Signal categorization by foraging animals depends on ecological diversity

  1. David William Kikuchi  Is a corresponding author
  2. Anna Dornhaus
  3. Vandana Gopeechund
  4. Thomas N Sherratt
  1. University of Arizona, United States
  2. Carleton University, Canada

Abstract

Warning signals displayed by defended prey are mimicked by both mutualistic (Müllerian) and parasitic (Batesian) species. Yet mimicry is often imperfect: why does selection not improve mimicry? Predators create selection on warning signals, so predator psychology is crucial to understanding mimicry. We conducted experiments where humans acted as predators in a virtual ecosystem to ask how prey diversity affects the way that predators categorize prey phenotypes as profitable or unprofitable. The phenotypic diversity of prey communities strongly affected predator categorization. Higher diversity increased the likelihood that predators would use a 'key' trait to form broad categories, even if it meant committing errors. Broad categorization favors the evolution of mimicry. Both species richness and evenness contributed significantly to this effect. This lets us view the behavioral and evolutionary processes leading to mimicry in light of classical community ecology. Broad categorization by receivers is also likely to affect other forms of signaling.

Data availability

All data for this study are present in the supporting files, and source code to produce the figures from those files is included in the Supplementary RMarkdown file.

Article and author information

Author details

  1. David William Kikuchi

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    For correspondence
    dwkikuchi@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7379-2788
  2. Anna Dornhaus

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Vandana Gopeechund

    Department of Biology, Carleton University, Ottawa, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Thomas N Sherratt

    Department of Biology, Carleton University, Ottawa, Canada
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (K12GM000708)

  • David William Kikuchi

Natural Sciences and Engineering Research Council of Canada

  • Thomas N Sherratt

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Consent process is described in the Methods. Human subjects research was carried out with the permission of the Carleton University Research Ethics Board-B under permit number 13385 14-0276.

Reviewing Editor

  1. Bernhard Schmid, University of Zurich, Switzerland

Publication history

  1. Received: November 28, 2018
  2. Accepted: April 24, 2019
  3. Accepted Manuscript published: April 25, 2019 (version 1)
  4. Version of Record published: May 10, 2019 (version 2)

Copyright

© 2019, Kikuchi 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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  1. David William Kikuchi
  2. Anna Dornhaus
  3. Vandana Gopeechund
  4. Thomas N Sherratt
(2019)
Signal categorization by foraging animals depends on ecological diversity
eLife 8:e43965.
https://doi.org/10.7554/eLife.43965
  1. Further reading

Further reading

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    Background: The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions-the United States, China, and Africa-differ from one another and from those at the global level.

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    Results: The numbers of human-infective virus species discovered in the United States, China, and Africa up to 2019 were 95, 80 and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was better predicted by land use and socio-economic variables than climatic variables and biodiversity, though the relative importance of these predictors varied by region. Map of virus discovery probability in 2010-2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high-risk areas as predicted by our model.

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    Funding: FFZ is funded by the Darwin Trust of Edinburgh (https://darwintrust.bio.ed.ac.uk/). MEJW has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 874735 (VEO) (https://www.veo-europe.eu/).