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This dataset represents the location and class density of potential annual grass risk within the Central Great Basin and Mojave Basin Ecoregion for the 2010 time period. This model represent a composite of multiple inductive (Maximum Entropy) models of varying annual grass cover using non-spectral landscape variables. Input Variables: elevation, aspect, distance to fire, geology, distance to hydric soils, distance to intermitant streams, landform, ombrotype, distance to perrenial streams, soil pH, density of primary roads, density of secondary/local roads, percent sandy soil, slope, thermotype. Classification of Model 0-No/Low Risk 1- < 5% Cover Risk 2- 5%-15% Cover Risk 3 - 15%-25% Cover Risk 4 - 25%-45% Cover...
This dataset represents the probability of occurrence for the sand dunes and sandy soils species assemblage within the Central Great Basin and Mohave Basin Ecoregion. This model represents the composite of multiple cross-validated inductive (Maximum Entropy) models of species distributions using non-spectral landscape variables. Input Variables: Elevation, NatureServe's ecological systems map, soil pH, percentage of coarse sands within soils, percentage of total sands within soil, distance to hydric soils, slope, geology, aspect, and available water holding capacity. Classification Model: 1 - High Potential Habitat NoData - Very Low Habitat Potential or "Non-Habitat"
This shapefile provides general range information for all landscape species for which distributions were mapped based upon models previously developed by the U.S. Geological Survey’s Southwest Regional Gap Analysis Project (SWReGAP). It was created to support the mapping of detailed distributions for these species. The range data provided here represents an amalgamation of range data from SWReGap, the California Wildlife Habitat Relationships database (http://www.dfg.ca.gov/biogeodata/cwhr/cawildlife.aspx ), NatureServe species distribution shapefiles, and expert opinion. SWReGap defined range based on 8-digit hydrologic units. Where it was necessarily to extend these ranges into California, Idaho, and/or Oregon,...
This data set contains distribution information for all terrestrial and aquatic reptiles, crocodilians, and turtles occurring in the United States and Canada.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
Summary lists by HUC10 watershed for CBR and MBR of Landscape Species, Local Species, Coarse Filter Species, and Assemblages, prepared by NatureServe from sensitive Element Occurrence (EO) and Observation data provided by NatureServe member programs for use in the BLM REA projects. Landscape species analyses are based on final distribution models created for the BLM REA projects. Bald Eagle and Golden Eagle data were supplemented by data downloaded from the Global Biodiversity Information Facility (http://www.gbif.org/).
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This shapefile provides general range information for all landscape species for which distributions were mapped based upon models previously developed by the U.S. Geological Survey’s Southwest Regional Gap Analysis Project (SWReGAP). It was created to support the mapping of detailed distributions for these species. The range data provided here represents an amalgamation of range data from SWReGap, the California Wildlife Habitat Relationships database (http://www.dfg.ca.gov/biogeodata/cwhr/cawildlife.aspx ), NatureServe species distribution shapefiles, and expert opinion. SWReGap defined range based on 8-digit hydrologic units. Where it was necessarily to extend these ranges into California, Idaho, and/or Oregon,...
This shapefile provides general range information for all landscape species for which distributions were mapped based upon models previously developed by the U.S. Geological Survey’s Southwest Regional Gap Analysis Project (SWReGAP). It was created to support the mapping of detailed distributions for these species. The range data provided here represents an amalgamation of range data from SWReGap, the California Wildlife Habitat Relationships database (http://www.dfg.ca.gov/biogeodata/cwhr/cawildlife.aspx ), NatureServe species distribution shapefiles, and expert opinion. SWReGap defined range based on 8-digit hydrologic units. Where it was necessarily to extend these ranges into California, Idaho, and/or Oregon,...
This is a 100-meter cell resolution raster dataset consisting of 1:750,000-scale surficial geology for California and 1:500,000-scale for Nevada, and parts of Utah, Colorado, New Mexico, and Arizona. This data set was aggregated from a compilation of geologic maps of the Southwest Regional GAP Analysis Project (ReGAP) along with California geology and the very southern portion of Oregon and Idaho geology.
This shapefile provides general range information for all landscape species for which distributions were mapped based upon models previously developed by the U.S. Geological Survey’s Southwest Regional Gap Analysis Project (SWReGAP). It was created to support the mapping of detailed distributions for these species. The range data provided here represents an amalgamation of range data from SWReGap, the California Wildlife Habitat Relationships database (http://www.dfg.ca.gov/biogeodata/cwhr/cawildlife.aspx ), NatureServe species distribution shapefiles, and expert opinion. SWReGap defined range based on 8-digit hydrologic units. Where it was necessarily to extend these ranges into California, Idaho, and/or Oregon,...
Scorecard analysis for terrestrial conservation elements and near-future landscape condition. The landscape condition score represents area weighted mean value based upon the combined count and condition score. sum(count*score) / sum(count) *where count equals the cell count and score is the condition value. NatureServe’s ecological integrity framework provides a practical approach to organize criteria and indicators for this purpose (Faber-Langendoen et al. 2006, Unnasch et al. 2008). This framework provides a scorecard for reporting on the ecological status of a given CE within a given location, and if needed, facilitates the aggregation and synthesis of the component results for broader measures of ecological...
Scorecard analysis for terrestrial conservation elements and near future landscape condition. The landscape condition score represents area weighted mean value based upon the combined count and condition score. sum(count*score) / sum(count) *where count equals the cell count and score is the condition value. NatureServe’s ecological integrity framework provides a practical approach to organize criteria and indicators for this purpose (Faber-Langendoen et al. 2006, Unnasch et al. 2008). This framework provides a scorecard for reporting on the ecological status of a given CE within a given location, and if needed, facilitates the aggregation and synthesis of the component results for broader measures of ecological...
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal Mean Annual Ground Temperature at 1 Meter Depth (°C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 2km spatial resolution. It represents the A2 emissions scenario and the spatial extent is the NOS REA study area.
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average January temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA...
This shapefile provides general range information for all landscape species for which distributions were mapped based upon models previously developed by the U.S. Geological Survey’s Southwest Regional Gap Analysis Project (SWReGAP). It was created to support the mapping of detailed distributions for these species. The range data provided here represents an amalgamation of range data from SWReGap, the California Wildlife Habitat Relationships database (http://www.dfg.ca.gov/biogeodata/cwhr/cawildlife.aspx ), NatureServe species distribution shapefiles, and expert opinion. SWReGap defined range based on 8-digit hydrologic units. Where it was necessarily to extend these ranges into California, Idaho, and/or Oregon,...
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average May temperature (in °C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from monthly totals, using the A2 emissions scenario. The spatial extent is clipped to the NOS REA study...
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average of spring (March, April, May) total precipitation (in millimeters) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean of seasonal totals calculated from monthly totals, using the A2 emissions...
Some of the YKL rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - The landscape condition model (LCM) evaluates the degree of human impact on the landscape. The current landscape condition was extracted to the current distribution of spruce forest. The intersection of the white spruce or black spruce CE distribution with the LCM suggests that over 94% of the total CE area is very high (intact)...
For each variable the per pixel change between the recent time slice (1981-2012) or future timslice (2050s) and the baseline (1900-1980) was calculated, identifying climate “deltas” for each pixel. Recent deltas are 800m resolution and use PRISM as the source dataset. Future deltas are 4km resolution and use ClimateWNA as the source dataset. Delta = later timeslice (recent or future) - baseline. Raster values are expressed in climate units either mm for precipitation or degrees c for temperature. delta ratio values are included for precipitation and CMD, which are ratios of change (1 = no change, < 1 = decreasing, > 1 = increasing).
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