Estimation of seasonal growth parameters using a stochastic Gompertz model for tagging data

Abstract

This paper addresses the problem of growth estimation from tagging data that exhibit large heterogeneity in size-increments. Precision in growth estimation is essential for stock assessment, especially for abalone fisheries, because they are managed in part by size limits. However, abalone growth is notoriously variable, changing dramatically between seasons and sites. It is also known that juvenile growth does not fit the commonly used yon Bertalanffy model. We present a modified deterministic Gompertz model for tagging data and three stochastic versions in which asymptotic length is a random parameter. The Kullback's informative mean was used to discriminate between models with respect to ..