1. Evolutionary Biology
  2. Physics of Living Systems
Download icon

A framework for studying behavioral evolution by reconstructing ancestral repertoires

  1. Damián G Hernández
  2. Catalina Rivera
  3. Jessica Cande
  4. Baohua Zhou
  5. David Stern
  6. Gordon J Berman  Is a corresponding author
  1. Centro Atómico Bariloche and Instituto Balseiro, Argentina
  2. Emory University, United States
  3. Janelia Research Campus, Howard Hughes Medical Institute, United States
Research Article
  • Cited 1
  • Views 1,011
  • Annotations
Cite this article as: eLife 2021;10:e61806 doi: 10.7554/eLife.61806

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

  1. Damián G Hernández

    Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche, Argentina
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8995-7495
  2. Catalina Rivera

    Department of Physics, Emory University, Atlanta, United States
    Competing interests
    No competing interests declared.
  3. Jessica Cande

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
  4. Baohua Zhou

    Department of Physics, Emory University, Atlanta, United States
    Competing interests
    No competing interests declared.
  5. David Stern

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1847-6483
  6. Gordon J Berman

    Department of Biology, Emory University, Atlanta, United States
    For correspondence
    gordon.berman@emory.edu
    Competing interests
    Gordon J Berman, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3588-7820

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.

Reviewing Editor

  1. Jesse H Goldberg, Cornell University, United States

Publication history

  1. Preprint posted: July 17, 2020 (view preprint)
  2. Received: August 5, 2020
  3. Accepted: September 1, 2021
  4. Accepted Manuscript published: September 2, 2021 (version 1)
  5. Version of Record published: September 16, 2021 (version 2)

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.

Metrics

  • 1,011
    Page views
  • 174
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Evolutionary Biology
    Chenlu Di et al.
    Research Article Updated

    Advances in genome sequencing have improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Recent genomic adaptation at disease genes has not been well characterized. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between mendelian, non-infectious disease genes and non-disease genes across distinct human populations from the 1000 Genomes Project. We find that mendelian disease genes have experienced far less selective sweeps compared to non-disease genes especially in Africa. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is almost non-existent at disease genes with higher recombination rates or lower numbers of associated disease variants. Because segregating recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. These results suggest that disease genes suffer from a transient inability to adapt as fast as the rest of the genome.

    1. Evolutionary Biology
    2. Microbiology and Infectious Disease
    Jennifer E Jones et al.
    Research Article Updated

    The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics.