Bayesian stock assessment using catch-age data and the sampling - Importance resampling algorithm

A Bayesian approach to fisheries stock assessment is desirable because it yields a probability density function (pdf) of population model parameters. This pdf can help to provide advice to fishery managers about the consequences of alternative harvest policies and convey uncertainty about quantities of interest such as population biomass. In stock assessment, catch-age data are commonly used to estimate population parameters. However, there are few catch-age analyses that use Bayesian methods. In this paper, we extend the sampling - importance resampling algorithm so that a pdf of population model parameters can be estimated using catch-age data and indices of relative abundance. We illustrate the procedure by estimating a 54-parameter pdf for yellowfin sole (Limanda aspera) in the eastern Bering Sea. The example demonstrates how catch-age data can markedly improve Bayesian estimation, and also illustrates the potential for significant biases in treating trawl survey abundance indices as absolute measures of stock size.