An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area.Downloads available: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Administrative+Forest+Boundaries

A map depicting the ceded lands of the Confederated Tribes of the Warm Springs Reservation and the current boundaries of the Warm Springs Reservation across the Ochoco National Forest and the state of Oregon.

Inventory methods, sources of data, data compilation techniques and analysis are described in detail in Andrews and Cowlin (1940), appendix D in PNW-GTR-584 and Cowlin and others (1942), appendix C in PNW-GTR-584. In brief, three basic procedures were used based on information available and land ownership. The national forests were cruised using intensive reconnaissance methods which consisted of mapping areas that are uniform as to type conditions and estimating the average volume per acre for each of these areas. The initial type mapping was done from both field work and aerial photographs. Approximately 30% of the land outside the national forests had recently been covered by intensive cruises by other organizations; these lands were adjustment cruised to adjust them to the standards used on national forest lands. The remaining areas were type mapped by driving roads and walking trails, using data from county records, and locating viewpoints to determine type boundaries and then cruised to determine age class, species composition, stocking, and volume. Additional surveys were done for cutover or recently burned areas. The original mapping was done for each county (scale (1:63,360) and then maps prepared for each quarter of each state. The quarter state maps were digitized by the Forest Inventory and Analysis group in the early 1990s and available as an ArcView shape file.Full citation for original report:Harrington, Constance A., comp. 2003. The 1930s survey of forest resources in Washington and Oregon. Gen. Tech. Rep. PNW-GTR-584. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 123 p. plus CD-ROM. https://doi.org/10.2737/PNW-GTR-584.

Forest resources in Washington and Oregon were surveyed in the 1930s by employees of the USDA Forest Service, Pacific Northwest Forest Experiment Station. As part of this process, forest cover maps were created on paper at an original scale of 1:253,440. Forest and land cover types recorded include classifications such as: agricultural, balsam fir mountain hemlock, cedar-redwood, deforested burns, Douglas-fir, hardwood, juniper, lodgepole pine, non-forest pine mix, ponderosa pine, recent cutover, spruce-hemlock, subalpine and non-commercial, water, etc. An additional subcategory classification is also provided which gives additional insight into tree size classes for conifers or species group for hardwoods. These forest and land cover types are provided as both a shapefile and geopackage for Washington and Oregon combined.The 1928 McSweeney-McNary Forestry Research Act (P.L. 70-466, 45 Stat. 699-702) directed the Secretary of Agriculture to make and keep current a comprehensive inventory and analysis of the nation's forest resources. The decision was made to begin the nationwide survey with the Douglas-fir region and shortly thereafter to expand to the other forested lands of Washington and Oregon. Surveys were conducted between 1930 and 1936. Results of these surveys were reported in many formats including quarter state maps (4 maps per state) as well as many printed reports.The history of this project and copies of some of the early results as well, were published in Harrington (2003) which included a CD with a digital map (an ArcView GIS shapefile) for all of Washington and Oregon.

OverviewORS 477.490 requires Oregon Sate University (OSU) and the Oregon Department of Forestry (ODF) to develop a statewide wildland-urban interface (WUI) map that will be used in conjunction with the statewide wildfire hazard map (ORS 477.490) by the Oregon State Fire Marshal to determine on which properties defensible space standards apply (ORS 476.392) and by the Building Codes Division to determine to which structures home hardening building codes apply (ORS 455.612).Rules directing development of the WUI are listed in OAR-629-044-1011 and 629-044-1016. A comprehensive description of datasets and geospatial processing is available at https://hazardmap.forestry.oregonstate.edu/understand-map. The official statewide WUI map is available on the Oregon Wildfire Risk Explorer at https://tools.oregonexplorer.info/viewer/wildfire.Following is an overview of the data and methods used develop the statewide WUI map.Wildland-Urban InterfaceCreating a statewide map of the WUI involved two general steps. First, we determined which parts of Oregon met the minimum building density requirements to be classified as WUI. Second, for those areas that met the minimum building density threshold, we evaluated the amount and proximity of wildland or vegetative fuels. Following is a summary of geospatial tasks used to create the WUI.Develop a potential WUI map of all areas that meet the minimum density of structures and other human development - According to OAR 629-044-1011, the boundary of Oregon’s WUI is defined in part as areas with a minimum building density of one building per 40 acres, the same threshold defined in the federal register (Executive Order 13728, 2016), and any area within an Urban Growth Boundary (UGB) regardless of the building density. Step One characterizes all the locations in Oregon that could be considered for inclusion in the WUI on building density and UGB extent alone. The result of Step One was a map of potential WUI which was then further refined into final WUI map based on fuels density and proximity in Step Two.Compile statewide tax lots.Map all eligible structures and other human development.Simplify structure dataset to no more than one structure per tax lotCalculate structure density and identify all areas with greater than one structure per 40 acresAdd urban growth boundaries to all the areas that meet the density requirements from the previous step.Classify WUI based on amount and proximity of fuel. The WUI is also defined by the density and proximity of wildland and vegetative fuels (“fuels”). By including density and proximity of fuels in the definition of the WUI, the urban core is excluded, and the focus is placed on those areas with sufficient building density and sufficient fuels to facilitate a WUI conflagration. Consistent with national standards, we further classified the WUI into three general classes to inform effective risk management strategies. The following describes how we refined the potential WUI output from step one into the fina

These rasters provide the local mean annual extreme low temperature from 1991 to 2020 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each _location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands.The classified rasters are then used to create print and interactive maps.Temperature station data for the 2023 edition of the USDA Plant Hardiness Zone Map (PHZM) came from many different sources. In the eastern and central United States, Puerto Rico, and Hawaii, data came primarily from weather stations of the National Weather Service and several state networks. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Mexico National Weather Service and the Global Historical Climate Network. The USDA PHZM was produced with PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom). Information on PRISM can be obtained from the PRISM Climate Group website https://prism.oregonstate.edu. Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked the draft maps for errors or biases. A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the 2012 PHZM (similar to 2023) are available from the articles listed below. Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264.Widrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19.

The Ochoco mule deer herd has overlapping migration corridors and summer ranges, but can be separated into three general subherds based on winter range locations. During spring, mule deer in the northern subherd use winter ranges north of the Maury Mountains, near Crooked River and the Ochoco Reservoir, and either migrate farther north to the Ochoco Mountains and Laughlin Table or east, past Twelvemile Table to the foothills of Snow Mountain. Mule deer in the middle subherd winter south of the Maury Mountains and have ranges near Bear Creek, Smoky Butte, and South Fork Crooked River. Some of the middle subherd, like the northern subherd, travel east to the base of Snow Mountain, Green Butte, and Juniper Ridge. Others migrate south to Sears Canyon, the Paulina Mountains, and the Oregon High Desert. Mule deer in the southern subherd have winter ranges adjacent to U.S. Highway 20 and migrate south to summer ranges along the Glass Buttes and the edge of the High Desert region. Habitats are similar across the Ochoco herd management unit where big sagebrush and mountain big sagebrush give way to encroaching western juniper followed by stands of ponderosa pine, lodgepole pine, and other mixed-conifer species as elevations increase. However, winter and residential ranges for the southern subherd feature more low sagebrush and agriculture and the summer ranges are less forested than the other two subherds. The Ochoco mule deer herd faces several challenges, including extreme to exceptional droughts that can force mule deer to compete with feral horses for water and high-quality browse. Home ranges and high-use migration corridors overlap the Liggett Table HMA and Big Summit Wild Horse Territory, which contain estimates of 170 and 130 feral horses, respectively—far more than the maximum AMLs of 25 and 57 horses (BLM, 2023b; K. Kern, U.S. Forest Service, written commun., 2023). The Ochoco herd also inhabits the Maury and Ochoco WMUs, which were included in the five-year Mule Deer Initiatives for 2010 and 2015, respectively, to improve conditions for mule deer, primarily through habitat restoration (ODFW 2015, 2020). Since improvement efforts started in 2010, ODFW, BLM, and NRCS SGI have worked together to remove 150,931 acres (61,079 ha) of western juniper and treat 5,912 acres (2,392 ha) with prescribed burns. Additionally, they completed 26 water development projects and constructed 24.28 mi (39.08 km) of wildlife-friendly fencing to protect riparian areas for mule deer fawning habitat and improve cattle distributions. These mapping layers show the location of the migration corridors for mule deer (Odocoileus hemionus) in the Ochoco population in Oregon. They were developed from 154 migration sequences collected from a sample size of 57 animals comprising GPS locations collected every 3-13 hours.

This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.

Candidate environmental variables evaluated for use in the MaxEnt cannabis grow-site distribution model at 90m resolution.

These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

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

This data set is obtained from the Forestry Group at Oregon State and it is an input to the analysis presented in this data set. Digital GNN imputation maps are provided as 30-m-resolution ArcGIS grids, where the grid value is a unique plot number that links to the plot database. Selected vegetation variables from the plot database are joined as items in the grid to facilitate viewing and exploratory spatial analysis. Metadata for the vegetation variables are included with the grids and in the plot database. Dates for maps developed from GNN species-size models are determined by the vintage of the satellite imagery used in their development.

Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This publication consists of the online version of a CD-ROM publication, U.S. Geological Survey Digital Data Series DDS-43. The data for this publication total 175 MB on the CD-ROM and 167 MB for this online version. This online version does not include the Acrobat Search index files. It also has a link rather than files for the Adobe Acrobat Reader installer mentioned below.

The Sierra Nevada Ecosystem Project was requested by Congress in the Conference Report for Interior and Related Agencies 1993 Appropriation Act (H.R. 5503), which authorized funds for a "scientific review of the remaining old growth in the national forests of the Sierra Nevada in California, and for a study of the entire Sierra Nevada ecosystem by an independent panel of scientists, with expertise in diverse areas related to this issue."

This publication is a digital version of the set of reports titled Sierra Nevada Ecosystem Project, Final Report to Congress published in paper form by the Centers for Water and Wildland Resources of the University of California, Davis. The reports consist of Wildland Resources Center Report No. 39 (Summary), No. 36 (Vol. I - Assessment summaries and management strategies), No. 37 (Vol. II - Assessments and scientific basis for management options), No. 38 (Vol. III - Assessments, commissioned reports, and background information), and No. 40 (Addendum). Vol. IV is a computer-based catalogue of all public databases, maps, and other digitally stored information used in the project. Vol. IV materials are listed under the SNEP name and available on the Internet from the Alexandria Project at the University of California at Santa Barbara and the California Environmental Resource Evaluation System (CERES) project of the Resources Agency of the state of California (see links below).

[Summary provided by the USGS.]

This data layer is an element of the Oregon GIS Framework. Ecoregions denote areas of general similarity in ecosystems and in the type quality, and quantity of environmental resources. This map depicts revisions and subdivisions of ecoregions that was compiled at a relatively small scale (Omernik 1987). Compilation of this map, performed at the larger 1:250,000 scale, was part of a collaborative project between the United StatesEnvironmental Protection Agency, National Health and Environmental EffectsResearch Laboratory (NHEERL)- Corvallis, OR., the U.S. Forest Service, Natural Resources Conservation Service, Washington State Department of Natural Resources and the Oregon Natural Heritage Program. The ecoregions and subregion are designed to serve as a spatial framework for environmental resource management. The most immediate needs by the states are for developing regulations, biological criteria and water quality standards, and for setting management goals for nonpoint-source pollution. Explanation of the methods used to describe the ecoregions are given in Omernik (1995), Griffith et al. (1994), and Gallan et al. (1989). This map is a draft product of one of a few regional interagency collaborative projects aimed at obtaining consensus between the EPA, the NRCS, and the USFS regarding alignments of ecological regions.

This data release consists of a compilation of previously published mineral potential maps that were used for the Sagebrush Mineral-Resource Assessment (SaMiRA) project. This information was used as guides for assessing mineral potential assessment of approximately 10 million acres in Idaho, Montana, Nevada, Utah, and Wyoming. Specifically, the compilation was used to identify the deposit types to be assessed and the deposit models to develop. The data release consists of georeferenced images of mineral potential maps and vector shapefiles of mineral potential tracts. The georeferenced images are presented in two formats: 1) as images within raster mosaic datasets in Esri geodatabases, and 2) as individual tiff images with an accompanying .csv data table. There are four geodatabases containing the raster mosaic datasets, one for each of the four SaMiRA report areas: North-Central Montana; North-Central Idaho; Southwestern and South-Central Wyoming and Bear River Watershed; and Nevada Borderlands. Tract map images are from BLM and Forest Service wilderness study summary reports, along with multiple other mineral potential reports that were done under the USGS CUSMAP program and for USGS assessments of USGS National Forests. The georeferenced images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text was imported into the raster mosaic dataset database as ‘Footprint’ layer attributes. This data is also included as a .csv table, which can be used in conjunction with the individual georeferenced tiff images. The data compiled into the tables contains the figure caption from the original map, online linkage to the source report when available, and information on the assessed commodities according to the legal definition of mineral resources—metallic, non-metallic, leasable non-fuel, leasable fuel, geothermal, paleontological, and saleable. The shapefiles were compiled from datasets which had different data structure schemes and which used two different types of assessment methodology. The BLM used qualitative categorical and others used the USGS quantitative 3-part form of assessment. The original GIS data was re-formatted so that all of the shapefiles had one of two consistent attribute table structures, one for reports that had quantitative data, and one for reports with qualitative data. A general attribute table structure was created which contained fields for information on the deposit type assessed, assessment rank, type of assessment, and tract name and identifier. For the attribute table of the quantitatively assessed reports which used the USGS 3-part form of assessment, we added additional fields for the deposit model name and number, probabilistic assessment results data, and estimators. We captured the original information as presented but also standardized nomenclature when we could and referred to the report text in some instances in order to fill in missing data into the descriptive data tables.

Fire perimeters 2000-2024. The national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the final fire perimeters datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, and CalFire. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies.2000-2023 fire perimeters were sourced from “InterAgencyFirePerimeterHistory All Years View” and 2024 fire perimeters were sourced from “WFIGS Interagency Fire Perimeters”, both of which are hosted on NIFC. This layer has been clipped to contain all fires that partially or completely occurred in Oregon and restricted to fires with a discovery date on or after 1/1/2000 for use in the SageCon Landscape Planning Tool on Oregon Explorer. QA/QC was performed to eliminate duplicate polygons based on incident names, however, some duplicate records may exist in the dataset because some fires had multiple incident names. The attributes table has been condensed to Incident name, polygon source, fire year, and GIS acres for simplicity.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The Klamath Basin mule deer herd contains an estimated 10,775 deer and features a mix of resident and migratory animals. Most winter ranges are adjacent to the California border near Bly and Lost River, California, in areas featuring western juniper, low shrublands, and early shrub-tree habitat. In spring, these mule deer either migrate northwest to regional national forest lands or northeast past South Fork Sprague River. Summer ranges contain ponderosa pine, mixed-conifer, and early shrub-tree habitat along with alfalfa and other agricultural crops. Notably, one mule deer migrated southeast into California near Goose Lake in May 2019 and spent a year near Deadhorse Reservoir before returning to Oregon in November 2020. Out of four mule deer outfitted with GPS collars during a separate capturing event, one migrates from Lake Albert to Lakeview, Oregon along U.S. Route 395 in spring. This stretch of U.S. Route 395 experienced an average annual daily traffic (AADT) value of 1,002 vehicles in 2018. Several other mule deer also cross sections of U.S. Highway 97, an even busier road that had an AADT value of 5,298 vehicles in 2018. From 2010 to 2022, ODOT recorded an average 65.7 mule deer-vehicle collisions per year along a 44.8 mi (72.1 km) section of U.S. Highway 97 north of Klamath Falls. Klamath Basin mule deer numbers are slowly declining, in part due to reduced summer forage quality (Peek and others, 2002). Forest fire suppression beginning in the 1920s increased canopy closure in the summer range, reducing preferred understory vegetation such as Purshia tridentata (antelope bitterbrush) and Ceanothus velutinus (snowbrush ceanothus). Without sufficient high-quality forage during drought years, mule deer become more reliant on agricultural fields near Klamath Falls as a dependable water source. Canopy closure also contributed to the severity of the 2021 Bootleg Fire, the third largest recorded fire in Oregon, which burned 413,765 acres (167,445 ha) north of Sprague River. These mapping layers show the location of the stopovers for mule deer (Odocoileus hemionus) in the Klamath Basin population in Oregon. They were developed from 24 migration sequences collected from a sample size of 11 animals comprising GPS locations collected every 5−13 hours.