Model uncertainty in the ecosystem approach to fisheries
Fisheries scientists habitually consider uncertainty in parameter values, but often neglect uncertainty about model structure. The importance of this latter source of uncertainty is likely to increase with the greater emphasis on ecosystem models in the move to an ecosystem approach to fisheries (EAF). It is therefore necessary to increase awareness about pragmatic approaches with which fisheries modellers and managers can account for model uncertainty and so we review current ways of dealing with model uncertainty in fisheries and other disciplines. These all involve considering a set of alternative models representing different structural assumptions, but differ in how those models are used. The models can be used to identify bounds on possible outcomes, find management actions that will perform adequately irrespective of the true model, find management actions that best achieve one or more objectives given weights assigned to each model, or formalise hypotheses for evaluation through experimentation. Data availability is likely to limit the use of approaches that involve weighting alternative models in an ecosystem setting, and the cost of experimentation is likely to limit its use. Practical implementation of the EAF should therefore be based on management approaches that acknowledge the uncertainty inherent in model predictions and are robust to it. Model results must be presented in a way that represents the risks and trade-offs associated with alternative actions and the degree of uncertainty in predictions. This presentation should not disguise the fact that, in many cases, estimates of model uncertainty may be based on subjective criteria. The problem of model uncertainty is far from unique to fisheries, and coordination among fisheries modellers and modellers from other communities will therefore be useful.
Authors: Hill, Simeon L., Watters, George M., Punt, Andre E., McAllister, Murdoch K., Le Quere, Corinne, Turner, John ORCID record for John Turner