Ecosystem Based Management for Marine Fisheries : An Evolving Perspective 9781139045094, 9780521519816

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Ecosystem-Based Management for Marine Fisheries Showing how big-picture patterns can help overcome the failures of conventional management, this book is ideal for students, researchers, and professionals involved with marine fisheries. It explores not only the current practice of the “ecosystem approach” to fisheries management, but also its critical importance to even larger perspectives. The first section gives a valuable overview of how more and more of the complexity of real-world systems is being recognized and involved in the management of fisheries around the world. The second section then demonstrates how important aspects of real-world systems, involving population dynamics, evolution, and behavior, remain to be taken into account completely. This section also shows how we must change the way we think about our involvement in, and the complexity of, marine ecosystems. The final chapters consider how, with the use of carefully chosen macroecological patterns, we can take important steps towards more holistic management of marine fisheries. ANDREA BELGRANO is a Senior Scientist with the Swedish Board of Fisheries, Institute of Marine Research, Lysekil, Sweden. His research deals with applying concepts of macroecology, food-web dynamics, and complex systems theory, mainly to marine systems and fisheries management. CHARLES W. FOWLER is Leader of the Systemic Management Studies Program at the National Marine Mammal Laboratory of the Alaska Fisheries Science Center (NOAA, NMFS) in Seattle. His research involves producing information to guide reality-based management towards sustainable human participation in ecosystems and the biosphere.

Ecosystem-Based Management for Marine Fisheries An Evolving Perspective A ndrea B elgrano Swedish Board of Fisheries, Institute of Marine Research, Lysekil, Sweden

C harles W. F owler National Marine Mammal Laboratory, Alaska Fisheries Science Center, Seattle, USA

ca m b r i d g e u n i v e r s i t y p r e s s

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Tokyo, Mexico City Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521519816 © Cambridge University Press 2011 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2011 Printed in the United Kingdom at the University Press, Cambridge A catalog record for this publication is available from the British Library Library of Congress Cataloging in Publication data Ecosystem based management for marine fisheries : an evolving perspective /  [edited by] Andrea Belgrano, Charles W. Fowler. p.  cm Includes bibliographical references and index. ISBN 978-0-521-51981-6 (hardback) 1.  Fishery management.  2.  Marine fishes – Ecology.  I.  Belgrano, Andrea, 1961–  II.  Fowler, Charles W. (Charles Winsor), 1941–  III.  Title. SH328.E28 2011 639.2–dc22    2010045707 ISBN 978-0-521-51981-6 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.

Contents

List of contributors  viii Foreword  xiii Alec D. MacCall

Acknowledgments for cover artwork  xvii

Introduction  1 Andrea Belgrano and Charles W. Fowler

Part I  Current forms of management  1. Food-web and climate-related dynamics in the Baltic Sea: present and potential future applications in fish stock assessment and management  9 Michele Casini, Christian Möllmann, and Henrik Österblom

2. Northwest Atlantic ecosystem-based management for fisheries  32 Jason S. Link, Alida Bundy, William J. Overholtz, Nancy Shackell, John Manderson, Daniel Duplisea, Jonathan Hare, Mariano Koen-Alonso, and Kevin Friedland

3. Alaska marine fisheries management: advances and linkages to ecosystem research  113 Patricia A. Livingston, Kerim Aydin, Jennifer L. Boldt, Anne B. Hollowed, and Jeffrey M. Napp

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4. A pragmatic approach for ecosystem-based fisheries assessment and management: a Korean marine ranch ecosystem  153 Chang Ik Zhang and Suam Kim

Part II  Elements of importance to management  5. Unintended consequences sneak in the back door: making wise use of regulations in fisheries management  183 Anne Maria Eikeset, Andries P. Richter, Florian K. Diekert, Dorothy J. Dankel, and Nils Chr. Stenseth

6. Population dynamic theory as an essential tool for models in fisheries  218 Mauricio Lima

7. Recovery of former fish productivity: philopatric behaviors put depleted stocks in an unforeseen deadlock  232 Henrik Svedäng, Massimiliano Cardinale, and Carl André

8. Boundary shifts: from management to engagement in complexities of ecosystems and social contexts  248 Peter J. Taylor

9. Civil society and ecosystem-based fisheries management: traditional roles and future opportunities  264 Tundi Agardy

Part III Using patterns  10. Science and management: systemically matching the questions  279 Charles W. Fowler and Larry Hobbs

11. Sustainability, ecosystems, and fishery management  307 Charles W. Fowler and Shannon M. McCluskey

Contents

12. On the path to holistic management: ecosystem-based management in marine systems  337 Andrea Belgrano and Charles W. Fowler

Afterword  357 Keith Brander

Index  362 The color plates are to be found between pages 238 and 239.

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Contributors

Tundi Agardy Executive Director Sound Seas Colrain, MA, USA

Carl André Department of Marine Ecology Göteborg University Strömstad, Sweden

Kerim Aydin National Oceanic and Atmospheric Administration National Marine Fisheries Service Alaska Fisheries Science Center Seattle, WA, USA

Andrea Belgrano Swedish Board of Fisheries Institute of Marine Research, Lysekil, Sweden

Jennifer L. Boldt Fisheries and Oceans Canada Pacific Biological Station Nanaimo, BC, Canada

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List of contributors

Keith Brander DTU Aqua – Danish Institute of Aquatic Resources Technical University of Denmark Charlottenlund, Denmark

Alida Bundy Department of Fisheries and Oceans Bedford Institute of Oceanography Dartmouth, NS, Canada

Massimiliano Cardinale Institute of Marine Research Swedish Board of Fisheries Lysekil, Sweden

Michele Casini Swedish Board of Fisheries Institute of Marine Research Lysekil, Sweden

Dorothy J. Dankel Institute of Marine Research, Bergen, Norway

Florian K. Diekert Centre for Ecological and Evolutionary Synthesis (CEES), Oslo, Norway

Daniel Duplisea Department of Fisheries and Oceans Institut Maurice-Lamontagne Mont-Joli Québec, Canada

Anne Maria Eikeset Centre for Ecological and Evolutionary Synthesis (CEES), Oslo, Norway

Charles W. Fowler National Oceanic and Atmospheric Administration National Marine Fisheries Service Alaska Fisheries Science Center National Marine Mammal Laboratory Seattle, WA, USA

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List of contributors

Kevin Friedland National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Narragansett, RI, USA

Jonathan Hare National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Narragansett, RI, USA

Larry Hobbs Inland Whale Bainbridge Island, WA, USA

Anne B. Hollowed National Oceanic and Atmospheric Administration National Marine Fisheries Service Alaska Fisheries Science Center Seattle, WA, USA

Suam Kim Pukyong National University Busan, Korea

Mariano Koen-Alonso Department of Fisheries and Oceans Northwest Atlantic Fisheries Centre St. John’s, Newfoundland & Labrador, Canada

Mauricio Lima Center for Advanced Studies in Ecology and Biodiversity Pontificia Universidad Católica de Chile Santiago, Chile

Jason S. Link National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Woods Hole, MA, USA

Patricia A. Livingston National Oceanic and Atmospheric Administration National Marine Fisheries Service

List of contributors Alaska Fisheries Science Center Seattle, WA, USA

Alec D. MacCall National Oceanic and Atmospheric Administration National Marine Fisheries Service Southwest Fisheries Science Center Santa Cruz, CA, USA

John Manderson National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Sandy Hook Highlands, NJ, USA

Shannon M. McCluskey Murdoch University Cetacean Research Unit Murdoch University Murdoch, Western Australia

Christian Möllmann Institute for Hydrobiology and Fisheries Science University of Hamburg Hamburg, Germany

Jeffrey M. Napp National Oceanic and Atmospheric Administration National Marine Fisheries Service Alaska Fisheries Science Center Seattle, WA, USA

Henrik Österblom Baltic Nest Institute, Stockholm Resilience Centre Stockholm University Stockholm, Sweden

William J. Overholtz National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center Woods Hole, MA, USA

Andries P. Richter Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, the Netherlands.

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List of contributors

Nancy Shackell Department of Fisheries and Oceans Bedford Institute of Oceanography, Dartmouth, NS, Canada

Nils Chr. Stenseth Centre for Ecological and Evolutionary Synthesis (CEES), Oslo, Norway

Henrik Svedäng Swedish Institute for the Marine Environment Göteborg, Sweden

Peter J. Taylor Programs in Science, Technology & Values Critical & Creative Thinking and Public Policy University of Massachusetts Boston, MA, USA

Chang Ik Zhang Pukyong National University Busan, Korea

Foreword a l e c d . m acca l l

The necessity of an ecosystem approach (EA) to fishery management has been gaining worldwide recognition in recent years. This concept provides an attractive alternative to viewing fisheries mainly as a profit-oriented economic enterprise, especially given a growing dissatisfaction with a long, mixed, and in some cases dismal record of resource overfishing, depletion, and collapse. Though it is intuitively appealing, the EA concept has been rather difficult to define formally. Yet, something called “an ecosystem approach” is now being attempted in many different fisheries systems – collectively characterized with as much variety as common ground. There are yet more proposals for how to implement an EA waiting to be put into practice. The diverse collection of examples in this book provides an overview of EA both in current practice and future potential, and may contribute a subjective sense of what EA actually is, whether or not we attempt a definition. Part of the difficulty in defining EA is that it reflects a set (or sets) of values, and human values notoriously elude formal definition. Such plausible concepts as “ecosystem health” are value-based, and it may be a false assumption that they can be measured and defined objectively. However, once the subjective terms of reference for EA have been established, some degree of conditional objectivity may become possible. Man is the only creature that frequently takes action based on an abstract, imagined, and even probabilistic future. Our thinking capacity is specialized for this unique talent, and with modern scientific and technological advances, our capability to anticipate possible future realities has reached truly astounding levels, especially in the physical sciences. But the level of understanding and predictability in the physical world far exceeds what can be accomplished in the natural biological world. In the physical world we have the benefit of

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Foreword understanding “laws of nature” that always work the same and confer precise predictability, at least in simple systems. In contrast, biological systems are extraordinarily complex and difficult to predict. Part of the problem is that individuals, populations, and ecosystems do not so much follow “rules of nature” but rather play “games of nature.” The basis for this sort of natural gamesmanship is evolution itself – the Darwinian law of biological nature (albeit probabilistic), that there are winners and losers at all levels of biological organization, and over time, the properties of the winners tend to propagate differentially. As Cro-Magnon man emerged from the last ice age into a more temperate world, the species was one of the biggest evolutionary-ecological winners in the history of the planet, quite possibly due to traits such as abstract future planning that were strongly adaptive in the harsh ice-age environment, but that found unanticipated utility in a milder and productive post-glacial environment. A question now facing humanity is, where does the evolutionary-­ecological game go next? Due to mankind’s own evolutionarily tentative success, the global environment is again changing on a scale equivalent to the end of the last ice age. With deforestation and desertification, continental climate patterns have changed measurably just in the last few centuries. Marine ecosystems are being impacted by industrialized harvesting at unprecedented levels. Atmospheric changes are generating both a warming trend and ocean acidification, with drastic consequences. Within this environmental turmoil, the ecological­evolutionary game will continue, despite current human ­dominance – in fact, involving the effects of that dominance. Mankind appears to be at the limit of its global ecological niche, given the rapidly declining ability of natural resources to sustain current human population growth rates. Technology is certainly a key factor in the ecological-­evolutionary game  – it is fair to say that the fate of all species (mankind included) now depends on what we do with it. More than generation of energy, extraction of raw materials, or improvement in agricultural yields, the science of ecology and its application to the management of human impact may ultimately be one of the most important aspects of mankind’s strategy for the coming rounds of the game. Never before has a species had the potential to understand the ecological-evolutionary game itself, and to apply that understanding to evaluating present management choices with regard to future probabilities. This is the essence of an ecosystem approach. Many fishermen and managers seem to consider EA to be a feel-good, annoying complication to the practical business of fishing. In the long run it is clearly more important than that, but its importance depends very much on the time frame or “planning horizon” for our management decisions. The great majority of people (including a significant fraction of fishery decision-makers) have

Foreword such a short planning horizon that their main criterion is little more than maintenance of the status quo. The corresponding planning horizon is perhaps on the order of one year, and this time scale has been appropriate for most human decisions for thousands of years. Surviving through the next winter, or the next drought, has long been a primary concern. A modern equivalent may be “making the next boat payment.” Maintaining the status quo has an additional political benefit in that it is easily understood and popular. But with modern technological fishing capacity it may have become a losing strategy in the game. For fisheries, status quo does not directly translate to “sustainable” except in the increasingly rare cases of very light exploitation. In the middle of the last century, fishery science developed predictive models of single-species populations, partly in response to evidence that unmanaged fisheries tended toward severe depletion, and that higher levels of sustained productivity could often be accomplished by placing restraints on fishing activity. Initially, the new goal was “maximum sustainable yield” (MSY) which was equivalent to the largest constant yield that can be maintained as an ongoing status-quo policy. It wasn’t long before the weakness of this concept became apparent – being the maximum, a constant harvest at this level renders the system unstable and will eventually fail because of natural fluctuations. Nonetheless, MSY is still a governing feature of many fishery management treaties and laws. Nowadays, the issue tends to be resolved by re-defining MSY. One method is to interpret it as the largest long-term average achievable under a more adaptive stabilizing catch policy. In the last few decades, quantitative single-species fishery models have achieved a fair degree of success (and some notable failures) in predicting responses to changes in fishing intensity. Consequently, fisheries managers now often have planning horizons of five to ten years, and status quo is just one alternative in a dynamic range of management actions. But fishery science and management is now undergoing yet another transition. With longer observational histories, the effects of fishing on interacting stocks and ecosystems are becoming measurable. Mandated attempts at stock rebuilding are forcing consideration of longer planning horizons and fishery interactions. These management complications, together with the uncertain future of entire ecosystems have given rise to a more comprehensive approach. The trend toward that approach is the subject of this book. But circumstances are overtaking us. There is general recognition of impending human-caused climate change, on a time scale that extends over centuries, and on a physical scale that is difficult to conceive, except for the perhaps ironic similarity to the end of the last glaciation that created our species. An “ecosystem approach” finally recognizes mankind as a powerful player in the

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Foreword natural game  – a player potentially as powerful as all of the rest of the biological players combined.1 It remains to be seen whether (or more accurately, for how long) man can sustain this status of ecological hegemony. There is no question that ecosystems will ultimately adjust and rearrange themselves in view of such a powerful influence. However when the intensity of human fishing pressure is combined with the scale and diversity of physical-biological changes associated with global climate change, massive ecosystem changes are likely to be seen sooner rather than later. Now, just as an ecosystem approach is becoming a mandate for management, humanity is beginning to grapple with the final level of ecological consideration: management of our participation in the global system. For a single species that has now placed itself equivalent to all other species on the planet, a global perspective is an imperative if humanity expects to find any semblance of ecological sustainability through the next few centuries. Alec D. MacCall Santa Cruz, CA

For example, this remarkable status is implied by the widespread rule-of-thumb that optimal fishing rates are similar to natural mortality rates (expressed as Fmsy = M), and this is equivalent to saying that at nominally optimal fishing intensities, human predation is equal to that of all other predators combined.

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Cover artwork

The artwork on the cover is based on a photograph of a rock carving of a fish found along the Norwegian coastline, near Hjemmeluft (www.verdensarvenialta.no/english/container3.htm). Worldwide, fish were more than simply a commodity to indigenous peoples. The macroecological patterns found in ecosystems around the Earth also have a subjective quality we only superficially appreciate in the graphs we use to represent them. The infinite behind the origins of observed phenomena promotes holism in management when such patterns are embraced as guiding information. This book aims toward that holism in moving toward, through, and beyond ecosystem-based management in marine environments. We thank the World Heritage Rock Art Centre, Alta Museum, Alta, and Professor Knut Helskog of Tromsø University Museum, Norway, for making this photo available.

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Introduction a n d r e a b e l g r a n o a n d c h a r l e s w. f o w l e r

This volume had its origins just after we published our joint paper on pattern-based policy (Belgrano and Fowler 2008). We were approached by Cambridge University Press with the proposal to publish a book on Ecosystembased Fisheries Management in marine systems. In agreeing to do so, the title we proposed was: “Ecosystem-based Management for Fisheries: Linking Patterns to Policy.” To achieve holism, the mission of being interdisciplinary has been stressed throughout a growing body of literature that presents tenets, pillars, or commandments to which management should adhere. We both had an abiding interest in macroecology. It seemed time to weave macroecology into the folds of management in what we referred to as “pattern-based” management, or systemic management, wherein deviations or abnormality observed in macroecological patterns serves as the basis for restorative action, or standards for sustainable human interactions with the non-human. Management involves maintaining such sustainability. With this in mind, our plan was to produce an edited volume that would add macroecology to the other disciplines that were already being seriously considered. We circulated our proposal to prospective authors, stressing the objective of making use of macroecological patterns. We hoped to have chapters broadly representative – geographically, scientifically,

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Introduction and across the spectrum of academic, governmental, and non-governmental organizations and authorship. The chapters that materialized accomplished many of these objectives. We fell short, however, in the objective of having a book in which most, or all, chapters dealt directly with macroecological patterns – particularly as they can be used in management. In taking advantage of what we had to work with, it became clear that the fields of both science and management remain in a state of transition wherein ecosystem-based management for fisheries is still seen largely as the objective rather than as one of many steps toward fully holistic management. As in the evolution of anything, management has to move through its individual steps and it became clear to us that managers, scientists, and stakeholders have not fully grasped the essence of ecosystem-based management for fisheries in order to move on to more inclusive perspectives. With this in mind, we had to address an important question:  “Given the situation in our world, as represented by the chapters we have in hand, what is being accomplished?” The answer seemed to be that we are, in fact, moving forward and that a more complete grasp of ecosystem-based management for fisheries will help perceive more clearly the steps that lie ahead. There is progress. We decided to adopt a new title: “Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective.” With this perspective, the chapters of this book treat various elements of management, various aspects of ecosystems, and contributions that research makes to understanding the complexity of both. Taylor’s chapter (Chapter 8) exemplifies the evolutionary aspect of progress being made; we consistently expand our frontiers as we make progress in the evolution of our thinking. Agardy (Chapter 9) emphasizes the human element of carrying out management with an eye toward achieving holism in the process; non-governmental organizations can be quite effective in contributing to change and educating stakeholders. Casini et al. (Chapter 1), Link et al. (Chapter 2), and Livingston et al. (Chapter 3) describe research and management in different areas of the globe to exemplify how complexity is now taken into account, especially in terms of generating models of ecosystems – in part, to predict the effects of fishing. Ecosystems are recognized as complex systems. Zhang and Kim (Chapter 4) provide an example of similar work undertaken in the context of an intensive set of human factors – also incredibly complex. Eikeset et al. (Chapter 5) make it clear that if we ignore the complexity of ecosystems, including the human component, we face a very real risk of consequences completely unforeseen; higher-order interactions are part of the complexity of systems and result in unintended reactions to management. Lima (Chapter 6) emphasizes the risk of carrying out management that neglects the complexity of population dynamics

A. Belgrano and C.W. Fowler and predator–prey systems; ignoring the subsystems of ecosystems is a source of serious risk. Similarly, Svedäng et al. (Chapter 7) emphasize the risk of ignoring the behavioral nature of species with populations that serve as integral components of ecosystems. Fowler and McCluskey (Chapter 11) use the declining trend in recommended fishing rates that accompany greater consideration of complexity, and relevant macroecological patterns, to estimate fishing rates that can be considered holistic – fishing rates that apply to individual species, species groups, ecosystems, or the marine environment. For further progress toward holism, Fowler and Hobbs (Chapter 10) show how to ask even more management questions and make use of macroecological patterns in each case, thus converting management to a process that is open to many options, each treated in a fully integral manner, including the option of treating the evolutionary impact of fishing directly. In our overview chapter “On the path to holistic management: ecosystembased management in marine systems” (Chapter 12), we show how the work represented by the other chapters fits into the evolution of management as it progresses, bit by bit, toward holism. The chapters of this book help substantiate the basic principles upon which progress toward holistic management depends. That progress includes progress toward the fully integrative aspects of holistic management that can be achieved by weaving use of macroecological patterns into the goal-setting process. The change that can happen, as facilitated through education (Chapter 9), has yet to be realized. We chose to organize the chapters of this book in a way that characterizes the progress being made as steps toward bringing macroecology, along with evolutionary ecology, into the realm of management. The first four chapters provide examples in which the complexity of ecosystems is known to include a large collection of different factors involving the physical environment, trophic interactions, food webs, and other ecological relationships. These chapters represent steps toward including the effects of humans in such systems. They also illustrate the kinds of science that are currently being used to make management decisions – often seen as very sophisticated attempts to bring ecosystems into the management process. The next five chapters present arguments for progress toward greater holism. Several stress the importance of including, or accounting for, greater complexity than is currently accomplished. In Chapter 5, Eikeset et al. argue that imperfections in the ways we account for complexity (including the complexity of human systems) result in unforeseen reactions to our management – many of which can be recognized as problematic. Evolutionary reactions to fishing are real and need to be accounted for in management. Lima (Chapter 6) argues that interactions among species that involve their basic population dynamics

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Introduction are elements of complexity that cannot be ignored in progress toward any form of ecosystem-based management. Svedäng et al. (Chapter 7) add behavior to the list of factors that have to be considered as fundamental elements in the complexity of ecosystems. Agardy (Chapter 9) describes how stakeholders, especially non-governmental organizations, are involved in management. This involvement is a critical component of both change toward greater holism in management (e.g., through education) and the means of weaving macroecology and macroecological patterns into the management process. Taylor (Chapter 8) makes a strong case that something like a different paradigm is needed in order to accomplish holistic management. This may occur as a stepwise process (pushing forward our boundaries) of which ecosystem-based management is a step in the right direction. Fowler and Hobbs (Chapter 10) draw upon the principles emergent from work such as that represented in the first nine chapters to bring added complexity into the management process. This is accomplished, in this case, by addressing as many management questions as possible with carefully chosen patterns, especially macroecological patterns in the case of ecosystem-based management. Fowler and McCluskey (Chapter 11) provide illustrative examples of how holism is involved in the setting of goals by addressing a number of specific management questions with macroecological patterns. Finally, our overview chapter (Chapter 12) synthesizes the structure of the book as it leads to the weaving of macroecological patterns into the management process and, at the same time, achieves holism heretofore not seen. In our 2008 paper, we used the symbol “∞” intentionally. Fully holistic means that nothing is excluded in the way management is carried out, in what is taken into account, and in the things to which management applies. The risk of extinction, all evolutionary processes, and the full spectrum of ecological dynamics are included  – as are all behavioral, hormonal, physiological, and molecular processes. All aspects of factors listed in the chapters of this book are included. In our paper, it was our strong contention that the integrative nature of natural patterns permits their being used for holistic management. It is our sincere hope that this book will provide material helpful to achieving holism in the management process – not simply in regard to marine fisheries, but in terms of management as it applies in all realms. We intend the meaning of the term holism to be that of the fully integral nature of natural patterns – it excludes nothing. When, and if, global society can get to a point of managing holistically, it is our hope that we have learned from experience in today’s world and that the lesson will be to remain open-minded about the possibility that even further steps will be necessary.

A. Belgrano and C.W. Fowler We extend our sincere thanks to all who contributed to the production of this volume. Our gratitude includes an appreciation of the work by all of the authors, as well as by all of the people whose help and support they received along the way. In our cases, these special contributions include our wives, Elisabeth and Jean. Alec MacCall very kindly agreed to write the Foreword and Keith Brander the Afterword; we very much appreciate their contributions. We are deeply thankful to the editorial staff at Cambridge University Press for all of their efforts, help and advice in the publication process (particularly: Denise Cheuk, Joanne Endell-Cooper, Jacqueline Garget, Helen Flitton, Chris Hudson, Dominic Lewis, Hilary Mead, and Lynette Talbot). Finally, we want to extend special compliments to Lesley Bennun (copy editor) for hours of work that greatly improved the quality of this book. Reference Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In Munoz, S.I. (ed.), Ecology Research Progress. Hauppauge, NY: Nova Science Publishers, pp. 5–31.

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PA RT I   CURRENT FORMS OF MANAGEMENT

Chapters 1 through 4 set the stage for the trend represented in this book; a trend in which more and more of the complexity of real-world systems is recognized and involved in management. The chapters of this section typify the sophistication brought to the process of science in support of management as it is currently implemented in many parts of the world. This sophistication often involves numerous elaborate models and complicated management plans. Such approaches exemplify a focus on ecological processes (e.g., predator– prey interactions, competition, trophic dynamics, and food webs) in which we humans inject ourselves by harvesting fish. Much of this work involves overt recognition of the complexity of natural systems and substantiates our understanding of complexity as a fundamental principle. Complexity is recognized, for example, in lists representing the numerous species with populations in ecosystems affected by human activities such as commercial fishing, pollution, and changes in acidity. Inherent to the factors important to the structure and function of ecosystems are environmental dynamics involving temperature, the circulation of currents, and a wealth of other physical and chemical factors. Such work is crucial to substantiating a variety of principles that are fundamental to progress toward more holistic management.

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Food-web and climate-related dynamics in the Baltic Sea: present and potential future applications in fish stock assessment and management michele casini, christian möllmann, and henrik österblom

Abstract The human influence on marine ecosystems is being recognized as a basis for extending the horizons of management. Historical anthropogenic influence has involved a wide variety of factors, including the effects of fishing on the dynamics of individual resource species. More inclusive complexity includes the interactions among species, and their interactions with other aspects of their biotic and physical environment. In this chapter, we review these elements of complexity for the central Baltic Sea. This ecosystem has a long history of human influence and its own special characteristics, due to its geographic location, geomorphic traits, and socio-political context. More and more of the complexity of this ecosystem is being recognized as scientists add to the wealth of documentation regarding the influence of surrounding terrestrial activities, monitor the dynamics of component populations, establish the effects of weather and climate, and illuminate the relationships among the various elements of the ecosystem. There is a great deal of historical information to characterize the changes that have occurred, not only among the various species making up the ecosystem, but also at the ecosystem level. Some of these have involved regime shifts, in part owing to climatic factors. Such

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Food-web and climate-related dynamics in the Baltic Sea significant changes involve more than one of the ecosystem’s trophic levels as well as physical features such as salinity, temperature, and oxygen concentration. We begin to understand some of the complexity of ecosystems when we recognize that such factors are not alone, however, and realize that trophic cascade dynamics and feedback loops are also involved. Of particular importance for fisheries management is the clarity with which we have observed, documented, and explained some of the effects of fishing, not simply as an influence on individual resource species, but on the ecosystem as a whole. Some of these ecological aspects are currently used in fish stock assessment and management in the central Baltic Sea, but recently acquired additional knowledge could be potentially used in the future to reach the goal of a sustainable exploitation of fisheries resources in this area. Introduction Human activities influence marine ecosystems in multiple and interactive ways (Jackson et al. 2001, Hughes et al. 2003, Halpern et al. 2008). Not only has commercial fishing a substantial impact on ecosystem structure and function by removing target fish biomass (Folke et al. 2004, Frank et al. 2005), but it also influences non-target species and habitats (see e.g., Browman et al. 2004). In addition, fish stock dynamics are influenced by variations in climate, introduced species, and water quality (Daskalov et al. 2007). The Baltic Sea is situated in a region where human impacts on the marine ecosystem have been substantial for over a century (Österblom et al. 2007, Eero et al. 2008). Extensive seal hunting, nutrient runoff from agriculture and municipalities, toxic compounds with a significant impact on top predators, high fishing mortality resulting from commercial and recreational fishing, and changes in climate, all have influenced this semi­enclosed sea. The Baltic is a particularly sensitive sea, being characterized by low salinities whose annual variations can have a large impact on species abundance, composition, and distribution (Voipio 1981). At the same time, as it is severely impacted by human activities and hydro-climatic conditions, it is also a sea with clear political boundaries. All nine bordering countries (except Russia) are part of the European Union (Fig. 1.1), leading to a common policy framework being available for joint action. The framework includes the Common Agriculture Policy, the Common Fisheries Policy, the Water Framework Directive (EC 2000) and the Marine Strategy Directive (EC 2008). These policies and directives all have a substantial impact on the sectors influencing the marine environment. The Marine Strategy Directive, in particular, has laid the foundation for integrating these tools toward the

M. Casini et al.

Fig. 1.1.  Map of the Baltic Sea. The central Baltic Sea (Baltic Proper) is marked in dark gray.

end of sustainable management of European seas. Management in the Baltic Sea is already ahead of the timetable laid out in the directive. The Helsinki Commission (HELCOM), the international governing environmental body for the Baltic Sea (also including Russia) has developed a joint action plan (the Baltic Sea Action Plan:  HELCOM 2007), addressing several of the relevant issues. Nevertheless, much remains to be done. There is however a relatively strong political will in the region to address marine issues and the Baltic can thus be said to be well poised to become a pilot area for implementing an ecosystem approach in a European context. In this chapter, we treat the concept of ecosystem-based management for fisheries (EBMF) focusing on its ecological aspects (food web and climate impact). In other words, EBMF is handled by taking into consideration the effects of fisheries on the ecosystem, and the effects of ecosystem on exploited fish and fisheries. We shortly present an account of the existing knowledge of the Baltic Sea ecosystem functioning and how this knowledge is currently used in fish stock assessment and fisheries management advice. Furthermore, we propose the potential use of additional information in the forthcoming assessment and management activities. We focus on the central Baltic Sea (the so-called Baltic Proper) because of the more detailed knowledge of this region if compared

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Food-web and climate-related dynamics in the Baltic Sea with other areas of the Baltic Sea. However, other areas will be also taken into consideration for comparison and completeness. Current ecosystem knowledge This section describes briefly the present knowledge of the general ecosystem functioning and specific ecological links in the Baltic Sea. The information provided here is based on data from the recent three decades (1974–2008), this choice being dictated by the fact that stock assessment data in the Baltic Sea are available for all the three main species – cod (Gadus morhua), herring (Clupea harengus), and sprat (Sprattus sprattus) – only during this period. Fish stock dynamics

In general, a high cod population is accompanied by low sprat population (ICES 2009a), since sprat is the main fish prey for cod. During periods of low cod biomass, a consequence of adverse hydro-climatic conditions and/or fishing, sprat is released from cod predation (Casini et al. 2008). During the last three decades, following the collapse of the cod stock, the sprat stock increased fourfold, shifting the Baltic from being cod-dominated to being sprat-­dominated (Fig. 1.2A). However, the sprat population is also favored by mild weather which supports sprat egg production and survival (Nissling 2004) and larval growth (Baumann et al. 2008) as well as the development of the main prey for larval sprat, the copepod Acartia spp. (Voss et al. 2003, Alheit et al. 2005). On the other hand, the decrease of the herring population during the past three decades is likely due to the synergistic effects of high fishing pressure (ICES 2009a), eutrophication-related degradation of coastal spawning grounds (Cederwall and Elmgren 1990), and decrease in mean body growth (Casini et al. 2010a; see next section) (Fig. 1.2B). The period considered here is characterized by low abundance of aquatic mammals, resulting from declines observed through most of the 1900s due to human activities (Österblom et al. 2007). Therefore, the potential effect of a large population of aquatic mammals on the fish community is not well understood. However, a modeling work by Österblom et al. (2007) predicted that in the presence of seals and harbor porpoises, as in the first half of the 1900s, both cod and clupeid populations could be maintained low by predation, at least at low levels of system productivity. Overall ecosystem change

Recently, Integrated Ecosystem Assessments (IEA) have been conducted for the central Baltic Sea (ICES 2008a) revealing a regime shift in the pelagic ecosystem during the late 1980s and early 1990s (Möllmann et al. 2009; Fig. 1.3),

M. Casini et al.

Sprat Herring Cod

3000 2500

600

1500

400

1000

200

500

0

60.0

1.4

50.0

1.2 1.0

40.0

0.8

30.0

0.6

20.0

0.4

10.0

0.2

0.0

0.0 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Clupeid mean weight, age 3 (g)

1000 800

2000

0

B

1200 Cod biomass (1000 tons)

3500

Cod mean weight, age 4 (kg)

Clupeid biomass (1000 tons)

A

Year Fig. 1.2.  Trends in total biomass and mean weight in the stock of sprat (whole Baltic Sea, SDs 22–32), herring (central Baltic stock, SDs 25–29, excl. Gulf of Riga) and cod (Eastern Baltic stock, SDs 25–32). Data from ICES 2009a.

similar to events detected in several North Pacific and North Atlantic marine ecosystems (e.g., Hare and Mantua 2000, Link et al. 2002, Beaugrand 2004, Choi et al. 2005, Weijerman et al. 2005). Two regimes (1974–1987 and 1994–2005) were identified, characterized by the opposite dominance of cod and sprat (see above), as well as the zooplankton species Pseudocalanus spp. and Acartia spp. (MacKenzie et al. 2008, Möllmann et al. 2008). Furthermore, a change in the dominance of phytoplankton from diatoms to dinoflagellates has been indicated (Wasmund et al. 1998, Alheit et al. 2005). The central Baltic Sea regime shift occurred in a transition period (1988–1993) characterized by low salinity and oxygen conditions as well as high temperatures and nutrient levels, eventually forcing the biotic part of the ecosystem into a new state (Möllmann et al. 2009). In addition to the physical and chemical conditions, unsustainable cod fishing pressure during the late 1980s contributed to the overall ecosystem changes, favoring the cod decrease and the

13

14

Food-web and climate-related dynamics in the Baltic Sea

Fig. 1.3.  Traffic-light plot representing the development of the central Baltic Sea ecosystem; time-series transformed into quintiles and sorted according to PC1; red represents high values while green represents low values of the respective variable. Modified from Möllmann et al. (2009). This figure is also reproduced in the color plate section.

consequent increase in sprat population, with further indirect changes down the food web (Casini et al. 2008, Möllmann et al. 2008; see next section). The integrated ecosystem assessment also provided indications that the observed changes can be described as a discontinuous regime shift where feedback loops stabilize the new regime which then represents a true alternative stable state (Scheffer et al. 2001, Scheffer and Carpenter 2003, Collie et al. 2004, Bakun and Weeks 2006). Plausible explanations for this phenomenon are the existence of trophic cascades, threshold dynamics, and prey-to-predator feedback loops (Casini et al. 2008, 2009, Möllmann et al. 2008, 2009; see below). Ecosystem effects of fishing – trophic cascades and other indirect effects An increasingly observed phenomenon involved in the reorganization of marine ecosystems is trophic cascading which involves a domination of

M. Casini et al. t­ op-down controls (Strong 1992). These processes have been shown in a wide variety of marine ecosystems (Pace et al. 1999; see also Casini et al. 2008 and references therein). Also in the Baltic Sea, large changes at the top of the food web (e.g., in the cod stock) have been shown to trigger chains of events propagating down the food web, leading to a number of direct as well as indirect effects on other trophic levels of the ecosystems. Fish–plankton

In the central Baltic Sea, the decrease in the cod stock has been one of the main causes of the zooplanktivorous sprat outburst (Casini et al. 2008), which in turn has altered the lower trophic levels. In fact, a species-level trophic cascade has been detected for spring decreasing the population of the copepod Pseudocalanus spp. (Möllmann et al. 2008), while for summer a community-level cascade appears in reduced total zooplankton biomass and increased phytoplankton biomass (Casini et al. 2008). In summer, moreover, the effects of the large sprat stock are also evident in other features of the zooplankton community, as altered species composition, stage composition, and vertical distribution (Casini et al. 2009). Herring, on the other hand, seems not to influence the open sea zooplankton (Casini et al. 2008), probably because of the omnivorous nature of the bigger individuals and the coastal distribution of the strictly zooplankton-feeding smaller herring. However, the generally lower stock abundance of herring, if compared with sprat, during the past three decades could also partially explain the lower impact of herring on zooplankton resources in this period. See Fig. 1.4 for a simplified conceptual food web of the central Baltic Sea ecosystem. Fish growth

Sprat and herring growth are heavily affected by density-dependent factors. The drastic increase of the sprat population in the early 1990s caused an abrupt decrease in both herring and sprat weight-at-age (Cardinale and Arrhenius 2000a, ICES 2009a, Casini et al. 2010a; Fig. 1.2B) as well as condition (Cardinale et al. 2002, Möllmann et al. 2005, Casini et al. 2006), caused by the strong intra- and inter-specific feeding competition among clupeids. The decrease in herring growth, however, could have been initiated and facilitated by a decrease in salinity and bottom oxygen, affecting respectively one of the main prey items for herring, the copepod Pseudocalanus spp. (Möllmann et al. 2003, Casini et al. 2010a), and the abundance of zoobenthic prey for larger herring. Arrhenius and Hansson (1993) showed, in fact, that a diet constituted of zoobenthos results in increased body growth of larger herring. Fishing activity, which selects larger and rapid-growing individuals, could also have contributed

15

16

Food-web and climate-related dynamics in the Baltic Sea

Fishery

Cod

Cod Density-dependent body growth

Herring

Density-dependent body growth

Sprat

Birds’ chicks growth

Sprat

Herring

Zooplankton

Zooplankton

Phytoplankton

Phytoplankton

Climate/ Hydrography & Nutrients

Top-down control and density effects

Climate and bottom-up control

Fig. 1.4.  Simplified ecological interactions in the central Baltic Sea. Continuous arrows indicate top-down links, whereas dashed arrows indicate bottom-up (including hydro-climatic effects) links. Top-down and bottom-up (including climate) interactions co-act in shaping food-web structure and dynamics.

to the observed reduction of clupeid mean weight and condition (Rahikainen and Stephenson 2004, Vainikka et al. 2009). Cod growth increased when the stock started to decrease in the 1980s (ICES 2009a; Fig. 1.2B), potentially indicating a density-dependent effect. However, since the early 1990s, cod growth decreased again despite an enduring low stock, this indicating that factors other than direct density-dependence have affected cod growth during the past 15 years. Top predators: seabirds and mammals

There are indications that changes in fish stock abundance and quality influence marine seabirds. The dramatic changes in the sprat stock during the past three decades, and the related decrease in sprat weight-at-age and condition, appears to have influenced a sprat-feeding seabird, the common guillemot (Uria aalge). In the Baltic Sea, the adults of this species feed their chicks almost exclusively with sprat and chicks’ weight seems to directly follow changes in sprat condition (Österblom et al. 2006; Fig. 1.4). The digestive capacity of the chicks appears ill equipped to deal with what is presumably reduced energy content of their prey. So far, there are no indications that a decrease in weight

M. Casini et al. has a negative influence on chick survival in the Baltic Sea (Kadin 2007), but the response of chick weight to sprat condition does provide yet another example of how changes in the food web are readily transferred among trophic levels. The possible effects of seabird consumption on fish stocks are currently not well known, but there are indications that marine mammals could have influenced fish stock dynamics in the early 1900s when nutrient levels were very low (Österblom et al. 2007). However, there are no indications that mammals (primarily gray seals) at current levels of abundance influence fish stocks directly to any detectable extent. Threshold dynamics and feedback loops

Apart from the general concurrent effects of bottom-up (including climate) and top-down processes, it has been shown that the relative strength of these two opposite forms of ecosystem dynamics and regulation can vary in the Baltic Sea (Casini et al. 2009, 2010a). For instance, a mechanistic investigation of the food web showed that the central Baltic Sea ecosystem can be found in two alternative configurations, in which either hydro-climatic forces or top-down processes, respectively, are the main regulators of zooplankton dynamics (Casini et al. 2009). Whether the ecosystem is in one configuration or the other depends on the abundance of the highly influential planktivore sprat, whose dynamics are in turn heavily affected by cod biomass (Casini et al. 2009) (Fig. 1.5). Similar threshold-like shifts have been observed in herring growth, being either climate- or density-dependent depending on the level of the sprat population (Casini et al. 2010a). This emphasizes the importance of large predatory fish in the maintenance of ecosystem functioning and resilience. As said before, a high cod population seems to be very effective in controlling sprat population via predation pressure. In turn, a high sprat population could potentially influence cod recruitment via predation on cod eggs (Köster and Möllmann 2000, Köster et al. 2001) and competition with cod larvae for zooplankton resources, especially the copepod Pseudocalanus spp. (Möllmann et al. 2008, Casini et al. 2008; Fig. 1.6). These prey-to-predator feedback loops, strengthened when the sprat abundance is high, may possibly contribute to cod recruitment failure even in periods of favorable hydro-climatic conditions for cod recruitment (Casini et al. 2009). Therefore, a high sprat stock may decouple cod recruitment from abiotic forcing and maintain the food web in a new stable state – a state which may be difficult to reverse without strong management action (Möllmann et al. 2008, Casini et al. 2009). Further evidence for a new “stable state” comes from a stage-structured biomass model for the cod–sprat interaction (Van Leeuwen et al. 2008). This study indicates that the lack of cod recovery in the 1990s–2000s could be explained by a stunted size

17

Food-web and climate-related dynamics in the Baltic Sea Cod-dominated

2.8 2.1

82 79

1.4 0.7

88

0.0 –0.7 –1.4

89 91

90 78 83 87 84 80 81 75 86 77

01

99 02 00

05 97

03 76

98

85 5

10

04 95

–2.1 0

B1

Sprat-dominated

PC1 zooplankton (44%)

PC1 zooplankton (44%)

A1

15

20

25

30

35

96

40

3.0

Cod-dominated Sprat-dominated

2.0 1.0 0.0

80

87 77

Cod-dominated Sprat-dominated Whole period

3.0 2.5 2.0 1.5 1.0 0.5 0.0 –1.0

–0.5

0.0

0.5

r (relation sprat abundance-PC1 zooplankton)

1.0

86

84 03

05 83

85

–2.0 –2

B2 2.5 Density distribution

A2 3.5

91 89

82

79

76

–1.0

45

78 81

–1

Sprat abundance (1010 individuals)

Density distribution

18

2.0

90 99 01 88 75 00 02 94 97 04 98 96 95

0 1 PC1 hydrology (59%)

2

Cod-dominated Sprat-dominated Whole period

1.5 1.0 0.5 0.0 –1.0

–0.5

0.0

0.5

1.0

r (relation PC1 hydrology-PC1 zooplankton)

Fig. 1.5.  Alternative dynamics of the central Baltic Sea ecosystem related to the dominance and subsequent collapse of the cod population. When cod dominate the system, the low sprat population is not able to significantly affect zooplankton, which is therefore driven by hydrological conditions. This situation drastically changes in situations of low cod biomass, when predation by the high sprat population replaces hydrology as the main regulator of zooplankton. The alternative dynamics are illustrated by changes in the relationship between sprat abundance and zooplankton (A1, A2), and between hydrology and zooplankton (B1, B2) in the scenarios of cod- and sprat-dominance. The vertical dashed line represents the ecological threshold separating the two scenarios. Modified from Casini et al. (2009).

distribution of the prey (i.e., sprat) providing insufficient food of the most suitable size for juvenile cod to grow and reproduce (Fig. 1.6). The low condition and energy content of sprat and herring in periods of low cod stock may also constitute a further mechanism hindering cod recovery via affecting cod body growth. However, beside these ecological feedback loops, it must be kept in mind that cod recruitment is also strongly affected by the parental stock size and age-structure, heavily shaped by fishing (Cardinale and Arrhenius 2000b). Ecological knowledge currently used in stock assessment and management Assessment

Little ecological knowledge is used currently in the assessment and short-term forecasts of Baltic Sea fish stocks (Table 1.1), although more

M. Casini et al.

Fig. 1.6.  Prey-to-predator feedback loops between cod and sprat. When the cod stock is high, the sprat stock is kept low by predation. For low cod stocks, the abundant sprat stock could hinder cod recovery by predation on cod eggs (1) and food competition with cod larvae (2). Moreover, the consequent lower condition (due to density-dependent processes) and changed size distribution (emergent Allee effect) of cod prey species (sprat and herring) would decrease, respectively, (a) the energy content of cod prey, and (b) the amount of prey of a suitable size for juvenile cod (3). These two last processes could explain the decrease in cod growth since the beginning of the 1990s. During the past two decades, after the drop of cod, the increased sprat stock has produced a drastic decline in both sprat and herring body growth.

information is available if compared with the vast majority of other marine ecosystems. Multi-species assessment models (previously Multi-Species Virtual Population Analysis, MSVPA; currently Stochastic Multi-Species model, SMS; ICES 2009a) have been used since the mid 1970s to estimate predation mortality of cod on herring and sprat, as well as cannibalism within the cod population, in the central Baltic Sea. Predation mortality by cod has been commonly used also in single-species stock assessment for herring and sprat (ICES 2009a). The North Atlantic Oscillation (NAO) winter index has been used in the forecast of the sprat year-class strength (ICES 2006), following the study by MacKenzie and Köster (2004) which found a significant correlation between sprat recruitment and spring sea temperature, the area of Baltic ice coverage, and the NAO. Temperature is important for sprat recruitment acting directly on egg survival (Nissling 2004) and indirectly enhancing the spring production of the main food for the larvae (Voss et al. 2003). Temperature and the biomass of the copepod Eurytemora affinis are used in the prediction of herring year-class strength in the Gulf of Riga (ICES 2009a). This planktonic species is considered to be crucial for herring recruitment in this area, owing to the fact that it is the main prey for larval herring. Higher

19

20

Food-web and climate-related dynamics in the Baltic Sea Table 1.1. Ecosystem information currently used and potentially usable in stock assessment, forecasts, and management for the central Baltic Sea. Future ecological Ecosystem knowl-

Cod

aspects poten-

edge used in

Ecosystem knowl-

assessment/ forecasts Cannibalism

Ecosystem knowl-

tially usable

edge potentially

edge used in

in assessment/

usable in advice

advice

forecasts

and management

Regime shift, pre- Prey-to-predator

(Multi-species

vious Biomass

models)

Reference

feedback loops

Prey-to-predator feedback loops

Points (Bpa, Blim) abandoned Sprat

Cod predation

Regime shift, pre- Density-dependent

Density-dependent

(Multi-species

vious Biomass

growth (inter-

growth (inter-

models)

Reference

and intra-spe-

and intra-

Points (Bpa, Blim)

cific) (also in S–R

specific)

abandoned

models)

North Atlantic Oscillation

Cod predation Herring

Cod predation

Density-dependent

Density-dependent

(Multi-species

growth (inter-

growth (inter-

models)

and intra-spe-

and intra-

Zooplankton (GoR)

cific) (also in S–R

specific)

Temperature (GoR)

models)

All

Cod predation

Seal predation

Seal predation

Hydrology (S–R

Hydrology

models)

Ecological thresholds Spatial patterns Ecological indicators

S–R, stock–recruitment. GoR, Gulf of Riga

water temperature in spring favors the development of this copepod, but also a longer spawning period and a more even distribution of herring spawning activity. After mild winters the abundance of zooplankton is also higher, thus ensuring better conditions for the feeding of herring larvae (see also Cardinale et al. 2009). Management

Recently, there has been a great deal of international work (at EC and ICES levels) with the objective of improving the management of Baltic Sea fish

M. Casini et al. stocks (ICES 2007, 2008b, 2009b). In these frameworks, the consideration of ecosystem dynamics (species interactions and hydro-climatic effects on stocks) has occupied an increasingly crucial role. It is of particular note that there has been an increasing awareness of the fact that appropriate Biological Reference Points for one species (e.g., cod, herring, or sprat) cannot be defined without considering the state of the other two species (ICES 2008b). Likewise, longlasting shifts in hydro-climatic conditions cannot be overlooked (ICES 2009b). Both processes (trophic interactions and hydro-climatic effects), acting either independently or in synergy with each other, may change the productivity of single species and, in turn, affect stock-recruitment relationships and the way the stocks respond to fishing (Lindegren et al. 2009). For the stocks in the Baltic Sea, management advice is given by ICES based on estimated Biological Reference Points. Fish stocks are then managed by the European Commission, primarily by issuing annual regulations in the form of TAC (Total Allowable Catch). However, the regime shift observed in the Baltic Sea in the early 1990s (see above) has produced a change in the use of the Biological Reference Points since 2008 (ICES 2008a). As a matter of fact, following analyses performed by different ICES expert groups (ICES 2007, 2008b, 2009b), it was decided to abandon the Biomass Reference Point for the Eastern Baltic cod stock (ICES 2008c) and Baltic sprat stock (ICES 2009a), because they were calculated using data collected under different ecological circumstances compared with those observed currently. In fact, in contrast to the previous ecosystem conditions, the circumstances after the early 1990s became more favorable for the sprat stock (e.g., higher temperature and lower predation by cod) and unfavorable for the cod stock (e.g., lower salinity and deep water oxygen levels as well as higher sprat stock), thus rendering the previously estimated Biological Reference Points invalid for these stocks (Table 1.1). Therefore, a multi-annual management plan for cod has been adopted since 2008 (EC 2007), having a certain fishing mortality of F = 0.3 as a target to be reached by reducing the TAC step by step every year. At the moment, this management strategy seems to have produced the desired effects (there has been a significant increase in cod biomass over the past two years; ICES 2009a), showing that the regulation of commercial catches is crucial to restoring and maintaining a healthy cod stock. Likewise, the development of a multi-annual management plan for sprat and herring, in which multi-species considerations and ecosystem effects would occupy a crucial part, is in progress (ICES 2009b). Potential future use of ecosystem knowledge in stock assessment and management A large amount of ecological information is available for the Baltic Sea ecosystem that has the potential to be used in fish stock assessment and

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Food-web and climate-related dynamics in the Baltic Sea management (Table 1.1). Generally, including environmental drivers could improve the fit of the Stock–Recruitment (S–R) relationship, and could be used to enhance the short- and medium-term forecasts, for all species (Köster et al. 2001, Axenrot and Hansson 2003, MacKenzie and Köster 2004). For cod, for example, the inclusion of salinity has been shown to improve the Ricker S-R model (Heikinheimo 2008). For sprat, a multiple regression model including spawning stock biomass and temperature, ice cover or indices of the North Atlantic Oscillation (NAO), improves the recruitment model (MacKenzie and Köster 2004). The use of these hydro-climatic parameters could also be used to predict sprat recruitment earlier than is now the case at the annual assessment meeting (MacKenzie et al. 2008). Biological parameters, and food web-related processes, can also be used to improve recruitment models. For example, there are indications that the weight-at-age of the spawners (a proxy for parental physiological condition), together with temperature, can be used as one of the predictors for central Baltic herring recruitment (Cardinale et al. 2009; Table 1.1). Knowledge of the factors affecting pelagic fish body growth is substantial and detailed in the Baltic Sea. Most of this information, however, rarely has been taken into consideration in stock assessment. Density-dependent growth of herring and sprat for example could be used in short- and medium-term forecasts to provide more precise predictions for biomass and spawning stock biomass of both species (see also ICES 2008b; Table 1.1). In this context, the inclusion of spatial considerations could also be implemented. For example, there are indications that when the cod stock is reduced due to abiotic changes (i.e., decrease in salinity) and fishing pressure, its population aggregates in the southern areas of the Baltic Sea where conditions are more favorable for its reproduction. These conditions trigger an increase in the sprat population prevalently in the northern areas, likely due to predation release, in turn causing a stronger density-dependent effect on clupeid growth in these areas (Casini et al. 2010b). This information, along with the knowledge of the natural geographical difference in growth rates of clupeids, could be used in the advice (Table 1.1). The importance of density-dependent mechanisms involving clupeid growth has been also emphasized in view of the potential establishment of an EC multi-annual management plan for pelagic fish (ICES 2009b). Seal populations (especially gray seals) are currently increasing at a very substantial rate (Karlsson et al. 2008), after a decrease during the 1900s due to hunting and pollution (Harding and Härkönen 1999). Modeling work has provided an indication that marine mammals could have influenced fish stock dynamics in the early 1900s when the system productivity was very low (Österblom et al. 2007). Therefore, if gray seals continue to increase (Fig. 1.7) and artificial

M. Casini et al. A

B

Fig. 1.7.  (A) Trends in counted seals in the Skagerrak-Kattegat and Baltic Sea; the Kalmar Sound is the area between the island of Öland and Sweden (modified from Karlsson et al. 2008). (B) Diet composition of gray seals in the Baltic Proper (samples collected between 2001 and 2005), presented as the proportion of consumed biomass with 95% confidence limits bootstrapped (based on 2000 bootstrap samples). Species that contribute < 5%, on average, to the consumed prey biomass have been excluded (data from Lundström et al. 2010).

nutrient input is substantially reduced (one of the main goals of HELCOM 2007), the effect on the fish community could be substantial (Hansson et al. 2007). Under these circumstances, predation mortality by these top predators on cod, herring, and sprat could be taken into account in the assessment of these three species (ICES 2009a; Table 1.1). The potential impact of seals on the fish stocks is exemplified in the Bothnian Sea (ICES SD 30) where it has been estimated that the amount of herring eaten by seals each year is higher than the annual catches of the Swedish fleet in this area (Anna Gårdmark, pers. comm.). As described above, cod recruitment could be hampered by excessively high populations of its main fish prey species (especially sprat) through several mechanisms: (a) predation on cod eggs, (b) competition with cod larvae for common zooplankton prey, (c) decrease in per capita abundance of the preferred prey size when the cod stock is at a low level (caused by a decrease

23

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Food-web and climate-related dynamics in the Baltic Sea in growth and fecundity of herring and sprat), i.e., an emergent Allee effect (De Roos et al. 2003, Van Leeuwen et al. 2008), and (d) decrease in prey (sprat and herring) energy content at high prey abundances (density dependence in clupeid body growth). Currently, the strength of these prey-to-predator feedback mechanisms (Fig.  1.6) on the cod stock are under investigation at national (Fiskeriverket 2008) and international (ICES 2008b, 2009b) levels. If these studies provide evidence for a significant negative effect of sprat on cod population, alternative counterintuitive ecosystem-based management actions, as a relative reduction of the sprat population, could be evaluated. However, the risks associated with an artificial suppression of sprat in the current Baltic ecosystem (e.g., risk of an explosion of jellyfish mass to occupy the open zooplanktivore niche) should be carefully assessed before such a management approach can take place. The synergistic effect of fishing, food-web dynamics, and hydro-climatic forces implies that advice on fishing mortality should be set taking into consideration the actual internal food-web state and external physical circumstances (Lindegren et al. 2009). The occurrence of thresholds in the ecosystem functioning (Casini et al. 2009, 2010a) implies that the management must be very adaptive and managers must be supplied continuously with updated information of the ecosystem state and especially of the main driving factors (Table 1.1). Quantitative thresholds, when properly determined, would provide straightforward reference points available for management decisions (Casini et al. 2010a). The information currently used, along with information that is potentially usable, in stock assessment and advice, as discussed above, is extremely relevant to the objective of a sustainable management of the fishery resources. This information, however, also should be integrated with other ecosystem objectives in order to reach the goals set by different policy bodies (see below). For example, the way the harvest of exploited fish stocks is managed has repercussion on all the other components of the food web, including eutrophication symptoms (e.g., risk of algal blooms; Casini et al. 2008), and nutrient dynamics (Hjerne and Hansson 2002), crucial aspects in the HELCOM agenda (HELCOM 2007). Ecological indicators

A further step towards developing an ecosystem approach to the management of marine resources for the Baltic Sea is the translation of the in-depth knowledge on the functioning of the ecosystem into an indicator-based management strategy (Hall and Mainprize 2004, Jennings 2005). Such a strategy includes the definition of strategic goals and ecological objectives which are to a large degree dependent on societal consensus. The available ecological knowledge can then be used to translate the ecological into operational objectives

M. Casini et al. which are reflected in specific indicators with characteristic reference targets and limits being the basis for management decisions (Jennings 2005). For the central Baltic Sea ecosystem such a strategy can be easily developed given the conceptual knowledge regarding the structure and function of the food web (see above and Fig. 1.3). Based on this conceptual understanding, different kinds of indicators (reflecting pressures, states, and response indicators; see Jennings 2005) can be selected. These can be for a single species targeted by the fishery, but should also reflect community changes (as the size-spectrum in fish communities) or ecosystem-wide reactions (see above; Hall and Mainprize 2004). Indicators of spatial changes occurring at the regional scale can also be evaluated (as suggested already for the central Baltic Sea; Casini et al. 2010b). Eventually, a set of indicators can be selected which together may form an early-warning system (Table 1.1). Such an approach would allow the adaptation of exploitation levels to, for example, changes in productivity in order to avoid unintended over-exploitation. Developing such a system requires, however, a thorough testing of the suitability of the combination of many possible indicators and respective reference points and targets. Such an exercise has still to be done for the Baltic Sea ecosystem. Steps forward in ecosystem management The ecosystem approach has been heavily promoted as a necessary approach by scientists, and has been incorporated, to varying degrees, in numerous policy documents. Now, politicians and managers are seeking advice from scientists regarding how this approach should be implemented (Rice 2005, Watson-Wright 2005). In the Baltic Sea, there has been a long tradition of systems research, and the understanding of Baltic Sea structure, function, and dynamics has advanced substantially during the most recent years. Ecosystem knowledge about the Baltic Sea is transferred to policy makers along different pathways, depending on the issue at hand, and the management institution asking for advice. In many marine regions, the focus has been on the impacts of fishing on marine ecosystems (e.g., bottom trawling, bycatch of target and non-target species, competition with marine predators). In the Baltic Sea there is also recognition of reciprocity. The dynamics, circumstances, and condition of ecosystems have a clear impact on fisheries (and fish stocks dynamics). Such factors include the status of the marine ecosystem, along with a variety of abiotic factors (nutrient levels, water quality, salinity, temperature, and deep water anoxia). There is thus a clear case for better integration of advice from the fisheries sector and the environment sector, and there are steps to move in this direction, albeit slow. Advice from ICES scientists on sustainable levels of fishing mortality are primarily dealt with by the Ministries of Fisheries (via

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Food-web and climate-related dynamics in the Baltic Sea the European Commission and stakeholders; Daw and Grey 2005) in a wellestablished annual cycle (advice on quotas, stakeholder consultations, and Council decisions), whereas issues and objectives related to the environment are handled primarily by the ministries of environment, throughout different HELCOM processes. Ecosystem considerations are increasingly becoming a part of ICES advice (e.g., multi-species assessment of quotas and discussions about reference points due to regime shifts; ICES 2008b) but there is currently no process established to deliver integrated assessments for the Baltic Sea to both the Ministers of Fisheries and Environment. However, the advisory structures within ICES are currently undergoing changes. One major problem is that the sectors influencing the Baltic Sea interact in numerous ways, and it is commonly difficult to separate cause and effect. The Marine Strategy Directive (EC 2008) can potentially promote coherence and consistency between the different policy-setting bodies, but there is a large need of common vision and common strategies shared by all. There are currently no mechanisms in place to deal with potential trade-offs. Potentially, this can be achieved by considering a number of possible trajectories for the future. Such scenario planning, with relevant stakeholders, could provide a mechanism for establishing priorities between potentially conflicting goals and help management bodies around the Baltic Sea find a common strategy. Before moving to an ecosystem approach, however, there is a clear case for starting with the scientific advice already at hand (Mace 2004). All indications from other regions where management is moving towards an ecosystem approach suggest that this approach, taking account of uncertainty and predator demands, results in smaller quotas than are currently implemented. If simple, single-species stock advice is being followed, and if fleet capacity is closer matched to systems capacity – that will be a necessary start. The capacity to implement an adaptive response to dynamic ecosystems with multiple tipping points must be a goal for management beyond those of traditional approaches, as hard as they themselves may be to achieve. Acknowledgments We thank Olle Karlsson and Karl Lundström who made Fig. 1.7 available. References Alheit, J., C. Möllmann, J. Dutz et al. 2005. Synchronous ecological regime shifts in the central Baltic and the North Sea in the late 1980s. ICES J. Mar. Sci. 62:1205–1215.

M. Casini et al. Arrhenius, F. and S. Hansson. 1993. Food consumption of larval, young and adult herring and sprat in the Baltic Sea. Mar. Ecol. Progr. Ser. 96:125–137. Axenrot, T. and S. Hansson. 2003. Predicting herring recruitment from young-of the-year densities, spawning stock biomass, and climate. Limnol. Oceanogr. 48:1716–1720. Bakun, A. and S.J. Weeks. 2006. Adverse feedback sequences in exploited marine systems: are deliberate interruptive actions warranted? Fish Fish. 7:316–333. Baumann, H., R. Voss, H.-H. Hinrichsen et al. 2008. Investigating the selective survival of summer- over spring-born sprat, Sprattus sprattus, in the Baltic Sea. Fish. Res. 91:1–14. Beaugrand, G. 2004. The North Sea regime shift: evidence, causes, mechanisms and consequences. Progr. Oceanogr. 60:245–262. Browman, H.I., P.M. Cury, R. Hilborn et al. 2004. Theme Section: perspectives on ecosystem-based approaches to the management of marine resources. Mar. Ecol. Progr. Ser. 274:269–303. Cardinale, M. and F. Arrhenius. 2000a. Decreasing in weight-at-age of Baltic herring (Clupea harengus) between 1986 and 1996: a statistical analysis. ICES J. Mar. Sci. 57:1–12. Cardinale, M. and F. Arrhenius. 2000b. The influence of stock structure and environmental conditions on the recruitment process of Baltic cod estimated using a generalized additive model. Can. J. Fish. Aquat. Sci. 57:2402–2409. Cardinale, M., M. Casini, and F. Arrhenius. 2002. The influence of biotic and abiotic factors on the growth of sprat (Sprattus sprattus) in the Baltic Sea. Aquat. Living Res. 15:273–281. Cardinale, M., C. Möllmann, V. Bartolino et al. 2009. Effect of environmental variability and spawner characteristics on the recruitment of Baltic herring Clupea harengus populations. Mar. Ecol. Progr. Ser. 388:221–234. Casini, M., M. Cardinale, and J. Hjelm. 2006. Inter-annual variation of herring (Clupea harengus) and sprat (Sprattus sprattus) condition in the central Baltic Sea: what gives the tune? Oikos 112:638–650. Casini, M., J. Lövgren, J. Hjelm et al. 2008. Multi-level trophic cascades in a heavily exploited open marine ecosystem. Proc. R. Soc. B 275:1793–1801. Casini, M., J. Hjelm, J.-C. Molinero et al. 2009. Trophic cascades promote thresholdlike shifts in pelagic marine ecosystem. Proc. Natl. Acad. Sci. USA 106:197–202. Casini, M., V. Bartolino, J.C. Molinero, and G. Kornilovs. 2010a. Linking fisheries, trophic interactions and climate: threshold dynamics drive herring Clupea harengus growth in the central Baltic Sea. Mar. Ecol. Progr. Ser. 413:241–252. Casini, M., G. Kornilovs, M. Cardinale et al. 2010b. Density-dependence regulates spatial and temporal patterns in the condition of central Baltic Sea clupeids. In press. Cederwall, H. and R. Elmgren. 1990. Eutrophication of the Baltic Sea: biological effects. Ambio 19:109–112.

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Food-web and climate-related dynamics in the Baltic Sea Choi, J.S., K.T. Frank, B.D. Petrie, and W.C. Leggett. 2005. Integrated ecosystem assessment of a large marine ecosystem: a case study of the devolution of the eastern Scotian Shelf, Canada. Oceanogr. Mar. Biol. Ann. Rev. 43:47–67. Collie, J.S., K. Richardson, and J.H. Steele. 2004. Regime shifts: can theory illuminate the mechanisms? Progr. Oceanogr. 60:281–302. Daskalov, G.M., A. Grishin, S. Rodionov, and V. Mihneva. 2007. Trophic cascades triggered by over fishing reveal possible mechanisms of ecosystem regime shifts. Proc. Natl. Acad. Sci. USA 104:10518–10523. Daw, T. and T. Gray. 2005. Fisheries science and sustainability in international policy: a study of failure in the European Union’s Common Fisheries Policy. Mar. Pol. 29:189–197. De Roos, A., L. Persson, and H.R. Thieme. 2003. Emergent Allee effects in top predators feeding on structured prey populations. Proc. R. Soc. Lond. B 270: 611–618. EC. 2000. Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for the Community action in the field of water policy. http://eur-lex.europa.eu/LexUriServ/LexUriServ. do?uri=CELEX:32000L0060:EN:NOT. Accessed June 2009. EC. 2007. Council regulation (EC) No. 1098/2007establishing a multiannual plan for the cod stocks in the Baltic Sea and the fisheries exploiting those stocks, amending Regulation (EEC) No. 2847/93 and repealing Regulation (EC) No. 779/97. Official Journal of the European Union L 248, September 22 2007, pp. 1–10. EC. 2008. Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). http:// eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32008L0056:EN:NOT. Accessed June 2009. Eero, M., F.W. Koster, and B.R. MacKenzie. 2008. Reconstructing historical stock development of Atlantic cod (Gadus morhua) in the eastern Baltic Sea before the beginning of intensive exploitation. Can. J. Fish. Aquat. Sci. 65:2728–2741. Fiskeriverket. 2008. Planktivore Management – Linking Food Web Dynamics to Fisheries in the Baltic Sea (PLANFISH). First Annual Report 2008. Folke, C., S. Carpenter, B. Walker et al. 2004. Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35:557–581. Frank, K.T., B. Petrie, J.S. Choi, and W.C. Leggett. 2005. Trophic cascades in a formerly cod-dominated ecosystem. Science 308:1621–1623. Hall, S.J. and B. Mainprize. 2004. Towards ecosystem-based fisheries management. Fish Fish. 5:1–20. Halpern, B.S., S. Walbridge, K.A. Selkoe et al. 2008. A global map of human impact on marine ecosystems. Science 319:948–952. Hansson, S., O. Hjerne, C. Harvey et al. 2007. Managing Baltic Sea fisheries under contrasting production and predation regimes: ecosystem model analyses. Ambio 36:265–271.

M. Casini et al. Harding, K.C. and T. Härkönen. 1999. Development in the Baltic grey seal (Halichoerus grypus) and ringed seal (Phoca hispida) populations during the 20th century. Ambio 28:619–627. Hare, S.R. and N.J. Mantua. 2000. Empirical evidence for North Pacific regime shifts in 1977 and 1989. Progr. Oceanogr. 47:103–145. Heikinheimo, O. 2008. Average salinity as an index for environmental forcing on cod recruitment in the Baltic Sea. Boreal Env. Res. 13:457–464. HELCOM. 2007. Helcom Baltic Sea Action Plan. www.helcom.fi/BSAP/en_GB/intro/ Accessed June 2009. Hjerne, O. and S. Hansson. 2002. The role of fish and fisheries in Baltic Sea nutrient dynamics. Limnol. Oceanogr. 47:1023–1032. Hughes, T.P., A.H. Baird, D.R. Bellwood et al. 2003. Climate change, human impacts, and the resilience of coral reefs. Science 301:929–933. ICES, International Council for the Exploration of the Sea. 2006. Report of the Baltic Fisheries Assessment Working Group (WGBFAS), ICES CM 2006/ACFM:24. ICES, International Council for the Exploration of the Sea. 2007. Report of the Workshop on Limit and Target Reference Points (WKREF), ICES CM 2007/ACFM:05. ICES, International Council for the Exploration of the Sea. 2008a. Report of the Working Group on Integrated Assessment of the Baltic Sea (WGIAB), ICES CM 2008/ BCC:04. ICES, International Council for the Exploration of the Sea. 2008b. Report of the Workshop on Reference Points in the Baltic Sea (WKREFBAS), ICES CM 2008/BCC:04. ICES, International Council for the Exploration of the Sea. 2008c. Report of the Baltic Fisheries Assessment Working Group (WGBFAS), ICES CM 2008/ACOM:06. ICES, International Council for the Exploration of the Sea. 2009a. Report of the Baltic Fisheries Assessment Working Group (WGBFAS), ICES CM 2009/ACOM:07. ICES, International Council for the Exploration of the Sea. 2009b. Report of the Workshop on Multi-annual Management of Pelagic Fish Stocks in the Baltic (WKMAMPEL), ICES CM 2009/ACOM:38. Jackson, J.B.C., M.X. Kirby, W.H. Berger et al. 2001. Historical overfishing and the recent collapse of coastal ecosystems. Science 293:629–637. Jennings, S. 2005. Indicators to support an ecosystem approach to fisheries. Fish Fish. 6:212–232. Kadin, M. 2007. Post fledging survival and sexual maturation in Common guillemot Uria aalge – the influence of biological and ecological factors. Degree project thesis, Dept. of Systems Ecology, Stockholm University, Stockholm. Karlsson, O., T. Härkönen, and B.-M. Bäcklin. 2008. Populationer på tillväxt. In Viklund, K., Tidlund, A., Brenner, U., and Svahn, K. (eds.), Havet 2008 – om miljötillståndet i svenska havsområden. Stockholm: Naturvårdsverket, pp. 91–92. Köster, F.W. and C. Möllmann. 2000. Trophodynamic control by clupeid predators on recruitment success in Baltic cod? ICES J. Mar. Sci. 57:310–323. Köster, F.W., H.-H. Hinrichsen, M.A. St. John et al. 2001. Developing Baltic cod recruitment models. II. Incorporation of environmental variability and species interaction. Can. J. Fish. Aquat. Sci. 58:1534–1556.

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Food-web and climate-related dynamics in the Baltic Sea Lindegren, M., C. Möllmann, A. Nielsen, and N.C. Stenseth. 2009. Preventing the collapse of the Baltic cod stock through an ecosystem-based management approach. Proc. Natl. Acad. Sci. USA 106:14772–14727. Link, J.S., J.K.T. Brodziak, S.F. Edwards et al. 2002. Marine ecosystem assessment in a fisheries management context. Can. J. Fish. Aquat. Sci. 59:1429–1440. Lundström, K., O. Hjerne, S.-G. Lunneryd, and O. Karlsson. 2010. Understanding the diet composition of marine mammals: grey seals (Halichoerus grypus) in the Baltic Sea. ICES J. Mar. Sci. 67:1230–1239. Mace, P.M. 2004. In defence of fisheries scientists, single-species models and other scapegoats: confronting the real problems. Mar. Ecol. Progr. Ser. 274:285–291. MacKenzie, B.R. and F.W. Köster. 2004. Fish production and climate: sprat in the Baltic Sea. Ecology 85:784–794. MacKenzie, B.R., J. Horbowy, and F.W. Köster. 2008. Incorporating environmental variability in stock assessment: predicting recruitment, spawner biomass, and landings of sprat (Sprattus sprattus) in the Baltic Sea. Can. J. Fish. Aquat. Sci. 65:1334–1341. Möllmann, C., G. Kornilovs, M. Fetter, F.W. Köster, and H.H. Hinrichsen. 2003. The marine copepod, Pseudocalanus elongatus, as a mediator between climate variability and fisheries in the central Baltic Sea. Fish. Oceanogr. 12:360–368. Möllmann, C., G. Kornilovs, M. Fetter, and F.W. Köster. 2005. Climate, zooplankton, and pelagic fish growth in the central Baltic Sea. ICES J. Mar. Sci. 62:1270–1280. Möllmann, C., B. Müller-Karulis, G. Kornilovs, and A.M. St. John. 2008. Effects of climate and overfishing on zooplankton dynamics and ecosystem structure: regime shifts, trophic cascades, and feedback-loops in a simple ecosystem. ICES J. Mar. Sci. 65:302–310. Möllmann, C., R. Diekmann, B. Müller-Karulis et al. 2009. Reorganization of a large marine ecosystem due to atmospheric and anthropogenic pressure: a discontinuous regime shift in the Central Baltic Sea. Global Change Biol. 15:1377–1393. Nissling, A. 2004. Effects of temperature on egg and larval survival of cod (Gadus morhua) and sprat (Sprattus sprattus) in the Baltic Sea – implications for stock development. Hydrobiologia 514:115–123. Österblom, H., M. Casini, O. Olsson, and A. Bignert. 2006. Fish, seabirds and trophic cascades in the Baltic Sea. Mar. Ecol. Progr. Ser. 323:233–238. Österblom, H., S. Hansson, U. Larsson et al. 2007. Human-induced trophic cascades and ecological regime shifts in the Baltic Sea. Ecosystems 10:877–889. Pace, M.L., J.J. Cole, S.R. Carpenter, and J.F. Kitchell. 1999. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14:483–488. Rahikainen, M. and R.L. Stephenson. 2004. Consequences of growth variation in northern Baltic herring for assessment and management. ICES J. Mar. Sci. 61:338–350. Rice, J.C. 2005. Implementation of the Ecosystem Approach to Fisheries Management – asynchronous co-evolution at the interface between science and policy. In Browman, H.I. and Stergiou, K.I. (eds.), Politics and socio-economics

M. Casini et al. of eco-system-based management of marine resources. Mar. Ecol. Progr. Ser. 300:265–270. Scheffer, M. and S.R. Carpenter. 2003. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18:648–656. Scheffer, M., S. Carpenter, J.A. Foley, C. Folke, and B. Walkerk. 2001. Catastrophic shifts in ecosystems. Nature 413:591–596. Strong, D.R. 1992. Are trophic cascades all wet? Differentiation and donor control in speciose ecosystems. Ecology 73:747–754. Vainikka, A., F. Mollet, M. Casini, and A. Gårdmark. 2009. Spatial variation in growth, condition and maturation reaction norms of the Baltic herring, Clupea harengus membras. Mar. Ecol. Progr. Ser. 383:285–294. Van Leeuwen, A., A.M. De Roos, and L. Persson. 2008. How cod shapes its world. J. Sea Res. 60:89–104. Voipio, A. 1981. The Baltic Sea. Elsevier Oceanography Series, 30. Amsterdam: Elsevier. Voss, R., F.W. Köster, and M. Dickmann. 2003. Comparing the feeding habits of co-occurring sprat (Sprattus sprattus) and cod (Gadus morhua) larvae in the Bornholm Basin, Baltic Sea. Fish. Res. 63:97–111. Wasmund, N., G. Nausch, and W. Matthäus. 1998. Phytoplankton spring blooms in the southern Baltic Sea – spatio-temporal development and long-term trends. J. Plankton Res. 20:1099–1117. Watson-Wright, W.M. 2005. Policy and science: different roles in the pursuit to common problems. In Browman, H.I. and Stergiou, K.I. (eds.), Politics and socio-economics of eco-system-based management of marine resources. Mar. Ecol. Progr. Ser. 300:291–296. Weijerman, M., H. Lindeboom, and A.F. Zuur. 2005. Regime shifts in marine ecosystems of the North and Wadden Sea. Mar. Ecol. Progr. Ser. 298:21–39.

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Northwest Atlantic ecosystem-based management for fisheries j a s o n s . l i n k , a l i d a b u n d y, w i l l i a m j . o v e r h o l t z , nancy shackell, john manderson, daniel duplisea, jonathan hare, mariano koen-alonso and kevin friedland

Abstract The northwest Atlantic has had a rich history of living marine resource exploitation. As stocks have undergone sequential depletion with some dramatic instances of stock declines in this region, there have been calls for evaluating and improving approaches to managing our use of these resources. As part of these calls and along with recognition that there are broader issues to consider when managing a fishery, the need for more holistic ecosystem-based approaches to manage living marine resources (LMRs) have been increasingly recognized. Here we present a history of LMR management for both Canadian and US waters. We also provide contextual information for the major ecosystems in this region, with synopses and descriptions of major biophysical features in the northwest Atlantic. After noting the main data sets in this region, we discuss some of the major ecosystem models produced by our institutions. Finally, we discuss current efforts to implement ecosystem-based marine fisheries management in the northwest Atlantic. Introduction There have been numerous prescriptions and admonitions to implement an ecosystem-based approach to the management of fisheries (EBMF; Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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J.S. Link et al. Larkin 1996, Link 2002a, 2002b, Garcia et al. 2003, Browman and Stergiou 2004, 2005). There have been relatively few instances where such an approach has been implemented to any significant extent (Pitcher et al. 2009), but the number is growing as fisheries scientists, managers, and stakeholders grapple with the specific details of how to do EBMF. As a discipline, and as a practice, we are now moving beyond the “whys” and “whats” of EBMF (Murawski 2007) and squarely facing the “hows.” That is, we are now well underway in the transition towards novel ways of assessing and managing living marine resources (LMR). We generally note (pers. obs.; pers. comm. with a wide range of global colleagues; Pitcher et al. 2009), and other chapters in this volume confirm, that a full implementation of EBMF is still distant but steps to that end are quite evident. What we provide in this chapter is a compilation of such steps in the northwest Atlantic. This is not an assertion that we are fully implementing EBMF in the northwest Atlantic. One of our main points, however, is that there have been concerted efforts that collectively have moved us toward that implementation. We provide a unique perspective from two countries that share contiguous marine ecosystems, Canada and the USA. We have attempted to provide an integrated view of commonalities – in both the biophysical and fishery (human) systems – as found in ecosystems from both countries rather than a more classical approach of describing these ecosystems and countries separately. Although some of the national distinctions will undoubtedly remain, we have emphasized a synthetic perspective in what we present. The seven ecosystems in the northwest Atlantic have a unique blend of common features, processes, and species coupled with some clear distinctions as seen in the range of ecosystems from the boreal Newfoundland–Labrador shelf system to the subtropical MidAtlantic Bight (Fig. 2.1). We also note that, in both countries, significant legislative and political emphasis has been placed on ecosystem-based management of LMRs. Applicable globally, but specifically intended for the USA, reports from the US Commission on Ocean Policy (2004) and the Pew Oceans Commission (2003) both noted the need for fisheries management to adopt an ecosystem-based approach. The updated Magnuson-Stevens Fishery Conservation and Management Act (2006; amended in 2008) called for an evaluation of ecosystem science as it pertains to the management of LMRs and their associated fisheries, and how best to incorporate such ecosystem considerations into management. In general, US fisheries organizations recognize the need to do so (Murawski 2007). Similarly, Canada’s Ocean Act (1997) has explicitly called for an ecosystem approach in the management of LMRs. Furthermore, since then, under the Fisheries Renewal initiative, the Department of Fisheries and Oceans (DFO; also known

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Northwest Atlantic ecosystem-based management 80°W

75°W

70°W

65°W

60°W

55°W

50°W

60°N

55°N

50°N

45°N

40°N

35°N

Fig. 2.1.  Map of the northwest Atlantic, with the seven major ecosystems denoted along with major features of interest. MAB, Mid-Atlantic Bight; SNE, Southern New England; GB, Georges Bank; GoM, Gulf of Maine; BoF, Bay of Fundy; SS, Scotian Shelf; GoSL, Gulf of St. Lawrence; NFLD, Newfoundland; LAB, Labrador.

as Fisheries and Oceans Canada) has implemented the Sustainable Fisheries Framework to incorporate precautionary and ecosystem approaches in fisheries management. The Framework consists of four main components:  conservation and sustainable use policies, economic policies, governance policies, and principles and planning and monitoring tools. Thus there is significant interest, demand, and even the beginnings of a mandate for ecosystem-based management of fisheries in both countries. By way of background, we provide a description of the major features and dynamics of the biophysics, LMR dynamics, and exploitation patterns from these ecosystems. We do so recognizing that the past events and understanding of these ecosystems will shape not only future states of these ecosystems but also how we will approach the management of their associated LMRs as we move towards an EBMF. We seek to learn from the past (status, mistakes, and successes) to better inform future endeavors. Our emphasis, although inclusive of the full range of taxa in the food web, is very much on upper trophic levels. Another reason we present this work, and provide a more extensive background beyond a summary of EBMF efforts, is to update information published

J.S. Link et al. in prior tomes covering the region. Backus (1987), Parsons (1993), Boreman et al. (1997), Fogarty and Murawski (1998), Breeze (2002), Breeze et al. (2002), Link and Brodziak (2002), and Zwanenburg et al. (2006) have all provided excellent summaries on the status and process of these ecosystems. Cognizant of the socio-economic facets at play in these ecosystems and their importance in EBMF, we largely focus on the biophysical and fisheries aspects of these ecosystems. One of our goals here is to provide a more contemporary and synthetic presentation of the state of these ecosystems, their associated LMRs, and the associated management thereof. Historical context: living marine resource exploitation context, history, and background Since their “discovery” of the Americas in the sixteenth century, Europeans have been exploiting the northwest Atlantic marine ecosystems for cod, whales, and other groundfish species (Kurlansky 1997). This started by sailing from Europe, then, as immigration escalated, from North America. Since the early twentieth century, access has been achieved by mechanized vessels and gear. During the early years of European exploitation, there was no fisheries management and no national boundaries to recognize. Fishing vessels fished the whole continental shelf, from the Mid-Atlantic Bight to Newfoundland and included, for example, French, Portuguese, English, and Spanish fleets. Although the nationalities participating in the fisheries have changed with time, the fishery was basically a “free for all,” effectively unregulated until the establishment of the International Convention for Northwest Atlantic Fisheries (ICNAF) in 1950 and the extension of maritime jurisdiction to 200 miles in the late 1970s. Thus, until relatively recently, the fisheries of the northwest Atlantic were an internationally shared resource (and to some extent, still so domestically), and the pattern of exploitation can be characterized as one of sequential depletion of fishery resources and large-scale changes in the relative abundance of different ecosystem components (Gough 1993, Parsons 1993, Fogarty and Murawski 1998, Link 2007). These changes pre-date the modern era and include the extirpation of the walrus (Odobenus rosmarus rosmarus) and the Atlantic gray whale (Eschrichtius robustus), coupled with the depletion of other marine mammal populations in the eighteenth century (e.g., gray seals; Waring et al. 2004, Clapham and Link 2006). The collapse of major fisheries, such as that for Atlantic halibut on Georges Bank, had occurred by the mid-nineteenth century, followed by sequential depletions across the Scotian Shelf (Breeze 2002). More recently (primarily in the 1960s), there have been a series of fishery declines initiated by the arrival of distant water fleets; groundfish stocks declined across the northwest

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Northwest Atlantic ecosystem-based management Atlantic under heavy exploitation. The sequence was repeated for small pelagic fish (principally Atlantic herring (Clupea harengus) and mackerel (Scomber scombrus)) and “other” fish stocks (including elasmobranchs, and large pelagics such as tuna and billfish) throughout the 1970s (Fogarty and Murawski 1998, Overholtz 2002). More recent changes are described below: the ecosystems we manage today have been shaped largely by fisheries exploitation and are very different from the ecosystems “discovered” in the sixteenth century (Heymans 2003, Lotze and Milewski 2004, Rosenberg et al. 2005). The first management of fisheries with any measures of consequence were policies implemented by the International Commission for Northwest Atlantic Fisheries (ICNAF), whose initial objective was to use science to maintain maximum sustainable catch (Halliday and Pinhorn 1996). To begin with, ICNAF imposed mesh size regulations in the trawl fishery, increasing from a restriction (minimum) of 73 mm to 114 mm in the 1950s. ICNAF then introduced total catch control in the 1960s. However, when distant water fleets started operating in the northwest Atlantic, with the consequent depletion of fish stocks, there was a clear need for even more direct controls on fishing. The establishment of the “two-tier” quota management system in 1974 by ICNAF (Pinhorn and Halliday 1990, Parsons 1993, Murawski et al. 1997) provided the nucleus for recovery of depleted stocks. This approach included explicit recognition of, and allowance for, bycatch, discarding practices, and inter-specific interactions (Brown et al. 1976) and, as such, was a real step toward an ecosystem approach; but unfortunately the two-tied system was never fully implemented. The extension of maritime jurisdiction to the 200-mile Exclusive Economic Zone (EEZ), in the late 1970s gave complete control of fisheries within that area to its associated nation. Since then, the DFO has governed fisheries activities in Canadian waters from north of Labrador to the Bay of Fundy, while the US National Marine Fisheries Service (NMFS) governs fisheries activities in US waters from the Gulf of Maine and Georges Bank to the Mid-Atlantic Bight (and beyond to the south). In waters outside the 200-mile zone, but in the northwest Atlantic, the North Atlantic Fisheries Organization (NAFO), which replaced ICNAF in 1980, is the international regulatory authority (Halliday and Pinhorn 1996). In Canada, quota-based management and limited-entry licensing have been the two main fisheries management tools, but the tool box also includes seasonal closures, area closures, and gear restrictions. The DFO’s policy priorities have changed over time, and reflect the context in which they were made. In the 1970s, with the extension of jurisdiction, the priorities were to establish control over fisheries in Canadian waters and to expand the capacity of the Atlantic fishing industry to harvest and process the resources within the EEZ. In the 1980s, after declines in the LMR of the late 1970s, the focus was

J.S. Link et al. on limiting increases in harvest rates and processing capacity. Policies were also developed to regulate the different fleet sectors and their interaction, to promote the independence of inshore fisheries, and to limit the concentration of ownership of fishing licenses. The widespread collapse of groundfish populations in the early 1990s led to moratoria on fishing, and the introduction of new policies (e.g., formalized co-management, individual quotas or enterprise allocations, and the diversion of effort into new fisheries for which new policies have been developed; Parsons 1993, DFO 2001). In the USA, quota-based management was maintained under the early years of extended jurisdiction but was replaced by more gear-specific measures (constraints on mesh size, legal size limits for fish, and short-term area and seasonal closures) in 1982. When these measures failed to adequately protect fishery resources, more restrictive measures (including the use of large-scale yearround closures and limits to days-at-sea) were added in 1994 (Murawski et al. 1997, Fogarty and Murawski 1998). Collective experience forces the acknowledgment that fishing changes ecosystems both globally and in the areas of interest to this chapter. The seven northwest Atlantic ecosystems discussed here are highly productive regions that have supported significant commercial fisheries for centuries (Sissenwine et al. 1984, Rosenberg et al. 2005, Rose 2007). Although there are regional differences, overall, the response of the northwest Atlantic ecosystems to over 500 years of exploitation has been very similar. The recent history of the component fish stocks has exhibited several iterations of classic cycles of excessive effort, and stock declines until reaching the point of sequential stock depletion (Gough 1993, Parsons 1993, Serchuk et al. 1994, Murawski et al. 1997, Fogarty and Murawski 1998, Link 2007). In this context, the major fishery-related events over the past several decades fall into a pattern that can be characterized loosely by the following sequence (Parsons 1993, Serchuk et al. 1994, Fogarty and Murawski 1998, Link and Brodziak 2002, Overholtz 2002, Bundy et al. 2009): • • • • • • • •

expanding catches from populations of small pelagic species by foreign fleets a continued increase in demersal groundfish catches a precipitous decline in small pelagic stocks a decline of some groundfish stocks an effective cessation of the small pelagic fisheries (and expulsion of foreign fleets) a continual series of overfishing on an ever-increasing array of groundfish an increase in elasmobranch stocks (not in the more northerly ecosystems) the beginnings of an increase in small pelagic stocks

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Northwest Atlantic ecosystem-based management • • • • • •

an establishment of elasmobranch fisheries (not in more northerly ecosystems) an increase in benthic invertebrate fisheries and stocks the persistence of groundfish stocks at moderate to low levels in the south and the collapse of groundfish in the north the persistence of groundfish fisheries at suboptimal yields in the south the decline of elasmobranch stocks and subsequently their fisheries, and the effective explosion of small pelagic stocks to record highs.

In response to these changes, fisheries have diversified to exploit a broad range of invertebrates and non-traditional species. While the various fisheries and their effects changed, the Canadian ecosystems all experienced large increases in pinniped populations, concurrently with elevated mortality on many fish stocks, leading to hypotheses that this is due, in some areas, to seal predation (Chouinard et al. 2005, Benoit and Swain 2008, Swain and Chouinard 2008). Meanwhile there have been notable changes to protected, endangered, and threatened species (PETS) or species at risk (SAR), with many in more critical condition than 50 years ago (Waring et al. 2004; Canada’s Species at Risk Act SARA).1 Additionally, shifts in non-targeted fauna such as some benthic and non-targeted fishes occurred (Link and Brodziak 2002, Choi et al. 2005), with some actually persisting at relatively stable levels or even increasing (Link and Brodziak 2002, Choi et al. 2005, Link 2007). This all occurred in the context of a changing regional physico-chemical dynamics; particularly long-term warming (Taylor and Bascunán 2001) and North Atlantic Oscillation (NAO) shifts (Drinkwater et al. 2003b). Ecosystem synopses and descriptions Physical description

The seven Atlantic ecosystems shown in Fig. 2.1 span a geographical range from 60° to 35° N, and thus represent a shift from the boreal Newfoundland–Labrador Shelf system to the subtropical Mid-Atlantic Bight. Circulation on the northwest Atlantic continental shelf is dominated by southwestward flows of cold buoyant fresh water derived from the Arctic and Labrador Seas and major riverine estuaries. The system is bounded on its eastern side by a shelf slope and the warm salty northward flowing Gulf Stream. The continental shelves are incised by a number of important http://www.dfo-mpo.gc.ca/species-especes/home_e.asp

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J.S. Link et al. gullies, channels, and drowned river valleys that serve as cross-shelf transport pathways for nutrient-rich shelf-slope water into the large deep basins of the Newfoundland Shelf, Scotian Shelf, and Gulf of Maine and onto the gently sloping Mid-Atlantic Bight Shelf. The effects of the cool, fresh Labrador Current are strongest in Newfoundland–Labrador areas and least in the MidAtlantic Bight where circulation is primarily driven by atmospheric wind and local buoyancy forcing (Epifanio and Garvine 2001). Tides and winds force ecologically significant variations in circulation and mixing on the synoptic southwestward flows on the northwest Atlantic region. Tidal amplitudes generally decrease with decreasing latitude in the northwest Atlantic (Cartwright et al. 1988) and, as a result, there is a north–south gradient in the relative importance of tidal forces in driving circulation and mixing processes on the shelf. Tidal forcing is the factor primarily responsible for persistent mixing and retention on shallow banks and shoals between New Brunswick, Canada, and southern Massachusetts. The Newfoundland–Labrador Shelf

The Newfoundland–Labrador Shelf is located in the northwest Atlantic and extends from the Hudson Strait in the north (latitude 60°00ʹ) to the Grand Banks (46°00ʹ) in the south, encompassing the North Atlantic Fisheries Organization (NAFO) management divisions 2GHJ3KLNO (Fig. 2.1). There are many shallow offshore banks separated by a series of channels and gullies and the circulation pattern over the shelf is dominated by the southward flowing Labrador Current (Hunt and Drinkwater 2005). Sea ice begins to form by December in the north and by March has reached its maximum spread south to the northern Grand Bank (Prinsenberg et al. 1997). In the southern regions, ice usually lasts from 1–2 months whereas at the northern end of the Labrador Shelf it lasts for an average of around 8 months. There are strong annual cycles in the water mass properties due to seasonal variations in atmospheric forcing, while decadal changes are linked to the NAO which can account for between 40–50% of the variance in sea ice, ocean temperatures, and shelf stratification off Labrador and Newfoundland. Temperatures range from 14 ºC in the surface waters in the southern areas of the region during the summer to near freezing in the ice-covered areas in winter. Nearbottom temperatures are strongly depth-dependent but generally cover the range from −1 ºC on some of the shallower banks to 4 ºC in the deeper regions of the shelf and along the slope. The Cold Intermediate Layer, CIL, generally defined by waters of temperatures 200 m). The shelf edge is characterized by a series of indentations and canyons, the largest of which, the Gully, is a marine protected area. The shelf extends from the Laurentian Channel in the northeast to the Fundian Channel in the southwest. Together the areas comprise NAFO management divisions 4VWX. The southern end of the Scotian Shelf is contiguous with the Bay of Fundy in Canadian waters and the Gulf of Maine and Georges Bank in US waters (Fig. 2.1). The Bay of Fundy is a large macro-tidal embayment and is oceanographically related to the greater Gulf of Maine. It is 270 km long and 60 km wide at its widest point. The Bay of Fundy encompasses offshore oceanic features with shallow banks and deep channels, and diverse coastal marine habitats including salt marshes and extensive mudflats. The hydrographic environment of the Scotian Shelf is governed largely by its location, near the confluence of three major currents of the northwest Atlantic, and its complex topography. The bottom topography consists of a series of submarine banks and cross-shelf channels along the outer shelf and basins and troughs along the central shelf that limit and guide the near-bottom flow (Zwanenburg et al. 2006). Overall, water properties have large seasonal cycles, east-west and inshore-offshore gradients, and vary with depth (Petrie et al. 1996). Annual variation in water temperatures on the Scotian Shelf and in the Gulf of Maine are among the most variable in the North Atlantic Ocean (Weare 1977). The eastern end of the Scotian Shelf consists mostly of cold fresh water from the Gulf of St. Lawrence and the Newfoundland Shelf and overall there is transport of water and organisms from the northeast towards the southwest. The eastern Scotian Shelf responds to the conditions in the Gulf of St. Lawrence and the Newfoundland Shelf, which are thought to reflect dynamics of water masses at more northerly latitudes (Zwanenburg et al. 2006). Water properties are thus characterized by large seasonal cycles plus strong vertical stratification (Drinkwater and Gilbert 2004). The deep basins on the central shelf are directly influenced by the slope water, where the water properties are determined by interactions between the Labrador Slope Water Current originating from the north, and the Gulf Stream, originating from the south. Consequently, the central and western parts of the Scotian Shelf are generally warmer than the eastern Scotian Shelf. The defining characteristic of the Bay of Fundy is the magnitude of tides, ranging from a mean height of 6 meters (maximum 8 m) in the outer bay to a mean height of 11.9 m (maximum 16 m) in the inner bay, the highest in the world. These high tides generate intense vertical mixing caused by bottom turbulence (Garrett et al. 1978) and generate high levels of marine productivity. High tides are the result of the narrowness of the mouth of the Bay, causing tides to travel faster. The combination of strong tidal currents (7–18 km/hour in

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Northwest Atlantic ecosystem-based management some areas) and complex bottom topography results in tidal rips, whirlpools, upwelling, and intense mixing throughout the region. Productivity is exceptionally high, and is greatest at the mouth of the Bay. A recent analysis (Drinkwater et al. 2003a, 2003b) indicates that salinities on the Scotian Shelf and Bay of Fundy during the 1990s were the lowest on record since the 1950s. At the same time, stratification was high and an extended period of cold subsurface (50–150 m) waters has been observed throughout much of the Gulf of St. Lawrence, the northeastern Scotian Shelf and the eastern Gulf of Maine. Relatively warm waters are seen in the deep basins and channels (Drinkwater and Gilbert 2004). Changes in temperature, such as the extended period of cooling that began in the mid-1980s on the eastern Scotian Shelf (a change that lasted until the early 1990s; Drinkwater et al. 2003a, 2003b), also impact productivity through reduced growth. This cold period is also associated with increased abundance of cold-water fish (capelin, turbot) and invertebrates (snow crab, shrimp) usually more prevalent in the colder waters of the Gulf of St. Lawrence and Newfoundland. The Gulf of Maine

The Gulf of Maine (GoM), a large (79 000 km2) marine basin, is located along the northeastern USA and Maritime Canada (Fig. 2.1). It is approximately 350 km long by 200 km in width and averages about 160 m in depth (Uchupi and Austin 1987). This area comprises NAFO management area 5Y. It is bounded to the northeast by Nova Scotia and New Brunswick, to the west by the states of Maine, New Hampshire, and Massachusetts, to the southwest by Cape Cod, and to the south and southeast by Georges Bank (Fig. 2.1). The region contains three deep basins, Georges, Jordan, and Wilkinson, ranging from 200–400 m in depth (Uchupi and Austin 1987). Circulation patterns in these basins are generally clockwise, but the overall pattern in the Gulf is from east to west with the entry of slope water through the Northeast Channel, exiting to the southwest through the Great South Channel. Bottom sediments in the Gulf are mostly silt to mud with a rocky coastline bordering the states (Poppe et al. 1989). Sea surface temperatures range from 2–5 ºC during winter and can be as high as 20 ºC during summer; bottom temperatures remain relatively cool year round, ranging between 4–6 ºC (Mountain and Holzwarth 1989, Taylor and Bascunán 2001). A steady supply of nutrients is available due to the movement of deep-slope water into the Gulf of Maine through the Northeast Channel, but summer stratification interrupts the nutrient cycle (Schlitz and Cohen 1984). Occasionally, nutrient-poor Labrador Shelf water is transported from the north due to an intense negative NAO anomaly and recently fresh water from the Gulf of St. Lawrence and Arctic melting has made incursions into the region (Townsend et al. 2006).

J.S. Link et al. Georges Bank

Georges Bank (GB) is a relatively large (44 000 km2) submerged marine plateau, as an extension of the continental shelf along the eastern USA that thrusts out into the northwest Atlantic ocean in a northeasterly direction (Fig. 2.1). It is approximately 250 km long by 125 km in width and is relatively shallow, averaging about 80 m in depth (Uchupi and Austin 1987). This area comprises NAFO management area 5Ze. The living and mineral resources of this diverse region are administered jointly by Canada and the USA, resulting from a decision by the world court in 1984 (Backus 1987). It is bounded to the east and northeast by Nova Scotia and the Northeast Channel, to the north by the Gulf of Maine, to the west-southwest by the Great South Channel, and to the south by the shelf slope and Gulf Stream (Fig. 2.1). Sediments on the Bank are diverse, ranging from silt to large boulders, but much of the area is comprised of sand and cobble (Uchupi and Austin 1987, Poppe et al. 1989). Surface temperatures during the winter average between 2–5 ºC and, during the late summer, can reach up to 20 ºC (Flagg 1987). Bottom temperatures are relatively cool during winter (3–5 ºC); because of mixing they reach a low of roughly 5 ºC on the shallow part of the Bank (Flagg 1987; Mountain and Holzwarth 1989; Taylor and Bascunán 2001). Due to Georges Bank’s unique location, nutrients are plentiful because of offshore upwelling along the shelfslope break and movements of slope water into the Gulf of Maine through the Northeast Channel (Townsend et al. 2006). Gulf of Maine waters to the north are a steady source of annual nutrients and vigorous tidal mixing promotes nutrient recycling via resuspension of bottom waters from the shallow portion of the Bank (Schlitz and Cohen 1984, Franks and Chen 2001). Georges Bank is highly productive and, due to its generally clockwise current pattern (Butman et al. 1987), nutrients and plankton are concentrated and retained on this relatively shallow plateau (Cohen et al. 1982, Drinkwater and Mountain 1997, Fogarty and Murawski 1998). During periods of stratification, the nutrient cycle can be temporarily interrupted, but this occurs seasonally and only in localized areas (Drinkwater and Mountain 1997). A specific feature of Georges Bank is associated with notable tidal forcing. Tidal forcing is the factor primarily responsible for persistent mixing and retention on shallow banks and shoals between New Brunswick, Canada, and Southern Massachusetts. In particular, tidal forcing results in the formation of a persistent front and clockwise gyre around Georges Bank, as well as continuous pumping and mixing of water and associated nutrients derived from deeper areas flanking the bank as noted above. These persistent tidal mechanisms serve to retain the egg and larval stages of biota inhabiting the Bank and continuously resupply nutrients to the system which remains well mixed even during the summer months when adjacent waters can be strongly

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Northwest Atlantic ecosystem-based management stratified. Mixing of deep water and nutrients from the great South Channel onto Nantucket Shoals occurs as a result of similar tidal pumping mechanisms (Townsend et al. 2006, Hu et al. 2008). Southern New England

Southern New England (SNE) extends from Hudson Canyon to Great South Channel and is a transitional region between the Mid-Atlantic Bight and Georges Bank. This area comprises NAFO management area 5Zw and 6A. Fresh water enters Southern New England from the Hudson River, Long Island Sound, Narragansett Bay, and numerous small coastal watersheds, but the volume is much less than what enters the Mid-Atlantic Bight from the Chesapeake and Delaware Bays (NOAA 1990). Salinity is low near shore and increases toward the shelf break. Temperature is homogeneous in fall and winter but is highly stratified in spring and summer (Mountain 2003). Tidal forcing in Southern New England is much less than on Georges Bank (Chen et  al. 2001) and the dominant low-frequency flow is southwestward from Georges Bank to the Mid-Atlantic Bight (Chapman et al. 1986). The area of shallow water (< 30 m) in SNE is proportionally less than in Mid-Atlantic Bight with the area of deeper water (60–100 m) proportionally greater. The average depth of Southern New England is about 70 m. Most of the Southern New England shelf is covered with sand, but as a result of the deeper water and relatively low currents, there is a greater proportion of area covered by silt and clay on Southern New England compared with the Mid-Atlantic Bight (Poppe et al. 1989, Hastings et al. 2000). The Mid-Atlantic Bight

The Mid-Atlantic Bight (MAB) is the only north–south long-shelf ecosystem of the four US areas; bounded in the north by the Hudson Canyon; in the south by Cape Hatteras; in the east by the Gulf Stream; and in the west by the coasts of New Jersey, Delaware, Maryland, Virginia, and North Carolina. The continental shelf is ~ 200 km wide in the New York Bight, and gradually narrows south of New Jersey to ~ 30 km off Cape Hatteras. This area comprises NAFO management area 6BC. The ecosystem includes the major estuaries, Hudson-Raritan River, Delaware River estuary, Chesapeake Bay, Pamlico Sound, and many smaller estuaries that affect hydraulics and productivity. The shelf has a mean depth of 58 m and is gently sloping except where drowned river valleys incise it. Mid-Atlantic Bight Shelf water characteristics are variable at a wide range of scales because the system lies in the middle latitudes at the confluence of the northward flowing Gulf Stream and southward flowing colder fresher waters derived from the Labrador Current (Chapman and Beardsley 1989). Atmospheric

J.S. Link et al. conditions and buoyancy forcing push water derived from the Labrador Current as far south as the New York Bight (Loder et al. 1998). The Gulf Stream meanders and rings impinge on the shelf primarily in the southern Mid-Atlantic Bight. Salinities on the shelf are < 34 ppt and variable as a result of inputs of fresher water from estuaries and the Gulf of Maine. Strong surface fronts are formed by buoyant water off major estuaries and along the 50 m isobath (Ullman and Cornillon 2001, Castelao et al., 2008a, 2008b). Seasonal heating and freshwater inputs produce strong vertical stratification from late spring through fall. Surface waters are warm during summer, but subsurface “Cold pool” water (≤ 8 °C) derived from winter tidal mixing in the Gulf of Maine and Nantucket Shoals flows southwest primarily along 50–80 m isobaths. The “Cold Pool” is persistent north of the Hudson Shelf Valley but extends as far south as Cape Hatteras in the spring (Houghton et al. 1982, Bignami and Hopkins 2003). Lower trophic levels Phytoplankton

There are large regional differences in primary production in the northwest Atlantic Ocean. The most obvious common pattern is the general onshore–offshore decrease in primary production. The other obvious pattern is a general increase in primary production from north to south. At a scale smaller than the regional ecosystems, there are areas of higher productivity including Georges Bank, inshore Mid-Atlantic Bight and Southern New England, northwestern Gulf of Maine, southwestern Gulf of St. Lawrence, and the Tail of the Grand Banks. Comprehensive time series of primary production are largely lacking, although in the northwest Atlantic there are two sources of valuable information. The longest times series of plankton across the northwest Atlantic is derived from the Continuous Plankton Recorders (CPR) data set, which are data collected from ships of opportunity (Sameoto 2004). The most reliable CPR data stem from the 1960s and early 1970s (early period) and 1991–2006 (later period). The CPR provides data on (a) color, which is an index of phytoplankton biomass, (b) dinoflagellates and diatoms, also an index of phytoplankton, and (c) large copepods representing Calanus stages 1–4, Calanus finmarchicus, C. ­glacialis, and C. hyperboreus following Frank et al. (2005; see below for discussion of zooplankton). The CPR observations are limited in that they measure only surface chlorophyll levels, and they only include larger phytoplankton that are retained on the instrument’s silk (Leterme et al. 2005), but they do indicate that in the northwest Atlantic, ocean color has increased from the 1960s to the 1990s (Sameoto 2004). Remotely sensed ocean color data are synoptic, global, and continuous and, as such, provide a very valuable tool for ecosystem approaches to management

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Northwest Atlantic ecosystem-based management (Platt et al. 2009). Although a relatively recent development, these data have been collected since 1997, and are thus providing time-series data at annual, seasonal, and spatial scales. Satellite-derived data have been used to examine the link between the timing of the spring bloom and haddock recruitment on the Scotian Shelf (Platt et al. 2003), the magnitude of the fall bloom and haddock recruitment on Georges Bank (Friedland et al. 2008), and shrimp growth and productivity in Newfoundland (Fuentes-Yaco et al. 2007), and have been proposed as ecosystem indicators (Polovina and Howell 2005, Platt and Sathyendrath 2008). Remotely sensed estimates of chlorophyll a and productivity, like the CPR, are surface estimates. However, methods have been developed to integrate chlorophyll depth profiles into these estimates, thus including chlorophyll productivity throughout the water column (Platt et al. 2009). Since primary production serves as the foundation for fisheries production in marine ecosystems, it is critical that work to quantify primary production throughout the water column be continued. Primary productivity has strong seasonal cycles that are governed by oceanographic conditions, including nutrient levels, stratification, vertical mixing, salinity, and temperature. These factors affect the timing of the spring bloom and the succession of phytoplankton species (Drinkwater et al. 2003b). During winter, low light levels limit phytoplankton production, and in the more northern ecosystems, winter ice cover and temperatures around 0 °C near the surface strongly limit primary production. All areas experience an intense spring phytoplankton bloom, generally accompanying seasonal warming during April, but intense stratification can preclude additional blooms during summer. Because of the strong vertical mixing in the Bay of Fundy and Georges Bank, stratification plays a lesser role in its plankton dynamics. A lesser fall bloom is observed in most years. Decadal trends in the spring bloom on the Scotian Shelf indicate that blooms start earlier now than they did in the 1960s and 1970s, and that spring blooms are now more intense and last longer. Data from the CPR indicate that phytoplankton has increased from the 1960s to the 1990s (Sameoto 2004), and that there are also decadal trends (Head and Sameoto 2007). The dynamics (timing and magnitude) of zooplankton, the main grazers of phytoplankton, determines the extent to which phytoplankton production is transferred to the pelagic food chain or goes directly, as phytodetritus, to the benthos. In the Mid-Atlantic Bight, variation in primary productivity also reflects the physical processes controlling nutrient transport, water column structure, and seasonal patterns of solar radiation. An early spring phytoplankton bloom occurs at the shelf slope front as light increases, the water column stabilizes, and nutrients are upwelled into the photic zone (Marra et al. 1990, Ryan et al. 1999). On the continental shelf, stratification and increasing light produces

J.S. Link et al. spring phytoplankton blooms in the vicinity of nutrient-rich estuarine plumes. During years with persistent sea breezes, coastal primary production is episodically high. This wind-driven upwelling associated with sea breezes appears to be the major source of inter-annual variability in primary production in the Mid-Atlantic Bight (Glenn 2004, Team 2008) and in the development of historical centers of episodic hypoxia. Some of this inshore production may be transported to the mid and outer shelf by the wind-driven surface transport mechanisms described above. A minor peak occurs in the fall phytoplankton bloom on the outer Mid-Atlantic Bight continental shelf as the stratification in the water column breaks down in the late fall (Yoder et al. 2002). Bacteria and microzooplankton

Bacteria and microzooplankton form the microbial loop and as such make up a significant part of the biotic web of marine ecosystems, specifically linking dissolved and particulate organic carbon to the mesozooplankton. There have been measurements of the abundance and grazing rates of bacteria and microzooplankton in the northwest Atlantic (e.g., Mousseau et al. 1996, Putland 2000) but the spatial and temporal extent of these studies has been limited. Estimates of bacterial abundances in the Gulf of Maine, Georges Bank, and Gulf of St. Lawrence are on the order of 0.2–2 × 1012 cells/m3 (0.015–0.02 g C/m3) (Lovejoy et al. 1996, Sieracki et al. 2006) and bacterial production is on the order of 0.005–0.010 g C/m3 (Caron et al. 2000). Microzooplankton is generally defined as the size fraction of zooplankton between 0.02–0.2 mm and is composed of protozoa, ciliates, flagellates, copepod nauplii, and the larvae of some benthic invertebrates. On the Scotian Shelf during winter, mean abundance ranges from 1.2–16.3 103 individuals/L (0.0016 to 0.007 μg C/m3). Link et al. (2008a, 2008b, 2008c) concluded that the microbial food web is extremely important in the energy flow of Georges Bank and it is clear that more research is needed to better define the abundance, distribution, production, and dynamics of this component of the ecosystem. Mesozooplankton

Mesozooplankton are small (0.2–2 mm) and represent a broad range of animals. One group of mesozooplankton are holoplanktonic species which spend their entire life cycle as plankton. The most important holoplanktonic mesozooplankton are copepods, and one of the most important copepod species in these systems is the abundant Calanus finmarchicus, which has a high energy content owing to its lipid reserves. A number of other organisms are also components of the holoplanktonic mesozooplankton including cladocerans, ostracods, mysids, euphausiids, chaetognaths, amphipods, and decapods.

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Northwest Atlantic ecosystem-based management Mesozooplankton are an important prey source for numerous species at higher trophic levels and play an important role in population and ecosystem dynamics. Atlantic mackerel recruitment in the Gulf of St. Lawrence has been linked to zooplankton production (Ringuette et al. 2002) and calving rates of the endangered North Atlantic right whale are thought to be linked to Calanus finmarchicus abundance in the Gulf of Maine zooplankton (Greene and Pershing 2007). Mesozooplankton also serve as a major conduit of energy through the ecosystem from primary producers and the microbial community to higher trophic levels. In the Scotian Shelf system, zooplankton have been estimated to produce ~50% of the consumed biomass (Bundy 2005). In the Georges Bank system, most of the energy available for higher trophic levels passes through mesozooplankon, and variability in mesozooplankton production substantially impacts production of small pelagics, highly migratory species, and baleen whales (Link et al. 2008a, 2008b). Mesozooplankton have been monitored in the northwest Atlantic Ocean for more than 40 years. One principal method is the CPR which represents the longest time series of plankton collected in the region (Jossi et al. 2003). Another principal method is randomized surveys of the Mid-Atlantic Bight, Southern New England, Georges Bank, and Gulf of Maine using a 0.61 m bongo frame equipped with a 0.333 mm mesh and towed obliquely. These surveys have been conducted since the mid-1970s and currently 120 stations are sampled six times per year (Kane 2007). On the Scotian Shelf, Gulf of St. Lawrence, and Newfoundland–Labrador Shelf, the Atlantic Zone Monitoring Program (AZMP) has sampled zooplankton abundance since the late 1990s, using a mixed fixed station and transect design with a 0.75 m ring net with 0.2 mm mesh net towed obliquely. Seven fixed stations are sampled every couple of weeks and 11 transects are sampled a couple times of year (Pepin et al. 2005). In addition to these large-scale monitoring programs, there have been numerous, more focused studies of zooplankton abundance, distribution, and production (e.g., the Global Ocean Ecosystem Dynamics (GLOBEC) program in Canada and the USA; Wiebe et al. 2002). A key result of the work noted above is the documentation of interdecadal changes in zooplankton abundance and community structure. Generally, Calanus finmarchicus decreased in the region in the 1990s, while Oithona spp. and Centropages hamatus increased in abundance. This pattern was found on the Newfoundland and Scotian Shelves (Head and Sameoto 2007), Gulf of Maine (Pershing et al. 2005), and Georges Bank (Kane 2007). Associated with these changes in abundance is a southward shift of boreal species during the 1990s including Calanus hyperboreus (Johns et al. 2001). This interdecadal variability has been linked to the increased downshelf advection in the Labrador Coastal

J.S. Link et al. Current related to changes in wind patterns and high latitude freshwater input (and by inference, climate change; Greene and Pershing 2007). Gelatinous zooplankton

Many animal phyla have taxa that are considered gelatinous zooplankton, which is a general name for fragile planktonic animals. Common groups include members of the phylum Cnidaria (e.g., jellyfish, hydromedusae, hydroids, siphonophores), Ctenophora (e.g., ctenophores), Chordata (e.g., larvaceans and salps), and Mollusca (e.g., pteropods). Owing to their fragile nature, extensive collection is a serious challenge and relatively little is known regarding the gelatinous zooplankton in the northwest Atlantic Ocean (see Pagès et al. 2006). However, there is growing recognition that these animals play an important role in the ecosystem (Frank 1986, Link et al. 2006, 2008b). There are only a handful of studies that examine the large-scale abundance and distribution of gelatinous zooplankton in the system. Link and Ford (2006), using dogfish stomach contents, describe an increase in the abundance and a northward shift in the distribution of ctenophores in the Mid-Atlantic Bight, Southern New England, Georges Bank, and Gulf of Maine regions. Pelagic hydroids have been observed over Georges Bank (Concelman et al. 2001), likely suspended into the water column by strong wind or tidal mixing, and may have an important predatory impact on copepod and larval fish production (Madin et al. 1996). Given the apparent importance of gelatinous zooplankton and the limited amount of information, additional research is necessary to understand their ecological role in the northwest Atlantic Ocean. Meroplankton

In addition to the broad range of animals that live their entire life in the plankton (holoplankton), a number of species occur in the plankton only during specific parts of their life history (referred to as meroplankton). Most marine animals have complex life histories – their life cycles are made up of morphologically distinct stages that use different habitats. A large number of marine invertebrates and fishes have planktonic larval stages including annelids (worms), molluscs (clams), gastropods (snails), bivalves (clams and scallops), decapods (crabs and shrimp), echinoderms (starfish and sea urchins), and most species of bony fish. In the northwest Atlantic Ocean, more than 700 species of fishes have larval stages that are planktonic (Fahay 2007). Further, there are more than 500 macrobenthic invertebrates in the southern extent of the northwest Atlantic Ocean (Theroux and Wigley 1998) and there are certainly additional macrobenthic invertebrate species on the eastern Scotian, Newfoundland, and Labrador Shelves – all as species that have planktonic larval stages. These life stages do not contribute significantly to the energy flow

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Northwest Atlantic ecosystem-based management in the ecosystem (Link et al. 2006), but they are important to the dynamics of both their and their predator populations. There have been several long-term monitoring programs primarily focused on the larval stages of exploited species (O’Boyle et al. 1984, Berrien and Sibunka 1999) and a multitude of more directed research programs (e.g., Canadian and US GLOBEC; Reiss et al. 2000, Mountain and Kane 2010). Studies of meroplankton will continue in support of the management of fisheries targeting individual species and may become more important as a measure of ecosystem diversity for a range of species in a range of habitats (Barber and Boyce 2006). Macrobenthos

There are also multiple species of large-bodied benthic species (macrobenthos at > 0.5 mm; Theroux and Wigley 1998) that inhabit the northwest Atlantic. Typically they are not well surveyed and our understanding of them only represents a small snapshot of the long-term, large-scale reality in the ecosystem. Yet what we do know is that the biomass of this group of organisms can be significant. The standing biomass of macrobenthos in the Gulf of Maine, especially gastropods and echinoderms, is very large, averaging about 72 g/m2 (Link et al. 2006), and around 120 g/m2 on the eastern Scotian Shelf (Bundy 2004). Megabenthos (species usually larger than 2 cm) biomass, particularly lobsters and crabs, is also relatively large averaging about 5 g/m2. Large standing stocks of macrobenthos, particularly crustaceans, gastropods, and echinoderms, are comparable to the standing stocks of demersal fishes on Georges Bank and Southern New England (Link et al. 2006). Several benthic species, such as lobster, scallops, and snow crabs, are now the target species of lucrative commercial fisheries. Upper trophic levels – fish

The fish fauna of the northwest Atlantic Ocean is regularly monitored using multi-species (research vessel; RV) trawl surveys in Canada and the USA. These surveys are designed to quantitatively monitor trends in abundance and distribution of fish and macroinvertebrate species inhabiting the region. All surveys have a stratified random design, and have a survey time series of up to 45+ years. The surveys are conducted in depths from approximately 27 to 366 m; however, greater depths are occasionally sampled in the canyons along the continental shelf break. Within each depth-region stratum, stations are assigned randomly and the number of stations allotted to a stratum is in proportion to its area (with a minimum of 2 per strata). A key difference between the ecosystems of the USA and Canada was the collapse of cod and some other groundfish fisheries in most Canadian systems in the early 1990s (except NAFO Divisions 4X and 3Ps). There has been an intense

J.S. Link et al. debate on the causes of the groundfish collapses and their subsequent lack of recovery. Many hypotheses have been put forward, ranging from overfishing, climate effects, predation by seals, food availability, and combinations of all of these (Myers et al. 1996, Shelton and Lilly 2000, Drinkwater 2002, Rose and O’Driscoll 2002). Many of these issues remain open for debate to this date. Nonetheless, there is ample recognition that overfishing played a major and significant role in the collapse of commercial stocks (Shelton et al. 2006), although it is also accepted that changes in ocean climate were also contributing factors to these declines (Drinkwater 2002). Cooler water temperatures were common during the last 30 years, but especially so in the early 1990s. The role of seals is still controversial. It is generally accepted that seals did not play a significant role in the collapse of these stocks, but their role in preventing recovery remains unclear (DFO 2008b). We discuss these themes in further detail below.

The Newfoundland–Labrador Shelf The fish community of the Newfoundland–Labrador Shelf was historically characterized by large demersal species, with cod being the dominant component of this assemblage. Most constituent species can be found throughout the entire region, including commercially important species like cod, capelin, Greenland halibut, and American plaice. However, with the exception of Greenland halibut, each one of these commercially important species has distinct management stocks in each subsystem (Bundy et al. 2000). Not surprisingly, the northern region is also characterized by the presence of cold-water species like Arctic cod, while the Grand Bank has temperate water species like yellowtail flounder and sand lance. Among forage fishes, capelin has a dominant role in the Newfoundland– Labrador marine food web. Arctic cod is also an important forage species in the northern area, while sand lance is important in the Grand Bank. Herring is another forage fish which is particularly relevant in inshore waters (Link et al. 2008b). The Newfoundland–Labrador Shelf ecosystem has been exploited for more than 500 years, with cod being the main target of these fisheries (Rice 2002, Rose 2004). Although there have been dramatic declines of many commercial stocks over the years, certainly the most dramatic one was the collapse of cod that occurred in the early 1990s (Rice 2002). Although the collapse of northern cod (2J3KL cod stock) is certainly the most visible and well-known example (Lilly 2008), many stocks underwent severe declines during this period (Fig. 2.2; Atkinson 1994, Gomes et al. 1995, DFO 2006). Overall, the total biomass of these

51

Campelen trawl

1000

100

100

10

10

1

1

0.1

Biomass (kg/tow) note the logarithmic scale

DFO Fall survey (selected strata)

Engels trawl NAFO Div. 2J3K

Southern Labrador - Northern Newfoundland shelf

Northwest Atlantic ecosystem-based management

0.1 1980

1985

1990

1995

DFO Spring survey (selected strata)

NAFO Div. 3LNO

0.01 1995

100

2005

2010

2000

2005

2010

10

10

1

1

0.1

0.1 0.01 1980

2000

100

1000

Grand Bank

52

1985

1990

1995

0.01 1995

Cod Greenland halibut (turbot)

Redfish Northern wolfish

American plaice

Yellowtail flounder

Fig. 2.2.  Survey biomass of the six major species in various DFO surveys of Newfoundland–Labrador. Note the change in gear in 1995.

fish declined by the mid 1990s to approximately 5–15% of what it was in the early to mid 1980s, as estimated based on research surveys (Fig. 2.3). The most severe reductions were observed in the northern area (Figs 2.2 and 2.3). During the same period, other concurrent changes included the increases of invertebrates like northern shrimp and snow crab (Lilly et al. 2000), as well as marine mammals like harp seals (Healey and Stenson 2000). Another important change during this period included the alteration of many biological characteristics in capelin, and its virtual disappearance from its original primary distributional area (Carscadden and Nakashima 1997, Mowbray 2002, DFO 2008a). Acoustic estimates of capelin abundance declined dramatically in 1991, and have remained low ever since. Conflicting information from different indices has given rise to controversy regarding whether capelin really collapsed or simply moved somewhere else (Carscadden and Nakashima 1997). Regardless, its availability in areas of known prior distribution has been significantly reduced since the early 1990s (DFO 2008a). Starting around 2003, the fish community of the Newfoundland–Labrador Shelf has been exhibiting signs of increasing populations. Although proper and complete comparisons with the pre-collapse period are not possible at the

J.S. Link et al. 500

Total fish biomass (kg/tow)

400 300 200 100 0 1980

1985

1990

1995

2000

2005

2010

2J3K (Fall survey – Engels trawl) 2J3K (Fall survey – Campelen trawl) 3LNO (Spring survey – Engels trawl) 3LNO (Spring survey – Campelen trawl) Fig. 2.3.  Total survey biomass of all the major species in various DFO surveys of Newfoundland–Labrador.

present time due to a gear change implemented in research surveys between 1994 and 1995 (an Engels trawl was replaced by a Campelen one), analysis of data from the more recent Campelen series indicates that the total fish biomass almost doubled between 1995 and 2007 (Fig. 2.3). Despite this increasing trend, the observed biomass still indicates that populations of the fish community are well below pre-collapse levels, especially if one considers that the Campelen trawl has a smaller mesh size, and hence a higher catchability for small-bodied creatures. Similarly, the acoustic index for capelin is also showing a positive trend in recent years, but it is still one order of magnitude below its pre-1991 level (DFO 2008a). At the present time it remains unclear if these positive trends can be interpreted as a prelude to long-term stock rebuilding or not. Current trends, however, still represent a positive sign and an opportunity for stock rebuilding if the fisheries are managed cautiously. Gulf of St. Lawrence

The fish community of the Gulf of St. Lawrence is composed of large populations of groundfish such as cod, plaice, white hake, and Greenland halibut (turbot) while spring spawning and fall spawning herring stocks and migratory mackerel dominate the pelagic system. In the northern Gulf of St. Lawrence, capelin has been an important component of the biomass and an important forage species. Large invertebrates include very abundant population of Pandalus shrimp in the north and in estuaries; snow crabs and lobsters are more abundant in the southern Gulf of St. Lawrence. A variety of bivalves can be quite abundant, as exemplified by the Stimpson surf clam (Mactromeris

53

54

Northwest Atlantic ecosystem-based management polynyma), softshell clams (Mya arenaria), and even Virginia oysters (Crassostrea virginica) in some of the warm inshore bays. Most of the fish species tend to make seasonal migrations to avoid cold winter temperatures, either exiting the Gulf completely or moving to deeper and slightly warmer waters in the Laurentian Channel. The abundant mackerel present in the summer months is considered part of the eastern Canada/US stock which winters off the eastern USA. Additionally, seasonal migrants have included bluefin tuna and swordfish, but such species were more noticeable when their populations were larger. The Gulf of St. Lawrence is not an overly diverse marine community. Seven species account for about 80% of the fish community biomass (Duplisea and Castonguay 2006). By including northern shrimp (Pandalus spp.) much of the biomass of the larger organisms in the system is accounted for. The total biomass of several species of fish in the Gulf of St. Lawrence was relatively high in the 1980s, declining precipitously until the mid 1990s when it started to increase again (Fig. 2.4). The total biomass, as estimated through surveys, has not recovered to previously observed levels but has stabilized; however, the dynamics of the most abundant species can be quite different from the concurrent dynamics of total biomass. The biomass of cod and redfish followed a trend similar to the total while turbot and shrimp show an inverse trend to cod and the total. The dynamics of the total biomass (Fig. 2.4) reflects the collective population dynamics of these four species. Cod, and especially redfish, are shrimp predators and it has been speculated that the decline of these groundfish in the 1980s released shrimp from predation, resulting in shrimp populations that soared (Worm and Myers 2003). Likewise, the turbot may have benefited both from the increased numbers of shrimp, and lack of other forms of competition with cod and redfish (Worm and Myers 2003). In the shallower southern Gulf, where shrimp are less important, there seems to be an important alteration of states going from total biomass being dominated by demersals to one where pelagic fish (herring and mackerel) are important (Swain and Sinclair 2000). That is, when pelagics are abundant, demersal fish (cod in particular) show depressed recruitment rates. Capelin is an important prey species for a variety of fish, particularly cod, and its abundance is currently slightly lower than in the 1980s. It has been suggested (Rose and O’Driscoll 2002) that lower capelin numbers could be an important factor in the slow recovery of cod in Newfoundland waters as these oily prey are essential for good condition and increasing reproductive potential among cod. Shrimp, though abundant, do not provide the same amount of energy for cod. How this applies to the Gulf of St. Lawrence remains unclear.

J.S. Link et al.

Fig. 2.4.  Survey biomass of the major species caught in the DFO groundfish survey of the northern Gulf of St. Lawrence. Survey vessel and gear changed in 1990 and again in 2004 and, while biomass is standardized through comparative tow experiments, changes at these points should not be over-interpreted. Species for which the series was unreliable from 1984–1990 do not have these points plotted. Lines are smooth splines (df = 5) through the series to remove the strong year effects that often occur in surveys (e.g., 2003 was anomalously high for most species).

There is currently growth in most stocks in the northern part of the Gulf, though fisheries are thought to be taking much of the production of cod each year (DFO 2008c). In the southern part of the Gulf, many groundfish species (particularly cod) are in a decreasing biomass trajectory even without fishing (Swain and Chouinard 2008). Single-species management is still the norm for these stocks though it is not clear if abundant prey (e.g., shrimp) and predator (e.g., cod) populations can co-occur (Collie and Gislason 2001). Reference

55

Northwest Atlantic ecosystem-based management 300 000 250 000 Metric Tons

56

Groundfish Pelagics Invertebrates

200 000 150 000 100 000 50 000 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year

Fig. 2.5.  Reported landings of groundfish, pelagics, and invertebrates in the Gulf of St. Lawrence (NAFO zones 4RST) from 1960–2004. From the NAFO landings database.

points currently being developed for single-species stock management may not sufficiently account for the possibility of inconsistency with multi-species considerations. Trends in landings of major species groups in the northern Gulf of St. Lawrence tend to mirror survey trends (Fig. 2.5). Groundfish landings dropped to effectively zero in only a few years in the early 1990s coinciding with the decline in cod and redfish. At the same time invertebrate landings increased notably, largely driven by increased shrimp yields. Pelagic fish landings also steadily increased but not to levels that were seen at their peak in 1970.

Scotian Shelf and Bay of Fundy Several analyses have been published concerning the decline in fish stocks and fisheries that occurred on the Scotian Shelf over the last two decades. The decline has been far more dramatic on the colder eastern Scotian Shelf (DFO 2003a, Choi et al. 2004, 2005, Bundy 2005, Frank et al. 2005) than on the warmer western Scotian Shelf (Zwanenburg et al. 2002, 2006, Shackell and Frank 2007). The Scotian Shelf has supported a wide range of fisheries for the last 500 years for between 30 and 60 species, including groundfish species such as cod, haddock, pollock, white hake, small pelagic species such as mackerel and herring, commercially valuable invertebrates such as lobsters, crab, clams, and scallops, and large pelagic species such as sharks, swordfish, and tunas. The eastern and western Scotian Shelf have similar exploitation histories, although there was a greater emphasis on groundfish in the east and on pelagics in the

J.S. Link et al. west (cf., figs 4–5 in Zwanenburg et al. 2002), yet only the groundfish fisheries on the eastern Scotian Shelf collapsed in the early 1990s. In the last 15 years, fishing effort has increased on such species as lobster, snow crab, shrimp, scallop, dogfish, periwinkles, and rockweed. More recently a suite of new fisheries have emerged. These include sea cucumber, sea urchin, whelks, red, rock, and jonah crabs, Atlantic and Arctic surf clams, sculpins, and hagfish. All are species that harvesters have sought to exploit as alternatives to species harvested earlier. The pace of developing new fisheries on species that were not harvested previously, in the context of the recent decline in traditional fisheries, may be too rapid to ensure their sustainability (Anderson et al. 2008). Total finfish biomass on the eastern Scotian Shelf averaged 358 000 tons (t) in the 1970s, 506 000 t in the 1980s, less than 300 000 t in the 1990s, and 257 000 t in the 2000s (Fig. 2.6A). Since the mid-1990s, estimated biomass remained low but stable, but it has decreased in the last few years to reach an historical low of 229 332 t in 2008. This represents less than 50% of the estimated biomass during the 1980s, and 65% of the 1970s biomass. These changes in biomass have been attributed to declines in taxonomic groups such as gadoids, flatfish, elasmobranchs, redfish, and other demersals. The only functional group that increased was the small pelagics. Averaged over the 38-year time series, cod, redfish, haddock, plaice, and silver hake were the five species with the highest average biomass (Fig. 2.7). Of these five species, all declined from the 1980s to the 1990s; only haddock has increased since then. When the data were aggregated by decade, there was less consistency; at one time or another, 11 species occur in the five most abundant species over the four decades. Only redfish and haddock were always among the top five species (Table 2.1). Although adjacent to the eastern Scotian Shelf, the western Scotian Shelf is represented by data depicting a healthier situation, since total estimated finfish biomass has gradually increased from around 268 000 t in the 1970s, to 380 000  t in the 1980s, 410 000 t in the 1990s, and 480 500 t in the 2000s (Fig. 2.6B). This is mainly due to increases in elasmobranchs, redfish, and small pelagics. The gadoids peaked during the 1980s, flatfish biomass remained relatively constant, and the other demersals decreased. Spiny dogfish, redfish, haddock, pollock, and cod were the species with the highest biomass in all decades except the 2000s when herring replaced cod (Table 2.1). The increase in elasmobranchs is due to the increase in spiny dogfish. Changes in life-history characteristics have been used to contrast and compare ecosystems. Analysis of survey data indicates that there has been a long-term reduction in the number of large fish and in the mean weight of individual fish in both the eastern and western Scotian Shelf from 1970 to 1999 (Zwanenburg et al. 2002). Shackell and Frank (2007) compared the

57

Northwest Atlantic ecosystem-based management 4VW

800 000

A

700 000 600 000

Others

Biomass (t)

500 000

Redfish Demersals

400 000

Elasmobranchs

300 000

Small pelagics Flatfish

200 000

Gadoids

100 000 0 1970

1975

1980

1985

1990

1995

2000

2005

4X

B 800 000 700 000 600 000

Biomass (t)

58

500 000 Others Redfish

400 000

Demersals 300 000

Elasmobranchs Small pelagics

200 000

Flatfish Gadoids

100 000 0 1970

1975

1980

1985

1990

1995

2000

2005

Fig. 2.6.  Survey biomass by functional groups for the eastern (A) (4VW) and ­western (B) (4X) Scotian Shelf.

eastern and western Scotian Shelf using a number of different metrics and concluded that, although the western Scotian Shelf appears more stable than the eastern Scotian Shelf, it is also following a similar pattern, possibly at a slower rate. They contrasted the condition and individual growth rate of four functional groups, large benthivores, medium benthivores, piscivores, and pelagic predators, over the time covered by the series of available data. They found that condition decreased in both the western and eastern Scotian Shelf for all groups except the pelagic predators, which included red hake and silver

J.S. Link et al. WSS Top five species in RV Survey A 450 000 400 000

Cod Redfish Haddock Plaice Silver hake

350 000

Biomass (t)

300 000 250 000 200 000 150 000 100 000 50 000 0 1970

1975

1980

1985

1990

1995

2000

2005

WSS Top five species in RV Survey Cod Spiny dogfish Redfish Haddock Pollock

B 450 000 400 000 350 000

Biomass (t)

300 000 250 000 200 000 150 000 100 000 50 000 0 1970

1975

1980

1985

1990

1995

2000

2005

Fig. 2.7.  Trends of the top five species for (A) the eastern Scotian Shelf and (B) western Scotian Shelf 1970–2008. Note years 2004 and 2007 are missing due to vessel changes.

hake. Growth rates, as measured by size-at-age, declined in both areas for haddock, pollock, and silver hake, whereas cod size-at-age only declined in the east. As prominent commercially important fish declined in the west, other species were able to compensate (largely due to warmer temperatures and higher base productivity) relative to the east. However, those authors warn

59

60

Northwest Atlantic ecosystem-based management Table 2.1. Five species with the highest biomass in the Canadian survey for each decade on the eastern Scotian Shelf and western Scotian Shelf.

ESS

WSS

1970s

1980s

1990s

2000s

1970–2008

1

Redfish

Cod

Redfish

Haddock

Cod

2

Cod

Haddock

Haddock

Herring

Redfish

3

Plaice

Redfish

Cod

Redfish

Haddock

4

Haddock

Pollock

Herring

Plaice

Plaice

5

Thorny skate

Plaice

Silver hake

Sandlance

Silver hake

1

Haddock

Spiny

Spiny

Spiny

Spiny

dogfish

dogfish

dogfish

dogfish

2

Redfish

Pollock

Redfish

Redfish

Redfish

3

Spiny dogfish

Haddock

Pollock

Haddock

Haddock

4

Pollock

Redfish

Haddock

Pollock

Pollock

5

Cod

Cod

Cod

Herring

Cod

that unless remedial actions are taken, the west is on a trajectory toward duplicating events in the east. Over-exploitation in large marine systems can induce trophic imbalances (e.g., Frank et al. 2007; Bundy et al. 2009). The eastern Scotian Shelf ecosystem has been profoundly altered and exhibits classic symptoms of changes involving a trophic cascade (Bundy 2005, Frank et al. 2005) and of “fishing down the food web” (Bundy 2005). Comparison of two ecosystem models indicated that although total productivity and total biomass of the ecosystem remained similar between the early 1980s and late 1990s, there were changes in predator structure, trophic structure, and energy flow, many of which were robust to uncertainty (Bundy 2005). The greatest change in the ecosystem is the switch from a demersal fish-dominated system to a system dominated by forage species, indicating a shift in trophic flow from the demersal to the pelagic side of the food web. The eastern Scotian Shelf has switched to an alternative state, dominated by small pelagics, invertebrates, and seals. This state, where the previously abundant cod are kept at low levels of abundance, is hypothesized to be maintained through a combination of cultivation/depensation effects, Allee effects, and a massive reduction in the size structure of what remains of the cod population (Bundy and Fanning 2005). In contrast, on the western Scotian, the top predator biomass has been relatively stable (though traditionally dominant commercial species such as cod have declined; Shackell and Frank 2007) yet the abundance of forage fish species has increased. This paradox is currently under investigation by examining

J.S. Link et al. the influence of diminishing predator size, as can occur from size-selective fishing, on trophic balance. Gulf of Maine

Major declines in groundfish and flounders occurred in the Gulf of Maine during 1961–1972 (Brown et al. 1976, Gabriel 1992). Analyses by Clark and Brown (1977) suggested an overall decline in biomass of over 40% in this region during 1963–1974, with overall declines in groundfish and flounders ranging from 25–85%, depending on species. Cluster analysis classified the Gulf of Maine as a separate zoographic region when the entire shelf area from Cape Hatteras to Nova Scotia was analyzed (Gabriel 1992). Total survey biomass for fall in the Gulf of Maine during 1963–2007 dropped rapidly in the early 1960s and then experienced a slow decline to about 60 kg/tow in 1992 (Fig. 2.8). Biomass recovered to an average of about 200 kg/tow during 2005–2007. Redfish, white hake, cod, and spiny dogfish were important species in terms of biomass during the 1960s through the early 1980s, while spiny dogfish and redfish dominated the biomass in the 1990s and 2000s (Fig. 2.9A). Redfish dominated the catch by number during the first half of the Gulf of Maine survey series (Fig. 2.9B). Redfish remained the dominant species, but Atlantic herring and spiny dogfish were also prominent during the latter part of the 1963–2007 time series.

Fig. 2.8.  Total autumn survey biomass (kg/tow) in the Gulf of Maine during 1963–2007.

61

Northwest Atlantic ecosystem-based management A

GOM Stratified Mean Weight per Tow 220.000 200.000

Weight (Kg) per Tow

180.000 160.000 140.000 120.000 100.000 80.000 60.000 40.000 20.000 0.000

1964

1968

1972

1976

1980

Spiny dogfish White hake

1984 1988 Year Atlantic herring Acadian redfish

1992

1996 2000

2004

Atlantic cod

GOM Stratified Mean Weight per Tow B 450.0 400.0 Numbers per Tow

62

350.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0

1964

1968

1972

1976

Spiny dogfish White hake

1980

1984 1988 Year Atlantic herring Acadian redfish

1992

1996

2000

2004

Atlantic cod

Fig. 2.9.  A. Autumn survey biomass (kg/tow) in the Gulf of Maine for five important species during 1963–2007. B. Autumn survey abundance (number/tow) in the Gulf of Maine for five important species during 1963–2007.

Georges Bank

Brown et al. (1976) documented a severe decline in fish biomass on Georges Bank following several years of intense fishery exploitation during the ICNAF era of 1961–1972. Subsequent analyses showed a decline of over 37% in the overall fish biomass of the Georges Bank region during 1963–1974 (Clark

J.S. Link et al. and Brown 1977, Gabriel 1992). Additional analyses measured fishing effort at over 900 000 hours/year fished in the early 1960s and showed that catch per unit effort (CPUE) had declined dramatically during 1960–1987 on Georges Bank (Mayo et al. 1992). Other studies focused on the relative persistence of assemblages in the region, documenting declines and recovery of several important groups of fishes (Overholtz and Tyler 1985, Gabriel 1992). Overholtz and Tyler (1985) showed that several groups of fishes consistently occupied spatial regions (depth zones) of the bank over time, while Gabriel (1992) showed similarly persistent trends as well as an extension of these groups to the south into Southern New England. The total autumn survey biomass on Georges Bank fluctuated considerably during 1963–2007 (Fig. 2.10). Biomass declined from about 200 kg/tow in the early 1960s to 60 kg/tow in 1971, followed by an increase to 180 kg/tow in the late 1970s and early 1980s. This period was followed by a slow decline to about 60 kg/tow in 1994 and finally a sustained recovery to roughly 200 kg/tow in 2007 (Fig. 2.10). The initial decline in total biomass during the 1960s was due mainly to several years of intense fishing on haddock and the recovery in the late 1970s and early 1980s occurred because of the short-term recovery of cod, two large year classes of haddock in 1975 and 1978, and a major increase in spiny dogfish (Fig. 2.11A). Spiny dogfish and winter skate dominated the survey

Fig. 2.10.  Total autumn survey biomass (kg/tow) on Georges Bank during 1963–2007.

63

Northwest Atlantic ecosystem-based management Georges Bank Key Indicator Mean Weight per Tow

A 160.00 Weight (Kg) per Tow

140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00

B

1964

1968

1972

1976

1980

1984 1988 Year

1992

1996

2000

2004

2000

2004

Georges Bank Key Indicator Mean Numbers per Tow 500.0 450.0 400.0

Numbers per Tow

64

350.0 300.0 250.0 200.0 150.0 100.0 50.0 0.0

1964

1968

1972

1976

1980

1984

1988

1992

1996

Year Spiny dogfish Atlantic cod

Winter skate Haddock

Atlantic herring

Fig. 2.11.  (A) Autumn survey biomass (kg/tow) on Georges Bank for five important species during 1963–2007. (B) Autumn survey abundance (number/tow) on Georges Bank for five important species during 1963–2007.

catches on Georges Bank in the mid 1980s, a period followed by low biomass of all five species during 1994. The subsequent recovery in biomass was dominated by herring and then finally by haddock and spiny dogfish after the turn of the century (Fig. 2.11A). The abundance series (number/tow) was initially dominated by haddock, then spiny dogfish, herring, and finally herring and haddock (Fig. 2.11B)

J.S. Link et al. Southern New England

Brown et al. (1976) showed that the populations of many major groundfish, flounders, and other species declined in the Southern New England region during 1961–1972. Clark and Brown (1977) suggested that this decline averaged 52% during 1963–1974 with a range of 6–99% for declining species. For example, cod declined 53%, haddock 99%, silver hake 43%, winter flounder 55%, and goosefish 18% during this time period. In other studies of the area, Southern New England tended to be aligned with Georges Bank in analyses that attempted to identify regions of persistent species composition during 1967–1988 (Gabriel 1992). Autumn survey of total biomass (kg/tow) declined slowly from an average of about 200 kg/tow in the early 1960s to an average of about 90 kg/tow in the mid 1970s (Fig. 2.12). After a slight recovery in the early 1980s, survey catches declined further to a low of about 35 kg/tow in 1994. Some recovery occurred in the 2000s with catches averaging a little over 90 kg/tow (Fig. 2.12). Spiny dogfish dominated survey biomass in the early and later parts of the 1963–2007 time series, and butterfish was somewhat important during the intermediate years (Fig. 2.13A). Butterfish and longfin squid dominated survey catches in number during most of the 1963–2007 period (Fig. 2.13B).

Fig. 2.12.  Total autumn survey biomass (kg/tow) in Southern New England during 1963–2007.

65

Northwest Atlantic ecosystem-based management SNE Stratified Mean Weight per Tow

A 270.000

Weight (Kg) per Tow

240.000 210.000 180.000 150.000 120.000 90.000 60.000 30.000 0.000

1964 1968

1972 1976

1980

1984

1988 1992

1996

2000 2004

Year Spiny dogfish Butterfish

Little skate Longfin squid

Yellowtail flounder

SNE Stratified Mean Numbers per Tow

B 2,400.0 2,100.0 Numbers per Tow

66

1,800.0 1,500.0 1,200.0 900.0 600.0 300.0 0.0

1964 1968

1972 1976

Spiny dogfish Butterfish

1980

1984 Year

1988 1992

Little skate Longfin squid

1996

2000 2004

Yellowtail flounder

Fig. 2.13.  (A) Autumn survey biomass (kg/tow) in Southern New England for five important species during 1963–2007. (B) Autumn survey abundance (number/tow) in Southern New England for five important species during 1963–2007.

Mid-Atlantic Region

The overall decline in biomass in the Mid-Atlantic area was apparently the largest of the four northeast US LME ecosystems with an estimated average 74% decrease occurring during 1963–1974 (Clark and Brown 1977, Gabriel 1992). Declines, ranging from 8–99%, were recorded for several species of

J.S. Link et al.

Fig. 2.14.  Total autumn survey biomass (kg/tow) for the Mid-Atlantic Bight region during 1967–2007.

groundfish, flounders, and other groundfish. For example, silver hake declined 78%, yellowtail flounder 99%, summer flounder 72%, winter flounder 93%, and goosefish 29% (Clark and Brown 1977). Other studies of this region suggest that the southern Mid-Atlantic Bight should be classified as a unique region based on the composition of fishes in the area (Gabriel 1992). Fall surveys of total biomass showed steep declines from a series high of 225  kg/tow in1968 to more of a slow steady decline to 20 kg/tow in 1993 (Fig. 2.14). Survey biomass recovered to an average of about 80 kg/tow in the 2000s. Species composition for five important fishes and squids favored butterfish and longfin squid in the early 1970s, Atlantic croaker, butterfish, and longfin squid in the 1980s and 1990s, and Atlantic croaker and longfin squid in the 2000s (Fig. 2.15A). Survey abundance (number/tow) was dominated by butterfish and longfin squid during the 1968–2007 period (Fig. 2.15B).

Upper trophic levels – marine mammals Pinnipeds

Seals found in the northwest Atlantic region include the harbor seal (Phoca vitulina), gray seal (Halichoerus grypus), harp seal (Pagophilus groenlandicus), and hooded seal (Cystophora cristata). Their distribution is northerly with the exception of harbor seals which are year-round inhabitants of the coastal

67

Northwest Atlantic ecosystem-based management MA Important Species Stratified Mean Weight per Tow

A 70.000

Weight (Kg) per Tow

60.000 50.000 40.000 30.000 20.000 10.000 0.000

1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 Year Spiny dogfish Atlantic croaker

Summer flounder Longfin squid

Butterfish

MA Important Species Stratified Mean Numbers per Tow

B 2000.0 1800.0 Numbers per Tow

68

1600.0 1400.0 1200.0 1000.0 800.0 600.0 400.0 200.0 0.0

1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 Year Spiny dogfish Atlantic croaker

Summer flounder Longfin squid

Butterfish

Fig. 2.15.  (A) Autumn survey biomass (kg/tow) for five important species for the Mid-Atlantic Bight region during 1967–2007. (B) Autumn survey abundance (number/tow) for five important species for the Mid Atlantic Bight region during 1963–2007.

waters of eastern Canada and Maine (Burns 2002) and occur seasonally along the southern New England and New York coasts from September through late May (Waring et al. 2004). The western North Atlantic population of gray seals inhabits waters from New England to Labrador, but are centered in the Sable Island region of Nova Scotia (Hall 2002, Zwanenburg et al. 2006). However, the Sable

J.S. Link et al. herd has increased notably (see below) and seals have expanded their distribution to the west and south. Some pupping has been observed along the coast of southwest Nova Scotia, and on several isolated islands along the Maine coast and in Nantucket-Vineyard Sound, Massachusetts (Waring et al. 2004, 2007). Harp seals are the most abundant pinniped in the northern Atlantic and Arctic Oceans (Lavigne 2002, Stenson et al. 2003) and have large populations on the Newfoundland–Labrador Shelf and in the Gulf of St. Lawrence; however, over the past decade during January to May, numbers of sightings and strandings have been increasing off the east coast of the USA from Maine to New Jersey (Waring et al. 2007). Hooded seals also have a more northerly distribution and occur throughout much of the northern North Atlantic and Arctic Oceans, preferring deeper water and occurring farther offshore than harp seals. Seasonal movements of pinnipeds are substantial, both among and in exchange with areas outside the ecosystems being described here. This is particularly true for the southern two regions (Southern New England and Mid-Atlantic Bight), where there are only a few pinnipeds found in the summer. Pinnipeds in the USA primarily inhabit the Gulf of Maine region, and rarely use the Georges Bank region (see Palka and Waring 2006). Baleen whales and odontocetes

Cetaceans (whales, dolphins, and porpoises) migrate across the entire northwest Atlantic including the ecosystems being characterized in this chapter. General descriptions of the distribution and habit of cetaceans in the north Atlantic are found in Waring and Palka (2002) and in Zwanenburg et al. (2006). More detailed descriptions for each species are found in Waring et al. (2004, 2007). Odontocetes (cetaceans with teeth) that utilize at least one of the ecosystems for some part of the year include: bottlenose dolphins (Tursiops truncatus), common dolphins (Delphinus delphis), beaked whales (Ziphius or Mesoplodon spp.), Risso’s dolphins (Grampus griseus), harbor porpoise (Phocena phocena), dwarf sperm or pygmy sperm whale (Kogia spp.), long-fin and short-fin pilot whales (Globicephala spp.), sperm whales (Physeter macrocephalus), spotted dolphins (Stenella frontalis), striped dolphins (S. coeruleoalba), and white-sided dolphins (Lagenorhynchus acutus). Baleen whales that utilize some part of the ecosystems for some part of the year include: fin whales (Balaenoptera physalus), sei whales (B. borealis), humpback whales (Megaptera novaeangliae), minke whales (B. acutorostrata), and right whales (Eubalaena glacialis). The seasonal distribution of cetaceans varies greatly by species. Biomass per area is dominated by baleen whales, even though there are fewer species of baleen whales than odontocetes. The cetacean biomass density is highest in the Gulf of Maine, Georges Bank, and Bay of Fundy, and lowest in the Mid-Atlantic Bight, and more northerly regions. Among the baleen whales,

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Northwest Atlantic ecosystem-based management the most common species are fin and humpback whales. Most odontocete biomass is comprised of pilot whales (long- and short-fin), common dolphins, and white-sided dolphins. Within US waters, the Gulf of Maine region has the lowest number of species but the most biomass. The Georges Bank and Southern New England regions have the most diverse composition of species. All the species found in the ecosystems south of the Gulf of Maine are also found offshore of the ecosystems. For some species, the abundance offshore is much greater than that estimated for the four US regions. The consumption rate per unit area is the highest in the Gulf of Maine and lowest in the Mid-Atlantic Bight. Baleen whales consume more than the odontocetes. Of the odontocetes, those on Georges Bank have the highest rate of consumption. Similar species occur in Canadian waters, where the Bay of Fundy provides important summer feeding and nursery grounds for the endangered North Atlantic right whale (Eubalaena glacialis). Further north, the beluga (Delphinapterus leucas), which is an Arctic and sub-Arctic species, occurs in the lower St. Lawrence Estuary in the Gulf of St. Lawrence. More detail can be found in Palka (2006). Protected species/species of concern

With increased mortality from fisheries (as well as other causes, some anthropogenic), it has become necessary to develop specific measures to protect marine species at risk of extinction. Canada’s Species at Risk Act (SARA) was created in 2002 to prevent wildlife species from becoming extinct).2 It requires Canada to at least try for the recovery of species at risk, especially those due to human activity, and to manage species of special concern, making sure they do not become endangered or threatened. SARA not only prohibits the killing, harming, harassing, capturing, or taking of species at risk, but also makes it illegal to destroy their critical habitats. The Act established the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) as an independent body of experts responsible for identifying and assessing species considered to be at risk. SARA classifies species into four categories of risk: extirpated (extinct in Canada), endangered, threatened, or of special concern. Most of the marine species listed to date are marine mammals, including the extirpated walrus (Odobenus rosmarus rosmarus) and the Atlantic gray whale (Eschrichtius robustus), the endangered blue whale (Balaenoptera musculus), North Atlantic right whale (Eubalaena glacialis), and the Scotian Shelf population of northern bottlenose whale (Hyperoodon ampullatus). Threatened species include the St. Lawrence Estuary population of beluga whales (D. leucas); the Atlantic fin whale (B. physalus) and northwest Atlantic killer whale (Orcinus orca) are species http://www.sararegistry.gc.ca/default_e.cfm

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J.S. Link et al. of concern. Five fish species are listed under SARA, two anadromous species which are both threatened (the Inner Bay of Fundy populations of Atlantic salmo (Salmo salar) and Atlantic whitefish (Coregonus huntsmani)) and three wolffish species, northern (Anarhichas denticulatus) and spotted wolffish (A. minor), which are threatened and Atlantic wolffish (A. lupus) which are of special concern. Several other species such as white hake, thorny skate, American plaice, smooth skate, and redfish are under COSEWIC review; Altantic cod and barndoor skaet are slated for review. Under SARA, the DFO must produce recovery strategies and action plans for aquatic species listed as endangered or threatened. Recovery strategies for endangered fish and marine species listed under SARA must be developed within a year of their addition to SARA lists. For threatened or extirpated species, recovery strategies must be developed within 2 years. In the USA, the Endangered Species Act (ESA) was established (in 1973) to similarly protect species from extinction. The Marine Mammal Protection Act (MMPA) of 1972 was also enacted to provide protection for these apex species. Species that are threatened or endangered in the US waters of the north Atlantic include the northern right whale (E. glacialis), blue whale (B. musculus), fin whale (B. physalus), sperm whale (Physeter macrocephalus), sei whale (B. borealis), humpback whale (M. novaeangliae), Altantic salmon (S. salar), shortnose sturgeon (Acipenser brevirostrum), Loggerhead turtle (Caretta caretta), Kemp’s Ridley turtle (Lepidochelys kempii), leatherback turtle (Dermochelys coriacea), and green turtle (Chelonia mydas). Similar to Canada’s SARA, under the ESA, if a species is listed, a plan to mitigate any threats and provide for recovery is required. All marine mammal species are monitored and managed under the MMPA to ensure similar recovery if stocks are depleted. Ecosystem modeling How we view ecosystems has changed in response to how we view the world around us (Simmons 2000) and our view is a product of the time and place that we live in (Thagard 1994, Castelo-Lawless 1995, Simmons 2000). It is useful to know how the prevailing world-view influences, as well as limits, how we conduct, and use the products of, our science. This is true, even if the only reason for such knowledge is to recognize areas of potential bias or perspectives we may be missing. Additionally, examining changing world-views over time and the science they produce can provide a set of unique insights into understanding (scientific or otherwise) that would otherwise be obfuscated by a narrower focus. Ecosystem modeling has moved from a food-web depiction based on farm machinery in the 1940s when the prevailing world-view was an

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Northwest Atlantic ecosystem-based management Table 2.2. Components of world-view spanning the different eras in which northwest Atlantic ecosystem models have been constructed, particularly on Georges Bank. US population and percentage rural from USCB (2006). 1930s–1950s

1960s–1970s

1970s–1980s

1990s–current

Farms and

Analog

Digital

Circuitry and

Mode of understanding Deterministic

Deterministic

Stochastic

Chaotic

Understanding

Mechanistic

Still

Integrative,

Operational paradigm

machinery

paradigm

Reductionist,

computers

mechanistic

mechanistic

Complexity

Simple

Relatively

Communication

Letters

Letters, phone

holistic

Moderate

Complex,

Phone

Email, cell

simple

highly MV phone

Max # lanes in major

2–4

6

Entire rooms

Corners of

8

12

Desktop

Pocket

US highways Size of computer

rooms % Rural

43.5

30.1

26.3

19.0

US human population

150

200

225

300

(millions) MV, multivariate.

­ gricultural-machine perspective (Thagard 1994, Curry 2000, Simmons 2000) a to a motherboard circuit diagram in contemporary times when the prevailing world-view has become one involving circuitry and computers (Table 2.2). In particular, our ability to conceptually and computationally address connectivity of ecosystems is much greater now than 60 years ago. As little as several decades ago we did not have the theory or the computing power to examine food webs in the holistic, integrated fashion that we do today. Thus, we see a significant shift in scientific thinking from reductionist, mechanistic, determinism to more connected, complex, and stochastic phenomena as we understand them today. Clarke’s (1946) representation of the Georges Bank food web provided an integrated perspective for oceanographers, marine ecologists, and fisheries scientists on the classical grazing food chain (diatoms–zooplankton–fish). In the early 1970s biological oceanographers developed a new paradigm for the ocean food web that emphasized the large pools of dissolved and particulate organic carbon and its utilization by microbes. The microbial community became the focus of models constructed by this research community. The Georges Bank

J.S. Link et al. model of the 1980s (Cohen et al. 1982, Sissenwine et al. 1984) incorporated some of these ideas on the potential role of detritus (non-living particulate organic carbon (POC)) and bacteria in supporting the benthic food chain. The primary focus, however, remained on the pelagic water column and the grazing food chain, using better estimates of primary production. Contemporary models, as developed to support an ecosystem approach to management, have begun ­re-connecting the conceptual approaches of oceanographers and fishery scientists to better appreciate how the ocean food web operates. The future challenge is to better integrate these perspectives. Ecosystem models can vary in complexity from extended single-species models (that is, single-species models with add-ons such as an environmental factor or predation-caused mortality), up to complex models that encompass selected aspects of the entire ecosystem. Different types of models are used to explore different questions and they contribute to the scientific information required for supporting management decisions in different ways. They are often used to further conceptual understanding, such as addressing the process-driven questions asked above. They also have the capacity to provide information required for strategic directions for management (which are long-range, broad-based, and linked to policy goals). Occasionally, ecosystem models can be used for tactical decision-making, as an evolving field of research (Townsend et al. 2008). Management for sustainable ecosystems needs to consider the impact of fisheries on ecosystems and vice versa. This means consideration of the impacts on target, bycatch, non-target species, and physical damage to habitat and food webs: these questions cannot be explored with single-species stock assessment models. Here we review the modeling efforts for the northwest Atlantic, starting with the simpler extended single-species models, then moving to more complex models. Extended single-species models

A suite of “minimum realistic” models (MRMs) have been developed in the USA (Plagányi 2007, Townsend et al. 2008; also known as Single Species (SS) add-ons) and these models seek to account for predation and its effects on a stock into a single-species assessment model. These have been both age or stage structured and bulk biomass or production models. These have ranged from providing context of stock biomass, tuning indices, sources of other mortality, to explicit estimates of additional (i.e., predation or M2) mortality. Examples of species where this has occurred are predominantly forage stocks, including Atlantic herring, Atlantic mackerel, butterfish, longfin squid, and northern shrimp (NEFSC 2007a, 2007b, Overholtz and Link 2007, Overholtz et  al. 2008, Link and Idoine 2009, Moustahfid et al. 2009a, 2009b).

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Northwest Atlantic ecosystem-based management Several of these models have been used as part of formal US stock assessment reviews, usually to provide context and estimates of predation mortality; the others are in various stages of development and research. For the most part, the way predation is added into these models is to treat it as an additional fleet. That is, predation by other species is treated collectively, but explicitly, as another source of removals. The data required are abundance of predators that consume from the stock of interest, stomach contents, estimated consumption rates, and diet composition estimates (in addition to the usual survey and fisheries catch data). The positive aspects of this approach are that such models are relatively simple conceptually and operationally, they use extant data, they are implemented in a familiar assessment and management context, they provide familiar (albeit modified) model outputs amenable to calculating biological reference points (BRPs), they improve the biological realism of assessment models, and they help to inform and improve stock assessments for species that may have been difficult to assess in the past. The negatives of this approach are that they run the risk common to all MRMs. More specifically, they may be missing a suite of complex interactions and non-linear responses caused by not including the full suite of interactions involved in a real-world ecosystem (e.g., the complex suite of coevolutionary interactions). They also have the potential to be controversial, by producing more conservative BRPs and emphasizing the potential for competition between predators and fleets that target these stocks without having a fuller modeling capability to fully address these trade-off issues. Single-species add-ons: ecological footprints

The models in this category attempt to account for the amount of food eaten by a fish stock. These estimates of energetic requirements (i.e., consumptive demands) at a given abundance level are then contrasted with estimates of the amount of food known to be available in the ecosystem from surveys and mass-balance system models. In many ways this is the same calculation as noted above for predatory removals; the difference here is that instead of summing across all predators feeding on a stock of fish, here we sum across all species serving as prey for the fish. These have been calculated for a wide range of groundfish, elasmobranch, and pelagic fish species, again mostly in the US ecosystems (Link and Garrison 2002, NEFSC 2007b, Tyrrell et al. 2007, Link and Sosebee 2008), though similar work is being developed for some Canadian stocks. A few sets of stocks (e.g., the skate complex, NEFSC 2007b, Link and Sosebee 2008; spiny dogfish, pollock, goosefish, NEFSC unpubl. data) are represented by such estimates that have gone through a formal stock assessment review; others are in various stages of

J.S. Link et al. development/research or have been calculated to estimate predatory removals of forage stocks (noted above). This approach remains an area of research interest. The data required are abundance of predators that eat the stock of interest, stomach contents, consumption estimates, and diet composition estimates (in addition to the usual survey and fisheries catch data). The positives and negatives of this approach are similar to those outlined above. Single-species add-ons: environmental considerations

The NMFS has begun to incorporate environmental considerations into population models, but not yet in a fully operational mode. These include changes in carrying capacity (K), growth rates (r), stock-recruitment relationships, or stock distribution relative to environmental conditions (Keyl and Wolff 2008). These have been done or are being done for a wide range of fish, mammal, and invertebrate species. With environmental terms in population models, it is possible to forecast the response of a population to climate change, thereby providing a long-term forecast that can inform EBMF (Fogarty et al. 2008, Hare et al. 2010). Brander and Mohn (2004) incorporated the North Atlantic Oscillation (NAO) into stock recruitment models of 13 cod stocks in the North Atlantic, recommending that in areas where the NAO had a strong effect, medium- and long-term assessments should consider likely future states of the NAO. Currently none of these models have been through formal review nor explicitly incorporated into a review process that directly informs management. Such modeling remains an active area of research and development. In addition to the needs of standard stock assessment, these approaches require appropriately (spatio-temporal) scaled environmental data such as temperature, depth, and salinity and the associated monitoring studies. The advantages of this approach are that the environmental data are usually available and relating them to stock dynamics typically takes advantage of commonly established statistical methods. These approaches also improve the biological realism of assessment models and allow for consideration of dynamics driven by factors typically outside of usual assessment considerations. The chief drawback of this approach is that the data are often auto-correlated without definitive causal mechanisms; the data may also often be collinear, and, short of exhaustive multivariate analysis, are difficult to untangle for useful stock projection. MSVPA-X

The “extended” multi-species virtual population analysis (MSVPA-X) is an expanded version of the ICES MSVPA model applied in Europe, which is, in effect, a series of single-species VPAs linked together via a feeding model. The

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Northwest Atlantic ecosystem-based management model has the ability to provide short-term forecasts. Most typically the model examines the stock dynamics of multiple species that are both predators and prey, particularly exploring the role of predatory removals of stocks relative to fishery removals. MSVPA-X models have been applied to two subsystems in the northwest Atlantic and are being developed for a third (Garrison and Link 2004, NEFSC 2006, Tyrrell et al. 2008). An MSVPA-X model for the mid-Atlantic region emphasizes menhaden as prey with three main predators and has gone through extensive peer review (NEFSC 2006). Outputs from that model have informed single-species assessments, particularly by providing time series of predation mortalities for the assessment of menhaden. A second MSVPA-X model applies to the Southern New England–Georges Bank–Gulf of Maine ecosystem (Tyrrell et al. 2008). It involves 19 species and emphasizes herring and mackerel as the major prey. It is still under development, with results anticipated to inform single-species assessments for herring and mackerel. The third is being developed for the southwestern Scotian Shelf/Bay of Fundy area with a focus on herring as prey. The data required for this approach include abundance estimates for predators that eat the stock of interest, stomach contents, consumption estimates, and diet composition estimates (in addition to the usual survey and fisheries catch data). The positive aspects of this approach effectively mirror those of the SS add-on with predation; namely it uses extant data, it is implemented in a familiar assessment and management context, it improves the biological realism of assessment models, and it helps to inform and improve stock assessment outputs. The key negative facet of this approach is that it is quite data intensive; there are many factors for which estimates are required for each species to parameterize the model. Multi-species production models: MS-PROD

A multi-species extension of the Schaeffer production model has been developed by the NMFS to include both predation and competition. The software development is ongoing, with a graphical-user interface (GUI) and mathematical simulation engine available (Link 2003, Gamble and Link 2009). This kind of model seeks to simulate the relative importance of predation, intraguild competition, between-guild competition, and fisheries removals. A model of this type has been parameterized for 25 species from the Georges Bank region, serving as a research tool, parameterized with empirically based values that can then explore sensitivities and scenarios for different considerations. It will not be used for management advice until it becomes feasible to fit

J.S. Link et al. estimates to time series of empirical data; however, it has proved useful in providing contextual information regarding the systems influenced by fisheries. The data required are initial biomass estimates, carrying capacities, predation and competition interaction terms, growth rates, and fishery removals. Again, some simulation results have been used as contextual information in the context of management of our use of LMRs (see Groundfish Assessment Review Meeting (GARM III) below; NEFSC 2008). The desirable aspects of this approach include the fact that it explicitly accounts for ecological processes in addition to the effects of fisheries and that lower trophic-level processes can be directly linked to estimates of carrying capacity. The limitations include the fact that some of the parameters, although empirically derived, are difficult to estimate. Another negative is that it does not currently replicate the empirically observed series data. Like most multi-species models, it is parameter intensive but less so than many other multi-species models given the simplicity of the model equation structure. Multi-species production models: AGG-PROD

This type of model is effectively the same as the MS-PROD model noted above, but initialized for aggregate groups of species (i.e., many species are not represented individually). The interactions with other system components for these groups have been parameterized both as functional guilds and taxonomically related species. The one distinction is that this kind of model simulates BRPs, and production at a more systemic or group level, rather than species level. This will be useful for considering two-tier quotas. The data needs, pros, and cons are the same as MS-PROD, with the caveat that amalgamation of parameters across groups remains a challenge. Again, some simulation results have been used as contextual information for management of fishery influence on LMRs (see GARM III below; NEFSC 2008). Ecopath with Ecosim

The Ecopath and Ecosim modeling tool (EwE) has been widely used to quantitatively describe aquatic systems and to explore the impacts of fishing ecosystems (Christensen and Pauly 1992, Christensen and Walters 2004, Coll et al. 2009). It is composed of a mass-balance model (Ecopath; Polovina 1984, Pauly et al. 2000; Christensen and Walters 2004, Christensen et al. 2005) from which temporal and spatial dynamic simulations can be developed (Walters et al. 1997). Mass-balance (Ecopath) models have been developed for the Newfoundland–Labrador Shelf (Bundy et al. 2000, Heymans 2003), the northern and southern Gulf of St. Lawrence (Morissette et al. 2009), the eastern Scotian Shelf (Bundy 2004, 2005), and for the Gulf of Maine, Georges Bank, Southern

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Northwest Atlantic ecosystem-based management New England, and Mid-Atlantic Bight ecosystems (Link et al. 2006, 2008a, 2008b). Ecosim models have been developed for the Newfoundland–Labrador Shelf (Bundy 2001) and the eastern Scotian Shelf. Data requirements for these models include estimates of biomass, production and consumption rates, catch and diets. These models were developed under specific projects in Canada (CDEENA, The Comparative Dynamics of Exploited Ecosystems in the Northwest Atlantic) and in the USA (EMAX, The Energy Modeling and Analysis eXercise). Further models are being developed for the western Scotian Shelf and the Bay of Fundy under DFO’s Ecosystem Research Initiative. These models have been used to further our understanding of ecosystem structure and functioning, comparative studies (spatial and temporal), to develop ecosystem indicators, and in various simulated perturbation experiments. The use of these models remains an area of research. Some results have been used as contextual information in a LMR management context (see GARM III below; NEFSC 2008). GOMAG

The NMFS is currently constructing a model of the Gulf of Maine ecosystem based on results from Ecopath modeling exercises (Link et al. 2006, 2008a, 2008b). The authors have structured the simulated system based on 16 aggregated biomass nodes spanning the entire trophic scale from primary production to seabirds and marine mammals (Overholtz and Link 2009). Parameters from the Ecopath model of the Gulf of Maine system were used to construct a simulation model using recipient controlled equations to model the flow of biomass and the biomass update equation used in Ecosim to model the annual biomass transition. Various performance measures and metrics such as throughput, total flow, biomass ratios (i.e., pelagic fishes to zooplankton), and trophic reference points (i.e., marine mammal biomass to pelagic fish biomass) can be tracked and compared with empirical information over the simulated time horizon. The model will be used to explore how the Gulf of Maine ecosystem might respond to both large- and small-scale changes to the trophic components and system drivers. Specifically, events such as climate change, various fishing scenarios, and system response to changes in the biomass of lower and upper trophic levels might be evaluated. GOMAG has not been through a formal model review. This remains a research tool and has not been used for management purposes. Bioenergetic-allometric models

Bioenergetic-allometric models are those based on the modeling framework originally developed by Yodzis and Innes (1992). This approach

J.S. Link et al. describes the dynamics of predator–prey systems in terms of biomass and relies on allometric relationships between vital rates (e.g., respiration, maximum consumption), and individual body mass to reduce and/or constrain the number of parameters to be estimated. The theory behind this approach has been expanded in two critical aspects. On the grounds provided by the metabolic theory of ecology (Gillooly et al. 2001, Brown et al. 2004), Vasseur and McCann (2005) incorporated the effect of temperature on vital rates for ectotherm species. More recently, De Roos et al. (2008) developed a way of incorporating juvenile and adult stages which captures most of the behavior of more complex physiologically structured models (e.g., food-dependent growth and maturation). This approach provides a high degree of flexibility for handling model complexity. Yodzis (1998) developed a 29-species mass-balance model for the Benguela system and used near-equilibrium perturbations to explore the effects of culling fur seals on fisheries yields. The same basic approach was used by Koen-Alonso and Yodzis (2005) to develop a fully dynamic minimum realistic model for key components of the Patagonia marine community. They used this application to explore the impact of uncertainty on predictions of the effects of different (theoretical) management regimes. Bioenergetic-allometric modeling is currently being applied to different systems in the Canadian Atlantic. Koen-Alonso and Bundy (2008) have developed a preliminary five-species model to explore the dynamics of core species of the eastern Scotian shelf marine community. This work allows for comparisons with the results of previous modeling exercises for this system (e.g., Bundy 2005). A more complex model, but still within a similar “minimum realistic/ maximum feasible” realm (Koen-Alonso 2009), is also being implemented for key stocks of the Newfoundland–Labrador marine community (Koen-Alonso et al. 2007). A single-species model based on this theoretical approach is being used to explore alternative hypotheses related to the impact of harp seals on northern cod (Buren et al. 2008). ATLANTIS

ATLANTIS (Fulton et al. 2004) is by far the largest, most complicated model in use in the region covered by this chapter. It was developed by colleagues in Australia and is a model involving environmental components. These include a simulated ocean with all its complex dynamics, a simulated monitoring and assessment process, a simulated set of ocean-uses (namely fishing), and a simulated management process. The dynamics represented in the model range from solar radiation to hydrodynamics, to include nutrient processes, growth (with age structure), feeding, settling, sinking, migration, fishery

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Northwest Atlantic ecosystem-based management captures, fleet dynamics, market valuation, regulation, and feedback among the various components of the model as appropriate. The NMFS has implemented ATLANTIS for the northeast US continental shelf ecosystem with 30 regional boxes, 5 depth layers per box, 12-hour time steps for 50 years, 45 biological groups, and 16 fisheries. The parameterization and initialization has required over 60 000 parameters and 140 000 initial values to estimate. A first level of calibration has been completed to ensure basic biophysical processes match observed dynamics. A second- and third-level calibration have also been completed, thus ensuring that fishing processes (catch and effort, respectively) are reasonable. Future scenarios of different management strategies are planned to follow completion of the third level of calibration. Although parameterized, initialized, and loosely tuned to empirical values, ATLANTIS is too complex, and was not designed to provide specific tactical management advice for a particular stock (e.g., a quota or effort limit). Rather, ATLANTIS is not only a research tool but a simulator to guide strategic management decisions and broader concerns. For instance, it has been used in other contexts to provide multi-species fishery advice and multi-sector oceanuse advice in support of ecosystem-based management (Smith et al. 2007). The northeast USA (NEUS) rendition of ATLANTIS has not been through a formal model review, although documentation of key parameters and calibration is forthcoming (NEFSC unpubl. data). The advantage of ATLANTIS is that it can incorporate multiple forms of myriad processes, it can emphasize those considerations and processes most appropriate for a given system, and it can be used to simulate an evaluation of management decisions to provide insight into what might happen in a real system. Another advantage is that it covers a wide range of biota and is quite flexible or adaptive to a range of key factors. The chief negative aspect of ATLANTIS is that it is unwieldy in its complexity and takes an inordinate amount of time to parameterize, initialize, calibrate, and run any particular application. Additionally, the validation routines and capabilities of ATLANTIS are minimal at best, requiring much further improvement. Implementation of ecosystem-based approaches in living marine resource management As previously noted, neither Canada nor the USA has yet developed a fully fledged approach to EBMF. Yet there have been endeavors that are important steps to that end. In this chapter we have briefly highlighted such efforts. These efforts encompass a wide range of activities, from the development of

J.S. Link et al. ecosystem indicators to objective-based approaches to specific applications of ecosystem models as described above. A key factor behind the need for ecosystem-based approaches to management is legislation such as the Oceans Act of 1997 in Canada and recent changes in the Magnuson-Stevens Reauthorization Act, 2006, 2008 in the USA (Murawski 2007). As this kind of legislation becomes more prevalent, and embraces more ecosystem-level principles, the approaches to the management of our use of fisheries resources will change. With these kinds of change, researchers continue to work to provide the scientific underpinnings that keep pace with the law. To do so, we will undoubtedly build upon the steps described above as well as those noted below. As the demand for EBMF increases, we recognize that a key element will be the venue, or framework (e.g., Integrated Ecosystem Assessments), within which such ecosystem-based management advice is provided, evaluated, and acted upon. The following examples represent a variety of options, some working within existing frameworks, and others proposed as modifications or alterations to develop novel approaches within which ecosystem-based management advice can be provided. Extended SS information

One can readily see from the various MRM modeling sections above how additional information could be used as an ecosystem-level application of management. Yet somewhat surprisingly, despite the large amount of contemporary effort applied to these issues, such information has rarely been utilized in a fisheries management context; use in stock assessments provide the best examples (NEFSC 2006, 2007a, 2007b, 2008). There is a great deal of information available, underlying mechanisms are mostly understood, and the data are no less certain than the data used otherwise. We suspect that the lack of familiarity and comfort level of including novel elements in stock assessment processes (as compared with the more typical survey and landings information) has been a challenge to the full implementation and utilization of such modified models. We also suspect that, particularly in terms of the effects of environmental factors, the challenge of predictability regarding the future states of ecosystems has limited their acceptance. Nevertheless, we are encouraged that such information has been evaluated, along with various MS models, in the context of stock assessment. These provide “contextual” assessments that are reviewed along with the primary assessment. Certainly more research is required, but a lot of such research is now at

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Northwest Atlantic ecosystem-based management the stage of proof of concept, sensitivity analysis, and model diagnostics rather than establishing or understanding basic mechanisms. Habitat closures and marine protected areas

In both Canada and the USA management options have included area closures bolstered by copious research on fishery habitat. In the USA, characterizing essential fish habitat (EFH) was mandated by the Magnuson-Stevens Reauthorization Act (MSRA). Essential fish habitat has been characterized generally for all managed, targeted fishery species, with technical memoranda over 35 species, some of which have been updated in more recent years (see Reid et al. 1999 for a description of the full series). Based upon the distributions of the various stocks of these species (determined from surveys), the collective “essential” habitat for all species has been noted as being quite vast, but we now have a better understanding of the dimensions of species habitat space and the interaction of habitats among species. Further, both the USA and Canada have used area closures as a fisheries management tool, some closures serving as ad hoc large-scale experiments (see summaries in Fisher and Frank 2002, Frank et al. 2004, Link et al. 2005, Murawski et al. 2005). Generally speaking the findings from these, and a broad array of associated works, indicate that the success, efficacy, or effectiveness of closures in temperate oceans depends upon the substrate type, and the mobility of organisms. For some species, differences are not readily discernable inside compared with immediately outside areas closed to fishing. However, for other species that are highly sessile (e.g., sea scallops) the differences in abundance and biomass inside versus outside are quite striking. Similarly, among habitats that are effectively sand barrens with very fast overlying currents, many show minimal difference inside versus outside closed areas. Conversely, habitats with cobble and boulder substrates tend to show notable differences in a wide range of metrics inside versus outside of areas closed to fishing. We suspect that EFH will continue to be monitored in the USA. We also suspect that area closures will also remain a viable management option. We simply assert that these are one of many such management tools for EBMF, but in and of themselves do not singularly constitute EBMF. Areas closed to fishing can certainly contribute to an ecosystem approach, and scale is an important determinant of their success. For most mobile, demersal, and pelagic fishes, however, these areas may be less efficacious than for their tropical, coral reef counterparts. In Canada, plans are underway for a network of marine protected areas (MPAs) to increase the ecological effectiveness and connectivity between

J.S. Link et al. individual marine protected areas in an effort to conserve and protect the structure and function of marine ecosystems. Marine protected areas will be identified, in part, through the identification of ecologically and biologically significant areas (EBSAs). Currently, there are five MPAs in the Canadian northwest Atlantic, only one of which, the Gully, is offshore. The Gully is a deep channel on the edge of the Scotian Shelf. This project is jointly implemented by Fisheries and Oceans Canada, Parks Canada, and Environment Canada3 and one objective is to link Canada’s MPA networks with those in the USA. Groundfish carrying capacity

The recent, formal Groundfish Assessment Review Meeting (GARM III) in the USA addressed two terms of reference for an ecosystem approach for the Gulf of Maine/Georges Bank fisheries: (a) (b)

Determine the production potential of the fishery based on food chain processes and estimate the aggregate yield from the ecosystem. Comment on aggregate single stock yield projections in relation to overall ecosystem production, identifying potential inconsistencies between the two approaches.

The background behind these moves involves the observation that recent fisheries management decisions (1994–2005) for fish stocks in the US Northeast Shelf LME have resulted in resurgence among some of the depleted fish populations (NEFSC 2008). A topic of some concern among various stakeholders is whether the ecosystem can support these elevated levels of biomass (i.e., BMSY) simultaneously for all the groundfish stocks, and more broadly, the entire fish community in the region. Analyses were completed for the Northeast Shelf LME to estimate the total system biomass, summed fisheries management target biomass, and compare results with other worldwide systems. Methods and analyses used data from either stock assessments or biomass-based approaches (Brodziak et al. 2004, NEFSC 2008). Information on the biological reference points for groundfish, other demersals, and small pelagic components of the US Northeast Shelf LME were summarized and compared with historical studies, recent energy budget analyses for the region, and similar metrics for comparable worldwide temperate ocean systems. Aggregate biological reference points (BRPs) were also estimated for important groups of groundfish, pelagic, and elasmobranch stocks on the Northeast Shelf LME with a surplus production model (Prager 1994). The technical basis

http://www.dfo-mpo.gc.ca/oceans-habitat/oceans/mpa-zpm/fedmpa-zpmfed/index_e.asp

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Northwest Atlantic ecosystem-based management for estimating aggregate BRPs for demersal and pelagic stocks is founded on the following observations: (1)

(2)

(3)

(4)

The energy available to demersal and pelagic fish from lower trophic levels is limited and shared by the entire community (Pauly and Christensen 1995, Pauly et al. 1998, 2002); therefore an aggregated approach may be warranted. Fish stocks have different productivities, making it difficult to attain single stocks objectives, therefore an average or aggregate quota may be more appropriate (May 1975). Due to biological and/or technological interactions, an aggregate quota may be more appropriate for managing suites of stocks (May 1975, Pope 1975, 1979, Fukuda 1976, Mayo et al. 1992). In mixed stock fisheries the effective catchability of each stock is different, therefore aggregate approaches are probably justified (Garrod 1973).

To address these terms of reference, a wide range of modeling approaches were applied. Results from EMAX (Ecopath and EcoNetwork) models were used to evaluate perturbation by fisheries and also to provide scaling of a magnitude comparable to similar studies from other marine ecosystems. MS-PROD and AGG-PROD were used to provide a simulation to contextualize multi-species yields. A trophic transfer model was used to estimate production capacity of the system to bound feasible limits. And finally, the production model, ASPIC, as parameterized for the aggregate fish community, was used to estimate values of maximum sustainable yield (MSY) and associated reference points for the entire groundfish and full fish community (NEFSC 2008). Here we present summary results, and refer the interested reader to NEFSC (2008) for further details. The estimated total MSY for the major groundfish species in the NEUS region is 144 977 mt (0.59 t/km2) and BMSY for this groundfish complex is 1 065 068 mt (4.32 t/km2; Table 2.3). If the entire demersal complex is summed, this equates to a biomass per unit area of 14.62 t/km2, about 24% higher than the 11.77 t/km2 estimated from a recent analysis for the 1996–2000 time period and compared with 10.6–17.04 t/km2 from historical studies for the Georges Bank ecosystem (Table 2.3; Cohen et al. 1982, Sissenwine et al. 1984, Link et al. 2006). The current target demersal biomass that the US Northeast Shelf LME needs to support is about 3.6 million mt (Table 2.3). The other demersal components of the ecosystem, excluding groundfish species and elasmobranchs, comprise about one-third of the total biomass (Table 2.3). Commercially important small pelagic fishes had a total MSY of 354 175 mt (1.4 t/km2) and total BMSY of 1 295 978 mt (5.24 t/km2 ; Table 2.3). If the other pelagic fish in this component are included (8.4 t/km2), this value is about 27% lower

J.S. Link et al. Table 2.3. Key aggregate parameters for the NEUS fish community, given as both Maximum Sustainable Yield (MSY) and BMSY in total (mt) and areal (t/km2) formats. Category

MSY (mt)

t/km2

BMSY (mt)

t/km2

Major groundfish

144 977

0.59

1 065 068

4.32

17 793

0.07

1 155 731

4.69

n/a

1 385 437

5.62

3 606 236

14.62

1 295 978

5.25

776 152

3.15

2 072 130

8.40

Elasmobranchs Other demersals

n/a

Total Pelagics

354 175

1.44

Other pelagics

n/a

n/a

Total

Table 2.4. Comparison of NEUS demersal and pelagic fish biomass densities relative to other marine ecosystems. System

Demersal B (t/km2)

Pelagic B (t/km2)

Gulf of Alaska

26.48

14.83

41.31

Bering Sea

44.85

7.44

52.30

Barents Sea

4.31

9.32

13.64

North Sea

8.87

10.15

19.02

Baltic Sea

2.13

19.07

21.20

Faroes

10.61

27.91

38.51

Newfoundland–Labrador

10.99

21.82

32.81

Gulf of St. Lawrence

21.78

24.08

45.86

6.85

23.39

30.24

Average

15.21

17.56

32.76

Northeast Shelf LME Target

14.62

8.40

24.48

Northeast Shelf LME Current

13.12

16.80

28.64

Scotian Shelf

Total t/km2

LME, Large marine ecosystem; Target, reference level; Current, contemporary value.

than the total pelagic biomass estimated during a recent analysis (11.43 t/km2) and compared with 9.1–17.3 t/km2 from historical studies for Georges Bank (Cohen et al. 1982, Sissenwine et al. 1984, Link et al. 2006). The other four pelagic components, excluding the commercial pelagic category, comprise about 40% of the total pelagic biomass and the current target pelagic biomass is about 2.1 million mt (Table 2.3). Pelagic and demersal biomass for nine temperate and boreal systems, including Canadian, European, and west US systems from various ecosystem modeling studies (NEFSC 2008) are compared in Table 2.4. The average demersal biomass was 15.2 t/km2, with a range between 2.1–44.9 t/km2, while the average for small pelagic species was 17.56 t/km2 with a range between

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Northwest Atlantic ecosystem-based management 7.4–27.9 t/km2; Table 2.4). Total target fish biomass (t/km2) for the Northeast Shelf LME is below the average for the nine other temperate marine systems (24.48 t/km2 vs 32.76  t/km2; Table 2.4). The target biomass for the demersal component is moderately lower than the average for the nine systems and is higher than six of the individual systems. However, for many of these other ecosystems the demersal component is depleted. The target pelagic biomass for the Northeast Shelf LME is well below the average for the nine other systems (Table 2.4). This estimate is lower than all but one of the pelagic system estimates for the other systems. The current demersal biomass is about 10% below the biomass reference level, while the current pelagic biomass is larger (by a factor of two) than the target biomass for pelagics. This is because the biomass of Atlantic mackerel and herring are currently considerably larger than the sum of their respective BMSY reference points, for the pelagic component. The total biomass for targeted species in the US Northeast Shelf ecosystem is 5.68 million mt, comprising 64% demersal species and 36% pelagic species. The groundfish stocks, commercial pelagic fishes, and elasmobranchs have similar BMSY biomass targets at 4.32, 5.25, and 4.69 t/km2, respectively. The LME biomass targets for pelagic and demersal fishes are similar in scale to biomass estimates from previous studies of the region. Overall, these and other aggregate production models (NEFSC 2008) similarly suggest that the estimated MSY level for all GARM species is lower than the sum of individual species MSY estimates and overall fishing mortality should be lower. If interactions among species are important, all species are unlikely to simultaneously be at BMSY, which is supported by several lines of evidence and multiple approaches that all suggest that this constraint is real. This is an active area of research and consideration of how best to incorporate this pattern into future stock assessments and related contexts, particularly how to address the tier-levels of such information (i.e., stock and aggregate). Examining pair-wise species interactions

One of the more common situations in which ecosystem considerations have been invoked in the Canadian and US northwest Atlantic are associated with trade-offs among species, particularly due to suspicions of predatory mortality. Thus we have increasingly needed to address various pair-wise species interactions. Examples that effectively pit one species against another via predation or competition are many, despite the highly complex nature of this food web (Link 2002c). For instance, there have been myriad calls to cull spiny dogfish due to the perception that it is a “voracious” predator of commercially and culturally important gadids (Link et al. 2002a). This continues despite the observation that spiny dogfish largely eat ctenophores and small pelagic fishes,

J.S. Link et al. rarely consuming (on the order of 10 out of 600 000 stomachs) commercially important groundfish species such as cod or haddock. Another example has been the perception of copious cod predation (Hanson and Lanteigne 2000) or white hake predation (Davis et al. 2004) on lobster, more suspected species interactions that have not been supported by data. We generally tend to discourage such pair-wise evaluations and rather explore them at the very least in an ESAM or multi-species modeling context. The one exception tends to be when there are marine mammals and fishes involved. Since in both countries there are laws addressing marine mammals and endangered species and laws addressing fisheries maximization, the need to explore instances when targeted species and marine mammals are thought to interact remains quite high. A prominent example in three Canadian northwest Atlantic ecosystems are the large increases seen in the populations of three species of pinnipeds over the last few decades and their impacts on fisheries:  gray seals on the eastern Scotian Shelf, gray seals, harp seals, and hooded seals in the Gulf of St. Lawrence, and harp seals and hooded seals in Newfoundland–Labrador. In parallel, all three areas have experienced stock collapses of some groundfish species and severe reductions in others. Since then, in all the collapsed species, natural mortality increased by at least double prior to the collapse, and did not drop when fishery harvests were reduced to levels that should have allowed rapid increase had natural mortality been at levels documented in the 1970s and 1980s. Great concern has been raised by the fishing industry concerning the increased seal populations and their impacts on the fishery. Serious debate has become prominent in the fisheries literature concerning the cause of the collapse of cod stocks and the role of fishing (e.g., Hutchings and Myers 1994, 1995, Myers et al. 1996) and the role of seals (Fu et al. 2001, Trzcinski et al. 2006, Chassot et al. 2009), and environmental factors (deYoung and Rose 1993). Seals have been implicated in the collapse of fish stocks, especially Atlantic cod, and identified as an impediment to their recovery (Bundy 2001, 2005, Fu et al. 2001, Chassot et al. 2009). From a fisheries point of view, the concern is about why Atlantic cod stocks are not recovering. There were groundfish moratoria in the early 1990s in all Canadian areas, but only that of the eastern Scotian Shelf groundfish has remained in place. In Newfoundland–Labrador, there was a directed inshore fishery from 1998–2002, and a recreational and “stewardship” fishery, reopened during 2006, remains open. In the northern and southern Gulf of St. Lawrence the fisheries were re-opened in the late 1990s and are now in a precarious state (DFO, 2007a, 2007b, Swain and Chouinard 2008). On the eastern Scotian Shelf (Fanning et al. 2003) there has been no recovery of cod and there is no directed fishery.

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Northwest Atlantic ecosystem-based management The clear correlation between the population trends of pinnipeds, and knowing that groundfish comprise a portion of their diet, has led to speculation that seals are one of the reasons fish stocks are not recovering. Seals are hypothesized to have five kinds of negative effects on prey populations:  (1) ­predation, (2) competition, (3) transmission of parasites causing increased mortality of fishes, (4) disruption of spawning to cause reduced reproductive success, and (5) other indirect effects on fish behavior caused by risk of seal predation (DFO 2008b). In addition, there is increasing concern about seals interfering with and damaging fishing gear and consuming fish caught in nets (DFO 2008b). Ecosystem modeling has been used to address questions of the trophic impact of seals on cod in all three areas. In the Newfoundland–Labrador Shelf, initial simulations based on a food-web model suggested that seals were a plausible factor for hindering cod recovery (Bundy 2001). However, a single-species model for cod which includes simultaneous bottom-up (capelin availability) and top-down (fisheries and predation by seals) factors suggests that fisheries and capelin availability are the most likely drivers of northern (2J3KL) cod dynamics (Buren et al. 2008). A third approach, using a bioenergetic-­allometric model currently under implementation (Koen-Alonso et al. 2007) may provide results that could further alter our understanding of this ecosystem. The nature of these interactions is still under active investigation and the recent (and still modest) upward trend in northern cod without any reduction in harp seal levels is of note. It presents a challenge to the interpretation that the key correlation between trends in fish and seals is evidence of a negative impact by pinnipeds. In the case of the northern Gulf of St. Lawrence cod there is some evidence that harp seal predation is slowing the recovery rate through consumption (Chassot et al. 2009). On the other hand, directed fisheries still occur on the stock even though its biomass is at least 50% below safe biological limits (Shelton et al. 2006). Simulations have shown that a significant decrease in recovery time of northern Gulf of St. Lawrence cod above the safe biological limit could be achieved via an aggressive seal-hunting scenario (Chassot et al. 2009) though it is unlikely that a hunt much larger than current levels would be acceptable to NGOs and the public and at the same time would not obey the precautionary harp seal management plan (DFO, online only).4 Harp seals, like most fish predators, tend to target the relatively small sizes of cod which have not yet recruited to the fishery. In this sense, one could view seals like a fishing fleet that is “growth overfishing,” i.e., more total biomass could be extracted www.dfo-mpo.gc.ca

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J.S. Link et al. from the population if the fish were allowed to grow in body size even though they would experience mortality over this time period (Chassot et al. 2009). One can therefore imagine that reductions in seal predation would likely lead to increased populations over the long run by allowing more of the population to reach spawning size. However, the danger here is that this minimum realistic model excludes most of the ecosystem and non-linear interactions which may result in a different outcome from that predicted. In the southern Gulf of St. Lawrence, gray seals (not harp seals) are the predominant seal species. The gray seal population in the southern Gulf of St. Lawrence is highly migratory and part of the northwest Atlantic population (see discussion on the Scotian Shelf below). This population has increased at a rate that, to some, is incredible. This growth has certainly led to increased consumption of fish by seals but it is unclear how this mortality compares with other sources. A body of evidence has been building over the past decade suggesting that this is a significant source of mortality on southern Gulf of St. Lawrence groundfish, particularly cod (Chouinard et al. 2005, Benoit and Swain 2008, DFO 2008b, Swain and Chouinard 2008). On the Scotian Shelf, the main pinniped species is the gray seal. Over the past three or four decades, the number of gray seals on the Scotian Shelf has increased from around 9000 in 1970 to 160 000 seals in 2003 (Trzcinski et al. 2006). The greatest increase is associated with the Sable Island colony, where population numbers have increased exponentially at an annual rate of 13% per year for the past four decades (Bowen et al. 2003). This increase is not unexpected as gray seals in both areas were recovering from low numbers resulting from hunting. This exponential rate of increase has since decreased (Bowen et al. 2005). However, the population has spread to the inshore where gray seals are becoming increasingly common and to the south, now occurring in the Bay of Fundy and Georges Bank. Several minimally realistic models have been developed to explore the impact of gray seals on eastern Scotian Shelf cod stock (NAFO division 4VsW cod). Mohn and Bowen (1996) used two models (constant ration or proportional ration) to describe the response of gray seal predation to changes in cod abundance under two assumptions about the level and pattern of age-specific natural mortality of cod. Under the assumptions of either model, gray seal predation mortality was estimated to be only 10–20% of the estimated mortality caused by the fishery. Fu et al. (2001) developed a separable population model that included interactions between cod, fisheries, and seals, treating seals as another fishery fleet. They concluded that predation mortality was not sufficient to cause the collapse of cod on the eastern Scotian Shelf but that low survival of young cod due to predation by gray seals, high mortality

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Northwest Atlantic ecosystem-based management of adults, and low recruitment were all factors that contributed to the failure of the 4VsW cod stock to recover. More recently, Trczinski et al. (2006) ­developed a statistical catch-at-age population model of cod, incorporating the gray seal population and their consumption requirements to explore why cod have not recovered. Like the earlier authors, they concluded that gray seals impose sufficient predation mortality on cod to hinder their recovery. Bundy and Fanning (2005) put the gray seal–cod interaction in a wider ecosystem context using a mass-balance ecosystem modeling approach (Ecopath with Ecosim). In addition to confirming the results of the above work, their results supported the cultivation-depensation hypothesis, whereby small forage fish such as sand lance and herring compete with small cod for prey, leading to food-limitation, poor condition, and low survival. Prior to the fishing down of cod and other groundfish, these large fish kept the abundance of small forage fish in check through top-down predation, thereby promoting the survival of their young. There have been calls by the fishing industry for a seal cull or hunt on the eastern Scotian Shelf to reduce the impact of gray seals on cod and other fish species; however, since a significant part of gray seals’ diet is small pelagics (DFO unpubl. data), this action could well lead to the opposite result, with cod and other groundfish experiencing far greater competition for prey, and possibly predation of their larval fish by small pelagics. Integrated management of Canada’s oceans and integrated ecosystem assessments

In 1997 Canada became the first country in the world to adopt comprehensive legislation for oceans management. Canada’s Oceans Act (1997) paved the way for the development of a national oceans strategy to guide the management of Canada’s aquatic ecosystems and provide the overall strategic framework for Canada’s oceans-related programs and policies. Canada’s Oceans Strategy is based on the principles of sustainable development, integrated management and the precautionary approach. Its central governance mechanism applies these principles through the development and implementation of Integrated Management plans. Integrated Management plans include ecosystem-based management, sustainable development, the precautionary approach, conservation, shared responsibility, flexibility, and inclusiveness. They directly involve stakeholders in the planning process, which is intended to be flexible and transparent. The three objectives of Integrated Management are (1) understanding and protecting the marine environment; (2) supporting sustainable economic opportunities; and (3) effective oceans governance. In Canada, the DFO has the overall mandate for integrated ocean management and the responsibilities for science, fish, and fish habitat management. The

J.S. Link et al. DFO has identified five large oceans management areas (LOMAs) with associated pilot Integrated Management initiatives. Three of these are situated in the northwest Atlantic. The eastern Scotian Shelf Integrated Management initiative (ESSIM; Rutherford et al. 20055) was the first plan to be announced in 1998. The Gulf of St. Lawrence Integrated Management initiative (GoSLIM) and Placentia Bay/Grand Banks Integrated Management initiative were subsequently announced. Many of these Integrated Management plans are still being developed, but the ESSIM strategic-level plan is published and provides long-term direction and commitment for integrated, ecosystem-based, and adaptive management of marine activities (DFO 2007a). The plan contains a comprehensive set of goals, objectives, and strategies for collaborative governance and integrated management, sustainable human use, and healthy ecosystems. It has been shaped and accepted by ocean stakeholders, supported and endorsed by government, and is Canada’s first integrated ocean management plan under the Oceans Act. It uses an objective-based management approach with associated indicators. There are three overarching objectives in the plan, under which all other objectives are nested: Collaborative Governance and Integrated Management, Sustainable Human Use, and Healthy Ecosystems (DFO 2007a). The latter goal is organized in the three interconnected themes of biodiversity, productivity, and marine environmental quality, each containing a set of more specific elements and supporting objectives. Several initiatives have contributed to the development of the scientific processes applicable to IM and to ecosystem-based management. As part of the National Integrated Management planning process in each LOMA, ecosystem overview and assessment reports were produced, summarizing and synthesizing known information concerning the ecosystem and providing an assessment of that system (e.g., DFO 2005, Zwanenburg et al. 2006). In parallel, a set of criteria were developed and used to identify ecologically and biologically significant areas (EBSAs; DFO 2004), degraded areas, ecologically and biologically significant species, and depleted species (DFO 2007b). These are used to help define ecosystem objectives, indicators, and, in the cases of EBSAs, to contribute to spatial management. Additionally, Integrated Ecosystem Assessments are planned for US ecosystems. These IEAs seek to assess the status of an ecosystem, cognizant of the major drivers or pressures influencing that system, and that status relative to some pre-established thresholds (Levin et al. 2009). Ecosystem modeling, ecological indicators, and adaptive management simulations (also known http://www.mar.dfo-mpo.gc.ca/oceans/e/essim/essim-intro-e.html

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Northwest Atlantic ecosystem-based management as management strategy evaluation, MSE) are all integral parts of an IEA approach. The status reports noted below, as well as some of the modeling noted in previous sections, will all contribute to these assessments. As in Canada, these are meant to be inclusive of the wide range of factors and processes that influence large marine ecosystems, but how focused on fisheries these IEAs will be, as compared to a broader inclusion of other ocean-use sectors, is still being explored. Ecosystem status reports and indicator development and usage

An IM or IEA approach to the regulation of our impact on ecosystems includes the use of ecosystem indicators to assess drivers, pressures, states, impacts and of the reaction of ecosystems to influential forces. Indicators are being widely used to support ecosystem-based management (Cury and Christensen 2005, Levin et al. 2009). There are a growing number of guidelines on how to select such indicators (Rochet and Trenkel 2003, Fulton et al. 2005, Rice and Rochet 2005, Rochet and Rice 2005, Methratta and Link 2006) and use them (Rice 2000, 2003, Link 2005). Various approaches have been developed, such as the “traffic light approach” (Halliday et al. 2001) and multivariate analyses (Link et al. 2002b). Link et al. (2002b) assessed the status of the northeast US continental shelf ecosystem using a suite of biotic, abiotic, and human indicators and tracked temporal changes in the system. That, and subsequent work (Link 2005, Methratta and Link 2006, EcoAP 2009), have identified key metrics which should be monitored over time as leading indicators of ecosystem change. On the Scotian Shelf, a multivariate assessment of the state of the eastern Scotian Shelf Ecosystem was conducted using a series of oceanographic and ecosystem indicators (DFO 2003a). This kind of assessment provides broad contextual advice for management, mainly by providing information of use in drawing attention to problems at the ecosystem level, such as the shift in size and species distribution of finfish, a shift in oceanographic condition, and a shift from a demersal to a pelagic system. A similar report is being developed for the western Scotian Shelf. From this preliminary work, a series of indicators are being routinely monitored to detect potential changes from a more community or systemic basis (e.g., DFO 2003a, Link 2005, EcoAP 2009) and provide shorter-term ecosystem advisories6 beyond the single stock level (indicators of which are also routinely monitored and assessed.7 A joint Ecosystem Overview Report (EOR) is also under development that describes major features and drivers of ecosystem dynamics that are germane to both countries. www.nefsc.noaa.gov/omes/OMES/ www.nefsc.noaa.gov/sos/

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J.S. Link et al. Much more to do: challenges and opportunities facing us The northwest Atlantic Shelf is one of the most studied portions of the world’s oceans, and yet there remain many unanswered questions concerning its structure and functioning. A major question remains: “Do we know enough about how these ecosystems function to practice ecosystem-based fisheries management?” Let us explore that question by focusing on one example topic as posed by a series of hypotheses. Plankton form the base of the food web and changes in the timing or duration of their cycles can affect productivity at higher trophic levels (Platt et al. 2003, Fuentes-Yaco et al. 2007, Friedland et al. 2008) through what are largely bottom-up processes. However, productivity at higher trophic levels may be more directly affected by the top-down effects of exploitation by both fisheries and other top-level predators. Both the eastern and western Scotian Shelves have witnessed reductions in biomass of dominant commercially exploited fish, much more dramatically on the colder eastern Scotian Shelf. In both areas, reduced condition and reduced size-at-age (Shackell and Frank 2007) reflect reduced productivity and stressed populations (Begg and Marteinsdóttir 2003, Ottersen et al. 2006, Brander 2008). These conditions may also lead to increased sensitivity to environmental factors such as climate change (Brander 2008, Perry et al. 2010). Using groundfish predator time series from throughout the North Atlantic, Frank et al. (2007) showed that the resilience of top groundfish predators to exploitation was lower in colder waters. Those groundfish stocks living in colder temperate temperatures collapsed faster than those in warmer temperate waters. Two competing hypotheses have emerged to explain the altered states observed in various ecosystems of the northwest Atlantic. Bundy (2005) and Frank et al. (2005) hypothesized that on the eastern Scotian Shelf, exploitation has led to a trophic cascade because the reduction of top groundfish predators resulted in an increase in forage fish, a decline in zooplankton, and ultimately an increase in phytoplankton. Bundy et al. (2009) observed similar patterns throughout the Canadian northwest Atlantic. In contrast, the increase in phytoplankton from the 1960s to the 1990s (Sameoto 2001) may be a result of changing oceanographic conditions. Low salinity water from the Arctic resulted in a regional downstream freshening of shelf waters as far south as the Mid-Atlantic Bight. Lower salinity would change stratification and circulation and is hypothesized to increase phytoplankton and small copepod abundance (Greene and Pershing 2007). Further, Calanus finmarchicus abundance in the Gulf of Maine is higher during cold years, while Centropages typicus and C. hamatus are dominant when it is warmer (Meise-Munns et al. 1990, Licandro et al. 2001, Kane 2007). While the cause of increased phytoplankton is unclear, it is clear that the

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Northwest Atlantic ecosystem-based management ecosystem has changed, and it is less clear whether or not these changes have resulted in a system that is stable. So in this context it is useful to note that Hunt and McKinnell (2006) clearly state that both top-down and bottom-up factors influence marine ecosystem dynamics. In many ways, this multi-faceted set of nuances has been confirmed for the US portions of the northwest Atlantic (Link et al. 2002b, EcoAP 2009). Certainly further ecosystem modeling to explore these process-related questions will shed more light on these and other related questions. We make no claim to having an exhaustive and perfect knowledge of all the processes in these and related marine ecosystems. However, we do assert that we know enough to make a few definitive statements. First, we concur with Hunt and McKinnell (2006) that there are multiple processes acting simultaneously in any ecosystem. The challenge has been to determine the relative importance of those processes (usually by partitioning variance in some multivariate sense) as they influence the dynamics of marine ecosystems, and tracking their associated dynamics over time and space. We recognize that it is prudent to avoid the trap of posing one hypothesis over another without accounting for the possibility of a hybrid among them (i.e., multiple, concurrent component processes) as a possibility. Second, we trust that as we continue our research on the ecosystems of the northwest Atlantic, we will elucidate novel processes and explore unknown factors, but also confirm key findings and principles. For instance, over­exploitation generally leads to depleted fish stocks; changes in primary production can be driven by large-scale oceanic phenomena; species that migrate from one area to another have impacts in both the systems they left and the ones they migrate to; the interplay between predators and prey remains dynamic and challenging given the complexities of marine food webs, etc. We know that coevolutionary dynamics are pervasive in ecosystems (especially including fishing; Conover and Munch 2002), yet our knowledge of the details of these interactions is miniscule compared with what we know about food webs. The point is that there are a wide range of patterns, processes, and principles that have either originated or been affirmed from studies in these ecosystems, and we aim for that to continue. Third, we also hope to elucidate those aspects of the ecosystems which we study that are understudied or under-determined. That includes such species as those associated with the microbial community, krill, most benthos (on a synoptic, broad-scale, real-time fashion), mesopelagics, and gelatinous zooplankton. Clearly, further work to understand and monitor those species will be invaluable for further insights into ecosystem functioning. Further, the vital rates of many species are known at only a cursory level, but as we continue to

J.S. Link et al. develop the various ecosystem models as noted above a better resolution of such rates will lead to enhanced parameterization of those models. Finally, and most germane to the topic of this book, we assert that one does not need perfect knowledge of every process to manage living marine resources from an ecosystem perspective. We highlighted the multiple hypotheses above to demonstrate that even though those different processes can be interpreted in different ways, there is acknowledgment that both those “opposing” processes (and myriad others like them) are important and need to be evaluated. And in that evaluation, a useful context for understanding fisheries can emerge. More importantly, we have demonstrated in the prior section that although we have not fully implemented EBMF, we have certainly taken steps to that end. We reiterate that the knowledge base to do so is extant. We also reiterate that doing EBMF is feasible, now, with information, tools, and approaches that are available. A recent evaluation of progress in implementing ecosystem-based management of fisheries in 33 countries placed the USA and Canada in the top ranks across a number of different criteria (Pitcher et al. 2009), indicating that both countries are doing relatively well in implementing EBMF. However, as we continue to move towards ecosystem approaches to fisheries management, several challenges remain and we very much recognize them. Yet we also assert that building upon the knowledge base we have and the examples of implementation to date, we are poised to more fully implement EBMF. Acknowledgments We thank A. Belgrano and C. Fowler who invited us to contribute this chapter. We thank the various institutes (NMFS and DFO) at which we work for maintaining and collecting some of the world’s most excellent fisheries data sets. We thank Patrick Ouellet, Rejean Dufour, and Claude Savenkoff for providing considerable information on the state of the Gulf of St. Lawrence. References Anderson, S.C., H.K. Lotze, and N. Shackell. 2008. Evaluating the knowledge base for expanding low trophic-level fisheries in Atlantic Canada. Can. J. Fish. Aquat. Sci. 65:2553–2571. Atkinson, D.B. 1994. Some observations on the biomass and abundance of fish captured during stratified-random bottom trawl surveys in NAFO Divisions 2J and 3KL, autumn 1981–1991. NAFO Science Council Studies 21:43–66. Backus, G.H. (ed.). 1987. Georges Bank. Cambridge, MA: MIT Press. Barber, P. and S.L. Boyce. 2006. Estimating diversity of Indo-Pacific coral reef stomatopods through DNA barcoding of stomatopod larvae. Proc. R. Soc. B Biol. Sci. 273:2053–2061.

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J.S. Link et al. Simmons, I. 2000. Making a mark: two thousand years of ecology, economy and worldview. J. Biogeogr. 27:3–5. Sissenwine, M.P., E.B. Cohen, and M.D. Grosslein. 1984. Structure of the Georges Bank ecosystem. Rapports et Proces-Verbaux des Reunions du Conseil International pour l’Exploration de la Mer 183:243–254. Smith, A.D.M., E.J. Fulton, A.J. Hobday, D.C. Smith, and P. Shoulder. 2007. Scientific tools to support the practical implementation of ecosystem-based fisheries management. ICES J. Mar. Sci. 64(4):633–639. Stenson, G.B., L.P. Rivest, M.O. Hammill, J.F. Gosselin, and B., Sjare. 2003. Estimating pup production of harp seals, Pagophilus groenlandicus, in the Northwest Atlantic. Mar. Mammal Sci. 19(1):141–160. Swain, D.P. and G.A. Chouinard. 2008. Predicted extirpation of the dominant demersal fish in a large marine ecosystem: Atlantic cod (Gadus morhua) in the southern Gulf of St Lawrence. Can. J. Fish. Aquat. Sci. 65:2315–2319. Swain, D.P. and A.F. Sinclair. 2000. Pelagic fishes and the cod recruitment dilemma in the Northwest Atlantic. Can. J. Fish. Aquat. Sci. 57:1321–1325. Taylor, M.H. and C. Bascunán. 2001. Description of the 2000 Oceanographic Conditions on the Northeast Continental Shelf. NEFSC CRD 01–01. Team, T.U. 2008. Eastern US continental shelf carbon budget. integrating models, data assimilation, and analysis. Oceanography 21:96–104. Thagard, P. 1994. Mind, society and the growth of knowledge. Philos. Sci. 61:629–625. Theroux, R. and R. Wigley. 1998. Quantitative composition and distribution of macrobenthic invertebrate fauna of the continental shelf ecosystems of the northeastern United States. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum NMFS 140. Townsend, D.W., A.C. Thomas, L.M. Mayer, M. Thomas, and J.A. Quinlan. 2006. Oceanography of the Northwest Atlantic continental shelf. In Robinson, A.R. and Brink, K.H. (eds.), The Sea. Vol. 14A, The Global Coastal Ocean. Cambridge, MA: Harvard University Press, pp. 119–116. Townsend, H.M., J.S. Link, K.E. Osgood et al. (eds.). 2008. Report of the NEMoW (National Ecosystem Modeling Workshop). Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum NMFS-F/SPO-87. Trzcinski, M.K., R. Mohn, and W.D. Bowen. 2006. Continued decline of an Atlantic cod population: how important is gray seal predation? Ecol. Applic. 16:66, 2276–2292. Tyrrell, M.C., J.S. Link, H. Moustahfid, and B.E. Smith. 2007. The dynamic role of pollock (Pollachius virens) as a predator in the Northeast US Atlantic ecosystem: a multi-decadal perspective. J. Northw. Atlant. Fish. Sci. 38:53–65. Tyrrell, M.C., J.S. Link, H. Moustahfid, and W.J. Overholtz. 2008. Evaluating the effect of predation mortality on forage species population dynamics in the Northwest Atlantic continental shelf ecosystem: an application using multispecies virtual population analysis. ICES J. Mar. Sci. 65:1689–1700. Uchupi, E. and J.A. Austin Jr. 1987. Morphology. In Bachus, R.H. (ed.), Georges Bank. Cambridge, MA: MIT Press.

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Northwest Atlantic ecosystem-based management Ullman, D.S. and P.C. Cornillon. 2001. Continental shelf surface thermal fronts in winter off the northeast US coast. Continent. Shelf Res. 21:1139–1156. United States Census Bureau (USCB). 2006. www.census.gov/ and www.census.gov/ population/www/censusdata/hiscendata.html. Accessed September 5, 2006. US Commission on Ocean Policy. 2004. An Ocean Blueprint for the 21st Century. Final Report. Washington, DC. Vasseur, D.A. and K.S. McCann. 2005. A mechanistic approach for modeling temperature-dependent consumer-resource dynamics. Am. Nat. 166:184–198. Walters, C.J., V. Christensen, and D. Pauly. 1997. Structuring dynamic models of exploited ecosystems from trophic mass-balance assessments. Rev. Fish Biol. Fish. 7:139–172. Waring, G.T. and D.L. Palka. 2002. North Atlantic marine mammals. In Perrin, W.E., Wursig, B., and Thewissen, J.G.M. (eds.), Encyclopedia of Marine Mammals. New York, NY: Academic Press, pp. 802–805. Waring, G.T., R.M. Pace, J.M. Quintal, C.P. Fairfield, and K. Maze-Foley. 2004. US Atlantic and Gulf of Mexico Marine Mammal Stock Assessments – 2003. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum NMFS-NE182. Waring, G.T., E. Josephson, C.P. Fairfield-Walsh, and K. Maze-Foley. 2007. US Atlantic and Gulf of Mexico Marine Mammal Stock Assessments – 2007. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum NMFS-NE-205. Weare, B.C. 1977. Empirical orthogonal function analysis of Atlantic Ocean surface temperatures. Q. J. R. Meteorol. Soc. 103:467–478. Wiebe, P.H., R. Beardsley, D. Mountain, and A. Bucklin, 2002. US GLOBEC Northwest Atlantic/Georges Bank Program. Oceanography 15:13–29. Worm, B. and R.A. Myers. 2003. Meta-analysis of cod-shrimp interactions reveals top-down control in oceanic food webs. Ecology 84:162–173. Yoder, J.A., S.E. Schollaert, and J.E. O’ Reilly. 2002. Climatological phytoplankton chlorophyll and sea surface temperature patterns in continental shelf and slope waters off the northeast US coast. Limnol. Oceanogr. 47:672–682. Yodzis, P. 1998. Local trophodynamics and the interaction of marine mammals and fisheries in the Benguela ecosystem. Ecology 67:635–658. Yodzis, P. and S. Innes. 1992. Body size and consumer-resource dynamics. Am. Nat. 139:1151–1175. Zwanenburg, K.C.T., W.D. Bowen, A. Bundy et al. 2002. Decadal changes in the Scotian Shelf large marine ecosystem. In Sherman, K. and Skjoldal, H.R. (eds.), Large Marine Ecosystems of the North Atlantic: Changing States and Sustainability. Amsterdam: Elsevier, pp. 105–150. Zwanenburg, K.C.T., A. Bundy, P. Strain et al. 2006. Implications of ecosystem dynamics for the integrated management of the Eastern Scotian Shelf. Can. Tech. Rep. Fish. Aquat. Sci. 2652.

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Alaska marine fisheries management: advances and linkages to ecosystem research patricia a. livingston, kerim aydin, jennifer l. boldt, anne b. hollowed and jeffrey m. napp

Abstract US marine fisheries management in Alaska has been cited worldwide as an example to highlight successful management that incorporates ecosystem considerations. A review of historical federal fisheries management and ecosystem advice in Alaska is provided that shows some of the more significant ecosystem-based management actions that have been taken. These include conservative exploitation rates, limits on bycatch and discards, habitat protection measures, endangered and protected species considerations, and including humans as part of the ecosystem. An important aspect of the US approach in Alaska is to assess not only fishing impacts on ecosystem components, but also the impacts of other pressures of human or natural origin on the ecosystem. In addition to human-induced ecosystem impacts, primarily through fishing, Alaska marine ecosystems are strongly influenced by climate variability. Understanding and quantifying the contributions of fishing and climate influences to ecosystem change have been important aspects of the research effort in this region. A variety of ecosystem indicators have been derived to evaluate and assess how various stressors may affect the ecosystem-based objectives of maintaining predator–prey relationships and energy flow, maintaining diversity, maintaining habitat, and incorporating/

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Alaska marine fisheries management monitoring the effects of climate change. Uncertainty regarding the mechanisms influencing ecosystem structure and function is influencing research efforts. A combination of field research and predictive modeling is leading to the design of management systems that are robust to a wide range of predictive uncertainty. Integrating social science and economic considerations into the ecosystem-based approach in Alaska is also a key consideration. Development of possible future scenarios, expansion of our forecasting capabilities, and identification of management choices that are robust to a wide range of future states are the focus of current efforts to improve ecosystembased US fisheries management in Alaska. Introduction Numerous guidelines and definitions have emerged worldwide with respect to an ecosystem-based approach to fisheries management. These are now being implemented at international, national, and regional levels (Pikitch et al. 2004, Garcia and Cochrane 2005, DeMaster et al. 2006, Marasco et al. 2007, Murawski 2007). In the USA, global interests are reflected in the reports of the US Commission on Ocean Policy (2004) and the Pew Ocean Commission (2003), which recommend that the current fishery management system rely more on science and incorporate an ecosystem-based management approach. An amendment in 2007 of the primary US legislation on fishery policy, the Magnuson–Stevens Fishery Conservation and Management Act, mandated that the US National Marine Fisheries Service (NMFS) examine the state of science for advancing and integrating ecosystem considerations in regional fishery management and make recommendations for improvements. A number of improvements have been identified and need to be initiated with respect to improving and maintaining fish and ecosystem surveys, process-oriented research, accounting for the effects of environmental variations (including climate), and development of integrated ecosystem assessments (Levin et al. 2009). An important ingredient to implementing an ecosystem approach to management of US marine fisheries is regionally based management (Sissenwine and Murawski 2004). Holliday and Gautum (2005) conclude that the goals of an ecosystem approach to fishery management include:  (1) prevent overfishing, (2) protect sensitive species, (3) conserve genetic diversity and structure, (4) conserve living marine resource habitat, (5) maintain trophic structure, (6) prevent systemic over-exploitation, and (7) improve knowledge of natural and anthropogenic processes controlling ecosystem structure and function to enable more accurate forecasts of living marine resources. Regional implementation of these goals will focus on species, habitats, and ecosystem processes

P. A. Livingston et al. that are of particular interest to that region, and will use management tools that are best suited to the local management regime. US marine fisheries management in Alaska has been cited worldwide as an example to highlight successful management that incorporates ecosystem considerations (Witherell et al. 2000, Pikitch et al. 2004, Marasco et al. 2007). These ecosystems are presently dominated by groundfish fisheries that are providing relatively stable sources of production, without the collapses seen in other regions. Ecosystem-based management measures implemented in the past include precautionary single-species harvest levels, bycatch monitoring and restrictions, habitat and food-web protection measures, and endangered species consideration, which have restricted directed fishing. In addition to human-induced ecosystem impacts, primarily through fishing, Alaska marine ecosystems are strongly influenced by climate variability at all trophic levels (Napp and Hunt 2001, Schumacher et al. 2003, Hunt et al. 2008). Understanding and quantifying the contributions of fishing and climate influences to ecosystem change have been important aspects of research efforts in this region. We provide a brief history of US ecosystem-based fishery management and advice in Alaska, highlight some of the current pressures and indicators of ecosystem status, describe ongoing scientific research and progress in understanding ecosystem processes, resilience, and thresholds, and discuss the challenges ahead for translating ecosystem research into management advice. History of federal fisheries management and ecosystem advice in Alaska Active US involvement in management of fishery resources in the exclusive economic zone (EEZ) off Alaska began with the passage of the MagnusonStevens Fishery Conservation and Management Act of 1976 (MSFCMA) and the establishment of the North Pacific Fishery Management Council (NPFMC) as one of the regional fishery management councils created under the Act. Fishery management plans (FMPs) in Alaska were soon developed and implementation continues through numerous amendments to these fishery management plans by the NPFMC. Groundfish fisheries in the eastern Bering Sea (EBS), Aleutian Islands (AI), and Gulf of Alaska (GOA) are the primary focus of research and management. The dominant catch species managed by the Federal government include gadids such as walleye pollock (Theragra chalcogramma) and Pacific cod (Gadus macrocephalus) and numerous flatfish and rockfish species (Fig. 3.1) (Livingston and Boldt 2008). The US Federal Government is also actively involved as a partner in the management of other important commercial species such as salmon and crab (managed by the State of Alaska) and Pacific halibut (Hippoglossus

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stenolepis) managed by the International Pacific Halibut Commission. A comprehensive evaluation of the groundfish fishery management program was completed in 2004, which identified nine areas for evaluating and improving the management system.1 These included: preventing overfishing, promoting sustainable fisheries and fishing communities, preserving the food web, managing incidental catch and reducing bycatch and waste, avoiding impacts to seabirds and marine mammals, reducing and avoiding impacts to habitat, promoting equitable and efficient use of fishery resources, increasing Alaska native consultation, and improving data quality monitoring and assessment. We describe here ecosystem-based management actions taken in both groundfish and crab fisheries that relate to some of these above areas. The focus will be on describing some of the more significant actions that relate to: (1) conservative exploitation rates, (2) limits on bycatch and discards, (3) habitat protection measures, (4)  endangered and protected species considerations, and (5) considering humans as part of the ecosystem (Witherell et al. 2000, Witherell 2004). Conservative exploitation rates

Conservative exploitation rates for groundfish and crab stocks have been implemented through a harvest control system that creates a buffer between maximum allowable biological catch and overfishing levels. The management system is comprised of a fishing mortality control rule and a biomass

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P. A. Livingston et al. control rule. The fishing mortality associated with maximum sustainable yield (FMSY) is treated as the overfishing limit (FOFL) rather than the target. A buffer is set between the limit for the fishing mortality rate (FOFL) and the target fishing mortality rate (FABC). Stock status relative to the biomass that produces maximum sustainable yield (BMSY) and a minimum stock size threshold (MSST) is annually reviewed to determine whether the stock is overfished and in need of a rebuilding plan. Spawner-per-recruit (SPR) proxies (Bxx%) for BMSY are used when the SPR relationship is uncertain. Following Clark (1991) the default proxy is B35% for crab and groundfish. To avoid reaching overfished status, the allowable level of fishing mortality is reduced when the stock size falls below the B40%2 for stocks managed using a SPR proxy or BMSY when the SPR relationship is known. An additional optimum yield (OY) provision is imposed on groundfish fisheries in the Gulf of Alaska and the eastern Bering Sea/Aleutian Islands (BSAI). In the BSAI, the OY limit is set at two million tons for the total catch of all groundfish combined. In most years, the BSAI cap has further limited the harvest of some stocks, particularly flatfish, to amounts well below the maximum allowable. This complex harvest control system is enforced through an extensive catch monitoring system that includes an at-sea observer data collection program. This program allows the State of Alaska and NMFS to implement in-season management of catch quotas in near real-time to prevent overfishing of target species, prohibited species, or non-target species. Conservative exploitation rates are set for some non-target groundfish species using the same harvest control system for target species described above. This provides protection to these species or species groups, in case they are ecologically important or have sensitive life-history features. Some of these species groups include sculpins, skates, sharks, squid and octopus, and some rockfish species. This system provides protection to these species, maintaining their biomass at higher levels than would be required under the US Endangered Species Act. Limits on bycatch and discards

The NPFMC policy restricts bycatch and discards in Alaska groundfish fisheries. The primary objective of bycatch limits for economically important species such as crab, Pacific halibut (Hippoglossus stenolepis), Pacific salmon (Oncorhynchus spp.), and Pacific herring (Clupea pallasi) is to protect these species, which are the target of other (non-groundfish) fisheries (Witherell and Pautzke 1997) and have been designated as “prohibited species” in the groundfish fishery. Gear restrictions and area closures have also been put in place to reduce

The biomass associated with a fishing mortality that would reduce the spawnerper-recruit to 40% of the unfished level.

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Alaska marine fisheries management gear conflicts and the bycatch of these species, which have been designated as “prohibited species” in the groundfish fishery. If a prohibited species cap is attained by a particular groundfish fishery, fishing immediately ceases, even if the target species’ catch limit has not been reached. Bycatch limits for a group of “forage species” are defined in the groundfish fishery management plans (FMPs) to prevent target fisheries from being initiated on those species. The group includes smelts (capelin (Mallotus villosus) and eulachon (Thaleichthys pacificus)), stichaeids, euphausiids, sandlance (Ammodytes hexapterus), sandfish (Trichodon trichodon), lanternfish (Myctophidae), and gunnels (Pholidae). These are generally species with high turnover rates, which have not been well-studied in the region. A maximum retention allowance (MRA) for forage fish within each groundfish fishery is set at 2% of the total fishery catch (for all species within the forage fish group in aggregate). Commerce in these species is currently prohibited except for the amounts under the MRA retained for fish meal production. This management tool is a food-web protection measure, protecting higher trophic level species (commercial, protected, and endangered) which rely on this forage group for food. Discarding of unwanted catch may have ecosystem implications by re-directing energy flow in marine food webs (Livingston et al. 2005). In the US groundfish fisheries off Alaska, discard has been reduced through improved utilization requirements. The requirement to retain all walleye pollock and Pacific cod caught in groundfish fisheries has been in place since 1998. This change alone has been responsible for reducing total discards in groundfish fisheries by about half. The current overall discard rate of groundfish in groundfish fisheries is about 10% for the eastern Bering Sea, Aleutian Islands, and Gulf of Alaska combined. Further reductions are expected as improved retention rules for other groundfish species become effective. Habitat protection

Federal fisheries within the USA are required to identify essential fish habitat (EFH) and habitat areas of particular concern (HAPC) and to take actions to protect and conserve these habitats. Essential fish habitat is defined as “those waters and substrate necessary to fish for spawning, breeding, or growth to maturity.” Habitat areas of particular concern are specific sites within EFH that are of particular importance to the long-term sustainability of managed species, are of a rare type, or are especially susceptible to degradation or development. In 2005, the NPFMC adopted a management policy that addressed these requirements. The NPFMC’s approach consists of numerous seasonal fishing area closures; furthermore, extensive year-round habitat closures have been put in place in the US EEZ off Alaska (Fig. 3.2) (Olson 2008). Mandating certain

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Fig. 3.2.  Year-round groundfish closures in the US Exclusive Economic Zone in the eastern Bering Sea, Aleutian Islands, and Gulf of Alaska in place as of 2008.

targets for the size of areas to close, without detailed analysis, clear objectives for closures, stakeholder involvement, and research and monitoring may not provide the intended benefits or result in successful implementation (Lindeboom 2000). However, the management approach in Alaska has attempted to incorporate all these elements in determining which areas to close. Historically, the closures were implemented to reduce conflicts between different fishing sectors (e.g., pot and trawl fisheries) and to reduce bycatch of prohibited species. More recently, closures have been implemented to protect ecological structure and function, conserve habitat, protect vulnerable stocks, and improve scientific understanding (Witherell and Woodby 2005), including closure of a substantial area of the northern Bering Sea. In 2009, the arctic region north of the Bering Strait (Beaufort and Chukchi Seas) off Alaska was added to the closures by the NPFMC under a new Arctic Fishery Management Plan. This plan takes a proactive approach by closing fisheries in this sensitive, poorly studied area. No new fishery can be opened in this region unless it meets established criteria. These criteria include obtaining sufficient biological information and assessments of the ecosystem impacts of potential fisheries north of the Bering Strait (Chukchi and Beaufort Seas). In most cases, seasonal closures have been implemented to reduce prohibited species bycatch in places and at times when catch rates of those species have been high. Some of the most substantial year-round fisheries closures are those around rookeries and haul-outs of the endangered Steller sea lion

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Alaska marine fisheries management (Eumetopias jubatus) and those established to protect vulnerable benthic habitat from trawl damage. Substantial year-round fishing closures for protecting coral and fish habitat now exist in the Aleutian Islands and in the Gulf of Alaska slope and seamount areas. Almost half of the US EEZ in Alaska is now closed to bottom trawling. There is an ongoing process for identifying additional sites for closure under the HAPC process. Decisions to protect must be based on one or more of the following considerations:  importance of the ecological function provided by the habitat, the extent to which the habitat is sensitive to human-induced environmental degradation, the extent to which development activities may be stressing the habitat types, and the rarity of the habitat type. The NPFMC issues periodic requests for proposals that provide for additional protection of habitat from adverse effects. This is a public process that involves stakeholders and scientific review to ensure successful implementation. Endangered and protected species considerations

A number of threatened or endangered species and critical habitats for these species occur in the US EEZ off Alaska. These species are afforded protection under the US Endangered Species Act (ESA). The ESA-listed species consist primarily of marine mammals and seabirds. Other marine mammal species are protected under the Marine Mammal Protection Act (MMPA). The general approach to fisheries management with respect to these species is designed to reduce direct takes of these species through time/area/gear restrictions and to protect foraging habitat through closures or through regulations prohibiting fisheries on forage species. Restrictions that have been placed on Alaska groundfish fisheries through ESA considerations are primarily for protection of the Steller sea lion and shorttailed albatross (Phoebastria albatrus). Even though the causes for the decline and continuing lack of recovery of the Steller sea lion population are still the source of considerable scientific debate (National Research Council 2003), Steller sea lion protection measures remain in place to reduce fishery interactions that may occur due to fishery competition for prey resources, vessel disturbance around rookeries, and direct mortality. These measures protect Steller sea lions in nearshore and critical habitat areas through fishing closures in certain areas and by directing the fishery away from these areas during periods of critical use by the protected species. Overall harvest of key Steller sea lion prey (walleye pollock, Atka mackerel, and Pacific cod) is regulated through a more conservative minimum stock size threshold (B20%) than the single-species biomass threshold in place for other groundfish stocks. If reached, this threshold would close directed fisheries sooner than in single-species management.

P. A. Livingston et al. As mentioned earlier, there are also restrictions on fisheries’ take of forage species, a food-web protection measure for upper trophic level species such as marine mammals. The primary management concern for the endangered short-tailed albatross is direct take in fisheries and very low take limits have been set. The primary groundfish fisheries with documented take of short-tailed albatross are demersal longline fisheries. Seabird bycatch mitigation devices have been required on vessels fishing this type of gear since since about 1998 and dramatic declines in the total number of bycaught seabirds from 2001 onward, including albatross, can be attributed to these measures (Fig. 3.3; Fitzgerald et al. 2008). This is an excellent example of successful implementation of an ecosystem-based

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Alaska marine fisheries management management action with stakeholder involvement, cooperative research with industry, and an active outreach and education program. Humans as part of the ecosystem

Ecosystem-based fishery management acknowledges that humans are part of the ecosystem. The North Pacific Fishery Management Council’s management approach is clearly outlined in its fishery management plans (NPFMC 2008). These state that fishery management needs to consider the impacts of management decisions on fishing communities and to ensure that fisheries are socially and economically viable through community-based or rights-based management, while protecting the long-term health of the resources and their ecosystem. Management actions are taken in an open and transparent public process for decision-making. All federally managed fisheries in the US EEZ off the coast of Alaska are managed under various limited entry programs (Witherell 2004). Implementation of these programs began around 1990 and has included individual quota programs for halibut and sablefish, cooperatives for the BSAI pollock fishery, and rationalization of the BSAI crab fishery. There is a community development quota program, which provides a portion of the quotas for fisheries to be allocated exclusively to designated Alaskan coastal communities. These management actions slow the race for fish (i.e., derby-style fishery) that is typically seen in non-rationalized fisheries, promote increased safety at sea, and provide benefits to and ensure sustainability of coastal communities. Additional ecosystem benefits are seen in these types of programs because fishery removals tend to be more spread out in space and time and industry has an improved ability to avoid bycatch. Salmon bycatch in the eastern Bering Sea pollock fishery is both a management and scientific issue. Chinook salmon bycatch in this fishery increased sharply from 2001 through 2007 and a variety of management measures from strict upper limits on total catch to spatial closures have been proposed and will be implemented in the coming years. Research continues to further elucidate the nature of the salmon bycatch with respect to stock of origin by fishery removal area and time. This will better identify the nature of the impacts to salmon stocks and provide improved protection measures, if needed. Gear research continues to refine salmon excluder devices that could be used in this fishery.

Assessing Alaska marine ecosystem status An ecosystem-based fishery management approach needs to consider not only fishing impacts on ecosystem components, but also the impacts of

P. A. Livingston et al. other pressures of human or natural origin on the ecosystem. The Bering Sea, Aleutian Islands, and Gulf of Alaska marine ecosystems are subject to two main pressures: climate and fishing. Therefore, the ability to separate climate from fishing effects on ecosystems is critical to direct the appropriate management or mitigation responses. In Alaska, annual assessments involve investigating the pressures ecosystems experience and indicators of the state of marine ecosystems (Boldt 2008). These assessments are also periodically used as a component of a much broader synthesis of the status of the entire North Pacific Ocean including marginal seas (PICES 2004). The primary intent of this Alaska Ecosystem Assessment (AEA) is to summarize and synthesize historical climate and fishing effects on the shelf and slope regions of the eastern Bering Sea/Aleutian Islands and Gulf of Alaska, and to provide an assessment of the possible future effects of climate and fishing on ecosystem structure and function. For the purposes of management, ecosystem information needs to be synthesized to provide a coherent view of ecosystem effects to clearly recommend precautionary thresholds, if any, required to protect ecosystem integrity. To this end, the AEA summarizes recent trends by distinct ecosystem properties that require consideration (Table 3.1). A suite of indicators must be involved in monitoring the state of an ecosystem, assessing the role of stressors on ecosystem state, and evaluating the efficacy of management measures (Link et al. 2002, Fulton et al. 2005, Rice and Rochet 2005). A “short” list of key indicators to track these aspects of ecosystem state and stressors relative to management objectives was therefore derived for the eastern Bering Sea, Aleutian Islands, and Gulf of Alaska ecosystem assessment using a stepwise framework, the Drivers, Pressure, Status, Impacts, Response (DPSIR) approach (Elliot 2002). In applying this framework we initially determined four objectives based on stated ecosystem-based management goals of the NPFMC: maintain predator–prey relationships and energy flow, maintain diversity, maintain habitat, and incorporate/monitor effects of climate change (Table 3.2, Fig. 3.4). Maintain predator–prey relationships and energy flow

Exploitation rates in Alaska ecosystems have been relatively conservative. At present, no groundfish stock is considered overfished and only one crab stock has that designation. Crab fishery management plans were updated in 2008 to reflect new, more scientifically based overfishing-level definitions. These definitions have greatly improved the scientific basis for setting overfishing limits for crab stocks and will provide greater protection to these stocks, which have experienced lower levels of productivity compared with groundfish

123

124

large-scale

Alaska and/or

Ice retreat Water column temperature

North Pacific Index

El Niño/Southern

bottom trawl survey

and juvenile salmon survey CPUE

Age-0 pollock, age-0 Pacific cod,

density, and diet

Age-0 pollock distribution, energy

to CPUE in summer

Biomass

Forage Fish

Forage species relative

Phytoplankton and nutrients

summer bottom trawl survey

biota relative to CPUE in

bottom trawl survey

Habitat of particular concern

environmental gradients

Distribution of rockfish along

Eddy kinetic energy

Water column temperature

Aleutian Islands

relative to CPUE in summer

Zooplankton

research

Essential fish habitat

research

on seafloor habitat

Effects of fishing gear

Sea level pressures

Sea surface temperatures

Habitat of particular concern biota

Ice extent

Arctic Oscillation Index

Oscillation Index

Bering Sea Pressure Index

Pacific Decadal Oscillation

Bering Sea

Nutrients and productivity

Habitat

Physical environment

Ecosystem status and trend indices

Attribute

Biomass

summer bottom trawl survey

biota relative to CPUE in

Habitat of particular concern

environmental gradients

Distribution of rockfish along

Wind mixing

Rainfall

Mixed layer depth

Eddy kinetic energy

Water column temperature

Gulf of Alaska

Table 3.1. Ecosystem status and trend indices and ecosystem-based management indices and information included in the Alaska Ecosystem Assessment.

125

Seabirds

Marine mammals

non-target fish

Benthic communities and

Recruits, biomass, spawning

Groundfish

trawl survey

survey sea lions

Seabird takes in fisheries

Seabird takes in fisheries and diet

distribution and diet

Information regarding distribution Information regarding

breeding chronology

Abundance, productivity, and

Steller sea lions

breeding chronology

Abundance, productivity, and

cetaceans

Information on ice seals and

St. George’s Island

born on the Pribilof Islands and

Number of northern fur seal pups

Regional counts of non-pup

to CPUE in summer bottom

CPUE in summer bottom trawl Regional counts of non-pup Steller

Miscellaneous species relative

Miscellaneous species relative to

distribution and diet

Information regarding

Seabird takes in fisheries

breeding chronology

Abundance, productivity, and

Information on harbor seals

Steller sea lions

Regional counts of non-pup

trawl survey

to CPUE in summer bottom

Miscellaneous species relative

mesh nearshore survey

of Alaska ADFG/NMFS large-

Bristol Bay red king crabs

mesh nearshore survey

of Alaska ADFG/NMFS small-

CPUE of various taxa in the Gulf

biomass, catch

Recruits, biomass, spawning

Trends in catch

CPUE of various taxa in the Gulf

biomass, catch

Recruits, biomass, spawning

Stock-recruit relationships for

Crab biomass

environmental variables

Flatfish distribution relative to

forcing

Winter-spawning flatfish and wind

biomass, catch

Trends in catch

Salmon

126

large-scale

Alaska and/or

and restore fish habitats

Ecosystem goal: maintain

diversity

Ecosystem goal: maintain

trawling

Areas closed to bottom

prohibited species

Time trends in bycatch of

fishing effort

Hook and line (longline)

species catch

BSAI time trends in non-target

discards

Time trends in groundfish

fishing effort

Hook and line (longline)

fishing effort

Hook and line (longline)

species catch

Time trends in non-target

discards

Time trends in groundfish

bottom trawl surveys

bottom trawl surveys

discards

fish and invertebrate taxa in

all fish and invertebrate taxa in

groundfish community Total catch-per-unit-effort of all

community BSAI Total catch-per-unit-effort of

and diversity of the

and diversity of the groundfish

Average local species richness

survival rate

BSAI Average local species richness

of groundfish recruitment and

recruitment and survival rate

Combined Standardized Indices

Gulf of Alaska

Indices of groundfish

Time trends in groundfish

of climate regimes

BSAI Combined Standardized

environmental knowledge

climate regimes

Alaska native traditional

Aleutian Islands

environmental knowledge of

Alaska native traditional

Bering Sea

Ecosystem-based management indices and information

indicators

Ecosystem or community

Attribute

Table 3.1. (cont.)

127

composition

Groundfish fleet

programs

Fishing overcapacity

of groundfish

population of the Gulf of

BSAI

Alaska

trends in the human

in the human population of the

Distribution and abundance

and overall exploitation rate

exploitation rate of groundfish Distribution and abundance trends

Total annual surplus production

production and overall

salmon, and scallop stocks

and status of groundfish, crab,

Fish Stock Sustainability Index

Trophic level of the catch

Pot fishing effort

effort

Groundfish pelagic trawl fishing

effort

Groundfish bottom trawl fishing

Service.

ADFG, Alaska Department of Fish and Game; BSAI, eastern Bering Sea/Aleutian Islands; CPUE, catch per unit effort; NMFS, National Marine Fisheries

are part of ecosystems

Ecosystem goal: humans

stocks

stocks

crab, salmon, and scallop

and status of groundfish,

Fish Stock Sustainability Index

Trophic level of the catch

Pot fishing effort

fishing effort

Groundfish pelagic trawl

fishing effort

Groundfish bottom trawl

BSAI Total annual surplus

salmon, and scallop stocks

groundfish, crab,

salmon, and scallop

and status of groundfish, crab,

Index and status of

Fish Stock Sustainability Index

Sea

community of the eastern Bering

Fish Stock Sustainability

the bottom trawl-caught fish

consumptive uses)

Community size spectrum of

Trophic level of the catch

consumptive and non-

sustainability (for

Ecosystem goal:

Pot fishing effort

effort

Groundfish pelagic trawl fishing

effort

Groundfish bottom trawl fishing

128 ecosystem services and systemlevel characteristics (e.g., predator demands, respiration), and catch levels high enough to cause the biomass of one or more guilds to fall below minimum biologically

functional guilds (combines: pelagic forage availability, removal of top predators, and energy removal)

demand

viability of ecologically important, non-resource species such as

fishery impact on forage

native species

Introduction of non-

enough to impair long-term

concentration of

native species, invasive species

introduction of one or more non-

levels high enough to cause viable

hull fouling organism exchange

Fishery vessel ballast water and

marine mammals and birds

Fishery concentration levels high

Spatial and temporal

acceptable limits

variability, taking into account

the natural level of abundance or

between critical

or shift in ratio

seafood

Fishery-induced changes outside

per capita

Availability, removal,

Significance threshold

and energy flow

Need for fishing;

Maintain predator–

Pressures/effects

prey relationships

Drivers

Objective

Invasive species observations

Total catch levels

forage species (qualitative)

Atka mackerel, herring, squid, and

concentration of fishery on pollock,

Degree of spatial/temporal

production ratios (“balance”)

Production rates and between-guild

guild and within each guild

Population status and trends of each

Sensitive species catch levels

and for entire ecosystem

Trends in catch and bycatch, by guild

Trophic level of the catch

Indicators

Table 3.2. Objectives, drivers, pressures and effects, significance thresholds, and indicators for fishery- and climate-induced effects on ecosystem attributes.

129

Maintain diversity

Northern fur seal pup production

and non-pup counts)

Steller sea lion population trends (pup

outside the range of natural variability observed for the system

a change in functional diversity

habitat) diversity

HAPC biota bycatch

guild disturbance)

Bottom gear effort (measure of benthic

(qualitative)

changes linked to fishing removals

Catch removals high enough to cause Guild diversity or size diversity

(trophic, structural

Effects on functional

(qualitative) Number of ESA listed marine species

levels below minimum biologically acceptable limits

thresholds, linked to fishing removals

to be kept from recovering from

species relative to MSST or ESA listing

(target, non-target) to fall below or

the biomass of one or more species Population levels of target, non-target

Catch removals high enough to cause Species richness and diversity

particularly on bottom organisms)

of unobserved gear mortality

demand

diversity

Effects on species

(qualitative)

production practices

Bottom gear effort (qualitative measure

to discard and offal production levels

due to fishery discarding and offal

Scavenger population trends relative

(quantitative for discards)

the range of natural variability

levels

or energy cycling that are outside

Trends in discard and offal production

biomass, respiration, production,

Long-term changes in system

seafood

per capita

Need for fishing;

Energy re-direction

130

change

change

Volume of cold pool Summer zooplankton biomass

on production and recruitment

groundfish recruitment and survival

Combined standardized indices of

index)

(PDO, AOI, NPI, and NINO 3.4 SST

North Pacific climate and SST indices

Ice indices (retreat index, extent)

recruitment of stocks

changes in productivity and/or

HAPC biota survey CPUE

HAPC biota catch

pot)

Fishing effort (bottom trawl, longline,

and resulting effects

currents, ice extent

ocean temperatures,

in changes in the

forcing resulting

Changes in climate that result in

limits.

minimum biologically acceptable

groundfish stocks Areas closed to bottom trawling

stock size threshold; NPI, North Pacific Index; PDO, Pacific Decadal Oscillation Index; SST, sea surface temperature.

AOI, Arctic Oscillation Index; CPUE, catch per unit effort; ESA, Endangered Species Act; HAPC, habitat areas of particular concern; MSST, minimum

climate

Change in atmospheric

cause a stock biomass to fall below

and other species

Concern about

change in HAPC biota that would

habitat, HAPC biota,

demand

high enough to cause a loss or

damage caused by fishing gear

effects on benthic

effects of climate

Incorporate/monitor

acceptable limits Catch removals high enough or

due to fishing gear

Habitat loss/degradation

seafood

Need for fishing;

Maintain habitat

Older age group abundances of target

(qualitative)

fall below minimum biologically

aggregations or larger fish

would cause the stock biomass to

Degree of fishing on spawning

genetic components of a stock that

a loss or change in one or more

Catch removals high enough to cause Community size diversity

diversity

Effects on genetic

Indicators

Significance threshold

Pressures/effects

per capita

Drivers

Objective

Table 3.2. (cont.)

131

Fig. 3.4.  Ecosystem assessment indicators of the eastern Bering Sea.

132

Alaska marine fisheries management BS Pelagic foragers biomass (t)

2.2e+07 2.0e+07 1.8e+07 1.6e+07 1.4e+07 1.2e+07

BS Pelagic foragers catch (t)

1600000 1400000 1200000 1000000

BS Catch Trophic Level

3.75 3.70 3.65 3.60 3.55

BS Fishing In Balance Index

3.0 2.5 2.0 1.5 1.0 0.5 0.0

Al Catch Trophic Level

4.0 3.9 3.8 3.7 3.6

Al Fishing In Balance Index

3.0 2.5 2.0 1.5 1.0 0.5 0.0

GOA Catch Trophic Level

4.4 4.3 4.2 4.1 4.0 3.9 3.8

GOA Fishing In Balance Index

0.8 0.6 0.4 0.2 0.0

1960

1977

1989

1999

2008

Fig. 3.5.  Time series of the biomass and catch (t) of the pelagic foragers guild in the Bering Sea (BS), and the trophic level of the catch and Fishing In Balance (FIB) index for the BS, Aleutian Islands (AI), and the Gulf of Alaska (GOA).

stocks. Some potential direct or indirect effects of fishing include changes in the abundance of species or guilds, the trophic organization of the food web, and the size structure and diversity of the fish community. To track potential effects of fishing, species in the eastern Bering Sea can be grouped into 12 guilds by their trophic role in the ecosystem (Aydin et al. 2007). These guilds span the trophic levels between phytoplankton and apex predators and include a separate pathway for pelagic and benthic components of the ecosystem. Current point estimates and trends in biomass, catch, and exploitation rates of these guilds are monitored for changes relative to longterm means and variances (Fig. 3.5).

.

P. A. Livingston et al.

The trophic level of a fishery has been suggested as a measure of overall topdown impact on the ecosystem by fishing; in particular, the notion of “fishing down the food web” has been popularized in recent years. Both the trophic level of the catch and Fishery In Balance (FIB) indices (Pauly et al. 2000) have been monitored in the eastern Bering Sea, Aleutian Islands, and Gulf of Alaska ecosystems to determine if fisheries have been “fished-down” in their food web by removing top-level predators and subsequently targeting lower trophic level prey (Fig. 3.5). The FIB index was developed to ascertain whether the trophic level of catches show trends that are a reflection either of deliberate choice of particular target species because of market or other economic issues, or of a “fishing down the food web” effect in which upper trophic level catch species are depleted due to strong fishing pressure. This index declines only when catches do not increase as expected when moving down the food web (i.e., lower trophic levels are more biologically productive), relative to an initial baseline year. Increases in the FIB index could indicate an expansion in the geographic range of fisheries (Pauly et al. 2001, Pauly and Palomares 2005) resulting in faster increases in catches than expected (Bhathal and Pauly 2008). Unlike other regions, such as the northwest Atlantic, the FIB index and the trophic level of the catch in the eastern Bering Sea, Aleutian Islands, and Gulf of Alaska have been relatively constant since the 1970s, suggesting an ecological balance in the catch patterns. Further examination of total catch by trophic level (see Essington et al. 2006) supports the idea that “fishing down the food web” has not occurred in Alaska, and there does not appear to have been a serial addition of lower trophic level species to the catches. The above predator-prey indices apply only to commercially exploited fish and invertebrate stocks. There are protection measures for endangered, threatened, and depleted marine mammal and seabird species in Alaska. Currently, nine species are listed as endangered, five species are listed as threatened, and three species are listed as depleted. One of those endangered species is the western stock of Steller sea lions, of which the adult females may be experiencing declines in reproductive rates that began in the early 1990s (Holmes and York 2003, Holmes et al. 2007). Although not formally listed under the ESA, the number of northern fur seal pups born on the Pribilof Islands is at or near their lowest level since the early 1900s (Towell et al. 2006). This trend cannot be fully explained by emigration or large-scale spatio-temporal environmental changes in the North Pacific Ocean. Thus, continued research to evaluate and identify the impacts of fishing on the endangered Steller sea lion and other top predators and to identify the factors preventing recovery of these populations is a high priority. While overall fishing levels may not be the ultimate factor, the space/time concentration of fisheries near foraging areas of centrally placed foragers may be a factor, partially mitigated by current area closures.

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134

Alaska marine fisheries management Maintain diversity

The diversity of an ecosystem can be affected by fishing through the removal of species and the removal of certain size classes of animals. Marine food-web relationships are strongly influenced by animal size. One important indicator of animal size diversity in the food web is the slope of the community size spectrum (CSS), the relationship between animal abundance and size. Fishing effects may result in changes of the CSS slope over time. For example, in an exploited fish community, larger fish may suffer higher fishing mortality than smaller individuals causing the size distribution to become skewed toward the smaller end of the spectrum (Zwanenburg 2000). The result is a change in CSS slope over time with increasing fishing pressure. Unlike other marine ecosystems, the eastern Bering Sea CSS slopes indicate that there has been no linear decreasing trend in groundfish size during 1982–2006 (Boldt et al. in review). Other indicators of diversity are the Shannon–Wiener diversity index and species richness. The effects of fishing on these diversity indices are, however, unclear (Livingston et al. 1999, Jennings and Reynolds 2000) and can be affected by natural variability in species distribution and abundance (Mueter 2007). Changes in eastern Bering Sea groundfish and invertebrate species richness may be related to environmental variability and have been attributed to changes in subarctic fish species distribution relative to the cold pool (region of shelf bottom water ≤ 2 °C; Mueter and Litzow 2008). Eastern Bering Sea groundfish and invertebrate species diversity, which has been low in recent years, may also be related to fish distribution (Mueter 2007) (Fig. 3.6). To address anthropogenic influences on biodiversity in Alaska marine ecosystems, the NPFMC is planning separate stock assessments for several species that were previously grouped together into an aggregate management category called “Other Species.” The Other Species management classification included taxonomically and ecologically unrelated species that have widely different life histories and assessments of this group lack detailed species-specific information regarding their population dynamics. The Council is poised to eliminate this category in favor of a more scientifically based management strategy for skates, sculpins, sharks, octopus, and squid. The NPFMC is also poised to add species into the fishery management plans that are frequently captured, such as grenadiers. These two actions will reduce anthropogenic influences on the biodiversity of the US EEZ off Alaska by dictating harvest levels based on the life-history features of these groups. Additionally, to help maintain biodiversity, the NPFMC has taken steps towards incorporating uncertainty in groundfish harvest control rules. A comprehensive environmental evaluation of the US groundfish fishery management

P. A. Livingston et al.

Fig. 3.6.  Time series of bottom trawl fishing effort (measured as the number of observer-viewed trawls) in the Bering Sea (BS), Aleutian Islands (AI), and Gulf of Alaska (GOA; Olson 2008); the Shannon–Wiener diversity index estimated for Bering Sea Shelf bottom trawl survey catches (Mueter 2008); species richness for Bering Sea Shelf bottom trawl survey catches (Mueter 2008).

regime off Alaska was conducted to guide future fisheries management policy in this region (NMFS 2004). This evaluation identified the need for developing harvest control rules that incorporate uncertainty. The reauthorization of the MSFCMA and its associated call for annual catch limits has renewed research on techniques for formally incorporating uncertainty into fisheries management. It is anticipated that by 2010, the NPFMC will review and modify the current harvest control rules for federal fisheries off Alaska to fully comply with

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136

Alaska marine fisheries management NMFS guidelines for implementation of the Act. These actions are likely to formalize precautionary adjustments to the harvest recommendations of Bering Sea/Aleutian Islands crab and scallop to ensure that there is a buffer between the FOFL and FABC. Maintain habitat

In addition to trawl closures that have been implemented to protect benthic habitat or reduce bycatch of prohibited species, groundfish fishing effort is monitored. Fishing effort is an indicator of damage to or removal of habitat-forming biota (termed “Habitat Areas of Particular Concern” (HAPC) biota in the fishery management plans), modification of non-living substrate, damage to small epifauna and infauna, and changes in benthic biodiversity by trawl or fixed gear. Fishing pressure in terms of bottom trawl effort appeared to stabilize at lower levels in the early to mid 2000s relative to the early 1990s as a result of rationalization programs (Fig. 3.6). Another result of this rationalization has been the spatial dispersion of trawl fisheries. It is difficult to assess the effects of fishing on benthic organisms and habitat (e.g., corals, seapens, seawhips, and anemones). Increased knowledge of habitat disturbance as a function of fishing intensity would improve our ability to make such assessments. Also, it would be beneficial to have improved knowledge of the importance of these biota as habitat for different species and life stages of fish. This research would include obtaining estimates of abundance and distribution for species that provide biotic habitat, particularly in areas currently untrawlable with standard survey gear. It would also be important to understand the influence of physical factors such as sediment type, bathymetry, and oceanography on the abundance and distribution of habitat biota. Furthermore, deriving an index that reflects the amount of fish habitat damaged by fishing gear and obtaining additional information on recovery rates of benthic habitat organisms, are both needed to better assess habitat impacts of fishing. A research plan for the newly closed northern Bering Sea research area will be developed in 2011. The research plan will provide a way to obtain baseline information on a relatively unfished area of the Bering Sea and allow systematic evaluation of the effects of fishing and recovery rates of benthic habitat. The NPFMC will also be requesting proposals to identify or protect additional habitat areas of particular concern in the coming year. Climate

Climate variability and global climate change exert strong pressure on North Pacific marine ecosystem structure and function (Fig. 3.7; Hare and Mantua 2000, Hunt et al. 2002, King 2005, Stabeno et al., 2006). These climate pressures influence the distribution and production of ecosystem components,

P. A. Livingston et al.

Fig. 3.7.  Time series of the Pacific Decadal Oscillation (PDO; annual mean), the Arctic Oscillation Index (AOI; annual mean), the Nino 3.4 index (annual mean), and the Bering Sea Ice Retreat Index (Bond and Overland 2008). The PDO is the leading mode of North Pacific sea surface temperature variability. The AOI is an index of the dominant pattern of non-seasonal sea-level pressure variations north of 20ºN. The Nino 3.4 index characterizes the state of the El Niño/Southern Oscillation. The Bering Sea (BS) Ice Retreat is the number of days after March 15 when the average ice concentration within a 2º by 2º box on the southeast Bering Sea shelf (56–58°N, 163–165°W) is > 10% of the total box area (Bond and Overland 2008).

in particular fish resources, and the fisheries that depend on them. Climate influences on fish recruitment can be viewed as a complex process of activating and constraining phenomena, both stochastic and deterministic (Bailey et al. 2005, Duffy-Anderson et al. 2005). Climate impacts on fish production can

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138

Alaska marine fisheries management either be direct or indirect through food-web effects. Direct effects of temperature on metabolism, growth, and distribution of fish are also observed (Farley et al. 2007, Mueter and Litzow 2008). Food-web effects can also occur through changes in the distribution and abundance of prey such as zooplankton and forage fish in the pelagic food web or detritus and benthic invertebrates in the benthic food web. Predators of, and competitors with, fish and shellfish may also be affected (e.g., Bailey 2000, Hollowed et al. 2000). Monitoring climate variability is necessary to understand changes that occur in the marine environment and may help predict potential effects on biota. In the North Pacific Ocean, interannual variability as measured by El  Niño/Southern Oscillation (ENSO) indices and quasi-decadal variability seen in the Pacific Decadal Oscillation index (PDO) are some of the most important (Fig. 3.7). Climate variability in the eastern Bering Sea is particularly evident in indices of sea level pressure (Arctic Oscillation Index, AOI), winter sea-ice extent, and temperature (PDO; Mantua et al. 1997, Stabeno et al. 1999, Overland and Stabeno 2004). Large-scale climate forcing affects sea ice and determines the size and location of the cold pool in the Bering Sea (region of shelf bottom water ≤ 2 °C). Seasonal sea ice is a defining characteristic of the Bering Sea Shelf (Stabeno et al. 2006, 2010). The presence of sea ice in winter and the timing of its retreat influence the timing of the spring bloom and bottom temperatures throughout the year (Stabeno et al. 2006, Hunt et al. 2008). Changes in the cold pool size and location affect the distribution of some fish species and the foraging of upper trophic level species (Ciannelli and Bailey 2005). Changes in the cold pool may also affect water column stratification, lower trophic level production, the seasonal availability and distribution of zooplankton, and community dynamics in the Bering Sea (e.g., Mueter et  al. 2007, Coyle et al. 2008, Hunt et al. 2008, Mueter and Litzow 2008, Spencer 2008). It is apparent that many components of the Alaskan ecosystem respond to variability in climate and ocean dynamics. Predicting changes in biological components of the ecosystem to climate changes, however, will be difficult until the mechanisms that cause the changes are understood. Some advances in that area will be described later in this chapter. Climate change and variability may be responsible, in part, for recent stress placed on particular populations within the system. For example, walleye pollock, a nodal species in the food web as well as the major commercial groundfish species, experienced a series of poor recruitment years coincident with the recent warm period (2000–2005). Poor pollock recruitment, as well as overall ecosystem considerations provided by scientists, prompted reduction of the recommended allowable biological catch beginning in 2008. Similarly, stocks

P. A. Livingston et al. of snow crab (Chionoecetes opilio) have generally declined during the warming after 1991 and the distribution of spawning females has contracted to the north with the general reduction in the cold pool (Zheng et al. 2001, Orensanz et al. 2004). Given the prevailing currents and directional transport and retention of crab larvae, it appears unlikely that the southern spawning group of females can re-establish itself (Parada et al. 2010). Future scientific and implementation challenges to ecosystembased fishery management in Alaska The evolution of perspective from prevention of overfishing single stocks to preserving marine ecosystems brings new challenges to stock assessment scientists (Hollowed and Bailey 2009) even in relatively data-rich areas such as Alaska. Making additional progress towards ecosystem-based fishery management means not only increasing our understanding of the mechanisms affecting individual or groups of species (e.g., specific relationships between climate and individual species), but it also means facing the very nature of causality and uncertainty within a complex adaptive ecosystem. Doing so will require a combination of existing and new field research and predictive modeling. Field research is needed to untangle the mechanisms that control ecosystem structure and function (Minobe 2000) and models are used to project the responses of species to changing conditions based on these mechanisms. It is not expected that “command and control” management policies will be developed to exploit precise predictions based on such efforts. Rather, this process aims to ensure that management systems are robust to a wide range of predictive uncertainty. Field research on the mechanisms linking climate and biology through long-term data collection programs (e.g., for fish early life-history examination; Kendall et al. 1996, Matarese et al. 2003) are critical to these efforts. For example, temporal series of estimated zooplankton abundance have shown that changes in the cold pool size and extent in the eastern Bering Sea may have influenced the production of lower trophic levels in recent years, particularly in the reappearance of an important forage species, Calanus marshallae, that has previously been tied to the southerly extent of sea ice and cold conditions (Baier and Napp 2003). The increased zooplankton biomass may have positive effects on zooplanktivorous fish, such as juvenile walleye pollock, and the endangered North Pacific right whale in the Bering Sea. As another example, Doyle et al. (2009) found coherent assemblages of late spring larvae from the northern Gulf of Alaska based on their early life-history characteristics and fluctuations in abundance with a high degree of synchrony within assemblages. Relationship

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Alaska marine fisheries management to physical variables (e.g., transport, winter temperatures, winds) displayed species and assemblage-specific patterns related to both local and basin-scale environmental forcing, and helped to identify environmental indicators that broadly apply or explain the success or failure of several species during their early life stage. For management purposes, field research is placed into a predictive (modeling) framework. For example, one might start with an age-structured (i.e., stock assessment) model of a single species and add interaction with climate and links to detailed studies and models of early life history and recruitment to forecast future stock conditions. This approach has been used to integrate climate indices into stock assessment; for example, advection indices have been used to predict flatfish recruitment in the Bering Sea (Wilderbuer et al. 2002). This “minimum realistic modeling” (MRM; Punt and Butterworth 1995) approach takes a traditional approach of seeking the most parsimonious explanation of observations to be modeled. This approach is most often used, and is most appropriate when the issue in question is improving already detailed singlespecies stock assessments. However, even if a control mechanism (e.g., a link between a climate index and fish production) is understood from the analysis of historical information, the nature of the explanations and the “control” of biological systems is multiplicative and non-linear (Hsieh et al. 2005), subject to positive feedback leading to rapid evolution towards new states (Ulanowicz 1999) and prone to switching of control mechanisms (Ciannelli et al. 2005, Duffy-Anderson et al. 2005). Under such circumstances, “prediction,” as one might perform with a quantitative model or a straightforward (e.g., correlative) hypothesis, is not only subject to wide margins of error but to wide deviations from basic management expectations (e.g., shifts in productivity which may lead to species baselines that differ from those under which policy and expectations were set). To ensure that current management policies are robust to such shifts, management strategy evaluations (MSEs) (Schnute et al. 2007) are becoming an increasingly important scientific endeavor in Alaska and elsewhere to evaluate the possible unintended consequences of management alternatives under such shifting baselines. These tools have been used, for example, to evaluate the performance of bycatch mitigation strategies (Stram and Ianelli 2009), and climate change impacts on fish and fisheries (A’mar et al. 2009, Hollowed et al. 2009). Management strategy evaluations test detailed, minimally realistic models for performance against a broader set of models that include a wide range of scenarios of how the ecosystem in question might function, change, or adapt to fisheries or variation in climate. These broader models start from a “big picture”; for example, such models may include interactions with “the whole ecosystem” or, in a predator/prey

context, the whole food web. In the direct context of marine ecosystem management, such models have been used extensively for over 30 years (e.g., for Georges Bank, Cohen et al. 1982 or for the Bering Sea, Aydin et al. 2007). The quantification of food-web interactions, such as shown for the Gulf of Alaska in Fig. 3.8 (Aydin et al. 2007), has been listed as an important component of developing ecosystem approaches to management (NMFS 1999, NRC 2006). The goal of such a model is to simulate a wide range of possible futures in place of precise predictions and expectations. The key to this work is not accepting a point estimate, but the examination of thousands or millions of simulations across a full range of possible ecosystem interactions. Current computing resources are just now making this level of analysis possible. The strength of combining a whole ecosystem approach with simulations of detailed model performances comes from examining management alternatives across a range of moderate or highly likely ecosystem conditions. In this sense, minimum realistic models may not be the best application from which to examine ecosystem approaches to management. Even when a single most likely explanation for a historical phenomenon may be uncovered, its historical-specific context may limit its informative power for the future (Gaichas 2006). Ecosystem model development and analysis should focus on providing insights into how to avoid management “surprises” (such as trophic cascades to undesirable species), whether the surprises would arise from not understanding the past, or from having insufficient imagination about the future. To this end, it is important to include sufficient complexity both in food-web structure (topology) and in functional relationships (responses) in the development of an ecosystem approach. Such models include an examination of the Gulf of Alaska food-web network structure for critical connections (Gaichas and Francis 2008), determining the range of properties in ecosystems which may assist in identifying critical ecosystem thresholds, comparative studies of food webs (e.g., Gaichas et al. 2009), or identifying the range of possible ecosystems in which fisheries policies produce more variable yields and susceptibility to climate variation (Gaichas 2006). There are also challenges for social science and economics with the need to identify the thresholds for economic viability and sustainable communities and how those would be impacted under various management alternatives. Economists and social scientists lag behind fisheries biologists in this area due to the complexity of the issues associated with trade-offs between potential user groups. Some progress has been accomplished through the formal recognition of coastal communities as a stakeholder in fisheries in Alaska. Economic data collection programs are essential to properly assess economic impacts of management actions and identify optimal management options. Economic data collection has been initiated in Alaska crab 141

Fig. 3.8.  A food web of the Gulf of Alaska large marine ecosystem (Aydin et al. 2007). Box size is proportional to the biomass in

each functional group. The width of each flow line is proportional to the volume of the flow (t/km2/year).

P. A. Livingston et al. fisheries and is envisioned to be expanded to groundfish fisheries in the near future. The interplay of ecosystem and management system complexities can be seen in several examples that are part of ongoing management efforts in the Alaska region: •

•

•

Under rationalized fisheries, federal fisheries managers and stakeholders jointly shoulder the burden of management. This shared management burden acts to sustain yield while preserving the ecosystem. An excellent example of this type of partnership between industry and federal managers is the Chinook salmon bycatch amendment recently adopted by the NPFMC. Chinook salmon bycatch in the Bering Sea pollock fishery increased sharply from 2001 through 2007, and a variety of management measures from strict upper limits on total catch to spatial closures will be implemented in the coming years. Research continues to further elucidate the nature of the salmon bycatch with respect to stock of origin by fishery removal area and time. Bycatch in the Gulf of Alaska is currently under review to assess whether additional protection of salmon, crab, and herring will be needed. Current research involves evaluating and assessing halibut mortality under different fishing conditions to determine if further reductions in mortality can be attained by changing fishing and bycatch processing procedures. Bycatch of non-target groundfish species is under additional scrutiny as new scientific information reveals a more complex stock structure and additional species than had previously been understood. For example, advances in genetic analytical techniques have shown that new rockfish species exist in the Aleutian Islands and Gulf of Alaska (Gharrett et al. 2005). Scientific challenges still exist in identifying essential stock components in time and space, devising easily implemented field identification techniques for species that lack unique external identification characteristics, and devising precautionary management procedures. For example, managing genetic diversity of Pacific cod within the Aleutian Island chain or between the Aleutian Islands and the eastern Bering Sea Shelf may become necessary due to new evidence for genetic distinction among fish captured in these areas (Cunningham et al. 2009). While the genetic tools for discriminating differences among fish are well developed, more attention needs to be devoted to stock assessment and management tools that can use these data.

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Federal scientists will continue to evaluate and identify the impacts of fishing on endangered species. The NPFMC has acted quickly to mitigate the direct impacts of fishing on endangered species. However, management measures to address the indirect interactions (competition and prey field disturbance) have lagged behind because of the difficulty of estimating biological thresholds for local removals – thresholds beyond which some undesirable amount or type of change in ecosystem status would occur. This is particularly true in the case of the western Steller sea lion population segment where process-oriented field research is needed to identify the factors currently preventing its recovery. Such research has been initiated but, due to the complex nature of the interactions, further spatial fishing experiments are needed to identify meaningful limits to fishery removals (Wilson et al. 2003, Hollowed et al. 2007).

Here, reporting the full range of uncertainty is not merely a challenge facing the scientist, it also challenges the policymaker. In fact, understanding the limitations on predictive capability in ecosystems is the first step towards informed management decision-making (Gaichas 2008). Communicating uncertainty in ecosystem responses to fishing has been the focus of recent research on Alaska marine ecosystems. This new era of management and evaluation of the skills of different management strategies is advantageous because it results in increased dialog between managers, stakeholders, and scientists. Outlook Despite the current overall conservative levels of fisheries exploitation in Alaska, and a set of management policies that emphasizes ecosystem approaches, it is important to realize that the Bering Sea, Gulf of Alaska, and Aleutian Islands are exploited ecosystems with a long history of use. High levels of removals include removals of pinnipeds, sea otters, and whales from the eighteenth through twentieth centuries (Gaichas 2006), and unrestricted ­fisheries for crabs, Pacific ocean perch, pollock, yellowfin sole, and other species, at least through the late 1970s. It is possible that many of these past events continue to influence the current ecosystem state at least indirectly. For example, past removals may have led to a less competitive environment or enabled particular species to take advantage of regime shifts or other climatic events. While the current state of Alaska marine ecosystems is not pristine, the management system in Alaska maintains practices that embrace ecosystem principles with the goal of sustainable exploitation of marine resources. It is important to ensure that the management system preserves a range of historical natural variability and robustness in ecosystem properties.

P. A. Livingston et al. There are gaps in understanding the system-level impacts of fishing and ­spatial/temporal effects of fishing on community structure and prey availability. Validation and improvements in system-level predator/prey models and indicators are needed along with research and models focused on understanding spatial processes (Bailey et al. 2010). Improvements in the monitoring system should include better mapping of corals and other benthic organisms, development of a system for prioritizing non-target species bycatch information in groundfish fisheries, and identification of genetic subcomponents of stocks. In the face of this uncertainty, additional protection of sensitive or rare ecosystem components such as corals or local spawning aggregations should be considered. Improvements in understanding both the nature and direction of future climate variability and effects on biota are critical (Hollowed and Bailey 2009). An indicator of secondary production or zooplankton availability would improve our understanding of marine ecosystem dynamics and in prediction of groundfish recruitment and survival. No significant adverse impacts of fishing on the ecosystem relating to predator–prey interactions and energy flow/removal, diversity, or habitat are noted in US fisheries off Alaska. There are, however, several cases where those impacts are unknown because of incomplete information on population abundance of certain species such as forage fish or habitat biota not well sampled by surveys. Identification of thresholds and limits through further analyses, research, and modeling is also needed to identify impacts. Incorporation of socio-economic indicators into this ecosystem assessment is needed in the future. Alaska marine ecosystem research has also benefited from the combined and cooperative attention of Federal, State, industry, and private research funding. When new, stable, long-term funding from a trust became available in the early 2000s, the recommendation was to leave a legacy of multidisciplinary, ecosystem-wide research (National Research Council 2003). The concept of vertically integrated ecosystem research (IERP) was first applied to a Bering Sea project by the North Pacific Research Board (NPRB) with leveraging from a complementary program from the US National Science Foundation (Bering Ecosystem STudy; BEST 2004) and base funding from Federal agencies such as the National Oceanic and Atmospheric Association (NOAA), the US Geodetic Survey, and the US Fish and Wildlife Service. Together the team of over 93 scientists will address the mechanisms that lead to the form and function of this ecosystem with the goal of linking ecosystem research directly to management objectives. Similar collaborative efforts are planned for the Gulf of Alaska starting in 2011. An ecosystem-based evaluation of the present and likely future fishing effects of groundfish fisheries operating under the current fishery

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Alaska marine fisheries management management constraints may provide understanding of the possible implications of the current management approach. As noted by Carpenter (2002), a limitation of ecological forecasts includes the uncertainty of predictions because the future probability distributions of drivers such as climate may be unknown or unknowable. Development of possible future scenarios, expansion of our forecasting capabilities within the space/time constraints that are relevant to human action, and identification of management choices that are robust to a wide range of future states are possible ways this assessment can be broadened in the future. Acknowledgments The authors wish to thank all the contributors over the years to the “Ecosystem Considerations” document of the North Pacific Fishery Management Council. Their data collection and analysis efforts are integral to the continued evolution of a framework for ecosystem-based fishery management in the Alaska region. References A’mar, Z.T., A.E. Punt, and M.W. Dorn. 2009. The impact of regime shifts on the performance of management strategies for the Gulf of Alaska walleye pollock (Theragra chalcogramma) fishery. Can. J. Fish. Aquat. Sci. 66:2222–2242. Aydin, K., S. Gaichas, I. Ortiz, D. Kinzey, and N. Friday. 2007. A comparison of the Bering Sea, Gulf of Alaska, and Aleutian Islands large marine ecosystems through food web modeling. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum 178. NTIS No. PB2008–107111. Baier, C.T. and J.M. Napp. 2003. Climate-induced variability in Calanus marshallae populations. J. Plankton Res. 25:771–782. Bailey, K.M. (2000). Shifting control of recruitment of walleye pollock Theragra chalcogramma after a major climatic and ecosystem change. Mar. Ecol. Progr. Ser. 198: 215–224. Bailey, K.M., L. Ciannelli, N.A. Bond, A. Belgrano, and N.C. Stenseth. 2005. Recruitment of walleye pollock in a physically and biologically complex ecosystem: a new perspective. Progr. Oceanogr. 67:24–42. Bailey, K.M., L. Ciannelli, M. Hunsicker, A. Rindorf, et al. 2010. Comparative analysis of marine ecosystems: Workshop on predator–prey interactions. Biol. Lett. in press. Bhathal, B. and D. Pauly. 2008. “Fishing down marine food webs” and spatial expansion of coastal fisheries in India, 1950–2000. Fish. Res. 91:26–34. Bering Ecosystem Study (BEST). 2004. Science Plan. Fairbanks, AK: Arctic Research Consortium of the USA. Boldt, J.L. (ed.). 2008. Ecosystem Considerations for 2009. Appendix C of the Bering Sea/ Aleutian Islands and Gulf of Alaska Groundfish Stock Assessment and Fishery Evaluation Report. Anchorage, AK: North Pacific Fishery Management Council.

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P. A. Livingston et al. Hsieh, C., S.M. Glaser, A.J. Lucas, and G. Sugihara. 2005. Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean. Nature 435:336–340. Hunt, G.L. Jr., P. Stabeno, G. Walters et al. 2002. Climate change and control of the southeastern Bering Sea pelagic ecosystem. Deep-Sea Res. 49:5821–5853. Hunt, G.L., Jr., P.J. Stabeno, S. Strom, and J.M. Napp. 2008. Patterns of spatial and temporal variation in the marine ecosystem of the southeastern Bering Sea, with special reference to the Pribilof Domain. Deep-Sea Res. II 55:1919–1944. Jennings, S. and J.D. Reynolds. 2000. Impacts of fishing on diversity: from pattern to process. In Kaiser, M.J. and de Groot, S.J. (eds.), Effects of Fishing on Non-target Species and Habitats. Oxford: Blackwell Science, pp. 235–250. Kendall, A.W.., Jr., J.D. Schumacher, and S. Kim. 1996. Walleye pollock recruitment in Shelikof Strait: applied fisheries oceanography. Fish. Oceanogr. 5(Suppl. 1): 4–18. King, J.R. (ed.). 2005. Report of the Study Group on Fisheries and Ecosystem Responses to Recent Regime Shifts. PICES Scientific Report No. 28. Sidney, BC: North Pacific Marine Science Organization. Levin, P.S., M.J. Fogarty, S.A. Murawski, and D. Fluharty. 2009. Integrated ecosystem assessments: developing the scientific basis for ecosystem-based management of the ocean. PLoS Biol. 7(1):e100004.doi:10.1371/journal. pbio.1000014. Lindeboom, H.J. 2000. The need for closed areas as conservation tools. In Kaiser, M.J. and deGroot, S.J. (eds.), The Effects of Fishing on Non-target Species and Habitats. Oxford: Blackwell Science, pp. 290–301. Link, J.S., J.K.T. Brodziak, S.F. Edwards et al. 2002. Marine ecosystem assessment in a fisheries management context. Can. J. Fish. Aquat. Sci. 59:1429–1440. Livingston, P.A. and J. Boldt. 2008. Trophic level of the catch. In Boldt, J. (ed.), Ecosystem Considerations for 2009. Appendix C of the Bering Sea/Aleutian Islands and Gulf of Alaska Groundfish Stock Assessment and Fishery Evaluation Report. Anchorage, AK: North Pacific Fishery Management Council, pp. 168–169. Livingston, P.A., L.-L. Low, and R.J. Marasco. 1999. Eastern Bering Sea ecosystem trends. In Sherman, K. and Tang, Q. (eds.), Large Marine Ecosystems of the Pacific Rim: Assessment, Sustainability, and Management. Malden, MA: Blackwell Science, pp. 140–162. Livingston, P.A., K. Aydin, J. Boldt, J. Ianelli, and J. Jurado-Molina. 2005. A framework for ecosystem impacts analysis using an indicator approach. ICES J. Mar. Sci. 62:592–597. Mantua, N.J., S.R. Hare, Y. Zhang, J.M. Wallace, and R.C. Francis. 1997. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78(6):1069–1080. Marasco, R.J., D. Goodman, C.B. Grimes et al. 2007. Ecosystem-based fisheries management: some practical suggestions. Can. J. Fish. Aquat. Sci. 64:928–939. Matarese, A.C., D.M. Blood, S.J. Picquelle, and J.L. Benson. 2003. Atlas of Abundance and Distribution Patterns of Ichthyoplankton from the Northeast Pacific Ocean and Bering Sea

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Alaska marine fisheries management and Brink, K.H. (eds.), The Sea. Vol. 14A, The Global Coastal Ocean. Cambridge, MA: Harvard University Press, pp. 1177–1212. Stabeno, P.J., J.M. Napp, C. Mordy, and T.E. Whitledge. 2010. Factors influencing physical structure and lower trophic levels of the eastern Bering Sea shelf in 2005: sea ice, tides and winds. Progr. Oceanogr. 85:180–196 Stram, D.L. and J.N. Ianelli. 2009. Eastern Bering Sea pollock trawl fisheries: variation in salmon bycatch over time and space. Am. Fish. Soc. Symp. 70:827–850. Towell, R.G., R.R. Ream, and A.E. York. 2006. Decline in northern fur seal (Callorhinus ursinus) pup production on the Pribilof Islands. Mar. Mam. Sci. 22:486–491. Ulanowicz, R.E. 1999. Life after Newton: an ecological metaphysic. BioSystems 50:127–142. US Commission on Ocean Policy. 2004. An Ocean Blueprint for the 21st Century. Final Report. Washington, DC: US Commission on Ocean Policy. Wilderbuer, T.K., A.B. Hollowed, W.J. Ingraham, Jr. et al. 2002. Flatfish recruitment response to decadal climatic variability and ocean conditions in the eastern Bering Sea. Progr. Oceanogr. 55:235–247. Wilson, C.D., A.B. Hollowed, M. Shima, P. Walline, and S. Stienessen. 2003. Interactions between commercial fishing and walleye pollock. Alaska Fish. Res. Bull. 10:61–77. Witherell, D. 2004. Report from the North Pacific Fishery Management Council. In Witherell, D. (ed.), Managing Our Nation’s Fisheries: Past, Present, and Future. Proceedings of a Conference on Fisheries Management in the United States Held in Washington, DC, November, 2003. Anchorage, AK: Fishery Management Council, pp. 129–150. Witherell, D. and C. Pautzke. 1997. A brief history of bycatch management measures for eastern Bering Sea groundfish fisheries. U.S. Mar. Fish. Rev. 59:15–22. Witherell, D. and D. Woodby. 2005. Application of marine protected areas for sustainable production and marine biodiversity off Alaska. U.S. Mar. Fish. Rev. 67:1–28. Witherell, D., C. Pautzke, and D. Fluharty. 2000. An ecosystem-based approach for Alaska groundfish fisheries. ICES J. Mar. Sci. 57:771–777. Zheng, J., G.H. Kruse, and D.R. Ackley. 2001. Spatial distribution and recruitment patterns of snow crabs in the eastern Bering Sea. In Kruse, G.H., Bez, N., Booth, A. et al. (eds.), Spatial Processes and Management of Marine Populations. Fairbanks, AK: Alaska Sea Grant Report No. AK-SG-01–02, pp. 233–255. Zwanenburg, K.C.T. 2000. The effects of fishing on demersal fish communities of the Scotian shelf. ICES J. Mar. Sci. 57:503–509.

4

A pragmatic approach for ecosystem-based fisheries assessment and management: a Korean marine ranch ecosystem chang ik zhang and suam kim

Abstract An integrated fisheries risk assessment method has been developed recently for assessing fisheries at the ecosystem level. It makes use of objectives, indicators, and reference points to assess the states of species, fisheries, and ecosystems. This pragmatic method for assessing fisheries at the ecosystem level involves a two-tier system to accommodate the quantity and quality of the available data. A number of indicators were selected for both tiers to assess systems for three management objectives, namely, sustainability, biodiversity, and habitat quality. Nested risk indices were developed to assess ecosystem status at the management unit level. The complexity and usefulness of the method were demonstrated by applying it to the “Tongyeong marine ranch” in Korea. It is hoped that assessment and management at this level will prevent significant and potentially irreversible changes in marine ecosystems caused by fishing, and aid efforts to recover depleted fish stocks in Korean marine ecosystems. Introduction The basic and inter-related problems of current world fisheries include the threat of over-exploitation of resources, over-capitalization or ­over-expansion

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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A Korean marine ranch ecosystem of fishing fleets, and the negative consequences of fisheries on the survival of marine life and associated habitats. The diverse, dynamic, and complex nature of fishery management problems necessitates the development of a process that accounts for more human interactions, as well as improved definition of biological indicators that can be applied. Resource managers, researchers, user groups, and other interested parties must be involved in the process to minimize conflict and maximize commitment to sustainable management (Zhang and Marasco 2003). Based on conventional assessments, the world’s oceans are at or near maximum sustainable fishery yields. Based on comparisons with other species (see Chapter 11) most or all of the world’s fish stocks are extremely overfished. This predicament is getting worse with time; conventional measures indicate that the number of over-exploited stocks increased by 2.5 times between 1980 and 1990 (Alverson and Larkin 1994). Much of the global harvest is accomplished by fishing for species at progressively lower trophic levels (Pauly et al. 1998). The prospect of increasing total sustainable yield is unlikely (Pauly and Christensen 1995). Under these circumstances, concern is growing over the effects of fishing on the world’s ecosystems. Fisheries are managed within a setting where decision-makers lack full information on, for example, fish population dynamics (see Chapter 6), interactions among species, the effects of environmental factors, and the impacts of human activity on fish and their ecosystems. Fisheries scientists and managers of our use of marine living resources have started recognizing the necessity of a paradigm shift in fisheries management from traditional single-species approaches to ecosystem-based approaches in recent years. Movement has occurred towards ecosystem-based fisheries management as a common theme in fishery management worldwide (Food and Agriculture Organization 2003, 2007, Garcia et al. 2003, Anonymous 20061). Basically, traditional single-species approaches consider only four population factors: recruitment, growth, natural mortality, and the population effects of fishing. The environment is considered relatively constant. In contrast, ecosystem-based approaches acknowledge the complexity of ecological interactions among related animals at various levels (Fig. 4.1). The stocks exploited by fisheries are parts of marine ecosystems where many species feed on one another. Therefore, sustainable fisheries must be based on a holistic view, such as the complexity represented in Fig. 4.1. Taking an ecosystem view includes the use of other managerial tools beyond fisheries regulation. Such additional tools include measures to enhance stocks and their productivity, accounting for the physical structure of their environment, and the establishment of marine protected areas. www.afma.gov.au/information/publications/education/pdfs/fs22_ebfmpdf

1

C.I. Zhang and S. Kim (a) Ecosystem Environment (E)

Recruitment (R) Growth (G)

Natural mortality (M) Fishing (F)

Fish Stock (S)

(b) Ecosystem Biotic environmental components, Abiotic environmental variables (climate changes, pollution, etc.)

Multi-species group Population interaction

Group interaction

Multi-species group

Population Population (R,G,M,F)

Fig. 4.1.  A schematic diagram of fish resources in a marine ecosystem. (a) Factors which control the abundance of a fish stock, and (b) Interactions of organisms with biotic and abiotic environments in a marine ecosystem (modified from Zhang 2002).

Many fisheries resources in Korean waters have been depleted due to overharvesting, marine environmental degradation, and a variety of unknown factors. Population biomass, annual catch, and catch per unit of effort for major fisheries resources have shown declining patterns. Moreover, some ecological indices have exhibited continuous declines, indicating changes in quantity as well as quality of fisheries resources. A variety of projects are underway to recover depleted fish resources in Korea, but these projects are operated separately, not systematically, nor consistently. The Korean government, therefore,

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A Korean marine ranch ecosystem has established a “Vision for Korean Fisheries,” with the objective of maintaining abundant and healthy marine ecosystems and, to the extent possible, prosperous fishing villages (MOMAF 2006). Four major goals for achieving this vision were identified. The first was rebuilding fishery resources based on an ecosystem approach. The other goals were: modifying fishery methodology, preventing harmful and illegal fishing activities, and improving marine environmental quality. This vision statement builds upon the “Act on the Conservation and Management of Marine Ecosystems” that came into force in 2007. It focused on the sustainable utilization of marine biological resources, protection of marine ecosystems, and conservation of marine biodiversity. Conventional management with its focus on managing target species, rather than on managing for true sustainability in the use of each species within its ecological context, has not been effective. Therefore, there is much interest in using an ecosystem-based approach in the assessment and management of fisheries in Korea. The purpose of this chapter is to introduce a pragmatic ecosystem-based fisheries assessment approach, and to document its application to a Korean marine ecosystem (henceforth this ecosystem and the management involved will be referred to as a “ranch”). Finally, we discuss current issues we face in management and suggest plausible ways to enhance systems involving ecosystem-based fisheries assessment and management in Korea. Background and history of ecosystem-based fisheries management Recognition of uncertainty and its potential consequences led to the adoption of the precautionary approach in the UNCED Rio Declaration (Principle 15), the 1995 UN Agreement on Straddling and Highly Migratory Fish Stocks, and the 1995 FAO Code of Conduct for Responsible Fisheries. The precautionary approach is focused on reducing the likelihood of fisheries having adverse impacts on marine resources and host ecosystems (FAO 1995). The World Summit on Sustainable Development (WSSD) recommended implementation of the ecosystem approach by 2010 (UN 2002). The consequences of overfishing are expressed in social, economic, cultural, and ecological changes. However, the ecological consequences of overfishing are often undocumented and may be poorly known or overlooked. The concept of ecosystem-based management of fisheries emerged in the 1982 United Nations Convention on the Law of the Sea. Agreements made at the 1992 United Nations Conference on Environment and Development refer, more specifically, to the ecosystem approach. This feature was carried forward by the 1995 United Nations Fish Stocks Agreement and the 1995 FAO Code of

C.I. Zhang and S. Kim Conduct for Responsible Fisheries. In 1999 the International Council for the Exploration of the Sea (ICES) convened a Symposium on the Effects of Fishing. It was agreed that the symposium contributed significantly to the understanding of the concept and provided some practical guidance on the application of the ecosystem approach. As the twenty-first century unfolds, the desire to move towards ecosystembased fisheries management is increasing. In the northeast Atlantic a scientific meeting was held in 2004 to determine how ICES would introduce an ecosystem approach in its activities (Anonymous 2004). The North Pacific Marine Science Organization (PICES) started discussing ecosystem-based management by establishing a study group in 2003. Further, a working group on ecosystembased management science and its application in North Pacific countries was created in 2005. In 2002 the World Summit on Sustainable Development recommended implementation of the ecosystem approach by 2010 (UN 2002). The Australian Fisheries Management Authority undertook an assessment of the risks that fisheries pose to the ecological sustainability of the marine environment of major Commonwealth fisheries (CSIRO 2005). Canada issued a policy in 2002 to provide guidance on the integrated management of marine fishery resources.2 As the result of concerns regarding the sustainability of marine ecosystems and the depletion of many fish species, the United States Commission on Ocean Policy recommended that the USA move towards ecosystem-based fisheries management in 2004.3 In view of these efforts, there have been a variety of studies stressing the importance of the implementation of the ecosystembased approach to fisheries in Korea (Zhang 2002, Huh and Zhang 2005, Zhang 2006). The desire to maintain abundant and healthy marine ecosystems led the Korean government to initiate actions to rebuild fishery resources based on an ecosystem approach (Zhang et al. 2009). Marine ecosystems and current fisheries management in Korea Three seas comprise Korean waters: the Yellow Sea, the East China Sea, and the East Sea (also called the Japan/East Sea). The Yellow Sea and the East Sea are semi-enclosed seas confined by the land masses of China–Korea and Japan– Korea–Russia, respectively. Major fisheries resources in the seas are straddling stocks migrating among the waters of the four countries. Most fish stocks are currently known to be over-exploited, and some stocks are depleted due to See www.oag-bvg.gc.ca/domino/reports.nsf/html/c2005090ce.html See www.oceancommission.gov/documents/full_color_rpt/welcome.html

2 3

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158

A Korean marine ranch ecosystem

Fig. 4.2.  Three management systems in a comprehensive ecosystem-based integrated fisheries management approach: the offshore large ecosystem management system, the coasted and inshore self-regulatory ecosystem management system, and the marine ranching ecosystem management system.

the increase in fishing intensity and over-capitalization of fishing fleets. Other probable reasons for the depletion are land development and coastal pollution, which destroy spawning and nursery grounds along the coastal regions. Identification of the ecosystem as a unit

The Korean government is currently developing a comprehensive ecosystem-based integrated fisheries management framework (MOMAF 2007b). The framework has what may be referred to as three management systems: the offshore large ecosystem management system, the coastal and inshore selfregulatory ecosystem management system, and the marine ranching ecosystem management system (Fig. 4.2). This management framework eventually will be designed for the enhancement and efficient management of fisheries in their use of resources, and thus it requires an in-depth understanding of the ecological interactions of major species in their relationships with predators,

C.I. Zhang and S. Kim competitors and prey species, the effects of climate on fish ecology, the complex interactions between fishes and their habitats, and the effects of fishing on fish stocks and their ecosystems. The offshore large ecosystem management system is designed to manage the use of resources by fisheries in offshore waters of Korea (Fig. 4.2). In this system the fishery is mainly composed of medium- or large-sized boats. The management of these fisheries would be implemented by the central government or the international regional management body. In this system, most major target species are transboundary species, so the assessment requires all the data of related fisheries and scientific surveys of all the adjacent nations. The Korean government encourages fishermen to establish a “Self-regulatory management community” in order to prevent illegal fishing operations, to enhance and protect their fishing grounds, and to jointly manage their fishery resources for themselves. As of 2007 a total of 579 units of the communities were established within this program (Fig. 4.2). The coastal and inshore self-regulatory community management system is designed to manage fisheries in inshore waters of Korea. The fishing patterns of inshore waters are diverse, which include boat fisheries, shellfish or seaweed culture fisheries, village cooperative fisheries, and diver fisheries. The management of these fisheries is to be implemented by the local self-regulatory communities, based on an ecosystem-based precautionary approach. However, assessments of the present status of fisheries resources in this inshore system indicate that they are depleted or over-exploited. Therefore, in addition to managing the fisheries and conserving the resources, management to enhance depleted resources and restore suitable environmental conditions within the ecosystem are essential. The central government, within this plan, will provide local communities with the skills and tools for managing their fishery and enhancing the related resources and ecosystems. The Korean government started a pilot marine ranching program to enhance marine fisheries resources, to protect and recover marine environments and fish habitats in the Tongyoung marine ranch in 1998 (Zhang et al. 2003). The government is now extending this program to four other places in Korea (Fig. 4.2). In this system, only limited fishing gear types, such as hooking or angling, are permitted to protect the ranching ecosystem. In this system, artificial reefs are created, larvae or juveniles of some resource organisms are released, and seaweed beds are constructed to restore fish habitats, in order to increase the survival and recruitment of fish stocks. Alternatively, physical structures are constructed to enhance the productivity of the ecosystem. The management of these fisheries is implemented by the local ranching community, with the ecosystem-based precautionary concept. To effectively implement proper

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A Korean marine ranch ecosystem management, specific management plans that include detailed management objectives and associated indicators and reference points have been developing for each management system, based upon a sound scientific knowledge. Current status of fish stocks and management measures

There are more than 250 exploited species harvested by about 80 000 fishing vessels using 37 (17 for coastal and 20 for offshore) gear types (Zhang and Marasco 2003). Although knowledge of the status of many species harvested in commercial fisheries is limited, stocks are generally considered to be declining. Several commercially valuable species, such as small yellow croaker (Pseudosciaena polyactis), white croaker (Argyrosomus argentatus), red sea bream (Pagrus major), sharp-toothed eel (Muraenesox cinereus), and some other demersal fish species are considered to be heavily exploited or even depleted. Zhang and Lee (2004) also indicated that the quality of fisheries catches has degraded, showing clear evidence of declining trophic levels in Korean marine ecosystems. For a long time Korea has relied on traditional control devices to manage its fisheries. The devices used include mesh size restriction, minimum size of fish limits, closed areas/seasons, boat licenses, and gear limitations. A number of difficulties are associated with traditional control devices. For example, mesh and minimum landing size regulations were adopted to avoid the dangers of harvesting fish before they reach full maturity. Such measures alone, however, have their problems. For example, they did not prevent fishing effort from increasing, since the number and power of vessels entering were not limited (MOMAF 2007a). Furthermore, size limitations do not address the genetic impact of such selectivity. In addition, regulations to increase the minimum mesh size further were resisted by fishermen. Immediate economic pressures stemming from short-term losses resulted in the discounting of future benefits likely to be realized from the introduction of such measures. Closed areas were implemented to allow stocks to reproduce and grow undisturbed by reducing fishing mortality. However, adequate funds for monitoring were not available. After ratifying the UN Convention on the Law of the Sea (UNCLOS) in 1996, the Korean government adopted a Total Allowable Catch (TAC)-based fisheries management system. This system was implemented on January 1, 1999. Currently, TAC-based management applies to ten species (common mackerel, Scomber japonicus; Jack mackerel, Trachurus japonicus; Pacific sardine, Sardinops melanostictus; queen crab, Hypothalassia armata; snow crab, Chionoecetes opilio; blue crab, Portunus trituberculatus; hen cockle, Mactra chinensis; spiny top shell, Turbo cornutus; pen shell, Atrina pectinata; common squid, Todarodes pacificus) for several related gear types (large purse seines, large offshore pots, diver fishery,

C.I. Zhang and S. Kim offshore gillnets, and community common fishery) operating under the system. The TAC-based management system has also some technical problems in the estimation of acceptable biological catch, monitoring, and enforcement. The system relies on assessments of fisheries resources based on traditional single-species approaches, rather than ecosystem-based approaches. Under this critical set of circumstances, the Korean government is now seriously considering the development of an ecosystem-based approach to fisheries management (MOMAF 2007a). Ecosystem-based fisheries assessment method An integrated fisheries risk assessment method was developed for the assessment of fisheries resources (Zhang et al. 2009). This method is a twotier analytical method:  Tier 1 is designed for data-rich situations, while Tier 2 applies to data-poor situations. This assessment method is composed of the following steps. The selection of the target management unit, i.e., ecosystem, fishery, or individual species, represents the first step. Once the management unit is selected, stakeholders and experts select management objectives. In the application for Korean fisheries, three objectives were utilized: sustainability, biodiversity, and habitat quality. Next, ecosystem indicators, along with target and limit reference points, are identified for each selected objective. The following considerations are used in the indicator selection process:  (1) ease of understanding by users, (2) susceptibility to influence through management of human activities, and (3) measurability, using existing data or currently monitored information. Indicators were identified for both data-rich (Tier 1) and data-poor (Tier 2) situations (Tables 4.1, 4.2, and 4.3). Relative weights for each indicator were obtained by conducting a series of expert workshops, considering:  (1) the importance for achieving the objectives, (2) scientific basis for estimating indicators and reference points, and (3) availability of data and information. Each weight is represented by one to three asterisks. For instance, “Biomass” was given a weight of three (represented by the number of asterisks in Table 4.1), and “Genetic structure” a weight of one under the sustainability objectives. The same indicators across Tier 1 and Tier 2 assessments can be weighted differently, depending on the situation. Both target and limit reference points for each indicator were established. The target reference point in this application corresponds to a state of each indicator that is considered desirable, while the limit reference point is defined as the limit beyond which the state of each indicator is not considered desirable (Tables 4.4 and 4.5). Species were assigned a status for each indicator to

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A Korean marine ranch ecosystem Table 4.1. Attributes and indicators for the sustainability objectives used in the ecosystem-based fisheries assessment approach in Korea. Tier 1 assessment uses quantitative analysis of rich scientific data, whereas Tier 2 assessment involves qualitative or semi-quantitative analysis of data-poor situations. The number of asterisks (*) reflects the subjective relative importance of the indicator, and is used to calculate objective risk indices (Zhang et al. 2009). Indicator Attribute

Tier 1

Biomass

Biomass

***

or CPUE

**

Fishing mortality or catch

Fishing intensity

Weight Tier 2

Weight

CPUE1

***

**

Restricted access

***

**

Fishery monitoring and

**

sampling Fishing method

**

Precautionary approach

**

and quality of stock assessment Size at first capture

Age at first capture

*

Size at entry

Habitat size

Habitat size

*

n/a

Community structure

FIB index2

*

n/a

Reproductive potential

FRP index 3

*

n/a

Productivity

Total production of

*

n/a

***

ecosystem4 Life history

n/a

Maximum age or age at

  characteristics

**

maturity Adult and juvenile habitat

*

overlap Management

n/a

Management plan for

**

fishery Recovery

n/a

Management of IUU5 fishery

*

Recovery plan and period

*

for depleted stocks Genetic structure

No. of spawning populations

*

Documentation of population structure

1

CPUE, Catch per unit effort.

2

FIB index (Fishery in balance): FIB = log( Yi ⋅ (1 / TE)TL i ) − log(Y0 ⋅ (1 / TE)TL0 ) (Pauly et al. 2000).

3

FRP index (Fish reproduction potential): FRP = log

4

Total production of the ecosystem (unit: mt/km2/year) (Christensen and Pauly 1992).

5

IUU, Illegal, unregulated, and unreported fishing. n/a denotes not applicable.

Yi ⋅ MRi Y ⋅ MR0 (Lee et al. 2007). − log 0 q ⋅ fi q ⋅ f0

*

C.I. Zhang and S. Kim Table 4.2. Attributes and indicators for the biodiversity objectives used in the ecosystembased fisheries assessment approach in Korea. Tier 1 assessment uses quantitative analysis of rich scientific data, whereas Tier 2 assessment involves qualitative or semiquantitative analysis of data-poor situations. The number of asterisks (*) reflects the subjective relative importance of the indicator, and is used to calculate objective risk indices (Zhang et al. 2009). Indicator Attribute

Tier 1

Weight Tier 2

Weight

Total bycatch

Bycatch rate

**

Bycatch

**

Total discards

Discard rate

**

Discard

**

Trophic level

Mean trophic level of

*

n/a

the community1 Diversity

Diversity index2

*

No. of species

**

Integrity of func-

Invasive/traditional

*

Changes in ratio of func-

*

tional groups

species in catch

Gear restrictions

n/a

tional groups in catch Gear restrictions and

and avoidance

avoidance tactics for

tactics

non-target species

**

  Mean trophic level from research surveys (Pauly et al. 1998).

1

N

  Diversity index: DI = −∑ Pj ⋅ ln Pj (Modified from Shannon and Wiener (1963)), where

2

j =1

N is the total number of individuals, Pj is proportion of each species. n/a denotes not applicable.

denote risk. If the state of the indicator was between the virgin state and the target reference point, then a score of “0” was assigned (Tables 4.4 and 4.5). When it was between the target and limit reference point, the risk score is calculated for each indicator as RS i = RS ma x

(

I target − I i I target − I limit

)

(1)

where RSi is the risk score for indicator i, RSmax is the maximum risk score “2,” Ii, Itarget, and Ilimit are estimated, target, and limit values for indicator i, respectively (Table 4.4). If it was beyond the limit reference point, then a score of “2” was assigned. Using the steps described above, information was produced that allowed for the construction of four different but nested indices useful for characterizing the status of the system (Fig. 4.3). These indices are the objectives risk index (ORI), species risk index (SRI), fishery risk index (FRI), and ecosystem risk index (ERI).

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A Korean marine ranch ecosystem Table 4.3. Attributes and indicators for the habitat quality objectives used in the ecosystem-based fisheries assessment approach in Korea. Tier 1 assessment uses quantitative analysis of rich scientific data, whereas Tier 2 assessment involves qualitative or semi-quantitative analysis of data-poor situations. The number of asterisks (*) reflects the subjective relative importance of the indicator, and is used to calculate objective risk indices (Zhang et al. 2009). Indicator Attribute

Tier 1

Habitat damage

Critical habitat damage

Weight **

Tier 2 Influence of fishing gear

rate Pollution rate of

Weight ***

on benthic habitat *

Pollution of habitat

**

Lost fishing gear

*

Lost fishing gear

**

Discarded wastes

Discarded wastes

*

Discarded wastes

*

Habitat protection

Areas of prohibited

*

Gear restrictions or

spawning and nursery grounds

fishing Habitat recovery

No. of artificial reefs

***

habitat closure *

Recovery of physically

*

damaged habitat Area of artificial sea-

*

Recovery of biologically

weed bed

*

damaged habitat

The objectives risk index (ORI) was defined as n

ORI =

∑ RS W i

i =0

i

n

∑W



(2)

i

i =1

where RSi is the risk score for indicator i (Tables 4.1–4.3), given the associated reference points (Tables 4.4 and 4.5). Wi is the weighting factor for indicator i, which is represented by the number described above (number of asterisks), and n is the number of indicators. For each species, objectives risk indices (ORI) are calculated for each objective, i.e., ORIS for sustainability, ORIB for biodiversity, ORIH for habitat quality. An overall species risk index (SRI) was calculated for each species and defined as the weighted sum of the objectives risk indices, SRI = λSORIS + λBORIB + λHORIH

(3)

where “λi” is the weighting factor for the ith ORI. The sum of λi s is 1.0. λi can vary with target species and was determined from the local experts’ opinions based on behavioral, ecological, and other fishery-related factors for the given ecosystem. The fishery risk index (FRI) is the weighted (in biomass of species)

165

Habitat

Biodiversity

Biomass

Sustainability

Invasive/traditional species in

Integrity of functional

Habitat damage

nursery ground (PG/G)14

Pollution rate of spawning and

(DH/H)13

Critical habitat damage rate

catch (I/T) 12

Diversity index11 (DI)

Diversity

group

Mean trophic level10 (TL)

Trophic level

No. of spawning populations (SP)

Genetic structure

Discard rate (D/C)9

Total production of ecosystem7 (P)

Productivity

Total discards

FRP index6

Reproductive potential

Bycatch rate (BC/C)8

FIB index5

Community structure

Total bycatch

Habitat size (H)

Habitat size

(PG/G) ≤ (PG/G)target

(DH/H) ≤ (DH/H)target

0.05(I/T)target

δ(I/T) ≤ 0.05(I/T)target

1.1(PG/G)target

(PG/G)target < (PG/G) ≤

1.1(DH/H)target

(DH/H)target < (DH/H) ≤

DI ≥ 0.9DItarget

TLtarget > TL ≥ TLlimit

1.05(D/C)target

(D/C)target < (D/C) ≤

1.05(BC/C)

(BC/C)target < (BC/C) ≤

SPtarget > SP ≥ 0.9SPtarget

Ptarget > P ≥ 0.9Ptarget

0.9FRPtarget

FRPtarget > FRP ≥

DI ≥ DItarget

TL ≥ TLtarget

(D/C) ≤ (D/C)target

(BC/C) ≤ (BC/C)target

SP ≥ SPtarget

P ≥ Ptarget

FRP ≥ FRPtarget

FIBtarget > FIB ≥ FIBlimit

Htarget > H ≥ 0.8Htarget

H ≥ Htarget FIB ≥ FIBtarget

ttarget > t ≥ 0.9ttarget

ABC < C ≤ MSY

F40% < F ≤ FMSY

CPUEMSY3

CPUEABC > CPUE ≥

B40% > B ≥ B35%

Between target and limit

t ≥ ttarget4

C ≤ ABC

Age at first capture (t)

F ≤ F40%

or Catch (C)

CPUE ≥ CPUEABC2

Fishing mortality (F)

B ≥ B40%

or CPUE1

Better than target

Biomass (B)

Indicator

Size at first capture

Fishing intensity

Attribute

Objective

Indicator status

(PG/G) > 1.1(PG/G)target

(DH/H) > 1.1(DH/H)target

δ(I/T) > 0.10(I/T)target

DI < 0.9DItarget

TL < 0.9TLlimit

(D/C)>1.05(D/C)target

(BC/C) > 1.05(BC/C)target

SP < 0.9SPtarget

P < 0.9Ptarget

FRP < 0.9FRPtarget

FIB > FIBlimit

H > 0.8Htarget

t < 0.9ttarget

C > MSY

F > FMSY

CPUE < CPUEMSY

B < B35%

Beyond limit

Table 4.4. Objectives, indicators, and status of indicator relative to target and limit reference points for Tier 1 ecosystem-based fisheries assessment approach (Zhang et al. 2009).

166 |N−Ntarget| ≤ 0.05Ntarget |A−Atarget| ≤ 0.05Atarget

No. of artificial reefs (N) Area of artificial seaweed bed (A)

Habitat recovery

MSY, Maximum sustainable yield.

t target: optimum age at first capture from Beverton and Holt yield-per-recruit analysis.

4

D is discards and C is total catch.

j

j

0.10Atarget

j

0.05Atarget < |A−Atarget| ≤

0.10Ntarget

0.05Ntarget < |N−Ntarget| ≤

PAtarget > PA ≥ 0.9PAtarget

1.1DWtarget

DWtarget < DW ≤

FRtarget < FR ≤ 1.1FRtarget

Between target and limit

Indicator status

|A−Atarget| > 0.10Atarget

|N−Ntarget| > 0.10Ntarget

PA < 0.9PAtarget

DW > 1.1DWtarget

FR > 1.1FRtarget

Beyond limit

I is catch of invasive species and T is catch of traditional species.

DH is damaged critical habitat area and H is total habitat area.

PG is polluted spawning and nursery ground and G is total spawning and nursery ground.

12

13

14

n/a denotes not applicable.

j =1

Diversity index: DI = −

11

∑ P ⋅ ln P (Modified from Shannon and Wiener (1963)), where N is the total number of individuals, P is proportion of each species.

N

Mean trophic level from research surveys (Pauly et al. 1998).

BC is bycatch and C is total catch.

9

10

Total production of ecosystem (unit: mt/km2/year) (Christensen and Pauly 1992).

Yi ⋅ MRi Y ⋅ MR0 (Lee et al. 2007). − log 0 q ⋅ fi q ⋅ f0

8

FRP index (Fish reproduction potential): FRP = log

7

6

FIB index (Fishery in balance): FIB = log( Yi ⋅ (1 / TE)TLi ) − log(Y0 ⋅ (1 / TE)TL0 ) (Pauly et al. 2000).

ABC, Acceptable biological catch.

3

5

CPUE, Catch per unit effort.

2

PA ≥ PAtarget

Prohibited area from fishing (PA)

Habitat protection

DW ≤ DWtarget

FR ≤ FRtarget

Lost fishing gear (Frequency, FR) Discarded wastes (DW)

Better than target

Indicator

Discarded wastes

Attribute

1

Objective

Table 4.4. (cont.)

167

inactive)

exists (≤30% of licenses

ods and some geographical

characteristics

maturity with juvenile

Adult habitat overlap

Low

respectively)

Low risk (25, >10,

at maturity unknown

catch immature or size

Greater than 20% of the

adopted

ary approach is not

ment, and precaution-

Inadequate stock assess-

evaluated

and patterns are not

Main fishing methods

sampling

Negligible monitoring or

Open access

available

CPUE data are not

Beyond limit

Table 4.5. Objectives, indicators, and status of indicator relative to target and limit reference points for Tier 2 ecosystem-based fisheries assessment approach (Zhang et al. 2009).

168

Biodiversity

Objective

community

Minor change in relative abundance of species in

Changes in ratio of

Integrity of func-

species have not occurred

Decreases in the number of

ant species missing locally

measurably and some import-

important species

with significant loss of

drastically altered

Ecosystem function

in several communities Ecosystem function altered

of species have occurred communities

Decreases in the number

trol of discards

Little monitoring or con-

trol of bycatches

Little monitoring or con-

or sharply declining

populations unknown

Number of spawning

are not operational

Recovery plan and period

Little regulation exists

operational

Management plan is not

Beyond limit

cies have occurred in some

Decreases in the number of spe-

fisheries

fisheries

catch

No. of species

Diversity

and controlled for some

and controlled for all

Discards are being monitored

fisheries

Discards are being monitored

and controlled for some

fisheries

Bycatches are being monitored

tored and controlled for all

Bycatches are being moni-

tions known and declining

functional groups in

Discard

Total discards

tional group

Bycatch

Number of spawning popula-

tions known and constant

reviewed

operational, but irregularly

Recovery plan and period are

Number of spawning popula-

reviewed

stocks Population structure

operational and annually

Recovery plan and period are

regulated

Some illegal fisheries exist

reviewed

annually All fisheries are legal and

ational, but irregularly

Management plan is oper-

Between target and limit

Indicator status

ational and reviewed

Management plan is oper-

Better than target

period for depleted

Recovery plan and

fishery

Management of IUU2

Total bycatch

Genetic structure

Recovery

Management plan for

Management fishery

Indicator

Attribute

Table 4.5. (cont.)

169

management plan in place

damaged habitat

CPUE, Catch per unit effort.

IUU, Illegal, unregulated, and unreported fishing.

damaged habitat

damaged habitat

recovered damaged habitat

Seaweed beds have partially

recovered damaged habitat

Artificial reefs have partially

planned

Artificial reefs have recovered

opment or habitat closures

ical habitat

Habitat avoidance gear in devel-

that avoid damage to crit-

Gear restrictions or closures

Some waste retained

ment plan in place No waste discarded

operations are recorded and

Recovery of biologically Seaweed beds have recovered

damaged habitat

Recovery of physically

2

Habitat recovery

habitat closure

Gear restrictions or

Habitat

protection

Discarded wastes

of gear lost during fishing

Type, quantity, and location

No recovery

No recovery

protection

or critical habitat

No gear restrictions

tity of waste retained

Little or unknown quan-

management plan

Little information and no

place

ing or recovery plan in

gear types lost and manage-

Sufficient knowledge of type,

Lost fishing gear

Polluted, but no monitor-

recovery plan in place

Serious impact (dredges)

avoidance tactics

Few gear restrictions or

Polluted, but monitoring or

fishing gear)

Identifiable impact (bottom

progress

tions and avoidance tactics in

Development of gear restric-

quantity, and location of

Monitored and unpolluted

surface fishing gears)

Negligible impact (mid-water,

ance tactics operational

Gear restrictions and avoid-

Pollution of habitat

on benthic habitat

Impact of fishing gear

Discarded wastes

Habitat damage

non-target species

tactics

1

Habitat

avoidance tactics for

Gear restrictions and

and avoidance

Gear restrictions

A Korean marine ranch ecosystem Ecosystem Fishery A Species 1 Objectives S

...

ORI

Objectives B

...

ORI

Objectives H

...

ORI

Objectives S

...

ORI

Objectives B

...

ORI

Objectives H

...

ORI

SRI

Species 2 FRI

...

SRI

Fishery B ERI ... MSI

Species 1 Objectives S

...

ORI

Objectives B

...

ORI

Objectives H

...

ORI

Objectives S

...

ORI

Objectives B

...

ORI

Objectives H

...

ORI

SRI

Species 2 FRI

...

SRI

...

170

Fig. 4.3.  Nested structure of risk indices used in the ecosystem-based fisheries assessment approach. ORI, objectives risk index; SRI, species risk index; FRI, fishery risk index; ERI, ecosystem risk index; MSI, management status index (Zhang et al. 2009).

average risk index for exploited species in each fishery. The ecosystem risk index (ERI) is defined as the weighted (in catch of each fishery) average of the fishery risk indices in an ecosystem. Objectives risk indices can be examined to determine the effectiveness of management in promoting sustainability, biodiversity, and habitat quality.

C.I. Zhang and S. Kim

Fig. 4.4.  Risk assessment diagram for ORI (objectives risk index) and SRI (species risk index) for the ecosystem-based fisheries assessment approach. Biodiversity– Sustainability plane at upper right, Sustainability–Habitat quality plane at upper left, Habitat quality–Biodiversity plane at lower left. The SRI scale is on the ­diagonal at lower right (Zhang et al. 2009).

A risk assessment diagram (Fig. 4.4) was created to facilitate consideration of the constructed indices. The four axes of the square represent each ORI (i.e., biodiversity, sustainability, habitat quality, and again biodiversity). The additional axis for biodiversity is designed to create three planes, namely, the sustainability–biodiversity plane (S–B plane) in the upper right quadrant, the habitat quality–sustainability plane (H–S plane) in the upper left quadrant, and the biodiversity–habitat quality plane (B–H plane) in the lower left quadrant. Further, a diagonal bisecting line in the lower right quadrant allows the plotting of the SRI value for each species. Therefore, four coordinates per species are labeled on the diagram. Each ORI’s score is specified to fall in the range 0 to 2. Each zone is given exactly the same area, with boundary values of 1.16 and 1.63. The probability of any random point falling anywhere within each zone of the square is the same. Codes are given to each square zone to facilitate the evaluation process. The inner zone is defined to range from 0 to 1.16, the middle zone from 1.16 to 1.63, and the outer zone from 1.63 to 2.00. So, three points are plotted in the upper right, upper left, and lower left quadrants for each species, corresponding to the three coordinate pairs. If

171

172

A Korean marine ranch ecosystem

Fig. 4.5.  Diagram showing objectives risk indices and species risk indices for the Tongyeong marine ranching area using ecosystem-based Tier 1 and Tier 2 fisheries assessment in (a) 1998 and (b) 2006. A, jacopever rockfish; B, black rockfish; C, red sea bream; D, common seabass; E, black sea bream; F, yellowtail; G, rock bream (Zhang et al. 2009).

any one of the ORI scores for any of the three two-component pairs falls into the outer zone, the resulting coordinate of the pair is assigned to the outer zone (Fig. 4.4). The three zones represent the degree of risk in the ecosystem. Any risk indicator falling into the outer zone is considered to be in need of special attention.

C.I. Zhang and S. Kim Identification of unit ecosystem/fishery/species Assessment of ecosystem/fishery/species based on two-tier system

Tier 1 assessment or Low risk/Tier 2 assessment

Medium or high risk/ Tier 2 assessment Tier 1 assessment

Establishment of management methods (Law/Act, policy, regulations, etc.) Enforcement of management methods and Evaluation by feed-back system Revision of management methods and enforcement Fig. 4.6.  A flowchart showing the procedure for the ecosystem-based fishery management system in Korea.

Changes in the status of any of the indices can be identified by examining the following management status indices (MSI). MSI O =

OR I t − ORI t OR I t

(4)

where MSIO measures changes in objectives, species, fishery, and ecosystem management status indices. The t and tʹ represent the initial and subsequent time periods being compared, respectively. Differences in risk indices can be statistically examined by a Wilcoxon paired-sample non-parametric test (Zar 1999).

Application and management implication: Tongyeong marine ranch In this chapter we demonstrate this method by applying it to Tongyeong marine ranch. The assessment analysis was conducted for each species before (1998) and after (2006) initiation of marine ranching activities. At Tongyeong marine ranch, the target species is jacopever rockfish (Sebastes schlegeli), which is taken in a pole and line fishery. This species was assessed by a Tier 1 analysis, since more scientific data are available and a quantitative stock assessment has been conducted for this species. A Tier 2 analysis was conducted for species taken as bycatch: black rockfish (Sebastes inermis), red sea bream (Pagrus major),

173

174

Black rockfish

2

FRI

Jacopever rockfish

1

Rock bream

Yellowtail

Black seabream

Common seabass

Red seabream

Species

Tier

0.381

0.538

1.101

1.769

Habitat

0.667 2006

1.889

Biodiversity

0.478

0.692

0.667

0.652

0.692

0.667

0.696

0.923

0.444

0.522

0.846

0.667

0.522

0.385

0.444

0.217

0.153

0.364

0.253

2006

1998

1.522

1.769

Habitat Sustainability

1.889

Biodiversity

1.769

Habitat 1.435

1.556

Biodiversity Sustainability

1.391

1.769

Habitat Sustainability

1.778

Biodiversity

1.538

Habitat 1.478

1.667

Biodiversity Sustainability

1.522

1.538

Habitat Sustainability

1.667

Biodiversity

0.750

Habitat 1.391

0.768

Sustainability

1.328

Biodiversity

1998

Sustainability

Objective

ORI

69.57

64.71

68.57

60.87

64.71

54.55

60.87

57.14

50.00

47.83

75.00

64.71

45.00

60.00

65.71

75.00

73.33

84.38

79.60

52.59

80.99

MSIO

***

**

***

***

**

***

***

**

***

***

**

***

***

**

***

***

**

***

**

NS

**

Signific­ance

1.725

1.696

1.570

1.673

1.574

1.531

0.948

1998

64.99

MSIF

0.561

0.670

0.684

0.629

0.678

0.348

0.256

2006

SRI

67.50

60.51

56.43

62.40

56.96

77.23

72.96

MSIS

***

***

***

***

***

***

***

Signific­ance

Table 4.6. Objectives risk index (ORI), species risk index (SRI), fishery risk index (FRI), and management status index (MSI) for the Tongyeong marine ranch using the ecosystem-based Tier 1 and Tier 2 fisheries assessment approach. Statistically significant differences between initial and subsequent MSI indices are denoted by *, **, and ***, corresponding to α = 0.05, 0.01, and 0.001 levels, respectively; NS denotes non-significance (Zhang et al. 2009).

C.I. Zhang and S. Kim common sea bass (Lateolabrax japonicas), yellowtail (Seriola quinqueradiata), and rock bream (Oplegnathus fasciatus), since quantitative data or information were not available for those species. Because scoring is an important step in the proposed assessment system, a few examples are introduced to help understand the process. For example, a target prohibited fishing area of 20 km2 was used in evaluating habitat states for jacopever rockfish (Sebastes schlegeli). Because there was no area of prohibited fishing in 1990, a score with the value of two was assigned to the indicator of prohibited area from fishing. For 2006, a value of zero is assigned because the area is determined to be within the favorable range constrained by the target reference point (Table 4.4). As an example of a Tier 2 assessment for black rockfish (Sebastes inermis), a “fishing method” score of two was assigned for the year 1998 because fishing methods and patterns were not evaluated at that time. All fishing methods and patterns were evaluated and changes monitored in 2006, resulting in a score of zero (Table 4.5). Detailed methods and data used for scoring other Tier 1 and Tier 2 indicators are explained in MOMAF (2007b). Once scores were assigned to all of the indicators, ORIs were calculated for each of the objectives. Objectives risk indices for jacopever rockfish ranged from 0.750 to 1.328 in 1998 (Table 4.6 and Fig. 4.5) and from 0.153 to 0.364 in 2006. Species risk index values were calculated using equation (3) with all λis assumed to be equal (0.33). The estimated SRI values for jacopever rockfish are 0.948 for 1998 and 0.256 for 2006, indicating a reduction in the risk level for this species between the two reference years. A Wilcoxon paired-sample non-parametric test was performed to determine the significance of the difference in ORIs and SRIs (Table 4.6) (Zar 1999). Differences in all risk indices were statistically significant between the two reference years, with the exception of the ORI associated with the biodiversity objectives for jacopever rockfish. Figure 4.6 shows a shift for this Tier 1 species from the middle zone in 1998 to the inner zone in 2006. The diagram for Tier 2 species also shows shifts of all species from the outer or middle zones in 1998 to the inner zone in 2006. The ORI and SRI values for all species in this group were lower for 2006 than for 1998 (Table 4.6). Fishery risk index values calculated using equation (4) are 1.101 for 1998 and 0.381 for 2006, indicating a 65.0% reduction in the risk level for the fishery between the two reference years. The pattern of ERI values is the same as those for FRI values in the Tongyeong marine ranch, because the pole and line fishery is the only one operating. In the procedure for the ecosystem-based fisheries management in Korea, the selection of the focal unit, i.e., ecosystem, fishery, or species, represents

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A Korean marine ranch ecosystem the first step in the application of the proposed approach described above (Fig.  4.6). Once the focal unit is identified, the assessment of the system of concern can proceed. As part of the process, all the available data must be identified and evaluated, and a decision should be made as to the appropriate analysis, either Tier 1 or Tier 2. In the two-tier system, in principle, target species are assigned to Tier 1 while other species are assigned to Tier 2. In the case of species with medium or high risks from a Tier 2 assessment, these species become subject to the Tier 1 assessment (Fig. 4.6). Having completed these steps, the assessment of the system can proceed. Upon completion of the assessment, the identification and implementation of appropriate management measures can begin. It will be useful to trace through the nested system to identify the subject or subjects which resulted in high risk scores. Once this process is completed, necessary corrective actions or enforcement measures can be identified and implemented. The final step in the process is the development of procedures capable of evaluating the effectiveness of management measures and revising the management system as needed. In each step the involvement of stakeholders is desirable to achieve consensus (Fig. 4.6). Discussion and conclusion The assessment method described above has proven to be useful, based on its application to the Tongyeong marine ranch. However, although the method has several advantages, it has some weaknesses. The major weakness is the difficulty of selecting appropriate indicators and reference points. Because some indicators are not fully studied, complicating the identification of reasonable reference points, more comprehensive studies on indicators and reference points are necessary. Continued research into ecosystem processes will enhance our ability to link management actions, indicators, and ecosystem responses. An additional weakness is the lack of explicit treatment of ­socio-economic considerations. Once a causal element has been identified, there is not always a process for automatically determining the appropriate management response that will result in reduced risk. However, awareness of a problem does stimulate a discussion as to how that risk might be reduced. Finally, whereas some indicators reflect an ecosystem response (e.g., fish biomass and Fishery in Balance (FIB) index), others (e.g., habitat protection measures and gear loss) are management actions lacking quantification of realized ecosystem benefits (Zhang et al. 2009). The following steps are suggested as means to implement ecosystem-based management for fisheries (EBMF) based on our experience. The first is the need

C.I. Zhang and S. Kim to define objectives in an ecosystem context. The second is the selection of a suite of ecosystem metrics and indicators. The third is the need to develop appropriate theory, models, and methods at the aggregate and system level to evaluate and assess indicators. The fourth is the need to expand monitoring activities to estimate parameters for applying developed theories or models. The fifth is the need to formalize the management of fisheries in the ecosystem context. Finally, the sixth is predicting and evaluating the effectiveness of management measures and revising the management system. While substantial progress has been made recently, large gaps in the information needed to support ecosystem-based management still exist. Fluharty and Cyr (2001) suggested three research themes required to implement this approach. The three recommended research themes are:  (1) increase understanding of the ecosystem effects of fishing, (2) monitor trends and dynamics of marine ecosystems, and (3) identify alternative governance systems and approaches. More recently, scientific issues in implementing ecosystem-based approaches were suggested to include the need to:  (1) improve predictive capability with regard to climate and human impacts on ecosystems, (2) develop a more explicit definition of ecosystem-based management objectives, (3) develop objective criteria and sensitive indicators to measure the success in achieving desired ecosystem state or condition, and (4) develop a more formalized decision-making framework (Jamieson and Zhang 2005). Ecosystem-based management for fisheries (EBMF) is an approach that reduces potential fishing impacts while at the same time allowing for the extraction of fish resources at levels sustainable for the ecosystem. Actually, forecasting the results of any management action is very important, but difficult because the dynamics of ecosystems are complex and poorly understood. Methods to design and evaluate operational management strategies have advanced considerably during the last decade (Sainsbury et al. 2000, Livingston et al. 2005). Complementing the assessment method described above will be research to develop a forecasting method based on the same concept as the method to evaluate the consequences of various management options ahead of implementing actual management measures. For example, if we reduce the TAC for species A, which risk indices for species B will be mostly affected? Or, as another example, if we release juveniles of a species, will the risk index for biodiversity be reduced? Which species will be most affected? Alternatively, if we want to reduce the risk index of a certain management unit, what will be the best management action? Another research subject would be to develop a tool to link this assessment method with some existing ecosystem models such as Ecosim (Walters et al. 1997). If we simulate risk indices on the changes in fishing mortality, we can directly utilize results

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A Korean marine ranch ecosystem of Ecosim as inputs for calculating risk scores of related species to estimate risk indices. Ecosystem-based management is an important complement to existing fisheries management approaches. Therefore, this approach should move forward now despite current uncertainties about ecosystems and their responses to human actions because the potential benefits of implementation are as large as or greater than the potential risks of inaction (Pikitch et al. 2004). When fishery managers understand the complex ecological and socio-economic envir­ onments in which fish and fisheries exist, they may be able to anticipate the effects that fishery management will have on the ecosystem and the effects that ecosystem change will have on fisheries. Management that takes ecosystem effects into account will require greatly expanded monitoring, improvement in the understanding of behavioral relationships among fishermen, the fish they catch, the predators and prey of the harvested species, and social and economic relationships among various resource users. However, there are many factors that can act to constrain EBMF efforts and its effectiveness. These factors are insufficient resources of money, time, and people, lack of common vision and goals, inter- and intra-agency conflict, lack of ecological knowledge, and lack of public participation. The research strategies for the EBMF should be developed and implemented as international or national interagency programs involving academic as well as government scientists. Because the ecosystem principles apply globally, each country should participate in international programs that further fisheries management objectives. A significant enhancement in resources (e.g., funding, research manpower, and fishery research vessels) will be required if these research themes are to be fulfilled. At the same time strong governmental actions to stop overfishing, protect habitats, and support expanded research and monitoring programs will be necessary to improve an ecosystem-based approach. References Alverson, D.L. and P.A. Larkin. 1994. Fisheries: fisheries science and management. In Voigtlander, C.D. (ed.), The State of the World’s Fishery Resources: Proceedings of the World Fisheries Congress, Plenary Session. New Delhi: Oxford and IBH Publishing, pp. 150–167. Anonymous. 2004. Report of the 13th dialogue meeting: advancing scientific advice for an ecosystem approach to management: collaboration amongst managers, scientists and other stakeholders. ICES Coop. Res. Rep. 267. Christensen, V. and D. Pauly. 1992. ECOPATH II: a software for balancing steady ecosystem models and calculating network characteristics. Ecol. Model. 61:169–185.

C.I. Zhang and S. Kim Commonwealth Scientific and Industrial Research Organization (CSIRO). 2005. Ecological Risk Assessment for Effects of Fishing; Case Study Instructions. (19/9/05) V8. Food and Agriculture Organization (FAO). 1995. Report of the Expert Consultation on Guidelines For Responsible Fisheries Management. FAO Fisheries Report No. 519. Rome: FAO. Food and Agriculture Organization (FAO). 2003. Fisheries Management: 2. The Ecosystem Approach to Fisheries. FAO Technical Guidelines for Responsible Fisheries 4. Suppl. 2. Rome: FAO. Food and Agriculture Organization (FAO). 2007. Models for an Ecosystem Approach to Fisheries. FAO Fisheries Technical Paper 477. Rome: FAO. Fluharty, D. and N. Cyr. 2001. Implementing ecosystem-based management of fisheries in the context of U.S. regional fisheries management: recommendations of the NMFS Ecosystem Principles Advisory Panel. CalCOFI 42:66–73. Garcia, S.M., A. Zerbi, C. Aliaume, T. Do Chi, and G. Lasserre. 2003. The Ecosystem Approach to Fisheries. Issues, Terminology, Principles, Institutional Foundations, Implementation and Outlook. FAO Fisheries Technical Paper 443. Rome: FAO. Huh, H.T. and C.I. Zhang. 2005. Ecosystem-based approaches to management of living marine resources and their environment – a new paradigm for managing fisheries resources. KAST Rev. Mod. Sci. Tech. 1:71–88. Jamieson, G. and C.I. Zhang (eds.). 2005. Report of the Study Group on Ecosystembased Management Science and its Application to the North Pacific. PICES Scientific Report 29. Lee, S.K., J.B. Lee, C.I. Zhang, and D.W. Lee. 2007. Fish reproduction potential indices in the coastal and offshore ecosystems in Korea. J. Kor. Fish. Soc. 40(1):24–30 (in Korean with English abstract). Livingston, P.A., K. Aydin, J. Boldt, J. Ianelli, and J. Jurado-Molina. 2005. A framework for ecosystem impacts assessment using an indicator approach. ICES J. Mar. Sci. 62:592–597. Ministry of Maritime Affairs and Fisheries (MOMAF). 2006. Vision for Korean Fisheries Development for the Next Five Years. Seoul, Korea: MOMAF (in Korean). Ministry of Maritime Affairs and Fisheries (MOMAF). 2007a. Statistical Yearbook of Marine Affairs and Fisheries. Seoul: MOMAF (in Korean). Ministry of Maritime Affairs and Fisheries (MOMAF). 2007b. Studies on the Ecosystembased Resource Management System. Seoul: MOMAF (in Korean with English abstract). Pauly, D. and V. Christensen. 1995. Primary production required to sustain global fisheries. Nature 374:255–257. Pauly, D., V. Christensen, J. Dalsgaard, R. Froese, and F. Torres, Jr. 1998. Fishing down marine food webs. Science 279:860–863. Pauly, D., V. Christensen, and C. Walters. 2000. Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci. 57:687–706. Pikitch, E.K., C. Santora, E.A. Babcock et al. 2004. Ecosystem-based fishery management. Science 305:346–347.

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A Korean marine ranch ecosystem Sainsbury, K.J., A.E. Punt, and A.D.M. Smith. 2000. Design of operational management strategies for achieving fishery ecosystem objectives. ICES J. Mar. Sci. 57:731–741. Shannon, C.E. and W. Wiener. 1963. The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press. United Nations (UN). 2002. Report of the World Summit on Sustainable Development. Geneva: United Nations. Walters, C., V. Chrisrensen, and D. Pauly. 1997. Structuring dynamics models of exploited ecosystems from trophic mass-balance assessment. Rev. Fish Biol. Fish. 7:139–172. Zar, J.H. 1999. Biostatistical Analysis (4th edn.). Upper Saddle River, NJ: Prentice-Hall International. Zhang, C.I. 2002. Prospect of ecosystem-based fisheries resource management. J. Kor. Soc. Fish. Res. 5:73–90. Zhang, C.I. 2006. A study on the ecosystem-based management system for fisheries resource in Korea. J. Kor. Soc. Tech. 42(4):240–258 (in Korean with English abstract). Zhang, C.I. and S.K. Lee. 2004. Trophic levels and fishing intensities in Korean marine ecosystems. J. Kor. Soc. Fish. Res. 6(2):140–152 (in Korean with English abstract). Zhang, C.I. and R.J. Marasco. 2003. New approaches in fisheries assessment and management under the Exclusive Economic Zone regime in Korea. Am. Fish. Soc. Symp. 38:685–693. Zhang, C.I., S.I. Lee, and J.M. Kim. 2003. Ecosystem-based management of fisheries: resources in marine ranching areas. J. Kor. Soc. Fish. Res. 6:71–83. Zhang, C.I., S. Kim, D. Gunderson et al. 2009. An ecosystem-based fisheries assessment approach for Korean fisheries. Fish. Res. 100:26–41.

PA RT I I   ELEMENTS OF IMPORTANCE TO MANAGEMENT

Chapters 5 through 9 introduce a variety of details important to management. It becomes clear that among the details are factors that, collectively, are impossible to account for completely in conventional approaches. This becomes obvious whether such factors involve the management of harvests taken from individual species, species groups, ecosystems, or complete ocean basins. These details involve the implementation of management, governance, and the involvement of stakeholders of various categories. The need to expand our perspective – the demand to expand the boundaries of our thinking – to include such detail is made obvious. Such progress involves accounting for the unintended consequences of our action (including a way to account for the unknowable, the unforeseeable, and the overall issue of risks – an infinite variety of risks). Evolutionary, behavioral, and social factors (including those of the human elements of ecosystems) count among the kinds of details that we must take into account. The details of population dynamics and related elements of life-history strategy cannot be ignored. In this section, the dilemma faced in conventional thinking begins to become obvious. The infinite set of factors we begin to acknowledge with work exemplified by Chapters 5 through 9 cannot all be included in models such as those described in the first section. It is impossible to accomplish a full accounting of all forms of complexity through conventional processes – the infinite eludes us in approaches. In spite of the seeming impasse, however, we are reminded, by work represented in both this and the preceding section, that a full accounting of complexity remains the objective as we continue along the path toward holistic forms of management.

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Unintended consequences sneak in the back door: making wise use of regulations in fisheries management a n n e m a r i a e i k e s e t , a n d r i e s p . r i c h t e r, f l o r i a n k . d i e k e r t , d o r o t h y j . d a n k e l , a n d n i l s c h r. s t e n s e t h

Abstract In this chapter we discuss the potential failure of simple management models. Analysing components of a complex adaptive system in isolation is often misleading. The fundamental complexity of the social and natural environment has to be fully accounted for if unpleasant surprises are to be avoided. We examine a list of general management tools used in real-world fisheries, arguing that the success of a given instrument depends not only on its inherent properties but also on the way these instruments are administered. Similarly, we address how uncertainty and the biological complexity of the resource system may result in unintended consequences, including unanticipated costs. This demonstrates that for each resource system, the informational constraints have to be considered. Hence, interdisciplinary research is mandatory in order to reach adequate management decisions for social–ecological systems. Introduction Marine fish stocks are renewable natural resources. They have the potential to provide food, income, and other services to mankind on a sustainable basis (Smith et al. 2010). Yet in reality, overfishing  – the wasteful

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Making wise use of regulations in fisheries management exploitation of marine resources – is a widespread observable fact (Jackson et al. 2001, Hilborn et al. 2003, Myers and Worm 2003, Worm and Myers 2004). On the one hand, there is no doubt that globally fisheries are in crisis (Clark 2006). On the other hand, how we can manage to rebuild global fisheries is still under debate (Worm et al. 2009). Interestingly, there are few cases of environmental policy wherein the gap between actual and potential performance is as large as in fisheries (Heal 2007). The underlying cause of overfishing is most often thought to be the open-access nature of many fisheries: each individual fisherman takes fish out of the ocean until the cost of catching one more fish exceeds the return of doing so. The fisherman has no incentive to leave fish as an investment for future harvesting; if the fisherman does not take the fish when they can be taken, another fisherman will. This problem is often described with the metaphor of the “tragedy of the commons” (Hardin 1968). Like most metaphors, it simplifies the true complexity of the problem. In this case it masks the two facets of overfishing that Munro and Scott (1985) defined as a “Class I problem” and a “Class II problem.” First, the Class I problem relates to excess fishing mortality; too many fish are harvested. Turned the other way around, too few fish are left in the oceans to reproduce. That is, future social and natural losses result from overstraining the replenishing potential of the resource. It resembles a “temporal trap” (Messick and McClelland 1983) as the concentration on today’s gains squanders obtainable gains in the future. Second, even when the government is aware of this problem and sets a Total Allowable Catch (TAC), too many boats will “race” to catch as much as possible until the TAC is reached. This is the Class II problem, where social and natural waste is the result of a perverse incentive structure brought about by the fact that fish can be appropriated only by the first fishermen to catch them, resembling a “social trap” (Messick and McClelland 1983). A symptom of this “rule of capture” (Boyce 1992) is the widespread overcapacity of fishing fleets. Decision-makers today meet challenges not previously experienced in the era of unregulated open-access fisheries (Homans and Wilen 1997). On the one hand, today’s decision-makers have more possibilities due to the increased level of knowledge. On the other hand, today’s managers are expected to uphold both biological and economic sustainability in an increasingly complex world (Clark 2006). Not all management instruments work in the same way:  while some solve the Class I problem, others overcome the Class II problem. Therefore, any management advice should specify whether it aims at solving a Class I problem, a Class II problem, or both. An excellent framework for analysing such complex social–ecological systems for sustainable management is given by Ostrom et al. (2007) and Ostrom (2009). A social–ecological system consists of four subsystems:  (i) resource

A.M. Eikeset et al.

Fig. 5.1.  An adaptation of Ostrom’s (2009) framework of core subsystems for a­nalyzing social–ecological systems in our marine fisheries context. Here, we emphasize the feedback loop between the resource system and resource units in their interactions with the users (black arrows) in the form of objectives, and the resulting management tools applied to the resource system and/or units (gray arrows).

system (e.g., a coastal fishery), (ii) resource units (e.g., fish stock), (iii) users (e.g., fishermen), and (iv) governance system (e.g., the specific laws and social norms in place) (Fig. 5.1). Within each subsystem, relevant variables can be identified to help map policy recommendations to specific system characteristics. Panaceas for resource management typically fail (Ostrom et al. 2007). This can happen as a result of a variety of factors, often in combination. Frequently, this occurs because of overweighing, or, alternatively, simply ignoring the importance of one of the subsystems. For example, a solution that focuses on the protection of resource units (RU), like biomass of a certain species, may fail because it does not take into account how it is affected by the response of users (U) to these regulations (see Fig. 5.1). It is crucial to recognize the occurrence of feedback and, to the extent possible, to identify particular feedback structures within and between the specific systems (Berkes et al. 1998, 2003). If management strategies are based on results derived from analyzing one of

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Making wise use of regulations in fisheries management the subsystems in isolation, the outcome may be very different than what the manager had in mind. These unintended consequences occur because overlooked or underemphasized issues will always find a way to sneak in through the back door. By this, we mean that models that look only at parts of the system lack important components that are present in reality. As a result, these models are inaccurate at best, but, often, they will also provide completely flawed results. It is conventional wisdom that every complex problem has an answer that is clear, simple, and wrong. We are obviously not the first ones who claim that this holds for fisheries as well. Wilson (1982), for instance, points out that objectives and forms of regulation would be very different from those proposed by the traditional economic view, when “complicating factors” were taken into account. This chapter proceeds in three steps. In Part A, we highlight the fundamental complexity of the social and natural environment relevant for fisheries management. In Part B, we discuss a list of management tools with regards to their ability to alleviate Class I and Class II problems. We argue that this depends not only on the inherent properties of a given instrument but also on the way an instrument is administered. In Part C, which also serves as a summary, we broadly categorize different sets of social and natural complexity. By constructing four stylized examples, we highlight that the adequacy of a given instrument in a given case is contingent on the specific structure of the costs of implementation and the difficulty of obtaining all relevant information.

Part A: Complexity Stakeholder participation and the social environment

Stakeholders can be defined as any member of society who has direct (primary stakeholders) or indirect (secondary stakeholders) interests, or stakes, in the actions of a fishery (Gray and Hatchard 2008). It is important to keep in mind that, in practice, managers and scientists often have hidden agendas themselves, in spite of their alleged neutrality (Jentoft and McCay 1995). Stakeholder participation can be an effective way to reconcile conflicting objectives (Dankel 2009). Through an active and assisted dialog process, objectives can be cognitively broken down and made more compatible (Follett 1955). For example, the objective “highest sustainable yield” could in fact be a symbol for a more specific objective; say “operating within a 10% profit margin over the next 5 years.” Likewise, the broad objective “ecosystem preservation” could be a symbol for a more specific objective like “a 50% decrease in the amount of trawling that has contact with bottom habitat.” Additionally, participation may

A.M. Eikeset et al. enhance the chances of reaching consensus and lead to better decisions due to the integration of the specific expertise these stakeholders have (Jentoft et al. 1998). Ideally, the outcome of such participation coincides with what would be best from society’s point of view, especially regarding long-term sustainability. Unfortunately this is not necessarily the case. Far too often, the voice that shouts loudest is heard best (Hatchard 2005), especially when some stakeholders have far more resources (financial funds as well as knowledge) than others (Esteban and Ray 2006, Mikalsen et al. 2007). In many cases it is impossible to distinguish an active debate among stakeholders from lobbying. Often stakeholders are willing to spend a substantial amount of money and time on influencing political decisions. This form of “rent-seeking” activity (Krueger 1974, Johnson and Libecap 1982, Bergland et al. 2002) is, of course, highly undesirable from the society’s point of view, but is often a well-established part of the political culture and therefore hard to eradicate. In spite of this, it would be naïve to conclude that all lobbying would cease if stakeholders were excluded from the present form of decision-making. This is especially true because the decision on whom to include and exclude is itself a political choice, making the process even less transparent (Mikalsen and Jentoft 2008). If primary stakeholders are involved in the decision-making process, they should therefore be made responsible and accountable (Berghöfer et al. 2008, Mikalsen and Jentoft 2008). Managers, on their side, should also be accountable and bear the full responsibility of their decisions while as in current forms of management they have often lost neutrality (Jentoft and McCay 1995). In part, this is because sustainable long-term management use of marine resources requires planning over a time horizon that is longer than the duration of political offices. Such challenges are made even more difficult by the fact that, often, policy makers use fisheries management as a vehicle to solve other political issues (and if these involve other environmental issues, it introduces artificial connections and relationships among the various elements of ecological systems). Prominent examples are regional development, employment, or simply redistribution of income. These are all legitimate political choices, but they do not necessarily fulfill the explicit management goals of a fishery. When management objectives have been identified and prioritized, scientists may present management trade-offs based on current knowledge of the fish stocks. But scientists are often confronted with large degrees of uncertainty (especially in regard to trade-offs and consequences involving other components of an ecosystem) that, especially when not successfully communicated, can disillusion stakeholders (Rosenberg 2007) and breed distrust towards scientists and their methods. Therefore, an open dialog process (Follett 1955) is a pertinent first step where different stakeholders and scientists can meet to

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Making wise use of regulations in fisheries management gain more knowledge of inherent trade-offs of the resource, data, and modeling involved to support management transparency and trust-building. In most cases, fisheries management is a top-down bureaucratic exercise with centralized control (Gray and Hatchard 2003, Prince 2003, Daw and Gray 2005); there is a tendency to disconnect the human system from the ecological system by not explicitly including the human component of ecosystems with all of its user groups. Since there are important feedbacks from the governance system to the users, including or excluding stakeholders will lead to institutional repercussions. Central intervention from authorities very often directly undermines existing norms of cooperation, lowers the willingness to obey these rules, and weakens stewardship motives. The literature has identified many cases where external interventions, intended to stimulate certain behavior, in fact eroded any motivation to voluntarily behave as intended (Frey et al. 1996, Deci et al. 1999, 2001, Frey and Jegen 2001, Gintis et al. 2005, Ostrom 2005, Frey and Stutzer 2006, Bowles 2008, Vollan 2008, Richter and van Soest, in prep.). This phenomenon, often referred to as “crowding-out,” holds especially for external incentives in the form of direct payments, but also for external control that signals distrust to the individual. This happens because individuals base their decision not only on financial considerations, but are also often intrinsically motivated to be a good member of society. A fisherman may, for instance, feel responsible or morally obliged to use nets that minimize bycatch: he may want to signal to others that he is a trustworthy person, who has high moral standards. Standard economic models typically ignore how moral motivation is affected by financial incentives. Instead, it is assumed that financial incentives come on top of moral motivation and, when the two are consistent, one would expect that it can only strengthen the overall incentive. The literature on crowding-out (where one motivation replaces another), however, has established that this assumption is often invalid because moral incentives and financial incentives are interlinked and therefore non-separable (Bowles 2008): a financial incentive directly affects, and often crowds-out (i.e., replaces) the incentive coming from moral motivation. If a fisherman suddenly receives money for using bycatch-minimizing nets, this external reward may supplant his moral motivation to use them voluntarily. As a result, he may still use more of such nets (if the financial incentive is large enough), but, in principle, it is possible that he will use less of them if the incentive is perceived to be too small. In principle, it is also possible for government policy to crowd-in (i.e., stimulate) good behavior through the decisions by fishermen other than simply to conform to policy. If banning nets that produce a lot of bycatch helps stigmatizing the use of them, a fisherman who is personally indifferent about

A.M. Eikeset et al. the problem of bycatch may not be indifferent towards social pressure and may try to comply with the social norm. Therefore, governmental policy can also help by supporting and evoking social values and public-spirited motives (Bowles 2008). Financial incentives are not alone in replacing voluntary actions; external control can do the same. In many cases, an individual obeys a certain social norm or law because he considers himself to be a good citizen, and not so much because he fears to be fined. Once the authorities start monitoring an individual frequently, he responds to this signal of distrust by non-compliance when he is not monitored. This can happen because he infers that he is simply not expected to comply by default, or he reciprocates this sign of mistrust by breaking the rules. In both cases, the individual sees the authorities as an opponent, rather than as a partner. This finding has been corroborated in economic experiments and distrust has aptly been called “the hidden cost of control” (Falk and Kosfeld 2006). Policy makers should take into account that any external intervention may have feedbacks not predicted by simple standard economic models. Some fairly simple rules can be used to try to minimize the negative consequences (Ostrom 2005, Frey and Stutzer 2006, Richter and van Soest, in prep.). First, policies that are designed in a way that reveals distrust towards users will most likely destroy any voluntary compliance that may have been present before (Anderson and Lee 1986, Sutinen and Kuperan 1999, Hatcher et al. 2000, Bowles 2008) and certainly inhibit additional voluntary compliance. Second, a law that is not perceived to be legitimate and fair, is less likely to be obeyed (Frey 1997, ch. 6). A good example comes from Denmark, where “fishers feel they are taken hostage by an illegitimate management system, and thus feel it is morally correct not to comply” (Raakjær Nielsen and Mathiesen 2003). In South Africa the government tried to reduce illegal fish landings by establishing formal rights for the local fishermen. But some fishermen had the feeling that the process was not fair and expressed their discontent by “protest fishing” (Hauck 2008). Similarly, economic experiments in the laboratory have shown that individuals indeed feel less obliged to comply with regulation by an institution that is perceived to be unfair (Kosfeld et al. 2009). Stakeholder participation can be an important way to achieve legitimacy (Jentoft et al. 1998, Hatcher et al. 2000, Dankel 2009). Such a participatory approach may build trust among users themselves, but it also contributes to trust between users and central authorities. Economic experiments have indeed shown that involving individuals in the process of institutional design leads to more efficient outcomes (Ostrom et al. 1992, 1994, Vyrastekova and van Soest 2003). On the other hand, if individuals fail to reach consensus, stakeholder

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Making wise use of regulations in fisheries management involvement can be counterproductive; the outcome can be less cooperative than if the individuals had never been involved in designing the institution (Sutter and Weck-Hannemann 2004, Tyran and Feld 2006). These findings from controlled experiments indicate that stakeholder participation can replace opposition with motivated stewardship, and increased compliance. But this will only be the case if an actual consensus is reached and the institution is designed in a fair way. Uncertainty and the biological environment

Worldwide marine fish stocks are declining (Worm and Myers 2004, Worm et al. 2006, 2009, FAO 2008,), leading to changes in ecosystem structure and functioning. After over-exploitation of large predatory species, fishermen may switch to target smaller prey species, making “fishing down the food web” a predominant threat to over-exploited marine systems (Pauly et al. 1998, 2002, Pauly and Palomares 2005). Habitat loss from trawl (the fishing net usually towed behind a fishing vessel) activity and bycatch (unintended mortality of non-targeted organisms caught in fishing gear) threatens populations of nontargeted species. This may be manifested as a reduction in species richness and ecosystem diversity (Armstrong and Falk-Petersen 2008). Fishing may also be effectively size-selective where larger fish are more likely to get caught, leading to age-truncation where younger age classes dominate the population and spawning stock biomass (Marshall et al. 2006, Ottersen 2008). Such juvenation and loss of age diversity may negatively affect recruitment and make stocks less robust or resilient to climate change and variability (Hsieh et al. 2006, Marshall et al. 2006, Ottersen 2006). Pertinent questions arise. How does fishing and changes in the environment, like climate change, affect inter- and intra-species interactions? In turn, how do these impact foodweb dynamics and ecosystems? For example, how do fisheries change stock vulnerability and resilience? Are there tipping points where, beyond a certain threshold, stock collapse is inevitable? And, if the stock collapses, what is the potential for recovery? Fishing can change the basic dynamics of exploited populations; for example, exploitation can result in larger variability in fish abundance, which may potentially pave the way to systematic declines in stock levels (Anderson et al. 2008, Stenseth and Rouyer 2008). A recent study that summarized the magnitudes of phenotypic change in fish, ungulates, invertebrates, and plants found that harvesting may produce rates of evolution up to 300% greater than in natural systems (Darimont et al. 2009). In commercial fish populations, changes in life-history traits, exemplified by maturation at earlier ages and smaller size, are greater when exposed to strong fishing pressure

A.M. Eikeset et al. (Sharpe and Hendry 2009). Such phenotypic changes may have a genetic component driven by the selection pressure caused by intense harvesting (Heino 1998, Heino and Godø 2002, Heino et al. 2002, Olsen et al. 2004, Dieckmann and Heino 2007, Marshall and McAdam 2007, Dunlop et al. 2009, Stenseth and Dunlop 2009). Potential effects of such genetic changes include the erosion of genetic and phenotypic diversity (Jørgensen et al. 2007). Therefore, fisheriesinduced evolution is of special concern because genetic changes may be difficult to reverse (Law and Grey 1989, Conover et al. 2009, Enberg et al. 2009). The extent to which fisheries-induced evolution occurs and how important it is compared with other factors are being debated (Hilborn 2006, Conover and Munch 2007, Jørgensen et al. 2007, Browman et al. 2008, Andersen and Brander 2009, Ozgul et al. 2009). However, addressing the genetic impact in such phenotypic changes is important if management is to be precautionary. Otherwise negative socio-economic and biological consequences from unnoticed fisheries-induced evolution (including coevolutionary effects on other species) could sneak in the back door. The identification and, where possible, the quantification of uncertainty in all the steps from data collection to model implementation is crucial to derive reliable projections for decision-making. In fisheries, the first level where uncertainty enters is in survey data and catch statistics, with cascading effects into models and model choice. Therefore, stock assessment (quantification of the number of fish in the sea) is a challenging, but crucial field of research. Models are continuously being improved or replaced. For example, survey estimates used in population models are not always consistent, and are difficult to reconcile with commercial catch statistics. To meet these challenges, as they involve uncertainty in marine science, state-space modeling, a statistical modeling framework, has become popular for use on data for many fish stocks (Millar and Methot 2002, Millar and Meyer 2002, Aanes et al. 2007, Bogaards et al. 2009, Lindegren et al. 2009, Swain et al. 2009, Eikeset et al. 2010). Choosing the level of model complexity is another challenging task: management has often focused on single-species populations, especially historically. However, it is progressively being recognized that single-species applications are inadequate for management decision-making when they exclude important multi-species feedbacks like predator–prey relationships within an ecosystem (Hjermann et al. 2007, Lindegren et al. 2009, Morissette et al. 2009). All of these factors contribute to the overarching principle of biological complexity of ecosystems. This principle contributes to the understanding of how fishing can create substantial change in ecosystems, to result in altered structure or function (e.g., lower biodiversity). Some of the changes may result in lower yield from the targeted fish; some changes may be hard or impossible to

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Making wise use of regulations in fisheries management reverse even if fishing ceases (Casini et al. 2009, Enberg et al. 2009, Lindegren et al. 2009). To meet the goals of adaptive management, models need to integrate the natural and social system as early as possible in order to provide knowledge and develop specific operational objectives for the resource. Part B: Fisheries management Many different tools for fishery management are available and have been applied and analyzed over the past decades. It is clear that what works well in one setting may lead to management failure in a different context (Brock and Carpenter 2007, Ostrom et al. 2007). Therefore, a key message is that a single best management instrument does not exist (Grafton 2000, Caddy and Seijo 2005, Degnbol et al. 2006, Jentoft 2006, Dankel et al. 2008, Ostrom 2008). Successful policy is not so much a question of inventing a new and magic strategy, but of adequately applying existing instruments to the specific situation at hand. However, this has proven to be difficult in the past. Management responsibility

An often overlooked question is not only what to manage, but how to manage. For example, a regulation on the Total Allowable Catch for one fishery may have very different effects, depending on whether it is agreed upon communally or administrated by a central government. A key ingredient of any successful management strategy is to provide the users with the right incentives. We will therefore take the question of how management is brought about as our principal characterization when portraying the management tools below. Afterwards, we will discuss specific management tools in more detail. Centralized management

The vast majority of industrialized fisheries are managed by a central authority (government) which stipulates laws and regulations that are legally binding. If users are caught violating these regulations, they face a penalty. This seems to be a straightforward bureaucratic approach, as the government by its very nature is equipped with the power to set up, monitor, and enforce a given set of rules. The costs of doing so can, however, be extremely high, and there is a real danger that users will be alienated. As a result, informal arrangements between the users may be crowded-out (i.e., replaced), and so may any willingness to comply with these laws. The “hidden cost of control” (Falk and Kosfeld 2006) in the form of distrust can be substantial. As a general rule, successful central management requires strong enforcement and monitoring. Therefore, even if a certain law or regulation can be easily formulated, it can be extremely difficult to implement and enforce it.

A.M. Eikeset et al. There is also the danger that unintended consequences of economic incentives will sneak in through the back door. If it is forbidden to land a species that is threatened and the fines for doing so are high, the users may throw it overboard when it comes on deck as bycatch. This may mask the overall effects on fishing on this particular species as conventional catch data used for stock assessments will not reflect bycatch discards. Co-management

In contrast to centralized management, co-management relies on a broader sharing of management responsibilities between governing systems (i.e., the state), research institutions and stakeholder groups. In fisheries discourse, co-management is presented as an alternative model which is reliant on stakeholder dialog and participation for cooperative management decisions between the state and other co-managers. A good review of the various forms of co-management is provided in Carlsson and Berkes (2005) and a review of implementation of fisheries co-management in developing countries can be found in Chuenpagdee and Jentoft (2007). In the context of fisheries, most research regarding co-management identifies legitimacy and stakeholder empowerment as important success factors of such a governance regime (Jentoft et al. 1998, 2009, Jentoft 2000a, 2000b, 2005, 2007, Jentoft and Mikalsen 2004, Chuenpagdee and Jentoft 2007, Pinkerton and John 2008, Armitage et al. 2009). Community-based management

Community-based management takes the co-management model a bit further from the top-down model and closer to a bottom-up management paradigm. The idea of community-based management is that the fishing community itself, separate from the state, decides on a harvesting strategy that is sustainable and profitable. This implies that the government deliberately steps down and relies on the community to develop management decisions. Actions may be legally non-binding, but still not purely voluntary, as they are based on social norms that may be enforced by fellow community members (Ostrom et al. 1992). Therefore, rule-compliance may be mandatory for members of the community and heavily sanctioned according to rules developed locally or at higher levels. This form of community-based management can be powerful, especially when users have close social ties and share the same norms and values. The government may, however, take a supportive role in giving scientific advice, by facilitating community meetings, or by encouraging desired behavior, such as promoting the use of nets that minimize bycatch. Many examples show that local users are able to agree on management decisions if certain conditions are met (McCay and Acheson 1987, Ostrom

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Making wise use of regulations in fisheries management 1990, Baland and Platteau 1996, Ostrom et al. 2002). While community-based management aims at upholding a harvesting strategy by social norms of cooperation, the actual harvesting strategy may take the form of a regulation of the mesh size (gear regulation), the number of days at sea (effort regulation), or of any other variable that defines the fishing process. Hence, the way a harvesting strategy is put into practice is not necessarily specific to the community-based approach. However, what is specific to community-based management is the explicit involvement of users in the process of deriving and implementing rules (Jentoft 2000b) (for example, via structured group consultations). It is worth pointing out that social norms often solve the “social trap” (Class II problem), but not necessarily the “temporal trap” (Class I problem). Fishermen may, for instance, take turns getting the best fishing spots (rather than competing for them), but may strongly resist joining a cooperative to achieve longterm sustainability (Taylor 1987). Norms of cooperation may even aggravate the Class I problem of over-exploitation. This may happen, for example, when norms are not aimed at sustainable management, but, instead, at lowering costs of exploitation (e.g., through sharing information about the location of the fishing grounds; Holm et al. 2000). In spite of this, community governance can be very effective and efficient, in particular when the users are able to pool their risks or when cooperative management helps lower costs of harvesting (Swallow et al. 1997). The literature on this topic includes several key variables that can be linked to the self­organizing capacity of a community and the sustainability of common-property regimes. A good synthesis is given by Ostrom (2009), who identifies a commonproperty regime to be successful when:  (i) the size of the resource system is moderate, (ii) the resource is neither too abundant, nor already exhausted, (iii) the system dynamics are predictable, (iv) the resource unit mobility is low, (v) the number of users is small, (vi) some users act as leaders, (vii) users hold common social norms and values, (viii) users have common knowledge about the system, (ix) the resource is very important to the users (in terms of livelihood or cultural value), and (x) the users have full autonomy for crafting collective-choice rules. By these standards, the chances for success of self-organized management for marine ecosystems are mixed (McClanahan et al. 2009). Some coastal (typically bay) fisheries can be successfully managed by a small community (Ostrom 1990, Schlager and Ostrom 1992, Schlager et al. 1994, Baland and Platteau 1996, Agrawal 2001, Ostrom et al. 2002, Ostrom et al. 2007), but when fish species are highly migratory and foreign fishermen are difficult to exclude, the prospects for community governance are rather bleak.

A.M. Eikeset et al. Management tools

Fisheries management can rely on a variety of tools (see Fig. 5.2 for an exposition of the tools we discuss). Good overviews can be found in Rettig (1995), Kahn (2005, ch. 10), and van Kooten and Bulte (2000, p. 94). We will distinguish between tools that are based on a command and control approach, such as fines for catching fish below a certain size limit, and tools that are based on financial incentives, such as imposing a tax on landings. Finally, we attempt to give an overview of the ongoing debate on tradable permits. These are a special class of tools based on financial incentives in that they aim to exploit the efficiency of decentralized competition by creating a market for harvesting rights. Management tools can be described and analyzed along several dimensions: one may ask whether a given class of policies aims to avoid the social or natural waste brought about by excessive harvesting (Class I problem), or changes the prevailing “rule of capture” (solve the Class II problem; see Fig. 5.2A). Alternatively one may ask whether a given instrument is robust to social and biological complexities, i.e., is it likely that it leads to unintended behavior from the fishermen, or is it likely that this instrument will lead to unintended changes in the resource or consequences for the ecosystem (Fig. 5.2B)? A management tool that targets the Class I problem should limit what is taken out of the water to ensure a sustainable stock for the future. This can be done by setting, for example, a Total Allowable Catch (TAC). A different angle is taken through input-centered instruments, which essentially control the way fish are taken from the ocean. An example would be to manage fishing capacity through controlling days at sea. Whether input or output controls perform better depends on many factors (Yamazaki et al. 2009); not all input controls are equally able to solve Class I or II problems and some of them are more likely to lead to unintended consequences than others. We will address this issue in the next section. Command and control approach

Let us first take a closer look at the tools that are used to control what is taken out of the water (output controls), before turning to controls that regulate the way of harvesting (input controls). The prime example and most ubiquitous output-centered instrument is a cap on the TAC (Clark 2006). That is, all harvesting of a given fish species is prohibited once the total allowable volume has been landed. While this may effectively protect the resource stock and, in principle, solve the Class I problem of overfishing, a TAC does not necessarily lead to an efficient use of the resource (Class II problem). Quite to the contrary, each fisherman has an incentive to catch as much as possible before the TAC is

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Making wise use of regulations in fisheries management A

Full TAC Capacity to solve Class I problem

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Mesh size Input regulations

Marine reserves

Virtual population units Taxes

Buy-backs Eco-labeling

Social norms Catch shares

Zero

B

Full Capacity to solve Class II problem

Tool

Biggest advantages

Biggest disadvantages

Challenges from the biological system

Challenges from the social system

TAC

Sets an effective limit on how much to be harvested

Requires substantial information about the ecosystem, and the realized catch

Uncertainty in stock assessment may over-estimate stock size

Race to fish; fishing down the food web induced by TAC on top predator, fishermen substituting to other (unregulated) species

Social norms

No central regulation needed; no crowdingout of voluntary actions

Scale-dependent, depends Adaptation to sudden on self-organizing capacity changes in the natural of a community environment can be slow

Minimum size limit

Administratively cheap and easy to monitor

Marine reserves Protect habitat and species

May lead to unintended size-selective response in the harvested population Users who bear the costs of foregone fishing are often not the ones who benefit

Input regulations

Administratively cheap and easy to monitor

Taxes

Highly efficient, effective, A lot of information on the and create revenues for fishing sector is needed to governments set a tax that leads to an intended catch

Buy-backs

Adaptation to sudden changes in the social environment (e.g., new and more efficient harvesting technology) can be slow

Migration pattern and spatial distribution may change

Only an indirect effect on the catch

May have strong distributional consequences for some users and, hence, lead to political opposition Fishermen substitute unregulated inputs for the regulated ones (effort creep) Strong resistance from incumbent fishermen

Very costly and often ineffective

In anticipation of a buy-back program users may increase capacity, leading to a situation that is worse than open access Price increase can lead to increased effort, proliferation of self-serving labels

Eco-labeling

Create consumer awareness and may provide positive spillovers to non-labeled fish

Can only have a complementary character

Virtual populations units

In theory, ensures perfect management

Unfeasible; requires perfect information at all levels; involves heavily virtual accounting; unclear scaling issues

Catch shares

A transferable quota solves the problem of overcapacity efficiently without central intervention

Effects on ecosystem depend fully on corresponding TAC; costly to administer and high monitory requirements; duration and issuance of catch shares has distributional consequences

Characteristics depend on TAC

Problem of bycatch, discarded catch, and substitution to non-controlled species; auctioned quotas evoke resistance from incumbent fishermen; anticipated windfall profits from the introduction of ITQs may lead to capacity increase, leading to a situation worse than open access; ambiguous whether catch shares crowd stewardship motives in or out

Fig. 5.2.  A classification of management tools and their characteristics. Panel A: Management tools and their capacity to solve Class I (excess fishing mortality) and Class II (overcapacity) problems. Panel B: Management tools and their general advantages, disadvantages, and challenges regarding biological and social ­complexity. TAC, Total Available Catch. ITQs, Individual Transferable Quotas.

A.M. Eikeset et al. filled and the fishery is closed for the rest of the season, leading to the infamous “race to fish” (Grafton et al. 2006). In the extreme case, this kind of derby fishery can lead to the complete dissipation of profits as price and quality of the landed fish deteriorate while harvesting costs are increasing (Homans and Wilen 1997). Moreover, a significantly shortened season often places serious strain on fishermen, gear, and environment. One of the most infamous examples is probably the North Pacific halibut fishery, in the 1980s, when the year’s catch was taken in 3–5 days after opening of the season, regardless of weather conditions (Homans and Wilen 2005). An additional problem with how TACs have been used is that they target individual species without consistent consideration of other species. Once the quota for one species is fulfilled, fishermen may shift to another one. The extreme case occurs when fishermen are “fishing down the marine food web” (Pauly et al. 1998, 2002, Pauly and Palomares 2005). It is not desirable to set the TAC every year on an ad hoc basis, because this leads to substantial economic uncertainty for the fishermen. It also requires time-consuming negotiations between countries (for shared stocks), or within any individual State’s governing system, which can be an obstacle when the stock has declined and a collapse needs to be prevented by prompt emergency actions. Therefore it is helpful for managers to have an adaptive management plan for how stocks should be exploited. One increasingly popular management tool with the mission of a sustainable exploitation pattern is the implementation of harvest control rules (HCRs). In this approach, the TAC is established through specific input variables, especially the size of the spawning stock biomass. An HCR is a feedback control rule that links a harvest scenario and a stock size (Sandal and Steinshamn 1997, 2001, Arnason et al. 2004). An HCR framework can be built on the precautionary principle by including reference points that are quantified and set to prevent over-exploitation and secure future stock recruitment by ensuring spawning stock biomass, or other selected indicators, to be above a defined precautionary limit (Beddington 2007). However, most HCRs in practice today retain the inadequacies of single-species approaches, with little, if any consideration of unintended consequences to other species and the ecosystem. Although often overlooked in the literature, it is important to acknowledge that the “race to fish” also is an influential factor in determining a fished stock’s age composition (Turvey 1964, Wilson 1982). Given that a fisherman deems that his own action has little influence on the overall outcome, he will have no incentive to avoid targeting young fish; he cannot be assured that he will have the benefit of gains from the investment of leaving a fish in the ocean so that it can grow, reproduce, and be harvested at a later time. Many fisheries

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Making wise use of regulations in fisheries management are indeed managed with minimum size limits that prohibit harvesting fish that are too young or too small. However, these size limits are almost always administered on an ad hoc basis and rarely take biological or economic criteria into account (Froese et al. 2008). Simulations from the Barents Sea cod fishery indicate that the profits could be more than doubled, simply by changing the mesh size (Diekert et al. 2009a). Another output-centered management approach that has sparked considerable interest this decade is the use of marine reserves. The aim is to provide a spatial or temporal refuge to particularly vulnerable or valuable life stages of a population. Examples could be a no-take zone around a highly productive and diverse coral reef, or a seasonal closure of the fishery during spawning. Sumaila et al. (2007) found that closing 20% of the high seas to fishing may have a relatively small decrease in the global reported marine fisheries catch (1.8%), while the gain from reserves would be maintenance of marine diversity and benefits for current and future generations. In principle, marine reserves can be very effective in preserving biodiversity (Sumaila and Alder 2001, Lubchenco et al. 2003), particularly in warm-water ecosystems (shallow water coral reefs) compared with temperate and cold open-water systems (Kaiser 2005). In spite of this, they can be quite inefficient, because adaptive behavior of fishermen harvesting outside the reserve may override the gains from protection (Hannesson 1998, Sanchirico and Wilen 1999). Alternatively, fish may migrate from densely populated, protected areas to less densely populated areas where they are harvested inefficiently. A large literature on marine reserves exists with considerable disagreement on the effectiveness of these methods (for overviews of this approach see Sanchirico et al. 2006 and Kaiser 2005). One has to take into account that users may have the incentive to undermine the establishment of a marine reserve that has the purpose of protecting an endangered species. Marine reserves therefore perform particularly poorly if they are not effectively controlled and clash with existing community customs. While it is important to analyze the ideal design of marine reserves, it is even more important to build community support for them (Kareiva 2006). Hence, one may conclude that marine reserves work best embedded in successful community-based management or co-management. It is pertinent to note that identifying and quantifying long-term consequences of an extinction of a species to an ecosystem and its related economic consequences is extremely difficult (Van Kooten and Bulte 2000, ch. 8 and 9). In general, the informational needs of output-centered instruments are demanding. The sustainability of a stock can only be ensured when its current size is accurately known, the total harvest can only be limited when the landings can be controlled, the fishing mortality can only be limited when it is

A.M. Eikeset et al. known which fish are targeted by the fishing gear, and special components of the stock can only be protected when their attributes are known. The advantage of output-centered management tools is of course that they directly target the defining characteristics of the system (i.e., how many and which fish to harvest, how many and which fish to leave in the ocean). In contrast, input-centered instruments essentially control what is used to take fish out of the ocean. Typical aspects of fishing that are managed by this class of instruments are days-at-sea, vessel length/width/tonnage, and gear restrictions. Although the number of active boats is just another dimension of inputs from the perspective of fish, it has the implication of turning regulated open access into regulated limited entry. “Closing the commons” (Hersoug 2005) may have considerable social side effects on employment, settlement, and the cultural landscape in general. Input regulations almost invariably lead to “effort creep” where fishermen substitute uncontrolled for controlled input. In the words of Wilen (1979, pp. 855–856) “we cannot necessarily simply limit ‘effort’ (a multidimensional notion) by, say, limiting tonnage or vessel numbers, or numbers of fishermen. With flexibility fishermen have the option to, and may, in fact, simply readjust other factors in their control to expand effort and subvert any imposed restrictions.” This is also referred to as “capital stuffing” (Clark 2006), which is indeed a widespread empirical observation. On the other hand, as Crutchfield (1979, p. 746) notes:  “The vessel is, after all, only a platform that carries harvesting equipment. There are obvious limitations on the extent to which additional capital investment … can increase catching power if key proxies for increased fishing power such as tonnage and length are constrained.” In spite of these limitations and drawbacks, input controls are often the easiest way to set an upper bound on what actually can be harvested. The informational needs for input-centered management are only moderate and this class of regulations provides flexible tools that can be adjusted to local circumstances. This makes them often the most practical management tools, especially in complex multi-species fisheries where the necessary information on biology and fleet structure is difficult to obtain. For example, the optimal harvest levels in a tropical multi-species fishery are often immensely hard to define and even harder to monitor (due to bounds on biological knowledge, technical ability, and institutional capacity). In contrast, a fisherman’s mesh size and length of his boat is fairly easy to observe. At the same time, however, these instruments have only an indirect impact on the actual resource stock. They are therefore not able to directly protect the resource stock (Class I problem), and they (by themselves) also do not change the perverse incentive structure (Class II problem).

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Making wise use of regulations in fisheries management Tools based on financial incentives

Taxes increase the cost of catching a fish and essentially determine the point where taking out another fish from the sea is no longer worthwhile. Managing a fishery via taxes works therefore only indirectly, as it requires extensive information about the economic components of the system. These extensive informational requirements are definitely a disadvantage (Arnason 1990). Yet if there is sparse information about the biological components of the system, Weitzman (2002) has argued that managing by prices (i.e., taxes) may actually be preferable to managing by quantities (i.e., quotas). It seems counterintuitive to use a tax instead of, for example, a TAC if the state of the stock is unknown. However, this result is based on the assumption that taxes can dampen harvesting activity effectively by making it more expensive. Albeit, there is – to the best of our knowledge – not one fishery which is managed by taxes as a specific instrument to solve Class I and Class II problems. The reason seems that taxes are often deemed to be politically infeasible (Scott 1979, Johnson and Libecap 1982, Brown 2000). In the words of Munro and Scott (1985, p. 662): “Fishermen are not noted for their reticence in using any and all political power at their command.” Another class of tools that draws on financial incentives is buy-back programs. Calls for measures to reduce overcapacity are often heard in relation to the observation that harvesting capacity in global industrial fisheries grew at a rate eight times greater than the rate of growth of landings over the two decades 1970–1990 (Greboval and Munro 1999). Buy-back programs ensure that boat owners are paid to take their boat permanently out of the fishery. Although these programs may be favored by the industry, their potential to perform in practice is limited, to say the least (Holland et al. 1999). First of all, it will most likely be the oldest and least efficient vessels that will be decommissioned initially. Therefore, efficiency is likely to be enhanced (Class II), but effects on over-exploitation will only be marginal (Class I). Second, owners will not withdraw unless sufficiently compensated, and in a limited entry fishery this implies granting boat owners payments far above original vessel costs (Clark 2006). Both these arguments hint that an effective reduction of fishing capacity via buy-back is likely to be very expensive. But to make matters worse, such a program could actually lead to extremes in capacity build-up if it is anticipated by the fishermen (Clark et al. 2005). And finally, buy-back programs may be next to useless in a global perspective if vessels that are taken out of one fishery are simply sold to be used in another fishery, touching on the “flags of convenience” phenomenon that is known to support illegal, unreported, and unregulated fishing.

A.M. Eikeset et al. Finally, a fairly recent market-based approach is eco-labeling. Based on the widely successful introduction of “dolphin free” labels that signaled the use of tuna-catching gear that avoided mammal bycatch (Teisl et al. 2002), the goal is to improve the harvesting pattern by changing the structure of the demand side. Non-governmental organizations such as the Marine Stewardship Council (MSC) award their labels to fisheries that fulfill an in-depth set of criteria for sustainable fishing. However, to be successful, this approach necessitates a substantive product demand (Gardiner and Viswanathan 2004); when only 1–2% of the consumers are receptive to such a label, its impact will most likely remain negligible. Moreover, it is prone to the proliferation of self-serving labels that are issued by the industry itself after adhering to significantly lower standards (Jacquet and Pauly 2007). Finally, if the label is not tied to the specific use of harvesting techniques, the label might be perceived by fishermen as a price premium, which could lead to increased effort (Gudmundsson and Wessells 2000). Hence, eco-labels will not be very effective if not embedded in a broader management plan. Nevertheless, they may have a complementary character, not the least of which would be raising awareness about the issue of sustainable fisheries.

Tradable permits

Tradable permits are a special case of market instruments. Individuals are endowed with harvesting rights, such as a catch quota, that they own as property. These permits can be sold or bought from other holders. The existence of a market for harvesting rights is appealing for at least two reasons. First, most people are very sensitive to financial incentives, making market instruments very effective. Second, in the absence of market failures, any market will allocate resources most efficiently without any central intervention and informational requirements. The central idea is that the externality at the root of the “tragedy of the commons” should be overcome by giving clear and well-defined property rights to those that harvest (Hannesson 2004, Grafton et al. 2006). Establishing a market for these rights would then effectively separate the individual harvesting decision from the development of the fish stock (Arnason 1990). However, whether tradable permits can in fact achieve their promise is actively debated. In the remaining part of the section we will give an overview of the main arguments assessing whether individual transferable quotas (ITQs) will eradicate overcapacity and the low profits obtained in the fishing sector (Class II problem), and at the same time, decrease the pressure on over-exploited fish stock (Class I problem).

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Making wise use of regulations in fisheries management While the notion of “clear” or “well-defined” property rights sounds good in theory, the practice is often much messier, making careful analysis necessary (Wilson 1982, Grafton 2000). In fact, property rights have several relevant dimensions, as pointed out by Schlager and Ostrom (1992, 1999). First, one may have the rights to enter a certain physical space, and extract resources. Second, one may hold the right to make management decisions, such as deciding to catch only fish above a certain size. Third, one may be able to exercise the right to enforce property rights by excluding others. Fourth, one may be able to transfer these property rights to a third party. Traditionally, economists favor an approach that ensures all of these rights, because this will maximize economic profits. The first three points make sure that the holder of the rights maximizes long-term benefits, while the last point ensures that the most efficient user will end up holding the rights. It is actually very difficult to come up with a policy tool that fulfills these criteria, since a necessary condition is that users take all consequences of harvesting into account so that the price for which one permit is traded in the market reflects the full value of the resource. To this end, it has been proposed that fishermen be provided with a right to manage their own part of the stock, bearing the full consequences of their own exploitation decision. This idea  – under the names “population stewardship right” (Gavaris 1996), “transferable dynamic stock rights” (Townsend 1995), or “virtual population units” (Lee and Gates 2007) – is indeed very appealing. However, as each fisherman would have to keep track of his own virtual stock, and the impact of his harvesting would have to attribute to the real overall stock development and recruitment, such a management tool is only feasible when there is full knowledge of the social and biological complexities. It is therefore unlikely this idea will become an available workhorse for managers reasonably soon. A much simpler and already widely used management tool is the use of ITQs or “catch shares.” These quotas give the exclusive right to harvest a certain amount of fish, but there is a wide variation in the actual implementation of this idea. In some fisheries, quotas are allocated to individuals by means of an annual auction. In others, the quota is tied to the fishing vessel, but the vessel may be bought or sold. Sometimes these quotas are issued in absolute values, but in most cases they are issued as a fraction of the Total Allowable Catch. Empirically, the track record of overcoming the race to fish by ITQs is indeed impressive (Grafton et al. 2006). For example, after ITQs were introduced in the North Pacific halibut fishery, the short season was lengthened to the whole year, with the effect that fresh fish was available for longer periods which resulted in considerable beneficial side effects (including much safer working conditions for the fishermen) in addition to more cost-effective harvesting (Homans and

A.M. Eikeset et al. Wilen 2005). Pinkerton and Edwards (2009), however, questioned the persistence of efficiency gains, mostly due to asymmetric information, imperfect capital markets, and other market distortions. Sometimes the fear is expressed that transferable quotas will end up in the hands of a few highly industrialized fishers and small, traditional boats will be driven out of the market. This is indeed likely to happen and it is important to understand that this is not a negative side effect of an ITQ, but the whole point of a transferable quota. Economic theory predicts that ITQs will most likely end up in the hands of the most efficient users and overcapacity will be reduced. In general, efficiency gains from ITQs will be higher compared with non-­transferable quotas if there is more heterogeneity among fishing techniques and boats. But this can cause unintended consequences since the most economically efficient user may be the one whose harvesting efforts are most detrimental to the environment. Moreover, the reallocation of fishing activity may create devastating effects on fishing communities and considerable political tensions (Helgason and Pálsson 1998, Pinkerton 2009). If society attaches cultural value to community life and small-scale family-owned fishing boats, the welfare losses could, in principle, be higher than any gains in efficiency. Another source of political tension relates to the duration of the quota. If the right to harvest is perpetual, the question of who exactly is the beneficiary becomes very important (Jentoft 2006). Selling quotas through auctions seems efficient and fair, but resistance from established fishermen can be expected to be very high. Individual transferable quotas that are given for free to incumbent, i.e., established, fisherman will most likely be welcomed by the recipients. But it seems unfair to transfer perpetually to a small number of people, wealth that, in principle, belongs to the whole society (Bromley 2009). Dividing the pie today can also be unfair to future generations. Giving the quotas away for free may create additional ­perverse incentives, especially if it is based on current capacity, an often-heard suggestion. In anticipation of an ITQ system, fishermen may be willing to incur losses to increase their capacity now, given that they may be rewarded with a valuable quota. In Iceland, anticipated free ITQs based on catch history may have led to increased fishing in the period before the quotas were actually distributed (Haraldsson 2008). This undermines not only progress toward solving the Class II problem but also aggravates the Class I problem. How do ITQs, in general, fare with respect to solving the Class I problem? Evidence seems to indicate that establishing ITQs indeed positively affects the long-term status of a stock (Grafton et al. 2006). Statistical analyses of 11 000 fisheries have indicated that the establishment of catch shares has reduced the probability of stock collapse (Costello et al. 2008, Heal and Schlenker 2008). It is,

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Making wise use of regulations in fisheries management however, notoriously difficult to disentangle institutional and economic reactions and performance. It is not unlikely that a general awareness among stakeholders has led to a management change (establishing ITQs) and the reduced stock collapse is the direct result of the same awareness rather than the management change. Also, the overall effect of ITQs on marine ecosystems is not unequivocal (Branch 2009). This may be due to a number of caveats: first, catch shares will not achieve efficiency when there are externalities (e.g., congestion of fishing spots) in the production process (Boyce 1992) or if the resource is of heterogeneous quality (Costello and Deacon 2007). Second, an incomplete coverage in terms of the principal target species may lead to a substitution of uncontrolled species for controlled ones (Grafton and McIlgorm 2009). Also, the related bycatch and discarding problem (Herrera 2005) may be substantial. On a more profound level, the allocation of catch shares alone could, of course, only overcome the problem of overfishing if, and only if, the TAC is set correctly. Someone who holds the right to harvest a fixed amount of fish, or a fixed fraction of a TAC, simply has no incentive to withdraw from that right. Sometimes the hope is expressed that ITQs will induce an expanded sense of stewardship on the part of the users (Grafton et al. 2006, Costello et al. 2008). The argument here is that an ITQ is a secure asset (like a share of a company) and if the fisheries collapse, the quota would be worthless. Therefore, ITQ owners will start caring about the state of the stock (their asset) and jointly agree on a lower quota. This view is probably overoptimistic, because the failure to reach consensus on what would be best for everyone (and especially, if the wellbeing of the ecosystem is a consideration) is exactly why most fisheries pose a social dilemma and are, hence, in crisis. From a theoretical perspective, the fact that ITQs may reduce the number of users, because less efficient users leave the industry, may help crowding-in stewardship motives. It is likely that a smaller number of users will find it easier to reach consensus on reducing exploitation. But, as established in the previous section, material incentives often crowd-out voluntary stewardship motives. Thus, it is essentially an empirical question of whether or not the lower number of users outweighs this crowding-out effect. Individual transferable quotas may be especially detrimental because they give fishermen an unambiguous enforceable right to harvest a certain amount of fish. One may even argue that buying “rights to destroy nature” are akin to medieval indulgences (Robert 1994) and therefore quite the opposite of progress toward stewardship based on social motivation. Summing up, it is clear that catch shares form an interesting group of management tools: they require regulatory activity in setting the overall harvesting

A.M. Eikeset et al. limit. Organizing and distributing the individual rights occurs at a central level, while the trading and changing of incentive structure happens at the individual level. However, the natural conditions that allow for ITQ management (high level of predictability) seem to be fulfilled only in a narrow set of circumstances in marine fisheries. Given that evidence to support the contention that ITQs do indeed induce stewardship motives is sparse, it seems wise to not take any irreversible steps. This links particularly back to the question of “how” a specific fishery is managed; even if the natural pre-conditions for successful ITQ management are present, it is important not to destroy effective informal arrangements. In general, establishing ITQs will not be cheap, as any catch share management implies considerable management costs. At times these may be prohibitively high (Grafton and McIlgorm 2009). Moreover, as an efficient catch-share system is expected to generate considerable profits, the distribution of catch shares may cause considerable political tensions (Hannesson 2004, Clark 2006). Last, but not least, it is clear that catch shares will be no global solution: roughly 50% of the world’s value from fisheries is taken from waters where either no single country has sufficient control to exclude other countries or where the country in question does not have the ability to institutionalize such a management scheme (Diekert et al. 2009b). Part C: Policy recommendations for four stylized examples Overfishing cannot be stopped with simple technical fixes (Degnbol et al. 2006). Neither Class I problems (the social and natural waste stemming from overstraining the replenishing potential of the resource), nor Class II problems (the social and natural waste which is the result of a perverse incentive structure brought about by the fact that fish can be turned into money only by the first person who catches it) will be solved by one instrument (see Fig. 5.2). Solving both simultaneously is even more complicated, if possible at all with current options. Remedies for over-exploitation require first, and foremost, agreement on what a given ecosystem is capable of delivering, thus the need for an explicit management objective (Dankel et al. 2008). This objective and the tools intended for its attainment will only be perceived as legitimate and fair when all stakeholders have the possibility to influence the decision process. In particular, external “incentives that appeal to self-interest may fail when they undermine the moral values that lead people to act altruistically or in other public-spirited ways” (Bowles 2008). However, not only the social subsystem, but also the resource subsystem is of fundamental complexity. To achieve true ecosystem-based management the larger context within which these subsystems occur must be taken into account. Not only direct ­human-induced

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Making wise use of regulations in fisheries management changes from resource use, but also natural changes to the resource’s environment and its qualitative properties will have a profound impact on the resource dynamics and its variability. The actual success of a given set of policies is thoroughly contingent on the specific circumstances (Sen 2009). Nevertheless, it is possible to broadly categorize different classes of biological and social settings that result in particular combinations of informational needs and transaction costs, and ultimately lead to different sets of policies that are recommendable. The first example is a hypothetical small-scale coastal fishery where fishermen know each other and have social ties on several levels outside of their professional activity (e.g., religious or community organizations, etc.). Fishing is a way of life and is done mostly by traditional means. The fishery mainly targets an autonomous stock which is not systematically affected by factors outside the fishery. Such a fishery would lend itself to informal management as many communal ties already are firmly established and little formal interaction would be needed to secure sustainable fishing. Indeed, outside intervention in a top-down manner (e.g., in the form of official government controls) could be viewed as an illegitimate intervention and could lead to a crowding-out of stewardship incentives. However, applied measures that are easily observed and enforced by the community itself, such as gear restrictions or minimum market sizes, could signal best practice and help to maintain a cooperative equilibrium. The second hypothetical example is a coastal fishery where fishermen may know each other but closer ties are confined to the professional level. Fishing is a way to make money and is pursued in a technologically advanced and industrial manner. The fishery is largely an autonomous stock which is not systematically affected by factors outside the fishery. Here, community management would be less effective, and such an industrialized fishery would lend itself better to market-based approaches such as ITQs. In fact, the technical efficacy of the fleet might make it necessary to externally control the amount of harvest in order to curb the Class I problem. Nonetheless it would still be instrumental to include fishermen and other stakeholders in management decisions, as this would significantly enhance the legitimacy of the overall TAC and other regulations. The latter would complement the ITQ system in order to minimize negative externalities. The third example would also be a coastal fishery where fishermen may know each other but ties are again confined to the professional level. As in the previous example, fishing is a way to make money and is pursued in a technologically advanced and industrial manner. However, the fishery consists of many different fish species that can replace each other in the market but

A.M. Eikeset et al. that are complementary in the water, constituting a complex ecosystem. In contrast to the second example, ITQs will be very costly in such a setting as they would have to involve most or all target species (to avoid substitution to uncontrolled species). To cope with the Class I problem, some form of limits on the volume of landing or on the amount of employed effort would still be needed. In addition, a temporal or spatial restriction on harvesting would be needed to protect the most vulnerable or productive parts of the system. Given the complexity of the resource(s), there would be a strong need for in-depth biological research. Again, stakeholder involvement in all stages of management and research would be crucial in order to enhance understanding and a sense of “ownership,” thereby stimulating joint responsibility for the fishery. The fourth example is a high-seas fishery, where individual fishermen do not know each other and fishing is highly industrialized and pursued internationally at a corporate level. The fishery consists of mainly one species, which is, however, highly migratory. Direct stakeholder participation will be very difficult in such a setting due to the distance separating them. At the same time, top-down management will be nearly impossible as there is no single central enforcing agency for the high seas. On the other hand, international agreements on the most proximate and easily observable measures (such as gear restrictions) might be possible and protect the sustainability of the fishery (albeit at an inefficient level). Additionally, pressure from consumers (e.g., mediated via eco-labeling) might provide further incentives to fishermen to harvest in a sound manner. In conclusion, sustainable fisheries management necessitates carefully identifying and disentangling all levels of biological and social complexity (Ostrom 2009). Management should be designed to avoid hidden assumptions and overlooked issues that result in unintended ramifications that sneak in through the back door. Moreover, it is crucial that the specific tools that are applied remain flexible and adaptable. It is, therefore, also very important to consider not only what is managed, but also how it is managed. It is mandatory that we account for how regulation is perceived and how it affects existing behavior based on incentives, social norms, or customs. The ideal would be a governance system where the objectives and tools are the result of a democratic involvement of all stakeholders. Yet, the fundamental challenge would be first, to set up the institutions necessary to keep such a system in place, and second, to make such a system robust to slow or sudden changes in the socio-economic (e.g., dominance by one interest group) or natural environment (e.g., climatic change). It remains paramount to recognize that we cannot wait for all uncertainties to resolve before action is taken. Rather, we need to apply the appropriate

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Making wise use of regulations in fisheries management available measures, by taking the salient biological features into account, bringing stakeholders on board, and then adapt management as the future unfolds. Acknowledgments We are grateful to Svein Jentoft, Kristina Raab, Alessandro Tavoni, and Daan van Soest for valuable comments and advice on an earlier version of the manuscript. Financial support for this work was provided to A.M. Eikeset, F.K. Diekert, D.J. Dankel, and N.C. Stenseth by the Norwegian Research Council. A.P. Richter is grateful to the Netherlands Organization for Scientific Research, NWO, for financial support as part of the Program on Evolution and Behavior. References Aanes, S., S. Engen, B.E. Saether, and R. Aanes. 2007. Estimation of the parameters of fish stock dynamics from catch-at-age data and indices of abundance: can natural and fishing mortality be separated? Can. J. Fish. Aquat. Sci. 64:1130–1142. Agrawal, A. 2001. Common property institutions and sustainable governance of resources. World Dev. 29:1649–1672. Andersen, K.H. and K. Brander. 2009. Expected rate of fisheries-induced evolution is slow. Proc. Natl. Acad. Sci. USA 106:11657–11660. Anderson, C.N.K., C.H. Hsieh, S.A. Sandin et al. 2008. Why fishing magnifies fluctuations in fish abundance. Nature 452:835–839. Anderson, L.G. and D.R. Lee. 1986. Optimal governing instrument, operation level, and enforcement in natural resource regulation: the case of the fishery. Am. J. Agric. Econ. 68:678–690. Armitage, D.R., R. Plummer, F. Berkes et al. 2009. Adaptive co-management for social-ecological complexity. Front. Ecol. Environ. 7:95–102. Armstrong, C.W. and J. Falk-Petersen. 2008. Food for thought – habitat-fisheries interactions: a missing link? ICES J. Mar. Sci. 65:817–821. Arnason, R. 1990. Minimum information management in fisheries. Can. J. Econ. 23:630–653. Arnason, R., L.K. Sandal, S.I. Steinshamn, and N. Vestergaard. 2004. Optimal feedback controls: comparative evaluation of the cod fisheries in Denmark, Iceland, and Norway. Am. J. Agr. Econ. 86:531–542. Baland, J.M. and J.P. Platteau. 1996. Halting Degradation of Natural Resources. Oxford: Clarendon Press for FAO. Beddington, J.R., D.J. Agnew, and C.W. Clark. 2007. Current problems in the management of marine fisheries. Science 316:1713–1716. Berghöfer, A., H. Wittmer, and F. Rauschmayer. 2008. Stakeholder participation in ecosystem-based approaches to fisheries management: a synthesis from European research projects. Mar. Pol. 32:243–253.

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A.M. Eikeset et al. Taylor, L. 1987. The river would run red with blood: community and common property in Irish fishing settlement. In McCay, B.J. and Acheson, J.M. (eds.), The Question of the Commons: The Culture and Ecology of Communal Resources. Tucson, AZ: University of Arizona Press, pp. 290–307. Teisl, M.F., B. Roe, and R.L. Hicks. 2002. Can eco-labels tune a market? Evidence from dolphin-safe labeling. J. Env. Econ. Manage. 43:339–359. Townsend, R.E. 1995. Transferable dynamic stock rights. Mar. Pol. 19:153–158. Turvey, R. 1964. Optimization and suboptimization in fishery regulation. Am. Econ. Rev. 54:64–76. Tyran, J.R. and L.P. Feld. 2006. Achieving compliance when legal sanctions are nondeterrent. Scand. J. Econ. 108:135–156. Van Kooten, G.C. and E.H. Bulte. 2000. The Economics of Nature: Managing Biological Assets. Oxford: Blackwell. Vollan, B. 2008. Socio-ecological explanations for crowding-out effects from economic field experiments in southern Africa. Ecol. Econ. 67:560–573. Vyrastekova, J. and D. van Soest. 2003. Centralized common-pool management and local community participation. Land Econ. 79:500–514. Weitzman, M.L. 2002. Landing fees vs harvest quotas with uncertain fish stocks. J. Env. Econ. Manage. 43:325–338. Wilen, J.E. 1979. Fisherman behaviour and the design of efficient fisheries regulation programs. J. Fish. Res. B. Can. 36:855–858. Wilson, J.A. 1982. The economical management of multispecies fisheries. Land Econ. 58:417–434. Worm, B. and R.A. Myers. 2004. Managing fisheries in a changing climate – no need to wait for more information: industrialized fishing is already wiping out stocks. Nature 429:15. Worm, B., E.B. Barbier, N. Beaumont et al. 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314:787–790. Worm, B., R. Hilborn, J.K. Baum et al. 2009. Rebuilding global fisheries. Science 325:578–585. Yamazaki, S., T. Kompas, and R.Q. Grafton. 2009. Output versus input controls under uncertainty: the case of a fishery. Nat. Res. Mod. 22:212–236.

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Population dynamic theory as an essential tool for models in fisheries mauricio lima

Abstract The state of world fisheries, and their impact on ecosystems, is certainly cause for trying to manage them differently. However, a focus on ecosystems to the exclusion of factors involved in basic population dynamics is extremely problematic. Any form of more holistic management cannot ignore fundamental insights gained from the study of single populations, predator–prey interactions, and the complexity of such systems. Theory helps understand the dynamics of populations and their interactions in the context of environmental circumstances. Such understanding emphasizes the essential importance of ensuring that such insight is taken into account in fisheries management. There are fundamental principles involved in the population dynamics of any species found in ecosystems, and the study of these dynamics continues to add to our appreciation of such principles. The concepts involved are basic components in our understanding of the larger system and cannot be ignored. They include the role and importance of competition, population regulation, predation, resource availability, cooperation, environmental variation, fishery impacts, and emergent patterns (stabilizing tendencies). This chapter explores a number of such factors and their fundamental nature in the dynamics inherent to population interactions and, ultimately, ecosystems. There is special

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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M. Lima attention to the role of fishing and its influence on population dynamics. This is not confined to simple single-species considerations, however, as the population dynamics of a single resource species is directly linked to that of its prey, competitors, and environmental variation. In all cases, population dynamics remains as a crucially important set of factors that cannot be neglected in fisheries management. Introduction The collapse of fish populations has been occurring at increasing rates during the last several decades (Jackson et al. 2001). This phenomenon is not a surprise if we take into consideration the huge impact of industrial-scale fishing on marine ecosystems (Pauly et al. 2002), especially when measured in comparison to non-human species (see Chapter 11). Overfishing is the main cause of the severe reduction observed in most of the exploited fish populations around the globe (Worm et al. 2009). Despite the fact that fishery science represents an applied branch of ecological theory, the last 50 years have shown a clear record of continuous collapses of managed fish stocks (Pitcher 2001). Two main causes can be identified as responsible for these failures. First, as stated by Beverton (1998), one group of explanations is connected with the fact that the fishing industry and managers have not taken the advice of scientists for determining fishing quotas and effort. Second, however, as Holt (1998) noted, the problem can be related with the kind of advice that fishery scientists have proposed. While the first group of causes deals with problems having political, sociological, and economic roots (Ludwig et al. 1993), the second group of causes is related to the kind of theory, models, and hypotheses used to understand, predict, and manage fish populations (the information provided; see Chapter 10). In this chapter, I will review the latter group of problems in fishery management. My motivation stems from the fact that ecology as a science has been frequently criticized for its lack of practical applications and predictive power (Peters 1991), and fishery biology is seen by many to be a prime example of how ecology fails to solve applied issues. Single-species versus ecosystem-based fishery management A common perception among fishery scientists is that fisheries management has been ineffective because the models being used were focused on maximizing extraction from a single species while ignoring fundamental components of the ecological system (prey, predators, habitat, climate, etc.; Pitcher 2001, Pikitch et al. 2004). To address this issue, ecosystem-based management

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Population dynamic theory for models in fisheries for fisheries (EBMF) is proposed as an attempt to implement management practices focused on the entire ecosystem rather than a particular species. An underlying assumption of this idea is that fish populations are very complex systems that interact with their environment. From a dynamic point of view, changes in fish numbers or biomass depend on large numbers of inter-related factors. The logic underlying this argument is that models used in single-­species fishery management are too simple to represent useful metaphors of “real” natural systems. Traditionally, two kinds of models have been used in fishery management: (1) those based on the concepts of stock productivity, surplus production, and maximum sustainable yield (MSY) (Quinn and Deriso 1999), and (2) those models based on the spawner-recruit relationships (stock-recruit models) developed from ecological assumptions affecting the reproductive processes (Ricker 1954, Beverton and Holt 1957). The basic assumption of both surplus production and stock recruitment models is the existence of an equilibrium population size (or biomass) of unexploited fishes. However, equilibrium or near-equilibrium dynamics can be caused only by the presence of negative feedback processes (e.g., competition) and limiting factors (Berryman 1999). For its part, the EBMF approach assumes that exploited populations are embedded in complex ecosystems involving large numbers of interactions. From this perspective, the sustainable management of fisheries can only be achieved if models used to harvest fish populations are able to include, or account for, other ecosystems components. In this chapter, my objective is to show how the theory of population dynamics (Royama 1992, Berryman 1999, Turchin 2003, Ginzburg and Colyvan 2004) sheds light on issues of basic importance in managing the harvest of fish populations. In particular, I want to emphasize the fundamentally important role of population theory in managing fisheries, despite the attitude of many biologists that there are no laws or reasonable theories for explaining natural systems (in particular marine ecosystems) or the idea that marine ecosystems are too complex to be described by simple models. The objective is to demonstrate that ecological theory and simple models can be useful for understanding and predicting the dynamics of fish populations. Finally, as a population dynamicist, I shall discuss how fisheries are managed today and compare two general approaches: single-stock fishery models and the multi-species stock assessment models. In this comparison, I will use population dynamics theory as a framework. Population dynamics theory Following the work of Royama (1977, 1992), other authors have proposed that there are a few simple principles that can explain much (or even

M. Lima most) of the apparent complexity observed in the fluctuations of natural populations (Berryman 1999, Turchin 2003, Ginzburg and Colyvan 2004). Although the experts often differ on the importance of the basic principles of population dynamics and how they should be formulated, I will use these principles (especially as formulated by Berryman 1999) to analyze how fisheries are managed and to compare the single-species and ecosystem-based approaches to management. Most ecologists agree that a basic principle (if not the most important principle) of population dynamics involves Malthusian growth (Berryman 1999, Turchin 2003, Ginzburg and Colyvan 2004) represented as the maximum per capita population growth rate. Regardless of ecological forces behind the expression of growth rates, they are a starting point for understanding population dynamics in nature and it is imperative that they be included in any conceptual basis for management and relevant theoretically based models. According to this “first principle” of population dynamics, all populations grow at constant logarithmic rates unless affected by other forces in their environment. This logarithmic growth rate is negatively influenced, for example, when population size grows and resource availability decreases on a per capita basis (competition principle). Similarly, resource availability can decrease as a consequence of the populations of consumer species becoming abundant and predation rates increasing even at low population densities of prey populations (predation and cooperation; Berryman 1999). Classical single-species fisheries models rely, in principle, on our understanding of competition as a basic component of processes that lead to population size (numbers or biomass) showing patterns of equilibrium or near-equilibrium. Predation also contributes to these patterns, and fishing (predation by humans) will reduce population size towards lower numbers where individuals are faced with more abundant resources (per capita) resulting in increased recruitment rates. Many have criticized management based on the assumption that these principles are sufficient for realistic decision-making. One of the most common criticisms relies on empirical evidence that marine ecosystems show a great deal of variability, often explained as the effects of stochastic processes, oceanographic variability, and inter-specific interactions (Spencer and Collie 1997a). The underlying reasoning behind these criticisms is that marine ecosystems are too complex to be managed by single-stock fishery models (Pikitch et al. 2004, Frid et al. 2006). Although it has to be recognized that single-stock fishery models are a simplistic metaphor of nature, the inclusion of more parameters and variables always fails to achieve a complete understanding of the causes of fish population dynamics. In fact, it is highly likely that many fish stocks are governed through dynamics in which simple first-order dynamics and limits of resource availability (food, refuge, etc.) count as primary factors. Some factors

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Population dynamic theory for models in fisheries are clearly much more influential than others. My point here is that the principles behind population dynamics can never be ignored, either as they are developed to better understand the underlying causes of population dynamics, or (and perhaps more importantly) to better understand their role in implementing adequate management options to assure long-term sustainability. One of the simplest cases imaginable involves a fish population limited by the availability of a particular resource where competition is the primary force regulating and limiting the size of the population. A proper and simple manner to represent this population involves plotting the per capita growth rates as a function of population density or biomass – what Royama (1977) defined as the reproductive curve or Berryman (1999) the R-function (Fig. 6.1). In such cases, single-stock models based on the competition principle (e.g., the logistic) often provide good representations of the pattern involving the ecological process underlying the dynamics of this population. Competition is sufficient to explain a great deal of the observed variability. Based on the understanding that competition is not the only factor behind observed dynamics, we can proceed to include what are accepted as other primary determinants. Thus, additional considerations should be included in the analyses. One is the extent and nature of the influence of variability in limiting factors. Inter-annual and long-term changes in climate and oceanography are known to be important

Fig. 6.1.  The relationships between per capita population growth rate and population size for a fish population limited by resources and regulated by competition. When fishing is only an exogenous perturbation (dotted line) the reproductive curve (R-function) moves in the y-plane as a vertical perturbation reducing the maximum per capita growth rate and the equilibrium population size.

M. Lima drivers for changes in food, habitat quality, or shelter and they can be included in the single-stock models to emphasize the importance of accounting for them in management practices. Additional complexity can arise because limiting factors can shift in time (5th principle; Berryman 1999). For example, there are several studies showing that fish populations respond to inter-annual climatic variability and large-scale regime shifts (Hare and Mantua 2000, Benson and Trites 2002). These observations bring out the inescapable conclusion that predicting population dynamics and supplying information useful to management must be done so as to account for such factors. They contribute to observed dynamics. As such, fisheries with catches from populations characterized by first-order dynamics and regulated by competition can be managed through using single-stock models (i.e., in the context of current approaches to management) to the extent that they capture the essential ingredients for understanding, predicting, and managing (Quinn and Collie 2005). Fishing is, in most cases, the most important extrinsic force acting on fish populations (Jackson et al. 2001), often orders of magnitude larger than that of other predators (see Chapter 11). Therefore, to understand and manage fisheries we need to understand the dynamics of the fishery fleet. For example, if fishing effort is constant in time, and the fleet behaves as a generalist or a highly mobile predator (capable of aggregating in high prey-density areas), a potential consequence is to diminish prey (fish) populations toward low levels and cause a new dynamic equilibrium point (Morris 1963, Holling 1965, Berryman 1999). Under this scenario, fish populations show a tendency to be stabilized by generalist predators at low densities and regulated by enemy-free space competition (Berryman 1999). Moreover, a generalist predator (functional responses of type III) can create one or two equilibriums (patterned dynamics) depending on the level of fishing effort (Fig. 6.2). If fishing mortality is high then a “predator pit” can be created deep enough to produce a low-density stabilizing tendency (“equilibrium”), which can have extremely important implications for management; the fish population would continue to vary, but at much reduced levels. In this scenario, the only way to bring about an increase in the fish population and an increase in sustainable biomass harvests is to substantially decrease fishing effort. Furthermore, even if the system is in the domain of one of the two particular equilibrium points, it can be moved toward the other domain by exogenous perturbations (Berryman and Kindlmann 2008). In many fishery collapses, the combination of overfishing and climatic variability has been seen as the primary causal factor (Alheit and Niquen 2004). In contrast, because many exploited fish species are preyed upon by generalist predators, it is possible that exploited stocks will show meta-stable dynamics without the effects of fishing (Fig. 6.3). This scenario was proposed

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Fig. 6.2.  The relationship between per capita population growth rates and ­population size, when the fishery behaves as a generalist or mobile predator able to aggregate in high-density areas of prey (type III functional responses). Here, a “predator pit” can be created at low densities of prey. If the fishing rate is high, then multiple equilibrium dynamics can be created with two stable (K’ and J) and one unstable equilibrium points (U).

Fig. 6.3.  When the fish population is preyed upon by generalist predators multiple equilibrium dynamics can be produced naturally. In these situations fishing represents an exogenous force and the system can alternate between high and low equilibrium points and show abrupt fluctuations.

by Steele and Henderson (1984), Spencer (1997), and Spencer and Collie (1997b) to explain the dramatic and sudden shift in abundances exhibited by many marine fish species. In this case, harvesting can move fish populations between the equilibrium points (Fig. 6.3). Under these circumstances,

M. Lima a reduction in the fishing effort or harvesting rate may not result in an increased population size (toward the high equilibrium point) because generalist predators can keep prey populations at the lower equilibrium (predator pit; Holling 1965). When fishing effort increases in response to economic forces (or other factors such as highly successful previous harvests), a delayed feedback can be created which leads fish population dynamics toward regular and large amplitude cycles (predator–prey cycles). It is interesting to note that predator–prey theory has been used very rarely in fisheries, despite the classic predator–prey model developed by Vito Volterra as an attempt to give explanation to fluctuations in the Adriatic fisheries after the First World War (Kingsland 1995). Cycles in marine fish or invertebrate populations can be the consequence of the destabilized forces imposed by the economic inertia behind fisheries (Berryman 1991); regular cycles in the numerical fluctuations of several exploited species have been documented (Bostford 1986, Higgins et al. 1997, Spencer and Collie 1997a). In these cases, the fishing effort and the fish stock are mutually connected by a feedback loop. Owing to this feedback, an integral management strategy is necessary to reduce the amplitude of fish oscillations which requires reducing the fishing effort or its variability (or both). It is also possible for a harvested fish species to be a specialized predator or prey and be embedded in a mutual feedback loop with its own prey/ predator. In this case, fisheries managers may be confronted with a fish stock showing high temporal variability, involving regular cycles and, potentially, high amplitude in the oscillations. In these cases, fishing can have different effects on the dynamics of the fish stocks, depending on whether or not it is the prey or the predator that is the harvested species. For cases when the harvested species is the prey, fishing can interact with the predators by amplifying the amplitude and the period of the prey cyclic oscillations. For example, this mechanism may be behind the large cyclic oscillations observed in the capelin stock of the Barents Sea. This would be a consequence of the combined effects of the predators (herring and cod) and fishing (Hjermann et al. 2004). On the other hand, harvesting a specialized predator will have important impacts on the prey population. The expected theoretical consequence is a release of predation pressure such that the prey population will increase in size until another limiting factor (or factors) influences the population growth rate. Recent studies appear to document clear examples of this mechanism (Casini et al. 2008, Möllmann et al. 2008, Lindegren et al. 2009). Finally, the role of cooperation (Allee effects) in fisheries deserves special attention. It had been postulated that Allee effects in harvested populations

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Population dynamic theory for models in fisheries are quite important, in particular fish populations (Courchamp et al. 1999, Stephens and Sutherland 1999). There are different ecological mechanisms that can trigger Allee effects at intermediate or low fish densities. First, because of schooling behavior in many fish species (often considered to be a response to predation; Sæther et al. 1996, Courchamp et al. 1999), lower per capita growth rates can be expected at low densities (Berryman 1999; Fig. 6.4a). A second potential mechanism causing Allee effects is related to low mating success at low densities. For example, data from North Atlantic cod (Gadus morhua) supports the hypotheses that fertilization rate declines with reduced abundance (Rowe et al. 2004). Under this scenario, harvest can lead to reduced populations and an unstable equilibrium point. Fishing can even be the cause of such critical threshold points, having what is referred to as a “vertical perturbation effect” (Roughgarden and Smith 1996). Cooperation (among the fish) at low abundance can be responsible for the collapse of many fisheries operating under a system of maximum sustainable yields or constant catch policy (Lande et al. 1994, Stephens and Sutherland 1999, Mullon et al. 2005). Although the empirical evidence appears to be weak, around 21% of the recorded collapses follow the expected pattern of a collapse caused by low unstable equilibrium points (Mullon et al. 2005). By their very nature, unstable equilibrium points seldom are to be observed in nature (Berryman 1999). To make matters worse, collapse in fisheries can be caused by human-induced Allee effects (Berec et al. 2006). Constant or slowly decreasing exploitation rates in spite of decreases in the population size of the resource species result in an increase in the per capita mortality rate by fishing at low abundances (Dulvy et al. 2003, Berec et al. 2006). This simple mechanism can produce an unstable low-density equilibrium point caused by a constant predator population exhibiting a type II predatory functional response (Holling 1965, Berryman 1999; Fig. 6.4b). Fishery science has been developing around a variety of basic concepts and ideas since the 1950s (Quinn and Collie 2005). One of the most important assumptions in this history is that fish stocks are near the “equilibrium” of the system and the only effect of fishing mortality is to reduce the abundance of the harvested population. Although fisheries scientists have long been aware of the multiple effects of fishing in marine communities (Pitcher 2001), the problem of dealing with this issue, using a more general and theoretical perspective, has been absent. During this time, the models used in fisheries research incorporated varying degrees of complexity by adding explanatory variables and exploring alternative sources of variability (see Quinn and Collie 2005 for a review). Despite these advances in accounting for environmental factors, most of the well-accepted general ideas and concepts derived from straightforward population dynamics theory are ignored

M. Lima

Fig. 6.4.  The relationship between per capita population growth rates and population size, (a) showing cooperation at low densities (weak Allee effects). However, under strong fishing pressure the Allee effect becomes stronger and a low unstable (high risk of extinction) equilibrium point can be created (dotted line); (b) an Allee effect can be induced by the fishery as soon as exploitation rates decrease at a slower rate than does the population size, the probability of any individual being caught increases. Exploitation rates can also increase as the fish population declines, for example, when rarity is associated with high value.

in applied fishery models. For example, although the concept of ­predator functional responses is quite well developed and used in the conventional management (control) of pest species, it has not been incorporated in the theoretical tool box of fisheries scientists (Morris 1963, Holling 1965, Royama 1977, 1992, Berryman 1999). Therefore, instead of trying to compare

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Population dynamic theory for models in fisheries single-species models with ecosystem models (as currently being proposed as a basis for ecosystem-based fishery management), I call for an acceptance and understanding of the magnitude of our ignorance regarding simple population dynamics, especially insofar as population dynamics is brought to bear in management (i.e., “conventional management”; see Chapter 10). There is an important need for simpler and theoretically based models as proper diagnostic tools for analyzing fish population fluctuations. I think that the theory behind population dynamics offers the proper conceptual background to develop simple models for understanding and predicting the dynamics of fish populations. The information to be gained from such work cannot be ignored in deciding what harvest rates are sustainable. History has shown that some single-­species population models, such as the Ricker (1954) SR model and the Beverton and Holt (1957) SR model, are firmly based in logistic law and the principle of competition. Such models often represent observed population dynamics very well in cases where populations are regulated by competition and limited by resource availability. However, it is important to recognize that limiting factors can shift in time and space (Berryman 1999); extrinsic factors influence the population dynamics of every species. Generalist predators can regulate prey populations at sparse densities and cause meta-stable dynamics (Holling 1965); predatory specialist species interact with their prey and cause population cycles; other exogenous factors can shift equilibrium points or change the maximum per capita growth rates (Royama 1992). These extrinsic factors operate in conjunction with intrinsic factors (e.g., cooperation at low densities can cause unstable equilibrium points; Berryman 1999, and others). I think that beyond any debate about the relative merits of single-stock or EBMF models is the more important question of how to bring our understanding of either to the management process. One of the main points of this chapter is that current forms of management fail to fully account for basic principles of population dynamics, partly because we have yet to develop a full understanding of these dynamics. Many things that are generally accepted among scientists who study populations are ignored in management. How can we develop and use models based in general theoretical arguments and how can they be used to predict and manage our harvesting of natural populations? The theory of population dynamics has matured considerably in the last two decades. This theory can offer a substantial (and crucially important) part of the background to be used for understanding and managing our harvests of fish populations embedded in ecological communities. A final comment: as substantiated elsewhere (Berryman 1991, Berryman and Lima 2006), I think that a proper analysis of empirical information is preferable

M. Lima to ecological modeling to inform the management of harvesting to achieve sustainability in fisheries. It is the only way to deduce the proper feedback structure to be responsive to change. It provides important insight to the importance of exogenous effects (e.g., climate), and how fishing influences fish stocks. Such an approach is essential for the analysis and a posteriori modeling of observed data to estimate model parameter and establish causal connections between variables (Berryman 1991). In summary, it is fundamentally important that we avoid management that focuses on ecosystems to the exclusion of populations and their dynamics: an essential part of what is brought to management must be our understanding of population dynamics. The science of population dynamics provides crucial insight to, and understanding of, populations that must be accounted for in management of fisheries. References Alheit, J. and M. Niquen. 2004. Regime shifts in the Humboldt Current ecosystem. Progr. Oceanogr. 60:201–222. Benson, A.J. and A.W. Trites. 2002. Ecological effects of regime shifts in the Bering Sea and eastern North Pacific Ocean. Fish Fish. 3:95–113. Berec, L., E. Angulo, and F. Courchamp. 2006. Multiple Allee effects and population management. Trends Ecol. Evol. 22:185–191. Berryman, A.A. 1991. Can economic forces cause ecological chaos? The case of the Northern Dungeness crab fishery. Oikos 62:106–109. Berryman, A.A. 1999. Principles of Population Dynamics and their Application. Cheltenham: Stanley Thornes. Berryman, A.A. and P. Kindlmann. 2008. Population Systems: A General Introduction. New York, NY: Springer. Berryman, A.A. and M. Lima. 2006. Deciphering the effects of climate on animal populations: diagnostic analysis provides new interpretation of Soay sheep dynamics. Am. Nat. 168:784–795. Bostford, L.W. 1986. Effects of environmental forcing on age structured populations: northern California Dungeness crab (Cancer magister) as an example. Can. J. Fish. Aquat. Sci. 43:2345–2352. Beverton, R. 1998. Fish, fact and fantasy: a long view. Rev. Fish Biol. Fish. 8:229–249. Beverton, R.J.H. and S.J. Holt. 1957. On the Dynamics of Exploited Fish Populations. London: HM Stationery Office. Casini, M., J. Lovgren, J. Hjelm et al. 2008. Multilevel trophic cascades in a heavily exploited open marine ecosystem. Proc. R. Soc. B 275:1793–1801. Courchamp, F., T. Clutton-Brock, and B. Grenfell. 1999. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14:405–410. Dulvy, N.K., Y. Sadovy, and J.D. Reynolds. 2003. Extinction vulnerability in marine populations. Fish Fish. 4:25–64.

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M. Lima Ricker, W.E. 1954. Stock and recruitment. J. Fish. Res. Board Can. 11:559–623. Roughgarden, J. and F. Smith. 1996. Why fisheries collapse and what to do about it. Proc. Natl. Acad. Sci. USA 93:5078–5083. Rowe, S., J.A. Hutchings, D. Bekkevold, and A. Rakitin. 2004. Depensation, probability of fertilization, and the mating system of Atlantic cod (Gadus morhua L.). ICES J. Mar. Sci. 61:1144–1150. Royama, T. 1977. Population persistence and density dependence. Ecol. Monogr. 47:1–35. Royama, T. 1992. Analytical Population Dynamics. London: Chapman and Hall. Sæther, B.E., T.H. Ringsby, and E. Röskaft. 1996. Life history variation, population processes and priorities in species conservation: towards a reunion of research paradigms. Oikos 77:217–226. Spencer, P.D. 1997. Optimal harvesting of fish populations with nonlinear rates of predation and autocorrelated environmental variability. Can. J. Fish. Aquat. Sci. 54:59–74. Spencer, P.D. and J.S. Collie. 1997a. Patterns of population variability in marine fish stocks. Fish. Oceanogr. 6:188–204. Spencer, P.D. and J.S. Collie. 1997b. Effect of nonlinear predation rates on rebuilding the Georges Bank haddock (Melanogrammus aeglefinus) stock. Can. J. Fish. Aquat. Sci. 54:2920–2929. Steele, J.H. and E.W. Henderson. 1984. Modeling long-term fluctuations in fish stocks. Science 224:985–987. Stephens, P.A. and W.J. Sutherland. 1999. Consequences of the Allee effect for behaviour, ecology and conservation. Trends Ecol. Evol. 14:401–405. Turchin, P. 2003. Complex Population Dynamics: A Theoretical/Empirical Synthesis. Princeton, NJ: Princeton University Press. Worm, B., R. Hilborn, J.K. Baum et al. 2009. Rebuilding global fisheries. Science 325:578–585.

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Recovery of former fish productivity: philopatric behaviors put depleted stocks in an unforeseen deadlock henrik svedäng, massimiliano cardinale, and carl andré

An attempt has been made to determine the spawning areas of the principal fish, plaice, herring, cod, haddock, to define the spawning migrations, the nurseries where the young fish develop, etc. It is hoped it may in this way be possible to find the general laws for the appearance of biological groups … Johan Hjort (1926)

Abstract It is widely recognized that knowledge of population structure and connectivity is vital for sustainable use of the world’s marine fisheries resources. The harvest of each individual population unit should ideally be managed to account for its specific level of productivity directly. Whereas most emphasis has been placed on identifying discrete population units, much less effort has been given to the mechanisms behind stock separation. Evidence is accumulating to indicate that philopatric behaviors (stock-specific behaviors) are important, in this regard, not only in other kinds of species, but also in marine free-­spawning fish species. Recent studies on herring and cod in the waters bounded by Norway, Sweden, and Denmark (Skagerrak–Kattegat) indicate that philopatric behavior is a key factor in shaping stock structure. Under such circumstances, an area depleted of its stock components can only slowly

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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H. Svedäng et al. be repopulated by fish from adjacent areas. Repopulation, for example, may depend on adults successfully aggregating and reproducing at new locations, with new migratory patterns that are dependent on new behavior. This can be a very time-consuming process, and the slow formation of new viable populations units results in a bleak forecast for population recovery. Detailed records from the longline fishery in the 1800s and trawl surveys in the Skagerrak– Kattegat since 1901 give what, to us, is irrefutable evidence of the transformation of a formerly productive eastern North Sea into an ichthyologic desert due to intense fishing. The barren nature of this aspect of ecosystems in this area is reinforced by the fact that, in spite of a yearly inflow of recruits for the species that have been eradicated, depleted areas remain abandoned because the homing behavior of the recruits that come from other units that remain productive is homing behavior that results in failure in their new habitat. Population thinking By necessity, the German fishery scientist Friedrich Heincke invented the modern concept of populations while studying the herring (Clupea harengus) stock complex in the North Sea region in the late nineteenth century (Sinclair and Solemdal 1988). In spite of the Darwinian evolutionary paradigm, biology still reflected a Platonic world of essentialistic species and individuals variably appreciated as elements that could be understood in the more modern context. The commercially important herring had been at the center of the debate for a long period of time:  it was argued that all herring found in the northeast Atlantic, possibly including also the northwest Atlantic, belonged to the same “group/shoal/stock,” which, furthermore, was supposed to be found in the Polar seas during winter after seasonal migrations. This interpretation fell under what was called “migration theory.” This theory assumed, for instance, that overfishing could not be a local phenomenon, because exhaustion of biomass at one locality was expected to soon be replenished from the common pool of individuals by migration. Compounding the unrealistic nature of the argument is the fact that it excludes meaningful levels of organization between the individual-level and the species-level. The Swedish zoologist Sven Nilsson had, in the early 1830s, dismissed the migration theory, and vigorously claimed the existence of a multitude of local herring “races” in the Skagerrak (Nilsson 1828). These local “tribes” were supposed to show very restricted migrations. The depletion due to overfishing of the local races was likely the reason behind the ending, in 1808, of the “herring period” in the county of Bohuslän in the eastern Skagerrak (Nilsson 1827). Heincke realized that the issue of segregation and local variability in abundance could never be resolved unless very detailed studies were performed and

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Philopatric behaviors put depleted stocks in a deadlock new ecological concepts developed in order to describe and analyze the occurrence and distribution of important commercial fish species. To make meaningful predictions about the dynamics of fish populations, analyses had to be focused on coherent groups of fish that showed similarities on relevant spatial and temporal scales and that were linked to common spawning areas. The birth of the population concept had enormous consequences, not only for fishery biology, but also for the development of ecological and evolutionary theory. Soon the notion of populations was recognized as a very powerful tool in describing the dynamics of fish stocks: year classes could be followed as they propagated from one year to the next (Hjort 1914); nursery areas and spawning areas were detected and linked with each other. Recent advances in population genetics, otolith chemistry, and tagging techniques have improved the ability to identify population units (Hunter et al. 2004, Wright et al. 2006, Hauser and Carvalho 2008). Genetic divergence is, in addition to population size and age structure, also dependent on gene flow wherein members from one population take part in the reproduction of another population. Even at moderate rates of gene flow among population units, genetic methods will often fail to detect genetically distinct populations (Waples et al. 2008), and hence stock units relevant to fisheries management may go unnoticed (Hauser and Carvalho 2008). Nevertheless, cod and herring, as well as other temperate marine fishes of great ecological and commercial importance, have been shown to be divided into subpopulations on remarkably small spatial scales (Ruzzante et al. 2006, Jorde et al. 2007, Reiss et al. 2009). Concurrent variation in ecologically important traits indicates that such small-scale populations are also adapted to local environmental conditions. Population-separating mechanisms The mechanisms that lead to population separation were, however, less clear to the researchers involved in the first steps towards comprehensive population thinking (Secor 2005). Population structures were identified and the complex life cycles were outlined, but what contributed to their origins and how were they maintained? Migration and homing behavior were recognized to be of key interest but the migratory and orientation abilities of fishes were also questioned; the issues were debated. After more than a century of investigation, it is now fairly clearly established that salmonid populations are separated, in large part, owing to philopatric behavior (Secor 2005). Salmonids provide much more convenient circumstances for studying homing behaviors due to the confinement of the egg, larval, and juvenile phases to easily identified and recognizable inland waters. Marking experiments have gradually shown that salmonid fish possess a keenly honed

H. Svedäng et al. ability to navigate as they cross entire ocean basins after leaving their home river and, yet, have the capacity to find and return to their natal spawning grounds for spawning. There is also compelling evidence of natal philopatry in eels (Anguilla sp.) and Atlantic bluefin tuna (Thunnus thynnus; Rooker et al. 2008), although the link between larval drift and return migration still needs to be validated.1 For all these fishes, the migration pattern, or the philopatric behavior, clearly defines the population unit (Secor 2005): whether a population is migratory or stationary, its individuals return to their natal spawning ground to reproduce. However, for other marine fishes, especially commercially important migratory species, the mechanisms behind population discreteness have been more difficult to explain. Since temperate marine fishes often show ontogenetic and seasonal shifts in habitat choice, direct observation of the entire life cycle is problematic when studying processes that shape population structure (Metcalfe 2006, Bradbury and Laurel 2007, Heath et al. 2008). For broadcast spawners at sea, there are at least two potential contributing factors for philopatric behavior. One involves imprinting during the very early life stages, possibly in combination with inherited behavioral responses, and the other involves population division in marine fishes that may result from physical forcing on the dispersal of larvae (Iles and Sinclair 1982, Bradbury and Laurel 2007, Knutsen et al. 2007a, 2007b, Heath et al. 2008). Oceanographic and environmental features such as bathymetry, temperature, and salinity boundaries may act as barriers to connectivity and gene flow, resulting in population differentiation (e.g., Ruzzante et al. 1998, Johannesson and André 2006) or that putative hybridization takes place in certain zones (Nielsen et al. 2003). As an extension of this mechanism for preserving metapopulation structure, socially transmitted behaviors have been suggested as a key factor to maintain population or spawning site diversity (e.g., McQuinn 1997). For Atlantic herring, it has been suggested that migration between spawning, wintering, and feeding grounds is a socially transferred behavior, where new year classes adopt the same migratory patterns as older herring cohorts (Corten 2002). Case study: the population architecture in the eastern North Sea Herring

In many places, recruits of herring from areas of different origin coexist in nursery or feeding areas. For example, the waters of the Skagerrak and Kattegat seem to function as nursery areas for a variety of gadoid and herring   See www.eeliad.com

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Philopatric behaviors put depleted stocks in a deadlock stocks originating from the adjacent North Sea or Baltic Sea (Munk et al. 1999, Svedäng 2003, Ruzzante et al. 2006). Thus, herring of local origin co-exist with juvenile and adult herring from both the western Baltic Sea and the North Sea (Ruzzante et al. 2006). Areas where herring spawn in the autumn are located in the western part of the North Sea along the English coast. After hatching, some of the resulting larvae drift into the Skagerrak where they stay between one and two years before swimming back for spawning in the North Sea. Similarly, larvae also drift to the Skagerrak from spring spawning areas in the Kattegat and, in particular, in the western Baltic. In addition to the larval drift into the Skagerrak, adult herring of the western Baltic population also undergo annual feeding migrations towards the Skagerrak during summer. In other words, in the summertime, adult herring in the Skagerrak could be of local origin or from the Kattegat/Western Baltic, whereas juvenile herring are mostly from the North Sea stock. During the winter, adult herring are to a large extent of local origin in the Skagerrak, whereas, among juvenile herring, the North Sea component dominates. In spite of extensive physical mixing that could significantly affect larval dispersal, the population structure is stable, showing significant genetic separation between all the listed stock components (Ruzzante et al. 2006). Taken together, these findings clearly point to philopatric behavior as the key factor in keeping herring populations distinct from one another in this area. It is also noteworthy that, in spite of extensive mixing during the juvenile stages, the genetically distinct populations seem to be preserved by migration to different spawning grounds, i.e., social transmission of migratory behaviors seems to be very unlikely. Atlantic cod

Based on a similar rationale, evidence for philopatry has been demonstrated for cod in the eastern part of the Skagerrak (Svedäng et al. 2007a). Disappearance of local spawning aggregations due to overfishing has provided a field experiment for analyzing homing behavior and recolonization processes. During these circumstances, high juvenile abundance coincides with permanent low adult cod abundance (Svedäng 2003, Svedäng and Svenson 2006). In search of an explanation, Svedäng (2003) and Cardinale and Svedäng (2004) hypothesized that juvenile cod in the eastern inshore waters of the Skagerrak are currently coming from offshore spawning areas, especially in the North Sea. Consistent with this explanation, Munk et al. (1999) presented data on larvae abundance of various gadoid species in the Skagerrak with origins in the North Sea. It can be concluded that cod larvae, together with other species, are in fact transported from the North Sea into the Skagerrak and the northern part of the Kattegat. This is consistent with, for example, the transportation of

H. Svedäng et al. glass eel into the eastern North Sea. Genetic analyses of juvenile cod together with ecological and oceanographic modeling have corroborated the hypothesis that recruits in the Skagerrak originate primarily in the North Sea (Knutsen et al. 2004, Stenseth et al. 2006). The unexpectedly low adult abundance following strong recruitment episodes is suggested to be due to migration of juvenile/ maturing fish as they return to areas other than the Skagerrak at a certain size or age (Pihl and Ulmestrand 1993, Svedäng 2003, Svedäng and Svenson 2006). A plausible explanation for such a series of linked observations in cod and herring is thus philopatric behavior in marine fishes, i.e., most offspring return to their natal spawning grounds regardless of distance. In contrast, according to the theory of physical forcing as a stock-separating mechanism (e.g., Nielsen et al. 2005), juvenile fish should remain and reproduce fairly close to the areas where they have been transported. As a consequence, some fish from the original population will “be lost at sea” once transported to a different area (Sinclair 1988). These two competing hypotheses were confronted with data from a recent archival tagging experiment. Tags were placed on prespawning cod in the Skagerrak–Kattegat area. Clear evidence was shown for non-random migrations towards spawning grounds in the North Sea (Svedäng et al. 2007a, 2007b). Cod tagged at different localities showed non-random, directional movements in agreement with the hypothesis that the cod population in this region comprises a mixture of resident and migratory stocks. The study showed pronounced differences in cod migratory behavior dependent on where they were tagged in the Skagerrak–Kattegat area. A large and increasing proportion of the tagged cod off the Skagerrak coast moved in a westerly direction after they were released. Some cod tagged in the Skagerrak migrated to the Kattegat, which could imply either straying (cf. Windle and Rose 2005), or homing of cod originating from the Kattegat. Most cod in the Kattegat showed very limited movement; the recapture positions were scattered around the release sites throughout the year, although some swam towards the Skagerrak/North Sea during the spawning period. Most return migration from the Kattegat towards the North Sea seems to take place at ages 2 to 3 years (Svedäng et al. 2007a). A question remains: can cod subpopulation structures occur on an even finer scale than those observed between the Kattegat and Skagerrak/North Sea? This question cannot be definitively addressed at this time. Preliminarily, however, such a differentiation within the Kattegat is probable. This contention is based on the occurrence of several separate spawning sites within the Kattegat (e.g., Vitale et al. 2008), and the fact that some previously important spawning sites such as Laholmsbukten and Skälderviken were abandoned by cod during the 1990s and have not been recolonized in spite of the presence of juvenile cod

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Philopatric behaviors put depleted stocks in a deadlock (Svedäng and Bardon 2003). Furthermore, there seems also to be a population separation between the hydrographically closely related areas of Kattegat and Öresund (ICES subdivision 23). Cod spawning taking place in the Öresund and the stock within the Öresund is demographically very different from the one in the Kattegat, presumably due to different exploitation patterns and efficient stock-separation mechanisms. A macroecological perspective on population diversity

In evolutionary terms, the most important factor favoring philopatric behaviors in salmonids is suggested to be the return of locally adapted individuals to suitable habitats (Hendry et al. 2004). For marine fishes such as the Atlantic cod or herring, reproduction is located in areas confined to the continental shelf. However, most of the sea, including the shelf, represents habitats that are not suitable for reproduction. A reproductive strategy that is easily triggered to result in spawning at maladaptive locations would not be viable. Very specific areas are more suitable for spawning. It is such sites that offer the opportunity for successful reproduction whether they are used by migratory or resident stocks (Iles and Sinclair 1982, Nordeide 1998, Robichaud and Rose 2004, Bradbury et al. 2008, Howell Huntting et al. 2008). The selective advantage of residency would favor such a strategy. The geographic separation of spawning sites thus leads to distinctive subpopulations and the separation of spawning aggregations. Such separation can easily explain the phenomenon of population richness in species such as cod and herring. Robichaud and Rose (2004) pointed out that the size of the stock is linked to migration pattern. Sedentary cod populations are small and their distribution is restricted whereas the largest stocks are linked to extensive migrations such as exemplified by the Northern cod along the Labrador coast and the Arctic cod in the Barents Sea. The small resident populations are of less economic importance but represent considerable value in evolutionary terms, as they may represent most the species’ genetic variability (Hauser and Carvalho 2008). In a macroecological perspective, the distribution of a multitude of spawning aggregations within species such as herring or cod could reflect an evolutionarily advantageous way of utilizing available food resources more efficiently (Ruzzante et al. 2006). The average level of recruitment may be enhanced by partitioning the overall population into a number of separate populations within a larger marine habitat wherein more juveniles are dispersed to the more suitable feeding areas. Within this scenario, the annual variation in recruitment success would be more consistent between spawning sites even though the mean level of reproductive success between populations may vary on longer time scales.

H. Svedäng et al. Regarding the portion of the cod stock in the Skagerrak/North Sea that is migratory, it may be hypothesized that spawning cod in the eastern North Sea constitute a subpopulation. This subpopulation can then be seen as one that utilizes the strong counter-clockwise water circulation in the Skagerrak for dispersal of their offspring. This would enhance their opportunity for utilization of large nursery areas. The return migrations to the eastern North Sea during the spawning period, as well as cod observed to be roaming around in the Skagerrak for feeding during other parts of the year, are consistent with the known life-history patterns of cod in this region. Food availability is favorable for growth in the eastern Skagerrak, with abundant stocks of crustaceans (Pandalus borealis, Nephrops norvegicus) and herring. Populations as behavioral entities The proximate causes for the natal homing behavior are still unknown for most species, but such behavior is known to occur. Alternative explanations for observed dynamics are often at odds with existing environmental factors. For fish, the complex migratory patterns as revealed for cod (Svedäng et al. 2007a) and herring (Ruzzante et al. 2006) in the Skagerrak–Kattegat area are not consistent with environmental cues, such as the prevailing direction of sea currents (Harden-Jones 1968). For marine fishes like herring and Atlantic cod, stock structure may thus be heavily behavioral and imprinted early in their life history, before juveniles of different origins intermingle in nursery areas. As observed for restocked eels at the glass eel stage taken from western Europe to, for instance, the Baltic Sea, the relocation seems to lead to complete disorientation and an inability to undertake the return migration to the Sargasso Sea (Westin 2003). In other words, although recognition of the position of the spawning site might be inherited, the return migration track seems to be imprinted through association with fish already familiar with the process during early life stages (Nischi and Kawamura 2005). If this is the case, an area currently depleted of its stock components can be repopulated by fish from adjacent areas only very slowly. Such a repopulation is dependent on straying adult fish which aggregate at new locations and give rise to new populations (cf. Bekkevold et al. 2007, Heath et al. 2008) by learning to follow what are, to them, new migratory routes that lead from areas of adequate foraging to areas where they can reproduce successfully. The disappearance and slow recovery of local cod stocks in the North Sea region (Svedäng 2003, Svedäng and Svenson 2006, Wright et al. 2006) and in the northwest Atlantic (Brattey et al. 2004, Wroblewski et al. 2005) is illustrative of such dynamics. Even in cases where inshore enclaves have shown signs of initial

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Philopatric behaviors put depleted stocks in a deadlock recovery, adjacent areas that were formerly equally productive have not been repopulated (Bundy and Fanning 2005). The recognition of situations in which stocks can be characterized as slow-changing behavioral population units is of obvious critical importance for their assessment and the management of their harvests (Smedbol and Stephenson 2001, Waples et al. 2008). Historical population structure

The Skagerrak–Kattegat has historically been an important fishing area. This is true, in particular, for herring inshore, but also, off the coast, for demersal fish such as cod, haddock, ling, halibut, rays, and skates since the Middle Ages (Haneson and Rencke 1923). The seine fishery for herring in inshore areas has been important; however, especially at times when the herring stocks had “disappeared,” of even more importance was longline gear as used until the twentieth century. The hasty development of the longline fishery in the mid 1800s led to a decline in demersal stocks, which was already notable in the 1870s. Each decline led to longline fishing in more distant fishing grounds (i.e., eventually to the west of the Shetlands), successively, specifically originating from the county of Bohuslän. At the beginning of the twentieth century trawl fishery was introduced. This, and other innovations in fishing technology, led to a rapid depletion of most demersal fish stocks in the eastern North Sea as early as the first part of the 1900s (Mollander 1950). In parallel with technological advances, fishery science became a prominent factor in fishing and its management. The Swedish Board of Fisheries recently compiled data for catch per unit of effort (CPUE) and individual size that were derived exclusively from bottom trawl surveys initiated in 1901, immediately before the onset of commercial trawl fisheries (Andersson 1954). This survey, combined with the International Bottom Trawl Surveys (IBTS) (Anon. 1992) and Swedish national bottom trawl surveys, extends the time series up to the present. The analysis presented here provides new insight to the former population complexity in the Skagerrak–Kattegat area for several gadoid species (Figs 7.1–7.3). Overall, abundance declined over time for three gadoid species: cod, haddock, and pollack. Periods of reduced fishing pressure, especially during the Second World War, resulted in noticeable effects on the demersal fish fauna. However, after the 1950s, raw abundance was not alone in declining; the number of adult fish aggregations also dwindled. The overall depletion progressed steadily from decade to decade. For pollack, it is now impossible to detect aggregations from the survey data. For haddock, one aggregation is still found in the western part of the Skagerrak; in the Kattegat no haddock aggregations are to be found, in spite of the fact that large aggregations were found

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Fig. 7.1.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for cod (Gadus morhua), in the Skagerrak–Kattegat (20 years average for 107 years) estimated between January and March (Cardinale et al. in prep.). This figure is also reproduced in the color plate section.

there in the beginning of the 1900s. For cod, some spawning aggregations still exist in the southeastern part of the Kattegat. However, abundance and most of the subpopulation complexity has been progressively eradicated (in parallel to what is observed in eastern Skagerrak inshore waters; Svedäng 2003, Svedäng and Svenson 2006). Tiny expectations?

The successive expiration of the ichthyological predatory guilds has thus considerably reduced the related productivity of some of the most important shelf areas in the North Atlantic. The regional predicament encountered in the eastern North Sea is illustrative of the situation when no, or almost no, populations are left. The adult abundance and stock structure of fish populations in the Kattegat and Skagerrak have not been replenished in spite of continuous inflowing recruits mainly from the North Sea. Cod, haddock, pollack, and possibly whiting recruits are transported from the North Sea into the Skagerrak

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Philopatric behaviors put depleted stocks in a deadlock

Fig. 7.2.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for haddock (Melanogrammus aeglefinus), in the Skagerrak– Kattegat (20 years average for 107 years) estimated between January and March (Cardinale et al. in prep.). This figure is also reproduced in the color plate section.

and Kattegat (Munk et al. 1999, Cardinale and Svedäng 2004). This inflow does not, however, alter the progressive desertification of these formerly productive sheltered seas, triggered by fishing on stocks characterized by their philopatric behaviors – characteristics that are clearly involved in making the stocks vulnerable to overfishing. Conclusions Previous work has resulted in the conclusion that numerous co-­ existing fish populations are the norm rather than the exception. This conclusion has been verified, to a very large extent, owing to an increased ability to identify separate populations (using a variety of techniques, including genetic analysis). In parallel, evidence is accumulating that shows that an important, if not the dominating, separating mechanism is philopatric behavior. This is now also being observed in broadcast-spawning fish such

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Fig. 7.3.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for pollack (Pollachius pollachius) in the Skagerrak–Kattegat (20 years average for 107 years) estimated between January and March (Cardinale et al. in prep.). This figure is also reproduced in the color plate section.

as temperate marine species. Environmental forcing as a proximate mechanism for shaping population structure, although important in some ways, may have been slightly exaggerated in the past (Sinclair 1988, Bradbury and Laurel 2007), to the degree that it is interpreted to exclude the effects of philopatric behavior. The connectivity between population units is clearly influenced by the flow of currents and their effect on the dispersal of early life stages. Socially transmitted spawning and migratory behaviors may seem to be contradicted by the mixing of juveniles at nursery areas where the genetic separation between population units is preserved. Historical data reveal that not only abundance but also former population richness were much higher than previously thought; the influence of protracted and excessive overfishing has been underestimated. The present depleted status of many stocks is not easily reversed (Anon. 2007) because, in large part, recolonization is likely to be rather small due to the slow process of establishing new viable spawning aggregations by the straying of adults (Hauser and Carvalho 2008). Thus,

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Philopatric behaviors put depleted stocks in a deadlock forecasts of stock recovery to former levels of abundance have to be seen as highly optimistic, in most or all cases, and biased by a lost memory of past population richness.

References Andersson, K.A. 1954. Fiskar och fiske i Norden. Vol. 1. Natur och kultur, Stockholm. Anon. 1992. Manual for International Bottom Trawl Surveys, Revision IV. ICES CM 1992/H:3/Addendum. Anon. 2007. Report of the ICES Advisory Committee on Fishery Management, Advisory Committee on the Marine Environment and Advisory Committee on Ecosystems, 2007. ICES Advice. Books 1–10. Bekkevold, D., L.A.W. Clausen, S. Mariani et al. 2007. Divergent origins of sympatric herring population components determined using genetic mixture analysis. Mar. Ecol. Progr. Ser. 337:187–196. Bradbury, I.R. and B.J. Laurel. 2007. Defining “natal homing” in marine fish populations. Mar. Ecol. Progr. Ser. 349:307–308. Bradbury, I.R., B.J. Laurel, D. Robichaud et al. 2008. Discrete spatial dynamics in a marine broadcast spawner: re-evaluating scales of connectivity and habitat associations in Atlantic cod (Gadus morhua L.) in coastal Newfoundland. Fish. Res. 2–3:299–309. Brattey, J., N. Cadigan, B.P. Healey et al. 2004. Assessment of the cod (Gadus morhua) stock in NAFO Subdivision 3Ps in October 2004. Canadian Science Advisory Secretariat No. 2004/083. Bundy, A. and L.P. Fanning. 2005. Can Atlantic cod (Gadus morhua) recover? Exploring trophic explanations for the non-recovery of the cod stock on the eastern Scotian Shelf, Canada. Can. J. Fish. Aquat. Sci. 62:1474–1489. Cardinale, M. and H. Svedäng. 2004. Modeling recruitment and abundance of Atlantic cod, Gadus morhua, in the eastern Skagerrak-Kattegat (North Sea): evidence of severe depletion due to a prolonged period of high fishing pressure. Fish. Res. 69:263–282. Cardinale, M., H. Svedäng, V. Bartolino, L. Maiorano, M. Casini, J. Hjelm, and H. Linderholm. (in prep.). Sequential population extinction of large predatory fish during the last century. Corten, A. 2002. The role of “conservatism” in herring migrations. Rev. Fish Biol. Fish. 11:339–361. Haneson, V. and K. Rencke. 1923. Bohusfisket. Skrifter utgivna till Göteborgs stads trehundraårsjubileum genom Jubileumsutställningens publikationskommitté. XIX. Göteborg. Harden-Jones, F.R. 1968. Fish Migrations. London: Edward Arnold. Hauser, L. and G.R. Carvalho. 2008. Paradigm shifts in marine fisheries genetics: ugly hypotheses slain by beautiful facts. Fish Fish. 9:333–362. Heath, M.R., P.A. Kunzlik, A. Gallego, S.J. Holmes, and P.J. Wright. 2008. A model of meta-population dynamics for North Sea and West of Scotland

H. Svedäng et al. cod – the dynamic consequencies of natal fidelity. Fish. Res. 93: 92–116. Hendry, A.P., V. Castric, M.T. Kinnison, and T.P. Quinn. 2004. The evolution of philopatry and dispersal – homing versus straying in salmonids. In Hendry, A.P. and Stearns, S.C. (eds.), Evolution Illuminated: Salmon and their Relatives. Oxford: Oxford University Press. Hjort, J. 1914. Fluctuations in the great fisheries of northern Europe. Rapport et Proces-Verbaux Réunions. Conseil International pour l´Exploration de la Mer 20:1–228. Hjort, J. 1926. Fluctuations in the year classes of important food fishes. Rapport et Proces-Verbaux Réunions. Conseil International pour l´Exploration de la Mer 1:1–38. Howell Huntting, W., M. Morin, N. Rennels, and D. Goethel. 2008. Residency of adult Atlantic cod (Gadus morhua) in the western Gulf of Maine. Fish. Res. 91:123–132. Hunter, E., J.D. Metcalfe, G.P. Arnold, and J.D. Reynolds. 2004. Impacts of migratory behaviour on population structure in North Sea plaice. J. Anim. Ecol. 73:377–385. Iles, T.D. and M. Sinclair. 1982. Atlantic herring: stock discreteness and abundance. Science 215:627–633. Johannesson, K. and C. André. 2006. Life on the margin – genetic isolation and loss of variation in a peripheral marine ecosystem. Mol. Ecol. 15:2013–2030. Jorde, P.E., H. Knutsen, S.H. Espeland, and N.C. Stenseth. 2007. Spatial scale of genetic structuring in coastal cod Gadus morhua and geographic extent of local populations. Mar. Ecol. Progr. Ser. 343:229–237. Knutsen, H., C. André, P.E. Jorde et al. 2004. Transport of North Sea cod larvae into the Skagerrak coastal populations. Proc. R. Soc. Ser. B 271:1337–1344. Knutsen, H., E.O. Olsen, L. Cianelli et al. 2007a. Egg distribution bottom topography and small-scale population structure in a coastal marine system. Mar. Ecol. Progr. Ser. 333:249–255. Knutsen, H., P.E. Jorde, O.T. Albert, A.R. Hoelzel, and N.C. Stenseth. 2007b. Population genetic structure in the North Atlantic Greenland halibut: influenced by oceanic current systems? Can. J. Fish. Aquat. Sci. 64:857–866. McQuinn, I.H. 1997. Metapopulations and the Atlantic herring. Rev. Fish Biol. Fish. 7:297–329. Metcalfe, J.D. 2006. Fish population structuring in the North Sea: evidence, processes and mechanisms. J. Fish Biol. Suppl. C. 69:48–65. Mollander, A.R. 1950. Swedish haddock fishery during three decades. Instit. Mar. Res. Ser. Biol. 1:1–40. Munk, P., P.-O. Larsson, D.S. Danielssen, and E. Moksness. 1999. Variability in frontal zone formation and distribution of gadoid fish larvae at the shelf break in the northeastern North Sea. Mar. Ecol. Progr. Ser. 177:221–233. Nischi, T. and G. Kawamura. 2005. Anguilla japonica is already magnetosensitive at the glass eel phase. J. Fish Biol. 67:1213–1224.

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Philopatric behaviors put depleted stocks in a deadlock Nielsen, E.E., M.M. Hansen, D.E. Ruzzante, D. Meldrup, and P. Grønskjær. 2003. Evidence of a hybrid-zone in Atlantic cod (Gadus morhua) in the Baltic and the Danish Belt Sea revealed by individual admixture analysis. Mol. Ecol. 12:1497–1508. Nielsen, E.E., P. Grønkjær, D. Meldrup, and H. Paulsen. 2005. Retention of juveniles within a hybrid zone between North Sea and Baltic Sea Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 62:2219–2225. Nilsson, S. 1827. Berättelse om fiskerierna i en del av Sverige och Norge, Kongl. Sv. Landtbruksakad:s handl. Stockholm 1827 (in Swedish). Nilsson, S. 1828. Förnyad underdånig berättelse om fiskerierna i Bohuslän. Sthlm, Kongl. tryckeriet. 4:o (in Swedish). Nordeide, J.T. 1998. Coastal cod and north-east Arctic cod – do they mingle at the spawning grounds in Lofoten? Sarsia 83:373–379. Pihl, L. and M. Ulmestrand. 1993. Migration pattern of juvenile cod (Gadus morhua) on the Swedish west coast. ICES J. Mar. Sci. 50:63–70. Reiss, H., G. Hoarau, M. Dickey-Collas, and W.J. Wolff. 2009. Genetic population structure of marine fish: mismatch between biological and fisheries management units. Fish Fish. doi: 10.1111/j.1467–2979.2008.00324.x. Robichaud, D. and G.A. Rose. 2004. Migratory behaviour and range in Atlantic cod: inference from a century of tagging. Fish Fish. 5:185–214. Rooker, J.R., D.H. Secor, G. De Metrio et al. 2008. Natal homing and connectivity in Atlantic bluefin tuna populations. Science 322:742–744. Ruzzante, D., C.T. Taggart, and D. Cook. 1998. A nuclear DNA basis for shelfand bank-scale population structure in northwest Atlantic cod (Gadus morhua): Labrador to Georges Bank. Mol. Ecol. 7:1663–1680. Ruzzante, D.E., S. Mariani, D. Bekkevold et al. 2006. Biocomplexity in a highly migratory marine pelagic fish. Proc. R. Soc. B 273:1459–1464. Secor, D.H. 2005. Fish migration and the unit stock: three formative debates. In Cadrin, S.X., Friedland, K.D., and Waldman, J.R. (eds.), Stock Identification Methods. Applications in Fishery Science. Burlington, MA: Elsevier Academic Press, pp. 17–44. Sinclair, M. 1988. Marine Populations: An Essay on Population Regulation and Speciation. Seattle, WA: University of Washington Press. Sinclair, M. and P. Solemdal. 1988. The development of “population thinking” in fisheries biology between 1878 and 1930. Aquat. Living Res. 1:189–213. Smedbol, R.K. and R. Stephenson. 2001. The importance of managing withinspecies diversity in cod and herring fisheries of the north-western Atlantic. J. Fish Biol. 59:109–128. Stenseth, N.C., P.E. Jorde, K.S. Chan et al. 2006. Ecological and genetical impact of larval drift: the Atlantic cod as an example. Proc. R. Soc B 273:1085–1092. Svedäng, H. 2003. The inshore demersal fish community on the Swedish Skagerrak coast: regulation by recruitment from offshore sources. ICES J. Mar. Sci. 60:23–31. Svedäng, H. and G. Bardon. 2003. Spatial and temporal aspects of the decline in cod (Gadus morhua L.) abundance in the Kattegat and eastern Skagerrak. ICES J. Mar. Sci. 60:32–37.

H. Svedäng et al. Svedäng, H. and A. Svenson. 2006. Cod (Gadus morhua L.) populations as behavioural units: inference from time series on juvenile cod abundance in the Skagerrak. J. Fish Biol. Suppl. C 69:151–164. Svedäng, H., D. Righton, and P. Jonsson. 2007a. Migratory behaviour of Atlantic cod Gadus morhua: natal homing is the prime stock-separating mechanism. Mar. Ecol. Progr. Ser. 345:1–12. Svedäng, H., D. Righton, and P. Jonsson. 2007b. Defining “natal homing” in marine fish populations; need for inference in fishery science: reply to Bradbury and Laurel (2007). Mar. Ecol. Progr. Ser. 347:309–310. Vitale, F., P. Börjesson, H. Svedäng, and M. Casini. 2008. The spatial distribution of cod (Gadus morhua L.) spawning grounds in the Kattegat, eastern North Sea. Fish. Res. 90:36–44. Waples, R.S., A.E. Punt, and J.M. Cope. 2008. Integrating genetic data into management of marine resources: how can we do it better? Fish Fish. 9:423–449. Westin, L. 2003. Migration failure in stocked eels Anguilla anguilla. Mar. Ecol. Progr. Ser. 254:307–311. Windle, M.J.S. and G.A. Rose. 2005. Migration route familiarity and homing of transplanted Atlantic cod (Gadus morhua). Fish. Res. 75:193–199. Wright, P.J., F.C. Neat, F.M. Gibb, I.M. Gibb, and H. Thordarson. 2006. Evidence for metapopulation structuring in cod from the west of Scotland and North Sea. J. Fish Biol. Suppl. C 69:181–199. Wroblewski, J., B. Neis, and K. Gosse. 2005. Inshore stocks of Atlantic cod are important for rebuilding the East Coast fishery. Coast. Manage. 33:411–432.

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Boundary shifts: from management to engagement in complexities of ecosystems and social contexts peter j. taylor

Abstract Three pictures of savanna ecology are used to draw attention to the explicit or implicit boundaries adopted by environmental and resource scientists and to the potential for researchers’ accounts to be confounded by the dynamics of what is left outside the boundaries. A series of boundary shifts are introduced through four vignettes that concern environmental research and the management of resources. Researchers can shift from a conventional scientific focus on refining models or representations of complexity to see themselves simultaneously representing and engaging within that complexity. From a focus on product  – established knowledge  – ecological researchers can embrace process, continually reassessing their knowledge, plans, and action proposals. Science can be seen as science-in-context, so that researchers become more self-conscious about their engagement within the complexity of the social situations that make it possible to do their research. Regarding this last shift, there is a tension between, on the one hand, taking seriously the creativity and capacity-building that seems to follow from well-facilitated participation of diverse people whose livelihood is directly dependent on the ecosystem, and, on the other hand, researchers’

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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P.J. Taylor professional identities and abilities as people who can contribute systematic analyses of changes that arise beyond the local region or at a larger scale than the local. Introduction Günter Grass’ 1959 novel, The Tin Drum, begins with its main character, Oskar declaring, “GRANTED:  I AM an inmate of a mental hospital …” (Grass 1963). Following this lead, let me make clear at the outset that I am writing from outside the boundaries of marine ecosystem management. Admittedly, I was a member of an EPA-funded “Marine Ecosystems Research Group” as a new graduate student. But I moved away from this project as I clarified the direction of my own inquiries into ecological complexity. Indeed, my research career has seen me progressively positioning myself outside the boundaries of groups and fields with which I had been associated at an earlier time. These shifts of position sometimes leave me wondering if I am crazy – in the sense of talking about a reality not recognizable to others. Nevertheless, the invitation to contribute to this volume lured me in. In this chapter I to try to say something as an outsider that readers might translate to speak to their own concerns about marine ecosystem management. Let us see if my above-water examples and ­discussion – the drums I beat – resonate for others. Boundaries My inquiries into ecological complexity have built from a question that I shall phrase in general and thus necessarily abstract terms: how can ecologists and resource scientists account for order arising out of “unruly complexity?” That is, out of the complexity of situations that build up over time from heterogeneous components and are embedded within wider dynamics, and in which there is an ongoing restructuring (Taylor 2005). The way we understand the world can change qualitatively as we shift our attention from uniform components to include the heterogeneous; from well-bounded dynamics to successive embeddings; and from current arrangements and recognizable endpoints to historical background and ongoing changes. Consider, for example, three perspectives of savanna ecology (Fig. 8.1). The first comes from an ecologist, Sharam:1 The Ecology of Savannas can be described as “Multiple Stable States.” Years ago, farmers and ranchers noticed that savannas tend to grow as www.serengeti.org/download/Plant_Ecology.pdf (viewed April 25, 2009).

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From management to engagement either an open grassland with almost no trees or as a dense woodland with many trees and less grass. They also noticed that transitions between these states were rapid and that it was difficult to maintain a mixture of the two states. Description of the Multiple Stable States: Scientists describe savanna ecology as “Multiple Stable States,” where there are two or more “States” that an area can live as. Each of those states supports and maintains itself and is thus “Stable.” To be exact: When a savanna has no trees (Grassland State), it grows massive amounts of grass which produces hot burns during the dry season. These fires kill vulnerable tree seedlings and effectively exclude trees, thus maintaining the grassland as a grassland. When a savanna has many trees (Woodland State), the trees use much of the water and sunlight before they can get to the grass. This reduces the amount of grass that can grow. The less grass there is, the fewer fires that can burn. Thus, the adult trees are safe and many small trees can grow, maintaining the woodland as a woodland. What causes savannas to switch between States: The old answer to the question of what caused savannas to switch from a woodland to a grassland state was “Elephants!” Research in the Masai Mara Reserve in Kenya has shown that the two most important factors for Acacia woodlands are Fire and Elephants. Fire, at sufficient frequency and temperature can remove trees from a woodland and push a savanna into a grassland state. Fire can then keep the grassland locked in a grassland state. Elephants can also keep a grassland locked as a grassland by eating any new tree seedlings that might grow. Elephants cannot, however, kill a large enough number of trees to push a woodland state into a grassland state. A quite different perspective on savanna ecology emerges in Pearce’s (2000) historical account of transformations that followed the entry of rinderpest virus into Ethiopia and from there into the rest of eastern and southern Africa in the 1880s. Tsetse flies like lush, extensive vegetation where adults can deposit their larvae. Before rinderpest arrived, the cattle on the plains kept the tsetse in check by grazing the grass sward very close and preventing tree seedlings and shrubs from growing more than a few centimetres high. But without cattle and other grazing animals, the

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Fig. 8.1.  Savanna with zebras and impalas, Serengeti National Park, Tanzania. © Dan L. Perlman/EcoLibrary.org (used with permission).

woody vegetation grows fast. “Within a season or two the pasture is transformed into woody grassland and shady thornbush thickets, creating ideal conditions for the spread of the tsetse fly,” says Reader [an historian] … After the rinderpest epidemic passed, wild animal populations rebounded much faster than the cattle, providing an animal host for the tsetse once more. The flies and the sleeping sickness they carried in turn kept humans and their cattle from returning to graze down the bush that was springing up. In a landscape suddenly highly conducive to the flies, tsetse spread fast … Today in much of eastern and southern Africa, there are two ecosystems, created and separated by people: areas where farmers and cattle herders reign and the bush and tsetse are tamed, and areas where the West’s peculiar vision of “wild” Africa holds sway, and the bush runs wild and tsetse flourishes. The truth is that the real world before the arrival of colonists was more like the former than the latter. To explain the dynamics of the savanna Pearce expands the boundaries of analysis, going as far back in history to the 1880s. The picture he paints can be complicated further by giving attention to the particularity of conditions, that is, to the heterogeneity in space and time of socio-environmental dynamics. Mara Goldman, a socio-environmental/conservation biology researcher

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From management to engagement now at the University of Colorado, states (pers. comm., 2006) that Pearce’s last line: … should read more like – the truth was more complicated and involved a continual battle by the former (herders and farmers trying to tame ‘the bush’) and the latter (the bush) … There are a lot of ecological factors that keep ‘nature’ from maintaining any kind of stability in these areas … For one, while livestock herding, combined with burning does keep the bush at bay, overgrazing in certain areas can also lead to bush encroachment. And variations in rainfall can completely change the vegetation in a given area (especially if for instance there is a persistent drought, combined with grazing and elephants, or very high rainfall combined with lack of animals/people … She continues, in more detail: [But when ecologists] talk about the current shift from woodlands to grassland as in fact a “natural” occurrence if we look at history – which shows a system that shifts back and forth from woodland to grassland because of several interacting factors – yes first there was rinderpest, combined with smallpox and drought, killing off a lot of the animals and the people, and then bush came in and created woodlands, then people came back and started farming, and herding, and burning, and the elephants (whose populations rose as people declined from the series of disasters) got pushed into the park as people began to return, and there was a skyrocketing of the wildebeest population both from a recovery of rinderpest, a creation of a vaccine for cattle (who kept transferring [rinderpest] to wildebeest), and an unusually high rainfall in the dry season in the 1970s. But the relationship is complex, and fluctuating wildlife numbers also impact the environment. For instance, fire and elephants can help push the woodland to grassland (even without people!), but a lot of wildebeest, who are grazers, and a good amount of rainfall so that elephants can graze (instead of browse), can remove enough of the undergrowth so that fires won’t spread as much and trees can come back. The preceding exercise in boundary-expanding could continue, so as to include, say, the politics and practical considerations that someone like Mara Goldman faces when she works in the field, in the turbulent cities where conservation and development NGOs are based, and back in the USA as she

P.J. Taylor makes her career. It would not be a difficult exercise – when prompted, most researchers can readily identify explicit and implicit conceptual boundaries in their accounts. When we include what is excluded and examine processes that cross those boundaries, it is possible, as the shifts from the original account to Pearce’s and Goldman’s indicate, for our understanding to change significantly. Of course, it would not be necessary to identify boundaries that can be crossed if it were not also the case that ecologists and environmental scientists can, and do, readily adopt explicit or implicit boundaries and study what is inside. This observation invites us to examine the measures researchers have to take in order to make sense of what is within the boundaries they adopt. At the same time, we might want to draw attention to the potential for researchers’ accounts to be confounded by what is left outside. This chapter does not advocate looking for a theory of everything, in which there are no boundaries, but acknowledges people’s efforts to make boundaries work for them, then progressively brings more considerations in. Four complementary ways of thinking about and stretching researchers’ boundary-making are presented. The theme of boundaries that can be crossed leads me to describe the four approaches as “frames” or “framings.”

Funding and expertise The first frame within which we can think about researchers’ ­boundary-making is an obvious one: boundaries are given by our funding and expertise – we do the best research we can under the terms of reference and deadlines set by the funding source and using the specific expertise we have. A common occupational hazard of working within this frame is the frustration of seeing the ways others constrain and use your research. Following is a personal example: in one of my early experiences of applied social-environmental research (in the late 1970s) we undertook detailed scientific analysis of an agricultural region in southeast Australia subject to salinization and economic decline. Projections of the economic and ecological future (which was my primary assignment) were straightforward as long as the model used to make the projections preserved the basic structure of the situation. When innovative possibilities, such as reforesting abandoned land, were considered, the analysis became difficult; it was left uncompleted even after a 3-month funding extension was secured. Moreover, the study was conducted at some distance  – geographic and sociological  – from those directly affected by the problems; the audience for the final analyses was small and attention to the

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From management to engagement report short-lived. The government ministry that commissioned the research was unable to implement the policy change that they had hoped the study would support:  increasing the price charged for irrigation water. The two or three person-years of research concerning the wider realm of agronomic, economic, environmental, and social factors appears to have left no mark on subsequent policy or research (see Taylor 2005). Mapping science-in-practice Ecologists and resource managers often complain about how their inquiries are channeled and their findings used selectively or forgotten. After one or two frustrating experiences like the one mentioned above, we could decide to expand the boundaries of our inquiries and investigate the sociological dynamics that shape what is taken to count as scientific knowledge and what is discounted or ignored. Under this second frame, I examined retrospectively (in the late 1980s) what had happened in the agricultural salinization study as well as in a second project, which involved US researchers in the mid 1970s building computer models of nomadic pastoralists in drought-stricken sub-Saharan Africa. In both cases I traced diverse interconnections between the various so-called technical decisions of the scientists and the social considerations that influence how scientists perform the resulting tasks. This allowed me to assess what would have been entailed in practice to modify the knowledge generated by the projects. The modeler in the US project on sub-Saharan Africa, as I interpreted his work, was dealing with a diverse set of considerations. These included, for example, the available computer compiler, published data, the short length of time both in the field and for the project as a whole, the work relations within the research team, the relationship of the USA’s efforts to other international involvement in the region, and the terms of reference set by the funding agency and its contradictory expectations of the project. Practical considerations for the US modeler or for me as we helped to build scientific knowledge (based on the models) involved commitments to certain actions; these actions implicated many other social agents and spanned social realms at local as well as much wider scales. In a variety of diverse and particular ways, we were imagining and engaging in social action at the same time as we were making knowledge about, or representing, the dynamics of the social and environmental situations being studied (Taylor 2005). This means that, although the modeling work in both projects could have been modified, such modifications would not have followed from a mere change of world-view on the part of the modelers. Advancing a different model or theory of what was

P.J. Taylor going on for the nomadic pastoralists would have involved many practical considerations and social negotiations. To employ this last insight proactively, I have conducted mapping workshops (starting in the late 1980s), which encourage participants to be more explicit and strategic about their efforts to modify the social context in which they conduct their research (Taylor 2005). These workshops begin by asking researchers to focus on a key issue – a question, dispute, or action in which the researcher is strongly motivated to know more or act more effectively. They then identify connections, things that motivate, facilitate, or constrain their inquiry and action. The ultimate goal is to analyze the diverse resources mobilized, or mobilizable, by the researcher. However, in the interest of exploring the range of potential resources, participants are not expected to evaluate carefully the significance of every connection before including it in their initial maps. Examples of things connected to the key issue might include theoretical themes, empirical regularities, methodological tactics, organisms, events, localities, agents, institutional facilities, disputes, and debated issues. Each researcher then draws a map, a pictorial depiction employing conventions of size, spatial arrangement, and perhaps color that allow many connections to be viewed simultaneously. The metaphor of a map is not intended to connote a scaled-down representation of reality. Instead a map serves as a guide for further inquiry or action – to show possible pathways for further investigation. It is important that mapping be undertaken in a workshop setting so that each participant’s thinking is exposed to questioning by other participants. The workshop interaction is intended to lead to participants clarifying and filtering the connections and, eventually, reorganizing their maps so as to indicate which connections represent actual significant resources. To illustrate this approach let me use one map drawn by a Finnish ecologist, who was studying carabid beetles in the leaf litter under trees (Fig. 8.2; note that this map has been streamlined and redrawn on a computer for publication and cannot do justice to the real-time experience of an actual workshop). The central issue on this ecologist’s map is very broad, namely, to understand the ecology of carabids in urban environments. Below this issue on the map many theoretical and methodological sub-problems are included, reflecting the conventional emphasis in science of refining one’s issue into specialized questions amenable to investigation. Above the central issue are various background considerations, larger and less specific issues, situations, and assumptions that either motivate work on the central issue or are related to securing support for the research. The ecologist’s research alone would not transform the urban public into recognizing that “nature is everywhere  – including in

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From management to engagement Challenge of combining local contingencies with general principles

Nature is everywhere, including the cities! Issue: recommendations for management of urban ecology are needed

Issue: urban ecology should be a proper subject for ecologists to study

ISSUE: understanding ecology of carabids in urban environments theoretical developments survey Issue: how to sample meaningfully sampling method exploratory data analysis

autecology

carabid assemblages

Funding

rural-urban gradients isolation or patches disturbance climate

habitat structure, dispersal and relative colonization, abundance, phenology, diversity population size Issue: minimum requirements (why are some spp. able to survive in urban areas?)

Fig. 8.2.  Redrawn map of considerations relevant to research on the ecology of carabid beetles in the city of Helsinki (from Taylor 2005, p. 150).

the cities,” but by combining the upward and downward connections, he was reminding himself that work on the background issues, not simply refining a working research hypothesis, would be necessary to ensure continuation of his research. In narrating some of the details of his map to the other workshop participants, the ecologist mentioned some additional history. Many of the ecologists with whom he collaborated had been studying forests, but the group lost their funding when the Forestry Department asserted that forest ecology was their exclusive domain. It did not matter that animals were barely considered in the forest ecology of the Department’s forestry scientists. The ecologists with whom he was working self-consciously turned their attention to the interconnected patches of forest that extend almost to the center of Helsinki, exploring novel sources of funding and publicity, including a TV documentary. The upward connections were thus a recurrent, if not persistent, influence on the ecologist who presented the map in Fig. 8.2 as he defined his specific research questions. Organized multi-person collaborative processes When we map the processes through which scientific knowledge is being made and highlight the heterogeneous resources involved in these

P.J. Taylor processes, we are drawing attention to multiple points at which scientists can engage differently in scientific practice to modify its outcomes. Of course, examining the wider social dimensions of our research provides no guarantee of being able to change the available resources to our advantage as we seek to establish a certain scientific account of what is going on. (Indeed, as it turned out, the Finnish ecologist was not able to complete his study of urban carabid ecology.) Whether any specific modifications are practical options depends on the position and resources of the specific scientists as they enter into negotiations with other relevant social agents. A proactive form of paying attention to these negotiations is evident in the concern with collaboration that has become significant in environmental planning and management since at least the 1990s (Margerum 2008). When I began to make sense of collaboration in environmental research I made a list of the variety of things such collaboration can mean. I now find it helpful to divide the list into two categories: the first reflecting the simple idea that collaboration aims for a sum of multiple parts; the second, the hope that something greater than the sum of those parts will emerge through their interaction (Table 8.1; Taylor et al. 2008).2 The greater-than-the-sum-of-the-parts objectives raise questions about the theory and practice of collaboration that need not be specific to environmental research:  why do well-facilitated group processes result in collaborators’ investment in the product of the processes? How can collaborators (or facilitators of collaboration) ensure that knowledge generated is greater than any single collaborator or sum of collaborators came in with? How does a person become skilled and effective in contributing to such outcomes? There is, moreover, an obvious flip side to these questions. What can we learn from interdisciplinary workshops and collaborations that fail to generate new knowledge or investment in the product, and that do not enhance participants’ ability to contribute to effective collaborations in the future? Many of us have seen time, energy, funds – as well as associated carbon footprint – poured into workshops in which the parts competed instead of adding together. The pressure for product can often contribute to a squelching process so that participants perpetuate familiar patterns of defending territory and speaking at cross-purposes; they head home without being enriched by perspectives and frameworks from other disciplines – and, in many cases, without any useful product emerging. The challenge is to move beyond grumbling about such frustrating experiences (which seem far from rare) and determine how to do better. In this spirit, we might expand the boundaries of our inquiries to include the generic questions in Table 8.1 (Schuman 2006, Taylor et al. 2008) together with specific issues

Adapted from www.faculty.umb.edu/pjt/ECOS.html (viewed August 21, 2008).

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From management to engagement Table 8.1. Why emphasize collaboration in environmental research?34 A. Sum of the Parts Combining multiple perspectives • When research is tied up with planning and management that involves meetings and networks of representatives of established and emerging stakeholder groups, research projects also need to integrate knowledge and questions from the different groups and kinds of research (Wondolleck and Yaffee 2000, Margerum 2008). • When researchers are concerned about social justice, they can shape their inquiries through ongoing work with, and empowerment of, people whose lives stand to be most affected by some change in social policy or technological development, such as digging of deep wells for irrigation (Greenwood and Levin 1998). • When the knowledge and research skills of more than one person/specialty are needed, multidisciplinary research teams can be established. • When the labor of research, especially in data collection, is beyond any research group, amateurs – citizen scientists3 – can be sought as collaborators (Barrow 2000). • Workshops and other organized multi-person collaborative processes in environmental research can constitute a self-conscious example of the mobilization of heterogeneous resources by diverse agents spanning different realms of social action (Taylor 2005, pp. 94ff; see previous section). Extending over time • The nature of environmental complexity means that ongoing assessment (as against a one-time analysis) is needed, so an ongoing organization or group can be formed to conduct the assessment, as recognized in the field of Adaptive Environmental Assessment and Management4 (Gunderson et al. 1995). Spanning distance • Researchers in separate projects and disparate locations can try to link their data into a larger picture using the tools of eco-informatics (Halpern et al. 2008). B. Greater than the Sum of the Parts (i.e., outcomes over and above A.) Generating new perspectives • Knowledge and further research questions can be generated that the collaborators (individually or in sum) did not have when they came in (Olsen and Eoyang 2001). Durable • Guided by skilful facilitators, collaborators can become invested in the plans, policy, and ongoing collaborations that emerge from the research (Stanfield 2002). Developing capacities • Collaborators develop skills and dispositions for collaboration in various settings, as warranted by the rise of citizen participation and of new institutions of civil society (Burbidge 1997, Taylor 2005).

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http://en.wikipedia.org/wiki/Citizen_science (viewed July 29, 2008). www.resalliance.org (viewed October 2, 2003).

P.J. Taylor related to the particular organized multi-person collaborative processes any of us may be involved in. Local versus translocal? My experience working within the first two frames and my thinking within the third frame led me, in the late 1990s, to pursue facilitation training at the Canadian Institute of Cultural Affairs (ICA). This training stimulated further thinking and inquiry regarding the underpinnings and limits of collaboration. The ICA’s techniques have been developed through several decades of “facilitating a culture of participation” in community and institutional development in many countries. Their work anticipated and now exemplifies the post-Cold War emphasis on a vigorous civil society, that is, of active institutions between the individual and the state and between the individual and the large corporation (Burbidge 1997). The ICA planning workshops involve a neutral facilitator leading participants through four phases  – practical vision, underlying obstacles, strategic directions, and action plans (Stanfield 2002). Most importantly, the ICA workshops aim to elicit participation in a way that brings insights to the surface and ensures the full range of participants are invested in collaborating to bring the resulting plans or actions to fruition. Such investment was evident, for example, after a community-wide planning process in the West Nipissing region of Ontario, 300 kilometers north of Toronto. In 1992, when the regional Economic Development Corporation (EDC) enlisted the ICA to facilitate this process, industry closings had increased the traditionally high unemployment to crisis levels. The EDC wanted specific plans, but it also sought significant involvement from community residents. Twenty meetings with over 400 participants moved through the first three phases  – vision, obstacles, and directions. The results were synthesized by a steering committee into common statements of the vision, challenges, and strategic directions. A day-long workshop attended by 150 community residents was then held to identify specific projects and action plans, and to engage various groups in carrying out projects relevant to them. A follow-up evaluation five years later found that it was not possible simply to check off plans that had been realized because the initial projects had spawned many others. Indeed, the EDC had been able to shift from the role of initiating projects to that of supporting them. It made more sense, therefore, to assemble the accomplishments under the headings listed in the original vision and strategy documents. Over 150 specific developments were cited, which demonstrated a stronger and more diversified economic base, and a diminished dependence on provincial and national government social welfare programs. What is especially

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From management to engagement noteworthy about this example is that the community came to see itself as responsible for these initiatives and developments, eclipsing the initial catalytic role of the EDC-ICA planning process. The EDC saw beyond their catalytic role and came to appreciate the importance of the emergent process and initiated a new round of facilitated community planning in 1999 (West Nipissing Economic Development Corporation 1993, 1999). I faced some difficult questions when I contrasted this case with my early experience in applied social–environmental research (briefly sketched earlier). Notice that the West Nipissing plan built from straightforward knowledge that the varied community members had expressed during the facilitated participatory process. Following the success the first time around, the process was repeated, which presumably allowed people to factor in changes and contingencies (e.g., the impact of the North American Free Trade Association and the decline in the exchange rate with the USA). Most importantly, the process has led community members to become invested in carrying out their plans and to participate beyond the ICA-facilitated planning process in shaping their own future. My own environmental research, in contrast, has drawn primarily on my skills in quantitative methods and not on interaction with the people directly affected by the environmental or resource issue in question. The question then: is there a role under the participatory planning approach for researchers to insert the translocal into participatory planning, that is, to contribute analysis of changes that arise beyond the local region or at a larger scale than the local? For example, suppose that I had moved to the agricultural region we studied and participated directly in shaping its future. I would still have translocal knowledge about the government ministry’s policy-making efforts, the data and models used in the economic analysis, and so on. Indeed, the local for researchers cannot be as place-based or fixed as it would be for most community members. How, then, can we take seriously the creativity and ­capacity-building that seems to follow from well-facilitated participation, but not conclude that we, as professionals or specialists, have to go local and focus all our efforts on one place? My reflection on this question led me to coin the term “flexible engagement.” This is intended to capture a challenge concerning process, rather than content, for researchers in any situation in which knowledge, plans, and policy are being made. It involves the question: how can we connect quickly with others who are almost ready to foster – formally or otherwise – participatory processes and, through the experience such processes provide their participants, to contribute to enhancing the capacity of others to do likewise? The term plays off the flexible specialization that arose during the 1980s, wherein transnational

P.J. Taylor corporations directed production and investment quickly to the most profitable areas and set aside previous commitments to full-time employees and their localities. Would flexible engagement constitute resistance to flexible specialization, or an accommodation with it? This remains an open question for me as I gradually develop tools for engaging flexibly (Taylor et al. 2008). Such questions became more pointed when I heard that, in late 2002, a major employer in the West Nipissing region, Weyerhaeuser, closed its containerboard plant. A local newspaper article (Haddow 2003) quoted a Weyerhaeuser spokesperson:  “[T]he decision to close the facility is not a reflection on the employees of Sturgeon Falls and their abilities and efforts … It was made for economic reasons beyond their control.” The spokesperson went on to explain that “the company’s preference would have been to keep all facilities running, but the market changes and current economic conditions forced their hand … If we as a company do not adapt, then we will not survive and none of our employees will have jobs.” The community sprang into action and threatened lawsuits, but the plant closure was not reversed. I hope to learn more about the community’s response. While I was planning a research trip to the region, discussion with colleagues involved in regional economic development (with a focus on technology-centered experiences and promises) led me to modify my thinking about the local – translocal contrast. The translocal side is not only about perspectives or knowledge, but can also encompass resources that could be brought to a locality or withdrawn and withheld from it. There is room to think about and to explain which aspect of the translocal comes into play – knowledge or resources; contributed or withheld  – and how they interact with solidarities forged through working and living together in particular places (Taylor 2005, pp. 210–213). Focusing on the tensions between the local and the translocal constitutes a fourth frame for acknowledging people’s efforts to make boundaries work for them as well as the ever-present potential for their accounts to be confounded by what is left outside. Conclusion The vignettes from ecological research and the management of resources included in this chapter, and the accompanying discussion, point to a series of boundary shifts. From a conventional scientific focus on refining models or representations of complexity researchers can come to see themselves simultaneously representing and engaging within that complexity. From a focus on product – established knowledge – ecological researchers can embrace process, responding to developments  – predicted and surprising alike  – by

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From management to engagement continually reassessing their knowledge, plans, and action proposals. Science can be seen as science-in-context, so that researchers become more self-conscious about their engagement within the complexity of the social situations that make it possible (or difficult) to do their research. On this last point, I have highlighted the tension between, on one hand, taking seriously the creativity and capacity-building that seems to follow from well-facilitated participation of diverse people whose livelihood is directly dependent on the ecosystem, and, on the other hand, researchers’ professional identities and abilities as people who can contribute analyses of changes that arise beyond the local region or at a larger scale than the local. This tension and these boundary shifts seem to me – although, granted, my work takes place on dry land – worth bringing to the surface in and beyond marine ecosystem management. Acknowledgments This chapter weaves in some material that has been published elsewhere, most notably Taylor (2005), but also Taylor (1999, 2009) and Taylor et al. (2008). Some of the thinking about collaborative processes is based on research supported by the National Science Foundation under grant SES–0551843. References Barrow, M.V. 2000. A Passion for Birds: American Ornithology after Audubon. Princeton, NJ: Princeton University Press. Burbidge, J. (ed.). 1997. Beyond Prince and Merchant: Citizen Participation and the Rise of Civil Society. New York, NY: Pact Publications. Grass, G. 1963. The Tin Drum. New York, NY: Pantheon. Greenwood, D.J. and M. Levin. 1998. Introduction to Action Research: Social Research for Social Change. Thousand Oaks, CA: Sage. Gunderson, L.H., C.S. Holling, and S.S. Light (eds.). 1995. Barriers and Bridges to the Renewal of Ecosystems and Institutions. New York, NY: Columbia University Press. Haddow, S.H. 2003. Gripped by uncertainty: Sturgeon Falls springs into action following the closure of its primary employer. Northern Ontario Business (January 1). Halpern, B.S., S. Walbridge, K.A. Selkoe et al. 2008. A global map of human impact on marine ecosystems. Science 319:948–952. Margerum, R.D. 2008. A typology of collaboration efforts in environmental management. Env. Manage. 41:487–500. Olson, E.E. and G.H. Eoyang. 2001. Facilitating Organizational Change: Lessons from Complexity Science. San Francisco, CA: Jossey-Bass. Pearce, F. 2000. Inventing Africa. New Scientist (August 12):30–33. Schuman, S. (ed.). 2006. Creating a Culture of Collaboration: The International Association of Facilitators Handbook. San Francisco, CA: Jossey-Bass.

P.J. Taylor Stanfield, R.B. 2002. The Workshop Book: From Individual Creativity to Group Action. Toronto: Canadian Institute of Cultural Affairs. Taylor, P.J. 1999. Mapping the complexity of social-natural processes: cases from Mexico and Africa. In Fischer, F. and Hajer, M. (eds.), Living with Nature: Environmental Discourse as Cultural Critique. Oxford: Oxford University Press, pp. 121–134. Taylor, P.J. 2005. Unruly Complexity: Ecology, Interpretation, Engagement. Chicago, IL: University of Chicago Press. Taylor, P.J. 2009. Infrastructure and scaffolding: interpretation and change of research involving human genetic information. Science as Culture 18, 435–459. Taylor, P.J., S.J. Fifield, and C.C. Young. 2008. Cultivating Collaborators: Concepts and Questions Emerging Interactively From An Evolving, Interdisciplinary Workshop. http:// www.faculty.umb.edu/pjt/08c.pdf (viewed April 25, 2009). West Nipissing Economic Development Corporation. 1993. Vision 20/20: Shaping our Futures Together. Executive Summary. West Nipissing Economic Development Corporation. 1999. Vision 2000 Plus. Executive Summary. Wondolleck, J.M. and S.L. Yaffee. 2000. Making Collaboration Work: Lessons from Innovation in Natural Resource Management. Washington, DC: Island Press.

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Civil society and ecosystem-based fisheries management: traditional roles and future opportunities tundi agardy

Abstract Fisheries management continues to improve and expand in scope, but increasingly governments cannot handle these management burdens alone. Governance with roots outside government, i.e., in civil society and in the private sector, can complement government-based fisheries management. Conservation or environmental non-governmental organizations (NGOs) are taking on increasing roles in this regard, including (1) raising public awareness, practicing advocacy, and pushing for policy reform; (2) setting priorities at regional or global scales; (3) performing monitoring, surveillance, and acting as watchdogs and whistleblowers; (4) bridging the chasm between science and management; (5) building capacity of local institutions to manage fisheries; (6)  channeling additional financial resources to fisheries and broader ecosystem management; and (7) creating demonstration models of ecosystem-based management for fisheries (EBMF), where all the above can come together. Civil society and fisheries management True ecosystem-based management for fisheries (EBMF) remains an elusive challenge. In the past, fisheries management was simpler and cheaper,

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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T. Agardy though not particularly effective – as we have seen from the newest Food and Agriculture Organization reports putting fully 80% of global commercial fisheries at or over sustainable levels (FAO 2009). The recent movement towards more effective fisheries management embodied in ecosystem-based management has carried a price – more and better science is needed to evaluate threats and investigate ecosystem feedbacks, a wider array of management tools need to be applied, integration of management is becoming ever more complex, and aiming for compliance across much wider arrays of users means greater surveillance and enforcement costs. Governments, once the sole watchdogs for commercial fisheries, cannot handle these management burdens alone. But this does not mean that ecosystembased management for fisheries is doomed to failure. Governance with roots outside government, i.e., in civil society and in the private sector, can complement government-based fisheries management. We have seen this played out hundreds of time already, through the self-regulation practiced by fishing cooperatives, in formalized public–private partnerships between government agencies and non-governmental organizations (NGOs), and in community-based marine management. This broader definition of governance takes some getting used to. While we traditionally think of governance as “government,” modern societies have built on more ancient traditions that allow for myriad synergistic forms of governance. Today, government practices conventional governance, while civil society (citizen’s groups, non-governmental organizations (NGOs), unions, cooperatives) works within government-established sets of rules to further push towards sustainability, and equity. And the business community rounds out the triumvirate of governance, such that the private sector increasingly has a role to play in ocean governance, even though that role is not yet well developed. Varying roles of conservation NGOs Civil society has a varying influence on, and ability to participate in, governance. In the management of fisheries, the roles of civil society are narrower, though still rather diverse. Conservation NGOs can assume many of these roles because in many, though not all, cases, they are able to be honest brokers to bring a bit of objectivity to the process. Environmental groups can maintain impartiality because they have neither a vested interest in claiming that fishing has no impact (as much of the industry would have us believe), nor in claiming that fisheries management is working (as most governments insist). Staff members of NGOs are increasingly versed in negotiation, and many are specifically trained in conflict resolution. They also often have the sort of multidisciplinary

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Civil society and EBFM: future opportunities training that allows them to view a problem from many different perspectives, unlike most specialists in academia. Thus, the ways in which NGOs move fisheries towards sustainability are diverse. Many small and/or local NGOs, and the regional offices of large international groups, work collaboratively with local fishing communities to help safeguard their livelihoods. In such cases, NGOs may offer the only substantial resistance to large-scale industrial fishing that can displace smallerscale, artisanal fishing that is, in all likelihood, closer to being sustainable. In fact, such NGOs may be the only kind of organization that can provide the catalyst needed to get different stakeholders together to forge communitybased conservation projects. Conservation groups have also been on the cutting edge of exploring innovative management and financing mechanisms for fisheries conservation (e.g., the use of limited entry schemes, individual transferable quotas, fishing cooperatives, alternative livelihood training, and “buy-back” programs that occur independent of the government). Conservation NGOs also provide a service and move fisheries towards greater sustainability by synthesizing the fisheries and ecosystem science in userfriendly ways – often buttressing traditional knowledge with equally important user knowledge. Finally, NGOs attempt to transcend local and national concerns, pushing for fisheries agreements and conservation programs that occur at the regional scales most appropriate to conserving shared or mobile fish stocks. Conservation NGOs have long been vilified by the fishing industry for being nature-centric rather than human-centric, and (in the opinion of many) putting the lives of fishes over the lives of those of humans eking out a living to care for their families. In contrast to this perception, more and more NGOs are now working to avoid antagonism by functioning as partners or collaborators, in part due to recognition by both the environmental community and the fishing industry that they have many conservation goals in common. Non-governmental organizations also share responsibilities for education and outreach, surveillance and enforcement, scientific research and monitoring, and managing visitor use in protected areas, among other management activities. The myriad roles NGOs and other institutions within civil society can play to promote or practice EBMF can be generalized, as summarized in Table 9.1. Civil society and public awareness If a common NGO approach to promoting EBMF can be said to exist (and this is a dangerous assumption, given the diversity of groups and their

T. Agardy Table 9.1. Key roles for non-governmental organizations (NGOs) in ecosystem-based management for fisheries (EBMF). 1.  Raising public awareness, practicing advocacy, and pushing for policy reform 2.  Setting priorities at regional or global scales 3.  Performing monitoring, surveillance, and acting as watchdogs and whistleblowers 4.  Bridging the chasm between science and management 5.  Building capacity of local institutions to manage fisheries 6.  Channeling additional financial resources to management 7.  Creating demonstration models of EBMF, where all the above can come together

approaches), it is to synthesize existing information, communicate it, and advocate for change in policy and regulations. In addition, some groups go beyond fisheries-by-fisheries management reform to advocate: (a) shifting the burden of proof when evaluating fishing impacts on ecosystems and (b) establishing strictly protected marine reserves to further our understanding and protect species, habitats, and ecological processes. Shifting the burden of proof has received recent attention in the fisheries management community but there remain misconceptions (Dayton 1998, Agardy 2000). Much of the conservation community advocates shifting this burden of proof in evaluating the prospective impacts of new fisheries, expanded fisheries, or new technologies and for regulators to permit such fisheries development only when proof of no likely impact exists. Non-governmental organizations and community groups have a critical role to play in communicating information about oceans, fisheries, and trends in resource availability. In some cases NGOs work to complement the efforts of government to raise awareness of such issues; in others NGOs play an antagonistic role, countering what some see as government propaganda meant to control community activities and expectations. Many pages could be written presenting an analysis of civil society and its role in public education and awareness-raising around fisheries, however, reallife illustrations of how this role is practiced may be more illuminating. Two examples: one of an NGO playing an advocacy role, the other of an NGO playing the role of information broker, show the wide range in which NGOs in particular, and civil society in general, can raise public awareness of EBMF, and use that public awareness to push for policy reform. The advocacy example takes advantage of the power of consumer choice in pushing marine fisheries towards ecosystem-based management, or greater sustainability. Seafood Choices, for example, was founded in 2001 as an international program that provides leadership and creates opportunities for change

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Civil society and EBFM: future opportunities across the seafood industry and ocean conservation community. According to their website,1 Seafood Choices helps the seafood industry – from fishermen and fish farmers to processors, distributors, retailers, restaurants, and food service providers  – to make the seafood marketplace environmentally, economically, and socially sustainable. A similar initiative is being undertaken by the Monterey Bay Aquarium, known as Seafood Watch. Seafood Watch allows consumers to access up-to-date information on which seafood products are sustainably harvested.2 To the extent that certification takes into consideration ecosystem impacts and thus promotes ecosystem-based management, these NGO-driven consumer choice initiatives have a hand in EBMF. The World Ocean Observatory (W2O) illustrates the role of NGO as information broker, having created and maintaining an internet-based site for exchanging information about the marine environment, providing educational services, and promoting public discourse about the world’s oceans. The World Ocean Observatory approaches the ocean as an “integrated, global, social system,” thereby relating the ocean to climate, biodiversity, fresh water, food, energy, human health, trade, transportation, policy, governance, finance, coastal development, and cultural traditions. According to their website,3 the W2O provides comprehensive links to ocean issues and organizations through its news service, monthly letter, directory, catalog of on-line resources, and interactive events focused on specific ocean questions, thus providing responsible science and information to institutions, experts, students and teachers, and curious citizens around the world. Its role in education and awarenessraising about EBMF is fulfilled by presenting synthesized materials on the topic, as well as extensive links to fisheries management publications, institutions, and initiatives. Setting priorities at regional or global scales Fisheries management issues are as diverse as the world’s fisheries themselves. Given the limited amount of resources available to marine managers and the urgent nature of many of the management challenges, strategic EBMF must be carried out with some form of priority-setting. Civil society, in particular through conservation NGOs, has a major role to play in helping establish those priorities, and in highlighting which geographic areas most need EBMF.

www.seafoodchoices.org/whoweare.php www.montereybayaquarium.org/cr/seafoodwatch.aspx 3 www.thew2o.net 1 2

T. Agardy The current emphasis on strategic approaches to marine fisheries management comes as a backlash against the opportunistic (and often ineffective) marine conservation initiatives of the past. Current emphasis is on scientifically sound and rigorous approaches to determining where EBMF projects should be launched and how they should be executed. Of the several approaches used by NGOs, two are noteworthy. One involves high-technology, decision-support, software-assisted methods. The other takes advantage of delphic processes utilizing expert opinion and local knowledge. These and other processes are utilized by NGOs to draw attention to problem areas where EBMF is needed. The expert opinion-driven process undertaken by The Nature Conservancy (TNC) in its marine priority-setting within the Latin America region provides a good example of marine conservation priority-setting, though its aim was not to expand EBMF so much as to highlight conservation needs and opportunities. This TNC effort, undertaken in 1999, worked to classify marine environments, develop methods for establishing geographic priorities, and, finally, identify high-priority conservation areas in the Latin America and Caribbean region. It was the third and final component of a larger effort undertaken by the Biodiversity Support Program (BSP), a USAID-funded consortium of the World Wildlife Fund, The Nature Conservancy, and the World Resources Institute. The study comprised two parts. The primary study consisted of the following steps:  delineating coastal biogeographic provinces; delineating coastal biogeographic regions, also called marine ecogregions; and ranking ecoregions within provinces. The second part consisted of a case study to identify and rank smaller “coastal systems” comprising the Central Caribbean ecoregion of the Tropical Northwestern Atlantic province, based on assessment of conservation and management needs.4 Environmental NGOs have also played an important role in flagging where fishing consistently occurs beyond sustainable limits, or where destructive fishing is an issue. Publishing maps of fisheries “hot-spots” or trouble areas captures the public’s attention, and that in turn can help to build political will for EBMF, or help direct funding for what are sometimes costly fisheries management activities. Performing monitoring, surveillance, and acting as watchdogs and whistleblowers One of the most crucial aspects of the management of our use of coastal and ocean resources is the monitoring, surveillance, and enforcement activities Information on the process is available at www.blackwell-synergy.com/links/ doi/10.1111/j.1523–1739.2004.00137.x/full

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Civil society and EBFM: future opportunities that ensure compliance with the wide variety of fisheries regulations that prevent overuse of resources or protect the wider ecosystems in which fisheries exist. Yet this potentially important role for civil society is often overlooked, in part because many of these activities were historically carried out by government enforcement and national security agencies. According to a National Academy of Sciences committee report on building capacity for ocean stewardship, lack of political will, particularly in developing nations puts enforcement of the rules and policies adopted by management programs low on the political agenda (National Research Council 2008). The capabilities necessary for such enforcement are often very weak to non-existent. For this reason, civil society can complement government-driven monitoring, surveillance, and even enforcement. Monitoring compliance, performing surveillance, and enforcing regulations are activities that are now increasingly being undertaken through a variety of arrangements between government, users or local communities, NGOs, and academia. The most suitable approach depends in large part on the culture and socio-political history of the particular place, as well as considerations of feasibility and expense. One example is the surveillance and enforcement of fisheries regulations at the Banco Chinchorro Biosphere Reserve in Mexico. This is performed through a co-management arrangement, in which the Mexican ministries of the Navy, Transport, and Environment work cooperatively with the World Wildlife Fund and fishing communities to support adequate enforcement (National Research Council 2008). People in the fishing community act as watchdogs, alerting the authorities to transgressions of regulations, and informing users of existing regulations. Bridging the chasm between science and management Due to the ballooning scope of global fisheries problems and conflicts, environmental NGOs are getting more and more involved in translating the science of fisheries management to make it more public and accessible, and using that science to resolve conflicts. In tackling fisheries issues, most organizations attempt to base their projects and advocacy on the best available scientific information. These groups sometimes undertake in-house scientific research, predictive modeling, and meta-analysis (Agardy 2002). In other cases, the NGOs are recipients of scientific information and act as liaisons between the scientific community, decision-makers, and the public. Key scientific information that forms the underpinnings of campaigns and field projects address three facets of EBMF and sustainability: (1) the levels of resource removal that can be realized without adverse impact on the ecosystem

T. Agardy given the particular environmental condition of the ecosystem at time of harvest, (2) the least invasive means by which that harvest can be undertaken at desired levels of harvest, such that habitat impacts and bycatch are minimized, and (3) the most appropriate stocks for large-scale harvest – those being stocks that are not the sole representatives of a deme or particular genetic structure and those for which the ecological role of the species is either not critically important or redundant (Agardy 2002). Additionally, NGOs can provide a service by bringing multi-dimensional science into the debate on what constitutes EBM and EBMF, since many of the larger NGOs have staff from a wide variety of social and natural science disciplines whose representatives are well versed in working together in a transdisciplinary or interdisciplinary fashion. Building capacity of local institutions to manage fisheries Developing adequate capacity to allow effective planning and execution of marine management is a key constraint to EBMF. “Capacity building” is now a widely used and accepted term, but in some ways it is a misleading one. Capacity for ecosystem-based management needs to be grown over time, not built as a building is built, by specialists who once having completed the building walk away from it, and do not stay to use it. Those involved with assisting countries to grow their capacities should see their role less as “constructors” of capacity (envisioning capacity as a static thing to be built), and more as catalysts spurring an open-ended and dynamic process. Given this catalytic role and the need for long-term investment strategies, there are opportunities for civil society to work in strategic ways to ensure the continued growth of capacity, so as to be able to respond effectively to changing coastal and ocean management challenges. Since environmental and social circumstances vary so widely around the world, there is no magic formula for growing capacity. There are, however, universal principles. Among the most important of these is committing to working with countries and institutions to fully understand the existing capacities, the constraints to expanding capacity, and the willingness and desire of institutions to expand their capacity for ocean management (National Research Council 2008). There are many avenues for increasing capacity, including investing in the knowledge, skill sets, leadership capabilities, competence, and willingness of individuals needed to contribute to managing ocean uses and fostering stewardship (National Research Council 2008). In addition to helping individuals develop the capacity to participate in planning and management, it is important

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Civil society and EBFM: future opportunities to assist institutions in the growth of their own capacity, including the capacity to retain enlightened, committed, and trained individuals. Channeling additional financial resources to management Ecosystem-based management for fisheries is an endeavor that takes place on a variety of time and spatial scales. The institutions that are charged with managing human impacts on the marine environment are varied, ranging from informal assemblages of concerned citizens to large bureaucracies. Similarly, the scale of investment in helping these institutions develop the capacity to address coastal and ocean management varies, from donors that invest at the project level in small sites, to donors that fund multi-year programs that span many sites, and sometimes even many countries. It would be a mistake to assume that only government agencies are involved in fisheries management. Private sector investments exist in coastal conservation, which in turn supports EBMF. An example is the privately owned and operated Chumbe Island Marine Park in Zanzibar, United Republic of Tanzania, where fisheries are regulated by the Park warden (commercial fishing is prohibited in Park waters, but Park staff influence fishing outside the Park as well by participating in planning processes in the wider region) (Ruitenbeck et al. 2005). Other examples include less direct private sector protection of ecosystem services, including fisheries management that is in part accomplished by funds that originate in the private sector. These include developer-financed ­conservation – take, for instance, the case where developers pay for the protection of mangrove forests that provide fish nursery habitat (in addition to protecting shorelines from storm events) or for restoration/rehabilitation projects, such as those undertaken as part of mitigation banking (essentially biodiversity offsets as allowed in the regulations of the USA, Australia, and the UK). Also included in this category would be public/private partnerships, such as municipal government teaming with Chambers of Commerce, or initiatives such as the Hilltops2Oceans project, launched by the United Nations Environment Programme in partnership with governments and the private sector in September 2002. Even conventional financing of public sector resource management has a link to the private sector in some cases, such as occurs with the generation of conservation funds through licensing fees for commercial and recreational fishing. In addition to government financing, there are many other mechanisms that may provide sustainable financing if the circumstances are right. For example, funding might be sustained by accessing a share of lottery revenues, dedicated revenues from wildlife stamps, tourist-related fees, fees for eco-labeling and

T. Agardy certification, fishing licenses and fishing access agreement revenues, fees for non-renewable resource extraction, fines for illegal activities, campaigns to establish trust funds, fees for bioprospecting, and income derived from local enterprises such as the sale of handicrafts (Agardy 2007). Another example of innovative financing to support direct community-level involvement in coastal and marine ecosystem protection is the growing movement of communities hiring watchdogs to monitor compliance with existing pollution and/or fishing regulations, and to publicly blow the whistle when infractions occur. Good examples of this approach are provided by projects in the USA, and include the pooling of community funds in Long Island and coastal Connecticut to underwrite the salary and expenses of a Soundkeeper, and similar programs in Narragansett Bay and along the Hudson River. Thus, governments need not shoulder the burden of coastal and ocean management alone. Civil society can be looked to for both fiscal support to grow capacities for management, and to provide valuable individual participation in shared management or co-management that effectively limits the impacts of human use on fisheries and on ecosystems more broadly. Creating demonstration models of EBMF Institutions of civil society are as diverse in their character, approach, and constituencies as the environmental problems they address. They variously function as purveyors of information, as translators of scientific and management language to the vernacular, as honest brokers (although their own value systems cause some to question their honesty), as advocates and lobbyists for certain types of reform or regulatory measures, and as adversaries to management agencies and industry through the use of environmental litigation. In many of these roles, environmental groups are seen as the antithesis to development, in opposition to business interests, and thwarting the needs of many user groups. However, environmental groups play an increasingly important non-adversarial role in demonstrating how conservation and sustainable use can be accomplished, through on-the-ground conservation projects that benefit users, community groups, business, and national interests. Environmental groups have for some years been demanding better information on the true, ecosystem-wide impacts of fisheries activity, particularly in cases where new fisheries are being launched, where major gear modifications are taking place, and/or where major expansion of fishing effort is occurring. In doing demonstration projects, NGOs then attempt to utilize this information to show how true EBMF can be instituted. Many groups use marine protected areas and fisheries reserves as a tool to strengthen management and to provide

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Civil society and EBFM: future opportunities control sites to further scientific understanding and promote adaptive management (Lester et al. 2009). Finally, environmental groups have played a key role in developing case studies where government bodies work in concert with user groups and communities to demonstrate how co-management can be achieved. In pushing for these approaches to marine conservation, environmentalists underscore the need for a holistic perspective on conservation problems, and a holistic approach to their solution. The role of civil society in EBMF, into the future The roles of various sectors of society in promoting and practicing management of our marine use are diverse and ever-changing. What is being practiced currently may have little resemblance to what lies ahead, as new tools are developed, and as the dynamics of political institutions and society shift. However, we can anticipate some changes in fisheries management and in governance, and postulate how the roles of civil society may evolve with those changes. An important barrier to fully engaging civil society in EBMF and using all aspects of governance to their full potential has been the conservatism of management agencies with jurisdiction over coastal and marine areas and resources. In general, such agencies have been slow to embrace innovative new ideas and unconventional co-management opportunities. One promising exception, however, has been the emergence of efforts to practice ocean zoning. The concept of ocean zoning is rising in popularity as managers struggle to slow or halt coastal degradation and over-exploitation of marine resources. Ocean zoning has implications for EBMF and the capacity needed to execute that management, for a number of reasons. First, ocean zoning is a potentially powerful tool to improve the management of our impact on coastal areas and more fully safeguard ecosystem services. Second, greater private sector investment could be spurred by zoning plans that provide information used to develop “trading zones” in more broadly zoned areas. Such trading zones could be areas where community institutions or NGOs have some management authority and could “sell” ecosystem services which they are protecting to “buyers,” or the industries that benefit from those services. An example in the fisheries realm would be the fishing industry making payments to a community-based NGO that protects fish nursery habitat, either by limiting their own use of that habitat or by actively managing others’ use of that habitat. Finally, making zoning operational will further coalesce a wide variety of institutions and actors that need to come together in order to make coastal and ocean governance effective and efficient.

T. Agardy The widely recognized failures of conventional marine fisheries management appear to be unrelated to the sophistication of the underlying science, the resources available for management and enforcement, or the desire of government agencies to get overfishing and destructive fishing under control. Instead, spiraling over-exploitation and over-capitalization seem tied to an unwillingness to develop the foundations for ocean stewardship or create new incentive structures for conservation. Better awareness of the potential roles of civil society in the ecosystem approach to fisheries, and harnessing that potential, may help reverse alarming trends in fisheries management worldwide. References Agardy, T. 2000. Effects of fisheries on marine ecosystems: a conservationist’s perspective. Bull. Mar. Sci. 66(3):875–888. Agardy, T. 2002. An environmentalist’s perspective on responsible fisheries: the need for holistic approaches. In Sinclair, M. and Valdimarsson, G. (eds.), Responsible Fisheries in the Marine Ecosystem. Wallingford: CABI Publishing, pp. 65–86. Agardy, T. 2007. Marine conservation banking and the brave new world before us. In Carroll, N., Fox, J., and Bayon, R. (eds.), Conservation and Biodiversity Banking. London: Earthscan Books, pp. 181–186. Dayton, P.K. 1998. Reversal of the burden of proof in fisheries management. Science 279:821–822. Food and Agricultural Organization (FAO). 2009. The State of World Fisheries and Aquaculture, 2008. Rome: FAO (available at www.fao.org). Lester, S.E., B.S. Halpern, K. Grorud-Colvert et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Progr. Ser. 384:33–46. National Research Council (NRC). 2008. Increasing Capacity for Stewardship of Oceans and Coasts: A Priority for the 21st Century. Washington, DC: National Academy Press. Ruitenbeek, J., I. Hewawasam, and M. Ngoile (eds.). 2005. Blueprint 2050: Sustaining the Marine Environment in Mainland Tanzania and Zanzibar. Washington, DC: The World Bank.

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Chapters 10 through 12 represent additional steps toward fully holistic management; however the progress is of a different kind from that of the previous two sections. Rather than substantiate principles, the progress represented in this section involves a different kind of thinking, to apply the principles substantiated in the first two sections. Progress toward holism is achieved, in part, by asking every management question we can possibly ask. By carrying out management based on carefully chosen patterns that match those questions, we bring the field of macroecology to the realm of management – adding to the interdisciplinary nature of science brought to management. Management based on carefully chosen patterns accomplishes a key step toward holism. This is achieved through the infinitely integral nature of such patterns – no detail is left out. In taking this approach, management is reality-based to include not only individual species, ecosystems, and the biosphere, but all systems. As such, this final section represents a substantial shift from the progress accomplished in current management practices. It represents a different kind of progress; it is not so much an extension of current advances as it is an alternative  – an alternative that fully embraces holism by being pattern-based rather than being based on stakeholder opinion. Among the factors taken into account in pattern-based management is the diversity of evolutionary forces at play in natural systems. Through the use of appropriate empirical patterns involving selectivity, we can implement management that directly addresses selectivity, while simultaneously accounting for all forms of natural selection (via the integral nature of patterns) – including the risks of extinction.

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Science and management: systemically matching the questions c h a r l e s w. f o w l e r a n d l a r r y h o b b s

The key to wisdom is knowing all the right questions. John A. Simone, Jr. The job is to ask questions – it always was – and to ask them as inexorably as I can. And to face the absence of precise answers with a certain humility. Arthur Miller The art and science of asking questions is the source of all knowledge. Thomas Berger Effective management always means asking the right question. Robert Heller

Abstract A primary objective of management is to achieve sustainable interactions and relationships. As individuals, this involves interactions with other individuals, other species, ecosystems, and the biosphere. It is the same for us as a species: management involves sustainable interactions – relationships with individuals, other species, ecosystems, and the biosphere (the non-human).

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Science and management: systemically matching the questions Two crucial steps in carrying out such management are: (a) to ask clear management questions and (b) to carefully express matching research questions. Central to the message of this chapter is the point that the pairing of such questions must be tightly coupled. There must be a pair of questions for each of our many interactions with the non-human in order to achieve sustainability. The pairing of management questions and scientific questions must involve a consistency far beyond anything achieved in today’s management. It must identify the kind of interaction and the levels of biological organization (e.g., individuals, species, and ecosystems) involved. Achieved effectively, the match between the two kinds of questions removes most of the barriers that currently exist between science and management  – barriers of human origin that not only prevent achieving sustainability but have also led to many of the problems we face today. The process of achieving sustainable human interactions with the non-human is complete, however, only when the match is extended to include the information used to guide management and management action itself. To the extent that we successfully achieve this consistency, we relieve the decision-making processes of human bias, human limitations, politics, and values secondary to that of sustainability and systemic health. Introduction Do we know how to link research and management questions? Do we know how to ask a management question and matching research question so that the resulting information best serves the management process? Are we aware of how often we ask research questions for which there is no matching management question? Do we recognize our mistake when we fail to find a sufficient match between the two – or when we ask one without the other? We contend that the answer to each of these questions is an emphatic “no” (Fowler and Hobbs 2009). We face a plethora of problems unearthed by scientists. Our planet is experiencing an ever-increasing list of ills such as an extinction crisis, global warming, overfishing, ocean acidification, deforestation, dying coral reefs, loss of top soil, depleted resources, acid rain, pollution by toxic chemicals, and compromised human health, to name a few. Humans are exerting a variety of abnormal genetic effects on species around us – exemplified by abnormal size selectivity in commercial fishing (Etnier and Fowler 2005, 2010). Is the failure to effectively link the questions asked in management and science a primary causal agent behind such problems? Are such failures the result of illogical thinking? Are these failures at the core of many, or perhaps most, problems that today’s scientists have brought to our attention? We contend that they are.

C.W. Fowler and L. Hobbs Scientists cannot provide information that meets management’s needs if management questions do not lead to the appropriate research questions. Without asking the right research questions, scientists can only provide information vulnerable to subjective interpretation, misuse, and error. Thus, if we are not asking either clear management questions, or clear research questions, we are destined to fail at the start – both are essential ingredients to realistic management. By asking the wrong questions, we create new problems. In this chapter we compare and contrast conventional management and systemic management. This comparison illustrates the lack of matching management and research questions in conventional approaches. By contrast, systemic management embodies a different way of thinking. In systemic management, questions are asked so that a match is achieved. This leads to solving, rather than causing, problems. After drawing this comparison, we consider how placing stakeholders in the role of asking questions rather than setting policy represents significant progress toward achieving objectivity and sustainability in the management process. Conventional management The difficulties of conventional management are nothing new to those with any experience in the way management decisions are made today. Norton’s (2005) story of working with the US Environmental Protection Agency is nothing out of the ordinary – a confusion of debate, lack of clarity, and administrative chaos. The case studies presented by Waltner-Toews et al. (2008) exemplify the inconsistency, conflict, and subjective nature of the decision-making processes in our current attempts to effectively use information. The conflict and lack of consensus behind current thinking is quite evident as exemplified by the diversity of opinion and perspectives presented in Browman and Stergiou (2004). We are familiar with such experiences and recognize the pattern in the flow of the decision-making process in management today as shown in Fig. 10.1. Typically, in Step 1, problems (e.g., an endangered species, genetic change, pollution, or depleted resources) are identified by managers, scientists, or the public. Often (as in the case of an endangered species), legislative mandates emphasize the importance of action – a source of intensity, stress, conflict, and polarization. To better understand conventional management, consider the example in which scientists have conducted studies to show that the population of a species of marine mammal is declining (e.g., the current decline in northern fur seals (Callorhinus ursinus), Towell et al. 2006, and the historical decline in Steller sea lions (Eumetopias jubatus), National Marine Fisheries Service 2008). The diet of

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Fig. 10.1.  Schematic representation of the processes involved in the decision-making of conventional management from the identification of a problem to taking action.

both species includes fish which the fishing industry maintains are important resources and crucial to their economic well-being (and possibly to the health of the economy in general). Step 1 is followed by an awareness that something needs to be done, but little if any effort is put into formulating a clear management question. Instead, we ask questions such as “What are the factors contributing to the decline?” This leads to our understanding that there are explanatory factors but explanation does not define a quantified objective. In conventional management, we fail to ask a well-defined management question, especially one that conforms to the overarching tenets and principles of management (asking what we should do, or can do, sustainably; Fowler 2003, 2009); Step 2 is given little attention. Thus, science/research questions are often asked without a corresponding management question  – often without attention to the matter of reversing the burden of proof (Dayton 1998; see also Chapter 9). Step 3 in conventional management involves getting more scientific information (often thought of as better information), reviewing the information in hand, and bringing stakeholders to the task of identifying needed information. In the case of declining pinniped populations, as examples of any declining population, stock identification may be emphasized; dietary studies may be used to examine food habits as they relate to fish taken in commercial fisheries; stock assessments may be carried out for both the pinniped and its prey species. Isolated elements of the connectivity of the system are often subjected to intense research. Effort in Step 3 is often focused on trying to determine if the decline is related to increased mortality or reduced reproduction or both (i.e., trying to answer questions involving explanation such as the question posed in the last paragraph). Information from such studies is often used in the construction of models of the system (all too often a model of the population extracted from the context of its ecosystem). A variety of the components of the system are identified and some are

C.W. Fowler and L. Hobbs given description; a variety of the processes and interactions among the components are also identified and some are measured. New research is often demanded. Definitive and responsible actions are often delayed by funding more scientific studies – studies that are usually descriptive or explanatory and do not answer clear management questions because such questions were not asked. The mass of data that can be brought to bear is synthesized with the best techniques available and with intensity reflective of legislation, public interest, and stakeholder concerns. Database managers accumulate data, bibliographies are produced, and panels of experts are formed (see Chapter 9) – often panels that ask questions pertinent to research but without matching management questions. In reaching Step 4, people with vested interests invariably choose data and models that substantiate their position, leading to heated debates. In the case of a declining marine mammal population, environmentalists stress the risks of the decline and representatives of the fishing industry stress the economic hardship of reducing commercial harvests. Human value systems play an integral and fundamental role. These often lead to court battles; legislators and politicians enter the fray – often demanding more research (usually asking for proof of relationships, or cause and effect). All stakeholders work toward a solution that is hoped will be mutually acceptable. Social factors, belief systems, and human limitations become integral to the process of setting goals (rather than serving as the basis for asking good management questions). Factors such as economic impact are made integral to decision-making by legislative mandate. Polarization becomes painfully evident and compromises are worked out with the intention of diplomacy, fairness, and justice – mostly with regard to humans or human systems. If there is a trade-off involving humans and other species, humans are usually given priority. The actions taken (Step 5) involve a complex set of implementing procedures, often taking advantage of lessons learned from past experience (see Chapter 9). The goals, however, are usually those produced in a process like that just described – a largely subjective human thought process. Thus, even though implementation and governance may be quite well developed as processes, the action taken is not holistic because of goals that are not holistic; they cannot represent a full consideration of the complexity of all of the realities before us (e.g., other species, ecosystems, the biosphere, and all of the processes involved in their interactions – including both the ecological and evolutionary). Neither the goals nor the action can be fully systemic in nature and, instead, address primarily the costs and benefits to humans. Often, few of the people involved in this process feel particularly satisfied or happy with the results. However, nobody thinks of themselves as being malicious, even though

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Science and management: systemically matching the questions an isolated few may experience the resulting management action as if it were the result of malice based on arguments made by others. If not outright malice, such interpretations often lead to strong differences of opinion. Polarized views are prominent. In conventional management, the action (Step 5), or change, required of stakeholders is often minimized, delayed, or avoided. It is in Step 4 of conventional management that we encounter a critical problem worthy of special emphasis. It is at this step where we see that goals are based on a conversion and consideration of information involving what we refer to as logical alchemy (Fowler and Hobbs 2009). The resulting advice is offered in units derived from information entirely different from that which a good management question would have specified (Fowler and Smith 2004, Belgrano and Fowler 2008, Hobbs and Fowler 2008, Fowler 2009, Fowler and Hobbs 2009). Ecological, behavioral, taxonomic, and even, in some cases, evolutionary information, is brought to the table. Economic, sociological, industrial, environmental, political, and historical factors are included. In other words, as much information as is humanly possible to think of is taken into account. The important point here is that we are confined to dealing only with what is considered possible in conventional thinking. We are dealing only with what is humanly possible when we rely so heavily on the human mind, for decision-making. A large variety of information and understanding is taken into account in processes that simultaneously, and unintentionally, also involve the ignorance of those involved; by default, human ignorance is manifest in the resulting policy. It is the human thought process that is inherently extremely vulnerable: human limitations and bias. It involves a conversion of information that is “alchemical” in nature (Belgrano and Fowler 2008; fallacious thinking, Fowler 2009, Fowler and Hobbs 2009). What is the alchemy of this process? Consider the case of a marine mammal population that is observed to be declining. Managers, scientists, and stakeholders may have the uneasy feeling that we should reduce the harvest of fish. They may even recommend a reduction in the commercial catch. However, the products of most research do not provide a direct estimate of how much that reduction should be. We would face this dilemma even if research were to conclusively prove that overfishing were causing the decline. There are no objectively derived estimates of a sustainable harvest expressed in directly applicable units that account for the complexity of the systems involved; holism remains beyond reach. Real sustainability (e.g., the sustainability of the entire suite of human and non-human species and their ecosystems) is usually undermined by the consideration of short-term goals related to selected human enterprises. In conventional thinking, the processes involved in Step 4 (selecting and converting information, Fig. 10.1; often referred to as translating research

C.W. Fowler and L. Hobbs to management advice, see Chapters 2, 3, and 9) are perceived to be difficult rather than impossible, challenging rather than erroneous, and the best that we can do rather than misleading. Thus, while retaining unrealistic hope, special emphasis is placed on training specialists to convert our understanding (and ignorance, mixed with bias, opinions, human values, and economic factors) to goals for management. This conversion is the matter of thought-based alchemy mentioned above. It is conducted with what are considered the best of intentions using very sophisticated procedures as exemplified by work described in a number of chapters in this book (e.g., Chapters 1–3). For our endangered marine mammal, a 10% reduction in the harvest of fish might be suggested by a team of recognized international experts, for example, as derived from an incomplete and selected combination of information (Belgrano and Fowler 2008). Actual management action, supported by time-honored procedures of involving people with vested interests might, instead, be a 5% reduction based on economic arguments – half of that recommended. In such a process, scientists recommending a 20% reduction can easily feel ignored in the face of a harvest reduction that is one-quarter of what they felt was reasonable! This lack of logic, or false equality, that we describe needs to be made clear. Throughout this process, the rate at which we harvest fish is one thing. It involves the ecological process of consumption. In contrast, information brought to the decision of how much to harvest is quite different. Such information often includes a variety of things such as estimates of recruitment to the populations of the resource species, population status of competing species, and projected economic impact on the fishing industry. However, recruitment is recruitment. That is, recruitment is not a harvest rate; information about recruitment must be converted (“translated”) to derive a target for the harvest rate. The population status of competing species is the population status of competing species, not a harvest rate; information about the population status of a competing species must also be converted to find a goal in terms of a harvest rate. The projected economic impact on the fishing industry, and information about the projected economic impact on the fishing industry, as it is used in conventional management, must be converted to derive a recommended harvest rate. In each case, the information is subject to alchemy in the flow of the processes in conventional management. Factors such as recruitment, competition, and economic forces are numerous and we are faced with dealing with them collectively as well as individually; they are all very real parts of the systems with which we are dealing. However, even combinations of such information must be converted and it is in combining information that we complicate our problems even more. When combining such information we encounter the combination of alchemical processes. This

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Science and management: systemically matching the questions results in a synergy of problems, or the “Humpty Dumpty” syndrome, that is immensely problematic in conventional management (the impossibility of realistically combining an incomplete set of fragmented information wherein each fragment is itself partial; Fowler 2003, 2009). As described above, conventional management is based on incomplete combinations of information (Norberg and Cumming 2008) that are chosen or selected in a way that requires conversion. Scientists, managers, and stakeholders play the role of alchemists in making such conversions. These conversions are seen as challenges that require intensified training (Brosnan and Groom 2006) rather than being recognized as impossible – a perpetuation of error-prone, illogical processes. Such efforts are all well intentioned and a great deal of sophistication is brought to the process. There is absolutely no malice intended – these efforts are aimed at improving what we are doing. Each one is an honest attempt to consider more complexity. They continue to support, however, rather than replace, current processes; they continue our attempt to accomplish what is logically impossible. The sophistication brought to the process results in sophisticated errors (Sáenz-Arroyo and Roberts 2008). In the end, we preserve a false equality – a major logical error. By failing to adopt a completely different approach, we continue to increase the number and severity of problems instead of solving them. Systemic management Can we find an alternative form of management that replaces conventional management?

We can. Figure 10.2 illustrates an entirely different approach to doing things – systemic management (Fowler 2003, 2009). It involves an entirely different way of thinking – one that calls for abandoning many of the thought processes behind setting goals in conventional management and replacing them with empirical observation. It requires what is essentially a complete shift in one of the roles of stakeholders. Systemic management starts and ends in the same places as conventional management. In each case, the last step remains management action – implemented through procedures, many of which have been established through a long history of trial-and-error (Chapter 9). In systemic management, however, the intermediate steps are entirely different. For both conventional and systemic management, we begin with what is identified as a problem – brought to the attention of the world by anyone. For example, consider again the observation of a declining marine mammal population identified as the problem in Step 1 – in this case a problem identified

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Systemic Management Fig. 10.2.  Schematic representation of the processes involved in the decisionmaking of systemic management from the identification of a problem to the action taken.

primarily by scientists in the context of national legislation in the USA (e.g., the Marine Mammal Protection Act or the Endangered Species Act). Step 2, in systemic management, depends on the combined involvement of stakeholders, scientists, and managers – all in collaboration, with interdisciplinary participation and effective communication to ask the right questions. Everyone is involved in the asking of a series of clearly framed, well-defined, specific management questions. Here, emotions, concerns, scientific principles, values, opinions, and theories lead effectively to many management questions. Here, many of the science questions that are posed in conventional approaches lead to management questions or their refinement (Fowler and Hobbs 2009). In our example, a documented declining marine mammal population gives rise to emotions, concerns, and the expression of human values – often bolstered by legislation. There is no magic that will make such factors disappear; they are real and they prove helpful by motivating us to ask good questions. Politics, values, and emotional reactions have been, and remain, part of our being human and serve well to generate good management questions. In Step 2 of systemic management, instead of ignoring these factors, they, both individually and collectively, serve to drive the formulation of clear management questions. They provide motivation; they are stimuli to ask questions  – management questions and not research or science questions (Fowler and Hobbs 2009). We see here a major distinction between the conventional and systemic approaches – differences in thinking. In systemic management, the mix of factors misused in conventional management provides the basis for asking and refining good management questions early in the process  – before such factors have a chance to influence decision-making directly. Thus, based on basic principles regarding ecological systems (e.g., knowing that competition and

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Science and management: systemically matching the questions predation involve the interactions of prey and predator populations), we use information about a declining population to ask management questions. Based on basic principles involving competition (i.e., when we harvest resources we are competing with other species – in particular any that consume the same resources), we might start with: “How much biomass should we leave (i.e., how much should we not harvest) so that it is available for the sustainability of other species and the ecosystem containing their populations?” We know that, when we harvest biomass, there is biomass that we do not harvest. We use what we harvest; other species use what we do not harvest. “What portion of the biomass produced annually within the marine mammal’s ecosystem should we not harvest each year so that it is available for the sustainability of the ecosystem and other species, including the marine mammal with the declining population?”1 This question meets the criteria for a good management question (i.e., meets the tenets of management; Fowler 2003, 2009). It addresses how we should limit ourselves (how we should manage our harvests as intransitive action; “through management of humans activities,” Chapter 4), has a quantitative character so that units are defined (portion of biomass production that is not harvested each year), and involves our interaction with the non-human (i.e., sharing resources with other species, ecosystems, and the biosphere). After we have posed a good management question, Step 3 of systemic management entails formulation of a research question (see Fowler and Hobbs 2009 for the distinction of the two types of questions). Scientists are heavily involved here, but other stakeholders (especially managers) participate in ensuring that the science question matches (is consonant2 with) the management question. The question posed in Step 2 of our example was: “What portion of the biomass produced annually within the marine mammal’s ecosystem should we not harvest each year so that it is available for the sustainability of the ecosystem and Questions such as: “What factors are contributing to the decline?” are often asked in conventional management. Such questions ignore the matter of burden of proof, and, being research questions rather than management questions, when answered, merely lead to explanation. Explanation, which verifies interconnectedness, is not management advice. Interconnectedness, as a basic principle, is assumed a priori in systemic management so that we proceed directly to management questions. Thus, questions concerning contributing factors can lead to management questions, but are not management questions (Fowler and Hobbs 2009). 2 Consonance, at this stage (and the focus of this chapter), involves very careful mapping of the management question to a research question. Consonance involves completely identical units of measure, identical logical types, and perfect isomorphism. It involves a one-to-one mapping, or congruence between management and research questions (Belgrano and Fowler 2008, Fowler 2009). This consonance is carried forward to research that reveals a pattern that is also fully consonant with the management question (Fowler and Hobbs 2009). 1

C.W. Fowler and L. Hobbs other species, including the marine mammal with the declining population?” The consonant research question is: “What portion of the biomass produced annually within the ecosystem is not consumed (i.e., not harvested) by nonhuman mammalian species of human body size?” Consonance is involved in the two questions because we are dealing with the same categories in framing the question (e.g., both the human and non-human species are mammalian predators – all are species). In other words, humans and marine mammals are species of the same taxonomic group; the specified body size is the same in each case. Furthermore, and very importantly, the questions involve the same units (portion of biomass produced that is not consumed annually) and the ecosystem identified is the same in each case. In essence, the research question is defined by the management question. Following the posing of each research question, research is carried out (Step 4) to provide an answer. In our example, research starts by producing estimates of the consumption rates by non-human mammalian species of human body size. Also essential are estimates of the production of biomass by the ecosystem. These data are then combined to estimate the portion of production not consumed (keeping in mind that the portion consumed and the portion not consumed sum to 1.0). The resulting pattern reveals limits to the variation observed in what is not consumed – limits to define the abnormal compared to the normal. This pattern is defined by both the management and the corresponding (consonant) research question. Consonance is carried forward so that the management question, the research question, and the pattern all match (no conversions are required). The pattern in our example is the pattern of ecosystem-level biomass production that is not consumed by each species of marine mammal of human body size. Such patterns are described and characterized as a normal activity of science. Research (especially theoretical and academic) often emphasizes the origins and explanation of such patterns to understand their complexity and integrative nature (often including an attempt to explain their origin). Explanation is often emphasized in proving connections, particularly connections wherein humans are causing problems – a failure to reverse the burden of proof. In Step 5, all stakeholders take action (through implementation and governance, see Chapter 9) to avoid the abnormal or pathological as revealed by the pattern when we compare humans to other species  – management involves everyone. If our harvest of biomass is abnormal, we have the responsibility of changing that harvest to achieve health and sustainability. The sustainability we refer to here is not confined to (but does include) the fishing industry as a short-term or anthropocentric objective; it is sustainability for all elements of the system: fisheries, humans, other species, ecosystems, and the species with

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Science and management: systemically matching the questions the declining population that led to the question at the outset – all in consideration of time scales well beyond those involved in conventional management. A closer look at systemic management The superficial nature of what we see above, as a preliminary description and comparison of conventional with systemic management, demands more depth. All of us involved in ‘resource management’3 are familiar with the details of conventional management; we are immersed in it. It is happening all around us; we are a systemic part of it – making change extremely difficult, especially with the tenacity with which we hold to the concept that stakeholders should be in the position of making decisions concerning goals in Step 4 (e.g., see Murawski and Matlock 2006, and Chapters 2 and 3). Systemic management is not confined to the current treatment of a declining marine mammal population. It is not confined to the endangered status of the spotted owl or to the Hayden penstemon (an endangered plant in North America). It involves our treatment of oceanic acidification, the extinction crisis, global warming, and the host of other problems science has brought to our attention. With the backdrop of a growing list of such problems, more of the detail involved in systemic management is helpful for comparison. Because readers have their own experiences of conventional management to draw upon, we now turn to a bit more detail regarding the progression of steps involved in systemic management, dwelling on those that are completely different from conventional management. Step 1

Step 1 is essentially the same for both conventional and systemic management. We are presented with a problem or management issue with which we must deal. There are many and none can be ignored; part of dealing with complexity is dealing with as many such issues as is possible to identify. Many will involve abnormalities among non-human species (e.g., abnormally low populations for endangered species), ecosystems (e.g., abnormally low mean trophic level), or the biosphere (e.g., abnormally high extinction rate). Because we humans do not have control over the myriad consequences of our actions on the non-human, “fixing” such abnormality is not an option in systemic management. Mitigating action instead of changing what we are doing (in cases where what we are doing is abnormal) is not an option. Abnormality Note the transitive nature of this term compared to the intransitive of systemic management. We do not manage other things in systemic management; we manage, instead, our interactions with other things (Fowler 2003, 2009).

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Systemic Management Fig. 10.3.  A schematic comparison of conventional and systemic management showing the difference in the role of stakeholders (Belgrano and Fowler 2008, Fowler 2009, Fowler and Hobbs 2009).

involving the non-human is treated as information that is used to drive us to asking questions confined to the sustainability of any human influence that has the slightest chance of contributing to any non-human abnormality we may observe (plus any we do not observe). Thus, questions asked in Step 2 are questions that involve limiting ourselves, finding sustainability, and interacting with the non-human consistently (i.e., we implement the tenets of management; Fowler 2003, 2009). Step 2

It is in Step 2 where we first find major differences between conventional and systemic management (Fig. 10.3). It involves posing and refining clear management questions. It involves accounting for complexity directly by asking as many management questions as possible. It involves a major change in the role of stakeholders compared with their role in conventional management. As mentioned above, one of the ways we account for complexity in systemic management is through the multiplicity of questions that can be asked (Step 2 in Fig. 10.2 and bottom row of Fig. 10.3). Another is the refinement of such questions; refining questions is one means of overtly accounting for complexity (Fowler and Hobbs 2009). Asking and refining management questions are parts of the second step in systemic management where stakeholders as a group play a crucial role along with managers and scientists. Each one uses their values,

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Science and management: systemically matching the questions insight, concern, and emotions to participate in the common goal of asking good management questions. All management questions must adhere to the tenets of management (Fowler 2003, 2009). Adhering to these tenets means approaching management by controlling ourselves (the intransitive of management; Fowler 2009) to avoid attempting the impossibility of controlling the non-human. The language and terminology we use changes: “predator control” becomes “managing our interactions and relationships with predatory species.” “Resource management” becomes a matter of “managing our use of resources.” “Ecosystem management” and “ecosystem-based management” become “managing our interactions and influence on ecosystems.” In our interactions with other species and ecosystems this means asking what we (humans) can do sustainably as normal parts of such systems in our interactions with, and influence on, all non-human systems. When it is suggested by conventional managers that we manipulate, modify, or try to “control” the non-human, we face a crucial decision. We can carry on with conventional management (e.g., “control” predators, pests, or diseases) or we can ask a truly systemic management question. Each manipulation, modification, or “control” in conventional approaches involves a variety of influences on various systems (e.g., mortality rate imposed on competitors or predators, or reduction in populations of such species). In the use of the systemic approach, on the other hand, we must ask, in each case, what level of each such influence is sustainable. In other words, in systemic management, we seek guidance for achieving sustainability in regard to each of the influences involved in conventional attempts to “control” so that management is that of controlling ourselves to avoid the abnormal rather than unrealistically trying to control the non-human. In the end, we must remain open to the option of having to prohibit many forms of conventional management (such as predator control) entirely. Multiple questions

Natural systems are complex and interconnected. One aspect of dealing with complexity involves the numerous management questions that can be asked. In addition to making it impossible to control them, the complexity of such systems leads to questions so numerous as to defy our imagination. In Step 2, all scientists, stakeholders, managers, politicians, and other interested parties are involved, thus bringing the complexity of human systems to the task. It may be that an environmental organization is concerned that the effects of fishing are contributing to the declining marine mammal population in our example. Rather than leading to a research question that would result in studies to prove such cause-and-effect, this leads to the management question: “At what rate can we sustainably harvest resource species X (where X is the

C.W. Fowler and L. Hobbs fish species that environmentalists have identified to be of concern)?” The fishing industry may be concerned about pollution. This prompts the question: “At what rate can we sustainably produce DDT?” (Distinct but similar questions would treat any other toxin that anybody, including the fishing industry representatives, may have in mind.) Evolutionary ecologists might suspect that the genetic effects of fishing are causing or contributing to the decline. In addition to the intensity of such effects (which emphasizes the importance of the question regarding sustainable harvest rate), such biologists might argue that selectivity by sex is an issue because of sexual dimorphism in the resource species. This leads to the question: “At what rates can we sustainably harvest males and females of resource species X (or, What would the ratio of males to females be in a sustainable harvest of species X)?” Many such questions can be asked – questions involving distribution of fishing, seasonal allocation of fishing, and depth of fishing. More global issues such as climate change can result in questions regarding sustainable CO2 production. Such questions are asked so as to account for the concerns of all stakeholders. Stakeholders may not be satisfied with the answers scientists provide, but satisfaction is one of the human values brought to current management in ways that are often counterproductive (e.g., short-term benefit at long-term costs; Fowler 2009). More such questions are considered in Fowler and Hobbs (2009) and Fowler (2009). Refinement

Refinement is part of the process of asking management questions in the second step. It involves another way in which complexity is taken into account – explicit treatment of factors stakeholders think might be involved. Refinement involves more clearly defining the management question using more specific terms to overtly account for more information. It also involves any set of non-consonant information scientists might consider relevant  – much of the research conducted today. We might begin with a general question such as: “At what rate can we sustainably harvest walleye pollock (Theragra chalcogramma)?” Some specificity has already been achieved; we have named a specific species (T. chalcogramma) and identified a particular process (harvesting or predation). We have identified ourselves as the predator species and have implicitly included the fact that we are a large mammal. Such factors can be made explicit. Walleye pollock occurs at a particular latitude and in a particular ecosystem, has characteristic life-history features, and a recognized adult body size. The management question can then be rephrased with greater specificity as: “At what rate can we sustainably harvest walleye pollock in the eastern Bering Sea, given its body size and the fact that we are large mammals?” Every question can be made more specific in this refinement process. This leads to questions refined in a way that assists scientists; they are

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Science and management: systemically matching the questions provided with clear direction for their research. A great deal of research, past and present, can be used in correlative analysis of information consonant with the management question. Step 3

Step 3, in systemic management, follows naturally from the posing of clear management questions. Specific research questions – questions that specify the research to be conducted – are formulated in this step. For example, we might start with the management question: “At what rate should we harvest males and females to achieve the sex ratio of a sustainable harvest from species X?” There are actually three management questions involved. One addresses the sex ratio of a sustainable harvest and the other two address the harvest of biomass or number of individuals – one rate for females, one for males. Each leads to its own research question. Thus, one research question involves the rates at which other species consume males, and the other the rates at which they consume females, both of the identified resource species (e.g., “At what rate do non-human mammalian species of human body size consume males (or females) of species X?” Importantly, we now also have a research question regarding sex ratio:  “What is the sex ratio of species X in the diets of nonhuman, mammalian, predators of human body size?” Each research question can then be refined in accord with its corresponding management question. For sex ratio, the sex ratio among fish consumed is measured, the species are identified, locations specified, and other conditions taken into account (note that thinking is critical to asking questions rather than setting policy). Both the management question and the research question involve sex ratio (e.g., number of males per female). Conversion is unnecessary when we get to Step 5; the biases and subjective nature of conventional management is avoided. Because natural patterns are integral parts of a natural system that embodies the laws of nature (Fowler 2009), normal consumption rates and sex ratios are set as goals. Achieving these goals achieves consistency in management (Hobbs and Fowler 2008). The associated patterns revealed by research contain information on what is normal, define the limits to natural variation, and provide examples of what is sustainable under existing conditions (Fowler and Hobbs 2002). The patterns are those to be analyzed in Step 4. Step 4

Step 4 involves the results of studies in which the research questions are addressed by scientists. For each research question, a pattern (often macroecological; Belgrano and Fowler 2008) is sought in natural systems, and, after being found, is characterized, and analyzed. The analysis often involves

C.W. Fowler and L. Hobbs analysis of variance and covariance – procedures familiar to statisticians. These analyses involve statistical models (Pilkey and Pilkey-Jarvis 2007) of the pattern defined in Step 3. The results are made available to managers with information about what is normal and what is not (e.g., see Fowler and Hobbs 2002, Fowler 2009, and Chapter 11). Assessment of human relationships with other species, ecosystems, and the biosphere can be made to determine if there is any abnormality on the part of humans. When human abnormality or pathology is discovered, science has revealed a problem that we can take action to solve. In such action, we take responsibility for our contribution(s) to problems we cause through systemic reactions to the abnormality of our influence. Complexity is involved in the multiplicity of these reactions and the systems involved. Step 5

The final step in systemic management is action that corrects any abnormality exhibited in human interactions with the non-human (Fowler 2003, 2008, 2009). It involves collective action on the part of everyone involved – all stakeholders taking action to change. There are quantitative goals established on the basis of the patterns used to define the normal compared with the abnormal (thus meeting the need for quantifiable objectives as one of the tenets of management; Fowler 2003, 2009). Step 5 involves achieving these goals (praxis, or undertaking management action; implementation and governance are involved, see Chapter 9). The action that is taken is action to achieve systemic sustainability and accounts for the complexity of everything involved. Stakeholder involvement It is important to emphasize the difference between conventional and systemic management in regard to the way stakeholders are involved. There is little difference between the first and last steps (Fig. 10.3) in replacing conventional with systemic management. We all notice problems and are involved in implementation (even though systemic action will often be toward goals quite different from goals in conventional management). There is by comparison, however, a major change in Steps 2–4. Here is where systemic management is completely different from conventional management; here is where stakeholders change roles. They, including scientists and managers, are removed from the misleading process of converting biased selections of what are almost always non-consonant pieces of information (as currently practiced in conventional management; Step 4 of Fig. 10.1 and the top row of Fig. 10.3). Instead, stakeholders take part in systemic management by being directly involved in asking the right management questions in Step 2 (Fig. 10.2, and bottom row of

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Science and management: systemically matching the questions Fig. 10.3). They are responsible for making sure that consonance is achieved and maintained from Step 2 to Step 5. The consonance ensures a realistic choice of, and use of, information. Stakeholders are involved in action that is consonant with what works in natural systems. It is easy to perceive the new role for stakeholder as counterintuitive. Steeped in conventional thinking, and driven to a large extent by emotions such as fear, many may see the altered role of stakeholders (including scientists and managers) as foolish. If we are to “consider” complexity, how can we not set our minds to the task of gathering data and then thinking about evolution, ecological interactions, and the economy? How do we account for the influence of our values without making them the basis for our decisions? How can we avoid trying to synthesize and combine all of the information at hand, when building models with existing information seems so logical (and involves a set of skills that have become so well developed)? In conventional thinking, when we see or use the words “account for,” “consider,” or “take into account,” we understand these processes as mental processes – processes carried out in human minds. If we are to account for the complexity of issues before us, how does removing humans from the “accounting” process (Step 4 in Fig. 10.1 and the top row of Fig. 10.3) help? In systemic management, the “accounting” happens systemically and we take advantage of it through very careful use of empirical information. As described earlier, and as developed in Fowler and Hobbs (2009), the empirical patterns themselves involve an integration of the complexity that needs to be taken into account (Belgrano and Fowler 2008); the “accounting” is done for us in the inherent nature of the patterns reflective of their emergence. Patterns consonant with management questions are integral parts of reality; their integral nature automatically accounts for all of the things that are involved in their emergence. Being integral, they integrate, or account for, everything involved. This “everything” is the infinite complexity of the things that contributed to the origin of the patterns, and that contributes to current dynamics characteristic of empirical patterns, as described in Belgrano and Fowler (2008)  – ­holism. Thus, using such patterns achieves the “integrative” of the last column of Table  2.2  – infinitely integral or fully holistic. We (especially scientists) get involved directly by refining questions and using correlative relationships within patterns  – much more a matter of observation than thinking. This is in contrast to conventional management wherein the “accounting” involves politics, opinion, and thinking that, by comparison, are all extremely vulnerable to human limitations, bias, and values. These lead to errors; illogical process is made part of the decision-making. By contrast, in systemic management, the emergent empirical patterns consonant with good management

C.W. Fowler and L. Hobbs questions contain an integration of information that accounts for the full variety of factors involved in their emergence (including human influence, beliefs, and values; Belgrano and Fowler 2008). As such, emergent patterns (rather than teams of experts, managers, and stakeholders with their human qualities) account for complexity  – all of the factors involved. Reality gets taken into account automatically through the integral nature of the patterns. The factors taken into account include historical management practices, economics, belief systems, values, politics, and vested interests as they played into past decisionmaking and management (Fowler 2009). In addition, the effects of all ecological mechanics (including any alternative states for any given ecosystem), the physical environment and its variation are all reflected in these patterns so that such factors need not be translated by special panels, stakeholder lobbying, politics, or economic pressures. Systemic management is, thus, also evolutionarily enlightened because all evolutionary dynamics, and natural selection at all levels are taken into account. Ecosystems – all ecosystems – are part of what is taken into account, thus making systemic management fully ecosystem-based, particularly when the questions being addressed are questions involving sustainable human influence on ecosystems. One of the features of empirical patterns is change – a characteristic that can be measured as part of their integral nature. Rates of change occur in patterns, including evolutionary change. Patterns of change, as with all patterns, reflect human influence along with all other contributing factors. Systems increasingly free of abnormal human impacts will result in a far better idea of what is fully sustainable than what we see now. What we see now, however, is a reflection of what is sustainable now, including change; what we see now provides initial goals to account for current human impacts. Thus, in systemic management (Fig. 10.2 and bottom row of Fig. 10.3), fears, emotions, insight, opinions, critical thinking, skilled communication, and understanding are used to ask questions rather than to set policy (recall that setting policy, in Step 4 of systemic management, is confined to the use of empirical information to avoid the abnormal). In Step 2, such factors are taken into account directly in asking questions. Policy, in systemic management, is based on increasingly well-defined empirical information consonant with the management question. For integral empirical patterns to be more useful and accurate in setting policy, scientists can look for correlative relationships involving factors such as environmental conditions (e.g., ambient temperature, season, latitude), life-history attributes of species involved (e.g., body size, adult mortality, generation time), alternative ecosystem states, and anthropogenic effects (e.g., past harvest rates, pollution levels, size selectivity). We know a priori that economic factors play a role in the origin of patterns; one of the

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Science and management: systemically matching the questions strongest arguments against change to avoid the abnormal involves economic impact. For example, reducing pollock harvests in the Bering Sea by 94% (Chapter 11) would have a huge economic impact on the fishing industry and would be forcefully resisted in conventional management. Another example is provided by the economic factors that contributed to the extirpation of whale populations by Soviet whalers during the last century (Berzin 2009)  – hardly an unexpected phenomenon (Clark 1973). Other value systems, beliefs, and politics play similar roles; they are major factors in the decision-making of conventional management and influence natural systems so as to be reflected in observed patterns. We cannot make such factors disappear or deny their impact. Their involvement in decision-making is often required by legislative mandate. Because of their roles in contributing to the origin of empirically observed patterns, such factors (including our ignorance and denial of them) are inherent to those patterns. Real-world integral patterns can be represented by statistical information (or statistical models as distinguished from simulation models; Pilkey and Pilkey-Jarvis 2007), often to directly account for contributing factors. Our values, beliefs, and politics do not serve as a basis for ignoring patterns and the human abnormality revealed when we compare humans with the non-human. With this perspective, science that reveals patterns consonant with the management question can be funded to produce information that answers management questions rather than simply adding to the monumental accumulation of information that adds to the verification of basic principles. Most such work is of varying degrees of relevance (Step 3, Fig. 10.1 and top row of Fig. 10.3), and impossible for us to unravel, interpret, and translate to policy correctly and objectively if it is not done through correlative analysis of consonant information. In following the path of the lower row of Fig. 10.3, people with vested interests have their concerns dealt with in the posing of clear and refined management questions followed by consonant research questions. Court battles would be confined to events involving failure to avoid the abnormal rather than in setting goals. Legislators and politicians make their contribution in posing questions. Funding non-consonant science as a delay tactic is not an option; there is no excuse for putting off the solving of problems that are clearly of human origin, specifically problems in the form of human abnormality. Scientists continue in their role of identifying problems, now with emphasis on problems of human abnormality – problems most directly solved by human action (management). All stakeholders are involved in a process aimed at achieving sustainability for systems at all hierarchical levels (individuals, species, groups of species, ecological communities, ecosystems,

C.W. Fowler and L. Hobbs the biosphere) and in regard to all of the ecological and evolutionary processes involved. Human limitations (e.g., effects of past mistakes and ignorance) are accounted for by being reflected in the patterns used for management, and do not directly influence the decision-making leading to ineffective or even harmful ongoing/future management action. Again, what we see now provides initial goals; in the future, systems with less abnormal human impact will better reveal systemic goals. Discussion We are witness to a history of management that fails to achieve sustainability, fails to adequately conserve resources, fails to find objectivity, fails to account for and overcome human limitation, and results in more problems than are solved. Observations of our environment show disturbing changes. Such changes are described by the scientific community in two of the very valid kinds of contributions made by science (observation and documentation). One of the responsibilities of science is to reveal problems. Scientists join all stakeholders in bringing problems to the attention of the public, managers, and policy-setting officials (Step 1 in both rows of Fig. 10.3). However, it is critical that information produced by science be realistically and objectively useful (and used) by managers so that problems can be solved rather than simply documented. Bringing a problem to the attention of managers places them in a bind if they are not given quantifiable goals and objectives for their actions (Fowler 2003, 2009). Hence, one of the qualities of a good management question is that of having units of measure that can be used to establish and distinguish the normal and the abnormal, to establish standards with which we can assess progress in management action, and provide researchers with the metrics to be used in research (e.g., portion of production consumed annually, tons of biomass consumed each year, portion of harvest made up of females, or portion of harvest made up of any particular phenotype). If managers are not asking the right questions, are not asking questions with enough specificity, or are not asking management questions at all, science cannot provide useful information. If scientists are producing information (even very good information) that is only tangentially or partially (and vaguely) related to the management question, the gulf between science and management is perpetuated (and conversion continues to be needed in Step 4 of conventional management, along with people trained to perpetuate the conversion process; Brosnan and Groom 2006). Scientists can produce useful information only if they understand, or are presented with, clear management

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Science and management: systemically matching the questions questions  – largely missing in today’s management. As developed by Fowler and Hobbs (2009), a clear understanding of the distinction between a science question and a management question helps define and then remove the gap between both science and management and between scientists and managers (see Chapter 9 for the role of non-governmental organizations in regard to this effort). This happens when we understand how to link the questions asked in management with questions asked in science and how to link questions asked in science with questions asked in management; it is a reciprocal process. A clearly framed management question defines the science needed to provide the answer. The last several decades have seen a variety of efforts to develop a new management paradigm that is ecosystem based. Part of the motivation for achieving such management is a result of the variety of problems that are observed in today’s world. Further motivation is found in the complexity of factors that should be taken into account in decision-making. We are also prompted to move toward different management by the realization that there is a hierarchical organization to nature in which we (the human species) are a part – along with other species. This line of thought extends the recognized need to include ecosystems in management to the need to include all levels of biological organization from individuals to the biosphere, i.e., the total complexity of all systems collectively. What are the questions that lead us to achieving management that accounts for and applies to the various hierarchical levels of biotic organization simultaneously? What are the scientific questions that lead to science that will provide the guidance we need? The questions we posed in Fowler and Hobbs (2009) serve as examples to illustrate the kind of question that needs to be asked: questions about sustainable influence on systems at each hierarchical level (individual, species, ecosystem, biosphere). The distinction between science and management is a distinction that helps define the respective questions in order to adequately deal with the complexity involved in the hierarchical structure of nature. This involves two things: the distinction of questions and hierarchical structure. To most, the distinction between science and management is anything but new; the distinction, along with most of what we present is, as Bateson (1979) called it, “… what every school boy knows …” Management involves establishing objectives, making decisions, and taking action. By contrast, science observes or monitors, predicts, characterizes, explains, understands, publishes, documents, and follows/develops appropriate methods and procedures. On the surface, it is clear that science produces information and management uses information.

C.W. Fowler and L. Hobbs When we look behind the scenes, however, we begin to encounter serious problems  – problems that involve the hierarchical structure of nature. Information about an ecosystem is rarely information about sustainable consumption within an ecosystem. As currently practiced, there are striking mismatches between information produced scientifically and the information needed in management. The information used is woefully inadequate to account for the complexity involved in decision-making; the information we choose to use in conventional management is not consonant with the management issues we face; we are often faced with errors of logical typing (Hobbs and Fowler 2008, Fowler 2009). Total primary production is total primary production; consumption of primary production is part of the fate of primary production. They are two different things. Faced with information that is not consonant with our management question, conversion is required to be useful; and the conversion process often brings to bear human values that completely overshadow any realistic management actions that would both account for the complexity of biological systems, such as ecosystems, and apply to our interactions with such systems to lead to sustainability for everything concerned. When we interact with ecosystems as a species, our management question must be posed such that it is clear that we are functioning as a species in interaction with an ecosystem. The harvest of biomass in the eastern Bering Sea is an example (see Chapter 11). The management question involves the sustainability of that interaction. The consonant research question involves consumption of biomass from the eastern Bering Sea by consuming species. It is a species/ecosystem interaction and the logical typing of the interaction must be consistent in framing the questions, conducting the research, and carrying out management. It is clear to most that management must be based on good science. However, it remains to be understood that errors are brought to the conversion of nonconsonant scientific information to guidance and management action (unless it is done through correlative processes using consonant information). It is currently taken for granted that our best option is to translate or convert scientific information to management action through the actions and involvement of human institutions (e.g., special panels, scientific committees, teams of industry representatives, and other organizations, Step 4, top row, Fig. 10.3 – including the niche for translational scientists; Brosnan and Groom 2006). It is assumed we have the skills, knowledge, and power to make such conversions. This ‘we’ includes scientists, managers, and other stakeholders exemplified by politicians, non-governmental organizations, citizens groups, and Native organizations – anyone involved in decision-making, policy-setting, and action. This misleading ‘translation and conversion’ of information is exemplified

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Science and management: systemically matching the questions by our use of information about production in attempts to address questions about consumption (harvests) in application of the maximum sustainable yield (MSY) approach to resource management (Fowler and Smith 2004, Belgrano and Fowler 2008, Hobbs and Fowler 2008). The alternative is to adopt an approach wherein little if any conversion is necessary  – thus largely removing human constructs (other than the value of sustainability and our representations of patterns), limitations, and bias from the process (i.e., systemic management, Fowler 2003; bottom row, Fig. 10.3). As is becoming increasingly clear, in current practice, the conversion of scientific information to guidance for management is subject to errors that mislead management and cause or contribute to the problems we face today. Is the gap between scientists and managers one of ineffective communication (Wilson 2006)? The errors behind observed problems may involve imperfect communication between scientists and managers; there is always room for improving communication. However, such issues are minor in comparison to a major problem in conventional management: communicating (or attempting to communicate) the wrong or misleading information. On the surface, at least, there appears to be little difficulty in the ability of the two groups to talk to each other and understand the information produced by science. However, in current forms of management, the choice of information to transfer from science to management is not the set of information management needs. People do not realize that management is asking for, and scientists are providing, misleading information (Fowler and Smith 2004, Belgrano and Fowler 2008). Perfect communication (and use) of the wrong information perpetuates problems rather than solves them. As sophisticated as the process of conventional management is, it is a process in which we make sophisticated mistakes (SáenzArroyo and Roberts 2008). Thus, rather than communication, the problem being defined here is the mismatch between the scientific information produced and its use in management. The mismatch involves an incorrect choice of information and results in a gulf between science and management. This gulf can be removed by finding a good match between science and management as defined for each specific management issue  – consonance among the relevant management question, the corresponding science question, the pattern used to reveal any human abnormalities, and the management action. It involves the correct choice of information; the precise choice of an integrative pattern to inform management is critical. With consonance, translation or conversion is achieved in correlative patterns and there is no need for panels, committees, or other organizations to translate information because further conversion is unnecessary. Current attempts to convert information (to overcome the mismatch) contribute to

C.W. Fowler and L. Hobbs the failures we see in today’s management; human error, judgment, values, opinions, and faulty logic prevail in attempts to link scientific information to management advice (Step 4, Fig. 10.1 and top row, Fig. 10.3). Fortunately, the mismatch is one that can be corrected. Solving this problem is a matter of clearly defining management questions early in the process so that they lead to clearly defined matching science questions (Step 2, Fig. 10.2 and bottom row, Fig. 10.3), guidance, and action. Although there must be a strict match between a management question and the corresponding science question, the two must be distinguishable. A key factor involves clearly posing management questions in a way that equally clearly defines the matching scientific questions (Fowler and Hobbs 2009). A desirable match between management question and its scientific counterpart involves, among other things, identical units (e.g., grams, tons/year, calories/hour, individuals/km2), and contextual circumstances. As is hopefully clear at this point (see also Fowler 2009, Fowler and Hobbs 2009) the two questions must be consonant, isomorphic, and congruent so that further conversion/translation is unnecessary in establishing good quantifiable goals. Summary Knowing how to ask good matching questions in management and science is a core element in solving the ecological problems we face today. Without it, solving problems involving the roles of economics, politics, human limitations, and anthropocentrism (with its cultural as well as evolved origins) will fail. In management, questions must adhere to the tenets of management by confining ourselves to achieving sustainability in our interactions and relationships, including those with the non-human. The posing of questions involves achieving a match between a specific management question and a specific science question. The match in such pairs of questions is consonance; that is, each pair has identical units and logical type and each question specifies similar circumstances and conditions. The more refined the pair of questions the more the circumstances and conditions are explicitly specified. In other words, through refinement, the factors involved are accounted for overtly rather than being lumped in a simple one-dimensional pattern of variation within limits. Overt treatment of correlative patterns revealed by science is simply an analysis of covariance – a statistical exercise that brings patterns into the management process. This is a matter of directly accounting for the various correlative factors, owing to the fact that the model of sustainability being used is an empirical model – an integral reality represented by statistics. These statistics (a statistical model; Pilkey and Pilkey-Jarvis 2007) are what we have to work with. The posing of questions that lead to these statistics needs to

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Science and management: systemically matching the questions be improved through the perspective we bring to the interface of management and science in Steps 2 and 3 of systemic management. Refinement in the pair-wise posing of management and science questions is a critical element in bringing out the best of science and demonstrating the ways science can contribute to effective management. It is key to embracing the field of macroecology in management as well as expanding the interdisciplinary horizons of management (often seen as an important goal for the management process, e.g., Ludwig et al. 2001). The wealth of scientific work to be done emphasizes the importance of science – true management science. The exposure of a pattern consonant with a specific management question results from the effective definition of the consonant scientific question. The information representing the pattern is the best scientific information to be used in management (filling the need for defining, and providing, the best scientific information for management; National Research Council 2004). The science that exposes the pattern is the best science for providing information to be used in management, followed by science that analyzes and characterizes such patterns, including the use of correlative variables derived from a variety of fields of science (Fowler 2009). Academic, exploratory, or theoretical science is very important to the refinement of paired scientific/management question through the exploration of correlative subpatterns in addition to establishing basic scientific principles. Involved, as a crucial form of research, are the scientific endeavors that expose problems that give rise to new management questions. When the problems exposed are problems of human abnormality compared to the non-human, we face the responsibility of taking action to solve them. The change we have presented in this chapter (moving from the top row of Fig. 10.3 to the bottom row) involves an entirely different way of ­thinking – a completely different approach to management. It means abandoning significant parts of historical approaches entirely and replacing them with different ways of doing things. Although the structural changes may seem simple and perhaps even trivial, they actually represent a fundamental change in thinking – a change from seeing the world as discrete manageable units to seeing it as complex systems that defy control at any level. Whether we have the self-control, the will, or sufficient wisdom to make such changes remains to be seen. Acknowledgments We thank Jason Baker, Judith Brown, Phil Clapham, Gary Duker, Lowell Fritz, Michelle Graves, Rod Hobbs, Jim Lee, Mike Sigler, and Allan Stoner for comments, editing, stimulating questions, and suggestions based on previous versions of this chapter.

C.W. Fowler and L. Hobbs References Bateson, G. 1979. Mind and Nature: A Necessary Unity. New York, NY: Dutton. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In Ecology Research Horizons. Hauppauge, NY: NOVA Publishers, pp. 5–31. Berzin, A.A. 2009. The truth about Soviet whaling. Mar. Fish. Rev. 70:4–59 (translated by Y.V. Ivashchenko). Browman, H.I. and K.I. Stergiou. 2004. Perspectives on ecosystem-based approaches to the management of marine resources. Mar. Ecol. Progr. Ser. 274:269–303. Brosnan, D.M. and M.J. Groom. 2006. The integration of conservation science and policy: the pursuit of knowledge needs the use of knowledge. In Groom, M.J, Meffe, G.K., and Carroll, C.R. (eds.), Principles of Conservation Biology. Sunderland, MA: Sinauer Associates, pp. 625–659. Clark, C.W. 1973. The economics of overexploitation. Science 181:630–634. Dayton, P.K. 1998. Reversal of the burden of proof in fisheries management. Science 279:821–822. Etnier, M.A. and C.W. Fowler. 2005. Comparison of Size Selectivity between Marine Mammals and Commercial Fisheries with Recommendations for Restructuring Management Policies. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum, NMFS-AFSC-159. Etnier, M.A. and C.W. Fowler. 2010. Size selectivity in marine mammal diets as a guide to evolutionarily enlightened fisheries management. N. Am. J. Fish. Manage. 30:588–603. Fowler, C.W. 2003. Tenets, principles, and criteria for management: the basis for systemic management. Mar. Fish. Rev. 65(2):1–55. Fowler, C.W. 2008. Maximizing biodiversity, information and sustainability. Biodivers. Conserv. 17:841–855. Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford: Oxford University Press. Fowler, C.W. and L. Hobbs. 2002. Limits to natural variation: implications for systemic management. Anim. Biodivers. Conserv. 25(2):7–45. Fowler, C.W. and L. Hobbs. 2009. Are we asking the right questions in management and science? Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum, NMFS-AFSC-202. Fowler, C.W. and T.D. Smith. 2004. Preface to the 2004 printing. In Fowler, C.W. and Smith, T.D. (eds.), Dynamics of Large Mammal Populations. Caldwell, NJ: Blackburn Press, pp. xiii–xxvi. Hobbs, L. and C.W. Fowler. 2008. Putting humans in ecology: consistency in science and management. Ambio 37:119–124. Link, J.S. 2005. Translation of ecosystem indicators into decision criteria. ICES J. Mar. Sci. 62:569–576. Ludwig, D., M. Mangel, and B. Haddad. 2001. Ecology, conservation, and public policy. Ann. Rev. Ecol. Syst. 32:481–517. Murawski, S.A. and G.C. Matlock (eds.). 2006. Ecosystem science capabilities required to support NOAA’s mission in the year 2020. Silver Spring, MD: US Department of Commerce, NOAA Technical Memorandum, NMFS-F/SPO-74.

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Science and management: systemically matching the questions National Marine Fisheries Service. 2008. Recovery Plan for the Steller Sea Lion (Eumetopias jubatus). Silver Spring, MD: National Marine Fisheries Service. National Research Council (NRC). 2004. Improving the Use of “Best Scientific Information Available” Standard in Fisheries Management. Washington, DC: National Academy Press. Norberg, J. and G.S. Cumming (eds.). 2008. Complexity Theory for a Sustainable Future. New York, NY: Columbia University Press. Norton, B.G. 2005. Sustainability: A Philosophy of Adaptive Ecosystem Management. Chicago, IL: University of Chicago Press. Pilkey, O.H. and L. Pilkey-Jarvis. 2007. Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future. New York, NY: Colombia University Press. Sáenz-Arroyo, A. and C. Roberts. 2008. Consilience in fisheries science. Fish Fish. 9:316–327. Towell, R.G., R.R. Ream, and A.E. York. 2006. Decline in northern fur seal (Callorhinus ursinus) pup production on the Pribilof Islands. Mar. Mammal Sci. 22(2):486–491. Waltner-Toews, D., J.J. Kay, and N.-M.E. Lister (eds.). 2008. The Ecosystem Approach: Complexity, Uncertainty, and Managing for Sustainability. New York, NY: Columbia University Press. Wilson, L.A. 2006. Communication between ocean scientists and policymakers: an analysis through the U.S. Ocean Policy review process. Dissertation submitted in partial fulfillment of the Doctor of Philosophy degree at Union Institute and University, Cincinnati, OH.

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Sustainability, ecosystems, and fishery management c h a r l e s w. f o w l e r a n d s h a n n o n m . m c c l u s k e y

Abstract Fisheries management is experiencing a trend in which harvest rates are being reduced as more of the principles of management are implemented, as more information emerges, and as more of the complexity of natural systems is taken into account. As recently as the late 1960s, there was overt and widespread acceptance of fishing mortality rates that were equivalent to natural mortality rates (F = M)  – an outdated standard that is still occasionally implemented today. From this extreme, reductions in fishing mortality rates of target resource species have been based on a variety of arguments, not the least of which is consideration of other species (especially endangered species) and ecosystems. What are sustainable harvest rates if we implement management principles completely and fully account for complexity? If we project current trends into the future, how much would we reduce current harvest rates to embody full sustainability? This chapter presents examples of the choice and use of empirical information for estimating harvest rates for fisheries so as to abide by established principles of management. We present measures of empirically observed rates of predation/consumption by various marine mammals as standards and reference points for fisheries management. These measures include recognized

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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Sustainability, ecosystems, and fishery management statistical parameters as well as maximized biodiversity. Such empirical standards are emergent from complexity and, through this emergence, fully account for the complexity behind their origin. Management that achieves the resulting goals is management that implements a key tenet of management: maintaining elements of systems within their normal range of natural variation. Fisheries would be managed with such guidance for harvests from individual resource species, groups of species, marine biotic communities, ecosystems, ocean basins, or the entire marine environment. Introduction and background Fisheries management is evolving (see Chapter 2). Harvests are being reduced to implement more and more of the recognized scientific principles and tenets of management. Such trends are closely associated with increased consideration of the complexity of factors that are important to decisionmaking, management, and policy. Here, we briefly review these trends to set the stage for considering an extrapolation of these trends to provide a look at where they are taking us. They are trends toward full, or holistic, ­sustainability – that is, sustainability of systems such as ecosystems and the biosphere, to include all of their component systems (e.g., species such as our own, along with our fisheries). In the 1960s, it was considered advisable to harvest fish populations with fishing mortality (F) equal to natural mortality (M) (Alverson and Pereyra 1969, Gulland 1970; and the closely related “potential yield formula” of Gulland 1971). The belief that it is safe to advise fishing rates equal to natural mortality (F = M) has been widely implemented (Clark 1991, Govender 1995). We continue to see evidence of M serving as a standard of reference (Mertz and Myers 1998). The rationale behind the F = M “rule of thumb” was based, in part, on consideration of the principles of population dynamics, and the relevant sciences of population biology as explained by Beddington and Cooke (1983; see also Gulland 1971). Throughout this history, the natural mortality rate (M) has been understood as a total that includes all biotic factors such as predation, disease, and senescence, combined with abiotic factors (e.g., weather and water quality). Also based on the principles of population dynamics is the concept of maximum sustainable yield (MSY) – related to the assumption that F could be set equal to M. This concept (MSY) was applied in fisheries management on the assumption that the maximum harvest of fish could be achieved if resource populations were reduced to stimulate density-dependent responses in population growth or productivity (Alverson and Pereyra 1969). In fisheries

C.W. Fowler and S.M. McCluskey management, acceptable reductions to achieve such production was (and often still is) between 25% and 50% of the virgin biomass of the population (MacCall 2009, Worm et al. 2009). From this limited point of view, it seemed logical to take advantage of the density dependence at play in the populations of all species. Increases in production by resource species stimulated by reductions in their populations were considered to be “surplus” and available for human use (Smith 1994). Beyond increased production, the consequences (including the unintended consequences; see Chapter 5) of such action were left largely unaddressed, even though a few such consequences were recognized (Gulland 1971). We see the results of ignoring such complexity in overfishing and habitat degradation (Botsford et al. 1997, National Research Council 1999, Jackson et al. 2001). Worldwide, such problems have led to the establishment of a set of guiding principles and tenets for management (Christensen et al. 1996, Mangel et al. 1996, Czech and Krausman 1997, Fowler et al. 1999, McCormick 1999, Fowler and Hobbs 2002, Fowler 2003, 2009). The often negative consequences of using simple population models in management have led scientists to build more complicated models and develop theories that attempt to reconstruct whole ecosystems and their dynamics. Such efforts have involved accounting for more complexity and have often led to advice to reduce harvests as part of the trend being observed. However, such advice is often offered without clear measures of how large the reductions should be (National Research Council 1999). Furthermore, in each case, modeled representations of the natural world are partial and when combined with other models and theories, those inaccuracies and shortcomings are compounded. It is impossible to combine, and weigh or establish the importance of partial information from the various disciplines of science and adequately account for reality (Fowler 2009). Many elements are left out of such models as exemplified by the many coevolutionary interactions among species. This situation (the Humpty Dumpty Syndrome, or the impossibility of realistically combining fragments of information each of which is partial, and collectively incomplete; Nixon and Kremer 1977, Dunstan and Jope 1993, Regal 1996, Horgan 1999; see Chapter 10) is insurmountable in conventional forms of management. Progress in management is impeded by the current use of man-made models and theories as incomplete representations of reality (Bateson 1979, McIntyre 1998, Schnute and Richards 2001, Pilkey and Pilkey-Jarvis 2007). Nevertheless, complexity, interconnectedness, and consequences are recognized as elements of real importance to management (Christensen et al. 1996, Mangel et al. 1996, Fowler and Hobbs 2002, Fowler 2003, 2009; see Chapter 5). Over the history of fisheries management, there has been recognition that

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Sustainability, ecosystems, and fishery management the population dynamics of any species are not realized in isolation from the numerous factors at play in natural systems. Criticism of the concept of MSY and conventional single-species approaches has often been voiced (Larkin 1977, Callicott and Mumford 1997), based in part on the fact that there is more to the complexity of natural systems than can be represented by population models. For example, it is now recognized that there are genetic effects of harvesting, some of which are clearly detrimental (Schaffer and Elson 1975, Nelson and Soulé 1986, Law and Grey 1989, Sutherland 1990, Smith et al. 1991, Horwood 1993, Kirkpatrick 1993, Law and Rowell 1993, Policansky 1993, Rijnsdorp 1993, Rowell 1993, Trippel 1995, National Research Council 1999, Conover and Munch 2002, Pauly et al. 2002, Myers and Worm 2003, Rosenberg 2003, US Commission on Ocean Policy 2004, Worm et al. 2006, Jørgensen et al. 2007). The size selectivity of fisheries is abnormal compared with that of non-human predators (Etnier and Fowler 2005, 2010). Furthermore, it is recognized that there are numerous unpredictable effects on ecosystems caused by reducing any species from pristine population levels. Accounting for complexity in a dynamic system is fundamental to achieving sustainability, especially in anything approximating a holistic approach. The sustainability of the human elements of complex systems (such as ecosystems) must not be ignored but cannot be allowed to overshadow the sustainability of the larger systems along with all of their components (Fowler 2009). The principles of sustainability appear in much of the theory of management (Christensen et al. 1996, Mangel et al. 1996, Czech and Krausman 1997, McCormick 1999, Fowler and Hobbs 2002), but are largely lacking in practice  – especially in regard to other species, ecosystems, and the biosphere. Management decisions continue to be made without fully accounting for such complexity. How do we surmount the impasse of the Humpty Dumpty Syndrome? How do we weigh various factors in proportion to their actual or inherent relative importance? How can we manage fisheries to fully account for the complexity of reality? How large are harvests that achieve sustainability that involves a full accounting of complexity? Although conventional management is not ill intended, it falls short (Fowler 2009; see Chapter 10). Managers and scientists continuously try to incorporate more complexity: more elements are added to models, additional factors are included in diagrams, and meetings of specialists draw up longer lists of factors to take into account, as exemplified by management described by many of the other chapters in this book (see especially Chapters 2, 3, and 4). However, such efforts are always incomplete. Considering ecosystems has been the focus of much discussion (National Marine Fisheries Service Ecosystem Principles Advisory Panel 1998, National Research Council 1999) and advisable levels of

C.W. Fowler and S.M. McCluskey sustainable fishing effort have dropped from the F = M standard of the late 1960s. MacCall (2009) provides concrete evidence that reductions needed in harvest rates are well beyond the relatively small reductions suggested in conventional management processes. Measures of population levels that produce at MSY rates have been proposed as lower limits to population reduction (Punt and Smith 2001), and Total Allowable Catches (TAC), Potential Biological Removal (PBR), and Optimal Yield (OY) are among the terms for advisable harvest levels to convey a sense of progress in avoiding the problems now recognized with attempting to achieve MSY. Even in these cases, however, the full complexity of natural systems eludes us. A more serious problem is that much of the progress that has been made in considering greater levels of complexity is mostly rhetoric (e.g., publications and meetings) rather than concrete action. The majority of fisheries management continues to be based on single-species considerations that outweigh consideration of larger-scale systems (e.g., ecosystems or the biosphere). The steps being made are small in comparison to those needed to completely account for complexity. For example, various genetic effects have been considered (Law and Grey 1989, Brown and Parman 1993, Grey 1993, Kirkpatrick 1993), but not in conjunction with a complete accounting for the entire suite of factors such as population dynamics (see Chapter 6), behavior (see Chapter 7), physiological processes, inter-specific interactions (especially coevolutionary interactions), and the various elements of the physical environment. Attempts have been made to develop multi-species approaches (Sissenwine and Daan 1991); however, none are generally accepted and universally applicable. Such considerations have added to suggested harvest reductions (Walters and Kitchell 2001). Predator/prey interactions have been considered (Francis et al. 1999) but not as a matter of accounting for predation among all of the predators and all of their prey in an ecosystem. Furthermore, predator/prey interactions are not fully integrated into management along with other important elements such as competition, coevolutionary interactions, and nutrient dynamics. As managers consider more and more complexity in their decision-making, there continue to exist huge gaps; a complete and objective accounting of complexity has not been achieved in conventional management. It is these gaps that stall appropriate action. Reduced harvests were suggested by Beddington and Cook (1983) in consideration of population variation. Reductions in harvest levels have been suggested by the National Research Council (1999) as a way of bringing more complexity into management. Such vague suggestions, however, do not quantify needed reduction nor do they fully integrate complexity into the decision-making process. Expanded single-species approaches have also failed in this regard. For example, fisheries management

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Sustainability, ecosystems, and fishery management to account for both direct and indirect effects of fisheries on endangered species often result in reduced harvests without objective grounds for establishing the extent of the reduction to fully account for the complexity of the systems involved. Management in consideration of ecosystems would involve all other species in the system, never simply a select few (for example, those we choose to represent in complex ecosystem models). All direct and indirect effects and their feedback (including evolutionary and coevolutionary effects) would be taken into account in a fully integrated (systemic) consideration of complexity. Management in consideration of ocean basins, and the marine environment as a whole, involves even greater complexity. The impasse of the Humpty Dumpty Syndrome mentioned earlier might remain insurmountable were it not for the fact that among the measurements and observations made by science is empirical guiding information that can be used to directly answer management questions (Chapter 10). If science were capable of building a complete representation of reality, it would be reality – the complete suite of factors at play, each in proportion to its actual intrinsic importance. In moving forward, a key step is to make each management question the basis for finding examples of what works rather than an incomplete set of partially related pieces to produce a simulated system or predicted future (Fowler and Hobbs 2009; Chapter 10). In this chapter we give examples of how to choose empirical information that meets the needs of management (exemplifying the process described in Chapter 10)  – the information that is needed in an extrapolation of trends observed in advice to reduce harvests. This integral information (found in macroecological patterns) is chosen to incorporate the complexity largely omitted in current practice while simultaneously adhering to established principles of management. The choice of empirical information is restricted to information that is consonant with the management question – a question which has to be asked so as to implement the tenets and principles of management (Belgrano and Fowler 2008, Fowler 2009, Fowler and Hobbs 2009). In this way, the empirical information used to establish goals for management accounts for thousands of years of evolutionary and coevolutionary adaptations. Using this kind of information automatically embraces all of the factors needed to make any natural system sustainably functional (Chapter 10). The resulting approach is systemic management (Fowler et al. 1999, Fowler and Hobbs 2002, Fowler 2003, 2009). When the management question involves sustainable consumption (harvest), systemic management uses information on consumption rates by other predators to find what is normal within the natural variation observed in such consumption rates. Commercial fishing rates are then compared to the pattern

C.W. Fowler and S.M. McCluskey of consumption rates established by other predators (usually in the form of frequency distributions; Fowler and Perez 1999). In the case of marine fisheries management, the predators considered are marine predators. This interspecific comparison is necessary because we (humans), and the effects that we have on other species, are bound by the same natural limits as any other living creature (the justification behind the core tenet of management requiring that we do what we can to avoid the abnormal; Pickett et al. 1992, Moote et al. 1994, Christensen et al. 1996, Mangel et al. 1996, National Marine Fisheries Service Ecosystem Principles Advisory Panel 1998, Francis et al. 1999, Uhl et al. 2000, Fowler and Hobbs 2002, Fowler 2009). Because these patterns (frequency distributions) are emergent integral products of the complexity of reality, guiding information based on them represents a full accounting of complexity that is impossible in conventional approaches (Fowler and Hobbs 2002, 2009; see Chapter 10). Management itself is a matter of changing what we (humans) do so as to position measures of human interactions with the non-human so as to fall within the normal range of natural variation (to implement the objective of avoiding the abnormal; Pickett et al. 1992, Moote et al. 1994, Christensen et al. 1996, Mangel et al. 1996, National Marine Fisheries Service Ecosystem Principles Advisory Panel 1998, Francis et al. 1999, Uhl et al. 2000, Fowler et al. 1999, Fowler and Hobbs 2002, Fowler 2003, 2009). Managing fisheries In the next several sections, we present data that would be used in several aspects of the systemic management of fisheries to exemplify its application – management as an extrapolation of trends toward sustainability under existing conditions. Figure 11.1 illustrates the concept, wherein other species are included as stakeholders in their own right (Fowler 2009). Carrying out management is a matter of relocating humans to fall within the normal range of natural variation among other species. This is based on the fact that patterns among other species represent an integration of, and therefore account for, complexity as explained above (see also Belgrano and Fowler 2008 and Chapter 10); all species are integral parts of the systems we are dealing with. Whether we choose to use a central tendency (e.g., mean or mode), to fall within specified statistical confidence limits, or maximize biodiversity (Fowler 2008) is not as important as the issue of avoiding the risks of unsustainable impacts on other systems (e.g., other species, ecosystems, the biosphere) that occur in reaction to abnormal human influence. The examples we present below include statistical parameters (e.g., both central tendencies and confidence limits) along with the maximization of biodiversity.

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Fig. 11.1.  A hypothetical probability distribution (pattern) of consumption rates among non-human species as it would be used in providing guidance to avoid abnormal harvest rates in the management of fisheries. Management would reduce catches in this hypothetical case by about 89% to correspond to rates that would maximize the biodiversity among consumption rates observed among all species as empirical examples of sustainability (Fowler et al. 1999). Among the alternative strategies would be to harvest at rates no greater than the geometric mean (requiring about a 99% reduction). Other alternative objectives include fishing at rates that correspond to the 0.95 confidence limit and the arithmetic mean among consumption rates observed for the non-human predators.

Any progression toward (or beyond) “ecosystem-based management” cannot ignore the need to continue managing the takes by fisheries from individual species. Beyond any single species, groups of species, ecological community, or ecosystem, there is the entire biosphere. Various levels of biological organization are involved in the systemic management of fisheries. These include managing our take from individual species (note that systemic management would replace conventional single-species approaches). Beyond single-species applications are multi-species groups, ecosystems, ocean basins, and the entire marine environment. The next sections are organized around these hierarchical levels wherein management is systemic in applying at all levels simultaneously and consistently. Harvests from individual species

Systemic management uses patterns (including macroecological patterns that can be represented by probability distributions) such as those shown in Figs 11.2–11.5 to establish sustainable harvest (consumption) rates from specific resource species based on the consumption rates of other

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Fig. 11.2.  Frequency distribution of “harvest” rates from walleye pollock by six species of marine mammals in the eastern Bering Sea compared to the harvest rate by primarily commercial fisheries, as based on data from the late 1980s (Livingston 1993, Fowler and Perez 1999). Fishing involved a consumption of pollock that was over 51 times greater than the mean consumption of pollock observed among the marines mammal specie, and 6.2 times greater than the upper, P = 0.95, confidence limit.

predators (Fowler et al. 1999, 2009, Fowler and Hobbs 2002). At the time, and under the circumstances that these estimates were produced, fisheries took 28 times as much walleye pollock (Theragra chalcogramma) in the Bering Sea as the average (arithmetic mean) marine mammal species based on raw estimates of consumption rates, and 51 times as much as the geometric mean (Fig. 11.2). Fisheries took 16.8 times as much as would be required to maximize the biodiversity among the consumers of pollock in this ecosystem (following the methodology of Fowler 2009, lower panel of Fig. 11.2). The total take by US fisheries for these data was 4.6 times as much as the combined take of all six species of marine mammal. Humans, through our fisheries, were (and presumably still are) statistical outliers (abnormal, or not

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Fig. 11.3.  Frequency distribution of “harvest” rates from herring by 12 species of marine mammals, birds, and fish in the northwest Atlantic compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from 1988–1992 (Overholtz et al. 1991). Fishing involved a consumption of herring that was 64 times greater than the geometric mean consumption of herring observed among the non-human species, and 8.8 times greater than the upper, P = 0.95, confidence limit.

Fig. 11.4.  Frequency distribution of “harvest” rates from hake by 11 species of marine mammals, seabirds, and fish in the northwest Atlantic compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from 1988–1992 (Overholtz et al. 1991). Fishing involved a consumption of hake that was 15 times greater than the mean consumption of hake observed among the non-human species, and 1.1 times greater than the upper, P = 0.95, confidence limit.

within the normal range of natural variation). To get to the 95th percentile of the probability distribution of consumption rates among other species would have required catches about 84% less than what were observed. If optimal sustainability is assumed to be that which maximizes biodiversity,

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Fig. 11.5.  Frequency distribution of “harvest” rates from mackerel by 16 species of marine mammals, birds, and fish in the northwest Atlantic compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from 1988–1992 (Overholtz et al. 1991). Fishing involved a consumption of mackerel that was 46 times greater than the mean consumption of mackerel observed among the non-human species, and 0.9 of the upper, P = 0.95, confidence limit.

the required reduction would have been about 94%. To achieve harvest rates equal to the geometric mean of the distribution shown in Fig. 11.2, catches of pollock would have been about 2% of recent levels (reductions in fishing of about 98%) to put consumption by humans on par with that among marine mammals. To match the arithmetic mean, the reduction would have been about 83%. Similar reductions would have been required for Atlantic herring (Clupea harengus), hake (Merluccius bilinearis), and mackerel (Scomber scombrus) of the northwest Atlantic. In view of the aberrant take in fisheries shown in Fig. 11.3, the take for herring would have been reduced by over 98% to correspond to the geometric mean (about 96% for the arithmetic mean) owing to the fact that commercial fisheries took between 26 (arithmetic) and 64 (geometric) times as much as the means among non-human consuming species. To maximize the biodiversity of this predator–prey system (i.e., with herring as prey), fisheries harvests would have been about 4.5% of observed rates. The harvests of hake and mackerel would also have been much less (reductions of about 82% (arithmetic), 93% (geometric), and 60% (biodiversity) would have been necessary for hake, and 77% (arithmetic), and 97% (geometric) for mackerel, to account for overfishing by factors of 5.5, 15, 2.5 and 4, and 46 respectively; Figs 11.4, 11.5). However, for mackerel, the harvest taken in fisheries was smaller than would have maximized biodiversity (at about 81% of what would have maximized biodiversity). The harvest of mackerel was 1.7 times

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Figures 11.6 and 11.7 show data for empirically observed sustainable consumption rates of biomass from two multi-species systems in comparison to the harvests taken by commercial fisheries. Systemic management of the Bering Sea harvests of biomass from finfish would have required that harvests be about 93% less than observed in order to maximize biodiversity (observed harvests were about 15 times the harvest level that would have maximized biodiversity of this multi-species predatory–prey system; Fowler 2008). Alternatively, to match the 0.95 confidence limit among non-human species, the harvest for this system by fisheries would have been reduced by only about 56%. This is in comparison to reductions of about 97% and over 99% (from 3% to less than 1% of recent harvests) based on the assumptions that arithmetic and geometric means, respectively, represent optimal sustainability. These comparisons, of course, apply today only insofar as the conditions for the system at the time the estimates were produced correspond to current conditions. For the hake, herring, and mackerel of the northwest Atlantic (as a multispecies group, Fig. 11.7), harvests were 14-fold too large to maximize the biodiversity of this system. Harvests would have had to have been less than 2% of

Fig. 11.6.  Frequency distribution of “harvest” rates from the finfish of the Bering Sea by 20 species of marine mammals compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from the late 1980s (Fowler and Perez 1999). Fishing involved a consumption of biomass that was over 135 times greater than the mean consumption of finfish observed among the non-human species, and 2.2 times greater than the upper, P = 0.95, confidence limit.

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Fig. 11.7.  Frequency distribution of “harvest” rates from hake, herring, and mackerel of the northwest Atlantic by 13 species of birds and marine mammals compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from 1988–1992 (Overholtz et al. 1991). Fishing involved a consumption of biomass that was about 74 times greater than the geometric mean consumption of these resource species by the non-human species, and 4.4 times greater than the upper, P = 0.95, confidence limit.

the observed levels in order to be equal to the geometric mean of “harvest” rates observed among the marine mammals and seabirds consuming from this group of species. We have shown both single-species application of systemic management in the previous section and multi-species applications in this section. It must be stressed that it is important not to restrict fishing regulations to our harvests from any one level in the hierarchical organization of nature. For example, a harvest established by the collective consumption of finfish by non-human species is not a basis for taking that harvest from one (e.g., economically preferred) finfish species. The harvests of individual species must be carried out with guiding information as presented in the previous section as well as guidance regarding the allocation of catches across the alternative prey species (also using empirical information as described in Fowler 1999). Harvests from ecosystems

Figures 11.8 and 11.9 show data regarding the limits to natural variation in the consumption of biomass from two different ecosystems. The observed takes by commercial fisheries should have been over 96% less than observed (based on raw consumption rates, shown in log scale in Fig. 11.8) in the Bering Sea, and about 98% less in the Georges Bank ecosystem in order to correspond to the geometric mean of the consumption rates among the ­non-human species

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Fig. 11.8.  Frequency distribution of “harvest” rates of biomass from the Bering Sea by 21 species of marine mammals compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from the late 1980s (Fowler and Perez 1999). Fishing involved a consumption of biomass that was over 27 times greater than the mean consumption within this ecosystem by the non-human species, and about 1.4 times greater than the upper, P = 0.95, confidence limit.

Fig. 11.9.  Frequency distribution of “harvest” rates of biomass from the Georges Bank ecosystem by 12 species of marine mammals compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from prior to the mid-1980s (Backus and Bourne 1986). Fishing involved a consumption of biomass that was about 57 times greater than the geometric mean consumption within this ecosystem by the non-human species, and 7.9 times greater than the upper, P = 0.95, confidence limit.

represented. A reduction in catches of over 90% would have been required to manage harvest in the Georges Bank ecosystem in order to maximize biodiversity. Similarly, the harvests of biomass in the eastern Bering Sea would have had to have been about 86% less than was observed.

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Fig. 11.10.  Frequency distribution of “harvest” rates of biomass from the north Atlantic Ocean basin by 16 species of cetaceans (consumption rates for humans not available; from Tamura and Ohsumi 1999).

Fig. 11.11.  Frequency distribution of “harvest” rates of biomass from the north Pacific Ocean basin by 25 species of cetaceans (consumption rates for humans not available; from Tamura and Ohsumi 1999).

Harvests from ocean basins

Figures 11.10 to 11.12 show data regarding the limits to natural variation for consumption rates among marine mammals (both cetaceans and pinnipeds) in three major ocean basins. Systemic management would require that the total worldwide harvest (see next section) be distributed by ocean basin such that totals for each basin fall within observed limits. For example, to maximize the biodiversity of the predator–prey system involving the take of fish in the north Atlantic (Fig. 11.10) the annual harvest in that region would be about 20.3 million metric tons. A partial implementation of systemic management is insufficient (not fully systemic). We need to address other questions (see Chapter 10), particularly the

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Fig. 11.12.  Frequency distribution of “harvest” rates of biomass from the southern hemisphere ocean basin by 13 species of cetaceans (consumption rates for humans not available; from Tamura and Ohsumi 1999).

Fig; 11.13.  Frequency distribution of the number of ocean basins occupied by 34 species of cetaceans, 22 of which are found in only one of the three regions represented by consumption data shown in Figs 11.14–11.15 (from Tamura and Ohsumi 1999).

questions of how much of the entire marine environment to set aside in protected status, how many ocean basins to harvest from, and where in each basin to conduct fishing. Figure 11.13 shows that we would be advised to harvest from only one of the three basins, but the observed limits in variation would not preclude harvesting from all three. Within each area open to harvesting, the total harvests, harvests from the various species groups, and harvests from individual species would be established as exemplified above. Harvests from the entire marine environment

Figures 11.14 and 11.15 show the total harvest from the world’s oceans by commercial fisheries (about 110 million metric tons per year; National

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Fig. 11.14.  Frequency distribution of “harvest” rates of biomass from the world’s oceans by 54 species of marine mammals compared with the harvest rate by primarily commercial fisheries (Fowler and Perez 1999). Fishing involves a consumption of biomass that is over 294 times larger than the mean consumption within the world’s oceans by the non-human species, and 3.0 times larger than the upper, P = 0.95, confidence limit.

Fig. 11.15.  Frequency distribution of “harvest” rates of biomass from the world’s oceans by 17 species of marine mammals (cetaceans of near-human body size) compared with the harvest rate by primarily commercial fisheries (from Tamura and Ohsumi 1999). Fishing involves a consumption of biomass that is over 154 times larger than the mean consumption within the world’s oceans by the nonhuman species, and 5.0 times larger than the upper, P = 0.95, confidence limit.

Research Council 1999) in comparison to the consumption rates by various groups of marine mammals. In applying recognized tenets of management, we would reduce the world harvest by over 89% to maximize the biodiversity of the predator–prey systems of the marine environment. It would need to be reduced by about 99% if we were to harvest at rates that correspond to the geometric

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Sustainability, ecosystems, and fishery management mean of consumption rates among the non-human species (a 67% reduction to fall within the 95th percentile of the probability distribution represented by Fig. 11.15). The total catches from all ocean basins would be restricted to fall within the limits of the natural variation of consumption rates such as these, just as the total for any one area would be managed on the basis of data such as those in the preceding section (Figs 11.10–11.13). Other aspects of management There are a wide variety of issues that must be addressed in management, including the size and placement of reserves (marine reserves in the case of fisheries management), and the allocation of catches (selectivity) across alternative resource species, age, sex, size (Etnier and Fowler 2005, 2010), space, and time (Fowler and Crawford 2004). The number of species harvested would need to be specified. These and other management questions would also be addressed systemically with guidance provided by empirical information (Fowler and Hobbs 2002; see Chapter 10). The sustainable allocation of harvests across alternative resource species through systemic management is presented in Fowler (1999); the allocation of harvests over seasons can be done in a parallel fashion (Fowler and Crawford 2004). When harvesting both the population of a predator and those of one or more of its prey species, the allocation of harvests across all species would be guided by the allocation of harvests among non-human species consuming the same resources. In its full implementation, systemic management would address all management questions in regard to the limits to natural variation (Fowler and Hobbs 2002; see Chapter 10) to go beyond the fisheries applications that we present herein to include other realms, environments, and issues (e.g., CO2 production, energy consumption, use of water, and over-population; Fowler and Perez 1999, Fowler and Hobbs 2002, Fowler 2003, 2009). The factors involved include elements of varying importance in their influence on the populations of the various fish species, and their ecosystems, directly or indirectly. Data requirements/choice of empirical information Figures 11.2, 11.6, and 11.8–11.16 are presentations of data for consumption rates by marine mammals. These examples emphasize one of our main points: the empirical information used must be consonant with the management question being addressed (see Chapter 10). These examples involve data on consumption rates for management questions involving consumption rates by our species as a mammalian species. This links data and question

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Fig. 11.16.  Frequency distribution of “harvest” rates from mackerel by 10 species of marine mammals in the northwest Atlantic compared with the harvest rate by primarily commercial fisheries as the take by humans in this area, based on data from 1988–1992 (Overholtz et al. 1991). Fishing involved a consumption of mackerel that was 62 times greater than the geometric mean consumption of mackerel observed among the non-human species, and 2.3 times greater than the upper, P = 0.95, confidence limit.

directly to avoid the use of models based on incomplete indirectly related data common in conventional management. However, linking data and question is never as simple as ensuring the use of data representing the same process (e.g., consumption); as introduced in the last paragraph, there is a need to account directly for other factors such as taxonomic category. Figures 11.3–11.5, and 11.7 include non-mammalian taxa such as birds and fish. Figure 11.16 represents only marine mammals – a subset of the data in Fig. 11.5. In particular, the data of Fig. 11.16 do not include the consumption estimated for spiny dogfish (Squalus acanthius), the only species that consumes more than humans in Fig. 11.5 (and a species particularly subject to the intensity of past human influence: see below). The data for every example above (Figs 11.2–11.16) serve to represent prevailing environmental conditions at the time they were collected, including human influence (e.g., the effects of existing fishing practices), measurement error, and the state of the ecosystem(s) involved. The factors mentioned in the previous paragraph illustrate three points. First, it is important to let systems such as the northwest Atlantic recover from abnormal human influence before data from the resulting circumstances can reliably represent sustainability in the absence of such influence. Second, marine mammals serve as better sources of guiding information than do birds or other non-mammalian species. Other mammal species (especially mammals of

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Sustainability, ecosystems, and fishery management near-human body size to account directly for the matter of body size) serve as the best empirical examples for guidance to our species because they are similar to humans in ways that help account for complexity as expressed in correlative relationships (Fowler et al. 1999, Fowler and Hobbs 2002, Fowler 2003, 2009; see Chapter 10). Thus, it is possible to directly account for the fact that predation rates may vary with factors such as taxonomic category, body size, metabolic rates, or trophic level. Third, it is important to account for environmental (and other) circumstances at the time of management. In the following, we amplify these three points without reiterating a long history of scientific focus on techniques for reducing statistical error and conducting science as best we know how – also a necessary consideration in order to conduct science that best serves management. Guidance requires systems free of abnormal human influence

Existing data account for the fact that the systems involved are influenced by abnormal human effects from our recent history (especially the last few hundred years; e.g., the northwest Atlantic with its increase in species such as the spiny dogfish, Fig. 11.5; Fogarty and Murawski 1998, Fowler 1999). In other words, they reflect the sustainability of systems that accounts for both human (e.g., pollution, fishing, CO2 production) as well as environmental impacts (e.g., climate change, solar radiation, seasonal variation) of all kinds. At least in theory, ecosystems experience alternative states, and presumably do so in response to human influence (Link 2002). Recovery of ecosystems from this influence will undoubtedly require decades (probably centuries). This means that a great deal of research can be conducted both as we (humans) regain a position within the normal ranges of natural variation for the ways we are aberrant at this stage and as the natural systems regain their normal variation within natural limits increasingly freed of abnormal human impact. Monitoring will be necessary both to observe the changes and collect relevant guiding information. If these systems were to be protected from the abnormal harvests we are currently imposing on them, other species might respond by increasing their populations, thus consuming more, and resulting in slightly higher estimates of sustainable takes for humans. Such changes would probably take decades and the current differences between consumption rates by humans in fisheries and those by non-human species could be somewhat less extreme that observed recently. Guidance requires species otherwise similar to humans

The choice of species to use as empirical examples of sustainability is important in providing the human species with useful guidance (Fowler et al.

C.W. Fowler and S.M. McCluskey 1999, Fowler and Hobbs 2002, Fowler 2003, 2009). For example, the seabirds or fish represented in Figs 11.3–11.5, and 11.7 do not provide the quality of guidance needed by a species like humans, given characteristics such as our body size, metabolism, and mobility – factors to take into account in direct consideration of complexity. This is illustrated in comparing Fig. 11.5 with Fig. 11.16. In Fig. 11.16, marine mammals, as a subset of species in Fig. 11.5, show fishing by humans to be even more clearly outside the normal range of natural variation (abnormal) than when fish and birds are included in the distribution. If we are outliers in comparison to all other categories of species, it is important to start the changes necessary to fall within the normal range of natural variation and then to identify appropriate harvest rates more specifically through the use of species otherwise comparable to humans as sources of guiding information as they respond to resulting shifts in ecosystem structure and function. This would include identification of appropriate metrics for the measure of advisable harvests (e.g., central tendencies, specific confidence limits, or the maximization of biodiversity) – keeping in mind the need to avoid fixed points so as to embrace the normal variation typical of complex systems. Accounting for prevailing circumstances

Environmental circumstances change; ecosystems change and may occur in different system-level states. If management is faced with a particular climate regime (such as an El Niño event) the guiding data best serving management needs would be data collected under similar conditions. When management is faced with an ecosystem in one of its alternative states, the data collected to address our management questions must be obtained under conditions of that ecosystem state. Thus, there are caveats to be considered in regard to using existing data, even though they provide a general idea of the magnitude of change needed to achieve sustainability (Fowler et al. 1999). It is clear, nevertheless, that the changes required of fisheries in most cases is so extensive that the time required to make such change will afford the opportunity for the studies needed to refine the estimates involved. Such large-scale alterations of current harvest levels will require extensive alterations in political, social, and economic realms, all of which will take time. Ultimately, natural limitations will force extensive declines of harvest rates as resource populations are over-exploited (whether to economic or ecological extinction), emphasizing the need to implement change as quickly as possible. For many management questions there are no data to provide direct guiding information in systemic management. To initiate a harvest of a previously

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Sustainability, ecosystems, and fishery management unharvested species of fish, for example, would require that studies be conducted to determine the mean and variation in consumption rates from such a species by its non-human predators. The resulting pattern would need to be adjusted to account for adding ourselves as a predator; sustainable consumption rates undoubtedly decline with the count of predator species (Fowler et al. 2009). Prior to obtaining empirical data specific to a resource species, estimates can be made through correlative patterns involving other species in relation to factors such as body size, life-history strategy, taxonomic group, and trophic level. In other words, interpolation or extrapolation based on information about the species in question can be used to take advantage of the consonant information on harvest/consumption rates from other species. Beyond simple harvest rates are the distribution of predatory mortality across age, sex, space, trophic level, and season to provide guidance in regard to the corresponding allocation of catches in fisheries. The determination of consumption rates at different population levels and under varying conditions in the physical environment would be necessary to be responsive to the respective changes encountered over time. Then there is the larger question of whether or not to initiate a new fishery at all. If the total biomass removal by fisheries (or the number of species consumed) for a given area already exceeds the normal range of natural variation among non-human species, then initiating a new fishery on a previously unharvested species in the area would be in violation of recognized principles of management, which require that we avoid abnormality in human interactions with other species (owing to the fact that we humans make ourselves components of the system through commercial fishing, and subject to the limits and complexity of the system; Fowler and Hobbs 2002). Discussion and summary Systemic management is an implementation of recognized tenets of management based on various principles developed primarily in the 1980s and 1990s (Christensen et al. 1996, Mangel et al. 1996, Czech and Krausman 1997, National Marine Fisheries Service Ecosystem Principles Advisory Panel 1998, Fowler 1999, 2009, Fowler et al. 1999, McCormick 1999, Fowler and Hobbs 2002). Many of these tenets and principles are mentioned in other chapters of this book (e.g., Chapter 3). One principle involves the limits of science and knowledge (Holt and Talbot 1978, Bateson 1979, Bartholomew 1986, Peters 1991, Ludwig et al. 1993, Brown 1994, National Research Council 1999), specifically the inability of conventional management to overcome the Humpty Dumpty Syndrome mentioned earlier. Information and observations can be used to better understand processes and parts of systems. However, selected pieces of data

C.W. Fowler and S.M. McCluskey and understanding cannot be combined, as is often done in current practice, to realistically represent systems as a whole (McIntyre 1998). Science cannot be asked to set policy (Mangel et al. 1996). The alternative is that of using the strengths of science to objectively and precisely observe and measure the limits to natural variation for guidance regarding information directly related to (consonant with; see Chapter 10) management questions in a way that accounts for complexity where reality is the model – advice that would be used by managers to set policy and bring about real change and achieve real sustainability. Managers are required to minimize risks and can no longer force systems (e.g., ecosystems) to meet unsustainable needs that result in the degradation of (including extinction within) such systems. Systemic management, exemplified by systemic fisheries management, as we describe it in this chapter, will require immense changes in policy and practice. Owing to the interconnected nature of natural systems, these changes cannot be carried out without concurrent changes in regard to other management questions (Fowler and Perez 1999, Fowler and Hobbs 2002, Fowler 2009). Systemic management takes into account the complexity of nature (Fowler et al. 1999, Fowler and Hobbs 2002, Belgrano and Fowler 2008; see Chapter 10). For each management question addressed, this happens automatically in that frequency distributions exemplified in the figures above are products of the contributing factors making up reality; each factor is represented in its relative level of importance in its contribution to these patterns. However, care must be taken to choose appropriate species and data for prevailing circumstances. Most difficult, however, is the matter of addressing the complete suite of questions that we face in management. We have to expand beyond managing single-species resource utilization sustainably. This must include the restriction of biomass extraction from ecosystems, multi-species systems, ocean basins, and the marine environment. Unsustainable fish harvests can be reduced intentionally or by harvesting until there are too few fish to harvest. However, systemic management must also go beyond fisheries management to include other issues. As difficult as it might seem to reduce fisheries takes to the extent indicated in the data presented above, addressing other aspects of management is even more challenging (e.g., CO2 production, energy consumption, use of water, and over-population; Fowler and Perez 1999, Fowler and Hobbs 2002, Fowler 2009 – all of the factors involved in the ways we influence non-human systems, including marine ecosystems. These other aspects of management often arise in the debate characteristic of conventional management (Step 4 in the process depicted in Fig. 10.1). An example is provided by the debate involved in continued attempts to undertake the outdated and overly simplistic notion of predator control.

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Sustainability, ecosystems, and fishery management A specific example is the debate concerning efforts by whaling nations to kill whales, rationalized as a way to make more marine resources available for human consumption (see Gerber et al. 2009). In this case, the debate serves well to generate genuine management questions such as: “At what rate can we sustainably harvest sperm whales (or any other species of whale)?” as explained in Chapter 10. Predator control, as an example of conventional management, is a good example of what Belgrano and Fowler (2008) call a “misdirected reductionism” wherein there is little if any consonance (see Chapter 10) between the information used (e.g., we know that competition occurs) and the management action taken (killing whales). The point here is that, instead of using data such as those presented in Figs 11.10–11.12 to argue for culling cetacean populations, these data serve as evidence of consumption rates that work sustainably in natural systems and allow for regulation of fisheries to achieve sustainable harvests. If predator control is a realistic option (it is unrealistic when it is an attempt to control the nonhuman; Fowler 2009), it must be guided by consonant information. Because predator “control” involves killing animals, any culling of cetaceans must be guided by information on the rates at which cetaceans are killed by their predators, not by information indicating that cetaceans consume resources that we might want to consume. One of the challenges for scientists is that of collecting more data of the kinds presented above, and improving the quality of the data that we have in hand. For example, in the data presented in Fig. 11.15, there are no killer whales (Orcinus orca) represented for the Pacific Basin – clearly an oversight that needs correcting even though this species may be too large for direct use in guiding humans (but useful in correlative relationships involving body size). The estimation of consumption rates for an entire species involves a great deal of effort, attested to by the work required to produce the results used to construct the estimates in the figures above. One of the requirements for adequate decision-making will be scientifically sound and defensible data. The direction toward which management needs to head is clear and the magnitude of the challenges before us is increasingly clear. While reducing our harvest rates, we have the responsibility of conducting more research to better define where we want to fall within the range of options for sustainability (statistical measures of central tendency or maximized biodiversity; Fig. 11.1). Thus, there is a way forward in spite of the imprecision of existing data and the resulting estimates of sustainability. It is up to us human managers as elements of ecosystems and the biosphere to apply systemic management to all management issues. With respect to fisheries, we conclude that the observed

C.W. Fowler and S.M. McCluskey trends in smaller advisable (sustainable) fishing rates are much smaller than currently recognized – often by orders of magnitude. Acknowledgments An earlier version of this chapter was a background paper accompanying a poster which we presented at the 2001 meeting of the North Pacific Marine Science Organization, in Victoria, BC, Canada. We wish to thank Gary Duker, Jean Fowler, James Lee, Alec MacCall, Staci Morton, and a number of anonymous reviewers for their help in reviews, comments, and suggestions on previous drafts of this chapter. References Alverson, D.L. and W.T. Pereyra. 1969. Demersal fish explorations in the northeastern Pacific Ocean – an evaluation of exploratory fishing methods and analytical approaches to stock size and yield forecasts. J. Fish. Res. Board Can. 26(8):1985–2001. Backus, R.H. and D.W. Bourne. 1986. Georges Bank. Boston, MA: MIT Press. Bartholomew, G.A. 1986. The role of natural history in contemporary biology. Bioscience 36:324–329. Bateson, G. 1979. Mind and Nature: A Necessary Unity. New York, NY: Dutton. Beddington, J.R. and J.G. Cooke. 1983. The Potential Yield of Fish Stocks. FAO Fisheries Technical Paper 242. Rome: FAO. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In Munoz, S.I. (ed.), Ecology Research Progress. Hauppauge, NY: NOVA Publishers, pp. 5–31. Botsford, L.W., J.C. Castilla, and C.H. Peterson. 1997. The management of fisheries and marine ecosystems. Science 277:509–515. Brown, J.H. 1994. The ecology of coexistence. Science 363:995–996. Brown, J.S. and A.O. Parman. 1993. Consequences of size-selective harvesting as an evolutionary game. In Law, R., McGlade, J.M., and Stokes, T.K. (eds.), The Exploitation of Evolving Resources. Proceedings of an International Conference held at Julich, Germany, September 3–5, 1991 (Lecture Notes in Biomathematics, 99). Berlin: Springer-Verlag, pp. 248–261. Callicott, J.B. and K. Mumford. 1997. Ecological sustainability as a conservation concept. Conserv. Biol. 11:32–40. Christensen, N.L., A.M. Bartuska, J.H. Brown et al. 1996. The report of the Ecological Society of America Committee on the Scientific Basis for Ecosystem Management. Ecol. Appl. 6:665–691. Clark, W.G. 1991. Groundfish exploitation rates based on life history parameters. Can. J. Fish. Aquat. Sci. 48:734–750.

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Sustainability, ecosystems, and fishery management Tamura, T. and S. Ohsumi. 1999. Estimation of Total Food Consumption by Cetaceans in the World’s Oceans. Tokyo: The Institute of Cetacean Research. Trippel, E.A. 1995. Age at maturity as a stress indicator in fisheries. Bioscience 45:759–771. Uhl, C., A. Anderson, and G. Fitzgerald. 2000. Higher education: good for the planet? Bull. Ecol. Soc. Am. 81:152–156. US Commission on Ocean Policy. 2004. Final Report: An Ocean Blueprint for the 21st Century. Washington, DC: US Commission on Ocean Policy. Walters, C. and J.F. Kitchell. 2001. Cultivation/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58:39–50. Worm, B., E.B. Barbier, N. Beaumont et al. 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314:787–790. Worm, B., R. Hilborn, J.K. Baum et al. 2009. Rebuilding global fisheries. Science 325:578–585.

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On the path to holistic management: ecosystem-based management in marine systems a n d r e a b e l g r a n o a n d c h a r l e s w. f o w l e r

Abstract Overfishing based on past management practices, combined with growing awareness of the complexity of the systems of which we are a part has motivated work toward management that is more holistic in its perspective. This book is a contribution to, and documentation of, progress being realized. It includes projections for significant next steps. Progress certainly involves attention to marine ecosystems. We need to manage our harvests from populations of fish with an approach that is ecosystem based. Partial progress involves knowing the need for sustainability in harvests taken from groups of species, including the populations of species that are components of ecosystems. It is important that such harvests be taken with an approach that is ecosystem based. More progress involves knowing that harvests of biomass taken from ecosystems need to be sustainable. These also must be taken with an approach that is ecosystem based. We are only now beginning to see that, not only must harvests be ecosystem based, they must also be evosystem based. This is necessary to account for both ecological complexity and the complexity of evolutionary and coevolutionary impacts of fishing throughout the systems affected. Progress involves knowing that both ecosystems and

Ecosystem-Based Management for Marine Fisheries: An Evolving Perspective, eds. Andrea Belgrano and Charles W. Fowler. Published by Cambridge University Press. © Cambridge University Press 2011.

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On the path to holistic management evosystems are parts of the biosphere; holistic management must also be biosphere based in all of its applications so the harvests, the biosphere, and all ecosystems are sustainable. The chapters of this book present a rich array of information and thinking that contributes to the principles upon which holistic management can be based. Progress is being realized as more and more of these principles are woven into the management process. Where is that progress headed – are we getting close or have we just begun? Overall, as outlined in several of the chapters, it appears that a great deal of change in thought and action has yet to be realized before we can call management holistic. Introduction In today’s world, people are increasingly aware of the serious nature of the problems that confront us. Of course, these are not confined to fisheries or the marine environment where increasing temperatures, overfishing, and ocean acidification are prime examples; problems confront us worldwide. There is a growing focus on changing management to avoid creating more such problems, to cease perpetuating such problems, and to rectify known problems. It is widely recognized that the systems involved, and of which we are a part, are extremely complex – so complex as to be beyond comprehension. In view of this complexity, the last several decades have been witness to concerted efforts to bring ecosystems into the realm of management, initially referred to as “ecosystem management” and later “ecosystem-based” management. Progress, in this regard, involved recognition that it is problematic to believe that we could extend control over other species to control over ecosystems; control over anything other than human influence is not an option. Our attempt to exert control over other species (e.g., “resource management”), is increasingly being recognized as a serious flaw of existing management practices that should not be extrapolated to the level of ecosystems (Fowler 2009). The complexity of the reality before us is experienced in the complexity of human subsystems (Chapter 5). More generally, it is reflected in the numerous scientific papers documenting the components and processes involved. Often, these publications stress the point that the components or processes which they bring to light are elements that should be taken into account in the process of management. Accounting for the complexity of the systems with which we interact was one of the primary concerns in the movement to expand beyond single-species approaches so prevalent historically and, in particular, in marine fisheries management. It involved expanding the boundaries of our paradigms (Chapter 8). How was this to be done? Another expansive and intensive set of

A. Belgrano and C.W. Fowler papers has been published to address this question. How do we proceed? It was recognized that part of the complexity we must deal with is the complexity of human systems that must be brought to the task of management (Chapters 5, 8, and 9). Tenets, pillars, criteria, commandments, and principles important to the management process abound in this literature – almost uniformly stressing the need to account for the complexities involved in all systems at all levels. This book is published at a time in which progress is being recognized. The chapters it contains are a tribute to that progress  – often emphasizing either the elements of complexity for which we must account or elements of the management process, or both. Progress is at the core of books with titles such as Gaining Ground:  In Pursuit of Ecological Sustainability (Lavigne 2006) and The Future of Fisheries Science in North America (Beamish and Rothschild 2009). We are in the process of working toward an approach that is holistic – management wherein the full complexity of systems (individual species, ecosystems, the biosphere and their combinations and interactions, along with our uncertainty about them) is taken into account. A common theme in the literature on management with regard to our use of natural resources is the concept and importance of holism – an expansion of current paradigms (Chapters 2, 4, 8, and 9). The holism being called for (Francis et al. 2007) can be characterized as being well beyond Leopold’s (1949) “thinking like a mountain.” Calls for interdisciplinary (or multidisciplinary) approaches to management are behind this aspect of the progress we are making  – management has its parts and each discipline emphasizes a part of the complexity important to management. Ecosystems (and marine ecosystems in particular), ocean basins, the marine environment, and the biosphere are parts of the reality we face and our management at each level is a component of the management toward which we are working. Biosphere-based management (Lubchenco et al. 1991) must be considered as an overarching component (ecosystems interact; Guerry 2005; Chapter 2) so that ecosystems, various species, and their component populations, with all of  their interactions, are parts not to be lost in the process. The focus on ecosystems, especially marine ecosystems, that provides the foundation for most of the chapters of this book, is a tribute to the progress being made on this component of management. This historical emphasis on ecosystems is experience that counts as progress toward the development of more holistic approaches to management to include the biosphere (without, of course, neglecting any subsystems or their components). We now recognize that these systems, along with all subsystems, interact through processes beyond widely accepted ecological relationships (e.g., ­predator–prey relationships). In addition to the food webs that are so much a part of such systems (and cannot be ignored; Chapter 2), there are also webs

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On the path to holistic management of evolutionary interactions – all of the coevolutionary processes. Evolutionary ecologists (Thompson 2005) are increasingly contributing to the understanding that the complexity of biotic systems includes evolutionary forces: in addition to ecosystems (and ecosystem-based management), we must recognize evosystems1 so that there is also an evosystem component to management. Fishing, for example, results in evolutionary effects (Chapter 5) similar to the effects of fishing on the demographics of populations and dynamics of food webs. This is not a realization that leads to replacing ecosystem-based management with evosystem-based management any more than management at the species or population levels can be replaced by ecosystem-based management. The evosystem perspective is a contribution to our understanding that the complexity of systems involves both food webs and coevolutionary webs. Both ecological and evolutionary principles must be parts of what is taken into account in the management process (Francis et al. 2007). Management must be ecosystem based and evosystem based simultaneously. Many of the chapters of this book document what is being done now and serve as examples of the progress that is being made, especially in terms of the steps toward holism that each represents. They present some of the elements critical to the construction of the foundation for holistic ­management – management yet to be fully realized. They recognize things that must be taken into account and contribute to change by emphasizing elements of importance in management. Where is this progress headed? What will management look like when it is fully holistic and accounts for all of the complexity inherent in the systems of which we are a part? Other chapters emphasize the processes involved in management, further contributing to the development of a more holistic form of management. One chapter (Chapter 11) involves an extrapolation of this progress to characterize what is seen as a future step in this progress  – management that the authors assert is much more holistic than is possible with current forms of management and applies to every question we choose to address (see Chapter 10 for the concept of The history of the term “evosystem” is not clear. One of the early uses of the term is found in Gordon et al. (1992) and it is also used by Hoctor et al. (2006) as part of the title of their chapter in a book on pine ecosystems. Samuel and Weng (2003) use the term in modeling work done in regard to evolutionary processes considered from a taxonomic perspective. The concept involved in the term “evosystem,” of course, dates back to Darwin and Wallace. It includes the evolutionary dynamics behind such systems as such dynamics involve natural selection at all levels, including selective extinction, or natural selection among species (Okasha 2006). Acceptance of an evosystem approach to management in conventional thinking would result in the production of models that might be called “EVOPATH” and “EVOSIM” models (comparable to the Ecopath and Ecosim models in use today regarding ecological dynamics; see Chapter 2).

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A. Belgrano and C.W. Fowler asking management questions). To achieve this holism, macroecological patterns are used directly. What progress is represented by the work being done? Basic principles of crucial importance to management emerge from the wealth of papers available in the scientific literature, including the chapters of this book. More principles emerge from the literature that treats management as a process. Such principles involve overarching truths or axioms that are known, or knowable, by virtually everyone. An example involves the lists of species presented in Chapters 2 and 3 – every ecosystem contains populations of an immense number of species from viruses to whales, from primary producers to top-level carnivores, and from local to widespread. In the list of species involved in ecosystems, humans are included among the elements that influence other species and the system as a whole (Chapters 1, 3, and 8). In principle, complexity involves diversity, species richness, and myriad characteristics beyond being listed or measured. A more generic, but highly related principle is that systems are made up of parts or subsystems (usually taken for granted when we list them). The ecosystems treated by most chapters of this book (see especially Chapters 1–4) are parts of the biosphere as are all species. These collections of species (including humans) use resources from their respective systems and are represented by populations that make up part of the complexity that is characteristic of such systems. Populations, in turn, contain individual organisms complete with their structural components (e.g., organs, cells, organelles, enzymes, and pheromones) and processes (e.g., physiological, behavioral, reproductive, neurological, embryological). In principle, there is a hierarchical structure; things are embedded in larger contexts (Chapter 8). Further complexity is emphasized by the common recognition of processes and interconnections among the populations, species, and individuals – including well-known processes such as predation, food-web dynamics, and physiological and behavioral processes (Chapter 7). Such processes are emphasized in Chapter 6 in which the author focuses on population dynamics and the very important message that such processes cannot be ignored in attempts to proceed toward the holism that includes ecosystems; holism is not achieved if population dynamics are not included. Such processes are accepted, in principle, by many of the modeling efforts mentioned in this book (e.g., Chapters 1–3). Interactions are at the core of the work presented in Chapter 1. In principle, these processes include the management conducted by humans (e.g., the management processes described in Chapters 8 and 9,

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Fig. 12.1.  Schematic diagram of the type found in the literature in attempts to represent various aspects of the complexity of systems. For ecosystems, such a diagram might represent the food web of interactions among the populations of the various species involved. For evosystems, such a diagram might represent the set of evolutionary and coevolutionary interactions among the populations of the various species involved. In either case, such a diagram (or the computer model it represents) can never be as complex as the actual system it represents. In all cases, such diagrams are manifestations of our understanding the principles of complexity and interconnectedness that are involved in such systems.

and shown in Fig. 4.6). Humans are components of (i.e., participants in) ecosystems, interacting and influencing other components in numerous complex interconnected ways. The complex nature of the systems with which we interact is, in principle, virtually never disputed. The more we study them, the more this complexity is appreciated, documented, and characterized. Such complexity is revealed by evolutionary ecologists who remind us of coevolutionary interactions. The “messy”2 diagrams of food webs (Fig. 12.1, see also Figs 3.8 and 1.4) are just as “messy” when they depict the evolutionary and coevolutionary interactions among the components of ecosystems and the biosphere. All of the chapters in this book, describing the various aspects of the ecosystems upon which they focus, contribute to the overarching principle that ecosystems are complex. The principle that individuals, species, ecosystems, and the biosphere are complex, both collectively, and as systems themselves, is indispensable in progress toward greater holism. Holism includes evolutionary and coevolutionary Term attributable to Jason Link (Atlantic States Marine Fisheries Commission, 2003. Linking multispecies assessments to single species management. Special Report No. 79. www.asmfc.org/publications/specialReports/sr79MultispeciesLinkages.pdf).

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A. Belgrano and C.W. Fowler processes in combination to neglect neither evolutionary nor ecological components of complex systems. Another principle emerges from the wealth of research and studies found in the scientific literature: systems have characteristics that can be measured whether they are ecosystems, individual species, or individual organisms. As described in Chapter 2 (see also Link et al. 2002) ecosystems can be described and characterized by their location, size, depth, mean trophic level, species richness, connectivity, productivity, geographic size, size distribution, diversity, and total biomass (see Chapter 4 for more ecosystem-level metrics). Such lists are virtually endless – again a tribute to the complexity before us. Likewise, species can be characterized by a long list of metrics. These include mean body size, trophic level, intrinsic rates of increase, behavior (Chapter 7), generation time, or population size (Chapter 6). Individuals are characterized by their body mass, respiratory rate, body temperature, or growth rate. The wealth of dimensions attributable to systems at each level reinforces the principle of complexity. In the study of these dimensions, another principle emerges: there are limits to the natural variation of the various characteristics, whether such characteristics are of individuals, species, or ecosystems. Patterns by their very nature do not show infinite variation. Each dimension occurs in patterns. These patterns are themselves characteristic of the systems with which we interact and of which we are a part. In developing models to represent complex systems, the principle of limited variation is inherent to the process of fitting models (parameterization) to empirical information. Predictions and behaviors by such models that do not mimic observed data (i.e., fall beyond the limits of observed patterns) are grounds for mistrusting the model. The overarching pattern that we are seeing here is that scientific endeavor has been critical to the process of establishing basic principles upon which there is general agreement that management should be based. Lists of species, lists of processes, and models involving both, all contribute to the foundation upon which we base our conclusion that systems are complex. The components are interconnected through relationships that are as innumerable as the components themselves. The complexity involved in interconnectedness is behind the unintended consequences of management action (Chapter 5). This is, in part, because the relationships, whether they are ecological (involving ecosystems) or evolutionary (involving evosystems), involve primary or direct interactions, secondary, tertiary, or higher-order interactions that are indirect. Thus, in Fig.  12.1, component #9 interacts with #5 (and reciprocally, #5 interacts with #9) by way of #24 and #10. The interactions involved can be of any kind, whether they are relationships we recognize today (e.g., behavioral,

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On the path to holistic management evolutionary, or ecological), or processes that we have yet to discover (the unknown  – widely recognized as very important to a holistic perspective as part of the uncertainty we need to take into account). Such interactions involve all relationships simultaneously, many of which are very non-linear. Every component of the system represented by Fig. 12.1 is inter-related with every other component  – not one component can be isolated from any other single component. Complex systems science provides validation for the principle behind such interconnectedness and its role in the emergence of system-level properties. Modeling plays a crucial role in leading to our understanding of this principle, an understanding that is also intuitively obvious, based on our experience of interconnectedness in human affairs. Modeling does two other things, each of which is also of critical importance in our progress toward a holistic form of management as management. Each involves a basic principle. First, modeling brings humility to the management process, a quality emphasized as critically important in much of the literature on the management process. Anyone who has constructed a simulation model quickly learns that it is impossible to include everything in the model. The lists of components (e.g., individuals, species, and species groups), the lists of processes, interactions, and inter-relationships, and the lists of qualities and characteristics are all superficial to the realities that models represent. We begin to understand the concept of uncertainty as it embraces the unknowable. The truth is more complicated than we perceive it to be (Chapter 8). As Egler (1977) said: “ecosystems are not only more complex than we think, but more complex than we can think.” In other words, we cannot think like a mountain, much less a universe, but yet, we press forward in search of management processes that will accomplish a full accounting of the reality with which we must contend. In the realm of principles, we add the principle that we humans are finite entities incapable of thinking of everything, knowing everything, or including everything in our models; it is the principle of human limitations. Where does this leave us in our objective of achieving a holistic form of management? As mentioned earlier, modeling is at the core of several of the chapters of this book. Modeling brings out a second principle of fundamental importance in the mission toward holism. Modeling demonstrates the emergence of patterns observed in nature. This principle, simply stated, is: empirical patterns are integral parts of nature (Appendix 12.1). It is the principle behind the ­holistic/integrative “understanding” paradigm listed in Table 2.2. As frequently stated, modeling (as with science in general) involves attempts to explain the emergent integral nature of patterns. Behind such work is the same principle stated a different way: there is a full explanation involved in the origin of every

A. Belgrano and C.W. Fowler pattern. As described in Belgrano and Fowler (2008), and in Chapter 10, being an integral part of nature means that all contributing factors involved in the origin of the pattern are inherently accounted for by the pattern itself. Again, we understand this principle through our experiences with modeling. When we construct simulation models to provide explanations or predictions of observed patterns, we are, within the confines of our ability to do so, integrating (synthesizing, or accounting for) all of what we understand to be involved in the origin of these patterns. When we finish the model to our satisfaction (at least to some level of satisfaction, or when we have exhausted our budget) the pattern we have recreated represents an integration of everything we have been able to put into the model. These modeling exercises bring us to understand that, in natural systems, the origin of the observed patterns is holistic. In nature, this origin is understood to include every minute detail of every factor, every process, and every contributing component of the system – what we would include in our models if it were possible to do so. As explained in Belgrano and Fowler (2008), Fowler (2009), and Chapter 10, the integral nature of empirical patterns involves the holism behind their origin and their observed characteristics. A simulation model is an incomplete integration; the empirical reality is a complete integration (Appendix 12.1). An empirically observed pattern involves the infinite set of factors involved in its formation (Belgrano and Fowler 2008, Fowler 2009). As such, the macroecological patterns used by the authors of Chapter 11 provide reality-based (holistic) information for guiding management regarding specific management questions. Chapter 10 extends this holism to all management questions we can ask. The patterns brought to bear in all cases are patterns that reflect holism in the combination of all of the principles substantiated by the other chapters. Thus, the interminable lists of components of systems (e.g., species, populations, processes, interactions, and influences) count among the factors integral (Appendix 12.1) to the empirical patterns found in nature  – in the systems for which we want to conduct management in a holistic way. These factors include the roles we play as the human component of systems and ways we carry out management – the things we humans do as parts of the complex systems in which we find ourselves. In principle, the decision-making described in several chapters (e.g., Chapters 8 and 9) is influential in the systems with which we interact and is reflected in the observed patterns characteristic of these systems. Human influence, in all of its complexity, is reflected in the means, modes, variance, limits, and dynamics of these patterns as it affects every dimension with which any specific pattern can be measured. Another principle emerges from the progress to which we are witness. It is a corollary to the principle of interconnectedness. It is the principle of

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On the path to holistic management limited control: it is beyond our capacity to change the fact that systems are interconnected. Changes brought about by one component of a system result in changes experienced by others (including changes initiated by fishing, as explained in Chapter 1). When we take management action, we cannot prevent our actions from having effects throughout the system, whether they be ecological (e.g., reactions by ecosystems) or evolutionary (e.g., reactions by evosystems). Returning to Fig. 12.1, if commercial fishing in a marine ecosystem is represented by component #1, its influence is experienced, in turn, by #6, #13, #20, #4 and back to #1; there is feedback and it cannot be avoided (Chapter 1). Furthermore, it occurs by way of a huge number of interconnected pathways – even in the simplistic representation of a complex system depicted in Fig. 12.1. In principle, we cannot control everything; we cannot alter the veracity of basic principles. Where is this progress taking us? There is little doubt that we continue to make progress toward greater holism. We know that fully holistic management will include not only marine ecosystems, but also terrestrial ecosystems and the biosphere (system-level principles apply in all realms; Chapter 8). Most of the chapters in this book focus on marine systems and represent part of the progress. Basic principles form the foundation of holistic management, and the chapters of this book, like the many other publications in the literature, contribute to the formulation of such principles. The more such principles are made integral to our management process, the further we advance toward the elusive goal of holistic management. Finding a way to incorporate all principles consistently is one of the challenges before us. The challenge of finding consistency, however, also brings progress. Consider one inconsistency with which we are confronted: calls for interdisciplinary approaches to management embody an acceptance of the complexity of natural systems while the principle of human limitations (we cannot know everything, think of everything, or include everything in our models) prevents holism in interdisciplinary policy-setting. The principle of human limitations forces us toward epistemological considerations – critical thinking that brings us to the conclusion that we cannot “think like a mountain.” It might be tempting to try “thinking like a universe,” yet that is even more unrealistic. Nevertheless, both the mountain and the universe, as well as all of the oceans, the biosphere, and every organism involved, and every process connecting them (physiological, ecological, or evolutionary), must be accounted for if we are to embrace holism.

A. Belgrano and C.W. Fowler Consider another inconsistency: a common goal or objective of the management process is to involve stakeholders in establishing goals. This mission of management is embodied in legislation (one example is the Magnuson-Stevens Fishery Management Act in the USA).3 It is heavily promoted in the literature (Meffe et al. 2006) and widely supported, as is implied in several chapters of this book (e.g., Chapters 1–3) and described directly in Chapters 4, 8, and 9. However, we are confronted again by the principle of human limitations. Human limitations prevent the achievement of holism through this aspect of stakeholder involvement; no matter how many stakeholders are brought to the process, decisions regarding ecosystem-level objectives, based on input generated in the minds of stakeholders, will always be incomplete. Not only will holism never be achieved in this manner, but stakeholder input, as the basis for policy, will always involve an anthropocentric bias – progress toward objectivity meets a dead-end when we involve the opinions of stakeholders as the basis for setting policy.4 Stakeholder-based policy is not ecosystem-based policy and makes holism impossible in conventional management. Progress toward holism, however, is not blocked by what may seem to be an impasse when we realize that stakeholder opinion does not serve as a realistic or holistic foundation for setting goals or establishing policy in management. A realistic option is provided by another general principle: patterns are integral parts of nature (see Chapter 10; Appendix 12.1). Patterns, such as macroecological patterns, reflect the complete set of factors behind their origin. Through our experience with modeling, we understand patterns as emergent from reality; order arises out of “unruly complexity” (Chapter 8). As outlined above, and described in more detail in Chapter 10 (see also Belgrano and Fowler 2008), the complexity inherent to such patterns is holistic; it involves the full and infinitely complex set of contributing factors. Their integral nature, in this regard, makes it so that natural patterns provide a full accounting for complexity as a matter of principle. This principle, as learned and understood through our experience with modeling, comes into play every time we use an empirical pattern to address a management issue to avoid the abnormal (Chapter 11; “through management of humans activities,” Chapter 4; involving all stakeholders, Chapter 9) and thus invoke the principle of finite limitations characteristic of patterns. In the process, we achieve one of the primary objectives of www.nmfs.noaa.gov/sfa/magact/MSA_Amended_2007%20.pdf A distinction being made here is opinion as a basis for setting policy in contrast to opinion regarding the process of decision-making. In thinking about the appropriate process, our opinions, as based on experience and basic principles, count heavily as experience that helps define what works, and, in this case, what works as a process for setting policy.

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On the path to holistic management management: the health and sustainability of all systems. In achieving holism, all elements of complex systems are given the opportunity to be stakeholders. Thus, people (as stakeholders) can be confined to asking management questions (Chapter 10), and carrying out management (implementing policy, governance; Chapter 9) rather than continuing with the processes in place today – processes increasingly recognized as flawed and behind many of the problems we face. Doing so embraces the principle of human limitations  – the fact that our finite minds are incapable of operating free of bias and cannot themselves account for the infinite quality of ultimate complexity, as laid out in Belgrano and Fowler (2008). The management questions generated can then be mapped to matching science questions (Chapter 10) leading to research that reveals natural patterns (including macroecological patterns) that also match the management question. These patterns are integral parts of the universe to account for all contributing factors (not simply those we are aware of ). It is a matter of principle that the patterns we see are integral parts of the complexity we wish to account for in our management, and, as such, they themselves account for complexity holistically. The progress we witness in today’s world, and as represented by the chapters of this book, is progress toward establishing foundational principles of critical importance to management – progress toward holism beyond ecosystem-based management to include evosystem principles, the complexity of the biosphere, and principles yet to be revealed. The principle of human limitations also prevents the asking of every management question. In principle, asking an infinite set of questions is impossible. However, every conceivable management question for which science can provide a matching (consonant5) pattern can be addressed holistically (Appendix 12.1; Chapter 10). The kind of advice that fishery scientists propose (Chapter 6) can be made realistic and holistic in parallel with the kind of advice scientists offer for any form of management. The integrative component of the last step in Table 2.2 is a key element. This makes it possible to begin making projections for what is at least another step toward addressing management issues that we already clearly recognize – issues such as the evolutionary/ coevolutionary effects of fishing. The progression outlined in Table 2.2 is headed toward holism, continuously expanding our boundaries (Chapter 8), and clearly depicting progress beyond which there may be further steps (Beck and Cowan 1996, Wilber 2001). Boundaries (Chapter 8) are continuously expanding.

See Chapter 10 regarding the concept of consonance. Most dictionary definitions (and lists of synonyms) include the concept of harmony: we are learning to live in harmony, or in accord, with nature.

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A. Belgrano and C.W. Fowler What happens when we push our boundaries beyond science that establishes principles and focus on research that provides information that can directly answer management questions (Chapter 10)? Can we predict how things will be when we have achieved full holism? Probably not. However, the authors of Chapter 11 note trends that are characteristic of progress being made toward greater holism in management. They present information characterized as much more holistic than information used in current forms of management – guidelines for harvests from marine populations of individual resource species, from groups of species, from ecosystems, and from entire ocean basins. We await the expansions of the boundaries of management (Chapter 8) to include other management questions (Chapter 10). In part, the extrapolation of today’s progress presented in Chapter 11 embraces the complexity of hierarchical interactions as depicted in Fig. 12.2 (i.e., implements the principles of hierarchical structure and associated complexity). In doing so, it addresses more complexity through asking management questions

Fig. 12.2.  Schematic diagram of the interactions among the various levels in the hierarchical organization of complex systems. To be holistic, the management questions we ask (see Chapter 10) must address the sustainability of each such interaction. The shaded area represents the infinite set of lines connecting all parts of a real-world system. These connections involve all interactions among all levels in the complexity of the reality behind this diagram. This complexity includes all things above the diagram (such as chemicals, molecules, and beyond), as well as below the diagram (to include the earth-system, and solar system and beyond). To achieve healthy systems, all such interactions would be free of abnormality as much as possible.

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On the path to holistic management (Chapter 10) to guide research that produces information regarding sustainability in the interactions among such structural categories. One example is finding a sustainable harvest from any group of marine fish, depending on how such groups are defined. Species can fall into taxonomic or functional groups. They can be categorized in behavioral groups (see Chapter 7 for behavioral features by which fish can be categorized), spatially associated groups, or groups that show similar dynamics. The species comprising the prey of a particular predator species make up a group. Species can be grouped by being physiologically similar, or of similar body size. Groups may also be defined as combinations of such dimensions. Groups, as a matter of principle, are something we are reminded of when, in our models, we “lump” species into groups (e.g., the copepods, phytoplankton, and bivalves grouped in Fig. 3.8, or the zooplankton and phytoplankton in Fig. 1.4, and the species groups of Fig. 4.1). Such groups interact (Fig. 12.2); they affect individual species and the ecosystems in which they occur. Our harvest of individual marine fish populations is another example (see Fig. 12.3). It is an example that involves a macroecological pattern (a pattern involving population dynamics; Chapter 6) and makes macroecology another of the disciplines brought to management. Historically, the standard of reference for acceptable fishing rates was (and often still is) the total natural mortality rate (often represented by the symbol M; Chapter 11). Based on patterns free of the errors inherent to the assumptions behind older standards, the total natural mortality rate can be used directly to estimate holistically sustainable harvest rates as shown in Fig. 12.3 (i.e., as a variable with which sustainable harvest rates are correlated; see the correlative refinement of Chapter 10). As expected, in this step toward accounting for complexity directly (in addition to the complexity indirectly accounted for by the integral nature of patterns; see Appendix 12.1), sustainable harvest rates increase with increasing total natural mortality. Using this yardstick, all of today’s observed harvest rates are seen to be unsustainable – often by an order of magnitude or more. As indicated in Chapter 9, conventional metrics indicate that as much as 80% are overfished; more holistically (Fig. 12.3), a much higher percentage is found to be unsustainable. These evaluations provide a substantive indication of how far we have to go in achieving holistic sustainability. The patterns illustrated in Fig. 12.3 also indicate that by harvesting sustainably, we directly improve the biodiversity of the system (third panel, Fig. 12.3), thus achieving more of the many objectives of management: including humans as part of the system (Chapter 3) and treating diversity directly – all parts of progress with major steps yet to be taken. The information involved in Fig. 12.3 is based on a variety of principles – principles that science has substantiated in the progress we are making and principles verified by the chapters of this book. Predator–prey interactions

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Fig. 12.3.  (a) Observed harvest rates for 44 species of marine fish populations as correlated with the estimated total natural mortality rate (M) for each species (filled circles; Mertz and Meyers 1998), and consumption rates by predators for five species of fish and three ungulates also in correlation with total mortality (open squares; from Fowler 2009, consumption rate = 0.16805 M). (b) The data shown in (a), here shown again in log10 scale. (c) The harvest rates of (a) and (b) and the points corresponding to the maximized biodiversity (open circles) for the consumption rates for the ungulates and five species of marine fish, illustrating how desirable biodiversity is achieved at the same time holistic sustainability is achieved (from Fowler 2009). Rather than serving as a standard of reference (see Chapter 11), M is used as a variable that accounts for much of the variation observed in empirical examples of holistic sustainability.

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On the path to holistic management are part of the complexity of marine ecosystems. They involve the population dynamics of both the predator and the prey (see Chapter 6) as well as the contributions of evolutionary interactions and dynamics – all as a matter of principle. Such factors contributed to the formation of the patterns depicted in Fig. 12.3; they are accounted for by the pattern – as a matter of principle. Biodiversity is inherent to such patterns – as a matter of principle. Human impacts are inherent to such patterns (including the longstanding influence we have had on our environment; Appendix 12.1) – as a matter of principle. As we incorporate more and more of the principles, tenets, pillars, and commandments found in the literature on the process of management, we make progress. Finding consistency among them (and finding that some principles result in rejecting some of what we had thought of as important qualities of the management process) is part of such progress. Finding application to every category of life (i.e., not simply individual species, ecosystems, evosystems, or the biosphere, but all, simultaneously – Fig. 12.2) is part of the progress. Finding application to every natural interaction (e.g., consumption, competition, evolutionary influence) is part of the progress. To the extent that all, simultaneously, are incorporated in our management, we make progress toward the holism we seek. Ecosystem-based management is part of management and cannot be used as an argument to ignore single-species applications, to avoid applications involving selectivity of harvesting, or to neglect any other influence we have on the various systems with which we interact. Evosystem-based management is part of management and cannot serve to replace ecosystem-based management; they both have to be part of holistic management – management that also has a biosphere-based component. The components of a holistic form of management include all applications, whether they be managing our interactions with ecosystems, species, groups of species, or the biosphere (Fig. 12.2). Macroecological patterns provide a basis for such holism (Chapter 11) for every management question we ask (Chapter 10). Synopsis The chapters of this book (like the ever-growing volume of literature published elsewhere) describe, list, characterize, and attempt to explain (at least partially) various elements of the complex systems for which we are held accountable in management if it is to be holistic. The principle of complexity stands substantiated. Involved in that complexity is the principle of interconnectedness: elements of complex systems are interconnected exactly as they are interconnected and the inter-relationships are interactive in the ways they

A. Belgrano and C.W. Fowler actually interact. Although this complexity is impossible to represent holistically in simulation models, our use of such models helps with understanding the nature and extent of such complexity. Modeling helps understand how complexity is involved in the emergence of natural patterns and is consequently taken into account when such patterns are used to guide management realistically – based on their integral nature (Appendix 12.1). Other principles emerge in such work. The principle of human limitations is obvious and often stated, as a matter of principle, following the experience of being incapable of including everything in our models – models of systems for which only the systems themselves embody the complexity with which we are confronted. Developing an approach that accounts for everything cannot be considered completely interdisciplinary if it ignores epistemology and our experience of the principle of human limitations. If we succeed in confining the effects of human limitations, in management, to that of being unable to ask all management questions, we see progress. If, at the same time, we ask every management question we can, we make progress. If we address every question we are capable of asking with information that provides a holistic answer, we make progress. In combination, we make a huge step toward the goal of holism – well beyond anything being implemented in today’s management not only in the number of management questions asked and addressed, but also in progress toward true sustainability for all systems with all of their components and processes. Confining stakeholders to the process of asking good management questions (and with answers in hand, carrying out management) not only adheres to the principle of human limitations but also brings management to a new level of objectivity (keeping in mind that science is itself subject to human biases and limitations; scientists are human). Progress is being made in: •

• •

Understanding how existing processes for setting goals in management are extremely vulnerable to the fallacy known as argumentum ad populum; groups can make mistakes. Recognizing the value of empirical information that demonstrates what is sustainable – holistically. Moving beyond science that merely establishes principle to science that provides management advice based on the realistic combination of all such principles.

Where is this progress headed? What will management look like when it is fully holistic and accounts for all of the complexity inherent in the systems of which we are a part? The answer to this question will become increasingly clear, the more management questions we ask, and the more we address those

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On the path to holistic management questions with holistically integral information analyzed to reveal information for increasingly specific management questions. Acknowledgments We would like to thank Jean Fowler, Lowell Fritz, Jeff Hard, and Larry Hobbs for invaluable reviews of earlier drafts of this chapter. The editorial assistance of Chris Baier, Gary Duker, and Jim Lee is gratefully acknowledged. References Abram, D. 1997. The Spell of the Sensuous: Perception and Language in a More-than-human World. New York, NY: Vintage Books. Beck, D.E. and C.C. Cowan. 1996. Spiral Dynamics: Mastering Values, Leadership and Change. Cambridge, MA: Blackwell Business. Beamish, R.J. and B.R. Rothschild (eds.). 2009. The Future of Fisheries Science in North America. New York, NY: Springer. Belgrano, A. and C.W. Fowler. 2008. Ecology for management: pattern-based policy. In Munoz, S.I. (ed.), Ecology Research Progress. Hauppauge, NY: Nova Science Publishers, pp. 5–31. Bridle, S. 2001. The divinization of the cosmos: an interview with Brian Swimme on Pierre Teilhard de Chardin. What is Enlightenment? 19:43–46. Egler, F. 1977. The Nature of Vegetation: Its Management and Mismanagement. Norfolk, CT: Aton Forest. Fowler, C.W. 2009. Systemic Management: Sustainable Human Interactions with Ecosystems and the Biosphere. Oxford: Oxford University Press. Francis, R.C., M.A. Hixon, M.E. Clarke, S.A. Murawski, and S. Ralston. 2007. Ten commandments for ecosystem-based fisheries scientists. Fisheries 32:217–233. Gordon, J., B. Bormann, and A. Keister. 1992. The physiology and genetics of ecosystems: a new target or “Forestry contemplates an entangled bank”. In Proceedings of the 12th North American Forest Biology Workshop., Sault Ste. Marie, Ontario. Ontario Ministry of Natural Resources, Ontario Forest Research Institute and Forestry Canada, Ontario Region, pp. 1–14. Guerry, A.D. 2005. Icarus and Daedalus: conceptual and tactical lessons for marine ecosystem-based management. Front. Ecol. Environ. 3(4):202–211. Hoctor, T.S., R.F. Noss, L.D. Harris, and K.A. Whitney. 2006. Spatial ecology and restoration of the longleaf pine evosystem. In Jose, S., Jokela, E., and Miller, D. (eds.), The Longleaf Pine Ecosystem: Ecology, Silviculture, and Restoration. New York, NY: Springer-Verlag, pp. 377–402. Lavigne, D. (ed.). 2006. Gaining Ground: In Pursuit of Ecological Sustainability. London, ON: International Fund for Animal Welfare. Leopold, A. 1949. A Sand County Almanac, and Sketches Here and There. New York, NY: Oxford University Press.

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Link, J.S., J.K.T. Brodziak, S.F. Edwards et al. 2002. Marine ecosystem assessment in a fisheries management context. Can. J. Fish. Aquat. Sci. 59:1429–1440. Lubchenco, J., A.M. Olson, L.A. Brubaker et al. 1991. The sustainable biosphere initiative: an ecological research agenda. Ecology 72:371–412. Maschner, H.D.G., M.W. Betts, J. Cornell et al. 2009. An introduction to the biocomplexity of Sanak Island, western Gulf of Alaska. Pac. Sci. 63:673–709. Meffe, G.K., M.J. Groom, and C.R. Carroll. 2006. Ecosystem approaches to conservation; responses to a complex world. In Groom, M.J., Meffe, G.K., and Carroll, C.R. (eds.), Principles of Conservation Biology. Sunderland, MA: Sinauer Associates, pp. 467–507. Mertz, G. and R.A. Myers. 1998. A simplified formulation for fish production. Can. J. Fish. Aquat. Sci. 55:478–484. Okasha, S. 2006. Evolution and the Levels of Selection. Oxford: Clarendon Press. Samuel, S.A. and G. Weng. 2003. Characterization of a branch of the phylogenetic tree. J. Theor. Biol. 220:457–468. Thompson, J.N. 2005. The Geographic Mosaic of Coevolution. Chicago, IL: University of Chicago Press. Wilber, K. 2001. A Theory of Everything: An Integral Vision for Business, Politics, Science and Spirituality. Boston, MA: Shambhala Center. William, J.R. 1998. The Life of Goethe: A Critical Biography. Oxford: Blackwell.

Appendix 12.1 Integral parts of reality

The complexity of being “integral” is nicely dealt with by David Abrams (1997); it is part of the understanding involved in traditional knowledge or wisdom attributed to aboriginal societies (and the basis for our description of the artwork chosen for the cover of this book). Being integral, as a part of nature, is involved in what philosophers and theologians often refer to as the “numinous” quality of things  – or the “sacred” nature of things as appreciated by aboriginal societies. It is behind our understanding of what we see as combinations of the contributions of “nature” and “nurture.” Aside from the experience of modeling, much of the field of science is devoted to explaining the origins (or emergence; Fowler 2009) involved in the integral nature of empirical patterns (Belgrano and Fowler 2008). Appendix 4.4 of Fowler (2009) provides an account of natural patterns interpreted as natural Bayesian integration.1 The “integral” nature of patterns involves the “ur-phenomena” or urphänomen described by Goethe as “… laws which do not reveal themselves through words   Available at: www.afsc.noaa.gov/Publications/misc_pdf/Fowler-book/Appendix04–4.pdf

1

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On the path to holistic management and hypotheses to the understanding, but through phenomena to the perception” (Williams 1998). Human influence is among the things that are integral to empirical patterns. It is particularly important to realize that human impacts involve a long history. Innumerable studies (e.g., Maschner et al. 2009) have described, explained, or otherwise documented the extensive and intensive effects that we humans have had on our environment during the hundreds of thousands of years of our evolution. Other studies involve the impact that we currently have. Overall, our impact has involved our thinking and beliefs (or “hominization” as conceived of by Teilhard Chardin; see Bridle’s (2001) interview with Brian Swimme). As described in Chapter 10, our impacts include today’s management actions and the underlying thinking and belief systems. All are themselves parts of reality that contribute, directly or indirectly, in small ways, or large, to the origin of the things we observe (and, in particular, to the origin of macro-ecological patterns) as integral parts of reality. Thus, as described in this chapter, the integral nature of natural patterns leaves nothing out of consideration. The “integration” involved is infinite (and therefore holistic), to account for everything just as it was involved, or is involved, in reality (and not necessarily as we assume it to be in the simulation models we use in attempts to explain what we observe).

Afterword keith brander

An Afterword should probably congratulate the authors, thank the readers for their attention, and gently drop the curtain with a few concluding summary remarks. However, the title and contents of this book clearly point to unfinished business and in the spirit of an evolving perspective I want to end with questions rather than answers. The Introduction proposes that “… a more complete grasp of ecosystem-based management for fisheries will help perceive more clearly the steps that lie ahead” and successive chapters provide a rich source of information and ideas on current approaches to ecosystem-based management for fisheries (EBMF). Many of the emergent issues are tackled in later chapters, particularly Chapter 12. My questions arise from the context within which EBMF has emerged and how this affects our developing view of sustainability and of our relationships with the natural world. The historical and cultural “framing” of EBMF plays a major role in our perception of the steps ahead and in our articulation of goals and objectives. In his Foreword Alec MacCall writes about the difficulty of defining an ecosystem approach (EA) and the concept of “ecosystem health” that goes with it, because they are based in human values. Attitudes towards nature (stewardship, the right to exploit natural systems, avoiding extermination) vary between different cultures and also over time within a culture (Thomas 1983). Let me try to be quite specific about what I mean by the context and framing of EBMF. The first obvious context is that EBMF deals with marine and not with terrestrial ecosystems. Our exploitation of terrestrial and marine ecosystems is fundamentally different and this is reflected in our attitudes towards them.

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Afterword In the sea we are still hunter-gatherers, notwithstanding the rapidly increasing technological sophistication of capture fisheries. We live on land and have gradually transformed ecosystems and landscapes for agriculture over the past ten millennia. The fact that we rely on the productivity of natural ecosystems in the oceans makes it unsurprising that the idea of an “ecosystem approach” arose there. The first international agreement requiring the adoption of an “ecosystem approach” was the 1980 Convention on the Conservation of Antarctic and Marine Living Resources. From there, the EA was gradually taken up in all major international fisheries agreements. In 2003, the FAO1 encouraged countries “to develop strategies, programmes and plans for agrobiodiversity in conformity with an ecosystem approach.” The EA has to struggle for a foothold on land, since it is swimming against the tide of agriculture. For centuries, humanity has been transforming ecosystems by getting rid of pests, predators, and competitors; irrigation, fertilization, and monoculture crops with highly selected traits add to anthropogenic impact. Much of the development of agriculture was thus about getting rid of the complexity of natural ecosystems and controlling them to provide conditions for increased production of food. The second context to keep in mind is that EBMF is only one component of a new management perspective that seeks to devise a coherent suite of measures to protect marine ecosystems against anthropogenic threats that arise from pollution, nutrients, shipping, litter, noise, mining, introduced species, coastal alteration, and climate change. Some of the impetus to develop the EA arose out of frustration with failures of fisheries management to control overfishing. Not only was management perceived as failing to protect the few commercial fish and shellfish stocks for which fisheries were supposed to be regulated, but it also did little to protect other vulnerable elements of marine ecosystems. Furthermore, the management of fisheries is recognized as an extremely expensive endeavor.2 This is not the place to discuss where the blame for overfishing and habitat degradation lies, but the political response has been to reposition fisheries management within a wider policy context that deals with all pressures on marine ecosystems (Rice and Ridgeway 2010). For example, the new European Marine Strategy Framework Directive (MSFD)3 aims to maintain Food and Agriculture Organization (FAO). 2003. Biodiversity and the ecosystem approach in agriculture, forestry and fisheries. (www.fao.org/DOCREP/005/Y4586E/ Y4586E00.HTM) 2 World Bank and Food and Agriculture Organization. 2008. The Sunken Billions. The Economic Justification for Fisheries Reform. Washington, DC:  Agriculture and Rural Development Department. The World Bank. (http://siteresources.worldbank.org/EXTARD/ Resources/336681–1224775570533/SunkenBillionsFinal.pdf) 3 EU (The European Parliament and the Council of the European Union). 2008. Marine Strategy Framework Directive. Directive 2008/56/EC of the European Parliament and 1

Afterword or restore ecosystems to a clean, healthy, and productive state that supports sustainable fisheries and biodiversity. It stipulates that the European Common Fisheries Policy shall take into account the environmental impacts of fishing and the objectives of the MSFD. The governance of fisheries is no longer a subject unto itself, with autonomous science and policy support; it is a part of wider environmental concerns. This of course has a major impact on the evolving perspective for EBMF. A great deal of work is now underway to define what we mean by a “healthy marine ecosystem” and what indicators can be used to measure our progress toward achieving or maintaining health. Indicators are being developed for biodiversity, non-indigenous species, commercially exploited species, food webs, eutrophication, seafloor integrity, contamination, pollution effects, marine litter, and underwater noise (e.g., see recent efforts by the EU).4 Similar activities are underway in relation to terrestrial systems (e.g., the Food and Agriculture Organization is developing an ecosystem approach to agriculture and forestry)1 and it is instructive to examine and draw lessons from the differences between terrestrial and marine activities. Are our values and attitudes toward sustainability and harvesting different on land and in the sea? Should they be different or should we be aiming for greater consistency? The pressures on marine and terrestrial ecosystems ultimately arise from the same source – the human population sequesters roughly one-third of the primary production in both realms. Is it possible to maintain this food supply while also restoring biodiversity and healthy ecosystems? What effect will changes in food supply from the sea have on terrestrial food production? The terrestrial debate takes a very different course from the marine debate because food production systems on land are under human control. Some topical questions that arise at the interface between terrestrial production ecology and conservation biology are: how can we achieve the optimal balance of agricultural production and biodiversity conservation? Can more intensive agriculture relieve the pressure to transform yet more uncultivated land? Can recent developments in trait-based ecology lead to new agro-ecosystems that help to meet the aims of both biodiversity conservation and food security (Brussaard et  al. 2010)? Two terms that help to structure the debate are transformation of the Council. Establishing a framework for community action in the field of ­marine environmental policy. Official Journal of the European Union L164:19–40. (http://eur-lex. europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:164:0019:0040:EN:PDF) 4 EU (Council of the European Union). 2010. Challenges for the good environmental status of the marine environment  – information from the Presidency and from the Commission. 10545/10. 4–6–2010. Brussels, June 4, 2010. (http://register.consilium. europa.eu/pdf/en/10/st10/st10545.en10.pdf)

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Afterword (terrestrial ecosystems have been transformed for agriculture and other human utility) and composition (how can we adjust the composition of the mix of transformed and untransformed ecosystems to get closer to our aims?). How would the debate about transformation and composition map out in a marine context? Should we be thinking about how to maximize food production from some parts of marine ecosystems by transforming them? There is a wealth of evidence to show that humans have been transforming marine ecosystems for hundreds of years, by selectively removing the large, vulnerable, slow-growing, late-maturing species (Brander et al. 2010). With regard to maximizing food production, there seem to be examples where this transformation has had a positive effect and others where it has been negative; herein lies an interesting topic for macroecological research and the evolving perspective on EBMF. I do not think we face a real choice between whether or not to transform marine ecosystems; like it or not we are doing that in any case.5 The choice is whether or not to carry out the science needed to anticipate the consequences of such transformations. This can help to stimulate a broad debate over risks, objectives, and benefits in order to influence the ongoing transformations in some desirable direction. A similar debate is already in progress over another aspect of transformation, namely the evolutionary consequences of fishing, including loss of genetic diversity and undesirable trait changes (Jorgensen et al. 2007). That debate has started to address the value-laden issue of how you define “desirable”? For example, if a large tasty fish species risks extinction due to overfishing then, from a biodiversity perspective a “desirable” outcome may be for it to evolve a small, poisonous life history, thus reducing the risk of being caught. The issue of composition is being addressed as part of the adoption of marine spatial planning and protected areas in many parts of the world. How much do they contribute to achieving the optimal balance between production and conservation, bearing in mind all the anthropogenic pressures on marine ecosystems? How can the composition of protected and less protected areas be adjusted to achieve the optimal balance? We may all regret the transformations that humans have inflicted on terrestrial ecosystems and seek ways to avoid a similar path for marine ecosystems. The timely and effective application of science and good governance through

Aquaculture, marine ranching, and other forms of controlled production are increasing rapidly and are likely to equal marine capture production within about 20 years. This is a rapid transformation and mistakes have been made because ecosystem impacts were not anticipated or not acted on.

5

Afterword EBMF and other necessary measures is undoubtedly an essential part of this. However, we must acknowledge that the dynamics and behavior of human populations ultimately determines the degree of protection that marine ecosystems are given. The material in this book provides an excellent background on existing practice in EBMF and where it is going. It will do much to stimulate new science and improved governance. References Brander, K., L.W. Botsford, L. Ciannelli et al. 2010. Human impacts on marine ecosystems. In Barange, M., Field, J.G., Harris, R., Hofmann, E., Perry, R.I., and Werner, F.E. (eds.), Marine Ecosystems and Global Change. Oxford: Oxford University Press, pp. 41–71. Brussaard, L., P. Caron, B. Campbell et al. 2010. Reconciling biodiversity conservation and food security: scientific challenges for a new agriculture. Curr. Opin. Environ. Sustain. 2:34–42. Jorgensen, C., K. Enberg, E.S. Dunlop et al. 2007. Ecology: managing evolving fish stocks. Science 318:1247–1248. Rice, J. and L. Ridgeway. 2010. Conservation of biodiversity and fisheries management. In Grafton, Q.R., Hilborn, R., Squires, D., Tait, M., and Williams, M.J. (eds.), Handbook of Fisheries Conservation and Management. New York, NY: Oxford University Press, pp. 139–149. Thomas, K. 1983. Man and the Natural World. London: Penguin Books.

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Index

aberrant. See abnormality abiotic factors, 25, 92, 308 accounting for conventionally, 17, 22, 92, 155 accounting for holistically, 4, 286–99, 313, 350–52, 355–56 changes, 22 forcing, 17 abnormality, 280, 289–91, 295, 297, 299, 302, 310, 313, 315, 317, 325–27 accounting for holistically, 4, 296–97, 350–52, 355–56 as information, See also: patterns as information sources, 291 avoiding, 289, 292, 295, 297–98, 304, 313–14, 328, 347, 349, See also: systemic management; sustainability contributing factors conventional management, 309–10,

313, 317–18, 320–21, 323, 350 accord, 294, See also: consonance accounting for complexity, 4, 291, 296–97, 310, 347, 350–52, 355–56, See also: complexity; infinite; reality based management in conventional management, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75 in integral information. See integral information in systemic management, 4, 296–97, 350–52, 355–56 directly, 287–88, 290–92, See also: patterns, correlative; questions, refining multiple questions, 280, 287, 290–92 indirectly, 4, 296–97, 313, 350–52, 355–56, See also: consequences,

315, 317–19, 322, 350, 352,

accounting for holistically; holism;

See also: conventional management

patterns, integral nature

human influence, See also: activities, human; beliefs/belief systems; economics; human influence human, 190, 315, 317–19, 321, 323, 350 as guidance, 280, 289–90, 292, 295, 297–98, 302, 304, 307–31, 350

362

treating systemically, 289, 291–92, 295,

acidification accounting for conventionally, xiv, 7, 280, 290, 338 accounting for holistically, 4, 296–97, 350–52, 355–56 action. See praxis

Index activities, human, 10, 12, 70, 80, 154, 157, 187, 206, 269–70,

area closure. See marine protected areas; praxis

See also: acidification; beliefs/belief

asking questions. See questions

systems; bureaucracy; conventional

assessment

management; harvesting; human influence; politics; praxis accounting for holistically, 4, 7, 296–97, 313, 350–52, 355–56 development, 120 fishing, xv, 15, 156, 200, 203, 273 governed, 36, 91, 161, 204, 266–67, 288, 347 illegal, 273 management, 11 marine ranching, 173 monitoring, 177 research, 289 adaptability. See management principles, adaptability adaptive management, 24, 26, 91, 192, 197, 274

accounting for holistically, 296–97, 350–52, 355–56 in conventional management, 74, 79, 81, 116, 136, 146, 159, 161, 175–77 ecosystem, 12, 14, 25, 90–92, 114, 123, 145, 153 multi-species, 26 of fishery impacts, 119 stock, 11, 18, 21–24, 73–76, 83, 134, 139–40, 143, 154, 156, 159, 173, 191, 220, 240, 282 stock, 12 in systemic management, 295 integral. See integral information is not guidance. See description; fallacy in conventional management axioms. See scientific principles

alchemy. See logical alchemy allocation, 328, See also: selectivity

bacteria, 73, See also: plankton 47

across age, 324

accounting for conventionally, 32–95

across sex, 324

accounting for holistically, 4, 296–97,

across size, 324 across species, 319, 324 across trophic level, 324 among fishers, 37, 122, 201–04 among seasons, 293, 324 sustainable, 293, 319, 324 allometry, 78, 88 accounting for conventionally, 78, 88 accounting for holistically, 289, 293–94, 297, 323, 326–28, 330, 343, 350, See also: patterns, correlative 289 anthropocentrism, 290, 303, 347, See also: beliefs/belief systems accounting for holistically, 4, 296–97, 350–52, 355–56 in conventional management, 283 anthropogenic factors, 70, 114, 134, 358, 360, See also: human influence aquaculture, 360

350–52, 355–56 Bayesian integrators, 355, See also: patterns, integral nature natural phenomena as, 4, 296–97, 313, 350–52, 355–56 use of. See patterns, as guidance; systemic management behavior, 3–4, 88, 164, 181, 239–40, 284, 311, 341, 343, 350 accounting for holistically, 4, 296–97, 350–52, 355–56 homing, 239 inherited, 235 migratory, 237 of fishing fleets, 223–24 of models, 79, 343 of stakeholders, 178, 188, 193, 195, 198, 207, 360 philopatric, 232, 234–38, 242

363

364

Index behavior (cont.) population, 239 schooling, 226 socially transmitted, 235 beliefs/belief systems accounting for holistically, 4, 283, 296–98, 350–52, 355–56 parts of conventional management, 283, 297–98, 308, 356 best scientific information conventional, 270 systemic, 304, 326, See also: patterns, as guidance bias, 244, 266, 284, 290, 299, 302–03, 311–12, 347, See also: objectivity; stakeholder-based management, bias xiii, 71 accounting for holistically, 4, 296–97, 350–52, 355–56 through belief systems. See beliefs/belief systems through values. See values, human biodiversity, 164, 171, 268, 341, 343, 350, 352, 359 accounting for holistically, 4, 296–97, 350–52, 355–56

biomass reference point, 21 maximum sustainable yield, 311, See also: maximum sustainable yield optimum yield, 117, 311 potential biological removal, 311 Total Allowable Catch, 21, 160, 177, 184, 192, 195, 197, 200, 202, 204, 206, 311 biomimicry, 307–31, 350, See also: predation, harvests that mimic biosphere, 277, 279, 283, 288, 290, 295, 299–300, 308, 310–11, 313–14, 330, 338–39, 341–42, 346, 348, 352 accounting for holistically, 279, 296–97, 313, 350–52, 355–56, See also: biosphere-based management as part of reality, 279, 283, 288, 290, 295–96, 299–300, 308, 310, 313–14, 338–39, 342, 346, 352, 356, See also: reality-based management 277 biosphere-based management in systemic management, 339, 352, See also: systemic management xvi, 338 biota, 43, 80, 125, 136, 138, 145

age, 190

birds. See seabirds

benthic, 136

body growth. See growth, somatic

conserving, 114, 156, 198, 359

body size, 92–93, 132, 134, 160, 190, 195,

dynamics, 134

198, 202, 206, 237, 240, 280, 289,

evolutionary forces, 277

See also: allometry 17, 23, 25, 47,

influenced by fishing, 134, 190–91 maintaining, 123, 127–28, 134, 153, 161, 170, 198 maximizing, 308, 313–18, 320–21, 323, 327, 330, 351

59, 88 bottom-up dynamics accounting for holistically, 4, 296–97, 350–52, 355–56 ecosystem, 16–17, 88, 93–94

measuring, 134–35, 163, 166

bottom-up management processes, 193

objectives, 163, 175

burden of proof. See management principles,

phenotypic, 191 population, 238 biological reference points, 21, 24, 74, 77, 83 Compare: reality-based reference points accounting for holistically, 296–97, 350–52, 355–56

reverse burden of proof bureaucracy, See also: government agencies; international organizations; nongovernmental organizations, 188, 192, 272 accounting for holistically, 4, 296–97, 350–52, 355–56

Index bureaucracy (cont.) conventional management, 281, 283–84, 298 systemic management, 289, 295, 297–98

climate. See climate change; weather/climate climate change, xv, xvi, 326, 358 accounting for conventionally, 49, 75, 78, 93, 123, 130, 136, 138, 140, 190, 222

catch limits, 117, 135, See also: harvesting, limits; praxis, catch limits catch policy. See praxis, catch policy cetaceans, 12, 69–70, 125, 321, 330, See also: marine mammals; whales change in management, 2, 33 smaller quotas, 26, 307 toward greater holism, 2–4, 32, 277, 296 change, ecosystem, 21, 25, 35, 37, 94, 115, 123, 128, 138, 155, 168, 195, 249, 327, See also: regime shift 10, 12 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 acidity, 7 biomass, 57 carrying capacity, 75 considered systemically, 297 decadal, 39

accounting for holistically, 4, 296–97, 313, 350–52, 355–56 treated systemically, See also: holism, through refinement, correlative refinement, 293 coevolution, 74, 191, 309, 311–12, 337, 340, 342, 348, See also: evosystems accounting for holistically, 4, 296–97, 350–52, 355–56 not accounted for conventionally, 74, 311 collapse. See depletion co-management, 37, 193, 198, 270, 273–74, 283, 285, See also: conventional management; stakeholders accounting for holistically, 4, 296–97, 350–52, 355–56 vulnerability, 285, See also: human limitations

diversity, 134, 136

commandments. See management principles

function, 190

communities, ecological, 12, 15, 25, 48,

indicators, 92 production, 139 productivity, 42, 93, 138 salinity, 22 stock abundance, 16, 51, 57 relative, 35, 52 structure, 48, 78, 132, 136, 190–91 trophic cascading, 14, 60 used systemically, 286, 290, 327–28 change, human as management, 284, 286–87, 291–92, 294–96, 304, 317–18, 320, 323, 350 ecological. See abnormality, avoiding; systemic management in thinking, 277, 286–87, 294, 296–97,

51–54, 72, 79, 83–85, 92, 94, 127, 132, 134, 138, 145, 162, 314 accounting for conventionally, 10–26, 355–56 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 community-based management, 159, 193, 198, 259–61, 266–67, 273, See also: stakeholders 122 accounting for conventionally, 10–26, 32–95, 114–46, 153–78, 183–208, 248–62, 264–75 accounting for holistically, 4, 296–97, 350–52, 355–56 competition, 7, 15, 17, 19, 23, 25, 54, 74,

304, 344, 346

76–77, 86, 88, 90, 120, 144, 178,

roles of NGOs, 265–68, 271

184, 195, 220–23, 228, 285, 287,

chemicals, 7, 13, 38, See also: nutrients accounting for holistically, 4, 296–97, 349–52, 355–56 toxic, 280, See also: pollution

311, 330, 352, See also: scientific principles, competition accounting for holistically, 4, 296–97, 313, 350–52, 355–56

365

366

Index complexity, See also: accounting for complexity; holistic; infinite; interconnectedness accounting for conventionally, 32–95,

examples, 219, 223, 279–304, 315, 317–19, 321–22, 350 explained/defined, 279–304 management question to action, 280, 302

114–46, 153–78, 183–208, 218–29,

management question to pattern, 289

232–44, 248–62, 264–75, 281–86,

management question to science, 288–89,

See also: models, in conventional management 10–26 accounting for holistically, 4, 296–97, 313, 343–50, 352, 356,

298, 300–01, 303 pattern to action, 302 science to pattern, 289, 302 control. See misdirected reductionism;

See also: empiricism; patterns, as

transitive management;

information sources

conventional management,

reality, 186, 277, 296, 312–13, 329, 338–39, 345, 347, 349, 355 congruence, 303, See also: consonance; questions, consonance consequences, See also: harvesting;

controlling the non-human; predator control control rules. See harvesting, control rules conventional management, 154, 156, 181, 227–28, 248, 255, 261, 265, 275,

unintended consequences

281–86, 290, 296, 298–99, 310, 329,

accounting for holistically, 4, 290, 292,

See also: human limitations; opinion-

295–97, 313, 324, 329, 350–52, 355–56 of fishing, 154, 156, 191, See also: consequences of management; unintended consequences accounting for conventionally, 177, 189, 202, 226, 309, 360 accounting for systemically, 297, See also: consequences, accounting for holistically consequences of management, 140 accounting for holistically, 4, 296–97, 313, 350–52, 355–56, See also:

based management; stakeholderbased management accounted for holistically, 296–97, 313, 345, 350–52, 355–56, See also: systemic management controlling the non-human ecosystem management, 25, 36, 141, 158, 264, 292, 338, 358–60 managing genetic diversity, 143 predator control, 88, 292, 329–30 See also: culling; predator control ranching, 153 resource management, 80, 185, 272, 290, 292

consequences, accounting for

details, 281–86

holistically

flaws, 183, 281–86, 295, 299, 301–03,

conventional management. See conventional management, accounting for holistically systemic management. See systemic management, accounting for itself consistency. See management principles, applied systemically, consistency consonance, See also: questions, consonance, 288–89, 303, 325

309–11, 313, 347, 350, 353, See also: fallacy in conventional management conversion, 284, See also: translation; logical alchemy correlative patterns, 296–98, 301–04, 328, 330, 350, See also: patterns, as information sources; patterns, macroecological

Index correlative refinement, 289, 293–94, 296–98, 302–04, 326, 328, 330, 350 culling, 79, 86, 88, 90, 330, See also: predator

in conventional management, 90, 122, 141, 178, 184, 186, 198, 207, 254, 259, 268, 282, 284–85

control; seals, culling; whales,

and evolutionary change, 191

culling

and extinction, 198

as misdirected reductionism, 330

and stakeholder reaction, 160, 283 as real factors, 35, 176, 200, 219, 225,

defects of conventional management.

261, 283

See errors in conventional

consequences of overfishing, 156

management; conventional

economic analysis, 260

management, flaws; fallacy in

economic models, 188

conventional management

economic policies, 34

depletion, xiii, xv, 36, 51, 91, 153, 157,

experiments, 189

159–60, 162, 168, 219, 223, 226,

maximizing profits, 202

232–44, See also: overfishing

parts of complexity, 178

accounted for holistically, 4, 296–97, 350–52, 355–56

profit orientation, xiii projections, 253

Atlantic halibut, 35

resource values, 117, 133, 238

caused by fishing, 94, 155, 157

socio-economic indicators, 145

cod, 12, 50, 89

theory, 203

demersal stocks, 86, 240

uncertainty, 197

groundfish, 37–38, 57, 87, 93, 115

unintended consequences, 193, 204

lower trophic level, 133

ecosystem change. See change, ecosystem

marine mammal populations, 35, 133

ecosystem health. See health

resources, 280–81

ecosystem structure, 7, 10, 25, 48, 78, 83, 93,

seabird populations, 133 sequential, 32, 35, 37 description accounting for holistically, 4, 296–97, 350–52, 355–56 in conventional management, 10–26, 32–95, 114–46, 153–78, 218–29 is not guidance, 283, 352, See also: fallacy in conventional management diversity, 360, See also: biodiversity xvi, 50, 123, 127, 132, 134, 143, 145, 198, 235, 277, 341, 343, 350 indices, 134–35, 163, 165–66, 168, 190, 341 dolphins. See cetaceans

114, 119, 123, 136, 139, 145, 162, 165, 190–91, 249 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 food web, 16, 25, 141 hierarchical, 249, 300–01, 341, 349–50 treating systemically, 327 trophic, 15, 17, 45, 48, 50, 60–61, 67, 77–78, 93, 114, 118, 121, 132–33, 138–39, 154, 290 ecosystem-based management as part of reality-based management, 337, 340, 343, 348, 352, See also: realitybased management calls for, 33, 114, 156–57, 309, 358 in conventional management, 10–26,

economics, See also: values, human accounting for holistically, 4, 296–98, 303, 327, 350–52, 355–56

32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75, 281–86

367

368

Index ecosystem-based management (cont.) in systemic management, 286–99, 319

Southern New England, 34, 44, 49, 63, 65–66, 69–70, 76

ecosystem application, 319

sustainable harvests from, 319

multi-species application, 318

West Nipissing, 259

single-species application, 314, 350 ecosystems accounting for conventionally, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75, 281–86 accounting for holistically, 4, 286–99, 319–20, 350–52, 355–56

Yellow Sea, 153–78 emergence, 4, 19, 24, 260, 274, 296–97, 308, 313, 344, 347, 353, 355, See also: patterns, integral nature; scientific principles:emergence accounting for holistically, 4, 296–97, 313, 350–52, 355–56 emotions, 189, 203, 287, 292, 296–97

Aleutian Islands, 114–46

accounting for conventionally, 296

as parts of reality, 277, 279, 283–84, 288,

accounting for holistically, 4, 297, 350–52,

297, 308, 310–11, 313–14, 329, 337, 339–40, 349, See also: reality-based management Baltic Sea, 10–26 Banco Chinchorro Biosphere Reserve, 270 Bay of Fundy, 41, 56

355–56 empiricism, 228, 255, 277, 286, 296–98, 303, 307–08, 312, 314, 318–19, 324, 326, 328, 343–45, 347, 351, 353, 355, See also: pattern-based management endangered species, 38, 48, 70–71, 87,

Chumbe Island Marine Park, 272

115–16, 118–20, 133, 139, 144,

East China Sea, 153–78

198, 281, 285, 290, 307, 312,

East Sea (Korea), 153–78 eastern Bering Sea, 114–46, 293, 301, 315, 320 Georges Bank, 34–35, 42–43, 46–49, 62–64, 69–70, 72, 76–77, 83–85, 141, 319–20 Gulf of Alaska, 114–46 Gulf of Maine, 42, 61, 69, 77, 83, 93

See also: depletion energy, xiv, 16, 18–19, 24, 47–49, 54, 60, 74, 78, 83, 118, 123, 125, 145, 268, 324, 329 accounting for conventionally, 10–26, 32–95, 114–46, 264–75 accounting for holistically, 4, 296–97, 350–52, 355–56

Gulf of St. Lawrence, 40, 53

enforcement. See praxis. enforcement

Hudson River, 44, 273

errors in conventional management, 281,

Kattegat, 232–44

286, 296, 301–02, See also: failures in

Masai Mara, 250

conventional management; fallacy

Mid-Atlantic, 66 Mid-Atlantic Bight, 39, 44, 67, 69–70, 93 Narragansett Bay, 44, 273 Newfoundland–Labrador Shelf, 38–39, 48–49, 51, 69, 77, 79, 87–88 North Sea, 232–44

in conventional management accounting for holistically, 4, 296–97, 350–52, 355–56 bias. See bias fallacy. See fallacy in conventional management

northwest Atlantic, 32–95

human, 303

Scotian Shelf, 35, 39–40, 46–50, 56, 70,

Humpty Dumpty Syndrome. See Humpty

76–77, 87, 89, 93 Skagerrak, 232–44

Dumpty Syndrome inconsistency. See inconsistency

Index errors in conventional management (cont.)

failures in conventional management,

lack of consonance, 281, 283–85, 301–03

183, 192, 219, 275, 280, 289, 298,

logical alchemy. See logical alchemy

303, 358 See also: conventional

logical typing, 24–25, 115, 270, 273,

management, flaws; errors in

284–85, 301, See also: conversion; logical alchemy; translation misdirected reductionism. See misdirected reductionism not holistic, 283 sophisticated mistakes. See sophisticated

conventional management; fallacy in conventional management fallacy in conventional management, 284, 286, 353, See also: errors in conventional management; misdirected reductionism

mistakes

argumentum ad populum, 285, 353

eutrophication, 12, 24, 359

false equality, See also: maximum

evolutionary influence of fishing, 3, 130,

sustainable yield; predator control

160, 190–91, 280, 310–11, 360,

fear. See emotions

See also: interactions, evolutionary

feedback, 14, 17–19, 24, 140, 185, 189, 191,

abnormal, 190 accounting for holistically, 4, 296–97, 350–52, 355–56 treating directly, 280, 310, 324 evosystems, xiv, 338, 340, 342–43, 346, 348, 352 accounting for holistically, 4, 286–99, 310, 324, 350–52, 355–56 as parts of reality, 338, 340, 343, 348–49, 352, See also: reality-based management evosystem-based management in reality-based management, 337, 340, 342–43, 348, 352 not accounted for conventionally, 74, 284, 311 explanation accounting for holistically, 4, 296–97, 350–52, 355–56 in conventional management, 10–26, 32–95, 114–46, 153–78, 218–29 is not guidance, 282–83, 352, See also: fallacy in conventional management extinction, 280, 290, 327, 329, 360

220, 225, 229, 265, 312, 346 accounting for conventionally, 14, 17, 19–20, 24, 140, 185, 220, 225, 265 accounting for holistically, 4, 296–97, 312, 350–52, 355–56 as part of complexity, 346 control rules, 197 delayed, 225 via governance, 188 fishing. See interactions; influence of fishing; harvesting accounting for holistically, 4, 296–97, 350–52, 355–56 ecosystem effects, 13–14, 37, 132, 153, 177 holistic guidance for, 307–31, 350 from ecosystems, 319 from individual species, 314 from marine environment, 322 from multiple species, 318 from ocean basins, 321 fishing down the food web, 60, 90, 133, 154, 190, 197, See also: human influence; interactions, influence of fishing; overfishing flexibility. See management principles, adaptability

accounting for conventionally, 70–71, 198, 227 accounting for holistically, 4, 277, 296–97, 313, 350–52, 355–56

gear restrictions. See praxis genetic effects of fishing. See evolutionary influence of fishing

369

370

Index governance. See praxis, governance

population, xiv, 20, 22, 55, 75, 77, 89,

government agencies, 145, 254, 265, 270, 272–75

221–22, 224–28, 308 somatic, 12, 15–19, 22, 24, 42, 46, 58, 79,

accounting for conventionally, 10–26, 32–95, 114–46, 153–78, 183–208, 248–62, 264–75

118, 138, 154, 239, 343 guidance conventional. See conventional

accounting for holistically, 4, 296–97, 313, 350–52, 355–56

management holistic, See also: pattern-based

Auistralian Fisheries Management

management; systemic

Authority, 157 conflict, 178

management examples, 307–31, 350–51

limitations, 265, See also: human limitations National Oceanic and Atmospheric Administration, 145 US Commission on Ocean Policy, 33, 114, 157 US Environmental Protection Agency, 281 US Fish and Wildlife Service, 145 US Geodetic Survey, 145 US National Marine Fisheries Service, 36, 114, 310 US National Science Foundation, 145 government policies, 71, 156, 259 accounting for conventionally, 10–26,

habitat accounted for conventionally, 10–26, 32–95, 114–46, 183–208, 218–29, 232–44, 274, 281–86 accounted for holistically, 4, 296–97, 313, 350–52, 355–56 accounted for systemically, 286–99 harvesting, xiv, 7, 36, 117, 200, 220, 223, See also: interactions; mortality, fishing; overfishing accounting for holistically, 4, 296–97, 307–31, 313, 350–52, 355–56 and life history, 134 consequences, 10, 202–03, 224, 226

32–95, 114–46, 153–78, 183–208,

consumption, 285

248–62, 264–75, 281, 283–84

control of methods, 195

accounting for holistically, 4, 296–97, 313, 350–52, 355–56 Act on the Conservation and Management of Marine Ecosystems, 156 Baltic Sea Action Plan, 11 Code of Conduct for Responsible Fisheries, 156 Common Agriculture Policy, 10 Common Fisheries Policy, 10 Marine Strategy Directive, 10, 26 Oceans Strategy, 90 Vision for Korean Fisheries, 156 Water Framework Directive, 10 growth

control rules, 116–17, 134, 197 economic costs, 194, 197, 283 equipment, 199 evolutionary impact. See evolutionary influence of fishing from ecosystems with holistic guidance, 319–21 from individual species, 24, 115, 232 with holistic guidance, 314–17, 325 from marine environment with holistic guidance, 322–23 from ocean basins with holistic guidance, 321–22 from species groups, 160, 199

accounting for holistically, 4, 296–97, 350–52, 355–56

with holistic guidance, 318–19 globally, 154, 200

Index harveting (cont.)

hominization, 356, See also: activities,

hierarchically, 181

human; belief/belief systems;

holistic guidance, 307–31

conventional management;

limits, 204, 207

economics; human influence;

established holistically, 307–31, 350 policy-setting, 193, 228–29 predators, 225 rights, 195, 201–04 strategy, 194 switching species, 57 techniques, 201, 271 unintended consequences, 183–208, See also: consequences, of fishing health, xiii, 21, 57, 91, 122–23, 156–57, 163,

thinking in integral patterns, 4, 296–97, 350–52, 355–56 human abnormality, See also: abnormality, human as guidance, See also: abnormality, human, as guidance human change. See change, human human influence, xiv, 10 accounting for holistically, 4, 291, 297,

282, 357, 359, See also: abnormality,

296–97, 299, 313, 325–26, 338, 345,

avoiding conventional

350–52, 355–56, See also: systemic

systemic, 280, 290, 348–49

management

hierarchical structure, 233, 341

as stakeholders. See stakeholders

hierarchy, 277, 279, 283, 288, 295, 299–301,

climate change, xv, See also: climate

308, 310, 314, 337, 339, 341–42, 349, 352, See also: complexity; scientific principles, hierarchical structure accounting for holistically, 279, 288, 295, 299–300, 308, 314, 339, 352 holism, 353, See also: patterns, integral

change, evolutionary, 94, 160, 181, 190–91, 280, See also: evolutionary influence of fishing; genetic effects of fishing accounted for holistically, 4, 296–97, 350–52, 355–56 on ecosystems, 242, See also: fishing,

nature 1–4, 284, 339, 341–42, 344,

ecosystem effects; harvesting,

346–49, 352

consequences

accounting for consequences. See consequences, accounting for holistically in systemic management, 4, 277, 296–97, 341, 345, 347, 350–52, 355–56 through multiple questions, 277, 340, 345 through refinement, 304, See also: questions, refining correlative refinement, 293–94, 304, 350 holistic management. See systemic management ecosystem application, 319–20 multi-species application, 318–19 ocean basin application, 322–24 single-species application, 314–18 holoplankton. See plankton

through conventional management. See conventional mangement; past decision-making through fishing. See harvesting; overfishing through lobbying, 187, 273, 297 via economics. See economics human limitations, 144, 265, 280, 283–84, 296, 299, 302–03, 344, 346–48, 353, See also: Humpty Dumpty Syndrome accounting for holistically, 4, 296–97, 313, 350–52, 355–56 in conventional management, 284 through bias. See bias human sustainability, xv–xvi, See also: sustainability, in systemic management, being normal

371

372

Index human sustainability (cont.) holistic management, 350, See also: systemic management maintaining status quo, 3, See also: conventional management xv humans–part of ecosystems accounting for conventionally, 10–26, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75, 281–86 accounting for systemically, xv, xvi, 286–99, 307–31, 350 of reality accounting for holistically, 4, 296–97, 350–52, 355–56 accounting for systemically, 286–99, 307–31, 350 Humpty Dumpty Syndrome, 33, 35, 91, 123, 259, 266–68, 286, 309–10, 312, 328, See also: models; synthesis accounting for holistically, 4, 296–97, 350–52, 355–56

marine protected areas, 83 models, 80–81, 88, 191 NMFS guidelines, 136 policies, 36 precautionary principle, 34, 159 progress, 33 research strategies, 178 rules, 194 seasonal closure, 119 sophistication, 7 systemic, 277, 289, 312–13, 321, 324, 328, 348, 353 inconsistency, 280 in conventional management, 57, 155, 191, 197, 281 infinite, 4, 181, 277, 296, 345, 347–49, 356, See also: complexity accounted for holistically, 4, 296–97, 313, 350–52, 355–56 information, See also: abnormality, as information; patterns as information sources; scientific principles, origins

implementation, 11, 22, 25–26, 114, 157, 159, 178, 283, 286, 327, 349, See also: praxis conventional, 159, 177, 181 area closure, 119, 122, 143, 160 catch quotas, 117 co-management, 193 costs, 186

about behavior, 232–44 about consumption guidance for harvesting, 307, 312, 314, 318–19, 321, 323–24, 326, 328, 330, 350 about ecosystems, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 301

ecosystem advice, 115

about evolution, 232–44

ecosystem-based management, 80, 95,

about management processes, 183–208,

114–15, 121, 139, 176–77, 220 enforcement, 176

232–44, 248–62, 264–75, 279–304, 337–54

exploitation rates, 116

about population dynamics, 218–29

gear restriction, 136

accounting for conventionally, 282,

habitat protection, 120

See also: thinking, conventional

harvest control rules, 197

10–26, 32–95, 114–46, 153–78,

integrated management plans, 90

183–208, 218–29, 232–44, 248–62,

ITQs, 202

264–75, 282–84

law, 192

accounting for holistically, 4,

limited entry, 122

296–97, 350–52, 355–56,

management options, 222

See also: systemic management

Index information (cont.) conversion, 285–86, See also: logical alchemy 284–85 guiding patterns, 289, 294–98, 302, 304,

account for the unknown, 312, 348 and explanation, 344 are holistically integral, 296, 345, 354 exclude nothing, 4

307–31, 351, See also: patterns, as

fully account for complexity, 313

information sources; best scientific

in statistical models, 298, 303

information

include change, 297

in asking questions, 279–304, 312, 321

include correlative subpatterns, 297

refining, 293–94, 296, 298, 303–04

infinitely integral, 277, 296

stimulating, 286–87, 290, 292–93

integral in principle, 344, 347

used systemically avoiding conversion, 288–89, 293–94, 307–31 with consonance, 289, 295, 307–31 integral information, 312 essential in principle, 225, See also: accounting for complexity real/consonant, 314, 317–19, 321, 323, 350, See also: patterns, integral nature abnormality. See abnormality, as information holistically integral, 4, 296–97, 313, 350–52, 355–56 in systemic management, 287, 289, 294, 297, 302, 307–31, 350 synthetic/non-consonant, 12, 25, 33, 72, 81, 90–92, 114, 153 in complex models. See models, in conventional management in conventional management, 14, 33,

parts are integral, 3–4, 296–97, 350–52, 355–56, See also: scientific principles, parts are integral integrity. See health interactions, 74, 77, 84, 88, 140, 144, 185, 220, 279–80, 283, 339, 341, 343–45, See also: interconnectedness abnormal, 190, 295, 310, 315, 317–19, 321, 323, 328 accounting for holistically, 4, 296–97, 350–52, 355–56 among ecosystems, 41 among fishers, 37 among species, 3, 17, 21, 36, 86–87, 89, 154, 190, 221, 311 coevolutionary, 74, 94, 309, 311, 342 direct, 343 ecological, 16, 154, 158, 283, 296 ecosystems, 141 evolutionary, 283, 340, 342, 352 food web, 141, 342

35, 91, 123, 145, 153, 259, 266, 268,

hierarchical, 349

283, 296, 345

higher order, 2

in sophisticated mistakes. See sophisticated mistakes integral parts, See also: patterns, integral nature

human, 1, 154 human influence, 279, See also: consequences, of fishing; harvesting

account for complexity, 313, 350, 353

indirect, 144, 343

account for everything, 345

influence of fishing, xv, 120, 154, 159,

account for evolution, 3 account for extinction, 277 account for human limitations, 283 account for laws of nature, 294 account for reality, 297

See also: consequences, of fishing; harvesting managing human, 292, 352, See also: systemic management 292 non-linear, 89

373

374

Index interactions (cont.)

Food and Agriculture Organization, 156, 265, 358–59

predator/prey, 7, 145, 288, 311, 350 sustainable, 279–80, 301, 303, 307–31, 350

Helsinki Commission, 11

trophic, 3

International Commission for the Northwest Atlantic Fisheries, 35–36,

with abiotic environmant, 155

62

interconnectedness, 91, 254–56, 292, 309, 329, 341–43, 345–46, 352,

International Council for the Exploration of the Sea, 21, 25–26, 75, 157

See also: complexity; interactions accounting for conventionally, 10–26,

International Pacific Halibut Commission, 116

32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75,

North Atlantic Fisheries Organization, 36, 39–40, 42

281–86 accounting for systemically, 4, 286–99,

North Pacific Marine Science Organization, 157

307–31, 350–52, 355–56 interdisciplinary basis for management, 183, 257, 271, 277, 287, 304, 339,

United Nations, 156 United Nations Environment Programme, 272

346, 353, See also: management principles, interdisciplinary basis

intransitive management, 288, 290, 292, 304, See also: systemic management

for management accounting for holistically, 4, 296–97,

isomorphism, 303, See also: consonance; questions, consonance

350–52, 355–56 in conventional management, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75, 281–86 biology, 40, 139, 199, 219, 233

law legislation, xv, 87, 156, 160, 185, 189, 192 accounting for conventionally, 10–26,

conservation biology, 251

32–95, 114–46, 153–78, 183–208,

ecology, 79, 159, 219, 248–50, 255, 257

248–62, 264–75

fishery biology, 234 forest ecology, 256 hydrology, 18 oceanography, 10–26, 32–95, 114–46, 153–78, 232–44, 281–86 topology, 141 in systemic management, 286–99 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 including epistemology, 2, 353 including macroecology, 277, 304 international organizations accounting for conventionally, 10–26, 32–95, 114–46, 153–78 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 European Commission, 21, 26

accounting for holistically, 4, 296–97, 313, 350–52, 355–56 Endangered Species Act, 71, 117, 120, 287 Magnuson–Stevens Fishery Conservation and Management Act, 33, 114–15, 135, 347 Magnuson–Stevens Reauthorization Act, 81–82 Marine Mammal Protection Act, 71, 120, 287 Oceans Act, 33, 81, 90–91 Species at Risk Act, 70 natural, xiv, 220, 228, 232, 294, 355 accounting for holistically, 4, 296–97, 350–52, 355–56 licensing. See praxis, licensing

Index life history accounting for holistically, 4, 296–97, 350–52, 355–56 in conventional management, 49, 57, 134, 139–40, 162, 239 in systemic management, 297, 328 limitations accounting for holistically, 4, 296–97, 313, 350–52, 355–56 human. See human limitations to variability, 347, See also: ecosystem structure; scientific principles, finite limitations logical alchemy, 284–85 accounting for holistically, 4, 296–97, 350–52, 355–56 avoiding. See consonance; systemic management conversion, 284, 286, 299, 301–02 misdirected reductionism, 330 translation, 24, 115, 270, 273, 284–85, 297–98, 301 logical typing. See errors in conventional management, logical typing; logical alchemy; translation

management principles, 34, 307, 328, 339, 352 account for complexity, 24, 73, 225, 248–62, See also: accounting for complexity integral information. See integral information adaptability, 90, 199, 207 applied conventionally, 90, 156, 271, 282, 312, 328, 338 include humans, 116, 122, 127, 221, 251, 266, 283, 341, See also: humans – part inconsistency, 57, 155, 191, 197 precautionary, 34, 88, 90, 115, 123, 136, 143, 156, 159, 162, 191, 197, See also: precautionary principle reverse burden of proof, 267, 282, 289 sustainability, 310 applied systemically, 312 accounting for reality, 4, 296–97, 350–52, 355–56 avoid abnormality, 1, 289, 292, 295, 297–98, 304, 308, 313–14, 328, 347, 349, See also: abnormality, avoiding; precautionary principle consistency, 291, 301, 314, 346

macroecology, 1, 4, 238, 277, 294, 304, 312, 347–48, 350, 356, 360, See also: patterns, macroecological accounting for holistically, 4, 296–97, 350–52, 355–56 in systemic management, 1, 294, 312, 314, 318–19, 321–22, 327, 341,

holism. See holism include humans, 280, 288–90, 292, 295, 298, 313, 315–20, 323, 325–28, 330, 341–42, 344–45, 350, 356, See also: humans – part reverse burden of proof, 288 sustainability, 279, 281–82, 284, 290–95,

345, 347–48, 350, 352, 356,

297–98, 300–01, 303, 307–31,

See also: pattern-based

337, 348–51, 353

management; systemic

holism, 32, 154, 274

management

interdisciplinary basis for management,

management accounting for holistically, 296–97, 326, 349–52, 355–56 conventional. See conventional management systemic. See systemic management management action. See praxis

1, 183, 257, 271, 339, 346, See also: accounting for complexity monitoring. See monitoring objectivity. See objectivity management questions, See also: questions, management examples, 288, 293–94

375

376

Index marine mammals accounting for holistically, 4, 296–97, 350–52, 355–56 as information sources, 289, 294, 307, 315–23, 325, 327, 350, See also: patterns, integral nature; ur-phenomena treated conventionally, 12, 16–17, 22, 35,

as explanatory, 355, See also: explanation complex, 7, 19–23, 83–92, 139, 261, 310 contribute to principles, 353 economic, 188–89 ecosystem, 2, 32, 60, 72, 94–95, 140–41, 177, 312 failures, 183 for lobbying, 283

52, 67–71, 75, 78, 87, 120, 125, 128,

incomplete, 181

133, 201

limitations, 186

treated systemically, 281–82, 284, 286, 288, 292, 330 marine protected areas, 41, 82–83, 154, 198, 267, 273, See also: praxis

manifest uncertainty, 191 minimally realistic, 140–41 model choice, 191 multi-species, 17, 19–20, 71–82

matching questions, 277, 279–304, 348,

not the reality represented, 353

See also: consonance; questions,

population models, xv, 282, 309

consonance

populatoin models, 310

maximizing biodiversity. See biodiversity, maximizing maximum sustainable yield as misdirected reductionism, 285,

predator–prey, 145 predictive, 253, See also: prediction refinement, 248 should be integrative, 192

302, 308, 311, See also: fallacy in

simulation, 298, 356

conventional management; logical

substantiate complexity, 353

alchemy

terrestrial, 254

in conventional management, xv, 36, 83–86, 117, 154, 166, 220, 302,

turnover, 191 in systemic management, 295, 298, 303,

308, 310, See also: conventional

329, 345, See also: consonance;

management, accounted for

patterns, statistical models

holistically

multi-species, 19, 75–77

meroplankton. See plankton

oceanographic, 237

mesozooplankton. See plankton

population, 75, 89–90, 140, 191, 218, 220,

microzooplankton. See plankton misdirected reductionism, 285, 330, See also: fallacy in conventional management; logical alchemy models, 343 bioenergetic, 78 economic, 188–89 ecosystem, 32, 60, 71, 77–79, 88, 140–41, 177, 228, 282, 309, 312 in conventional management, 12, 140, 145, 177, 188, 219–29, 237, 254, 296, 325, 343–50 as artificial representations, 309, 342

222, 224, 227–28, 309–10 predator–prey, 145 stock-recruitment, 22, 75, 220, 228 monitoring, 34, 48, 50, 75, 115, 117, 119, 123, 138, 145, 161–62, 177–78, 189, 192, 264, 266–67, 269–70, 326 mortality, 10, 38, 73, 89–90, 120, 282, 292, 297 disease/parasites, 88 fishing, 21, 24–25, 70, 86, 89, 116–17, 128, 134, 143, 160, 162, 177, 184, 190, 198, 223, 226, 307–08 natural, 87, 89, 154, 307–08, 350–51

Index See also: conventional management;

Mortality (cont.)

stakeholder-based management

predation, 19, 23, 73–74, 86, 89–90, 328 MPAs. See marine protected areas MSY. See maximum sustainable yield

conflict, 284 opinions, 164, 266, 269, 277, 281, 284–85, 287, 296–97, 303, 347,

multi-species harvests, 56, 83, 118, 160, 199,

See also: opinion-based management;

318, See also: harvesting multiple

politics

species; harvesting species groups; systemic management, multi-

accounted for holistically, 4, 296–97, 350–52, 355–56

species application

over-exploitation. See overfishing natality. See reproduction

overfishing, xiii, 13, 37, 51, 60, 88, 90, 94,

NGOs. See non-governmental organizations

114, 116, 133, 139, 153, 155–57,

non-governmental organizations, 2, 4, 88,

159, 178, 183–84, 190, 194–95, 197,

201, 252, 264–71, 273–74,

200–01, 204–05, 219, 223, 233, 236,

300–01

242–43, 270, 275, 280, 284, 309, 327, 338, 358

accounting for holistically, 4, 296–97, 313, 350–52, 355–56

accounting for holistically, 4, 296–97, 313, 350–52, 355–56

Biodiversity Support Program, 269 Hilltops2Oceans, 272

conventional criteria, 117, 123, 350

Marine Stewardships Council, 201

preventing, 116–17, 197

Nature Conservancy, 269

systemic criteria, 154, 315–20, 323, 325, 350

Pew Ocean Commission, 33, 114 Seafood Choices, 268

overharvesting. See overfishing

Seafood Watch, 268

oxygen

World Ocean Observatory, 268 World Resources Institute, 269 World Wildlife Fund, 269–70 nutrients, 10, 13, 17, 23–25, 39–40,

accounting for conventionally, 13, 15, 21 accounting for systemically, 4, 296–97, 313, 350–52, 355–56

42–43, 46, 79, 125, 311, 358, See also: chemicals accounted for holistically, 4, 296–97, 313, 350–52, 355–56

paradigm, 72, 193, 233 paradigm shift, 4, 154, 300, 338–39, 344 past decision-making, See also: conventional management

objectivity, xiii, 177, 281, 284, 298–99, 311–12, 347 as a goal, 177 in conventional management, 265 lacking. See errors in conventional management, bias in systemic management, 353, See also: stakeholders, systemic roles 329 ocean zoning. See praxis, ocean zoning opinion-based management, 164, 266, 269, 277, 281, 285, 296, 303, 347,

accounting for holistically, 4, 296–97, 299, 350–52, 355–56, See also: systemic management pathology. See abnormality pattern-based management, 1, 277, See also: systemic management patterns, 221–23, 226, 239, 297, 347, 350, See also: pattern-based management as guidance, 277, 289, 295, 302–03, 312, 314, 347, 353 as information sources, 4, 296–97, 350–52, 355–56

377

378

Index patterns (cont.) consonance, 289, 298, 304 contributing factors, 4, 296, 352 complexity, 296, See complexity; holism;

treated conventionally, 10, 22, 158, 164, 273 treated systemically, 281, 293, 297 population discreteness, 235

infinte; interconnectedness; reality;

and harvesting, 242

ur-phenomena

behavioral, 232, 234, 239, 242

human influence, 352, See: activities, human; beliefs/belief systems; economics; human activities nothing left out, 4, 296–97, 350–52, 355–56 correlative, 303, 328, See correlative patterns; patterns, macroecological exploitation, 35, 37, 133, 155, 159, 175, 197, 201, 238 integral nature, 4, 277, 294, 296–97,

genetic, 234, 238 habitat, 234, 238 population-dynamics, 154, 181, 218, 221, 223, See also: competition; growth, population; mortality; recruitment; reproduction; survival accounted for holistically, 4, 296–97, 350–52, 355–56 density-dependence, 15–17, 19–20, 22, 24, 222–24, 226, 308–09

308, 313, 329, 344–45, 347–48,

porpoises. See cetaceans

350–52, 355–56, See also: holism;

praxis, 181, See also: implementation

marine mammals, as information; ur-phenomena macroecological, 1, 3–4, 294, 312, 314, 341, 345, 348, 350, 352 migratory, 233, 235, 238–39 statistical models, 295, 298, 303

catch limits conventional, 80, 114–17, 116–17, 119, 122, 134, 138, 141, 160, 183, 192, 195, 201–02, 226, 284, 310 systemic, 314, 316, 319–20, 324

phytoplankton. See plankton

catch policy, xv, 21, 36–37

pillars. See management principles

conventional

pinnipeds, 38, 67, 69, 87–89, 144, 282, 321, See also: marine mammals; seals plankton, 13, 15, 17–20, 23, 43, 45–47, 49, 72, 78, 93–94, 125, 132, 138–39, 145, 350 accounting for conventionally, 10–26, 32–95, 114–46 accounting for holistically, 4, 296–97, 350–52, 355–56 politics, 280, 297–98, 303, See also: opinionbased management accounting for holistically, 4, 287, 296–98, 350–52, 355–56 in conventional management, 252, 296 pollution, 7, 358–59, See also: acidification; chemicals, toxic accounted for holistically, 4, 296–97, 326, 350–52, 355–56

quotas, 26 enforcement, 117, 161, 176, 192–93, 202, 206, 265–66, 269–70, 275 gear restrictions, 36–37, 117, 120, 159–60, 163, 194, 197, 199, 206–07 governance, 283 legislation. See law, legislation licensing, 36–37, 160 marine protected areas, 36–37, 83, See also: marine protected areas ocean zoning, 274, See also: marine protected areas quotas, 26, 36–37, 77, 80, 84, 117, 122, 197, 200–04, 219, 266 seasons, 37 size limits, 37, 160, 162, 195, 202, 206 systemic, 295, 302, 313, 324, 328, 348, 353

Index precautionary principle, 191 in conventional management, 34, 88, 90, 115, 123, 136, 143, 156, 159, 162, 197 in systemic management, 287, 289, 295, See also: abnormality, avoiding

is not guidance, 312, See also: fallacy in conventional management principles. See management principles; scientific principles proof. See management principles, reverse burden of proof

predation, 21, 23, 51, 76, 87–90, 221, 293, 341, See also: mortality, predation accounting for conventionally, 12, 17–20, 22, 38, 54, 73–74, 76–77, 88–90, 221, 225–26, 292, 311 accounting for holistically, 4, 289, 294,

questions, xiv, 73, 192, 203, 205, 228, 237, 249, 255, 257–58, 260–61, 280, 289 asking, 281 examples, 288–89, 293–94

296–97, 307–08, 313, 326, 350–52,

consonance, 279–304

355–56

conventional, 73, 88, 93–94, 190, 204, 268,

harvests that mimic, 315, 318–19, 321, 350, See also: biomimicry predator control, 88, 90, 292, 330, See also: misdirected reductionism culling seals, 79, 88–90 culling sharks, 86 culling whales, 330 prediction as a product of science, 300 in conventional management, 139–40,

282 management, 3–4, 277, 281, 287, 291 defined, 288 matching. See consonance; matching questions refining, 294, 296, 298, 303–04, 350, See also: correlative refinement 287, 291, 293 research, 256, 258, 281, 288–89, 294 quotas. See harvesting, limits; praxis

194, 219, 223, 261, 270, 312 economic future, 253 economic impact, 285 ecosystem states, 81 effects of climate change, 138, 177

reality, See also: patterns accounting for holistically, 4, 296–97, 313, 350–52, 355–56 reality-based management, 277, 296–97,

effects of fishing, 2

303, 309, 313, 329, 339, 344–45,

effects of management, 79, 177

347, 356, See also: patterns, integral

effects of predation, 12, 89

nature; systemic management

for decision-making, 191

ecosystem application, 319–20

ITQ dynamics, 203

multi-species application, 318–19

limitations, 144, 146, 189

ocean basin application, 322–24

multi-species, 76

single-species application, 314–18

population dynamics, 220, 223, 228, 234 population responses, 75

reality-based reference points. Compare: biological reference points

recruitment, 22, 140, 145

maximized biodiversity, 308, 313–18,

resource abundance, 114

320–21, 323, 327, 330, 350–51,

stock abundance, 18, 22, 75, 140 stock yield, 83

See also: biodiversity, maximizing recruitment, 17, 19, 21–23, 46, 54, 75, 90,

subject to error, 140

127, 137–38, 140, 145, 154, 159,

variability, 141

190, 197, 202, 220–21, 237–38, 285

year-class strength, 19

as misdirected reductionism, 285

379

380

Index reductionism. See misdirected reductionism refining questions, See also: correlative refinement regime shift, 12–14, 20–21, 26, 127, 144, 223, 327, See also: change, ecosystem accounting for conventionally, 12–14, 20–21, 26, 144, 223 accounting for holistically, 4, 296–97, 350–52, 355–56 treating systemically, 327 regulations

scientific principles, 3–4, 81, 94, 144, 178, 277, 287, 298, 304, 308, 341, 343, 346, 348–50 accounting for holistically, 4, 296–97, 350–52, 355–56, See also: systemic management competition, 221–22, 228, 288, See also: competiton consequences are systemic, 309, 343, See also: consequences; unintended consequences

input control, 199

ecological, 340

limited landings, 198

ecosystems are complex, 74, 79, 86, 94,

size limits, 198 relationships. See interconnectedness reproduction, 22, 54, 88, 133, 162, 165–66, 220, 222, 234, 238, 282, 341, See also: recruitment

139, 141, 143–44, 154, 159, 177–78, 183–84, 186, 191, 199, 202, 205, 207, 220–21, 223, 248–49, 252 ecosystems have dynamics, 17, 21, 48, 92, 94, 145 ecosystems have structure. See ecosystem

research describing, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62 explaining, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 289 research questions, See also: questions, research

structure emergence, 4, 296–97, 344, 350–52, 355–56, See also: emergence; patterns, integral nature evolutionary, 340, 352, See also: interactions, evolutionary evosystem, 348, See also: evosystems explanation, 14, 140–41, 219, 225,

consonant examples, 289, 294

236–37, 239, 251, 282, 289, 344,

conventional, 88, 94, 190, 204, 237, 250,

See also: scientific principles,

282 risk, See also: endangered species; fishing, ecosystem effects accounted for conventionally, 10–26, 32–95, 114–46, 153–78, 183–208, 218–29, 232–44, 248–62, 264–75 accounted for holistically, 4, 296–97, 350–52, 355–56 extinction. See extinction

emergence finite limitations, xiv, 46, 84, 90, 220–23, 225, 228, 294, 314, 327–28, 343, 345, 347, See also: ecosystem structure; scientific principles, ecosystems have structure fishing changes ecosystems, 37 hierarchical structure, 249, 341, 349–50 human limitations, 328, 344, 346–48, 353 interconnectedness, 188, 341–42, 344, 352,

salinity, 10, 13, 15, 21 accounting for conventionally, 22, 25, 44, 46, 75, 93, 235 accounting for systemically, 4, 296–97, 350–52, 355–56 science. See research

See also: interconnectedness limited control, 345 natural systems are complex, 7, 191, 341–42, 352, See also: complexity origins, 338, See also: emergence; explanation; information

Index scientific principles (cont.) parts are integral, 344, 347–48, 352,

shortcomings of conventional management. See errors in conventional

See also: integral parts; patterns,

management; conventional

integral nature; ur-phenomena

management, flaws; fallacy in

populations exhibit dynamics, 221–22, 228, 308

conventional management size limits. See praxis

predation. See predation

size selectivity. 134, See also: selectivity

systems have dimensions, 343

sophisticated mistakes, 183, 285–86, 302,

universal, 346 variability, 223 variance within limits, 343, See also: ecosystem structure seabirds, 16, 78, 116, 120–21, 125, 316–17, 319, 325, 327 accounted for conventionally, 16–17, 78, 116, 120–21, 125, 128, 133 accounting for holistically, 4, 296–97, 350–52, 355–56 seals, 12, 17, 22, See also: pinnipeds; predator control accounting for conventionally, 23, 35, 51, 60, 67, 69, 79, 87–90, 125, 282 accounting for systemically, 4, 296–97, 350–52, 355–56 culling, 79, 88–90 seasonal variation, 39, 41, 43, 45–46, 68, 138, 235 accounting for conventionally, 10–26,

See also: errors in conventional management stakeholder-based management, 347, See also: community-based management; human limitations; opinion-based management accounting for holistically, 4, 296–97, 350–52, 355–56 bias, 71, 280, 284–85, 294–96, 347–48, 353, See also: bias ignorance, 284–85, See also: stakeholders, ignorance lobbying, 187 power, 193, 200, 267, 301 stakeholders, 188, 190, 193, 204–05, 207–08, 258, 299 behavior, 178 co-management, 193 concerns, 83, See also: question asking conventional roles, 4, 26, 90–91,

32–95, 114–46, 153–78, 232–44,

119–20, 122, 143–44, 161, 176,

248–62

181, 186, 189, 206, 281–86, 347,

accounting for holistically, 4, 296–97, 313, 350–52, 355–56

See also: opinion-based management; stakeholder-based management

distribution, 69

defined, 186, 301

fishing rates, 293

educating, 2

management, 117

ignorance, 2, See also: human limitations

migration, 54, 69, 233

including fishing communities, 141

seasons, See also: praxis closed to fishing, 36, 118–19, 160, 197–98 selectivity, See also: allocation accounting for conventionally, 15, 61, 160, 190–91, 310, 360 accounting for holistically, 4, 277, 296–97, 313, 324, 350–52, 355–56 self-control, 288, See also: intransitive management

including the non-human, 313, 348 praxis, 313, 347 systemic roles, 281, 286–99, 348, 353 survival, 12, 17, 19, 89–90, 127, 145, 154, 159, See also: mortality sustainability, xvi, 353 holistic, 308, 310, 326, 329, 348, 350, 353 human. See human sustainability

381

382

Index sustainability (cont.) in conventional management. See management principles, applied conventionally in systemic management, See also: management principles, applied systemically, sustainability being normal, 1, 289, 291–92, 294–95,

ecosystem application, 319–20, See also: ecosystem-based management Bering Sea, 318–20 Georges Bank, 319 northwest Atlantic, 318 marine environment application, 322

297–98, 304, 308, 313, 316, 326–28,

multi-species application, 318–19

347

ocean-basin application, 321

synergy, 257 accounting for holistically, 4, 296–97, 350–52, 355–56 in conventional management, 286 in reality, 12, 21, 24, 265, 286 synthesis, 123, 259, 266–68, 283, 296,

reality-based management, 277, 296–97, 313, 329, 345, 356 role of opinions, 287, 297 single-species applications, 314 size selectivity, 324 systemic overfishing

345, See also: Humpty Dumpty

ecosystem, 319–20

Syndrome; integral information,

marine environment, 323

synthetic/non-consonant;

single-species, 315, 317

models

species-group, 318

systemic management, 154, 286–95, 302, 312–14, 328–29

systemic reference points. See reality-based reference points

accounting for conventional management, 4, 296–97, 313, 350–52, 355–56 accounting for itself, 4, 296–97, 313, 350–52, 355–56 accounting for reality, 4, 296–97, 313, 350–52, 355–56

technology, xiv, 240, 261 accounting for holistically, 4, 296–97, 350–52, 355–56 temperature, 7, 338 accounting for conventionally, 13,

allocation over seasons, 324, 328

19–22, 25, 39–44, 46, 51, 54, 59,

as avoiding abnormality. See abnormality,

75, 79, 93, 125, 137–38, 140, 235,

avoiding as holistic management. See holistic management as implementing consonance. See consonance as maximizing biodiversity. See biodiversity, maximizing as objective management. See objectivity as pattern-based management. See patternbased management as reality-based management. See realitybased management details, 290–95

250, 297 accounting for systemically, 4, 296–97, 350–52, 355–56 body, 343 change, xiv tenets. See management principles thinking, xiii, 2, 72, 181, 233, 253, 255, 277, 280–81, 286–87, 304, 338–39, 344, 346, 356 accounting for holistically, 4, 296–97, 313, 350–52, 355–56 conventional, 181, 281, 284, 296 systemic, 294, 296–97

Index thinking (cont.) vs seeing, 283, 286, 296–97, 353, See also: empiricism thresholds accounting for holistically, 296–97, 350–52, 355–56 in conventional management, 14, 17–18, 24, 91, 115, 117, 120, 123, 128, 141, 144–45, 190, 226 top-down dynamics accounting for holistically, 4, 296–97, 350–52, 355–56 ecosystem, 15–17, 88, 90, 93–94, 133 top-down management processes, 188, 193, 206–07

unintended consequences, 140, 181, 183, 186, 193, 195, 197, 203, 309, See also: consequences accounting for holistically, 4, 296–97, 350–52, 355–56 upwelling accounting for conventionally, 42–43, 46 accounting for systemically, 4, 296–97, 350–52, 355–56 ur-phenomena, 355, See also: complexity, accounting for holistically; information; marine mammals, as information; patterns, integral nature

total natural mortality, See also: mortality, natural accounted for conventionally, 87, 154, 307–08, 350–51 in correlative analysis, 350 transitive management, 36, 80, 88, 115, 134, 139, 141, 143, 153–54, 156, 158, 185, 219, 221, 227, 232, 249, 261–62, 264, 271–72, 289–90, 292, See also: conventional management 25, 33 translation, 24–25, 115, 266, 270, 273, 284–85, 297–98, 301–02,

values, human, xiii, 189, 193–94, 205, 280, 285, 293, 297–98, 357, 359, See also: economics accounting for objectively, 4, 296–97, 350–52, 355–56 in conventional management, 273, 283, 296, 301, See also: beliefs/belief systems motivating questions, 287, 292 vulnerability of conventional management to anthropocentrism. See anthropocentrism

See also: conversion; logical

to bias. See bias

alchemy

to causing abnormality, 307–31

trophic cascading. See change, ecosystem, trophic cascading

to disrupting evolution, 191, 280, 310, See evolutionary influence of fishing

uncertainty, 183, 187 accounting for conventionally, 26, 60, 79, 134–35, 139, 144–46, 156, 190–91, 197, 261 accounting for systemically, 94, 145–46, 155, 239, 339, 344 unknowable accounting for holistically, 4, 296–97, 350–52, 355–56 accounting for systemically, 344

to error. See errors in conventional management to fallacy. See fallacy in conventional management to human limitations. See human limitations to human values, 280, 283, 285, 287, 293, 296–97, 301, 303 to thinking. See thinking, vs seeing to unintended consequences. See consequences, of fishing

383

384

Index weather/climate, xiv, 327, See also: climate change accounting for conventionally, 10–26, 51, 114–15, 123, 127, 136, 139–40, 144, 146, 159, 177, 197, 219, 229, 268 accounting for holistically, 4, 296–97, 350–52, 355–56 whales, See also: cetaceans

accounting for systemically, 4, 296–97, 350–52, 355–56 as information sources, 321–22, 330 culling, 330, See also: fallacy in conventional management; misdirected reductionism; predator control treating systemically, 330

accounting for conventionally, 35, 48, 69–70, 144, 330

zooplankton. See plankton

Fig. 1.3.  Traffic-light plot representing the development of the central Baltic Sea ecosystem; time-series transformed into quintiles and sorted according to PC1; red represents high values while green represents low values of the respective variable. Modified from Möllmann et al. (2009).

Fig. 7.1.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for cod (Gadus morhua), in the Skagerrak–Kattegat (20 years average for 107 years) estimated between January and March (Cardinale in prep.).

Fig. 7.2.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for haddock (Melanogrammus aeglefinus), in the Skagerrak– Kattegat (20 years average for 107 years) estimated between January and March (Cardinale in prep.).

Fig. 7.3.  Historical trend in the spatial distribution of adult fish density (kg/km2) and aggregation pattern for pollack (Pollachius pollachius) in the Skagerrak–Kattegat (20 years average for 107 years) estimated between January and March (Cardinale in prep.).