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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","accessURL":"http://doi.org/10.5066/F72J6916","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.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":"2026-09-17T00:00:00Z","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":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f4b10392-4467-4c6d-8ff9-40413f6c875d","harvest_record_raw":"https://catalog.data.gov/harvest_record/f4b10392-4467-4c6d-8ff9-40413f6c875d/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":"2026-09-24T00:59:20.796891","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":8.073565,"_sort":[1790211505463,8.073565,1,"6a4c71a5-46da-44e9-8095-6d4d5e4ded3a"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jordan S. Read","hasEmail":"mailto:jread@usgs.gov"},"description":"Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: glm_setup, morphometry, time, output, init_profiles, meteorology, bird_model, inflow, outflow, snowice, which are defined below.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7DV1H10","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.57d30e7ee4b0571647d113e2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d30e7ee4b0571647d113e2","keyword":["IA","IL","IN","Illinois","Indiana","Iowa","MI","MN","Michigan","Minnesota","SD","South Dakota","US","USGS:57d30e7ee4b0571647d113e2","United States","WI","Wisconsin","biota","climate change","elevation","environment","inlandWaters","modeling","reservoirs","temperate lakes","thermal profiles","water"],"modified":"2026-09-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.8589114267623, 48.7289513243629, -83.0573307815185, 41.7553570685206","theme":["geospatial"],"title":"Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes"},"description":"Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: glm_setup, morphometry, time, output, init_profiles, meteorology, bird_model, inflow, outflow, snowice, which are defined below.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/f13019d9-3334-472d-a7ee-dd15b909e675","harvest_record_raw":"https://catalog.data.gov/harvest_record/f13019d9-3334-472d-a7ee-dd15b909e675/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d30e7ee4b0571647d113e2","keyword":["IA","IL","IN","Illinois","Indiana","Iowa","MI","MN","Michigan","Minnesota","SD","South Dakota","US","USGS:57d30e7ee4b0571647d113e2","United States","WI","Wisconsin","biota","climate change","elevation","environment","inlandWaters","modeling","reservoirs","temperate lakes","thermal profiles","water"],"last_harvested_date":"2026-09-24T00:58:25.463378","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-configuration-a-large-scale-database-of-modeled-contemporary-and-future-water-temper","spatial_centroid":{"lat":45.939513622025984,"lon":-91.33827916866478},"spatial_shape":{"coordinates":[[[-96.8589114267623,48.7289513243629],[-96.8589114267623,41.7553570685206],[-83.0573307815185,41.7553570685206],[-83.0573307815185,48.7289513243629],[-96.8589114267623,48.7289513243629]]],"type":"Polygon"},"theme":["geospatial"],"title":"Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes","type":"dataset"},{"_score":9.54983,"_sort":[1790211356537,9.54983,0,"4684a7ff-c595-4fc4-8094-5fdc7921855c"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Kristin Trippe","hasEmail":"mailto:kristin.trippe@ars.usda.gov"},"description":"This repository contains source code for a set of decision support tools to be used for selecting a biochar type and application rate. \nThe tool set includes:\n- Biochar Property Explorer This tool allows users to compare the biochars in our database by graphing their physiochemical properties. We compiled laboratory- and commercially-produced biochars from PNW-relevant feedstocks and measured physiochemical properties related to fertilizer value, liming value, carbon sequestration, and particle size, following IBI-recommended protocols. These data are in the file BiocharData_PNWComputed.csv. Users can also enter test data for their own biochar to view its classification following the protocol developed by Camps-Arbestain et al. (2015). \n- Biochar Selection Tool This tool guides users to assess their soil needs, select the most appropriate type of biochar, and establish an appropriate amendment rate. This tool requires two data files, BiocharData_PNWComputed.csv and CropFertilizerGuide_Aug_26_2017.txt\n- Cost-Benefit Analysis Tool This tool guides users through a cost-benefit analysis to assess whether biochar could be a good investment.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1UUUQZY","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.5ada1a56e4b0e2c2dd294a4e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ada1a56e4b0e2c2dd294a4e","keyword":["Idaho","Oregon","USGS:5ada1a56e4b0e2c2dd294a4e","Washington","biota","decision support methods","environment","external research support","soil chemistry"],"modified":"2026-09-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-125.8594, 41.7713, -114.2578, 48.6910","theme":["geospatial"],"title":"Biochar decision support tools created in RShiny: pnwbiochar"},"description":"This repository contains source code for a set of decision support tools to be used for selecting a biochar type and application rate. \nThe tool set includes:\n- Biochar Property Explorer This tool allows users to compare the biochars in our database by graphing their physiochemical properties. We compiled laboratory- and commercially-produced biochars from PNW-relevant feedstocks and measured physiochemical properties related to fertilizer value, liming value, carbon sequestration, and particle size, following IBI-recommended protocols. These data are in the file BiocharData_PNWComputed.csv. Users can also enter test data for their own biochar to view its classification following the protocol developed by Camps-Arbestain et al. (2015). \n- Biochar Selection Tool This tool guides users to assess their soil needs, select the most appropriate type of biochar, and establish an appropriate amendment rate. This tool requires two data files, BiocharData_PNWComputed.csv and CropFertilizerGuide_Aug_26_2017.txt\n- Cost-Benefit Analysis Tool This tool guides users through a cost-benefit analysis to assess whether biochar could be a good investment.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/0da96fdf-0cc0-4a44-9379-ea29f371c9dc","harvest_record_raw":"https://catalog.data.gov/harvest_record/0da96fdf-0cc0-4a44-9379-ea29f371c9dc/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ada1a56e4b0e2c2dd294a4e","keyword":["Idaho","Oregon","USGS:5ada1a56e4b0e2c2dd294a4e","Washington","biota","decision support methods","environment","external research support","soil chemistry"],"last_harvested_date":"2026-09-24T00:55:56.537343","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":"biochar-decision-support-tools-created-in-rshiny-pnwbiochar","spatial_centroid":{"lat":44.53918,"lon":-121.21876},"spatial_shape":{"coordinates":[[[-125.8594,41.7713],[-125.8594,48.691],[-114.2578,48.691],[-114.2578,41.7713],[-125.8594,41.7713]]],"type":"Polygon"},"theme":["geospatial"],"title":"Biochar decision support tools created in RShiny: pnwbiochar","type":"dataset"},{"_score":8.18627,"_sort":[1790211251997,8.18627,0,"0fb588f9-a565-4767-aa64-1d53c9caa94d"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Anne Nolin","hasEmail":"mailto:nolina@geo.oregonstate.edu"},"description":"We used the observed historical meteorology and mean modeled snow-water-equivalent for the reference period (1989-2011) and mean modeled snow-water-equivalent under four climate change scenarios, T2, T2P10, T4 and T4P10.\nIn the T2 scenario the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record in the reference period meteorology, and this data is then used as input to the model. In the T2P10 scenario, the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2\u00b0C to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T4 scenario: the observed historical (reference period) meteorology is perturbed by adding +4oC to each daily temperature record in the reference period meteorology, and this data is then used as input to the model. The T4P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +4oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13RCMYM","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.775dc252-e75f-4785-8f4f-326e5bcda283.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_775dc252-e75f-4785-8f4f-326e5bcda283","keyword":["Deschutes River Basin","Oregon","SWE","USGS:775dc252-e75f-4785-8f4f-326e5bcda283","climate change","climatologyMeteorologyAtmosphere","effects of climate change","environment","external research support","geospatial datasets","modeling","precipitation (atmospheric)","snow water equivalent"],"modified":"2026-09-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-121.9009, 44.1159, -121.4008, 44.8245","theme":["geospatial"],"title":"Historical and Climate\u2011Scenario Snow\u2011Water Equivalent Conditions and Change Metrics under T2, T2p10, T4, and T4p10 Scenarios for the Upper Deschutes River Basin, Oregon"},"description":"We used the observed historical meteorology and mean modeled snow-water-equivalent for the reference period (1989-2011) and mean modeled snow-water-equivalent under four climate change scenarios, T2, T2P10, T4 and T4P10.\nIn the T2 scenario the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record in the reference period meteorology, and this data is then used as input to the model. In the T2P10 scenario, the observed historical (reference period) meteorology is perturbed by adding +2oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T2P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +2\u00b0C to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model. T4 scenario: the observed historical (reference period) meteorology is perturbed by adding +4oC to each daily temperature record in the reference period meteorology, and this data is then used as input to the model. The T4P10 scenario: the observed historical (reference period) meteorology is perturbed by adding +4oC to each daily temperature record, and +10% precipitation to each daily precipitation record in the reference period meteorology, and this data is then used as input to the model.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/76787789-eb04-49c7-bb69-557f26d9516f","harvest_record_raw":"https://catalog.data.gov/harvest_record/76787789-eb04-49c7-bb69-557f26d9516f/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_775dc252-e75f-4785-8f4f-326e5bcda283","keyword":["Deschutes River Basin","Oregon","SWE","USGS:775dc252-e75f-4785-8f4f-326e5bcda283","climate change","climatologyMeteorologyAtmosphere","effects of climate change","environment","external research support","geospatial datasets","modeling","precipitation (atmospheric)","snow water equivalent"],"last_harvested_date":"2026-09-24T00:54:11.997672","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":"historical-and-climatescenario-snowwater-equivalent-conditions-and-change-metrics-under-t2-9e067","spatial_centroid":{"lat":44.39934,"lon":-121.70085999999999},"spatial_shape":{"coordinates":[[[-121.9009,44.1159],[-121.9009,44.8245],[-121.4008,44.8245],[-121.4008,44.1159],[-121.9009,44.1159]]],"type":"Polygon"},"theme":["geospatial"],"title":"Historical and Climate\u2011Scenario Snow\u2011Water Equivalent Conditions and Change Metrics under T2, T2p10, T4, and T4p10 Scenarios for the Upper Deschutes River Basin, Oregon","type":"dataset"},{"_score":9.103693,"_sort":[1790210959989,9.103693,0,"b2dc0e78-5423-4f6b-bf34-528d91d21013"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Christina A Murphy","hasEmail":"mailto:camurphy@usgs.gov"},"description":"Species distribution models are useful for wildlife management by identifying suitable habitats and communities for focal species. Identifying these key relationships may allow for predictions of a species in undersurveyed or unmanaged areas, providing a way to fill data gaps and save resources. However, when species or community distribution models are limited in spatial extent (i.e., to a state or other locality), results may not be applicable across an entire range. Flora and fauna experience different environmental and biotic relationships across biogeographic spatial gradients, making range-wide models important for regional management and status determination. In the study that accompanies this data release, we explore how inferences differ between range-wide versus spatially-restricted distribution models of the Yellow Lampmussel (Lampsilis cariosa), a U.S. federal At-Risk freshwater mussel species, and the community of fishes and other mussels it occurs with using stacked species distribution models (SSDMs). We collated co-occurring freshwater mussel data and host fish data from across L. cariosa\u2019s range and made predictions at the range-wide (n = 16 states; ~750,000 km^2) and, when possible (n \u2265 50 observations), state (n = 5 states; ~100,000 +/- 30,000 SD km2/state) spatial extents. Our state-based SSDMs had similar spatial predictions to our range-wide model, but different relationships between abiotic and biotic variables and L. cariosa. These different relationships are likely due to the \u201cniche truncation\u201d effect of SSDMs across smaller spatial extents, or local responses to the environment that are nonrepresentative across space, and the uneven availability of host fish and co-occurring mussel distribution data. Our range-wide model predicted L. cariosa presence in Vermont, New Hampshire, and Delaware, three states where L. cariosa is presumed to not occur (Vermont) or is presumed extirpated (New Hampshire, Delaware). By avoiding effects of niche truncation, allowing predictions into undersurveyed areas, and providing a range-wide model to integrate with future modeling or survey efforts, our study supports the value of range-wide modeling efforts and applications for management.\nWe grouped our model predictions from the range-wide and state-based models to three different hydrologic unit code (HUC) watershed levels: HUC8, HUC10, and HUC12. Therefore, there are 6 shapefiles associated with this data set (range-wide [rw] across 3 scales, state-based [sb] across 3 scales). The .csv file titled \"data_sources.csv\" indicates where survey and museum records came from, including institutions and points of contact, as well as the date each database was queried.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13GJUGN","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.69b2e30bb66b012bf56c2917.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_69b2e30bb66b012bf56c2917","keyword":["Ecology","USGS:69b2e30bb66b012bf56c2917","atlantic slope","distribution","freshwater mussels","niche truncation","northeast united states","random forest","range","southeast united states","stacked species distribution model"],"modified":"2026-09-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-84.5731, 30.6774, -66.1830, 47.5717","theme":["geospatial"],"title":"Yellow Lampmussel stacked species distribution model results across the Atlantic Slope, U.S."},"description":"Species distribution models are useful for wildlife management by identifying suitable habitats and communities for focal species. Identifying these key relationships may allow for predictions of a species in undersurveyed or unmanaged areas, providing a way to fill data gaps and save resources. However, when species or community distribution models are limited in spatial extent (i.e., to a state or other locality), results may not be applicable across an entire range. Flora and fauna experience different environmental and biotic relationships across biogeographic spatial gradients, making range-wide models important for regional management and status determination. In the study that accompanies this data release, we explore how inferences differ between range-wide versus spatially-restricted distribution models of the Yellow Lampmussel (Lampsilis cariosa), a U.S. federal At-Risk freshwater mussel species, and the community of fishes and other mussels it occurs with using stacked species distribution models (SSDMs). We collated co-occurring freshwater mussel data and host fish data from across L. cariosa\u2019s range and made predictions at the range-wide (n = 16 states; ~750,000 km^2) and, when possible (n \u2265 50 observations), state (n = 5 states; ~100,000 +/- 30,000 SD km2/state) spatial extents. Our state-based SSDMs had similar spatial predictions to our range-wide model, but different relationships between abiotic and biotic variables and L. cariosa. These different relationships are likely due to the \u201cniche truncation\u201d effect of SSDMs across smaller spatial extents, or local responses to the environment that are nonrepresentative across space, and the uneven availability of host fish and co-occurring mussel distribution data. Our range-wide model predicted L. cariosa presence in Vermont, New Hampshire, and Delaware, three states where L. cariosa is presumed to not occur (Vermont) or is presumed extirpated (New Hampshire, Delaware). By avoiding effects of niche truncation, allowing predictions into undersurveyed areas, and providing a range-wide model to integrate with future modeling or survey efforts, our study supports the value of range-wide modeling efforts and applications for management.\nWe grouped our model predictions from the range-wide and state-based models to three different hydrologic unit code (HUC) watershed levels: HUC8, HUC10, and HUC12. Therefore, there are 6 shapefiles associated with this data set (range-wide [rw] across 3 scales, state-based [sb] across 3 scales). 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Pesticides and DBP were analyzed at the USGS Organic Chemistry Research Laboratory (OCRL) in Sacramento, California. Inorganic constituents were analyzed at the USGS Redox Chemistry Laboratory (RCL) in Boulder, Colorado, and the USGS Analytical Trace Element Chemistry Laboratory (ATECL) in Boulder, Colorado. Microbiological pathogens were analyzed by the USGS Michigan Bacteriological Research Laboratory (MIBaRL) in Lansing, Michigan. Gross Alpha and Gross Beta radionuclides were analyzed at the New Jersey Department of Health (NJDOH) in Ewing, New Jersey. Based on the results from the gross alpha and beta analyses, two additional samples were collected and analyzed by NJDOH for radium isotopes in 2023.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P9676QON","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.655ceefdd34ee4b6e05cc527.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_655ceefdd34ee4b6e05cc527","keyword":["New York","PFAS","USGS:655ceefdd34ee4b6e05cc527","United States","cyanotoxins","disinfection byproducts","drinking water use","environment","environmental health (human)","geoscientificInformation","health","inorganic analytes","microbiology","organic contaminants","pesticides","point-of-use exposure","radionuclides","tapwater","volatile organic compounds","water quality"],"modified":"2026-09-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"theme":["geospatial"],"title":"Target-chemical concentrations and microbiological results in tapwater, New York, 2022-23"},"description":"This data release contains the concentration and quality-assurance results for inorganic constituents, as well as organic compounds (volatile organic compounds [VOC], per- and polyfluoroalkyl substances [PFAS], pesticides, and disinfection byproducts [DBP]), radionuclides, cyanotoxins and microbiological results analyzed in water-quality samples from 19 residential tapwater locations in New York, by the U.S. Geological Survey (USGS) Environmental Health Program (EHP). Nineteen samples (18 private wells, one public supply) were collected in New York between October 17-18, 2022. Samples were collected from private residences therefore site locations are not available because of privacy concerns. Pharmaceuticals, VOC, and PFAS were analyzed at the USGS National Water Quality Laboratory (NWQL) in Denver, Colorado. Cyanotoxins were analyzed at the Organic Geochemistry Research Laboratory (OGRL) in Lawrence, KS. Pesticides and DBP were analyzed at the USGS Organic Chemistry Research Laboratory (OCRL) in Sacramento, California. Inorganic constituents were analyzed at the USGS Redox Chemistry Laboratory (RCL) in Boulder, Colorado, and the USGS Analytical Trace Element Chemistry Laboratory (ATECL) in Boulder, Colorado. Microbiological pathogens were analyzed by the USGS Michigan Bacteriological Research Laboratory (MIBaRL) in Lansing, Michigan. Gross Alpha and Gross Beta radionuclides were analyzed at the New Jersey Department of Health (NJDOH) in Ewing, New Jersey. Based on the results from the gross alpha and beta analyses, two additional samples were collected and analyzed by NJDOH for radium isotopes in 2023.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/b2c1c7fc-ac65-43dc-ae53-b289ccfa1c36","harvest_record_raw":"https://catalog.data.gov/harvest_record/b2c1c7fc-ac65-43dc-ae53-b289ccfa1c36/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_655ceefdd34ee4b6e05cc527","keyword":["New York","PFAS","USGS:655ceefdd34ee4b6e05cc527","United States","cyanotoxins","disinfection byproducts","drinking water use","environment","environmental health (human)","geoscientificInformation","health","inorganic analytes","microbiology","organic contaminants","pesticides","point-of-use exposure","radionuclides","tapwater","volatile organic compounds","water quality"],"last_harvested_date":"2026-09-24T00:48:28.911375","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":"target-chemical-concentrations-and-microbiological-results-in-tapwater-new-york-2022-23","spatial_centroid":null,"spatial_shape":null,"theme":["geospatial"],"title":"Target-chemical concentrations and microbiological results in tapwater, New York, 2022-23","type":"dataset"},{"_score":10.153866,"_sort":[1790210832346,10.153866,0,"d3b5939b-a558-4247-90b8-85f0deb2ce35"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Shannon Brewer","hasEmail":"mailto:shannon.brewer@okstate.edu"},"description":"This dataset contains water temperature and flow calculations from the lower Tallapoosa River catchment. Data was derived from U.S. Geological Survey Gauges and placed water temperature and level loggers.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13DP9K6","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.6a79f9db1ba49b951e8402cb.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a79f9db1ba49b951e8402cb","keyword":["Alabama","Discharge","River","Tallapoosa River","USGS:6a79f9db1ba49b951e8402cb","Water temperature","environment"],"modified":"2026-09-16T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-86.3498, 32.3521, -85.7565, 32.6347","theme":["geospatial"],"title":"Thermal and hydrologic measurements associated with the lower Tallapoosa River catchment"},"description":"This dataset contains water temperature and flow calculations from the lower Tallapoosa River catchment. Data was derived from U.S. Geological Survey Gauges and placed water temperature and level loggers.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/918e9bea-1a5d-460d-9da1-20aa4593ceb8","harvest_record_raw":"https://catalog.data.gov/harvest_record/918e9bea-1a5d-460d-9da1-20aa4593ceb8/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a79f9db1ba49b951e8402cb","keyword":["Alabama","Discharge","River","Tallapoosa River","USGS:6a79f9db1ba49b951e8402cb","Water temperature","environment"],"last_harvested_date":"2026-09-24T00:47:12.346756","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 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futures based on SLAMM model outputs, NLCD land cover and the projected distribution of sea levels for 2050. 2) a table of parcel identification numbers (without georeference) with parcel size (Ha) and sub-group identity. 3) Optimization-model derived reserve design portfolios that define the Pareto-optimal frontier for each sub-group and for four budget scenarios along axes of reserve design benefits and risk.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P91XU2ON","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.5ce598e0e4b0bc180232e9ae.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ce598e0e4b0bc180232e9ae","keyword":["Cape Romain","Modern Portfolio 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reserve design portfolios that define the Pareto-optimal frontier for each sub-group and for four budget scenarios along axes of reserve design benefits and risk.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/63bffb2b-f8f0-4ec2-b23e-caf34cf60191","harvest_record_raw":"https://catalog.data.gov/harvest_record/63bffb2b-f8f0-4ec2-b23e-caf34cf60191/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5ce598e0e4b0bc180232e9ae","keyword":["Cape Romain","Modern Portfolio Theory","South Carolina","South Carolina Coastal","USGS:5ce598e0e4b0bc180232e9ae","decision support methods","ecosystem services valuation","environment","geospatial datasets","mathematical modeling","risk assessment","spatial 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All mapped sequoia groves in Sequoia-Kings Canyon National Park and two groves from Yosemite National Park, Merced and Mariposa, are included. For each grove, we calculated aspect, slope, the stream network, height above nearest drainage (HAND), depth to water (DTW), maximum elevation deviation (DEVmax), profile and planform curvature, and heat load index (HLI). These indices were chosen because of their relevance in determining soil moisture across a landscape. Derived topographic parameters were calculated using 1 m resolution DEMs generated in 2016 and 2019 and resampled to 3 m and a combination of ArcGIS Pro (v 3.3.2) Tools, ArcPy Scripts, Whitebox GAT tools, and R scripts. The stream networks, which are used in the calculation of HAND and DTW, were determined using a flow accumulation threshold of 2000 meters squared based on a literature-accepted range and chosen specifically based on knowledge of the areas of interest. The neighborhood size range for DEVmax were calculated at two scales, allowing for the identification of both local and more global minima and maxima. Squares centered at each pixel range from 9 to 303 meters and 9 to 1203 meters wide. The neighborhood size chosen by the algorithm at each pixel maximizes the deviation from the average. For both profile and planform curvature, neighborhood sizes of 100 and 150 m were selected based on knowledge of the heterogeneity of the landscape. Files that specify the neighborhood size are included for reference. All files included are GeoTIFF files.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P18BXVHZ","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.65dcd2c3d34eb64889e195df.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_65dcd2c3d34eb64889e195df","keyword":["Sequoia National Park","USGS:65dcd2c3d34eb64889e195df","Yosemite National Park","digital elevation models","elevation","environment","imageryBaseMapsEarthCover","mathematical modeling","remote sensing","soil moisture","streamflow"],"modified":"2026-09-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-119.8800, 36.2900, -118.5400, 37.9900","theme":["geospatial"],"title":"Digital Elevation Model (DEM) Derived Topographic Indices Across Sequoia Groves in Sequoia-Kings Canyon National Park and Yosemite National Park (ver. 2.0, September 2026)"},"description":"This dataset provides topographic indices derived from 1 m resolution DEMs for sequoia groves in both Sequoia-Kings Canyon and Yosemite National Parks. All mapped sequoia groves in Sequoia-Kings Canyon National Park and two groves from Yosemite National Park, Merced and Mariposa, are included. For each grove, we calculated aspect, slope, the stream network, height above nearest drainage (HAND), depth to water (DTW), maximum elevation deviation (DEVmax), profile and planform curvature, and heat load index (HLI). These indices were chosen because of their relevance in determining soil moisture across a landscape. Derived topographic parameters were calculated using 1 m resolution DEMs generated in 2016 and 2019 and resampled to 3 m and a combination of ArcGIS Pro (v 3.3.2) Tools, ArcPy Scripts, Whitebox GAT tools, and R scripts. The stream networks, which are used in the calculation of HAND and DTW, were determined using a flow accumulation threshold of 2000 meters squared based on a literature-accepted range and chosen specifically based on knowledge of the areas of interest. The neighborhood size range for DEVmax were calculated at two scales, allowing for the identification of both local and more global minima and maxima. Squares centered at each pixel range from 9 to 303 meters and 9 to 1203 meters wide. The neighborhood size chosen by the algorithm at each pixel maximizes the deviation from the average. For both profile and planform curvature, neighborhood sizes of 100 and 150 m were selected based on knowledge of the heterogeneity of the landscape. Files that specify the neighborhood size are included for reference. 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Predictor variables include measurements and classifications related to canyon physiography (canyon width, average dunefield slope, dominant underlying geomorphic landform), climate (dominant aeolian sand transport direction, Great Basin Desert versus Mojave Desert climate zone designation), river hydrology and geomorphology (pre-dam river width, exposed channel from typical dam operations, distance from dunefield centroid to river centerline, river mile downstream of Lees Ferry, river flow direction, aeolian sand transport direction relative to river flow direction). These data were compiled from a combination of field observations, remotely sensed data, geospatial data, and previously published modeled data. 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These data were compiled from a combination of field observations, remotely sensed data, geospatial data, and previously published modeled data. 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To translate these water surface elevations to inundation depths, we subtracted a merged terrain model consisting of floodplain LIDAR and bathymetric surveys of the river channel. We completed these calculations for an 800 kilometer stretch of the Missouri River, spanning from Rulo, Nebraska to the river's confluence with the Mississippi River. This approach resulted in a 29,000+ day time series of inundation depths across the floodplain using grid cells with 30 meter spatial resolution. This dataset presents 17 metrics for each of two scenarios, one using a baseline timeseries of stages from the HEC-RAS simulation and one using a timeseries of stages adjusted to account for changes in discharge under one possible climate change scenario. These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water surface elevation across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. Lastly, we include the base elevation layer that we generated to calculate depth of inundation from interpolated water-surface elevations.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7HM56KG","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.5a0f755de4b09af898d09bc2.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_5a0f755de4b09af898d09bc2","keyword":["Iowa","Kansas","Lower Missouri","Missouri","Nebraska","USGS:5a0f755de4b09af898d09bc2","environment","floodplains","floods","geospatial datasets","inlandWaters","river systems","streamflow AND mathematical modeling","wetland ecosystems"],"modified":"2026-09-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-95.4290546039, 38.5006200584, -90.0550813057, 40.0709909628","theme":["geospatial"],"title":"Science to Inform Management of Floodplain Conservation Lands under Non-Stationary Conditions"},"description":"Within large-river ecosystems, floodplains serve a variety of important ecological functions. 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These metrics are calculated on a per pixel basis and encompass a variety of temporal criteria generally relevant to flora and fauna of interest to floodplain managers, including, for example, the average number of days inundated per year within a growing season. We also include a series of maps of water surface elevation across the floodplain by return interval for each scenario, and the minimum return interval at which each pixel is inundated. 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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":"https://doi.org/10.5066/F76971RH","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.582e25cce4b04d580bd405d0.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_582e25cce4b04d580bd405d0","keyword":["Baeolophus ridgwayi","Callipepla squamata","Cassin\u2019s Sparrow","Colinus virginianus","Cyrtonyx montezumae","Gymnorhinus cyanocephalus","Juniper Titmouse","Lagopus leucurus","Lesser Prairie-Chicken","Mexican Spotted Owl","Montezuma Quail","Northern/Masked Bobwhite Quail","Peucaea cassinii","Pinyon Jay","Scaled Quail","Strix occidentalis lucida","Tympanuchus pallidicinctus","USGS:582e25cce4b04d580bd405d0","White-tailed Ptarmigan","biota","birds","climatologyMeteorologyAtmosphere","geospatial datasets"],"modified":"2026-09-17T00:00:00Z","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 bird species in South Central USA"},"description":"This dataset contains the result of the bioclimatic-envelope modeling of nine bird species -- Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin\u2019s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus) -- 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/f4231b8a-7432-4bfb-896f-c8bfb4919611","harvest_record_raw":"https://catalog.data.gov/harvest_record/f4231b8a-7432-4bfb-896f-c8bfb4919611/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_582e25cce4b04d580bd405d0","keyword":["Baeolophus ridgwayi","Callipepla squamata","Cassin\u2019s Sparrow","Colinus virginianus","Cyrtonyx montezumae","Gymnorhinus cyanocephalus","Juniper Titmouse","Lagopus leucurus","Lesser Prairie-Chicken","Mexican Spotted Owl","Montezuma Quail","Northern/Masked Bobwhite Quail","Peucaea cassinii","Pinyon Jay","Scaled Quail","Strix occidentalis lucida","Tympanuchus pallidicinctus","USGS:582e25cce4b04d580bd405d0","White-tailed Ptarmigan","biota","birds","climatologyMeteorologyAtmosphere","geospatial datasets"],"last_harvested_date":"2026-09-24T00:30:18.732675","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-bird-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 bird species in South Central USA","type":"dataset"},{"_score":10.153866,"_sort":[1790209751474,10.153866,0,"a82c0c8c-6482-4907-b32c-40e744e2bb50"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Megan Behnke","hasEmail":"mailto:mibehnke@alaska.edu"},"description":"To explore how cryospheric change around Alaska could impact energy available for fish growth in rivers, data from published studies and archives were compiled to run the Aquatic Trophic Productivity (ATP) model. 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In this data release the following data are provided for 602 morphologically identified and genotyped Lake Superior ciscoes: sample location including state and country, ecoregion, collection date, geographic coordinates, sampling gear, collector; morphological and genetic species identifications; morphological conformation scores; Q-scores; measurement data for 38 morphometric characters and counts; USGS specimen number; MEL_ID (Molecular Ecology Lab Identifier); and National Center for Biotechnology Information (NCBI) accession numbers for genetic data.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P1R4SNN5","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.6a07284eb66b01b8f8e903c3.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a07284eb66b01b8f8e903c3","keyword":["Coregonine","Coregonus","Coregonus artedi","Coregonus clupeaformis","Coregonus hoyi","Coregonus kiyi","Coregonus nigripinnis","Coregonus reighardi","Coregonus zenithicus","DNA sequencing","Great Lakes","Lake Superior","Natural Resource Management","USGS:6a07284eb66b01b8f8e903c3","animals","aquatic biology","biodiversity","biota","environment","fish","freshwater ecosystems","genetic diversity","genetics","genotype","ichthyology","inlandWaters","meristics","morphology (biological)","native species","species diversity"],"modified":"2026-09-21T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-92.6807, 46.3166, -84.1553, 49.1530","theme":["geospatial"],"title":"Morphological and genetic data for the Lake Superior cisco complex, 2006-2021"},"description":"Contained in this data release are the core data elements of our study of the contemporary diversity of the cisco complex of Lake Superior as described by Koelz (1929): Coregonus artedi, C. hoyi, C. kiyi, C. zenithicus, C. reighardi, and C. nigripinnis (Gorman et al. 2026). Fish were collected over a broad range of habitats and geographic regions of Lake Superior during 2003-2021. Most collections were conducted by the U.S. Geological Survey and the Department of Fisheries and Oceans Canada and supplemented by collections from The Nature Conservancy and state and tribal partners. A subset of 602 fish collected from years 2006-2021 were identified morphologically, genotyped, and measured morphometrically. 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Ball","hasEmail":"mailto:lbball@usgs.gov"},"description":"Hydrologic and geophysical data were collected in 2023-2025 near the Perigo Mine in Gilpin County, Colorado. Two boreholes (P9 and P10) were drilled in the fall of 2023. The drill site is located on a forested mountain hillslope near abandoned mining infrastructure at an elevation of about 2,970 meters. The boreholes were continuously cored using a wireline HQ-sized coring system through Quaternary soil, colluvium, and Precambrian-aged gneiss bedrock. In-situ injection-based hydraulic tests were performed during drilling while advancing using a single-packer apparatus in tandem with an in-line flow meter and interval pressure monitoring. Borehole geophysical logging was performed in the open holes. Each borehole was completed as four vertically discrete nested monitoring wells (P9A-D and P10A-D). Pressure transducers have been deployed in these wells along with pre-existing monitoring wells (P5 and P8) to monitor groundwater levels and temperature. Additionally, shallow soil moisture and temperature monitoring data were collected at 15 locations across the hillslope to better understand infiltration dynamics.\nThis data release includes borehole location and well completion information from P9 and P10. The hydraulic test data directory contains in-situ packer test data collected during drilling and laboratory permeability/porosity results from selected core samples. The geophysical log directory contains borehole geophysical logs collected prior to well completion. The hydrologic monitoring data directory contains groundwater level and temperature data for fall 2023 to summer 2025. The soil moisture and temperature data directory contains shallow monitoring data for fall 2023-fall 2024.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P149XR6J","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.68b0dbb8d4be02739177e93f.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_68b0dbb8d4be02739177e93f","keyword":["Colorado","Colorado Division of Reclamation, Mining, and Safety","DRMS","Dakota Hill","GGGSC","Gamble Gulch","Geology, Geophysics, and Geochemistry Science Center","Gilpin","MRP","Mineral Resources Program","Perigo (historical)","Roosevelt National Forest","U.S. Geological Survey","USGS","USGS:68b0dbb8d4be02739177e93f","University of Wyoming","borehole logging","core analysis","drilling and coring","environment","geoscientificInformation","groundwater","groundwater level","hydrogeology","permeability","porosity"],"modified":"2026-09-16T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-105.534507, 39.878868, -105.528252, 39.881091","theme":["geospatial"],"title":"Hydrologic and borehole geophysical data from the Perigo Mine site, Gilpin County Colorado"},"description":"Hydrologic and geophysical data were collected in 2023-2025 near the Perigo Mine in Gilpin County, Colorado. Two boreholes (P9 and P10) were drilled in the fall of 2023. The drill site is located on a forested mountain hillslope near abandoned mining infrastructure at an elevation of about 2,970 meters. The boreholes were continuously cored using a wireline HQ-sized coring system through Quaternary soil, colluvium, and Precambrian-aged gneiss bedrock. In-situ injection-based hydraulic tests were performed during drilling while advancing using a single-packer apparatus in tandem with an in-line flow meter and interval pressure monitoring. Borehole geophysical logging was performed in the open holes. Each borehole was completed as four vertically discrete nested monitoring wells (P9A-D and P10A-D). Pressure transducers have been deployed in these wells along with pre-existing monitoring wells (P5 and P8) to monitor groundwater levels and temperature. Additionally, shallow soil moisture and temperature monitoring data were collected at 15 locations across the hillslope to better understand infiltration dynamics.\nThis data release includes borehole location and well completion information from P9 and P10. The hydraulic test data directory contains in-situ packer test data collected during drilling and laboratory permeability/porosity results from selected core samples. The geophysical log directory contains borehole geophysical logs collected prior to well completion. The hydrologic monitoring data directory contains groundwater level and temperature data for fall 2023 to summer 2025. The soil moisture and temperature data directory contains shallow monitoring data for fall 2023-fall 2024.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/449f5688-3b72-47d0-843b-edea92d34602","harvest_record_raw":"https://catalog.data.gov/harvest_record/449f5688-3b72-47d0-843b-edea92d34602/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_68b0dbb8d4be02739177e93f","keyword":["Colorado","Colorado Division of Reclamation, Mining, and Safety","DRMS","Dakota Hill","GGGSC","Gamble Gulch","Geology, Geophysics, and Geochemistry Science Center","Gilpin","MRP","Mineral Resources Program","Perigo (historical)","Roosevelt National Forest","U.S. Geological Survey","USGS","USGS:68b0dbb8d4be02739177e93f","University of Wyoming","borehole logging","core analysis","drilling and coring","environment","geoscientificInformation","groundwater","groundwater level","hydrogeology","permeability","porosity"],"last_harvested_date":"2026-09-24T00:14:00.919002","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":"hydrologic-and-borehole-geophysical-data-from-the-perigo-mine-site-gilpin-county-colorado","spatial_centroid":{"lat":39.87975719999999,"lon":-105.532005},"spatial_shape":{"coordinates":[[[-105.534507,39.878868],[-105.534507,39.881091],[-105.528252,39.881091],[-105.528252,39.878868],[-105.534507,39.878868]]],"type":"Polygon"},"theme":["geospatial"],"title":"Hydrologic and borehole geophysical data from the Perigo Mine site, Gilpin County Colorado","type":"dataset"},{"_score":10.169591,"_sort":[1790208742942,10.169591,0,"28566861-8d65-4d5d-8e73-8355b983184e"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Erica Stuber","hasEmail":"mailto:erica.stuber@usu.edu"},"description":"This raster dataset provides estimated ecosystem condition scores, representing the difference between current conditions and reference/baseline conditions, for Utah, USA for the year 2024. The data are analogous to LANDFIRE's Vegetation Departure Scores (which are calculated using LANDFIRE Succession-Class raster data, and LANDFIRE Biophysical Settings raster data), but we used custom processing of the underlying LANDFIRE data to generate scores at higher resolution.\nLANDFIRE Succession Class (S-Class) maps at 30 m resolution were summarized within 300 m analysis cells. Each 300 m cell contains the proportional cover of seven vegetation classes: A (early development), B (mid-development closed), C (mid-development open), D (late-development open), E (late-development closed), UN (uncharacteristic native), and UE (uncharacteristic exotic). S-classes for each ecosystem type consider the type, cover, and height of vegetation, and whether the vegetation is characteristic of ecosystem type. The dominant LANDFIRE Biophysical Setting in each 300 m cell was used to identify reference-condition expectations. \nThe Ecosystem Condition score raster provides a single, integrated departure score ranging from 0 to 100, where larger values indicate greater departure from reference conditions. The score is based on the same ecological departure framework used by the LANDFIRE Vegetation Departure product and the Fire Regime Condition Class (FRCC) methodology.\nTo calculate the score, vegetation conditions within each 300 m grid cell are first summarized across seven LANDFIRE Succession Classes (S-Classes): A, B, C, D, E, UN, and UE. Current proportions of each S-Class are compared with the proportions expected under reference conditions for the corresponding ecosystem type. Individual S-Class departures are then combined into a single aggregate Ecosystem Condition score that represents the overall degree of departure from the ecosystem's expected reference condition.\nECO Score:\n100 - sum(Ci - Ri)\nWhere Ci = The current percentage of the 300m resolution landscape in successional/structural class i. And Ri = The historical reference percentage of the landscape in successional/structural class i.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13EXQGF","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.6a96d08b1ba49b45440e95de.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a96d08b1ba49b45440e95de","keyword":["USGS:6a96d08b1ba49b45440e95de","biota","ecosystem monitoring","ecosystem restoration","environment","terrestrial ecosystems"],"modified":"2026-09-15T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-114.0400, 36.9900, -109.0400, 42.0000","theme":["geospatial"],"title":"2024 geospatial Ecosystem Condition (ECO) Score layer for Utah, USA"},"description":"This raster dataset provides estimated ecosystem condition scores, representing the difference between current conditions and reference/baseline conditions, for Utah, USA for the year 2024. The data are analogous to LANDFIRE's Vegetation Departure Scores (which are calculated using LANDFIRE Succession-Class raster data, and LANDFIRE Biophysical Settings raster data), but we used custom processing of the underlying LANDFIRE data to generate scores at higher resolution.\nLANDFIRE Succession Class (S-Class) maps at 30 m resolution were summarized within 300 m analysis cells. Each 300 m cell contains the proportional cover of seven vegetation classes: A (early development), B (mid-development closed), C (mid-development open), D (late-development open), E (late-development closed), UN (uncharacteristic native), and UE (uncharacteristic exotic). S-classes for each ecosystem type consider the type, cover, and height of vegetation, and whether the vegetation is characteristic of ecosystem type. The dominant LANDFIRE Biophysical Setting in each 300 m cell was used to identify reference-condition expectations. \nThe Ecosystem Condition score raster provides a single, integrated departure score ranging from 0 to 100, where larger values indicate greater departure from reference conditions. The score is based on the same ecological departure framework used by the LANDFIRE Vegetation Departure product and the Fire Regime Condition Class (FRCC) methodology.\nTo calculate the score, vegetation conditions within each 300 m grid cell are first summarized across seven LANDFIRE Succession Classes (S-Classes): A, B, C, D, E, UN, and UE. Current proportions of each S-Class are compared with the proportions expected under reference conditions for the corresponding ecosystem type. Individual S-Class departures are then combined into a single aggregate Ecosystem Condition score that represents the overall degree of departure from the ecosystem's expected reference condition.\nECO Score:\n100 - sum(Ci - Ri)\nWhere Ci = The current percentage of the 300m resolution landscape in successional/structural class i. And Ri = The historical reference percentage of the landscape in successional/structural class i.","distribution_titles":["Digital Data","Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/aca9470d-7b60-4b90-80e8-44c389e9d5dd","harvest_record_raw":"https://catalog.data.gov/harvest_record/aca9470d-7b60-4b90-80e8-44c389e9d5dd/raw","has_download":true,"has_spatial":true,"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a96d08b1ba49b45440e95de","keyword":["USGS:6a96d08b1ba49b45440e95de","biota","ecosystem monitoring","ecosystem restoration","environment","terrestrial ecosystems"],"last_harvested_date":"2026-09-24T00:12:22.942895","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":"2024-geospatial-ecosystem-condition-eco-score-layer-for-utah-usa","spatial_centroid":{"lat":38.994,"lon":-112.04},"spatial_shape":{"coordinates":[[[-114.04,36.99],[-114.04,42.0],[-109.04,42.0],[-109.04,36.99],[-114.04,36.99]]],"type":"Polygon"},"theme":["geospatial"],"title":"2024 geospatial Ecosystem Condition (ECO) Score layer for Utah, USA","type":"dataset"},{"_score":8.106066,"_sort":[1790208540856,8.106066,2,"6cae55a6-1ac9-453a-9715-a3c3302922ac"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"Jordan S. Read","hasEmail":"mailto:jread@usgs.gov"},"description":"Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: ice_duration_days, ice_on_date, ice_off_date, winter_dur_0-4, coef_var_30-60, coef_var_0-30, stratification_onset_yday, stratification_duration, sthermo_depth_mean, peak_temp, gdd_wtr_0c, gdd_wtr_5c, gdd_wtr_10c, bottom_temp_at_strat, schmidt_daily_annual_sum, mean_surf_jas, max_surf_jas, mean_bot_jas, max_bot_jas, mean_surf_jan, max_surf_jan, mean_bot_jan, max_bot_jan, mean_surf_feb, max_surf_feb, mean_bot_feb, max_bot_feb, mean_surf_mar, max_surf_mar, mean_bot_mar, max_bot_mar, mean_surf_apr, max_surf_apr, mean_bot_apr, max_bot_apr, mean_surf_may, max_surf_may, mean_bot_may, max_bot_may, mean_surf_jun, max_surf_jun, mean_bot_jun, max_bot_jun, mean_surf_jul, max_surf_jul, mean_bot_jul, max_bot_jul, mean_surf_aug, max_surf_aug, mean_bot_aug, max_bot_aug, mean_surf_sep, max_surf_sep, mean_bot_sep, max_bot_sep, mean_surf_oct, max_surf_oct, mean_bot_oct, max_bot_oct, mean_surf_nov, max_surf_nov, mean_bot_nov, max_bot_nov, mean_surf_dec, max_surf_dec, mean_bot_dec, max_bot_dec, which are defined below.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7DV1H10","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.57d9e887e4b090824ffb1098.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_57d9e887e4b090824ffb1098","keyword":["IA","IL","IN","Illinois","Indiana","Iowa","MI","MN","Michigan","Minnesota","SD","South Dakota","US","USGS:57d9e887e4b090824ffb1098","United States","WI","Wisconsin","biota","climate change","environment","inlandWaters","modeling","reservoirs","temperate lakes","thermal profiles","water"],"modified":"2026-09-17T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-96.8589114267623, 48.7289513243629, -83.0573307815185, 41.7553570685206","theme":["geospatial"],"title":"Thermal metrics: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes"},"description":"Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. 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The data consist of: 1) size and disease state of sea stars, 2) counts of sea stars, and 3) taxonomic classification of sea stars.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/F7513WCB","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.ASC486.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_ASC486","keyword":["Alaska","Alaska Peninsula","Animals/Invertebrates","Aquatic ecosystems","Biospheric indicators","Biota","Community ecology","Echinoderms","Ecosystem monitoring","Environment","Gulf of Alaska","Intertidal environments","Intertidal zone","Katmai National Park and Preserve","Kenai Fjords National Park","Marine ecosystems","Marine invertebrates","Nearshore ecology","Prince William Sound","Sea star","USGS:ASC486"],"modified":"2026-09-14T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-156.643, 57.938, -145.415, 61.122","theme":["geospatial"],"title":"Rocky Intertidal Sea Star Size, Count, and Disease Data from Prince William Sound, Katmai National Park and Preserve, and Kenai Fjords National Park"},"description":"This is a child item of the USGS Data Release: https://doi.org/10.5066/F7513WCB.\n\t These data are part of the Gulf Watch Alaska (GWA) long-term monitoring program, nearshore monitoring component. 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This page provides data for the Tree and Shade Canopy performance measure.

Sustainable growth for the City of Tempe is influenced by many factors, including the development and maintenance of our tree canopy. An adequate canopy provides shade, cooling, reduces water evaporation, and is an indication of a healthy and well-maintained environment. As per the Urban Forestry Master Plan, this data represents tree canopy in parks, street right-of-ways, and municipally owned and managed facilities.

View this performance measure dashboard at performance.tempe.gov. Select Sustainable Growth & Development, then select the link for Performance Measure 4.11.

 

Data Dictionary

 

Additional Information 

 
Source: 
 
Contact:\u202f Craig Hayton
 
Contact E-Mail:\u202fCraig_Hayton@tempe.gov
 
Data Source Type:\u202f CSV
 
Preparation Method:\u202f Tracked by staff quarterly
 
Publish Frequency:\u202f Annual
 
Publish Method:\u202fManual
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This page provides data for the Tree and Shade Canopy performance measure.

Sustainable growth for the City of Tempe is influenced by many factors, including the development and maintenance of our tree canopy. An adequate canopy provides shade, cooling, reduces water evaporation, and is an indication of a healthy and well-maintained environment. As per the Urban Forestry Master Plan, this data represents tree canopy in parks, street right-of-ways, and municipally owned and managed facilities.

View this performance measure dashboard at performance.tempe.gov. Select Sustainable Growth & Development, then select the link for Performance Measure 4.11.

 

Data Dictionary

 

Additional Information 

 
Source: 
 
Contact:\u202f Craig Hayton
 
Contact E-Mail:\u202fCraig_Hayton@tempe.gov
 
Data Source Type:\u202f CSV
 
Preparation Method:\u202f Tracked by staff quarterly
 
Publish Frequency:\u202f Annual
 
Publish Method:\u202fManual
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