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Specifically, we provide estimates for soil moisture (monthly, seasonal, and annual), trends of spring and growing season soil moisture (Theil-Sen estimates), soil temperature and moisture regimes (STMRs; discrete classes defined by United States Department of Agriculture [USDA] Natural Resources Conservation Service [NRCS]), seasonal Thornthwaite moisture index (TMI; precipitation minus PET), and seasonality of TMI and soil moisture (30-meter rasters). Moisture values were estimated using our spatial implementation of the Newhall simulation model that relies on the Thornthwaite-Matter-Sellers potential evapotranspiration (PET) index. Among many enhancements, our application is the first known soil-climate model to include the effects of snow (for example, sublimation, snowmelt, attenuated evaporation, and insulation from air temperatures). Notably, we developed procedures that facilitate data substitution using spatial_nsm, supporting many use cases and flexibility, such as assessing projected climate scenarios. Our results provide evidence of the utility of spatially explicit soil-climate products, which could support subsequent use for modeling and managing ecosystem, habitat, and species distributions. For example, we demonstrated soil-climate properties had significant correlations with vegetation patterns: soil moisture variables predicted sagebrush (R^2 = 0.51), annual herbaceous plant cover (R^2 = 0.687), exposed soil (R^2 = 0.656), and fire occurrence (R^2 = 0.343). These statistical results suggested the data captured distributions of soil moisture and STMRs that can explain landscape and vegetation patterns. \nRefer to the Cross Reference section for all citations referenced in metadata supporting methods. 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The crystalline-rock aquifer underlies the Milford-Souhegan glacial-drift (MSGD) aquifer\n(a high water-producing aquifer) and the Savage Municipal Water-Supply Well Superfund site. \nResidential water-supply wells are within one-quarter of a mile of the PCE-contaminated \nmonitoring wells and many are likely installed in similar rock types and formations as those of\nthe monitoring wells. The need to understand and quantify flow and transport in the crystalline-\nrock aquifer is crucial in assessing strategies for remediation. The current, area-wide model \nsimulates flow in the crystalline-rock aquifer and covers a much larger area than previous models\nwith the goal of improving the computation of groundwater flow from distal locations to the \nresidential wells and the area. 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In addition, a previously\ndeveloped model (https://doi.org/10.3133/sir20045176 and https://doi.org/10.3133/ofr20121079)\nwas used with MOC3D to evaluate the solute-transport of tetrachloroethylene (PCE). In 2010 \nPCE, a chlorinated volatile organic compound, was detected in groundwater from monitoring \nwells tapping the deep (more than 300 feet below land surface) fractures in a crystalline-rock \naquifer. The crystalline-rock aquifer underlies the Milford-Souhegan glacial-drift (MSGD) aquifer\n(a high water-producing aquifer) and the Savage Municipal Water-Supply Well Superfund site. \nResidential water-supply wells are within one-quarter of a mile of the PCE-contaminated \nmonitoring wells and many are likely installed in similar rock types and formations as those of\nthe monitoring wells. The need to understand and quantify flow and transport in the crystalline-\nrock aquifer is crucial in assessing strategies for remediation. The current, area-wide model \nsimulates flow in the crystalline-rock aquifer and covers a much larger area than previous models\nwith the goal of improving the computation of groundwater flow from distal locations to the \nresidential wells and the area. 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The SPCMSC Geologic Core and Sample Database includes geologic cores and samples collected beginning in the 1970s to present day, from study sites across the world. This database captures metadata about samples throughout the USGS Science Data Lifecycle: including field collection, laboratory analysis, publication of research, and archival or deaccession. For more information about the USGS Science Data Lifecycle, see USGS Open-File Report 2013-1265 (https://doi.org/10.3133/ofr20131265). The SPCMSC Geologic Core and Sample Database also includes storage locations for physical samples and cores archived in a repository (USGS SPCMSC or elsewhere, if known). The majority of the samples and cores in this database come from field activities associated with the SPCMSC and have been assigned a field activity number (FAN) in the field activity management and data inventory tool for USGS Coastal and Marine Hazards and Resources Program (CMHRP) Coastal and Marine Geoscience Data System (CMGDS), https://cmgds.marine.usgs.gov/. Some cores and samples were retroactively assigned FANs based on existing metadata and published information. Cores and samples without FANs indicate there is insufficient information regarding collection of the core(s) or sample(s) needed in order to assign a field activity number in CMGDS. Please see the supplemental information section of the metadata for more information about FANs. All samples and cores contained in this database are described in published research. The database contains a link to the FAN page within the CMGDS for each sample or core where associated publications can be accessed. 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The SPCMSC Geologic Core and Sample Database includes geologic cores and samples collected beginning in the 1970s to present day, from study sites across the world. This database captures metadata about samples throughout the USGS Science Data Lifecycle: including field collection, laboratory analysis, publication of research, and archival or deaccession. For more information about the USGS Science Data Lifecycle, see USGS Open-File Report 2013-1265 (https://doi.org/10.3133/ofr20131265). The SPCMSC Geologic Core and Sample Database also includes storage locations for physical samples and cores archived in a repository (USGS SPCMSC or elsewhere, if known). The majority of the samples and cores in this database come from field activities associated with the SPCMSC and have been assigned a field activity number (FAN) in the field activity management and data inventory tool for USGS Coastal and Marine Hazards and Resources Program (CMHRP) Coastal and Marine Geoscience Data System (CMGDS), https://cmgds.marine.usgs.gov/. Some cores and samples were retroactively assigned FANs based on existing metadata and published information. Cores and samples without FANs indicate there is insufficient information regarding collection of the core(s) or sample(s) needed in order to assign a field activity number in CMGDS. Please see the supplemental information section of the metadata for more information about FANs. All samples and cores contained in this database are described in published research. The database contains a link to the FAN page within the CMGDS for each sample or core where associated publications can be accessed. 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Post-processing steps applied to all output layers include: removal of small detections below 12 square meters, geometric regularization, and systematic manual QA editing comprising deletion of false detections, manual digitizing of missed buildings, and splitting of merged multi-building polygons. All footprint layers were geometrically adjusted to a common 2025 reference frame using a matched-pair median translation approach. A companion settlement growth analysis workbook (Settlement_Growth_Analysis.xlsx) provides derived metrics for each site and epoch, including building count, density, built-up coverage ratio, mean and median building size, and compound annual growth rates. This release includes 18 building footprint shapefiles, six study site boundary shapefiles, and the growth analysis workbook. 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Seven additional MODFLOW-2000 scenarios (numbered 7-13), using this updated and recalibrated model, were developed to evaluate different withdrawal strategies which are included in this data release: (7) Mount Pleasant Waterworks bringing online a new well (located at the old well 5 location) at 3.51 million gallons per day (Mgal/d) in 2025; (8) Maximizing withdrawals from Mount Pleasant Waterworks wells 2 and 5 (3.51 Mgal/d each) in 2020 and 2025, respectively; (9) Same as Scenario 7, but removing well 3 from production in 2025; (10) Same as Scenario 9, but removing well 4 from production in 2025 (11) Same as Scenario 7, but converting well 3 to an injection well in 2025 (12) Same as Scenario 11, but converting well 4 to an injection well in 2030; and (13) Same as scenario 8, but with two injection wells added (one in 2025 and one in 2035) to Mount Pleasant Waterworks well field. Nine alternate simulations for scenarios 11-13 (three MODFLOW and six MODPATH) were done to evaluate the effects of different porosity on the groundwater flow system, water levels, and the time-of-travel of particles from injection wells to the main water source. 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This model was originally developed in 2007, by Petkewich and Campbell (https://pubs.usgs.gov/publication/sir20075126), then updated and recalibrated to conditions from 1900 to 2015. Results of six previous scenario simulations (scenarios 1-6) for the Mount Pleasant Water Works are published in a U.S. Geological Survey (USGS) Scientific Investigations Report (https://doi.org/10.3133/sir20175128). The archived model input and output files are available in a USGS data release (https://doi.org/10.5066/F7S181FC). Seven additional MODFLOW-2000 scenarios (numbered 7-13), using this updated and recalibrated model, were developed to evaluate different withdrawal strategies which are included in this data release: (7) Mount Pleasant Waterworks bringing online a new well (located at the old well 5 location) at 3.51 million gallons per day (Mgal/d) in 2025; (8) Maximizing withdrawals from Mount Pleasant Waterworks wells 2 and 5 (3.51 Mgal/d each) in 2020 and 2025, respectively; (9) Same as Scenario 7, but removing well 3 from production in 2025; (10) Same as Scenario 9, but removing well 4 from production in 2025 (11) Same as Scenario 7, but converting well 3 to an injection well in 2025 (12) Same as Scenario 11, but converting well 4 to an injection well in 2030; and (13) Same as scenario 8, but with two injection wells added (one in 2025 and one in 2035) to Mount Pleasant Waterworks well field. Nine alternate simulations for scenarios 11-13 (three MODFLOW and six MODPATH) were done to evaluate the effects of different porosity on the groundwater flow system, water levels, and the time-of-travel of particles from injection wells to the main water source. 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The data package includes five tables: 1) characteristics of bird survey sites including environmental conditions and habitat classifications, 2) behaviors and habitat associations of all shorebirds and avian predatory species detected at each survey site, 3) a list of all birds species (i.e., passerines, shorebirds, predatory birds, etc.) detected at each survey site, 4) definitions of habitat classification codes, and 5) bird species codes and taxonomy. Additionally, shapefiles for each point count location are also included in this data release.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13M7THW","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.6a43e5121ba49b97f88a8712.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a43e5121ba49b97f88a8712","keyword":["Alaska","Anchorage","Animals/Vertebrates","Avian abundance","Biogeography","Biological productivity","Biota","Birds","Community ecology","Community structure","Cook Inlet","Distribution","Environment","Habitat preferences","Habitats","Migratory birds","Seasonal distribution","Species richness","Susitna Flats State Game Refuge","Terrestrial ecosystems","USGS:6a43e5121ba49b97f88a8712","Wildlife"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-152.4243, 60.3596, -148.8428, 61.6586","theme":["geospatial"],"title":"Inventory of Shorebirds and other Bird Species in the Upper Cook Inlet Region of Alaska, 1996 and 1998"},"description":"This data release contains tabular data pertaining to population surveys of shorebirds, predatory birds (i.e., raptors, cranes, and gulls), and other bird species (i.e., passerines) conducted across the Upper Cook Inlet region of Alaska during 1996 and 1998. 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See Section 2.5.2 WHCWG (2012) for additional information pertaining to development of the four landscape integrity-derived resistance rasters. Adjacent core areas within 100 km Euclidean distance of one another were connected.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13NGXBJ","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.0f45a6ff-f1ef-4c9c-a87c-d9081225feaa.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_0f45a6ff-f1ef-4c9c-a87c-d9081225feaa","keyword":["British Columbia","Columbia Plateau","Idaho","Oregon","Pacific Northwest","USGS:0f45a6ff-f1ef-4c9c-a87c-d9081225feaa","Washington","climate change","core areas","environment","external research support","geospatial datasets","landscape integrity","normalized least-cost corridor","pre-SM502.8","wildlife habitat connectivity"],"modified":"2026-09-10T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.8623, 46.4998, -115.4036, 49.0049","theme":["geospatial"],"title":"Landscape integrity HCA and corridors from four integrity-derived resistance surfaces"},"description":"This raster combines linkages developed from four landscape integrity-derived resistance surfaces: linear, low sensitivity, medium sensitivity, and high sensitivity. 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No data are provided here. The geospatial outlines and herd size estimates of Chukchi Sea autumn walrus haulouts, interpreted from Satellite imagery, have been combined into a single updated U.S. Geological Survey data release:  https://doi.org/10.5066/P9CSM0KN\n      This data release contains maps, geospatial files, and a table of the satellite imagery types with the dates when they were collected and examined to interpret the presence of, and area occupied by, walruses at terrestrial haulouts. Estimates of the land area occupied by walruses are provided based on interpretation by experienced image reviewers. The images are from a variety of Earth observing satellite imagery sources collected over coastal areas of the Chukchi Sea (northwestern Alaska and northeastern Russia) in autumn when walruses come ashore in large numbers to rest in the absence of sea ice. Earth observing imagery sources used in this data release include (but are not limited to) optical imagery collections by: (1) the European Space Agency's Sentinel-2 mission and (2) the Plant Labs Planet Scope constellation, as well as synthetic aperture radar imagery collected by: (1) European Space Agency's Sentinel-1 mission; (2) the DLR (German Aerospace Agency) TerraSAR-X satellite (X-band), and (3) the Umbra Space satellite constellation (X-band).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P97NFTDU","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.ASC557.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ASC557","keyword":["Alaska","Animals/vertebrates","Biota","Cape Serdtse-Kamen","Carnivores","Chukchi Sea","Chukotka","Coastal ecosystems","Environment","Field inventory and monitoring","Image collections","Mammals","Marine ecosystems","Marine mammals","Migratory species","Odobenus rosmarus divergens","Pacific Walrus","Pinniped","Point Lay","Remote sensing","Russia","Satellite imagery","Seals/sea lions/walruses","Seasonal distribution","USGS:ASC557","Walrus","Wildlife"],"modified":"2024-06-29T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-171.7, 66.9, -163.0, 69.6","theme":["geospatial"],"title":"SUPERSEDED: Pacific Walrus Coastal Haulout Occurrences Interpreted from Satellite Imagery, 2023"},"description":"This data release has been SUPERSEDED. No data are provided here. The geospatial outlines and herd size estimates of Chukchi Sea autumn walrus haulouts, interpreted from Satellite imagery, have been combined into a single updated U.S. Geological Survey data release:  https://doi.org/10.5066/P9CSM0KN\n      This data release contains maps, geospatial files, and a table of the satellite imagery types with the dates when they were collected and examined to interpret the presence of, and area occupied by, walruses at terrestrial haulouts. Estimates of the land area occupied by walruses are provided based on interpretation by experienced image reviewers. The images are from a variety of Earth observing satellite imagery sources collected over coastal areas of the Chukchi Sea (northwestern Alaska and northeastern Russia) in autumn when walruses come ashore in large numbers to rest in the absence of sea ice. 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Areas are delineated using results from analyses of seasonal migratory movements and habitat characteristics conducive to movements, particularly between silver sagebrush and big sagebrush habitats. \n\nLMHMA: Part of the Unique Habitat Management Area (UHMA) as identified in the MT/DK 2025 GRSG Approved RMP Amendment/ROD. Little Missouri HMA (LMHMA) is an identified core area by the states of Montana and North Dakota. This area contains high-quality GRSG habitat in Montana and encompasses the remaining GRSG habitat in North Dakota. A substantial portion of the area is the Cedar Creek Anticline unitized oil and gas field. Formerly occupied habitat has been converted or degraded, an outbreak of West Nile Virus impacted bird numbers, and GRSG are challenged by being on the periphery of their range. Unique management and a focus on restoration efforts is needed to maintain connectivity of sagebrush and GRSG habitat between Montana and North Dakota. \n\nSCHMA: Part of the Unique Habitat Management Area (UHMA) as identified in the MT/DK 2025 GRSG Approved RMP Amendment/ROD. South Carter HMA (SCHMA) is an identified core area by the state of Montana. This area has a high density of bentonite mining. Unique management is needed to balance GRSG habitat and mineral development in the short term, while planning for longer-term reclamation. \n\nOHMA: Areas in Nevada and Northeastern California, identified as other unmapped habitat in the Proposed RMP/Final EIS, that are within the Planning Area and contain seasonal or connectivity habitat areas. \n\nSHMA: GRSG stewardship habitat areas in Wyoming that are generally characterized by large percentages of private land, existing disturbance and prior and existing rights, and fragmented landscapes that continue to support substantial populations of GRSG, provide important connections between populations, and are important for maintaining GRSG populations.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://blm-egis.maps.arcgis.com/sharing/rest/content/items/7a3378b8ba4040238384c36695c40256/info/metadata/metadata.xml?format=iso19139","mediaType":"text/xml","title":"ISO-19139 metadata"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/csv?layers=0","format":"CSV","mediaType":"text/csv","title":"CSV"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/excel?layers=0","format":"XLSX","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Excel"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/featureCollection?layers=0","format":"TXT","mediaType":"text/plain","title":"Feature Collection"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/filegdb?layers=0","format":"ZIP","mediaType":"application/zip","title":"File Geodatabase"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/geojson?layers=0","format":"GeoJSON","mediaType":"application/vnd.geo+json","title":"GeoJSON"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/gpkg?layers=0","format":"ZIP","mediaType":"application/geopackage+sqlite3","title":"GeoPackage"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/kml?layers=0","format":"KML","mediaType":"application/vnd.google-earth.kml+xml","title":"KML"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/shapefile?layers=0","format":"ZIP","mediaType":"application/zip","title":"Shapefile"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/api/download/v1/items/7a3378b8ba4040238384c36695c40256/sqlite?layers=0","format":"GDB","mediaType":"application/geopackage+sqlite3","title":"SQLite"},{"@type":"dcat:Distribution","accessURL":"https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-westernus-grsg-range-for-fire-response","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"},{"@type":"dcat:Distribution","accessURL":"https://services1.arcgis.com/KbxwQRRfWyEYLgp4/arcgis/rest/services/BLM_Natl_WesternUS_GRSG_Range_for_Fire_Response/FeatureServer/0","format":"ArcGIS GeoServices REST API","mediaType":"application/json","title":"ArcGIS GeoService"}],"identifier":"https://blm-egis.maps.arcgis.com/home/item.html?id=7a3378b8ba4040238384c36695c40256&sublayer=0","issued":"2026-09-11T17:32:37Z","keyword":["Adaptive Management","Allocation Decision Analysis","Anthro Mountain","BLM","Bureau of Land Management","CA","CO","California","Colorado","Core Areas","GHMA","GRSG","Geospatial","Greater sage-grouse","HUB-GRSG","ID","IHMA","Idaho","LCHMA","MT","Management","Montana","ND","NV","Nevada","North Dakota","OHMA","OR","Oregon","PHMA","Plan","Public Lands","RHMA","ROD","Record of Decision","SD","Sage-Grouse","South Dakota","UDH","US","USFS","UT","Undesignated Habitat","United States","Update","Utah","WA","WY","Washington","Wildlife","Wyoming","biota","boundaries","environment","location"],"landingPage":"https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-natl-westernus-grsg-range-for-fire-response","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2026-09-11T21:05:38Z","programCode":["010:000"],"publisher":{"@type":"org:Organization","name":"Bureau of Land Management"},"spatial":"-118.0169,46.0219,-110.5686,41.1925","theme":["geospatial"],"title":"BLM Natl WesternUS GRSG Range for Fire Response"},"description":"This dataset represents multiple consolidated submissions of Greater Sage-Grouse (GRSG) data to represent habitat management areas, range, and potential habitat from the Department of Interior (DOI) Bureau of Land Management (BLM) and US Fish and Wildlife Service (USFWS). 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Full dataset can be found here: https://services1.arcgis.com/KbxwQRRfWyEYLgp4/arcgis/rest/services/BLM_Natl_WesternUS_GRSG_ROD_HabitatMgmtAreas_Feb_2026/FeatureServer/0 \n\n(2) USFWS GRSG Priority Areas for Conservation (PACs), which includes Bi-State GRSG. Full dataset can be found here: https://gis-fws.opendata.arcgis.com/datasets/grsg-priority-areas-for-conservation-pacs/explore?location=43.446373%2C-112.913086%2C5 \n\n(3) GRSG Current Range used by the USFWS during the 2015 Status Review. Current Range is defined as areas believed to be currently occupied. Data map can be found here: https://www.usgs.gov/media/images/greater-sage-grouse-range; and current GRSG range data is available from multiple sources including here: https://www.sciencebase.gov/catalog/item/56f96693e4b0a6037df06034 \n\nThe following habitat management areas were used in the creation of this feature class: \n\nPHMA: Areas identified as having the highest habitat value for maintaining sustainable GRSG populations and include breeding, late brood-rearing, and winter concentration areas. \n\nGHMA: Areas that are occupied seasonally or year-round and are outside of PHMAs. \n\nIHMA: Areas in Idaho that provide a management buffer for and that connect patches of PHMAs. IHMAs encompass important areas of generally moderate to high habitat value habitat or populations but that are not as important as PHMAs. \n\nLCHMA: Areas in Colorado that have been identified as broader regions of linkage connectivity important to facilitate the movement of GRSG and maintain ecological processes. \n\nCHMA: Part of the Unique Habitat Management Area (UHMA) as identified in the MT/DK 2025 GRSG Approved RMP Amendment/ROD. Connectivity HMA (CHMA) are areas that provide regions of connectivity important to facilitate long-distance movements of GRSG and maintain ecological processes, including between priority populations, adjacent states, and across international borders, including but not limited to Montana Connectivity areas. This HMA boundary represents where stopover sites may exist, likely within a matrix of degraded or converted habitat or non-habitat (such as in Montana general habitat within the HiLine). Areas are delineated using results from analyses of seasonal migratory movements and habitat characteristics conducive to movements, particularly between silver sagebrush and big sagebrush habitats. \n\nLMHMA: Part of the Unique Habitat Management Area (UHMA) as identified in the MT/DK 2025 GRSG Approved RMP Amendment/ROD. Little Missouri HMA (LMHMA) is an identified core area by the states of Montana and North Dakota. This area contains high-quality GRSG habitat in Montana and encompasses the remaining GRSG habitat in North Dakota. A substantial portion of the area is the Cedar Creek Anticline unitized oil and gas field. Formerly occupied habitat has been converted or degraded, an outbreak of West Nile Virus impacted bird numbers, and GRSG are challenged by being on the periphery of their range. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7VM49FN","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5867e06de4b0cd2dabe7c768.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e06de4b0cd2dabe7c768","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Mule deer","Rocky Mountains","USGS:5867e06de4b0cd2dabe7c768","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.049289, 41.894474, -108.528169, 49.005950","theme":["geospatial"],"title":"Potential climate change impacts on mule deer connectivity in the U.S. Northern Rockies"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0bd3aa3c-7c35-4b89-9b14-ec29aa67421b","harvest_record_raw":"https://catalog.data.gov/harvest_record/0bd3aa3c-7c35-4b89-9b14-ec29aa67421b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e06de4b0cd2dabe7c768","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Mule deer","Rocky Mountains","USGS:5867e06de4b0cd2dabe7c768","United States","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-12T00:25:28.434980","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"potential-climate-change-impacts-on-mule-deer-connectivity-in-the-u-s-northern-rockies","spatial_centroid":{"lat":44.739064400000004,"lon":-113.640841},"spatial_shape":{"coordinates":[[[-117.049289,41.894474],[-117.049289,49.00595],[-108.528169,49.00595],[-108.528169,41.894474],[-117.049289,41.894474]]],"type":"Polygon"},"theme":["geospatial"],"title":"Potential climate change impacts on mule deer connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":11.321112,"_sort":[1789172720076,11.321112,1,"1b47995f-8fb7-4561-8a50-138495dbcd3c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Center for Large Landscape Conservation","hasEmail":"mailto:tyler@largelandscapes.org"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7VM49FN","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5867da61e4b0cd2dabe7c75a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867da61e4b0cd2dabe7c75a","keyword":["Connectivity","Environment and Conservation","Grizzly bear","Idaho","Montana","Rocky Mountains","USGS:5867da61e4b0cd2dabe7c75a","United States","Wyoming","climate change","environment","natural resource management"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.049289, 41.894474, -108.528169, 49.005950","theme":["geospatial"],"title":"Potential climate change impacts on grizzly bear connectivity in the U.S. Northern Rockies"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d540a905-8bfa-413e-91c7-c5df98e5947b","harvest_record_raw":"https://catalog.data.gov/harvest_record/d540a905-8bfa-413e-91c7-c5df98e5947b/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867da61e4b0cd2dabe7c75a","keyword":["Connectivity","Environment and Conservation","Grizzly bear","Idaho","Montana","Rocky Mountains","USGS:5867da61e4b0cd2dabe7c75a","United States","Wyoming","climate change","environment","natural resource management"],"last_harvested_date":"2026-09-12T00:25:20.076786","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"potential-climate-change-impacts-on-grizzly-bear-connectivity-in-the-u-s-northern-rockies","spatial_centroid":{"lat":44.739064400000004,"lon":-113.640841},"spatial_shape":{"coordinates":[[[-117.049289,41.894474],[-117.049289,49.00595],[-108.528169,49.00595],[-108.528169,41.894474],[-117.049289,41.894474]]],"type":"Polygon"},"theme":["geospatial"],"title":"Potential climate change impacts on grizzly bear connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":12.296835,"_sort":[1789172563104,12.296835,0,"610744f6-e148-447a-ae3c-220f4452d0d5"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kenneth G. Boykin","hasEmail":"mailto:kboykin@nmsu.edu"},"description":"This dataset contains the result of the bioclimatic-envelope modeling of the two reptile species -- Rio Grande Cooter (Pseudemys gorzugi) and Gray-Checkered Whiptail (Aspidoscelis dixoni) -- in the South Central US using the downscaled data provided by WorldClim.  We used five species distribution models (SDM) including Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines (MARS) and ensembles to develop the present day distributions of the species based on climate-driven models alone.  We then projected future distributions of the species using data from four climate models: Community Climate System Model version 4 (CCSM4), Hadley Centre Global Environment Model version 2-Earth System (HadGEM2-ES), Model for Interdisciplinary Research on Climate version 5 (MIROC5), and Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR).  We ran the climate models according to two greenhouse gas concentration pathways (RCP2.6 and RCP8.5).  Datasets in this file are the results for models RCP2.6 and RCP8.5 for the years 2050 and 2070.  It shows a comparison of ensembles of suitable bioclimatic conditions between present day and future day.  The dataset shows areas where ensembles agree and suitable conditions are stable (stable represented in green), future ensemble projects new suitable conditions (gain represented in yellow), present ensemble may be converted to unsuitable in the future (loss represented in red), and areas where conditions are unsuitable in the future (non represented in gray).","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7XS5SJH","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.583324a0e4b046f05f211a7d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_583324a0e4b046f05f211a7d","keyword":["Aspidoscelis dixoni","Gray-Checkered Whiptail","Pseudemys gorzugi","Rio Grande Cooter","USGS:583324a0e4b046f05f211a7d","bioclimatic-envelope","biota","climatologyMeteorologyAtmosphere","ecology","geospatial datasets","herpetofauna","modeling"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-112.586128217, 31.332172208, -106.486128241, 35.4988388585","theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for reptile species in South Central USA"},"description":"This dataset contains the result of the bioclimatic-envelope modeling of the two reptile species -- Rio Grande Cooter (Pseudemys gorzugi) and Gray-Checkered Whiptail (Aspidoscelis dixoni) -- in the South Central US using the downscaled data provided by WorldClim.  We used five species distribution models (SDM) including Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines (MARS) and ensembles to develop the present day distributions of the species based on climate-driven models alone.  We then projected future distributions of the species using data from four climate models: Community Climate System Model version 4 (CCSM4), Hadley Centre Global Environment Model version 2-Earth System (HadGEM2-ES), Model for Interdisciplinary Research on Climate version 5 (MIROC5), and Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR).  We ran the climate models according to two greenhouse gas concentration pathways (RCP2.6 and RCP8.5).  Datasets in this file are the results for models RCP2.6 and RCP8.5 for the years 2050 and 2070.  It shows a comparison of ensembles of suitable bioclimatic conditions between present day and future day.  The dataset shows areas where ensembles agree and suitable conditions are stable (stable represented in green), future ensemble projects new suitable conditions (gain represented in yellow), present ensemble may be converted to unsuitable in the future (loss represented in red), and areas where conditions are unsuitable in the future (non represented in gray).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/c214b52f-aa11-4dbd-b868-15da1509fa6e","harvest_record_raw":"https://catalog.data.gov/harvest_record/c214b52f-aa11-4dbd-b868-15da1509fa6e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_583324a0e4b046f05f211a7d","keyword":["Aspidoscelis dixoni","Gray-Checkered Whiptail","Pseudemys gorzugi","Rio Grande Cooter","USGS:583324a0e4b046f05f211a7d","bioclimatic-envelope","biota","climatologyMeteorologyAtmosphere","ecology","geospatial datasets","herpetofauna","modeling"],"last_harvested_date":"2026-09-12T00:22:43.104874","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"projected-future-bioclimate-envelope-suitability-for-reptile-species-in-south-central-usa","spatial_centroid":{"lat":32.9988388682,"lon":-110.14612822659998},"spatial_shape":{"coordinates":[[[-112.586128217,31.332172208],[-112.586128217,35.4988388585],[-106.486128241,35.4988388585],[-106.486128241,31.332172208],[-112.586128217,31.332172208]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for reptile species in South Central USA","type":"dataset"},{"_score":11.321112,"_sort":[1789172392353,11.321112,1,"f4275485-1d12-4073-9635-208abaef3e99"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Center for Large Landscape Conservation","hasEmail":"mailto:tyler@largelandscapes.org"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. The U.S. Northern Rockies still support a full suite of native wildlife, and survival of these populations depends on connected landscapes. Connected landscapes support current migration and dispersal as well as future shifts in species ranges that will be necessary for species to adapt to our changing climate. Working in partnership with state and federal resource managers and private land trusts, we sought to: 1) understand how future climate change may alter habitat composition of landscapes expected to serve as important connections for wildlife, 2) estimate how wildlife species of concern are expected to respond to these changes, 3) develop climate-smart strategies to help stakeholders manage public and private lands in ways that allow wildlife to continue to move in response to changing conditions, and 4) explore how well existing management plans and conservation efforts are expected to support crucial connections for wildlife under climate change. We assessed vulnerability of eight wildlife species and four biomes to climate change, with a focus on potential impacts to connectivity. Our assessment provides some insights about where these species and biomes may be most vulnerable or most resilient to loss of connectivity and how this information could support climate-smart management action. We also encountered high levels of uncertainty in how climate change is expected to alter vegetation and how wildlife are expected to respond to these changes. This uncertainty limits the value of our assessment for informing proactive management of climate change impacts on both species-specific and biome-level connectivity (although biome-level assessments were subject to fewer sources of uncertainty). We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7VM49FN","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5867e1a1e4b0cd2dabe7c76c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e1a1e4b0cd2dabe7c76c","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867e1a1e4b0cd2dabe7c76c","United States","Wyoming","climate change","environment","natural resource management","shrubland ecosystems"],"modified":"2026-09-09T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-117.049289, 41.894474, -108.528169, 49.005950","theme":["geospatial"],"title":"Potential climate change impacts on shrub connectivity in the U.S. Northern Rockies"},"description":"Establishing connections among natural landscapes is the most frequently recommended strategy for adapting management of natural resources in response to climate change. 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We offer suggestions for improving the management relevance of future studies based on our own insights and those of managers and biologists who participated in this assessment and provided critical review of this report.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/19953783-ec84-48cb-8bf3-740f691b7b24","harvest_record_raw":"https://catalog.data.gov/harvest_record/19953783-ec84-48cb-8bf3-740f691b7b24/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5867e1a1e4b0cd2dabe7c76c","keyword":["Connectivity","Environment and Conservation","Idaho","Montana","Rocky Mountains","USGS:5867e1a1e4b0cd2dabe7c76c","United States","Wyoming","climate change","environment","natural resource management","shrubland ecosystems"],"last_harvested_date":"2026-09-12T00:19:52.353348","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"potential-climate-change-impacts-on-shrub-connectivity-in-the-u-s-northern-rockies","spatial_centroid":{"lat":44.739064400000004,"lon":-113.640841},"spatial_shape":{"coordinates":[[[-117.049289,41.894474],[-117.049289,49.00595],[-108.528169,49.00595],[-108.528169,41.894474],[-117.049289,41.894474]]],"type":"Polygon"},"theme":["geospatial"],"title":"Potential climate change impacts on shrub connectivity in the U.S. Northern Rockies","type":"dataset"},{"_score":7.497505,"_sort":[1789172321181,7.497505,0,"d194c2f4-a213-4004-9213-c17ee9e16d54"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adrienne M. 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Miller","hasEmail":"mailto:bwmiller@usgs.gov"},"description":"Agent-based models (ABMs) and state-and-transition simulation models (STSMs) are two classes of simulation models that have proven useful for understanding the processes underlying complex, dynamic ecosystems and evaluating practical questions about how ecosystems will respond to different scenarios of global change and environmental management. ABMs can simulate many types of agents (i.e., autonomous units, such as wildlife, livestock, people, or viruses) and are advantageous because they can represent agent characteristics, decision-making, adaptive behavior, mobility, and interactions, and can capture feedbacks between agents and their environment. STSMs are flexible and intuitive models of landscape dynamics that can track landscape attributes and management scenarios, and integrate diverse data types (e.g., output from correlative and mechanistic models). 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The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.\n\nWhile every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. 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The vector data file is included in \"Faults_MontereyCanyon.zip,\" which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nFaults in the Monterey Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S\u2013N1\u201309\u2013MB and S\u20136\u201311\u2013MB. 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Cochran, eds.), 2016, California State Waters Map Series\u2014Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016\u20131072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.\nFaults in the Monterey Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S\u2013N1\u201309\u2013MB and S\u20136\u201311\u2013MB. 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The model was calibrated using parameter estimation (PEST) by \nfitting estimated drawdowns to simulated drawdowns from 16 multiple-well aquifer tests. Water-level models \nwere used to estimate drawdowns from continuous water-level data collected during multiple-well aquifer \ntesting. This USGS data release contains all of the input and output files for the simulations described \nin the associated model documentation report (http://doi.org/10.3133/sir20165151). 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The model was calibrated using parameter estimation (PEST) by \nfitting estimated drawdowns to simulated drawdowns from 16 multiple-well aquifer tests. Water-level models \nwere used to estimate drawdowns from continuous water-level data collected during multiple-well aquifer \ntesting. This USGS data release contains all of the input and output files for the simulations described \nin the associated model documentation report (http://doi.org/10.3133/sir20165151). This data release \nalso includes (1) preprocessing Microsoft Excel macros, FORTRAN executables, and associated input \ndata files for creating the groundwater-flow models; and (2) post-processing FORTRAN executables for \ngenerating model output files.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5ced7dfa-ec95-4123-b891-650c79b8569f","harvest_record_raw":"https://catalog.data.gov/harvest_record/5ced7dfa-ec95-4123-b891-650c79b8569f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_bb08a70e-38dd-4b9c-aed4-352879cd5ecb","keyword":["Groundwater","Groundwater Model","MODFLOW","MODFLOW-2005","Nevada","Nevada Test Site Area 19","Nevada Test Site Area 20","Nye County","PEST","Pahute Mesa","USGS:bb08a70e-38dd-4b9c-aed4-352879cd5ecb","environment","geoscientificInformation","inlandWaters","usgsgroundwatermodel"],"last_harvested_date":"2026-09-10T22:50:54.298137","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":5,"publisher":"U.S. Geological Survey","slug":"modflow-2005-and-pest-models-used-to-simulate-multiple-well-aquifer-tests-and-characterize","spatial_centroid":{"lat":37.068,"lon":-116.63799999999999},"spatial_shape":{"coordinates":[[[-117.21,36.62],[-117.21,37.74],[-115.78,37.74],[-115.78,36.62],[-117.21,36.62]]],"type":"Polygon"},"theme":["geospatial"],"title":"MODFLOW-2005 and PEST models used to simulate multiple-well aquifer tests and characterize hydraulic properties of volcanic rocks in Pahute Mesa, Nevada","type":"dataset"},{"_score":7.345406,"_sort":[1789080653623,7.345406,1,"9c9dcd93-1d95-4a2a-8403-2a7b5cedbe9d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Emily A. Himmelstoss","hasEmail":"mailto:ehimmelstoss@usgs.gov"},"description":"The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010 and 2014. The first new shoreline for the State includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha\u2019s Vineyard are from a 2012 USACE Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha\u2019s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates.  Some of rates include a proxy-datum bias correction, this is indicated in the filename with \u201cPDB\u201d. The rates that do not account for this correction have \u201cNB\u201d in their file names.  The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9RRBEYK","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5c644d31e4b0fe48cb37294b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c644d31e4b0fe48cb37294b","keyword":["Atlantic Coast","Baseline","CMGP","Coastal and Marine Geology Program","DSAS","Digital Shoreline Analysis System","Massachusetts","New England","North America","Shoreline","Shoreline Change","U.S. Geological Survey","USGS","USGS:5c644d31e4b0fe48cb37294b","United States","WHCMSC","Woods Hole Coastal and Marine Science Center","coastal processes","environment","geoscientificInformation","geospatial datasets","oceans"],"modified":"2026-04-20T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-70.989790, 42.343469, -70.590365, 42.875152","theme":["geospatial"],"title":"Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0"},"description":"The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. 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The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. 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","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/348b0c69-9218-4f77-8816-314ab928278d","harvest_record_raw":"https://catalog.data.gov/harvest_record/348b0c69-9218-4f77-8816-314ab928278d/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_4b5602e7-db46-427a-bc5c-2b78473ef771","keyword":["Idaho","Net primary productivity","Owyhee County","Reynolds Creek Experimental Watershed","South Idaho","USGS:4b5602e7-db46-427a-bc5c-2b78473ef771","climate change","environment","external research support","modeling"],"last_harvested_date":"2026-09-10T22:50:46.420660","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":0,"publisher":"U.S. Geological Survey","slug":"field-measurements-biogeochemical-model-input-files-climate-data-and-simulation--1984-2015","spatial_centroid":{"lat":43.55288,"lon":-115.09715999999999},"spatial_shape":{"coordinates":[[[-119.2456,41.5086],[-119.2456,46.6193],[-108.8745,46.6193],[-108.8745,41.5086],[-119.2456,41.5086]]],"type":"Polygon"},"theme":["geospatial"],"title":"Field measurements, biogeochemical model input files, climate data, and simulation output for aspen sites in the Reynolds Creek Experimental Watershed, ID, USA (1984-2015).","type":"dataset"},{"_score":5.3858767,"_sort":[1789080646190,5.3858767,1,"867f55dd-d860-4bf2-9b7a-e85bbb4db39d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"U.S. Geological Survey, Alaska Science Center","hasEmail":"mailto:gs-ak_asc_datamanagers@usgs.gov"},"description":"This data release includes 5 child items with photos and videos taken during low altitude photo survey transects in northwest Alaska, July 2013. Three flight transects were conducted from small aircraft over the National Park Service's Arctic Network (ARCN; Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and Noatak National Preserve) and the U.S. Fish and Wildlife Service's Selawik National Wildlife Refuge.\n      The aerial photo surveys were flown for the WildCast Project (WILDlife Potential Habitat ForeCASTing), a collaboration of the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, and U.S.D.A. Forest Service. WildCast was devised to provide models for projecting future land cover and wildlife habitat conditions in northwest Alaska under potential scenarios of climate change, and to provide an image database for future change-comparison research. More information is available at: https://www.usgs.gov/centers/alaska-science-center/science/wildlife-potential-habitat-forecasting-framework-wildcast#overview\n      Child Item 1: \"Flight Path GPS Logs and Browse Maps of Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 2: \"Nadir Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 3: \"Oblique Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 4: \"Nadir Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 5: \"Oblique Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9KFIRWQ","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.611595d4d34e3267c61166ce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_611595d4d34e3267c61166ce","keyword":["Aerial Photography","Alaska","Arctic Network","Bering Land Bridge National Preserve","Biota","Cape Krusenstern National Monument","Climate Change","Coastal Ecosystems","Ecotypes","Environment","Frozen Ground","Gates of the Arctic National Park","Gates of the Arctic National Preserve","Geography","Geomorphic Landforms/Processes","GeoscientificInformation","Image Analysis","Image Collections","ImageryBaseMapsEarthCover","InlandWaters","Kobuk Valley National Park","Land Cover","Land Surface","Land Use and Land Cover","Land Use/Land Cover","Landscape","Low Altitude Air Photo","Low Altitude Video","Noatak National Preserve","Northwest Alaska","Northwest Arctic Borough","Photogrammetry","Selawik National Wildlife Refuge","Tundra Ecosystems","USGS:611595d4d34e3267c61166ce","Vegetation","Videography"],"modified":"2024-10-08T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-169.22, 64.78, -149.05, 68.87","theme":["geospatial"],"title":"Low-Altitude Photographic Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013"},"description":"This data release includes 5 child items with photos and videos taken during low altitude photo survey transects in northwest Alaska, July 2013. Three flight transects were conducted from small aircraft over the National Park Service's Arctic Network (ARCN; Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and Noatak National Preserve) and the U.S. Fish and Wildlife Service's Selawik National Wildlife Refuge.\n      The aerial photo surveys were flown for the WildCast Project (WILDlife Potential Habitat ForeCASTing), a collaboration of the U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, and U.S.D.A. Forest Service. WildCast was devised to provide models for projecting future land cover and wildlife habitat conditions in northwest Alaska under potential scenarios of climate change, and to provide an image database for future change-comparison research. More information is available at: https://www.usgs.gov/centers/alaska-science-center/science/wildlife-potential-habitat-forecasting-framework-wildcast#overview\n      Child Item 1: \"Flight Path GPS Logs and Browse Maps of Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 2: \"Nadir Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 3: \"Oblique Photographs Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 4: \"Nadir Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"\n      Child Item 5: \"Oblique Videos Taken During Low-Altitude Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013\"","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1cab0fd1-c709-4fba-ae6e-fca92161ab2e","harvest_record_raw":"https://catalog.data.gov/harvest_record/1cab0fd1-c709-4fba-ae6e-fca92161ab2e/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_611595d4d34e3267c61166ce","keyword":["Aerial Photography","Alaska","Arctic Network","Bering Land Bridge National Preserve","Biota","Cape Krusenstern National Monument","Climate Change","Coastal Ecosystems","Ecotypes","Environment","Frozen Ground","Gates of the Arctic National Park","Gates of the Arctic National Preserve","Geography","Geomorphic Landforms/Processes","GeoscientificInformation","Image Analysis","Image Collections","ImageryBaseMapsEarthCover","InlandWaters","Kobuk Valley National Park","Land Cover","Land Surface","Land Use and Land Cover","Land Use/Land Cover","Landscape","Low Altitude Air Photo","Low Altitude Video","Noatak National Preserve","Northwest Alaska","Northwest Arctic Borough","Photogrammetry","Selawik National Wildlife Refuge","Tundra Ecosystems","USGS:611595d4d34e3267c61166ce","Vegetation","Videography"],"last_harvested_date":"2026-09-10T22:50:46.190129","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"low-altitude-photographic-transects-of-the-arctic-network-of-national-park-units-and--2013","spatial_centroid":{"lat":66.41600000000001,"lon":-161.152},"spatial_shape":{"coordinates":[[[-169.22,64.78],[-169.22,68.87],[-149.05,68.87],[-149.05,64.78],[-169.22,64.78]]],"type":"Polygon"},"theme":["geospatial"],"title":"Low-Altitude Photographic Transects of the Arctic Network of National Park Units and Selawik National Wildlife Refuge, Alaska, July 2013","type":"dataset"},{"_score":6.775566,"_sort":[1789080645974,6.775566,1,"05a37ef8-fcb4-4857-b0a8-73e6994534f2"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Tim McCrink","hasEmail":"mailto:tim.mccrink@conservation.ca.gov"},"description":"This map shows the potential of widespread slope failures, in terms of Newmark displacement (measured in centimeters), triggered by a M7.0 scenario earthquake on the Hayward Fault in the 10-county area surrounding the San Francisco Bay region, California. The cumulative downslope displacement of hillslopes is calculated using a simplified Newmark rigid sliding block slope stability model utilizing four primary datasets: a regional-scale geologic map of the study area, geologic strength parameters compiled as part of the California Geological Survey Seismic Hazard Mapping Program, earthquake shaking data from the USGS ShakeMap developed for this scenario, and 10-meter digital elevation data from the USGS 2009 National Elevation Dataset.The seismic-landslide hazard potential map covers the counties of Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Santa Cruz, Solano, and Sonoma. The slope failures are triggered by a hypothetical earthquake with a moment magnitude of 7.0 occurring on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay part of California\u2019s San Francisco Bay region.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7RN363Z","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5910b0ade4b0e541a03ac88a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5910b0ade4b0e541a03ac88a","keyword":["Acceleration Ratio","Alameda County, Contra Costa County, Marin County, Napa County, San Francisco County, San Mateo County, Santa Clara County, Santa Cruz County, Solano County, Sonoma County","California","Earthquake Hazards","Earthquake-induced Landslide Hazards","Hayward Fault","Newmark Displacement","USA","USGS:5910b0ade4b0e541a03ac88a","Yield Acceleration","economy","environment","geoscientificInformation","health","planningCadastre","society","structure","transportation","utilitiesCommunication"],"modified":"2020-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-123.537399, 36.815789, -121.20505, 38.884942","theme":["geospatial"],"title":"Landslide Displacement in the San Francisco Bay Region.                  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The slope failures are triggered by a hypothetical earthquake with a moment magnitude of 7.0 occurring on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay part of California\u2019s San Francisco Bay region.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5150da9b-d2b4-4c06-9ba6-6b6ef28206a9","harvest_record_raw":"https://catalog.data.gov/harvest_record/5150da9b-d2b4-4c06-9ba6-6b6ef28206a9/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5910b0ade4b0e541a03ac88a","keyword":["Acceleration Ratio","Alameda County, Contra Costa County, Marin County, Napa County, San Francisco County, San Mateo County, Santa Clara County, Santa Cruz County, Solano County, Sonoma County","California","Earthquake Hazards","Earthquake-induced Landslide Hazards","Hayward Fault","Newmark Displacement","USA","USGS:5910b0ade4b0e541a03ac88a","Yield Acceleration","economy","environment","geoscientificInformation","health","planningCadastre","society","structure","transportation","utilitiesCommunication"],"last_harvested_date":"2026-09-10T22:50:45.974345","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"landslide-displacement-in-the-san-francisco-bay-region-the-haywired-earthquake-scenario","spatial_centroid":{"lat":37.643450200000004,"lon":-122.6044594},"spatial_shape":{"coordinates":[[[-123.537399,36.815789],[-123.537399,38.884942],[-121.20505,38.884942],[-121.20505,36.815789],[-123.537399,36.815789]]],"type":"Polygon"},"theme":["geospatial"],"title":"Landslide Displacement in the San Francisco Bay Region.                  The HayWired Earthquake Scenario","type":"dataset"},{"_score":9.375885,"_sort":[1789080643480,9.375885,1,"8f54c98e-34c7-4d1e-b976-da84570aed30"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kylle Roy","hasEmail":"mailto:kroy@usgs.gov"},"description":"We designed two new samplers for monitoring airborne particulates, including fungal and fern spores and plant pollen, that rely on natural wind currents (Passive Environmental Sampler) or a battery operated fan (Active Environmental Sampler). Both samplers are modeled after commercial devices such as the Rotorod\u00ae and the Burkard\u00ae samplers, but are more economical and require less maintenance than commercial devices. We compared our two new samplers to Rotorod\u00ae samplers using Xyleborus spp. boring dust (frass) known to contain fungi responsible for Rapid Ohia Death. The comparison was done in a large outdoor field cage to determine relative effectiveness of the three samplers for capturing windblown boring dust. The dataset contains measurements of boring dust particles that were captured by the three types of samples over the course of twelve trials.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9QN1HBT","description":"Landing page for access to the data","format":"XML","mediaType":"application/http","title":"Digital Data"},{"@type":"dcat:Distribution","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.5c1c1d65e4b0708288c7ac25.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c1c1d65e4b0708288c7ac25","keyword":["Environmental DNA","Environmental Sampler","Frass","Metrosideros polymorpha","ROD","Rapid Ohia Death","Spore Traps","USGS:5c1c1d65e4b0708288c7ac25","Xyleborus","environment"],"modified":"2020-08-27T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-155.096827, 19.695607, -155.091505, 19.700617","theme":["geospatial"],"title":"Frass Length Measurements during Caged Sampler Type Comparisons"},"description":"We designed two new samplers for monitoring airborne particulates, including fungal and fern spores and plant pollen, that rely on natural wind currents (Passive Environmental Sampler) or a battery operated fan (Active Environmental Sampler). Both samplers are modeled after commercial devices such as the Rotorod\u00ae and the Burkard\u00ae samplers, but are more economical and require less maintenance than commercial devices. We compared our two new samplers to Rotorod\u00ae samplers using Xyleborus spp. boring dust (frass) known to contain fungi responsible for Rapid Ohia Death. The comparison was done in a large outdoor field cage to determine relative effectiveness of the three samplers for capturing windblown boring dust. The dataset contains measurements of boring dust particles that were captured by the three types of samples over the course of twelve trials.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/88a4f2c7-15e8-4544-856e-bfadebbcb160","harvest_record_raw":"https://catalog.data.gov/harvest_record/88a4f2c7-15e8-4544-856e-bfadebbcb160/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c1c1d65e4b0708288c7ac25","keyword":["Environmental DNA","Environmental Sampler","Frass","Metrosideros polymorpha","ROD","Rapid Ohia Death","Spore Traps","USGS:5c1c1d65e4b0708288c7ac25","Xyleborus","environment"],"last_harvested_date":"2026-09-10T22:50:43.480771","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"143529f7-2eef-4a07-b227-93ac9e84fad8","logo":"https://raw.githubusercontent.com/GSA/logo/master/doi.png","name":"Department of the Interior","organization_type":"Federal Government","slug":"doi"},"parent_identifier":null,"popularity":1,"publisher":"U.S. Geological Survey","slug":"frass-length-measurements-during-caged-sampler-type-comparisons","spatial_centroid":{"lat":19.697611000000002,"lon":-155.09469819999998},"spatial_shape":{"coordinates":[[[-155.096827,19.695607],[-155.096827,19.700617],[-155.091505,19.700617],[-155.091505,19.695607],[-155.096827,19.695607]]],"type":"Polygon"},"theme":["geospatial"],"title":"Frass Length Measurements during Caged Sampler Type Comparisons","type":"dataset"}],"sort":"last_harvested_date"}