A length-based Bayesian stock assessment model for the New Zealand abalone Haliotis iris

Abstract

We describe a length-based Bayesian model for stock assessment of the New Zealand abalone Haliotis iris (paua). We fitted the model to five data sets: catch-per-unit-effort (CPUE) and a fishery-independent survey index, proportions-at-length from both commercial catch sampling and population surveys, and tag–recapture data. We estimated a common component of error and used iterative re-weighting of the data sets to balance the residuals, removing the arbitrary data set weightings used in previous assessments. Estimates at the mode of the joint posterior distribution were used to explore sensitivity of the results to model assumptions and input data; the assessment itself was based on marginal posterior distributions estimated from Markov chain–Monte Carlo simulation. Assessments are presented for two stocks in the south of New Zealand. One may be recovering after recent catch reductions; the other is over-exploited and likely to decline further. Assessment for the first stock was robust; assessment for the second stock was sensitive to the CPUE data and may be too optimistic. We discuss future directions and potential problems with this approach.