Individual growth estimation in elasmobranchs: the multi-model inference approach

Abstract

Elasmobranchs play an important role in marine ecosystem and worldwide fisheries. Accurate and quantitative description of growth is crucial in modeling the demography and fisheries stock assessment. This study reviews the quantitative methods (asymptotic, non-asymptotic, and generalized), algorithms, and criteria for the model selection applied for growth modeling in elasmobranchs. We analyzed and contrasted the criteria for model selection, mainly between model selection using r2 and information theoretic approach. In marine organisms, the Akaike information criterion (AIC) has been frequently used as a measure of the relative goodness of fit of different growth models, applied to data from different species such as: Dasyatis americana, Carcharhinus acronotus, Carcharhinus plumbeus, Heterodontus portusjacksoni, Malacoraja senta, Mustelus asterias and Mustelus mustelus. We suggest the use of AIC to select the best growth model in elasmobranchs studies.