An application of the Bayesian approach to stock assessment model uncertainty

Abstract

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 managementeach 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.

Discriminating alternative stock–recruitment models and evaluating uncertainty in model structure

“…They cautioned that using information criteria (such as AIC) can be misleading. These inconsistent results can cause disagreement over which models should be used for a particular stock or management decision (Patterson, 1999;Hammond and O'Brien, 2001).…”

What model suits ecosystem-based fisheries management? A plea for a structured modeling process

“…Therefore, to reduce uncertainty, ecosystem modelers usually examine the quality and compatibility of the inputs (Håkanson 2003;Murawski 2007), assume constancy in ecological processes (Magnứsson 1995;Bogstad et al 1997), and reduce or aggregate the species and inter-species links that are considered (Plagányi 2007). The total uncertainty of a model can be evaluated through the quality of outputs according to statistical parameters (e.g., standard deviation), classifications of input origin (e.g., a mere guess, imported from similar system or a precise estimate on local data; Pauly et al 2000), methods of sensitivity estimation (Hammond and O'Brien 2001;Kavanagh et al 2004), or/and calculations of error propagation (Snowling and Kramer 2001).…”

Baltic Herring Fisheries Management: Stakeholder Views to Frame the Problem

“…Structural uncertainty refers to the basic lack of knowledge about the components, dynamics, and internal interactions of a system , Charles 1998, De Young et al 1999, Walker et al 2003, and manifests as ambiguity, confusion, and controversy in management discussions (Charles 1998, Hammond and O'Brien 2001, Gréboval 2002.…”

“…The models can be merged into a graphical meta-model and extended into a quantitative form by adding probabilistic information related to the strength of the links between factors. The most advanced possibility is to build an extensive meta-model of the system using the model-averaging technique (Hammond andO'Brien 2001, Mäntyniemi et al 2009a). This would require observations to be compared with the models to weight the different models according to their correspondence with data (Mäntyniemi et al 2009a).…”

“…This has led to the questioning of conventional biological stock assessments as the basis for fisheries management (Hammond and O'Brien 2001, Schnute and Richards 2001, Degnbol and McCay 2007, Kraak et al 2010 and to proposals to expand the knowledge base toward a more holistic approach by incorporating interactions with other species, ecosystem characteristics, and socioeconomic issues (Schnute and Richards 2001, FAO 2003, Garcia and Charles 2007, CEC 2008. For this purpose, approaches need to be developed that allow the crossing of disciplinary lines, perspectives, forms of knowledge, and scales (Degnbol and McCay 2007, Garcia and Charles 2007.…”