. These examples reveal that, in many cases, a link can theoretically encompass multiple kinds of interactions, depending on the perspective from which we look at the association. Ecological network analysis has typically ignored this aspect, but things are now changing. We will see later in this book how the simultaneous existence of different kinds of links connecting the same nodes—or pointing to nodes out of the network under consideration, as in the case of the links between a cleaner fish and its client’s parasites—is now becoming an interesting object of investigation in the field of multilayer networks [15, 16]. A fundamental distinction in network theory is that between unipartite and bipartite networks. In a unipartite network, each node can be simultaneously a target and

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a source of interactions to other nodes. Food webs are typical unipartite ecological networks. Each species in a food web can be both consumed by and consume other species. Note that this is a general, defining network property that does not necessarily apply to each node. Some specific nodes in the network might act exclusively as resources (e.g. primary producers), and others might act only as consumers (e.g. top predators). In some food web representations, such nodes are artificially linked to generic nodes in the network representing detritivores, scavengers and decomposers. By contrast, in bipartite networks, one can distinguish nodes into two different categories. Nodes from one category can interact with (i.e. have links to) nodes in the other category, but interactions between nodes of the same category are not considered. Typical examples are plant–pollinator and host–parasite networks. The distinction between unipartite and bipartite networks is irrespective of link directionality. Both unipartite and bipartite networks can be either directed or undirected. For example, social and contact networks (such as networks representing encounters between individual members of a population) are unipartite and undirected. Networks of this kind are widely studied in ecology, especially in the context of ethological and epidemiological research [17, 18], even if they are not central to the questions we are addressing here. Co-occurrence networks connecting species with a significant tendency to be found in the same localities (i.e. co-occur) are another example of unipartite, undirected networks [19]. Mutualistic networks, such as plant– pollinator networks, are bipartite and undirected, while antagonistic networks such as host–parasite are bipartite and directed. Ideally, reconstructing an accurate network requires identifying the realized interactions between all possible pairs of nodes (e.g. species). The number of possible interactions changes depending on the kind of network (unipartite vs bipartite) we are considering. In a unipartite network (e.g. a food web), given n species, the number of possible pairwise interactions is n 2 . It becomes n × (n − 1) if we exclude the case of species interacting with themselves, the so-called self links which, in food webs, identify interactions that, by the way, are quite common in nature [20]. In a bipartite network with n 1 species belonging to the first category (e.g. plants), and n 2 species belonging to the second category (e.g. pollinators), the number of possible interactions is n 1 × n 2 . In real-world situations, identifying the complete network would ideally require sampling/observing all of the n species of a unipartite network, and either the n 1 or n 2 species of a bipartite network. For example, reconstructing the food web of a hypothetical lake community with n species would require examining the stomach content of all the n species (either visually or taking advantage of advanced molecular techniques, such as metabarcoding [21]). In a plant–pollinator network, one could identify all pollination links by sitting in front of each of the n 1 flower species in the community, taking notes of the visiting pollinators. That would be probably more convenient than running after the n 2 pollinators and take note of the flowers they visit. That is, in general, the choice of focusing on either the first or the second category of nodes in a bipartite network does not necessarily depend on the relative size of n 1 compared to n 2 but is

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affected by practical aspects. For instance, it is an obvious practice to assess links in a host–parasite network by sampling and screening hosts for parasites. Despite this apparent simplification, the task of building a comprehensive and accurate ecological network in the real world is often humongous, and trickier as species richness increases. In most cases, only a small subsample of local diversity has been investigated. The issue is not just qualitative. That is, it does not refer only to whether a given species has been sampled or not most often what reduces the accuracy and realism of an ecological network is a limited sampling effort. A relatively small number of sampled individuals cannot ensure that a fair fraction of extant interactions has been identified, nor that the identified interactions are ecologically relevant. Still, identifying the minimum number of individuals to be examined is complex, casespecific, and possibly requires empirical assessment. A possible solution is estimating the number of detected interactions for increasing levels of sampling effort and then using this information to build accumulation curves (see Fig. 4.1) [22]. This approach is analogous to the common practice in biodiversity assessment of estimating species richness by collecting samples of increasing size (either in terms of surveyed area or collected individuals) until no species are added to the list [23]. Yet, despite being intuitive and straightforward, it is not a standard procedure in ecological network reconstruction. Besides being comprehensive, we need the list of interactions to be reliable. But often, the plain observation of interactions could be deceiving, leading to the identification of “false” links. For example finding a parasite in a certain organism does not necessarily imply the existence of a parasitic relationship between the two. Especially in trophically transmitted parasites, an infected intermediate host is often eaten by the “wrong” predator, representing a dead end for the parasite development. In many cases, when this happens, the parasite simply dies because unable to escape the host immune defences. Sometimes, however, parasites can survive and possibly lead to the wrong identification of a host–parasite pair, as it is not always possible or straightforward to assess whether the host is a dead end for the parasite or, instead, a suitable host. To further complicate the situation, there are also intermediate cases where parasites can survive and wait in hosts not ideal for their further development until something happens that straightens up things. Such temporary hosts are called “paratenic”. Unlike dead-end hosts, paratenic hosts often play an essential ecological role in parasite populations’ survival, giving parasites extra time to find their final host. Although it would be better to exclude those links from networks in most cases, there might be specific situations where—depending on the ecological questions tackled—it might make sense to include them. Another potential source of “false” links derives from the fact that symbiotic relationships offer evolutionary windows of opportunity to cheaters [25]. Plants attract pollinators through a wide range of visual and chemical clues. How this reflects on flower beauty in terms of colours is evident, but the evolution of chemical strategies is as much extraordinary and, in some case, far from common knowledge. For example, by producing chemical clues (i.e. smell) typical of carrions, some flowers could attract necrophagous insects [26]. Some more romantic flower will provide various attractive clues to insects looking for sexual partners [27, 28]. However,

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Fig. 4.1 Accumulation curve of interaction richness in a plant–pollinator network with increasing sampling effort (i.e. plants). Dark-dashed lines indicate the expected number of interaction richness according to Chao 2 estimator [24], while grey-dashed lines indicate the estimator standard error. The number of observed interactions is 728, while the number of estimated interactions is 1323. The accumulation curve predicts that 2160 additional samples (i.e. monitored flowers) would have been needed to detect 80% of the estimated total number of interactions. To detect 90% of the estimated interactions, the number of additional samples goes up to 6144 and skyrockets to 16974 to identify all interactions. Adapted from [22], with kind permission from John Wiley and Sons

while appearance and smell can induce a pollinator to “try” a given flower, regular visits will only be encouraged by flowers offering a high reward in terms of nectar [29]. Pollinators learn to identify flowers producing good, abundant nectar based on some features such as the flower colour or smell. Therefore, they tend to visit them more often than other flowers. Generating nectar costs energy to a flower, which goes at the expense of other functions, such as the amount of pollen produced. Thus, there is a delicate balance between the energetic investment of producing a certain amount of nectar and the increased pollination benefit deriving from the flower’s appeal towards pollinators. Some plants might circumvent the trade-off by imitating some of the features which attract pollinators (e.g. floral display) while offering no or little reward (e.g. nectar) to the deceived visitors [30–32]. Of course, a pollinator will be disappointed by visiting a cheater, so that it will learn quite rapidly how to discriminate cheaters from “honest” flowers [33]. Similarly, cheating could also evolve in insects. There, cheaters consist of visitors that consume nectar from a flower without entering into contact with the floral anthers or stigma. To remove nectar, cheating visitors might or might not damage corollas

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(by piercing or biting holes). In the first case, they are called nectar robbers, while in the latter case, where the lack of pollination is due to a morphological mismatch between the flower and the visitor, they are called nectar thieves [34–36] Similar examples show well how not many things in nature are as simple as they appear. The event of an insect visiting a flower might hide multiple levels of undisclosed ecological and co-evolutionary complexity. In turn, this complexity implies that identifying a comprehensive and reliable set of “true” interactions is challenging and requires much more study effort than sitting in front of a flower and taking notes of visiting bees—which is a complicated, time-consuming task in itself. And yet, this steep challenge represents only one step towards building realistic ecological networks. Besides the distinctions between unipartite and bipartite, and between directed and undirected networks, another fundamental categorization is that of weighted vs unweighted networks. A consumer would likely depend on resources to different degrees. For example, we may imagine a situation where a predator obtains 90% of the energy it needs from a single prey species and the remaining percentage from several other species. This situation is substantially different from that where a consumer uses several resources with no particular preferences. May [37] identified as key to the stability of complex ecosystems a community pattern where species having few associations interact strongly with their partners, while species having many partners interact weakly with them. Thus knowing the relative strength of links connecting resources to consumers is fundamental to investigate network structure. Different networks could look identical from a “qualitative” perspective (i.e. in terms of presence/absence of links) but highly different from a quantitative one (i.e. when the strength of interactions is considered). In practical terms, this translates into attributing weights to network edges1 . Unfortunately, if identifying species interaction is hard, quantifying them is even more complicated. Consequently, information about interaction strength is not available in many cases, leaving us to deal with unweighted networks. Imagine the situation of identifying the set of pollinators using a given flower. As previously discussed, to map all interactions, one should spend hours in the field compiling a list every single insect that visited the target flower. However this would be an easy task compared to assessing the degree of interaction properly. For that, one should identify and note down every single visit to a given flower by a specific pollinator within a fair amount of time. Then one should replicate the experiment to ensure that particular conditions (e.g. time of the day, climate) have not biased the measures. In the same way, when assessing host–parasite interactions, to have a truly faithful representation of ecological relationships, one should evaluate not only if a given interaction is possible, but also how often it is realized. This would require first to examine enough individuals of each species to assess the frequency a given parasite infects a certain host. Then, for the hosts found infected, one should test whether the target parasite/s can develop. Then, for the hosts found infected, one should test 1

Since weights can correspond to very different properties and assume different values, it is common practice to rescale them between [0, 1].

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whether the target parasite can develop. And finally, one should assess how often the parasite is successfully transmitted from the host to the next one in the life cycle (if any). Collecting enough information to quantify the frequencies of these events— assuming, of course, that we already have proper and comprehensive knowledge of the parasite ecology—would be challenging even under controlled situations, let alone in the wild. Yet, this should ideally represent the target of a parasitological survey aiming to identify the detailed structure of a host–parasite network in detail. Not considering this information, and using instead “binary” interactions can lead not only to a partial picture but even to a biased one. For example, a much relevant pattern in the study of ecological networks is nestedness. The concept of nestedness, which I have already mentioned, and which will recur several times throughout this book, was first developed in the biogeographical context to identify the tendency for the species composition of a given community to be a subset of more diverse communities [38]. When applied to ecological networks, nestedness identifies the tendency for nodes to share interacting partners (e.g. pollinators’ tendency to use overlapping sets of flowers and the tendency of flowers to be serviced by similar pollinators)[12]. Various studies suggest that nestedness is common in ecological networks independently from interaction types. For example, a study focusing on three networks representing cleaning interactions in coral reef communities found that cleaning networks seem to exhibit structural patterns (e.g. nestedness) similar to those observed in plant–pollinator networks [39] (Figs. 4.4,4.5). However, accounting for interaction weights substantially changes this picture. More generally, many ecological networks result nested in their binary structure but not in their quantitative one. From an ecological perspective, this reveals that the qualitative overlap in associations does not reflect into the quantitative one, which suggests a limited overlap in consumers’ use of resources [40]. This mismatch highlights how the lack of quantitative information on interaction strength might not only affect our ability to detect a given pattern, but also mislead pattern identification. Additional conceptual subtleties further complicate the issue. In particular, assuming the frequency of interactions as a proxy for interaction weights, which is the most common approach, could not always be the best choice. As emphasized by the above discussion about cheating, the only observation of how often a (true) pollinator visits a flower could provide incomplete or even deceiving information. The actual strength of interactions is affected by the efficacy of the pollination process, which, in turn, is affected by co-evolutionary dynamics. A pollinator perfectly adapted to a given flower will be more effective in pollinating it than a poorly adapted pollinator. However, if the two pollinators visit the flower with the same frequency, an observer unaware of the differences in plant–pollinator co-adaptation might be tempted to attribute to the two interactions the same weight in the network. In many cases, it would be more meaningful to consider as interaction weight the “benefit” a species provides to its partner. To do this, we would first have to define what we mean with benefit and how to quantify it. The relative contribution a mutualist provides to its partner’s fitness could offer a simple definition but not a straightforward quantification solution.

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In the simplified case of a highly specialized interaction, where a pollinator can use only one plant species, and where the plant species can be serviced only by that pollinator (as in the case of several yucca/yucca moth interactions [41]), the absence of either the plant or the pollinator would reduce the partner’s fitness to zero—assuming that the plant has no alternative ways for reproduction other than pollination and that the pollinator has no alternative sources of energy besides those provided by the target plant, or cannot reproduce without it. However, a plant is often used by more than one pollinator [42], and a pollinator can use different plants [43]. In these uni- or bidirectional generalism situations, evaluating the individual contributions of single interactions to overall species fitness might be complex. To add complexity to the issue, quantifying the relative effect of interactions on species fitness (in terms of reproduction ability) is further complicated by the multitude of environmental and ecological factors affecting species fitness to a different degree and in different directions. These factors include other kinds of interactions, such as antagonistic ones as parasitism, herbivory and predation. Looking at the glass half full, there are potential approaches that permit us to cope at least partially with the above complexity and identify alternative proxy measures to weigh links in a more ecologically meaningful way than the plain count of interaction events does. For example, as mentioned before, the most common way a flower rewards a pollinator is through nectar [44]. Thus, one may assess the specific levels of reward obtained by a pollinator visiting different flowers in terms of sugar mass [45]. Similarly, one may quantify various aspects defining the performance of pollinators. Potential performance measures are pollination intensity (the number of pollen grains that a specific pollinator deposits on stigma in a single visit [46]), and pollination efficiency (fraction of the pollen carried by a pollinator and deposited on the floral stigma in a single visit [47]). However, since there is no unique recipe to define efficiency (see Table 1 in [48]), the application of one definition or another might lead to different networks. Similar reasonings to those made in the previous paragraphs for plant–pollinator systems are critical when focusing on host–parasite relationships. The effects of different parasites on the same host can be highly variable. Some parasites can be present in large numbers on a given host without strongly influencing its fitness (e.g. fecundity). In contrast, even a single specimen of a “castrator” parasite can annihilate its host’s reproductive ability. In doing that, the parasite transforms its host into a walking dead with zero fitness, which constitutes the parasite’s extended phenotype [49, 50]. This strategy is quite common among parasites, with typical examples of parasitic castrators being larval trematodes infecting snails and parasitic barnacles infecting decapod crustaceans [50]. The most famous parasitic barnacle, which was described in 1836 [51], is Sacculina carcini, a parasite of the common littoral crab Carcinus maenas. Many other species have been described since then, with the common feature of castrating their hosts (see Fig. 4.2)[52].

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Fig. 4.2 X-ray microcomputer tomography showing the position of a castrator parasitic barnacle (Peltogaster sp.) in a hermit crab (Pagurus pubescens). A dorsal view of the host is shown. The part of the parasite inside the host (“interna”, composed by a high number of tubules the parasite uses to obtain nourishment from the host) is coloured in green, while the part of the parasite outside the host (“externa”, containing the parasite’s reproductive organs) is coloured in orange. Modified from [53]. With kind permission from Elsevier

The strategy of parasitic castration is an evolutionary response to the classic virulence trade-off: parasites can grow more and increase their fecundity by increasing their hosts’ consumption. Yet, in doing that, parasites can shorten the host’s lifespan, which, from the parasite’s perspective, generates a fundamental trade-off between host consumption and longevity [54]. In targeting (and limiting) their consumption to the sole energy a host allocate to the reproduction, parasites achieve the rewarding goal of obtaining energy from their host without reducing its longevity. Other co-evolutionary trajectories could lead parasites to a different degree of virulence towards target hosts. A classical idea supported by conventional wisdom is that virulence should decrease through time and reduce the chances that parasites go coextinct by depleting their hosts’ populations. This view, however, has been criticized because of relying on group selection [55]. A more realistic hypothesis is that selection will increase a parasite’s reproductive rate instead of reducing its virulence. In this scenario, the balance between the parasite’s reproductive rate and its virulence might lead to different outcomes. There are many examples where virulence tends to decrease through time, but also other examples going in the opposite direction [56]. This matter is broad and complex, going beyond the scope of this chapter. The relevant message here is that different parasites can have very diverse effects on their hosts and that this information should be accounted for while building/examining a host–parasite network.

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All this considered, a realistic weighting of links in interaction networks would require combining the observed frequency of interactions with their effect on interacting partners. Although this would be an ambitious task per se, another additional aspect virtually doubles the challenge. The potential asymmetry in the reciprocal effects of one interacting species on another (e.g. a pollinator obtaining more reward from a flower than the benefit it brings to it) implies that a realistic network should have two links between any pair of interacting nodes. Furthermore, each link should receive a weight according to the respective source and target perspectives. However, and to no surprise, this approach is very far from standard practice. Even if we succeeded in attributing multiple links to interacting pairs and meaningful weights to links, we could still be quite far from having a truthful representation of ecological reality. An additional source of complexity (and uncertainty) stems from the fact that ecological networks are not static entities. Instead, they exhibit both short- and long term dynamics. All these aspects make the task of building a realistic ecological network exceptionally time consuming. The moment we are done with sampling and observations (for instance, having identified all food items in a large set of fish stomach contents), the network structure could have substantially changed already. Accounting for temporal network dynamics introduces further theoretical complications and additional constraints to data collection. The theoretical complications derive mainly from the long-term network dynamics and particularly from the fact that the current structure of a network results from his history. I will discuss this aspect in detail in the following chapters. For the moment, let us just say that since most current ecological settings have been strongly altered by human activity, trying to infer general ecological rules from structural patterns of extant ecological networks could be deceiving. This is because the structure we observe in current ecological networks is likely already far from natural. From a less theoretical perspective, the existence of short-term network dynamics (e.g. seasonality) makes the timing of collection/network reconstruction crucial to interpreting patterns. For example, the structure of plant–pollinator networks in temperate climates is naturally subjected to high seasonal variability in both the identity and weight of interactions [57, 58]. This additional complexity highlights how much information we need to have a comprehensive picture of natural systems’ ecological processes. And there might be more, possibly stemming from other forms of interactions that we have not yet discovered or from oversimplifications in current assumptions and paradigms (Fig. 4.3). We attribute interactions to different ecological categories based on somehow arbitrary definitions. For example, a lot has been written on the blurred boundaries in the definition of parasitism [59]. Some studies have emphasized how going beyond those distinctions could reveal important, underrated aspects of natural system structure and dynamics. For example, considering parasitism simply as a consuming strategy [60] permits to integrate parasites into food webs. The addition of parasites results in substantial changes in network structure, by increasing dramatically the density of connections [61, 62]; see Fig. 2.2. Yet, the difficulties in collecting truthful and exhaustive data, including different kinds of interactions covering the full spectrum

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Fig. 4.3 Temporal variation in the structure of interaction networks. (a) binary matrices of incidence, with rows corresponding to pollinators, columns corresponding to plants, and observed interactions as black squares. (b, c) distributions of pollinator and plant phenophases throughout a flowering season (with day 1 being June 21). Length of rectangles in panels b and c indicates, respectively, the observed temporal extent of insect flower visitation activity, and of plant flowering (with each bar corresponding to a single species). Reproduced from [58], with kind permission from John Wiley and Sons

of the parasitism–mutualism continuum, hinder a broad application of this kind of approach. A strong separation between studies focusing on different interaction types is still pervasive in ecological network research. In the next chapter, we will focus on this aspect, examining one of the main obstacles towards a more unified field: the strong separation between the study of bipartite networks (e.g. plant–pollinator or host– parasite networks) and that of unipartite networks (such as food web). Furthermore, we will explore the many exciting paths for the future development of ecological network science that the emerging field of multilayer networks is opening [16].

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Fig. 4.4 a An ideal, perfectly nested network, where the set of interacting partners of each node is a perfect subset of the sets of partners of nodes having more interactions. b–d Nested structure in different networks representing the interactions between cleaners and their clients in three reef ecosystems, and particularly: Abrolhos Archipelago, Western South Atlantic, five cleaners and 35 client species; Bonaire, Netherlands Antilles, Caribbean, six cleaners and 50 client species; Saint Croix, US Virgin Islands, Caribbean, four cleaners and 32 client species. Reproduced from [39], with kind permission from the Royal Society

Fig. 4.5 Adult cleaner wrasse Labroides dimidiatus inspects for cleaning an adult of its cheating counterpart, the mimetic blenny Aspidontus taeniatus. From [63]. Photo by D.R. Robertson (1972), Great Barrier Reef, doi:10.1371/journal.pone.0054939.g001

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Summary Networks offer a convenient model to represent countless real-world situations. The use of networks as a framework to explore ecological complexity has become increasingly popular in the last couple of decades. The study of food webs, which has a long history in ecology, has been complemented by a partially distinct and entirely novel research field focusing on direct, pairwise mutualistic and antagonistic interactions (such as pollination and parasitism). There are obvious conceptual (and sometimes structural) differences between networks that map different kinds of ecological interactions. However, even networks aimed at representing the same ecological entity (e.g. a plant–pollinator system) can be substantially different in terms of the quality and quantity of information they convey. Such differences originate, in most cases, from the challenges in obtaining the relevant ecological data. For example, we might build a plant–pollinator network by spending a few hours in a field looking at flowers and their visiting insects during a single spring day, taking note of each observed visit. Alternatively, we might perform multiple campaigns using sophisticated equipment and performing complementary laboratory experiments. The first network would include basic (and possibly unreliable) information on non-verified interactions between a small set of flowers and insects. It will provide no additional ecological information, such as details on link weights. By contrast, the latter network, built through a substantially higher effort, might include that information level. However, in most real-world cases, technical limitations make it very hard to retrieve all the information that we should ideally include in an ecological network to make it “truly realistic”. Consequently, ecologists often focus on networks that are likely telling only one part of the story. Although the exercise remains extremely valuable, considering the potential data limitations is a crucial step: caution is needed before generalizing results obtained on networks with low informative content since those results might be possibly reversed when additional information is included in the networks.

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Chapter 5

Integrating Interaction Types

The previous chapter discussed how different ecological networks can represent the same system of interacting species with varying level of detail. Ideally, the more features we add to a network (such as interaction direction and strength), the higher its realism in representing the natural world. Yet, in many cases, achieving such complexity is hampered by practical obstacles in obtaining the necessary information. For this reason, most networks used in ecological studies offer highly simplified versions of the systems they aim at representing. For example, several highly influential studies have used mutualistic ecological networks providing the simplest possible representation of species interactions, that is, unweighted links between flowers (or plant seeds) and their pollinators or (seed-dispersers) [1, 2]. By contrast, only a few studies use networks including interaction weights [3]. This tendency is not surprising considering the many challenges of adding interaction weights to plant–pollinator networks (as detailed in the previous chapter). Depending on the hypotheses under study, even the information provided by “basic”, unweighted and undirected networks could be enough to obtain valuable ecological insights. Yet, it is essential to acknowledge the potential limitations of the simplified picture provided by unweighted links and consider that different outcomes could emerge when more information (e.g. interaction weights) is associated with the network [3, 4]. Besides the crucial questions related to the amount of information we need to define pairwise interactions between species properly, there is another fundamental conceptual gap separating how we try to represent complex ecological systems using networks and their true essence. This gap emerges from the fact that most studies on ecological networks either focus on specific kinds of networks (e.g. plant–pollinator, host–parasite) or, when considering multiple types of networks, treat them as separate entities. This simplification, which had been necessary to permit ecologists to move the first steps in a new, unexplored territory, had remained pervasive, generating substantial constraints in the field. © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_4

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Antagonistic (e.g. host–parasite) networks are obviously different entities than mutualistic (e.g. plant–pollinator) ones. But how strong is this separation? In the previous chapter, I have mentioned an article examining various ecological networks depicting client-cleaner interactions in three coral reef ecosystems. The study’s main finding is the consistency between the structural patterns of the examined cleaning networks and the patterns (e.g. nestedness) commonly observed in plant–pollinator networks [5]. Throughout the text, the authors recognized that “…client–cleaner interactions do not necessarily imply mutual benefits for both species. In fact, cleaner species may act as parasites in some ecological communities. Therefore, only a subset of recorded interactions in these networks is unambiguously mutualistic”. Yet, they decided not to consider this distinction “since there is a wide gradient of mutually beneficial effects from pure mutualism to pure antagonism or amensalism, and all them potentially influence network build-up and evolution” [5]. This conceptual step appears crucial and applies also, for instance, to cheating interactions in plant–pollination systems. Interactions of this kind should be considered parasitic since, for example, a cheating flower will obtain benefit at the expenses of its pollinator without providing it with a reward (such as nectar [6, 7]). From a broader co-evolutionary perspective, those interactions belong, in principle, to the plant–pollination network. However, the choice of whether to include those antagonistic interactions in a mutualistic plant–pollinator (or client–cleaner) network depends on the specific purposes for which the network is built and analysed. And even when the goals are clear, the choice could be difficult and not entirely free from subjectivity. To circumvent this issue—and perhaps avoid taking a decision—some have argued that, since the mere observation of an insect visiting a flower does not provide definitive information on the nature of the interaction, one should use the term “visitation”, instead of pollination networks [8]. On the one hand, this seems like recognizing that the assessment of ecological interactions is a difficult task that requires a substantial effort going far beyond superficial observation. On the other hand, it might also lead to the dangerous conclusion that a cautionary semantic adjustment could fix biases with deeper conceptual roots. Despite their differences, at least, mutualistic and host–parasite networks have been investigated within the same analytical framework (e.g. quantifying their structure in terms of nestedness, segregation or modularity [9–11]). Conversely, many technical and theoretical aspects of the newer ecological network field have evolved independently from the older food web field. This distinct evolution is most likely a direct consequence of the fact that mutualistic and host–parasite ecological networks belong to a class of networks (bipartite networks) separate from the class food webs belong to (unipartite networks). As previously mentioned, we can unequivocally attribute nodes in bipartite networks to two different categories (e.g. plants and pollinators, hosts and parasites), and draw links between two members of different categories but not between members of the same category. By contrast, in unipartite networks, this unambiguous attribution is impossible since a node can simultaneously be a consumer for some other resources in the network and a resource for some other consumers. Food webs, where a predator can be prey to other species, clearly belong to this network category.

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Such a distinction is more than formal since it has led to the development of separate analytical tools applicable to one network category only. Depending on the hypotheses under examination, in many cases, it makes perfect sense to consider host–parasite or plant–pollinator interactions without considering trophic links. Still, the lack of a unifying analytical framework makes it difficult to obtain a comprehensive view on processes going on at the ecosystem level, where the formal distinctions that help us put things in order are, at best, very blurred. In his seminal papers, Lindeman [12, 13] introduced and formalized several aspects that have become fundamental in the food web field, such as the concept of trophic level, and made some essential steps towards the current representation of trophic networks. It has been clear for a long time that food webs could be examined in the formal framework of network/graph theory [14]. Much of early work on food webs has focused on the flow of energy through food web chains [15], which is a topic of general interest in network science, with applications in various fields, such as transportation [16] and information science [17]. Many measures of food web structure commonly used by ecologists are directly derived from network theory. For example, species’ trophic levels are usually measured based on path length (i.e. the number of steps moving from one node to another through network links) between the target species and basal resources. The shortest path length between a consumer and basal resources is often used to define the consumer’s trophic level, with alternative measures being the longest or the average of the shortest paths between the consumer and basal resources [18]. While food web scientists have naturally looked into network/graph theory to find analytical tools, the study of bipartite networks has remained isolated. It has developed independently various techniques that, in the end, often resulted not too distant or even analogous to those produced in “general” network science. At least in its infancy, the analysis of bipartite networks has broadly benefitted from theory and tools developed for the study of presence–absence species/locality binary matrices, such as those commonly used to synthesize the distribution (in terms of occurrences) of a set of species across a group of localities/islands. In fact, a bipartite network not only can be easily represented in the same way as a binary species/locality matrix (with localities and occurring species being replaced, for example, by flowers and pollinators), but the two entities also have significant conceptual parallels that stem from island biogeography theory and that have been at the centre of debate for a while [19]. These parallels apply most clearly to host–parasite interactions since it is pretty easy to identify a host as an island for its parasites (even if there are many caveats about this point [20]) but might also extend to other kinds of associations. A typical analysis that has been developed in the context of species/locality matrix and that has become very popular in the study of ecological network structure is nestedness. A species/locality matrix is nested when the set of species inhabiting a given locality is always a perfect subset of the species inhabiting a more biodiverse locality [21]. This pattern implies that the rarest species will be present only in the species-richest area, while the most common species will be present in all areas. The second most common species will be present in all areas except the second most rich area and so on (Fig. 5.1). In a fundamental paper, Wirt Atmar and Bruce Patterson

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Fig. 5.1 Presence–absence matrix showing the distribution of phyllostomid bats across Gatún Lake islands, Panama. Each column corresponds to an island, while each row corresponds to a bat species. The matrix is “packed”, that is it has been sorted in decreasing order of row and column marginal totals. Reproduced from [24], with kind permission from John Wiley and Sons

[22] formalized the idea that a nested matrix can serve both to obtain an overall rank of species extinction risk and to identify the most vulnerable species per island (Fig. 5.2). The exercise is not particularly enlightening for a perfectly nested matrix, where the least vulnerable species is simply the one occurring at most islands, and the most vulnerable one is the rarest species. Yet, applying this criterion to real-world matrices may reveal interesting, non-obvious patterns, hence providing important insights for species conservation in insular settings (or, in general, in a fragmented habitat). These ideas have promoted hundreds of studies in the last three decades, and a lively debate revolves around the concept of nestedness, its definition and computation [23]. When translated to ecological networks, the concept of nestedness acquires a distinctive meaning, even if some similarities with the biogeographical context are maintained. If I had to point out one seminal work about nestedness in bipartite ecological networks, I would undoubtedly choose “The nested assembly of plantanimal mutualistic networks” [1]. In that work, which is now a classical (and one of the most cited) papers in the field, Bascompte and co-authors examined nestedness in several plant–pollinator networks. Consistent with the biogeographical concept,

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Fig. 5.2 In a hypothetical maximally nested matrix, species are ordered according to their respective extinction probabilities (increasing left to right). In a given island, the rightmost species will be the most extinction-prone. Similarly, the probability of extinction for each species, P(e), increases progressively from top to bottom. Reproduced from [22], with kind permission from Springer Nature

they defined nestedness as the tendency for the plants visited by a given pollinator of being a smaller subset of the plants visited by any more generalist pollinator in the network, as well as the tendency of the pollinators visiting a given plant to be a smaller subset of any plant visited by more pollinators. For their analyses, Bascompte et al. used the original, entropy-based measure of nestedness, “matrix Temperature” [22]. This metric was later on criticized for various technical reasons [25]. Nevertheless, the requirement of having sets of interacting partners of decreasing size was maintained in what soon became the most popular nestedness measure, an index called “NODF” (an acronym for “Nestedness based on Overlap and Decreasing Fill”) [26]. However, in more recent work, a different idea of nestedness has emerged in the specific context of network analysis, focusing on the overlap in interactions (e.g. tendency for plants to share pollinators) and free from the Russian doll constraint of having progressively smaller interaction subsets. For example, Bastolla et al. [2] have proposed to measure nestedness as:  η= 

i< j

ni j

i< j min(n i , n j )

(5.1)

There, η is the nestedness measured for one of the two components of a bipartite network (e.g. for plants in a plant–pollinator network); n i j is the number of shared interactions (e.g. pollinators) between (plant) species i and j; while n i and n j are the total numbers of interactions (pollinators) for (plant) species i and j. It is clear from the formula that situations where n i j = n i = n j (i.e. pairs of plants having identical sets of associated pollinators) contribute positively to nestedness, while their contribution to nestedness would be considered null by NODF). Using this measure, the authors developed a theoretical framework identifying nestedness as key to ecological networks’ stability due to the potential reduction

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in competition for resources emerging from the benefits brought simultaneously to the shared resource by competing mutualistic partners. For example, two pollinators competing for a flower will both help the flower to proliferate; thus, even if competing for the resource, we can consider the two pollinators also as indirectly cooperating [2]. Apart from the potential importance of nestedness for the stability of ecological networks (actually, a still debated concept [27–29]), it is interesting that Bascompte’s paper generated, for the first time, a solid methodological parallel between the study of bipartite networks, and that of presence–absence binary matrices. The idea of describing the distribution of pollinators on plants or parasites on hosts in the same way as the distribution of species across localities provided researchers interested in biogeographical issues with a new window of opportunity to explore a completely different field using familiar analytical tools. This was a good thing. But it also had the side effect of encouraging researchers to use available, standard tools (developed in a different context and with other purposes) instead of exploring new (and possibly more formally sound) techniques developed by “full-time” network scientists from physics and mathematics. This had also substantially contributed to the widespread tendency to consider bipartite ecological networks as separate entities from food webs. Most nestedness metrics, including NODF, have been designed to be used only on bipartite networks. Other authors, including myself, have attempted to introduce metrics working also on unipartite (e.g. trophic) networks [10, 30, 31]. Still, the bipartite-only metrics remain the most popular and straightforward choice for researchers approaching nestedness for the first time and not primarily interested in technicalities. Similarly, few studies have taken advantage of existing measures to investigate nested patterns in food webs. One potential factor that complicates the shift from bipartite-only nestedness to a more general nestedness theory (not constrained by network kind) lies in the fact that the idea of nestedness and its ecological implications are intuitive in a bipartite network, but not that much in a food web. Another example demonstrating the strong separation between the study of unipartite and bipartite networks comes from the concept of network motifs, which are small subgraphs (including a few, usually three or four nodes, and the links connecting them) within a network. The number of nodes involved in a motif determines the motif order. For example, in a directed network, a subgraph where two nodes have a link pointing to the same edge and are linked to one another by a directed link forms a motif of order three. Motifs are relevant because they offer a simple, systematic way to identify specific processes recurring in a network. Assessing their relative frequency can tell us something about the mechanisms regulating network dynamics. When the concept was first introduced, it was applied to food webs [32] and to various other kinds of networks (transcriptional gene regulation, neuron connectivity, electronic circuits, World Wide Web), but not to bipartite networks [33]. One apparent reason for that is the lack of links between nodes belonging to one of the two

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categories of a bipartite network (e.g. a node between two plants or two pollinators). Such lack, which makes a bipartite network different from a unipartite one, reduces the number of possible motifs, making their investigation less appealing. However, studies from fields other than ecology have highlighted how one can identify several motifs in bipartite networks as well, and how they can provide interesting insights into network structure and processes [34] (Fig. 5.3). Yet, the potential of this idea has only recently started to be explored in bipartite ecological networks [35, 36]. Interestingly, the idea behind motifs is closely related to one of the most studied patterns in species/locality presence–absence matrices, that is, “checkerboardness”. Checkerboardness quantifies the frequency of situations—checkerboards—where given two species and two localities, one species is present in the first locality but not in the second one, while the other species is present only in the second locality (Fig. 5.4). A classical—and controversial—idea in ecology is that competitive exclusion, a process resulting in the lack of a weaker competitor in a locality where a strong antagonist is present, is a major determinant of community composition [37]. Therefore, ecologists have often investigated the role of competition in shaping ecological communities by assessing the frequency of checkerboards in a metacommunity matrix (e.g. representing species’ distribution across the islands of an archipelago). This approach, however, has caused heated debates [38]. Let us consider again cleaning symbioses. From a broader ecological perspective, it is clear how parasites play a fundamental role in their regulation. Parasites not only represent the prey for cleaner fish but, in a certain way, they also bring clients to cleaners. The more parasites a fish has, the more it will be prone to visit a cleaner. At a large scale, it has been observed that parasite abundance can affect cleaners’ behaviour. In particular, scientists have shown that, when ectoparasites are scarce,

Fig. 5.3 Some of the possible motifs that one could detect in a bipartite network. Reproduced from [34] under a Creative Commons Attribution 4.0 International License (https://creativecommons. org/licenses/by/4.0/).

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lock locz

spi ... 1 ... ... ... ... ... 0 ...

spj 0 ... ... ... 1 ...

Fig. 5.4 A “checkerboard” in a species per locality matrix. The ith species is present in the kth locality, but not in the zth locality, while the jth species occurs in the zth but not in the kth locality. Redrawn from [37], with kind permission from Springer Nature

clients have an increased chance to be bitten by the cleaner fish Labroides dimidiatus, as a consequence of the cleaner’s attempts to feed on client mucus [39]. Moreover, since cleaners are among the few predators for parasites, an ongoing evolutionary arms race promotes the emergence of adaptations making parasites less detectable by cleaners. Cleaner fish are mainly active during the day. Some parasites that can move from a host to another, such as gnathiid copepods (which are actually micropredators, as mosquitoes), may avoid predation by having nocturnal habits and hiding in the benthos when inactive [40]. Other strategies commonly adopted by parasites include reducing their detectability through immobility, site-specificity (i.e. the infection of host body locations out of the cleaners’ reach), or mimicry. As a graduate student and during my PhD, I have spent many hours searching for monogenoidean flatworms in fish gills. I have always been amazed by how difficult it was to detect them. In many cases, I have noticed a tendency of parasites (especially the tropical marine ones) for having shape and colour exceptionally resembling gill lamellae. Similar adaptations are not limited to fish parasites. For example, various avian feather lice species (Phthiraptera: Ischnocera) have evolved colourations matching their bird hosts’ plumage. This form of co-evolution might be driven by the selective pressure exerted by active, inter- or infraspecific parasite removal activities. This hypothesis is indirectly supported by the fact that head lice, which are not targeted by grooming, have not evolved cryptic colourations [41]. Cleaning activity is only one of the many ecological processes where parasites become prey. Concomitant predation, that is, the ingestion of a prey infected by a parasite, is the most common way parasites are consumed. In some specific cases, similar events are functional to parasites’ development (e.g. trophic transmission). But this requires the predator to be a suitable host for the co-ingested parasite, which is not always the case. Depending on the ecological setting and local biodiversity, there could be many events in which an infected host is consumed by a predator where parasites cannot survive. In such cases, parasites contribute energy to the consumer in the same way the targeted prey does. Since parasites are, in general, tiny organisms, one may think that their energetic contribution in concomitant predation events is negligible compared to that of their hosts. However, we have to consider that, under natural conditions, it is common to have high abundances of parasites within single hosts. In such cases, when consumers prey on a heavily infected host, parasites’ energetic contribution becomes important. For example, nematodes may occupy substantial portions of their host’s intestine. I suggest those having doubts about what I mean by “substantial” to do a quick web

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search for Ascaris lumbricoides. It will also be immediately apparent why I refrained from adding a picture here. Concomitant predation is not the only way parasites contribute energy to consumers. Many parasites have free-living stages, such as eggs or larvae, which are eaten at high frequencies by vertebrates and invertebrates [42]. One of the most challenging tasks for a parasite is finding the “right” host. It is common for a parasite to be highly prolific to increase the chances of this happening. Consequently, the abundant parasite offspring is likely to play an essential role in food webs, a matter that scientists have historically overlooked [42–44]. As if that was not enough, besides being both consumers and resources for freeliving species, parasites can also be hosts to other parasites. This kind of association, known as hyperparasitism [45], is quite common in nature and generates additional, elusive links in ecological networks. Although challenging, considering those aspects simultaneously and mapping mutualistic, antagonistic and trophic interactions into a single network would provide a more comprehensive and realistic picture. A few studies have tried to move beyond strict network categorization and explore the simultaneous effects of multiple kinds of resource–consumer interactions within a single analytical framework. However, these few attempts provide strong evidence that taking this challenging step is fundamental for a deeper understanding of ecological complexity. For example, as previously mentioned, it has been shown how including parasites substantially alters the structure of food webs [43]. On the one hand, the observed changes in the degree distribution (i.e. the distribution of the number of links per node) and other typical food web metrics after the inclusion of parasites are in line with those expected from the resulting increase in network size and complexity. This consistency suggests that parasites, in this regard, have similar effects on network structure to those of free-living species. On the other hand, however, adding parasites alters the frequency of motifs involving three (free-living and parasitic) nodes due to parasites being additional resources for their hosts’ predators. Furthermore, other structural changes are brought by the fact that the feeding niches of many parasites are broader and more segregated than those of free-living species, possibly resulting from parasites’ life-cycle complexity and small body sizes [46]. One major obstacle to moving forward in this direction is that attempting to combine different interactions types forces us to deal either with a multidimensional network space hard to treat and interpret or with a lack of sufficient or proper data. It is already difficult to have accurate information on a specific set of ecological interactions, let alone on multiple interaction kinds. This problem does not depend only on the increased effort required to collect additional data but also on the fact that studying different types of interactions requires, in general, different methodological approaches. This need makes it unlikely that the researchers involved in a project dealing with a specific kind of interactions will also have the expertise to collect information on other interaction types. For example, scientists working on the reconstruction of plant–pollinator networks might not have the interest, equipment or skills necessary to collect data on trophic or host–parasite interactions. Still, there

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Fig. 5.5 Example of a multilayer network with a total of four nodes and four layers, identified by the combination of two hypothetical dimensions. For example, each layer can map the identity of interactions between nodes in two different localities (X, Y ) and at two different moments (A, B). Edges can connect nodes both within and between layers (a). A synthetic representation of intra- and interlayer edges is provided in panel b, with the first being represented by solid lines and the latter by dotted lines. Reproduced from [51] (doi:10.1093/comnet/cnu016) under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)

might be some situations where collecting multiple interaction data would be feasible at a small additional cost. For instance, with minor adjustments, studies that aim to reconstruct trophic interactions using metabarcoding of stomach contents or environmental DNA ([47–49]) could also produce valuable information on host–parasite interactions. However, the most obvious way to advance the field would be to create multidisciplinary research consortia to build comprehensive networks entailing multiple interaction kinds. Despite the many challenges it poses, this appears to be the main direction where network science will develop in the next future [50]. The study of interconnected systems has been the object of much attention from an engineering perspective under the general notion that the interconnectedness of different parts of infrastructures can increase the risk of cascading failures and generate processes substantially different from those observed in isolated systems. In the last decade, the interest for networks with multiple layers/types of edges (Fig. 5.5), as well as for “networks of networks” has grown in various fields, also generating issues related to terminology and the formal mathematical framework [51]. In ecology, multilayer networks might offer a novel valuable resource to model various settings. Here, layers can represent, for example, different interaction types, localities, communities or times. Layers can be interconnected by different kinds of relationships between nodes. For example, the relationship may be temporal, with each layer representing the status of a food web at a given moment. In that case, intralayer edges represent trophic links between species (as in a typical food web), while interlayer links connect species to themselves in an ordinal way. That is, each species in a layer corresponding to the food web at time t is linked to itself in the layer mapping the food web at time t − 1. The interlayer links represent the obvious dependency between a species’ population at a given time and earlier population’s states. Alternatively (or in addition), different layers may correspond to different

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localities. In that case, we may use interlayer edges to link species to themselves across localities and represent potential metapopulation connectivity. As discussed in previous paragraphs, there is an increasing need to overcome the limitations of investigating the ecological effects of different kinds of species interactions separately [43, 52, 53]. Multilayer networks offer a potential solution to the issue since they allow the representation of the different interaction types (mutualism, parasitism, trophic interactions) in separate layers. For example, one might include all species in each layer (regardless of their participation in a specific kind of interaction) and then create links between interacting partners in different layers. There, each pair of layers would correspond to a particular type of interaction. Alternatively, layers might include only the species participating in a specific interaction kind and their links. For example, one layer might include pollinated plants and pollinators plus plant–pollinator links, while another layer might consist of plants and herbivores feeding on those plants, with interlayer edges connecting to themselves the species participating in multiple interactions (Fig. 5.6a–d). Nodes do not necessarily need to represent species. A potential insightful multilayer network representation is one where: each layer corresponds to a species’ population; nodes within the layer correspond to individuals of the target species; intralayer edges indicate some relationship between nodes (e.g. social interactions or spatial proximity); and interlayer edges represent interspecific interactions at the individual level (Fig. 5.6e). Another valuable application of multilayer networks is representing metacommunities, with individual layers mapping local interactions and interlayer edges mapping dispersal between communities. This design is similar to the one described above for spatial food webs, but here interlayer links represent metapopulation connectivity [50] (Fig. 5.6f). These examples are not exhaustive. If monolayer networks offer a flexible tool to represent a wide variety of real-world settings, multilayer networks bring such flexibility to a new level. However, the possibility offered by multilayer networks to represent highly complex and comprehensive ecological settings does not necessarily imply immediate gains in understanding the underlying processes at play [54]. The ecological relevance of many structural metrics is still debated for monolayer networks, let alone for multilayer ones. As an example, you might consider the broad discussion about the potential role of nestedness for ecological network stability and hence promoting biodiversity I mentioned in previous chapters [2, 27–29]). Let alone for multilayer ones. Furthermore, it is intuitive that the amount of information and effort needed to build a multilayer network is much larger than that required to construct a monolayer network. One potential field where multilayer networks can find a straightforward application is that of modelling co-extinction cascades. I will provide details about the available techniques devoted to this purpose (in monolayer networks) in a dedicated chapter. However, in a rough simplification, the idea is to model the effect of the progressive removal of nodes from a network, taking into account that consumers (or obligate mutualists) cannot survive without their resources (or mutualistic partners)

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Fig. 5.6 Examples of multilayer ecological networks. a Layers correspond to different points in time, with intralayer edges indicating ecological interactions (e.g. trophic links), and interlayer edges connecting species to themselves (if present at different times); b layers correspond to localities, with intralayer edges indicating ecological interactions, and interlayer edges linking (to themselves) species present at multiple localities; c layers correspond to different kinds of ecological interactions (e.g. one layer indicates mutualistic interactions, while another one indicates prey–predator interactions), with intralayer edges mapping pairwise species interactions, and interlayer edges connecting (to themselves) species participating to different (individual) networks (e.g. a pollinator that is also a prey to other species); d as in c, layers correspond to different kids of interactions, but here all nodes are reported in all layers (and connected to themselves across layers), with intralayer edges mapping the actual ecological interactions for a given interaction type (e.g. plant–pollinator interactions); e nodes correspond to individuals of a given species, and each layer reports some kind of interactions (such as social contacts or spatial proximity) between the individuals, while interlayer edges correspond to interspecific interactions mapped at the individual level; f layers correspond to different communities, with intralayer edges indicating local interactions (e.g. trophic links), and interlayer edges indicating metapopulation connectivity in terms of species dispersal/colonization patterns. Modified from [50], with kind permission from Springer Nature

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and that, therefore, the removal of nodes can result in secondary (co-)extinctions. A typical procedure is to take a plant–pollinator (or host–parasite) network and then keep track of the persistence of pollinator (or parasite) species as plant (or host) species are progressively removed from the network [55, 56]. In a multilayer network, we can enrich the exercise by considering how the effect of species loss in one layer can propagate to other layers through interlayer edges (such as the edges connecting species participating to different interaction types, see Fig. 5.5c). For example, one can consider a two-layer network with the first layer mapping the mutualistic interactions between plants and pollinators; the second one mapping the antagonistic interactions between leaf-miner parasitoids and plants; and interlayer edges connecting plant species present in both layers to themselves. Typical co-extinction experiments on this ecological system would either consist of testing the plant community’s robustness to the loss of pollinators (vice versa) or the response of parasitoid species to the loss of their plant hosts. However, the multilayer framework makes it possible to conduct a more complex (and perhaps more interesting) experiment where (i) pollinators are removed (simulating, for example, the detrimental effect of pesticides [57]); (ii) secondary extinctions in plant species following flower visitor/pollinator loss are tracked; and (iii), finally, co-extinctions of parasitoids following the loss of their plants are simulated (Fig. 5.7).

Fig. 5.7 Simulating co-extinctions in a two-layer network with the first layer mapping the (mutualistic) interactions between plants and pollinators and the second one mapping the (antagonistic) interactions between leaf-miner parasitoids and plants, and interlayer edges connecting (to themselves) plant species present in both layers. Two scenarios of co-extinctions are presented. The first one (dotted line) shows the decline in parasitoid diversity due to secondary (co-) extinctions following the progressive removal of plants; while the latter shows the loss of parasitoid diversity due to “tertiary” co-extinctions following the secondary loss of their plant hosts triggered by the removal of pollinator species. Modified from [50], with kind permission from Springer Nature

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At the time I am writing, this modelling approach is in an early phase. Still, I am confident that, in the next future, thanks to the increasing data availability and the progress in our fundamental understanding of (monolayer) ecological networks, the use of multilayer networks to investigate patterns of diversity loss will become common. Whether or not this prediction will prove to be correct, there is little doubt that the new generations of ecologists will have necessarily to pick up the gauntlet of assembling a comprehensive theory of biodiversity and ecosystems stability rooted in the simultaneous consideration of multiple interaction kinds. As there is no technical or theoretical reason preventing the combination of unipartite and bipartite networks into multilayer entities (as anticipated in the studies mentioned above combining host–parasite interactions and food webs [43, 46]), this might also help to overcome some of the issues stemming from the theoretical and technical separation of studies dealing with either of the two network types. On a final note, this chapter has focused on integrating different kinds of resource– consumer interactions. However, an additional outstanding challenge is integrating niche and network theories. That is, how can we take simultaneously into account the interactions between groups of resources and consumers and the interactions within these groups? Or, speaking in food–web language, how can we integrate the study of interactions between different trophic levels (which is the focus of food web/ecological network theory) with the interactions within trophic levels (which is the focus of niche theory?). The topic is still in its infancy, and there are multiple, big open questions that need to be tackled [58]. The results of recent research combining a theoretical and experimental approach in a plant–pollinator community have revealed that both plant–pollinator interactions (i.e. across trophic levels) and the competitive interactions within plants and pollinators (i.e. within trophic levels) play a fundamental role for biodiversity. That is “[...] the effects occurring within and across trophic levels percolate across the two scales. Thus, work that does not take into account this integration can be underestimating or overestimating species persistence” [59]. Such integration is likely one of the next frontiers of theoretical and computational ecology, yet the importance of within-trophic-level interactions (e.g. competition) for species co-existence and biodiversity persistence has been already recognized by and explored in various important studies (e.g. [60, 61]). Chapter 13 will be dedicated specifically to this topic.

Summary Due to the many challenges associated with collecting comprehensive information defining the nature and intensity of pairwise relationships between interacting species, often we have to rely on networks that provide a highly simplified picture of reality. Limitations arise, for example, from using undirected or unweighted links to represent scenarios where the strength of interactions is not homogeneous or not symmetrical between interacting partners. These, however, are not the only limitations hindering the accuracy of our representation of real-world systems. A compelling

References

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problem in the field of ecological networks lies in the tendency to treat different kinds of interactions as separate entities (i.e. as separate networks). For example, most studies that use network analysis to investigate the mechanisms by which biodiversity emerges and persists focus on mutualistic (mainly plant–pollinator and seed disperser) networks. A pervasive theoretical and conceptual separation exists between the field of bipartite ecological networks and that of food webs. In a bipartite network, nodes can be divided into two distinct categories, such as plants and pollinators, and links exist between but not within members of each type. Conversely, in food webs, which belong to the class of unipartite networks, each node can be potentially linked to any other node in the network, including itself. Such a conceptual (and methodological) separation has limited our ability to investigate ecosystemwide processes that depend on or are mediated by ecological interactions since, in reality, most species belong simultaneously to multiple networks. For example, a pollinator insect can be prey to a bird, a consumer for some plants and a host for a parasitoid. It is becoming increasingly clear that considering the different roles of a species in different interaction networks simultaneously is very important to improve our understanding of how complex natural systems work and how they can respond to perturbations and species loss. In particular, the recent advances in the field of multilayer (or “multiplex”) networks achieved in the physics context offer valuable opportunities for the development of a much-needed, holistic theory of ecological interactions.

References 1. Bascompte J et al (2003) The nested assembly of plant-animal mutualistic networks. Proc Nat Acad Sci 100(16):9383–9387 2. Bastolla U et al (2009) The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458(7241):1018–1020 3. Kaiser-Bunbury CN et al (2010) The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol Lett 13(4):442– 452 4. Staniczenko PPA, Kopp JC, Allesina S (2013) The ghost of nestedness in ecological networks. Nat. Commun. 4:1391 5. Guimarães PR et al (2007) The nested structure of marine cleaning symbiosis: is it like flowers and bees? Biol Lett 3(1):51–54 6. Bell G (1986) The evolution of empty flowers. J Theoret Biol 118(3):253–258 7. Thakar JD et al (2003) Nectarless flowers: ecological correlates and evolutionary stability. Oecologia 136(4):565–570 8. Ballantyne G, Baldock KCR, Willmer PG (2015) Constructing more informative plantpollinator networks: visitation and pollen deposition networks in a heathland plant community. 282(1814):20151130 9. Fortuna MA et al (2010) Nestedness versus modularity in ecological networks: two sides of the same coin? J Animal Ecol 79(4):811–817 10. Strona G, Veech JA (2015) A new measure of ecological network structure based on node overlap and segregation. Methods Ecol Evol 6(8):907–915 11. Joppa LN et al (2010) On nestedness in ecological networks. Evol Ecol Res 12:35–46

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12. Lindeman RL (1941) Seasonal food-cycle dynamics in a senescent lake. Am Midl Nat 26:636– 726 13. Lindeman RL (1942) The trophic-dynamic aspect of ecology. Ecology 23(4):399–417 14. Gallopin GC (1972) Structural properties of food webs. Sys Anal Simul Ecol 2:241–282 15. Levine S (1980) Several measures of trophic structure applicable to complex food webs. J Theoret Biol 83(2):195–207 16. Ford LR, Fulkerson DR (1956) Maximal flow through a network. Canad J Mathe 8(3):399–404 17. Ahlswede R et al (2000) Network information flow. IEEE Trans Inf Theo 46(4):1204–1216 18. Williams RJ, Martinez ND (2004) Limits to trophic levels and omnivory in complex food webs: theory and data. Am Nat 163(3):458–468 19. Kuris AM, Blaustein AR, Alio JJ (1980) Hosts as islands. Am Nat 116(4):570–586 20. Strona G, Fattorini S (2014) A few good reasons why species-area relationships do not work for parasites. BioMed Res Int 2014:271680 21. Patterson BD, Atmar W (1986) Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol J Linnean Soc 28(1–2):65–82 22. Atmar W, Patterson BD (1993) The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia 96(3):373–382 23. Ulrich W, Almeida-Neto M, Gotelli NJ (2009) A consumer’s guide to nestedness analysis. Oikos 118(1):3–17 24. Meyer CFJ, Kalko EKV (2008) Bat assemblages on Neotropical land-bridge islands: nested subsets and null model analyses of species co-occurrence patterns. Diversity Distrib 14(4):644– 654 25. Strona G et al (2014) Nestedness for dummies (NeD): a user friendly web interface for exploratory nestedness analysis. J Stat Softw 59(1):1–9 26. Almeida-Neto M et al (2008) A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117(8):1227–1239 27. Saavedra S et al (2011) Strong contributors to network persistence are the most vulnerable to extinction. Nature 478(7368):233–235 28. James A, Pitchford JW, Plank MJ (2012) Disentangling nestedness from models of ecological complexity. Nature 487(7406):227–230 29. Saavedra S, Stouffer DB (2013) “Disentangling nestedness” disentangled. Nature 500(7463):E1–E2 30. Jonhson S, Domínguez-García V, Mu´noz MA (2013) Factors determining nestedness in complex networks. PloS One 8(9):e74025 31. Cantor M et al (2017) Nestedness across biological scales. PloS One 12(2):e0171691 32. Bascompte J, Melián CJ (2005) Simple trophic modules for complex food webs. Ecology 86(11):2868–2873 33. Milo R et al (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827 34. Saracco F et al (2015) Randomizing bipartite networks: the case of the world trade web. Sci Rep 5:10595 35. Simmons BI et al (2019) Motifs in bipartite ecological networks: uncovering indirect interactions. Oikos 128(2):154–170 36. Simmons BI et al (2019) bmotif: a package for motif analyses of bipartite networks. Methods Ecol Evol 10(5):695–701 37. Stone L, Roberts A (1990) The checkerboard score and species distributions. Oecologia 85(1):74–79 38. Connor EF, Collins MD, Simberlof D (2013) The checkered history of checkerboard distributions. Ecology 94(11):2403–2414 39. Bansemer C, Grutter AS, Poulin R (2002) Geographic variation in the behaviour of the cleaner fish Labroides dimidiatus (Labridae). Ethology 108(4):353–366 40. Grutter AS (2002) Cleaning symbioses from the parasites perspective. Parasitology 124(7):65– 81 41. Bush SE et al (2010) Evolution of cryptic coloration in ectoparasites. Am Nat 176(4):529–535

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Chapter 6

Modelling Co-extinctions

Modelling how the effects of species extinction can propagate from resources to consumers is fundamental to map future trajectories of biodiversity loss under different scenarios. Various approaches have been proposed for this purpose, based on different assumptions making them more or less conservative. With “conservative”, I refer to how much the target model is cautious in hypothesizing the magnitude of secondary effects following primary extinctions. Moving from conservative to permissive assumptions permits representing a wide range of possible co-extinction scenarios. These span from a worst-case where even the extinction of a few species generates a domino effect eventually leading to the collapse of the entire system under study; to a best-case scenario where the impact of species loss does not extend beyond primary extinctions. Unfortunately, the “truth”, that is what we should expect to happen in a real-world system or, even better, what is happening right now, and possibly playing a crucial role for the future of earth’s biodiversity, might not lay in the middle. Instead, it might be much closer to the worst than the best-case scenario [1]. But, before we get to the bad news, we first need to delve into the technical modelling aspects, as these can generate hard constraints in our understanding of past, present and future co-extinction processes. Most co-extinction models focus on network structure or, better, use the network as a map to identify the paths through which the effects of species loss can propagate throughout communities. In previous chapters, we saw how networks can be handy to model the natural world. Still, we also identified many limits for this purpose, as well as possible strategies to overcome them and increase realism. Such strategies involve both an improvement in the quality of the information provided by pairwise interaction links (e.g. by adding realistic weights going beyond the plain, observed frequency of interactions) and, at a broader scale, the integration of different kinds of species interaction within an organic theoretical framework (e.g. multilayer networks). Those aspects are relevant for the way one can model co-extinctions through networks. Since current, commonly applied approaches have been developed based on available networks, they suffer from some of the conceptual and practical limi© Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_5

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tations which apply to network representation. Yet, their extension to an improved next generation of realistic ecological networks would be pretty straightforward. In other words, the limitations of the modelling approaches appear a weaker knowledge bottleneck than acquiring primary network information. A last, important caveat is that co-extinctions are not the only kind of secondary effect that “primary” losses (such as those caused, for example by the direct impact of habitat destruction or climate change) can trigger. Here, I mean co-extinctions as the disappearance of consumers following the partial or complete depletion of their resources. Further in this book, I will dedicate some space to higher order interactions representing the indirect effect that one species can have on the pairwise interactions between other species. These offer another fundamental pathway through which the impact of species loss can propagate from one species to another, even in the absence of “visible” paths in the interaction network [2]. The most straightforward and conservative approach to model co-extinction sits on the general assumption that a consumer cannot live without resources. When applied to a network, this translates into a two steps cycle. First, species (i.e. nodes in the network) subjected to primary extinctions (e.g. caused by human overharvesting) are removed. Second, all the other species/nodes left with no resources (secondary extinctions) are also removed. Secondary extinctions are then treated as the primary ones, and, again, all the nodes left with no resources are removed. The process is repeated until either the network is left with no nodes (i.e. when diversity is annihilated) or it reaches a new stable state, with all consumers having some resource left to rely on [3, 4]. One important technical aspect of this approach may help clarify its conceptual limitations. Let us consider bipartite networks (such as the ones Fig. 6.1 is referring to), where one can categorize nodes into two separate classes with no internal connections. There, the assumption that a consumer will go extinct only after the complete depletion of its resources prevents de facto the occurrence of chains of extinctions longer than one cycle. As a basic example, we can consider a toy network with three plant species (P1, P2, P3) and three insect pollinator species (I 1, I 2, I 3) (Fig. 6.2). Plant P1 is serviced by all pollinators, while plants P2 and P3 are only associated, respectively, to insects I 2 and I 3 (Fig. 6.2a). For any scenario where one species goes extinct, regardless of which one, in the simple co-extinction model described above, there is no possibility that diversity loss goes beyond a single round of secondary extinctions. For instance, if we remove plant P1, the only species to go extinct will be insect I 1, while the other two pollinators will remain unaffected (Fig. 6.2b). Similarly, if we remove either plant P2 or P3, we will have the only extinction of either insect pollinator I 2 or I 3. Still, no additional co-extinctions in the opposite direction will be triggered (Fig. 6.2c). More in general, in any bipartite network, for any primary extinction of a set of resources, we will observe secondary extinctions only in consumers specialized on those resources. This expectation is true even if we assume that both sets/classes of nodes (e.g. plants and pollinators) constitute a fundamental resource for the nodes in the other class: after the primary extinctions and the resulting, first-order secondary

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pollinator exncon (%) Fig. 6.1 Co-extinction trajectories for two “visitation” networks, that is networks of interactions between plants and insects built based on the only observation of insects visiting flowers, thus not providing certain information on the actual ecological nature of the specific plant–insect relationships (a Pikes Peak, Rocky Mountains, Colorado, USA, from [5]; b prairie–forest transition of western Illinois, USA, from [6]). Networks were disassembled by the sequential removal of all insect species. Plants were considered extinct when no associated visitor/potential pollinator was left in the network. The different lines represent the outcomes of distinct sequences of pollinator removal. Solid lines track the expected decline in plant diversity following pollinator loss when pollinators are removed from the most to the least linked to plants (a worst-case scenario for plant persistence). Dashed lines track to the opposite scenario, where pollinators are removed from the least to the most connected; dotted lines track the average plant persistence when pollinators are removed in random order, hence serving as a reference scenario. See the next chapter for a thorough discussion about how different sequences of node removal affect co-extinction trajectories. Modified from [3], with kind permission from the Royal Society

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Fig. 6.2 In bipartite networks, under a co-extinction model assuming that species are lost only after the complete depletion of their resources, chains of extinctions longer than one step cannot take place. This is exemplified in a toy network with three plant species (P1, P2, P3) and three insect pollinator species (I 1, I 2, I 3) (a). If one removes plant P1 from the network, insect I 1 will go extinct (assuming that plant P1 is its only resource available), while I 2 and I 3 will be unaffected (b). If also plant P2 is lost, I 2 will go co-extinct, and then the system will reach new stability, with no further co-extinction triggered (c). Removing insects instead of plants would generate matching events. In any case, the model implicitly does not permit an extinction triggered by the full loss of associated partners in either plants or insects to propagate back to the group where the primary extinction took place

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Probability of extinction of consumer

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Fig. 6.3 Examples of different potential responses of consumers to resource depletion. πi is the baseline probability of extinction for the consumer (i.e. the probability of going extinct when all resources are fully available). a: a consumer goes extinct only when left with no resources; b: consumer’s extinction risk increases linearly with resource depletion; c: consumer’s extinction risk grows slowly for very low and very high resource depletion and faster (linearly) for intermediate resource depletion; d: consumer’s risk grows rapidly for very low and very high resource depletion and slows down for intermediate resource depletion; e: consumer’s risk grows very rapidly when the first resources are lost and then slowly approaches 1 as more resources disappear; f: consumer’s risk grows slowly until most resources are lost and then speeds up substantially as resource depletion approaches completion. Modified from [7], with kind permission from John Wiley and Sons

extinctions, no consumers will remain without resources, and the extinction cascade will inevitably die out. There are some apparent problems related to this simplified procedure. In particular, assuming the complete depletion of resources as a necessary condition for secondary extinctions might not offer a truthful representation of how natural systems function. In some cases, even a slight reduction in a given resource’s availability might be enough to drive associated consumers to extinction. Thus, a more realistic approach would be modelling a species’ probability to go extinct as some function of resource availability (Fig. 6.3), instead of as a threshold response to the complete disappearance of its resources. The shape and complexity of this function might vary depending on several specific factors related to consumers’ ecology. For example, we can think of some direct and indirect consequences that a specialized herbivore could experience following a decline in its primary/preferred food resource availability. The herbivore will spend more energy to find food while experiencing more intraspecific (and possibly interspecific) competition over the resource and an increased risk of being targeted by predators. Increased mortality deriving from predation and possibly the reduced fecundity caused by energy deficiency will

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lead to a progressive reduction in population size. A small population size might make finding a sexual partner more difficult, generating the need for an even higher energy investment (e.g. more wandering). It might also further increase interspecific competition and predation risks and cause inbreeding (with all the associated detrimental consequences). Similar, multifaceted effects and intertwined processes might be particularly relevant in highly diverse systems where competition for resources is intense and where the survival of a given species (and of the community itself) depends on delicate equilibria. We can easily imagine situations where a slight reduction in resource availability affects consumers on multiple levels for various ecological settings. For example the population dynamics of directly transmitted parasites are closely related to their hosts’ density. When this falls below a certain level, parasite populations can no longer be sustained, and this is likely to happen well before the last available host has gone extinct. The mode a parasite is transmitted from a host to the following one in its life cycle combines with host availability to determine the parasite’s probability of surviving, completing its development and eventually reproducing. Let us put aside for a moment parasite generalism (how many host species a parasite can use at a given life stage) and parasite life-cycle complexity (how many intermediate hosts the parasite must use to reach sexual maturity). Instead, let us focus on the simple case of a host-specific parasite with direct development, that is a parasite using only one host of a particular species to develop and reproduce. Many factors affect a similar parasite’s chances to find the “right” host in a community. These include specific abilities to localize the host (e.g. based on chemical, visual or mechanical cues) or substantial overlap in habitat occupancy (i.e. the host and parasite live in close contact with one another, which increases the chances for infection). Some parasites have good motility and can search actively for a host. Although valuable, such an ability has energetic costs and exposes the parasite to the risk of becoming prey to a non-host species [8]. Other parasites, instead, have reduced motility and wait for the host to be close enough before attempting to colonize it. For instance some tick species behave as “hunters” by running across open ground and attacking potential hosts. By contrast, many other ticks use an “ambush” strategy by climbing on vegetation and waiting for transient hosts. In the latter strategy, infection attempts are triggered by chemical stimuli (such as hosts’ emissions of CO2 or NH3 ) or body heat [9]. Regardless of the different infection strategies (e.g. hunting vs. ambushing), it is intuitive that a decline in host density will reduce a tick’s likelihood of success in infecting a host, while increasing the probability of parasite co-extinction. This notion is conceptually close to basic epidemiological concepts. One essential epidemic measure is the basic reproduction rate (R0), which indicates the number of secondary infections produced by an infected host. Ideally, an epidemic will persist for R0 ≥ 1, while it will die out for R0 < 1. Many aspects determine R0 of a given pathogen in a given host population. Among these, the density of susceptible hosts plays a fundamental role, and, intuitively, R0 for a given pathogen can fall below 1 far before the host population is annihilated [10].

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Considering again the simplified example of a directly transmitted, host-specific parasite, we can use the R0 criterion to determine the threshold for parasite persistence in a host population. In theory, the hypothetical parasite population will be doomed to extinction as soon as the average chance of an individual parasite infecting a host will be less than 1/β, with β being the average number of a tick’s offspring capable of reaching maturity. As stated above, to achieve an equilibrium where the parasite population remains stable, β should be ≈1. However, this does not necessarily mean that a slight reduction in the individual chances of a parasite finding a host would set them on the path towards co-extinction. Oscillations, even strong ones, should be expected as the result of natural antagonistic population dynamics. But, when the reduction in host availability is too prolonged or intense, a species’ fate could be set well before its eventual disappearance. At the same time, it is fundamental to consider that real-world scenarios are far more complicated than our simplified examples. Other factors, such as climate, can affect to a different degree different stages in the life cycles of host and parasite populations (Fig. 6.4) [11], making it very difficult to understand and model how oscillations in host availability might affect parasite populations. We might have cases where the synergy between different environmental/ecological drivers speeds up co-extinction processes and opposite situations where, instead, compensatory mechanisms mitigate co-extinctions. This complexity leaves us with many doubts about the current state of things, let alone the future. Given the critical decline many extant species has faced to date, it is reasonable to assume that many “associate” species (e.g. parasites or, more in general, dependent symbionts) have gone extinct already. Quoting Crawford S. Holling, “Individuals die, populations disappear, and species become extinct. That is one view of the world. But another view of the world concentrates not so much on presence or absence as upon the numbers of organisms and the degree of constancy of their numbers” [12]. In 1989, Estes et al. [13] discussed the consequences of three different types of extinction processes: global, local and ecological extinctions. Ecological extinction is the reduction of a species’ abundance to such a low level that the species can no longer have significant interactions with other species in the community. Local extinction is the disappearance of a species from part of its natural range. Global extinction is the complete loss of a species from our planet. The three processes can be seen as three incremental steps across the same path. Species decline locally until ceasing to contribute to community dynamics; then, they go locally extinct; and, when multiple local extinctions have occurred across the whole species’ range, they go globally extinct. The ecological extinction of a species might have different effects on a given system. In some cases, it might happen completely unnoticed, but in other ones, it might even lead to the disappearance of other species. Some authors have referred to situations where decreasing in population abundance of a given species cause extinctions in other species before the declining one as “functional extinctions” [14]. From a modelling perspective, implementing functional extinctions is complicated because it implies knowledge of species’ population dynamics. As an approximation, one could set a threshold in the number/amount of resources that, if lost,

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Fig. 6.4 Input and output (predicted number of feeding individuals per stage of development) parameters for a model of population dynamics of the African tick Rhipicephalus appendiculatus. The diagram shows that the complexity in factors involved goes far beyond the hosts’ population dynamics (which affect the feeding probability of the parasite’s different life stages directly). In this particular model, host availability is assumed constant over time, which permits to focus on the effects of environmental factors on parasite population dynamics. Reproduced from [11], with kind permission from Cambridge University Press

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would doom a consumer to co-extinction. In principle, this approach does not necessarily require information on interaction weights. For example one could assume that a consumer would go extinct when a certain fraction of the initial number of interactions is lost. However, as discussed in previous chapters, it is an oversimplification to assume that all pollinators contribute equally to the reproduction of a plant. In a realworld setting, we might have an insect species (I 1) which relies almost exclusively on a given plant species (P1) for its survival. We might also have another plant (P2) with a much smaller population than P1 that is also (and exclusively) pollinated by I 1. In such a situation, and in the absence of possible compensatory mechanisms, the rapid loss of plant P1 (due, for instance, to the spread of a species-specific disease) might drive pollinator I 1 to extinction despite the persistence of plant P2. Indeed, plant P2 might even go extinct before the disappearance of plant P1 and pollinator I 1, due to mechanisms similar to those described above for host–parasite interactions (as in the case of a host population density critically low and no longer permitting parasite transmission). This simplified scenario illustrates the importance of accounting for interaction weights. A straightforward way to implement this in a co-extinction model is assuming a certain threshold for the decrease in total interaction weight between a target node and its associates, after which secondary extinctions happen. One possible threshold can be the sum of weights of all edges pointing from resources to the target consumer or from mutualistic species to the symbiotic partner benefiting from the association. Following a similar approach, researchers simulated co-extinctions in 13 weighted mutualistic networks (eight plant–pollinator networks and five seeddisperser networks) in different central Europe regions. They assumed that interaction frequencies are proportional to the functional dependencies of animals on plants and vice versa. Then, they explored different thresholds (25, 50 and 75%) for the loss of total interaction frequency triggering a node co-extinction. In their model, in a hypothetical situation of a pollinator associated with three plants with respective weights 0.2, 0.3 and 0.5, for a co-extinction threshold of 50%, a pollinator would go extinct after the disappearance of the first two plants, or for any combination of extinction events involving the third plant. They concluded that plant extinctions due to climate change are more likely to cause secondary animal extinctions than the other way around [15]. Depending on the specific system under study and modelling details, interactions weights might need to be standardized to represent the relative importance of resources associated with a given species, under the assumption that those represent the totality of available resources (hence, summing up to 1). As an example, we can consider a network where weights are attributed based on observed visitation events. There, a possible strategy to standardize the the weight of a link connecting the i-th pollinator to the j-th plant (representing the total number of visits of pollinator i to plant j) could be dividing it by the total number of visits of pollinator i to any plant. In mutualistic networks, where links represent the bidirectional beneficial effects of symbiotic partners, the standardization process might apply to both directions. Thus, in a plant-pollinator network, starting from the visitation count matrix, one might derive two distinct adjacency matrices of standardized interaction

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Table 6.1 Example of standardization of a hypothetical weighted ecological network mapping the visiting frequency of pollinators to plants Plants P1 P2 P3 Total (a) Count of interactions Pollinators I1 I2 I3 I4 I5 Total (b) Weigths for pollinators Pollinators I1 I2 I3 I4 I5 Total (c) Weights for plants Pollinators I1 I2 I3 I4 I5 Total

3 2 6 1 1 13

5 2 0 1 0 8

0 0 8 1 3 12

8 4 14 3 4

0.38 0.50 0.43 0.33 0.25 1.89

0.63 0.50 0.00 0.33 0.00 1.46

0.00 0.00 0.57 0.33 0.75 1.65

1 1 1 1 1

0.23 0.15 0.46 0.08 0.08 1

0.63 0.25 0.00 0.13 0.00 1

0.00 0.00 0.67 0.08 0.25 1

0.86 0.40 1.13 0.29 0.33

The upper table (a) reports the raw number of visits. Those are quoted by the total number of flowers visited by each pollinator to obtain standardized interaction weights from a pollinator’s perspective (b). Similarly, the number of visits received by a plant species from each pollinator is quoted by the total number of visits received by the target plant to obtain interaction weights from a plant’s perspective (c)

weights representing the relative dependency of pollinators on plants and vice versa (Table 6.1). One can then take into account both matrices simultaneously while simulating co-extinction processes, keeping track of how they change as species are lost and identifying which species fall below a pre-selected interaction threshold (Table 6.2). Although being a first step towards increasing realism, this approach is still far from perfect. One obvious limitation lies in the challenge of choosing a “proper” coextinction threshold. Large thresholds would generate co-extinction trajectories not too distant from those observed in models assuming complete resource depletion as the only mechanism triggering secondary losses, possibly leading to a severe underestimation of the importance of co-extinctions for biodiversity loss. Conversely, using small thresholds would generate the opposite effect, potentially leading to an overestimation of secondary extinctions. Furthermore, it is reasonable to assume

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Table 6.2 Example of co-extinction event in the hypothetical weighted plant–pollinator network of Table 6.1 P1 P2 P3 Total (a) Weigths for pollinators Pollinators I1 I2 I3 I4 I5 Total (b) Weights for plants Pollinators I1 I2 I3 I4 I5 Total

0.00 0.00 0.00 0.00 0.00 0.00

0.63 0.50 0.00 0.33 0.00 1.46

0.00 0.00 0.57 0.33 0.75 1.65

0.63 0.50 0.57 0.67 0.75

0.00 0.00 0.00 0.00 0.00 0

0.63 0.00 0.00 0.13 0.00 0.75

0.00 0.00 0.67 0.08 0.25 1

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A co-extinction threshold of 50% is used, meaning that secondary extinctions are triggered whenever the total interaction weight of a given species declines by at least a half in respect to the initial value. After extinction of plant P1, pollinator I 2 goes co-extinct (since, its total interaction weight is reduced to 0.5). Pollinator I 2 is then removed from the plants’ interaction weight matrix. Since no plant experiences a reduction in its total interaction weight larger than 50% in respect to the initial weight, no further extinction is triggered, and the system reaches new stability

that different species could have different thresholds, i.e. that some species would go extinct after a considerably smaller relative reduction in total interaction weight than other species. Thus, any choice of the co-extinction threshold would be necessarily biased in one direction or another. However, one advantage of using the threshold is the possibility to circumnavigate these limitations by generating multiple scenarios to evaluate the robustness and variability of model results under a broad range of ecological assumptions. Using an alternative, more complex approach, researchers modelled population dynamics of species in a network with systems of differential equations. They showed that the frequency of functional extinctions (defined, as above, as the disappearance of a species following the decline of another species before the latter has gone numerically extinct) is higher than the frequency of primary extinctions in both real and modelled food webs [14]. In their approach, the authors approximated the population dynamics of the i − th species in an ecological network with S species using a generalized Rosenzweig-MacArthur model:  dNi a˜ i j N j ) = Ni (bi + dt j=1 S

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Fig. 6.5 Example of functional extinctions simulated in a food web from the Baltic Sea using a multispecies, population dynamic model. The arrows indicate a species in the network for which the mortality rate is progressively increased. The crosses indicate the first species in the network that is lost following the manipulation. The three panels show different possible outcomes. In the left one, the focal species (i.e. the one subjected to increased mortality) disappears before causing secondary extinctions. In the middle panel, a species directly linked to the focal one goes extinct. In the right panel, the decline in focal species’ population drives another, indirectly linked species to extinction. Modified from [14], with kind permission from Springer Nature

Ni is the number of individuals of species i at time t; bi is the rate of change of species i (which is the per capita growth rate for primary producers and the per capita mortality rate for consumers); a˜ i j is the per capita effect of species j on the intrinsic rate of change of species i. The term a˜ i j can represent different ecological mechanisms and, particularly, competitive or prey–predator interactions, hence being a placeholder for many possible functions (see [14]). Modelling co-extinctions in this framework is straightforward. The decline in the population size of a given species i in the network is simulated by increasing its mortality rate progressively. At each step of mortality increase, equilibrium population densities for all species in the network are obtained by solving the differential equation system. The “updated” densities are then used as initial densities in the next step, and the procedure is reiterated keeping track of the extinctions in the network. This approach differs fundamentally from the previously discussed ones based on tracking node removal’s effect across network links. While those approaches permit only to evaluate the direct (“first order”) effect of primary extinctions, using systems of differential equations permits tracking also the loss of species not directly connected to the declining one (“higher order” extinctions) (Fig. 6.5). The relevance of higher order interactions for biodiversity maintenance (and loss) will be the subject of a dedicated chapter. However, it is important to emphasize here how the contribution of higher order extinction in the global diversity loss could be very high (Fig. 6.6), which raises additional problems regarding the limitations of modelling co-extinctions focusing on direct, pairwise interactions only.

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trophic level Fig. 6.6 Outcomes of functional co-extinction simulations in three real-world food webs (top left: Baltic Sea food web; top right: Lake Vättern; bottom left: Tropic Sea web; for details, refer to the original paper [14]). Each food web was represented in the simulations using a multispecies population dynamic model. The authors conducted replicates to evaluate how increasing the mortality of a particular species in the network affected the other species’ persistence. They increased the focal species’ mortality rate until either the focal species or any other species in the network went extinct. The bar plots provide a visual comparison between the frequency of functional coextinctions affecting, alternatively, the focal species (black), a species directly linked to the focal species (grey), or a species not directly linked to the focal one (white). Modified from [14], with kind permission from Springer Nature.

One common feature, and possibly a substantial limitation, of the different approaches described above, is that they consider ecological networks as nonresponsive to change. In the case of co-extinction models not explicitly accounting for population dynamics (e.g. [3]), that is quite evident. There, the reference point for evaluating co-extinction events is usually derived from the initial network status. This might be reasonable at the beginning of a simulation but becomes more unrealistic as species disappear from the network. Conversely, models accounting also for multispecies population dynamics consider individual responses of extant populations to species decline/loss [14]. Yet, even in this case, the structure of the network mapping species interactions is assumed to be rigid.

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A more realistic approach would be providing species in the network with some degree of adaptability/plasticity in their interactions with other species. The underlying idea is that observed network links (and their weight) could represent just a subset of all possible ecological interactions. Available information on species interactions is often far from being complete, both in terms of species included in the network and links connecting them. Missing species from a network could lead to an over or an under-estimation of co-extinction events. We might mistakenly predict a species’ extinction because our network does not include resources-available in the real world-which would permit the target species’ survival. But we might as well overlook many co-extinction events by excluding from the network some of the interacting partners strictly depending on resources undergoing primary extinctions. If and how the two errors compensate one another is hard to say. Furthermore, it is often tough to identify the actual dimension of generalism. Even assuming that we have complete information about the resources used by a consumer, this does not necessarily exclude that the consumer, if deprived of some of them, would shift to alternatives. Changes in diet, especially in generalist consumers, are common and driven by resource availability. For instance a study focusing on the diet of the generalist predator yellowfin tuna (Thunnus albacares) showed a substantial shift in the species’ trophic habits towards the end of the twentieth century, as a consequence of large scale changes in pelagic communities. In particular, in the 1990s, the diet of yellowfin tuna was mainly based on sizeable epipelagic fish prey. However, in the 2000s, the decline of such resources, paired with an increase in the availability of smaller mesopelagic species and crustacean, forced yellowfin tunas to shift their food preferences to the latter [16]. Changes in resource–consumer associations can also happen in other, seemingly less flexible contexts, such as host–parasite networks. Events of “host-shifts” in which a parasite invades a previously unexploited host are not rare at the evolutionary scale and likely one of the leading forces (besides cospeciation) driving parasite diversification [17]. However, host-shift could be negligible at the ecological scale, particularly in the context of rapid environmental change, which is the typical setting of most co-extinction studies [18]. For this, it is essential to distinguish between hostspecific and host-opportunistic parasites. This distinction combines with the scale of observation, generating several possible scenarios [19]. Some parasites may be consistently host-specific across different geographical scales, using a few hosts in each locality, all of which belonging to a limited set of species (Fig. 6.7P1). However, this case is substantially different from that of parasites that use a few hosts in each locality but rely on different species from place to place, hence having a large host range globally (Fig. 6.7P2). Other parasites may be host-opportunistic at the local scale, with a relatively large set of hosts used in each locality. However, if the set of hosts remains consistent across the parasite’s whole geographical range, then the parasite can be considered host-specific at the regional scale (Fig. 6.7P3). In that situation, the overall set of hosts the parasite uses is indeed narrow when compared to that of a parasite which is host-opportunistic at both the local and the regional scale (hence, using large, not necessarily overlapping sets of hosts in each locality; see Fig. 6.7P4).

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Both local and regional/global host specificity vs. host opportunism strategies are important factors that affect the realism of co-extinction simulations. Modifications in host availability at the local scale are not likely to influence local and regional hostspecific parasites (Fig. 6.7P1, P2) since those will not be able to replace host species lost to primary extinctions with other previously unexploited hosts. Conversely, a globally host-opportunistic parasite (Fig. 6.7P4) could be more prone to be affected by host availability and more flexible in its associations. That is it might be ready to shift rapidly to different hosts—such as hosts that were already present in the community at a low abundance when/if these experience a significant demographic increase following the extinction of competitors (e.g. the former preferred hosts for the global opportunistic parasite). Therefore, while, in the first case, a co-extinction model would not need to account for possible shifts in host–parasite associations, the opposite would be true in the latter case.

Fig. 6.7 Different, hypothetical scenarios of parasite–host specificity and opportunism at the local and global scale. Each panel shows the host range (with four host species considered, A-D) of a different parasite (P1-P4) across three locations (Site 1-Site 3). Parasite P1 is host-specific at both the local and the regional scale; it uses a small and consistent set of host species in each location. Host sets are also “nested”, that is the set of hosts used in any location is a subset of richer host sets. Parasite P2 is host-specific at the local scale but opportunistic at the regional scale; it uses only a few hosts in each location but many different hosts globally. Parasite P3 is opportunistic at the local scale while specific at the regional scale; it uses many hosts in each location, but, as P1, its host range in any locality is a subset of the richest host range. Parasite P4 is opportunistic at both local and regional scales, using many hosts in each location, and with the identity of those hosts being variable between locations. Modified from [19], with kind permission from Elsevier

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The same reasoning virtually applies to any system of interacting species. Different degree of flexibility in specialization characterizes any resource–consumer interaction. However, discriminating between the various combinations of specificity and opportunism across different geographical scales is challenging in most natural systems. In the case of parasites, while we often have information about the identity of host–parasite interactions, we rarely have reliable data on their geographical coverage. Unequal sampling emerging from logistic impediments can lead to deep gaps in the knowledge of parasite distributions. Other biases might derive from funding opportunities: it is much easier to obtain resources for studying parasites that have relevance for human health, which often means focusing on hosts capable of transmitting zoonotic diseases [20]. Thus, quite often, the kind of specificity/opportunism we tend to consider is that at the regional scale and, for most parasites, it is difficult to assess the consistency (or lack of) of host–parasite associations across different localities. Back to trophic interactions, as mentioned above, many species can have a flexible diet. In contrast, others can exhibit various trophic specialization degrees, but many confounding factors make it challenging to discriminate between the two situations. A consumer can feed on a resource more often than on another simply because it tastes better or can be accessed with less effort. This preference, however, does not mean that the consumer could not survive by using another suboptimal resource that, perhaps, does not taste as good or is currently less abundant, and, therefore, less accessible and likely to be consumed. We may easily imagine a situation where a herbivore uses two competing plants. The populations of the two competing plants may oscillate in abundance depending on the season so that the herbivore may use preferentially one or another plant depending on their momentary availability. Assessing the consumer’s dependence on the two plants based on a single observation may result in biased information. For example ruminants do not forage at random but create their own “diet” by selecting a subset of plant species among those available. For that, they use multiple criteria, such as: nutritional content of food; the presence of potentially harming chemicals (e.g. toxic substances produced by plants to protect themselves from herbivores); physical aspects affecting consumption (e.g. presence of woody parts or canopy shape/architecture—the leaves of a tall tree are less accessible than grass); temporal/spatial variability, which may limit resource accessibility [21]. The problem of identifying whether a species relies on a given set of resources because those are the only ones it can access at the specific time of observation or, instead, because those are the only resources it can use (regardless of availability) has no straightforward solution. Controlled experiments might yield important insights. These could consist of removing some resources from a system (e.g. in a laboratory mesocosm or larger scale manipulative experiments) and then measuring the effect on the consumer’s population. For instance in a field manipulation experiment conducted in plant–pollinator networks in subalpine meadows in the Rocky Mountains of Colorado, removing a single pollinator species led to an increased generalism in the remaining pollinators [22].

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The study was conducted in 20 plots of 20 × 20 metres over two consecutive summer growing seasons (2010–2011). Scientists first monitored pollination activities in each plot in a natural (i.e. control) setting with no manipulation applied. Then, they removed the most abundant species of bumblebee (Hymenoptera: Apidae: Bombus) from each plot. The identity of the removed species varied from plot to plot. Scientists removed all visible individuals of the target species and started collecting data after one hour. During each survey, one team member ensured that bees of the target species did not move into the plot from outside the site boundaries [22]. Finally, the scientists quantified the dynamic specialization (floral fidelity) of the remaining bumblebees, as well as alterations in their pollination activity (pollen carriage and deposition) and plant reproductive function (in terms of seed output). At the end of the experiments, the removed bumblebees were reintroduced to their respective plots. The experiments resulted in a significant decrease in floral fidelity rates, carriage and deposition of conspecific pollen and seed production in manipulated plots compared to control ones. Bees increased their visits between plants of different species by 156%, which resulted in them carrying 17.5% more mixed species pollen loads relative to controls and in a reduced probability of depositing the “right” pollen on a given plant species. In turn, this led to a reduced seed production, with the mean seed count per flower reduced by 32% in the larkspur Delphinium barbeyi (Ranunculaceae), that was chosen as reference species, being common and potentially visited by the near totality of bumblebees in the study system (Fig. 6.8). Another hypothetical experimental approach may consist of presenting selectively different resources to the consumer to verify if it can potentially use them. Experiments of this kind have a long history in ecology, even if these have been usually conducted more to understand species’ food preferences and consumption rates (with particular attention to herbivory) than to discriminate between observed and potential interactions [23]. By presenting a broad spectrum of resources to a consumer, one can obtain information on its trophic niche’s boundaries. Analogous experiments are standard in parasitological studies, where hosts are exposed to different pathogens to evaluate their susceptibility. Many of such studies have revealed how bringing together hosts and parasites that are not commonly co-occurring can put in question previous assumptions on host specificity [24]. An intriguing aspect of this approach (when applied to any kind of ecological network, not necessarily to host–parasite systems) is that when testing potential interactions, one is not limited to the set of species inhabiting the target locality. This freedom offers valuable opportunities to get insights into various timely issues, such as the impact of invasive species on native communities and the potential effectiveness (and associated risks) of biological control. For instance, laboratory experiments were conducted to test the possible use of the parasitic barnacle Sacculina carcini as a control agent for an invasive species, the European green crab Carcinus maenas on the west coast of North America. In their native range, S. carcini is a natural enemy for the green crab, thus constituting a potential option to reduce invasive green crab populations. However, introducing species as a means of biological control is a hazardous strategy, which might often produce harsh detrimental consequences on

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Fig. 6.8 Schematic representation of a study aimed at investigating the potential effect of pollinator loss on plants. Scientists conducted field experiments in subalpine meadows in the Rocky Mountains of Colorado, USA, by selectively removing the most abundant bumblebee species from several plots and then assessing the effect of such removal on pollination activities within the plot compared to the pre-manipulation stage. The graphs below the scheme report quantitative results. Floral fidelity was quantified as the relative proportion of bees visiting only one species of plant. Pollen carriage indicates the relative proportion of bees carrying pollen from only one species of plant. Pollen deposition indicates the proportion of conspecific pollen loads carried by a single bee. The last (right) plot reports raw seed production values per flower. Modified from [22], with kind permission from the National Academy of Sciences

non-target, native species [25]. Controlled experiments where four native California crab species were exposed to S. carcini warned that this could have also been the case for introducing the alien parasitic barnacle in North America: all of the native species resulted susceptible to S. carcini, with infection resulting in high mortality rates. Interestingly, the parasite was not able to produce an “externa”—their reproductory organ—in any of the infected native California crabs. Thus, in the hypothetical case of a S. carcini invasion (either assisted or accidental) in California, its survival

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would depend exclusively on the alien green crab, with native California crabs constituting collateral targets. In a long-term, co-adaptation perspective, such a situation could either promote the evolution of higher host selectivity in the invading parasite (i.e. the ability to avoid dead-end hosts) or expand its host range. The latter would require eluding the host’s immune defensive system and gaining the ability to reproduce in the novel hosts. In any case, thinking about potential interactions between species that we do not commonly find together adds a new element of complexity to the way we can model co-extinctions. This aspect is usually overlooked in “standard” co-extinction models, which, in most cases, look at progressive removal of species while neglecting the possible addition of new nodes and interactions to the network. The issue is of primary importance, and I will treat it extensively in Chap. 14. From a modelling perspective, accounting for plasticity in species interactions is relatively straightforward. Referring to the previously described co-extinction model where a consumer goes extinct after the total frequency of its interactions falls below a pre-selected threshold, we might implement plasticity by permitting the reallocation of a fraction of lost interactions to species persisting in the network [15]. The idea is that the reduction in competition over some resources following the loss of consumers can increase resource availability for the remaining species. However, there are multiple choices involved in the modelling implementation, and each is possibly characterized by a certain degree of subjectivity. First, one should decide how much of the resources made available after extinction events should be reallocated to remaining consumers. The most optimistic scenario (in terms of species persistence) is that all of them are redistributed among extant consumers, but intermediate scenarios of partial redistribution might be more ecologically realistic. The next decision is which species in the network should be the recipient/s of the resource. For this, information regarding the actual consumers’ generalism would be vital to make the model realistic. Otherwise, one can take general assumptions. These span from a conservative view where interactions can only be redistributed to consumers already linked to the target resource (by increasing the weights of existing links); to a permissive one where all consumer in the network can receive the resource (with redistribution resulting also in the generation of novel links in the network). Intermediate scenarios would require the the identification of specific rules controlling the resource reallocation process. Once the total amount of resource to be redistributed and the recipients have been identified, there are different options regarding the specific amount of resource each species should receive. A straightforward approach would be redistributing available resources among eligible consumers equally. Alternatively, one might reallocate resources at random, for example assigning them to each species according to the broken stick model [26]. Still, more “informed” alternatives exist, such as reallocating the resource proportionally to the initial interaction weights (i.e. before species loss), assuming those as a proxy for the species’ ability to use different resources.

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The multiplicity of these choices, combined with co-extinction thresholds, generates many possible model setups that might lead to different outcomes. For instance when comparing the vulnerability of a set of plant–pollinator and seed-disperser networks to either animal or plant extinctions, researchers combined: (i) three possible co-extinction thresholds, permitting consumers to tolerate the loss of respectively 25, 50 and 75% of cumulative interaction weight before going extinct; (ii) four different values indicating the fraction of available resource to be reallocated among suitable recipients (0, 25, 50 and 100%); and (iii) two rules for network plasticity, with resource reallocation permitted either to any possible interacting partner in the network or to only partners already using the resource being reallocated [15]. This led to 24 different parameter combinations (for each considered scenario of climate change). In that case, simulation results were relatively consistent across the parameter space, with the networks resulting, on average, more vulnerable to removing plant species than to removing animal species. That is they showed a higher probability that the climate change-induced loss of a plant could trigger secondary extinctions of animal species than the other way around (Fig. 6.9). But there might be less clear-cut cases where contrasting model outcomes complicate results’ interpretation. Some authors refer to the reallocation process as “rewiring” [15]. Yet, the term has a different meaning in network science, indicating, in general, the process of modifying the position of links in a network (Fig. 6.10)[27]. For example in a plant– pollinator network, rewiring an edge would consist of replacing a plant associated with a pollinator with another plant in the network (hence, leaving the total number of links in the network unchanged).1 If the addition of novel species to a system can have hardly predictable effects on the structure and dynamics of the interaction network, the same is valid for reintroducing species lost previously. A famous example is the reintroduction of wolves in Yellowstone, which has likely contributed to significant, ecosystem-wide modifications [29]. However, there are also examples showing how species loss can bring systems to an alternate, stable ecological status which cannot be easily reverted. An empirical study focusing on two marine subtidal communities in two close islands (Malgas Island and Marcus Island) in South Africa [30] is a good one. Anecdotal evidence indicated a previous abundance of spiny lobsters in both sites until some unclear event (possibly oxygen depletion) drove the lobster population in Marcus Island to local extinction. Later on, fishing was closed at both islands, providing scientists with a valuable natural experiment. The marine community at Malgas Island, the site where lobsters survived local extinction, was a kelp forest with lobster dominating the macroinvertebrate population. Conversely, the community at Marcus Island, where lobster went extinct, was characterized by a rocky habitat dominated by molluscs, with extensive mussel beds and abundant populations of whelks but virtually no lobsters.

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reallocaon % Fig. 6.9 Network robustness to plant versus animal extinctions in different modelling setups. Co-extinction thresholds (25, 50 and 75%) indicate the cumulative fraction of interaction weight a consumer can lose before going extinct. Reallocation rules indicate how resources freed up by some consumers’ extinction were redistributed among remaining interacting partners. In the “constrained” reallocation scenario, redistribution of a given resource took place only among partners already using it. In the “unconstrained” scenario, available resources were reallocated to all extant consumers. Four different values (25, 50, 75 and 100%) were used to determine the fraction of resource (i.e. cumulative interaction weight) freed up by consumer extinctions that could be reallocated to extant species. Note that the scenario of unconstrained, full (100%) resource reallocation is omitted, as highly unrealistic and not informative: in such a scenario, all consumers would persist until the last resource is lost and then would go all extinct simultaneously. Bar plots indicate the mean differences (±1 s.e.) across 13 pollination and seed-dispersal networks between the impact of plant extinctions compared to animal extinctions. Positive values (red bars) indicate that networks are more vulnerable (i.e. collapse faster) when plants go extinct than when animals go extinct. Negative values (blue bars) indicate the opposite situation. Modified from [15] under a Creative Commons Attribution 4.0 International Licence (https://creativecommons.org/licenses/by/4.0/).

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Fig. 6.10 Example of a random rewiring process applied to a network with regular structure. Nodes and the edges connecting them to their nearest neighbour are selected one by one in a clockwise direction. With probability p, the selected edge is disconnected from its target node and reconnected to another node chosen with random probability among all nodes in the network (with the additional rule that the process must not generate duplicated edges). After one complete lap (i.e. when all nodes have been selected once), the procedure is repeated by selecting, this time, the edges connecting the target node to its second-nearest neighbours (clockwise). At intermediate values of p, rewiring transforms the initial regular network into a “small-world” network, that is a network where the distance (in terms of steps across network links) between a given node and any other one is minimal. For increasing values of p, the network becomes random. Reproduced from [27], with kind permission from Springer Nature

Experiments consisting of maintaining caged lobsters in local conditions (feeding them on different species of whelks) demonstrated that lobster from Malgas Island could also survive in Marcus Island, which raised the question of why their distribution remained limited to the first island. Searching for an answer, the authors released 1000 rock lobsters taken from Malgas Island to Marcus Island. Almost immediately, the released lobsters were attacked and consumed by swarms of whelks. After a week, whelks had killed all lobsters. Additional experiments confirmed this unexpected result, with each released lobster being attacked by more than 300 whelks, killed in 15 min, and ultimately consumed in less than one hour, leaving little doubt that the predator–prey role had been reversed in Marcus Island (Fig. 6.11). The rock lobster study offers a strong case supporting the idea that as diversity goes lost, networks’ dynamic response to perturbation might result in unpredictable outcomes. Marcus Island’s ecological network, far from being a simple modification of Malgas network, represents a new status or, to use the authors’ words, one among the multiple possible states of the same ecosystem [30]. As similar situations might be common in natural systems, identifying proper strategies to account for ecosystem feedback response to perturbations following species loss appears critical to advancing the co-extinction field (from both a theoretical and modelling perspectives). On a final note, an important outcome of co-extinction models is the relative extinction risk of individual species/network nodes. This can be obtained, for example

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Time Fig. 6.11 Reversal of prey–predator trophic role in Marcus Island compared to Malgas Island (South Africa). Arrows go from prey to predators. After rock lobsters (Jasus lalandii) went extinct in Marcus Island, possibly due to a temporary event of oxygen depletion, they could no longer recolonize the site. Reintroduction experiments demonstrated that the reason for that was the new trophic status in the community of the gastropod Burnupena papyracea, whose population had become abundant enough to wipe out any lobster trying to recolonize the area. Redrawn from [30]

by performing multiple co-extinction simulations in the same network keeping track of how often (and when in the simulation) a given node goes co-extinct. As mentioned in various parts of this book (see, for instance, Chap. 8), the relative vulnerability of nodes can be then used to inform conservation or test eco-evolutionary hypotheses [18]. In this context, the use of Bayesian networks has been proposed as an elegant way to obtain species’ vulnerability values in food webs without the need to perform simulations [7]. A Bayesian network is a set of random variables connected by conditional probabilities. When applied to model extinctions in food webs, the random variables represent the presence or absence of a species (i.e. they can take the value of either extant or extinct), and feeding interactions represent probabilistic dependencies between resources and consumers. In such representation, a node/species’ extinction probability depends on the extinction probability of its resources, and so on. Thus, depending on network structure, each consumer will have a set of conditional extinction probabilities depending on the state (extant/extinct) of its resources. One advantage of using Bayesian networks is that they permit to model different forms of consumer’s response to the loss of resources (see Fig. 6.3). Another one is the possibility of computing a species’ “marginal” probability of extinction ( pi ) analytically. Such pi value expresses the species’ extinction risk when considering all the possible states of all species in the network simultaneously. The computation is made tractable by the sparse nature of most real-world food webs (i.e. as real-world food webs are far from being fully connected, the number of species’ possible states does not sky-rocket). For each node in the network, one conditional extinction probability refers to the case

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where all the resources of the target species are present. This can be considered the “baseline” extinction probability ( pi i ), which does not depend on network structure (and that might ideally be attributed to nodes based on informed  criteria, such as IUCN threat status). The sum of baseline extinction probabilities ( πi ) quantifies the expected number of extinctions not due to ecological dependencies. By contrast, the sum of marginal extinction probabilities ( pi ) quantifies the predicted number of extinctions due to the network structure. Thus, if the total number of species in the   pi offers a measure of network robustness naturally varynetwork is S, the ratio S− S− πi ing in [0, 1]. Such a measure quantifies the extent to which ecological dependencies amplify the effect of primary extinctions. The many challenges introduced in this chapter seem to point to the conclusion that achieving realism in modelling co-extinction by manipulating a network (e.g. by removing nodes) and recording how it responds to manipulation is hardly feasible. This conclusion stems from the fact that networks can be often incomplete or only partially representative of natural systems’ complexity. Furthermore, networks can adapt to change, but how they do this can be largely independent of their structure. In theory, to take this aspect into account, after removing nodes (species) from the network, a modeller should use complementary approaches to identify structural changes following species loss and triggered by processes that the simplified network representation cannot capture. But since these complementary approaches (e.g. empirical experiments or different kinds of simulations such as agent-based models) have the potential to provide comprehensive insights going beyond those attainable from co-extinction models, what is the point of modelling the co-extinction, then? A simple answer to this question is that, in many cases, realistic complementary approaches are out of reach due to lack of data availability or implementation/computational challenges. By contrast, simulating co-extinctions in a network is quite straightforward, and, with due caveats, it is possible regardless of network completeness and level of detail. Furthermore, as previously discussed, even if some modelling choices are arbitrary (such as identifying the extent of resource loss a consumer can tolerate before going co-extinct), the exploration of vast parameter space is, in general, possible at a low computational cost. Still, apparent trade-offs exist between the breadth of such exploration and the intelligibility of the results. However, there is a more compelling aspect to be taken into account, which is the actual goal of co-extinction models. In many cases, this is far from making specific predictions. The true power of co-extinction models lies in providing a simple and flexible tool to assess the “relative” robustness of networks to species loss. Here, “relative” refers to comparing different networks using the same coextinction models, or to comparing the different responses of the same network to different species loss scenarios and co-extinction mechanisms. The next chapter will dig into this last aspect to clarify which insights we can obtain from exploring network response to alternative species extinction sequences. This will provide the

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conceptual bases needed to understand why current ecological networks are highly vulnerable to environmental change [18], and why warming our planet is possibly the most effective way to disrupt them [1].

Summary A simple way to model co-extinctions in resource–consumer networks (such as host– parasite networks or food webs) is: (i) removing nodes according to a particular criterion (either random or informed); and then, (ii) identifying which other nodes in the network are left without interacting partners (and, hence, doomed to co-extinction). Although intuitive, this approach has some significant limitations, deriving from its simplified view of processes that are dominated by complex dynamics. For example a consumer can go extinct even before the complete depletion of its resources, as in the case of a parasite no longer able to find a suitable host when those became rare. Similarly, the resources associated with a consumer can have unequal importance for its subsistence. Additionally, there could be scenarios where a consumer escapes extinction even after losing all of its resources by switching to other, previously neglected resources. Designing co-extinction models with an increasing degree of complexity can provide more realistic scenarios. To this end, one can consider the weights of species interactions, assume specific thresholds for resource depletion triggering consumer extinctions and permit a dynamic reallocation of interactions following species loss. Different modelling setups make it also possible to explore a wide range of hypotheses regarding ecological networks’ robustness to species loss. A completely different yet very informative modelling approach consists of modelling multispecies population dynamics. In that case, the network of interactions is used to build a system of differential equations linking mortality and birth rates of species in the community. Although this overcomes many of the limitations associated with plain node removal models, it comes at a high cost regarding the information it requires for the equations’ parametrization. Consequently, the approach is mainly used to investigate theoretical questions using simulated data, while it has limited application in empirical studies. In general, real-world examples show how the modelling tools we have at hand to model co-extinctions might fall short in capturing the actual complexity of natural systems, and describing how perturbations triggered by species loss can propagate across interaction networks. These limitations call for both caution in interpreting results obtained with available techniques and for a further effort to advance the current theoretical and simulation framework. All this considered, despite the limitations, co-extinction simulations offer a straightforward and flexible tool to explore the robustness of ecological networks to different perturbations and constitute a cornerstone of ecological network theory.

References

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References 1. Strona G, Bradshaw CJA (2018) Co-extinctions annihilate planetary life during extreme environmental change. Sci Rep 8(1):16724 2. Levine JM et al (2017) Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546(7656):56 3. Memmott J, Waser NM, Price MV (2004) Tolerance of pollination networks to species extinctions. Proc Royal Soc London B: Biolog Sci 271(1557):2605–2611 4. Dunne JA, Williams RJ (2009) Cascading extinctions and community collapse in model food webs. Philosoph Trans Royal Soc London B: Biolog Sci 364(1524):1711–1723 5. Clements FE, Long FL (1923) Experimental pollination: an outline of the ecology of flowers and insects, vol 336. Carnegie Institution of Washington 6. Robertson C (1929) Flowers and insects: lists of visitors of four hundred and fifty three flowers. Carlinville: Privately published 7. Eklöf A, Tang S, Allesina S (2013) Secondary extinctions in food webs: a Bayesian network approach. Meth Ecol Evol 4(8):760–770 8. Welsh JE et al (2017) Parasites as prey: the effect of cercarial density and alternative prey on consumption of cercariae by four non-host species. Parasitology 144(13):1775–1782 9. Sonenshine DE, Mather TN (1994) Ecological dynamics of tick-borne zoonoses. Oxford University Press 10. Krkošek M (2010) Host density thresholds and disease control for fisheries and aquaculture. Aquacul Environ Interact 1(1):21–32 11. Randolph SE, Rogers DJ (1997) A generic population model for the African tick Rhipicephalus appendiculatus. Parasitology 115(3):265–279 12. Holling CS (1973) Resilience and stability of ecological systems. Ann Rev Ecol Systemat 4(1):1–23 13. Estes JA, Duggins DO, Rathbun GB (1989) The ecology of extinctions in kelp forest communities. Conservat Biol 3(3):252–264 14. Säterberg T, Sellman S, Ebenman B (2013) High frequency of functional extinctions in ecological networks. Nature 499(7459):468 15. Schleuning M et al (2016) Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat Commun 7:13965 16. Olson RJ et al (2014) Decadal diet shift in yellowfin tuna Thunnus albacares suggests broadscale food web changes in the eastern tropical Pacific Ocean. Marine Ecol Prog Ser 497:157–178 17. Alcala N et al (2017) Host shift and cospeciation rate estimation from co-phylogenies. Ecol Lett 20(8):1014–1024 18. Strona G, Lafferty KD (2016) Environmental change makes robust ecological networks fragile. Nat Commun 7:12462 19. Krasnov BR et al (2011) Beta-specificity: the turnover of host species in space and another way to measure host specificity. Int J Parasitol 41(1):33–41 20. Hopkins ME, Nunn CL (2010) Gap analysis and the geographical distribution of parasites. In: Morand S, Krasnov B (eds) The biogeography of host-parasite interactions. Oxford University Press, Oxford, pp 129–142 21. Provenza FD, Balph DF (1990) Applicability of five diet-selection models to various foraging challenges ruminants encounter. In: Hughes RN (ed) Behavioural mechanisms of food selection. NATO ASI Series (Series G: Ecological Sciences), vol 20. Springer, Berlin, Heidelberg, pp 423–460 22. Brosi BJ, Briggs HM (2013) Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc Nat Acad Sci 110(32):13044–13048 23. Peterson CH, Renaud PE (1989) Analysis of feeding preference experiments. Oecologia 80(1):82–86 24. Poulin R, Keeney DB (2008) Host specificity under molecular and experimental scrutiny. Trends Parasitol 24(1):24–28

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25. Simberloff D, Stiling P (1996) Risks of species introduced for biological control. Biolog Conservat 78(1–2):185–192 26. MacArthur RH (1957) On the relative abundance of bird species. Proc Nat Acad Sci 43(3):293– 295 27. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440 28. Carstens CJ, Berger A, Strona G (2018) A unifying framework for fast randomization of ecological networks with fixed (node) degrees. MethodsX 5:773–780 29. Dobson AP (2014) Yellowstone wolves and the forces that structure natural systems. PLoS Biol 12(12):e1002025 30. Barkai A, McQuaid C (1988) Predator-prey role reversal in a marine benthic ecosystem. Science 242(4875):62–64

Chapter 7

Extinction Sequences

The previous chapter explored different approaches to model how the effect of primary extinctions can propagate across networks of ecological interactions and possibly cause further species loss. In the simplest co-extinction model, primary extinctions are simulated by removing one or more nodes (i.e. species) from the network. Then, the consumers left with no resources are removed as well, and this two-step procedure is reiterated until there are no more co-extinction events or all species have been lost from the network [1, 2]. A fundamental modeller’s decision in this procedure is selecting the nodes to be removed1 and the order in which to remove them. Such a decision is affected by the specific goal/s of the co-extinction simulation. Although these might vary substantially from one study to another, we can identify two major categories. The first one refers to studies that try to model/predict a given network’s response to specific perturbations. The second one refers to studies aimed at identifying the intrinsic/theoretical robustness of an ecological network. In the first case, the sequence of primary extinctions would be realistic, trying to capture the identity of species that are reasonable candidates to go extinct in a network, as well as their relative risk of extinction. Examples of realistic sequences of primary extinctions include removing nodes according to different vulnerability measures such as those provided by the International Union for Conservation of Nature (IUCN). Using this approach, scientists have hypothesized, for instance, the effect of species extinctions in the food web of Serengeti National Park [4] (Fig. 7.1). Apart from available conservation indexes, one can use many possible criteria to rank species according to their vulnerability and obtain an informed host extinction (i.e. node removal) sequence for co-extinction simulations. For example one could assume a positive relationship between species body mass and extinction risk [5] 1

Note that, in co-extinction models based on multispecies population dynamics, the nodes are not removed. Instead, their corresponding populations are reduced by increasing mortality rates [3]. However, for simplicity, in this chapter, I will only refer to node removal. © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_6

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Fig. 7.1 Effect of sequential species loss according to their extinction risk as assessed by the Red List of Threatened Species (IUCN 2009) on a food web from Serengeti National Park. The food web includes 321 species (belonging to 29 different orders). Species were first aggregated into “trophic species” based on similarity in resources/consumers and on higher-order taxonomical classification, and then disaggregated into multiple nodes according to body size. The aggregation/disaggregation process resulted in 86 nodes (and 547 links). The first matrix (a) shows the pristine food web. The other matrices show the cumulative loss of nodes after the progressive removal of all species listed by IUCN as endangered (b); endangered or vulnerable (c); endangered or vulnerable or near threatened (d). Columns correspond to consumers, while rows correspond to resources. Dots indicate feeding interactions, with empty ones indicating interactions lost following species removal according to their IUCN status. Modified from [4], with kind permission from John Wiley and Sons

and, hence, explore extinction sequences where species are removed from largerto smaller-bodied species [4]. Another possibility is combining environmental niche modelling with climate projections to hypothesize expected sequences of host extinctions. For this, one could identify the species that will struggle most in future, and the expected time they will be no longer able to cope with the predicted, novel climatic conditions [6]. Following this approach, in a global scale study on more than four hundred species of parasitic worms, researchers applied ecological niche modelling to both the parasites and their hosts. In this way, the authors concluded that 5 to 10% of the investigated parasite species could be doomed to extinction by 2070 as not tolerant to the predicted novel conditions. The percentage goes up to 30% when one accounts for co-extinctions [7].

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pollinator extinction (%) Fig. 7.2 Examples of survival curves in a plant–pollinator network (from Pikes Peak, Rocky Mountains, Colorado, USA, from [8]). The authors disassembled the network by the sequential removal of all insect species. They considered plants extinct when left with no associated insect visitor/potential pollinator. The different lines represent the outcomes of distinct sequences of insect removal. In particular, the bottom line tracks the expected decline in plant diversity following pollinator loss when pollinators are removed from the most to the least linked to plants (ideally, a worst-case scenario for plant persistence). The upper line tracks to the opposite scenario, where pollinators are removed from the least to the most connected (the best-case co-extinction scenario). The middle line tracks the average plant persistence when pollinators are removed in random order, hence serving as a reference scenario. The robustness of the target network to the different extinction sequences can be quantified by computing the area under the respective curve. Modified from [2], with kind permission from the Royal Society

When co-extinctions are used, instead, to investigate the theoretical, intrinsic robustness of a network to species loss, the removal of nodes is usually performed based on some of the features defining the nodes’ structural role in the network (such as their number of connections). Theoretical sequences of species extinctions permit obtaining standardized co-extinction trajectories that are very useful for comparisons. Typically, in this kind of approach, all nodes (or at least all nodes corresponding to resources) in the network are subsequentially removed in a given order (underlying specific assumptions), while the resulting decline in consumer diversity is tracked. This procedure permits drawing a “survival” curve that models the fraction of extant consumers (or associate species) vs. the fraction of removed resources (or symbiotic partners). The larger the area under that curve, the more robust the network towards the loss of resources. Here, the initial expectation of how the network will respond to the selected extinction sequence stems from theoretical knowledge and hypotheses. For example, one would expect a network to be much robust to the removal of nodes from the least to the most connected ones, while fragile to the removal of nodes from the most to the least connected [1, 2] (Fig 7.2). Removing nodes (species) in random order offers a straightforward way to compare the robustness in different networks. Also, it provides a null model against which to test network robustness compared to realistic/informed extinction sequences and hence assess the relative network vulnerability to specific extinction drivers (see, for example [2]). In many situations, it might also be interesting to identify the theoretical

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boundaries of robustness for a given system, ideally corresponding to how a network would respond to either a best or a worst-case scenario of species loss. Here, “best” refers to a scenario where a high number of primary extinctions is needed before co-extinctions are triggered. This can be achieved by removing nodes from the least to the most important one for network persistence. By contrast, a worst-case scenario would be one where co-extinctions are triggered early, with the most important species being removed first, followed by progressively less important ones. The increasing availability of large volumes of data, paired with the opportunity to analyse them through the lens of network analysis, has encouraged scientists to explore strategies aimed at identifying network vulnerabilities and improving network robustness towards perturbations and attacks [9]. A whole branch of network physics addressed the issue directly, leading to optimal strategies to secure networks against failure or dismantle them more efficiently. The problem has been widely explored in the context of percolation theory [10]. Let us take a regular grid where each cell can be either occupied or not and where two adjacent occupied cells (either one above the other or one next to the other) are considered connected. Let us define a cluster as a set of cells in which each cell is connected to at least one other cell in the cluster. Suppose, each cell has the same probability of being either empty or occupied ( p). For small values of p (close to 0), it will be improbable to obtain a cluster spanning from one grid edge to another (e.g. from the upper to the lower or from the left to the right boundary). Chances for that to happen will increase as p gets closer to 1 (Figs. 7.3 and 7.4). We can model the same problem by looking at the probability that a giant component (i.e. a single large cluster including most network nodes) will emerge by connecting nodes at random with probability p. The probability at which the giant component’s emergence is observed ( pc ) is known as the percolation threshold. It is intuitive that by wisely selecting which nodes to connect instead of creating connections at random, one might sensibly speed up a giant component’s emergence. Similarly, looking at the issue from the opposite perspective, it is intuitive that one might break apart the giant component into smaller pieces faster and more efficiently by selecting critical nodes than by removing nodes at random. Incidentally, these concepts have real-world implications important not only to evaluate network vulnerability to extinction but also in the context of infectious disease ecology and epidemiology [12]. The theory behind node removal criteria is key to the identification of efficient “immunization strategies”, where immunization refers here to the process of breaking apart a network’s giant component into sensibly smaller, isolated clusters [13]. For example one important problem is that of identifying a small set of nodes permitting to maximize the efficacy of a vaccination campaign based on social interactions (letting alone for a moment the ethical implications of such a “selective” vaccination) and reduce the risk for epidemic outbreaks [14, 15]. The most straightforward feature defining the importance of a network node is its degree, which is the number of links connecting the target node to other nodes in the network. However, although node degree is an intuitive and easy to compute proxy for node importance, it is a purely local property based on the target node’s neigh-

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Fig. 7.3 Percolation in a two-dimensional grid of 25 × 25 cells. In each alternative version of the grid, each cell has a probability p of being occupied. The upper panels represent the grid in a matrix-like format, while the lower panels represent it as a network. In the latter, two cells are connected one to another if sharing one edge in the grid. In both representations, empty grid cells are white, occupied cells belonging to the largest cluster of connected cells are black, and occupied cells not belonging to the largest connected cluster are grey. With p = 0.6, percolation happens and the grid’s upper boundary becomes connected to the lower one. This event translates into the emergence of a “giant component” in the grid’s network representation

bourhood only. Thus, in some cases, focusing on node degree might be a suboptimal criterion to inform management decisions or draw conclusions about network-wide processes and lead to biased conclusions. Two nodes in a network could have identical degrees, the same number of neighbours and yet very different importance for network processes such as co-extinctions. Such a difference might emerge, for example when the two nodes belong to different parts of the network, with one node being in a peripheral area compared to the other one or with the two nodes being part of two distinct clusters with diverse size. Or, it might derive from substantial differences in the properties of the nodes belonging to the respective sets of target nodes’ neighbours. For instance, we can imagine a situation where one of the two target nodes is linked to highly connected nodes, and the other node is linked to nodes poorly connected to the rest of the network. A solution to these issues is using criteria that take into account more significant portions of the network topology going beyond the immediate neighbours of target nodes (Fig. 7.5).

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Fig. 7.5 “Collective influence” (C I ) of the i-th node in a network. The measure provides an optimal criterion to select which nodes will determine, if removed, the fastest disruption of the network’s giant component (i.e. a cluster of nodes for which at least one link to another node in the cluster exists, and including most nodes in the network). C I (i) is computed by identifying a “ball” including all j nodes having a shortest path distance to i (i.e. the minimum number of steps through network’s nodes needed to move from j to i) less or equal than . The measure takes into account the focal node degree (ki , the number of nodes connected to node i) and the degree of all other nodes within the ball. The larger is , the more accurate is the estimation of a node’s collective influence, and the more rapid is the dismantling of a network’s largest connected component following the subsequent removal of nodes in reverse order of their C I . Modified from [14], with kind permission from Springer Nature

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An important aspect is that networks with different topological properties and showing diverse structural transitions for increasing complexity/connectance [16] will respond differently to varying node removal criteria. Modelling theoretical trajectories of network disassembly (either according to random or topologically wise node removal) can provide a frame of reference helpful to put in context more realistic co-extinction trajectories (as in the above example about the food web of Serengeti National Park). Furthermore, the best and worst-case scenarios can also offer important insights for conservation planning. Even without considering the entire co-extinction sequence, the mere identification of which nodes in a network could have the most considerable impact on its persistence might offer an obvious criterion for conservation prioritization. It is intuitive how potential mismatch situations between nodes’ topological importance in a network and their current vulnerability might help identify possible routes for improvement in ongoing conservation strategies. It is essential to clarify that breaking apart the giant component is not necessarily equivalent to collapsing an ecological network. Yet, it is intuitive that the two issues are closely related since the fragmentation of a network will most likely result in the extinction of many species left with no resources. As a proof of concept, I have explored this issue in one of the largest plant–pollinator networks available, mapping the interactions between 1429 flower-visiting insects and 456 plant species near Carlinville, Illinois, between 1887 and 1916 [17]. I have replicated 100 times the procedure of disassembling the network by progressively removing all plant species one after another in random sequence. After each removal, I kept track of both the fraction of nodes in the network’s largest connected component and the overall decline in species diversity, considering as co-extinct all the pollinator species left with no links to plants. The reduction in the size of the largest connected component was almost indistinguishable from the diversity decline, meaning that, at least in this specific network, whenever a species gets disconnected from the giant component, it is also doomed to extinction (Fig. 7.6). In other networks, however, differences between the two trajectories might be observed. In trophic networks, in particular, we might anticipate a relatively faster decline in diversity compared to giant component size than that expected for undirected ecological networks. This expectation stems from the fact that consumers with no paths to basal resources will go extinct even if still belonging to the giant component. However, consistent with the idea that a modular structure might increase network robustness against local perturbations [18], we might envision situations where fragmenting the giant component and increasing modularity could improve network stability. In fact, by using a bioenergetic resource–consumer model to simulate multispecies population dynamics within food webs, researchers have shown that compartmentalization increases the persistence of multitrophic food webs. The key mechanism behind this positive relationship lies in the buffering action that food web compartments exert against the propagation of extinctions throughout network links [19]. Most of the measures used to define the topological importance of nodes in a network could also be good indicators of nodes’ contribution to network robustness against species loss. The PageRank™ algorithm provides an interesting example in

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that regard. Today the PageRank™ algorithm is just one among many criteria used by Google to answer users’ search queries soundly and shows the most relevant pages at the top of a web search result page. But, initially, it was at the basis of the first Google prototype and played a leading role in determining Google’s success [20]. PageRank™ takes into account the number of links pointing to the target page and those links’ relative importance that, in turn, depends on the number of other pages pointing at them. Thus, high PageRank™ if linked by pages that, in turn, have also a high PageRank™ . This poses an interesting chicken-and-egg computational problem, since it means that we need to know PageRank™ values to compute PageRank™ values. However, this can be solved using a reiterative procedure. Thus, the algorithm starts by assigning placeholder values to the pages and then visits all nodes iteratively in the network (i.e. all web pages), adjusting their value until completion. Formally, the PageRank™ score of a given page A is computed as:   P R(Tn ) P R(T1 ) + ··· + P R(A) == (1 − d) + d C(T1 ) C(Tn ) where n is the number of pages with at least one link pointing to page A and d is a socalled damping factor which determines how much PageRank™ score is transmitted from one page to another, as well as the minimum PageRank™ value for a given page (which is equal to 1 − d for a page with no incoming links). The original choice for the damping factor was d = 0.85, setting the minimum PageRank™ score to 0.15 [20].

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It turned out that the PageRank™ score is very efficient in ranking species according to their importance for food web persistence. Using PageRank™ in this context only requires a few minor modifications to the target food web, namely the addition of a “root” node linked to primary producers and to which each species in the network is connected, which represents the detritus cycle. The authors performed in silico experiments using a simple co-extinction model where consumers are removed from a network when left with no resources. They showed that removing species according to their PageRank™ drives food webs to collapse way faster than removing species in several other possible sequences. In most cases, the PageRank™ criterion identified a sequence of node removal as much destructive as the worst possible one. The latter was determined using a genetic algorithm (as a computationally accessible alternative to a heuristic approach consisting of evaluating all possible sequences, which are N ! for a food web with N species) [21]. To compare the efficiency of their network disassembly method against alternative ones, the authors focused on network robustness as defined above (area under the curve of the proportion of species extinct for an increasing proportion of species removed, Fig. 7.2). The alternative criteria of node removal were: (1) node degree, with nodes removed from the most to the least connected; (2) closeness centrality, with nodes having a short distance to many nodes in the network removed first; (3) betweenness centrality, with nodes located on the shortest path between many pairs of nodes removed first; (4) node dominance, with nodes removed from the strongest to the weakest “dominator” (where a given x node dominates another y node if all the paths from basal resources to node y intersect node x, which implies that the removal of node x will determine the extinction of node y). The PageRank™ algorithm resulted consistently more efficient than the other criteria, leading to an average2 maximum extinction area (for 12 real-world food webs examined) virtually identical to the most destructive sequence identified by the genetic algorithm (0.95 ±0.04 S.D.). Yet, the improvement in efficiency offered by PageRank™ compared to other criteria is far from dramatic. The least performing criterion was closeness centrality that resulted in an average of maximum extinction areas of 0.78 ±0.11, which still depicts a quite rapid collapse of the food webs. The dominance criterion led to results very close to those obtained with PageRank™ (0.92 ±0.11), and even the most trivial criterion of removing nodes in reverse order of their number of connections led to results (0.80 ±0.12) not too far from the most destructive method [21] (Fig. 7.7). More in general, several methods developed in physics might be overkill when applied to model co-extinctions. The underlying uncertainty in species interaction data and the many simplified assumptions of co-extinction models (see the previous chapter) are likely to affect the outcome of co-extinction simulations much more than minor differences in extinction sequences resulting from choosing one specific node ranking criterion over another one. Nevertheless, such methods offer a rich, 2

In case of ties in the node ranking, the authors explored all sequences (up to half a million) deriving from the choice of a particular sequence of removal of nodes having equal rank. This exploration led to multiple alternative extinction areas emerging from the disassembly of a given network.

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Fig. 7.7 Extinction areas (y-axes) for a set of twelve real-world food webs disassembled by removing species (x-axes) according to different criteria. Criteria of node removal are: D—nodes are removed from the most to the least connected; Clos—nodes are removed in order of their closeness centrality; Betw—nodes are removed in order of their betweenness centrality; Dom—nodes are removed from the strongest to the weakest “dominator”; Eig1, Eig2—nodes removed according to their PageRank™ ; the difference between the two alternative criteria, Eig1 and Eig2, is that the first uses the original networks, while the latter uses simplified versions of the networks where redundant links are removed first; GA—genetic algorithm identifying the sequence of node removal leading to the largest theoretical extinction area. All the algorithms to identify the sequence of node removal are “greedy”, meaning that the ranking scores are recalculated for all nodes in the network after each node removal. When ties were present, all possible removal sequences were explored (up to half a million), yielding many different extinction areas. The minimum, mean and maximum recorded extinction area for each criterion are reported in different colours (red, blue and black). See text for additional details. Image by Stefano Allesina and Mercedes Pascual, from [21] (https://doi.org/ 10.1371/journal.pcbi.1000494.g003). Published under a Creative Commons Attribution Licence (http://creativecommons.org/).

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largely unexplored suite of measures that might have high ecological relevance and, therefore, importance for conservation planning. A fundamental lesson that we can learn from how physicists have been approaching the issue is that the more we move our focus far from single nodes, the better insights we get into networks’ resilience to perturbations [14]. This implies that species-specific actions where conservation targets are defined based on expert assessment or popular demand (e.g. focusing on charismatic species) have fewer chances of successfully protecting ecosystems than holistic strategies focusing on ecological networks as organic entities. Information availability often limits the latter approach, but this, more than being a justification to maintain current conservation strategies, should promote the investigation of ecological networks (and of ecological complexity in general) from a subject of interest for theoretical ecologists to one of the top targets for conservation.

Summary Simulating progressive, multiple species extinctions in ecological networks while keeping track of the network’s response to subsequent species loss can be an informative exercise. However, the nature of the information emerging from such exercise strongly depends on the identity of nodes (species) removed from the network and the order in which we remove them. For example by replicating the experiment of removing nodes one after another in random order in different ecological networks, one can get an idea of how the networks compare in robustness against a generic form of perturbation. Yet, informative criteria can be used instead of random node removal. For example one could remove species in decreasing order of their current vulnerability to extinction as assessed by IUCN. Similarly, one could combine ecological niche and global circulation models to estimate relative species vulnerability to future climate change and then use those predictions to identify extinction sequences. Although these “informed” approaches can provide a more realistic picture of the potential paths diversity loss might take in the near future compared to random simulations, identifying boundaries (i.e. a best and a worst-case scenarios) for the many potential trajectories of collapse in a given network is essential to put specific predicted patterns into perspective. Knowledge developed in the physics context offers sophisticated and efficient techniques to identify either the most or the least detrimental sequences of species extinction, that is sequences either maximizing or minimizing the speed at which a given network approaches collapse following progressive species removal. In doing that, those techniques also offer novel, largely overlooked tools for conservation. Determining which nodes are crucial for network persistence (at any stage of network dismantling) appears as a straightforward criterion for identifying conservation targets which has received very little consideration to date.

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References 1. Dunne JA, Williams RJ (2009) Cascading extinctions and community collapse in model food webs. Philosoph Trans Royal Soc of London B: Biolog Sci 364(1524):1711–1723 2. Memmott J, Waser NM, Price MV (2004) Tolerance of pollination networks to species extinctions. Proc Roy Soc London B: Biol Sci 271(1557):2605–2611 3. Säaterberg T, Sellman S, Ebenman B (2013) High frequency of functional extinctions in ecological networks. Nature 499(7459):468 4. de Visser SN, Freymann BP, Olff H (2011) The Serengeti food web: empirical quantification and analysis of topological changes under increasing human impact. J An Ecol 80(2):484–494 5. Cardillo M et al (2005) Multiple causes of high extinction risk in large mammal species. Science 309(5738):1239–1241 6. Strona G, Bradshaw CJA (2018) Co-extinctions annihilate planetary life during extreme environmental change. Sci Rep 8(1):16724 7. Carlson CJ et al (2017) Parasite biodiversity faces extinction and redistribution in a changing climate. Sci Adv 3(9):e1602422 8. Clements FE, Long FL (1923) Experimental pollination: an outline of the ecology of flowers and insects, vol 336. Carnegie Institution of Washington 9. Schneider CM et al (2011) Mitigation of malicious attacks on networks. Proc Nat Acad Sci 108(10):3838–3841 10. Stauffer D, Aharony A (2018) Introduction to percolation theory. CRC Press 11. Erdós P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5(1):17–60 12. Davis S et al (2008) The abundance threshold for plague as a critical percolation phenomenon. Nature 454(7204):634–637 13. Pastor-Satorras R, Vespignani A (2002) Immunization of complex networks. Phys Rev E 65(3):036104 14. Morone F, Makse HA et al (2015) Influence maximization in complex networks through optimal percolation. Nature 524(7563):65–68 15. Strona G et al (2018) The intrinsic vulnerability of networks to epidemics. Ecolog Model 383:91–97 16. Cohen R, Ben-Avraham D, Havlin S (2002) Percolation critical exponents in scale-free networks. Phys Rev E 66(3):036113 17. Robertson C (1929) Flowers and insects: lists of visitors of four hundred and fifty three flowers. Carlinville: Privately published 18. May RM (1972) Will a large complex system be stable? Nature 238(5364):413–414 19. Stouffer DB, Bascompte J (2011) Compartmentalization increases food-web persistence. Proc Nat Acad Sci 108(9):3648–3652 20. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comp Netw ISDN Syst 30(1–7):107–117 21. Allesina S, Pascual M (2009) Googling food webs: can an eigenvector measure species’ importance for coextinctions? PLoS Comput Biol 5(9):e1000494

Chapter 8

The Specialization Paradox

Monogenoidea1 is one of the most diverse groups of fish parasites [2, 3]. These worldwide distributed parasites are commonly found on the skin and in the gills of most freshwater and marine fish. Less frequently, they infect amphibian hosts and one species, Oculotrema hippopotami, as suggested by its name, inhabits the eye of hippopotamuses [4]. Monogenoideans have a simple life cycle. Most of them have free-swimming ciliated larvae that attach and develop on one or a few specific hosts. Once mature they release eggs that hatch in the water, closing the cycle [3] (Fig. 8.1a). Some of them (and particularly Gyrodactilidae) are viviparous, with an interesting ability named polyembryogenesis. An adult can carry in the uterus a fully developed individual already containing a developing embryo, like a Russian doll [5] (Fig. 8.1b). In general, monogenoideans are not particularly harmful to their hosts, even if there are examples of severe outbreaks that led to high host mortality in aquaculture settings and wild fish populations [6]. One peculiarity of monogenoideans is their high host specificity; so high that some researchers have even argued that their occurrence can serve as a criterion for the taxonomic identification of their hosts [8]. Although this sounds a bit pointless in the molecular taxonomy era, it exemplifies how much parasitologists perceive monogenoideans’ specificity as a distinctive trait of the group. I had a first-hand experience of this. Indeed, my first research interests revolved around this parasite group. Back in 2004, as a graduate student, I was introduced to the topic by my thesis supervisor, Paolo Galli. A few years later, in August 2009, I was with Paolo—who had become my PhD advisor by that time—in Cape Town, attending the 6th International Symposium on Monogenea. To most, this might sound like an obscure venue, but it is pretty much The Venue for monogenoidean specialists. At some point, at a late-afternoon 1

There is an unresolved debate about the correct name for the class, whether Monogenea or Monogenoidea [1]. I am following here the advice from Walter A. Boeger and Delane C. Kritsky, for whom I have deep respect and admiration, and refer to the group as Monogenoidea [2]. © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_7

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Fig. 8.1 Simple life cycles of monogenoidean parasites. Oviparous monogenoideans (a, Dactylogyrus sp.) release eggs in the water. When those hatch, free swimming larvae (called oncomiracidia) look for a suitable host where to develop and, eventually, reproduce. Viviparous monogenoideans (b, Macrogyrodactylus sp.) generate fully developed individuals which, in turn, can enclose a developing embryo [5]. Illustration by M. Luo, reproduced from [7], with kind permission from the Royal Belgian Institute of Natural Sciences

coffee break, Paolo tried to impress the people in the room by showing them how to open a beer without a bottle opener. Observing him crashing the neck of the bottle against the side of a table put me in the right mood to deliver my talk the day after. I cannot recall precisely the details of my speech, apart from the fact that it was about host specificity. But I do remember well what happened next, which provided me with a unique opportunity to realize how much the idea of monogenoidean specificity is deep-rooted in the community of monogenoidean specialists. Although such a community is relatively broad for the topic, it is still small enough for the leading experts to fit in a conference room. Most of them were sitting there

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when I talked. It was question & answer time, and, at first, I was delighted to see that my results had caught the interest of the French professor Louis Euzet, who had been an authority in the field of parasitology. Between 1950 and the time of our interaction, he had described more than 200 new species of monogenoidean parasites. He was 86 at the conference time (sadly he passed away four years after, in September 2013). He asked his question in French, and even with my poor knowledge of the language, I understood what he was saying. One of his collaborators, however, took the initiative to translate the question for the whole audience. He said something very close to “The Professor said – and I agree – that generalist monogeneans do not exist. If you find the same monogenean species in two different host species, you have either misidentified the hosts or the parasites”. That was not exactly a question, so when the chairman, Marty Deveney, asked me if I wanted to respond, I took the diplomatic path and said something generic and harmless. Despite Professor Euzet’s strong position, there are several cases where monogenoideans are not strictly specialists, with their host range including at least a few hosts. And there are also notable examples of very generalist monogenoidean species. Among them, Neobenedenia melleni (Fig. 8.2—a species of great interest being a common pathogen of aquarium and farmed marine fish—has been reported from more than 100 teleost species from more than 30 families [9]. However, molecular evidence suggests that many populations of N. melleni are genetically separated, and that the species, instead of a supergeneralist, might be a complex of more hostspecific, distinct species [10, 11]. Bringing this topic in, I somehow satisfied the professor. However, this was not exactly an appraisal of his strong position. Of course, the interesting genetic variability of N. melleni populations does not imply a fish or parasite misidentification for every case where the same monogenoidean species is detected on two different host species. Still, the generalism of N. melleni remains an exception and does not erase the fact that the level of specificity observed in monogenoideans is remarkable. Other parasite groups show less specificity, but, in general, there are many more parasites using few hosts than generalist ones [12] (Fig. 8.3). The commonness of specialization in host–parasite associations (which also extends to other kinds of ecological interactions, with an example being diet specialization in herbivores [13], Fig. 8.4) suggests that having a narrow host range could be a more stable strategy than being a generalist. From an evolutionary perspective, this is puzzling. A primary goal for a parasite is that of finding a proper host in which to reproduce. Furthermore, a parasite would also benefit from dispersing long distances and colonizing different habitats to increase the species’ survival chances by geographical range expansion. Having a broad host range might look like an excellent strategy to achieve these goals since it would permit a parasite to have many resources at reach and to be successful independently from the individual fate of any of those. However, an empirical clue pointing at this reasoning’s fallacy is that we do not have such a superparasite in the real world. On the contrary, as highlighted above, the opposite situation of highly specialized parasites is pervasive. One general explanation for the commonness of specialization is that nature imposes trade-offs between the range of resources a consumer can use and its effi-

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Fig. 8.2 Ventral view of a whole adult of Neobenedenia melleni (MacCallum, 1927) Yamaguti, 1963. aa, Anterior attachment organ; ah, anterior hamulus; as, accessory sclerite; g, germarium; h, haptor; ho, booklet; m, marginal valve; ic, internal fertilization chamber; oo, ootype; p, pharynx; pe, penis; ph, posterior hamulus; t, testis; te, tendon; u, uterus; vd, vas deferens; vf, vitelline follicle; vr, vitelline reservoir. Modified from [9], with kind permission from Taylor & Francis

ciency in using each of them. Thus, specialized consumers wouldt benefit from their superior ability to exploit their few resources. Such a trade-off might promote a continuum of specialization starting from organisms that are very good at doing one thing and extending towards multitask organisms, whose increasing generalism is paired with decreasing efficiency in using each resource. This concept is closely related to one of the conditions Sir Robert May indicated as key for the (mathematical) stability of complex systems [14]: “. . .this suggests that within a web species which interact with many others [. . .] should do so weakly [. . .], and conversely those which interact strongly should do so with but a few species”.

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Fig. 8.3 Histograms showing the distribution known host range (log-transformed number of host species used by a parasite species) in parasites of fish (a) and terrestrial vertebrates (b). Host ranges have been aggregated for different groups of fish parasites (including Acantocephala, Cestoda, Monogenea, Nematoda and Trematoda; symbols above the swordfish silhouette, from left to right). From [12], used under a Creative Commons CC-BY-NC-ND (http://creativecommons.org/), with kind permission from Elsevier

Fig. 8.4 Distribution of diet breadth (counts of the numbers of herbivores associated with different numbers of host plant families) for Lepidoptera (a) and all other herbivores (b) from a large data set of plant–insect interactions including thousands of herbivore species from 17 localities representative of temperate and tropical habitats in both the Western and Eastern Hemispheres. Adapted from [13], with kind permission from the National Academy of Sciences

A broadly accepted evolutionary hypothesis is that of specialization as a derived condition, with specialists evolving from generalists more often than the other way around. This idea, paired with the one that organisms capable of using multiple resources can better face unpredictable environmental challenges [15], suggests that specialization might be an evolutionary dead end limiting the possibility of a species to adapt to changing conditions [61]. Furthermore, specialization appears as a prominent driver of co-extinction risk. Let us consider the simplest co-extinction model we have defined in Chap. 7, where a consumer goes extinct after losing all of its resources. It is intuitive that, all the rest being equal, the most specialized consumers will be the most endangered since the loss of a few resources will be enough to drive them extinct [16]. Since the risks associated with ecological dependency are not a novelty of our times (although possibly now more substantial than in the past, as we

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will see), the question arises about how specialization has become so diffused if it complicates species’ survival by increasing their co-extinction risk. There are a few aspects to be considered to resolve this apparent paradox. Let us start from the principle, that is, from how a symbiotic interaction gets established in (co-)evolutionary terms. In a conceptual simplification, the probability of developing some specific ecological interaction depends on how often two species get in contact. Intuitively, besides being at the basis of the development of specialization, the likelihood/frequency of encounters between potential partners is also fundamental for the maintenance of the symbiosis.2 The evolution of specialization in using a particular resource, be it in trophic, mutualistic or parasitic interactions, requires the resource on which the consumer specializes to be continuously available during the process. Considering the tradeoffs between generalism and efficiency in using resources, we expect specialized parasites to increase their fecundity and develop better host-finding ability in the long run. The key for this to happen would be that specialized parasites persist for enough evolutionary time to allow the trade-off advantages to get fixed in the population. For parasites specialized on very common hosts, this appears perfectly feasible. A specialized parasite using a common host might persist (without being outcompeted by more generalist parasites) for a long time even without having fecundity and hostfinding ability higher than the other hosts in the population. This time might permit it to develop the improved host-finding ability and fecundity needed to survive future host availability declines. Host local abundance is an important determinant of interaction probability. There are, however, multiple aspects that determine the frequency and the consistency of interaction events between potentially co-evolving host–parasite pairs. Among them, an obvious one is the host’s geographical range. If a host is abundant in a given locality but has a narrow geographical range, it will be less dependable to a consumer for several intuitive reasons. Locally, both the host and the parasite population will be more at risk of extinction than the host having a broader geographical range, with multiple biogeographical and phylogeographical mechanisms affecting local population dynamics. Another aspect (or dimension) defining resource dependability, besides local abundance and geographical range, is time (Fig. 8.6). However, time has multiple subdimensions, such as long-term persistence (i.e. persistence over evolutionary time3 ), lifespan duration and seasonal availability. It is intuitive that a species that has been around for a long time due to its evolutionary success, which has a long lifespan and is active and available all year round constitutes a very reliable resource for a 2

It is also essential to keep in mind that the mechanisms regulating a symbiotic relationship are not statical and can evolve through time. For example, it is common to have a progressive decrease in virulence in host–pathogen interactions. 3 With persistence over evolutionary time, I refer to that of an evolutionary lineage. When species go extinct, their lineages can survive and possibly permit the survival (and co-speciation) of associated species.

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consumer. Conversely, deviations from this ideal scenario generate different challenges for a consumer. The lack of resources’ persistence over evolutionary time is a critical aspect, as it can prevent the evolution of specialized interactions or cancel them through co-extinction. The lifespan of a resource poses different challenges for consumers, depending on the resource–consumer interaction nature. For a predator, a short lifespan in the prey could be advantageous since, under certain circumstances, the prey population would be continuously renovated. The picture would be more complex and largely unpredictable for a host–parasite relationship, with different co-evolutionary trajectories possibly emerging in response to the same pressures. Furthermore, the lifespan of a host could change during co-evolution in response to parasite virulence. For example, a theoretical study has shown that high parasite virulence can determine the simultaneous emergence of two opposite strategies within the same host population. The first strategy is to develop rapidly to increase the chances of reproducing before being infected. This comes, however, at a high cost, which is a substantial reduction in fecundity. The latter, opposite strategy is that of a slow development paired with high fecundity. This leads to only a few hosts remaining uninfected and reaching maturity. However, those hosts are prolific enough to sustain the population [17]. Cyclical or irregular fluctuations in population abundances, such as those observed in some insects and annual plants, can be associated with/result from/promote the emergence of various kinds of synchronous or asynchronous ecological co-adaptation between resource and consumers. For example, plants would benefit from producing leaves in times of the year characterized by a low abundance of leaf-eaters. Conversely, insects relying on certain plants would benefit from maximizing the overlap between their life-cycle stages depending on a particular resource and the availability of that resource (e.g. plant leaves, flowers, fruits or seeds). These opposite forces can generate arms-race co-evolutionary scenarios. Besides, they might be sources of ecosystem vulnerability. There is a growing interest in how rapid climate change might affect plant phenology and in the resulting ecosystem implications [18]. Alterations in the timing of different developmental stages of plants might lead to unpredictable, detrimental ecosystem effects mediated by biotic interactions. For example, a longer dry season (a common consequence of global change) might induce a delayed production of leaves, possibly exposing them to high abundances of leaf-eating consumers and leading to severe damages. At the same time, a delay in plant life cycle will also determine a later reproduction. Such a situation might generate a mismatch between the time plants are ready for pollination and the time pollinators are most abundant, eventually leading to a sensible reduction in seed germination [19] (Fig. 8.5). The situation can be even more complex for life cycles spanning multiple years. For example, some cicadas species (Magicicada spp.) from North America spend most of their long life feeding on the roots of forest trees. Only at the end of their development, which took either 13 or 17 years depending on the species, they emerge in huge numbers. After emergence, adults survive for only a few weeks, dedicated to reproduction. Scientists have hypothesized that cicadas can satiate predators by emerging in huge numbers simultaneously, compensating for their high vulnerability

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Fig. 8.5 Hypothetical outcomes of climate-induced shifts in plant phenology with implications for conservation. A longer than usual dry season might delay the timing of either leafing or reproduction (assuming that the first rains after a dry season are the trigger for leafing or flowering). A delay in leafing activity might result in an increasing overlap between leaf availability and peak insect abundance, leading to increased herbivory damage and a reduction in plant fitness. Similarly, a delay in flowering might reduce the overlap between plant flowering and pollinator activity. The plant–pollinator mismatch would negatively affect plant reproductive success and fruit production, which might have detrimental consequences for frugivores. At the same time, the reduced fruit production and seed dispersal would have negative effects on plant recruitment. Adapted from [19], with kind permission from Elsevier

[20]. Another added advantage deriving from having a prime number of years as the diapause duration would be maintaining genetic separation between different species, and populations (which are called “broods” [21]) having different times of emergence. The prolonged diapause generates several challenges against the development of specialized interactions targeting Magicicadas, as these do not constitute a reliable resource for consumers (predators or parasitoid) with shorter life cycles. There is, however, a notable exception, provided by a fungus (Massospora cicadina). As in other cases of host–pathogen co-evolution and host manipulation by parasites, the specialization of this interaction is very sophisticated and efficient. At the early phase of emergence, cicadas infected by the fungus (“Stage I” infected cicadas) produce haploid conidiospores. Those can infect other adult cicadas (“Stage II” infected cicadas). Afterwards, Stage II infected cicadas release diploid resting spores in the

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soil, which will eventually close the fungal life cycle by infecting the next generation of cicada nymphs 13 or 17 years later. Thus, Stage I Massospora fungal infection is acquired by nymphs as those emerge from the soil (with a similar proportion of male and female nymphs becoming infected by the fungus at this phase [22]). Differently, Stage II infections are transmitted from one adult cicada to another through contacts. The fungus can increase this latter transmission process by modifying male adult cicadas’ behaviour, inducing them to exhibit a wing-flick signalling behaviour that females typically use to attract sexual mates. The signalling attracts other males, promoting male-to-male copulation attempts facilitating the spread of infection across the population [23]. Other forms of discontinuous life cycles might be even more challenging in promoting the development of specialized interactions. In particular, this applies to species capable of entering stages of cryptobiosis when environmental conditions became no longer tolerable, such as, for example, tardigrades, rotifers, and various crustacean groups (such as Cladocera and Copepoda). In theory, this implies that situations of high environmental stress could be, in the long run, beneficial to species capable of dormant (and resistant) stages. Those might, in fact, experience release from their natural enemies once conditions became again favourable, and the species can interrupt their dormancy. In practice, however, it has been shown that dormant stages might be common also in the natural enemies of target species. For example, Daphnia spp. cladocerans can form diapausing stages to cope with unfavourable abiotic of biotic conditions (the latter include, for example, overabundance of predators). An experiment exposed specimens of Daphnia magna to sediments of different ages and from different localities. In all cases, the exposition led to the cladocerans’ infection by various pathogens, showing that the sediment included a rich and long-lasting bank of spores [24]. These last examples, with consumers specializing over species having limited availability through time, might seem to provide arguments against the general idea— at the basis of this chapter—that the emergence of ecological specialization is strongly affected by resource dependability. Yet, when considering the different dimensions of a resource’s temporal dependability from a potential consumer’s perspective, continuity in availability—as opposed to discontinuous life cycles involving long diapauses or open-ended dormant stages—is just one of several dimensions determining species’ dependability. Among these, the most important one is possibly persistence over evolutionary time, which is a clear, necessary condition for the happening of co-evolutionary processes. Trade-offs and compensations are likely to exist between the different dimensions. It is intuitive how discontinuity in life cycles or a reduction in lifespan are evolutionary responses to ecological challenges that can be rewarding for the species in the long run. Thus, a species might have low dependability for resources in a short-term/ecological temporal scale but high dependability in the long term evolutionary perspective, eventually permitting the emergence of specialized interactions. For example, the interactions between periodical cicadas and their parasitic fungus might have developed more in response to the long-term stability of cicada’s atypical life cycle than despite the lack of short-term (e.g. seasonal) cicada availability. Yet, individual dimensions of species vulnerability might have critical

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Fig. 8.6 Dimensions determining the dependability of a resource from a consumer’s perspective (such as local abundance, persistence over evolutionary time and geographical range, which are also proxies for resources’ vulnerability to extinction) affect consumers’ chances to evolve a specialized interaction with that resource. This promotes the emergence of ecological networks where highly specialized consumers rely on resources with low vulnerability to extinction; and where resources with low risk of extinction support a high diversity of consumers with varying degree of specialization. From [12], used under a Creative Commons CC-BY-NC-ND (http://creativecommons.org/), with kind permission from Elsevier

effects in determining the nature of resource–consumer interactions, while compensation between different dimensions is likely limited. Referring to the above example, it is true that periodical cicadas have a specialized natural enemy in the fungus M. cicadina which, however, represents a notable exception [23]. All this considered, it seems legit to assume that a given species will have more chances to get specialized on dependable resources in terms of local abundance and space and time. It is also intuitive that the elements contributing to a species’ dependability (in the above terms) might be proxies of species vulnerability to extinction, corresponding, for instance, to some of the main criteria used by the IUCN for their assessment. When the two aspects are combined, it follows the expectation that consumers will preferentially develop specialized associations with resources with low vulnerability to extinction [25, 26] (Fig. 8.6). I initially tested this idea on different groups of fish parasites. For this, I computed the average specificity (in terms of host range breadth) of all parasite species known from a given host. Then I compared these values with the corresponding estimates of host (fish) vulnerability. For the latter, I used a measure of vulnerability to fishing

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Fig. 8.7 Comparison between specificity (expressed as the logarithm of host range) of different groups of fish parasites (A: acantocephalans; C: cestodes; M: monogenoideans; N: nematodes; T: trematodes) and the vulnerability of their hosts [27]. Panel a summarizes parasite–host range (log transformed). Parasitic groups are sorted from the most host-specific (monogenoideans), to the least host-specific (acantocephalans). Panel b shows parasite extinction risk, which was computed as the product of the extinction probabilities of a parasite’s host species (e.g. the extinction risk of a parasite associated to two hosts having respective vulnerabilities of 0.5 and 0.2 was quantified as 0.5 × 0.2 = 0.1). Panel c shows host vulnerability, while panel d summarizes the information by showing the relationship between parasites’ host range and average vulnerability of the corresponding hosts. Adapted from [25], with kind permission from Springer Nature

based on several aspects of fish life-history traits and ecology that proved quite efficient in predicting fish population decline [27]. The analysis confirmed the starting hypothesis, with host-specific parasites being found mainly on hosts having low vulnerability (Fig. 8.7). In that first paper [25], we concluded that the negative relationship between the specificity of parasites using a particular host and the host vulnerability (i.e. the tendency for host-specific parasites to use hosts with low vulnerability) was a possible solution to the so-called paradox of missing co-extinctions [28]. The paradox expresses the contrast between the idea that co-extinctions4 are one leading cause 4

i.e. the extinction of consumers following the depletion of their resources, such as the disappearance of a parasite species following the demise of its host/s.

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of diversity loss and the minimal number of documented co-extinction events. Our work showed that one possible explanation for the mismatch between expectation and reality was that previous co-extinction theory was not considering potential, non-random patterns of association between consumer specialization and resource vulnerability. Taking this aspect into account revealed a straightforward mechanism that increases the robustness of host–parasite networks to species loss and, in turn, explains why real-world examples of co-extinctions are scarce. A few years after the paper was published, as it sometimes happens when spending years on a specific research topic, new analyses and results changed my perspective. I realized I might have been too optimistic in assuming that resource–consumer networks’ robustness can explain the low number of documented co-extinctions. We do not need any particularly complex explanation for that. Considering our limited knowledge of diversity and ecological interactions and our even more limited knowledge about primary extinctions, it is apparent that our chances of detecting co-extinctions are meagre. To date, IUCN has assessed the conservation status of around 115,000 species. Among these, more than 31,000 species are threatened with extinction. As of 2021, the number of known extinctions is of about 900 species (plus around 80 species extinct in the wild but still surviving in human-managed facilities such as zoos, natural reserves and botanical gardens). To put this number in perspective, we should consider current global estimates of species diversity. There is no consensus on how many species are there on Earth [29]. Some recent estimates go from a very conservative one of around 2 million species on Earth (not counting bacteria) [30] to one exceeding 100 million species [31]. Even considering the lower estimate, it emerges that we have minimal information on species conservation status, accounting for less than 6% of global diversity. Furthermore, the available information is far from being uniformly distributed across different taxa and ecological groups. Most notably, many obvious targets of co-extinction (such as parasites or mutualistic invertebrates) are virtually unassessed ([32, 33]). So, to put it simply, as we are likely missing most primary extinctions, there is no surprise in our almost unexisting record of co-extinction events. On the one hand, I was still fully convinced that consumers’ tendency to specialize on dependable resources was an important mechanism permitting the emergence and persistence of complex ecological networks with varying degrees of specialization. On the other hand, however, I got more and more convinced that co-extinctions are happening and play a lead role in the ongoing mass extinction. This shift of perspective encouraged me to reconsider some aspects of our analysis on fish parasites [25]. As a first step, I looked further into alternative ways to assess host vulnerability and the potential effects of this choice on assessing host–parasite networks’ robustness. In particular, I compared the fish vulnerability measure I have mentioned above [27] (and used in the 2013 paper [25]) with the fish vulnerability to extinction according to the IUCN Red List assessment. The first measure (which is used by FishBase [34]) determines fish vulnerability to fishing by combining fish life-history traits and ecology, providing a continuous vulnerability value ranging from 0 to 100 (low to high vulnerability). The latter measure identifies different threat categories for extant species (from “Least Concern” to “Critically endangered”). Thus, I attributed to each

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IUCN class a numerical value, using a geometric progression, giving a vulnerability score of 1 to Least Concern species, 2 to Near Threatened, 4 to Vulnerable, 8 to Endangered and 16 to Critically Endangered (as in [35]). Then I compared the two different vulnerability metrics for more than 2500 bony fish species. I also compared the two measures with both life-history traits and the geographic range of the species. Quite surprisingly, I found out that the two measures were completely uncorrelated (see Table 8.1) [36]. This mismatch reflects the fact that the two measures quantify fundamentally different aspects of vulnerability. The metric used by FishBase accounts explicitly for various species life-history traits and hence looks at species’ vulnerability also in evolutionary terms, combining it with the current species geographical range [27]. Differently, the IUCN measure takes into consideration other criteria that are more focused on the current endangerment status of a species [37] (as also evident from Table 8.1). These results made me wonder if the relationship between parasite–host specificity and host vulnerability we had observed for fish parasites [25] would have remained valid for different measures of host vulnerability. Some quick tests revealed that this was not the case. There was no apparent relationship between parasite specificity and their hosts’ extinction risk when assessed by IUCN criteria. The discovery was disappointing at first, but it was also an opportunity to put the dependability–specificity theory into discussion and formulate novel hypotheses. The unexpected result was revealing some aspects with important implications for conservation. The observed deviation from the parasite specificity/host vulnerability relationship was not questioning the idea of ecological networks becoming robust thanks to consumers’ tendency to specialize on dependable resources. Still, it provided quantitative evidence that such structural robustness was no longer valid in the face of a redistributed extinction risk not consistent with the dependability in place during network co-evolutionary history (for which fish life-history traits offered a proxy [25]). Realizing this encouraged me to start searching for examples of highly specialized associations that are now in danger of co-extinction due to recent changes in host dependability changes. I found many, some of which depict quite peculiar eco-evolutionary settings. I am reporting here just a few of them. A monogenean parasite named Pseudodiplorchis americanus has developed a symbiosis with a very challenging host, the desert toad Scaphiopus couchii. This amphibian occurs in the Sonoran Desert of Arizona and California. To survive, adult toads bury in the sand and wait for summer thunderstorms to create temporary ponds

Table 8.1 Comparisons between FishBase and IUCN fish vulnerability, fish life-history traits, and fish geographic range FB IUCN L K Y Ym T AOO EOO FB IUCN

– 0.02

0.02 –

0.83∗ −0.05

−0.95∗ 0.84∗ −0.03 0.06

0.92∗ 0.05

0.18∗ 0.05

0.23∗ -0.29∗

0.21∗ −0.32∗

Correlations are quantified as Spearman’s rho correlation coefficients. L: body length; K: growth rate; Y: lifespan; Ym: age at maturity; T: trophic level; AOO: species’ ranges quantified as areas of occupancy; EOO: species’ ranges quantified as extent of occurrences. *: p j, Hii = 0.5 ∀ i : 0 → S, and with H ji = 1 − Hi j ∀ i, j. Then I modelled multispecies population dynamics following the model described in Fig. 12.6 [19]. I assumed identical death rate and fertility for all species (di = f i = 1 ∀ i : 0 → S) and used tournaments between three randomly sampled seeds to determine the species replacing a dead tree. In all simulations,

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Fig. 12.6 Simple community model used to illustrate the importance of higher-order interactions in stabilizing population dynamics. The community consists of a theoretical forest where tree species die with probability di and produce seeds according to fecundity f i . All the seeds produced by forest trees enter a common pool from which candidate recruits are sampled at random to fill in canopy gaps opened by tree deaths. A competition matrix H defines the probability of a seedling from one species to win the competition (for space) with a seedling of another species if they attempt to fill in the same gap in the canopy. The seedling which will fill in the gap in the canopy is selected through a “tournament” between three species. Seedlings from two random species, i and j, engage in competition, with the winner being the first species with probability Hi j , and the latter species with probability 1 − Hi j . Then, the winner engages in competition with a third species, k, with the final winner being determined again according to the probabilities detailed by matrix H . Reproduced from [19], with kind permission from Springer Nature

as expected (see [19]), ≈ 50% of the initial diversity went rapidly extinct (with an average number of species persisting at the equilibrium of 9.92), while the remaining populations approached the attractive equilibrium points (see Fig. 12.7, left panels). After the survival populations reached stability (5000 steps), I removed one species from the community. After that, I modelled population dynamics for an additional 5000 steps. Then, I took note of the fraction of species in the community experiencing secondary extinction after removing the first species. For all simulated community, I replicated the same experiment by removing, each time, one different species (chosen among the species that did not go extinct at the very beginning of the simulation). In this way, I explored all possible scenarios of secondary extinction per community following a single species loss event (and was, therefore, able to identify a best- and worst-case scenario).

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Fig. 12.7 Effect of higher-order interactions in stabilizing multispecies population dynamics. The underlying model for the dynamics presented in the right column is the one from [19], also presented in Fig. 12.6. In the model, which accounts for higher-order interactions, a dead tree is replaced by the winner of a tournament played by three seeds sampled at random from a common pool. Instead, the dynamics presented in the middle column derive from a version of the model accounting only for pairwise competitive interactions, where the recruit is identified between only two seeds sampled at random from the seed pool. The diagrams on the right indicate the (pairwise) competitive relationships between the species (that is, the competition matrix H ; see text for details). When the replacement of dead trees is controlled by pairwise interactions only, populations cycle neutrally around equilibrium points (right panels). Higher-order interactions make the equilibrium points attractive, eliminating the wide oscillations of the neutral cycles (left panels). Reproduced from [19], with kind permission from Springer Nature

This simple set of simulations provide numerical support to the idea that whenever a species goes lost from a community, detrimental, secondary effects on species diversity should be expected even without taking into account direct ecological dependencies (such as resource-consumer interactions). In the worst-case scenarios, removing one species led to the loss of 58 ± 12% (mean ± standard deviation) of the remaining community diversity. In the best-case scenario, secondary extinctions still involved 17 ± 9% of the species. On average, removing one species resulted in the secondary loss of 33 ± 5% of the diversity in place before the primary extinction event. The latest value indicates that, on average, more than three species are expected to go extinct in the modelled communities following any single species removal event. What should we expect if further extinctions happen following the first loss? That is, how many extinctions can a hypothetical system controlled exclusively by higherorder interactions (that is, free from pairwise ecological dependencies such as those between plants and pollinators) tolerate before collapsing? I explored these additional questions by generating 100 random communities as in the previous examples (starting with an initial diversity of 50 species). In each simulated community, I removed progressively one species at a time every

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removed species Fig. 12.8 Trajectories of diversity loss following secondary extinctions triggered by random species removal and propagating across the community through higher-order interaction links. The plot summarizes the trends observed in 100 experiments. In each experiment, I generated a random community of 50 species, and associated it to a competition matrix H with each entry Hi j sampled at random from a uniform distribution U [0, 1] ∀ i > j, Hii = 0.5 ∀ i : 0 → S, and with H ji = 1 − Hi j ∀ i, j. Each entry Hii of the competition matrix H defines the probability for the i-th species to win the competition with a j-th species. Multispecies population dynamics were simulated following the model described in Fig. 12.6 [19]. I assumed identical death rate and fertility for all species (di = f i = 1 ∀ i : 0 → S) and used a tournament involving three randomly sampled seeds as the criterion to determine the identity of the seedling replacing a dead tree, and hence filling a gap in the canopy. I ran dynamics for a preliminary period of 5000 steps. During those steps, as expected (see [19]), ≈ 50% of the initial diversity went rapidly extinct (with an average number of species persisting at the equilibrium of 21.5), while the remaining populations approached quite rapidly the attractive equilibrium points (see Fig. 12.7, left panels). Then, I removed one species chosen at random among extant ones, run the dynamics for another 5000 steps and took note of the remaining diversity. I reiterated this process until all species went extinct (following either artificial removal or secondary extinctions). The black line indicates the mean trajectory of diversity loss across the 100 simulations. The grey area indicates the variation interval defined by standard deviation. The green and magenta lines correspond to the trajectories of minimum and maximum loss, respectively

5000 steps, keeping track of the overall persisting diversity following both primary and secondary extinctions (that is, direct species removals and all higher-order coextinctions triggered by them). Since, as in previous examples, only around a half of the initial diversity persisted at stability, I started removing species and keeping track of patterns of diversity loss only after an initial burn-in period of 5000 steps. Thus, I randomly selected the species to be removed from the set of populations still extant at equilibrium and computed relative diversity loss and species removal based on the size of that set (and not the initial diversity of 50 species). The trajectories of diversity loss show how secondary extinctions deriving exclusively from indirect (higher-order) interactions can bring a stable system to collapse

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very rapidly. On average, the removal of less than 40% of species (42% in the bestcase scenario, and 19% in the worst-case scenario) caused complete community annihilation, while as little as 11% of species removal reduced by more than a half the extant diversity (Fig. 12.8). Despite being based on a highly simplified representation of the complex processes regulating multispecies population dynamics, these simulations highlight the importance of a severely underrated mechanism of diversity loss. The pervasiveness of higher-order interactions in natural systems goes far beyond indirect competition relationships. Thus, the co-extinction trajectories of Fig. 12.8 warn that even accounting for the effect of pairwise (e.g. resource–consumer) interactions when modelling local and global diversity loss patterns [2] might provide an overly optimistic view on the actual robustness of natural systems towards direct extinction drivers. Higher-order interactions are typically elusive. I have already mentioned monogenoidean parasites (see Chap. 9), which are parasites using mainly fish and a few other hosts from different taxa (such as hippopotami, amphibians, and octopuses [20]). Monogenoideans have a simple life cycle, needing just one host to develop and reproduce, and they are highly host-specific. On the one hand, the simple life cycle reduces their risk to go extinct following the loss of one among the intermediate hosts needed for reproduction (see Chap. 11). On the other hand, the high host-specificity constitutes a challenge for their long-term survival, as they might be driven co-extinct by the loss of the few species on which they are specialized. One way monogenoidean parasites have dealt with this challenge in evolutionary terms is by developing efficient strategies to find a proper host. A few hypotheses have been formulated about how those strategies might work, partially supported by experimental studies. Although monogenoidean larvae can move thanks to the presence of bacteria-like cilia (called “oncomiracidia”), they are not strong swimmers. Thus, to succeed in the infection, they need to be very efficient in identifying the right host when this happens to be close, not to waste the opportunity. Some studies suggest that mechanisms increasing spatial and temporal overlap are vital factors to make this happens. Monogenoideans increase their chances to find the right host through adaptations which increase their probability of being close to a proper host at an appropriate time. Such adaptations might result in synchronization between their activities and those of the hosts. For example, it has been suggested that chemical, mechanical or visual cues can induce egg hatching in monogenoidea. These include substances released by hosts, water movements and shadows. The clues inducing egg hatching are thought to be very specific, permitting monogenoidean larvae to distinguish between suitable and unsuitable hosts [21, 22]. When I think about recognizable fish movements and the release of specific chemicals in the water, the first image that comes to my mind is that of a fish hunted down by another. In such an ecological setting, the chased fish would probably make some “routine” escape movements to elude predation and release substances typical of the situation (due to fear or aimed at deterring) [23]. Similar clues would provide monogeneans with a suite of strong signals which might trigger the hatching. However, there is a potential aspect that would make such a strategy suboptimal for parasites.

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Colonizing a host just before its predation would result in certain death for a parasite with a simple life cycle. Thus, the strategy could be rewarding only if the chased prey often manages to escape predation, which could be, in fact, quite common. Especially, if we consider fish living in large schools, the attack of a predator will stimulate a reaction in all the members of the group but only one or a few individuals will be eaten [24]. Should this hypothesis be true, it would imply that, for some parasites, the clue/s triggering the hatching would not originate from the behaviour or physiology of a single species but, instead, from an interspecific interaction. Furthermore, such a scenario would provide the target parasite with the dual opportunity to colonize either the predator or the prey involved in the interaction. By combining a rich fish–parasite dataset with ecological data about fish prey– predator interactions, I found indirect evidence supporting this idea. After controlling for potential confounding aspects (such as geographical and habitat overlap between hosts and parasites, as well as host–parasite co-evolution), I found that monogenoidean species known to parasitize a given fish are more likely to be also found on predators of that fish than expected by chance [25]. On the one hand, the ability to infect both a predator and its prey provides an advantage to a parasite, as it might double the parasite’s chances to end up in a proper host. Besides, infecting a predator might enhance the parasite’s dispersal ability, as predators usually have a broader range than their prey. Such advantages are conceptually similar to those a parasite might experience from increasing life cycle complexity (see Chap. 11). On the other hand, in this hypothesized ecological setting, the parasite’s survival depends on the interactions between its prey and predator. This kind of relationship can be identified as a higher-order interaction and visually represented as a link pointing from one monogenoidean species to another link connecting the prey host to the predator host (Fig. 12.9). As we broaden our view of natural systems’ complexity, similar scenarios where the interaction between species is mediated by other species and/or other interactions become more and more common. I have mentioned the fascinating life cycle of nematomorphs earlier in the book (see Chap. 3). These parasites induce their hosts (mainly orthopterans, such as crickets or grasshoppers) to commit suicide by jumping into the water. When that happens, the parasites leave their arthropod host and go looking for a sexual mate to complete their life cycle. The abandoned, drowning host becomes a potential exogenous source of food for aquatic predators. The effects of this additional source of food are not negligible at the ecosystem level [26]. The trophic link ideally pointing from a predator to the suicidal host depends on the interactions between nematomorphs and their hosts. Should nematomorphs go locally extinct in a given area, the trophic link might go lost as well, with aquatic predators having no more access to the potential preys represented by the horsehair worms’ hosts, regardless of their actual local abundance. This is a specific example. Still, in general, manipulative strategies used by parasites to increase their chances of being trophically transmitted to their final hosts are so common in nature [27] that ignoring them when focusing on food web structure and dynamics means missing an essential part of the story [28, 29].

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Fig. 12.9 Hypothetical higher-order interaction linking a monogenoidean parasite to the trophic interaction between a predator fish and its prey. Solid arrows indicate resource → consumer relationships (between the prey and predator fish and between the monogenoidean parasite and its hosts). The dashed arrow indicates the higher-order interaction between the monogenoidean parasite and the prey–predator interaction between the two fish species. The parasite might use such higher-order interaction as a clue for attempting host infection. Adapted from [25], with kind permission from John Wiley and Sons

Another example supporting the commonness and ecological relevance of higherorder interactions is provided by cleaning symbioses, which are pretty common in most ecosystems. In the marine environment, this kind of symbiosis usually involves fish and sea turtles (acting as “clients”) associated with other fish or shrimps (acting as cleaners) [30]. There are also documented cases of lesser studied forms of interactions (such as that between the topsmelt Atherinops affinis as a cleaner of the grey whale, Eschrichtius robustus [31]). By eating, among the other, parasites from their clients, cleaners can affect parasite communities [32]. Thus, the interactions between the client and its parasites are mediated by the cleaner species. Notably, the effect is not just limited to the actual individual being cleaned, since by reducing the number of parasites on that host, the cleaning fish also limits the potential parasite offspring coming from that host. In this way, a single cleaner individual can affect host–parasite interactions extending beyond its client’s population, possibly involving different host species targeted by the same parasites. Looking at the issue from a different, complementary perspective, it is clear that cleaners rely on the interactions between hosts and parasites, as this determines their food availability. Still, this is not the whole story, as other aspects of this symbiosis can generate additional indirect interactions. Different forms of co-evolution between cleaners and their clients ensure that cleaners are efficient in picking parasites and food residuals from the client without producing any harm (especially when cleaning happens in delicate body parts such as gills), and that clients restrain from eating their cleaners.

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As a strategy to reduce predation risk, some cleaner fish manifest their good intentions by performing a sort of dance where they oscillate their body and repeatedly touch the potential clients. This ritual is significantly more intense when the cleaners interact with hungry (more threatening) piscivorous clients than when they interact with satiated ones [33]. Body colour patterns might also be important signals used by cleaners to increase their chances to be identified as non-food by predator clients. In particular, it has been noticed that cleaner fish show statistically more yellow and blue patterns than other fish [34]. From the opposite perspective, clients often exhibit behaviours that can reassure cleaners and made their task more manageable. However, despite this idyllic façade, cleaning symbioses are also an evolutionary game that offers bilateral opportunities for cheating. This is particularly obvious from the clients’ side, as these have many opportunities to eat the cleaners during the interaction. Still, it applies also to cleaners that have the chance to act as micropredators (such as a mosquito) and eat tiny bits of the host’s tissue or mucus instead of performing the expected parasite removal [35]. Furthermore, other species have exploited the evolutionary opportunity emerging from cleaning symbioses. An example is that of the blennid fish Aspidontus taeniatus, which is known as “false cleaner-fish” as it mimes the appearance of the “true” cleaner fish Labroides dimidiatus to get close to unaware clients and feed on their tissues. Thus, the micropredatory relationship between the false cleaner and its “prey” (i.e. the deceived clients) depends on and affects the relationship between the honest cleaners and their clients. When the success and persistence of a species depend on the interactions between other species, extinctions can be triggered by the only loss or modification of existing ecological associations (that is, even if the species involved do not go extinct). For example, we can imagine a hypothetical situation where false cleaner fish experience a sudden demographic explosion in a given location. This would possibly result in the rapid emergence of hostility in the cheated clients against both false and true cleaners. In turn, in the worst case, this might lead to the extinction of both honest (Labroides dimidiatus) and cheating (Aspidontus taeniatus) cleaners. Similar reasonings apply to the case of trophically transmitted parasites, whose survival and reproduction depend on the trophic interactions between intermediate and final hosts. The previous example regarding the role of horsehair worms in generating food web links between their suicidal, arthropod hosts and aquatic predators focused on the effects of parasitic manipulation on prey–predator interactions. However, we can look at the same process from a different perspective and focus on how prey–predator interactions affect parasite transmission. In that regard, when evaluating the extinction risk of a trophically transmitted parasite, we need to account not only for the probability of hosts going extinct but also for the probability of interactions to disappear. This means that even rearrangements in the trophic web structure not involving the loss of species may produce extinctions in trophically transmitted parasites. Overall, these examples highlight how identifying resource–consumer dependencies might be not enough to hypothesize, model, reconstruct or predict the pathways that might lead to diversity loss in complex communities. Even if not directly causing secondary extinctions, the potential alterations in population dynamics following the

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loss or modification of higher-order interaction patterns might increase the vulnerability of some species to other threats. Then, extinctions triggered by higher-order interactions can, of course, generate additional losses by depriving consumers of their resources. These considerations call for integrating models accounting for resource– consumer (second-order) interactions with higher-order interaction theory. The challenges of obtaining sufficient empirical information to achieve this goal in real-world communities are exceptional, but the problem might be tractable from a theoretical (mathematical) perspective. Recent, rapid developments in ecological theory [36, 37], as well as the growing interest in co-extinction processes, leave me optimistic that we might get there sometime soon. However, until that moment, when modelling diversity loss focusing on either resource-consumer or higher-order interactions, the least we can do is assuming that we might be severely overestimating community robustness to species extinctions.

Summary The previous chapters have focused on the direct effects of resource loss on consumers. From that perspective, co-extinction events occur (either locally or globally) whenever a consumer runs out of resources, such as in the case of a plant left with no pollinators, a parasite left with no hosts, or a predator left with no prey. However, such direct “bottom-up” effects might not be the only way diversity loss can trigger further extinctions. Indeed, top-down regulatory effects (such as, for example, how a predator affects indirectly plant diversity by modifying plant–herbivore interactions) can have significant consequences at the ecosystem level. Higher-order interactions, which occur when the (pairwise) interaction between two species is modulated by either a third species or by other interactions, might play a fundamental role in the emergence and maintenance of diversity in natural communities, as supported by theoretical and, to a lesser extent, empirical work. This poses the interesting question of whether (and to what degree) higher-order interactions can generate additional paths through which the effects of species loss can propagate across ecological systems in detrimental ways and possibly result in further extinctions. Simulations conducted on a simple multispecies population dynamics analytical framework suggest that higherorder interactions can substantially amplify primary extinctions and that higher-order co-extinctions might represent a fundamental, yet largely neglected, driver of global diversity loss.

References 1. Pace ML et al (1999) Trophic cascades revealed in diverse ecosystems. Trends Ecol Evol 14(12):483–488

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2. Strona G, Bradshaw CJA (2018) Co-extinctions annihilate planetary life during extreme environmental change. Sci Rep 8(1):16724 3. Dawkins R, Krebs JR (1979) Arms races between and within species. Proc Roy Soc Lond Ser B Biol Sci 205(1161):489–511 4. Yoder JB, Nuismer SL (2010) When does coevolution promote diversification? Am Nat 176(6):802–817 5. Abrams PA (1992) Why don’t predators have positive effects on prey populations? Evol Ecol 6(6):449–457 6. Packer C et al (2003) Keeping the herds healthy and alert: implications of predator control for infectious disease. Ecol Lett 6(9):797–802 7. Primack RB, Kang H (1989) Measuring fitness and natural selection in wild plant populations. Ann Rev Ecol Syst 20(1):367–396 8. Holt RD, Bonsall MB (2017) Apparent competition. Ann Rev Ecol Evol Syst 48:447–471 9. Holt RD (1977) Predation, apparent competition, and the structure of prey communities. Theor Population Biol 12(2):197–229 10. Schoen ER et al (2015) Temperature and depth mediate resource competition and apparent competition between Mysis diluviana and Kokanee. Ecol Appl 25(7):1962–1975 11. Terborgh J et al (2001) Ecological meltdown in predator-free forest fragments. Science 294(5548):1923–1926 12. Schmitz OJ (2003) Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol Lett 6(2):156–163 13. Heck KL, Valentine JF (2007) The primacy of top-down effects in shallow benthic ecosystems. Estuaries Coasts 30(3):371–381 14. Carpenter SR et al (1995) Biological control of eutrophication in lakes. Environ Sci Technol 29(3):784–786 15. Vollenweider RA (1976) Advances in defining critical loading levels for phosphorus in lake eutrophication. In: Memorie dell’Istituto Italiano di Idrobiologia, Dott. Marco de Marchi Verbania Pallanza 33:53–83 16. Abrams PA, Ginzburg LR (2000) The nature of predation: prey dependent, ratio dependent or neither? Trends Ecol Evol 15(8):337–341 17. Schmitz OJ, Krivan V, Ovadia O (2004) Trophic cascades: the primacy of trait-mediated indirect interactions. Ecol Lett 7(2):153–163 18. Bairey E, Kelsic ED, Kishony R (2016) High-order species interactions shape ecosystem diversity. Nat Commun 7:12285 19. Grilli J et al (2017) Higher-order interactions stabilize dynamics in competitive network models. Nature 548(7666):210 20. Yamaguti S et al (1963) Monogenea and Aspidocotylea. In: Systema helminthum, vol 4, Interscience Publishers, John Wiley and Sons, Inc., New York and London 21. Whittington ID, Kearn GC (1988) Rapid hatching of mechanically-disturbed eggs of the monogenean gill parasite Diclidophora luscae, with observations on sedimentation of egg bundles. Int J Parasitol 18(6):847–852 22. Whittington ID, Kearn GC (2011) Hatching strategies in monogenean (Platyhelminth) parasites that facilitate host infection. Integ Compa Biol 51(1):91–99 23. Chivers DP, Smith RJF (1998) Chemical alarm signalling in aquatic predator-prey systems: a review and prospectus. Eco-Sci 5(3):338–352 24. Partridge BL (1982) The structure and function of fish schools. Sci Am 246(6):114–123 25. Strona G (2015) The underrated importance of predation in transmission ecology of direct lifecycle parasites. Oikos 124(6):685–690 26. Sato T et al (2012) Nematomorph parasites indirectly alter the food web and ecosystem function of streams through behavioural manipulation of their cricket hosts. Ecol Lett 15(8):786–793 27. Robert P (2010) Parasite manipulation of host behavior: an update and frequently asked questions. Adv Study Behav 41:151–186 28. Lafferty KD, Dobson AP, Kuris AM (2006) Parasites dominate food web links. Proc Nat Acad Sci 103(30):11211–11216

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29. Lafferty KD et al (2008) Parasites in food webs: the ultimate missing links. Ecol lett 11(6):533– 546 30. Vaughan DB et al (2017) Cleaner fishes and shrimp diversity and a re-evaluation of cleaning symbioses. Fish Fish 18(4):698–716 31. Swartz SL (1981) Cleaning symbiosis between topsmelt, Atherinops affinis, and gray whale, Eschrichtius robustus, in Laguna San Ignacio, Baja California Sur, Mexico. In: Fishery bulletin United States, National Marine Fisheries Service 79(2):360 32. Johnson PTJ et al (2010) When parasites become prey: ecological and epidemiological significance of eating parasites. Trends Ecol Evol 25(6):362–371 33. Grutter AS (2004) Cleaner fish use tactile dancing behavior as a preconflict management strategy. Curr Biol 14(12):1080–1083 34. Cheney KL et al (2009) Blue and yellow signal cleaning behavior in coral reef fishes. Curr Biol 19(15):1283–1287 35. Poulin R, Vickery WL (1995) Cleaning symbiosis as an evolutionary game: to cheat or not to cheat? J Theor Biol 175(1):63–70 36. Bartomeus I et al (2021) Experimental evidence of the importance of multitrophic structure for species persistence. Proc Nat Acad Sci 118(12):e2023872118 37. Godoy O et al (2018) Towards the integration of niche and network theories. Trends Ecol Evol 33(4):287–300

Chapter 13

Biological Invasions

The world is becoming smaller every day. We move people and goods so fast and so far that even keeping track of such movements is becoming increasingly difficult. This has dramatic effects on ecosystems, stemming from at least three different processes. First, building the infrastructures (such as roads) needed for transportation has several short-, mid- and long-term impacts on natural habitats and biodiversity [1]. Second, shipping and travelling produce a considerable amount of pollution. For instance, the overall carbon footprint of an individual is disproportionally affected by the number of air trips taken.1 Regardless of personal mobility, consuming products from distant localities can have a much worse environmental impact (at least in terms of emissions associated with transportation) than consuming local products. Third—and this will be the main focus of this chapter—whenever people, vehicles and goods travel around the globe, there are high chances that biological material (individual organisms at different stages of development, or propagules such as plant seeds, fungi spores, bacteria endospores) is moved as well, generating a substantial risk for biological invasions. While some of this biological material is moved deliberately, most translocations happen inconspicuously. Often, organisms that are moved from one area to another die either during transportation or shortly after the arrival. There are small odds that a translocated organism will tolerate the challenging conditions of the travel and even smaller chances that it will end up in an environment compatible with its ecological requirements. However, although more an exception than a rule, it is possible that an organism would survive a long trip and find suitable environmental conditions in a far-away location. One significant source of biological invasions consists of planktonic organisms moved around the world by ballast water. Big cargo ships are designed to have 1

Paradoxically, scientists tend to travel more than the average person to do fieldwork and attending meetings. This makes their carbon footprint often worse than that of people who do not care much about atmospheric science, climate change, habitat loss or biodiversity conservation [2]. © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_12

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optimal cruising performance and stability when fully charged. Consequently, when they deliver their cargo, they become too light to be capable of efficient navigation. For this reason, they have special compartments that they can fill with seawater to achieve optimal balance. The problem is that the water collected in one harbour is then discharged in the next one (where the ship loads a new cargo). Since the latter harbour can be thousands of kilometres far away from the first, the process provides a myriad of eggs, larvae, juveniles and adult organisms with a unique opportunity to colonize a new world. We should also consider that the transportation process itself might promote the “natural” selection of invasive species. The oxygen depletion and extreme temperatures that the organisms have to face during the long trips may preselect resistant species able to adjust to novel/harsh conditions, hence increasing their chances to succeed in colonization.2 When colonization succeeds, the consequences for the native community can be dramatic, so that the term “biological invasion” looks appropriate to describe the phenomenon [4]. There are many potential ways biological invasions can affect biodiversity, and an entire book might not be enough to cover all of them (actually, several books have been written on the subject, see, for example, [5, 6]). The aim of this chapter is more contained and is that of providing a simplified, schematic overview of the main effects that invasion processes may have on local and global diversity, focusing, in particular, on ecological interactions. We can look at biological invasions from either the invader’s perspective or that of the invaded ecosystem. Usually, there is more emphasis on the latter angle, and the invading species are just considered pests to get rid of. However, investigating invaders’ ecology might help understand why some invasions are more successful than others, and overcome the simplistic generalization that all invaders need to be eradicated outside their native range. Such a view is rapidly becoming anachronistic in a world where the proportion of alien species compared to that of native ones is quickly growing, and where global biogeography is being redesigned by the humanaided translocation of species beyond their natural dispersal means and across barriers that have been effective for millions of years [7]. In many cases, getting rid of an invader is not possible. Identifying the reasons for an invader’s success, possibly starting from investigating its ecology in its native range, could be key to design possible management strategies and predict/understand the effect of the invasion process on the native (invaded) community. Although the amount of potential invaders that are moved every day is huge, the chances of succeeding in an invasion are meagre. There are many subsequent challenges that a candidate invader would have to tackle before establishing itself into a novel area. Apart from surviving the trip and ending up in a favourable environmental setting, the colonizer would need immedi2

Due to the seriousness of this problem, the International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM) has been adopted in 2004 [3], to establish standards and procedures (such as water filtering and UV light sterilization) to prevent the spread of harmful aquatic organisms from one region to another.

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ate access to resources (e.g. food and shelter). Then, of course, plain survival would be meaningless if not leading to reproduction. This additional requirement shrinks the environmental/climatic window since the ranges of conditions permitting reproduction are usually considerably narrower than a species’ environmental tolerance limits. And, of course, reproduction will only be possible if the invader had arrived together with at least one potential sexual partner; or if the invader can reproduce without a mate (as in the case, for example, of plant seeds, or pregnant females or individuals capable of parthenogenesis). However, even if a single or a few organisms succeed in producing further generations of invaders, the size of the founder population remains a fundamental aspect determining the possibility of success in the long term, since it constrains the genetic variability. By increasing the homozygosity of segregating deleterious recessive alleles while reducing loci with heterozygote advantage, inbreeding might produce a severe reduction in fitness. On top of that, a small founder population size might lead to a stochastic increase in the frequency of deleterious alleles. Since these mechanisms protect local communities from invasions, the cases in which invaders are successful in establishing themselves into novel areas despite starting with a small founder population are regarded as paradoxical. These include situations where the invader can hybridize with local species or has some genetic characteristics emerging from its evolutionary history reducing its susceptibility to inbreeding depression [8]. The key to a successful invasion is not just obtaining resources to survive, such as food to eat or a safe spot to rest or reproduce. Successful invaders are those that manage to establish themselves within the intricate system of direct and indirect ecological links connecting all species of the native community. The effects of this process could be unpredictable and do not determine only the success (or failure) of the invasion but also its impact on the invaded ecosystem. Before we get to analyse the complex effects of invaders on ecological networks/food webs, let us focus on a simplified picture of how they can affect biodiversity. In a best-case scenario, an invader might enter a community without causing any local extinction, thus leading to a net increase in local diversity. In a less optimistic scenario, the invader establishes itself outcompeting a local species (i.e. driving it to local extinction). This will leave local diversity unchanged but might impact regional and global diversity. The outcompeted species might become more vulnerable to extinction (if present elsewhere) or even extinct if endemic of the invaded locality. In an even worse scenario where the invader drives to extinction more than one local species, the biodiversity balance will be negative (both locally and globally). In general, we can assume that the negative scenarios, where the arrival of alien species causes a diversity decline in the native population, are the most common. The IUCN threat classification scheme v. 3.0 identifies 12 major threats to species survival. An analysis of all the extinction events documented by IUCN (and for which an extinction cause was given) for plants, amphibians, birds, mammals and reptiles (215 species listed as “EX”, extinct, plus 32 species listed as “EW”, extinct in the wild) identified alien species as a factor associated to extinction in 58% of all EX species, and 31% of all EW species. The relative importance of alien species

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as an extinction driver varied between different groups. In particular, alien species were less critical in plant extinctions (being listed as an extinction driver in 27% of EX + EW species) than in vertebrate losses (being listed as essential drivers in 65% of amphibian extinctions; 60% of bird extinctions; 67% of mammal extinctions; and 57% of reptile extinctions). Overall, alien species were listed as an important extinction cause in more than half of assessed species (54%, 134/247 species) [9]. Diversity loss following an invasion can be often paired with a reduction in the overall local biomass. This process might be mediated by complex ecological mechanisms acting at the community level. For instance, a study focusing on insect productivity in a native Azorean forest strongly impacted by alien species demonstrated how, although not associated with a decrease in insect abundance, the degree of plant invasion was negatively correlated with plant and insect diversity. Notably, associations between smaller insect species and alien plants replaced those between large insect species and native plants, which led to a substantial reduction in insect biomass [10]. More interesting than the plain numerical balance of diversity loss caused by biological invaders are the mechanisms from which such loss originates. Those can be direct effects stemming from (pairwise) competition for resources or excessive predatory pressure exerted by the invader on some local species. Other less apparent mechanisms might arise from higher-order interactions (see Chap. 12). Here, the arrival of a new species might destabilize the dynamical equilibrium between coexisting species. This, in turn, might result in secondary extinctions. According to the mechanisms described in previous chapters, the loss of species triggered by the invader can result in further secondary extinctions. Thus, depending on where an invading species “enters” a food web, the results, in terms of ecological impact, might vary a lot, spanning from very detrimental to possibly beneficial. The latter outcome would manifest, for instance, when the arrival of a new species provides an additional node and additional links to the local ecological network and increases its robustness. This situation might materialize when the invader is a basal resource (e.g. a plant) that succeeds in establishing itself in the new area without causing secondary extinctions and becomes a novel resource for local species [11]. The rarity of such events depends, among the others, on the fact that native species have to face multiple challenges to use an alien resource, many of which originate from lack of co-adaptation. Lack of co-adaptation is also at the basis of various potentially detrimental consequences an invasion can have on the native community. In particular, invasive species can make native species’ behavioural decision-making rules no longer adaptive, generating an “evolutionary trap” [12]. A notable example regards the cane toad (Bufo marinus), a large anuran native to South and Central America. The cane toad has been introduced in several localities of the Pacific as a biological control agent by the sugar cane industry. In Australia (eastern Queensland), the first introduction took place in 1935. Since then, cane toad populations have spread widely, and their range in the Australian continent now vastly exceed 1M km2 [13]. All the toad life stages are toxic, which generates an obvious risk for a wide range of native predators that have no behavioural or physiological defences to cope with

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that. However, the first studies (dating back to the 40–50) dealing with the potential impact of invasive species on local fauna focused on the top-down effects of the introduction. In particular, they investigated the effect of the predatory activity of the cane toad on farmed honey bees (Apis mellifera), which caused a substantial economic loss for apiarists. Only later, starting from the 60s, anecdotal information about the detrimental effect of the presence of toads on native predators began to build up, with observations of decline in reptiles (lizards, monitors and snakes), as well as in birds, and in marsupial carnivores (Dasyurus spp.) [14]. Besides being responsible for declines in populations, this new challenge has also promoted rapid evolutionary adaptation. Scientists examined hundreds of museum specimens belonging to four Australian snake species collected over almost a century, all coming from areas (in Queensland) colonized by cane toads. Of the four selected species, two were considered at high death risk following ingestion of the toad, while two were considered at low risk. The risk was assessed based on head size and body mass, under the starting assumption that snakes with a large head and a small body size have a higher risk of ingesting a toad large enough to be fatal compared to snakes with a smaller head (and hence capable of eating only small toads) and larger body size (and therefore more tolerant to the toxic effects). They found that the two vulnerable species (Pseudechis porphyriacus and Dendrelaphis punctulatus) showed a steady progressive reduction in head/gape size, paired to a progressive increase in body length with time since exposure to toads. Conversely, the two less vulnerable species showed no particular change, which strongly supports the hypothesis of a rapid evolutionary adaptation in native predators following the biological invasion of a toxic prey [15]. In some cases, ecological traps can act more subtly, with the native consumers initially experiencing ephemeral benefits from using a novel resource. The consequences of such situations are hardly predictable. One possible outcome is the emergence of fragile scenarios of strict ecological dependencies, eventually exposing native consumers to a high risk of local extinction. A notable example is that of the relationship observed around the margins of a spring-fed meadow at 1800 m elevation in Carson City, Nevada, between a sedentary, isolated population of the native butterfly Euphydryas editha, and an invasive flowering plant, Plantago lanceolata. Plantago lanceolata is native to Eurasia and has been introduced in many parts of the world (and in the study site) as a forage plant by cattle ranchers. A series of studies conducted in the 80s identified a rapid increase in the relative abundance of butterflies showing a marked preference for P. lanceolata over native species constituting the ancestral diet of E. editha (from around 7% in 1982 to around 50% by 1990). A comparative study of the dietary preferences of nearby populations of E. editha inhabiting areas where the alien plant had not been introduced revealed that these used mainly the native plant species Collinsia parvijiora. About 10% of their individuals were able to use the alien species P. lanceolata, but none of them showed a preference for it. This result suggests that the preference for the alien plant evolved in native butterflies following their exposure to the novel resource [16]. Further experiments also showed a progressive increase in the number of individuals refusing to use the native plant C. parvijiora when this was the only one provided to them in controlled

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experiments. The authors of the study (published in 1993) speculated that a further increase of specialization of the native butterfly on the alien resource would have put butterflies at high risk of local extinction due to the strong dependency of P. lanceolata availability on human management [17]. The prevision materialized in little more than a decade. In 2005 cattle-grazing ceased in the meadow. To develop, E. editha larvae need to regulate their body temperature (raising it to 30–35degC) by basking in the sunlight. Lacking to receive enough insolation might be fatal for the butterflies, preventing them from reaching maturity. By 2007, grasses growing freely in the abandoned meadow had embedded most of P. lanceolata plants, forcing E. editha larvae to wander among dense vegetation in a desperate, often worthless, search for sunlight. Subsequent surveys failed to detect butterflies from 2008 to 2012, providing strong evidence for the species’ local extinction. In 2013, however, the site was naturally recolonized by E. editha specimens feeding exclusively on native hosts, bringing the system back to its starting point, and possibly ready to repeat the anthropogenic evolutionary cycle [18]. Although, as highlighted above, it is uncommon that alien species become a reliable resource for native species, there are documented situations where alien species have become important resources for other alien species. For example, a study conducted in the south-eastern USA showed that the abundance of an invasive ant (Solenopsis invicta) native from South America correlated positively with the abundance of an invasive Asian hemipteran, the honeydew-producing mealybug Antonina graminis. In turn, the abundance of the mealybug correlated to that of an invasive African host grass, Cynodon dactylon [19]. Although in a different ecological setting, a similar scenario was observed in the San Francisco Estuary in California. Scientists examined the gut content of an Asian alien fish, the shimofuri goby (Tridentiger bifasciatus), which was most likely introduced in California through ballast waters, and first collected in the area in 1985. They found that fundamental elements of the shimofuri goby’s diet consisted of two alien species not exploited by other resident fishes. In particular, shimofuri gobies fed preferentially on the cirri of the barnacle Balanus improvisus, a species native to the Atlantic coast of North America and first collected in San Francisco Bay in 1853; and on the hydroid Cordylophora caspia, a species introduced from Europe. Other resident fish in the area only marginally consumed the hydroid, which accounted for less than 2% of their total dietary volume, while accounting for 18–23% of shimofuri’s diet [20]. These examples highlight how often the only way invaders can establish themselves into an existing ecological network is by altering its structure, either by eliminating nodes or by creating novel links. However, it would be an oversimplification to assume that all invaders are evil. The abundance of alien species in seemingly “healthy” ecosystems demonstrate how, under certain circumstances, invaders can become well integrated into the invaded communities. We may imagine a gradient of ecological settings spanning from one where highly detrimental invaders drive a system to rapid collapse to an ideal situation where an invader has beneficial effects at the ecosystem level by providing missing services and enhancing stability, diversity and productivity.

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The position of an invader in such a theoretical gradient depends on its ability to find a proper place into the ecological functional space [21]. This ability is determined both by properties of the invaded ecosystem (e.g. the quality and quantity of resources available, system maturity, local species diversity and climate). There is also evidence that this aspect might be a fundamental determinant of the outcome of an invasion, with successful invaders being, in fact, species capable of exploiting free niches and filling in gaps in the ecological space. This concept is closely related to the limitingsimilarity hypothesis by MacArthur and Levins, which identifies species similarity as a fundamental limiting factor for population abundances due to the detrimental effect of interspecific competition [22]. The original seed for the idea possibly dates back to Darwin’s Origin of Species. Darwin first hypothesized that plants would have an advantage in becoming naturalized in a given land if closely related to indigenous species since such relatedness might come together with an intrinsical (evolutionary) adaptation to local conditions. However, when he tested this idea by comparing raw numbers of species in native and alien genera from data he had available, he found that the opposite was apparent. He then proposed that, since closely related species are more likely to compete for the same resources, a colonizer not related with local species might experience a selective advantage by fitting into a poorly exploited ecological niche [23]. This hypothesis is not universally accepted, and counterexamples where alien species phylogenetically close to native species are more likely to succeed in an invasion than species with no relatives are present in literature [24]. Still, it seems to provide a reasonable explanation for the success of “Lessepsian” species, which are migrants from the Red Sea that have been entering (and establishing themselves into) the Mediterranean since the opening of the Suez Canal in 1869. The phenomenon has been promoted in recent times by the increase in Mediterranean water temperature. Studies comparing functional traits of native and Lessepsian fish species now well established in Mediterranean waters have shown that the latter have a morphology under-represented in the native community. Since the morphometric features of a species can be considered a proxy for its ecological role, these results suggest that the successful invaders were indeed those able to fill in empty niche space [25]. Further analyses accounting for a broader suite of functional traits corroborated this hypothesis: invaders from the Red Sea tend to occupy relatively free ecological niches and, therefore, do not engage in intense competition with local species. However, as the number of successful invasion events increases, this pattern is getting weaker. Recent invaders show an increasing tendency for competing with native species, often resulting in the local extirpation of the latter. One example is that of the Japanese tiger prawn Marsupenaeus japonicus, native to the Red Sea, which outcompeted the native prawn Melicertus kerathurus [26]. This tendency is setting up the stage for a future Mediterranean Sea increasingly similar, both in species composition and ecological structure, to the Red Sea [27]. A study focusing on alien and native plant diversity in permanent grasslands across France confirms these results and provides additional insights. Consistent with what observed for Lessepsian species, researchers found that trait similarity between native and alien species reduces the chances of invasion. Besides, the study revealed

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that biotic and phylogenetic similarity between potential invaders and resident alien species has the opposite effect. That is, invaders have more chances to establish themselves in a target community in case another “similar” invader is already there [28]. Many possible caveats are associated with these conclusions, emerging from the complexity of potential scenarios of interactions between native species, resident invaders and novel invaders, and from differences in how we assess biotic similarity. However, it is intuitive that the modifications in native communities deriving from a given biological invasion event can generate both challenges and opportunities for future invaders. From a more general perspective, diversity loss can empty ecological niches and possibly facilitate the successful establishment of alien species. In that context, we might see the effect of alien species in terms of replenishing the functional space as positive. Nevertheless, since alien species can be themselves responsible for local extinctions, the process might generate a loop where invasions cause diversity loss promoting further invasions causing more diversity loss. This self-reinforcing mechanism might be just one among various possible ways invaders can pave the way to other invaders. As mentioned above, there are examples of novel trophic interactions emerging between invaders, with an alien species becoming a fundamental resource permitting the establishment and persistence of another invader [19, 20]. However, the synergy between invasions can exploit other ecological mechanisms than trophic ones. Often the effect of an invader goes far beyond the consumption of native resources and can also extend to physical modification of the local environment. For example, alien grazers may rapidly lead to soil denudation. But other effects may be more subtle. Many invading plants can be outcompeted, in natural conditions, by local plants. Nevertheless, the introduction of alien ungulates can create situations that are favourable to exotic species. For example, a study conducted on an island in Patagonia (Argentina) showed how the browsing activity of introduced deers reduced by 77% the growth of native tree (Austrocedrus chilensis) saplings compared to deer-free control areas (obtained by setting 400 m 2 exclosures). By contrast, browsing had a much weaker effect on the growth of the exotic tree Pseudotsuga menziesii sapling (with only a 3.3% of reduction compared to control areas) [29]. In general, it is well established that various forms of disturbances besides herbivory, such as fires, soil disturbance and nutrient addition, can increase community invasibility [30]. The idea that the synergic effect of alien colonizers might induce substantial changes in an invaded community is formalized in the “invasional meltdown hypothesis”. This proposes that positive interactions among invaders can trigger population level feedbacks intensifying the impact of established invaders and promoting further invasions [31]. A remarkable example of this phenomenon is provided by the invasion of the crazy ant Anoplolepis gracilipes in the rain forest of remote Christmas Island, in the Indian Ocean. Ants reached the island in the 1930s and persisted at relatively low population densities for several years. However, towards the end of the century, something changed, and ant supercolonies took over the island. The expansion of ants had a dramatic, direct effect on the populations of the native, omnivore red

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land crab. The crab used to play a fundamental ecological role, being the dominant consumer on the forest floor, hence regulating seedling recruitment, seedling species composition, litter breakdown and the density of litter invertebrates. Ants killed most crabs at invaded sites, reducing the average density of red crab burrows to 0.03/m 2 , which is more than 40 times less than the density observed at uninvaded sites (one crab per square metre). Ants also established mutualistic associations with generalist scale insects. This synergic action resulted in a substantial reduction of growing shoots on canopy trees and a much higher frequency of tree dieback. When tree did not die, their performance was nevertheless impaired by the insects’ deposition of excess honeydew on leaf surfaces which, in turn, promoted the development of sooty moulds, with adverse effects on photosynthesis [32]. As the establishment of novel ecological (mutualistic or resource–consumer) interactions might amplify the effect of invaders and pave the way to further arrivals of exotic species, losing interactions from the locality of origin can also play an essential role in determining the outcome of an invasion. This concept is central to the “enemy release hypothesis” (ERH), which identifies a lead factor behind the success of invasive species in the absence of natural enemies (e.g. herbivores or parasites) in the invaded communities [33]. The idea that the ERH can provide a universal explanation to the success of biological invasions has been criticized [34]. Yet, the ecological implications of losing antagonistic interactions from the native area (and possibly establishing novel ones in the area of colonization) are intriguing, especially when focusing on host–parasite systems. Most living species (and individual organisms) are parasitized to some degree. There could be situations where invaders bring along their parasites while invading a novel area [35, 36]. In that case, the alien parasites have a chance to expand their host range by infecting local hosts, hence becoming co-invaders [36]. However, in many cases, and especially in the marine realm, colonizers move in their juvenile phase, which makes them often free from parasitic infections. Furthermore, even when infected migrants invade a locality, there are multiple ways they can lose their parasites once at the destination. For example, the initial number of colonizers might be too small to ensure efficient transmission processes, and hence the survival of parasites throughout the first phases of the invasion. Also, parasites with a complex life cycle might be deprived of either intermediate or final hosts to complete their development and reproduce [37]. Parasite-free, newly arrived hosts provide native parasites with novel chances of host-switch and host range expansion [38]. Such events can lead to different outcomes. Native parasites might be highly virulent on alien hosts due to the lack of co-evolution [39]. However, it might also happen that invaders are immune to native parasites. This situation might be beneficial not only to the invader but also to local hosts. The invaders might act as resistant targets for the parasites, which might create a dilution effect reducing the efficiency of parasite transmission and overall infection rates [40]. Understanding and generalizing the ecosystem level implications of these different scenarios is challenging. A schematic summary of potential outcomes is shown in Fig. 13.1.

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Fig. 13.1 In this schematic representation, individuals from one population (blue frogs) move to a new area inhabited by a native population of a different, related species (green frogs). The circles at the bottom present different potential outcomes of this hypothetical biological invasion in the context of host–parasite relationships: a Invaders lose their parasites, experience a benefit from enemy release and do not establish relationships with local parasites. Alien hosts might also act as resistant targets for local parasites, reducing their transmission rates [40]. b Invaders lose their parasites while establishing novel relationships with local parasites; local parasites might benefit from host range expansion. By reducing the fitness of alien hosts, native parasites might also reduce the chances that the latter outcompete native hosts. c Invaders maintain their parasites, and those establish new symbioses with local hosts (co-invasion, [36]). This might bring benefits to the alien hosts by reducing the fitness of local competitors. d Invaders maintain their parasites and establish novel relationships with native parasites, while the alien parasites establish novel relationships with local hosts. e Invaders do not lose their parasites, they do not establish novel relationships with local parasites, and their parasites do not establish novel associations with local hosts. This lack of enemy release might reduce the impact of the invaders on the native community. Reproduced from [41], with kind permission from Elsevier.

The potential positive effects of enemy release are not limited to host–parasite relationships. Often, the most decisive ecological consequences of release from natural enemies occur when invaders are freed from their natural predators. Most of these cases regard mammals moved to islands, with the introduction and acclimatization of rabbits in Australia being one of the most striking (and disruptive) examples [42]. Other mammal introductions that have resulted in dramatic effects on native insular ecosystems are those of rats and cats, which have led to many documented local extinctions of several birds and reptiles (and, most likely, to the loss of many plants and invertebrates) [43]. When an invader is a consumer, its effects on the native community are mainly a consequence of its trophic ecology. The examples above of invasive rabbits, rats and cats are representative of the three main different trophic strategies, namely herbivory (rabbits, but also domestic goats), omnivory (rats) and carnivory (cats). The invaders’ trophic strategy will partly determine the direction (top-down or bottom-up) of the

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perturbations resulting from the introduction across food web links. However, as illustrated in the previous examples, complex scenarios can emerge on a per case basis. Additional complications arise from the fact that, as discussed in the previous chapter, the establishment of new (or the disruption of extant) indirect, higher-order interactions might have significant consequences for the overall community stability and biodiversity. When invaders are plants, they might alter extant vegetation, which might have substantial bottom-up effects, such as the extinction of specialized herbivores unable to switch to novel resources to face local species losses. These can be the consequence of direct (pairwise) outcompetition or might result from the alteration of extant, higher-order competitive equilibria. I tested this by using the same modelling framework described in the previous chapter, simulating multispecies population dynamics in a virtual, random community of trees according to a simple model where, each time a tree dies, another one fills in the canopy gaps. I set the initial community diversity to 25 species. I identified pairwise competition relationships between tree species in form of a competition matrix H with each entry Hi j (indicating the probability of the i-th species to win competition against the j-th species) sampled at random from a uniform distribution U [0, 1] ∀ i > j, Hii = 0.5 ∀ i : 0 → S, and with H ji = 1 − Hi j ∀ i, j. To simulate population dynamics, I followed the model described in the previous chapter, Fig. 12.6 [44]. Thus, I assumed identical death rate and fertility for all species (di = f i = 1 ∀ i : 0 → S), and a tournament involving three randomly sampled seeds as the criterion to determine the identity of the seedling replacing a dead tree, and hence filling a gap in the canopy. After 5000 steps into the model (burn-in time needed for the dynamics to stabilize), as expected, only around half of the species survived in any simulation (on average, 12.12 ±2.7S.D.). At that point, I added to the system an “alien” species. I assigned to the species a random competitive strength (sampled from U [0, 1]) relative to any other species in the community. I set its initial abundance to 5% of the total abundance of extant species. After the introduction, I modelled community dynamics for 5000 steps and then quantified the effect of the invasion on the overall diversity. At that point, I removed the alien species and ran the model for another 5000 steps, after which I took note of extant diversity. In 38% of cases, the invasion resulted in a net reduction of diversity, as more than one species went extinct following the introduction of the alien species. In 59% of cases, the introduction did not alter local diversity, that is, either the invasive species failed to establish itself and did not drive any local species to extinction; or the invasive species replaced exactly one local species. Only in 3% of cases, the alien species established itself without driving local species to extinction, increasing overall diversity. On average, the introduction of the alien species led to a reduction of 9.7% of initial diversity (±17.5 S.D.), with a maximum recorded loss of 92.3%. Eradicating the invasive species resulted very often (46% of the cases) in a further reduction of diversity, while it had no effect on 52% of cases and provided benefit to local diversity only on 2% of simulations. Although this is an oversimplified model of reality, it offers food for thought on the fundamental topic of how to deal with the global problem of invaders. The idea that

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Fig. 13.2 Simulated dynamics of a system including a superpredator (cat), a mesopredator (rat) and a prey (birds) under different management options. If we remove cats from the system (a), rats drive birds to extinction. If we eradicate rats, cats and birds can coexist, reaching an equilibrium (b). If the system is left unmanaged, cats keep the number of rats low, reducing rats’ pressure on birds, and the three groups can coexist (c). In some cases, cats can eradicate rats, turning scenario (c) into (b). Adapted from [43], with kind permission from John Wiley and Sons

eradicating an invader can paradoxically lead to further species loss is not mine, having also been proposed in the context of systems controlled by resource–consumer interactions. In particular, models suggest that getting rid of invasive superpredators (such as cats) from a given locality might result in a demographic explosion of mesopredators which, in turn, might drive their prey to extinction. This concept is known as the “mesopredator release” hypothesis [43] (Fig. 13.2). Thus, the more cautious idea that removing invasive species (especially in isolation) can result in unpredictable ecosystem changes is progressively replacing the simplified assumption of a general positive effect of alien species eradication on native biota [45]. These examples highlight how alien species can promote the emergence of novel communities where species that had been evolving for a long time in reciprocal isolation coexist in combinations and with relative abundances never observed before [46]. At the global scale, this might have already substantially altered long-standing biogeographical patterns. For example, a large-scale study on the native and alien distributional ranges of terrestrial gastropods revealed how the traditional biogeographical realms reflecting limitations in species natural dispersal abilities are no longer consistent with the current gastropod distribution. This is instead explained mainly by climate drivers and secondarily by distance and trade relationship between localities. These results indicate that human-mediated dispersal can break down biogeographic barriers and that future biogeographical patterns will be most likely the result of a combination of climate and socio-economic factors [7]. A more general study reached similar conclusions, showing how invasions are promoting a global-scale homogenization, division and redefinition of zooregions in amphibians, mammals and birds [47]. These findings support the idea that dispersal abilities might no longer limit species distribution on the Earth. Thus, if we look at a species’ successful establishment in a novel area as a two-step process—reaching a given locality and succeeding

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in colonization—global, human-aided species mobility will increasingly make the second step the main limiting factor. Conservation biologists agree with decisionmakers that moving species around the globe is at best very dangerous and, at worst, critically detrimental to the invaded ecosystems. Clearly, for the latter, the interest in the topic is not always motivated by the desire to protect native communities. An obvious motivation for preventing alien species from jumping from one locality to another is their potential to be harmful to native species of economic importance. History has taught us the hard way that alien pathogens might have devastating effects on species of primary agricultural significance. However, it would be nonsensical to assume that such events are just a consequence of bad luck or divine wrath. Our blind management of natural resources often creates the conditions for the perfect storm by generating scenarios of extreme low taxonomical and genetic diversity, which is the ideal substrate for the transmission of pathogens and the burst of large uncontrollable outbreaks. Although this concept seems obvious, it does not discourage us from making the same mistakes repeatedly. During the great Irish potato famine, 25% of the Irish population died or was forced to migrate following a potato blight that hit the country between 1845 and 1850. This historical tragedy highlights the inherent risk of dedicating entire landscapes to growing specific products [48]. However, in many parts of the world, we can see entire regions dedicated to monocultures, according to a blind management strategy focusing on short-term profit. There are two essential differences between what is happening now and how it was back in 1850. We have more technology to protect crops from diseases. But we are also moving pathogens worldwide at an unprecedented rate, which is often enough to make our protection practices ineffective. The consequences of emerging disease outbreaks can have huge economic impacts. We continue to look at those kinds of events as unexpected and unpredictable, as we seem unable to learn an easy lesson from nature. This attitude stems in part from a misleading—yet quite common—view on natural systems. The ecological rules governing communities are entirely independent of what we have done in the last few thousands years. Although visually appealing and emotionally moving, a well-managed vineyard is not far from a shopping centre on a hypothetical gradient of naturalness. Same for olive orchards. The millenary tradition of olive farming in Italy is precisely that: a human tradition. It started a long time ago, and from the perspective of olive farmers, olive trees have always been there, meaning that they were there before the farmer was born. Thus, the dramatic mortality due to a pathogenic, vector-borne bacterium named Xylella fastidiosa, likely introduced from Costa Rica and now threatening other European countries, including Spain and France, is a significant social problem but cannot be considered as a natural disaster. The shame is on us, who planted one olive tree next to another throughout millennia, until we transformed a natural landscape into a vast, highly vulnerable monoculture [49]. On us, who move tropical plants from a continent to another, to grow them in our overheated apartments. With this, I am not suggesting that we should embrace the pessimistic view that we can do nothing about biological invasions or that we have to accept them as an

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atonement for our wrong choices. Yet, we have to be realistic in that the problem is deeply rooted in the modern world. Unless substantial changes take place globally in socio-economic systems (which is, at best, extremely unlikely in the short term), it is tough to be optimistic about future trends of invasions. Realizing this calls for a thoughtful reconsideration of how to deal with the problem. Enhancing control against the involuntary, human-aided movement of alien species is one obvious route to try limiting the risk of invasions. Still, the increasing global mobility makes the chances of missing some translocations high despite the monitoring efforts. Similarly, many recent cases show that even intense eradication campaigns might fail (with dramatic economic impacts) or, when successful, might have unpredictable ecosystem-wide effects [45]. Milder forms of management can be beneficial, but depending on the specific setting, obtaining a clear picture of such actions’ cost–benefit balance might be not straightforward. All this considered, a valuable option left would be improving the resistance of ecosystems against invaders, that is, reducing their “invasibility”. The detrimental effects of biological invaders on native communities can generate a self-feeding loop promoting further invasions and speeding up biotic homogenization. Despite some of the caveats discussed above, it is, in general, reasonable to assume biodiversity as an essential defence against the successful establishment of alien species [50]. Thus, redistributing part of the effort currently dedicated to preventive control and management/eradication of alien species to the conservation (or even on the enrichment) of local (possibly native) diversity might be our best bet to break out from that loop.

Summary Species dispersal ability has played a lead role in millions of years of evolutionary, biogeographical and ecological processes, eventually leading to modern communities. However, the increasing global mobility is now boosting the frequency of events where species are translocated to localities far beyond their natural dispersal range. For the large majority, such events do not lead to the successful establishment of the species which are moved. Still, the rare cases where colonization is successful can dramatically affect the invaded, native communities. Although there is an agreement (and strong support from data) on the idea that biological invasions have, in most cases, a harsh effect on the biodiversity and stability of invaded communities, predicting the potential outcomes of invasions on a per case basis is exceptionally challenging. Many ecological aspects (and a fair amount of stochasticity) combine to determine either the success or the failure of an invasion and the potential effect of successful invaders on native communities. In this context, species interactions (both pairwise and higher order) play a fundamental role. From a basic perspective, it is intuitive that a consumer species will not colonize a locality out of its current range if that locality does not offer it access to appropriate resources. Furthermore, successful invaders can become resources for other consumers. The possible scenar-

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ios emerging from those mechanisms can be complex. For example, in some cases, the colonizer might act as a novel resource for newcoming alien species. It has also been hypothesized that the release from natural enemies can provide alien species with an important ecological advantage within the invaded communities. However, interactions between a colonizer (and possibly the associated species moving with it, such as its parasites) and local species can also emerge, producing novel ecological scenarios. The possibility of driving those new settings back to a pre-invasion status is uncertain, and there is a substantial risk that at least some of the ecosystem modifications originating from invasion events are irreversible. This poses important conservation questions, particularly regarding the need and potential benefits of actions aimed at eradicating well-established invaders. Simple model simulations suggest that, in some cases, eradication of invaders might even cause further damage to communities. However, it is clear that generalizations in that context can be dangerous and that each case calls for specific management approaches (including, in some contexts, no management action at all). The many challenges of implementing effective control and management strategies in the face of increasing globalization call for a thoughtful reconsideration of how to deal with the problem. Various evidence suggests that local biodiversity might protect native communities from invasions. Therefore, extinction caused by invaders might activate a self-feeding loop promoting further invasions and hence fueling global biotic homogenization. Looking at the issue from this perspective, and given the uncertainty associated with the potential success of control, management and eradication actions, protecting and enhancing local diversity might be a win-win way to break out from that loop.

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11. Walther G-R et al (2009) Alien species in a warmer world: risks and opportunities. Trends Ecol Evol 24(12):686–693 12. Schlaepfer MA et al (2005) Introduced species as evolutionary traps. Ecol Lett 8(3):241–246 13. Urban MC et al (2007) The cane toad’s (Chaunus [Bufo] marinus) increasing ability to invade Australia is revealed by a dynamically updated range model. Proc Roy Soc B Biol Sci 274(1616):1413–1419 14. Phillips BL, Brown GP, Shine R (2003) Assessing the potential impact of cane toads on Australian snakes. Conserv Biol 17(6):1738–1747 15. Phillips BL, Shine R (2004) Adapting to an invasive species: toxic cane toads induce morphological change in Australian snakes. Proc Nat Acad Sci 101(49):17150–17155 16. Thomas CD et al (1987) Incorporation of a European weed into the diet of a North American herbivore. Evolution 41(4):892–901 17. Singer MC, Thomas CD, Parmesan C (1993) Rapid human-induced evolution of insect-host associations. Nature 366(6456):681 18. Singer MC, Parmesan C (2018) Lethal trap created by adaptive evolutionary response to an exotic resource. Nature 557(7704):238 19. Helms K, Hayden CP, Vinson SB (2011) Plant-based food resources, trophic interactions among alien species, and the abundance of an invasive ant. Biol Inv 13(1):67–79 20. Matern SA, Brown LR (2005) Invaders eating invaders: exploitation of novel alien prey by the alien shimofuri goby in the San Francisco Estuary. California. Biol Inv 7(3):497–507 21. Shea K, Chesson P (2002) Community ecology theory as a framework for biological invasions. Trends Ecol Evol 17(4):170–176 22. Macarthur R, Levins R (1967) The limiting similarity, convergence, and divergence of coexisting species. Am Nat 101(921):377–385 23. Darwin C (1859) The origin of species. J Murray Lond 24. Daehler CC (2001) Darwin’s naturalization hypothesis revisited. Am Nat 158(3):324–330 25. Azzurro E et al (2014) External morphology explains the success of biological invasions. Ecol lett 17(11):1455–1463 26. Galil BS (2007) Loss or gain? Invasive aliens and biodiversity in the Mediterranean Sea. Mar Pollut Bull 55(7–9):314–322 27. Givan O et al (2017) Trait structure reveals the processes underlying fish establishment in the Mediterranean. Glob Ecol Biogeogr 26(2):142–153 28. Sheppard CS et al (2018) It takes one to know one: similarity to resident alien species increases establishment success of new invaders. Divers Distrib 24(5):680–691 29. Relva MA, Nunez MA, Simberloff D (2010) Introduced deer reduce native plant cover and facilitate invasion of non-native tree species: evidence for invasional meltdown. Biologica Invasions 12(2):303–311 30. Hobbs RJ, Huenneke LF (1992) Disturbance, diversity, and invasion: implications for conservation. Conserv Biol 6(3):324–337 31. Green PT et al (2011) Invasional meltdown: invader-invader mutualism facilitates a secondary invasion. Ecology 92(9):1758–1768 32. O’Dowd DJ, Green PT, Lake PS (2003) Invasional ’melt-down’on an Oceanic Island. Ecol Lett 6(9):812–817 33. Keane RM, Crawley MJ (2002) Exotic plant invasions and the enemy release hypothesis. Trends Ecol Evol 17(4):164–170 34. Colautti RI et al (2004) Is invasion success explained by the enemy release hypothesis? Ecol Lett 7(8):721–733 35. Galli P et al (2005) Introduction of alien host-parasite complexes in a natural environment and the symbiota concept. Hydrobiologia 548(1):293–299 36. Lymbery AJ et al (2014) Co-invaders: the effects of alien parasites on native hosts. Int J Parasitol Parasites Wildl 3(2):171–177 37. Torchin ME, Mitchell CE (2004) Parasites, pathogens, and invasions by plants and animals. Front Ecol Environ 2(4):183–190 38. Prenter J et al (2004) Roles of parasites in animal invasions. Trends Ecol Evol 19(7):385–390

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Chapter 14

Artificial Intelligence and the Future of Biodiversity

In 1965, Hubert Dreyfus, a professor of philosophy at MIT, wrote in an essay [1]: “Alchemists were so successful in distilling quicksilver from what seemed to be dirt, that after several hundred years of fruitless effort to convert lead into gold, they still refused to believe that on the chemical level one cannot transmute metals. To avoid the fate of the alchemists, it is time we asked where we stand. Now, before we invest more time and money on the information-processing level, we should ask whether the protocols of human subjects suggest that computer language is appropriate for analyzing human behaviour”. There, he was questioning the optimistic views on the future frontiers of artificial intelligence (AI) by contemporary computer scientists such as Edward A. Feigenbaum and Julian Feldman, who had just issued the first anthology on AI, entitled “Computer and thought” [2]. One of Dreyfus’ assertions was the impossibility of a computer to beat a human at chess. In 1967, several MIT students and professors challenged him to play a game against a computer program developed by their colleague Richard Greenblatt. The program was capable of evaluating ten positions per second. Eventually, the computer won. The outcome might sound not surprising to us now, but our lack of surprise is biased by more than 50 years of technological development. Back then, the victory of the computer was all but certain. Although Dreyfus was not a chess master, he did pretty well in the match for the audience’s joy. Thus, although the computer victory set a milestone in the history of AI, many more years and advances separate that event from the moment we witnessed a computer capable of surpassing the most skilled human beings. The famous victory of the IBM computer Deep Blue against Kasparov in 1996 represented the first time a computer defeated a world champion. In the 2000s and beyond, computer programs became able to beat the strongest opponents even when run by a cell phone. In October 2015, a computer program called AlphaGo, developed by Google DeepMind, beat a human professional Go player, Fan Hui, who had won the last three European Go championships before the match. That was an incredible © Springer Nature Switzerland AG 2022 G. Strona, Hidden Pathways to Extinction, Fascinating Life Sciences, https://doi.org/10.1007/978-3-030-86764-5_13

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achievement because the game of Go was one of the last standing outposts where human ability was still superior to computers’ one. Although the game of Go has simpler rules than chess, it is much more complex in terms of possible trajectories a match can take. From a quantitative perspective, while the estimated number of different possible games for chess is ≈10120 , the number of possible Go games is ≈10761 . Although both numbers are enormous, the latter is arguably much larger than the first one. To create the artificial champion, scientists used state-of-the-art techniques based on neural networks and Monte Carlo tree search programs simulating thousands of random games of self-play [3]. Thanks to those fascinating achievements, it is becoming more apparent how we can use AI to approach scientific questions. Genetic algorithms and machine learning can find relationships between data even when those are far from being intuitive. Artificial neural networks (ANNs) such as those used by AlphaGo are inspired by the complexity of the human brain, which processes information through hundreds of billions of interconnected neurons working in parallel. In an ANN, different layers (specifically, an input layer, one or more often several “hidden” layers and an output layer) of nodes (“neurons”) are connected by weighted links. Information flows in one direction from input to output nodes across the hidden layers [4]. In a fully connected neural network such as that depicted in Fig. 14.1, each node in one layer is linked to all nodes in the next layer. Still, other architectures are possible, as it happens, for example, in “convolutional” neural networks, which are typically used in image analysis [5]. Each node in one layer receives, as inputs, real numbers representing the output from the connected nodes in the preceding layer (or the initial input provided to the ANN), adjusted by the weights of the connections between the target node and the preceding ones in the information flow. The node sums up these inputs and applies a nonlinear function to the total before transmitting the processed information to the connected nodes in the next layer (again, adjusted based on connection weights) until the information reaches the output layer. For example, in an image recognition problem, the input might be the brightness value of each pixel in the image (thus, each input node will correspond to a pixel), and the output might be the probability that the image depicts a cat. To work efficiently (i.e. identify correctly whether the target image shows a cat), ANN needs to be trained. Without entering into technical details, during this process, the network is provided with “known” input; its performance in producing the expected output is evaluated; and weights of the connections are progressively adjusted to improve the network’s predictive/classification ability [6]. Despite the name and the source of inspiration, neural networks (and AI/machine learning techniques in general) are still very far from simulating human consciousness and emotions, a target that is still firmly grounded in the science fiction realm [4]. However, the examples above show how, given enough raw computational power and proper design, computers can perform better than humans in specific tasks. In a game such as chess or Go, specific tasks consist of taking decisions to achieve a goal, that is, winning the match.

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output layer

input layer

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Fig. 14.1 Simplified representation of a “deep feedforward”, fully connected artificial neural network. Network nodes (“neurons”) process the information they receive (in the form of real numbers) from the nodes in the preceding layer and transmit the processed result to the connected nodes in the next layer. The connections between nodes have different weights, which are progressively adjusted during the training phase where the network is “taught” to provide the expected output, such as, for example, the correct classification of an image

From this perspective, AI looks like a potential panacea for most human problems. In our everyday life, we are continuously facing the challenge of making decisions. Some of those are trivial ones, such as what to wear or what to eat for breakfast. Other ones are more important ones, such as whether to get some medical treatment. Very often, our mind gets stuck in the decision process, which could be very uncomfortable: How often does procrastination wear us out? What is going on in our mind during those moments of freeze where we struggle to anticipate the emotions we might experience as a consequence of our next choice [7] is not much different from what Deep Blue does when playing a chess game. Even if, in most cases, it is much more efficient than us in doing that. Given a specific arrangement in the board, Deep Blue tries to evaluate all the moves it might make and all the possible consequences. In doing that, Deep Blue goes beyond the opponent’s first response and figures out potential developments of the game far ahead in terms of players’ moves. A sample of Deep Blue’s behaviour in different positions against Garry Kasparov (New York, 1997) suggests that some of the computer’s searches were capable of reaching as deep as 40 single player’s moves ahead (with

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the maximum depth depending on the position of pieces on the board) [8]. Such a “predictive” ability (which was state of the art at that time but obsolete now) is possibly shared only by a few chess masters. All this considered, would not it be great if a computer could relieve us from the daily pain of making choices? At least, two fundamental problems are complicating this issue. The first one is known as the “black-box” problem. The rapid development of AI techniques, paired with the dramatic increase in available computational power (thanks, particularly to the increasing end-user accessibility to parallel computing), has made the computers’ decision process extremely complex. So much that understanding the reasons behind a given answer by the AI is becoming incredibly challenging, which leaves us with two choices. Either we trust the black box or we do not. For example, a typical application of AI is automated image recognition applied to satellite imagery. Satellites are capable of acquiring huge quantities of data that humans cannot visually examine. In that context, a powerful solution is using AI to select potentially interesting images for further examination by human experts. As the algorithms dedicated to this task become more efficient (and more complex), it also becomes increasingly difficult to understand the criteria behind the selection. The lack of a “mechanistic” explanation of the decision process generates doubts regarding the alignment of purposes between the computer and the man behind it. A critical step in this kind of AI-fueled automated processes is the training procedure. Failing to present the AI with a set of situations representative of the full spectrum of settings it will have to interpret afterwards might result in unexpected responses. Odd outcomes might result from the fact that, while machine learning techniques are good at interpolating (or generalizing), they struggle with extrapolation. Once trained on a specific problem, the AI will be efficient in solving slight variations of the target problem. But, if the variations become substantial, turning the problem into a different one, the AI will have, in most cases, no clue about how to solve it (even if new techniques are exploring solutions to overcome such limitations [9]). The issue is not just about using the broadest possible range of training data to maximize the spectrum of situations experienced by the AI and, hence, its potential ability to interpret new data. An additional complication arises from the fact that assembling a training dataset is not a trivial task and might sometimes produce unexpected results due to the involuntary introduction of systematic, hardly detectable biases. A computer program called Deep Dream, which was developed to provide a visualization of how a neural network “thinks”, can help better understand the issue [10]. When presented an image, the program (which had been preventively trained using a large set of images) makes a first attempt to identify known objects within the image. The image is then progressively modified to be more consistent with the prediction (Fig. 14.3). For example, if one proposes a generic landscape image to the software, the neural network might mistakenly “see” a cat in the clouds. Deep Dream will, therefore, slightly modify the image to better match the first interpretation. Specifically, it will modify the clouds to increase their resemblance to a cat (i.e. it will slightly

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Fig. 14.2 Deep Dream’s rendition of the picture of Toughie, the last living specimen of Rabbs’ fringe-limbed treefrog, Ecnomiohyla rabborum (see also Chap. 1). Original photo by Brian c Brian Gratwicke (2011), used under a CC BY 2.0 (https:// Gratwicke from DC, USA.  creativecommons.org/licenses/by/2.0/). The image was modified using the Deep Dream software (https://github.com/google/deepdream) [10]

“catify” them). Then the image will be presented again to the neural network, with the possibility that the previous modifications make the network see something new in respect to the last step. Deep Dream will modify the image again according to this reinterpretation, and so on, with the result of producing beautiful, although sometimes disturbing, abstract images (Fig. 14.2). Since many of the available Deep Dream implementations (as the one used to produce Fig. 14.2) have been trained mainly using animal pictures, they tend to see animals everywhere. Thus, the resulting images are populated by strange animal-like creatures (such as the sort of iguana coming out from Toughie’s leg and the birdlike creatures popping out in the background in Fig. 14.2). Apart from the potential artistic applications of the procedure, Deep Dream offers an intriguing way to verify if a neural network has learned how to extract the essence of things from the training data. By increasingly modifying a picture according to its original interpretation, the neural network provides us with a visual representation of the features it considers relevant for the classification. For example, in an experiment, researchers trained the neural network using a large set of dumbbells pictures. They then asked the network to identify objects in images full of random noise and modify the images progressively according to the first and successive interpretations. As expected, the resulting images included dumbbells, but, in all of them, tiny hands and wrists were coming out from dumbbell handles [10] (Fig. 14.4). This seemingly harmless example illustrates how things can go easily wrong if we decide to trust the black box without looking at what is inside it. For a game of chess or Go, trusting the black box might be wise, as making the suggested moves

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Fig. 14.3 Testing what neural networks learn from a training process. The training consists of showing to the neural network many examples of a target item. The expectation is that the network will eventually manage to extract the essential features of the item while disregarding the non-essential ones. If we provide a so-trained neural network with an image full of (nearly) random noise (with just a few constraints making the image’s statistics similar to those of natural images, for example, by imposing correlation between neighbouring pixels), we will trick the network into seeing in the image one or more entities of the target item. However, the classification will have low confidence. Progressive small modifications of the image ensuring a monotonic increase in the classification confidence will eventually lead to a visual representation of the neural network’s abstract idea of the target item. The picture shows the initial random noise figure and the final figures obtained by applying the above procedure to neural networks trained with different categories of objects (hartebeest, measuring cups, ants, ...). Adapted from [10] (https://web.archive.org/web/20150703064823/ http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html) under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/ by/4.0/)

Fig. 14.4 How Deep Dream thinks dumbbells look like. The image was obtained by first training the neural network with a set of images of real dumbbells and then providing it with a (nearly) random noise image (with just a few constraints making the image’s statistics similar to those of natural images, for example by imposing correlation between neighbouring pixels). The neural network progressively converted the random image into the abstract idea of a dumbbell distilled from the training data. Unexpectedly, such an abstract idea includes also tiny hands and wrists coming out from dumbbell handles. Adapted from [10] (https://web.archive.org/web/20150703064823/ http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html) under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/ by/4.0/)

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would most likely lead us to victory. But would it be as wise to do the same in a more complex decisional context, not embedded into a narrow set of game rules? Specifically, could we use help from AI for conservation purposes? What are the opportunities—and the risks—of relying on AI in an attempt to reduce biodiversity loss? In many life science fields, AI (especially in the form of machine learning techniques) is now becoming mainstream, particularly as a response to the new challenges emerging from big data availability. More than one decade ago, scientists showed that computers can identify autonomously natural laws from empirical dataset [11], and that robots can formulate and test scientific hypotheses [12]. However, only a few attempts to date have been made to apply these cutting edge technologies to the ecological context (see, for example, [13]). The fast-growing amount of ecological data, such as, for example, species occurrences [14], seems to be particularly suitable to the application of AI techniques. Notwithstanding, the potential value coming from this approach is put in question by the limitations, biases and errors, which, unfortunately, are typical of large ecological datasets (see, for instance, [15]). At least in the perception of full-time scientists, this issue is particularly compelling for data collected outside “proper” scientific campaigns, as it happens in the context of citizen science surveys [16]. Data quality does not represent the only aspect that creates heat among ecologists. The historical separation between theoretical and empirical ecologists [17] has become weaker and weaker with the increasing availability of user-friendly tools for analysis and modelling. Nowadays, even the most hardcore field ecologists have at least basic coding/programming skills, giving them instant access to a wide array of statistical tools. Nevertheless, researchers who have been building their career by collecting data in the field and conducting empirical experiments often remain sceptical towards the appropriateness and the efficacy of investigating large-scale ecological patterns and processes sitting in front of a computer screen. Here, the scepticism is more oriented towards the necessary simplifications of a macro-ecological approach, which sometimes leads to general results that are not valid in specific settings. One obvious limitation of macro-ecological studies is that, in most cases, they cannot be validated by experiments [18]. In this context, the application of AI might even fuel further scepticism by producing complex hypotheses and results hard to be interpreted, besides offering little to no information on how these were achieved. The increasing availability of ecological information is not limited to large-scale datasets of species distribution patterns. Instead, novel, fast-developing technologies provide new ways to collect data rapidly and at a low cost in specific ecological settings. The advantage consists of obtaining information faster and more comprehensively (i.e. at a higher spatial or temporal resolution, and in better detail) than it would be possible with traditional techniques. For example, in the past, the primary way to reconstruct a species’ diet was by examining stomach contents visually, trying to identify all food items. Now, molecular techniques as metabarcoding make it possible to perform the same task automatically (and with better accuracy), which is a first fundamental step towards the reconstruction of highly detailed food webs

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[19–21]. Similarly, environmental DNA permits to automatically identify stream community composition from samples of water or detritus [22]. In reality, those techniques pose challenges in their implementation and are still quite expensive, but it is undebatable that they offer substantial advantages compared to classic approaches. Had I spent a few decades collecting samples in the field with traditional methods or classifying organisms by looking through a microscope for countless hours, it would be natural for me to consider new automatic techniques as soulless short cuts. But, as much frustrating as it might appear, this is how scientific and technological progress work. One danger that new methods bring along is that we fall into the trap of relying entirely on them, forgetting the importance of taking a field trip, observing organisms under a microscope, or conducting simple laboratory experiments. I firmly believe that it is through these empirical activities that scientific curiosity and inspiration can thrive and persist and that wholly replacing them with automated, more efficient procedures might have unexpected, detrimental effects on the future of scientific thinking. In addition to collecting biological data, we can benefit from different automatic tools to acquire large amounts of environmental/climatic/chemical data. This offers many possibilities to investigate ecosystem dynamics through AI (and particularly machine learning techniques). For example, researchers studying the dynamics of a temperate Swiss lake phytoplankton community used a scanning flow cytometer to quantify the density of cyanobacteria and eukaryotic algae by automatic characterization of the scattering and pigment fluorescence of individual phytoplankton cells. In parallel, they used a floating platform in the middle of the lake, sampling automatically water at six different depths every four hours, also measuring hourly several physical parameters such as temperature, pH, conductivity and oxygen. Besides, a weather transmitter installed on the platform’s roof monitored atmospheric parameters (temperature, pressure, wind speed and direction, rain and humidity) every hour for all the experiment duration. Water samples for chemistry (nutrients) and zooplankton samples for the quantification of biomass were collected twice a week. Measurements were taken during all summer and autumn of two consecutive years. In the end, the authors had to deal with around 30 environmental variables and a large dataset that would have been hardly tractable only a few years ago. Using machine learning, they were able to generate models capable of predicting with a high degree of confidence phytoplankton temporal community dynamics [23]. This example illustrates how the integration of automatic data retrieval with AI techniques might offer unprecedented opportunities. A recent review on the potential application of deep learning (i.e. the branch of machine learning dealing with artificial neural networks) in ecology suggests that the field is still in its infancy (with less than 100 papers available on the subject as of 2019, most of which focusing on image recognition). Yet, the interest is rapidly growing (Fig. 14.5a), and the possibilities are vast (Fig. 14.5b) [24]. A common feature of ecological systems is their inherent complexity. Still, the extent to which one can identify and measure the variables controlling and modulating such complexity varies substantially from one setting to another. Considering

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Fig. 14.5 Recent trends and potential applications of deep learning techniques in ecology. The bar plots in panel (a) report the number of ecological publications using deep learning in the period 2000–2019, with different colours corresponding to different types of deep learning techniques. The category “Other” refers to studies where the type of technique was not identifiable, while “N/A” refers to studies dealing with but not using deep learning methods. Panel (b) reports potential ecological fields of application of deep learning techniques (with concrete examples). Adapted from [24], with kind permission from John Wiley and Sons

this aspect is crucial when evaluating the potential application of AI techniques to different ecological contexts. The phytoplankton example described above refers to a complex system strongly controlled by chemical and physical variables that can be precisely identified and measured. Thus, the study’s main result, the high predictability of phytoplankton cell density over hours to months [23], although remarkable, does not come as a total surprise. In some way, we can draw analogies between the phytoplankton study and other ones dealing with a non-mainstream AI technique called “symbolic regression”, which provides a method for the automatic discovery of equations explaining complex processes. The idea behind symbolic regression is that of combining building blocks consisting of basic functions (such as arithmetic and logical operators) and constants to explore a broad space of mathematical expressions in search for a simple and accurate model fitting the input data. To test the potential of symbolic regression, scientists used motion tracking to record the angles and angular velocities of a double pendulum over time. Then they applied symbolic regression to those data. Although free from any knowledge of physics, geometry and kinematics, the method (re)discovered several equations describing important natural laws [11] (Fig. 14.6), as a confirmation of the potential of symbolic regression, a more recent implementation of the method1 was able to rediscover 100 equations from the Feynman Lectures on Physics [26]. The motion capture of the pendulum position at each moment is conceptually closer to the automatic measurement of environmental data in the phytoplankton study. In both cases, the data retrieved are precise and minimally impacted by noise, 1

“...a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques.” [25]

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L1 θ1

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6 4 2 0 −2 −4 −6 6

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Fig. 14.6 Identification of physical laws through symbolic regression. The angles and angular velocities of a double pendulum over time are captured using motion tracking. Then equations capable of describing relationships between these variables are searched for by the algorithm, which, even without prior knowledge of physics, kinetics or geometry, finds exact theoretical laws starting from basic mathematical building blocks. Redrawn from [11]

which was critical to the AI’s success. Similarly, encouraging results were obtained in a proof-of-concept ecological study that applied symbolic regression to three datasets of time series documenting the population dynamics of the protist Paramecium grown either in isolation (i) or together with the predatory ciliate Didinium (ii); and the population dynamics of the flour beetle Tribolium (iii). There, symbolic regression was able to discover dynamical rules explaining most of the variance in the data (84–95%) [27]. Similar examples (and especially time series obtained under controlled laboratory conditions) depict situations substantially different from typical real-world ecological settings. These are often characterized by high complexity and ruled by many factors (including stochasticity) that are either unknown or difficult to be measured, making it extremely difficult for the AI to generate a meaningful model. In some cases, machine learning techniques have indeed yielded promising results. For instance, machine learning successfully reconstructed networks of interactions between plants and pollinators based on their traits [28]. But, depending on the eco-

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logical system and the questions under investigation, results have been varying and often not particularly encouraging [29]. Problems emerge not only from the limitations of some categories of big data for which gaps and inaccuracies are to be expected (such as large datasets of species occurrences [15]). They also arise from potentially confounding issues and complications intrinsic to the nature of the data, which might persist regardless of how accurate and precise their collection and measurement are. Consider the highly detailed data we can retrieve using emerging molecular techniques such as metabarcoding or environmental DNA. Even if the information provided by these techniques (such as the specific content of a fish stomach) can be exhaustive and precise, various confounding factors might complicate deriving processes from patterns. This might be relevant, for example, when identifying trophic rules to be used in different contexts for automatic reconstruction of food webs. There are many ways a given organism can end up in a fish stomach, and not all of those are related to trophic ecology. Parasites would be likely present, together with various materials possibly not relevant for the target species’ diet. Individual species’ preferences, behavioural responses regulating prey–predator interactions, local environmental and ecological variability might all substantially affect what we will find in a fish stomach. Depending on the complexity of the ecological setting under investigation, obtaining detailed information about these aspects might be extremely challenging. But disregarding them, and simply asking the AI to figure this complexity out on its own from the “raw” data would be a risky strategy and most likely lead to unreliable results. Imagine a situation where the double pendulum is brought around the room by a playful dog. Would the AI be still able to distil natural laws from the data, or will it end up seeing a banana in an unordered cloud of points? All this considered, the overarching complexity of natural systems seems still very far from being crackable. This, in turn, seems to question the actual feasibility of relying on AI to tackle conservation issues. Although the idea that AI might play a fundamental role in natural resource management and policy analysis was already being speculated decades ago [30], to date, only a few studies have started to explore its potential. For example, recent work has shown that AI techniques can help identify hypothetical, optimal management strategies in food webs and improve conservation outcomes. In particular, the optimal management (species prioritization) approach identified by the AI through a greedy heuristic largely exceeded the performance (in terms of the number of species surviving) of other strategies based on food web structural properties or other criteria (such as cost–benefit trade-offs) [31]. However, the topic is still at a very theoretical stage. An AI-fueled tool capable of producing an “objective” list of conservation targets from ecological information is still a vision. Nevertheless, for other more specific and less ambitious tasks, novel technologies and the development of dedicated AI procedures have been producing promising results. Automated data collection through drones or camera traps, paired with image classification based on machine learning, is becoming an increasingly valuable resource for animal surveys in remote areas and in the fight against poachers (see, for example [32–34]). In a less applicative context, machine learning techniques have been used to assess the threat status of data-deficient species, reveal-

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ing that most of those (>60%) might be actually at risk of going extinct unnoticed [35]. Furthermore, the use of AI-derived techniques might contribute substantially to future technological development in renewable energy [36], which has clear important environmental and ecological implications. Nevertheless, it might still be a while before a computer relieves us from the burden of deciding which species should live or die. But, what if, when that moment comes, the AI decides that a panda is not worth conservation? Actually, the AI would not be the first to come up with this suggestion [37, 38], but the example helps convey the point. Assuming that technological development will force us, sooner or later, to leave the driving seat, it will be fundamental that the goals behind the AI’s choices are not ill-defined and bringing good to humanity and our planet. Conceptually, this issue is the same as training a neural network, which comes with the same risk of misunderstandings. While most of humanity struggles with everyday problems, a growing minority sees the planetary environmental crisis and climate change as vital issues. A narrow elite seems not to care about both, as long as these are not threatening the current unequal global distribution of richness and power [39]. And an even smaller minority is identifying a different problem—the “existential risk”—as the most important one for the future of humanity [40, 41]. The logic of this minority is not flawed. It is hard to think that something can matter more for a species than its survival, which is also consistent with the laws of nature. The existential risk does not only include the disappearance of humans from Earth, but also their failure in reaching a stage of technological maturity, or a scenario where technological maturity is reached, but in a “ill” way (e.g. with humans living as slaves of intelligent machines). Furthermore, if we define humanity as Earth-originating intelligent life, then the existential risk might be averted even in case Homo sapiens will evolve into another species—if such species will reach technological maturity [41]. Considering that we are constantly evolving (even if at an extremely slow pace), human speciation is an expected outcome of a long-time persistence of humanity permitted by technological realization. This expectation applies also to the possibility that future technologies will transform humans into different entities transcending the current biological concept of species. We also have to consider another possibility, which is the chance for the unexpected, independent evolution of an intelligent life form capable of technological development from another branch of the tree of life. The history of human evolution is highly debated and why we, Homo sapiens, are not sharing the Earth with other species of comparable intellectual abilities (particularly the Homo neanderthalensis) is still unclear. The classical hypothesis that we exterminated H. neanderthalensis populations is now being replaced by the idea that climate change could have instead played a fundamental role in the extinction of our close relative. In any case, considering how we are currently managing wildlife and natural resources, it seems reasonable to assume that, should a competitor of ours appear, we would not hesitate to get rid of it. The current extinction rate, one thousand times higher than the natural one and vastly surpassing speciation rate [42], is enough to reduce dramatically the chances for this to happen. Assessing the actual probability that another form of intelligent

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life with abilities comparable or higher than humans will evolve might be a fundamental research goal since the risks stemming from losing this opportunity could be even superior to that of human extinction. We might achieve some insights into that direction by a comparative analysis on the emergence of different levels of cognitive abilities across living organisms and by taking advantage of the increasing scientific knowledge of the genetic basis of animal intelligence. Here, considering the temporal scale is very important: when referring to the existential risk, we are not talking about what to expect for the next hundred, or thousand, or even million years. We are talking about expected events in a cosmological time scale [41], a time span certainly long enough for evolution to do a proper job in generating (new) intelligent life capable of reaching technological maturity. Looking at the issue from a more human-sized perspective, we might compare current trends in the loss of genetic diversity (and evolutionary history [43]). This approach could permit us to obtain a picture of how much we are reducing the chances that novel human-level or beyond intelligent forms will evolve in a reasonably near future (i.e. in the next few millions years). Many vital questions arise when we accept the possibility that another humanlevel or beyond intelligence might evolve. The most obvious is what could be the consequences for humanity, assuming we will be here to witness the event. Would this pose an immediate existential risk? If we consider the issue by taking our evolutionary history as a model, it would appear reasonable to assume that the new intelligent form will try to get rid of us as we possibly did with the Neanderthal (most likely succeeding in the task). However, we might look at the issue from a different angle. Suppose humans will face a permanent stagnation scenario of technological development (either reaching a plateau of submature development or with recurring events of collapse and recovery). Could the evolution of an alternative form of intelligence offer a chance to overcome the stagnation, either by helping us surpass our limits or being benign enough to coexist? Does the risk of being eradicated by the younger intelligence justify targeted effort to prevent its potential development? Or, instead, do the potential benefits deriving from its help in escaping permanent stagnation make running the risk worth, or even encourage us trying to maximize the chances for this to happen? From an even broader, moral perspective, if we consider the highest possible value that of many, long, fulfilling lives in a technological mature human civilization, should we attribute the same value to the fulfilling lives of another, younger, possibly unrelated species? Embracing this idea would encourage us to consider expanding the concept of existential risk to the whole genetic pool of Earth and would make reducing species extinction one of humanity’s most important objectives. There are also other aspects linking the idea of existential risk to the ongoing global environmental crisis. Despite being overflowed by technology, we still give great value to the wilderness. Thus, it is fundamental to hypothesize how environmental degradation/habitat loss trends will be affected by the potential rise of a superhuman AI. What could possibly be her “ecological niche”? Will the AI fit in a natural world, or will it exploit natural resources and destroy natural habitat to grow, eventually sterilizing the whole planet?

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In the latter case, it is hard to see a different future for human beings than one where they will be only allowed to experience virtual simulations of their “ancestral” world. However, the uniqueness of personal perception of reality is a fundamental pillar of human nature. It lays at the basis of any form of art and drives our daily existence. The set of synthetic experiences the AI might provide us with could be wide enough to give the impression of authenticity, but this begs the question of whether a simulated nature would be enough for a fulfilling life. A negative answer to this question implies a flawed realization and hence a substantial existential risk. In turn, it highlights the vital need, for the future of humanity, to ensure that preserving natural resources will be one of AI’s primary goals. This perspective also offers a way to reconcile current efforts in ecology and conservation with the work on AI development and humanity’s future. The current gap between the fields is surprising and not helpful. It is becoming more apparent how the collapse of natural systems, paired with the demographic momentum, will determine huge societal problems. Those could slow down the technological development and increase the existential risk by raising the chances for human extinction (following global catastrophes triggered by global change) or, more likely, bring humanity back to a dark era and possibly ground it in a scenario of permanent stagnation. These aspects, taken together, make it clear that targeting global environmental problems is key to reduce existential risk both in terms of permanent stagnation, extinction and flawed realization. As I write these lines, I see that they sound more appropriate to the opening chapter of a science fiction book than to the closure one of an ecological essay. Yet, underestimating the transformative power of technology and its fundamental role in shaping the future of our planet might easily add up to the list of biggest humanity’s mistakes. As it is happening for global population growth, also technological development is proceeding at an exponential, ever-accelerating pace. Every moment we hesitate in recognizing the emergency we are living in brings us closer to the point of no return. Two big questions arise from this consideration: Whether there is enough time left to steer out of the collision, and how we can do that? As for the first question, some state that we might already be beyond the point of no return. But, I do not want to close this book with a pessimistic note, so I would rather assume there is still time. For the latter question—how we avoid collapse—an easy rhetorical answer might be by enhancing the opportunities, facing the challenges and learning to navigate the risks of technological development. But the truth is that there is no silver bullet. Throughout these pages, I have done my best to pass the message that the world is amazingly complex and that we might never fully understand how natural systems work. Can technology—in the form of an AI—help us there? Possibly, but there is still a long way to go, at least if compared to the breakneck speed of the ongoing collapse. Is the overarching complexity a valid reason to give up our attempts to figure out the elusive mechanisms making the world run? Or, from a more personal perspective, would this be a reason for me to give up on science and look for a “real” job? Well, if I believed that, I would not have spent the last four years working on this book. In Japan, becoming a sushi chef, an “itamae”, requires many years of training. Depending on their ability—and on their mentor’s will—apprentices might spend

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years just washing rice before being promoted to other duties. That is to say, even a seemingly basic task might hide invisible layers of complexity, hence requiring time and dedication to be mastered. Now, in ecology, we are facing the no-exit situation of being left with little time to learn and a desperate need to become masters. We do not have years to practise our rice-washing abilities. Nevertheless, we cannot skip learning the bases before we move on. We have to keep in mind that our rice might be far from perfection and that this might affect the further steps of the preparation. Thus, as our understanding of the natural world improves and as technologies advance, it becomes fundamental not only to identify ambitious targets in unexplored territories but also to look back at the road that brought us here. Maybe we have missed something important along the way. Maybe there are paths we did not see. I hope these pages might have inspired you, dear Reader, to consider starting your own journey, and carry on the exploration. I will.

Summary The rapid technological development is generating unprecedented opportunities for scientific research. In particular, the development of artificial intelligence (AI) techniques, with computers becoming increasingly independent in performing tasks and—to a certain degree—taking decisions, is opening new frontiers. Machine learning techniques are now mainstream in many fields of research. For instance, they provide exceptional aid in automatic image classification of remote sensing imagery and look very promising as tools for identifying non-trivial relationships in large, multivariate datasets. However, as AI spreads across multiple scientific disciplines, awareness is increasing about the potential risk of relying on the “black box”, trusting predictions with a limited understanding of how these had been achieved. Studies focusing on the application of AI to specific contexts (image classification) have revealed that such risks are real: even when an AI is accurately trained, its thought processes might take unexpected directions. Furthermore, although AI has proven very efficient in specific settings, its applicability to complex ecological scenarios (e.g. as a tool to identify mechanisms and drivers of ecosystem stability and species diversity, or inform conservation decisions) seems still quite distant. Considering the issue from a much broader perspective and looking at the potential role of AI in the future of Earth’s natural systems, important philosophical considerations emerge. Regardless of the fate of humanity, averting the environmental crisis is necessary to reduce the so-called existential risk, which entails both the risk of human extinction and the risk that humanity will not reach technological maturity or will reach it in an ill way. Ensuring that future AIs will align with this goal should be therefore considered as a global priority.

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