Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 4.5)

Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP4.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation seasonality (coefficient of variation in monthly precipitation totals), long-term winter precipitation variability, and long-term summer precipitation variability. The conversion to principal components both standardizes and accounts for covariation in climate variables, while emphasizing the most important climate gradients across the landscape. Raster layers representing each principal component form the input to Climate Distance Mapper (https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/), an interactive R Shiny application for matching seed sources with restoration sites. Plant populations are commonly adapted to local climate gradients and frequently exhibit a home-site advantage. For this reason, climate information may serve as a proxy for local adaptation in restoration designs. Climate Distance Mapper allows users to rank the suitability of seed sources for restoration sites by displaying multivariate climate distances (incorporating climate principal components) from user-supplied input points to the surrounding landscape. The application provides functions to match seed sources with current or future climate, guide sampling effort for large scale seed collections, and partition the landscape into suitable areas for different seed sources. These data support the following publication: Shryock, D.F., DeFalco, L.A., and T.C. Esque. 2018. Spatial decision-support tools to guide restoration and seed sourcing in the Desert Southwest. Ecosphere 9(10):e02453.

[
  "010:12"
]

[
  "geospatial"
]