Shryock, D.F DeFalco, L.A. Esque, T.C. 20190903 raster digital data Denver, CO U.S. Geological Survey data release https://doi.org/10.5066/P9R8YKL0 Shryock, D.F. DeFalco, L.A. Esque, T.C. 2018 publication Ecosphere vol. 9, issue 10 n/a Wiley https://doi.org/10.1002/ecs2.2453 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. These data were obtained and formatted as input to Climate Distance Mapper, an R shiny application. The data are intended to be used only within this application. 20400101 20701231 existing data sources Complete None planned -120.3440 -105.3440 42.3572 31.3322 The raster grids encompass four regions in the arid southwestern United States as defined by combinations of, or renaming of, the EPA’s Omernik level III ecoregion polygons (Omernik and Griffith 2014): the Sonoran Desert (Sonoran Basin and Range combined with Madrean Archipelago), Mojave Desert (Mojave Basin and Range), Colorado Plateau (Colorado Plateaus combined with Arizona / New Mexico Plateau), and Southern Great Basin (Central Basin and Range). ISO 19115 Topic Category climatologyMeteorologyAtmosphere USGS Thesaurus precipitation geospatial analysis atmospheric and climatic processes USGS Metadata Identifier USGS:5d6423a6e4b01d82ce9870a0 Getty Thesaurus of Geographic Names United States Arizona California Colorado Nevada New Mexico Utah Colorado Plateau Great Basin Mojave Desert Sonoran Desert None. Please see 'Distribution Info' for details. The authors of these data require that users direct any questions pertaining to appropriate use or assistance with understanding limitations and interpretation of the data to the individuals/organization listed in the Point of Contact section. U.S. Geological Survey, Western Ecological Research Center mailing address

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Sacramento CA 95819 United States 916-278-9485 916-278-9475 gs-b-werc_data_management@usgs.gov Funding: U.S. Bureau of Land Management, Native Plan Materials Program Principal components analysis was conducted using R v. 3.4.3. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. https://www.R-project.org/ Download by visiting website and using preferred CRAN mirror. R Core Team 2017 3.4.3 Tools Software Vienna, Austria R Foundation for Statistical Computing A formal accuracy assessment has not been conducted. Each raster was visually checked to ensure that they covered the full extent. Principal component values were calculated for areas that fell within the desired ecoregions as defined by EPA's Omernik level III polygon (Omerick and Griffith 2014), the complete description can be found in the Description of Geographical Extent. Areas that fell outside were due to elevation or other factors and were not included in the analysis. These areas are indicated with a value of NoData. Wang T. Hamann A. Spittlehouse D. Carroll C. 2015 raster digital data https://adaptwest.databasin.org/pages/adaptwest-climatena http://cfcg.forestry.ubc.ca/projects/climate-data/ http://climatewna.com/climatena_map/ Digital and/or Hardcopy 1980 2010 publication date AdaptWest Project, ClimateNA ClimateNA was used to derive climate values at locations within the study extent Monthly precipitation and temperature values were obtained from the program ClimateNA (Wang et al. 2016), which downscales PRISM climate data (Daly et al. 2008) and corrects for elevational variation. We used 30-year averages for the 1980-2010 normal period to represent the current climate and 30-year averages for the 2040-2070 normal period to represent future climate. Future climate normals were based on an ensemble average of three models from the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report for future projections (IPCC 2014), under the RCP8.5 (high emissions) and RCP4.5 (moderate emissions) representative concentration pathways. The future climate models included CCSM4 (Community Climate System Model, version 4.0), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model, version 3), and HadGEM2-ES (Hadley Centre Global Environmental Model, version 2 Earth System). From the monthly precipitation and temperature values, we derived 12 composite climate variables for further analysis. These included: summer precipitation (average precipitation received from May - Oct), winter precipitation (average precipitation received from November - April), mean annual precipitation, precipitation seasonality (coefficient of variation in monthly precipitation totals over the course of a year), winter precipitation variability (coefficient of variation in annual winter precipitation received from 1950 – 2000), summer precipitation variability (coefficient of variation in annual summer precipitation received from 1950 – 2000), mean annual temperature, summer maximum temperature (maximum temperature of the warmest month), winter minimum temperature (minimum temperature of the coldest month), annual temperature range (average of the monthly temperature ranges (monthly maximum minus monthly minimum), and temperature seasonality (coefficient of variation in monthly average temperatures throughout the course of a year). All precipitation variables were expressed in millimeters, and all temperature variables were expressed in degrees Celsius. We performed a principal components analysis (PCA) on the 12 composite climate variables to derive five principal components which together accounted for more than 90 % of the total variability in the 12 composite climate variables. This analysis was conducted via the “prcomp” function in R v3.4.3 (R Core Team 2018). The PCA model of current climate was used to predict scores on each principal component for the same 12 climate variables in the 2040-2070 period for the RCP8.5 and RCP4.5 scenarios. PCA scores for each principal component were transferred to raster grids, with one raster grid representing each principal component. Current climate raster grids have the suffix "pca1", "pca2", etc., where the number indicates the order of principal components. Future raster grids have the additional suffix "rcp85" or "rcp45" to denote the future climate scenario (RCP8.5 2040-2070, RCP4.5 2040-2070, respectively). Unknown Lab Four regions in the arid southwestern United States as defined by combinations of, or renaming of, the EPA’s Omernik level III ecoregion polygons. James M. Omernik Glenn E. Griffith 20140916 publication Environmental Management vol. 54, issue 6 n/a Springer Nature ppg. 1249-1266 http://dx.doi.org/10.1007/s00267-014-0364-1 Lab Climate data were obtained from the program ClimateNA (version 5.40). We used 30-year averages for the 1980-2010 normal period to represent the current climate and for the 2040-2070 normal period to represent future climate. Tongli Wang Andreas Hamann Dave Spittlehouse Carlos Carroll 20160608 publication PLOS ONE vol. 11, issue 6 n/a Public Library of Science (PLoS) ppg. e0156720 http://dx.doi.org/10.1371/journal.pone.0156720 Lab Downscales PRISM climate data and corrects for elevational variation. Christopher Daly Michael Halbleib Joseph I. Smith Wayne P. Gibson Matthew K. Doggett George H. Taylor Jan Curtis Phillip P. Pasteris 20080312 publication International Journal of Climatology vol. 28, issue 15 n/a Wiley ppg. 2031-2064 http://dx.doi.org/10.1002/joc.1688 Lab Future climate normals were based on an ensemble average of three models from the Coupled Model Intercomparison Project phase 5 (CMIP5) database corresponding to the 5th IPCC Assessment Report for future projections, RCP8.5 (high emissions), and RCP4.5 (moderate emissions) scenario. The future climate models included CCSM4 (Community Climate System Model, version 4.0), GFDL-CM3 (Geophysical Fluid Dynamics Laboratory Climate Model, version 3), and HadGEM2-ES (Hadley Centre Global Environmental Model, version 2 Earth System). IPPC 2014 publication Cambridge, United Kingdom, and New York, NY, USA Cambridge University Press Raster Grid Cell 1323 1800 5 0.008333333330000003 0.008333333330309901 Decimal seconds WGS_1984 WGS 84 6378137.0 298.257223563 Climate_PCA_rcp45_2040-2070.tif A five-band composite of the Principal Components 1-5 for the climate period 2040-2070, RCP4.5 scenario. Producer defined Value for Band 1 Unique numeric PCA scores contained in each raster cell for Principal Component 1 Producer defined -8.9420003890991 6.1690001487732 Value for Band 2 Unique numeric PCA scores contained in each raster cell for Principal Component 2 Producer defined -13.744000434875 6.2820000648499 Value for Band 3 Unique numeric PCA scores contained in each raster cell for Principal Component 3 Producer defined -7.2919998168945 4.5929999351501 Value for Band 4 Unique numeric PCA scores contained in each raster cell for Principal Component 4 Producer defined -7.3829998970032 3.2400000095367 Value for Band 5 Unique numeric PCA scores contained in each raster cell for Principal Component 5 Producer defined -2.779000043869 7.5450000762939 U.S. Geological Survey, ScienceBase mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data (raster) https://doi.org/10.5066/P9R8YKL0 None 20200830 U.S. Geological Survey, Western Ecological Research Center Cartographic Technician mailing address

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Sacramento CA 95819 United States 916-278-9483 916-278-9475 gs-b-werc_data_management@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999