Data from: Environmental variation predicts patterns of phenotypic and genomic variation in an African tropical forest frog
Miller, Courtney (2020), Data from: Environmental variation predicts patterns of phenotypic and genomic variation in an African tropical forest frog, Dryad, Dataset, https://doi.org/10.5061/dryad.m129c0s
Central African rainforests are predicted to be disproportionately affected by future climate change. How species will cope with these changes is unclear, but rapid environmental changes will likely impose strong selection pressures. Here we examined environmental drivers of phenotypic and genomic variation in the central African puddle frog (Phrynobatrachus auritus) to identify areas of elevated environmentally-associated turnover where populations may have the greatest capacity to adapt. We also compared current and future climate models to pinpoint areas of high genomic vulnerability where allele frequencies will have to shift the most in order to keep pace with future climate change. Analyses of body size, relative leg length, and head shape suggest that seasonal aspects of temperature and precipitation significantly influence phenotypic variation, whereas geographic distance and precipitation seasonality are the most important drivers of SNP allele frequency variation. However, neither landscape barriers nor the effects of past Pleistocene refugia had any influence on genomic differentiation. Most phenotypic and genomic differentiation coincided with key ecological gradients across the forest-savanna ecotone, montane areas and a coastal to interior rainfall gradient. Areas of greatest vulnerability were found in the lower Sanaga basin and southeastern region of Cameroon. In contrast with past conservation efforts that have focused on hotspots of species richness or endemism, our findings highlight the importance of preserving environmentally heterogeneous landscapes to preserve putatively adaptive variation and ongoing evolutionary processes in the face of climate change.
National Science Foundation, Award: OISE-1243524