Journal Article

An application of the Bayesian approach to stock assessment model uncertainty

T. R. Hammond and C. M. O'Brien

in ICES Journal of Marine Science

Published on behalf of ICES/CIEM

Volume 58, issue 3, pages 648-656
Published in print January 2001 | ISSN: 1054-3139
Published online January 2001 | e-ISSN: 1095-9289 | DOI:
An application of the Bayesian approach to stock assessment model uncertainty

More Like This

Show all results sharing these subjects:

  • Environmental Science
  • Marine and Estuarine Biology


Show Summary Details


Bayesian methods have a number of advantages that make them especially useful in the provision of fisheries management advice: they permit formal decision analysis, and they facilitate the incorporation of model uncertainty. The latter may be particularly useful in the management of contentious fisheries, where different nations and interest groups may suggest alternative assessment models and management–each likely to imply different findings, even when using the same data. Such situations might be approached in a number of different ways. For example, one might attempt to choose a best model from all those available and to base decisions on it alone. Alternatively, one might make decisions that lead to acceptable outcomes under all envisaged models; or one could reach decisions that are good on average (where average is taken over the set of all competing models and is weighted by a measure of how well each model coheres with available information). This last approach is advocated in this paper, and a Bayesian technique for achieving it is presented and discussed. The main points of the paper are illustrated with a hypothetical application of the technique to the rebuilding of the biomass of haddock by a selective culling of seals.

Keywords: decision analysis; Bayesian networks; model uncertainty; ecosystem effects; fisheries management

Journal Article.  0 words. 

Subjects: Environmental Science ; Marine and Estuarine Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.