A Bayesian hierarchical analysis of stockrecruit data: quantifying structural and parameter uncertainties

Abstract

Stock–recruit functions are important in fisheries stock assessment, but there is often uncertainty surrounding the appropriate stock–recruit model and its parameter values. Combining different stock–recruit data sets of related species through Bayesian hierarchical analysis can decrease these uncertainties and help to characterize appropriate stock–recruit forms and ranges of plausible parameter values. Two different stock–recruit functions (Beverton–Holt and Ricker) have been parameterized in terms of the steepness, which is a parameter that is comparable between populations. In the hierarchical analysis, the prior probability distribution of parameters for the cross-population variation in steepness is determined through a concise model structure. By calculating the Bayes' posteriors for alternative model forms, model uncertainty is accounted for. This methodology has been applied to Atlantic salmon (Salmo salar) stock–recruit data to provide predictions for the steepness of the stock–recruit function for Baltic salmon for which no stock–recruit data exist.