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GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Categories: Data;
Types: ArcGIS REST Map Service,
Map Service;
Tags: Biodiversity,
Conservation,
GAP,
Gap Analysis,
Species Distribution,
This table contains information about the amount of each threat analysis attribute, indicator and metric quantified for the 6th level Hydrological Unit (HUC12). These attributes were calculated for the entire HUC 12, the streams within the HUC 12, or the riparian corridor within the HUC 12. The attributes were calculated using ArcMap Tools These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use. The User is encouraged to carefully consider the content of the metadata file associated with these data.
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 consists of raster distribution maps for terrestrial vertebrate species in Alaska. Individual species distribution maps were developed using the best available known occurrence points for each species and modeled using MaxEnt software and a series of environmental predictor variables. Output maps were clipped...
Rating based on Percent of HUC GAP Status 1 or 2. This geodatabse set contains the attributes and indicators associated with aquatic EI were categorized by size, landscape context, and conditionset in the Middle Rockies Ecoregion. The data for the Aquatic EI used information from the Aquatic Threat analysis performed by the Missouri River Assesment Program (MoRAP). The MoRAP aquatic threat analysis contains information about each threat analysis attribute, indicator and metric arbitrarily quantified (1,2,3) for the 6th level Hydrological Unit (HUC12). These attributes were calculated for the entire HUC 12, the streams within the HUC 12, or the riparian corridor within the HUC 12. For this analysis MoRAP used arbitrary...
These data sets contain the attributes and indicators associated with aquatic EI and were categorized by size, landscape context, and conditionset in the Northwestern Plains Ecoregion. The data for the Aquatic EI used information from the Aquatic Threat analysis performed by the Missouri River Assesment Program (MoRAP). The MoRAP aquatic threat analysis contains information about each threat analysis attribute, indicator and metric arbitrarily quantified (1,2,3) for the 6th level Hydrological Unit (HUC12). These attributes were calculated for the entire HUC 12, the streams within the HUC 12, or the riparian corridor within the HUC 12. For this analysis MoRAP used arbitrary values were assigned good =3, fair =2 and...
This map summarizes information presented in the other chapters of the report, including background information on the Bureau of Land Management and Rapid Ecoregional Assessments (REAs), and the REA components that are addressed by the Wyoming Basin REA. In addition, we provide two-page summaries for each Change Agent (development, invasive species, fire, and climate change) and Conservation Element (species and communities) assessed by the Wyoming Basin REA. The REA?s provide an assessment of 1) baseline conditions for long-term monitoring of broad-scale conditions and trends; 2) landscape-level intactness of ecological communities, habitats for priority species, and the ecoregion overall; and 3) a predictive capacity...
USGS Historical Quadrangle in GeoPDF.
GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Categories: Data;
Types: ArcGIS REST Map Service,
Map Service;
Tags: Biodiversity,
Conservation,
GAP,
Gap Analysis,
Species Distribution,
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
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 consists of raster distribution maps for terrestrial vertebrate species in Alaska. Individual species distribution maps were developed using the best available known occurrence points for each species and modeled using MaxEnt software and a series of environmental predictor variables. Output maps were clipped...
Generally, the mapping of land cover is done by adopting or developing a land cover classification system, delineating areas of relative homogeneity (basic cartographic objects), then labeling these areas using categories defined by the classification system. More detailed attributes of the individual areas are added as more information becomes available, and a process of validating both polygon pattern and labels is applied for editing and revising the map. This is done in an iterative fashion, with the results from one step causing re-evaluation of results from another step. In its coarse filter approach to conservation biology (e.g., Jenkins 1985, Noss 1987), gap analysis relies on maps of dominant natural land...
GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Vectorized version of the raster model with the PJ and Conifer interface areas merged across the west. A raster model was developed to identify sagebrush land cover which is in close proximity to conifer land cover, thus suggesting a risk for conifer encroachment. To achieve this end product the following general steps were taken: 1. Extract sagebrush land cover types from GAP/ReGAP data. 2. Extract conifer land cover types (excluding those corresponding to pinyon, juniper, and pinyon-juniper) from GAP/ReGAP data. 3. Reclassify and add these raster datasets. 4. Conduct a focal statistics operation. 5. Multiply the above product by the extracted, reclassified sagebrush raster to identify sagebrush cells adjacent...
Types: Downloadable;
Tags: BER,
BLM,
Baseline Environmental Report,
Bureau of Land Management,
Conifer,
GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Categories: Data;
Types: ArcGIS REST Map Service,
Map Service;
Tags: Biodiversity,
Conservation,
GAP,
Gap Analysis,
Species Distribution,
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 consists of raster distribution maps for terrestrial vertebrate species in Alaska. Individual species distribution maps were developed using the best available known occurrence points for each species and modeled using MaxEnt software and a series of environmental predictor variables. Output maps were clipped...
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 consists of raster distribution maps for terrestrial vertebrate species in Alaska. Individual species distribution maps were developed using the best available known occurrence points for each species and modeled using MaxEnt software and a series of environmental predictor variables. Output maps were clipped...
GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Categories: Data;
Types: ArcGIS REST Map Service,
Map Service;
Tags: Biodiversity,
Conservation,
GAP,
Gap Analysis,
Species Distribution,
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
GAP distribution models represent the areas where species are predicted to occur based on habitat associations. GAP distribution models are the spatial arrangement of environments suitable for occupation by a species. In other words, a species distribution is created using a deductive model to predict areas suitable for occupation within a species range. To represent these suitable environments, GAP compiled existing GAP data, where available, and compiled additional data where needed. Existing data sources were the Southwest Regional Gap Analysis Project (SWReGAP) and the Southeast Gap Analysis Project (SEGAP) as well as a data compiled by Sanborn Solutions and Mason, Bruce and Girard. Habitat associations were...
Categories: Data;
Types: ArcGIS REST Map Service,
Map Service;
Tags: Biodiversity,
Conservation,
GAP,
Gap Analysis,
Species Distribution,
This table contains information about the amount of each threat analysis attribute, indicator and metric quantified for the 6th level Hydrological Unit (HUC12). These attributes were calculated for the entire HUC 12, the streams within the HUC 12, or the riparian corridor within the HUC 12. The attributes were calculated using ArcMap Tools. These data are provided by Bureau of Land Management (BLM) "as is" and may contain errors or omissions. The User assumes the entire risk associated with its use of these data and bears all responsibility in determining whether these data are fit for the User's intended use. The User is encouraged to carefully consider the content of the metadata file associated with these data.
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