Use of Bayesian hierarchical models to estimate northern abalone, Haliotis kamtschatkana, growth parameters from tag-recapture data

Bayesian hierarchical models were developed to estimate the growth parameters of northern abalone, Haliotis kamtschatkana, using tag-recapture data with a mixture of single and multiple recaptures. Individual variability in the growth parameters L-infinity and k of the von Bertalanffy model was incorporated in the analyses. The models developed fit the data well based on the Bayesian p-values. Variability in L-infinity for individuals was high relative to the variability in L. for the population, and variability in k for individuals was about the same as the variability in k for the population. Simulations showed that estimates of the growth parameters were accurate (relative biases <5%), when variability in both L-infinity and k or just in L. was accounted for. The true values of the parameters, L-infinity and k, were contained in the estimated 95% credibility intervals in 90-94 out of 100 simulation runs on 100 simulated data sets. Overall, allowing for variability for both L-infinity and k resulted in moderately more accurate estimates than allowing for just L-infinity On the contrary, estimates were unreliable when variability in just k was considered. Using the WinBUGS software program, the calculation procedure was rather simple irrespective of which growth parameter was modeled with variability. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.