2.2 Environmental datasets
We extracted environmental data for each sampling location from publicly
available geospatial raster layers (Table S1). These 26 environmental
data layers included 19 bioclimatic variables summarizing the mean,
maximum, minimum and range values of temperature and precipitation
across the study region (Fick & Hijmans, 2017), four vegetation layers
(Huete et al., 2002), elevation (Rodriguez et al., 2006), net primary
productivity (NPP; Running et al., 2015), and potential
evapotranspiration (PET; Mu et al., 2011). We then tested for
collinearity in these raster layers (Table S2) using the
‘removeCollinearity’ function in the R package virtualSpecies to
select a subset of variables where no two variables had a Pearson’s
correlation coefficient > 0.7, resulting in the following
twelve uncorrelated variables: isothermality (i.e. temperature evenness;
BIO3), minimum temperature of coldest month (BIO6), mean temperature of
driest quarter (BIO9), precipitation seasonality (BIO15), precipitation
of wettest quarter (BIO16), precipitation of warmest quarter (BIO18),
precipitation of coldest quarter (BIO19), elevation, percent tree cover,
leaf area index (LAI), NPP, and PET. Future projections of bioclimatic
variables were taken from aggregated global climate models (Sesink Clee,
2017) for two representative concentration pathways (RCPs) 2.6 and 8.5,
projected for year 2080 based on the Intergovernmental Panel on Climate
Change (IPCC) 5th assessment report. RCP 2.6
represents a “best case” scenario in that global mean temperature is
projected to rise by 0.3 to 1.7 °C by the late-21st century, whereas RCP
8.5 represents a “worst case” scenario and projects global mean
temperatures to rise by 2.6 to 4.8°C. All climate layers have a spatial
resolution of 30 arc seconds (approximately 1km2).