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Beaulieu M. Capturing wild animal welfare: a physiological perspective. Biol Rev Camb Philos Soc 2024; 99:1-22. [PMID: 37635128 DOI: 10.1111/brv.13009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/29/2023]
Abstract
Affective states, such as emotions, are presumably widespread across the animal kingdom because of the adaptive advantages they are supposed to confer. However, the study of the affective states of animals has thus far been largely restricted to enhancing the welfare of animals managed by humans in non-natural contexts. Given the diversity of wild animals and the variable conditions they can experience, extending studies on animal affective states to the natural conditions that most animals experience will allow us to broaden and deepen our general understanding of animal welfare. Yet, this same diversity makes examining animal welfare in the wild highly challenging. There is therefore a need for unifying theoretical frameworks and methodological approaches that can guide researchers keen to engage in this promising research area. The aim of this article is to help advance this important research area by highlighting the central relationship between physiology and animal welfare and rectify its apparent oversight, as revealed by the current scientific literature on wild animals. Moreover, this article emphasises the advantages of including physiological markers to assess animal welfare in the wild (e.g. objectivity, comparability, condition range, temporality), as well as their concomitant limitations (e.g. only access to peripheral physiological markers with complex relationships with affective states). Best-practice recommendations (e.g. replication and multifactorial approaches) are also provided to allow physiological markers to be used most effectively and appropriately when assessing the welfare of animals in their natural habitat. This review seeks to provide the foundation for a new and distinct research area with a vast theoretical and applied potential: wild animal welfare physiology.
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Affiliation(s)
- Michaël Beaulieu
- Wild Animal Initiative, 5123 W 98th St, 1204, Minneapolis, MN, 55437, USA
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2
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Houston AI, Fromhage L, McNamara JM. A general framework for modelling trade-offs in adaptive behaviour. Biol Rev Camb Philos Soc 2024; 99:56-69. [PMID: 37609707 DOI: 10.1111/brv.13011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/24/2023]
Abstract
An animal's behaviour can influence many variables, such as its energy reserves, its risk of injury or mortality, and its rate of reproduction. To identify the optimal action in a given situation, these various effects can be compared in the common currency of reproductive value. While this idea has been widely used to study trade-offs between pairs of variables, e.g. between energy gain versus survival, here we present a unified framework that makes explicit how these various trade-offs fit together. This unification covers a wide range of biological phenomena, highlighting similarities in their logical structure and helping to identify knowledge gaps. To fill one such gap, we present a new model of foraging under the risk of predation and damage accumulation. We conclude by discussing the use and limitations of state-dependent optimisation theory in predicting biological observations.
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Affiliation(s)
- Alasdair I Houston
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
| | - Lutz Fromhage
- University of Jyväskylä, PO Box 35, Jyväskylä, 40014, Finland
| | - John M McNamara
- School of Mathematics, University of Bristol, Fry Building, Woodland Road, Bristol, BS8 1UG, UK
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3
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An L, Grimm V, Sullivan A, Turner II B, Malleson N, Heppenstall A, Vincenot C, Robinson D, Ye X, Liu J, Lindkvist E, Tang W. Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109685] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Budaev S, Kristiansen TS, Giske J, Eliassen S. Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing. ROYAL SOCIETY OPEN SCIENCE 2020; 7:201886. [PMID: 33489298 PMCID: PMC7813262 DOI: 10.1098/rsos.201886] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/04/2020] [Indexed: 05/08/2023]
Abstract
To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.
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Affiliation(s)
- Sergey Budaev
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Tore S. Kristiansen
- Research Group Animal Welfare, Institute of Marine Research, PO Box 1870, 5817 Bergen, Norway
| | - Jarl Giske
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
| | - Sigrunn Eliassen
- Department of Biological Sciences, University of Bergen, PO Box 7803, 5020 Bergen, Norway
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5
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Freire CA, Cuenca AL, Leite RD, Prado AC, Rios LP, Stakowian N, Sampaio FD. Biomarkers of homeostasis, allostasis, and allostatic overload in decapod crustaceans of distinct habitats and osmoregulatory strategies: an empirical approach. Comp Biochem Physiol A Mol Integr Physiol 2020; 248:110750. [DOI: 10.1016/j.cbpa.2020.110750] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 06/22/2020] [Accepted: 06/22/2020] [Indexed: 12/20/2022]
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Weidner J, Jensen CH, Giske J, Eliassen S, Jørgensen C. Hormones as adaptive control systems in juvenile fish. Biol Open 2020; 9:bio046144. [PMID: 31996351 PMCID: PMC7044463 DOI: 10.1242/bio.046144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022] Open
Abstract
Growth is an important theme in biology. Physiologists often relate growth rates to hormonal control of essential processes. Ecologists often study growth as a function of gradients or combinations of environmental factors. Fewer studies have investigated the combined effects of environmental and hormonal control on growth. Here, we present an evolutionary optimization model of fish growth that combines internal regulation of growth by hormone levels with the external influence of food availability and predation risk. The model finds a dynamic hormone profile that optimizes fish growth and survival up to 30 cm, and we use the probability of reaching this milestone as a proxy for fitness. The complex web of interrelated hormones and other signalling molecules is simplified to three functions represented by growth hormone, thyroid hormone and orexin. By studying a range from poor to rich environments, we find that the level of food availability in the environment results in different evolutionarily optimal strategies of hormone levels. With more food available, higher levels of hormones are optimal, resulting in higher food intake, standard metabolism and growth. By using this fitness-based approach we also find a consequence of evolutionary optimization of survival on optimal hormone use. Where foraging is risky, the thyroid hormone can be used strategically to increase metabolic potential and the chance of escaping from predators. By comparing model results to empirical observations, many mechanisms can be recognized, for instance a change in pace-of-life due to resource availability, and reduced emphasis on reserves in more stable environments.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Jacqueline Weidner
- University of Bergen, Department of Biological Sciences, Postboks 7803, N-5020 Bergen, Norway
| | | | - Jarl Giske
- University of Bergen, Department of Biological Sciences, Postboks 7803, N-5020 Bergen, Norway
| | - Sigrunn Eliassen
- University of Bergen, Department of Biological Sciences, Postboks 7803, N-5020 Bergen, Norway
| | - Christian Jørgensen
- University of Bergen, Department of Biological Sciences, Postboks 7803, N-5020 Bergen, Norway
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7
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Budaev S, Jørgensen C, Mangel M, Eliassen S, Giske J. Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00164] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Scott R, MacPherson B, Gras R. A comparison of stable and fluctuating resources with respect to evolutionary adaptation and life-history traits using individual-based modeling and machine learning. J Theor Biol 2018; 459:52-66. [PMID: 30243755 DOI: 10.1016/j.jtbi.2018.09.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 09/08/2018] [Accepted: 09/17/2018] [Indexed: 10/28/2022]
Abstract
There are three non-mutually-exclusive key strategies evolved by gene pools to cope with fluctuating food resource availability, including evolutionary adaptation, phenotypic plasticity, and migration. We focus primarily on evolutionary adaptation and behavioral plasticity, which is a type of phenotypic plasticity, resulting in life-history changes as ways of dealing with fluctuations in food resource availability. Using EcoSim, a predator-prey individual-based model, we compare individuals with stable food resources with those in environments where there are fluctuating food resources in terms of adaptation through behavioral plasticity and evolution. The purpose of our study is to determine whether evolution and behavioral plasticity truly play a role in adapting to an environment with fluctuating food resources, as well as to determine whether there are specific gene divergences between gene pools in fluctuating food resource environments versus gene pools where food resources are relatively stable. An important result of our study is that individuals in environments that are unstable with respect to food resource availability exhibited significant differences in behaviors versus those in environments with stable food resources. Although behavioral plasticity facilitates a rapid response to unstable food conditions, our study revealed the evolution of perceptual traits such as vision range in reaction to fluctuating food resources, indicating the importance of evolution in adapting to unstable resource environments in the long run. Moreover, using decision trees, we found that there were significant behavioral gene divergences between individuals in environments with fluctuating food resources as opposed to individuals in environments with stable food resources.
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Affiliation(s)
- Ryan Scott
- University of Windsor, School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada.
| | - Brian MacPherson
- University of Windsor, Department of Biology, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada.
| | - Robin Gras
- University of Windsor, School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada; University of Windsor, Great Lakes Institute for Environmental Research, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada.
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MacPherson B, Mashayekhi M, Gras R, Scott R. Exploring the connection between emergent animal personality and fitness using a novel individual-based model and decision tree approach. ECOL INFORM 2017. [DOI: 10.1016/j.ecoinf.2017.06.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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10
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McKenzie DJ, Axelsson M, Chabot D, Claireaux G, Cooke SJ, Corner RA, De Boeck G, Domenici P, Guerreiro PM, Hamer B, Jørgensen C, Killen SS, Lefevre S, Marras S, Michaelidis B, Nilsson GE, Peck MA, Perez-Ruzafa A, Rijnsdorp AD, Shiels HA, Steffensen JF, Svendsen JC, Svendsen MBS, Teal LR, van der Meer J, Wang T, Wilson JM, Wilson RW, Metcalfe JD. Conservation physiology of marine fishes: state of the art and prospects for policy. CONSERVATION PHYSIOLOGY 2016; 4:cow046. [PMID: 27766156 PMCID: PMC5070530 DOI: 10.1093/conphys/cow046] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 08/17/2016] [Accepted: 09/13/2016] [Indexed: 05/24/2023]
Abstract
The state of the art of research on the environmental physiology of marine fishes is reviewed from the perspective of how it can contribute to conservation of biodiversity and fishery resources. A major constraint to application of physiological knowledge for conservation of marine fishes is the limited knowledge base; international collaboration is needed to study the environmental physiology of a wider range of species. Multifactorial field and laboratory studies on biomarkers hold promise to relate ecophysiology directly to habitat quality and population status. The 'Fry paradigm' could have broad applications for conservation physiology research if it provides a universal mechanism to link physiological function with ecological performance and population dynamics of fishes, through effects of abiotic conditions on aerobic metabolic scope. The available data indicate, however, that the paradigm is not universal, so further research is required on a wide diversity of species. Fish physiologists should interact closely with researchers developing ecological models, in order to investigate how integrating physiological information improves confidence in projecting effects of global change; for example, with mechanistic models that define habitat suitability based upon potential for aerobic scope or outputs of a dynamic energy budget. One major challenge to upscaling from physiology of individuals to the level of species and communities is incorporating intraspecific variation, which could be a crucial component of species' resilience to global change. Understanding what fishes do in the wild is also a challenge, but techniques of biotelemetry and biologging are providing novel information towards effective conservation. Overall, fish physiologists must strive to render research outputs more applicable to management and decision-making. There are various potential avenues for information flow, in the shorter term directly through biomarker studies and in the longer term by collaborating with modellers and fishery biologists.
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Affiliation(s)
- David J. McKenzie
- Centre for Marine Biodiversity Exploitation and Conservation, UMR MARBEC (CNRS, IRD, IFREMER, UM), Place E. Bataillon cc 093, 34095 Montpellier, France
| | - Michael Axelsson
- Department of Biological and Environmental Sciences, University of Gothenburg, Medicinaregatan 18, 413 90 Gothenburg, Sweden
| | - Denis Chabot
- Fisheries and Oceans Canada, Institut Maurice-Lamontagne, Mont-Joli, QC, CanadaG5H 3Z4
| | - Guy Claireaux
- Université de Bretagne Occidentale, UMR LEMAR, Unité PFOM-ARN, Centre Ifremer de Bretagne, ZI Pointe du Diable. CS 10070, 29280 Plouzané, France
| | - Steven J. Cooke
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON, CanadaK1S 5B6
| | | | - Gudrun De Boeck
- Systemic Physiological and Ecotoxicological Research (SPHERE), Department of Biology, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
| | - Paolo Domenici
- CNR–IAMC, Istituto per l'Ambiente Marino Costiero, 09072 Torregrande, Oristano, Italy
| | - Pedro M. Guerreiro
- CCMAR – Centre for Marine Sciences, Universidade do Algarve, 8005-139 Faro, Portugal
| | - Bojan Hamer
- Center for Marine Research, Ruder Boskovic Institute, Giordano Paliaga 5, 52210 Rovinj, Croatia
| | - Christian Jørgensen
- Department of Biology and Hjort Centre for Marine Ecosystem Dynamics, University of Bergen, 5020 Bergen, Norway
| | - Shaun S. Killen
- Institute of Biodiversity,Animal Health and Comparative Medicine, College of Medical,Veterinary and Life Sciences, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK
| | - Sjannie Lefevre
- Department of Biosciences, University of Oslo, PO Box 1066,NO-0316 Oslo,Norway
| | - Stefano Marras
- CNR–IAMC, Istituto per l'Ambiente Marino Costiero, 09072 Torregrande, Oristano, Italy
| | - Basile Michaelidis
- Laboratory of Animal Physiology, Department of Zoology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Göran E. Nilsson
- Department of Biosciences, University of Oslo, PO Box 1066,NO-0316 Oslo,Norway
| | - Myron A. Peck
- Institute for Hydrobiology and Fisheries Science, University of Hamburg, Olbersweg 24, Hamburg 22767, Germany
| | - Angel Perez-Ruzafa
- Department of Ecology and Hydrology, Faculty of Biology, Espinardo, Regional Campus of International Excellence ‘Campus Mare Nostrum’, University of Murcia, Murcia, Spain
| | - Adriaan D. Rijnsdorp
- IMARES, Institute for Marine Resources and Ecosystem Studies, PO Box 68, 1970 AB IJmuiden, The Netherlands
| | - Holly A. Shiels
- Core Technology Facility, The University of Manchester, 46 Grafton Street, Manchester M13 9NT, UK
| | - John F. Steffensen
- Marine Biological Section, Department of Biology, University of Copenhagen, Strandpromenaden 5, DK-3000 Helsingør, Denmark
| | - Jon C. Svendsen
- Section for Ecosystem-based Marine Management, National Institute of Aquatic Resources (DTU-Aqua), Technical University of Denmark, Jægersborg Allé 1, DK-2920 Charlottenlund, Denmark
| | - Morten B. S. Svendsen
- Marine Biological Section, Department of Biology, University of Copenhagen, Strandpromenaden 5, DK-3000 Helsingør, Denmark
| | - Lorna R. Teal
- IMARES, Institute for Marine Resources and Ecosystem Studies, PO Box 68, 1970 AB IJmuiden, The Netherlands
| | - Jaap van der Meer
- Department of Coastal Systems, NIOZ Royal Netherlands Institute for Sea Research and Utrecht University, PO Box 59, 1790 AB Den Burg, Texel, The Netherlands
| | - Tobias Wang
- Department of Zoophysiology, Aarhus University, 8000 Aarhus C, Denmark
| | - Jonathan M. Wilson
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), University of Porto, 4050-123 Porto, Portugal
| | - Rod W. Wilson
- Biosciences, College of Life & Environmental Sciences, University of Exeter, ExeterEX4 4QD, UK
| | - Julian D. Metcalfe
- Centre for Environment,Fisheries and Aquaculture Science (Cefas), Lowestoft Laboratory, Suffolk NR33 0HT, UK
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van der Post DJ, Franz M, Laland KN. Skill learning and the evolution of social learning mechanisms. BMC Evol Biol 2016; 16:166. [PMID: 27553961 PMCID: PMC4995764 DOI: 10.1186/s12862-016-0742-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 08/16/2016] [Indexed: 11/23/2022] Open
Abstract
Background Social learning is potentially advantageous, but evolutionary theory predicts that (i) its benefits may be self-limiting because social learning can lead to information parasitism, and (ii) these limitations can be mitigated via forms of selective copying. However, these findings arise from a functional approach in which learning mechanisms are not specified, and which assumes that social learning avoids the costs of asocial learning but does not produce information about the environment. Whether these findings generalize to all kinds of social learning remains to be established. Using a detailed multi-scale evolutionary model, we investigate the payoffs and information production processes of specific social learning mechanisms (including local enhancement, stimulus enhancement and observational learning) and their evolutionary consequences in the context of skill learning in foraging groups. Results We find that local enhancement does not benefit foraging success, but could evolve as a side-effect of grouping. In contrast, stimulus enhancement and observational learning can be beneficial across a wide range of environmental conditions because they generate opportunities for new learning outcomes. Conclusions In contrast to much existing theory, we find that the functional outcomes of social learning are mechanism specific. Social learning nearly always produces information about the environment, and does not always avoid the costs of asocial learning or support information parasitism. Our study supports work emphasizing the value of incorporating mechanistic detail in functional analyses. Electronic supplementary material The online version of this article (doi:10.1186/s12862-016-0742-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel J van der Post
- Center for Social Learning and Cognitive Evolution, School of Biology, St Andrews University, Harold Mitchell Building, St Andrews, KY16 9TH, UK.
| | - Mathias Franz
- Leibniz Institute for Zoo and Wildlife Research, Alfred-Kowalke-Straße 17, Berlin, 10315, Germany
| | - Kevin N Laland
- Center for Social Learning and Cognitive Evolution, School of Biology, St Andrews University, Harold Mitchell Building, St Andrews, KY16 9TH, UK
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Eliassen S, Andersen BS, Jørgensen C, Giske J. From sensing to emergent adaptations: Modelling the proximate architecture for decision-making. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2015.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Ward TD, Algera DA, Gallagher AJ, Hawkins E, Horodysky A, Jørgensen C, Killen SS, McKenzie DJ, Metcalfe JD, Peck MA, Vu M, Cooke SJ. Understanding the individual to implement the ecosystem approach to fisheries management. CONSERVATION PHYSIOLOGY 2016; 4:cow005. [PMID: 27293757 PMCID: PMC4825417 DOI: 10.1093/conphys/cow005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 01/25/2016] [Accepted: 02/08/2016] [Indexed: 05/20/2023]
Abstract
Ecosystem-based approaches to fisheries management (EAFMs) have emerged as requisite for sustainable use of fisheries resources. At the same time, however, there is a growing recognition of the degree of variation among individuals within a population, as well as the ecological consequences of this variation. Managing resources at an ecosystem level calls on practitioners to consider evolutionary processes, and ample evidence from the realm of fisheries science indicates that anthropogenic disturbance can drive changes in predominant character traits (e.g. size at maturity). Eco-evolutionary theory suggests that human-induced trait change and the modification of selective regimens might contribute to ecosystem dynamics at a similar magnitude to species extirpation, extinction and ecological dysfunction. Given the dynamic interaction between fisheries and target species via harvest and subsequent ecosystem consequences, we argue that individual diversity in genetic, physiological and behavioural traits are important considerations under EAFMs. Here, we examine the role of individual variation in a number of contexts relevant to fisheries management, including the potential ecological effects of rapid trait change. Using select examples, we highlight the extent of phenotypic diversity of individuals, as well as the ecological constraints on such diversity. We conclude that individual phenotypic diversity is a complex phenomenon that needs to be considered in EAFMs, with the ultimate realization that maintaining or increasing individual trait diversity may afford not only species, but also entire ecosystems, with enhanced resilience to environmental perturbations. Put simply, individuals are the foundation from which population- and ecosystem-level traits emerge and are therefore of central importance for the ecosystem-based approaches to fisheries management.
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Affiliation(s)
- Taylor D. Ward
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, CanadaK1S 5B6
- Corresponding author: Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6.
| | - Dirk A. Algera
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, CanadaK1S 5B6
| | - Austin J. Gallagher
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, CanadaK1S 5B6
| | - Emily Hawkins
- Department of Biology, University of Ottawa, 30 Marie-Curie Private, Ottawa, ON, CanadaK1N 9B4
| | - Andrij Horodysky
- Department of Marine and Environmental Science, Hampton University, Hampton, VA 23668, USA
| | - Christian Jørgensen
- Department of Biology and Hjort Centre for Marine Ecosystem Dynamics, University of Bergen, PO Box 7803, Bergen 5020, Norway
| | - Shaun S. Killen
- Institute of Biodiversity, Animal Health, and Comparative Medicine, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK
| | - David J. McKenzie
- Equipe Diversité et Ecologie des Poissons, UMR5119 Ecologie des Systèmes Marins Côtiers, Université Montpellier, Place Eugène Bataillon, Montpellier cedex 5 34095, France
| | - Julian D. Metcalfe
- Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft Laboratory, Suffolk NR33 0HT, UK
| | - Myron A. Peck
- Institute of Hydrobiology and Fisheries Science, Center for Earth System Research and Sustainability, Olbersweg 24, Hamburg 22767, Germany
| | - Maria Vu
- Department of Biology, University of Ottawa, 30 Marie-Curie Private, Ottawa, ON, CanadaK1N 9B4
| | - Steven J. Cooke
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON, CanadaK1S 5B6
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