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¹ Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas

Obtaining best possible estimates of individual growth parameters is essential in studies 15 of physiology, fisheries management, and conservation of natural resources because growth is a key 16 component of population dynamics. In the present work we use data of an endangered fish species 17 to demonstrate the importance of selecting the right data error structure when fitting growth models 18 in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of 19 California increasingly studied in recent times because of a perceived threat of extinction. Previous 20 works estimated individual growth using the von Bertalanffy model assuming a constant variance 21 of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit 22 the von Bertalanffy and Gompertz models. We found consistent significant differences between the 23 constant and nonconstant error structure scenarios and provide an example of the consequences 24 using the growth performance index ?’ to show how using the wrong error structure can produced 25 growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and 26 other related species we recommend using the observed error structure to obtain the growth rate k 27 and asymptotic length L

Palabras clave: Multimodel inference; error structure; Totoaba; growth performance

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