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