Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians

Author:

Amador Luis1ORCID,Arroyo‐Torres Irvin1ORCID,Barrow Lisa N.1ORCID

Affiliation:

1. Museum of Southwestern Biology and Department of Biology University of New Mexico Albuquerque New Mexico USA

Abstract

AbstractAimIntraspecific genetic variation is key for adaptation and survival in changing environments and is known to be influenced by many factors, including population size, dispersal and life‐history traits. We investigated genetic variation within Neotropical amphibian species to provide insights into how natural history traits, phylogenetic relatedness, climatic and geographic characteristics can explain intraspecific genetic diversity.LocationNeotropics.TaxonAmphibians.MethodsWe assembled data sets using open‐access databases for natural history traits, genetic sequences, phylogenetic trees, climatic and geographic data. For each species, we calculated overall nucleotide diversity (π) and tested for isolation by distance (IBD) and isolation by environment (IBE). We then identified predictors of π, IBD and IBE using random forest (RF) regression or RF classification. We also fitted phylogenetic generalized linear mixed models (PGLMMs) to predict π, IBD and IBE.ResultsWe compiled 4052 mitochondrial DNA sequences from 256 amphibian species (230 frogs and 26 salamanders), georeferencing 2477 sequences from 176 species that were not linked to occurrence data. RF regressions and PGLMMs were congruent in identifying range size and precipitation (σ) as the most important predictors of π, influencing it positively. RF classification and PGLMMs identified minimum elevation as an important predictor of IBD; most species without IBD tended to occur at higher elevations. Maximum latitude and precipitation (σ) were the best predictors of IBE, and most species without IBE occur at lower latitudes and in areas with more variable precipitation.Main ConclusionsThis study identified predictors of genetic variation in Neotropical amphibians using both machine learning and phylogenetic methods. This approach was valuable to determine which predictors were congruent between methods. We found that species with small ranges or living in zones with less variable precipitation tended to have low genetic diversity. We also showed that Western Mesoamerica, Andes and Atlantic Forest biogeographic units harbour high diversity across many species that should be prioritized for protection. These results could play a key role in the development of conservation strategies for Neotropical amphibians.

Funder

National Science Foundation

Publisher

Wiley

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

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