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Predicting genetic biodiversity in salamanders using geographic, climatic, and life history traits

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  • Danielle J Parsons
  • Abigail E Green
  • Bryan C Carstens
  • Tara A Pelletier

Abstract

The geographic distribution of genetic variation within a species reveals information about its evolutionary history, including responses to historical climate change and dispersal ability across various habitat types. We combine genetic data from salamander species with geographic, climatic, and life history data collected from open-source online repositories to develop a machine learning model designed to identify the traits that are most predictive of unrecognized genetic lineages. We find evidence of hidden diversity distributed throughout the clade Caudata that is largely the result of variation in climatic variables. We highlight some of the difficulties in using machine-learning models on open-source data that are often messy and potentially taxonomically and geographically biased.

Suggested Citation

  • Danielle J Parsons & Abigail E Green & Bryan C Carstens & Tara A Pelletier, 2024. "Predicting genetic biodiversity in salamanders using geographic, climatic, and life history traits," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-20, October.
    • Handle: RePEc:plo:pone00:0310932
    DOI: 10.1371/journal.pone.0310932
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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. H. Christoph Liedtke & John J. Wiens & Ivan Gomez-Mestre, 2022. "The evolution of reproductive modes and life cycles in amphibians," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Pascal Peduzzi, 2019. "The Disaster Risk, Global Change, and Sustainability Nexus," Sustainability, MDPI, vol. 11(4), pages 1-21, February.
    5. Jessica Gurevitch & Julia Koricheva & Shinichi Nakagawa & Gavin Stewart, 2018. "Meta-analysis and the science of research synthesis," Nature, Nature, vol. 555(7695), pages 175-182, March.
    6. Wickham, Hadley, 2011. "The Split-Apply-Combine Strategy for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i01).
    7. Shiran Abadi & Dana Azouri & Tal Pupko & Itay Mayrose, 2019. "Model selection may not be a mandatory step for phylogeny reconstruction," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
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