Alpine climate and soils heterogeneity data and simulation results

We developed metrics of climatic and edaphic heterogeneity, using principal components analyses and the shoelace algorithm, and added elevation range. We applied commonality analysis to partition the unique and shared explanation of the observed vascular plant species richness  among selected metrics. A simulation was developed to separate the relative importance of area and heterogeneity at different extents and representations of spatial nestedness, and the heterogeneity – effective area tradeoff was evaluated by altering spatial discreteness. The simulations revealed that heterogeneity was consistently more important, but less so among smaller areas. This qualitative pattern was maintained regardless of whether and how nestedness was represented. The heterogeneity – effective area tradeoff occurred in a few simulations of more discrete habitats., Data for 8 climate variables from CHELSA and 11 soils variables from ISRIC (uppermost layer only) were used to produce metrics of environmental heterogeneity. The first two axes were used to plot the points for each of the 23 regions in 2D PCA (PC-ORD v7) space. Between 5 and 15 points that outlined the area occupied by the points were selected to create a polygon, and the shoelace algorithm was applied to them to calculate the area. The data show the two PCA axes split out for each region, the points selected for the shoelace calculation, and the area. In some cases where the points were in two distinct clusters the areas were measured separately and summed. The complete CHELSA data are available at Dryad: http://dx.doi.org/doi:10.5061/dryad.kd1d4. The ISRIC data are available at https://www.isric.org/explore/isric-soil-data-hub under ISRIC’s own applicable access categories: CC-BY-NC and CC-BY, and their policies are described at https://www.isric.org/about/data-policy The data from C..., Excel can be used to access the data file. Alternatively, files can also be accessed using Apache OpenOffice.