A superensemble approach for managing data-limited fisheries

Many fisheries around the world are not formally assessed, and for these fisheries it is hard to know whether they are overfished or not, and how much to fish to ensure that fishing remains sustainable. A suite of models has been developed that can be applied to fisheries where the only data available are time series of catches, but there is no information on trends in actual fish numbers. In a new study, these models are combined into a “superensemble” of models, in the same way that hurricane forecast models are combined to make predictions, and then tested with different types of rules for setting catches. The superensemble was able to reduce the risk of overfishing, but often resulted in very small catches, compared to continuing catches at current levels. In addition, when rules were based on controlling fishing effort, they were robust to errors in the data-limited catch-only models; but when rules were based on controlling fishing catches, this often resulted in depletion and overfishing. Thus the new superensemble approach offers another tool for better management of the many fisheries worldwide for which data collection is difficult and expensive. The paper is led by Jessica Walsh of Simon Fraser University, includes former SAFS postdoc Sean Anderson and current SAFS researcher Merrill Rudd as coauthors, and appears in the journal Fish and Fisheries.

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