Estimating uncertainty in fish stock assessment and forecasting

Kenneth Patterson, Robin Cook, Chris Darby, Stratis Gavaris, Laurence Kell, Peter Lewy, Benoît Mesnil, André Punt, Victor Restrepo, Dankert W. Skagen, Gunnar Stefánsson

Research output: Contribution to journalArticle

112 Citations (Scopus)

Abstract

A variety of tools are available to quantify uncertainty in age-structured fish stock assessments and in management forecasts. These tools are based on particular choices for the underlying population dynamics model, the aspects of the assessment considered uncertain, and the approach for assessing uncertainty (Bayes, frequentist or likelihood). The current state of the art is advancing rapidly as a consequence of the availability of increased computational power, but there remains little consistency in the choices made for assessments and forecasts. This can be explained by several factors including the specifics of the species under consideration, the purpose for which the analysis is conducted and the institutional framework within which the methods are developed and used, including the availability and customary usage of software tools. Little testing of either the methods or their assumptions has yet been done. Thus, it is not possible to argue either that the methods perform well or perform poorly or that any particular conditioning choices are more appropriate in general terms than others. Despite much recent progress, fisheries science has yet to identify a means for identifying appropriate conditioning choices such that the probability distributions which are calculated for management purposes do adequately represent the probabilities of eventual real outcomes. Therefore, we conclude that increased focus should be placed on testing and carefully examining the choices made when conducting these analyses, and that more attention must be given to examining the sensitivity to alternative assumptions and model structures. Provision of advice concerning uncertainty in stock assessments should include consideration of such sensitivities, and should use model-averaging methods, decision tables or management procedure simulations in cases where advice is strongly sensitive to model assumptions.

Original languageEnglish
Pages (from-to)125-157
Number of pages33
JournalFish and Fisheries
Volume2
Issue number2
Early online date21 Aug 2001
DOIs
Publication statusPublished - 29 Aug 2001

Keywords

  • bayes
  • bootstrap
  • fish stock
  • fisheries assessment
  • model-averaging
  • Monte Carlo
  • uncertainty

Cite this