A Machine Learning Framework Integrating DeepLabCut and SimBA for Quantifying Aggressive Behavior in Swimming Crab Portunus trituberculatus

The swimming crab Portunus trituberculatus is a commercially important species in aquaculture, and its aggressiveness strongly influences productivity, making quantitative assessment a priority. However, such an assessment is hindered by behavioral complexity and a lack of high-throughput approaches. Here, we present a machine learning framework for quantifying aggressive behavior using DeepLabCut-SIMBA (Simple Behavioral Analysis). By converting video data into quantifiable durations of aggression-related events, we established a Time-weighted Aggression Index (TAI) that enables standardized, high-throughput assessment. The TAI showed strong concordance with conventional methods, confirming its validity. To demonstrate its applicability, we examined the correlation between TAI and serotonin levels, revealing distinct physiology-behavior associations. We then stratified crabs into three aggression categories based on TAI and conducted pairwise fighting trials. The frequency of aggressive interactions differed significantly across group combinations, indicating that TAI-based grouping effectively predicts behavioral outcomes. This framework offers a reliable, scalable tool for behavioral phenotyping and practical value for selective breeding programs.