Environmental Analysis Data: These data were compiled to investigate the complex interactions between environmental gradients and geographic distance across the Intermountain West of the western United States. Due to complex topography, physiographic heterogeneity, and complicated relationships with large bodies of water, spatial autocorrelation of environmental similarity may be expected. We provide an R script (VarioAnalysis.R) that uses four associated data files (annualprecip.csv, annualSWA.csv, annualtemp.csv, key.csv) to reproduce Figure 3 in Massatti et al. 2020 (see Larger Work Citation). The data files contain information on yearly soil water availability, temperature, and precipitation, which are summed or averaged and used to test autocorrelations using semi variograms. There is also a shapefile (see Source Data) and raster (RasterbySiteID.tif) that ties all of the site-specific information together and places data into a spatial context. The script and data were developed, extracted, and/or compiled by R.K. Shriver. Genetic Analysis Data: These data were compiled to assess the relationship between genetic differentiation and geographic distance in the Intermountain West of the western United States. Included are 14 files: 13 tab-delimited text files that detail species-specific data and one R script (czi.R) that uses data within the 13 files to reproduce Figures 1 and 2 in Massatti et al. 2020 (see Larger Work Citation). Species-specific files include site names, location information (latitude/longitude), and information on which genetic population each site belongs to according to the original publication document (see Table 1 in the Larger Work Citation). The R script is annotated to provide important information regarding how the analyses work and how they can be modified if users want to tailor analyses to other geographic regions. The script and data were developed, extracted, and/or compiled by R. Massatti.

Environmental Analysis Data: These data were compiled to investigate the complex interactions between environmental gradients and geographic distance across the Intermountain West of the western United States. Due to complex topography, physiographic heterogeneity, and complicated relationships with large bodies of water, spatial autocorrelation of environmental similarity may be expected. We provide an R script (VarioAnalysis.R) that uses four associated data files (annualprecip.csv, annualSWA.csv, annualtemp.csv, key.csv) to reproduce Figure 3 in Massatti et al. 2020 (see Larger Work Citation). The data files contain information on yearly soil water availability, temperature, and precipitation, which are summed or averaged and used to test autocorrelations using semi variograms. There is also a shapefile (see Source Data) and raster (RasterbySiteID.tif) that ties all of the site-specific information together and places data into a spatial context. The script and data were develo ...