A framework for studying behavioral evolution by reconstructing ancestral repertoires
Abstract
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.
Data availability
All behavioral region information is submitted with the article and will be posted publically, if accepted, on GitHub (https://github.com/bermanlabemory/behavioral-evolution). The original video data are too large to post (tens of TB), but will be made available upon request.
Article and author information
Author details
Funding
National Institute of Mental Health (MH115831-01)
- Gordon J Berman
Human Frontiers Science Program (RGY0076/2018)
- Gordon J Berman
Howard Hughes Medical Institute
- Jessica Cande
- David Stern
- Gordon J Berman
Research Corporation for Science Advancement (25999)
- Gordon J Berman
National Science Foundation (1806833)
- Catalina Rivera
Ministerio de Ciencia y Tecnología, Gobierno de la Provincia de Córdoba
- Damián G Hernández
The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Hernández et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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