Automated profiling of social behaviors to assess the genetic basis of evolution of aggressive behaviors in .

Across the animal kingdom, social behaviors such as aggression are critical for survival and reproductive success. While there is significant variation in social behaviors within and between species, the genetic mechanisms underlying natural variation in social behaviors are poorly understood. A central challenge to investigating the mechanisms contributing to the evolution of social behaviors is that these behaviors are typically complex, making them a challenge to quantify. The Mexican tetra, is a powerful model for investigating the evolution of traits, as it is a single species that exists as populations of eyed, river-dwelling surface fish and blind cave-dwelling fish. The blind cavefish have evolved morphological and behavioral differences compared to surface fish, including reduced aggression. Here, we developed and validated an automated machine learning pipeline that integrates pose-estimation and supervised behavioral classification to track and quantify aggression-associated behaviors-striking, following, and circling. Using this pipeline, we established that these behaviors are quantitatively different between surface and cave fish during juvenile stages in , similar to what was observed previously in adults. Moreover, assessment of these aggressive behaviors in surface-cave F2 hybrid fish revealed that striking and following are strongly positively correlated, while striking and circling are negatively correlated, suggesting that these behaviors evolved through some shared genetic mechanisms. These findings demonstrate the power of automated tracking and behavioral phenotyping in multiple fish in and establish a foundation for future studies investigating the genetic basis of evolution of social behaviors.