Developing predictive growth models for Asian seabass using four generations of data

Abstract

Growth is an important trait in aquaculture breeding programs and production. Cost-effective and precise measurement of growth traits provides valuable information for growth monitoring, feed management, stocking density, size grading, and health management. However, in aquaculture species, precise measurement of growth traits is challenging. Predictive growth models based on large data sets have the potential to tackle this challenge. Here, we developed predictive growth models for the analysis of growth parameters for the Asian seabass using the dataset from four separate generations (F1-F4), aged between 90- and 768-days post-hatch (dph) in a 20-year selective breeding program. To analyze the length-weight relationship of the fish, the equation W=aLb was used to estimate the parameters a and b. Our results showed that there were high positive correlations between body length and weight in each generation. In addition, to explore the relationships between body weight and ages, for each generation of fish, we fitted two different models: the von Bertalanffy Growth Function (VBGF) and the Gompertz model to analyze the Age-Weight Relationship (AWR). Out of the two, the VBGF, showed a higher goodness of fit. These developed predictive growth models, in combination with digital imaging, will reduce the cost and time for measuring growth traits for breeding programs of Asian seabass, and the effective management of commercial production.