A framework for studying behavioral evolution by reconstructing ancestral repertoires

TL;DR

This paper introduces a computational framework to reconstruct ancestral behavioral repertoires in fruit flies, revealing how behaviors evolve together and are influenced by internal states, advancing understanding of behavioral evolution's genetic basis.

Contribution

The study presents a novel method combining behavioral measurement and statistical modeling to infer ancestral behaviors and their co-evolution, offering new insights into behavioral evolution.

Findings

Intraspecific variation linked to behavioral 'mood' states

Identified groups of behaviors that evolved together

Framework enables studying genetic basis of behavioral evolution

Abstract

Although extensive behavioral changes often exist between closely related animal species, our understanding of the genetic basis underlying the evolution of behavior has remained limited. Here, we propose a new framework to study behavioral evolution by computational estimation of ancestral behavioral repertoires. We measured the behaviors of individuals from six species of fruit flies using unsupervised techniques and identified suites of stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the suites of behaviors exhibited by ancestral species, as well as the intra- and inter-species behavioral covariances. We found that much of intraspecific behavioral variation is explained by differences between individuals in the status of their behavioral hidden states, what might be called their "mood." Lastly, we propose a method to identify groups of behaviors that appear to have evolved together, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the genetic basis of behavioral evolution.