Estimating species composition and quantifying uncertainty in multispecies fisheries: hierarchical Bayesian models for stratified sampling protocols with missing data

Abstract

Accurate landing statistics are among the most important data for the management of sustainable fisheries. For many fisheries, however, estimating species-specific landings and the associated uncertainty can be difficult, especially in the case of complex multispecies fisheries. Here we develop general and flexible methods for estimating species-specific landings, motivated by the mixed-species California groundfish fishery. We describe Bayesian generalized linear and hierarchical models for estimating species compositions from port sampling data and illustrate the application of each to several examples from California fisheries. Our hierarchical modeling approach provides a coherent statistical framework that can provide estimates of landings and uncertainty in the face of sparse and missing sampling data that compliment existing procedures for estimating landings. Furthermore, our methods provide ways to compare alternative model formulations and to maintain estimates of uncertainty when landings are aggregated across temporal or spatial scales. Our model structure is applicable to fisheries worldwide.