Fisheries management under climate and environmental uncertainty: control rules and performance simulation

Author:

Punt André E.12,A'mar Teresa3,Bond Nicholas A.4,Butterworth Douglas S.5,de Moor Carryn L.5,De Oliveira José A. A.6,Haltuch Melissa A.7,Hollowed Anne B.3,Szuwalski Cody1

Affiliation:

1. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA

2. CSIRO Wealth from Oceans Flagship, GPO Box 1538, Hobart, TAS 7001, Australia

3. Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Seattle, WA 98115, USA

4. Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA 98105, USA

5. Marine Resource Assessment and Management Group (MARAM), Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701, South Africa

6. CEFAS Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK

7. Northwest Fisheries Science Center, National Marine Fisheries Service, 2725 Montlake Boulevard East, Seattle, WA 98112, USA

Abstract

Abstract The ability of management strategies to achieve the fishery management goals are impacted by environmental variation and, therefore, also by global climate change. Management strategies can be modified to use environmental data using the “dynamic B0” concept, and changing the set of years used to define biomass reference points. Two approaches have been developed to apply management strategy evaluation to evaluate the impact of environmental variation on the performance of management strategies. The “mechanistic approach” estimates the relationship between the environment and elements of the population dynamics of the fished species and makes predictions for population trends using the outputs from global climate models. In contrast, the “empirical approach” examines possible broad scenarios without explicitly identifying mechanisms. Many reviewed studies have found that modifying management strategies to include environmental factors does not improve the ability to achieve management goals much, if at all, and only if the manner in which these factors drive the system is well known. As such, until the skill of stock projection models improves, it seems more appropriate to consider the implications of plausible broad forecasts related to how biological parameters may change in the future as a way to assess the robustness of management strategies, rather than attempting specific predictions per se.

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

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