Data from: Environmental variation predicts patterns of phenotypic and genomic variation in an African tropical forest frog

Miller, Courtney, University of New Orleans

cmiller504@gmail.com

Published Aug 14, 2020 on Dryad. https://doi.org/10.5061/dryad.m129c0s

Cite this dataset

Miller, Courtney (2020). Data from: Environmental variation predicts patterns of phenotypic and genomic variation in an African tropical forest frog [Dataset]. Dryad. https://doi.org/10.5061/dryad.m129c0s

Abstract

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.

Usage notes

Morphometric data for P. auritus

Sample attributes, linear morphometric and geomorphometric data, corresponding environmental data

Pauritus_morphometrics.csv

VCF file of SNPs generated with STACKs

We used the bioinformatics software pipeline, STACKS v.1.44 (Catchen et al., 2011; Catchen, Hohenlohe, Bassham, & Amores, 2013) to process the restriction-site-associated DNA markers (RAD-tags) and generate single nucleotide polymorphism (SNP) datasets. First, we executed the “process_radtags” program in STACKS to demultiplex and trim sequence reads by the P1 barcodes and remove low quality reads (Phred quality score less than 20). After removing PCR duplicates with the “clone_filter” script, the processed reads were used to generate RAD loci without a reference genome using “denovo_map.pl” (parameter settings: m = 3 M = 5 n = 4). We empirically determined these parameters to limit the impact of over-splitting loci (see Harvey et al., 2015; Ilut, Nydam, & Hare, 2014). This involved running the de novo assembly over a wide range of values of M (1–8) with “ustacks”. From these runs, we selected a value of M = 5 since we observed that the percentage of homozygous and heterozygous loci reached a plateau at this value and thus minimized over-splitting of alleles for the final SNP calling. Stacks calls SNPs (“sstacks”) within RAD loci using a multinomial-based likelihood model that estimates the likelihood of two most frequently observed genotypes at each site and performs a standard likelihood ratio test using a chi-square distribution (Catchen et al., 2011; Hohenlohe et al., 2010). For SNP inference, we used the default alpha significance level of 0.05. Paralogous loci that stacked together were identified and removed by subsequent quality control steps built into STACKS (max number of stacks per loci (m) = 3; Harvey et al., 2015; Ilut et al., 2014). After the preliminary assembly of catalog loci using “denovo_map.pl”, we ran the STACKS correction mode (rxstacks-cstacks-sstacks) using the bounded SNP model with a 0.05 upper bound for the error rate. The “rxstacks” program made corrections to genotype and haplotype calls based on population information, rebuilt the catalog loci and filtered out loci with average log likelihood ratio of < 8.0.

Pauritus_SNPs.vcf

Funding

National Science Foundation, Award: OISE-1243524

Location

Gabon

Cameroon