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This project was designed to understand ecosystem structure as a driver of climatic, habitat, and hydrological services in heterogeneous restored wetlands. 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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","geospatial datasets","herpetofauna","modeling"],"modified":"2026-09-03T00:00:00Z","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/3b46a8f6-e3ee-47b3-a24e-ce90d4093d13","harvest_record_raw":"https://catalog.data.gov/harvest_record/3b46a8f6-e3ee-47b3-a24e-ce90d4093d13/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","geospatial datasets","herpetofauna","modeling"],"last_harvested_date":"2026-09-05T19:09:32.877056","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":30.131657,"_sort":[1788635244447,30.131657,0,"a70f89bd-ead2-4136-8689-a6780dd9b757"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Alison Meadow","hasEmail":"mailto:meadow@arizona.edu"},"description":"This data is from a survey of participants in the Arizona wine industry conducted in 2021. Participants in the Arizona wine industry generally include wine grape growers, wine makers, winery owners, vineyard or winery employees, and viticulture students. The data were collected in order to inform a broader economic analysis of the Arizona wine industry, which had been requested by a number of industry members during engagement with University of Arizona and Arizona Cooperative Extension researchers. The data were also collected in order to gauge the use of climate data presented to the Arizona wine industry in two Growing Season in Review workshops in 2019 and to identify possible future opportunities for climate and soil scientists at University of Arizona to engage with the Arizona wine industry.\nQuestions pertained to sales and production costs (for both vineyards and wineries), growing practices (vineyards), and marketing channels and revenue sources (wineries). The survey also asked questions about respondents\u2019 participation in grape growing workshops held by researchers at the University of Arizona, as well as respondents\u2019 interest in continued research on the wine industry\u2019s contribution to the state economy, the links between climate and viticulture, and the effects of soil health on wine grapes.\nInvitations to participate in the survey were sent to any person associated with the wine industry, as identified by contact lists from the Arizona Wine Growers Association, the Arizona Vignerons\u2019 Alliance, and researchers at the University of Arizona. The survey was sent out in mid-2021 and recipients included members of each association, individuals that have attended University of Arizona wine grape growing workshops and events, individuals that currently receive the University\u2019s Climate Viticulture Newsletter, or others that have otherwise been identified as associated with the industry. An email was sent to all individuals on these lists, resulting in 243 invitations to participate in the survey. Of these, 52 responded to a majority of the survey for an overall response rate of 21%. 82 people responded to at least part of the survey. Those responses are included in this dataset as well.\nThe data contains information about expenses and revenues from various components of the wine industry including sales and production, growing practices, and marketing - as well as revenue sources The survey also collected feedback from industry members about whether and how they would like to engage with University of Arizona and Arizona Cooperative Extension researchers on issues related to viticulture and climate in the state. This data could be used to pinpoint economic conditions for the Arizona wine industry in 2021. It could use used to compare Arizona's wine industry to other state's wine industries.\nThese data provide a snapshot of a subset of participants in the Arizona wine industry as of 2021. The respondents do not constitute a representative sample. While we presume that the data provided by respondents was accurate to the best of their ability, these findings are not necessarily replicable or generalizable beyond this group of respondents. These data have been deidentified to protect the privacy of survey respondents.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/h8xg-yc91","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.62ba0506d34e8f4977cc9f5c.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62ba0506d34e8f4977cc9f5c","keyword":["USGS:62ba0506d34e8f4977cc9f5c","biota","climate","climate change","external research support","soil health","viticulture"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.82, 31.33, -109.04, 37.0","theme":["geospatial"],"title":"Survey of Participants in the Arizona Wine Industry in Summer 2021 for the purposes of understanding their data needs"},"description":"This data is from a survey of participants in the Arizona wine industry conducted in 2021. Participants in the Arizona wine industry generally include wine grape growers, wine makers, winery owners, vineyard or winery employees, and viticulture students. The data were collected in order to inform a broader economic analysis of the Arizona wine industry, which had been requested by a number of industry members during engagement with University of Arizona and Arizona Cooperative Extension researchers. The data were also collected in order to gauge the use of climate data presented to the Arizona wine industry in two Growing Season in Review workshops in 2019 and to identify possible future opportunities for climate and soil scientists at University of Arizona to engage with the Arizona wine industry.\nQuestions pertained to sales and production costs (for both vineyards and wineries), growing practices (vineyards), and marketing channels and revenue sources (wineries). The survey also asked questions about respondents\u2019 participation in grape growing workshops held by researchers at the University of Arizona, as well as respondents\u2019 interest in continued research on the wine industry\u2019s contribution to the state economy, the links between climate and viticulture, and the effects of soil health on wine grapes.\nInvitations to participate in the survey were sent to any person associated with the wine industry, as identified by contact lists from the Arizona Wine Growers Association, the Arizona Vignerons\u2019 Alliance, and researchers at the University of Arizona. The survey was sent out in mid-2021 and recipients included members of each association, individuals that have attended University of Arizona wine grape growing workshops and events, individuals that currently receive the University\u2019s Climate Viticulture Newsletter, or others that have otherwise been identified as associated with the industry. An email was sent to all individuals on these lists, resulting in 243 invitations to participate in the survey. Of these, 52 responded to a majority of the survey for an overall response rate of 21%. 82 people responded to at least part of the survey. Those responses are included in this dataset as well.\nThe data contains information about expenses and revenues from various components of the wine industry including sales and production, growing practices, and marketing - as well as revenue sources The survey also collected feedback from industry members about whether and how they would like to engage with University of Arizona and Arizona Cooperative Extension researchers on issues related to viticulture and climate in the state. This data could be used to pinpoint economic conditions for the Arizona wine industry in 2021. It could use used to compare Arizona's wine industry to other state's wine industries.\nThese data provide a snapshot of a subset of participants in the Arizona wine industry as of 2021. The respondents do not constitute a representative sample. While we presume that the data provided by respondents was accurate to the best of their ability, these findings are not necessarily replicable or generalizable beyond this group of respondents. These data have been deidentified to protect the privacy of survey respondents.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/30a17d73-ba00-4a6d-a524-c4eec478a1c6","harvest_record_raw":"https://catalog.data.gov/harvest_record/30a17d73-ba00-4a6d-a524-c4eec478a1c6/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_62ba0506d34e8f4977cc9f5c","keyword":["USGS:62ba0506d34e8f4977cc9f5c","biota","climate","climate change","external research support","soil health","viticulture"],"last_harvested_date":"2026-09-05T19:07:24.447510","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":"survey-of-participants-in-the-arizona-wine-industry-in-summer-2021-for-the-purposes-of-und","spatial_centroid":{"lat":33.598,"lon":-112.508},"spatial_shape":{"coordinates":[[[-114.82,31.33],[-114.82,37.0],[-109.04,37.0],[-109.04,31.33],[-114.82,31.33]]],"type":"Polygon"},"theme":["geospatial"],"title":"Survey of Participants in the Arizona Wine Industry in Summer 2021 for the purposes of understanding their data needs","type":"dataset"},{"_score":29.082043,"_sort":[1788635090300,29.082043,1,"e1cf63ea-cac7-49f0-be33-45dd67b5d604"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Luke Winslow","hasEmail":"mailto:lwinslow@usgs.gov"},"description":"It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale.\nThis data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July, SthermoD_mean_JAS, SthermoD_mean, lake_average_temp, peak_lake_average_temp, lake_average_temp_JAS, mean_epi_temp, mean_hypo_temp, mean_surf_temp, mean_bottom_temp, peak_surf_temp, peak_bottom_temp, mean_surf_temp_JAS, mean_bottom_temp_JAS, mean_bottom_temp_365, mean_surf_temp_365, mean_1m_temp, mean_surf_JA, GDD_wtr_5c, GDD_wtr_10c, volume_mean_m_3, simulation_length_days, mean_volumetric_temp, kd, out_val calculated for 2210 lakes.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/F7028PN4","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.57473491e4b07e28b663d822.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57473491e4b07e28b663d822","keyword":["007","012","US","USGS:57473491e4b07e28b663d822","United States","WI","Wisconsin","climate change","environment","hydrodynamic model","inlandWaters","lakes","limnology","water clarity","water quality","water temperature"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.91, 42.48, -86.75, 47.54","theme":["geospatial"],"title":"Wisconsin Lake Temperature Metrics Stable Clarity"},"description":"It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale.\nThis data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July, SthermoD_mean_JAS, SthermoD_mean, lake_average_temp, peak_lake_average_temp, lake_average_temp_JAS, mean_epi_temp, mean_hypo_temp, mean_surf_temp, mean_bottom_temp, peak_surf_temp, peak_bottom_temp, mean_surf_temp_JAS, mean_bottom_temp_JAS, mean_bottom_temp_365, mean_surf_temp_365, mean_1m_temp, mean_surf_JA, GDD_wtr_5c, GDD_wtr_10c, volume_mean_m_3, simulation_length_days, mean_volumetric_temp, kd, out_val calculated for 2210 lakes.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/63cec2ed-5be2-4b11-9fd4-2e254fd79f86","harvest_record_raw":"https://catalog.data.gov/harvest_record/63cec2ed-5be2-4b11-9fd4-2e254fd79f86/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57473491e4b07e28b663d822","keyword":["007","012","US","USGS:57473491e4b07e28b663d822","United States","WI","Wisconsin","climate change","environment","hydrodynamic model","inlandWaters","lakes","limnology","water clarity","water quality","water temperature"],"last_harvested_date":"2026-09-05T19:04:50.300138","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":"wisconsin-lake-temperature-metrics-stable-clarity","spatial_centroid":{"lat":44.504,"lon":-90.446},"spatial_shape":{"coordinates":[[[-92.91,42.48],[-92.91,47.54],[-86.75,47.54],[-86.75,42.48],[-92.91,42.48]]],"type":"Polygon"},"theme":["geospatial"],"title":"Wisconsin Lake Temperature Metrics Stable Clarity","type":"dataset"},{"_score":20.080853,"_sort":[1788634881825,20.080853,0,"f3d26010-cccb-46f4-9506-001aaf0b7c9f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"This is a spatially-explicit state-and-transition simulation model of rangeland vegetation dynamics in the southwest South Dakota study site. The study site encompasses part of multiple jurisdictions, including Badlands National Park, Buffalo Gap National Grasslands, and Pine Ridge Indian Reservation. It represents key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition (i.e., distribution of ecological community phases). The model was built using the ST-Sim software platform. \nFrom http://wiki.syncrosim.com/index.php?title=Main_Page: ST-Sim allows users to develop and run spatially-explicit, stochastic state-and-transition simulation models (STSMs) of vegetation change, and is designed to simulate and compare possible vegetation conditions across a landscape over time by considering the interaction between succession, disturbances and management. ST-Sim is the latest in a 20-year lineage of STSM development tools that includes the Vegetation Dynamics Development Tool (VDDT), the Tool for Exploratory Landscape Scenario Analysis (TELSA), and the Path Landscape Model (Path). ST-Sim is intended as an upgrade to Path: in addition to all of the previous Path features, ST-Sim also provides a new option to run raster-based, spatially-explicit simulations.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7T1524X","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.58e7ae48e4b09da6799c0e55.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_58e7ae48e4b09da6799c0e55","keyword":["Badlands National Park","Bison","Buffalo Gap National Grassland","Grazing","Northern Great Plains","South Dakota","State-and-transition simulation model","USGS:58e7ae48e4b09da6799c0e55","cattle","climate change","environment","fires","geospatial datasets","invasive species","livestock","modeling","scenario planning"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-103.1759, 43.1995, -101.4480, 44.0587","theme":["geospatial"],"title":"State-and-Transition Simulation Model of Rangeland Vegetation in Southwest South Dakota (1969-2050)"},"description":"This is a spatially-explicit state-and-transition simulation model of rangeland vegetation dynamics in the southwest South Dakota study site. The study site encompasses part of multiple jurisdictions, including Badlands National Park, Buffalo Gap National Grasslands, and Pine Ridge Indian Reservation. It represents key vegetation types, grazing, exotic plants, fire, and the effects of climate and management on rangeland productivity and composition (i.e., distribution of ecological community phases). The model was built using the ST-Sim software platform. \nFrom http://wiki.syncrosim.com/index.php?title=Main_Page: ST-Sim allows users to develop and run spatially-explicit, stochastic state-and-transition simulation models (STSMs) of vegetation change, and is designed to simulate and compare possible vegetation conditions across a landscape over time by considering the interaction between succession, disturbances and management. ST-Sim is the latest in a 20-year lineage of STSM development tools that includes the Vegetation Dynamics Development Tool (VDDT), the Tool for Exploratory Landscape Scenario Analysis (TELSA), and the Path Landscape Model (Path). 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Climate change refugia were considered to be those areas projected to be climatically suitable now and in 2080. These maps are intended to inform management plans for refugia conservation throughout the northeastern United States, especially in protected areas including national parks, and can be used to support both climate adaptation efforts for focal species in protected areas individually and facilitate cross-boundary collaborations as species ranges shift across parks in the Northeast.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P16X6UKM","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.3b355ab9-bf4c-4b6c-a1c8-dab89366d5c7.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_3b355ab9-bf4c-4b6c-a1c8-dab89366d5c7","keyword":["USGS:3b355ab9-bf4c-4b6c-a1c8-dab89366d5c7","biota","climate adaptation","climate change","climate change refugia","climate envelope","climate niche","effects of climate change","environment","geospatial datasets","species range shifts"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-83.6792, 34.5292, -66.4792, 49.3625","theme":["geospatial"],"title":"Maps of the climate envelopes of 9 focal species in the northeastern United States in 2020 and 2080 under RCP 8.5"},"description":"We developed maps of of projected present and future climate niches for 9 focal species (including 4 plants [common bearberry, Bebb's sedge, highland rush, and shrubby five-fingers], 2 birds [grasshopper sparrow and black-throated green warbler], and 3 salamanders [blue-spotted salamander, jefferson salamander, and marbled salamander]) to identify climate change refugia, areas on the landscape relatively buffered from contemporary climate change. Climate change refugia were considered to be those areas projected to be climatically suitable now and in 2080. 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The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). The statewide analysis quantifies current connectivity patterns for Washington State and adjacent areas in British Columbia, Idaho, Oregon and a small portion of Montana. Available WHCWG raster data include model base layers, resistance, cost-weighted distance, landscape integrity networks, focal species networks, and focal species guild networks. Grid cell size is 100meters x 100meters. Habitat concentration areas, landscape integrity core areas, and linkage maps reside in raster and vector format. Project background can be found in the report: Washington Wildlife Habitat Connectivity Working Group (WHCWG). 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Online linkage: http://www.waconnected.org. 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The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). The statewide analysis quantifies current connectivity patterns for Washington State and adjacent areas in British Columbia, Idaho, Oregon and a small portion of Montana. Available WHCWG raster data include model base layers, resistance, cost-weighted distance, landscape integrity networks, focal species networks, and focal species guild networks. Grid cell size is 100meters x 100meters. Habitat concentration areas, landscape integrity core areas, and linkage maps reside in raster and vector format. Project background can be found in the report: Washington Wildlife Habitat Connectivity Working Group (WHCWG). 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Online linkage: http://www.waconnected.org. This metadata record covers 6 datasets, for 6 different species: GUGU: Wolverine (Gulo gulo), LYCA: Canada Lynx (Lynx canadensis), MAAM: Pacific Marten (Martes americana), ODHE: Mule Deer (Odocoileus hemionus), ORAM: Mountain Goat (Oreamnos americanus), and URAM: Black Bear (Ursus americanus).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/129e8d7c-9f50-445f-933f-0f9065138e66","harvest_record_raw":"https://catalog.data.gov/harvest_record/129e8d7c-9f50-445f-933f-0f9065138e66/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_574734ffe4b07e28b663d828","keyword":["USGS:574734ffe4b07e28b663d828","biota","climate change","external research support","geospatial datasets","habitat","pre-SM502.8","wildlife"],"last_harvested_date":"2026-09-05T18:52:40.928524","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":"normalized-least-cost-corridors-statewide-analysis-for-six-vertebrae-species-in-the-pacifi","spatial_centroid":{"lat":46.924,"lon":-121.62},"spatial_shape":{"coordinates":[[[-124.76,45.54],[-124.76,49.0],[-116.91,49.0],[-116.91,45.54],[-124.76,45.54]]],"type":"Polygon"},"theme":["geospatial"],"title":"Normalized least-cost corridors, statewide analysis for six vertebrae species in the Pacific Northwest","type":"dataset"},{"_score":27.23599,"_sort":[1788634357708,27.23599,0,"d4038ec1-2954-42c6-a812-d558e4a02fe6"],"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 six mammal species -- (a) New Mexican Jumping Mouse (Zapus hudsonius luteus), (b) Northern Pygmy Mouse (Baiomys taylori), (c) Gunnison's Prairie Dog (Cynomys gunnisoni), (d) Black-tailed Prairie Dog (Cynomys ludovicianus), (e) American Pika (Ochotona princeps), and (e) Swift Fox (Vulpes velox) -- 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","description":"The metadata original format","downloadURL":"https://data.usgs.gov/datacatalog/metadata/USGS.583322bee4b046f05f211a6b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_583322bee4b046f05f211a6b","keyword":["American Pika","Baiomys taylori","Black-tailed Prairie Dog","Cynomys gunnisoni","Cynomys ludovicianus","Gunnison's Prairie Dog","New Mexican Jumping Mouse","Northern Pygmy Mouse","Ochotona princeps","Swift Fox","USGS:583322bee4b046f05f211a6b","Vulpes velox","Zapus hudsonius luteus","bioclimatic-envelope","biota","climate change","climatologyMeteorologyAtmosphere","geospatial datasets","modeling"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-112.5861, 31.3322, -106.4861, 35.4988","theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for mammal species in South Central USA"},"description":"This dataset contains the result of the bioclimatic-envelope modeling of the six mammal species -- (a) New Mexican Jumping Mouse (Zapus hudsonius luteus), (b) Northern Pygmy Mouse (Baiomys taylori), (c) Gunnison's Prairie Dog (Cynomys gunnisoni), (d) Black-tailed Prairie Dog (Cynomys ludovicianus), (e) American Pika (Ochotona princeps), and (e) Swift Fox (Vulpes velox) -- 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":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ac5f2efd-8a07-47bf-bcac-3f977a8b2f51","harvest_record_raw":"https://catalog.data.gov/harvest_record/ac5f2efd-8a07-47bf-bcac-3f977a8b2f51/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_583322bee4b046f05f211a6b","keyword":["American Pika","Baiomys taylori","Black-tailed Prairie Dog","Cynomys gunnisoni","Cynomys ludovicianus","Gunnison's Prairie Dog","New Mexican Jumping Mouse","Northern Pygmy Mouse","Ochotona princeps","Swift Fox","USGS:583322bee4b046f05f211a6b","Vulpes velox","Zapus hudsonius luteus","bioclimatic-envelope","biota","climate change","climatologyMeteorologyAtmosphere","geospatial datasets","modeling"],"last_harvested_date":"2026-09-05T18:52:37.708750","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-mammal-species-in-south-central-usa","spatial_centroid":{"lat":32.99884,"lon":-110.1461},"spatial_shape":{"coordinates":[[[-112.5861,31.3322],[-112.5861,35.4988],[-106.4861,35.4988],[-106.4861,31.3322],[-112.5861,31.3322]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projected future bioclimate-envelope suitability for mammal species in South Central USA","type":"dataset"},{"_score":29.240461,"_sort":[1788634343328,29.240461,2,"e3548643-25f3-4a5b-a4fd-8457c75c4e4d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Luke Winslow","hasEmail":"mailto:lwinslow@usgs.gov"},"description":"It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale.\nThis data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July, SthermoD_mean_JAS, SthermoD_mean, lake_average_temp, peak_lake_average_temp, lake_average_temp_JAS, mean_epi_temp, mean_hypo_temp, mean_surf_temp, mean_bottom_temp, peak_surf_temp, peak_bottom_temp, mean_surf_temp_JAS, mean_bottom_temp_JAS, mean_bottom_temp_365, mean_surf_temp_365, mean_1m_temp, mean_surf_JA, GDD_wtr_5c, GDD_wtr_10c, volume_mean_m_3, simulation_length_days, mean_volumetric_temp, kd, out_val calculated for 2210 lakes.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/10.5066/F7028PN4","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.57473446e4b07e28b663d81a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57473446e4b07e28b663d81a","keyword":["007","012","US","USGS:57473446e4b07e28b663d81a","United States","WI","Wisconsin","climate change","environment","hydrodynamic model","inlandWaters","lakes","limnology","water clarity","water quality","water temperature"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.91, 42.48, -86.75, 47.54","theme":["geospatial"],"title":"Wisconsin Lake Temperature Metrics Decreasing Clarity"},"description":"It is well recognized that the climate is warming in response to anthropogenic emission of greenhouse gases. Over the last decade, this has had a warming effect on lakes. Water clarity is also known to effect water temperature in lakes. What is unclear is how a warming climate might interact with changes in water clarity in lakes. As part of a project at the USGS Office of Water Information, several water clarity scenarios were simulated for lakes in Wisconsin to examine how changing water clarity interacts with climate change to affect lake temperatures at a broad scale.\nThis data set contains the following parameters: year, WBIC, durStrat, max_schmidt_stability, mean_schmidt_stability_JAS, mean_schmidt_stability_July, SthermoD_mean_JAS, SthermoD_mean, lake_average_temp, peak_lake_average_temp, lake_average_temp_JAS, mean_epi_temp, mean_hypo_temp, mean_surf_temp, mean_bottom_temp, peak_surf_temp, peak_bottom_temp, mean_surf_temp_JAS, mean_bottom_temp_JAS, mean_bottom_temp_365, mean_surf_temp_365, mean_1m_temp, mean_surf_JA, GDD_wtr_5c, GDD_wtr_10c, volume_mean_m_3, simulation_length_days, mean_volumetric_temp, kd, out_val calculated for 2210 lakes.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/93f65afe-a523-451d-a9d3-0c5782d6b688","harvest_record_raw":"https://catalog.data.gov/harvest_record/93f65afe-a523-451d-a9d3-0c5782d6b688/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57473446e4b07e28b663d81a","keyword":["007","012","US","USGS:57473446e4b07e28b663d81a","United States","WI","Wisconsin","climate change","environment","hydrodynamic model","inlandWaters","lakes","limnology","water clarity","water quality","water temperature"],"last_harvested_date":"2026-09-05T18:52:23.328381","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":2,"publisher":"U.S. Geological Survey","slug":"wisconsin-lake-temperature-metrics-decreasing-clarity","spatial_centroid":{"lat":44.504,"lon":-90.446},"spatial_shape":{"coordinates":[[[-92.91,42.48],[-92.91,47.54],[-86.75,47.54],[-86.75,42.48],[-92.91,42.48]]],"type":"Polygon"},"theme":["geospatial"],"title":"Wisconsin Lake Temperature Metrics Decreasing Clarity","type":"dataset"},{"_score":10.005785,"_sort":[1788633838347,10.005785,3,"30d6a47c-37a5-4443-8ca5-c3934d6963dd"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Scott Carleton","hasEmail":"mailto:scott_carleton@fws.gov"},"description":"Grassland birds are among the most imperiled bird guilds in North America. Scaled quail (Callipepla squamata) are a semi-arid grassland bird whose populations have declined over the past half century. 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Three sets of output files were produced at different temporal resolutions: (1) hourly files with all model outputs (files in the \u201chourly\u201d directory); (2) daily files containing data at 15-minute increments for surface pressure, precipitation, 2-meter air temperature, 2-meter water vapor mixing ratio, and u- and v- components of windspeed at all levels (files in the \u201c15min\u201d directory); and (3) daily files with minimum, maximum, mean, and standard deviation values for selected of surface variables (files in the \u201cxtrm\u201d directory). The simulation time of the model is referenced to Coordinated Universal Time (UTC) rather than local time. The Entity and Attribute element of this metadata record documents the data dictionaries for all the variables in each of the three sets of output files.\nThe output files are approximately 120 terabytes (TB) in volume and are archived on the U.S. Geological Survey's Black Pearl tape drive system. Data files are provided via a Globus Access Portal (linked under Related External Resources and below herein). Globus* is a fast, secure, and reliable way to move large data files and will automatically resume transfers when there are network disruptions. Learn more at https://www.globus.org/data-transfer.\nhttps://app.globus.org/file-manager?origin_id=7b183415-9453-4f5d-ab8b-2092c98e5711&amp;origin_path=%2F&amp;two_pane=true.\n* Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P143UNYX","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.687e68a6d4be027f6b8de278.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_687e68a6d4be027f6b8de278","keyword":["Alabama","Florida","Georgia","Lower Mississippi","Mississippi","North Carolina","South Atlantic-Gulf","South Carolina","Tennessee","USGS:687e68a6d4be027f6b8de278","atmospheric and climatic processes","high resolution climate simulation","hydroclimate","hydrology","mathematical simulation","precipitation","regional climate model","time series datasets","weather research and forecast model"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-90.70026, 23.324265, -76.499756, 36.62347","theme":["geospatial"],"title":"Regional reanalysis for the southeastern United States at a 4-kilometer and daily to sub-hourly resolution, 1975-2024"},"description":"A regional reanalysis of weather for the southeastern United States at a 4-kilometer spatial resolution and daily to sub-hourly resolution was produced by the U.S. Geological Survey in cooperation with the Florida Flood Hub for Applied Research and Innovation. 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Data files are provided via a Globus Access Portal (linked under Related External Resources and below herein). Globus* is a fast, secure, and reliable way to move large data files and will automatically resume transfers when there are network disruptions. 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While it is known that water clarity changes can alter lake water temperatures, it is unknown if frequently observed water clarity trends are sufficient to meaningfully impact the thermal trajectories of diverse lake populations. Using process-based modeling and empirical observations, this study demonstrates that water clarity changes of about 1% per year amplifies or suppresses warming at rates comparable to climate-induced warming. These results demonstrate that trends in water clarity, which are occurring in many lakes, may be as important as rising air temperatures in determining how waterbodies respond to climate change.\nThese data support the following publication:  \nJordan S. Read, Luke A. Winslow, Gretchen J.A. Hansen, Jamon Van Den Hoek, Paul C. Hanson, Louise C. 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While it is known that water clarity changes can alter lake water temperatures, it is unknown if frequently observed water clarity trends are sufficient to meaningfully impact the thermal trajectories of diverse lake populations. Using process-based modeling and empirical observations, this study demonstrates that water clarity changes of about 1% per year amplifies or suppresses warming at rates comparable to climate-induced warming. These results demonstrate that trends in water clarity, which are occurring in many lakes, may be as important as rising air temperatures in determining how waterbodies respond to climate change.\nThese data support the following publication:  \nJordan S. Read, Luke A. Winslow, Gretchen J.A. Hansen, Jamon Van Den Hoek, Paul C. Hanson, Louise C. Bruce, 2014, Simulating 2368 temperate lakes reveals weak coherence in stratification phenology: Ecological Modeling, http://dx.doi.org/10.1016/j.ecolmodel.2014.07.029.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/a53f98c2-98b9-431f-bb85-4e2967a9d414","harvest_record_raw":"https://catalog.data.gov/harvest_record/a53f98c2-98b9-431f-bb85-4e2967a9d414/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5735f0f2e4b0dae0d5df6c67","keyword":["USGS:5735f0f2e4b0dae0d5df6c67","climate change","environment","lakes","pre-SM502.8"],"last_harvested_date":"2026-09-05T18:16:13.313755","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":"climate-warming-of-wisconsin-lakes-can-be-either-amplified-or-suppressed-by-trends-in-wate","spatial_centroid":{"lat":44.315999999999995,"lon":-90.44800000000001},"spatial_shape":{"coordinates":[[[-92.88,42.48],[-92.88,47.07],[-86.8,47.07],[-86.8,42.48],[-92.88,42.48]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate warming of Wisconsin lakes can be either amplified or suppressed by trends in water clarity","type":"dataset"},{"_score":20.81449,"_sort":[1788631796422,20.81449,0,"79aa5f01-cab1-4354-ae5d-003be68b71b6"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kimberley Davis","hasEmail":"mailto:Kimberley.davis@usda.gov"},"description":"This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the projections, climate and time since fire varies by year but other predictors stay constant at fixed values. Distance to seed source was set at 50 m and fire severity, measured as the differenced normalized burn ratio (dNBR), was set at 400 for all projections. Because we hold distance to seed source and fire severity constant, the post-fire recruitment index is interpreted as the climate suitability for post-fire recruitment, under the given scenario. We recognize that post-fire recruitment is also influenced by other local factors that are unaccounted for in our models, including biotic interactions, such as herbivory and competition, and abiotic factors, such as substrate, topography and soil moisture.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/hzh5-6h93","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.633f1aebd34e342aee062eee.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_633f1aebd34e342aee062eee","keyword":["Pinus ponderosa","Pseudotsuga menziesii","USGS:633f1aebd34e342aee062eee","biota","environment","external research support","fires","geospatial datasets","modeling","post-fire regeneration","tree regeneration","wildfire"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-120.5420, 31.3161, -103.2715, 48.9514","theme":["geospatial"],"title":"Maps of post-fire conifer recruitment from: Fire-catalyzed vegetation shifts in ponderosa pine and Douglas-fir forests of the western United States"},"description":"This dataset includes spatial projections of the post-fire recruitment index for ponderosa pine (Pinus ponderosa) and Douglas-fir (Pseudotsuga menziesii) using climate data from different time periods (1980-1989, 1990-1999, 2000-2009, 2010-2014) and a future climate scenario of a global mean increase in temperature of two degrees Celsius. The post-fire recruitment index varies from 0 to 1 and represents the proportion of the first five years following wildfire that had climate suitable for regeneration of the given species. We chose a five-year window because the majority (69%) of recruitment across all sites in the dataset used to build our recruitment models occurred within the first five post-fire years. In the projections, climate and time since fire varies by year but other predictors stay constant at fixed values. Distance to seed source was set at 50 m and fire severity, measured as the differenced normalized burn ratio (dNBR), was set at 400 for all projections. Because we hold distance to seed source and fire severity constant, the post-fire recruitment index is interpreted as the climate suitability for post-fire recruitment, under the given scenario. We recognize that post-fire recruitment is also influenced by other local factors that are unaccounted for in our models, including biotic interactions, such as herbivory and competition, and abiotic factors, such as substrate, topography and soil moisture.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ed2483da-0f1d-4043-a36a-05d43e4e6956","harvest_record_raw":"https://catalog.data.gov/harvest_record/ed2483da-0f1d-4043-a36a-05d43e4e6956/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_633f1aebd34e342aee062eee","keyword":["Pinus ponderosa","Pseudotsuga menziesii","USGS:633f1aebd34e342aee062eee","biota","environment","external research support","fires","geospatial datasets","modeling","post-fire regeneration","tree regeneration","wildfire"],"last_harvested_date":"2026-09-05T18:09:56.422487","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":"maps-of-post-fire-conifer-recruitment-from-fire-catalyzed-vegetation-shifts-in-ponderosa-p","spatial_centroid":{"lat":38.37022,"lon":-113.6338},"spatial_shape":{"coordinates":[[[-120.542,31.3161],[-120.542,48.9514],[-103.2715,48.9514],[-103.2715,31.3161],[-120.542,31.3161]]],"type":"Polygon"},"theme":["geospatial"],"title":"Maps of post-fire conifer recruitment from: Fire-catalyzed vegetation shifts in ponderosa pine and Douglas-fir forests of the western United States","type":"dataset"},{"_score":25.38983,"_sort":[1788631793300,25.38983,0,"2e2ad70c-1d2a-4ff7-87fe-63fc3a71da24"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kristina Stinson","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Maple syrup is produced from the sap of sugar maple collected in the late winter and early spring. Native American tribes have collected and boiled down sap for centuries, and the tapping of maple trees is a cultural touchstone for many people in the northeast and Midwest. Because the tapping season is dependent on weather conditions, there is concern about the sustainability of maple sugaring as climate changes throughout the region. Our research addresses the impact of climate on the quantity and quality of maple sap used to make maple syrup. Sap was sampled at 6 sites across the native range of sugar maple over 2 years as part of the ACERnet collaboration. At each site we sampled 15-25 mature sugar maple trees, and an additional 10 red maple trees at 3 sites. Sap from mature trees was collected using traditional gravity tapping methods following accepted tapping guidelines for gravity tapping. Xylem sap was collected from mid-February through late April, depending on the site, on all days of sap flow. Sap volume and sugar content were measured for each tree during each collection at site. Sap samples were then frozen and send to the Food and Health Lab at Montana State University for analysis of Total Phenolic Concentration as an additional measure of quality. A sub-set of samples from each site were analyzed for individual secondary metabolites.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9PF7WF8","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.5d67f076e4b0c4f70cf15c27.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5d67f076e4b0c4f70cf15c27","keyword":["Acer saccharum","USGS:5d67f076e4b0c4f70cf15c27","biota","climate change","forest ecosystem service","maple syrup","modeling","predictive model","yield"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-87.0811, 37.0108, -70.6885, 48.4309","theme":["geospatial"],"title":"Sap Quality at Study Sites in the Northeast"},"description":"Maple syrup is produced from the sap of sugar maple collected in the late winter and early spring. 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Sap volume and sugar content were measured for each tree during each collection at site. Sap samples were then frozen and send to the Food and Health Lab at Montana State University for analysis of Total Phenolic Concentration as an additional measure of quality. 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Scaled quail (Callipepla squamata) are a semi-arid grassland bird whose populations have declined over the past half century. We monitored scaled quail in New Mexico to study the effects of habitat, temperature and precipitation on survival of scaled quail adults, nests, and broods. Seasonal nest survival (39.4%) had a positive relationships with increasing average weekly maximum temperature and grass density, and negative relationships with increasing average minimum temperature and percent bare ground. Seasonal brood survival (49.0%) had a negative relationship with increasing average weekly minimum and maximum temperature, and with increasing precipitation. These results illustrate the importance of managing ground cover for scaled quail to ensure adult survival and successful recruitment. Ground cover provides protection from thermal and precipitation related stress, as well as for visual obstruction from predators. This data release has three child items corresponding to the data and metadata files on adult, brood and nest survival of scaled quail.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7MP52J0","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.5a903969e4b06990606407aa.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a903969e4b06990606407aa","keyword":["New Mexico","USGS:5a903969e4b06990606407aa","biota","climate change","scaled quail","southwestern US","southwestern United States"],"modified":"2026-09-03T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-107.4490, 31.7982, -106.8750, 32.1849","theme":["geospatial"],"title":"Effects of Climate on Scaled Quail Reproduction and Survival"},"description":"Grassland birds are among the most imperiled bird guilds in North America. Scaled quail (Callipepla squamata) are a semi-arid grassland bird whose populations have declined over the past half century. We monitored scaled quail in New Mexico to study the effects of habitat, temperature and precipitation on survival of scaled quail adults, nests, and broods. Seasonal nest survival (39.4%) had a positive relationships with increasing average weekly maximum temperature and grass density, and negative relationships with increasing average minimum temperature and percent bare ground. Seasonal brood survival (49.0%) had a negative relationship with increasing average weekly minimum and maximum temperature, and with increasing precipitation. These results illustrate the importance of managing ground cover for scaled quail to ensure adult survival and successful recruitment. Ground cover provides protection from thermal and precipitation related stress, as well as for visual obstruction from predators. 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The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped sagebrush-occupied habitats from SWReGAP landcover (USGS 2004).\nCurrent occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as the appropriate sagebrush type. These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current 2035 \n1 Lost &gt;= 0.56 &lt; 0.34 \n2 Threatened &gt;= 0.56 &gt;= 0.34 and &lt; 0.56 \n3 Persistent &gt;= 0.56 &gt;= 0.56 \n4 Emergent &lt; 0.56 &gt;= 0.56 \n0 none of the above \nwhere: 0.56 is the average probability of occurrence value from the 3 scenarios, current timeframe, where vaseyana is known to occur (using SWReGAP landcover). 0.34 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1W8ENLD","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.5c0934cbe4b0815414d0c467.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c0934cbe4b0815414d0c467","keyword":["USGS:5c0934cbe4b0815414d0c467","biota","climate change","climatologyMeteorologyAtmosphere"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.1583, 36.9167, -101.9500, 41.0833","theme":["geospatial"],"title":"Artemisia tridentata spp. vaseyana Feast/Famine scenario change categories (2035)"},"description":"Projected suitable habitat models were constructed in Maxent (version 3.3; Phillips et al. 2004, 2006) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped sagebrush-occupied habitats from SWReGAP landcover (USGS 2004).\nCurrent occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as the appropriate sagebrush type. These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current 2035 \n1 Lost &gt;= 0.56 &lt; 0.34 \n2 Threatened &gt;= 0.56 &gt;= 0.34 and &lt; 0.56 \n3 Persistent &gt;= 0.56 &gt;= 0.56 \n4 Emergent &lt; 0.56 &gt;= 0.56 \n0 none of the above \nwhere: 0.56 is the average probability of occurrence value from the 3 scenarios, current timeframe, where vaseyana is known to occur (using SWReGAP landcover). 0.34 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/5bc26585-6f81-4b46-99cc-72741fbe6442","harvest_record_raw":"https://catalog.data.gov/harvest_record/5bc26585-6f81-4b46-99cc-72741fbe6442/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c0934cbe4b0815414d0c467","keyword":["USGS:5c0934cbe4b0815414d0c467","biota","climate change","climatologyMeteorologyAtmosphere"],"last_harvested_date":"2026-09-04T19:16:44.686456","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":"artemisia-tridentata-spp-vaseyana-feast-famine-scenario-change-categories-2035","spatial_centroid":{"lat":38.58334,"lon":-106.27498},"spatial_shape":{"coordinates":[[[-109.1583,36.9167],[-109.1583,41.0833],[-101.95,41.0833],[-101.95,36.9167],[-109.1583,36.9167]]],"type":"Polygon"},"theme":["geospatial"],"title":"Artemisia tridentata spp. vaseyana Feast/Famine scenario change categories (2035)","type":"dataset"},{"_score":8.843673,"_sort":[1788549279268,8.843673,1,"593a12b1-f5e6-4f2c-af88-235498fbd7c8"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Seth M. 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Cover was determined using sub-pixel classifications of two Landsat scenes from path 36, row 38 (centered on latitude: 31.7470, longitude: -111.3981) and path 37, row 38 (31.7470, -112.9431) that encompass Tucson, AZ.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://doi.org/10.5066/F7959FNF","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.d369daae-76ad-4e64-8dd2-fbc980696a1d.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_d369daae-76ad-4e64-8dd2-fbc980696a1d","keyword":["Sonoran Desert; southern Arizona","USGS:d369daae-76ad-4e64-8dd2-fbc980696a1d","aridity","biota","climate change","desert","environment","geospatial datasets","land degradation","remote sensing","shrub encroachment","southern Arizona"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-113.8600, 31.3375, -110.4700, 32.3930","theme":["geospatial"],"title":"Shifts in Perennial grass in southern Arizona, 1989 - 2009"},"description":"This dataset includes the cover of three vegetation types: perennial grasses, creosote bush (Larrea tridentata), and leguminous trees (Prosopis velutina, Parkinsonia microphylla, Parkinsonia florida) in 1989, 1995, 1999, 2005, and 2009 across southern Arizona. 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CSC_model_files contains the baseline Cleve Creek model, T-only change model, and T-P change model. Model_results.rar is from R-RHESSYS model of climate projections of drought during the next 30 years (2016-2035) for the Cleve Creek watershed in Nevada.  Within the .rar packaging are .dat files that contain outputs including net primary productivity (NPP), leaf area index (LAI), actual evapotranspiration (AET), soil\nmoisture, groundwater level, streamflow, snow pack (as snow water equivalent, SWE). \nGeographic information:  Site is Cleve Creek, a headwaters basin to Spring Valley in\nEastern Nevada. Lower left corner of model grid is 701984 m (east) and 4342483 m\n(north) NAD 83 zone 11.  The grid is oriented North-South and extends 143 rows and\n115 columns with each cell 100 m by 100 m.  NAD83, zone 11N, datumD_North_American1983.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14HLZC4","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.56feafa1e4b0328dcb7dec33.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_56feafa1e4b0328dcb7dec33","keyword":["Cleve Creek","Great Basin","Nevada","Spring Valley","USGS:56feafa1e4b0328dcb7dec33","climate change","environment","hydrologic response","modeling","pre-SM502.8","vegetation response"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.6607, 39.2054, -114.5276, 39.3369","theme":["geospatial"],"title":"Linking climate, hydrology and ecological changes at intermediate timescales in Cleve Creek, Eastern Nevada"},"description":"This collection contains GSFLOW and R-RHESSys model input and output files. 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NAD83, zone 11N, datumD_North_American1983.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/ac06a926-51cd-4387-8007-412d10516645","harvest_record_raw":"https://catalog.data.gov/harvest_record/ac06a926-51cd-4387-8007-412d10516645/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_56feafa1e4b0328dcb7dec33","keyword":["Cleve Creek","Great Basin","Nevada","Spring Valley","USGS:56feafa1e4b0328dcb7dec33","climate change","environment","hydrologic response","modeling","pre-SM502.8","vegetation response"],"last_harvested_date":"2026-09-04T19:13:27.447811","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":"linking-climate-hydrology-and-ecological-changes-at-intermediate-timescales-in-cleve-creek","spatial_centroid":{"lat":39.257999999999996,"lon":-114.60746000000002},"spatial_shape":{"coordinates":[[[-114.6607,39.2054],[-114.6607,39.3369],[-114.5276,39.3369],[-114.5276,39.2054],[-114.6607,39.2054]]],"type":"Polygon"},"theme":["geospatial"],"title":"Linking climate, hydrology and ecological changes at intermediate timescales in Cleve Creek, Eastern Nevada","type":"dataset"},{"_score":19.718248,"_sort":[1788549078084,19.718248,1,"33f501ac-5add-4609-85ec-f51e53ace5f1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Adam J Terando","hasEmail":"mailto:aterando@usgs.gov"},"description":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. 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These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current  2035 \n1 Lost  &gt;= 0.83  &lt; 0.52 \n2 Threatened &gt;= 0.83  &gt;= 0.52 and &lt; 0.83 \n3 Persistent &gt;= 0.83  &gt;= 0.83 \n4 Emergent &lt; 0.83  &gt;= 0.83 \n0 none of the above\nwhere: 0.83 is the average probability of occurrence value from the 3 scenarios, current timeframe, where PIED is known to occur (using LANDFIRE vegetation). 0.52 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1W8ENLD","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.5c093fb2e4b0815414d0e101.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c093fb2e4b0815414d0e101","keyword":["USGS:5c093fb2e4b0815414d0e101","biota","climate change","climatologyMeteorologyAtmosphere"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.1583, 36.9167, -101.9500, 41.0833","theme":["geospatial"],"title":"Pinus edulis Feast/Famine scenario change categories (2035)"},"description":"Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. 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These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current  2035 \n1 Lost  &gt;= 0.83  &lt; 0.52 \n2 Threatened &gt;= 0.83  &gt;= 0.52 and &lt; 0.83 \n3 Persistent &gt;= 0.83  &gt;= 0.83 \n4 Emergent &lt; 0.83  &gt;= 0.83 \n0 none of the above\nwhere: 0.83 is the average probability of occurrence value from the 3 scenarios, current timeframe, where PIED is known to occur (using LANDFIRE vegetation). 0.52 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2b05608f-0b96-4176-b7cc-9f8929df1f32","harvest_record_raw":"https://catalog.data.gov/harvest_record/2b05608f-0b96-4176-b7cc-9f8929df1f32/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c093fb2e4b0815414d0e101","keyword":["USGS:5c093fb2e4b0815414d0e101","biota","climate change","climatologyMeteorologyAtmosphere"],"last_harvested_date":"2026-09-04T18:57:01.754350","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":2,"publisher":"U.S. Geological Survey","slug":"pinus-edulis-feast-famine-scenario-change-categories-2035","spatial_centroid":{"lat":38.58334,"lon":-106.27498},"spatial_shape":{"coordinates":[[[-109.1583,36.9167],[-109.1583,41.0833],[-101.95,41.0833],[-101.95,36.9167],[-109.1583,36.9167]]],"type":"Polygon"},"theme":["geospatial"],"title":"Pinus edulis Feast/Famine scenario change categories (2035)","type":"dataset"},{"_score":9.661032,"_sort":[1788548208142,9.661032,2,"5ed45c4d-edef-45dd-9c5c-05fe1c52fed0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Matt Germino","hasEmail":"mailto:mgermino@usgs.gov"},"description":"To test experimental warming effects, we used and enhanced the Snake River Plain (SRP) Warming Experiment. 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At Grand Teton National Park (GTNP) on cobbly alluvium soils in the Pilgrim Creek basin, we established 3 control and 3 warmed frames in a ~ acre area having low (little) sagebrush ( A. arbuscula ssp. thermopola) and a high abundance of native forbs and scarce grasses such arrowleaf ( Balsamorhiza) and buckwheat ( Eriogonum), in May 2010. The Teton site is pristine and was not fenced, but all other sites had 1.5 m tall barbed wire fences to exclude livestock and the BOP sites additionally had chicken wire fencing to exclude small mammals. Frames were removed just prior to and just following permanent winter snowpack accumulation at GTNP (only).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14FNDCN","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.a341e9e1-114c-4387-8d33-ac1396eb7823.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_a341e9e1-114c-4387-8d33-ac1396eb7823","keyword":["Big wyoming sagebrush","Great Basin","Idaho","Pacific Northwest","USGS:a341e9e1-114c-4387-8d33-ac1396eb7823","climate change","environment","sagebrush"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-116.9698, 42.3265, -110.3676, 44.0554","theme":["geospatial"],"title":"Snake River Plain (SRP) Warming Experiment Data"},"description":"To test experimental warming effects, we used and enhanced the Snake River Plain (SRP) Warming Experiment. 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At Grand Teton National Park (GTNP) on cobbly alluvium soils in the Pilgrim Creek basin, we established 3 control and 3 warmed frames in a ~ acre area having low (little) sagebrush ( A. arbuscula ssp. thermopola) and a high abundance of native forbs and scarce grasses such arrowleaf ( Balsamorhiza) and buckwheat ( Eriogonum), in May 2010. The Teton site is pristine and was not fenced, but all other sites had 1.5 m tall barbed wire fences to exclude livestock and the BOP sites additionally had chicken wire fencing to exclude small mammals. Frames were removed just prior to and just following permanent winter snowpack accumulation at GTNP (only).","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/94ffd017-61ff-4974-a38b-255ae0acba21","harvest_record_raw":"https://catalog.data.gov/harvest_record/94ffd017-61ff-4974-a38b-255ae0acba21/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_a341e9e1-114c-4387-8d33-ac1396eb7823","keyword":["Big wyoming sagebrush","Great Basin","Idaho","Pacific Northwest","USGS:a341e9e1-114c-4387-8d33-ac1396eb7823","climate change","environment","sagebrush"],"last_harvested_date":"2026-09-04T18:56:48.142974","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":2,"publisher":"U.S. Geological Survey","slug":"snake-river-plain-srp-warming-experiment-data","spatial_centroid":{"lat":43.018060000000006,"lon":-114.32892},"spatial_shape":{"coordinates":[[[-116.9698,42.3265],[-116.9698,44.0554],[-110.3676,44.0554],[-110.3676,42.3265],[-116.9698,42.3265]]],"type":"Polygon"},"theme":["geospatial"],"title":"Snake River Plain (SRP) Warming Experiment Data","type":"dataset"},{"_score":10.005785,"_sort":[1788547926270,10.005785,0,"ccaa21d7-3f4f-42fc-a71f-92b8f8e53cde"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"This raster was clipped from the raw NLCC raster for this species according to the linkage width cutoff listed in Table 2.2 WHCWG (2012). As with the statewide analysis (see WHCWG 2010), the normalized least-cost corridor algorithms produced wall-to-wall linkage maps, with everygrid cell in the study area having a value that represented its deviation from the nearest least-cost movement route. This necessitated creating maps that displayed only values from zero (the optimum modeled route) to a species-specific linkage width cutoff to identify areas that contribute most to connectivity between each HCA pair. Because of the smaller extent of this analysis and the finer-scale data that were available, we chose cutoff values (Table 2.2, WHCWG 2012) that produced linkage zone widths that were somewhat narrower than those mapped at the statewide scale, while being mindful of the intent that linkage zones serve not only focal species, but other species and processes as well. Keeping linkage zones reasonably wide also acknowledges that there is still considerable uncertainty in GIS base data, resistance models, and other parameters used in our modeling process. We did not wish users of our products to assume that very narrow corridors necessarily indicate the areas most important for wildlife movement. This metadata record covers 2 datasets, for 2 different species: AMTI; Tiger salamander (Ambystoma tigrinum) at a 5km analysis and ODHE: Mule Deer (Odocoileus hemionus) at a 20km analysis.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1ZJ7VW5","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.57473e4ce4b07e28b663d87b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57473e4ce4b07e28b663d87b","keyword":["USGS:57473e4ce4b07e28b663d87b","biota","climate change","external research support","geospatial datasets","habitat","pre-SM502.8","wildlife"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.7600, 45.5400, -116.9100, 49.0000","theme":["geospatial"],"title":"Normalized least cost corridors, Columbia Plateau analysis for two species in the Pacific Northwest"},"description":"This raster was clipped from the raw NLCC raster for this species according to the linkage width cutoff listed in Table 2.2 WHCWG (2012). As with the statewide analysis (see WHCWG 2010), the normalized least-cost corridor algorithms produced wall-to-wall linkage maps, with everygrid cell in the study area having a value that represented its deviation from the nearest least-cost movement route. This necessitated creating maps that displayed only values from zero (the optimum modeled route) to a species-specific linkage width cutoff to identify areas that contribute most to connectivity between each HCA pair. Because of the smaller extent of this analysis and the finer-scale data that were available, we chose cutoff values (Table 2.2, WHCWG 2012) that produced linkage zone widths that were somewhat narrower than those mapped at the statewide scale, while being mindful of the intent that linkage zones serve not only focal species, but other species and processes as well. Keeping linkage zones reasonably wide also acknowledges that there is still considerable uncertainty in GIS base data, resistance models, and other parameters used in our modeling process. We did not wish users of our products to assume that very narrow corridors necessarily indicate the areas most important for wildlife movement. 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For each species, we mapped important habitat connections between core patches of habitat using three different modeling approaches (Connectivity Analysis Toolkit (CAT), Circuitscape, and Linkage Mapper) that incorporated three types of resistance layers (expert opinion, niche modeling, and empirical data for the black bear only). The result was 21 sets of important connections, one for each of the species-resistance-connectivity algorithm combinations we analyzed. \nIn \"Connectivity for Climate Change in the Southeastern United States\" data set, we present the results of overlaying all of the connections in the 21 sets of results on current climate suitability, as well as future climate suitability. The data here show the change in suitability under climate change for all connections mapped in our study.\nIn \"key landscape connections under urban growth\" data set, we present the results of overlaying all of the connections in the 21 sets of results on maps of current urbanization as well as future projections of urbanization. The data here show the change in the proportion of each connection that is urbanized for all connections mapped in our study. The geographic domain of the urbanization projections we used did not cover the entire extent of our connectivity modeling effort. 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Spatially, the larger project uses raster data covering the full range of sugar maple across the northeastern US, but the summary figure represents point data (as an example) at a sugarbush farm in central Wisconsin.  The results show that dynamically downscaled models fail to adequately forecast absolute values of future conditions but do capture potential changes in year-to-year variability \u2014 a metric of particular concern to producers as it challenges planning.  Statistically downscaled models, while they do adequately capture the absolute range in future conditions (i.e., produce reasonable values, i.e., all positive, as would be expected in a warming climate), they dampen the effects on year-to-year variability.  In summary, both downscaling methods have strengths and weaknesses, and both provide useful information to producers.  Therefore, complete information requires both downscaling types.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13V2P66","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.5ce5798ee4b0bc180232e7ce.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ce5798ee4b0bc180232e7ce","keyword":["USGS:5ce5798ee4b0bc180232e7ce","climatologyMeteorologyAtmosphere","effects of climate change"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.5000, 35.1000, -59.7000, 49.4000","theme":["geospatial"],"title":"Data supporting the study of Impacts of Downscaled Climate Model Selection on Projections of Maple Syrup Tapping Season"},"description":"A final summary figure summarizing the results of a study that compares two different downscaling techniques in terms of how they project future change in various aspects of the maple syrup tapping season.  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Munson","hasEmail":"mailto:smunson@usgs.gov"},"description":"This dataset includes the cover of perennial grasses in 1989, 1995, 1999, 2005, and 2009 across southern Arizona. Cover was determined using sub-pixel classifications of two Landsat scenes from path 36, row 38 (centered on latitude: 31.7470, longitude: -111.3981) and path 37, row 38 (31.7470, -112.9431) that encompass Tucson, AZ.","distribution":[{"@type":"dcat:Distribution","accessURL":"http://dx.doi.org/doi:10.5066/F7959FNF","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.576c2a1fe4b07657d1a26fdb.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_576c2a1fe4b07657d1a26fdb","keyword":["USGS:576c2a1fe4b07657d1a26fdb","aridity","climate change","desert","land degradation","shrub encroachment"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-113.86, 31.3375, -110.47, 32.393","theme":["geospatial"],"title":"Shifts in Perennial grass in southern Arizona, 1989 - 2009"},"description":"This dataset includes the cover of perennial grasses in 1989, 1995, 1999, 2005, and 2009 across southern Arizona. 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The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped sagebrush-occupied habitats from SWReGAP landcover (USGS 2004). \nCurrent occupied habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as the appropriate sagebrush type. These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current 2035 \n1 Lost &gt;= 0.46 &lt; 0.21 \n2 Threatened &gt;= 0.46 &gt;= 0.21 and &lt; 0.46 \n3 Persistent &gt;= 0.46 &gt;= 0.46 \n4 Emergent &lt; 0.46 &gt;= 0.46 \n0 none of the above\nwhere: 0.46 is the average probability of occurrence value from the 3 scenarios, current timeframe, where wyomingensis is known to occur (using SWReGAP landcover). 0.21 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1W8ENLD","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.5c093c23e4b0815414d0d31e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c093c23e4b0815414d0d31e","keyword":["USGS:5c093c23e4b0815414d0d31e","biota","climate change","geoscientificInformation"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.1583, 36.9167, -101.9500, 41.0833","theme":["geospatial"],"title":"Artemisia tridentata spp. wyomingensis Feast/Famine scenario change categories (2035)"},"description":"Projected suitable habitat models were constructed in Maxent (version 3.3; Phillips et al. 2004, 2006) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. 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The complexities of wildfire drivers, including anthropogenic ignitions, novel fuel types, and extreme heterogeneity in climate over short spatial scales, pose significant challenges to fire risk assessment and fire management, and conservation and restoration of endemic biodiversity. 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Fire regimes in Hawaii have shifted from very infrequent wildfire occurrence prior to human arrival to greatly increased frequency, intensity, and size over the past 100+ years, almost all of which is driven by anthropogenic ignitions and wildland fuels associated with invasive species, particularly grasses. Recent fire science has greatly increased understanding of contemporary drivers of fire in Hawaii; however, the social dimensions and historical perspectives from Hawaiian language primary sources have not been integrated into synthetic understanding of fire in Hawaii. The regional Pacific Island Climate Adaptation Science Center Future of Fire project focuses on how Hawaiian language informs contemporary wildfire science in the Hawaiian Islands. We synthesized how fire and subsequent impacts on natural resources have changed over time by utilizing Hawaiian language archival materials for historical records on fire history and perceptions of these impacts. The complexities of wildfire drivers, including anthropogenic ignitions, novel fuel types, and extreme heterogeneity in climate over short spatial scales, pose significant challenges to fire risk assessment and fire management, and conservation and restoration of endemic biodiversity. As island communities face increased wildfire risk due to climate change and continued plant invasion, collaborative bio-cultural stewardship approaches to adaptation and mitigation will be critical to wildfire management.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/4a01c191-5af2-4e7b-8db8-b7d8c33fc2e5","harvest_record_raw":"https://catalog.data.gov/harvest_record/4a01c191-5af2-4e7b-8db8-b7d8c33fc2e5/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_f7db61ec-b469-4188-8df0-bfaa6e49f4c2","keyword":["Hamakua","Hawaii","Kapaa","Kauai","Kealia","Newspaper","USGS:f7db61ec-b469-4188-8df0-bfaa6e49f4c2","Wildfire","biota","external research support","wildfires"],"last_harvested_date":"2026-09-04T18:24:33.537870","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":"selected-hawaiian-language-newspaper-articles-relating-to-wildfires-in-1877-and-1901","spatial_centroid":{"lat":20.32,"lon":-158.07},"spatial_shape":{"coordinates":[[[-160.35,18.8],[-160.35,22.6],[-154.65,22.6],[-154.65,18.8],[-160.35,18.8]]],"type":"Polygon"},"theme":["geospatial"],"title":"Selected Hawaiian language newspaper articles relating to wildfires in 1877 and 1901","type":"dataset"},{"_score":33.417297,"_sort":[1788545801978,33.417297,0,"64ce589d-64a3-45fd-b984-bdd54b87f9f1"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Projected current and future potential distribution for several vertebrate species, based on correlative bioclimatic models and projected changes in vegetation biomes. Bioclimatic models were built using the Random Forest algorithm. Projected changes in vegetation were also modeled using the Random Forest algorithm but were produced by Rehfeldt et al. (2012). Projected current distribution is based on the average climate conditions for the years 1961-1990. Projected future distributions are based on average climate conditions for the years 2070-2099 using downscaled (30-second or ~1-kilometer resolution) climate projections from two Global Circulation Models: CGCM3.1 (T47) and UKMO-HadCM3. Both projections use the A2 emissions scenario and are from the CMIP3 (Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report) family of climate simulations.\nDue to changing climatic conditions, species ranges are expected to shift throughout the course of this century. Modeling subsequent shifts in suitable habitat for animal species, and the resulting changes in species assemblages, represent critical information for resource planners and managers. Developing robust suitability models for large geographic areas can be challenging, in part due to insufficient sampling data and to computational limits associated with modeling large geographies at a fine-grained spatial resolution. To overcome these challenges, I developed a method to model habitat suitability in which I built correlative climate suitability models for 366 terrestrial animal species at a relatively coarse spatial resolution for the entire North American continent using species range maps and 23 bioclimatic variables. I then applied the models to both current and projected future climate data downscaled to a moderately fine resolution for western North America. I refined the resulting climate suitability projections by applying a filter that limited suitability to areas in which suitable biomes were projected to be present. I verified my modeling results using an independent species occurrence data set, finding a median accuracy rate of 70%. I found that incorporating information about biomes into the models resulted in projections of larger climate-driven changes in suitability\u2014on average a difference of about 10%. My results also indicate that study species are more likely to see climate-driven losses than gains in habitat suitability. The percentage of study species projected to undergo a significant net decrease in habitat suitability was double the percentage projected to experience a net increase. These results highlight the shortcomings of many broad-scale models and highlight the need to take finer scale vegetation patterns into account. They also indicate that while many animal species could potentially benefit from climate-change induced increases in habitat suitability, the majority of species may suffer from substantial decreases, complicating future conservation efforts.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13QNMZS","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.9901eac2-ba5e-40a5-bf2a-3c919d8f9b14.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9901eac2-ba5e-40a5-bf2a-3c919d8f9b14","keyword":["Bioclimatic Model","British Columbia","Climate Adaptation","Climate Change","Climatic Niche Model","Idaho","Montana","Range Shift Model","Species Distribution Model","USGS:9901eac2-ba5e-40a5-bf2a-3c919d8f9b14","Washington","climate change","environment","external research support","geospatial datasets","pre-SM502.8"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.5042, 46.4958, -113.9958, 53.0042","theme":["geospatial"],"title":"Projected habitat suitability for several vertebrate species in the Pacific Northwest based on projected climatic suitability, projected vegetation, and current  land use"},"description":"Projected current and future potential distribution for several vertebrate species, based on correlative bioclimatic models and projected changes in vegetation biomes. Bioclimatic models were built using the Random Forest algorithm. Projected changes in vegetation were also modeled using the Random Forest algorithm but were produced by Rehfeldt et al. (2012). Projected current distribution is based on the average climate conditions for the years 1961-1990. Projected future distributions are based on average climate conditions for the years 2070-2099 using downscaled (30-second or ~1-kilometer resolution) climate projections from two Global Circulation Models: CGCM3.1 (T47) and UKMO-HadCM3. Both projections use the A2 emissions scenario and are from the CMIP3 (Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report) family of climate simulations.\nDue to changing climatic conditions, species ranges are expected to shift throughout the course of this century. Modeling subsequent shifts in suitable habitat for animal species, and the resulting changes in species assemblages, represent critical information for resource planners and managers. Developing robust suitability models for large geographic areas can be challenging, in part due to insufficient sampling data and to computational limits associated with modeling large geographies at a fine-grained spatial resolution. To overcome these challenges, I developed a method to model habitat suitability in which I built correlative climate suitability models for 366 terrestrial animal species at a relatively coarse spatial resolution for the entire North American continent using species range maps and 23 bioclimatic variables. I then applied the models to both current and projected future climate data downscaled to a moderately fine resolution for western North America. I refined the resulting climate suitability projections by applying a filter that limited suitability to areas in which suitable biomes were projected to be present. I verified my modeling results using an independent species occurrence data set, finding a median accuracy rate of 70%. I found that incorporating information about biomes into the models resulted in projections of larger climate-driven changes in suitability\u2014on average a difference of about 10%. My results also indicate that study species are more likely to see climate-driven losses than gains in habitat suitability. The percentage of study species projected to undergo a significant net decrease in habitat suitability was double the percentage projected to experience a net increase. These results highlight the shortcomings of many broad-scale models and highlight the need to take finer scale vegetation patterns into account. They also indicate that while many animal species could potentially benefit from climate-change induced increases in habitat suitability, the majority of species may suffer from substantial decreases, complicating future conservation efforts.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/d949c686-9ef5-4f75-ad89-007d4028a748","harvest_record_raw":"https://catalog.data.gov/harvest_record/d949c686-9ef5-4f75-ad89-007d4028a748/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9901eac2-ba5e-40a5-bf2a-3c919d8f9b14","keyword":["Bioclimatic Model","British Columbia","Climate Adaptation","Climate Change","Climatic Niche Model","Idaho","Montana","Range Shift Model","Species Distribution Model","USGS:9901eac2-ba5e-40a5-bf2a-3c919d8f9b14","Washington","climate change","environment","external research support","geospatial datasets","pre-SM502.8"],"last_harvested_date":"2026-09-04T18:16:41.978232","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-habitat-suitability-for-several-vertebrate-species-in-the-pacific-northwest-base","spatial_centroid":{"lat":49.09916,"lon":-120.90083999999999},"spatial_shape":{"coordinates":[[[-125.5042,46.4958],[-125.5042,53.0042],[-113.9958,53.0042],[-113.9958,46.4958],[-125.5042,46.4958]]],"type":"Polygon"},"theme":["geospatial"],"title":"Projected habitat suitability for several vertebrate species in the Pacific Northwest based on projected climatic suitability, projected vegetation, and current  land use","type":"dataset"},{"_score":25.68738,"_sort":[1788545654687,25.68738,0,"5c50eb85-9c33-4200-9d87-8e73226692bf"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Michelle Fink","hasEmail":"mailto:michelle.fink@colostate.edu"},"description":"Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped J. osteosperma habitats from LANDFIRE existing vegetation (version 1.3.0). Current JUOS habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as JUOS. These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current  2035 \n1 Lost  &gt;= 0.90  &lt; 0.55 \n2 Threatened &gt;= 0.90  &gt;= 0.55 and &lt; 0.90 \n3 Persistent &gt;= 0.90  &gt;= 0.90 \n4 Emergent &lt; 0.90  &gt;= 0.90 \n0 none of the above\nwhere: 0.90 is the average probability of occurrence value from the 3 scenarios, current timeframe, where JUOS is known to occur (using LANDFIRE vegetation). 0.55 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1W8ENLD","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.5c09258ce4b0815414d0bca9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c09258ce4b0815414d0bca9","keyword":["USGS:5c09258ce4b0815414d0bca9","biota","climate change","climatologyMeteorologyAtmosphere"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-109.1583, 36.9167, -101.9500, 41.0833","theme":["geospatial"],"title":"Juniperus osteosperma Feast/Famine scenario change categories (2035)"},"description":"Projected suitable habitat models were constructed in randomForest (R package, version 4.6-10) using a set of presence points for the species derived from element occurrence and herbarium records, together with temperature, precipitation, and soil variables. The current distribution used modeled historic period (1970-2000) climate variables from the appropriate matching GCM model run. These model parameters were then used with projected climate data to get future (2020-2050) modeled suitable habitat for each scenario. Modeled past suitable habitat and modeled future suitable habitat are combined to show areas of change, using various thresholds to distinguish change categories, as well as current mapped J. osteosperma habitats from LANDFIRE existing vegetation (version 1.3.0). Current JUOS habitat is represented as areas with probability greater than the all-scenario average model-reported threshold (sensitivity = specificity) AND currently mapped as JUOS. These probability threshold levels were also applied to projected future habitat (since we have no \u201cfuture\u201d mapping), with the final model was classified as: \nValue Habt Class Current  2035 \n1 Lost  &gt;= 0.90  &lt; 0.55 \n2 Threatened &gt;= 0.90  &gt;= 0.55 and &lt; 0.90 \n3 Persistent &gt;= 0.90  &gt;= 0.90 \n4 Emergent &lt; 0.90  &gt;= 0.90 \n0 none of the above\nwhere: 0.90 is the average probability of occurrence value from the 3 scenarios, current timeframe, where JUOS is known to occur (using LANDFIRE vegetation). 0.55 is the average probability of occurrence value from the 3 scenarios, current timeframe, where the model specificity = the model sensitivity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/1498e372-b858-410d-9f21-9be19a0ab079","harvest_record_raw":"https://catalog.data.gov/harvest_record/1498e372-b858-410d-9f21-9be19a0ab079/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5c09258ce4b0815414d0bca9","keyword":["USGS:5c09258ce4b0815414d0bca9","biota","climate change","climatologyMeteorologyAtmosphere"],"last_harvested_date":"2026-09-04T18:14:14.687726","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":"juniperus-osteosperma-feast-famine-scenario-change-categories-2035","spatial_centroid":{"lat":38.58334,"lon":-106.27498},"spatial_shape":{"coordinates":[[[-109.1583,36.9167],[-109.1583,41.0833],[-101.95,41.0833],[-101.95,36.9167],[-109.1583,36.9167]]],"type":"Polygon"},"theme":["geospatial"],"title":"Juniperus osteosperma Feast/Famine scenario change categories (2035)","type":"dataset"},{"_score":30.775694,"_sort":[1788545500083,30.775694,0,"8b2631e5-876b-488b-8922-8b5c5ef43508"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Collection of model inputs and outputs examining different combinations of management and climate scenarios run in southeastern Oregon. The objectives of this project are to explore how climate and land management might interact to shape future vegetation and wildlife habitat, and determine what management actions will likely maximize habitats for key species. Climate scenarios include no climate change, HadGEM global circulation model, representative concentration pathway 8.5, NorESM global circulation model, representative concentration pathway 8.5 (NorESM), MRI global circulation model, representative concentration pathway 8.5 (MRI).","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P142KWFC","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.a1f4de45-06b1-46af-90e1-e52c5d49026a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_a1f4de45-06b1-46af-90e1-e52c5d49026a","keyword":["Great Basin","Pacific Northwest","Southeast Oregon","USGS:a1f4de45-06b1-46af-90e1-e52c5d49026a","biota","climate change","environment","external research support","geospatial datasets","habitats","land management","modeling","state and transition modeling","vegetation change","vulnerability assessment","wildlife habitat"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.6463, 41.9478, -116.6953, 45.3066","theme":["geospatial"],"title":"Future Sage-Grouse Habitat Scenarios, Southeast Oregon Study Area, 2007-2096"},"description":"Collection of model inputs and outputs examining different combinations of management and climate scenarios run in southeastern Oregon. The objectives of this project are to explore how climate and land management might interact to shape future vegetation and wildlife habitat, and determine what management actions will likely maximize habitats for key species. 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This metadata record documents multiple individual datasets, specifically the change from historical (1950-2005) for 12 hydrometerological variables projected by 3 Global Circulation Models (GCM) over 2 future time periods, for one Representative Concentration Pathway (RCP 8.5)\nThe variables are:\nWater Deficit, Spring (March-May)\nWater Deficit, Summer (July-September)\nPotential Evapotranspiration, Spring (March-May)\nPotential Evapotranspiration, Summer (July-September)\nTotal Runoff, Summer (June-August)\nTotal Runoff, Spring (March-May)\nSoil Moisture, Summer (July-September)\nEvapotranspiration, Spring (March-May)\nEvapotranspiration, Summer (July-September)\nLength of the Snow Season\nSpring (April 1st) Snowpack\nLate Spring (May 1st) Snowpack\nPercentage of Winter Precipitation Captured in April 1st Snowpack\nThe three GCMs  are:\nCanESM2\nCNRM-CM5\nCCSM4\nThe two time periods are:\n2050s (2040-2069)\n2080s (2070-2099)","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P14BFQDH","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.1cc1aaa5-1180-4d94-96fc-e9d9a1ee2e0b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_1cc1aaa5-1180-4d94-96fc-e9d9a1ee2e0b","keyword":["USGS:1cc1aaa5-1180-4d94-96fc-e9d9a1ee2e0b","biota","climate change","climate projections","climatologyMeteorologyAtmosphere","ecosystem services","external research support","geospatial datasets","pre-SM502.8"],"modified":"2026-09-02T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.4531, 42.0330, -111.6211, 49.2678","theme":["geospatial"],"title":"Integrated scenarios of the future Northwest U.S. environment: hydrometerological projections for 2050s and 2080s, CMIP5 models, RCP 8.5"},"description":"Projected change from historical (1950-2005)  in several hydrometerological variables under three Global Circulation Models for two time periods (2050s and 2080s) under RCP 8.5. 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f773de882ce20f3301008a2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773de882ce20f3301008a2","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773de882ce20f3301008a2","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Woody Debris on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. 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Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region.  We collected stream temperature and stream drying to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region.  We collected stream temperature and stream drying to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used stream discharge (flow) data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. Discharge (cubic meter/second) was collected throughout the eight populations across the three seasons (summer, fall, spring) for two years.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f773c9182ce20f330100894.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773c9182ce20f330100894","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773c9182ce20f330100894","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influence of Stream Discharge on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used stream discharge (flow) data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. Discharge (cubic meter/second) was collected throughout the eight populations across the three seasons (summer, fall, spring) for two years.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/bb515b6a-63f1-4293-a9d2-99f7bd7661b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/bb515b6a-63f1-4293-a9d2-99f7bd7661b4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773c9182ce20f330100894","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773c9182ce20f330100894","biota"],"last_harvested_date":"2026-09-03T19:08:14.849569","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":"influence-of-stream-discharge-on-eight-populations-of-rio-grande-cutthroat-trout-in-northe","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influence of Stream Discharge on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":14.86113,"_sort":[1788462272126,14.86113,0,"5aeeea19-80fb-4702-a904-a5c738ac17da"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f776c6c82ce20f3301009e6.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f776c6c82ce20f3301009e6","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f776c6c82ce20f3301009e6","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influences of Water Quality on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). 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All but one of the frogs recaptured gained weight after 17 days post-release (average gain = 0.28 \u00b1 0.13 g), suggesting that transmitter/harness setup did not affect foraging behavior. The average daily distance travelled per individual was 0.76 \u00b1 0.22 m, being significantly higher for translocated individuals (1.19 \u00b1 0.35 m). 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We also took note of climate drivers mentioned and details on species vulnerability and adaptive capacity.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/27bb89f0-e6c5-4b74-b83d-952cecc47a0a","harvest_record_raw":"https://catalog.data.gov/harvest_record/27bb89f0-e6c5-4b74-b83d-952cecc47a0a/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64147d6fd34eb496d1ceb497","keyword":["Africa","Arctic Ocean","Asia","Atlantic Ocean","Australia","Europe","Global","North America","Pacific Ocean","South America","USGS:64147d6fd34eb496d1ceb497","biodiversity","boundaries","climate change","depth","distribution","distribution shift","effects of climate change","elevation","global change","global warming","habitat extent","latitude","occupancy","precipitation","range","species","species redistribution","temperature","vulnerability","warming"],"last_harvested_date":"2026-09-03T18:48:14.472744","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":3,"publisher":"U.S. Geological Survey","slug":"core-contractions-or-range-expansions-database-global-database-of-species-range--1802-2019","spatial_centroid":{"lat":-18.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-90.0],[-180.0,90.0],[180.0,90.0],[180.0,-90.0],[-180.0,-90.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"CoRE (Contractions or Range Expansions) Database: Global Database of Species Range Shifts from 1802-2019","type":"dataset"},{"_score":14.6293125,"_sort":[1788460971786,14.6293125,0,"0c94e6d6-8ef2-42fb-b5be-2a3837088248"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f773fd782ce20f3301008ad.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773fd782ce20f3301008ad","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773fd782ce20f3301008ad","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.5454, 35.9157, -105.3369, 36.6772","theme":["geospatial"],"title":"Influences of Water Chemistry on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/786ac359-5fad-4f3a-ab1c-83a9c56c52f2","harvest_record_raw":"https://catalog.data.gov/harvest_record/786ac359-5fad-4f3a-ab1c-83a9c56c52f2/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f773fd782ce20f3301008ad","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f773fd782ce20f3301008ad","biota"],"last_harvested_date":"2026-09-03T18:42:51.786286","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":"influences-of-water-chemistry-on-eight-populations-of-rio-grande-cutthroat-trout-in-northe","spatial_centroid":{"lat":36.220299999999995,"lon":-106.06199999999998},"spatial_shape":{"coordinates":[[[-106.5454,35.9157],[-106.5454,36.6772],[-105.3369,36.6772],[-105.3369,35.9157],[-106.5454,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Influences of Water Chemistry on Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":23.617172,"_sort":[1788460599451,23.617172,0,"4058c750-ea06-4308-bb43-903202d36d12"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Climate change influences apex predators in complex ways, due to their important trophic position, capacity for resource plasticity, and sensitivity to numerous anthropogenic stressors. Bald eagles, an ecologically and culturally significant apex predator, congregate seasonally in high densities on salmon spawning rivers across the Pacific Northwest. One of the largest eagle concentrations is in the Skagit River watershed, which connects the montane wilderness of North Cascades National Park to the Puget Sound. Using multiple long-term datasets, we evaluated the relationship between local bald eagle abundance, chum and coho salmon availability and phenology, and the number and timing of flood events in the Skagit River. We analyzed both changes over time as a reflection of climate change impacts, as well as differences between managed and unmanaged portions of the river. We found that peaks in chum salmon and bald eagle presence have advanced at remarkably similar rates (~0.45 days/year), suggesting synchronous phenological responses within this trophic relationship.Yet the temporal relationship between chum salmon spawning and flood events, which remove salmon carcasses from the system, has not remained constant. This has resulted in a paradigm shift whereby the peak of chum spawning now occurs before the first flood event of the season rather than after. The interval between peak chum and first flood event was a significant predictor of bald eagle presence: as this interval grew over time (by nearly a day per year), bald eagle counts declined, with a steady decrease in bald eagle observations since 2002. River section was also an important factor, with fewer flood events and more eagle observations occurring in the river section experiencing direct hydroelectric flow management.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P91VEXEW","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.5b7daa78e4b045b1dc7beb95.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b7daa78e4b045b1dc7beb95","keyword":["North Cascades National Park","Skagit River","USGS:5b7daa78e4b045b1dc7beb95","bald eagle","biota","climate change","flood","hydroelectric","phenology","salmon","trophic interaction"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-127.0020, 45.5410, -116.9824, 49.7990","theme":["geospatial"],"title":"Biological and Hydrological Data from the Skagit River Ecosystem (Washington, USA) 1968-2016"},"description":"Climate change influences apex predators in complex ways, due to their important trophic position, capacity for resource plasticity, and sensitivity to numerous anthropogenic stressors. Bald eagles, an ecologically and culturally significant apex predator, congregate seasonally in high densities on salmon spawning rivers across the Pacific Northwest. One of the largest eagle concentrations is in the Skagit River watershed, which connects the montane wilderness of North Cascades National Park to the Puget Sound. Using multiple long-term datasets, we evaluated the relationship between local bald eagle abundance, chum and coho salmon availability and phenology, and the number and timing of flood events in the Skagit River. We analyzed both changes over time as a reflection of climate change impacts, as well as differences between managed and unmanaged portions of the river. We found that peaks in chum salmon and bald eagle presence have advanced at remarkably similar rates (~0.45 days/year), suggesting synchronous phenological responses within this trophic relationship.Yet the temporal relationship between chum salmon spawning and flood events, which remove salmon carcasses from the system, has not remained constant. This has resulted in a paradigm shift whereby the peak of chum spawning now occurs before the first flood event of the season rather than after. The interval between peak chum and first flood event was a significant predictor of bald eagle presence: as this interval grew over time (by nearly a day per year), bald eagle counts declined, with a steady decrease in bald eagle observations since 2002. River section was also an important factor, with fewer flood events and more eagle observations occurring in the river section experiencing direct hydroelectric flow management.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2dbd1f3a-3c96-4906-bce5-f7cb0d14b3ce","harvest_record_raw":"https://catalog.data.gov/harvest_record/2dbd1f3a-3c96-4906-bce5-f7cb0d14b3ce/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5b7daa78e4b045b1dc7beb95","keyword":["North Cascades National Park","Skagit River","USGS:5b7daa78e4b045b1dc7beb95","bald eagle","biota","climate change","flood","hydroelectric","phenology","salmon","trophic interaction"],"last_harvested_date":"2026-09-03T18:36:39.451374","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":"biological-and-hydrological-data-from-the-skagit-river-ecosystem-washington-usa-1968-2016","spatial_centroid":{"lat":47.2442,"lon":-122.99416},"spatial_shape":{"coordinates":[[[-127.002,45.541],[-127.002,49.799],[-116.9824,49.799],[-116.9824,45.541],[-127.002,45.541]]],"type":"Polygon"},"theme":["geospatial"],"title":"Biological and Hydrological Data from the Skagit River Ecosystem (Washington, USA) 1968-2016","type":"dataset"},{"_score":39.046246,"_sort":[1788460377052,39.046246,0,"685ebf21-e0fd-4f74-b9fb-33c4d2e94c64"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"These data include interview scripts/protocols and notes and transcripts resulting from group and individual interviews with individuals involved with the generation and application of climate adaptation science across the southeastern US. Exact audiences, topics, and data formats varied across the rounds of data collection. Text density and spacing also varies but, in total, this dataset includes over 65 pages of typed notes and transcriptions of data recorded from the interviews.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/a940-6y31","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.9b823210-dadd-44fd-9c26-3a3c985fb2e9.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9b823210-dadd-44fd-9c26-3a3c985fb2e9","keyword":["Co-production","Evaluation","Interview","USGS:9b823210-dadd-44fd-9c26-3a3c985fb2e9","biota","social sciences","society"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-91.6300, 24.4800, -75.2000, 36.6400","theme":["geospatial"],"title":"Interview Data Collected from 2019 to 2021 for the Development of a Survey to Measure Use of Climate Adaptation Science by Management Partners in the Southeastern US"},"description":"These data include interview scripts/protocols and notes and transcripts resulting from group and individual interviews with individuals involved with the generation and application of climate adaptation science across the southeastern US. Exact audiences, topics, and data formats varied across the rounds of data collection. Text density and spacing also varies but, in total, this dataset includes over 65 pages of typed notes and transcriptions of data recorded from the interviews.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f8f3fa24-62b2-4600-91fa-e639d96f7ae1","harvest_record_raw":"https://catalog.data.gov/harvest_record/f8f3fa24-62b2-4600-91fa-e639d96f7ae1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_9b823210-dadd-44fd-9c26-3a3c985fb2e9","keyword":["Co-production","Evaluation","Interview","USGS:9b823210-dadd-44fd-9c26-3a3c985fb2e9","biota","social sciences","society"],"last_harvested_date":"2026-09-03T18:32:57.052297","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":"interview-data-collected-from-2019-to-2021-for-the-development-of-a-survey-to-measure-use-","spatial_centroid":{"lat":29.344,"lon":-85.05799999999999},"spatial_shape":{"coordinates":[[[-91.63,24.48],[-91.63,36.64],[-75.2,36.64],[-75.2,24.48],[-91.63,24.48]]],"type":"Polygon"},"theme":["geospatial"],"title":"Interview Data Collected from 2019 to 2021 for the Development of a Survey to Measure Use of Climate Adaptation Science by Management Partners in the Southeastern US","type":"dataset"},{"_score":17.68956,"_sort":[1788459871406,17.68956,3,"410f2a69-37d7-4e80-9d07-95332f4f8e0a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jaime A Collazo","hasEmail":"mailto:jaime_collazo@usgs.gov"},"description":"Amphibians are vulnerable to extinction owing, partly, to altered physiological processes induced by projected global warming and drying.  Understanding the mechanisms behind their responses is essential to formulate adaptation strategies for their conservation.  Puerto Rico harbors 15 endemic Eleutherodactylus frogs considered vulnerable to extinction due to poor vagility and sensitivity to environmental variability.  Herein are reported the effects of four temperature treatments (15, 20, 25, and 30 degrees Centigrade) on metabolic rates associated with specific dynamic action (SDA) and standard metabolic rates (SMR) of four representative species of Eleutherodactylus employing a respirometer.  All species in either experiment increased their excretion of CO2 with increasing temperature.  CO2 excretion rates were higher immediately post-ingestion, subsiding to low levels by the third day (72 hours).  SMR excretion rates of E. juanariveroi and E. antillensis increased up to 20 degrees Centigrade and then curbed.  Rates of E. coqui increased linearly, whereas rates of E. wightmanae increased markedly from 20 to 25 degrees Centigrade, perishing at 30 degrees Centigrade.  E. antillensis, E. wightmanae and E. juanariveroi exhibited a change in metabolic rates between 20 degrees Centigrade and 25 degrees Centigrade, the same range where occupancy shifts from lower to higher probability for all species.  Climate projections suggest that species will be exposed to 2-3 additional hours during evenings at \u226525 degrees Centigrade below 300 m, and about 1 hour at 400-500 m.  Species occurring in low elevations (\u2264400 m) may have to compensate for the additional energy expenditure induced by increased exposure and adjust their evening time budget.  A continuing warming trend could begin to infringe on habitats of high elevation specialists like E. wightmanae.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P940RY1P","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.65009778d34ed30c2057f6e5.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65009778d34ed30c2057f6e5","keyword":["Adaptation Strategy","Climate Change","Coqu\u00ed","Coqu\u00ed  llanero","Coqu\u00ed chur\u00ed","Coqu\u00ed melodioso","Eleutherodactylus","Eleutherodactylus antillensis","Eleutherodactylus coqui","Eleutherodactylus juanariveroi","Eleutherodactylus wightmanae","Physiology","Puerto Rico","Specific Dynamic Action","Standard Metabolic Rate","USGS:65009778d34ed30c2057f6e5","West-Central Puerto Rico","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-67.00094, 18.14352, -66.97879, 18.21016","theme":["geospatial"],"title":"Physiological Analysis of Eleutherodactylus Specimens in West-Central Puerto Rico, 2021-2022"},"description":"Amphibians are vulnerable to extinction owing, partly, to altered physiological processes induced by projected global warming and drying.  Understanding the mechanisms behind their responses is essential to formulate adaptation strategies for their conservation.  Puerto Rico harbors 15 endemic Eleutherodactylus frogs considered vulnerable to extinction due to poor vagility and sensitivity to environmental variability.  Herein are reported the effects of four temperature treatments (15, 20, 25, and 30 degrees Centigrade) on metabolic rates associated with specific dynamic action (SDA) and standard metabolic rates (SMR) of four representative species of Eleutherodactylus employing a respirometer.  All species in either experiment increased their excretion of CO2 with increasing temperature.  CO2 excretion rates were higher immediately post-ingestion, subsiding to low levels by the third day (72 hours).  SMR excretion rates of E. juanariveroi and E. antillensis increased up to 20 degrees Centigrade and then curbed.  Rates of E. coqui increased linearly, whereas rates of E. wightmanae increased markedly from 20 to 25 degrees Centigrade, perishing at 30 degrees Centigrade.  E. antillensis, E. wightmanae and E. juanariveroi exhibited a change in metabolic rates between 20 degrees Centigrade and 25 degrees Centigrade, the same range where occupancy shifts from lower to higher probability for all species.  Climate projections suggest that species will be exposed to 2-3 additional hours during evenings at \u226525 degrees Centigrade below 300 m, and about 1 hour at 400-500 m.  Species occurring in low elevations (\u2264400 m) may have to compensate for the additional energy expenditure induced by increased exposure and adjust their evening time budget.  A continuing warming trend could begin to infringe on habitats of high elevation specialists like E. wightmanae.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6e2461d1-e1b5-4f77-8106-1d929920fea1","harvest_record_raw":"https://catalog.data.gov/harvest_record/6e2461d1-e1b5-4f77-8106-1d929920fea1/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65009778d34ed30c2057f6e5","keyword":["Adaptation Strategy","Climate Change","Coqu\u00ed","Coqu\u00ed  llanero","Coqu\u00ed chur\u00ed","Coqu\u00ed melodioso","Eleutherodactylus","Eleutherodactylus antillensis","Eleutherodactylus coqui","Eleutherodactylus juanariveroi","Eleutherodactylus wightmanae","Physiology","Puerto Rico","Specific Dynamic Action","Standard Metabolic Rate","USGS:65009778d34ed30c2057f6e5","West-Central Puerto Rico","biota"],"last_harvested_date":"2026-09-03T18:24:31.406606","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":3,"publisher":"U.S. Geological Survey","slug":"physiological-analysis-of-eleutherodactylus-specimens-in-west-central-puerto-ric-2021-2022","spatial_centroid":{"lat":18.170175999999998,"lon":-66.99208},"spatial_shape":{"coordinates":[[[-67.00094,18.14352],[-67.00094,18.21016],[-66.97879,18.21016],[-66.97879,18.14352],[-67.00094,18.14352]]],"type":"Polygon"},"theme":["geospatial"],"title":"Physiological Analysis of Eleutherodactylus Specimens in West-Central Puerto Rico, 2021-2022","type":"dataset"},{"_score":41.11916,"_sort":[1788459658996,41.11916,0,"6add1eb4-67bd-456e-a413-24c1fc6dea84"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Diana Zamora-Reyes","hasEmail":"mailto:dzamora-reyes@usgs.gov"},"description":"This data release includes the downscaled climate inputs from twenty-four Weather Generator Scenarios and hydrologic outputs using the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), with a 270 by 270-meter spatial resolution at a monthly time steps from water years 1916 to 2018 for the Santa Ana River watershed. \nThe twenty-four Weather Generator Scenarios are defined as: \nScenario 1: Baseline\nScenario 2: -25% average (ave) precipitation (ppt), +2 degrees Celsius (C)\nScenario 3: -25% ave ppt, +3C\nScenario 4: -25% ave ppt, +4C\nScenario 5: -25% ave ppt, +5C\nScenario 6: -12% ave ppt, +1C\nScenario 7: -12% ave ppt, +2C\nScenario 8: -12% ave ppt, +3C\nScenario 9: -12% ave ppt, +4C\nScenario 10: -12% ave ppt, +5C \nScenario 11: 0% ave ppt, +1C\nScenario 12: 0% ave ppt, +2C\nScenario 13: 0% ave ppt, +3C\nScenario 14: 0% ave ppt, +4C\nScenario 15: 0% ave ppt, +5C\nScenario 16: +12% ave ppt, +1C\nScenario 17: +12% ave ppt, +2C\nScenario 18: +12% ave ppt, +3C\nScenario 19: +12% ave ppt, +4C\nScenario 20: +12% ave ppt, +5C\nScenario 21: +25% ave ppt, +2C\nScenario 22: +25% ave ppt, +3C\nScenario 23: +25% ave ppt, +4C\nScenario 24: +25% ave ppt, +5C\nThis data release provides outputs from twenty-four Weather Generator Scenarios. For each scenario, three datasets are included: (1) monthly climate variables, (2) hydrology BCM variables, and (3) water-year summaries (72 datasets in total). The monthly climate variables child items contain precipitation (PPT), maximum air temperature (TMX), minimum air temperature (TMN), and potential evapotranspiration (PET). The monthly hydrology BCM variables child items contain actual evapotranspiration (AET), climatic water deficit (CWD), snowpack or snow water equivalent (PCK), recharge (RCH), runoff (RUN), and soil moisture storage (STR). The water-year summaries child items contain annual average summaries of each of the monthly climate and monthly hydrology BCM variables for each scenario.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1QGH9FX","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.6a0657fcb66b01f7f6adc53a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0657fcb66b01f7f6adc53a","keyword":["Atmospheric and Climatic Processes","California","Evaporation","Geospatial Datasets","Hydrology","Mathematical Modeling","Modeling","Permeability","Precipitation (atmospheric)","Santa Ana","Snow and Ice Cover","Soil Moisture","Streamflow","Surface Water (non-marine)","Transpiration","USGS:6a0657fcb66b01f7f6adc53a","United States","Water Budget","Water Cycle","Water Resources","Watershed Management","climatologyMeteorologyAtmosphere","elevation","geoscientificInformation"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-118.0003, 33.5547, -116.5584, 34.3869","theme":["geospatial"],"title":"Santa Ana River 270-meter Basin Characterization Model using Weather Generator Scenarios - Monthly Climate and Hydrology"},"description":"This data release includes the downscaled climate inputs from twenty-four Weather Generator Scenarios and hydrologic outputs using the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), with a 270 by 270-meter spatial resolution at a monthly time steps from water years 1916 to 2018 for the Santa Ana River watershed. \nThe twenty-four Weather Generator Scenarios are defined as: \nScenario 1: Baseline\nScenario 2: -25% average (ave) precipitation (ppt), +2 degrees Celsius (C)\nScenario 3: -25% ave ppt, +3C\nScenario 4: -25% ave ppt, +4C\nScenario 5: -25% ave ppt, +5C\nScenario 6: -12% ave ppt, +1C\nScenario 7: -12% ave ppt, +2C\nScenario 8: -12% ave ppt, +3C\nScenario 9: -12% ave ppt, +4C\nScenario 10: -12% ave ppt, +5C \nScenario 11: 0% ave ppt, +1C\nScenario 12: 0% ave ppt, +2C\nScenario 13: 0% ave ppt, +3C\nScenario 14: 0% ave ppt, +4C\nScenario 15: 0% ave ppt, +5C\nScenario 16: +12% ave ppt, +1C\nScenario 17: +12% ave ppt, +2C\nScenario 18: +12% ave ppt, +3C\nScenario 19: +12% ave ppt, +4C\nScenario 20: +12% ave ppt, +5C\nScenario 21: +25% ave ppt, +2C\nScenario 22: +25% ave ppt, +3C\nScenario 23: +25% ave ppt, +4C\nScenario 24: +25% ave ppt, +5C\nThis data release provides outputs from twenty-four Weather Generator Scenarios. For each scenario, three datasets are included: (1) monthly climate variables, (2) hydrology BCM variables, and (3) water-year summaries (72 datasets in total). The monthly climate variables child items contain precipitation (PPT), maximum air temperature (TMX), minimum air temperature (TMN), and potential evapotranspiration (PET). The monthly hydrology BCM variables child items contain actual evapotranspiration (AET), climatic water deficit (CWD), snowpack or snow water equivalent (PCK), recharge (RCH), runoff (RUN), and soil moisture storage (STR). The water-year summaries child items contain annual average summaries of each of the monthly climate and monthly hydrology BCM variables for each scenario.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/17c96fc8-05e5-43e8-8d6a-2702bb1f26b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/17c96fc8-05e5-43e8-8d6a-2702bb1f26b4/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a0657fcb66b01f7f6adc53a","keyword":["Atmospheric and Climatic Processes","California","Evaporation","Geospatial Datasets","Hydrology","Mathematical Modeling","Modeling","Permeability","Precipitation (atmospheric)","Santa Ana","Snow and Ice Cover","Soil Moisture","Streamflow","Surface Water (non-marine)","Transpiration","USGS:6a0657fcb66b01f7f6adc53a","United States","Water Budget","Water Cycle","Water Resources","Watershed Management","climatologyMeteorologyAtmosphere","elevation","geoscientificInformation"],"last_harvested_date":"2026-09-03T18:20:58.996966","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":"santa-ana-river-270-meter-basin-characterization-model-using-weather-generator-scenarios-m","spatial_centroid":{"lat":33.88758,"lon":-117.42354},"spatial_shape":{"coordinates":[[[-118.0003,33.5547],[-118.0003,34.3869],[-116.5584,34.3869],[-116.5584,33.5547],[-118.0003,33.5547]]],"type":"Polygon"},"theme":["geospatial"],"title":"Santa Ana River 270-meter Basin Characterization Model using Weather Generator Scenarios - Monthly Climate and Hydrology","type":"dataset"},{"_score":50.59848,"_sort":[1788459516126,50.59848,1,"963c0492-364a-44cb-8b67-3498117ca7cb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"Carbon sequestration and biodiversity are tightly linked, but many models projecting carbon storage change do not account for the role biodiversity plays in the sequestration capacity of terrestrial ecosystems. Here, we link a macroecological model projecting changes in vascular plant richness with empirical biodiversity-biomass stock relationships, to assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. Data presented here include global raster files of plant species loss by ecoregion, biomass loss by ecoregion, and carbon loss by ecoregion. Estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050.This data release is associated with the publication Biodiversity loss reduces global terrestrial carbon storage published in Nature Communications.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13WUFMU","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.65fc456dd34e64ff1548d31b.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65fc456dd34e64ff1548d31b","keyword":["Biodiversity","Biodiversity-ecosystem functioning relationships","Climate change","Conservation","Scenario Planning","USGS:65fc456dd34e64ff1548d31b","economy","geospatial datasets"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-180.0000, -60.0000, 180.0000, 90.0000","theme":["geospatial"],"title":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050"},"description":"Carbon sequestration and biodiversity are tightly linked, but many models projecting carbon storage change do not account for the role biodiversity plays in the sequestration capacity of terrestrial ecosystems. Here, we link a macroecological model projecting changes in vascular plant richness with empirical biodiversity-biomass stock relationships, to assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. Data presented here include global raster files of plant species loss by ecoregion, biomass loss by ecoregion, and carbon loss by ecoregion. Estimates are what is expected over the long term, when ecosystems approach their new equilibrium states, based on climate and land-use changes projected for 2050.This data release is associated with the publication Biodiversity loss reduces global terrestrial carbon storage published in Nature Communications.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/6a1f27f0-36d6-4fd6-ad35-3b9f189da074","harvest_record_raw":"https://catalog.data.gov/harvest_record/6a1f27f0-36d6-4fd6-ad35-3b9f189da074/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65fc456dd34e64ff1548d31b","keyword":["Biodiversity","Biodiversity-ecosystem functioning relationships","Climate change","Conservation","Scenario Planning","USGS:65fc456dd34e64ff1548d31b","economy","geospatial datasets"],"last_harvested_date":"2026-09-03T18:18:36.126588","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":"model-outputs-highlighting-how-biodiversity-loss-reduces-global-terrestrial-carbon-st-2050","spatial_centroid":{"lat":0.0,"lon":-36.0},"spatial_shape":{"coordinates":[[[-180.0,-60.0],[-180.0,90.0],[180.0,90.0],[180.0,-60.0],[-180.0,-60.0]]],"type":"Polygon"},"theme":["geospatial"],"title":"Model outputs highlighting how biodiversity loss reduces global terrestrial carbon storage based on climate and land-use changes projected for 2050","type":"dataset"},{"_score":55.33768,"_sort":[1788459309718,55.33768,0,"14e1dec9-54a3-41be-a015-1f667aae44eb"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adapation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"These datasets include the components of and results from the Rarity and Climate Sensitivity Index (RCS) and occurrence records used to calculate the index for 29 stream fishes native to the Pacific Northwest (Washington, Idaho, Oregon) of the United States. The RCS is an index that ranks species\u2019 intrinsic sensitivity to climate change based on their area of occurrence and climate niche breadth, the range of environmental conditions for a given species. The RCS uses point occurrences to calculate both metrics. We compiled point occurrences from a variety of sources. Final point occurrences were filtered for quality assurance and received expert review (details in occurrence dataset metadata). We calculated RCS metrics at two spatial grains: 1) Hydrologic Unit Code (HUC) level 12 watersheds and 2) 1 km buffered occurrence points and stream segment level. The area of occurrence for each species was calculated as the total watershed area of all HUC 12 watersheds containing any of the final set of occurrence points or the total area of 1 km buffer around the final set of occurrence points. We calculated climate niche breadth using two sets of environmental variables at both spatial grains, bioclimatic and stream level. Bioclimatic level was calculated by extracting annual mean precipitation, maximum temperature of the warmest month, and minimum temperature of the coldest month from the area of occurrence using the the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset. Stream level was calculated by extracting mean August stream temperature, mean stream baseflow, and either predicted streamflow permanence probability or predicted streamflow permanence class from all streams within the watershed area of occurrence or the nearest stream to each point occurrence for the 1 km grain. Stream level data was extracted using compiled streamflow permanence, water temperature, and modeled streamflow data (Sando and Schultz, 2022). Climate niche breadth from both levels is calculated as the area-weighted standard deviation for each variable. The RCS is calculated from the scaled and combined species\u2019 area of occurrence and climate niche breadth, such that an intrinsically sensitive species has a small area of occurrence and a narrow niche breadth.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9WE05SV","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.64da96cbd34ef477cf3ee729.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_64da96cbd34ef477cf3ee729","keyword":["Idaho","North America","Oregon","Pacific Northwest","USGS:64da96cbd34ef477cf3ee729","United States","Washington","area of occupancy","biota","climate niche breadth","climatologyMeteorologyAtmosphere","community ecology","environment","fish","inlandWaters","multispecies study","rarity","vertebrates","vulnerability"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-124.9022, 41.1278, -109.7625, 52.8807","theme":["geospatial"],"title":"Occurrence, Rarity and Climate Sensitivity Index, and Components of 29 Fishes Native to the Pacific Northwest, USA"},"description":"These datasets include the components of and results from the Rarity and Climate Sensitivity Index (RCS) and occurrence records used to calculate the index for 29 stream fishes native to the Pacific Northwest (Washington, Idaho, Oregon) of the United States. The RCS is an index that ranks species\u2019 intrinsic sensitivity to climate change based on their area of occurrence and climate niche breadth, the range of environmental conditions for a given species. The RCS uses point occurrences to calculate both metrics. We compiled point occurrences from a variety of sources. Final point occurrences were filtered for quality assurance and received expert review (details in occurrence dataset metadata). We calculated RCS metrics at two spatial grains: 1) Hydrologic Unit Code (HUC) level 12 watersheds and 2) 1 km buffered occurrence points and stream segment level. The area of occurrence for each species was calculated as the total watershed area of all HUC 12 watersheds containing any of the final set of occurrence points or the total area of 1 km buffer around the final set of occurrence points. We calculated climate niche breadth using two sets of environmental variables at both spatial grains, bioclimatic and stream level. Bioclimatic level was calculated by extracting annual mean precipitation, maximum temperature of the warmest month, and minimum temperature of the coldest month from the area of occurrence using the the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset. Stream level was calculated by extracting mean August stream temperature, mean stream baseflow, and either predicted streamflow permanence probability or predicted streamflow permanence class from all streams within the watershed area of occurrence or the nearest stream to each point occurrence for the 1 km grain. Stream level data was extracted using compiled streamflow permanence, water temperature, and modeled streamflow data (Sando and Schultz, 2022). Climate niche breadth from both levels is calculated as the area-weighted standard deviation for each variable. 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This dataset contains results from a simple process-based model, PC2FM, to derive projected analog fire regimes with respect to the potential fire probability concept. This concept is based on the potential energy and fuels available in the background environmental state under pre-industrial conditions for the coterminous US. To map climate-fire analog futures, three key relevant variables are used in addition to fire probability derived from PC2FM: annual temperature, annual precipitation, and precipitation seasonality. Projections of the climate-fire analogs are provided for 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5) for two time periods (2040-2069 and 2070-2099) and 655 protected areas in the conterminous U.S.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13FFXWV","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.65ccd3aed34ef4b119cb3b5a.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65ccd3aed34ef4b119cb3b5a","keyword":["Analog mapping","Climate change adaptation","USGS:65ccd3aed34ef4b119cb3b5a","Wildland fire","climatologyMeteorologyAtmosphere","environment","fires","geoscientificInformation","geospatial datasets","protected areas"],"modified":"2026-07-24T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-126.7383, 23.4834, -66.0938, 50.0642","theme":["geospatial"],"title":"Climate-fire analog mapping to inform adaptive management strategies for wildland fire in protected areas of the conterminous U.S."},"description":"Managing and adapting to changing wildland fire regimes due to human-caused global warming can be facilitated through the use of analog mapping of potential climate-influenced outcomes. 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Projections of the climate-fire analogs are provided for 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5) for two time periods (2040-2069 and 2070-2099) and 655 protected areas in the conterminous U.S.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/100e8247-bb63-4732-a909-0c669fbf5b83","harvest_record_raw":"https://catalog.data.gov/harvest_record/100e8247-bb63-4732-a909-0c669fbf5b83/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65ccd3aed34ef4b119cb3b5a","keyword":["Analog mapping","Climate change adaptation","USGS:65ccd3aed34ef4b119cb3b5a","Wildland fire","climatologyMeteorologyAtmosphere","environment","fires","geoscientificInformation","geospatial datasets","protected areas"],"last_harvested_date":"2026-09-03T18:14:12.600320","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":2,"publisher":"U.S. Geological Survey","slug":"climate-fire-analog-mapping-to-inform-adaptive-management-strategies-for-wildland-fire-in-","spatial_centroid":{"lat":34.115719999999996,"lon":-102.4805},"spatial_shape":{"coordinates":[[[-126.7383,23.4834],[-126.7383,50.0642],[-66.0938,50.0642],[-66.0938,23.4834],[-126.7383,23.4834]]],"type":"Polygon"},"theme":["geospatial"],"title":"Climate-fire analog mapping to inform adaptive management strategies for wildland fire in protected areas of the conterminous U.S.","type":"dataset"},{"_score":8.041479,"_sort":[1788458965528,8.041479,1,"7cabad6e-bd43-43a5-8765-f83a2f0ddbf0"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kaitlyn Strickfaden","hasEmail":"mailto:kstrickfaden@uidaho.edu"},"description":"Snow conditions are changing dramatically in the mountains of the interior Pacific Northwest, including eastern Washington, northern Idaho, and western Montana. 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Managers also require an index of winter severity that includes information on temperature, snow depth, and snow hardness at relevant spatial and temporal scales to adapt management strategies for seasonal conditions. \n \nThis project seeks to advance the understanding of how snow conditions vary and how such variation affects both species of greatest conservation need (e.g., wolverine, hoary marmot, western bumble bee, and mountain goat) and species of economic and recreational importance (e.g., elk and moose) in forests spanning the rain-snow transition zone in the interior Pacific Northwest. To do this, researchers created new tools that managers can use to estimate snow depth, map areas of late season snow (known as \u201csnow refugia\u201d), and estimate winter severity for ungulate species such as elk and moose. Researchers used these novel datasets to predict winter range habitat use by deer and elk in Idaho and to identify linkages between ungulate survival and winter severity. These data were used to create a model predicting snow disappearance dates (SDD) at camera sites and across our entire study area to identify priority areas of conservation for snow-dependent wildlife. The model predicted high-elevation areas, north-facing aspects, and cold-air pools retained snow latest. These data were also used to model the probability of deer presence at camera sites dependent on snow conditions, and it was determined that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.\n \nThe results of this project will be directly applicable to federal (U.S. Fish and Wildlife Service), state (Idaho Department of Fish and Game), and tribal (Coeur D\u2019Alene Tribe) managers in the region. Providing natural resource managers with tools to identify locations of snow retention for sensitive and listed species is critical for identifying habitats to conserve or modify in order to facilitate species recovery. 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Researchers used these novel datasets to predict winter range habitat use by deer and elk in Idaho and to identify linkages between ungulate survival and winter severity. These data were used to create a model predicting snow disappearance dates (SDD) at camera sites and across our entire study area to identify priority areas of conservation for snow-dependent wildlife. The model predicted high-elevation areas, north-facing aspects, and cold-air pools retained snow latest. These data were also used to model the probability of deer presence at camera sites dependent on snow conditions, and it was determined that deer respond negatively to increased snow density and respond slightly positively to increased snow hardness.\n \nThe results of this project will be directly applicable to federal (U.S. Fish and Wildlife Service), state (Idaho Department of Fish and Game), and tribal (Coeur D\u2019Alene Tribe) managers in the region. Providing natural resource managers with tools to identify locations of snow retention for sensitive and listed species is critical for identifying habitats to conserve or modify in order to facilitate species recovery. 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Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We used otolith and multistate capture-mark-recapture data collected along a temperature and stream drying gradient to determine how these environmental constraints influence life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f77376c82ce20f330100872.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f77376c82ce20f330100872","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f77376c82ce20f330100872","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.6223, 35.9157, -105.2930, 36.8533","theme":["geospatial"],"title":"Fish Diets from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. 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Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. Precipitation, however, had little effect on RGCT population dynamics and was likely influenced by the uncharacteristically wet years (2016-2017) during this study. Regardless, our results suggest that RGCT persistence depends on temperature-influenced diversity in life-history trait expression (e.g., longevity, age-at-maturity). Plasticity in trait expression is an important life-history characteristic of coldwater species, and may be vital for trailing edge populations to persist in a changing climate.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/60ad2a7f-d83d-440d-a60a-1850dd609d76","harvest_record_raw":"https://catalog.data.gov/harvest_record/60ad2a7f-d83d-440d-a60a-1850dd609d76/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f77376c82ce20f330100872","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f77376c82ce20f330100872","biota"],"last_harvested_date":"2026-09-03T18:05:30.942415","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":"fish-diets-from-eight-populations-of-rio-grande-cutthroat-trout-in-northern-new-mexico","spatial_centroid":{"lat":36.29074,"lon":-106.09058},"spatial_shape":{"coordinates":[[[-106.6223,35.9157],[-106.6223,36.8533],[-105.293,36.8533],[-105.293,35.9157],[-106.6223,35.9157]]],"type":"Polygon"},"theme":["geospatial"],"title":"Fish Diets from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico","type":"dataset"},{"_score":11.919667,"_sort":[1788457910162,11.919667,0,"ab282729-720b-41e3-9dcb-b8730f0e967f"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Climate Adaptation Science Centers","hasEmail":"mailto:casc-data@usgs.gov"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. Rio Grande cutthroat trout (Oncorhynchus clarkii virginalis, RGCT) occupy arid southwestern U.S.A. streams at the southern-most edge of all cutthroat trout distributions; thus making RGCT particularly vulnerable to the anticipated warming and drying in this region. However, RGCT may possess a portfolio of life-history traits that aide in their persistence, attributes commonly observed in trailing edge populations. We tagged RGCT across eight populations in 2016 and 2017 and used this  capture-mark-recapture data to determine how environmental constraints influenced life-history trait expression (length- and age-at-maturity), demography, and extirpation risk in RGCT populations from northern New Mexico, U.S.A. We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.21429/kbmw-6z60","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.5f7ccda382ce1d74e7db55ca.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5f7ccda382ce1d74e7db55ca","keyword":["Drought","Growth","Southwestern Cutthroat Trout","Survival","USGS:5f7ccda382ce1d74e7db55ca","biota"],"modified":"2026-09-01T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.6168, 35.9157, -105.2380, 36.9148","theme":["geospatial"],"title":"Fish Length, Weight, and Unique Identification from Eight Populations of Rio Grande Cutthroat Trout in Northern New Mexico"},"description":"The impacts of climate change on cold water species will likely manifest in populations at the trailing edge of their distribution. 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We found the rate at which RGCT reached maturity was highest at warm to intermediate stream temperatures, which was the demographic trait most strongly linked to RGCT persistence. Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. 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Interestingly, older life-stages contributed more to population growth as temperatures decreased, providing further evidence of strong temperature effects controlling life-history trait expression in RGCT. 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Column name definitions are available in \"reported_fields.csv\"","downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_samples.csv","format":"comma-separated values","mediaType":"application/vnd.ms-excel","title":"Sample Properties"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/appendix.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Appendix A"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/reported_fields.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_chemistry.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Analytical Chemistry Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_samples.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Sample Properties"}],"identifier":"ark:/88434/mds2-2431","issued":"2021-11-09","keyword":["BDEs","Environment and Climate","ML","NIST Biorepository","PBDEs","PCBs","Pacific Ocean","bird","chemistry","chemometric","eggs","genetics","heavy metals","inorganic","machine learning","mercury","organic","pesticides","seabird","stable isotopes","tissues","trace elements"],"landingPage":"https://data.nist.gov/od/id/mds2-2431","language":["en"],"license":"https://www.nist.gov/open/license","modified":"2021-07-07 00:00:00","programCode":["006:045"],"publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"references":["https://doi.org/10.6028/jres.126.028"],"spatial":"[115.0, 0.0, 283.0, 66.0]","temporal":"1999-05-01/2010-12-31","theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Environment:Environmental health","Environment:Marine science","Information Technology:Data and informatics"],"title":"Data Supporting \"Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010\""},"description":"Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.","distribution_titles":["DOI Access for Data Supporting \"Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010\"","Data Dictionary","Analytical Chemistry Data","Appendix A","Sample Properties","SHA256 File for Appendix A","SHA256 File for Data Dictionary","SHA256 File for Analytical Chemistry Data","SHA256 File for Sample Properties"],"harvest_record":"https://catalog.data.gov/harvest_record/51ac6be0-81ea-4271-954d-36ddbe71827c","harvest_record_raw":"https://catalog.data.gov/harvest_record/51ac6be0-81ea-4271-954d-36ddbe71827c/raw","has_download":true,"has_spatial":true,"identifier":"ark:/88434/mds2-2431","keyword":["BDEs","Environment and Climate","ML","NIST Biorepository","PBDEs","PCBs","Pacific Ocean","bird","chemistry","chemometric","eggs","genetics","heavy metals","inorganic","machine learning","mercury","organic","pesticides","seabird","stable isotopes","tissues","trace elements"],"last_harvested_date":"2026-09-02T19:18:09.244669","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"176f2a2d-ca9b-41f2-8df3-d93096ebdb85","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nist.png","name":"National Institute of Standards and Technology","organization_type":"Federal Government","slug":"nist"},"parent_identifier":null,"popularity":3,"publisher":"National Institute of Standards and Technology","slug":"data-supporting-seabird-tissue-archival-and-monitoring-project-stamp-data-from-1999-2010","spatial_centroid":null,"spatial_shape":null,"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Environment:Environmental health","Environment:Marine science","Information Technology:Data and informatics"],"title":"Data Supporting \"Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010\"","type":"dataset"},{"_score":8.565277,"_sort":[1788376278838,8.565277,77,"0b63eab1-1185-4f25-bf74-e9adc6ca98b4"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"Mike.Roos@seattle.gov","hasEmail":"mailto:open.data@seattle.gov"},"description":"Seattle\u2019s Building Energy Benchmarking Program (SMC 22.920) requires owners of non-residential and multifamily buildings (Greater than 20,000 square feet) to track energy performance and annually report to the City of Seattle. Annual benchmarking, reporting, and disclosing of building performance are foundational elements of creating more market value for energy efficiency.\n\nPer Ordinance (125000), starting with 2015 energy use performance reporting, the City of Seattle is making the data for all buildings greater than 20,000 SF available annually. This dataset contains benchmarking records for all buildings required to report for years 2015-2024.\n\nIf you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://cos-data.seattle.gov/api/views/teqw-tu6e/columns.json","describedByType":"application/json","downloadURL":"https://cos-data.seattle.gov/api/v3/views/teqw-tu6e/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://cos-data.seattle.gov/api/views/teqw-tu6e/columns.xml","describedByType":"application/xml","downloadURL":"https://cos-data.seattle.gov/api/v3/views/teqw-tu6e/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://cos-data.seattle.gov/api/v3/views/teqw-tu6e/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://cos-data.seattle.gov/api/views/teqw-tu6e","issued":"2024-12-13","keyword":["benchmarking","building energy","buildings","climate change","commercial buildings","electricity","energy","energy star","eui","fossil fuel","fossil gas","gas","greenhouse gas","multifamily","multifamily buildings","office of sustainability and environment","ose","steam"],"landingPage":"https://cos-data.seattle.gov/d/teqw-tu6e","modified":"2026-08-28","publisher":{"@type":"org:Organization","name":"data.seattle.gov"},"theme":["Built Environment"],"title":"Building Energy Benchmarking Data, 2015-Present"},"description":"Seattle\u2019s Building Energy Benchmarking Program (SMC 22.920) requires owners of non-residential and multifamily buildings (Greater than 20,000 square feet) to track energy performance and annually report to the City of Seattle. 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This dataset contains benchmarking records for all buildings required to report for years 2015-2024.\n\nIf you have questions or comments on the data, email us at energybenchmarking@seattle.gov and include Open Data in the subject line.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/396f5714-1967-49fb-87ca-6330031f727d","harvest_record_raw":"https://catalog.data.gov/harvest_record/396f5714-1967-49fb-87ca-6330031f727d/raw","has_download":true,"has_spatial":false,"identifier":"https://cos-data.seattle.gov/api/views/teqw-tu6e","keyword":["benchmarking","building energy","buildings","climate change","commercial buildings","electricity","energy","energy star","eui","fossil fuel","fossil gas","gas","greenhouse gas","multifamily","multifamily buildings","office of sustainability and environment","ose","steam"],"last_harvested_date":"2026-09-02T19:11:18.838838","organization":{"aliases":["washington"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"a706cca3-8032-4bcb-89c8-6f2d14fa1137","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/seattlewa.png","name":"City of Seattle","organization_type":"City Government","slug":"seattle-wa"},"parent_identifier":null,"popularity":77,"publisher":"data.seattle.gov","slug":"building-energy-benchmarking-data-2015-present","spatial_centroid":null,"spatial_shape":null,"theme":["Built Environment"],"title":"Building Energy Benchmarking Data, 2015-Present","type":"dataset"},{"_score":19.48583,"_sort":[1788375399586,19.48583,0,"52e706f6-a6f2-4e64-862a-28a705db307f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"mikewynne","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"This is the plant list used by the SF Plant Finder (https://sfplanninggis.org/plantsf/).  \n\nThe San Francisco Plant Finder is a resource for gardeners, designers, ecologists and anyone who is interested in greening neighborhoods, enhancing our urban ecology and surviving the drought. The Plant Finder recommends appropriate habitat-building plants for sidewalks, gardens and roofs that are adapted to San Francisco's unique environment and climate.\n\nThe plants in the database include California natives and Mediterranean climate exotics. A large subset of the California natives are actually local San Francisco natives. We strongly recommend local natives since they provide the best habitat for local pollinators and other wildlife with whom they have co-evolved. San Francisco natives are the most closely adapted to the climate and environment of the San Francisco peninsula of course, and so they are the best in terms of water and soil conservation, ecosystem health, and overall sustainability.\n\nThe geographic boundaries for plant communities used in SF Plant Finder are here: https://data.sfgov.org/d/27u4-a5b3","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vmnk-skih/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vmnk-skih/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vmnk-skih/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/vmnk-skih","issued":"2025-07-10","keyword":["environment","green connections","planning","plant","plantfinder","plants","sf plant finder"],"landingPage":"https://data.sf.gov/d/vmnk-skih","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2025-07-10","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Plant Finder Data"},"description":"This is the plant list used by the SF Plant Finder (https://sfplanninggis.org/plantsf/).  \n\nThe San Francisco Plant Finder is a resource for gardeners, designers, ecologists and anyone who is interested in greening neighborhoods, enhancing our urban ecology and surviving the drought. The Plant Finder recommends appropriate habitat-building plants for sidewalks, gardens and roofs that are adapted to San Francisco's unique environment and climate.\n\nThe plants in the database include California natives and Mediterranean climate exotics. A large subset of the California natives are actually local San Francisco natives. We strongly recommend local natives since they provide the best habitat for local pollinators and other wildlife with whom they have co-evolved. San Francisco natives are the most closely adapted to the climate and environment of the San Francisco peninsula of course, and so they are the best in terms of water and soil conservation, ecosystem health, and overall sustainability.\n\nThe geographic boundaries for plant communities used in SF Plant Finder are here: https://data.sfgov.org/d/27u4-a5b3","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1aa0febb-120d-4928-bf02-f70ef3e232b3","harvest_record_raw":"https://catalog.data.gov/harvest_record/1aa0febb-120d-4928-bf02-f70ef3e232b3/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/vmnk-skih","keyword":["environment","green connections","planning","plant","plantfinder","plants","sf plant finder"],"last_harvested_date":"2026-09-02T18:56:39.586390","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"san-francisco-plant-finder-data","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Plant Finder Data","type":"dataset"},{"_score":7.956642,"_sort":[1788375398889,7.956642,4,"374f22e6-71ff-41c8-ad59-226d7c041e6e"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/vc6r-v7av","issued":"2024-03-28","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"landingPage":"https://data.sf.gov/d/vc6r-v7av","modified":"2026-08-28","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory"},"description":"<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the  <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s  <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the  <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6bc8420b-1643-4be1-b0ee-0dd225c00c2f","harvest_record_raw":"https://catalog.data.gov/harvest_record/6bc8420b-1643-4be1-b0ee-0dd225c00c2f/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/vc6r-v7av","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"last_harvested_date":"2026-09-02T18:56:38.889822","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":4,"publisher":"data.sf.gov","slug":"san-francisco-municipal-natural-gas-equipment-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory","type":"dataset"},{"_score":10.18502,"_sort":[1788375386975,10.18502,0,"f38ec6b1-0e62-4fbc-a758-65f0796265bd"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65\u2010decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO\u2019s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director\u2019s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/qxw2-ncq3/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/qxw2-ncq3/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/qxw2-ncq3/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/qxw2-ncq3","issued":"2023-11-20","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/qxw2-ncq3","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2026-07-28","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Transportation"],"title":"Aircraft Noise Climates"},"description":"<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65\u2010decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO\u2019s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director\u2019s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0b548d66-940d-4bba-99f1-2bcef74ea3af","harvest_record_raw":"https://catalog.data.gov/harvest_record/0b548d66-940d-4bba-99f1-2bcef74ea3af/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/qxw2-ncq3","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:26.975410","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"aircraft-noise-climates","spatial_centroid":null,"spatial_shape":null,"theme":["Transportation"],"title":"Aircraft Noise Climates","type":"dataset"},{"_score":14.373923,"_sort":[1788375384045,14.373923,0,"e263fdc3-2713-4c14-a744-42bfd7933ea5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The purpose of the San Francisco Municipal Greenhouse Gas Inventory is to measure and track departmental greenhouse gas emissions as part of the City's climate action strategy. Per Environment Code Chapter 9, this data is collected and calculated by the Department of the Environment.\n\n\nNote: Data as of 10/20/18. San Francisco municipal greenhouse gas inventory for Fiscal Years 2012 per the California Air Resources Board's Local Government Operations Protocol Version 1.1 (May 2010). Third-party verification of Fiscal Year 2012 which was completed in March 2015 is available at http://sfenvironment.org/download/fiscal-year-2012-municipal-ghg-inventory-memo","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/pxac-sadh/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/pxac-sadh/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/pxac-sadh/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/pxac-sadh","issued":"2016-01-21","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/pxac-sadh","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-06-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Greenhouse Gas Inventory"},"description":"The purpose of the San Francisco Municipal Greenhouse Gas Inventory is to measure and track departmental greenhouse gas emissions as part of the City's climate action strategy. Per Environment Code Chapter 9, this data is collected and calculated by the Department of the Environment.\n\n\nNote: Data as of 10/20/18. San Francisco municipal greenhouse gas inventory for Fiscal Years 2012 per the California Air Resources Board's Local Government Operations Protocol Version 1.1 (May 2010). Third-party verification of Fiscal Year 2012 which was completed in March 2015 is available at http://sfenvironment.org/download/fiscal-year-2012-municipal-ghg-inventory-memo","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/a9776654-4081-44cb-9f6c-c9ca70b207aa","harvest_record_raw":"https://catalog.data.gov/harvest_record/a9776654-4081-44cb-9f6c-c9ca70b207aa/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/pxac-sadh","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:24.045675","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"san-francisco-municipal-greenhouse-gas-inventory","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"San Francisco Municipal Greenhouse Gas Inventory","type":"dataset"},{"_score":11.321318,"_sort":[1788375380108,11.321318,0,"728f25d0-74a8-420f-aae0-5c267fe1aec8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"Alex Morrison","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"Heat and air quality issues caused by climate change and gas-powered vehicles affect San Francisco communities differently. Tree canopy that would buffer the effects is not equally distributed due to historic racial inequities in infrastructure investment.\n\nTo positively affect public health and leverage new federal funding sources, the City and other stakeholders are planning for green infrastructure investments, such as tree planting, sidewalk landscape zones, cool pavement, structural shading,\ngreen schoolyards, and increased areas of stormwater management. This dataset identifies locations where these strategies could have the highest benefit\nto community health and make the most effective use of City investment.\n\nSF Public Works mapped a combination of environmental and health data to identify the priority zones. The study layers exposure to fine particulate matter (PM2.5), satellite temperature readings from a recent heat wave, and tree canopy data to identify where exposure is the highest. To further refine the prioritization zone, data was added for residents experiencing asthma or diabetes hospitalizations\nwhich are both exacerbated by heat and air quality issues. \n\nThis created two final maps focused on heat and air quality that combine environmental data and human health. These maps were combined to produce the final priority zones.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/nn26-kuy2/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/nn26-kuy2/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/nn26-kuy2/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/nn26-kuy2","issued":"2024-06-20","keyword":["untagged"],"landingPage":"https://data.sf.gov/d/nn26-kuy2","modified":"2024-06-21","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"HAQR Priority Green Infrastructure Zones"},"description":"Heat and air quality issues caused by climate change and gas-powered vehicles affect San Francisco communities differently. Tree canopy that would buffer the effects is not equally distributed due to historic racial inequities in infrastructure investment.\n\nTo positively affect public health and leverage new federal funding sources, the City and other stakeholders are planning for green infrastructure investments, such as tree planting, sidewalk landscape zones, cool pavement, structural shading,\ngreen schoolyards, and increased areas of stormwater management. This dataset identifies locations where these strategies could have the highest benefit\nto community health and make the most effective use of City investment.\n\nSF Public Works mapped a combination of environmental and health data to identify the priority zones. The study layers exposure to fine particulate matter (PM2.5), satellite temperature readings from a recent heat wave, and tree canopy data to identify where exposure is the highest. To further refine the prioritization zone, data was added for residents experiencing asthma or diabetes hospitalizations\nwhich are both exacerbated by heat and air quality issues. \n\nThis created two final maps focused on heat and air quality that combine environmental data and human health. These maps were combined to produce the final priority zones.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/6d254ee6-5c2a-4d45-aab6-d337b3ccbd8a","harvest_record_raw":"https://catalog.data.gov/harvest_record/6d254ee6-5c2a-4d45-aab6-d337b3ccbd8a/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/nn26-kuy2","keyword":["untagged"],"last_harvested_date":"2026-09-02T18:56:20.108177","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"haqr-priority-green-infrastructure-zones","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"HAQR Priority Green Infrastructure Zones","type":"dataset"},{"_score":11.205373,"_sort":[1788375372830,11.205373,0,"857be423-36b5-410e-9739-045558e27105"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFEBO Help Desk","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"<strong>As of March 20, 2026, this dataset will no longer update. To access new and historical data going forward, navigate to the <u><a href=\"https://data.sfgov.org/d/bfhx-j6n5/\">dataset here</a></u>.</strong>\n\n<strong>A. SUMMARY</strong>\nSan Francisco\u2019s Existing Buildings Energy Performance Ordinance requires owners of non-residential buildings over 10,000 square feet to annually benchmark and disclose energy performance. On behalf of City agencies, the San Francisco Public Utilities Commission (SFPUC) benchmarks and reports energy use for a portfolio of approximately 500 public facilities buildings. The performance of public facilities can be examined in an interactive report at bit.ly/SFMunicipalBenchmarking, and annual reports from 2011-present are available there as well.\n \nThis dataset presents the energy performance and basic characteristics for public facilities that is visualized by the SFPUC\u2019s interactive report.  \n \nIn addition, energy performance data for non-municipal buildings (i.e. commercial buildings of 10,000 square feet or larger, and multifamily & mixed-use buildings of 50,000 square feet or larger) is available at: bit.ly/ExistingBuildingsReport\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nIn compliance with California Energy Benchmarking Regulations (CA Public Resources Code Section 25402.10 and CCR Title 20 Section 1680), and San Francisco Existing Buildings Energy Ordinance (Environment Code Chapter 20), the San Francisco Public Utilities Commission provides energy benchmarking services on behalf of municipal facilities. Details for public facilities are compiled from city records, and energy usage is compiled from utility records; related metrics such as energy use intensity are calculated from the combination of such records. Data is subjected to quality assurance validation prior to publication. For additional information regarding data sources and assumptions, please review the \"Data Sources and Assumptions\" page of the Municipal Facilities Energy Benchmarking dashboard: https://bit.ly/SFMunicipalBenchmarking.\n\n<strong>C. UPDATE PROCESS</strong>\nUpdated Annually.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/k3fc-45qw/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/k3fc-45qw/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/k3fc-45qw/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/k3fc-45qw","issued":"2024-10-25","keyword":["climate change","energy","environment","sustainability"],"landingPage":"https://data.sf.gov/d/k3fc-45qw","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2026-03-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"[DEPRECATED] San Francisco Municipal Energy Benchmarking"},"description":"<strong>As of March 20, 2026, this dataset will no longer update. To access new and historical data going forward, navigate to the <u><a href=\"https://data.sfgov.org/d/bfhx-j6n5/\">dataset here</a></u>.</strong>\n\n<strong>A. SUMMARY</strong>\nSan Francisco\u2019s Existing Buildings Energy Performance Ordinance requires owners of non-residential buildings over 10,000 square feet to annually benchmark and disclose energy performance. On behalf of City agencies, the San Francisco Public Utilities Commission (SFPUC) benchmarks and reports energy use for a portfolio of approximately 500 public facilities buildings. The performance of public facilities can be examined in an interactive report at bit.ly/SFMunicipalBenchmarking, and annual reports from 2011-present are available there as well.\n \nThis dataset presents the energy performance and basic characteristics for public facilities that is visualized by the SFPUC\u2019s interactive report.  \n \nIn addition, energy performance data for non-municipal buildings (i.e. commercial buildings of 10,000 square feet or larger, and multifamily & mixed-use buildings of 50,000 square feet or larger) is available at: bit.ly/ExistingBuildingsReport\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nIn compliance with California Energy Benchmarking Regulations (CA Public Resources Code Section 25402.10 and CCR Title 20 Section 1680), and San Francisco Existing Buildings Energy Ordinance (Environment Code Chapter 20), the San Francisco Public Utilities Commission provides energy benchmarking services on behalf of municipal facilities. Details for public facilities are compiled from city records, and energy usage is compiled from utility records; related metrics such as energy use intensity are calculated from the combination of such records. Data is subjected to quality assurance validation prior to publication. For additional information regarding data sources and assumptions, please review the \"Data Sources and Assumptions\" page of the Municipal Facilities Energy Benchmarking dashboard: https://bit.ly/SFMunicipalBenchmarking.\n\n<strong>C. UPDATE PROCESS</strong>\nUpdated Annually.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/c0fbdaaf-1042-4dcd-9884-7b8a7c021926","harvest_record_raw":"https://catalog.data.gov/harvest_record/c0fbdaaf-1042-4dcd-9884-7b8a7c021926/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/k3fc-45qw","keyword":["climate change","energy","environment","sustainability"],"last_harvested_date":"2026-09-02T18:56:12.830800","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":0,"publisher":"data.sf.gov","slug":"deprecated-san-francisco-municipal-energy-benchmarking","spatial_centroid":null,"spatial_shape":null,"theme":["Energy and Environment"],"title":"[DEPRECATED] San Francisco Municipal Energy Benchmarking","type":"dataset"},{"_score":26.52007,"_sort":[1788375350368,26.52007,3,"10888954-a747-4e51-a777-34b34c453c43"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"SFDPH Open Data","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the \"An Assessment of San Francisco\u2019s Vulnerability to Flooding & Extreme Storms\" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdf\n\nData dictionary can be found in the attachments section of the metadata.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/cne3-h93g/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/cne3-h93g/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/cne3-h93g","issued":"2016-03-25","keyword":["@sfclimatehealth.org","climate change","community resiliency","dph","flood","health assessment","health impacts","public health","san francisco climate and health program","sea level rise","sfclimatehealth.org"],"landingPage":"https://data.sf.gov/d/cne3-h93g","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-03-13","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability"},"description":"The San Francisco Department of Public Health Flood Health Vulnerability Index is a composite index that measures the spatial distribution and relative vulnerability of San Francisco communities to the health impacts of flood inundation and extreme storms. The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the \"An Assessment of San Francisco\u2019s Vulnerability to Flooding & Extreme Storms\" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdf\n\nData dictionary can be found in the attachments section of the metadata.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/7705d5b6-a21a-4f45-a424-d311db779f24","harvest_record_raw":"https://catalog.data.gov/harvest_record/7705d5b6-a21a-4f45-a424-d311db779f24/raw","has_download":true,"has_spatial":false,"identifier":"https://data.sf.gov/api/views/cne3-h93g","keyword":["@sfclimatehealth.org","climate change","community resiliency","dph","flood","health assessment","health impacts","public health","san francisco climate and health program","sea level rise","sfclimatehealth.org"],"last_harvested_date":"2026-09-02T18:55:50.368499","organization":{"aliases":["sf","california"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png","name":"City of San Francisco","organization_type":"City Government","slug":"san-francisco-ca"},"parent_identifier":null,"popularity":3,"publisher":"data.sf.gov","slug":"san-francisco-flood-health-vulnerability","spatial_centroid":null,"spatial_shape":null,"theme":["Health and Social Services"],"title":"San Francisco Flood Health Vulnerability","type":"dataset"},{"_score":27.44969,"_sort":[1788375348420,27.44969,3,"4b00ede5-99cb-4808-987b-f1277b8edf48"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"The purpose of the San Francisco Communitywide Greenhouse Gas Inventory is to measure and track greenhouse gas emissions to determine progress towards meeting the City's climate action goals. The Department of the Environment collects this data from various sources and calculates the emissions per current greenhouse gas protocols. This data supports San Francisco's climate change planning and mitigation strategies.\n\nNote: Greenhouse gas emissions were calculated based on the ICLEI 2012 U.S. Community Protocol Version 1.0. San Francisco inventories are completed in accordance with the ICLEI U.S. Community Protocol (USCP) for Accounting and Reporting of Greenhouse Gas Emissions. The methodology and sectors tracked were third party verified in inventory year 2012. The subsequent inventories are completed according to the guidance of the verifiers. The third-party verification memo for 2010 is available at http://sfenvironment.org/download/2010-community-greenhouse-gas-inventory-3rd-party-verification-memo-march-2013 and for 2012 at http://sfenvironment.org/download/2012-community-greenhouse-gas-inventory-3rd-party-verification-memo-january-2015. In 2015, the City began reporting its emissions to C40 to improve its GHG emissions inventory by using a newer protocol to estimate emissions referred to as the Global Protocol for Community-Scale Greenhouse Gas Emissions Inventories (GPC). GPC is a framework unifying emissions inventories globally while incorporating new categories to track. San Francisco has been tracking its emissions since 1990; hence, it continues to use the ICLEI USCP. Today, San Francisco continues to disclose emissions under the GPC framework for reporting purposes to and compliance with the Global Covenant of Mayors (GCOM).","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/btm4-e4ak/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/btm4-e4ak/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/btm4-e4ak/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.sf.gov/api/views/btm4-e4ak","issued":"2018-10-31","keyword":["carbon emissions","climate","climate change","communitywide","environment","ghg inventory","greenhouse gas emissions","san francisco climate action strategy","sustainability"],"landingPage":"https://data.sf.gov/d/btm4-e4ak","license":"http://opendatacommons.org/licenses/pddl/1.0/","modified":"2024-06-20","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Communitywide Greenhouse Gas Inventory"},"description":"The purpose of the San Francisco Communitywide Greenhouse Gas Inventory is to measure and track greenhouse gas emissions to determine progress towards meeting the City's climate action goals. The Department of the Environment collects this data from various sources and calculates the emissions per current greenhouse gas protocols. This data supports San Francisco's climate change planning and mitigation strategies.\n\nNote: Greenhouse gas emissions were calculated based on the ICLEI 2012 U.S. Community Protocol Version 1.0. San Francisco inventories are completed in accordance with the ICLEI U.S. Community Protocol (USCP) for Accounting and Reporting of Greenhouse Gas Emissions. The methodology and sectors tracked were third party verified in inventory year 2012. The subsequent inventories are completed according to the guidance of the verifiers. The third-party verification memo for 2010 is available at http://sfenvironment.org/download/2010-community-greenhouse-gas-inventory-3rd-party-verification-memo-march-2013 and for 2012 at http://sfenvironment.org/download/2012-community-greenhouse-gas-inventory-3rd-party-verification-memo-january-2015. 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