Inferring behavioral homologies from dynamical models

Linking the evolution of animal behavior to the genes underlying it has proven challenging, largely due to our inability to find representations of behavior that allow for inter-species comparisons. Animals exhibit variability in many different traits across species, but certain traits are relatively conserved. These traits are known as homologies or homologous structures, and quantitatively identifying these homologies in behavior could provide a new approach for understanding the evolution of behavior. Here, we measure the behavioral repertoires of six fruit fly species, finding both the frequency of behavioral performance, as well as their temporal dynamics. By fitting dynamical models to these transitions, we can reproduce the summary statistics of our dataset, including long timescale dynamics and hierarchical structure. We show that features of these models can be used to define such homologies, providing future avenues for exploring the genetic basis of behavioral evolution.