AndreaBozzo/ceres-open-data-index huggingface dataset:
FWS_R5_FR_LGLFWCO_AIS_EDM_Ichthyoplankton_PublicView

Ichthyoplankton (larval fish) surveys are a component of the U.S Fish and Wildlife Services (USFWS) Lower Great Lakes Fish and Wildlife Conservation Office (LGLFWCO) Aquatic Invasive Species (AIS) Early Detection and Monitoring (EDM) program. Larval fish surveys were included in the EDM program starting in 2014 with the hopes of increasing AIS detections by focusing on sampling the most abundant life-stage of fishes. Surveys occurred annually from 2014 - present, excluding 2020 due to COVID-19 restrictions which prevented sampling for that year. Sampling focuses on harbors and rivers of the lower Great Lakes (Erie and Ontario) as determined by a [risk-based prioritization framework for AIS in the Great Lakes]. Samples are collected using larval seine nets (500 µm) and plankton nets (500 µm) that are towed from the stern or pushed from the bow of a vessel for a short duration (usually 5 minutes). At one point, light traps were also used but are no longer apart of the current sampling gear suite. Samples are preserved using 95% ethanol (EtOH) which allows for tissue and DNA preservation. Samples are processed for fish identification in a few different ways: ethanol decant, tissue homogenate and taxonomically. Ethanol decant and tissue homogenizing are genetic identification methods that are provided by the Northeast Fishery Center (NEFC). Taxonomic identifications are conducted in-house (LGLFWCO) using available keys for larval fishes of the Great Lakes region. Recently, ethanol decants have been the primary method of identification due the efficiency of sample processing. The information within this dataset is geospatial in nature and documents larval fish sampling events. Both abiotic and biotic data is collected for each individual sampling event. Taxonomic and genetic identifications of eggs and larvae are to the lowest possible taxa. Genetic identification results only represent presence/absence of a species detection. Taxonomic identification results could include the number of species in the sample. Although infrequent, some early stage juvenile fish are represented in this data set. This data set contains survey information from 2017 - present (excluding 2020). Although collections began in 2014, the early years of implementation resulted in inconsistencies requiring their exclusion here.
SRU_GUSG_Map

The CED (Conservation Efforts Database https://conservationefforts.org/) has implementing an option for Gunnison Sage Grouse CED data providers to report conservation efforts that are conducted on private lands. This option provides spatial ambiguity to alleviate concerns of too much spatial detail representing private landowners’ efforts. This option allows CED data providers to pick a predetermined spatial SRU (Sagebrush Reporting Unit) instead of submitting the explicit effort boundary. These SRU are to be large enough to provide spatial ambiguity and obscure private landowner locations.
FWS WSFR Nevada Bighorn Sheep Herds

This layer shows the general distribution of Bighorn Sheep herds in Nevada. Data provided by the Nevada Department of Wildlife. Herds are labeled by subspecies, approximate date established, and associated mountain range. This map is part of the Storymap "Bringing Bighorns Back from the Brink" developed for the USFWS Wildlife and Sport Fish Restoration Program's "Partner with a Payer" initiative. This layer is intended to show a generalized view of bighorn sheep in Nevada; it is not intended as a resource for specific boundaries. For a direct link to Nevada's publicly available geospatial data, visit the Nevada Department of Wildlife's GIS Data Clearinghouse.
FWS WSFR Bighorn Sheep Translocations Nevada

This layer shows the translocation of bighorn sheep in Nevada over time as point data in a time series. Data provided by the Nevada Department of Wildlife. Translocations are labeled by date, subspecies, and quantities of sheep released. This app is part of the Storymap "Bringing Bighorns Back from the Brink" developed for the USFWS Wildlife and Sport Fish Restoration Program's "Partner with a Payer" initiative. For a direct link to Nevada's publicly available geospatial data, visit the Nevada Department of Wildlife's GIS Data Clearinghouse.
Black Carp Working Group Data: HUC2 Watershed Boundaries

The HUC 2 unit watershed boundaries were extracted from the U.S. Geological Survey (USGS) National Hydrology Dataset from the Watershed Boundary Data set. The hosted feature layer is being optimized to draw better at different scales. Please refer to the USGS National Hydrology site for more information: https://www.usgs.gov/core-science-systems/ngp/national-hydrography/about-national-hydrography-products?qt-science_support_page_related_con=0#qt-science_support_page_related_con This data set is for display purposes only and is to be used for the FWS Black Carp working group only.
Black Carp Working Group Data: HUC8 Watershed Boundaries

The HUC 8 unit watershed boundaries were extracted from the U.S. Geological Survey (USGS) National Hydrology Dataset from the Watershed Boundary Data set. Please refer to the USGS National Hydrology site for more information: https://www.usgs.gov/core-science-systems/ngp/national-hydrography/about-national-hydrography-products?qt-science_support_page_related_con=0#qt-science_support_page_related_con This data set is for display purposes only and is to be used for the FWS Black Carp working group only.
bats_labo_150

This is a feature set depicting estimated bat activity level for Eastern Red Bats (Lasiurus borealis), modeled using the Great Lakes bat acoustic dataset (downloadable from https://www.fws.gov/radar/acoustic/index.html and also available on the BatAmp platform). A series of spatio-temporal models were built with decision tree regression on various spatial covariates (land cover, distance from physiographic features, geography) and temporal covariates (weather, time of night, time of season) with bat pass counts (at locations specified in the Bat Sites point layer) as the response. The covariate-specific responses identified by the models were then used to predict bat pass rates across the region based on spatial characteristics, under a set of static temporal conditions associated with above-average bat activity levels. The values of each shape indicate the predicted activity level on a relative scale, taking a categorical value from a range of 24 different values. The activity levels depicted represent Eastern Red Bats (Lasiurus borealis). A thorough description of the modeling methodology can be provided in the manuscript "Spatial models of acoustic data identify Great Lakes coasts and northern forests as bat activity hotspots" by Heist et al. (available on request from FWS Radar Project [fws.gov/radar] or Kevin Heist [kevinwheist@gmail.com])
bats_laci_150

This is a feature set depicting estimated bat activity level for the Hoary Bat (Lasiurus cinereus), modeled using the Great Lakes bat acoustic dataset (downloadable from https://www.fws.gov/radar/acoustic/index.html and also available on the BatAmp platform). A series of spatio-temporal models were built with decision tree regression on various spatial covariates (land cover, distance from physiographic features, geography) and temporal covariates (weather, time of night, time of season) with bat pass counts (at locations specified in the Bat Sites point layer) as the response. The covariate-specific responses identified by the models were then used to predict bat pass rates across the region based on spatial characteristics, under a set of static temporal conditions associated with above-average bat activity levels. The values of each shape indicate the predicted activity level on a relative scale, taking a categorical value from a range of 24 different values. The activity levels depicted represent the Hoary Bat (Lasiurus cinereus). A thorough description of the modeling methodology can be provided in the manuscript "Spatial models of acoustic data identify Great Lakes coasts and northern forests as bat activity hotspots" by Heist et al. (available on request from FWS Radar Project [fws.gov/radar] or Kevin Heist [kevinwheist@gmail.com])
bats_mtb_150

This is a feature set depicting estimated bat activity level for the migratory tree bats (Hoary Bat (Lasiurus cinereus), Eastern Red Bat (L. borealis), and Silver haired bat (Lasionycteris noctivagans)) modeled using the Great Lakes bat acoustic dataset (downloadable from https://www.fws.gov/radar/acoustic/index.html and also available on the BatAmp platform). A series of spatio-temporal models were built with decision tree regression on various spatial covariates (land cover, distance from physiographic features, geography) and temporal covariates (weather, time of night, time of season) with bat pass counts (at locations specified in the Bat Sites point layer) as the response. The covariate-specific responses identified by the models were then used to predict bat pass rates across the region based on spatial characteristics, under a set of static temporal conditions associated with above-average bat activity levels. The values of each shape indicate the predicted activity level on a relative scale, taking a categorical value from a range of 24 different values. The activity levels depicted represent migratory tree bats (Hoary Bat (Lasiurus cinereus), Eastern Red Bat (L. borealis), and Silver haired bat (Lasionycteris noctivagans)). A thorough description of the modeling methodology can be provided in the manuscript "Spatial models of acoustic data identify Great Lakes coasts and northern forests as bat activity hotspots" by Heist et al. (available on request from FWS Radar Project [fws.gov/radar] or Kevin Heist [kevinwheist@gmail.com])
bats_myotis_150

This is a feature set depicting estimated bat activity level for bats of the genus Myotis (Myotis lucifugus, M. septentrionalis) modeled using the Great Lakes bat acoustic dataset (downloadable from https://www.fws.gov/radar/acoustic/index.html and also available on the BatAmp platform). These species were grouped as the calls used to identify the two species are difficult to distinguish from one another. A series of spatio-temporal models were built with decision tree regression on various spatial covariates (land cover, distance from physiographic features, geography) and temporal covariates (weather, time of night, time of season) with bat pass counts (at locations specified in the Bat Sites point layer) as the response. The covarite-specific responses identified by the models were then used to predict bat pass rates across the region based on spatial characteristics, under a set of static temporal conditions associated with above-average bat activity levels. The values of each shape indicate the predicted activity level on a relative scale, taking a categorical value from a range of 24 different values. The activity levels depicted represent bats of the genus Myotis (Myotis lucifugus, M. septentrionalis). A thorough description of the modeling methodology can be provided in the manuscript "Spatial models of acoustic data identify Great Lakes coasts and northern forests as bat activity hotspots" by Heist et al. (available on request from FWS Radar Project [fws.gov/radar] or Kevin Heist [kevinwheist@gmail.com])
LandAcquisition_updateApril2020

In July 2017, a Natural Resource Damage Assessment and Restoration (NRDAR) settlement was approved by the United States District Court for the Western District of Virginia. The NRDAR trustees, U.S. Fish and Wildlife Service and the Commonwealth of Virginia, are working to fund and complete restoration projects that will benefit the same types of natural resources that were injured for the future enjoyment of the community. This dataset shows the general locations of DuPont NRDAR restoration projects (selected as of December 2017 ). The dataset was created as a geographic representation of land protection project locations within the impacted watershed to indicate general location of pending and ongoing projects. This dataset should not be used for legal purposes or to show precise locations. The dataset was created to allow interested public to view potential restoration locations and as a way for the Trustees to share status updates for ongoing projects as they progress or change. It is a graphical representation and has limitations of accuracy.
Driving Directions

Route and directions for Driving Directions Route Details * 363.55 minutes * 332.96 miles * Route start time: Wed Jun 14 2017 15:54:00 GMT-0500 (Central Daylight Time) Directions 1. Start at W4279 Headquarters Road Mayville, WI 53050 2. Go east on Headquarters Rd toward CR-Z 3. Make sharp left on CR-Z 4. Turn left on WI-49 (CR-Z) 5. Turn left on WI-68 (Fox Lake Rd) 6. Turn right on E State St (WI-68) 7. Continue on WI-33 (E State St) 8. Turn left on Cascade Mountain Rd 9. Arrive at W10040 Cascade Mountain Road Portage, WI 53901, on the left 10. Depart W10040 Cascade Mountain Road Portage, WI 53901 11. Go back west on Cascade Mountain Rd 12. Turn right on WI-33 (Kinney Rd) 13. Turn left onto ramp and go on I-90 W / I-94 W toward Wis Dells 14. At exit 61 bear right onto ramp to WI-80 toward Necedah / New Lisbon 15. Make sharp right on WI-80 (E Bridge St) 16. Turn left on 9th Ave 17. Turn left on 35th St 18. Turn right on 8th Ave 19. Turn left on 28th St W 20. Turn right on 7th Ave 21. Turn left on 22nd St 22. Continue on 7th Ave 23. Turn right on 20th St (Grand Dyke Rd) 24. Bear right on Headquarters Rd 25. Arrive at N11385 Headquarters Rd, Necedah, Wisconsin, 54646, on the right 26. Depart N11385 Headquarters Rd, Necedah, Wisconsin, 54646 27. Go back northwest on Headquarters Rd 28. Bear left on 20th St (Grand Dyke Rd) 29. Turn left on 7th Ave 30. Make sharp right on WI-21 (State Road 21) 31. Turn left on CR-PP (County Highway PP) 32. Turn right onto ramp and go on I-90 W / I-94 W toward Tomah 33. At exit 45 take ramp on the left and go on I-90 W toward Tomah / La Crosse 34. At exit 4 take ramp on the right to US-53 N / WI-157 toward La Crosse / Onalaska 35. Turn right on US-53 N (WI-157 W) 36. Take ramp on the right to WI-35 N / The Great River Road toward Holmen (CR-HD) / Trempealeau 37. Turn left on WI-35 N (WI-35-BR N) 38. Turn right at 3rd St / Main St to stay on WI-35 N (Main St) 39. Turn left on 10th St 40. Turn right on Pine St 41. Continue on Park Rd 42. Turn right on West Prairie Rd (W Prairie Rd) 43. Turn left on Refuge Rd 44. Arrive at W28488 Refuge Rd, Trempealeau, Wisconsin, 54661, on the right 45. Depart W28488 Refuge Rd, Trempealeau, Wisconsin, 54661 46. Go back northeast on Refuge Rd 47. Turn left on West Prairie Rd (W Prairie Rd) 48. Turn right on WI-35 (WI-54) 49. Turn left on Todd Rd 50. Turn left on WI-93 (State Road 35 54) 51. Turn left toward St Paul 52. Take ramp and go on I-94 W 53. At exit 19 take ramp on the right to US-63 toward Baldwin / Ellsworth 54. Turn right on US-63 N (10th St) 55. At roundabout, take the third exit to proceed on WI-64 W (Highway 64) 56. Turn left on 95th St 57. Finish at 1764 95th Street, New Richmond, WI 54017, on the right
Sammamish Kokanee NHD Streams

Regional map showing Lake Sammamish and Tributaries. Hydrography source data is from USGS (https://nhd.usgs.gov/). Feature layer generated from running the Find Existing Locations solutions for Washington State NHD Hydrography-NHD Rivers. Expression Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Issaquah Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'East Fork Issaquah Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Tibbetts Creek' or Washington State NHD Hydrography-NHD Rivers where Permanent_Identifier is '148889234' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012000339' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012001631' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012001600' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012004740' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012001568' or Washington State NHD Hydrography-NHD Rivers where ReachCode is '17110012001563' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'George Davis Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Ebright Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Pine Lake Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Zackuse Creek' or Washington State NHD Hydrography-NHD Rivers where Permanent_Identifier is '148888780' or Washington State NHD Hydrography-NHD Rivers where Permanent_Identifier is '148888783' or Washington State NHD Hydrography-NHD Rivers where Permanent_Identifier is '148888784' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Lewis Creek' or Washington State NHD Hydrography-NHD Rivers where GNIS_Name is 'Squibbs Creek'
USFWS Lands

This data layer depicts lands and waters administered by the U.S. Fish and Wildlife Service (USFWS) in Wisconsin. It may also include inholdings that are not administered by USFWS. The intended application of this data layer is as a cadastral framework for use with other data layers in GIS and mapping applications. It is specifically not intended to be used as a land survey or representation of land for conveyance or tax purposes. The primary source for this information is the USFWS Realty program, current in May 2017 and not beyond. The audience of this map is the general public, particularly those with an interest in Migratory Bird Conservation Fund acquisitions within the NWR system
bats_epfulano_150

This is a feature set depicting estimated bat activity level for Silver haired bats (Lasionycteris noctivagans) and the Big brown bats (Eptesicus fuscus) modeled using the Great Lakes bat acoustic dataset (downloadable from https://www.fws.gov/radar/acoustic/index.html and also available on the BatAmp platform). These species were grouped as the calls used to identify the two species are difficult to distinguish from one another. A series of spatio-temporal models were built with decision tree regression on various spatial covariates (land cover, distance from physigraphic features, geography) and temporal covariates (weather, time of night, time of season) with bat pass counts (at locations specified in the Bat Sites point layer) as the response. The covariate-specific responses identified by the models were then used to predict bat pass rates across the region based on spatial characteristics, under a set of static temporal conditions associated with above-average bat activity levels. The values of each shape indicate the predicted activity level on a relative scale, taking a categorical value from a range of 24 different values. The activity levels depicted represent Silver haired bats (Lasionycteris noctivagans) and the Big brown bats (Eptesicus fuscus). A thorough description of the modeling methodology can be provided in the manuscript "Spatial models of acoustic data identify Great Lakes coasts and northern forests as bat activity hotspots" by Heist et al. (available on request from FWS Radar Project [fws.gov/radar] or Kevin Heist [kevinwheist@gmail.com])
SALS_Tool_final_patch_ranks_MARCH2020

This tool identifies the suite of patches (8680) with potential to support Saltmarsh Sparrows in the near and long term. The patches are ranked according to a formula that assesses the relative importance of a variety of factors known to positively or negatively influence Saltmarsh Sparrow populations (see Influence Factors tab). Patches in darker green colors are assumed to provide higher quality habitat than patches in lighter green colors and indicate important areas to focus conservation attention. This prioritization does not factor current density or abundance of Saltmarsh Sparrows into the results. Rather, it ranks patches according to their suitability to provide high quality Sparrow habitat according to expert opinion. This app includes This map shows tidal complexes that have been identified as “Resilient Migration Space” by The Nature Conservancy. Complexes with resilience scores of Average or above (e.g., “Slightly Above Average,” “Above Average,” “Far Above Average”) for the 3.0-ft. and 6-ft. sea level rise scenarios are displayed along with a protected areas layer. If a migration space unit was associated with more than one tidal complex, the highest resilience score was assigned. Click for areas for more information. For more about The Nature Conservancy Resilient Coastal Sites click here: https://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/climate/CoastalResilience/Pages/Resilient-Coastal-Sites--for-Conservation-across-the-Northeast-and-Mid-Atlantic-Seaboard.aspx
fl_WPRa_lo

This is a contour feature set depicting a surface of predicted migration intensity (Window Passage Rate, WPR) in units of targets per square km per hour. Predictions are based on data collected from 23 spring and 22 fall locations throughout the Great Lakes by the US Fish and Wildlife Service Avian Radar Team. Predictions were generated by sampling targets from the lowest 4 of 11 areas ("windows") of radar return arrayed altitudinally with consistently low interference (radar "clutter"). This method allowed for greater comparability among sites as it helps control for site effects in radar returns due to clutter. Window data were further corrected for level of effort (e.g. time radar on, precipitation interference) for each site. Predictions were generated by interpolating overall WPA during this season with landscape variables (% water cover, % forest cover, night light illumination, latitude, longitude, and proximity to a Great Lake) as spatial predictors of WPR. Fields Value_Min and Value_Max contain the minimum and maximum predicted WPR values for each of 20 passage rate categories. Fields SE and CoV contain the standard error and covariance of the predicted WPR for each cell. See Supplementary Information for details. The purpose of this dataset is to provide a broad view of differences in aerial migration intensity across the Upper Midwest Great Lakes region. This dataset may be used to identify general areas of greater and lesser airspace use by birds and bats. These estimates may also reflect differences in potential stopover intensity or use of terrestrial resources.
fl_WPRa_hi

This is a contour feature set depicting a surface of predicted migration intensity (Window Passage Rate, WPR) in units of targets per square km per hour. Predictions are based on data collected from 23 spring and 22 fall locations throughout the Great Lakes by the US Fish and Wildlife Service Avian Radar Team. Predictions were generated by sampling targets from the highest 2 of 11 areas ("windows") of radar return arrayed altitudinally with consistently low interference (radar "clutter"). This method allowed for greater comparability among sites as it helps control for site effects in radar returns due to clutter. Window data were further corrected for level of effort (e.g. time radar on, precipitation interference) for each site. Predictions were generated by interpolating overall WPA during this season with landscape variables (% water cover, % forest cover, night light illumination, latitude, longitude, and proximity to a Great Lake) as spatial predictors of WPR. Fields Value_Min and Value_Max contain the minimum and maximum predicted WPR values for each of 20 passage rate categories. Fields SE and CoV contain the standard error and covariance of the predicted WPR for each cell. See Supplementary Information for details. The purpose of this dataset is to provide a broad view of differences in aerial migration intensity across the Upper Midwest Great Lakes region. This dataset may be used to identify general areas of greater and lesser airspace use by birds and bats. These estimates may also reflect differences in potential stopover intensity or use of terrestrial resources.