R scripts used to perform analyses described in journal article.

Annotated R scripts required to run GDM on plant distribution data

R scripts for modeling and mapping genomic variation

R scripts to use gradient forest and generalized dissimilarity modeling to model and map genomic variation under current and future climate

gradientForests: Table of environmental / spatial predictors and minor allele frequencies

Data used to fit gradient forest models for reference and candidate SNPs from 31 balsam poplar populations

GDM: Table of Fst distances and geographic / environmental data

Data used to fit generalized dissimilarity models for reference and candidate SNPs from 31 balsam poplar populations. Tables is in GDM site-pair format, with Fst distances for each SNP dataset

Plant species distribution, seed dispersal traits, and environmental data for northern Europe

Plant distribution data from Atlas Florae Europaeae. Environmental data from worldclim (climate) and, for soils, Batjes, N. H. 1997 A world dataset of derived soil properties by FAO–UNESCO soil unit for global modelling. Soil Use and Management 13, 9–16. See publication for details

Plant species distribution, seed dispersal traits, and environmental data for southwest Australia

Plant distribution data from the Western Australia Herbarium. Environmental data from worldclim (climate) and the Australian Natural Resources Data Library (soils). See publication for details

Modeled vs. NHD stream density metrics for each HUC12 watershed in the study area.

<p>In (A) modeled stream density is compared to NHD stream density, and in (B) modeled channel head density is compared to NHD channel head density. Although there is a strong correlation, modeled streams exhibit many more small streams per unit area, each with its own channel head.</p

Stream density maps for HUC12 watersheds in the study region.

<p>(A) NHD stream density was more uniform and lower than (B) the stream density calculated from MaxEnt after smoothing, connecting discontinuous segments, and merging with NHD. (C) The percent change in stream density highlights the effects of urban areas and other areas with poor quality NHD stream maps. (D) Spatial variation in stream density can be explained in part by differences in geology between physiographic provinces.</p

The mean and variance of stream density for HUC12 watersheds by physiographic province (CP = Coastal plain; PD = Piedmont; BR = Blue ridge; RV = Ridge and valley; and AP = Appalachian plateau).

<p>The mean and variance of stream density for HUC12 watersheds by physiographic province (CP = Coastal plain; PD = Piedmont; BR = Blue ridge; RV = Ridge and valley; and AP = Appalachian plateau).</p

Local slope vs. Log₁₀(catchment area) for bins of increasing catchment area (black dots) across the entire survey region and for the locations of channel heads (colored dots).

<p>Characteristics of this plot have been discussed previously in the literature [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074819#B21" target="_blank">21</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074819#B53" target="_blank">53</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074819#B64" target="_blank">64</a>], and are covered briefly in the text.</p