AndreaBozzo/ceres-open-data-index huggingface dataset:
chsk_gradient

This dataset features the NetMap processed streamlines for the Chilkat Watershed, providing a representation of the regions stream network. The streamlines are derived from advanced geospatial analyses, offering critical insights into the hydrological landscape of this ecologically rich region. Regionally derived intrinsic potential models have been developed and summarized, highlighting the potential habitat quality for anadromous fish species. These models assess the upstream effects of various geomorphic and hydrological attributes, providing valuable information for habitat restoration and conservation efforts. This layer is intended for use by resource managers, conservationists, and researchers involved in aquatic habitat assessments, restoration planning, and watershed management. It serves as a vital tool for identifying priority areas for conservation and understanding the ecological dynamics of stream systems.
Migratory Bird Rehabilitation Facilities

List of federally permitted migratory bird rehabilitation facilities. This dataset populates an app available to the public to assist in locating a federally-permitted migratory bird rehabilitation facility near you. Addresses and phone numbers are available to facilitate treatment of injured birds. For more information, please contact the content administrator. Maintained by the US Fish and Wildlife Service Migratory Bird Program. Data source: ePermits.fws.gov Updated: April 2025 For more information on the USFWS Migratory Bird Permit Offices, please visit our [website]. A federal migratory bird rehabilitation permit is required to take, transport, and/or temporarily possess sick, injured, and orphaned migratory birds for rehabilitation purposes.
NatureScape Landscape Conservation Design, Southern Appalachians

Systematic conservation planning is well suited to address the many large-scale biodiversity conservation challenges facing the Appalachian region. However, broad, well-connected landscapes will be required to sustain many of the natural resources important to this area into the future. If these landscapes are to be resilient to impending change, it will likely require an orchestrated and collaborative effort reaching across jurisdictional and political boundaries. The first step in realizing this vision is prioritizing discrete places and actions that hold the greatest promise for the protection of biodiversity. Five conservation design elements covering many critical ecological processes and patterns across the Appalachian Landscape Conservation Cooperative (LCC) geography were identified. These elements include large interconnected regions as well as the broad landscapes that connect them. Small areas that are likely to contain larger ecological significance than their size would suggest were also identified. There are eight ‘locally connected cores’ identified. These areas are locally significant (irreplaceable) and also have high internal local connectivity: 1. Cumberland Plateau – Chattanooga Local Core 2. Daniel Boone Local Core 3. Nashville Basin Local Core 4. Hoosier – Interior Low Plateau Local Core 5. Mammoth Cave – Campbellsville Local Core 6. Cumberland Gap – Big South Fork – Chickamauga Local Core 7. Southern Finger Lakes – Alleghany Plateau Local Core 8. Lower Tennessee – Bankhead – Wheeler Local Core. ‘Regional linkages’ are region-scale corridors that connect large cores. Three were mapped: 1. Northern Cumberland – Blue Ridge Linkage (Connecting S. Blue Ridge to Central Appalachian Core to the north). 2. Southern Cumberland – Blue Ridge Linkage (Connecting S. Blue Ridge to Central Appalachian Core to the south) 31 3. Northern Sandstone Ridges Linkage (Connecting Central Appalachian Alleghany Regional Core to Delaware Water Gap – Catskills Regional Core)
AR-LA West Gulf Coastal Plain Open Pine Delivery Priority Tool (FY26)

FOR non-AGOL ACCOUNT HOLDERS, DOWNLOAD THIS GEOSPATIAL DATA HERE: [https://gis-fws.opendata.arcgis.com/search?tags=lmvjv] AR-LA West Gulf Coastal Plain Open Pine Delivery Priority Tool (FY26) delivery priority tool was created by compiling the following with partner review: · WGCPO Open Pine Decision Support Model 2.1 landscape o based on open pine priority bird landscape files o all area with priority value over .201 based on feedback from RCPP delivery team, filtered to contiguous priority landscapes over 50,000 acres ( RCW secondary core population threshold), buffered by 2 miles (Bachman's dispersal distance), · Building landscapes concept o Anchor points plus buffer - defined as existing 245+ acre properties managed for open pine woodland/savanna/prairie - 60 BA or less with fire with 2 mile buffers (Bachman's dispersal in open pine landbird plan) o Within 5 miles of known/documented RCW · Other priorities - where does it make sense to focus work based on soils/other priorities among partners o blackland prairie soils with 2 miles buffers o saline barrens in LA with 2 mile buffers o AGFC Conservation Opportunity Areas § sandhills COA (and expanded concept to NW LA across state lines) explicit boundary § blackland prairie COA explicit boundary
USFWS Wildlife Spill Reponse Survey Tracks

The U.S. Fish and Wildlife Service participates in responses to wildlife impacted by oil spills. This digital data collection form and subsequent dataset contains information regarding the search and reporting of oiled wildlife following a spill. Partner agencies (federal, state, NGO, etc.) can participate in this reporting system and feed information directly to the incident command team during spills. The intention of this system is to provide first responders and situation leaders with real-time information to make decisions during developing spill scenarios. The submitted responses will feed into dashboards, maps, and reports that can be used for decision making. The dataset consists of both point and polygon geometry data along with stand alone tables. Polygon data represents the general perimeter of the search area during the spill response. Point data represents the specific location of individual wildlife that was observed during the search. Tabular data represents three related tables comprised of the following: general spill site information, species-specific wildlife information, and individual animal information. Role and Responsibilities Data Trustee: Anne Condon (anne_condon@fws.gov) Data Steward: Jo Ann Banda (joann_banda@fws.gov) * If data needs edited, deleted or updated, please reach out to JoAnne directly to facilitate those requests. Feature Layer for the USFWS Wildlife Spill Response Survey. This layer is captured in the QuickCapture App named “ USFWS Wildlife Spill Reponse Survey Tracks” and linked the to the Survey123 app “USFWS Wildlife Spill Response Survey- Smartphone Layout” and “USFWS Spill Response Survey- Tablet Layout” (see below for links). Associated QuickCapture Layers: * Map - [https://fws.maps.arcgis.com/home/item.html?id=3ab21a3c1a2c4049929459602b80821f] * QuickCapture Project Layer - [https://fws.maps.arcgis.com/home/item.html?id=52b61562cb6647f2be4d9ed716fbec61] Associated Survey123 Layers: * Feature Layer - [https://fws.maps.arcgis.com/home/item.html?id=ac90cf8355354d3191c800e7ca61eed8] * Map - [https://fws.maps.arcgis.com/home/item.html?id=050788507efd48c4a804db7933807785] * Form (Smartphone) - [https://fws.maps.arcgis.com/home/item.html?id=134bd49adf724962b3e164d5b4045987] * Form (Tablet) - [https://fws.maps.arcgis.com/home/item.html?id=e1e669f0bf5349e2abef28ba4e56f51d] * Dashboard - [https://fws.maps.arcgis.com/home/item.html?id=e17300bb5ffc4b62b468df0381b52cf5] ServCat Project: [https://iris.fws.gov/APPS/ServCat/Reference/Profile/183897]
Montana Mesonet Stations

Geographic web layer representation of the Montana Mesonet web resource https://mesonet.climate.umt.edu/api/stations/?type=json. The Montana Climate Office (MCO) installed 6 weather and soil moisture monitoring stations in 2016 as part of the Montana Research and Economic Development Initiative (MREDI). The Mesonet was designed to support decision-making for statewide drought assessments, precision agriculture and rangeland and forested watershed management. Since 2016 the network has grown to over 100 stations through support from private landowners, watershed groups, tribes, state agencies and grants from federal entities. In 2020 the MCO was awarded a contract from the U.S. Army Corps to add 205 additional stations. The new stations will be installed every 500 square miles in central and eastern Montana to improve drought assessments and flood forecasting - in the protection of lives and property. See https://climate.umt.edu/mesonet/ for more information. Citation: Montana Mesonet, Montana Climate Office 2025. Data accessed from https://mesonet.climate.umt.edu/api/stations/?type=json on 12/15/2025.
Sediment Risk Index (SRI) Stream Crossing Surveys

The Sediment Risk Index was developed as a model for prioritizing stream crossings in the Choctawhatchee watershed within southeast Alabama from 2006 to 2007. Over seventy-three variables were collected from stream crossings including information on waterway conditions, crossing structures, road approaches, and roadside soil erosion features that promote sediment delivery. These findings were summarized into a 12-metric index (the SRI), modeled after Karr’s Index of Biotic Integrity (IBI) for fish and other assemblages (Karr, 1981). The goal of condensing stream crossings data into an index was to reduce the time and manpower investment of collecting data. Each metric of the SRI is standardized with scores 1 (most-impacted), 3 (moderately-impacted), or 5 (least-impacted) (Stewart et al., 2006) The scores for the 12 metrics are summed into a single SRI value ranging from 12 to 60, which is used for determining site rank and condition.
Gain and Loss of Ecological Integrity 2001-2021

File-based downloads available at [https://www.sciencebase.gov/catalog/item/67aa1820d34e329fb204348b] Conservation of species’ mobility and ecological integrity is necessary for the productivity of the sagebrush biome in the western United States. Building on the recently developed Sagebrush Conservation Design (SCD) that mapped sagebrush ecological integrity (SEI)—defined as the higher cover of sagebrush and perennial grass and reduced threats due to invasive annual grass, tree encroachment, and human disturbance—we modeled the structural connectivity of sagebrush ecosystems to better incorporate the role of landscape-level processes into assessments of integrity. Because integrity can vary spatially, as well as temporally, we quantified both interannual variability and trends in variability in SEI from 2001–2021. We used the resultant map to identify areas with high structural landscape connectivity (i.e., “well-connected cores”), then determined the coincident core sagebrush areas (CSAs) that represent functioning sagebrush ecosystem with few landscape threats, and growth opportunity areas (GOAs) that represent functioning systems impacted by one or more threats as originally defined and mapped in the SCD. We found that CSAs were located in areas with higher landscape connectivity, and the biome-wide average of SEI declined by 30% from 2001 to 2021, although the structural connectivity biome-wide declined one-third less (by 20%). CSAs located in areas with high connectivity had 25% higher SEI values on average than those with low connectivity, and the trend in declining SEI values was slower. Our datasets of landscape connectivity can be combined with other SCD products to provide a broader ecosystem context—both spatially and temporally. Our results can be used to inform, refine, focus, and prioritize conservation and management efforts to those CSAs and GOAs we identified as particularly well connected and which may be more resilient to recently altered dynamics and declines—those that will serve to anchor efforts to conserve the sagebrush biome in light of changing land use and climate.
Connecticut River Watershed Boundary

This dataset represents the Connecticut River Watershed Boundary and is from the Watershed Boundary Dataset (WBD). The WBD defines the areal extent of surface water drainage to a point, accounting for all land and surface areas. Watershed Boundaries are determined solely upon science-based hydrologic principles, not favoring any administrative boundaries or special projects, nor particular program or agency. The intent of defining Hydrologic Units (HU) for the Watershed Boundary Dataset is to establish a baseline drainage boundary framework, accounting for all land and surface areas. At a minimum, the WBD is being delineated and georeferenced to the USGS 1:24,000 scale topographic base map meeting National Map Accuracy Standards (NMAS). Hydrologic units are given a Hydrologic Unit Code (HUC). For example, a hydrologic region has a 2-digit HUC. A HUC describes where the unit is in the country and the level of the unit. The document "Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD)" can be found here: http://pubs.usgs.gov/tm/tm11a3/pdf/WBD-Ed3_052212.pdfA hydrological unit is a drainage area delineated to nest in a multi-level, hierarchical drainage system. Its boundaries are defined by the hyrographic and topographic criteria that delinate an area of land upstream from a specific point on a river, stream, or similar surface waters. The hydrologic units are only synonymous with class watersheds when their boundaries include all the source area contributing surface water to a single defined outlet point.Please see http://nhd.usgs.gov/wbd.html for more information
RCT Core Geospatial

This map service supports the River Conditions Tool (https://uppermissouriheadwaters.org/UMHW_RiverConditions/index.html). It contains sub-layers (Stream Section Endpoints, Stream Gages, SNOTEL Sites, Stream Sections, Lakes/Reservoirs, Basins/Large Geographies, Watershed, etc.) to run the River Conditions Tool (RCT). Data creation involved input from local groups (conservation districts, watershed groups, stewardship councils, etc.), then USFWS R6 Science Applications and Montana State University compiled the data. Leveraging the investment of the USFWS R6- Science Applications’ funding, the RCT supports stakeholders in drought management planning by improving access to US Geological Survey, Montana Fish Wildlife and Parks, and Montana Dept. of Natural Resources and Conservation (DNRC), Colorado Division of Water Resources (DWR) stream gage data. These data drive RCT functionality for eight subbasins within the Missouri Headwaters Basin. The RCT can be linked or embedded on websites for easy access to real-time stream conditions such as temperature and flow, and river status (e.g., restrictions, closures).
Northeast State's Conservation Opportunity Areas, 2015

This map represents the identified Conservation Opportunity Areas (COAs) for the Northeast states that were developed in 2015 based on the State Wildlife Action Plans (SWAPs). SWAPs are developed by U.S. states and territories to conserve wildlife and habitat before they become too rare or costly to restore. In 2005, all 50 States and five U.S. Territories developed a SWAP with updates occuring every 10 years. Each plan includes the identification of Species of Greatest Conservation Need for that state and deliniates COAs. COAs indicate where conservation actions are needed and are designed to coordination conservation between partners and guide actions based on goals. Each state develops COAs independently of each other. For more information on state specific SWAPs and COAs visit: [https://northeastwildlifediversity.org/swap].
2001 - 2020 Sagebrush Ecological Integrity (SEI) Changes

File-based data for download:https://www.sciencebase.gov/catalog/item/65564cbad34ee4b6e05c47fc This layer calculated changes between the first and last time steps from the Sagebrush Conservation Design dataset. Calculations were done by adding the first and second time step rasters using the Raster Calculator tool in ArcGIS Pro. The later raster was reclassified with the following values Non-Rangeland Areas = 0, Core Sagebrush Areas = 10, Growth Opportunity Areas = 20, Other Rangeland Areas = 30. This created a raster showing change with the following values. Non-Rangeland to Non-Rangeland = 0Core to Non-Rangeland =1, Growth to Non-Rangeland = 2,Other to Non-Rangeland = 3Non-Rangeland to Core = 10Core to Core = 11Growth to Core = 12Other to Core = 13Non-Rangeland to Growth = 20Core to Growth = 21Growth to Growth = 22Other to Growth = 23Non-Rangeland to Other = 30Core to Other = 31Growth to Other = 32Other to Other = 33The purpose of these data are to provide a biome-wide, consistent, quantitative information about changes in sagebrush core habitat and growth areas. These data may be used to enable better prioritization of landscapes for conservation, and to inform which treatments or other conservation actions are appropriate in specific areas.Original Data cited as:Doherty, K., Theobald, D.M., Holdrege, M.C., Wiechman, L.A., and Bradford, J.B., 2022, Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design: U.S. Geological Survey data release, https://doi.org/10.5066/P94Y5CDV.Supporting literature for original dataset:Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2922, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.
A Strategic and Science-Based Framework for Management of Invasive Annual Grasses in the Sagebrush Biome - Cores Only

File-based data available at: https://www.sciencebase.gov/catalog/item/676d8256d34e3ce2f6084e91 In the last 20 years, the North American sagebrush biome has lost over 500 000 ha of intact and largely intact sagebrush plant communities on an annual basis. Much of this loss has been associated with expansion and infilling of invasive annual grasses (IAGs). These species are highly competitive against native perennial grasses in disturbed environments, and create fuel conditions that increase both the likelihood of fire ignition and the ease of wildfire spread across large landscapes. Given the current rate of IAG expansion in both burned and unburned rangelands, we propose a range-wide paradigm shift from opportunistic and reactive management, to a framework that spatially prioritizes maintenance of largely intact, uninvaded areas and improvement of invaded habitats in strategic locations. We created a framework accompanied by biome-wide priority maps using geospatial overlays that target areas to MAINTAIN large, uninvaded areas as natural resource anchors through activities to prevent IAGs, IMPROVE areas where management success in restoring large, intact landscapes is most likely, and CONTAIN IAG infestations where necessary. We then offer three case studies to illustrate the use of these concepts and map products at multiple scales. Our map products operate at the biome scale using regional data sources and additional data sources will be needed to inform local conservation planning. However, the basic strategic management principles of (1) maintaining the intact and uninvaded areas that we can least afford to lose to IAGs, (2) improving areas where we have a reasonable likelihood of restoration success, and (3) containing problems where we must, are timely, relevant, and scalable from the biome to local levels.
Classified Annualized Burn Probability within Sagebrush Ecological Integrity Growth Opportunity Areas and Low Resilience or Resistance Map

For millennia, wildfire has helped shape the sagebrush biome of the western United States. Over recent decades, historical fire regimes have been altered by several factors, including contemporary climate and fuel conditions, leading to the loss or degradation of hundreds of thousands of hectares (ha) of sagebrush each year. In response to wildfire threats, extensive fuel treatment investments are proposed across the region. To help inform strategic and cost-effective investments, we conducted a quantitative assessment of wildfire risk for the sagebrush biome. We used a geospatial fire modeling approach, customized for the sagebrush biome, to estimate spatially explicit burn probability and expected average annual area burned within three Sagebrush Ecological Integrity classes under the Sagebrush Conservation Design: Core Sagebrush Areas (CSAs), Growth Opportunity Areas (GOAs), and Other Rangeland Areas. We further used indices of ecological resilience to disturbance and resistance to invasive grasses to characterize fire risk and recovery potential. Our approach indicates that nearly 530,000 ha are likely to burn in a typical contemporary fire year across the highest integrity Sagebrush Ecological Integrity classes (7% in CSAs and 31% in GOAs). Of the CSAs and GOAs likely to burn, nearly 9000 and 66000 ha, respectively, are expected to have low resilience or resistance and therefore highest loss potential. Cost-effective conservation investments should include wildfire protection for high-integrity sagebrush with low resilience or resistance. Protection objectives may be met with strategically placed fuel breaks intended to enhance fire prevention and containment efforts. Fuel treatments, including prescribed fire and mechanical activities outside of fuel breaks, are by contrast best suited for high-integrity areas with relatively high resilience and resistance. Those activities should be risk-informed and intended to maintain or improve ecological integrity and resilience to wildfire rather than to exclude fire altogether. For ease of interpretation, we created a classed version of the continuous BP dataset using breaks that produced four categories similar to the R|R data: 1) low, 2) moderately low (mod-low), 3) moderate, and 4) moderately high to high (mod-high). The low probability class included values up to 0.0001, which represents a 1-in-10000 annualized probability of burning under contemporary conditions for its maximum pixel value. The maximum pixel values in the mod-low, moderate, mod-high, and high categories were 0.001, 0.01, 0.1, and 1, respectively, such that each represented a ten-fold increase in BP. However, the maximum BP value for the entire analysis area was 0.112, and there were very few pixels in the highest (>0.1) category—hence the reason that the moderately high and high classes were combined to form the mod-high class. These data were used to create Figure 3 B–D in Crist et al. 2024.