Stock Identification Methods: Applications in Fishery Science

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Stock Identiﬁcation Methods Applications in Fishery Science

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Stock Identiﬁcation Methods Applications in Fishery Science Edited by

Steven X. Cadrin Kevin D. Friedland John R. Waldman

AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Editorial Coordinator: Kelly Sonnack Publishing Services Manager: Andre Cuello Project Manager: Justin Palmeiro Cover Design: Eric DeCicco Composition: Best-Set Text Printer: Maple Press Cover Printer: Phoenix Color Elsevier Academic Press 200 Wheeler Road, Burlington, MA 01803, USA 525 B Street, Suite 1900, San Diego, California 92101-4495, USA 84 Theobald’s Road, London WC1X 8RR, UK This book is printed on acid-free paper. Copyright © 2005, Elsevier Inc. All rights reserved. Except Chapter 21; Copyright © 2005, Mark B. Bain No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: [email protected]. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting “Customer Support” and then “Obtaining Permissions.” Library of Congress: Application submitted. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 0-12-154351-X For all information on all Academic Press publications visit our Web site at www.books.elsevier.com Printed in the United States of America 04 05 06 07 08 09 9 8 7 6 5 4 3 2 1

ANNOTATED TABLE OF CONTENTS

Foreword by Michael Sissenwine

xi

Contributors

xiii

PART

I

Introduction 1. Stock Identiﬁcation Methods: An Overview, by Steven X. Cadrin, Kevin D. Friedland, and John R. Waldman A brief introduction to stock identiﬁcation, including the historical development of the ICES Working Group. 2. Deﬁnition of Stocks: An Evolving Concept, by John R. Waldman A more detailed introduction to the ﬁeld, including technical deﬁnitions. 3. Fish Migration and the Unit Stock: Three Formative Debates, by D. H. Secor A review of ecological and historical issues related to stock connectivity and metapopulations. 4. Environmental and Genetic Inﬂuences on Stock Identiﬁcation Characters, by Douglas P. Swain, Jeffrey A. Hutchings, and Chris J. Foote An overview on the major categories of stock identiﬁcation approaches and their relative strengths for identifying stocks.

3

7

17

45

v

vi

Annotated Table of Contents

PART

II

Life History Traits 5. The Use of Early Life Stages in Stock Identiﬁcation Studies, by Jonathan A. Hare A description of methods that examine geographic range and distribution during early life history, including planktonic stages as well as juvenile and adult stages. 6. Life History Parameters, by Gavin A. Begg A review and critique of approaches that use differences in ontogenetic rates to distinguish stocks.

89

119

PART

III

Natural Marks—Morphological Analyses 7. Morphometric Landmarks, by Steven X. Cadrin A description of techniques used to measure and analyze general morphometry, including traditional multivariate morphometrics and more advanced geometric analyses. 8. Morphometric Outlines, by Steven X. Cadrin and Kevin D. Friedland A review and critique of methods that describe shape of outlines for structures such as scales and otoliths, and how methods are used to distinguish individuals with differently shaped features. 9. Analyses of Calciﬁed Structures: Texture and Spacing Patterns, by Kevin D. Friedland and Steven X. Cadrin A review of methods used to analyze spacing patterns of circuli on scales, otoliths, and vertebrae through image analysis, including digital photomicrograph examples. 10. Meristics, by John R. Waldman An evaluation of using the number of discrete morphological elements (e.g., number of vertebrae, ﬁn rays) for identifying stocks, with illustrative examples.

153

173

185

197

vii

Annotated Table of Contents

PART

IV

Natural Marks—Environmental Signals 11. Parasites as Biological Tags, by K. MacKenzie and P. Abaunza A summary of how parasitological analysis has been used to discriminate stocks.

211

12. Otolith Elemental Composition as a Natural Marker of Fish Stocks, by Steven E. Campana A review of rapidly developing techniques that use chemical composition of secreted hard parts to identify environmental differences and individuals that inhabited different habitats throughout their life history.

227

13. Fatty Acid Proﬁles as Natural Marks for Stock Identiﬁcation, by O. Grahl-Nielsen A description of a relatively new method for determining different populations according to fatty acids in tissues, with demonstrations on ﬁnﬁsh and marine mammals.

247

PART

V

Natural Marks—Genetic Analyses 14. Chromosome Morphology, by Ruth B. Phillips A review and critique of techniques for detecting differences among stocks through inspection of chromosome form, including several example photomicrographs.

273

15. Genetic Analysis: Allozymes, by M.-L. Koljonen and R. Wilmot A description of traditional electrophoretic methods, with many examples of stock identiﬁcation applications.

295

16. Mitochondrial DNA, by Antonios Magoulas A comprehensive review and protocol for detecting mitochondrial genetic characters and analyzing stock differences.

311

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Annotated Table of Contents

17. Use of Nuclear DNA in Stock Identiﬁcation: Single-Copy and Repetitive Sequence Markers, by Isaac Wirgin and John R. Waldman A review and critique of methods using single-copy, coding and noncoding, repetitive nuclear DNA for stock identiﬁcation.

18. Random Ampliﬁed Polymorphic DNA (RAPD), by P. J. Smith A review of polymerase chain reaction and RAPD techniques, which have had a rapidly increased application for stock identiﬁcation in recent years.

19. Ampliﬁed Fragment Length Polymorphism (AFLP), by Zhanjiang (John) Liu A description of a relatively new technique with great potential for stock identiﬁcation, including a comparative review with other genetic approaches.

331

371

389

PART

VI

Applied Marks 20. Internal and External Tags, by J. A. Jacobsen and L. P. Hansen A summary and critique of conventional tagging methods and their application for identifying stocks.

415

21. Electronic Tags, by Mark B. Bain A description of rapidly developing techniques involving telemetry and archival tags.

435

22. Otolith Thermal Marking, by Eric C. Volk, Steven L. Schroder, and Jeffrey J. Grimm A description of relatively new methods involving thermal signatures on ﬁsh otoliths, with photomicrographs illustrating their application for stock identiﬁcation.

447

ix

Annotated Table of Contents

PART

VII

Stock Identiﬁcation Data Analysis 23. Experimental Design and Sampling Strategies for Mixed-Stock Analysis, by Mary C. Fabrizio A protocol for sampling and a description of how sampling issues affect precision and accuracy of stock composition analysis.

467

24. An Introduction to Statistical Algorithms Useful in Stock Composition Analysis, by Michael H. Prager and Kyle W. Shertzer An evaluation of methods used to determine the contributions of different stocks in mixed-stock samples.

499

25. Classical Discriminant Analysis, Classiﬁcation of Individuals, and Source Population Composition of Mixtures, by Jerome Pella and Michele Masuda A description of linear discriminant analysis, with focus on stock identiﬁcation applications.

517

26. Neural Networks Used in Classiﬁcation with Emphasis on Biological Populations, by Saul B. Saila An introduction to a relatively new method of data analysis, with illustrative examples for identifying stocks. 27. Maximum Likelihood Estimation of Stock Composition, by Jon Brodziak A review of stock identiﬁcation applications using maximum likelihood to estimate contributions of different stocks in mixed-stock samples. 28. Estimation of Movement from Tagging Data, by Carl James Schwarz A description of analytical methods used to examine mark-recapture data for identifying stocks and quantifying interchange rates among stocks.

553

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591

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Annotated Table of Contents

PART

VIII

Application of Stock Identiﬁcation Data in Resource Management 29. Stock Identiﬁcation for Conservation of Threatened or Endangered Species, by Michael A. Banks A summary of how information on stock structure is used in resource management decisions. 30. The Role of Stock Identiﬁcation in Formulating Fishery Management Advice, by Cornelius Hammer and Christopher Zimmermann A description of how information on stock structure is considered in advice on stock status and management alternatives. 31. Identifying Fish Farm Escapees, by Peder Fiske, Roar A. Lund, and Lars. P. Hansen A review of the issue of escaped ﬁsh from aquaculture operations and a protocol for monitoring methods.

Index

609

631

659

681

FOREWORD

Fisheries scientists and managers use the term stock so frequently that you would think we know all there is to know about it. We speak of stock assessments, stock structure, spawning stock biomass, stock-recruitment relationships, stock complexes, stock production models, and so forth. I think it is fair to say that most scientists and managers take “stock” for granted as if it were sufﬁcient to rely on the old adage that “we’ll know one when we see it.” The truth is that its impossible to know how many scientiﬁc conclusions or ﬁshery management decisions may have been led astray by assuming we were seeing a stock that wasn’t! Information on the stocks is needed to meet objectives of ﬁsheries management to achieve sustainable yield, avoid recruitment failures, rebuild overﬁshed stocks, as well as to conserve threatened and endangered species. With growing acceptance of the need to conserve biodiversity (including genetic diversity), apply a precautionary approach, and operationalize the ecosystem approach (which places greater emphasis on spatial distributions and place-based management, such as MPAs), know what is and is not, a stock has never been more important. For more than a decade, the International Council for Exploration of the Sea, Stock Identiﬁcation Methods Working Group has been promoting standard protocols for sampling, data processing, and analytical methods, for data being generated by both traditional methods (e.g., meristics and morphometrics, traditional tags, parasites as natural tags) and new technologies (otolith chemistry, molecular genetics, electronic tags). The result is this comprehensive volume prepared by an outstanding team of international scientists. It addresses the stock concepts, historical development, applications to ﬁsheries science and management, use of natural marks (some traditional and some recently developed techniques), genetics, recent advances in tagging technology, and analytical methods. Although this volume will be a valuable reference for years to come, I think that we should all be excited by the prospect of innovative advances in the near future that surely will render some of the conclusions in the book out of date. xi

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Foreword

The scientists responsible for the volume, and ICES as the sponsor of the Working Group, do not want to rest on their laurels. Advances in biochemistry, analytical chemistry, and electronics (including microtechnology with nanotechnology on the horizon) foretell a very productive era unfolding when it comes to stock information in support of better science and better resource management. Speaking as both the President of ICES and the Chief Science Advisor for the U.S. National Marine Fisheries Service, I look forward to important and exciting discoveries in the future. Michael Sissenwine

CONTRIBUTORS

P. ABAUNZA, Instituto Espanol de Oceangraﬁa, Santander, Spain MARK B. BAIN, Center for the Environment, Cornell University, Ithaca, New York, USA MICHAEL A. BANKS, Coastal Oregon Marine Experiment Station, Hatﬁeld Marine Science Center, Department of Fisheries and Wildlife, Oregon State University, Newport, Oregon, USA GAVIN A. BEGG, CRC Reef Research Centre, James Cook University, Townsville, Queensland, Australia JON BRODZIAK, National Marine Fisheries Service, Woods Hole, Massachusetts, USA STEVEN X. CADRIN, National Marine Fisheries Service, Woods Hole, Massachusetts, USA STEVEN E. CAMPANA, Marine Fish Division, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada MARY C. FABRIZIO, National Marine Fisheries Service, Highlands, New Jersey, USA PEDER FISKE, Norwegian Institute for Nature Research, Trondheim, Norway CHRIS J. FOOTE, Department of Fisheries and Aquaculture, Malaspina University-College, Nanaimo, British Columbia, Canada O. GRAHL-NIELSEN, Department of Chemistry, University of Bergen, Bergen, Norway JEFFREY J. GRIMM, Washington Department of Fish and Wildlife, Olympia, Washington, USA xiii

xiv

Contributors

EVIN D. FRIEDLAND, NOAA Cooperative Marine Education and Research Program, University of Massachusetts, Amherst, Massachusetts, USA CORNELIUS HAMMER, Federal Research Centre for Fisheries, Institute for Baltic Sea Fisheries, Rostock, Germany M.-L. KOLJONEN, Finnish Game and Fisheries Research Institute, Helsinki, Finland L. P. HANSEN, Norwegian Institute for Nature Research, Oslo, Norway JONATHAN A. HARE, NOAA National Ocean Service, Center for Coastal Fisheries and Habitat Research, Beaufort, North Carolina, USA JEFFREY A. HUTCHINGS, Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada J. A. JACOBSEN, Faroese Fisheries Laboratory, Tórshavn, Faroe Islands ZHANJIANG (JOHN) LIU, The Fish Molecular Genetics and Biotechnology Laboratory, Department of Fisheries and Allied Aquacultures and Program of Cell and Molecular Biosciences, Aquatic Genomics Unit, Auburn University, Auburn, Alabama, USA ROAR A. LUND, Norwegian Institute for Nature Research, Trondheim, Norway K. MACKENZIE, School of Biological Sciences, Department of Zoology, The University of Aberdeen, Aberdeen, Scotland, United Kingdom ANTONIOS MAGOULAS, Hellenic Centre for Marine Research, Institute of Marine Biology and Genetics, Heraklion, Crete, Greece MICHELE MASUDA, National Marine Fisheries Service, Auke Bay, Alaska, USA JEROME PELLA, National Marine Fisheries Service, Auke Bay, Alaska, USA RUTH B. PHILLIPS, Washington State University, Vancouver, Washington, USA MICHAEL H. PRAGER, National Marine Fisheries Service, Beaufort, North Carolina, USA SAUL B. SAILA, University of Rhode Island, Graduate School of Oceanography, Narragansett, Rhode Island, USA STEVEN L. SCHRODER, Washington Department of Fish and Wildlife, Olympia, Washington, USA CARL JAMES SCHWARZ, Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada

Contributors

xv

D. H. SECOR, Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, USA KYLE W. SHERTZER, National Marine Fisheries Service, Beaufort, North Carolina, USA P. J. SMITH, National Institute of Water and Atmospheric Research Ltd., Wellington, New Zealand DOUGLAS P. SWAIN, Department of Fisheries and Oceans, Gulf Fisheries Centre, Moncton, New Brunswick, Canada ERIC C. VOLK, Washington Department of Fish and Wildlife, Olympia, Washington, USA JOHN WALDMAN, Hudson River Foundation for Science and Environmental. Research, New York, York; currently, Biology Department., Queens College, The City University of New York, New York, New York, USA R. WILMOT, National Marine Fisheries Service, Juneau, Alaska, USA ISAAC WIRGIN, Department of Environmental Medicine, New York University School of Medicine, Tuxedo, New York, USA CHRISTOPHER ZIMMERMAN, Institute for Sea Fisheries, Hamburg, Germany

PART

Introduction

I

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CHAPTER

1

Stock Identiﬁcation Methods— An Overview STEVEN X. CADRIN,* KEVIN D. FRIEDLAND,† AND JOHN R. WALDMAN‡ *National Marine Fisheries Service, Woods Hole Massachusetts, USA †University of Massachusetts, Amherst, Massachusetts, USA ‡Queens College, The City University of New York, New York, New York, USA

References

Stock identiﬁcation is an interdisciplinary ﬁeld that involves the recognition of self-sustaining components within natural populations and is a central theme in ﬁsheries science and management. The obvious role of stock identiﬁcation is as a prerequisite for the tasks of stock assessment and population dynamics, because most population models assume that the group of individuals has homogeneous vital rates (e.g., growth, maturity, and mortality) and a closed life cycle in which young ﬁsh in the group were produced by previous generations in the same group. Because stock structure and delineation are uncertain, the reliability of stock assessments, and therefore the effectiveness of ﬁshery management, is severely limited for many ﬁshery resources. There are also roles for stock identiﬁcation in ﬁshery science that may be equally important but less obvious. Any study that wishes to represent a living resource through ﬁeld sampling, or even laboratory studies, should consider the species’ population structure in the sampling and analytical design. Whether the research concerns general life history, growth, physiology, or diet, the population of inference and its stock components should be identiﬁed. Therefore, stock identiﬁcation can be viewed as a prerequisite for any ﬁshery analysis, just as population structure is considered a basic element of conservation biology (Thorpe et al., 1995). Despite its importance, stock identiﬁcation remains one of the most confusing subjects in ﬁsheries science for nonspecialists, with a wide variety of approaches and conﬂicting terminologies and interpretations. There have been some excellent reviews of stock identiﬁcation research, including concise overviews (Simon and Larkin, 1972; Templeman, 1982; Pawson and Jennings, 1996) and conference proceedings that include various case studies (Ihssen et al., Stock Identiﬁcation Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

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1981; Kumpf et al., 1987; Begg et al., 1999). However, a synthetic overview of stock identiﬁcation was not previously available, and a focus on application of stock identiﬁcation results to ﬁshery science and management was generally lacking. Many of the case studies on stock identiﬁcation are result-oriented and narrowly focused, and overview perspectives lack the detail needed to guide researchers. Furthermore, in recent years signiﬁcant advances have been made in many approaches to stock identiﬁcation. In 1992, the International Council for the Exploration of the Sea (ICES) established a “Study Group on Stock Identiﬁcation Protocols for Finﬁsh and Shellﬁsh Stocks,” chaired by Kevin Friedland, to review methodologies of stock identiﬁcation and develop a protocol for the application of stock identiﬁcation results. The Study Group was organized in an open format to invite a wide participation of experts on stock identiﬁcation to summarize the various approaches. Over the next decade, the group expanded to the “Stock Identiﬁcation Methods Working Group,” currently co-chaired by Kevin Friedland, John Waldman, and Steve Cadrin, and developed a volume of contributions that is aimed at synthesizing the many disciplines involved in stock identiﬁcation. An outline of major stock identiﬁcation approaches and applications was formed and authors were solicited to draft chapters that review each speciﬁc method, with emphasis on recent advances, review of benchmark case studies, critique of strengths and weaknesses, and guidance for effective protocol. The support of this work by the ICES community reﬂects the continuing leadership of ICES in oceanographic and ﬁshery research. After all, it was an early ICES committee that ﬁrst promoted stock identiﬁcation as an important consideration for ﬁshery science in the late 1800s (Smith, 1994). This book introduces a wide variety of methods and provides guidance and example applications. As described in the introductory chapters, the deﬁnition of the term stock is somewhat conditional on methodology, and different methods offer complementary perspectives on population structure. Accordingly, the “stock concept” evolved as methodological approaches evolved, from early morphological methods through the development of genetic techniques and the increased power to detect environmental signals in tissues. The compendium of methodological reviews (sections II to VI) was designed to serve as a resource for researchers interested in comparative studies in stock identiﬁcation as well as a general introduction for all scientists and managers of natural resources. Methodological chapters are not necessarily comprehensive reviews but focus more on historical development, benchmark case studies, critique of current issues, and prescriptions for the most effective protocols for stock identiﬁcation. Section VII, Stock Identiﬁcation Data Analysis, offers insights into a variety of statistical procedures and provides guidance on their proper application. Considerations for proper sampling, data treatments, and interpretations are dis-

Stock Identiﬁcation Methods—An Overview

5

cussed. The ﬁnal section describes how stock identiﬁcation information is used in resource management, illustrating the difﬁculties and limitations, and reviewing case studies in which stock identiﬁcation played a central role. One theme that emerged throughout the development of this volume is the strength of interdisciplinary analyses. Over the history of stock identiﬁcation, new methods were developed and promoted as better ways to approach stock identiﬁcation, often leading to equivocal information from competing methodological camps. However, when results from each approach are viewed in the context of what precise aspect of stock structure they reveal (deﬁned in this volume), a more holistic view with multiple perspectives is possible, providing more reliable information for resource management. As new methods continue to emerge, their results should be considered, along with those from traditional approaches, in improving our ability to study stock structure. Finally, despite its importance in the development of ﬁshery advice and management, stock identiﬁcation continues to be an afterthought. The ﬁshery science community has a habit of building assessments from back to front, often giving only cursory treatment to stock identiﬁcation, and in the name of being expeditious, population vital rates are estimated without regard to lingering questions about stock structure. We hope this volume not only will provide source material to improve the quality of stock identiﬁcation research, but also will stimulate new research on stocks being assessed without the beneﬁt of reliable stock identiﬁcation.

ACKNOWLEDGMENTS We thank the ICES community, particularly David Grifﬁth and Mette Bertelson, for their support throughout this endeavor. We are also grateful to Dave Cella, Kelly Sonnack, and Chuck Crumly for their assistance in the publication.

REFERENCES Begg, G., Friedland, K. D., and Pearce, J. B. 1999. Stock identiﬁcation—its role in stock assessment and ﬁsheries management. Fisheries Research 43: 1–8. Ihssen, P. E., Bodre, H. F., Casselman, J. M., McGlade, J. M., Payne, N. R., and Utter, F. 1981. Stock identiﬁcation: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences 38: 1838–1855. Kumpf, H. E., Vaught, R. N., Grimes, C. B., Johnston, A. G., and Nakamura, E. L. 1987. Proceedings of the Stock Identiﬁcation Workshop. NOAA Tech. Mem. NMFS-SEFC-199. Pawson, M. G. and Jennings, S. 1996. A critique of methods for stock identiﬁcation in marine capture ﬁsheries. Fisheries Research 25: 203–217. Simon, R. C. and Larkin P. A. (eds.). 1972. The stock concept in Paciﬁc salmon. H. R. MacMillan Lectures in Fisheries. University of British Columbia.

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Smith, T. D. 1994. Scaling Fisheries: A Science Driven by Economics and Politics 1855–1955. Cambridge University Press, Cambridge, UK. Templeman, W. 1982. Stock discrimination in marine ﬁshes. NAFO SCR Doc. 82/IX/79. Thorpe, J., Gall, G., Lannan, J., and Nash, C. 1995. Conservation of Fish and Shellﬁsh Resources: Managing Diversity. Academic Press, San Diego, CA.

CHAPTER

2

Deﬁnition of Stocks: An Evolving Concept JOHN R. WALDMAN Hudson River Foundation for Science and Environmental Research, New York, New York Currently, Biology Department, Queens College, The City University of New York, New York, New York, USA

I. Introduction II. Deﬁning the “Stock”: An Evolving Concept A. Early Viewpoints B. Late Twentieth-Century Deﬁnitions C. Stocks and Conservation Biology III. Some Fundamental Issues in Deﬁning Stocks A. Genotypic vs. Alternative Approaches B. Negative Results C. Congruence IV. Conclusions References

I. INTRODUCTION Fisheries science is based on the notion of an idealized “unit stock,” a discrete entity with its own origin, demographics, and fate. Fish stock assessment is the science of estimating certain key population dynamics parameters to better manage the unit stock. If the rates of growth, natural mortality, reproduction, and present ﬁshing mortality can be estimated, their effects may be combined in order to evaluate the effects of changes in the ﬁshing mortality rate. An understanding of stock structure is necessary for designing appropriate management regulations in ﬁsheries where multiple stocks are differentially exploited (Ricker, 1981). Stock identiﬁcation is an essential partner to stock assessment—unit stocks cannot be assessed unless they are circumscribed, that is, their boundaries deﬁned in relation to other units of the same species. At its core, stock identiﬁcation is the process that seeks to identify coherent units of individuals that have complete-to-partial discreteness in space or time Stock Identiﬁcation Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

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from their congeners. These units have certain characteristics that render them fundamental to management: (1) they occupy their own physical life history circuit, including spawning grounds that are geographically or temporally unique; (2) they experience their own natural demographic inﬂuences, such as mortality suffered from a particular suite of predators; (3) their complete-to-partial isolation allows ﬁne tuning of their morphological and genetic characteristics to their particular environmental circumstances; and (4) their abundances and life history characteristics respond to their own sets of unnatural inﬂuences, such as ﬁsheries and contamination of their habitats. Before identifying these units, ﬁsheries science must grapple with deﬁning them, a task that remains confusing and problematic. Deﬁnitions of both parts of the term stock identiﬁcation are muddled, that is, “stock” and “identiﬁcation,” pattern and process. But of the two, process is far more easily speciﬁed. Concerning process, although the term stock identiﬁcation is often used to represent an entire realm of investigation, in a narrower sense it may be taken to mean only the initial identiﬁcation of units within a species, whereas stock discrimination means the process of classifying individuals or collections of individuals to those units. The term stock composition analysis is an extension of stock discrimination where the proportions of unit stocks to a mixed-stock ﬁshery are estimated. It is common that stock identiﬁcation and stock discrimination are used interchangeably. Stock composition analysis is also referred to as mixed-stock analysis and relative contribution analysis. Concerning pattern or structure in ﬁshery resources, however, deﬁnitions abound. Royce (1972) believed there were “. . . a bewildering array of semantic problems because there is little agreement on the meaning of the words used to deﬁne groups in the hierarchy with the rank of subspecies and below. . . .” Reaching a consensus in this area remains immensely difﬁcult. Older categories beneath subspecies that have largely fallen out of favor among ﬁshery biologists include subpopulation, race, strain, breed, and variety. A more current hierarchy below subspecies is illustrated by Secor (this volume; Fig. 3-1) that includes metapopulation, population, contingent, year-class, school, brood, and individual. A fundamental problem in deﬁning stocks (and other biological categories such as species) is assuming the optimal balance between precision and generality: too much precision and the deﬁnition will not be robust enough to serve all situations, too general and the deﬁnition will have little utility. This conundrum runs throughout the history of stock deﬁnition. Kutkuhn (1981) observed that “. . . the literature is replete with observations and opinions on the concept, character, and implications of something we commonly refer to as the (ﬁsh) ‘stock.’ But nowhere, insofar as I can determine, has any body of authority, as is often established to perform such functions, acclaimed a deﬁnition of it.” Nonetheless, many deﬁnitions have been proposed.

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II. DEFINING THE “STOCK”: AN EVOLVING CONCEPT

A. EARLY VIEWPOINTS Secor (this volume, Chapter 3) reviews notions of unit stock and population thinking, noting how the unit stock arose as a means of practically deﬁning vital rates and renewal rates pertinent to geographic areas where ﬁsheries were undertaken. Dahl (1909) viewed stock as a source of ﬁsh, that is, a speciﬁc portion of a population that is inﬂuenced by an anthropogenic activity that affects population productivity. This deﬁnition is purely operational in which the portion of ﬁsh exposed to the ﬁshery deﬁnes the stock. This category might be referred to as a “harvest stock.” The baseline for establishment of a harvest stock is not nature’s knife (i.e., isolation and the process of differentiation) and its natural subdivisions below that of species. Instead, a harvest stock is deﬁned by human interaction with the species, cutting across and grouping whichever natural subdivisions (lineages) happen to encompass a particular ﬁshery.

B. LATE TWENTIETH-CENTURY DEFINITIONS Despite its beginnings as an operationally deﬁned ﬁsheries term, the term stock began to drift from the practical toward more theoretical deﬁnitions that recognized the microevolutionary subdivisions occurring below the species level, regardless of whether ﬁsheries are promulgated. A transitory deﬁnition was provided by Larkin (1972), who wrote that a stock is “. . . a population of organisms which, sharing a common gene pool, is sufﬁciently discrete to warrant consideration as a self-perpetuating system which can be managed.” Both a common gene pool and management are explicit in this deﬁnition. However, deﬁnition of the stock by the ﬁshery alone continued in the Magnuson Fishery Conservation and Management Act of 1976 in which it meant a species, subspecies, geographic grouping, or other category of ﬁsh capable of management as a unit (Fox and Nammack, 1995). The decades-long conundrum over the deﬁnition of stock brought about an ambitious symposium held in 1980 in Ontario, Canada, to consider that subject. Indeed, the papers originating from the Stock Concept International Symposium (Canadian Journal of Fisheries and Aquatic Sciences, 1981, vol. 33, no. 12) provide a ﬁne summary of the state of our perceptions of stock at that time. Several observations can be made. One is that the issue was far from settled at, or even after, the gathering—the resultant papers reﬂect a wide diversity of unreconciled opinion. Another was a movement from utilitarian deﬁnitions to ones that seek to outline naturally occurring units. A third was a sometimes expressed desire to base the deﬁnition on genetics, a response, in part, to the growing wealth

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of genotypic knowledge gained through the application and maturation by 1980 of protein electrophoretic analysis. Among these papers, Booke (1981) tackled the issue head on, wishing ambitiously to propose a working deﬁnition of stock that would be useful for all ﬁsh species. He offered one that he recognized was highly general, that is, “a species group, or population, of ﬁsh that maintains and sustains itself over time in a deﬁnable area.” He then suggested more precisely that a genotypic stock is a population of ﬁsh maintaining and sustaining Castle–Hardy–Weinberg equilibrium. And that if his preferred genotypic stock characterization is not possible, a phenotypic stock “. . . has to be recognized as a group, or population of ﬁsh maintaining characteristics which are expressed in one or more ways depending on the type of environment or domicile.” Bailey and Smith (1981) echoed this genotypic and phenotypic dichotomy, deﬁning stocks as local populations that maintain recognizable genetic differentiation by separation of their spawning place or time, but also stating that “. . . a (phenotypic) stock has to be recognized as a group, or population, of ﬁsh maintaining characteristics which are expressed in one or more ways depending on the type of environment or domicile.” However, their genotypic criterion contrasted with Booke’s in that it was based on differentiation among units, whereas Booke mentioned “group” and “population” but did not deﬁne them as differentiated (but he did uniquely invoke Castle–Hardy–Weinberg equilibrium, a withinpopulation measure). Another attempt was made by Casselman et al. (1981), who stated that a stock is “. . . a population of ﬁsh that behaves as a cohesive unit whose members exhibit common responses to environmental conditions within its geographic boundaries.” They believed this includes but is not restricted to population units that are reproductively isolated, which is less restrictive than many other deﬁnitions and which conceivably could embrace other concepts such as a regional stock or metapopulation. The deﬁnition of stock made at this symposium, which I believe was most robust, yet sufﬁciently speciﬁc to be useful, was offered by Ihssen et al. (1981), who proposed that a stock is “. . . an intraspeciﬁc group of randomly mating individuals with temporal or spatial integrity.” This deﬁnition places an upper boundary at the species level yet allows for a kind of operational deﬁnition anywhere below that boundary at whatever level temporal or spatial integrity is displayed; that is, by this deﬁnition various units of interest expressed by Royce (1972) as groups in the hierarchy with the rank of subspecies and below (such as metapopulation, population, or contingent) would qualify, depending on the pattern expressed and the degree of technical stringency applied to the problem. A newer and less restrictive deﬁnition was offered by Hilborn and Walters (1992) in which stocks are considered as arbitrary groups of ﬁsh large enough

Deﬁnition of Stocks: An Evolving Concept

11

to be essentially self-reproducing, with members of each group having similar life history characteristics.

C. STOCKS

AND

CONSERVATION BIOLOGY

Beyond its operational deﬁnitions linked to ﬁsheries, stocks may be considered to be units below species that are naturally occurring and of interest to managers and scientists beyond reasons concerning harvest. With its emphasis on preserving biodiversity, the relatively new science of conservation biology is intimately involved with identifying these below-species units. Many of the technical approaches to resolving stocks for ﬁsheries’ purposes are also applicable to the goals of conservation biology. However, new terms have emerged that border on, yet differ from, more traditional ﬁsheries’ stock concepts. Primary among these today is the “evolutionarily signiﬁcant unit” (ESU) of the biological species, a concept reﬁned for Paciﬁc salmon under the general demands of the U.S. Endangered Species Act to conserve genetic diversity within and between species (Waples, 1991, 1995). The ESU as deﬁned by Waples (1991) is a population that (1) is substantially reproductively isolated from other conspeciﬁc reproductive units, and (2) represents an important component in the evolutionary legacy of the species. The ﬁrst criterion is essentially the deﬁnition of a stock. But the second criterion attempts to answer a question with respect to a population’s evolutionary legacy, that is, if the population became extinct, would this represent a signiﬁcant loss to the ecological-genetic diversity of the species? Thus, the ESU goes beyond the stock concept in that its designation includes a qualitative and often difﬁcult judgment (King and Ludke, 1995) about each unit’s importance. Other deﬁnitions of ESU beyond Waples (1991) were reviewed by Fraser and Bernatchez (2001). They argued that no single approach works best in all situations, but that each has its strengths and weaknesses under different circumstances. Thus, they propose “adaptive evolutionary conservation” in which alternative ESU criteria are ﬁt to situational circumstances.

III. SOME FUNDAMENTAL ISSUES IN DEFINING STOCKS

A. GENOTYPIC

VS.

ALTERNATIVE APPROACHES

Some workers have referred to the genotypic approach to deﬁning stocks as if it is the avatar of stock identiﬁcation (e.g., Booke, 1981). Genotypic approaches do offer many advantages to alternative techniques, including permanence across an individual’s life cycle, freedom from environmental modulation, often large

12

John R. Waldman

substrates of variation, and usually, nonfatal sampling (Wirgin and Waldman, 1994). But it is important not to be so swayed by these advantages as to dismiss the fact that any consistently detectable differences among stocks are both valid and useful; that is, a stock is a stock if a marker discriminates among them, even if these differences are partly or completely environmental in origin. Indeed, there are situations in which it is conceivable that nongenetic approaches might be preferable. One such situation is when there are simple, qualitative characteristics, such as observations of different timings of runs of an anadromous species in a river; another might be an obvious phenotypic marker available, such as a color difference. Another is when newly formed populations through restocking or natural colonization do not yet show genetic differences despite their discreteness. Yet another is where stock identiﬁcation information can emerge from mark-recapture work conducted for stock assessment purposes. (However, it would help the stock identiﬁcation ﬁeld if more controlled “common garden” rearing experiments in which the environment was held constant were conducted in order to gain a better understanding of genetic and nongenetic control of stock differences.) Today’s recognition of the complementarity of genotypic and phenotypic approaches contrasts with the apparently short-lived view espoused by Marr (1957) who believed that “subpopulations” are characterized by genetic selfsustainment, whereas stocks are populations or portions of populations of which all members are characterized by similarities that are not heritable, but are induced by the environment. The scientist interested in identifying ﬁsh stocks has a large number of tools available in the kit. Genotypic approaches may be highly prominent among them, but they have not supplanted the remainder.

B. NEGATIVE RESULTS Kutkuhn (1981) summarized the scientiﬁc process of stock identiﬁcation. First, one needs indication of the existence of stocks, from which a hypothesis is mounted (vs. the null hypothesis of a single stock). Second, a well-conceived survey must be undertaken. Finally, an efﬁcient discrimination technique must be applied to the collections. Each of these stages may be nulliﬁed by ﬂaws that result in a failure to falsify the null hypothesis, including defective study logic, faulty survey techniques, and questionable classiﬁcation procedures (Kutkuhn, 1981). Notwithstanding the ﬁrst two problems, it is possible in a study that a competent classiﬁcation procedure was applied but that it lacked the sensitivity to detect evidence for the stocks at a statistically signiﬁcant level; that is, a careful and earnest effort using one approach yields negative results (Type 1 error). Should analysis stop there and the absence of discrete stocks be accepted?

Deﬁnition of Stocks: An Evolving Concept

13

My answer is—only with caveats. Nonexistence cannot be proved and, in a formal sense, what has occurred is that the null hypothesis has not been falsiﬁed. It is entirely possible that a different stock identiﬁcation approach would falsify the null hypothesis. Unfortunately, there is no hard-and-fast rule to know how far to proceed with additional analyses. Most often, resource limitations preclude any, or at least, much more analysis. But it is important to make a distinction in such cases where negative results have been obtained: The existence of stocks has not been disproved, only the null hypothesis of a single stock has not been falsiﬁed. Also, although failure to falsify the null hypothesis may be less compelling to the investigator, such negative results should nonetheless be published inasmuch as they still provide valuable information about the nature of the species studied and the sensitivities of the stock identiﬁcation approaches employed.

C. CONGRUENCE Falsiﬁcation of the null hypothesis of a single stock implies that multiple stocks exist, and much of what we know about the existence of particular stocks is derived from the successful application of single stock identiﬁcation approaches. But why might one approach be successful in detecting multiple stocks, whereas another is unsuccessful? That is, why do different stock identiﬁcation approaches have different sensitivities to a particular stock problem? One factor is the “depth” of the stock division being addressed. As anyone who has examined branching diagrams of relationships among ﬁsh stocks has observed, stocks show subdivisions with different levels of relatedness, sometimes within a metapopulation structure (Stephenson, 1999). For example, several anadromous ﬁsh stocks in rivers that drain into a common bay or sound may group as a regional stock, yet there may be tributary stocks within one of these single rivers. This depth is a product of space and time, that is, the greater the degree and length of isolation, the greater the depth of the stock division. But differentiation of stocks is estimated by examining a wide variety of phenotypic and genotypic features, and each of these features may undergo microevolution at their own rate (Waldman, 1999). Fish have literally been “taken apart” down to the level of DNA in the search for useful stock discrimination characters. Some of these features respond rapidly to differentiating forces (e.g., ﬁn ray counts, nuclear DNA microsatellites), whereas others are more conservative (e.g., vertebral counts, the coding region of any nuclear DNA gene). Thus, a particular stock identiﬁcation problem involves choosing among approaches that address features of the ﬁsh that compose these stocks, which are passing through differing microevolutionary trajectories—some of which may be applicable to the problem (offering a high signal-to-noise ratio) and others which may be less, or not at all applicable.

14

John R. Waldman

Application of additional stock discrimination techniques (a holistic approach) has several advantages to single approach studies (Begg and Waldman, 1999). One advantage is that one of these techniques may detect stock structure where others fail to do so. Another is that greater conﬁdence is gained when contrasting approaches provide congruent results. Also, additional levels of stock structure may be observed with approaches that offer different sensitivities. Finally, multiple applications of discrete approaches to individual stock identiﬁcation problems provide more empirical information on the relative merits of alternative approaches to this still developing ﬁeld.

IV. CONCLUSIONS Since the advent of modern ﬁsheries science, the concept of the unit stock has remained the same while the practice of identifying ﬁsh stocks has undergone signiﬁcant change. The unit stock is still an idealized fundamental unit which, when correctly identiﬁed, allows estimation of important population parameters without the biases that result from incorporation of unknown components of additional unit stocks. But the stock searched for in the stock identiﬁcation process may not always be a unit stock. Under an early, and presently minority, point of view, this does not matter; that is, a harvest stock is simply “what is caught” and so the ﬁshery itself deﬁnes the stock. Secor (1999) noted that the increased technical ability to discriminate ﬁsh populations has led to synonomy between population and stock. He argues, however, that this classical deﬁnition still has validity in the sense that accessibility is implicit in stock deﬁnitions and that accessibility is mediated largely by within-population ecological attributes such as migratory modalities. How consideration of these within-population differences will affect future notions of stock is unclear, but it is likely that much new information will emerge as powerful new tools are employed, such as microelemental analysis of otoliths (e.g., Secor et al., 2001) and stable isotope analysis (e.g., Adams et al., 2003). However, the numerous modern deﬁnitions of stock reﬂect an evolution toward naturally deﬁned divisions or lineages within species that has occurred for at least two reasons, one philosophical and one practical. The philosophical rationale is that lineage-based divisions correspond more closely with the notion of the idealized unit stock. Although deﬁning such stocks involves necessary steps beyond letting the ﬁshery itself delimit the stock, and these steps may be costly and technically challenging (sometimes producing unclear results), they nonetheless represent a search for fundamental biological units, not units of convenience. The practical reason is the century-long trend toward improved stock identiﬁcation capabilities; that is, the tool kit has grown considerably larger and far more technically keen, as evidenced by this volume.

Deﬁnition of Stocks: An Evolving Concept

15

Earlier ﬁsheries scientists had no choice but to consider harvest stocks inasmuch as stock discrimination techniques did not exist and so synonomy between stock and population was not even possible. Today a wealth of techniques can be applied to discriminate among stocks, and in many instances these results can be applied to estimate their proportions in mixed stocks. Thus, the possibly “mixed” harvest stock may be quantitatively decomposed to its constituent unit stocks. Begg and Waldman (1999) underscored the analogy between the surprisingly difﬁcult problems in deﬁning species (e.g., Mayden and Wood, 1995) and in deﬁning stocks. Deﬁning the more recently conceptualized ESU presents similar problems (Fraser and Bernatchez, 2001). All of these notions have shifted and diversiﬁed over time partly in response to changes in thinking about evolution (albeit, at different levels) and to technological advances. Yet, despite the challenges in deﬁning them, on the operational level, all are usually discriminated without much debate. If a single stock deﬁnition is needed, I favor that of Ihssen et al. (1981), but others may be more appropriate in particular cases. Deﬁnitions of stock will likely continue to evolve as management requirements change and technologies advance.

ACKNOWLEDGMENTS Thanks to Steven Cadrin, Kevin Friedland, and David Secor for their insightful comments on the manuscript.

REFERENCES Adams, C., Fraser, D., McCarthy, I., Shields, S., Waldron, S., and Alexander, G. 2003. Stable isotope analysis demonstrates ecological segregation in a bimodal size polymorphism in Arctic charr from Loch Tay, Scotland. Journal of Fish Biology 62: 474–481. Bailey, R. M. and Smith, G. R. 1981. Origin and geography of the ﬁsh fauna of the Laurentian Great Lakes basin. Canadian Journal of Fisheries and Aquatic Sciences 38: 1539–1561. Begg, G. A. and Waldman, J. R. 1999. An holistic approach to ﬁsh stock identiﬁcation. Fisheries Research 43: 35–44. Booke, H. E. 1981. The conundrum of the stock concept—are nature and nurture deﬁnable in ﬁshery science? Canadian Journal of Fisheries and Aquatic Sciences 38: 1479–1480. Casselman, J. M., Collins, J. J., Crossman, E. J., Ihssen, P. E., and Spangler, G. R. 1981. Lake whiteﬁsh (Coregonus clupeaformis) in the Great Lakes region. Canadian Journal of Fisheries and Aquatic Sciences 38: 1772–1789. Dahl, K. 1909. The problem of sea ﬁsh hatching. Special Part B, No. 5. In J. Hjort (ed.). Rappurt sur les Travaux de Commission A, Dans La Période 1902–1907, Conseil Permanent International pour l’Exploration de La Mer. Rapports et Procès-Verbaux, Vol. X. Fox, W. W., Jr. and Nammack, M. F. 1995. Conservation guidelines on signiﬁcant population units: responsibilities of the National Marine Fisheries Service. American Fisheries Society Symposium 17: 419-422.

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Fraser, D. J. and Bernatchez, L. 2001. Adaptive evolutionary conservation: towards a uniﬁed concept for deﬁning conservation units. Molecular Ecology 10: 2741–2752. Hilborn, R. and Walters, C. J. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York. 570 pp. Ihssen, P. E., Booke, H. E., Casselman, J. M., McGlade, J. M., Payne, N. R., and Utter, F. M. 1981. Stock identiﬁcation: materials and methods. Canadian Journal of Fisheries and Aquatic Sciences 38: 1838–1855. King, T. L. and Ludke, J. L. 1995. A national biological service perspective on deﬁning unique units in population conservation. American Fisheries Society Symposium 17: 425–429. Kutkuhn, J. H. 1981. Stock deﬁnition as a necessary basis for cooperative management of Great Lakes ﬁsh resources. Canadian Journal of Fisheries and Aquatic Sciences 38: 1476–1478. Larkin, P. A. 1992. The stock concept and management of Paciﬁc salmon. In R. C. Simon and P. A. Larkin (eds.), The Stock Concept in Paciﬁc Salmon. H. R. MacMillan Lectures in Fisheries. University of British Columbia, Vancouver, BC, pp. 11–15. Marr, J. C. 1957. Contributions to the study of sub-populations of ﬁshes. U.S. Fish and Wildlife Service Special Scientiﬁc Report—Fisheries 208: 1–6. Mayden, R. L. and Wood, R. M. 1995. Systematics, species concepts, and the evolutionary signiﬁcant unit in biodiversity and conservation biology. American Fisheries Society Symposium 17: 58–113. Ricker, W. E. 1981. Changes in the average size and average age of Paciﬁc salmon. Canadian Journal of Fisheries and Aquatic Sciences 38: 1636–1656. Royce, W. F. 1972. Introduction to the Fishery Sciences. Academic Press, New York. 351 pp. Secor, D. H. 1999. Specifying divergent migrations in the concept of stock: the contingent hypothesis. Fisheries Research 43: 13–34. Secor, D. H., Rooker, J. R., Zlokovitz, E., and Zdanowicz, V. S. 2001. Identiﬁcation of riverine, estuarine, and coastal contingents of Hudson River striped bass based upon otolith elemental ﬁngerprints. Marine Ecology Progress Series 211: 245–253. Stephenson, R. L. 1999. Stock complexity in ﬁsheries management: a perspective of emerging issues related to population sub-units. Fisheries Research 43: 247–249. Waldman, J. R. 1999. The importance of comparative studies in stock analysis. Fisheries Research 43: 237–246. Waples, R. S. 1991. Paciﬁc salmon, Oncorhynchus spp. and the deﬁnition of “species” under the endangered species act. Marine Fisheries Review 53: 11–22. Waples, R. S. 1995. Evolutionarily signiﬁcant units and the conservation of biological diversity under the Endangered Species Act. American Fisheries Society Symposium 17: 8–27. Wirgin, I. I. and Waldman, J. R. 1994. What DNA can do for you. Fisheries 19(7): 16–27.

CHAPTER

3

Fish Migration and the Unit Stock: Three Formative Debates D. H. SECOR Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, USA

I. The Unit Stock and Population Thinking II. Debate 1. Early Cod Hatchery Enhancement: Local or Global Effects on Fisheries? III. Debate 2. The Parent Stream Theory: Directed Migrations or Local Wanderings? A. Natural Tags B. Marking Experiments C. Transplant Experiments D. The Year-Class Phenomenon (Again) E. Ocean Studies F. The Parent Stream Theory and Population Thinking IV. Debate 3. The Eel Problem: Who Contributes to Reproduction? A. Mediterranean vs. North Atlantic Origin of Eels B. North Atlantic Eels: Speciation or Population Structure? C. Failed Adults D. “Freshwater Eels” and Contingent Thinking E. More Eel Problems and Population Thinking V. Summary References

I. THE UNIT STOCK AND POPULATION THINKING Modern ﬁsheries science emerged through the early efforts of the International Council for the Exploration of the Sea (ICES) to understand factors that caused Stock Identiﬁcation Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

17

18

D. H. Secor

ﬂuctuations in important coastal ﬁsheries (Smith, 1994; Smed and Ramster, 2002). A major research goal early on for ICES scientists was to establish relationships among ﬁsh distribution, behavior, and environmental factors (Nakken, 2002). Late nineteenth-century investigations on Atlantic herring by Heincke (1898) supported a widely held view that many local races structured the distribution of herring and their availability to ﬁsheries. Local ﬁsheries might then have local effects. This so-called modern migration theory displaced an earlier view of panmixia in cod and other coastal ﬁshes. In contrast to these earlier ideas, ICES scientists discovered that ﬁsh became accessible to ﬁsheries according to population renewal processes structured over large but distinct geographic regions (Hjort, 1914). The unit stock arose as a means of practically deﬁning vital rates and renewal rates pertinent to geographic regions where ﬁsheries were undertaken. Early use of the term was by Dahl (1909), who deﬁned stock as the source of ﬁsh. Later, Russell (1931), in his catch equation, deﬁned stock as the exploitable portion of a population. Stock as an operational deﬁnition could be tailored for regions where vital rates and recruitment were deemed homogenous (Gulland, 1983; Cushing, 1995). Thus, while population thinking underlies the way we deﬁne and manage stocks, stocks themselves are not ecological entities per se; they are operationally deﬁned by the geographic extent of anthropogenic effects and other practical considerations. Levels of biological structure relevant to stock issues can range from species to brood (Fig. 3-1). In some instances, lineage (species to population levels) may be critical in deﬁning management units, as is the case when large reductions occur to abundance and threaten maintenance of the underlying gene pool. This often entails an evolutionary perspective that can extend millions of years and across ocean basins. In other cases, ecological time and spatial scales may be of greatest relevance. For instance, a manager may need to know how important a given habitat is as a source for recruits. Here, stocks are deﬁned according to regions of production, and biological levels such as contingents, cohorts, and shoals may be important (e.g., Beck et al., 2001). Despite the operational deﬁnition of stocks, there is little doubt that population structure is fundamental to how we assess and manage living resources (Sinclair, 1988; Sinclair and Smith, 2002). Population thinking arose during the early twentieth century as scientists contended with Hjort’s discovery of population cycles common over large coastal regions (Solemdal and Sinclair, 1989). Issues remained as to which geographic scales were relevant to populations (scale and entity), which behaviors contributed to population structure (migration, life cycle closure, and philopatry), and the consequences of population structure (or alternatively, the consequence of deviations from life cycle closure). Here, I introduce three formative debates, which relate to pattern, process, and consequence. These include: (1) the effectiveness of cod hatcheries in fjords of southern

19

Fish Migration and the Unit Stock: Three Formative Debates Region of Coherence

Years of Coherence

Stock Entity

107 Species

106 Subspecies

105 Metapopulation

104 103 102 10

Population Contingent Year-class School Brood

Individual

FIGURE 3-1. Temporal and spatial domains, and levels of biological organization relevant to the unit stock. Here, the unit stock is operationally deﬁned by the anthropogenic effects of interest (Gulland, 1983).

Norway; (2) evaluation of the parent stream theory for Paciﬁc salmon; and (3) the eel problem: Who contributes to population renewal processes?

II. DEBATE 1. EARLY COD HATCHERY ENHANCEMENT: LOCAL OR GLOBAL EFFECTS ON FISHERIES? Johan Hjort’s pioneering observation of population response to periodically strong year-classes remains a principal paradigm that guides ﬁsheries science and management. To make this revelation, Hjort and his team had to turn away from prevailing theory, which stipulated local races and local effects (Sinclair and Solemdal, 1988), and discover a new way of thinking about the renewal process. Several scientists and historians have argued that Hjort’s “population thinking” may have originated from practical considerations on the effectiveness of hatchery releases of larval cod into Norwegian fjords over a century ago (Solemdal et al., 1984; Sinclair and Solemdal, 1988; Smith, 1994; Schwach, 1998; Secor, 2002; Sinclair and Smith, 2002). Did these releases produce local effects in a fjord? Or were released cod swamped by natural juvenile production within the fjord or, perhaps, from outside the fjord?

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D. H. Secor

By the mid-1890s the Flødevigen hatchery under the leadership of G. M. Dannevig was releasing millions of cod larvae into southern Norway fjords. Early development of egg fertilization and hatching led to the belief that “. . . cod fry artiﬁcially hatched have the power and energy to live, grow, and develop when set at liberty and left to care for themselves in their natural element, the sea” (Rognerud, 1887). From 1890 to 1906, 23.3 billion yolk-sac larvae were released into small fjords, sounds, and bays in southern Norway (Solemdal et al., 1984). Hjort questioned the effectiveness of these releases and a study was commissioned in 1903 by the Norwegian Parliament (Smith et al., 2002) that required the participation by both Dannevig and Hjort. Hjort assigned Norwegian scientist K. Dahl to work with Dannevig. During spring 1904 and 1905, millions of cod larvae were released into two fjords, and subsequent summertime juvenile abundances were estimated through beach seine sampling. These abundances were compared to summertime abundances in 1903, when no releases were made (Table 3-1). Dannevig reported increased juvenile abundances associated with the releases for 1904–1905 in comparison to 1903, the control year. Dahl, on the other hand, investigated other fjord systems that did not receive hatchery larvae in 1904 and 1905. In 1904, large numbers of juveniles were encountered in the fjords Dahl surveyed regardless of whether releases were made. But in 1905, the abundance of juveniles was relatively scarce in all fjords, again independent of whether larvae were released or not (Table 3-1). Dahl concluded (1909) that, “. . . the formation of the ﬁsh stock (the ﬁsh supply) in these fjords is not really much dependent on the spawning and hatching taking place in the fjord itself, it is more dependent on the quantities of fry brought by currents (the circulation of fry).” Thus, the same study resulted in conﬂicting conclusions. Parliament

TABLE 3-1. Number of Released Larvae (“Fry”) into Different Fjords as Part of G. M. Dannevig’s Cod Hatching Programa Søndeled Fjord

Year

Larvae released

1903 0 1904 33.5 ¥ 106 1905 33 ¥ 106

Helle Fjord

Sandnes Fjord

Støle Fjord

Cod per haul

Larvae released

Cod per haul

Larvae released

Cod per haul

Larvae released

6.3 33.7 11.4

0 0 10 ¥ 106

5.4 10.9 1.5

— 0 0

— 49 4.1

— 0 0

Cod per haul

Christiania Fjord Larvae released

Cod per haul

— — — 112 20 ¥ 106 10.8 2.7 20 ¥ 106 1.9

a The study on the effectiveness used “cod per haul” (number of juveniles collected using beach seines) to index juvenile abundance among years for months of July and August. Dannevig emphasized interannual comparisons in Søndeled and Helle Fjords, 1903–1905, using 1903 as a control year. K. Dahl emphasized between Fjord contrasts in 1904 and 1905 using Sandnes, Støle, and Christiania Fjords as controls. Data from Dahl, 1909 (p. 31).

Fish Migration and the Unit Stock: Three Formative Debates

21

assigned a committee to evaluate the opposing conclusions, which determined that the uncertainty in the ﬁndings dictated continued study. Hatchery releases of cod and juvenile monitoring continued into modern times, and the issue of the effectiveness of hatchery releases of cod into fjords remains a point of contention (e.g., Smith et al., 2002). The diverging views on the effectiveness of hatchery releases, which persist today (e.g., Secor et al., 2000, 2002), center on scale. Dahl maintained that ﬂuctuations in juvenile cod abundances had to be thought about at a spatial scale larger than a fjord. Dannevig subscribed to the modern migration theory, which prescribed restricted movements inshore and offshore by numerous local races (Heincke, 1898; Smith, 1994). Dahl (1909) speciﬁed the theoretical underpinnings of Dannevig’s program: “. . . the importance of the size of the waters was reduced by accepting the doctrine that each area of the sea, even the smallest, possessed its own tribe of ﬁsh. These tribes were supposed to be highly local during the whole life of the individuals. They were easily injured by overﬁshing and had to be replaced by the aid of man.” Based on his studies of differing stages of Norwegian coastal cod, Dahl (1909) thought it “just to consider the ﬁsh stock of a considerable stretch of coast as belonging to the whole of the area.” How could relevant spatial domains be applied to issues of stock renewal? The answer came through migration studies. Because adult cod do not reside the year round in spawning or nursery habitats, ontogenetic and seasonal migration patterns were needed to link stage-speciﬁc distribution patterns. Through early tagging studies, Hjort (1909) conﬁrmed seasonal migrations between Lofoten Island spawning grounds and far-off Barents Sea feeding habitats. For Atlantic herring, Hjort and his colleagues used “certiﬁcates of origin”—unique optical patterns of scale annuli—to chart the seasonal migrations to and from spawning grounds. Based on herring studies, Hjort argued that different stages or sizes of herring located in different regions were in fact members of the same population (Secor, 2002). Such studies, informed by the hatchery investigation, provided Hjort with the critical spatial domain needed to combine samples across relevant scales and observe decadal cycles in abundance of Norwegian herring (Hjort and Lea, 1914). Thus, Hjort used complex life cycles to reveal the spatial scale relevant to stock renewal processes. But how then were complex life cycles maintained? Resolution of this issue came with early studies of the parent stream theory in Paciﬁc salmon.

III. DEBATE 2. THE PARENT STREAM THEORY: DIRECTED MIGRATIONS OR LOCAL WANDERINGS? Prior to the twentieth century, naturalists, commercial ﬁshermen, and anglers long wondered: Where do spawning Paciﬁc and Atlantic salmon originate? Does

22

D. H. Secor

a stream’s spawning run represent a random mixture of mature ﬁsh migrating in from the coast? Or, does each spawning run represent the return of a generation of salmon to their parent’s stream? The speciﬁc migration and spawning behaviors associated with salmon reproduction would seem to represent adaptations that only lineage could guarantee. Thus, the parent stream theory became dogma well in advance of scientiﬁc evidence. Two central issues required resolution: (1) Do salmon home to natal streams? (2) Where do spawning runs of maturing adults originate—from distant oceanic waters or from regional coastal waters? For Paciﬁc salmon, scientiﬁc investigations and debate related to these issues were concentrated during the ﬁrst half of the twentieth century starting with C. H. Gilbert’s important work on natural tags (circuli patterns in scales) and homing for sockeye salmon, and culminating with work on ocean migrations by the International North Paciﬁc Fisheries Commission (Neave, 1964; Royce et al., 1968). Nevertheless, much research remains to be done on mechanisms of migration, which remain controversial (Hansen and Quinn, 1998). Is homing to natal streams obligatory or facultative in Paciﬁc and Atlantic salmon? With 100 years of hindsight, this may seem like a ridiculous question, but as the famous ichthyologist Jordan (1887) pointed out, “It seems more probable than parent stream theory that the young salmon hatched in any river mostly remain in the ocean, within a radius of 20, 30, or 40 miles of its mouth.” This idea was in keeping with the modern migration theory, prevalent in Europe during the late nineteenth century, which viewed seasonal disappearances of diadromous and coastal ﬁshes as restricted migrations to deeper near-shore coastal regions rather than the result of extensive ocean migrations. In this view, homing is not due to directed behaviors ensuring philopatry, but is due only to proximity.

A. NATURAL TAGS Gilbert provided early evidence for the parent stream theory in a series of reports on Fraser River sockeye salmon (1914–1919) with an early application of natural tags. Like his contemporary Hjort (1909, 1914), Gilbert developed a demographic approach based on annuli in scales to explain cycles of abundance. Further, like Hjort, he used scale circuli patterns during the ﬁrst year of life as certiﬁcates of origin. Distinct frequencies of circuli that were associated with the early freshwater juvenile period occurred among tributaries and, on occasion, even within tributaries (Fig. 3-2; Gilbert, 1919). Comparisons between juveniles and adults captured at Fraser River spawning localities showed the same number of circuli corresponding to the freshwater juvenile period, leading Gilbert (1915) to conclude, “Examination of the scales had removed any possible doubt that the progeny of the Fraser River ﬁsh return to the Fraser at their maturity, and that this is true also of the ﬁsh of each of the large river basins.”

23

Number of First Growth Season Circuli

Fish Migration and the Unit Stock: Three Formative Debates

A

30

A D

B C

E

20

10

0

Birkenhead Harrison Hatchery Pitt Lake Hatchery FishCanyon Morris Creek Skookumchuck

Spawning Locality FIGURE 3-2. Box-whisker plots of circuli counts of Fraser River juvenile sockeye salmon, collected in 1918 (Table III; Gilbert, 1919). Not shown due to low sample size (n = 12) is the Hanceville locality. Sample sizes for other sites ranged from 40 to 153. Letters represent similar and discrete statistical groupings (ANOVA; Tukey post hoc test; a = 0.05).

Were these natural tags the result of environment or lineage? Gilbert argued for both causes. He believed that heredity ﬁxed racial differences existed among tributaries, but also stated that “. . . during their life in freshwater salmon are subjected to obviously diverse external conditions. These are most marked perhaps, between salmon inhabiting different watersheds, but undoubtedly exist, if in less degree, between those that live for a year or more in different lakes belonging to the same river basin” (Gilbert, 1915). Although speculative, reconciliation of these two apparently diverging views may have come from Gilbert’s appreciation for trait variance within species and populations: “. . . the peculiarities of each race in the matter of habitat do not lie outside but within the total range of variation as found in other river basins. Nothing new to the species, then, is found in a rare phenomenon, within the range of variation exhibited elsewhere” (Gilbert, 1914). Thus, Gilbert may have recognized well in advance of population genetics (e.g., Fisher, 1930) that heredity leads to modalities in trait expression rather than invariant race-speciﬁc traits. To refute Jordan’s criticism of the parent stream theory, it was insufﬁcient to only recognize trait differences between tributaries—this could be due to local

24

D. H. Secor

ambits rather than philopatry. Initial evidence for philopatry came from observations of low straying rates between adjacent spawning tributaries. For instance, from many thousands of sockeye salmon examined from Rivers Inlet Race, only 24 were observed to exhibit different scale patterns, which were attributed to the adjacent Fraser River race. Gilbert (1917) concluded, “. . . the spawning runs in these streams and tributaries enjoy practical isolation, each from every other. . . .” The question of hatcheries underlay the early work on Paciﬁc salmon, as it did with Norwegian cod. As previously described, Dahl (1909) and Hjort (1909) used the issue of scale to argue against local beneﬁts due to local releases of cod. In contrast, Gilbert’s early work suggested that hatcheries could have local beneﬁts. Gilbert (1915) advised that for hatcheries to be effective, “every stream must receive its own quota of fry.” Using natural tags to evaluate philopatry entails a troubling conundrum. What if the salmon of two tributaries exhibit the same certiﬁcate of origin? Gilbert (1919) noted two possible causes: Racial segregation has not occurred, or segregation has occurred, but has not resulted in divergence in the natural tag. Gilbert and others (e.g., Neave, 1964; Harden Jones, 1968; see also Waldman, 1999) have used multiple phenotypic traits (e.g., growth, age at maturity, length of life in freshwater, color, quality of ﬂesh) to reduce the chance that common certiﬁcates of origin for different races will be misinterpreted. Still, the parent stream theory entails lineage and natural tags can only provide circumstantial evidence. Later, Huntsman (1937a), a chief antagonist of the parent stream theory, would argue strongly against the use of natural tags in support of philopatry: “The characters that have been used to distinguish ‘races’ in species of marine ﬁshes, such as herring and cod, are being demonstrated to result from the action of the environment on the individual during its lifetime, so that it seems doubtful whether there are heritable differences between the populations of different districts.”

B. MARKING EXPERIMENTS Tagging should provide advantages in investigating homing because origins (spawning tributaries and localities) can be identiﬁed without the reliance of distinguishing natural tags. A central problem in early marking experiments for Paciﬁc and Atlantic salmon was low rates of returning adults in comparison to tagged and released juveniles. Return rates of adults from a marked sample of juveniles ranged from 0% to 10% in early studies. What then happened to the majority of tagged juveniles? Were these lost due to straying to other systems, tagging and release mortality, mortality at sea, ﬁshing mortality, or were they

Fish Migration and the Unit Stock: Three Formative Debates

25

somehow otherwise “lost at sea,” as Huntsman and others believed? Interestingly, this debate remains important in investigations on homing mechanisms in salmon. Geolocation homing mechanisms entail high overall ocean mortality but efﬁcient (>90%) homing by adult survivors (Quinn and Groot, 1984; Quinn and Dittman, 1990). Random walk models imply low ocean losses prior to spawning migrations, but less efﬁcient (<10%) homing by adult survivors (Saila and Shappy, 1963; Jamon, 1990). Presumably in such models those adults that do not home, wander and are lost at sea. In his compendium of migration studies, Harden Jones (1968) reviewed decades of early marking experiments, but highlighted the Cultus Lake experiment on Fraser River sockeye salmon conducted by Foerster (1936) as being instrumental in providing evidence for homing, and experiments conducted by A. L. Pritchard as evidence against substantial straying to adjacent tributaries. In successive years, Foerster’s group marked (adipose ﬁn clip) ~105,000 and 365,000 sockeye juveniles as they migrated seaward through a restricted outlet from Cultus Lake. For the larger release, a total of 12,803 returning salmon were recovered over a 3-year period. Of these, 71% were recovered from salmon canneries, which obtained salmon from commercial ﬁsheries in coastal areas approaching the Fraser River. The remaining 2,856 ﬁsh returned to Cultus Lake. Thus, those salmon not captured in coastal ﬁsheries homed to Cultus Lake. Pritchard marked a portion (<5%) of pink salmon during their seaward migration from McClinton Creek in northern British Columbia (Queen Charlotte Islands) during the 1930s. In 1940, he examined returns in McClinton Creek and immediately adjacent streams for ﬁn clips. In McClinton Creek, 781 of 35,521 (2.2 ± 0.1%) ﬁsh were marked. In four adjacent streams, only two marked ﬁsh were observed of 4,075 ﬁsh examined (0.04%). Early marking experiments showed evidence for directed spawning migrations from coastal areas (Foerster, 1936) and lack of straying to adjacent streams. Still, these studies can show strong biases due to low recapture rates, unknown tagging effects, ocean and ﬁshing mortality, and recapture reporting rates. Further, we still cannot conﬁdently distinguish the role of proximity vs. philopatry. For instance, might the propensity of Cultus Lake sockeye salmon to home be due to the sampling domain, which did not include the ocean or coastal approaches to rivers other than the Fraser River itself? Further, subsequent tagging studies showed higher incidence of straying than indicated by the Pritchard study, leading Huntsman (1937b) to later question the prevalence of straying: “In quite a number of instances salmon marked or tagged in one river have been recaptured in another. . . .” These issues would motivate a more manipulative approach to test straying rates by transplanting salmon across distant watersheds.

26

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C. TRANSPLANT EXPERIMENTS Many early transplant studies addressed practical issues such as restoring depleted runs or enhancing ﬁsheries, but Snyder (1931) deliberately tested the parent stream theory by transplanting marked Chinook salmon juveniles of Sacramento River progeny into the Klamath River, and vice versa. Of 25,000 marked Sacramento juveniles released into the Klamath River, 10 were recovered at sea and 50 were recovered in the Klamath River. None of the 15,000 Klamath River juveniles released into the Sacramento River were recovered outside of coastal areas. This result was evidence that in one instance, spawning adults return to the stream in which they experienced their early growth rather than the stream where their parents resided. If the parent stream theory implied that progeny return to the stream of parents regardless of early rearing environments, then Snyder’s experiment and subsequent ones showing similar results refuted the popular theory. From a practical viewpoint, transplant and marking experiments did support the view that anthropogenic effects on spawning and rearing habitats of Paciﬁc salmon would directly affect returns of subsequent generations.

D. THE YEAR-CLASS PHENOMENON (AGAIN) Much like Norwegian and North Sea herring, region-speciﬁc recruitment cycles in Paciﬁc salmon supported the view that populations were spatially structured. Contemporary to Hjort’s (1914) important observation of the impact of the 1904 year-class on Norwegian herring ﬁsheries, Gilbert (1914) noted large interannual variation in Fraser River sockeye spawning runs with a 4-year dominant yearclass cycle. Based on scales, differences in age structure were noted between the Fraser Rivers Inlet races. Gilbert attributed these cyclical patterns to differential reproductive success among years, which were tributary-speciﬁc. For the upper Fraser River, this cycle was interrupted by construction of a train corridor through Hell’s Canyon, which resulted in a large rock fall into the river (see Smith, 1994, for a review of this case study). Gilbert lamented that the 1917 spawning run “. . . was deprived to a large extent by the progeny of up-river spawners. . . . Four years before, on their ascent of the river, these had been arrested at the canyon and died in the lower river without laying their eggs.” Here, local anthropogenic inﬂuences provided strong circumstantial evidence for the signiﬁcance of homing on population dynamics. To varying degrees the localized inﬂuences of dams, hatcheries, pollution, and ﬁsheries in spawning tributaries have a larger impact on anadromous salmon than other temperate coastal ﬁshes due to the precise nature of their homing (e.g., Schaller et al., 1999).

Fish Migration and the Unit Stock: Three Formative Debates

27

While recruitment cycles and local effects showed that population structure was associated with homing within river systems, they did not provide support for the parent stream theory per se. We have yet to address the second issue prompted by the theory: Where do spawning runs of maturing ﬁsh originate?

E. OCEAN STUDIES In 1937, University of Toronto Professor A. G. Huntsman, in two letters to the journal Science, established himself as a principal skeptic of the parent stream theory. In particular, he was critical of the view that the return spawning migrations begin in distant ocean feeding grounds. “I maintain that the return of salmon to their native rivers from distant feeding grounds in the ocean is not only merely a theory, but one with a frail basis” (Huntsman, 1939). Huntsman’s view was similar to that of Jordan (see previously) and similar in concept to the modern migration theory of the late nineteenth century. Atlantic salmon (and presumably Paciﬁc salmon) moved into coastal waters, proximate to the spawning river’s “zone of inﬂuence.” Once outside of this zone, salmon became lost and may have moved to other rivers or traveled out to sea. Very little was known about ocean distributions of salmon. Why should we expect salmon to occur outside of coastal waters where they were principally harvested? Understanding the oceanic occurrences of salmon would require a decade of concerted ocean studies, which came about due to the rapid emergence of ocean purse-seine and long-line ﬁsheries for Paciﬁc salmon. Harden Jones (1968) cast Huntsman as a stalwart skeptic who presented important challenges and uncertainties to the widely held, but poorly supported parent stream theory. Indeed, Huntsman stated, “For questioning homing, I have suffered the fate of a heretic.” Still, Huntsman held a pretty high standard for this theory throughout his career. Initially, he argued against homing based on natural tag studies, suggesting that there was little evidence for directed homing migrations within tributaries (Huntsman, 1937a, b); then when presented with such evidence, he focused on uncertainties in oceanic migrations and the mechanism of homing: “Homing is a very deﬁnite feature in salmon migration, but it is the end of wandering rather than a directive factor” (Huntsman, 1950). And when multiple mark-recapture data showed that when an individual tagged as a juvenile occurred in distant ocean waters and then was recaptured as an adult in the natal river, Huntsman (1952) argued that “Recognition of home water does not direct the ﬁsh home, but may stop the wandering. Very foreign water may actually be avoided by the ﬁsh.” Huntsman held the view that while some salmon could home from distant waters, most did not. His contemporaries also thought that many salmon must be “lost at sea” (e.g., Saila and Shappy, 1963; Harden

28

D. H. Secor

Jones, 1968). Such views were in part reﬂective of a lack of directed studies on oceanic distributions of Atlantic salmon, a situation that would soon be remedied for Paciﬁc salmon by the International North Paciﬁc Fisheries Commission. In 1955, a convention by ﬁshing nations on North Paciﬁc salmon (Canada, Japan, and the United States) formed the International North Paciﬁc Fisheries Commission and initiated migration and distribution studies of salmon in the North Paciﬁc Ocean and Bering Sea. Data and analyses from the resulting studies conducted in the late 1950s and early 1960s remain fundamental to knowledge and understanding of Paciﬁc salmon oceanic migrations (Pearcy, 1992; Groot and Margolis, 1998). Further, these studies yielded a new dynamic understanding about salmon migration and oceanography. For Atlantic salmon, ﬁsherydependent mark-recapture studies on Atlantic salmon have occurred over the past several decades, but due to the limited scale of these investigations, patterns of oceanic distribution and oceanic migration circuits remain poorly understood (Hansen and Quinn, 1998). Neave (1964) and Royce et al. (1968) summarized much of the Commission’s research ﬁndings for pink, sockeye, and ocean-run steelhead, which included trawling directed at intercepting the migrations of juveniles, extensive mark recapture, and ocean ﬁshery (purse-seine and long-line) data. These sources of evidence left little doubt as to the oceanic dependency of local races. Royce et al. (1968) described entire oceanic migration circuits, the underlying oceanography, and possible mechanisms leading to homing. These oceanic migration circuits occurred as single annual cycles for pink salmon, but were repeatedly undertaken as annual cycles by longer-lived salmons (Fig. 3-3). “Downstream” (conﬂuent with major ocean current) migration circuits were described for British Columbia pink salmon in the Alaska Gyre; for eastern Kamchatka pink salmon in the current system centered on the Bering Sea and North Paciﬁc Ocean; and for Bristol Bay pink salmon in the Bering Sea and North Paciﬁc Ocean (Fig. 33). Neave (1964) focused on migration trajectories from oceanic aggregations to spawning grounds. Based on Japanese high sea ﬁsheries data, he observed “. . . well ordered direction and timing of the movements towards appropriate spawning grounds . . . ,” and concluded that “. . . the evacuation of the high seas is not a random affair. . . .” Thus, the weight of evidence from directed North Paciﬁc studies countered the idea that salmon were lost at sea: “. . . we believe that the salmons’ migrations could not be performed if they migrated or drifted at random . . .” (Royce et al., 1968).

F. THE PARENT STREAM THEORY and POPULATION THINKING The parent stream theory or philopatry is a ﬁrst principle in ﬁsheries science. There is no more important assumption that underlies the management of ﬁshery

Fish Migration and the Unit Stock: Three Formative Debates

29

FIGURE 3-3. Circuits of oceanic migrations of Bristol Bay sockeye salmon based on landings and tagging data. From Figure 17, Royce et al. (1968).

stocks. As Huntsman and Harden Jones emphasized, the parent stream theory not only requires homing, but also requires a complex life cycle. A ﬁsh must leave natal areas to juvenile and adult habitats and then return. The spatial ambit and temporal frequency of this life cycle was codiﬁed in the migration triangle, conceived by Harden Jones (1968) and later modiﬁed by D. H. Cushing (Secor, 2002). For marine ﬁshes, Cushing (1975) elaborated the migration triangle to include the concept of hydrographic containment, that life stages and migrations were constrained within oceanographic systems that favored denatant migrations of juveniles away from spawning areas and countranatant migrations by adults back to spawning areas (Fig. 3-4), a view favored by Harden Jones (1968) based on the many case studies he examined. According to the views of philopatry and hydrographic containment, salmon were not lost or randomly cast about the oceans, but were distributed in time and space according to a complex life cycle designed to guarantee high levels of philopatry. Once such patterns were elucidated, recruitment cycles could be well matched with the inﬂuence of ﬁsheries that were arrayed across the migration circuit. Similarly, other natural and anthropogenic inﬂuences that varied in time and space could be allocated in proportion to their effect on a given spawning population. The studies on Paciﬁc salmon were taken as demonstrative of a general pattern for temperate and boreal marine ﬁshes: “In temperate seas ﬁsh usually return to the same spawning ground each year at the same season, much as do Paciﬁc salmon” (Cushing, 1995, p. 86).

30

D. H. Secor

Adult Stock

t en

Denatant

itm

A

Spawning area

cru Re

De na tan Co t ntr an ata nt

C

B

Nursery area

FIGURE 3-4. Migration triangle as depicted by D. H. Cushing (1982). Cushing substantially modiﬁed F. W. Harden Jones’s (1968) migration triangle, which depicted seasonal migrations of adults rather than an ontogenetic circuit (Secor, 2002).

Despite remaining issues related to the assumption of the parent stream theory and population substructure (see last section), marine ﬁsheries research and management during the last half of the twentieth century moved to a typological view of stock structure and ﬁsh migrations. Migration studies and stock structure studies were directed at determining for each population a single spawning area, a single nursery area, and a single migration triangle (Stephenson, 2002). This approach has been substantially aided with the advent of biochemical markers, some of which represent population lineage and are thus more directly applicable to the issue of parental natality. An interesting late twentieth-century theory related to philopatry was the “member-vagrant” hypothesis (Iles and Sinclair, 1982; Sinclair, 1988), which countered the idea of denatant dispersal of juveniles, but rather emphasized the importance of larval habitats with physical retention features. Those larvae/juveniles that failed to arrive in prescribed nursery areas were “vagrants”—lost at sea. In this view, populations could persist only if spawning behaviors guaranteed that a large fraction of larvae arrived at nursery areas where retention would occur. Interestingly, Harden Jones doubted that single population circuits would survive generations. First, in consideration of homing migrations, “Homing could be a disadvantage when ﬁsh persist in returning to spawn in an area . . . where conditions have become unfavorable. . . . The only biological insurance against this is a satisfactory level of straying and a multiplicity of spawning grounds. . . .” Further, in consideration of population substructure, “. . . the capacity to meet change lies, not in the ﬂexibility of each unit, but the multiplicity of units.” These observations were remarkably prescient of metapopulation and contingent concepts, which have only emerged during the past decade to meet some of the shortcomings of the parent stream theory.

Fish Migration and the Unit Stock: Three Formative Debates

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IV. DEBATE 3. THE EEL PROBLEM: WHO CONTRIBUTES TO REPRODUCTION? Population thinking in ﬁsheries science largely developed outside ideas related to the role of races in natural selection (e.g., Darwin, 1859). Rather than evolutionary groups, races (populations) were conceived as ecological entities—groups of individuals that had common natality and migration patterns and were therefore affected similarly by exploitation, climate, and environment. Later, the widespread use of neutral lineage markers would depend on theoretical developments of microevolution. Still, stock structure studies were not explicitly related to evolution or speciation. Sinclair (1988) provided an initial theoretical basis for the role of evolution in stock structure by suggesting that population richness was itself a selected attribute, deﬁned by oceanographic retention areas that favored larval survival. In addition, recent emphasis on conservation and biodiversity of aquatic resources has emphasized the relationship between population structure and endangerment of exploited species (e.g., Waples, 1995). Still, there remains little cross-fertilization between ﬁsheries scientists and evolutionary biologists who work on ﬁsh (Conover, 2000). As recent evidence, consider that a series of papers devoted to the species concept in ﬁshes, published in the journal Fish and Fisheries [Vol. 3(3); Stauffer et al., 2002], failed to address the role of population thinking in the species concept. Indeed, the journal issue did not contain a single reference to population or stock structure in ﬁshes. Within this dearth of studies on the species concept and population thinking in ﬁsheries, there exists the so-called eel problem. During the past century, the linked problems of natality and migrations of Atlantic eels have required simultaneous thought about population structure and speciation. Indeed, debate on the role of population structure and speciation remains active for Anguilla species and important for their management (e.g., Tsukamoto and Aoyama, 1998; Tsukamoto et al., 2002). Recurring as part of the resolution to the eel problem is the view that adult eels in major portions of their range do not reproduce (Tucker, 1959; Harden Jones, 1968; Tsukamoto et al., 1998), or that some eels disproportionately contribute to reproduction dependent on the habitats to which they recruit (Limburg et al., 2003). That some eels are destined never to reproduce seems a peculiar idea, but such proposals have been advanced for salmon due to ocean wanderings (see previously) and for vagrant ﬁshes that range too far from their migration triangle (Sinclair, 1988). Might some juvenile/adult habitats and associated stocks require constant subsidy from reproduction from adults that grew elsewhere? Managing the effects of exploitation would engender risk if reproducers were selectively harvested or otherwise affected (Castonguay et al., 1994), an issue now attracting consideration in the design of exploitation refuges and marine protected areas.

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D. H. Secor

A. MEDITERRANEAN vs. NORTH ATLANTIC ORIGIN

OF

EELS

Well after Aristotle’s musings on spontaneous generation of eels in mud, evidence for the marine origins of eels began with the Italian naturalist G. B. Grassi, who recognized that Leptocephalus brevirostrum was in fact the larval form (termed leptocephalus) of Anguilla (Grassi and Calandruccio, 1897). Based on local collections of leptocephali, Grassi assumed that Anguilla spawned in the Straits of Messina (narrow straights north of Sicily). Proximity between inland freshwater ﬁsheries for eels and coastal spawning areas (the modern migration theory) implied local populations of eels. “If Grassi’s hypothesis was correct, there were likely to be problems of over-ﬁshing if too many eels were trapped on their way to the sea, the implication being that each river was supplied by elvers derived from a local spawning ground” (Harden Jones, 1968). Schmidt, through decades of directed ocean collections of the pelagic leptocephali, discovered that eels in the Mediterranean and North Atlantic originated in the Sargasso Sea (Schmidt, 1922). The Danish scientist’s efforts in tracking ever-decreasing sizes of leptocephali across the Atlantic represented important pioneering research in ﬁsheries oceanography (Jakobsson, 2002). Schmidt argued for a single spawning region in the Sargasso Sea for eel stocks throughout Northern and Southern Europe. Evidence against a separate Mediterranean spawning location included: (1) similar myomere counts for leptocephali captured in the Mediterranean with those captured in the Northeast Atlantic; (2) increased leptocephalus size inside the Mediterranean in comparison to outside the Mediterranean, implying older ages and longer periods of dispersal from the Sargasso Sea; and (3) earlier seasonal occurrences of leptocephali outside of the Strait of Gibraltar in comparison to those captured off Italy.

B. NORTH ATLANTIC EELS: SPECIATION POPULATION STRUCTURE?

OR

That the European eel life cycle must include distant oceanic spawning in the Sargasso Sea was a surprising discovery; one not well accommodated by the emphasis of racial differences and population structure emerging from the ICES community (Hjort, 1909; 1914). If all eels, from the Baltic to the Mediterranean, were the result of spawning in the Sargasso and years of larval drift along the Gulf Stream, then all eel ﬁsheries were dependent on one panmictic population: species and population were one and the same. Further, the Sargasso Sea not only included all European eel A. anguilla spawning, but also proved to be the only spawning region for the congeneric American eel A. rostrata. Indeed, in many of Schmidt’s samples both species (distinguished by myomere counts) co-occurred as leptocephali. This ﬁnding presented a particularly difﬁcult conundrum: Over

Fish Migration and the Unit Stock: Three Formative Debates

33

generations of presumed co-occurrence of spawning in the Sargasso Sea what led to maintenance of distinct eel species? Schmidt’s hypothesis was that leptocephali of European eels were entrained into the Gulf Stream and drifted for 2 to 3 years until they reached regions of Northern Europe (earlier) and the Mediterranean (later), whereupon they metamorphosed into glass eels and entered the estuaries and rivers of Europe. Juvenile habitats in North America were substantially more proximate to the Sargasso Sea, and leptocephali could be expected to reach most of these habitats within one year. Thus, if the leptocephalus development rates and stage durations were substantially different between American and European eels, then speciation might be maintained over generations. Evidence for this came from (1) drift bottle studies, which suggested leptocephalus drift must be substantially greater for eels arriving in European as opposed to North American estuaries; (2) European leptocephali that were over 33% longer at metamorphosis than American eels; (3) slower growth and development rates of European eels vs. American eels based on an assumed overlapping spawning season during spring (Harden Jones, 1968) (for instance, when European eels passed Bermuda, they were not competent to ingress into freshwater); and (4) sample sizes of A. rostrata leptocephali collected by Schmidt that were substantially less than A. anguilla leptocephali, corresponding to differences in ﬁshery yields between the two species. (While early twentieth-century harvest records are probably insufﬁcient to make this claim, production of European eels is likely higher. From 1980 to 2000, ﬁshery yields were an order of magnitude higher for A. anguilla in comparison to A. rostrata.) Still, without direct observations of spawning eels, these lines of evidence only provided circumstantial support for speciation in North Atlantic eels.

C. FAILED ADULTS In an inﬂuential Nature article, Tucker (1959) played the lone role of skeptic by suggesting an alternative set of explanations for regulation of eel stocks produced by Sargasso Sea spawning. Eel species were not distinct, but represented different phenotypes due to environmentally mediated meristic characters. He proposed that temperature differences across the large region over which small leptocephali had been observed in the Sargasso Sea could generate meristic differences in vertebral (myomere) counts. Different origins within the Sargasso Sea not only resulted in different dispersal fates (North America or Europe), but also associated vertebral counts. But this proposal was not the most controversial part of Tucker’s thesis. He forcefully argued that the distance for adults to migrate from European waters to the Sargasso Sea was too vast. “European eels . . . do not succeed in returning to the ancestral spawning ground, but perish in their own

34

D. H. Secor

continental waters.” Tucker believed that stocks of so-called European eels were sustained by American eel parentage. The idea of failed reproduction was similar to the view that much Paciﬁc salmon production was lost at sea (see previously), but here stock structure of eels had consequence: If stocks failed in North America, so too would they fail in Europe. A scientiﬁc debate on Tucker’s proposal quickly emerged. Chief criticisms were against temperature affecting stable differences in myomere counts in American and European eels and Tucker’s view that “. . . Europe and North Africa are regularly colonized by eels of American ancestry doomed to perish in a fruitless suicide-migration” (Tucker, 1959). D’Ancona (1959) argued that Tucker’s hypothesis of temperature gradient differences would produce continuous variation in myomere counts rather than the observed bimodal pattern unless there was a threshold or critical temperature. Tesch (1977) and Harden Jones (1968) argued that expected temperature effects on vertebral counts from salmonid studies would lead to a difference of less than one vertebral count rather than seven, which would be required to explain the observed difference between European and American eels. In recent decades, karyotyping and other genetic studies (Passakas, 1981; Lintas et al., 1998) have determined the separation of species on grounds other than meristics. The issue of whether European eels can successfully return to the Sargasso Sea has been more difﬁcult to resolve and remains an active area of speculation. Tucker remarked that it was surprising that no emigrating silver (mature) eels had ever been observed in or near the Strait of Gibraltar. Others stated that the reason for an apparent absence of silver eels there and elsewhere was lack of ﬁsheries and appropriate gear to observe returning eels (D’Ancona, 1959; Deelder, 1960). From an energetics perspective Tucker articulated this colorful metaphor: “. . . the advanced modiﬁcations of the European eel appear to be ill-adapted to . . . a return journey. We may indeed know that a motorist is in the best of health but, as we watch him driving down a mountain road, intoxicated and without brakes, we can still legitimately say that his celebration was premature that he is likely to reach his destination.” Did European eels have sufﬁcient reserves to undertake a 3,000- to 4,000-km migration to the Sargasso Sea? Based on telemetry studies, Tesch (1977) argued that with a fat content of >20%, and using advantageous currents, an adult eel could accomplish a 6,000-km migration in 5 months. More recent studies have failed to deﬁnitively resolve whether energetic stores are sufﬁcient to undertake spawning migrations from waters as distant as the Baltic Sea. Svedäng and Wickström (1997) observed that many eels occurring in the Baltic Sea had limited fat reserves (<10%) and proposed that most of these eels may delay maturation and migration to the Sargasso Sea. Further, they believed that transformation to the adult silver eel form and related migration behaviors may be reversible. Van Ginneken and

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vanden Thillart (2000) conducted swimming metabolism studies on large eels and predicted that only 40% of the total fat reserves of a 2-kg eel with a 20% fat content would be utilized for a 6,000-km spawning migration. Still, a 2-kg eel would represent an extremely large eel. For a more typical size of 500 g, approximately 125% of fat reserves would be required to accomplish a 6,000-km spawning migration. Limburg et al. (2003) estimated a range of possible costs for eels of different sizes and origins, of which only a subset would be able to accomplish a 6,000-km spawning run based on energy reserves. While the issue of speciation in eels has been resolved through genetic studies, there remains the question of whether more distant yellow eel stocks such as those in the Baltic Sea substantially contribute to the pool of spawners.

D. “FRESHWATER EELS”

AND

CONTINGENT THINKING

Anguilla eels are commonly referred to as freshwater eels because it is assumed that their longest juvenile period, the yellow eel-phase, is dependent on freshwater habitats. Still, important ﬁsheries are centered in estuaries (ICES, 2001), and scientists have suggested that brackish water regions may represent better growth habitats than freshwater systems (Helfman et al., 1987). In a recent letter to Nature, Tsukamoto et al. (1998) made the noteworthy discovery that some A. anguilla complete their life cycles without ever moving into freshwater. Further, based on a small sample of silver eels collected from the North Sea and using otolith microconstituent analysis, they concluded that freshwater eels did not contribute to the spawning population. Tsukamoto’s speculation on failed reproduction by freshwater eels was controversial, and subsequent analysis of a larger sample indicated that some Baltic Sea silver eels did in fact originate from freshwater eels (Limburg et al., 2003). Still, estuarine and marine eels made up the majority of these silver eels. Tsukamoto et al.’s observation of divergent life cycles within eel stocks has been conﬁrmed for several Anguilla species worldwide based on otolith microconstituent analysis (Arai et al., 2000; Tzeng et al., 2000; Tsukamoto and Arai, 2001; Limburg et al., 2003; Morrison et al., 2003). Thus, the issue of how freshwater vs. brackish and marine habitats contribute to reproduction is important for eel management, particularly because freshwater and estuarine systems are differentially affected by exploitation and habitat degradation (ICES, 2001). The observed modalities of individuals, within the same population, structured by discrete migration is well epitomized by eels, but in fact has been observed frequently for other marine species. Secor (1999) termed groups of individuals structured by lifetime migration patterns as contingents [the term contingent was based on early usage by Hjort (1914) and Gilbert (1914)] and proposed that

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contingents represented important components of population substructure, particularly in strategies of spatially explicit ﬁsheries and habitat management. This is exempliﬁed by a recent proposal to establish exploitation refuges in freshwater eel habitats where little exploitation now occurs but could develop in the future (ICES, 2001). If freshwater contingents produce relatively few reproducing adults, then such a management strategy would prove ineffective (Morrison and Secor, 2003).

E. MORE EEL PROBLEMS

AND

POPULATION THINKING

The issue of speciation mechanisms for Atlantic eels has resurfaced in recent research. Wang and Tzeng (2000) observed hatch dates, which were fairly discrete for each species. Although differing substantially in season, direct observations of small leptocephali by McCleave and Kleckner (1987) also supported the view of allopatric spawning by A. anguilla and A. rostrata. While the assumption of daily increment formation in otoliths has not been substantiated, analyses indicate that dispersal is of much shorter duration for European eels (9–18 months) than suggested by Schmidt and other early investigators (LecomteFiniger and Yahyaoui, 1989; Wang and Tzeng, 2000). This shorter duration has called into question dispersal mechanisms for European eel leptocephali that now have an insufﬁcient larval duration to allow transport by the Gulf Stream. McCleave et al. (1998) have proposed an alternative route by prevailing northeast ﬂow from northeast regions of the Sargasso Sea. In contrast to recent oceanographic and otolith studies, genetics studies have further muddied the issue of population structure in Atlantic eels. While A. rostrata remains consistently different from A. anguilla when genetic markers are used (e.g., Tsukamoto and Aoyama, 1998; Lehmann et al., 2000), Wirth and Bernatchez (2001) interpreted microsatellite DNA as evidence for population structure within A. anguilla. They observed a latitudinal gradient in genetic differentiation and suggested that population structure was maintained through differential seasonality in silver eel return migrations. In a second independent study, further evidence was given for nonrandom genetic structure of A. anguilla stocks (Daemen et al., 2001). Still, a more conservative genetic marker (mitochondrial DNA) showed genetic homogeneity of A. anguilla (Lintas et al., 1998), and the conclusion of population structure in Atlantic eels remains controversial, in part because it is difﬁcult to conceive of a likely mechanism to retain population structure. Flaming controversy, an Italian scientist has sought to redeem history (Casellato, 2002): “[Recent genetic studies] could lead to support of . . . Grassi, who was convinced that European eels could reproduce in the Mediterranean Sea.” The mystery of speciation and population structure in Atlantic eels will endure until spawning eels are captured (Tsukamoto et al., 2003). Issues of how eel

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stocks contribute to overall reproduction remain very active and important, particularly as both species have experienced large declines in ﬁshery yields during the past 20 years (ICES, 2001). The question of who reproduces, epitomized by eel investigations over the past century, has become critical as we become more reliant on management strategies of regional ﬁshing allocation, exploitation refuges, and habitat protection. The eel example also demonstrates that such strategies may sometimes necessitate alternatives to “population thinking” (e.g., contingent and metapopulation thinking).

V. SUMMARY These three examples of population structure in marine ﬁshes support the strongly held views of Hjort and Gilbert that dynamics of living resources occurred within spatial domains deﬁned by population migration circuits. There can be no doubt after decades of genetic study that salmon and most other marine ﬁshes show varying degrees of homing. Fish commonly return to restricted spawning regions, and migration trajectories are seasonal and connect important reproductive, nursery, feeding, and winter habitats. Still, Hjort and Gilbert also noted an important source of biodiversity within the migration circuits. Nordland “fat” herring undertook a different migration circuit than the other Norwegian herring; a contingent of anadromous sockeye salmon did not partake in oceanic migrations. Later, Tsukamoto observed that some freshwater eels in fact persisted in marine waters throughout their life history. These deviations have been ignored during the ﬁrst century of ﬁsheries science, in part due to the view that populations are each deﬁned by a single migration triangle (Secor, 2002). Biodiversity in spatial behaviors within populations can have signiﬁcant consequences on recruitment rates to regional ﬁsheries, efﬁciency of hatchery enhancement programs, anthrophogenic effects due to degraded or lost habitats, exposure of ﬁsh to contaminants, and overall resiliency of a population or metapopulation to longer term climate or anthropogenic change. Here, Booke’s (1981) call to operationally deﬁne stock based on the issue at hand is prudent. Regardless of the complexity of population structure, stock can be deﬁned at scales appropriate to the question at hand. Still, an increased focus on resiliency and conservation by policy makers and managers will mean that stock deﬁnition will encompass a broader perspective in space and time (Fig. 3-1). Further, many of the assumptions underlying population thinking remain unveriﬁed or inadequate. Consider the following: 1. In several presumably large subocean basin populations, it is increasingly evident that local renewal processes may be important to population and metapopulation resiliency. Large temperate ﬁsheries for Norwegian spring-spawning herring, Japanese sardine, and Scotian Shelf Atlantic

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cod have all experienced severe range contraction into local coastal or shelf regions during nadirs of population abundance (Hutchings, 1996; Watanabe and Wada, 1997; Holst et al., 2002). Thus, during decadal phases of collapsed abundance, local subpopulations may be critical as sources for the next population expansion. Also, through use of oceanographic models and otolith microconstituent analysis, retentive life cycles (“self-recruitment”) have been observed in pelagic spawning species despite the large potential for advection, drift, and straying (Jones et al., 1999; Thorrold et al., 2001; Swearer et al., 2002; Sponaugle et al., 2002). Thus, local processes may be important in sustaining large opensystem populations. 2. Knowledge of the underlying mechanism of homing is incomplete for salmon and unstudied for the vast majority of ﬁshes managed as unit stocks. For salmon, Great Lake studies support the role of olfaction as a local/regional means for identifying natal stream (Hasler and Scholz, 1983), but mechanisms of oceanic migration remain unknown and speculative (Hansen and Quinn, 1998). Still, rapid progress is being made in the use of electronic tags in oceanic migration studies, which may provide critical clues about possible homing mechanisms (Friedland et al., 2001). Indeed, the lack of a certain homing mechanism in salmon has kept Huntsman’s views of oceanic wanderers alive (Jaman, 1990; but see Quinn and Groot, 1984, for evidence against this view). Thus, in salmon and other marine ﬁshes there remains no complete explanation for the most important process underlying the unit stock—philopatry. 3. The emphasis on natural and lineage markers in deﬁning the unit stock has not always provided useful resolution in stock structure issues. The confusion in using different types of markers should highlight that stock is not always clearly deﬁned (Begg and Waldman, 1999). Also, markers result from processes that range in scale from ecological to generational to evolutionary (Waldman, 1999). Thus, deﬁning stock in an operational manner should dictate which markers are used (phenotypic vs. lineage), rather than vice versa. Further, we should not take the term stock to be synonymous with population, as has been done in the past (e.g., Booke, 1981; Pitcher and Hart, 1982). Stock (Schmidt, 1909: “source of ﬁsh”) is a speciﬁc portion of a population that is inﬂuenced by an anthropogenic activity that affects population productivity (Russell, 1931; Ricker, 1975; Secor, 1999). Similarly, tests of the parent stream theory using natural or genetic markers do not test the same assumptions related to natality— natural tags focus on source habitat; genetic markers focus on source lineage. It should be noted that the use of natural tags and electronic tags in recent years has seen rapid progress in better tests of homing in marine ﬁshes and in more precisely quantifying anthropogenic

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inﬂuences on ﬁsh populations (e.g., Block et al., 1999; Zlokovitz and Secor, 1999). 4. Alternative migratory pathways remain poorly accommodated in ﬁsheries and habitat management. Unusual occurrences that were previously viewed as anomalies, strays, or vagrants were ignored because they did not match expectations for a given migration circuit. The “multiplicity of units” within a population (Harden Jones, 1968) or contingent structure was recognized early but forgotten for many decades, which favored research and management directed in support of a more monolithic view of migration—the migration triangle. The role of contingents and strays in ﬁshery problems has again emerged with the advent of changed management goals and new stock identiﬁcation methods (Secor, 1999; Secor et al., 2001; Limburg et al., 2001; Tzeng et al., 2000).

ACKNOWLEDGMENTS Support for preparation of this manuscript came from the National Science Foundation (OCE0324850). I thank J. Waldman and A. Folkvord for comments on an earlier draft. This is Contribution No. 3774 of the University of Maryland Center for Environmental Science.

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CHAPTER

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Environmental and Genetic Inﬂuences on Stock Identiﬁcation Characters DOUGLAS P. SWAIN,* JEFFREY A. HUTCHINGS,† AND CHRIS J. FOOTE‡ *Department of Fisheries and Oceans, Gulf Fisheries Centre, Moncton, New Brunswick, Canada, † Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada, ‡ Department of Fisheries and Aquaculture, Malaspina University-College, Nanaimo, British Columbia, Canada

I. Introduction II. Factors Underlying Intraspeciﬁc Patterns of Phenotypic and Genetic Diversity III. Meristic Characters A. Environmental Inﬂuences B. Genetic Inﬂuences C. Variation Among Populations IV. Morphometric Characters A. Environmental Inﬂuences B. Genetic Inﬂuences C. Variation Among Populations V. Life History Characters A. Environmental Inﬂuences B. Genetic Inﬂuences C. Variation Among Populations VI. Conclusions References

I. INTRODUCTION Characters used to identify ﬁsh stocks can be divided into three groups: those that are purely genetic, those that are purely environmental, and those that may reﬂect both genetic and environmental variation. Early studies characterized Stock Identiﬁcation Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

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stocks on the basis of phenotypic variation in life-history, meristic, morphometric, and life history traits. These characters are quantitative genetic traits, typically controlled by many genes and affected by the environment in which those genes are expressed (Falconer, 1981; Hard, 1995). They also are generally related to ﬁtness and thus, molded by natural and sexual selection, they reﬂect local adaptation (e.g., Carvalho, 1993; Hard, 1995; Conover, 1998). Over the past 40 years, severa lmolecular genetic techniques have been developed to directly examine genetic variation within and between groups (e.g., protein allozymes, mitochondrial DNA, microsatellite DNA). These genetic markers usually are assumed to be neutral or nearly neutral to selection (Carvalho and Hauser, 1994; McKay and Latta, 2002), although this is not always the case (e.g., Karl and Avise, 1992; Pogson et al., 1995; Streelman et al., 1998; Merila and Crnokrak, 2001). Finally, environmental markers, such as the elemental composition of otoliths, have also been used to delineate stock structure in recent decades (e.g., Thresher, 1999; Campana et al., 2000). These markers, signatures of the habitats or areas occupied at each life history stage, are usually thought to reﬂect purely environmental differences between groups of ﬁsh, although it has been suggested that genetic effects also may contribute to differences between individuals, stocks, or species (Kalish, 1989; Thresher et al., 1994). In this chapter, we focus on a comparison between the “traditional” approaches to stock identiﬁcation using phenotypic characters, in particular meristic, morphometric, and life history traits, and the newer approaches using molecular genetic markers. The strengths and weaknesses of the different approaches depend on the working deﬁnition of the term stock. A wide range of deﬁnitions has been used, spanning a continuum from the “ﬁshery stock,” a group of ﬁsh exploited in a speciﬁc area, to the “genetic stock,” a reproductively isolated unit, genetically different from other such units (Carvalho and Hauser, 1994). Most commonly, a stock is considered equivalent to a population, at least partly reproductively isolated from other populations, and genetically different from them as a result of adaptation to its local environment (e.g., MacLean and Evans, 1981). However, the importance of delineating “phenotypic” stocks (Booke, 1981), groups of ﬁsh characterized by phenotypic differences that may be entirely environmentally induced, is being increasingly emphasized (e.g., Shepherd, 1991; Haddon and Willis, 1995; Jerry and Cairns, 1998; Lowe et al., 1998; Cadrin, 2000). For example, Cadrin and Friedland (1999) argue that intraspeciﬁc groups with persistent phenotypic differences in life history traits need to be recognized in stock assessment and ﬁsheries management, even if these differences do not reﬂect genetic differentiation. Meristic, morphometric, and life history characters are clearly appropriate for delineating phenotypic stocks. In this chapter, we focus on the advantages and disadvantages of these types of characters for delineating genetically distinct, locally adapted stocks.

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II. FACTORS UNDERLYING INTRASPECIFIC PATTERNS OF PHENOTYPIC AND GENETIC DIVERSITY Genetic divergence between intraspeciﬁc groups depends on the level of gene ﬂow between the groups, genetic drift, and selection (Endler, 1986). Genetic drift refers to the random ﬂuctuation in allele frequencies that arises from the sampling of gametes in ﬁnite populations. Gene ﬂow is the change in allele frequency that results from the movement of gametes or individuals between groups. Gene ﬂow reduces or prevents differentiation, while drift promotes it. Selection can do either, depending on whether the same or different genotypes are favored in the different groups. On evolutionary time scales, neutral molecular evolution between reproductively isolated groups appears to proceed at a steady pace, with the extent of neutral genetic differentiation between groups being a measure of the time since they became isolated (Wilson et al., 1977; Clayton, 1981). However, gene ﬂow is a potent force opposing neutral genetic differentiation between groups that are not reproductively isolated. The exchange of a single migrant per generation will prevent different neutral alleles from being nearly ﬁxed in different populations (Wright, 1931), although a higher exchange rate is required to maintain the same allele frequencies between populations (Allendorf and Phelps, 1981; Adkison 1995). Given the sample sizes usually employed in stock identiﬁcation studies, Carvalho and Hauser (1994) argue that even a small number of migrants per generation is sufﬁcient to prevent detectable heterogeneity in neutral genetic markers. While neutral genetic markers provide an important tool for identifying the extent of reproductive isolation between groups and for determining phylogenetic relationships among isolated groups, they may not accurately reﬂect quantitative genetic divergence between groups in adaptive traits (Hard, 1995; Volpe and Ferguson, 1996; Conover, 1998). Genetic divergence in response to natural selection can be rapid (e.g., Reznick et al., 1997; Conover and Munch, 2002; Koskinen et al., 2002) and can occur despite regular gene ﬂow that would prevent the accumulation of neutral genetic differences (Carvalho, 1993; Allendorf, 1995). Reed and Frankham (2001) reported only a weak correlation between molecular and quantitative measures of genetic variation and concluded that molecular markers do not accurately measure differentiation between populations as a result of natural selection. Karhu et al. (1996) found that Finnish Scots pine populations showed a high degree of genetic differentiation in adaptive quantitative traits but little differentiation in molecular markers. Similarly, genetic divergence in quantitative traits tended to be much stronger than molecular genetic (microsatellite) differentiation in recently evolved grayling populations (Koskinen et al., 2002). Finally, there is strong evidence of extensive adaptive genetic divergence among four morphs of sympatric Arctic charr, Salvelinus alpinus, in developmental, life history, behavioral, and morphological traits (Skulason et al., 1989,

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1993, 1996, 1999; Snorrason et al., 1994), yet there is relatively little variation in neutral molecular traits (Magnusson and Ferguson, 1987; Danzmann et al., 1991; Volpe and Ferguson, 1996). In some cases, natural selection may also inﬂuence the divergence between populations in molecular markers. In cases where greater divergence is indicated by microsatellite DNA than by allozymes, it has been suggested that the allozyme loci are subject to stabilizing selection acting to maintain similar genotypic frequencies in different populations (e.g., Karl and Avise, 1992; Pogson et al., 1995). In other cases, rapid divergence in allozyme loci has been attributed to contrasting selection pressures between populations (e.g., Vuorinen et al., 1991). However, in most cases, molecular genetic markers, in particular noncoding microsatellite DNA variants, are thought to be largely neutral to selection (Carvalho and Hauser, 1994). Local adaptation and differentiation into genetically distinct populations or stocks are generally thought to be less signiﬁcant in marine ﬁshes than in freshwater or anadromous ﬁshes (e.g., Blankenship and Leber, 1995; Pawson and Jennings, 1996). This view stems from the lack of obvious physical barriers to migration in the sea and the high vagility of most marine ﬁshes, either as migratory adults or as planktonic eggs or larvae. Consistent with this view, molecular genetic studies suggest lower genetic diversity in marine ﬁshes than in anadromous and freshwater ﬁshes (Gyllensten, 1985; Ward et al., 1994). However, gene ﬂow may be limited despite the absence of physical barriers (e.g., Tallman and Healey, 1994; Wood and Foote, 1996; Skúlason et al., 1999), and genetic differentiation in adaptive traits may occur despite gene ﬂow (e.g., Wood and Foote, 1996; Foote et al., 1997). Genetic differences in phenotypic traits related to ﬁtness have been identiﬁed between populations of marine ﬁshes by the few studies that have tested for such differences (e.g., Conover and Present, 1990; Svåsand et al., 1996; Schultz et al., 1996; Puvanendran and Brown, 1998; Purchase and Brown, 2000). Studies of the Atlantic silverside, Menidia menidia, provide an example of extensive evidence for local adaptation in a marine ﬁsh (Conover, 1998). This species is widely distributed along the east coast of North America, with few physical barriers to gene ﬂow, a neustonic larval stage, and migratory adults. Genetic differentiation in life history and morphological traits has been demonstrated at ﬁne geographic scales even though molecular genetic studies provide little evidence of population subdivision from Florida to Maine (Conover, 1998). The main advantage in using life history and morphological traits in studies of population structure is that these traits are often related to ﬁtness and respond to selection, and thus may reveal genetic differentiation not evident in neutral genetic traits. Their main disadvantage results from phenotypic plasticity, the ability of a genotype to produce different phenotypes across an environmental gradient (Bradshaw, 1965; Stearns, 1989; Thompson, 1991; Schlichting and Pigliucci, 1998; Debat and David, 2001). Two types of plasticity have been

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distinguished (Schmalhausen, 1949). One type involves an environmentally cued switch in developmental programs, producing one phenotype in one set of environmental conditions and a different phenotype in a second set of conditions (Levins, 1963; Smith-Gill, 1983). Usually assumed to be adaptive, examples of this type of plasticity include the predator-induced shell dimorphism of some barnacles (Lively, 1986) and the female “morphotypes” found in some rotifers (Gilbert, 1980). The second type involves an effect of the environment on the rate or degree of expression of a given developmental program, rather than a switch between programs, and has been termed phenotypic modulation (SmithGill, 1983). Plasticity in life history and morphological traits of ﬁshes appears to be mostly of this type. Although often considered nonadaptive, reﬂecting a failure to buffer development against environmental perturbations (Smith-Gill, 1983), recent research has focused on the possibility that this type of plasticity is also an adaptation to environmental heterogeneity in the phenotypes favored by selection (e.g., Thompson, 1991; Via et al., 1995; Schlichting and Pigliucci, 1998). Phenotypic plasticity can be described as a norm of reaction (Schmalhausen, 1949; Schlichting and Pigliucci, 1998), a function that expresses how the mean phenotypic value of a given genotype changes with the environment (Fig. 4-1). Reaction norms provide information on the magnitude of plasticity in a trait, the presence of genotype ¥ environment (G ¥ E) interactions in the phenotypic expression of a trait, and the extent to which the additive genetic variance or

FIGURE 4-1. Hypothetical norms of reaction of two different genotypes to an environmental variable. The reaction norms specify the mean phenotype produced at each level of the environmental variable. Variation about the mean phenotype produced by a particular genotype in a particular environment generally occurs as a result of developmental noise (Waddington, 1957). Genotype ¥ environment interaction is indicated by the intersection of the reaction norms. Common garden experiments conducted only in the vicinty of points A and B would fail to identify the difference between the two genotypes. Reprinted from Swain and Foote (1999) with permission from Elsevier.

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heritability changes with the environment. Crossing reaction norms, indicative of G ¥ E interaction, suggest the presence of genetic variation in plasticity. If that genetic variation is additive (i.e., heritable), then selection can produce changes to the shapes of reaction norms, resulting in population differences in responses to environmental change. Phenotypic differences between groups in the wild may reﬂect genetic differentiation, environmental differences, or a combination of the two (Thompson, 1991). Differences that are consistent with expected or known differences in selection pressure are sometimes taken as evidence for genetic differentiation (e.g., Fleming and Gross, 1989 ). However, these patterns may be entirely environmentally induced (e.g., Swain et al., 1991), reﬂecting adaptive phenotypic plasticity (e.g., Bradshaw, 1965; Meyer, 1987; Schlichting and Pigliucci, 1998; Robinson and Parsons, 2002) rather than genetic divergence. An experimental approach is needed to disentangle the genetic and environmental components of phenotypic variation (Thompson, 1991; Conover and Schultz, 1995). Two approaches have been used: “common garden” experiments and reciprocal transplants. In common garden experiments, individuals from the different areas or populations are reared in the same controlled environments (e.g., Tallman, 1986; Kinnison et al., 2001; Koskinen et al., 2002). Ideally, a series of environments, spanning the range of conditions experienced in the wild, are used to delineate reaction norms and test for genetic differences in plasticity between groups. The use of a number of levels for the controlled environmental factors also reduces the chances that a genetic difference is overlooked because experiments are conducted at the intersection of reaction norms (e.g., points A and B in Fig. 4-1). In reciprocal transplant experiments, individuals from each population are placed together in each of their natural habitats (e.g., Robinson and Wilson, 1996). This approach has the advantage that genotypes are tested in each of the environments experienced in the wild, but has the disadvantage that care must be taken to avoid the escape of transplanted genotypes. Maternal effects and other preexperimental environmental inﬂuences pose a difﬁculty for both approaches. Differences due to these effects can be avoided by using offspring of parents that have themselves been reared in common environments (Conover and Schultz, 1995), though this will not be possible for many ﬁshes. Alternatively, reciprocal hybrids (e.g., Hatﬁeld, 1997; Craig and Foote, 2001) or quantitative genetic analyses (e.g., Koskinen et al., 2002) can be used to distinguish between genetic and maternal effects. The possibility that phenotypic similarity reﬂects genetic differentiation is rarely considered in stock identiﬁcation studies. However, this situation is likely to exist when phenotypic expression depends on environmental conditions and stabilizing selection favors the same phenotype in groups developing in different environments (e.g., Bervan et al., 1979; Bervan and Gill, 1983; Conover and Present, 1990; Tallman and Healey, 1991; Billerbeck et al., 2001). In this

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situation, selection will favor genetic differentiation between the groups to counteract the differing environmental inﬂuences. This geographic pattern, in which differences between areas in the genetic inﬂuences on a trait oppose the differences in environmental inﬂuences, has been termed countergradient variation (Levins, 1968; Conover and Shultz, 1995). The alternative pattern, in which genetic and environmental differences reinforce each other, has been termed cogradient variation. Numerous examples of cogradient have been identiﬁed (see below), perhaps because the phenotypic differences produced by cogradient variation have tended to attract the attention of ecologists and evolutionary biologists. Even though countergradient variation may often be overlooked because of the phenotypic similarity that it produces, a growing number of cases have been demonstrated in marine, anadromous, and freshwater ﬁshes (Conover and Present, 1990; Wood and Foote, 1990; Tallman and Healey, 1991; Present and Conover, 1992; Nicieza et al., 1994; Schultz et al., 1996; Schultz and Conover, 1997; Conover et al., 1997; Arendt and Wilson, 1999; Purchase and Brown, 2000; Lankford et al., 2001; Craig and Foote, 2001).

III. MERISTIC CHARACTERS

A. ENVIRONMENTAL INFLUENCES Meristic characters are the numbers of discrete, serially repeated, countable characters such as vertebrae, gill rakers, and ﬁn rays (e.g., Waldman, this volume). Phenotypic plasticity of meristic characters has been studied extensively (reviewed by Lindsey, 1988). The developmental environment can have a great effect on the number of parts formed in ﬁsh. However, early in ontogeny, the number of parts is ﬁxed and remains unchanged regardless of subsequent changes in the environment. Some meristic characters (such as the number of vertebrae) are ﬁxed relatively early in development, usually well before hatching, while others (such as median ﬁn rays counts) may remain labile to environmental inﬂuences even well after hatching. The number of parts may be ﬁxed well before the ﬁnal number is visible and countable (cf. Lindsey et al., 1984). Meristic characters are inﬂuenced by a wide variety of environmental factors including salinity, light, and dissolved oxygen (see review by Lindsey, 1988), but the inﬂuence of temperature has been the most widely studied (e.g., Heuts, 1949; Tåning, 1952; Lindsey, 1962; Lindsey and Harrington, 1972; Ali and Lindsey, 1974). Norms of reaction to temperature are most commonly negative (i.e., more parts produced at colder temperatures) for both vertebrae and ﬁn rays, though U-shaped responses (i.e., minimum number at an intermediate temperature) are also common for vertebrae while positive or arched responses are frequent for ﬁn rays (Lindsey, 1988).

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Attempts have been made to delimit the developmental period when meristic characters are labile to environmental inﬂuences by transferring embryos between temperatures at various developmental stages (e.g., Tåning, 1944; Ali and Lindsey, 1974). This work has led to the deﬁnition of “sensitive” periods before and after which meristic counts are not labile, and “supersensitive” periods when a sudden temperature change produces a “shock effect” on meristic counts (e.g., Tåning, 1952). However, subsequent work has cast doubt on the ideas of shock effects and of sensitive periods before which meristic count is unaffected by environmental inﬂuences. Instead, meristic number appears to be continuously labile to environmental inﬂuences from fertilization to the ﬁnal ﬁxation of count, with no special periods of supersensitivity (Lindsey, 1988). Meristic characters can also be affected by environmental inﬂuences acting on gametes before fertilization. The temperatures experienced by parents prior to fertilization affected the numbers of vertebrae and ﬁn rays formed in offspring in the zebraﬁsh Brachydanio rerio (Dentry and Lindsey, 1978) and in the cyprinodont ﬁsh Rivulus marmoratus (Swain and Lindsey, 1986a). Likewise, the number of parts formed in R. marmoratus differed between offspring produced soon or long after the onset of oviposition in parents (Swain and Lindsey, 1986b). These prefertilization inﬂuences need to be considered when interpreting the results of common garden experiments. Meristic differences between groups of ﬁsh reared in the same environment may not indicate genetic differences, but instead reﬂect different prefertilization environmental inﬂuences acting on genetically similar parents.

B. GENETIC INFLUENCES Despite the strong environmental inﬂuences on meristic traits, there appears to be a strong genetic component to meristic variation within populations. High heritabilities (0.4–0.9) have been reported for a variety of meristic characters, including the numbers of vertebrae, ﬁn rays, spines, and gill rakers (e.g., Hagen, 1973; Kirpichnikov, 1981; Hagen and Blouw, 1983; Tave, 1984; Leary et al., 1985; Beacham, 1990; Foote et al., 1999). Although polygenically controlled, usually by many genes (Kirpichnikov, 1981; Hatﬁeld, 1997), these characters may in some cases be inﬂuenced by relatively few loci with major effects (Hagen and Blouw, 1983; Leary et al., 1984). Hagen (1973), Leary et al. (1985), and Foote et al. (1999) found no evidence for maternal or sex-linked effects on meristic traits. A number of common environment experiments have also revealed genetic variation in phenotypic plasticity of meristic characters (e.g., Ali and Lindsey, 1974). The high heritabilities reported for meristic characters indicate a large store of additive genetic variation within populations available for response to selection. Because natural selection is expected to deplete additive genetic variation, high

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heritability in a trait is sometimes assumed to indicate that the trait is not closely related to ﬁtness. However, a link with ﬁtness has been demonstrated for many meristic characters (e.g., Hagen and Gilbertson, 1973; Blouw and Hagen, 1984b; Swain, 1992b; Day and McPhail, 1996). Additive genetic variance in meristic characters appears to be maintained within populations by spatial, temporal, and ontogenetic heterogeneity in selection pressures (e.g., Reimchen, 1980; Swain, 1992b; Foote et al., 1999; Reimchen and Nosil, 2002).

C. VARIATION AMONG POPULATIONS Widespread patterns in meristic variation occur among populations of related ﬁshes. For example, vertebral number tends to be higher in populations occurring at higher latitudes or in colder waters (Jordan’s rule; Jordan, 1892) as well as in those with greater maximum body lengths (pleomerism; Lindsey, 1975). Gill raker number tends to be higher in limnetic forms than in benthic forms (e.g., Lindsey, 1981; McPhail, 1984, 1992; Snorrason et al., 1994; Hatﬁeld, 1997), with the difference associated with prey size and the efﬁciency with which prey are handled (Lavin and McPhail, 1986; Schluter, 1993; Day and McPhail, 1996). Differences between areas or populations in spine and lateral plate numbers of sticklebacks appear to be related to differences in the intensity or type of predation (e.g., Hagen and Gilbertson, 1972; Reimchen, 1980; Blouw and Hagen, 1984a). These widespread trends suggest that meristic traits may be subject to natural selection. Because of their link to ﬁtness through effects on survival under predation (e.g., Hagen and Gilbertson, 1973; Blouw and Hagen, 1984b), meristic differences between populations in traits like spine and lateral plate numbers are usually expected to have a genetic component, and this has been conﬁrmed by common environment experiments (McPhail, 1992). Gill raker number also has clear adaptive signiﬁcance through effects on feeding efﬁciency, and controlled rearing experiments have generally conﬁrmed a genetic component to differences between populations or morphs (Svärdson, 1979; McPhail, 1984, 1992; Snorrason et al., 1994; Hatﬁeld, 1997; Foote et al., 1999). In contrast to gill raker number and meristic traits related to the body armature of sticklebacks, the adaptive signiﬁcance of many meristic traits such as the numbers of vertebrae and ﬁn rays has been obscure. For example, Fowler (1970) argued that, within narrow limits, the precise vertebral count is without selective signiﬁcance, and that, within these limits, it may be more adaptive to let the number of vertebrae vary in response to environmental changes than to ﬁx the number genetically. Concordant with this view, geographic variation in meristic traits is often attributed to environmental differences rather than to genetic differentiation (e.g., Brander, 1979; Templeman, 1981; Beacham, 1984; Shepherd,

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1991), particularly when molecular genetic markers suggest genetic homogeneity between areas (Pepin and Carr, 1993). In contrast, Barlow (1961) argued that genetic differences may often underlie the meristic dissimilarities between populations or races of ﬁsh. He suggested that these differences may result from selection acting on correlated physiological processes, arguing that it is unlikely that “the addition or subtraction of a few elements would materially affect the probability of the survival of a ﬁsh.” However, effects of the precise vertebral phenotype on swimming performance and survival under predation have been demonstrated in larval ﬁshes and suggest that the trends observed among populations in this meristic trait reﬂect local adaptation to contrasting selection pressures acting directly on the number of vertebrae (Swain, 1988, 1992a,b). Common environment experiments have indicated a strong genetic component to geographic variation in the numbers of vertebrae and ﬁn rays in the few cases examined (references in Lindsey, 1988; Taylor and Foote, 1991; Billerbeck et al., 1997). For example, Billerbeck et al. (1997) revealed a genetic basis to latitudinal variation in vertebral number in the Atlantic silverside using common garden experiments. Maternal effects and other prefertilization inﬂuences were ruled out by using offspring of parents that had themselves been reared in a common environment. In this case, genetic and environmental inﬂuences were cogradient, since there was also a negative relationship between vertebral number and rearing temperature. However, this environmental effect was slight compared to the strong genetic differences between populations. These genetic differences occurred despite little differentiation in molecular genetic traits (Conover, 1998).

IV. MORPHOMETRIC CHARACTERS

A. ENVIRONMENTAL INFLUENCES Morphometric characters describe aspects of body shape. In contrast to meristic characters, they are continuous variables and depend on body size. Thus, a key step in measuring morphometric characters is disentangling shape from size (e.g., Rohlf and Bookstein, 1987). Morphometric characters also typically show ontogenetic changes associated with allometric growth (Gould, 1966). These ontogenetic changes in body shape may be particularly rapid at key life history stages, such as metamorphosis from larval to juvenile body forms, smoltiﬁcation in salmon, and sexual maturation. Unlike meristic characters, which are ﬁxed early in life, morphometric characters may be labile to environmental inﬂuences throughout life (e.g., Wainwright et al., 1991). Although environmental inﬂuences on morphometric characters have not been as well studied as those on meristic characters, a number of inﬂuential factors have been identiﬁed. Body shape in ﬁshes can be modiﬁed by

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rearing temperature (Martin, 1949; Beacham, 1990), water velocity (Imre et al., 2002), quantity of food (Currens et al., 1989) and type of food or feeding mode (Meyer, 1987, 1990; Witte et al., 1990; Wimberger 1991, 1992; Wainwright et al., 1991; Day et al., 1994; Robinson and Wilson, 1995; Day and McPhail, 1996). Structures made of bone remodel and change shape depending on the stresses imposed on them (Lanyon, 1984; Lanyon and Rubin, 1985). These changes are usually considered to be adaptive (Lanyon and Rubin, 1985). Plasticity in trophic morphology induced by diet or feeding mode is usually assumed to result from bone remodeling in response to differences in loading regime. Other environmental inﬂuences may involve heterochrony, changes in the relative timing of developmental events (Meyer, 1987) such as switches between growth stanzas (Martin, 1949). Phenotypic plasticity in morphometric traits may often be adaptive (Robinson and Parsons, 2002). Predator-induced changes in body shape of crucian carp Carassius carassius provide a striking example of adaptive plasticity in morphology. In the presence of northern pike Esox lucius, a predator of carp, crucian carp develop a deeper body (Brönmark and Miner, 1992). This change in body form occurs in response to chemical cues released by piscivores fed a diet of ﬁsh (Brönmark and Pettersson, 1994) and reduces the vulnerability of carp to predation by pike, a gape-limited predator (Nilsson et al., 1995). Adaptive plasticity may also contribute to the morphological differences between the benthic and limnetic forms observed in a variety of ﬁsh taxa (Robinson and Wilson, 1994; Robinson and Parsons, 2002). Coexisting limnetic and benthic forms of stickleback (Gasterosteus sp.) in Enos and Paxton Lakes, British Columbia, provide an intensively studied example. The limnetic forms have more and longer gill rakers; shallower bodies and heads; longer heads, snouts, and upper jaws; and larger eyes than the benthic forms (McPhail, 1984, 1992). Foraging success is superior for the limnetic forms in open-water habitats feeding on zooplankton and for the benthic forms in benthic habitats feeding on benthic prey (Bentzen and McPhail, 1984; Schluter, 1993). Common environment experiments indicate that the differences in morphology are inherited (McPhail, 1984, 1992; Hatﬁeld, 1997), but the two forms also exhibit morphological plasticity in the adaptive direction (Day et al., 1994). Each species more closely resembled the other when raised on the latter’s diet. Diet-induced plasticity resulted in improved foraging efﬁciency (Day and McPhail, 1996) and reduced the morphological gap between species by 30% to 60% (Day et al., 1994). Similar diet-induced plasticity has been demonstrated in the cichlids Geophagus brasiliensis and G. steindachneri (Wimberger, 1991, 1992). Fish fed brine shrimp nauplii (a planktonic prey) developed longer snouts, larger eyes, longer and shallower heads, longer paired ﬁns, and shallower bodies and tails than those fed chironomid larvae (a benthic prey). Similarly, changes in feeding orientation induced morphological differences in guppies Poecilia reticulata (Robinson and Wilson, 1995). Guppies feeding on ﬂoating food developed

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a more fusiform shape than those feeding on food attached to a plate on the bottom. Morphological differentiation associated with trophic specialization has also been extensively studied in Arctic char Salvelinus alpinus and involves both genetic differences and phenotypic plasticity (e.g., Skúlason et al., 1989, 1993, 1996; Snorrason et al., 1994). Finally, Meyer (1987) reported extensive phenotypic plasticity in trophic morphology resulting from differences in diet and feeding mode in the cichlid Cichlasoma managuense. This plasticity paralleled the differences in morphology used to divide cichlids into functional groups. Adaptive phenotypic differences between groups of ﬁsh may thus reﬂect plasticity instead of indicating genetic differentiation between the groups.

B. GENETIC INFLUENCES Like meristic characters, morphometric traits are quantitative genetic characters, generally thought to be inﬂuenced by many genes of small individual effect, though some adaptive morphometric differences may be explained by relatively few genes of large effect (Hatﬁeld, 1997). Estimates of the heritability of morphometric characters range between low and moderate values. Riddell et al. (1981) reported heritabilities less than 0.1 for morphometric traits in Atlantic salmon. The average heritability reported for morphometric characters of chum salmon ranged between 0.3 and 0.6, depending on rearing temperature (Beacham, 1990). Lavin and McPhail (1987) reported heritabilities between 0.19 and 0.84 for morphometric characters in the threespine stickleback G. aculeatus. Grudzien and Turner (1984) reported a heritability of 0.44 for the mouth width polymorphism in Ilyodon. Genetic variation for morphological plasticity has also been demonstrated whenever it has been tested in ﬁshes (Robinson and Parsons, 2002).

C. VARIATION AMONG POPULATIONS Body shape in ﬁshes is generally thought to reﬂect adaptation to their ecological niches. Associations between body shape and ecological variables are commonly observed among populations of related ﬁshes. Correlations between body shape and the extent of stream residence in Paciﬁc salmon provide a well-studied example. Body shape and the extent of stream residence are correlated among the species of Oncorhynchus (see Scott and Crossman, 1973, p. 145), among life history types of Chinook salmon O. tshawytscha (Carl and Healey, 1984), and between ecological types (stream- vs. lake-rearing) of coho salmon O. kisutch (Swain and Holtby, 1989). Forms with longer stream residence have deeper bodies with larger, more brightly colored median ﬁns. This pattern presumably

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reﬂects contrasting selection pressures between stream and open-water habitats. In streams, both as territorial juveniles and spawning adults, selection may be predominantly for burst swimming and agonistic performance (e.g., Fleming and Gross, 1989). A deep body with large median ﬁns is advantageous for burst swimming (Webb, 1978, 1984; Taylor and McPhail, 1985b) and during agonistic interactions (Holtby et al., 1993). In open waters (and during migrations to and from the sea), selection may be predominantly for sustained swimming performance, favoring a fusiform or streamlined body shape. Differences in body shape between interior and coastal populations of coho salmon are also consistent with these predictions. Interior coho, which must undertake longer migrations to and from the sea, are more streamlined, with shallower bodies and smaller median ﬁns (Taylor and McPhail, 1985a), and have superior sustained swimming performance and inferior burst swimming performance in comparisons with coastal populations (Taylor and McPhail, 1985b). Similar differences in body shape and swimming performance have been demonstrated between sockeye salmon and kokanee, the anadromous and nonanadromous forms of O. nerka (Taylor and Foote, 1991). Body shape also differs between wild and hatchery populations of coho salmon. Both as juveniles (Taylor, 1986; Swain et al., 1991) and as adults (Fleming and Gross, 1989), wild salmon have deeper bodies and larger median ﬁns than do hatchery salmon. These differences are in the direction predicted from the expected differences in selection between wild and hatchery salmon (Fleming and Gross, 1989; Swain et al., 1991). Numerous other patterns in body shape have been identiﬁed between populations of related ﬁshes. As previously noted, predictable differences in trophic morphology and general body shape occur between pelagic and benthic forms of related ﬁshes (e.g., McPhail, 1984, 1992; Robinson and Wilson, 1994). Other examples include associations between body shape and stream velocity in Atlantic salmon Salmo salar (Riddell and Leggett, 1981), spawning stream size in chum O. keta, pink O. gorbuscha, and sockeye salmon O. nerka (Beacham, 1984; Beacham et al., 1988: Hamon et al., 2000; Quinn et al., 2001), and predation intensity in Galaxias platei (Milano et al., 2002). Such differences among stocks can result in differential exploitation rates, with increased risk of stock extinction, in cases where morphological variation is related to catchability by a ﬁshery (Hamon et al., 2000). Because morphometric differences often result from changes in the timing of developmental events, they can be an indication of life history differences between groups, differences that are central to the assessment and management of exploited ﬁsh stocks (Cadrin, 2000). Differences in body shape between areas, populations, or morphs could reﬂect genetic differences, phenotypic plasticity, or a combination of both factors. A genetic component to these differences has generally been demonstrated when it has been tested for by controlled rearing in common environments (Riddell et al., 1981; Todd et al., 1981; McPhail, 1984; Taylor and McPhail, 1985a; Taylor

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and Foote, 1991; McPhail, 1992; Robinson and Wilson, 1996; Hatﬁeld, 1997). An environmental component has also been found in most cases examined. Genetic differences and phenotypic plasticity both contribute to trophic polymorphism in pumpkinseed sunﬁsh Lepomis gibbosus (Robinson and Wilson, 1996) and in stickleback species pairs (Day et al., 1994). In each case, environmental and genetic inﬂuences are cogradient, both operating in the adaptive direction. However, morphological differences between groups that appear to be adaptive given known or expected differences in natural selection between the groups are no guarantee that there is a genetic component to the differences. For example, differences in body shape of juvenile coho salmon between hatchery and wild populations, differences that appear to be adaptive given the expected differences in selection between the two types of salmon, are entirely environmentally induced (Swain et al., 1991). Much of the genetic variation in body shape between ﬁsh populations may be overlooked because of the tendency to focus research on the phenotypic differences observed between areas or groups of ﬁsh (Tallman and Healey, 1991; Conover and Schultz, 1995). Body shape is similar between early- and latespawning stocks of O. keta in the wild (Tallman and Healey, 1991) but differs distinctly between the stocks when reared in common laboratory environments (Tallman, 1986). Tallman and Healey (1991) suggested that stabilizing selection favored the same body shape in the two populations, resulting in genetic differences between the populations to compensate for the differences in their developmental environments. Given the demonstrated inﬂuences of the environment on body shape in ﬁshes (see above), phenotypic similarity between groups of ﬁshes developing in different environments may be more likely to reﬂect genetic differentiation than genetic homogeneity. A danger in using multivariate morphometric analyses to discriminate stocks of ﬁshes is that slight differences between groups in individual characters, possibly just sampling artifacts, can result in statistically signiﬁcant but biologically insigniﬁcant differences between groups in multivariate analyses with large sample sizes and many characters (Cadrin, 2000; e.g., Bowering, 1988; Bowering et al., 1998). For example, Bowering et al. (1998) found statistically signiﬁcant morphometric differences between all 18 samples of American plaice Hippoglossoides platessoides that they examined. Sample sizes were large, averaging 241 ﬁsh per sample. Bowering et al. (1998) concluded that a large body of additional biological evidence did not support the ﬁne-scale stock separation suggested by the morphometric differences. Morphological differences with clear adaptive signiﬁcance or ontogenetic causes that are stable over time, persisting between repeat samples taken over a number of years, provide a more meaningful basis for stock separation than differences occurring among a single collection of samples and lacking any evident ecological signiﬁcance (Cadrin, 2000).

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V. LIFE HISTORY CHARACTERS

A. ENVIRONMENTAL INFLUENCES Life history traits reﬂect the ways in which individuals vary their stage- or age-speciﬁc expenditures of reproductive effort in response to intrinsic and extrinsic factors that inﬂuence survival and fecundity. As such, life histories reﬂect the expression of traits most closely related to ﬁtness, such as age and size at maturity, number and size of offspring, and longevity, and the timing of the expression of those traits throughout an individual’s life. Within an evolutionary context, natural selection is predicted to favor those individuals whose age-speciﬁc rates of survival and fecundity generate the highest rate of genotypic increase, commonly expressed as either r or R0 (Stearns, 1992; Roff, 2002). When expressed at the individual level, r and R0 represent ﬁtness. When expressed at the population or stock level, they represent rates of population growth, a parameter that reﬂects a broad range of characteristics of interest to ﬁsheries scientists, including sustainable rates of exploitation, resilience following population collapse, and probability of extinction (Hutchings, 2002). Despite this rather clear and fundamental link between individual life histories and population growth rate, life history traits have been underrepresented as means of distinguishing putative stocks. Given that the harvest rates that a stock can sustain are ultimately a function of that stock’s maximum rate of increase, it would seem logical to distinguish putative stocks on the basis of their life histories. However, the observation that the environment can affect life history traits appears to have contributed unduly to the relatively infrequent application of life history traits as metrics of stock identity. In ﬁshes, as with most indeterminately growing organisms, the inﬂuence of the environment on life history traits is realized primarily through factors that affect body size and the rate at which body size changes throughout an individual’s life. Thus, an environmental factor, such as density, food supply, or temperature, that directly inﬂuences size can be expected to have some concomitant impact on several life history traits. A key question is whether these concomitant changes reﬂect phenotypic or genetic correlations among traits; in many cases, they almost certainly represent both. Body size has a positive inﬂuence on many life history traits. Perhaps most notable among these associations is the observation that larger females produce more eggs than smaller females (Wootton, 1998; Roff, 2002). Larger individuals also tend to produce larger eggs, a correlation that holds for a number of species, such as Atlantic cod (Chambers and Waiwood, 1996), Atlantic herring, Clupea harengus (Hempel and Blaxter, 1967), capelin, Mallotus villosus (Chambers et al., 1989), and striped bass, Morone saxatilis (Zastrow et al., 1989). Increased

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body size is also associated with lower mortality in several ﬁshes (Hutchings, 1994; Schluter, 1995; Schultz et al., 1998; Post and Parkinson, 2001). Reﬂecting both individual size at age and the rate at which that size is attained, few parameters have greater inﬂuence on the life history traits of ﬁshes than growth rate. One of the best-documented associations between growth rate and life history is the observation that fast-growing individuals mature earlier in life than slower growing individuals (Alm, 1959; Hutchings, 1993a; Fox, 1994; Trippel et al., 1995; Godø and Haug, 1999). Independent of its effect on body size, growth rate can also potentially affect fecundity. Scott (1962), for example, reported that rainbow trout (Oncorhynchus mykiss) fed ad libitum produced more eggs per unit body mass than individuals fed a restricted diet. Regarding offspring size, there is evidence that growth rate in early life can both negatively (Jonsson et al., 1996) and positively (Morita et al., 1999) inﬂuence egg size in Atlantic salmon and white-spotted char (Salvelinus leucomaenis), respectively. Although the life history consequences of phenotypic changes to growth rate are reasonably well understood, it is not clear how growth rate affects survival independent of its inﬂuence on body size. To some extent, this depends on the scale at which the association is examined. One of the classic life history invariants in ﬁsh is that denoted by K/M. Representing the ratio of a metric of growth (from the von Bertalanffy growth equation) to the instantaneous rate of mortality, its invariance across taxa implies that species or populations characterized by rapid individual growth are also characterized by high mortality (Beverton and Holt, 1959; Charnov, 1993). At the proximate level, negative consequences to survival may be effected by physiological, metabolic, and developmental costs associated with compensatory growth, deﬁned as accelerated growth following a period of retarded growth (Metcalfe and Monaghan, 2001). For example, rapid growth during larval development is associated with delays in cranial ossiﬁcation in pumpkinseed sunﬁsh (Lepomis macrochirus), leading to reduced survival early in life (Arendt and Wilson, 2000). From an ecological perspective, faster growing individuals may place themselves at greater risk of predation if faster growth can only be achieved by riskier foraging behavior (Holtby and Healey, 1990; Werner and Anholt, 1993). Thus, environmental inﬂuences on life history traits are realized primarily through correlated responses to life history generated by environmentallyinduced variation in body size and individual growth rate. Indeed, given their heritable basis, their intimate links with ﬁtness, and their manifestation above the level of the individual as population growth rate (r), the utility of using life history characters as metrics of stock identiﬁcation merits reexamination.

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B. GENETIC INFLUENCES Life history traits are quantitative traits that vary continuously and are controlled by the expression of many genes, each having a small effect (Roff, 2002). Across taxa, one analysis of 341 wild, outbred animal populations revealed that the heritability of life history traits frequently exceeds 0.30 (Mousseau and Roff, 1987). In ﬁshes, studies have consistently revealed substantive genetic variation for life history traits, particularly in salmonids (Gjedrem, 1983; Gjerde, 1984, 1986; Robison and Luempert, 1984; Roff, 2002). Moderately high heritabilities are also suggested by comparatively rapid changes in life history characters produced by intense selection pressures. In the laboratory, these selection responses are most evident in aquaculture breeding programs as a consequence of the intentional selecting and breeding of only those individuals possessing characteristics desired by the aquaculture industry (e.g., faster growth rate, delayed maturity). For example, artiﬁcial selection has produced changes in growth rate of 10% per generation in Atlantic salmon, Salmo salar (Gjoen and Bentsen, 1997) and coho salmon, Oncorhynchus kisutch (Hershberger et al., 1990). Experimentally, after subjecting Atlantic silverside (Menidia menidia) to intensive size-selective harvesting, Conover and Munch (2002) documented signiﬁcant changes in size-atage after only four generations, caused by differential selection of genotypes with slower or faster rates of growth. Genetic changes resulting from selection on one life history trait can produce genetic changes in other traits (Sinervo and Svensson, 2002). These changes, a consequence of correlated selection, result when traits are genetically correlated with one another. Genetic correlations express the extent to which two characters are determined by the same genes. Estimates of genetic correlations between life history traits in ﬁshes have been limited primarily to the aquaculture literature (e.g., Su et al., 2002). Genetic differences in life history traits can also be generated by genotypically variable thresholds that appear to determine age and size at maturity in some ﬁshes, notably those that adopt alternative reproductive strategies (Taborsky, 1994). There is good evidence, for example, that alternative mating strategies in salmonids have both a genetic and an environmental component. Comparing the incidence of jacking (early maturity) among male progeny sired by jacks and by “hooknose” (late-maturing) Chinook salmon (Oncorhynchus tshawytscha), Heath et al. (1994) estimated the heritability of jacking to be about 0.4. Breeding experiments also suggest that the incidence of parr maturity is higher among the progeny of mature male parr than it is among those of anadromous males (Thorpe et al., 1983; Gjerde, 1984; Glebe and Saunders, 1986). Whatever the genetic basis to alternative life histories, it is clear that environmental factors are also of signiﬁcant importance. In particular, there is often a signiﬁcant correlation between growth rate and/or condition and the likelihood of adopting the

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small-male strategy (Myers et al., 1986; Thorpe, 1986; Bohlin et al., 1990; Metcalfe, 1998). Several authors have suggested that adoption of alternative strategies in male salmonids depends on whether an individual’s growth rate in early life exceeds that speciﬁed by a growth-rate threshold, that is, that the strategies are conditional upon an individual’s state (Leonardsson and Lundberg, 1986; Thorpe, 1986; Hazel et al., 1990; Hutchings and Myers, 1994). In the quantitative genetic sense, threshold traits describe characters that are determined by alleles at multiple loci and that can be assigned to one of two or more distinct classes (Roff, 1998). The loci affecting threshold traits are assumed to each have some small effect on a trait that varies continuously. For alternative strategies in salmonids, the continuously varying trait may be the concentration of a hormone, the amount of lipid deposition, or a metabolic efﬁciency (Thorpe, 1986; Metcalfe, 1998). Genotypes expressing less than the threshold value of this underlying trait will express one phenotype, while those exceeding the threshold will express the alternative phenotype. Growth-rate thresholds can be modeled as norms of reaction for age at maturity. The existence of substantive differences in the incidence in parr maturity among families reared in a common environment (Glebe and Saunders, 1986; Herbinger, 1987) suggests that differences in reaction norms for the probability of parr maturity exist among individuals in the same population.

C. VARIATION AMONG POPULATIONS Across species, life history traits vary tremendously in ﬁshes. For example, age at maturity ranges from weeks in annual cyprinodonts (Simpson, 1979) to decades in dogﬁsh sharks (Saunders and McFarlane, 1993). Size at maturity varies from less than 1 cm in some gobies (Winterbottom and Emery, 1981) to several meters in whale sharks (Helfman et al., 1997). Fecundity ranges between 2 in some elamobranchs to millions in many broadcast-spawning marine ﬁsh. And egg size differs 100-fold, varying from 0.3 mm in the surfperch, Cymatogaster aggregata (Kamler, 1992) up to 30 mm in mouth-brooding catﬁshes (Tyler and Sumpter, 1996). Signiﬁcant differences in life history traits are not limited to among-species comparisons, and it is this within-species variability upon which stock differentiation could be undertaken (Begg et al., 1999; Begg, this volume, Chapter 6). Among populations of Atlantic salmon (Salmo salar), for example, egg size can vary threefold, age at maturity by one order of magnitude, size at maturity can differ 14-fold, and fecundity can vary almost 500-fold (Hutchings and Jones, 1998). Among marine ﬁshes, the Atlantic cod (Gadus morhua) can also exhibit wide-ranging differences in age at maturity (2–7 years; Brander, 1994; Trippel et al., 1997), length at maturity (35–85 cm; Patriquin, 1967; Morris and Green,

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2002), and size-speciﬁc fecundity (Marteinsdottir and Begg, 2002; McIntyre and Hutchings, 2003). There is considerable evidence to suggest that population variation in life history reﬂects adaptation by ﬁshes to local environments (see reviews by Hindar et al., 1991; Taylor, 1991; Carvalho, 1993; Conover and Schultz, 1997). Smallscale life history variation among populations of brook trout, Salvelinus fontinalis, on Cape Race, Newfoundland, serves to provide one such example (Ferguson et al., 1991; Hutchings, 1991, 1993a,b, 1994, 1996, 1997). Brook trout inhabiting these rivers are unexploited, do not interbreed, do not differ in density, and are uninﬂuenced by interspeciﬁc competition or predation (Ferguson et al., 1991; Hutchings, 1993a). Comparing the most divergent populations, females in Freshwater River mature, on average, more than a full year earlier than those in Cripple Cove River at a ﬁvefold smaller weight. Regarding reproductive effort, relative to Cripple Cove River females, the smaller Freshwater River females allocate more than twice as much body tissue to gonads, produce signiﬁcantly more eggs, and produce 40% larger eggs (all comparisons corrected for body size; Hutchings, 1991, 1993a, 1996). These population differences in brook trout life history can be attributed to the consequences of food supply, and possibly habitat, to age-speciﬁc rates of survival and fecundity (Hutchings, 1991, 1993a,b, 1994, 1996, 1997), assertions supported by recent genetic evidence of directional selection on body size in response to sizeselective overwinter mortality and individual differences in growth (Wilson et al., 2003). Adaptive population variation in life history is the result of natural selection, acting within populations, favoring those genotypes whose age-speciﬁc rates of survival and fecundity generate the highest per capita rate of increase, or ﬁtness. One of the fundamental premises of life history theory is that natural selection acts on age-speciﬁc expectations of producing future offspring (Fisher, 1930) in response to environmental and genetic inﬂuences on age-speciﬁc survival and fecundity. Thus, if population variation in life history is adaptive, one can assume that this is a consequence of differential selection responses to environments that have different effects on survival and/or fecundity. The potential for ﬁshing to effect signiﬁcant evolutionary change within a population is no different from that of any other form of predator-induced mortality that differentially affects the survival of individuals of different ages and sizes. The question is not whether ﬁshing represents a primary selective pressure effecting genetic change in exploited ﬁsh populations—clearly it must. As Rijnsdorp (1993) put it, ﬁsheries are large-scale experiments on life history evolution. Irrespective of the causal factors, life history responses to selection are manifested primarily by population differences in (a) age and size at maturity, (b) reproductive effort, and (c) phenotypic responses to environmental change, that is, plasticity.

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1. Age at Maturity Age at maturity reﬂects an evolutionary compromise between the costs and beneﬁts to ﬁtness of reproducing comparatively early or late in life (Hutchings, 2002; Roff, 2002). Beneﬁts associated with early maturity include increased probability of surviving to reproduce and an increased rate of gene input into the population, resulting in reduced generation time. However, early maturity can also result in reduced fecundity and/or postreproductive survival because of the smaller body size typically associated with earlier maturity within a population. By contrast, the primary cost of delaying one’s initial spawning is the increased risk of death prior to reproduction. The primary ﬁtness advantage to delaying maturity in ﬁshes is the larger initial body size attained by individuals when they ﬁrst reproduce. Selection is predicted to favor earlier maturity with reductions in either the ratio of adult to juvenile survival (i.e., survival from birth until age at ﬁrst reproduction) or with increases in the variance in adult survival relative to the variance in juvenile survival (Cole, 1954; Gadgil and Bossert, 1970; Schaffer, 1974a,b). Intuitively these predictions make sense. As external mortality, that is, that unassociated with reproduction, at potentially reproductive ages increases, selection is expected to favor those individuals (genotypes) that reproduce prior to those ages, thus increasing their probability of contributing genes to future generations. A similar argument can be made for environmental perturbations that increase the variance in survival at potentially reproductive ages, increased variance in survival being associated with increased uncertainty in an individual’s (genotype’s) persistence. These predictions are generally presumed to hold true for ﬁshes. Leggett and Carscadden (1978) examined population differences in age at maturity among ﬁve populations of American shad, Alosa sapidissima, from Florida, United States, north to New Brunswick, Canada. They found that males and females in the northern populations, for which they presumed juvenile mortality to be more variable than that in southern populations, matured as much as 11 and 14% older, respectively, than their southernmost counterparts. Similarly, Hutchings and Jones (1998) reported a negative correlation between temporal variance in adult survival and age at maturity in anadromous Atlantic salmon. Reznick et al. (1990), documenting selection responses to predator-induced changes to mortality in guppies, Poecilia reticulata, found age at maturity among males and females to be 17 and 7% higher, respectively, in the high juvenile mortality environment relative to that in the environment characterized by comparatively low juvenile mortality. Fox and Keast (1991), comparing life histories of pumpkinseed sunﬁsh, Lepomis gibbosus, populations subjected to either high or low overwinter mortality, documented 1- to 2-yr reductions in age at maturity among males and females in the high mortality environments. And age at maturity in

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bluegill sunﬁsh, Lepomis macrochirus, populations exposed to high juvenile predation is reported greater than that in populations that experience comparatively low juvenile mortality (Belk, 1995). The predominant changes to life history associated with ﬁshing are reduced age and size at maturity, the latter often being a simple consequence of the former, although increases in both characters might occur under certain circumstances (Heino, 1998; Rochet, 1998). The rapidity with which many of these changes occur within stocks is consistent with the hypothesis of a phenotypically plastic response to exploitation. In theory, reductions in density effected by ﬁshing should lead to reduced competition for resources, resulting in an increase in individual growth rate and possibly body condition. Given the widely documented negative association between individual growth rate and age at maturity in ﬁshes (e.g., Alm, 1959; Roff, 1992; Hutchings, 1993a), a comparatively rapid decline in age at maturity can be explained as a plastic response to increases in individual growth. However, while some short-term changes in age at maturity appear to be linked to increases in individual growth rate and/or condition and can potentially be explained as plastic responses to ﬁshing, others are not. One example is that of the northern stock of Atlantic cod extending from southeastern Labrador to the northern half of Newfoundland’s Grand Bank. Between the mid-1980s and the mid-1990s, female median age at maturity declined by more than 1 year, a reduction of approximately 17% (Lilly et al., 2001). However, these changes were not associated with either faster individual growth rate or improved condition (Lilly et al., 2001). Hutchings (1999) suggested that the most parsimonious explanation for these changes was an extremely rapid differential reduction of late-maturing genotypes, by severe overﬁshing, relative to that experienced by early-maturing genotypes. The high exploitation rates experienced by other Northwest Atlantic cod were also sufﬁciently high to effect genetic selection responses to life history traits, possibly favoring earlier maturity and also slower growth (Sinclair et al., 2002) as a consequence. Thus, as a life history metric of stock identiﬁcation, age at maturity would be a useful trait to consider, given its heritable basis, its responsiveness to selection, and its close correspondence with individual ﬁtness and population growth rate. 2. Reproductive Effort, Fecundity, and Egg Size Metrics of reproductive effort may also be useful as tools for distinguishing stocks, particularly if used in conjunction with other life history traits. Reproductive effort can be deﬁned as the proportion of total energy devoted to the physiological and behavioral aspects of reproduction, measured across a biologically meaningful time period (Hirshﬁeld and Tinkle, 1975) such as gonad development, movement/migration to spawning grounds, reduction of feeding prior to and

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concomitant with reproduction, mate competition, nest construction, or parental care. A common surrogate of reproductive effort is the gonadosomatic index, or GSI, that is, the weight of an individual’s gonads relative to that individual’s body weight. Among species, GSI ranges from as little as 0.2% in male Tilapia spp. (Helfman et al., 1997) and 0.5% in male white sticklebacks, Gasterosteus sp. (C-A. Smith and J. A. Hutchings, unpublished data) to as much as 47% in female European eels, Anguilla (Kamler, 1992). Among populations, GSI in females has been reported to differ 1.5 times in brook trout (Hutchings, 1993a) and twofold among Northwest stocks of Atlantic cod (McIntyre and Hutchings, 2003). Population differences in GSI will be reﬂected by differences in size-speciﬁc fecundity (number of eggs per unit of body mass), differences in absolute or size-speciﬁc egg size, or both. Regarding the former, there are surprisingly few comparisons among populations, although one recent study of Atlantic cod reported that size-speciﬁc fecundity can differ several-fold among stocks, depending on maternal size (Marteinsdottir and Begg, 2002). Population differences in average egg size have often been considered a proxy for adaptive variation. But, as noted elsewhere (Hutchings, 1991; Reznick and Yang, 1993), the relationship between offspring size and offspring survival must differ among environments, or among populations, for environment- or population-speciﬁc egg size optima to exist. Various hypotheses have been proposed to explain population variation in egg size and fecundity in ﬁshes. Many of these center on proposed selection responses to species- and age-speciﬁc differences in the quality of parental care (Sargent et al., 1987) and to seasonal (Rijnsdorp and Vingerhoed, 1994; Trippel, 1998), population (Kaplan and Cooper, 1984; Hutchings, 1991, 1997), and individual (Jonsson et al., 1996) differences in access to food resources. Quinn et al. (1995) suggested that among-population variation in sockeye salmon, Oncorhynchus nerka, egg size can be explained as adaptive responses to differences in the size composition of incubation gravel, arguing that the positive association between egg size and substrate size may be related to the latter’s inﬂuence on dissolved oxygen supplies relative to the surface-to-volume ratio constraints of eggs. Although somewhat more problematic to compare among populations, it has recently been shown that within-female variability in egg size can differ signiﬁcantly among populations, and that there is good reason to believe that these differences are adaptive. In a study of 10 brook trout populations, Koops et al. (2003) found that there is less egg size variability, both within and among females, when environments are more predictable, and hypothesised that females use variability in egg size to offset the cost of imperfect information about their environment when producing smaller eggs. Population differences in reproductive effort are thought to be associated with adaptive responses to age at maturity. For example, a decline in survival during

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potentially reproductive ages (adult survival), relative to that during prereproductive ages (juvenile survival), is predicted to favor genotypes that increase reproductive effort, in addition to maturing earlier in life (Gadgil and Bossert, 1970; Law, 1979). Evidence of such a direct response to selection has been forthcoming from Reznick et al.’s (1990) work on Trinidad guppies. From 30 to 60 generations after a shift in predator-induced mortality from adults to juveniles, guppies responded to the presumed increase in the ratio of adult to juvenile survival by reducing reproductive allotment and by increasing age at maturity. Life history comparisons among populations have also supported these predictions concerning effort and maturity. Comparing pumpkinseed sunﬁsh from ﬁve populations that experienced either high or low levels of overwinter mortality, Fox and Keast (1991) found males and females inhabiting high-mortality environments to mature earlier and to have signiﬁcantly higher GSIs than those inhabiting low-mortality environments. Among brook trout populations in Newfoundland, declines in the ratio of adult to juvenile survival are associated with earlier maturity and increase in reproductive effort, as approximated by GSI (Hutchings, 1993a). A negative association between age at maturity and reproductive effort (GSI and size-speciﬁc fecundity) has also been reported for yellow perch, Perca ﬂavescens (Jansen, 1996). Reductions in adult survival attributable to ﬁshing should also, in theory, be predicted to increase reproductive effort. Although there has been considerably less attention directed to such changes, there is evidence that temporal changes in size-speciﬁc fecundity in the orange roughy, Hoplostethus atlanticus, may reﬂect a life history response in reproductive effort to ﬁshing. Between 1987 and 1992, when the roughy stock off east Tasmania was reduced by 50%, individual fecundity increased 20% on average (Koslow et al., 1995). Law (1979) reported a 60% increase in the fecundity of 3-year-old northern pike, Esox lucius, 12 years after an experimental harvest in Lake Windermere, U.K. 3. Phenotypic Plasticity and Norms of Reaction When life history trait optima differ among environments inhabited largely at random with respect to genotype within and among generations, selection can be expected to act on the way in which a genotype alters its life history in response to environmental change; that is, selection will act on a genotype’s norm of reaction (Schmalhausen, 1949; Via and Lande, 1985; Schlichting and Pigliucci, 1998). Such adaptive phenotypic plasticity may underlie many life history responses by ﬁsh to environmental change, notably to nongenetic variation in individual growth rate, but also to differences in temperature, habitat quality, and food supply. Experimental studies have revealed how reaction norms for juvenile growth rate (a proxy for environmental change) in brown trout (Salmo trutta) (Einum

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and Fleming, 1999) and juvenile survival in brook trout (Hutchings, 1991) can be inﬂuenced by egg size, and there is corresponding evidence that growth rate in early life can negatively (Jonsson et al., 1996; Morita, et al., 1999) and positively (Morita et al., 1999) inﬂuence egg size as an adult in Atlantic salmon and white-spotted char (Salvelinus leucomaenis), respectively. By modeling reaction norms as threshold traits, researchers have been able to account for the inﬂuence of both environmental and genetic inﬂuences on age and size at maturity when explaining the maintenance of conditional alternative mating strategies in Atlantic salmon (Salmo salar) (Hutchings and Myers, 1994) and coho salmon (Oncorhynchus kisutch) (Hazel et al., 1990). Comparatively few studies have examined population variation in the plasticity of life history traits, that is, population differences in the ways in which individuals alter their life history in response to environmental change. Using optimality theory, Hutchings (1996) predicted the ﬁtness consequences associated with different ages at maturity and different rates of juvenile growth (a proxy for environmental change), permitting the construction of norms of reaction for age at maturity for different populations of brook trout in southeastern Newfoundland. In one population (Freshwater River), age at maturity was invariant with growth rate (individuals being favored to mature as early in life as possible), while in the other populations age at maturity was inversely related to growth. The adaptive basis for these population differences in reaction norms underscores the point that the ﬁtness advantages of delaying maturity (increased fecundity and higher overwinter survival because of larger body size) are inevitably balanced by the probability of realizing those beneﬁts. Thus, Freshwater River females appear to be favored to mature early in life, regardless of growth rate, because the survival costs of delaying maturity are too high relative to the apparently marginal beneﬁts of increased fecundity. The existence of genetic variation in the shapes of reaction norms in some taxa (Schlichting and Pigliucci, 1998; Pigliucci, 2001) raises the possibility that plastic responses by individuals to environmental change can be modiﬁed by selection. Furthermore, if natural selection can act on reaction norms, it is possible that selection induced by anthropogenic activities, such as ﬁshing, also may be important. The adaptive signiﬁcance of interpopulation differences in plasticity, and the possibility that selection is responsible for these differences, has recently been examined for ﬁve Norwegian populations of grayling (Haugen, 2000a,b,c; Haugen and Vøllestad, 2000, 2001). Since their introduction from Lesjaskogsvatn into Hårrtjønn and Øvre Mærrabottvatn in 1910, grayling have since dispersed among several other lakes in south-central Norway, including Aursjøen and Osbumagasinet. Over a period of time ranging from 9 to 22 grayling generations, there has been considerable divergence in life history among these

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populations, notably with respect to age at maturity, size at maturity, and fecundity (Haugen, 2000a,b,c). Population differences in life history have manifested themselves as differences in the shapes of reaction norms for age and size at maturity (Haugen, 2000b). Delayed age at maturity is associated with smaller size at maturity for grayling in four populations, with the population-level reaction norms crossing in state space. Interestingly, the reaction norm for grayling in Øvre Mærrabottvatn expresses an invariance in age at maturity with changes to size at maturity, all individuals maturing at age 3. Consistent with the explanation discussed previously for population differences in reaction norms for age at maturity in Newfoundland brook trout, there appears to be a direct association between the steepness of the grayling reaction norms and average adult mortality (Haugen, 2000b). For individuals aged 4 through 8 years, the instantaneous rate of mortality, Z, was highest (0.77) for Øvre Mærrabottvatn grayling, those apparently favored to reproduce as early in life as possible. By contrast, the reaction norms with the shallowest slopes, encompassing the greatest ranges of ages, are those for Hårrtjønn and Aursjøen grayling, which have the lowest rates of mortality (Z = 0.36 for both). To test the hypothesis that population differences in reaction norms are a result of selection, acting over a comparatively short period of time (9–22 generations), Haugen and Vøllestad (2000) undertook a common garden experiment in which they reared grayling from three different populations under the same experimental conditions in the laboratory. Speciﬁcally, they measured survival and speciﬁc growth rate during the ﬁrst 180 degree-days of exogenous feeding at three different temperatures. These temperatures corresponded to the average temperatures experienced by grayling in each of the three populations during this stage of life in the wild. The common garden experiments revealed signiﬁcant genetic differences in reaction norms for survival and growth rate among populations. Survival declined with temperature for the “cold” population (Aursjøen), peaked at the intermediate temperature for the “medium” population (Lesjaskogsvatn), and increased with temperature for the “warm” population (Hårrtjønn). Thus, survival was highest at the temperatures typically experienced by these grayling during this phase of their lives in the wild. There were also clear differences in plasticity for growth rates among population, although the fastest growth rates were not always achieved at the temperatures experienced by grayling in the wild (given the existence of life history trade-offs, this need not be unexpected). Thus, life history research on grayling has revealed genetic differences in reaction norms for life history traits among populations, providing data consistent with the hypothesis that the shapes of at least some reaction norms [temperature-dependent survival in early life) are adaptive and are the product of natural selection (see also the work by Hendry et al. (1998) on sockeye salmon, O. nerka].

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The results are also in agreement with research indicating that natural selection can take place over relatively short (10–20 generations) periods of time (Hendry and Kinnison, 1999). The question of whether ﬁshing can change the shapes of reaction norms by selection has received comparatively little attention. Reznick (1993) hypothesized that the primary effect may be to change the elevation of the reaction norms, assuming that ﬁshing would select against individuals genetically predisposed to mature at large body sizes. Considering how ﬁshing might affect the slopes of reaction norms, Hutchings (1993b, 1997, 2002) used age-speciﬁc survival and fecundity data on brook trout populations to predict how reaction norms for age, size, and effort at maturity might change in response to increases in adult mortality. As ﬁshing mortality increased, selection was predicted to favor a ﬂattening of reaction norms, notably for age and effort at maturity, such that individuals would be favored to reproduce as early in life as possible and to expend the maximum amount of effort at that age, irrespective of growth rate. Flattening of the reaction norm for age at maturity would be expected as the probability of realizing the ﬁtness beneﬁts of delayed maturity declines with increases in mortality due to ﬁshing. Similarly, as longevity declines with increased ﬁshing pressure, selection should favor increases in reproductive effort. It seems reasonable to conclude that ﬁshing can result in selective changes to reaction norms in heavily exploited populations (Hutchings, 2002). These changes may involve changes to both the slopes and the elevations of reaction norms for several life history traits. Detecting such changes, however, will be exceedingly difﬁcult, given the near absence of research on phenotypic plasticity and reaction norms on commercially exploited ﬁshes. Perhaps the primary reason that life history traits have not formed the primary basis for distinguishing stocks lies in the presumption that life history variability is predominantly environmental in origin. If so, then within the context of establishing management strategies for the purpose of providing protection for genetically distinguishable units, the use of life history variation thus becomes problematic. On the other hand, given their clear links to individual ﬁtness and population rates of increase, one could argue that it would be prudent to assume that life history variation among putative stocks has a genetic basis until demonstrated otherwise.

VI. CONCLUSIONS Molecular genetic markers and quantitative phenotypic characters both have advantages and disadvantages for delineating ﬁsh stocks. The main advantage to molecular markers is that they are direct measures of genetic differences, unaffected by any environmental differences between groups. Thus, a molecular

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genetic difference between groups is an unambiguous indication of genetic differentiation. These markers are also usually assumed to be neutral to selection, and are thus useful for identifying reproductively isolated groups and for determining the phylogenetic relationships among groups. However, this selective neutrality is the main drawback of molecular markers for delineating ﬁsh stocks. Selection can result in rapid genetic divergence, much more rapid than expected as a result of mutation and genetic drift. Selection can also generate genetic differentiation between groups in the face of gene ﬂow. Thus, adaptive genetic differences between recently diverged groups or between incompletely isolated groups are not likely to be reﬂected by neutral molecular genetic differences. The main advantage to quantitative phenotypic characters such as life history and morphological traits is that these traits are generally related to ﬁtness and respond to natural selection. Thus, local adaptation, rapid adaptive divergence between recently separated groups, and genetic differences maintained by selection in the face of gene ﬂow may all be reﬂected in these traits. The main disadvantage to phenotypic characters is that they are subject to environmental as well as genetic inﬂuences. Thus, differences between groups in these characters, even differences that appear to be adaptive, may reﬂect phenotypic plasticity rather than genetic differentiation. An experimental approach, in which individuals from different areas or groups are reared in common environments, is needed to disentangle these two sources of phenotypic variation. Common environment experiments to identify the genetic and environmental components of phenotypic variation are not feasible for many ﬁsh species, particularly marine ﬁshes. In these cases, a precautionary approach would be to tentatively treat groups characterized by persistent phenotypic differences as separate stocks, recognizing that these differences may be environmentally induced rather than genetically based. This is particularly true when differences are in life history traits affecting productivity and responses to exploitation. A difﬁculty with this approach is that when countergradient variation occurs, genetic variation among groups will be cryptic, manifest by phenotypic similarity rather than by differences. When common environment experiments cannot be conducted to disentangle genetic and environmental inﬂuences and uncover hidden countergradient variation, the best recourse may be to adopt a ‘holistic’ approach, employing a broad spectrum of complementary techniques (Begg and Waldman, 1999).

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Thompson, J. D. 1991. Phenotypic plasticity as a component of evolutionary change. Trends in Ecology and Evolution 6: 246–249. Thorpe, J. E. 1986. Age at ﬁrst maturity in Atlantic salmon, Salmo salar: freshwater period inﬂuences and conﬂicts with smolting. In D. J. Meerburg (ed.), Salmonid Age at Maturity. Canadian Special Publication of Fisheries and Aquatic Sciences 89, pp. 7–14. Thorpe, J. E., Morgan, R. I. G., Talbot, C., and Miles, M. S. 1983. Inheritance of developmental rates in Atlantic salmon, Salmo salar L. Aquaculture 33: 119–128. Thresher, R. E. 1999. Elemental composition of otoliths as a stock delineator in ﬁshes. Fisheries Research 43: 165–204. Thresher, R. E., Proctor, C. H., Gunn, J. S., and Harrowﬁeld, I. R. 1994. An evaluation of electronprobe microanalysis of otoliths for stock delineation and identiﬁcation of nursery areas in a southern temperate groundﬁsh, Nemadactylus macropterus (Cheilodactylidae). Fishery Bulletin 92: 817–840. Todd, T. N., Smith, G. R., and Cable, L. E. 1981. Environmental and genetic contributions to morphological differentiation in ciscoes (Coregoninae) of the Great Lakes. Canadian Journal of Fisheries and Aquatic Sciences 38: 59–67. Trippel, E. A. 1998. Egg size and viability and seasonal offspring production of young Atlantic cod. Transactions of the American Fisheries Society 127: 339–359. Trippel, E. A., Buzeta, M. L., and Hunt, J. J. 1995. Evaluation of the cost of reproduction in Georges Bank Atlantic cod (Gadus morhua): utility of otolith back-calculation. In D. H. Secor, J. M. Dean, and S. E. Campana, (eds.), Recent Developments in Fish Otolith Research. University of South Carolina Press, Columbia, SC, pp. 599–610. Trippel, E. A., Morgan, M. J., Fréchet, A., Rollet, C., Sinclair, A., Annand, C., Beanlands, D., and Brown, L. 1997. Changes in age and length at sexual maturity of northwest Atlantic cod, haddock and pollock stocks, 1972–1995. Canadian Technical Report of Fisheries and Aquatic Sciences No. 2157. Tyler, C. R. and Sumpter, J. P. 1996. Oocyte growth and development in teleosts. Reviews in Fish Biology and Fisheries 6: 287–318. Via, S. and Lande, R. 1985. Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution 39: 505–522. Via, S., Gomulkiewicz, R., De Jong, G., Scheiner, S. M., Schlichting, C. D., and Van Tienderen, P. H. 1995. Adaptive phenotypic plasticity: consensus and controversy. Trends in Ecology and Evolution 10: 212–217. Volpe, J. P. and Ferguson, M. M. 1996. Molecular genetic examination of the polymorphic Arctic charr Salvelinus alpinus of Thingvallavatn, Iceland. Molecular Ecology 5: 763–772. Vuorinen, J., Næsje, T. F., and Sandlund, O. T. 1991. Genetic changes in a vendace Coregonus albula (L.) population, 92 years after introduction. Journal of Fish Biology 39 (suppl. A): 193–201. Waddington, C. H. 1957. The Strategy of the Genes. Allen and Unwin, London, UK, 262 pp. Waldman, J. R. this volume, Chapter 10. Wainwright, P. C., Osenberg, C. W., and Mittelbach, G. G. 1991. Trophic polymorphism in the pumpkinseed sunﬁsh (Lepomis gibbosus Linnaeus): effects of environment on ontogeny. Functional Ecology 5: 40–55. Ward, R. D., Woodwark, M., and Skibinski, D. O. F. 1994. A comparison of genetic diversity levels in marine, freshwater, and anadromous ﬁshes. Journal of Fish Biology 44: 213–232. Webb, P. W. 1978. Fast-start performance and body form in seven species of teleost ﬁsh. Journal of Experimental Biology 74: 211–226. Webb, P. W. 1984. Form and function in ﬁsh swimming. Scientiﬁc American 251: 72–82. Werner, E. E. and Anholt, B. R. 1993. Ecological consequences of the trade-off between growth and mortality rates mediated by foraging activity. American Naturalist 142: 242–272.

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Wilson, A. C., Carlson, S. S., and White, T. J. 1977. Biochemical evolution. Annual Review of Biochemistry 46: 573–639. Wilson, A. J., Hutchings, J. A., and Ferguson, M. M. 2003. Selective and genetic constraints on the evolution of body size in a stream-dwelling ﬁsh. Journal of Evolutionary Biology 16: 584–594. Wimberger, P. H. 1991. Plasticity of jaw and skull morphology in the neotropical cichlids Geophagus brasiliensis and G. steindachneri. Evolution 45: 1545–1563. Wimberger, P. H. 1992. Plasticity of ﬁsh body shape. The effects of diet, development, family and age in two species of Geophagus (Pisces: Cichlidae). Biological Journal of the Linnean Society 45: 197–218. Winterbottom, R. and Emery, A. R. 1981. A new genus and two new species of gobiid ﬁshes (Perciformes) from the Chagos Archipelago, central Indian Ocean. Environmental Biology of Fishes 6: 139–149. Witte, F., Barel, C. D. N., and Hoogerhoud, R. J. C. 1990. Phenotypic plasticity of anatomical structures and its ecomorphological signiﬁcance. Netherlands Journal of Zoology 40: 278–298. Wood, C. C. and Foote, C. J. 1990. Genetic differences in the early development and growth of sympatric sockeye salmon and kokanee (Oncorhynchus nerka), and their hybrids. Canadian Journal of Fisheries and Aquatic Sciences 47: 2250–2260. Wood, C. C. and Foote, C. J. 1996. Evidence for sympatric genetic divergence of anadromous and nonanadromous morphs of sockeye salmon (Oncorhynchus nerka). Evolution 50: 1265–1279. Wootton, R. J. 1998. Ecology of Teleost Fishes. Chapman & Hall, London, UK. Wright, S. 1931. Evolution in Mendelian populations. Genetics 16: 97–159. Zastrow, C. E., Houde, E. D., and Saunders, E. H. 1989. Quality of striped bass (Morone saxatilis) eggs in relation to river source and female weight. Rapports et Procès-Verbaux des Réunions, Conseil International pour l’Exploration de la Mer 191: 34–42.

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5

The Use of Early Life Stages in Stock Identiﬁcation Studies JONATHAN A. HARE NOAA National Ocean Service, Center for Coastal Fisheries and Habitat Research, Beaufort, North Carolina, USA

I. The Stock Concept, Again II. Role of ELS Information in the Stock Concept A. ELSs and Discreteness of Spawning B. ELSs and Genetic Population Structure C. ELSs and Phenotypic Traits III. Examples of ELS Information in the Deﬁnition of Stocks A. Atlantic Herring in the Northwest Atlantic Ocean B. Japanese Eel in the Western Paciﬁc Ocean C. American Lobster in the Northwest Atlantic Ocean IV. Conclusions and Future Directions A. Increased Use of Geostatistics B. An Ecosystem Framework for the Inclusion of ELSs in Stock Identiﬁcation C. Modeling and Measuring Actual Planktonic Transport D. Spatial Population Models and the Stock Concept Acknowledgments References

I. THE STOCK CONCEPT, AGAIN A stock is a group of individuals for which population parameters can be meaningfully estimated for speciﬁc management applications, typically ﬁshery stock assessments (Brown et al., 1987; Carvalho and Nigmatullin, 1998; Begg and Waldman, 1999). The purpose of a ﬁshery stock assessment is to use various statistical and mathematical calculations to make quantitative predictions about Stock Identiﬁcation Methods Copyright © 2005 by Elsevier. All rights of reproduction in any form reserved.

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the status of exploited ﬁshes and their response to alternative management options (Hilborn and Walters, 1992). The parameters used in ﬁshery stock assessments include, but are not limited to, fecundity, reproductive rate, recruitment, immigration/emigration rate, age structure, size structure, growth rate, and natural mortality rate, as well as ﬁshing mortality rate, catchability, and accessibility. As such, stock has no strict biological deﬁnition (Brown et al., 1987), and the concept of stock lies at the intersection of biological organization and human activities (Secor, 1999). A main purpose of stock deﬁnition is to design management strategies that prevent the depletion of “weaker” stocks that might be harvested at an optimal rate appropriate for “stronger” stocks (Begg et al., 1999a). Misleading management strategies can originate from stock assessment models if several stocks or only a portion of a stock are the components actually modeled. Thus, stocks must be identiﬁed and then managed as discrete units (Kutkuhn, 1981; Brown et al., 1987; Begg et al., 1999a). There are two general methods by which stocks are deﬁned: by genotype and by phenotype (see Booke, 1981). A “genotypic” stock consists of randomly interbreeding members of a species whose genetic integrity persists whether they remain spatially and temporally isolated as a group or segregate for breeding and otherwise mix freely with other “genotypic” stocks (Kutkuhn, 1981). Thus, a “genotypic” stock is synonymous with an ecological population, which is deﬁned as “a group of conspeciﬁc organisms that occupy a more or less well-deﬁned geographic region and exhibit reproductive continuity from generation to generation” (Futuyma, 1986). In contrast, “phenotypic” stocks are deﬁned as intraspeciﬁc groups that differ in the expression of certain characters owing to environmental or genetic effects. The use of phenotypic characters in stock identiﬁcation diminished with the improvement in genetic techniques (Kutkuhn, 1981; Ihssen et al., 1981). However, the identiﬁcation of “phenotypic” stocks still has value, particularly when the characters used to distinguish stocks are the same parameters used in ﬁsheries stock assessments (e.g., growth, mortality, and maturity). The combined approach of multiple characters examining differing aspects of the spatial relationship of groups of individuals through time has led to the increasing use of a “holistic” approach, combining both “genotypic” and “phenotypic” concepts to identify stocks (Begg and Waldman, 1999; Smith et al., 2002). Several variations on the “genotypic” and “phenotypic” stock concepts have been proposed. One that warrants discussion, particularly in light of the “holistic” approach, is Clark’s (1968) and Secor’s (1999) “contingent” stock: a group of individuals that maintain spatial and temporal integrity by engaging in a distinct pattern of migration not shared by individuals of other contingents. Fish following a distinct pattern of movement through space and time will experience the same environment and interbreed, thereby potentially satisfying the conditions of “genotypic” and “phenotypic” stocks. Fish that are lost from a “contingent” potentially provide gene ﬂow to other “contingents,” sensu “vagrants” of Sinclair’s

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(1988) member-vagrant hypothesis. Whether contingents are ultimately used to deﬁne stocks is unknown, but the “contingent” concept is attractive because it combines ideas regarding distribution and dispersion/migration throughout the life cycle, which are important elements of several unifying hypotheses of ﬁsh population structure: Harden Jones’s (1968) migration triangle, Sinclair’s (1988) member-vagrant hypothesis, MacCall’s (1990) basin model, and Tsukamoto and Aoyama’s (1998) and Tsukamoto’s et al. (2002) migration loop hypothesis. Early life stages (ELSs) can be useful in identifying stocks in context of the “genotypic,” “phenotypic,” and “contingent” stock concepts. The purpose of this chapter is to review the use of planktonic ELSs, in the deﬁnition of ﬁsh and invertebrate stocks. Planktonic ELSs include eggs, larvae, and, in some species, juveniles. The emphasis will be on marine systems, however, the material should be relevant to freshwater systems as well. This chapter is not meant to provide a deﬁnitive review of all instances where ELS information has been used in stock delineation, nor is it a review of the methodology for collecting ELS data; any interested readers are referred to Heath (1992) and Wiebe and Benﬁeld (2003) for reviews of the latter topic. The intent is to review the role of ELS information in the stock concept, provide some examples of the application of ELS information to stock delineation, and conclude with a discussion of the future of ELS studies in stock identiﬁcation.

II. ROLE OF ELS INFORMATION IN THE STOCK CONCEPT All three stock deﬁnitions discussed above are in one form or another based on spatially and temporally discrete groups of individuals at some point during their life cycle. “Genotypic” stocks imply isolation, at least during spawning. “Phenotypic” stocks imply different phenotypes resulting from genotypic or environmental differences during the time when the phenotypic characters develop. The “contingent” concept bridges the “genotypic” and “phenotypic” deﬁnitions and implies discrete distributions during the life cycle (Fig. 5-1). Most marine ﬁshes and invertebrates have planktonic stages that last from days to months (Fig. 5-2). These planktonic eggs and larvae disperse over large areas owing to transport by ocean currents (Scheltema, 1986), and the traditional view is that larvae with longer planktonic durations are dispersed over larger areas. This traditional view carries to the stock concept: greater larval dispersal leads to more mixing of ELSs among spawning locations, which results in less stock structure. This simple view of larval dispersal and stock structure is partially supported by population genetic studies of marine invertebrates and ﬁsh. Invertebrate species with direct development (i.e., crawling juveniles developing directly from benthic egg masses) exhibit greater spatial genetic structure compared to related

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Jonathan A. Hare Eggs & Preadult Spawning Larvae Juvenile & Adult Spawning

“Genotypic”

“Phenotypic”

Latitude

“Contingent”

ng Lo

itu

de

Time

FIGURE 5-1. Conceptualization of three stock concepts. The “genotypic” stock concept emphasizes discrete spawning from generation to generation. ELSs are used to infer discrete spawning locations. The “phenotypic” stock concept emphasizes the phenotypic expression of characters, typically during the preadult and adult stages, but also larval and juvenile characters. The “contingent” stock concept emphasizes spatially discrete distributions integrated over the entire life cycle. “Genotypic” stocks are often explained in terms of larval transport mechanisms that maintain discrete larval distributions. Here, preadult is deﬁned as juvenile ﬁsh that use adult habitats rather than spatially discrete juvenile nursery habitats (presentation modiﬁed from Secor, 1999).

species with planktonic larvae (Yamada, 1989; Parsons, 1996). Similarly, ﬁsh with shorter planktonic durations have more genetic differences among locations than ﬁsh with longer planktonic durations owing to limited mixing across space (Doherty et al., 1995; Riginos and Victor, 2001). However, the inverse relation between planktonic duration and the amount of spatial population structure is not universal. Shulman and Bermingham (1996) found no evidence for a relationship between population structure and planktonic duration in a large-scale

Number of species examined

The Use of Early Life Stages in Stock Identiﬁcation Studies

90 80 70 60 50 40 30 20 10 0

wk <1

mo –2 k 1w

93

mo >2

Larval duration FIGURE 5-2. Distribution of larval durations in demersal marine ﬁsh and invertebrate species (modiﬁed from Bradbury and Snelgrove, 2001, with permission). Fish are displayed as solid bars and invertebrates as empty bars.

study of several ﬁsh species in the Caribbean (see also Lacson, 1992). One resolution to this apparent dichotomy was readily pointed out by Bradbury and Snelgrove (2001): planktonic duration is only a proxy for actual planktonic transport. Actual planktonic transport, which includes the successful arrival to juvenile habitat, is a function of multiple interacting factors: spawning, advection, dispersion, egg and larval survival, egg and larval behavior, stage durations and the distribution of juvenile habitat. Spawning time and location initiates the transport process (e.g., Iles and Sinclair, 1982; Checkley et al., 1999). Eggs and larvae are then advected and dispersed (actual physical dispersion) in the threedimensional ﬂow ﬁeld (e.g., Werner et al., 1997; Epifanio and Garvine, 2001). The vertical distribution of eggs and larvae modify the outcome of transport in vertically structured ﬂows (e.g., Cowen et al., 1993, 2000; Hare et al., 1999), and some species are able to affect their horizontal distributions through active swimming (e.g., Stobutzki and Bellwood, 1997; Leis and Carson-Ewart, 1997; Kingsford et al., 2002). Egg and larval survival are an important component of larval transport (e.g., Helbig and Pepin, 1998a,b; Cowen et al., 2000; Hare et al., 2002) and are affected by the abiotic environment, feeding conditions, and predation (e.g., Bailey and Houde, 1989; Dower et al., 1997, 2002; Pepin et al., 2002). Stage durations determine the time over which transport occurs and can be ﬁxed or variable (e.g., Cowen, 1991; Bradbury and Snelgrove, 2001). The distribution of ELSs must then intersect with appropriate juvenile habitat to make the transition from larvae to juvenile (e.g., Etherington and Eggleston, 2000; Steves et al., 2000; Chant et al., 2000). Thus, actual planktonic transport—the

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survival of individuals from spawning to arrival at suitable juvenile habitat—is distinct from potential larval dispersal. Actual planktonic transport is an important element in the population dynamics of marine organisms, as well as in the formation of “genotypic,” “phenotypic,” and “contingent” stocks. Many overriding theories of marine population biology involve ELSs: member vs. vagrant, open vs. closed populations, long-distance transport vs. local retention, recruitment limitation vs. density-dependence (Sinclair, 1988; Chesson, 1998; Armsworth, 2002; Strathmann et al., 2002). Local larval retention contributes to membership in a group, closed populations, and potentially density-dependent dynamics. Long-distance transport contributes to vagrancy, open populations, and potentially recruitment-limited dynamics. These contrasts are a matter of scale (Sinclair, 1988), but deﬁne the importance of ELSs to the structure of marine populations. Broadly speaking, “genotypic” stocks form where exchange of individuals among stocks is limited—actual planktonic transport is only one form of exchange that occurs during the life cycle. The primary use of ELSs in stock identiﬁcation pertains to “genotypic” stocks. Egg and larval distributions are used to deﬁne discrete spawning areas and evaluate the potential for mixing among spawning areas during the planktonic stages. A second use of ELSs involves quantifying actual larval transport to test hypotheses resulting from genetic studies of stock structure—many genetic studies use generalizations of actual larval transport to support their ﬁndings. A third use of ELSs in stock identiﬁcation involves characters from ELSs to deﬁne “phenotypic” stocks. A more detailed discussion of these three applications of ELSs to stock identiﬁcations follows.

A. ELSS

AND

DISCRETENESS

OF

SPAWNING

The distribution of eggs and early larvae indicate the spatial and temporal distribution of spawning. When the distributions of these stages are spatially discrete at one time, there is an indication of distinct spawning areas; when distributions are spatially continuous at one time, there is an indication of continuous spawning or mixing of eggs and larvae following distinct spawning. This application of ELSs to stock deﬁnition is the most common and is usually used in the context of deﬁning “genotypic” stocks (Neilson et al., 1988). As an example, Gaughan et al. (2002) found a near continuous spatial occurrence of sardine (Sardinops sagax) eggs along southwest Australia. In addition, Gaughan et al. (2001) used larval ages and measures of eastward ﬂow along South Australia and concluded there was potential for long-range larval transport from west to east along the coast. Both studies provided evidence of a single stock. In contrast, earlier studies found spatial differences in allozymes and otolith chemistry, indicating separate stocks (Edmonds and Fletcher, 1997, cited in Gaughan et al., 2001). These apparently conﬂicting results can be explained. For example,

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a panmictic population with freely mixing ELSs can experience localized selection that results in different allozyme frequencies among areas, or can experience local differences in the environment that result in spatial differences in otolith chemistry. Recognizing these conﬂicting results and the attributes of the different stock identiﬁcation techniques, Gaughan et al. (2002) concluded that management must give consideration to smaller-scale reproductive units, within a single breeding stock. The results and conclusions of Gaughan et al. (2001, 2002) emphasize the general limitation of egg and larval distribution studies in identifying “genotypic” stocks. “Genotypic” stocks require that most individuals spawned in an area either remain in that area to spawn or return to that area to spawn. Egg and larval distributions can be used to determine if spawning is spatially and temporally distinct. Movement of eggs and larvae after spawning can be inferred from age or stage distributions, model results, or actual observations (e.g., Gaughan et al., 2001), but it is impossible through ELS studies alone to determine if individuals remain in or return to their spawning location. The separation of ELSs in space and time further confounds conclusions regarding stock identiﬁcation. For example, based on larval distributions, Kendall and Walford (1979) concluded that blueﬁsh (Pomatomus saltatrix) spawn in the spring on the southeast United States shelf and in the summer on the northeast United States shelf. Two stocks could be inferred from the distinct distribution of ELSs, but Chiarella and Conover (1991) found spring-spawned blueﬁsh spawning in the summer, thereby refuting the possibility of “genotypic” stocks. A real problem when spawning is separated in space and time is determining the individual patterns in reproduction and migration (Hare and Cowen, 1993). The blueﬁsh example demonstrates important limitations of ELSs applied to stock identiﬁcation. For the “genotypic” stock deﬁnition, spawning distribution and mixing after spawning can be inferred, but the patterns of individual spawning and homing are not addressed. For the “contingent” stock deﬁnition, separation in time and space during ELSs provides only a partial picture of the temporal and spatial distribution of individuals throughout the life cycle (see Fig. 5-1).

B. ELSS

AND

GENETIC POPULATION STRUCTURE

The results of genetic stock identiﬁcation studies are often discussed in the context of dispersal during ELSs. Typically when genetic differences are found among locations, retention mechanisms for larvae within locations are identiﬁed. When genetic differences are not found among locations, mechanisms of larval transport among locations are discussed. As an example, Ruzzante et al. (1999) reviewed a series of studies that showed signiﬁcant differences in genetic composition among Atlantic cod (Gadus morhua) at a variety of geographic scales in the northwest Atlantic Ocean. They concluded that topographically inﬂuenced

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gyre-like circulation on cod spawning banks retain cod eggs, larvae, and juveniles, and facilitate late-juvenile/adult spawning site ﬁdelity, which in turn contributes to the observed genetic differences. Similarly, Stepien (1999) found signiﬁcant genetic divergences in Dover sole (Microstomus paciﬁcus) from Alaska to southern California and hypothesized that the observed differences result from barriers to larval dispersal or local retention of larvae in currents. As a ﬁnal example, Planes (1993) explained the genetic structure of convict surgeonﬁsh (Acanthurus triostegus) in the context of prevailing currents in French Polynesia and hypothesized that the existence of gyres around islands could cause local recruitment of larvae. The examination of ELS distributions and, more explicitly, the tracking of ELSs through time provides a method by which these hypotheses can be tested (see Drouin et al., 2002).

C. ELSS

AND

PHENOTYPIC TRAITS

Phenotypic stocks are most often deﬁned using characters from juvenile and adult ﬁsh collected at different locations. The range of characters used is large and includes body and otolith morphometrics (e.g., Swain and Foote, 1999; Cadrin and Friedland, 1999) and life history traits (e.g., Begg et al., 1999b). Analyses then test whether there are spatial differences in the traits in question. Phenotypic traits of eggs and larvae are rarely used in the deﬁnition of “phenotypic” stocks. Jamieson and Phillips (1993) provided one example. Speciﬁcally, the vertical distribution and size-at-settlement of Dungeness crab (Cancer magister) larvae was different between locations on the shelf and in the Straits of Georgia. These phenotypic differences would limit the mixing of larvae between locations, potentially contributing to genotypic separation. Larval morphometrics also have been used in stock identiﬁcation similar to adult morphometrics. Harding et al. (1993) used seven morphometric measurements of American lobster (Homarus americanus) larvae from the northwestern Atlantic Ocean and identiﬁed three spatial groups, inferring three “phenotypic” stocks. However, Harding et al. (1993) also found a signiﬁcant effect of temperature on the morphometric variables, and once this effect was removed, two spatial groups were identiﬁed. By removing the effects of a dominant environmental variable, Harding et al. (1993) extended their phenotypic analysis closer to “genotypic” stock identiﬁcation, but they point out that maternal effects, food resources, or other unknown environmental factors could cause the spatial differences in temperature-corrected morphometrics. In addition to behavior and morphometrics, other phenotypic and life history traits exhibited by larvae have been used in stock identiﬁcation studies. Burke et al. (2000) examined a suite of larval and early juvenile characters of summer ﬂounder (Paralichthys dentatus), including ingress patterns, size and develop-

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mental stage-at-ingress, meristics, and laboratory growth and survival. They concluded that observed differences support the hypothesis of two “phenotypic” stocks separated at Cape Hatteras, North Carolina. As another example, Castello and Castello (2003) argued that larval ﬁsh growth is controlled by the environment, and differences in growth can be used as a proxy for differences in the environment experienced. They found variable larval growth in anchovy (Engraulis anchoita), providing evidence for distinct stocks. Although ELS phenotypic traits are not commonly used in stock identiﬁcation, this approach may become more important because of recent studies demonstrating the importance of maternal and ELS traits to subsequent juvenile and adult traits and survival (Meekan and Fortier, 1996; Hare and Cowen, 1997; Searcy and Sponaugle, 2000, 2001).

III. EXAMPLES OF ELS INFORMATION IN THE DEFINITION OF STOCKS There are a number of examples of ELS information used in stock identiﬁcation. Some have already been discussed brieﬂy as they pertain to the value of ELSs in stock identiﬁcation. In this section, three case studies are presented where ELSs made a signiﬁcant contribution to the deﬁnition of stocks. The one overriding theme is that ELSs do not provide a comprehensive approach to stock identiﬁcation. Rather, ELSs contribute to a holistic approach, one using multiple characters pertaining to different facets of the stock concept (Begg and Waldman, 1999).

A. ATLANTIC HERRING

IN THE

NORTHWEST ATLANTIC OCEAN

Atlantic herring (Clupea harengus) provides the quintessential example of ELSs used in stock identiﬁcation. Atlantic herring are widely distributed on both sides of the North Atlantic. Since the late 1800s, “stocks” of herring have been identiﬁed based on speciﬁc spawning areas and times (see Sinclair, 1988). Primary spawning locations off the northeast United States include the Maine coast, Jeffreys Ledge, Nantucket Shoals, and Georges Bank (Fig. 5-3A). Iles and Sinclair (1982), Sinclair and Iles (1985), and Sinclair (1988) proposed that discrete spawning occurs in locations that promote larval retention and permit the persistence of larval distributions for a few months after hatching (Fig. 5-3B). Following the larval stage, juveniles and prespawning adults mix, but adults then home to their natal spawning areas. Thus, these discrete spawning areas represent distinct “genotypic” stocks. Just as larval distributions were used to support the existence of Atlantic herring stocks based on spawning areas, larval distributions also were used to refute aspects of the hypothesis. Fortier and Gagné (1990) found that Atlantic

A

B

C

< 2 wks

5-8 wks

2-5 wks

> 8 wks

FIGURE 5-3. (A) Spawning and (B) larval distributions of Atlantic herring (Clupea harengus) in the Gulf of Maine region of the northwestern Atlantic Ocean (from Sinclair, 1988, with permission). These data were used to support stock identiﬁcation based on spawning areas. (C) Composite larval Atlantic herring distributions by approximate age from 1971 to 1975 (from Smith and Morse, 1993). There are six gradations in the grayscale representing 0 (white, 1–2, 2–5, 5–10, 10–25, >25 (black) larvae 10m-2. Distributions indicate mixing of larvae after approximately 8 weeks between the Georges Bank and Nantucket Shoals spawning groups.

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herring larvae spawned in the St. Lawrence River during the spring were retained near spawning grounds, whereas larvae spawned during the fall were advected away from spawning grounds. Other studies have found that Atlantic herring larvae were advected away from spawning areas (e.g., Townsend et al., 1986; Chenoweth et al., 1989), bringing into question the retention of Atlantic herring larvae near spawning grounds. Based on nearly 20 years of ichthyoplankton data from the northeast United States continental shelf, Smith and Morse (1993) found evidence of discrete distributions of larvae <5 weeks old on Georges Bank and Nantucket Shoals, but also found evidence for mixing between the groups of larvae between 5 and 8 weeks old (Fig. 5-3C). The questions become: What is the actual transport of Atlantic herring larvae from discrete spawning areas and do adults home to natal spawning areas? Genetic stock identiﬁcation studies do not support the hypothesis that discrete spawning areas of Atlantic herring form “genotypic” stocks. Allozyme and restriction fragment length polymorphism data found little or no genetic difference between Atlantic herring from separate spawning locations (see Smith and Jamieson, 1986; Kornﬁeld and Bogdanowicz, 1987). Similarly, McPherson et al. (2001) found limited genetic differences between Georges Bank and coastal Nova Scotia Atlantic herring using microsatellites, but did ﬁnd differences between these two groups and Atlantic herring from the Cape Breton Lakes region of northern Nova Scotia. These genetic studies support the hypothesis of one, or at least only a few, genotypic stocks of Atlantic herring in the northwest Atlantic Ocean. McQuinn (1997) attempted to reconcile the two points of view—multiple stocks deﬁned by multiple discrete spawning areas vs. multiple discrete spawning areas but limited number of stocks. McQuinn (1997) framed his reconciliation in the metapopulation concept and pointed out that an important issue is the fate of individuals that “stray” from their natal spawning location. Thus, strays or migrants between local populations (i.e., spawning locations) are integral to the structure and dynamics of the larger metapopulation. Stocks in a metapopulation concept are reviewed elsewhere (Secor, 2004), and management of Atlantic herring does not currently use a metapopulation model, but managers are aware of the spatial complexity of Atlantic herring population structure (Stephenson et al., 2001). In the future, studies of actual planktonic transport between spawning locations will play a role in building the “metapopulation” stock concept for Atlantic herring in the northwest Atlantic Ocean.

B. JAPANESE EEL

IN THE

WESTERN PACIFIC OCEAN

The Japanese eel (Anguilla japonica) is distributed from south of Taiwan, throughout Japan, and along the coasts of China and Korea (see Fig. 5-4). Matsui (1957)

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FIGURE 5-4. Ages of Japanese eel (Anguilla japonica) elvers by latitude. Ages increase with latitude from 20°–25°N to 30–36°N, suggesting larvae could be from the same source, with age increasing with distance along the larval transport route. Age data from 20°–25°N were from elvers collected in Taiwan (Tzeng and Tsai, 1992). Age data from 30°–36°N were from elvers collected in Japan (Tsukamoto, 1990).

(cited in Tzeng and Tsai, 1992) proposed that Japanese eel spawn south of Okinawa and east of Taiwan, implying that there is one stock in the western Paciﬁc. Kuo (1971) and Chen (1975) (cited in Tzeng and Tsai, 1992) hypothesized a second spawning area southwest of Taiwan, based on the observation that elvers (i.e., larvae transforming into juveniles) were more abundant along the northern and western coasts of Taiwan (i.e., the opposite side of Taiwan from the presumed spawning location). Two aspects of the distribution of Japanese eel ELSs support the one stock hypothesis. First, ichthyoplankton surveys in the western Paciﬁc found progressively smaller and younger leptocephali (i.e., the larval stages of eel) eastward from Taiwan (Kajihara, 1988; Ozawa et al., 1989; Tsukamoto et al., 1989; Tsukamoto, 1992; Kimura et al., 1994). If there were two spawning locations, young larvae would be expected closer to Taiwan (Tsukamoto, 1992; Kimura et al., 1994). Second, Tzeng and Tsia (1992) hypothesized that if there were two stocks, then there would be older elvers on the southern and eastern Taiwanese coast, which result from spawning east of the Philippines, and younger elvers on the northern and western Taiwanese coasts, which result from more localized spawning southwest of Taiwan. Tzeng and Tsia (1992) found, however, that there were not signiﬁcant age differences among Taiwanese estuaries. In a similar study along the southern coast of Japan, Tsukamoto (1990) found similar ages among

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locations. Combining the age data from Japan and Taiwan indicates that the age of elvers entering estuaries increases as distance along the presumed larval transport route increases (Fig. 5-4) (see Kimura et al., 1994 for more detail on larval transport). Cheng and Tzeng (1996) also found a cline in ages at estuarine ingress, supporting the previous conclusions of Tsukamoto (1990) and Tzeng and Tsia (1992). A subsequent genetic study found no evidence for spatial or temporal genetic heterogeneity in glass eels (i.e., juvenile stage) from Taiwan, China, and Japan, providing further support for the one stock hypothesis (Ishikawa et al., 2001). Two explanations can be provided for the pattern of increasing age of eels from east to west. Either larvae are spawned in the east and transported west or spawning occurs over time from west to east, along the transport route. These two explanations for the same data illustrate the problem of analyzing distribution data, recognized by Andrewartha and Birch (1954): mapping the distribution at one time provides a static description confounding the effects of spawning, advection, dispersion, and survival. Knowing the source of larvae and mapping their distribution repeatedly over time provides an explicit deﬁnition of spawning, advection, dispersion, and survival. However, in the case of Japanese eel, Cheng and Tzeng (1996) found that speed of the Kuroshio Current coupled with the distances between sampling sites agreed with the difference in ages of estuarine ingress, supporting the hypothesis of one spawning site with subsequent larval transport along the transport route. The ability to interpret larval distributions and larval ages in the context of physical oceanography strengthens the inferences regarding actual planktonic transport, whether samples are collected at one time or at multiple times (see Hare and Cowen, 1991; Cowen et al., 1993).

C. AMERICAN LOBSTER

IN THE

NORTHWEST ATLANTIC OCEAN

American lobster (Homarus americanus) is distributed in the northwest Atlantic Ocean from Labrador to North Carolina, from the coast to the shelf edge. Lobster were traditionally harvested by an inshore trap ﬁshery, but an offshore trawl ﬁshery started in the 1950s and an offshore trap ﬁshery developed in the 1970s (Lockhart and Estrella, 1997). Concern grew that the heavily ﬁshed inshore lobsters depended on larval supply from offshore lobster, and the spread of ﬁshing into offshore areas could be a signiﬁcant threat to the inshore ﬁshery (Lockhart and Estrella, 1997). Fogarty (1998) demonstrated that even limited supply of lobster from offshore could explain the continued resilience of inshore lobster to very high ﬁshing mortality rates. The issue is one of stock structure: are inshore and offshore lobster components of the same stock? A number of studies have examined potential mixing of lobster between inshore and offshore areas and have reached different conclusions. Tagging

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studies found movement of lobsters from offshore to inshore (e.g., Cooper and Uzmann, 1971). Morphometric studies have found differences (Cadrin, 1995), and no differences (Harding et al., 1993; Cadrin, 1995), between inshore and offshore samples. However, morphometric differences were found between Gulf of St. Lawrence lobsters and individuals from more southern locations (Harding et al., 1993). Finally, genetic studies indicated little difference between inshore and offshore samples, but found some evidence for differences between Gulf of St. Lawrence samples and more southern samples (e.g., Tracey et al., 1975; Harding et al., 1997). ELSs of lobster also were used to examine the issue of stock delineation between inshore and offshore lobsters. Harding and Trites (1988) used larval surveys and drift card releases to infer that larvae spawned offshore (over Browns Bank) would be transported inshore (into the Bay of Fundy and along the coast of Maine). Katz et al. (1994) described an offshore to inshore gradient in the ontogenetic stages of larval lobster (Fig. 5-5A), suggesting offshore to inshore transport (or an inshore to offshore pattern of spawning with little transport). They developed a two-dimensional circulation model that indicated offshore to inshore transport of larvae could occur through a combination of advection, windinduced surface ﬂow, and directional swimming by late-stage larvae (Fig. 5-5B). Incze and Naimie (2000) examined lobster larval transport in the Gulf of Maine region using a three-dimensional numerical circulation model. Results showed examples of long-distance transport and local retention and indicated the potential importance of diurnal sea breeze in the cross-isobath transport of larvae. Incze and Naime (2000) did not expressly examine the cross-shelf transport of lobster larvae, but their model indicated the potential for mixing of larvae between locations. Data derived from ELSs contribute to the conclusion that inshore and offshore groups of lobster do not function independently and should be managed as one unit stock. Although there are data that support “phenotypic” stock separation (e.g., Cadrin, 1995), the precautionary approach and analyses of Fogarty (1998) indicate that larval supply from offshore needs to be considered in the management of the heavily exploited inshore lobster ﬁshery. One spatial difference that appeared in most comparisons was lobsters from the Gulf of St. Lawrence were distinct from lobsters further south. Harding et al. (1997) hypothesized that genotypic and phenotypic differences might occur as a result of “one-way” larval transport from the Gulf of St. Lawrence, forming a partial hydrographic barrier to larval transport. ELS studies could be used to test this stock hypothesis through broader-scale surveys of berried females and larvae, coupled with a larger spatial domain three-dimensional circulation model to examine actual planktonic transport.

A

B

FIGURE 5-5. (A) Cross-shelf distribution of American lobster (Homarus americanus) larvae by stage. The data show an increase in stage from offshore to inshore, suggesting onshore transport. PL: postlarvae. (B) Simulated transport of American lobster larvae as simulated by average currents, winddriven ﬂow, and diurnal horizontal swimming behavior (from Katz et al., 1994, with permission). 103

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IV. CONCLUSIONS AND FUTURE DIRECTIONS The strength of ELSs to stock identiﬁcation is their part in a holistic approach— one tool to examine the mechanisms responsible for forming “genotypic” stocks or to provide a set of characters for examining “phenotypic” stock structure. Further, understanding actual larval transport provides one piece of the contingent puzzle (see Fig. 5-1). Speciﬁcally, ELSs provide a variety of information applicable to stock identiﬁcation. Egg and larval distributions deﬁne spawning areas (Iles and Sinclair, 1982; Gaughan et al., 2002). Size, age, and stage of larvae used in combination with physical oceanographic data provide a measure of actual planktonic transport and assist in the interpretation of distributions (Smith and Morse, 1993; Katz et al., 1994; Gaughan et al., 2001). Genetic studies on larvae provide another facet to examining genetic population structure (Ruzzante et al., 1999) and for testing hypotheses regarding the role of planktonic transport in maintaining genetic population structure (Drouin et al., 2002). Larval traits contribute to the identiﬁcation of phenotypic stocks (Harding et al., 1993; Burke et al., 2000). In the future, improvements in modeling and quantifying exchange between local groups will enhance our ability to identify stocks and, ultimately, manage marine populations. In particular, research and development in four areas will greatly improve the application of ELSs to stock identiﬁcation: increased use of geostatistics in the analysis of ELS distributions, a system-level framework for the application of ELSs to stock identiﬁcation, bio-physical modeling of actual planktonic transport, and the development of a “metapopulation” stock concept.

A. INCREASED USE

OF

GEOSTATISTICS

Future stock identiﬁcation studies using egg and larval distributions will beneﬁt from increased use of geostatistics. Begg et al. (1999b) qualitatively examined egg, larval, juvenile, and adult distributions and combined this information with quantitative measures of other life history parameters to evaluate stock structure of Atlantic cod, haddock (Melanogrammus aegleﬁnus), and yellowtail ﬂounder (Limanda ferruginea). The qualitative examination of ELS distribution could have been improved by the use of quantitative analysis of the distribution data. The application of geostatistics to egg and larval distribution data is relatively new. Geostatistics are used to examine and quantify the spatial patterns of a given variable, such as abundance. Several studies have applied geostatistical techniques to quantify the distribution of ELSs. Fletcher and Sumner (1999) determined the spatial scale of different early stages of sardine (Sardinops sagax) using two-dimensional spatial correlograms (see also Bez and Rivoirard, 2001). Grioche et al. (2001) used variograms to deﬁne the links between larval distributions and

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environmental variables. Although these studies did not address stock identiﬁcation questions, the approaches could be used to quantitatively examine the link between egg, larval, and juvenile distributions. Such use would strengthen the inferences regarding the discreteness or lack of discreteness of ELSs spawned in particular areas and thereby contribute to stock identiﬁcation studies.

B. AN ECOSYSTEM FRAMEWORK IN STOCK IDENTIFICATION

FOR THE INCLUSION OF

ELSS

Most applications of ELSs to stock identiﬁcation involve a single species. However, in a given ecosystem, the ELSs of different species that are released or hatch in the same location at the same time will be subjected to the same physical processes of advection and dispersion. Subsets of these ELSs may interact differently with the physical environment owing to differences in vertical or horizontal behavior, survival, planktonic duration, or transition to juvenile habitats, and as a result their planktonic distributions will change accordingly. These spatially and temporally co-occurring groups of eggs and larvae are termed assemblages (Cowen et al., 1993), and by comparing distributions among and within assemblages in terms of life history similarities, with concomitant measures of the physical environment, insights can be gained into the processes that affect ELS distribution, transport, and, ultimately, the spatial structure of marine populations (Cowen et al., 1993; Hare et al., 2001). The use of ELSs in stock identiﬁcation will beneﬁt from an assemblage/ecosystem approach. For example, the stock structure of species on the northeast United States shelf has been studied extensively: Atlantic cod (e.g., Begg et al., 1999b; Ruzzante et al., 1999), haddock (e.g., Begg, 1998; Begg et al., 1999b), Atlantic herring (e.g., Kornﬁeld and Bogdanowicz, 1987; Sinclair, 1988), American lobster (e.g., Harding and Trites, 1988; Harding et al., 1997), yellowtail ﬂounder (e.g., Neilson et al., 1988; Begg et al., 1999b), American plaice (e.g., Neilson et al., 1988), and witch ﬂounder (e.g., Neilson et al., 1988). Many of these studies refer to ELSs and stock identiﬁcation. Doyle et al. (1993) deﬁned larval assemblages on the northeast United States shelf, and the occurrence of managed species in discrete larval assemblages (Table 5-1) indicates that the processes contributing to the formation of larval distributions of some of these species are similar (e.g., Atlantic cod, haddock, and American plaice). To the extent that ELSs affect stock structure, there should be similarities in the stock structure of species that are part of the same larval assemblage (e.g., Atlantic cod, haddock, and American plaice). Identifying these similarities and developing and testing hypotheses regarding the processes that result in similarities and/or dissimilarities in stock structure among species will strengthen the basis for understanding stock structure in marine systems. Through such comparisons of ELSs and stock structure

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TABLE 5-1. Larval Assemblage Membership of Species of Management Concern on the Northeast United States Continental Shelf as Deﬁned by Doyle et al. (1993)a Species Atlantic herring Clupea harengus Atlantic cod Gadus morhua Haddock Melanogrammus aegleﬁnus American plaice Hippoglossoides platessoides Atlantic mackerel Scomber scombrus Witch ﬂounder Glyptocephalus cynoglossus Yellowtail ﬂounder Limanda ferruginea Gooseﬁsh Lophius americanus Summer ﬂounder Paralichthys americanus Atlantic menhaden Brevoortia tyrannus Atlantic croaker Micropogonias undulatus

Spring

Summer

A1

Fall

Winter

A5

A7

A5

A1 A1 A2 A2

A4

A2

A4

A3

A4 A6 A6 A6

a

Species shown here are a subset of the total number of species examined by Doyle et al. (1993), and the assemblages indicated here have been renumbered from Doyle et al. (1993) for clarity. Analysis based on ichthyoplankton sampling conducted on the northeast continental shelf from 1977 to 1987. The same number indicates membership in the same larval assemblage.

of a suite of managed species occurring in an ecosystem, stock deﬁnition in individual species will improve, and management in general will move from a single species emphasis toward an ecosystem approach (National Marine Fisheries Service, 1999).

C. MODELING AND MEASURING ACTUAL PLANKTONIC TRANSPORT The next great improvement in the application of ELSs to stock identiﬁcation will come from quantifying actual planktonic transport through both modeling and observation. Modeling of larval transport is already well developed in the form of individual based bio-physical models (see review by Werner et al., 2001).

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Individual-based models, as applied to marine ﬁsh populations, were developed initially to examine individual feeding and the population consequences of feeding and growth (e.g., Beyer and Laurence, 1980; Laurence, 1985). Roughly during the same period, sophisticated and realistic circulation models were developed (e.g., Blumberg and Mellor, 1987), which initially predicted paths of passive or ﬁxed-depth particles, but then incorporated more realistic vertical and horizontal behaviors (Werner et al., 1993; Hare et al., 1999). The two approaches— trophodynamic and transport—were recently combined (Hermann et al., 1996; Hinckley et al., 2001), and as these models become more integrated and more complex, they will capture more of the processes involved in determining actual planktonic transport. The next step is applying the coupled, bio-physical individual-based models to stock identiﬁcation questions. As an example, Skogen et al. (1999) examined stock separation in northeast Atlantic blue whiting (Micromesistius poutassou) using a three-dimensional circulation model. They released ﬁxed-depth particles in the model domain from the southwest England shelf, along the shelf west of Ireland, to the northern shelf of Scotland, and then quantiﬁed the direction of transport. They found that southward drift occurred for particles released in the southern portion of the domain and northward drift occurred for particles in the northern portion of the domain, demonstrating potential stock separation on the basis of discrete transport of planktonic stages. However, over the 20 years examined, the “line” separating the two groups varied by 200 km, indicating a zone of mixing between the two potential stocks. In the end, the results were equivocal, but provide some evidence for differences in larval transport based on spawning area. In the future, coupling the stock identiﬁcation focus of Skogen et al. (1999) with more realistic models of actual planktonic transport will provide a powerful approach for addressing stock identiﬁcation questions based on ELSs. Another hurdle facing the use of ELSs in stock identiﬁcation is observing and quantifying actual larval transport from known source locations. The traditional view of long-distance transport and large, open marine populations is giving way to the view of spatially complex marine populations that are created in part by a continuum of long-distance transport and local retention (e.g., Cowen et al., 2000). However, without being able to follow larvae spawned at known locations, it is impossible to quantify actual larval transport and, as a result, impossible to quantify planktonic connectivity between spawning groups. A number of methods exist for marking larvae in the ﬁeld (see review by Thorrold et al., 2002). Markers can be either artiﬁcial or natural. A marker not discussed by Thorrold et al. (2002) is larvae that are transported beyond their spawning range. Larvae expatriated from the southeast to northeast United States shelf illustrate longdistance and cross-shelf transport mechanisms (Cowen et al., 1993; Hare et al., 2002), and larvae that spawn on the shelf and enter estuarine nursery habitats illustrate mechanisms of estuarine ingress and retention (Churchill et al., 1999;

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Forward et al., 1999). Although there are major logistical obstacles to actually following larvae in the ﬁeld (Thorrold et al., 2002), the application of marked eggs and larvae in the ﬁeld to study actual planktonic transport will greatly enhance the application of ELSs to stock identiﬁcation.

D. SPATIAL POPULATION MODELS

AND THE

STOCK CONCEPT

The growing awareness of the spatially complex structure of marine populations led to the reintroduction of the “contingent” stock hypothesis (Secor, 1999) and the call for a metapopulation approach to stock identiﬁcation (McQuinn, 1997; Thorrold et al., 2001; Smedbol and Wroblewski, 2002; but see Smedbol et al., 2002 for a critique of the term metapopulation as applied to marine ﬁshes). A critical element of these stock concepts is quantifying the exchange of individuals among separate groups. Even limited exchange will result in genetic homogeneity between the groups (Whitlock and McCauley, 1999), but from the point of view of population dynamics and stock identiﬁcation, different groups may exhibit different dynamics, even with exchange. In the future, stock identiﬁcation based on the “contigent” or “metapopulation” concept depends on quantifying exchange rates during all stages of the life history. A relevant example comes from weakﬁsh (Cynoscion regalis) along the east coast of the United States. Currently, ﬁshery management assumes one stock along the east coast of the United States, based on genetic homogeneity of allozymes, mtDNA, microsatellites, and intron markers (Crawford et al., 1989; Graves et al., 1992; Cordes and Graves, 2003). However, Thorrold et al. (2001) demonstrated signiﬁcant homing to estuarine nursery areas by adults using otolith microchemistry, and indicated that management would be improved by the application of a spatially structured population model to weakﬁsh along the east coast of the United States (Fig. 5-6). Rates of exchange between juvenile and adult stages can be quantiﬁed from data in Thorrold et al. (2001). However, rates of exchange between spawning and the juvenile stage are unknown. Rowe and Epifanio (1994a,b) found that weakﬁsh larvae are retained within estuaries owing to interactions between larval vertical distributions and estuarine ﬂow ﬁelds. However, weakﬁsh eggs and larvae have been collected on the inner shelf (e.g., Cowen et al., 1993; Berrien and Sibunka, 1999), indicating potential exchange of ELSs between estuarine systems. To fully apply spatially structured concepts to stocks and ﬁshery management, rates of exchange need to be quantiﬁed throughout the life cycle. In the case of weakﬁsh, this would require examining the transport and survival of larvae between estuaries along the east coast of the United States (Fig. 5-6). The “metapopulation” concept of Atlantic herring (Clupea harengus) offered by McQuinn (1997) presents similar challenges: the need to estimate the rate of

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First Year Second Year S E L J JO S A AO S Thorrold et al. (2001) ? ? Peconic Bay

?

?

?

?

?

?

?

?

Delaware Bay

Chesapeake Bay

Pamlico Sound

Coastal Georgia FIGURE 5-6. Schematic of life history of weakﬁsh (Cynoscion regalis) along the east coast of the United States. Spawning (S) occurs in or near estuaries (Daniels and Graves, 1994; Luczkovich et al., 1999). Mixing of eggs (E) and larvae (L) between estuarine spawning areas is unknown. Larvae do exhibit selective tidal stream transport that would result in larval retention within estuaries (Rowe and Epifanio, 1994a,b), but larvae have also been collected on the continental shelf (Cowen et al., 1993; Berrien and Sibunka, 1999), which could lead to transport between estuaries. Juveniles (J) use estuarine nursery habitats through the summer and into the fall. Juveniles then leave estuaries and move southward and offshore to juvenile overwintering grounds (JO) (Wilk, 1974), where ﬁsh from different spawning estuaries mix. Adults (A) also mix during winter (AO), but return to their juvenile estuaries with 65% to 80% ﬁdelity (Thorrold et al., 2001). There is apparently enough exchange to limit genetic differentiation between spawning sites (Cores and Graves, 2001), but the rate of natal homing indicates substantial population structure (Thorrold et al., 2001). To develop a spatially explicit population model, information regarding exchange of eggs and larvae between estuaries is needed.

exchange among local populations within a metapopulation. Here the modeling approaches previously described would be quite useful. For example, James et al. (2002) used a regional-scale hydrodynamic model to examine larval transport and metapopulation structure of reef ﬁsh in the Great Barrier Reef Marine

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Park. Future integration of biophysical models with spatial population structure concepts will truly advance the use of ELSs in stock identiﬁcation. The concepts of member-vagrant, open vs. closed populations, long-distance transport vs. local retention, and recruitment limitation vs. density dependence are all related (Sinclair, 1988; Armsworth, 2002; Strathmann et al., 2002) and relevant to spatially structured populations and the deﬁnition of stocks, particularly the “contingent” concept. With regard to ﬁsheries species, however, the easy trap is to focus on these concepts from the point of view of ELSs and ignore connections during other parts of the life cycle. The assumption that the planktonic larvae stage is the only stage of exchange between spatially separated groups is valid for sessile species. However, homing in weakﬁsh (Thorrold et al., 2001) and Atlantic herring (McQuinn, 1997) serves as an important reminder that most ﬁshery species move during all life stages, and that these movements are important to the spatial structure of the population. This reminder of the implementation of ELSs to stock identiﬁcation also serves to reiterate that the distribution and traits of ELSs are only one piece of the life cycle, and that the whole life cycle must be considered to examine spatial structure of ﬁsh stocks. ELSs can only be used to infer “genotypic” or “phenotypic” stocks; these stages do not provide direct evidence of generational consistency in spawning and rarely do they exhibit traits directly relevant to ﬁsheries management. Early life history stages should only be used in combination with other approaches (Begg and Waldman, 1999).

ACKNOWLEDGMENTS First, I thank Gavin Begg, Steve Cadrin, Kevin Friedland, and John Waldman for spurring me to think about early life stages and stock identiﬁcation. Emma Jugovich (Center for Coastal Fisheries and Habitat Research) provided assistance accumulating literature. Allyn Powell (Center for Coastal Fisheries and Habitat Research), Frank Hernandez (Center for Coastal Fisheries and Habitat Research), and Dariusz Fey (Morski Instytut Rybacki) reviewed earlier drafts of the manuscript. Katsumi Tsukamoto (Ocean Research Institute, The University of Tokyo), Bill Overholtz (Northeast Fisheries Science Center), and Mike Fogarty (Northeast Fisheries Science Center) reviewed and improved the sections on Japanese eel, Atlantic herring, and American lobster, respectively. Finally, Dennis Hedgecock (University of Southern California) contributed to my thoughts and to ﬁnding literature regarding exchange and genetic structure in marine populations.

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Bailey, K. M. and Houde, E. D. 1989. Predation on eggs and larvae of marine ﬁshes and the recruitment problem. Advances in Marine Biology 25: 1–67. Begg, G. A. 1998. A review of stock identiﬁcation of haddock, Melanogrammus aegleﬁnus, in the Northwest Atlantic Ocean. Marine Fisheries Review 60(4): 1–15. Begg, G. A. and Waldman, J. R. 1999. An holistic approach to ﬁsh stock identiﬁcation. Fisheries Research 43: 35–44. Begg, G. A., Friedland, K. D., and Pearce, J. B. 1999a. Stock identiﬁcation and its role in stock assessment and ﬁsheries management: an overview. Fisheries Research 43: 1–8. Begg, G. A., Hare, J. A., and Sheehan, D. D. 1999b. The role of life history parameters as indicators of stock structure. Fisheries Research 43: 141–163. Berrien, P. and Sibunka, J. 1999. Distribution patterns of ﬁsh eggs in the U.S. northeast continental shelf ecosystem, 1977–1987. NOAA Technical Report NMFS 145. Beyer, J. E. and Laurence, G. C. 1980. A stochastic model of larval growth. Ecological Modeling 8: 109–132. Bez, N. and Rivoirard, J. 2001. Transitive geostatistics to characterize spatial aggegrations with diffuse limits: an application on mackerel ichthyoplankton. Fisheries Research 50: 41–58. Blumberg, A. F. and Mellor, G. L. 1987. A Description of a Three-Dimensional Coastal Ocean Circulation Model. Three-Dimensional Coastal Ocean Models. American Geophysical Union, Washington, D.C. 208 pp. Booke, H. E. 1981. The conundrum of the stock concept—are nature and nurture deﬁnable in ﬁshery science? Canadian Journal of Fisheries and Aquatic Sciences 38: 1479–1480. Bradbury, I. R. and Snelgrove, P. V. 2001. Contrasting larval transport in demersal ﬁsh and benthic invertebrates: the roles of behavior and advective processes in determining spatial pattern. Canadian Journal of Fisheries and Aquatic Sciences 58: 811–823. Brown, B. E., Darcy, G. H., and Overholtz, W. 1987. Stock assessment/stock identiﬁcation: an interactive process. NOAA Technical Memorandum NMFS-SEFSC 199: 1–19. Burke, J. S., Monaghan, J. P., and Yokoyama, S. 2000. Efforts to understand stock structure of summer ﬂounder (Paralichthys dentatus) in North Carolina, USA. Journal of Sea Research 44: 111–112. Cadrin, S. X. 1995. Discrimination of American lobster (Homarus americanus) stocks off southern New England on the basis of secondary sex character allometry. Canadian Journal of Fisheries and Aquatic Sciences 52: 2712–2723. Cadrin, S. X. and Friedland, K. D. 1999. The utility of image processing techniques for morphometric analysis and stock identiﬁcation. Fisheries Research 43: 129–139. Carvalho, G. R. and Nigmatullin, C. M. 1998. Stock structure analysis and species identiﬁcation. FAO Fisheries Technical Papers 376: 199–232. Castello, L. and Castello, J. P. 2003. Anchovy stocks (Engraulis anchoita) and larval growth in the SW Atlantic. Fisheries Science 59: 409–421. Chant, R. J., Curran, M. C., Able, K. W., and Glenn, S. M. 2000. Delivery of winter ﬂounder (Pseudopleuronectes americanus) larvae to settlement habitats in coves near tidal inlets. Estuarine, Coastal and Shelf Science 51: 529–541. Checkley, D. M., Jr., Ortner, P. B., Werner, F. E., Settle, L. R., and Cummings, S. R. 1999. Spawning habitat of the Atlantic menhaden in Onslow Bay, North Carolina. Fisheries Oceanography 8 (suppl. 2): 22–36. Chen, T. S. 1975. Preliminary study of the elver of Anguilla japonica in Taiwan. China Fisheries Monthly 268: 11–19. Cheng, P. W. and Tzeng, W. N. 1996. Timing of metamorphosis and estuarine arrival across the dispersal range of the Japanese eel Anguilla japonica. Marine Ecology Progress Series 131: 87–96. Chenoweth, S. B., Libby, D. A., Stephenson, R. L., and Power, M. J. 1989. Origin and dispersion of larval herring (Clupea harengus) in coastal waters of eastern Maine and southwestern New Brunswick. Canadian Journal of Fisheries and Aquatic Sciences 46: 624–632.

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