Abstract:
The size-selectivity of fishing gear is of particular interest to fisheries managers, aiding in the reduction of by-catch and increase of profits. Size-selectivity models have typically been fitted using generalised linear models (GLMs) and mixed-effects models implemented using software such as SAS. We investigate a Bayesian approach to fitting these models, with special focus on the comparability of frequentist and Bayesian models. We look at several case studies, which are used to establish the validity of our models by comparing our results to those published in previous analyses. Markov chain Monte Carlo (MCMC) diagnostic tests and posterior predictive checks (PPCs) for overdispersion are explored, and random-effects are shown to be the preferred method of modelling overdispersion in the data. Having formulated a general model, several extensions are investigated. The Poisson distribution is used as an alternative likelihood function, allowing the models to make use of any population size distribution information, and multiple random-effects models are implemented that were previously found to be too complex. Semiparametric selection curves—notably basis splines—are used with the constraint that they be continuous, non-decreasing functions of length. A new R package, BSM, is introduced as a simple tool for fitting many of the Bayesian selectivity models discussed in this thesis.