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Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
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Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
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Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
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Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
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A raster dataset of the appropriate management levels of horses in BLM Herd Management Areas (HMAs) and USFS Wild Horse and Burro Territories (WHBT) within Reslience and Resistance classes within the GRSG management zones. This dataset was made by adding the Resilience and Resistance raster to a raster of horse AML levels.
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A raster dataset representing conifer cover within the Western United States. This dataset was made reclassifying the LANDFIRE 1.3 EVT layer into "Conifer-dominated Ecological Systems with Little to No Sagebrush", and "Conifer-dominated Ecological Systems Likely to Expand into Sagebrush".
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Information about economic activity was obtained from the National Cohesive Wildland Fire Management Strategy (cohesivefire.nemac.org). Data were derived from the USDA Economic Research Service to create a county-level measure of Dominant Economic Activity (county economic dependence). This describes the most prevalent kind of economic activity, which includes activities from farming, mining, and manufacturing to government employment and the service industry. The Appalachian economy is diverse and geographically variable; for example, manufacturing is spread throughout the region, whereas mining activities are located more centrally. Data are from 2004.The mission of the USDA Economic Research Service is to inform...
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The Forest Health Technology Enterprise Team (FHTET) was created by the Deputy Chief for State and Private Forestry in February 1995 to develop and deliver forest health technology services to field personnel in public and private organizations in support of the Forest Service’s land ethic, to “promote the sustainability of ecosystems by ensuring their health, diversity, and productivity.” This dataset shows the total basal area of all tree species as square feet per acre.For more information: http://www.fs.fed.us/foresthealth/technology/nidrm2012.shtml
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global second-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Classified probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Classification is based on 4 probability cutoff levels with category 1 being low habitat suitability and category 4 being high habitat suitability. Categorized probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce categorized probability raster see report. https://www.fws.gov/science/catalog
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Classified probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Classification is based on 4 probability cutoff levels with category 1 being low habitat suitability and category 4 being high habitat suitability. Categorized probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce categorized probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Classified probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Classification is based on 4 probability cutoff levels with category 1 being low habitat suitability and category 4 being high habitat suitability. Categorized probability data is created from fitting a global second-order model to county level raster data. For details on model fitting and data used to produce categorized probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global second-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
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Classified probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Classification is based on 4 probability cutoff levels with category 1 being low habitat suitability and category 4 being high habitat suitability. Categorized probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce categorized probability raster see report. https://www.fws.gov/science/catalog
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Probability of suitable habitat for Black Tailed Prairie Dogs for each cell of raster. Probability is measured from 0 to 1 with 0 being low habitat suitability and 1 being high suitability. Probability data is created from fitting a global third-order model to county level raster data. For details on model fitting and data used to produce probability raster see report. https://www.fws.gov/science/catalog
map background search result map search result map Cheyenne KS Third Order Resource Selection Function Stevens KS Third Order Resource Selection Function Dundy NE Third Order Resource Selection Function Lincoln NE Third Order Resource Selection Function Cheyenne NE Third Order Categorized Resource Selection Function Chase NE Third Order Resource Selection Function Mora NM Third Order Categorized Resource Selection Function Larimer CO Third Order Resource Selection Function Morgan CO Third Order Categorized Resource Selection Function Morton KS Second Order Categorized Resource Selection Function Boulder CO Second Order Resource Selection Function Elbert CO Second Order Resource Selection Function Total Basal Area of All Tree Species 2012 Western United States Conifer Cover Raster Temperature (Mean: Annual) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2070-2100 - RCP8.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP4.5 - Min Horse AML Levels of BLM HMAs and USFS WHBTs within Resilience and Resistance Classes Raster Dominant Economic Activity USDA Economic Research Service Morton KS Second Order Categorized Resource Selection Function Stevens KS Third Order Resource Selection Function Cheyenne NE Third Order Categorized Resource Selection Function Chase NE Third Order Resource Selection Function Boulder CO Second Order Resource Selection Function Dundy NE Third Order Resource Selection Function Cheyenne KS Third Order Resource Selection Function Morgan CO Third Order Categorized Resource Selection Function Mora NM Third Order Categorized Resource Selection Function Lincoln NE Third Order Resource Selection Function Elbert CO Second Order Resource Selection Function Larimer CO Third Order Resource Selection Function Dominant Economic Activity USDA Economic Research Service Total Basal Area of All Tree Species 2012 Horse AML Levels of BLM HMAs and USFS WHBTs within Resilience and Resistance Classes Raster Western United States Conifer Cover Raster Temperature (Mean: Annual) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2070-2100 - RCP8.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP4.5 - Min