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

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

  • 2,255
    Page views
  • 306
    Downloads
  • 5
    Citations

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

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

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

  1. Damián G Hernández
  2. Catalina Rivera
  3. Jessica Cande
  4. Baohua Zhou
  5. David Stern
  6. Gordon J Berman
(2021)
A framework for studying behavioral evolution by reconstructing ancestral repertoires
eLife 10:e61806.
https://doi.org/10.7554/eLife.61806
  1. Further reading

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Guillem Santamaria, Chen Liao ... Joao B Xavier
    Research Article Updated

    Microbes have disproportionate impacts on the macroscopic world. This is in part due to their ability to grow to large populations that collectively secrete massive amounts of secondary metabolites and alter their environment. Yet, the conditions favoring secondary metabolism despite the potential costs for primary metabolism remain unclear. Here we investigated the biosurfactants that the bacterium Pseudomonas aeruginosa makes and secretes to decrease the surface tension of surrounding liquid. Using a combination of genomics, metabolomics, transcriptomics, and mathematical modeling we show that the ability to make surfactants from glycerol varies inconsistently across the phylogenetic tree; instead, lineages that lost this ability are also worse at reducing the oxidative stress of primary metabolism on glycerol. Experiments with different carbon sources support a link with oxidative stress that explains the inconsistent distribution across the P. aeruginosa phylogeny and suggests a general principle: P. aeruginosa lineages produce surfactants if they can reduce the oxidative stress produced by primary metabolism and have excess resources, beyond their primary needs, to afford secondary metabolism. These results add a new layer to the regulation of a secondary metabolite unessential for primary metabolism but important to change physical properties of the environments surrounding bacterial populations.

    1. Evolutionary Biology
    2. Neuroscience
    Antoine Beauchamp, Yohan Yee ... Jason P Lerch
    Research Advance Updated

    The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously, we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Mars et al., 2018b). Here, we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.