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Haroon Mumtaz & Konstantinos Theodoridis, 2015.This framework merged the strengths of different approaches to estimate growth parameters in harvested fish populations, considering modeling of individual variability of length-at-age, Bayesian inference, and distribution of errors from the Student-t model.
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Comparisons indicated that a Student-t model with mixed effects describes best back-calculated data regarding pink cusk-eel. Considering several information criteria, and comparing males and females, we have found that males grow significantly faster than females, and that length-at-age for both sexes exhibits extreme length observations. The proposed method was applied and compared to the standard methods using back-calculated length-at-age data for pink cusk-eel (Genypterus blacodes) off Chile. We presumed that errors in the VBGF can be assumed as a Student-t distribution, given the abundance of individuals with extreme length values. In this paper, we combine recent studies in non-Gaussian distributions and a Bayesian approach to model growth variability using back-calculated data in harvested fish populations. Modern statistical methods evaluate individual variation usually from mark-recapture data, and the parameters describing this function are estimated using empirical Bayes methods assuming Gaussian error. Trajectories of individual growth can be inferred using either mark-recapture or back-calculation of length-at-age from growth marks in hard body parts such as otoliths. The von Bertalanffy growth function (VBGF) with random effects has been widely used to estimate growth parameters incorporating individual variability of length-at-age.