Abstract
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.
Highlights
Growth is the most important aspect in species demographic analysis
The von Bertalanffy growth model (VBGM) is the most used because [1] introduced the idea of using it for stock assessment
The main purpose of this study was to compare the previously used hypothesis of variability at age, assumed as constant, and depensatory and compensatory approaches in growth models against the observed variance proposed in this study
Summary
Growth is the most important aspect in species demographic analysis. The importance of growth is reflected in the extensive literature on individual growth in fisheries, aquaculture, and ecological studies. Increases in stock biomass are directly correlated to the individual’s growth and how they grow is a response to the environmental conditions in timing or location. This is one of the reasons why growth studies for particular species are assessed annually or geographically in the same year. Fishery and aquaculture studies are very common to gather information on ages and sizes (length, weight, etc.) to be later modeled or interpreted via mathematical equations. The basic principle is to predict the size (length, weight, etc.) as functions of age This empirical equation defines growth by balancing the negative and positive (anabolism and catabolism) processes within individuals
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