Machine learning-based aggressiveness assessment model construction for crabs: A case study of swimming crab Portunus trituberculatus

Aggressiveness trait-based selection is crucial for alleviating interspecies cannibalism in economic crab species and enhancing survival rates in aquaculture. However, there is a lack of efficient and simple methods for assessing aggressiveness. In this study, we measured aggressiveness of the swimming crab Portunus trituberculatus through repeated mirror tests and fighting experiments. Factor analysis and the K-means algorithm were used to assess aggressiveness quantitatively and qualitatively. A combination of multiple linear regression and support vector machine (SVM) analyses was employed to construct an aggressiveness assessment model for swimming crabs and explore the relationship between aggressiveness and fighting ability. The results showed significant correlations among repeated aggressive behaviors (attacking, chela extending, defending, crossing, reverse walking, and freezing). Aggression score was significantly correlated with fighting behaviors, and there were significant differences in fighting abilities among different levels of aggressiveness. This suggested that aggressive behaviors are consistent within individuals and that aggressiveness, as a personal trait, affects the fighting ability of swimming crabs. Aggression score (Y) and clustering results of K-means can serve as assessment indicators of aggressiveness. The predictive variables for the quantitative assessment model were relative movement distance (X1) and freezing duration (X2). The adjusted R-square of the optimized quantitative model was 0.72, it also had the smallest Sigma, AIC, MSE, and RMSE values and the best fitting regression equation, which was Y = 0.023X1 – 0.001X2 – 0.002. The predictor variables for the qualitative assessment model were relative movement distance, freezing frequency, and duration. SVM was used to construct the qualitative model, and the prediction accuracy was 92%, sensitivity was 84%, and specificity was 100%, indicating the model has a good classification and prediction effect. The machine learning-based aggressiveness assessment model constructed in this study provides a behavioral method for the selection and high-throughput measurement of economic crab species with excellent aggressiveness traits, giving it important industrial application value.