fishes Article Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters Marcelo V. Curiel-Bernal 1,2, E. Alberto Aragón-Noriega 2 , Miguel Á. Cisneros-Mata 1,* , Laura Sánchez-Velasco 3, S. Patricia A. Jiménez-Rosenberg 3 and Alejandro Parés-Sierra 4 1 Instituto Nacional de Pesca y Acuacultura, Calle 20 No. 605-Sur, Guaymas 85400, Sonora, Mexico;
[email protected] 2 Unidad Guaymas del Centro de Investigaciones Biológicas del Noroeste, S.C. Km 2.35 Camino a El Tular, Estero de Bacochibampo, Guaymas 85454, Sonora, Mexico;
[email protected] 3 Instituto Politécnico Nacional-Centro Interdisciplinario de Ciencias Marinas, Av. Instituto Politécnico Nacional s/n, Playa Palo de Santa Rita, La Paz 23096, Baja California Sur, Mexico;
[email protected] (L.S.-V.);
[email protected] (S.P.A.J.-R.) 4 Departamento de Oceanografía Física, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Tijuana-Ensenada 3918, Ensenada 22860, Baja California, Mexico;
[email protected] * Correspondence:
[email protected]; Tel.: +52-622-22-25925 Abstract: Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of Citation: Curiel-Bernal, M.V.; California increasingly studied in recent times due to a perceived threat of extinction.