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bioRxiv · 10.1101/2023.06.15.545105

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

Amador, L.·Arroyo-Torres, I.·Barrow, L. N.
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Abstract

AimIntraspecific genetic variation is key for adaptation and survival in changing environments and is known to be influenced by many factors, including population size, migration, and life history traits. We investigated genetic variation within Neotropical amphibian species to provide insights into how natural history traits, phylogeny, climatic, and geographic characteristics influence intraspecific diversity. LocationNeotropics. TaxonAmphibians. MethodsWe assembled datasets using open-access databases for natural history traits, genetic sequences, phylogenetic trees, climatic, and geographic data. For each species, we calculated overall nucleotide diversity ({pi}) and tested for isolation by distance (IBD) and isolation by environment (IBE). We then identified predictors of {pi}, IBD, and IBE using Random Forest (RF) regression or RF classification. To incorporate phylogenetic relationships, we fitted phylogenetic generalized linear mixed models (PGLMMs) to predict {pi}, IBD, and IBE. ResultsWe compiled 4,052 mitochondrial DNA sequences from 256 amphibian species (230 frogs and 26 salamanders), georeferencing 2,477 sequences from 176 species that were not linked to occurrence data. RF regressions and PGLMMs were congruent in identifying range size and precipitation ({sigma}) as the most important predictors of {pi}. RF classification and PGLMMs identified minimum elevation as an important predictor of IBD, and maximum latitude and precipitation ({sigma}) as the best predictors of IBE. Main conclusionsThis study unified machine learning and phylogenetic methods and identified predictors of genetic variation in Neotropical amphibians. 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 harbor 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.

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Amador, L., Arroyo-Torres, I., Barrow, L. N.. 2023-06-15. Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians. https://doi.org/10.1101/2023.06.15.545105

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