Improving Growth Predictions in Aquaculture through an Improved Bioenergetics Model Incorporating Feed Composition and Nutrient Digestibility for Largemouth Bass (Micropterus salmoides)

Abstract

Bioenergetics models serve as mechanistic tools to predict growth by linking energy intake, metabolic expenditure, and nutrient partitioning. However, traditional models rely primarily on gross energy (GE) intake, thereby oversimplifying the effects of feed composition and nutrient availability on fish growth. This work therefore proposed a refined bioenergetics model incorporating nutrient-specific digestibility coefficients (ADCs) and feed composition and tested using a compiled dataset (n=235; 165 for calibration and 70 for independent validation) and a field experimental dataset of largemouth bass (Micropterus salmoides). We first optimized parameters of a gross energy intake-based bioenergetics model, increasing R2 from 0.62 to 0.96 and thereby providing a calibrated foundation for subsequent refined model. The refined model demonstrated superior predictive performance on the compiled dataset (R2 = 0.97) with RMSE = 19.86 g and MAE = 10.31 g), representing reductions of 4.13% in RMSE and 19.98% in MAE and a 1.03% increase in R2 compared with the optimized GE-based model. In the field experiment, the refined model achieved high predictive accuracy (R2 = 0.98 and 0.97), whereas the optimized GE-based model showed poor performance (R2 = 0.33 and 0.06 respectively). This study is, to our knowledge, the first bioenergetics framework for largemouth bass that decomposes feed composition and nutrient-specific ADCs to compute macronutrient-resolved digestible energy, enabling formulation-aware growth prediction and nutrient-oriented optimization.