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 statistical model used is extended to estimate abundance from commercial catch-at-age data for many of the major commercial fish species in the world. The model combines commercial catch-at-age data and research survey estimates of fish abundance; extends the model to allow correlated errors among ages within a year for the survey estimates of fish abundance. Method is formulated for modeling the fishing mortality on the oldest ages of the fish caught. Estimates are obtained using maximum likelihood. The level of correlation among ages is sufficiently large to produce large biases in the standard methods for some stocks. The statistical model that includes correlated errors greatly reduces bias and increases efficiency if the correlation in the estimation error is large.