This dataset was developed by Chris Stermer (CDFG - RAP Program). No original metadata were located, but the following is an abstract from a document describing the product: We conducted field surveys for Willow Flycatchers (Empidonax traillii brewsterii) in 1997 and 1998, from June 15 through July 31, within the McCloud Flats region of Siskiyou County, California. A Geographic Information System (GIS) was used to predict potentially suitable habitat to survey prior to field visits. We used a GIS to model willow flycatcher habitat within our study area from remotely sensed data and digitally mapped data layers. Spatially explicit data used in our predictions included a vegetation map (a vegetation classification derived from Landsat 5 Thematic Mapper imagery), a Digital Elevation Model (DEM), a slope gradient model, and a stream layer. Seventy-seven Willow Flycatcher territories were found during our surveys. Nine of the territories were located within a large montane meadow complex (Bigelow Meadows) known to have Willow Flycatchers, the remaining territories (68) were predicted using a GIS pattern analysis. We characterized vegetation within .07 ha circular plots centered on sixty-six territories located in 1997. Riparian thickets > 2 m in height was the most abundant vegetation type, making up 53% of the vegetation within the plots. Twenty-one percent of the vegetation was a composite of live green grasses and forbs. A pattern based habitat predictability model was developed using the 66 territories located in the 1997 field season as image training sites. The model integrated two environmental variables found to have predictive capability: (1) composition of vegetation classes, and; (2) slope gradient. An accuracy assessment indicated the model was 94% correct when predicting suitable habitat greater than 6 ac. We concluded that Landsat Thematic Mapper imagery, when applied in conjunction with other landscape data, was an effective technique to identify suitable Willow Flycatcher habitat for our study area. Currently, this technique is being used by the California Department of Fish and Game to identify habitat throughout Northern California. This dataset was modified on May 17, 2005 by Eric Haney of CDFG - Information Services branch. Modifications included addition of a Site_ID Field, and fields representing UTM Northing and Easting coordinates (using NAD83 Datum). These fields were added to assist in an effort to field validate the dataset. Note that not all UTM coordinates are located within habitat polygons. Depending on the irregular shape of the polygons, some of the utm coordinates are located outside the boundaries. These coordinates are only to be used for coarse navigational purposes. While there is no publication date planned, Region 1 staff are working to validate the model results.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

This dataset represents the consolidated submissions of GRSG habitat management areas from each individual BLM ARMP & ARMPA/Records of Decision (ROD) and for subsequent updates as of February 2025. Please contact the BLM Montana State Office for details on how sage-grouse management is applied in these areas. This dataset represents the consolidated submissions of GRSG habitat management areas from each individual BLM ARMP & ARMPA/Records of Decision (ROD) and for subsequent updates. These data were submitted to the BLM’s Wildlife Habitat Spatial Analysis Lab in March 2016 and were updated for UT in April of 2017, WY in October of 2017 (Lander and Bighorn EIS) and May 2022 (Buffalo and NinePlan EIS); CO in February of 2020, NVCA in July 2022, and Oregon in August 2022.

February 2025: Colorado and Oregon submitted signed ROD data for GRSG habitat in Feb 2025 - these feature classes replaced previous CO and OR data in the Aug 2022 version.

August 2022 Update: OR - New habitat data was submitted by Oregon EIS as part of the Allocation Decision Analysis data call in 2022. Data that was submitted earlier was updated to reflect SFA designations in Aug 2022.

July 2022 Update: NVCA - New habitat data was submitted by NVCA as part of the Allocation Decision Analysis data call in 2022.

May 2022 Update: WY - New habitat data was submitted by Wyoming EISs Buffalo and Nine Plan as part of the Allocation Decision Analysis data call in 2022.

February 2020 Update: CO - In February 2016, the Associated Governments of Northwest Colorado (AGNC) hired a consultant (Olsson) to help further refine CPW’s greater sage-grouse habitat maps in Northwest Colorado. The Olsson consultation team, have utilized CPW’s contemporary and rigorous habitat models and developed their own to produce revised PHMA and GHMA habitat data. These spatial datasets (i.e., habitat maps) are specifically designed to meet the management intent of the ARMPA and have been produced for formal submittal to the BLM for incorporation into Northwest Colorado Land and Resource Management Plans. The updated habitat delineations for NWCO include Undesignated Habitat (UDH) to address concerns surrounding the management of privately held irrigated agricultural lands. The BLM's NWCO Sage-Grouse Plan has no management decisions associated with this habitat designation.

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 of Fillmore and Salt Lake Field Offices. Appendix I of the ARMPA includes “hard-wired” changes in management that were finalized in the 2015 Record of Decision, listed in Appendix I Table I.1 (Specific Management Responses). The PHMA in the Sheeprocks population has changed as a result of this, and the change is reflected in this data.

The following habitat management areas were used in the creation of this feature class:

PHMA: Areas identified as having the highest habitat value for maintaining sustainable GRSG populations and include breeding, late brood-rearing, and winter concentration areas.

GHMA: Areas that are occupied seasonally or year-round and are outside of PHMAs.

IHMA: Areas in Idaho that provide a management buffer for and that connect patches of PHMAs. IHMAs encompass areas of generally moderate to high habitat value habitat or populations but that are not as important as PHMAs.

OHMA: Areas in Nevada and Northeastern California, identified as unmapped habitat in the Proposed RMP/Final EIS, that are within the Planning Area and contain seasonal or connectivity habitat areas.

RHMA: Areas in Montana EISs with ongoing or imminent impacts containing substantial and high-quality GRSG habitat that historically supported sustainable GRSG populations. Management actions would emphasize restoration for the purpose of establishing or restoring sustainable GRSG populations. Areas are delineated using key, core, and connectivity data or maps and other resource information.

LCHMA: Areas in CO EIS that have been identified as broader regions of connectivity important to facilitate the movement of GRSG and maintain ecological processes.

UDH: In CO EIS, An Undesignated Habitat management prescription was developed to address concerns surrounding the management of privately held irrigated agricultural lands.

Anthro Mountain: An additional 41,200 acres of National Forest System lands in the Anthro Mountain portion of the Carbon Population Area in Utah EIS that are managed as neither PHMA nor GHMA. These areas are identified as “Anthro Mountain.” In the BLM’s ARMPA, these areas are considered split-estate, where the BLM merely administers the mineral estate.

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

This grid represents habitat suitability based on the California Wildlife Habitat Relationships (CWHR) expert opinion habitat suitability rankings by habitat type. Habitat suitability rankings (ranging 0-100) for each combination of habitat type, size class (dbh) and cover class (density) were applied to the 30 m vegetation grid using CWHRs Bioview. These values were then averaged across grid cells to create a 270 m grid consistent with that used in the Maxent models for the project. The grid was then symbolized to represent low (0-50), medium (50-75) and high (75-100) habitat suitability. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.

The Sheeprocks (UT) was revised to resync with the UT habitat change as reflected in the Oct 2017 habitat data, creating the most up-to-date version of this dataset. Data submitted by Wyoming in February 2018 and by Montana and Oregon in May 2016 were used to update earlier versions of this feature class. The biologically significant unit (BSU) is a geographical/spatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual states/EISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areas/core habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLM's GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographical/spatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority and/or Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority. Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also. California and Nevada's BSUs were developed by Nevada Department of Wildlife's Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevada's Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding). PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat. PGH is defined as Greater sage-grouse Occupied Range outside of PPH. Datasets used to create PPH and PGH: Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06/09/2012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacant/unknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. Vacant or Unknown Habitat: Suitable habitat for sage-grouse that is separated (not contiguous) from occupied habitats that either: 1) Has not been adequately inventoried, or 2) Has not had documentation of grouse presence in the past 10 years Potentially Suitable Habitat: Unoccupied habitats that could be suitable for occupation of sage-grouse if practical restoration were applied. Soils or other historic information (photos, maps, reports, etc.) indicate sagebrush communities occupied these areas. As examples, these sites could include areas overtaken by pinyon-juniper invasions or converted rangelandsUpdate October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat and management zones, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh” polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond” were removed from GrSG habitat. Oregon submitted updated BSU boundaries in May 2016 and again in October 2016, which were incorporated into this latest version. In Oregon, the Core Area maps and data were developed as one component of the Conservation Strategy for sage-grouse. Specifically, these data provide a tool in planning and identifying appropriate mitigation in the event of human development in sage-grouse habitats. These maps will assist in making

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

This polygon feature class represents the spatial extent and boundaries for the Utah Greater Sage-Grouse (GRSG) seasonal habitats, as modeled by the Utah State University Kohl Model Version 5. The original habitat model is clipped to a habitat buffer created by the Utah Division of Wildlife Resources. The data contains a clipped polygon for nesting, summer, and winter habitat respectively. Process of creating habitat model, as described by the authors: The process of creating the seasonal habitats layer is described below.1. Creation of statewide general habitat model for GRSG (2016). In 2016, researchers at Utah State University (USU) developed a statewide general sage-grouse habitat map using a database of hundreds of lek locations and more than 20,000 GRSG telemetry locations collected statewide from 1998 – 2014. The map depicted habitat suitability on a scale from 0 to 100 at 1 km spatial resolution, based on comparing environmental (vegetation, topography, soils, climate) and anthropogenic (developed land cover, road density, powerline density) conditions at active lek and sage-grouse use locations versus inactive lek and random background locations statewide. Because multiple telemetry locations were often associated with a single brood-rearing or non-breeding bird, the median values of environmental and anthropogenic variables at these telemetry locations were used in the model. A random forest model was used to create the map (Breiman 2001, Cutler et al. 2007). Random forests is a highly accurate non-parametric classification technique that predicts the probability of an outcome (in this case, habitat vs non-habitat) by averaging the results of many classification trees, each of which is trained on a random subset of the available data. The general habitat map was reclassified into ‘habitat’ and ‘non-habitat’ classes such that habitat areas captured 99% of all sage-grouse use locations. These general habitat areas were used as a mask in order to constrain preliminary predictions of seasonal habitats, described below.2. Create preliminary seasonal GRSG habitat models (Jan - May 2017). Telemetry locations in the database were classified into three seasonal habitat types based on time of year and type of GRSG use. Breeding habitat was defined as areas used by GRSG engaged in lekking, nesting, and early brood-rearing, from March 1 – June 14. Summer habitat was defined as areas used by brood-rearing and non-breeding GRSG from June 15 – August 31. The June 15 cutoff date between breeding and summer use locations was selected based on the temporal distribution of nesting and brooding use locations. Winter habitat was defined as areas used by non-breeding GRSG from November 1 – February 29. As in the general habitat modeling approach, environmental conditions at annual brood-rearing or non-breeding locations associated with the same bird were measured as medians over the multiple locations. Seasonal habitats were modeled using the same predictors as the general habitat model, with the addition of distance to leks due to its association with breeding habitat. A random forest model was used to estimate the suitability of general habitat areas statewide (from step 1 above) for breeding, summer, and winter use. For each seasonal use class, a suitability threshold was selected such that 85% of all seasonal use locations were captured in the resulting seasonal habitat map. This resulted in models that were neither overly restrictive nor overly liberal. To reduce the 'salt and pepper’ effect of isolated or scattered habitat pixels, a 3x3 km smoothing window was applied to each of the seasonal habitat layers, assigning the majority value (habitat or non-habitat) to the center pixel. 3. DWR biologists reviewed seasonal models (April - June 2017). An overview of the general and seasonal mapping methodology and preliminary maps was presented to GRSG biologists and managers from the Utah Division of Wildlife Resources (UDWR), Bureau of Land Management (BLM), and Forest Service (FS) at Utah State University in Logan on 4/21/17. Feedback at the meeting led to a few minor changes to the seasonal mapping methods. Because the breeding seasonal use model was not picking up areas around all active leks, distance to leks was dropped as a predictor variable from the seasonal habitat random forest model, and a 3 km buffer around all active leks was manually included in the breeding habitat model. Updated seasonal use models were sent to UDWR on 5/17/17, where they were made available for review by biologists with local area knowledge. An ArcGIS Online webpage was used to share the models with biologists, and allow for them to provide recommended additions / deletions to areas captured by the models. Accompanying the spatial data was an 8 minute webinar communicating the modelling procedure. UDWR returned updated seasonal use models with biologists’ comments, additions, and deletions to USU researchers on 6/6/17. Most but not all areas in the state received substantive feedback and comments from UDWR biologists. 4. Additional review of seasonal use models and final edits performed (July – August 2017). USU researchers reviewed biologist edits on 7/13/17. Most of the areas flagged to be added/removed from the seasonal use models seemed appropriate and helpful. But, there were a few areas UDWR biologists identified to be deleted from the models that USU researchers considered questionable. These areas were identified and shared with UDWR managers on 7/13/17. UDWR managers indicated a desire to have another face to face meeting to make a final determination on areas to include / exclude from the seasonal use models. This meeting occurred on 8/8/17, at which it was determined that it would be preferable to have the final seasonal habitat products reflect both use and potential suitability, as opposed to only areas of known use. This led to rejecting some areas flagged for deletion by biologists, as biologist comments indicated they were conceptualizing the map as primarily a use map only. Numerous small edits were made to the seasonal use layers, including several edits to include seasonal use locations not captured by preliminary models. Finally, all single, isolated habitat pixels were removed from the map. References: Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.Cutler, D.R., T.C. Edwards, K.H. Beard, A. Cutler, K.T Hess, J.C. Gibson and J.J. Lawler. 2007. Random forests for classification in ecology. Ecology 88(11):2783-2792.This dataset was used in the preparation of the Greater Sage-grouse (GRSG) 2018 DEIS planning maps and acreages initiated by Secretarial Order (SO) 3353 of June 7, 2017 and the Notice of Intent (NOI) of October 11, 2017. Recipient Principal Investigator/Project Manager: Terry A. Messmer, Jack H. Berryman Institute, Utah State University, 5230 Old Main Hill, Logan, Utah 84322-5230, Phone 435-797-3975, Fax 435-797-3796, E-mail terry.messmer@usu.edu Project Co-PIs and Staff: Dave Dahlgren, Eric Thacker, Kris Hulvey. Michel Kohl (Post-Doc), and Ben Crabb (GIS staff)

Open the Data Resource: https://www.chesapeakeconservation.org/our-work/goal-mapping/habitat Open the Map Viewer: https://chesbay.maps.arcgis.com/apps/instant/basic/index.html?appid=e961d8ef2367436d8a3603b3491f0b2b Open the Map Service:https://cicgis.org/arcgis/rest/services/CCP/Habitat/MapServer The Chesapeake Conservation Partnership Habitat Model is a suitability model aimed at identifying a network of large natural areas and corridors sufficient to allow nature to respond to a changing climate and land development, support thriving populations of native wildlife, migratory birds, fish and plants, and sustain at-risk species. This map focuses on four mappable resources: The lotic core network (rivers and streams), lentic core network (lakes and ponds), aquatic buffers and a terrestrial core-connector network. The data layer includes the following: Aquatic Core Network: This consists of the streams, lakes and ponds that are intact, connected and support a wide diversity of aquatic species and ecosystems. The core areas are based on the Index of Ecological Integrity and are scaled to HUC 6 watersheds and were developed through a set of regional analyses that assess the physical and biological value of aquatic systems and species across the Northeast region. Aquatic Buffers: Aquatic buffers surround the aquatic (both lotic and lentic) cores. Buffers represent the areas estimated to have a strong influence on the integrity of the aquatic cores based on watershed processes. Terrestrial Core-connector Network: Core areas are intact, well-connected places that, if protected, will continue to support a broad diversity of fish, wildlife, plants and the ecosystems on which they depend. These include especially intact, resilient examples of each major ecosystem type. They contain widespread ecosystems such as hardwood forests, rare natural communities and important habitat for a variety of fish, wildlife and plants. Core areas are stratified by HUC 6 watersheds. Core areas are linked together by a network of connectors. The connectors allow movement of animals and plants from one core area to another, and establish a flow pattern for ecological features and processes, as landscape conditions and climate change. Note: The initial terrestrial core-connector network did not seem to sufficiently address landscape features adjacent to tidal waters that contribute to tidal aquatic habitat integrity. To partially address this, a National Wetlands Inventory derived potential black duck habitat layer was used to supplement the terrestrial core network. Future deliberations of the partnership will consider ways to strengthen the assess and prioritize the habitat conservation value of wetlands and terrestrial habitat adjacent to tidal waters.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The northern Sierra Nevada foothills wildlife connectivity project modeled wildlife corridors for 9 focal species between 238 landscape blocks within the northern Sierra Nevada foothills and neighboring ecoregions. We followed the least-cost corridor techniques described by Beier et al. (2007). This analysis identified the least-cost corridor, or the best potential route for each species, between neighboring landscape blocks. The data needed for a least-cost corridor analysis are a resistance raster and landscape blocks. The resistance raster is the inverse of the species distribution model (SDM) output (i.e., Maxent or BioView habitat models, which rank habitat suitability across the landscape from 0-100 for each species). We identified habitat patches for each focal species within each landscape block, and connected those habitat patches using the least-cost corridor models. The least-cost corridor model does not identify barriers, risk and dispersal. We removed urban areas and areas of unsuitable/non-restorable habitat from the corridors and then inspected the corridor to make sure they were continuous. We examined the amount of predicted suitable habitat in each corridor, and measured the distance between habitat patches within each corridor to make sure it was within the maximum dispersal distance for that focal species. If the corridors did not meet these rules then habitat patches on the border of the corridor were added to meet the selection requirements. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].