A framework for studying behavioral evolution by reconstructing ancestral repertoires

eLife

Catalina Rivera¹  Gordon J Berman²  Damián G Hernández³  Baohua Zhou⁴  Jessica Cande⁵  David L Stern⁵ 

[1] Department of Physics, Emory University, Atlanta, United States;Department of Physics, Emory University, Atlanta, United States;Department of Biology, Emory University, Atlanta, United States;Department of Physics, Emory University, Atlanta, United States;Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche, Argentina;Department of Physics, Emory University, Atlanta, United States;Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States;Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States;

关键词: evolution;    behavior;    GLMM;    unsupervised learning;    D. melanogaster;   

DOI  :  10.7554/eLife.61806

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual’s behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, 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 mechanistic basis of behavioral evolution.

【 授权许可】

CC BY