Predicting species capacity to respond to climate change is an essential first step in developing effective conservation strategies. However, conservation prioritization schemes rarely take evolutionary potential into account. Ecotones provide important opportunities for diversifying selection and may thus constitute important reservoirs of standing variation, increasing the capacity for future adaptation. Here we map patterns of environmentally-associated genomic and craniometric variation in the central African rodent Praomys misonnei to identify areas with the greatest turnover in genomic composition. We also project patterns of environmentally-associated genomic variation under future climate change scenarios to determine where populations may be under the greatest pressure to adapt. While precipitation gradients influence both genomic and craniometric variation, craniometric variation is also affected by changes in vegetation structure. Areas of elevated environmentally-associated genomic and craniometric variation overlap with zones of rapid ecological transition underlining their importance as reservoirs of evolutionary potential. We find that populations in the Sanaga river basin, central Cameroon and coastal Gabon are likely to be under the greatest pressure from climate change. Lastly, we make specific conservation recommendations on how to protect zones of high evolutionary potential and identify areas where populations will be the most susceptible to climate change.

Genomic DNA was extracted from 132 individuals, sampled from 10 populations across Gabon and Cameroon. The genomic data was generated using Restriction Site-Associated DNA sequencing (rad-seq). Processing of RAD-seq data was carried out using the Stacks v 1.48 pipeline (Catchen et al. 2013). SNPs were called running STACKS with the following parameters: m=3, M=3, n=4. Rare SNPs with a minor allele frequency (MAF) of 0.05 or below were filtered and discarded. The remaining SNPs were further filtered to include only a single, randomly selected SNP for each RAD locus.

Morphometric data was generated from 169 individuals sampled from 10 populations across Gabon and Cameroon. The skull was extracted from sampled individuals and preserved in 70% ethanol. Each skull was photographed and digitized using Image J (Klingenberg). Homologous landmarks were chosen to cover all functional areas of the skull, including 20 landmarks on the dorsal surface and 16 landmarks on the ventral surface. The effects of size, position and orientation were removed, and covariance matrices describing shape variation were generated from the resulting Procrustes shape coordinates. Principal Components Analysis (PCA) was used to summarise inter-individual shape variation and the first three principal components describing dorsal and ventral shape variation were retained.

The genomic data is stored in a VCF file ("m3M3n4_allpops_FinalSNPset.vcf"). The GPS coordinates for sampling localities are stored in a separate text file ("GPS_coords.txt").

The morphometric data includes raw landmark data ("Dorsal_Raw_skull_data.csv" and "Ventral_Raw_skull_data.csv") as well as the first three principal components describing each of dorsal ("Dorsal_PCscores.csv") and ventral ("Ventral_PCscores.csv") skull shape.