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Evaluation of performance of alternative management models of Pacific salmon (Oncorhynchus spp.) in the presence of climatic change and outcome uncertainty using Monte Carlo simulations

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An important management challenge is to maintain productive populations of Pacific salmon (Oncorhynchus spp.), despite highly variable environments and our weak understanding of future climatic conditions and mechanisms that link them to salmon. This understanding could be improved by including environmental covariates in salmon population models and applying advanced “meta-analyses” to large data sets to better estimate underlying functional relationships. However, the performance of such models needs to be determined in the context of an overall system. We therefore simulated a 15-population salmon fishery system and compared the performance (in terms of catch and an index of conservation concern) of 10 forecasting and stock assessment models, ranging from simple to complex, by stochastically simulating components of a salmon fishery using a “closed-loop simulation” (or “management strategy evaluation”) under a variety of plausible future climatic scenarios. We found that complex models perform better in some situations. However, their incremental benefits are small and are swamped by the large variability in outcomes of management actions caused by “outcome uncertainty”, which reflects noncompliance of fishing vessels with regulations as well as variation in catchability. Reduction of this outcome uncertainty should therefore be a top priority, as should evaluations of more complex stock assessment models before adopting them.

C’est un défi important de gestion que de maintenir des populations très productives de saumons du Pacifique (Oncorhynchus spp.) malgré les environnements très variables et une faible compréhension des conditions climatiques futures et des mécanismes qui les relient aux saumons. Cette compréhension pourrait être améliorée si on ajoutait des covariables environnementales dans les modèles démographiques de saumons et si on faisait des «méta-analyses» des grands ensembles de données afin de mieux estimer les relations fonctionnelles sous-jacentes. Cependant, la performance de tels modèles doit être déterminée dans le contexte d’un système global. Nous avons donc simulé un système de pêche de saumons comprenant 15 populations et comparé la performance (en ce qui a trait à la capture et à un indice de préoccupation pour la conservation) de 10 modèles de prévision et d’évaluation des stocks, allant de simples à complexes. Nous simulons de façon stochastique les composantes d’une pêche à saumons à l’aide d’une «simulation en boucle fermée» (ou d’une «évaluation des stratégies de gestion») dans une variété de scénarios climatiques plausibles dans le futur. Les modèles complexes fonctionnent mieux dans certaines circonstances. Cependant, leur bénéfice additionnel est petit et masqué par l’importante variabilité des résultats des actions de gestion causée par «l’incertitude des résultats» qui reflète le fait que les navires de pêche ne se conforment pas aux règlementations et que la capturabilité est variable. On devrait donc donner une forte priorité à la réduction de cette «incertitude des résultats»; il en va de même des évaluations des modèles plus complexes d’estimation des stocks avant leur adoption.

Document Type: Research Article

Publication date: December 1, 2009

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  • Published continuously since 1901 (under various titles), this monthly journal is the primary publishing vehicle for the multidisciplinary field of aquatic sciences. It publishes perspectives (syntheses, critiques, and re-evaluations), discussions (comments and replies), articles, and rapid communications, relating to current research on cells, organisms, populations, ecosystems, or processes that affect aquatic systems. The journal seeks to amplify, modify, question, or redirect accumulated knowledge in the field of fisheries and aquatic science. Occasional supplements are dedicated to single topics or to proceedings of international symposia.
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