The Standard Deviation Structure as a New Approach to Growth Analysis in Weight and Length Data of Farmed <i>Lutjanus guttatus</i>
Sergio G. Castillo-Vargasmachuca,
Eugenio Alberto Aragón-Noriega,
Guillermo Rodríguez-Domínguez,
Leonardo Martínez-Cárdenas,
Eulalio Arámbul-Muñoz,
Álvaro J. Burgos Arcos
Affiliations
Sergio G. Castillo-Vargasmachuca
Doctorado en Ciencias Biológico Agropecuarias, Universidad Autónoma de Nayarit, Carretera Tepic-Compostela Km 9, Xalisco 63780, Nayarit, Mexico
Eugenio Alberto Aragón-Noriega
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
Guillermo Rodríguez-Domínguez
Facultad de Ciencias del Mar, Universidad Autónoma de Sinaloa, Paseo Claussen S/N, Mazatlán 82000, Sinaloa, Mexico
Leonardo Martínez-Cárdenas
Doctorado en Ciencias Biológico Agropecuarias, Universidad Autónoma de Nayarit, Carretera Tepic-Compostela Km 9, Xalisco 63780, Nayarit, Mexico
Eulalio Arámbul-Muñoz
Doctorado en Ciencias Biológico Agropecuarias, Universidad Autónoma de Nayarit, Carretera Tepic-Compostela Km 9, Xalisco 63780, Nayarit, Mexico
Álvaro J. Burgos Arcos
Programa de Ingeniería en Producción Acuícola, Universidad de Nariño, Ciudadela Universitaria Torobajo, Calle 18 No. 50-02, Pasto 520001, Nariño, Colombia
In the present study, size-at-age data (length and weight) of marine cage-reared spotted rose snapper Lutjanus guttatus were analyzed under four different variance assumptions (observed, constant, depensatory, and compensatory variances) to analyze the robustness of selecting the right standard deviation structure to parametrize the von Bertalanffy, Logistic, and Gompertz models. The selection of the best model and variance criteria was obtained based on the Bayesian information criterion (BIC). According to the BIC results, the observed variance in the present study was the best way to parametrize the three abovementioned growth models, and the Gompertz model best represented the length and weight growth curves. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.
