A MAXIMUM-LIKELIHOOD APPROACH FOR ESTIMATING GROWTH FROM TAG-RECAPTURE DATA

The Fabens method is commonly used to estimate growth parameters k and l infinity in the von Bertalanffy model from tag-recapture data. Howe ver, the Fabens method of estimation has an inherent bias when individ ual growth is variable. This paper presents an asymptotically unbiasse d method using a maximum likelihood approach that takes account of ind ividual variability in both maximum length and age-at-tagging. It is a ssumed that each individual's growth follows a von Bertalanffy curve w ith its own maximum length and age-at-tagging. The parameter k is assu med to be a constant to ensure that the mean growth follows a von Bert alanffy curve and to avoid overparameterization. Our method also makes more efficient use nf thp measurements at tno and recapture and inclu des diagnostic techniques for checking distributional assumptions. The method is reasonably robust and performs better than the Fabens metho d when individual growth differs from the von Bertalanffy relationship . When measurement error is negligible, the estimation involves maximi zing the profile likelihood of one parameter only. The method is appli ed to tag-recapture data for the grooved tiger prawn (Penaeus semisulc atus) from the Gulf of Carpentaria, Australia.