Zusammenfassung: Estimation of von Bertalanffy growth parameters has received considerable attention in fisheries research. Since Sainsbury much of this research effort has centered on accounting for individual variability in the growth parameters. In this paper we demonstrate that, in analysis of tagging data, Sainsbury's method and its derivatives do not, in general, satisfactorily account for individual variability in growth, leading to inconsistent parameter estimates (the bias does not tend to zero as sample size increases to infinity). The bias arises because these methods do not use appropriate conditional expectations as a basis for estimation.

Zusammenfassung: The Bayes posterior probability distribution is a powerful way to represent uncertainty in fisheries stock assessments, and can be calculated for key population and policy parameters of practically any population dynamics model. But the calculation is unwieldy when probabilities are to be assigned to a large grid of parameter combinations. The computational burden can be reduced substantially by analytically integrating over at least two “nuisance parameters” that occur in most assessment models: the observation error variance and the catchability coefficient. This simplification allows the analyst and manager to focus more easily on population parameters (stock size, slope of recruitment curve) that are of direct policy interest.