2.8 Mapping genomic turnover and predicting patterns of genomic vulnerability under future climate change
We evaluated the importance of environmental variables as predictors of environmentally-associated genomic turnover and spatialized these patterns across the study region using GF modeling within the packagegradientForest in R (Ellis et al., 2012) (Appendix 1). Response variables were individual SNP minor allele frequencies within each population (only SNPs with MAF above 0.05 were used, N=3092). Predictor variables were represented by the same environmental variables that were included in the GDM along with latitude and longitude. GF uses a machine-learning algorithm to divide the biological data into different bins (i.e. different values of allele frequencies), with partitions occurring at several split values along each environmental variable. This binning is performed for every SNP, weighting each SNP individually according to its fit to the model (i.e. R2) before aggregating across all SNPs. GF determines the “split importance’ by measuring the amount of biological variation explained by a given split value (e.g. between 26 and 27°C), which is then cumulatively summed along each gradient to construct turnover functions (Fitzpatrick & Keller, 2015). The top three environmental variables in modeling genomic turnover from a total of 2000 regression trees were used to predict and map environmentally-associated turnover across the study region using a random grid of 100,000 sample points. To ensure that our GF model was performing better than random, we shuffled the environmental-predictor matrix to generate 200 randomized datasets and compared the number of SNP loci with R2 positive values to the mean R2 value across SNP loci using GF models describing variation in the real versus randomized datasets.
Lastly, we predicted future environmentally-associated genomic variation based on GF models under projected climate change for the year 2080, RCPs 2.6 and 8.5, representing ”best” and ”worst” cases, respectively. To map predicted changes of genomic variation associated with environment, we subtracted future GF predictions from current predictions using QGIS v.2.18. Areas where environmentally-associated genomic variation changed the least are considered to have low genomic vulnerability while areas where they change the most are considered to have high genomic vulnerability.
3. RESULTS