Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic datasets, in combination with machine
learning frameworks, offer great potential to answer long-standing
questions in ecology and evolution. One such question has intrigued
population geneticists, biogeographers, and conservation biologists: What
factors determine intraspecific genetic diversity? This question
is challenging to answer because many factors may influence genetic
variation, including life history traits, historical influences, and
geography, and the relative importance of these factors varies across
taxonomic and geographic scales. Furthermore, interpreting the influence
of numerous, potentially correlated variables is difficult with
traditional statistical approaches. To address these challenges, we
analyzed repurposed data using machine learning and investigated
predictors of genetic diversity, focusing on Nearctic amphibians as a case
study. We aggregated species traits, range characteristics, and
>42,000 genetic sequences for 299 species using open-access scripts
and various databases. After identifying important predictors of
nucleotide diversity with random forest regression, we conducted follow-up
analyses to examine the roles of phylogenetic history, geography, and
demographic processes on intraspecific diversity. Although life history
traits were not important predictors for this dataset, we found
significant phylogenetic signal in genetic diversity within amphibians. We
also found that salamander species at northern latitudes contain lower
genetic diversity. Data repurposing and machine learning provide valuable
tools for detecting patterns with relevance for conservation, but
concerted efforts are needed to compile meaningful datasets with greater
utility for understanding global biodiversity.