The U.S. Geological Survey (USGS) modeled the distribution of terrestrial ecosystems for the contiguous United States using a standardized, deductive approach to associate unique physical environments with ecological systems characterized in NatureServe's Ecological Systems of the United States classification (Comer et al., 2003). This approach was first developed for South America (Sayre et al., 2008) and is now being implemented globally (Sayre et al., 2007). Unique physical environments were delineated from a massive biophysical stratification of the nation into the major structural components of ecosystems: biogeographic regions (Cress et al., 2008c), land surface forms (Cress et al., 2008a), surficial lithology (Cress et al., 2008d), and topographic moisture potential (Cress et al., 2008b). Each of these structural components was mapped for the contiguous United States and then spatially combined to produce ecosystem structural footprints which represented unique abiotic (physical) environments. Among 49,168 unique structural footprint classes, 13,482 classes which met a minimum pixel count threshold (20,000 pixels) were aggregated into 419 NatureServe ecosystems through semi-automated labeling process using rule set formulations for attribution of each ecosystem. UPDATE: A newer terrestrial ecosystems datalayer, "World Terrestrial Ecosystems (WTE) 2020", is now available at: https://doi.org/10.5066/P9DO61LP. This datalayer is a global raster dataset at a 250 m spatial resolution where 431 ecosystem types are identified and mapped. Each ecosystem type is a unique combination of vegetation/land cover, climate region, and landform.

The U.S. Geological Survey (USGS) modeled the distribution of terrestrial ecosystems for the contiguous United States using a standardized, deductive approach to associate unique physical environments with ecological systems characterized in NatureServe's Ecological Systems of the United States classification (Comer et al., 2003). This approach was first developed for South America (Sayre et al., 2008) and is now being implemented globally (Sayre et al., 2007). Unique physical environments were delineated from a massive biophysical stratification of the nation into the major structural components of ecosystems: biogeographic regions (Cress et al., 2008c), land surface forms (Cress et al., 2008a), surficial lithology (Cress et al., 2008d), and topographic moisture potential (Cress et al., 2008b). Each of these structural components was mapped for the contiguous United States and then spatially combined to produce ecosystem structural footprints which represented unique abiotic (physical) environments. Among 49,168 unique structural footprint classes, 13,482 classes which met a minimum pixel count threshold (20,000 pixels) were aggregated into 419 NatureServe ecosystems through semi-automated labeling process using rule set formulations for attribution of each ecosystem. UPDATE: A newer terrestrial ecosystems datalayer, "World Terrestrial Ecosystems (WTE) 2020", is now available at: https://doi.org/10.5066/P9DO61LP. This datalayer is a global raster dataset at a 250 m spatial resolution where 431 ecosystem types are identified and mapped. Each ecosystem type is a unique combination of vegetation/land cover, climate region, and landform.

The U.S. Geological Survey (USGS) has generated land surface form classes for the contiguous United States. These land surface form classes were created as part of an effort to map standardized, terrestrial ecosystems for the nation using a classification developed by NatureServe (Comer and others, 2003). Ecosystem distributions were modeled using a biophysical stratification approach developed for South America (Sayre and others, 2008) and now being implemented globally (Sayre and others, 2007). Land surface forms strongly influence the differentiation and distribution of terrestrial ecosystems, and are one of the key input layers in the ecosystem delineation process. The methodology used to produce these land surface form classes was developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's (1964a, 1964b) land surface form classification, which allowed the use of 30-meter source data and a 1 km2 window for neighborhood analysis (True 2002, True and others, 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True 2002). Slope is classified as gently sloping or not gently sloping using a slope threshold of 8%, local relief is classified into five classes (0-15m, 15-30m, 30-90m, 90-150m, and >150m), and eight landform classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks, and low mountains) were derived by combining slope class and local relief. The USGS implementation of the MoRAP methodology was executed using the USGS 30-meter National Elevation Dataset (NED) and an existing USGS slope dataset. In this implementation, a new land surface form class, the high mountains/deep canyons class, was identified by using an additional local relief class (> 400m). The drainage channels class was derived independently from the other land surface form classes. This class was derived using two of Andrew Weiss's slope position classes, "valley" and "lower slope" (Weiss 2001, Jenness 2006). The USGS implemented Weiss's algorithm using the 30-meter NED and a 1 km2 neighborhood analysis window. The resultant drainage channel class was combined into the final land surface forms dataset.

The U.S. Geological Survey (USGS) has generated land surface form classes for the contiguous United States. These land surface form classes were created as part of an effort to map standardized, terrestrial ecosystems for the nation using a classification developed by NatureServe (Comer and others, 2003). Ecosystem distributions were modeled using a biophysical stratification approach developed for South America (Sayre and others, 2008) and now being implemented globally (Sayre and others, 2007). Land surface forms strongly influence the differentiation and distribution of terrestrial ecosystems, and are one of the key input layers in the ecosystem delineation process. The methodology used to produce these land surface form classes was developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's (1964a, 1964b) land surface form classification, which allowed the use of 30-meter source data and a 1 km2 window for neighborhood analysis (True 2002, True and others, 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True 2002). Slope is classified as gently sloping or not gently sloping using a slope threshold of 8%, local relief is classified into five classes (0-15m, 15-30m, 30-90m, 90-150m, and >150m), and eight landform classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks, and low mountains) were derived by combining slope class and local relief. The USGS implementation of the MoRAP methodology was executed using the USGS 30-meter National Elevation Dataset (NED) and an existing USGS slope dataset. In this implementation, a new land surface form class, the high mountains/deep canyons class, was identified by using an additional local relief class (> 400m). The drainage channels class was derived independently from the other land surface form classes. This class was derived using two of Andrew Weiss's slope position classes, "valley" and "lower slope" (Weiss 2001, Jenness 2006). The USGS implemented Weiss's algorithm using the 30-meter NED and a 1 km2 neighborhood analysis window. The resultant drainage channel class was combined into the final land surface forms dataset.

Gap Analysis Project (GAP) habitat maps are predictions of the spatial distribution of suitable environmental and land cover conditions within the United States for individual species. Mapped areas represent places where the environment is suitable for the species to occur (i.e. suitable to support one or more life history requirements for breeding, resting, or foraging), while areas not included in the map are those predicted to be unsuitable for the species. While the actual distributions of many species are likely to be habitat limited, suitable habitat will not always be occupied because of population dynamics and species interactions. Furthermore, these maps correspond to midscale characterizations of landscapes, but individual animals may deem areas to be unsuitable because of presence or absence of fine-scale features and characteristics that are not represented in our models (e.g. snags, vernal pools, shrubby undergrowth). These maps are intended to be used at a 1:100,000 or smaller map scale. These habitat maps are created by applying a deductive habitat model to remotely-sensed data layers within a species’ range. The deductive habitat models are built by compiling information on species’ habitat associations and entering it into a relational database. Information is compiled from the best available characterizations of species’ habitat, which included species accounts in books and databases, primary peer-reviewed literature. The literature references for each species are included in the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository. For all species, the compiled habitat information is used by a biologist to determine which of the ecological systems and land use classes represented in the National Gap Analysis Project’s (GAP) Land Cover Map Ver. 1.0 that species is associated with. The name of the biologist who conducted the literature review and assembled the modeling parameters is shown as the "editor" type contact for each habitat map item in the repository. For many species, information on other mapped factors that define the environment that is suitable is also entered into the database. These factors included elevation (i.e. minimum, maximum), proximity to water features, proximity to wetlands, level of human development, forest ecotone width, and forest edge; and each of these factors corresponded to a data layer that is available during the map production. The individual datasets used in the modeling process with these parameters are also made available in the ScienceBase Repository (see the end of this Summary section for details). The "Machine Readable Habitat Database Parameters" JSON file attached to each species habitat map item has an "input_layers" object that contains the specific parameter names and references (via Digital Object Identifier) to the input data used with that parameter. The specific parameters for each species were output from the database used in the modeling and mapping process to the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository. The maps are generated using a python script that queries the model parameters in the database; reclassifies the GAP Land Cover Ver 1.0 and ancillary data layers within the species’ range; and combines the reclassified layers to produce the final 30m resolution habitat map. Map output is, therefore, not only a reflection of the ecological systems that are selected in the habitat model, but also any other constraints in the model that are represented by the ancillary data layers. Modeling regions were used to stratify the conterminous U.S. into six regions (Northwest, Southwest, Great Plains, Upper Midwest, Southeast, and Northeast). These regions allowed for efficient processing of the species distribution models on smaller, ecologically homogenous extents. The 2008 start

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

GAP species range data are coarse representations of the total areal extent a species occupies, in other words the geographic limits within which a species can be found (Morrison and Hall 2002). These data provide the geographic extent within which the USGS Gap Analysis Project delineates areas of suitable habitat for terrestrial vertebrate species in their species habitat maps. The range maps are created by attributing a vector file derived from the 12-digit Hydrologic Unit Dataset (USDA NRCS 2009). Modifications to that dataset are described here < https://www.sciencebase.gov/catalog/item/56d496eee4b015c306f17a42>. Attribution of the season range for each species was based on the literature and online sources (See Cross Reference section of the metadata). Attribution for each hydrologic unit within the range included values for origin (native, introduced, reintroduced, vagrant), occurrence (extant, possibly present, potentially present, extirpated), reproductive use (breeding, non-breeding, both) and season (year-round, summer, winter, migratory, vagrant). These species range data provide the biological context within which to build our species distribution models. Versioning, Naming Conventions and Codes: A composite version code is employed to allow the user to track the spatial extent, the date of the ground conditions, and the iteration of the data set for that extent/date. For example, CONUS_2001v1 represents the spatial extent of the conterminous US (CONUS), the ground condition year of 2001, and the first iteration (v1) for that extent/date. In many cases, a GAP species code is used in conjunction with the version code to identify specific data sets or files (i.e. Cooper’s Hawk Habitat Map named bCOHAx_CONUS_2001v1_HabMap).

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

This geospatial layer is a spatial index for the CED (Conservation Efforts Database https://conservationefforts.org/), serving as a spatial framework for summary reports by area (a.k.a. polygon). In addition, this SRU (Sagebrush Reporting Unit) data is an option for data providers to provide spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts efforts and to protect Personally Identifiable Information. This option allows CED data providers to pick a predetermined SRU instead of submitting the explicit effort boundary. These SRUs are large enough to provide spatial ambiguity and obscure private landowner locations. This SRU data is in the format of a GIS polygon layer and is an aggregate of USGS partner’s lek cluster layer, BLM HAF data modified by Oregon, Idaho layers, and CED development team modification for CED purposes.