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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...
The BLM GRSG ROD/ARMP/ARMPA habitat management areas include Priority Habitat Management Areas (PHMA), General Habitat Management Areas (GHMA), Important Habitat Management Areas (IHMA – Idaho only), Other Habitat Management Areas (OHMA – NV only), Linkage Connectivity Habitat Management Areas (LCHMAs – NWCO only), Restoration Habitat management Areas (RHMAs – Montana only), and Anthro Mountain (Utah only) from the final plan data in the western U.S. Sixteen Environmental Impact Statements (EIS) were referred to for these datasets, which were updated for UT in April of 2017 and for WY in October of 2017. These data are provided by Bureau of Land Management (BLM) “as is” and might contain errors or omissions. The...
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October 2017 Update: WY - On October 27, 2017 the WY state director signed maintenance actions for the Wyoming Sage-Grouse ARMPA, Buffalo RMP, Cody RMP, and Worland RMP that changed WY PHMA boundaries, bringing them into consistency with the Wyoming Core Areas (version 4) from the current Governor's executive order 2015-4. The updated PHMA boundaries were also adopted by the Lander RMP.April 2017 Update: UT - The interagency team reconvened in late 2016 to review State of Utah GRSG populations and the BLM’s 2015 and 2016 wildfire data. Of the ten soft triggers and seven hard triggers evaluated, only one population soft trigger and one population hard trigger have been met, both within the Sheeprocks population area...
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Land Use Plan (LUP) boundaries for the Greater Sage-Grouse (GRSG) National Planning Effort in the Rocky Mountain Region. Modifications were incorporated (see below) based on clarification on the correct boundaries to use from BLM planners. Additionally, definitions for EISs each LUP is included in for the Sage-grouse effort were added. Acreage calculations for each LUP as well as each full EIS were provided. EIS definitions were approved by BLM Planners on 7/19/12.Note that individual RMPs that are subsets of other RMPs are designated as "part of xxxx RMP". These polygons are overlapping polygons in the dataset. Rocky Mountain answers to LUP Polygon Selection questions (6/20/12): From:Schardt, Randall D Sent:Friday,...
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Map of harvester ant relative abundance developed from a probability of occurrence map using multi-scale vegetation, abiotic, and anthropogenic features. These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release.
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This fire risk assessment was conducted to understand how resilience and resistance and sage-grouse breeding bird habitat may inform wildland fire management decisions including preparedness, suppression, fuels management and post-fire recovery for western sagebrush communities. The assessment is based on the premise that risk = probability of a threat and the consequences of that threat (negative or positive). Fire risk was determined by the probability of a large wildfire and the consequences of fire on greater sage-grouse breeding habitat. These consequences were modified by the capacity of sage-grouse habitat to be resilient and thus recover from fire processes, and be resistant to invasive annual grasses. The...
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More than 23,000 field measurements of cheatgrass cover were acquired from various agencies and research groups across the study area and reviewed for use in the mapping effort. A subset of 6650 field measurements were used to statistically evaluate relationships between cheatgrass and a suite of 50 biophysical and remotely sensed (NDVI) variables. Variables were examined for pairwise correlation and highly correlated variables were discarded from the analysis. Eighteen of the 50 variables were selected to construct a Generalized Linear Model to classify cheatgrass occurrence. A decision rule based on strength of class membership was used to classify the study area as containing either 0-2% or >2% cheatgrass cover....
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nv_lvl4_moderatescale: Nevada hierarchical cluster level 4 (moderate-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in...
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wy_lvl3_moderatescale: Wyoming hierarchical cluster level 3 (moderate-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result...
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Potential future greater sage-grouse (Centrocercus urophasianus) habitat restoration was projected (2018-2068) for three sage-grouse Priority Area for Conservation (PACs) populations located along the northwestern, central, and eastern edge of the Great Basin using outputs from a spatially explicit state-transition simulation model (STSM) developed for sagebrush ecosystems. These datasets, for the Klamath Oregon/California, USA (KLAM) sage-grouse population, include: 1) a set of 78 categorical raster layers illustrating a time series (decade intervals) of potential future habitat, and 2) a set of 15 uncategorized raster layers illustrating potential change in habitat classification across space, after simulating...
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This map shows the potential current distribution of Greater Sage-grouse, in the context of current and near-term terrestrial intactness and long-term potential for climate change and energy development.
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Climate affects both the demographics of the Greater sage-grouse bird and the condition and long-term viability of their habitats, including sage-steppe communities. This project builds on collaboration among federal land managers, state wildlife biologists, scientists, and other organizations to create a long-term framework for implementing adaptive management for the sage-grouse. The study examined factors that might be limiting grouse numbers and will investigate components of weather patterns in relation to projected climate change models. Precipitation and temperature, as well as variables such as evaporation and soil moisture, will be considered. Overall, the project focused on (1) providing workshops to foster...
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This three-band, 30-m resolution raster contains sagebrush vegetation types, soil temperature/moisture regime classes, and large fire frequencies across greater sage-grouse population areas within the Colorado Plateau sage-grouse management zone. Sagebrush vegetation types were defined by grouping together similar vegetation types from the LANDFIRE biophysical settings layer. Soil moisture and temperature regimes were from an USDA-NRCS analysis of soil types across the greater sage-grouse range. Fire frequencies were derived from fire severity rasters created by the Monitoring Trends in Burn Severity program. The area of analysis included the greater sage-grouse populations areas within specific management zones....
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We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned.
This metadata references the polygonal ARC/INFO GIS cover showing the current and historic distribution of potential habitat, or range, of the Greater Sage-grouse (Centrocercus urophasianus) and Gunnison Sage-grouse (Centrocercus minimus) in Western North America. This data was initially researched and compiled by Dr. Michael A. Schroeder, research biologist for the Washington State Department of Fish and Wildlife. The initial draft of current and historic range data was mapped and submitted to state, federal, or provincial natural resource agencies and other experts for review, comment, and editing. The final product represents the best available science and expert review available at the time of compilation. Definition...
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This map presents limiting factors for Greater Sage-grouse for an area in the northern part of the ecoregion. This was used for the sage-grouse insert in the final report.


map background search result map search result map Integrating Climate and Biological Data into Management Decisions for the Greater Sage-­Grouse and their Habitats 6States_sg_subpopulations_sagemap Colorado Plateau REA Conservation Elements - Terrestrial Species: Greater Sage-Grouse Colorado Plateau REA Conservation Elements - Terrestrial Species: Greater Sage-Grouse Limiting Factors Colorado Plateau Image of Sagebrush Types, Soil Regime Classes, and Fire Frequencies (1984-2013) Fire Risk Assessment for the Greater Sage-Grouse Raster Cheatgrass Across the Range of the Greater Sage-Grouse Precipitation (Proportion July - Sep) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP8.5 - Min Precipitation (Mean: Dec - Mar) - 2070-2100 - RCP4.5 - Mean Precipitation (Mean: Dec - Mar) - 2020-2050 - RCP8.5 - Max Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Nevada), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 3 (Wyoming), Interim BLM GRSG BER: Land Use Plan (LUP) Boundaries for GRSG National Planning Effort - Rocky Mountain Region (polygon) BLM Western U.S. GRSG ARMP and ARMPA Habitat Management Areas, October 2017 Update Klamath Oregon/California time series (2018-2068) of potential habitat and 50-year change Harvester ant relative abundance in the Wyoming Basins Ecoregional Assessment area Time-Varying Greater Sage-Grouse Habitat Selection and Survival Indices in the Bi-State Region of California and Nevada Klamath Oregon/California time series (2018-2068) of potential habitat and 50-year change Time-Varying Greater Sage-Grouse Habitat Selection and Survival Indices in the Bi-State Region of California and Nevada Colorado Plateau Image of Sagebrush Types, Soil Regime Classes, and Fire Frequencies (1984-2013) Integrating Climate and Biological Data into Management Decisions for the Greater Sage-­Grouse and their Habitats Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 3 (Wyoming), Interim Colorado Plateau REA Conservation Elements - Terrestrial Species: Greater Sage-Grouse Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 4 (Nevada), Interim Colorado Plateau REA Conservation Elements - Terrestrial Species: Greater Sage-Grouse Limiting Factors BLM GRSG BER: Land Use Plan (LUP) Boundaries for GRSG National Planning Effort - Rocky Mountain Region (polygon) Harvester ant relative abundance in the Wyoming Basins Ecoregional Assessment area 6States_sg_subpopulations_sagemap Fire Risk Assessment for the Greater Sage-Grouse Raster BLM Western U.S. GRSG ARMP and ARMPA Habitat Management Areas, October 2017 Update Cheatgrass Across the Range of the Greater Sage-Grouse Precipitation (Proportion July - Sep) - 2020-2050 - RCP4.5 - Mean Precipitation (Mean: Apr - June) - 2020-2050 - RCP8.5 - Min Precipitation (Mean: Dec - Mar) - 2070-2100 - RCP4.5 - Mean Precipitation (Mean: Dec - Mar) - 2020-2050 - RCP8.5 - Max