Increasing biological realism of fisheries stock assessment: towards hierarchical Bayesian methods

Excessively high rates of fishing mortality have led to rapid declines of several commercially important fish stocks. To harvest fish stocks sustainably, fisheries management requires accurate information about population dynamics, but the generation of this information, known as fisheries stock assessment, traditionally relies on conservative and rather narrowly data-driven modelling approaches. To improve the information available for fisheries management, there is a demand to increase the biological realism of stock-assessment practices and to better incorporate the available biological knowledge and theory. Here, we explore the development of fisheries stock- assessment models with an aim to increasing their biological realism, and focus particular attention on the possibilities provided by the hierarchical Bayesian modelling framework and ways to develop this approach as a means of efficiently incorporating different sources of information to construct more biologically realistic stock-assessment models. The main message emerging from our review is that to be able to efficiently improve the biological realism of stock-assessment models, fisheries scientists must go beyond the traditional stock-assessment data and explore the resources available in other fields of biological research, such as ecology, life-history theory and evolutionary biology, in addition to utilizing data available from other stocks of the same or comparable species. The hierarchical Bayesian framework provides a way of formally integrating these sources of knowledge into the stock-assessment protocol and to accumulate information from multiple sources and over time. Key words: Bayesian statistics, fisheries management, harvesting, life histories, overfishing, stock assessment. Des taux de mortalite excessifs de poissons ont conduit a des declins rapides de plusieurs stocks de poissons commerciaux importants. Afin de recolter de facon durable les stocks de poissons, l'amenagement des pecheries necessite une information precise sur la dynamique des populations, mais la generation de cette information, connue sous le nom d'evaluation des stocks de poissons, repose traditionnellement sur la conservation plutot qu'a des approches de modelisation conduites a partir de donnees precises. Afin d'ameliorer l'information disponible pour l'amenagement des pecheries, on observe une demande croissante pour l'ameliorer du realisme biologique des pratiques d'evaluation des stocks et pour mieux incorporer la connaissance et la theorie biologique disponibles. Les auteurs examinent le developpement de modeles d' evaluation des stocks de poissons avec l'objectif d'augmenter leur realisme biologique, et de porter une attention particuliere sur les possibilites provenant du cadre de modelisation bayesien ainsi que les facons de developper cette approche comme moyen d'incorporer efficacement differentes sources d' information permettant de construire des modeles plus realistes pour l' evaluation des stocks. Le principal message emergeant de cette revue est a l'effet que pour arriver a ameliorer efficacement le realisme des modeles d'evaluation des stocks, les specialistes des pecheries doivent aller au-dela des donnees traditionnelles d' evaluation des stocks et explorer les ressources disponibles dans d'autres champs de recherche biologique, comme l'ecologie, la theorie du cycle vital et la biologie evolutive, en plus d' utiliser les donnees disponibles a partir d'autres stocks de la meme ou d' especes comparables. Le cadre bayesien hierarchique fournit une facon d'integrer formellement ces sources de connaissances dans le protocole d'evaluation des stocks et d' accumuler de l' information a partir d'autres sources avec le temps. Mots-cles : statistiques bayesiennes, amenagement des pecheries, recolte, cycle vital, surpeche. [Traduit par la Redaction]
Introduction Numerous commercially exploited fish stocks have rapidly declined as a result of excessively high rates of fishing mortality (FAO 2010). The demographic consequences of overexploitation have been realised through [...]