Maximum likelihood estimation of growth and growth variability from tagging data

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Abstract
A maximum likelihood approach is described for the analysis of growth increment data derived from tagging experiments. As well as describing mean growth this approach allows the separate estimation of measurement error and growth variability, and uses mixture theory to provide an objective way of dealing with outliers. The method is illustrated using data for Pacific bonito (Sarda chiliensis) and the growth variability model is compared to other published models. The difference between growth curves derived from tagging and age‐length data is emphasised and new parameters are given for the von Bertalanffy curve that have better statistical properties, and represent better the growth information in tagging data, than do the conventional parameters.