Introduction Stock assessment methods for sustainable fisheries

This special volume of the ICES Journal compiles contributions from the World Conference on Stock Assessment Methods for Sustainable Fisheries (July 2013, Boston, USA). The conference was the product of a strategic initiative on stock assessment methods that engaged many national and regional fishery management organizations to assure that scientists can apply the most appropriate methods when developing management advice. An inclusive workshop was designed to evaluate the performance of a variety of model categories by applying multiple models to selected case study data as well as simulated pseudo-data that had realistic measurement error. All model applications had difficulties in recovering the simulated stock and fishing mortality trends, particularly at the end of the assessment time series, when they are most important for informing fishery management. This general result suggests that the next steps in evaluating the performance of stock assessment methods should include stock status relative to sustainable reference points, catch advice, multi-model consideration, and alternative management procedures. Recognition of the limitations of conventional stock assessment methods should promote further development of data-limited approaches, methods with time-varying parameters, or spatial complexity, and a more revolutionary shift towards the application of multispecies and ecosystem models. The contributions in this volume address methodological themes that are expected to improve the scientific basis of fishery management. Furthermore, the limitations of stock assessment methods and associated uncertainty should be more extensively considered in fishery management strategies and tactical decisions. Recommendations developed during the conference called for the establishment of a global initiative to synthesize regional advances, form guidance on best practices, promote strategic investments, and highlight research needs for fish stock assessments.