Evidence map›Paper›PMID 27293757›Full record

ArticleConservation physiology2016

Understanding the individual to implement the ecosystem approach to fisheries management.

Taylor D Ward, Dirk A Algera, Austin J Gallagher, Emily Hawkins, Andrij Horodysky, Christian Jørgensen, Shaun S Killen, David J McKenzie, Julian D Metcalfe, Myron A Peck and 2 more

Abstract read
In one paragraph

Article in Conservation physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Methods to estimate marine functional connectivity: A primer.Ecological applications : a publication of the Ecological Society of America · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Taylor D WardFish Ecology and Conservation Physiology Laboratory, Department of Biology , Carleton University , 1125 Colonel By Drive, Ottawa, ON , Canada K1S 5B6.
Dirk A AlgeraFish Ecology and Conservation Physiology Laboratory, Department of Biology , Carleton University , 1125 Colonel By Drive, Ottawa, ON , Canada K1S 5B6.
Austin J GallagherFish Ecology and Conservation Physiology Laboratory, Department of Biology , Carleton University , 1125 Colonel By Drive, Ottawa, ON , Canada K1S 5B6.
Emily HawkinsDepartment of Biology , University of Ottawa , 30 Marie-Curie Private, Ottawa, ON , Canada K1N 9B4.
Andrij HorodyskyDepartment of Marine and Environmental Science , Hampton University , Hampton, VA 23668 , USA.
Christian JørgensenDepartment of Biology and Hjort Centre for Marine Ecosystem Dynamics , University of Bergen , PO Box 7803, Bergen 5020 , Norway.
Shaun S KillenInstitute of Biodiversity, Animal Health, and Comparative Medicine, Graham Kerr Building , University of Glasgow , Glasgow G12 8QQ , UK.
David J McKenzieEquipe Diversité et Ecologie des Poissons, UMR5119 Ecologie des Systèmes Marins Côtiers , Université Montpellier , Place Eugène Bataillon, Montpellier cedex 5 34095 , France.
Julian D MetcalfeCentre for Environment, Fisheries and Aquaculture Science (Cefas) , Lowestoft Laboratory , Suffolk NR33 0HT , UK.
Myron A PeckInstitute of Hydrobiology and Fisheries Science , Center for Earth System Research and Sustainability , Olbersweg 24, Hamburg 22767 , Germany.
Maria VuDepartment of Biology , University of Ottawa , 30 Marie-Curie Private, Ottawa, ON , Canada K1N 9B4.
Steven J CookeFish Ecology and Conservation Physiology Laboratory, Department of Biology , Carleton University , 1125 Colonel By Drive, Ottawa, ON , Canada K1S 5B6.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Conservation behaviourecosystem-based managementecosystem modelsfisheries managementindividual-based modelsindividual-level variation

Identifiers

PMID27293757
PMCPMC4825417

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.