Modeling the individual growth of the Gulf corvina, Cynoscion othonopterus (Pisces: Sciaenidae), using a multi-model approach

Abstract

The Gulf corvina, Cynoscion othonopterus, is an endemic sciaenid of the Upper Gulf of California and Colorado River Delta biosphere reserve. It is a high-value fishery resource in this region. To sustainably manage this resource, it is necessary to address its individual growth pattern. Previous studies on C. othonopterus growth have fitted the von Bertalanffy growth model (VBGM) without examining alternative models. In this study, the main objective was to analyze the individual growth of the Gulf corvina via a multi-model inference (MMI) approach rather than simply applying the VBGM. Five growth models—VBGM, logistic, Gompertz, Schnute, and Schnute–Richards—were tested. The parameters of each model and their confidence intervals (CI) were computed using the maximumlikelihood method. The MMI was used to average the asymptotic length (L_∞). The best-fitting model was selected using the Akaike information criterion, and the Schnute–Richards growth model best described the growth of the Gulf corvina. The L_∞ values obtained via MMI averaged 735.0 mm (95% CI: 730.4–739.5 mm). The conclusion is that the growth of C. othonopterus exhibits a biphasic pattern that is better described by a higher-parameter model such as the Schnute–Richards model.