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ElkProductionArea is an ESRI SDE Feature Class showing elk production areas. This dataset represents that part of the overall range of elk occupied by the females from May 15 to June 15 for calving. Only known areas are mapped and this does not include all production areas for the Data Analysis Unit. This information was derived from field personnel. A variety of data capture techniques were used including drawing on mylar overlays at 1:50,000 scale USGS county mapsheets and implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Dataset was acquired from a third party...
In a step towards implementing our Vision, SREP completed a statewide assessment of wildlife linkages in collaboration with the Colorado Department of Transportation (CDOT), the Federal Highway Administration, The Nature Conservancy, and Colorado State University, identifying and prioritizing wildlife linkages across the state of Colorado. The goal of this work is to provide transportation planners, state and federal agencies, community leaders, engineers, and conservationists with a statewide vision for reconnecting habitats that are vital for maintaining healthy populations of native species. Both the Federal Highway Administration and CDOT have begun promoting wildlife crossings in their transportation plans...
These raster data represent the results of a case study in Arizona on how vertebrate richness metrics can be used with existing state and federal guidance in wind and solar energy facility siting. Each of the four geodatabases (see Cross References) contain eight native terrestrial wildlife group models in Arizona: 1) all vertebrates, 2) amphibians, 3) reptiles, 4) birds, 5) mammals, 6) bats, 7) raptors and 8) long-distant migratory birds. An XML workbook is included that lists all terrestrial native vertebrate species in Arizona which cross-walks these species to the name of the National Gap Analysis Project species distribution model.
This data set includes the relative production scenarios for eight (8) grass species based on linear models from Epstein, et al. (1998). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis; BOGR) and buffalo grass (Bouteloua dactyloides; BODA); mixedgrass prairie: sideoats grama (Bouteloua curtipendula; BOCU) and little bluestem (Schizachyrium scoparium; SCSC); tallgrass prairie: big bluestem (Andropogon gerardii; ANGE) and Indiangrass (Sorghastrum nutans; SONU); and semiarid grasslands: black grama (Bouteloua eriopoda; BOER) and tobosagrass (Pleuraphis mutica; PLMU). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided...
The Forest and Rangeland Ecosystem Science Center 's mission is to provide scientific understanding and the technology needed to support sound management and conservation of our nation's natural resources, with emphasis on western ecosystems. The scientists from FRESC capitalize on their diverse expertise to answer critically important scientific questions shaped by the equally diverse environments of the western United States. FRESC scientists collaborate with each other and with partners to provide rigorous, objective, and timely information and guidance for the management and conservation of biological systems in the West and worldwide. Research activities are concentrated in Washington, Oregon, Idaho, Nevada,...
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
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 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 of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
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
Scorecard analysis for terrestrial conservation elements and 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 integrity at...
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 dataset represents the probability of occurrence for the gypsum 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, distance to gypsum soils, soil pH, geology, NatureServe's ecological systems map, available water holding capacity, aspect, and slope. Classification Model: 1 - High Potential Habitat NoData - Very Low Habitat Potential or "Non-Habitat"
This data set contains distribution information for all birds occurring in the Western Hemisphere. The goal of this project is to make this distributional information freely available to the public to inform conservation and other landuse decisions. A Memorandum of Understanding signed by NatureServe (then known as the Association for Biodiversity Information), The Nature Conservancy/Migratory Bird Program, Conservation International/CABS, World Wildlife Fund-US, and in 2000, with the subsequent addition of Environment Canada, governed the initial development and guidelines for sharing of these data. The MOU expired in 2003, but NatureServe, the compiler of the data set, continues to maintain the data largely in...
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...
This layer represents the scorecard of one indicator of ecosytem integrity. This ecosystem assessment is for SNK 2010 landscape condtion (LCM). 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 integrity at broader scales.
This layer represents the scorecard of one indicator of ecosytem integrity. This ecosystem assessment is for SNK 2010 landscape condtion (LCM). 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 integrity at broader scales.
This data set contains vector lines and polygons representing the shoreline and coastal habitats of Western Alaska classified according to the Environmental Sensitivity Index (ESI) classification system. This data set comprises a portion of the ESI for Western Alaska. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.
Types: Downloadable;
Tags: BLM,
Bureau of Land Management,
Coastal Zone Management,
Coastal resources,
DOI,
Some of the SNK 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 dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and baseline climate conditions, and then projecting these correlations into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-5 show...
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