Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Critical Habitat for Threatened and Endangered SpeciesThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Fish and Wildlife Service (FWS) feature layer, displays proposed and designated critical habitat under the U.S. Endangered Species Act. According to the FWS, "When the Fish and Wildlife Service proposes a species for listing under the Endangered Species Act, we are required to consider whether there are geographic areas that contain essential features on areas that are essential to conserve the species." Those areas may be proposed for designation as critical habitat. Critical habitat is a term defined and used in the Act. It is a specific geographic area(s) that contains features essential for the conservation of a threatened or endangered species and that may require special management and protection. Critical habitat may include an area that is not currently occupied by the species but that will be needed for its recovery. An area is designated as “critical habitat” after the FWS publishes a proposed Federal regulation in the Federal Register and receives and considers public comments on the proposal. The final boundaries of the critical habitat are also published in the Federal Register. Federal agencies are required to consult with the FWS on actions they carry out, fund, or authorize to ensure that their actions will not destroy or adversely modify critical habitat. These areas provide notice to the public and land managers of the importance of these areas to the conservation of a listed species. Special protections and/or restrictions are possible in areas where Federal funding, permits, licenses, authorizations, or actions occur or are required.Canada Lynx and Atlantic SalmonData currency: current federal service (USFWS Critical Habitat)NGDAID: 3 (FWS Critical Habitat for Threatened and Endangered Species Dataset)OGC API Features Link: Not AvailableFor more information: USFWS Threatened & Endangered Species Active Critical Habitat ReportFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Biodiversity and Ecosystems Theme Community. Per the Federal Geospatial Data Committee (FGDC), Biodiversity and Ecosystems is defined as pertaining to, or describing, "the dynamic processes, interactions, distributions, and relationships between and among organisms and their environments".For other NGDA Content: Esri Federal Datasets

This data package contains two types of data for the Jornada Experimental Range (JER) from 1915 to 1952: 1) shape files containing polygons and attribute tables that represent the pasture configurations on the Jornada Experimental Range and 2) monthly stocking data from these pastures. The livestock represented in the stocking data comprise cattle, horse, sheep, and goats. Grazing goats were infrequent and are grouped with sheep in the source data. As such for this data set, they are included in the sheep category. Stocking data are expressed in animal unit months (AUM), which is based on metabolic weight.This data package provides finer resolution AUM data than knb-lter-jrn.210412001, which presents the annual stocking data for the entire JER from 1916 to 2001. The stocking data in this package begins in June of 1915 and continues through December of 1952, the last year for which the researchers on this project have verified and digitized historical pasture configurations on the JER.https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-jrn&identifier=210412001

When a species is proposed for listing as endangered or threatened under the Endangered Species Act, the U.S. Fish and Wildlife Service must consider whether there are areas of habitat believed to be essential the species conservation. Those areas may be proposed for designation as critical habitat. Critical habitat is a term defined and used in the Act. It is a specific geographic area(s) that contains features essential for the conservation of a threatened or endangered species and that may require special management and protection. Critical habitat may include an area that is not currently occupied by the species but that will be needed for its recovery. An area is designated as critical habitat after the Service publishes a proposed Federal regulation in the Federal Register and receives and considers public comments on the proposal. The final boundaries of the critical habitat are also published in the Federal Register.

Analysis condicted by ABR Inc.–Environmental Research & Services.Data from ADFG/BLM/NSB and ConocoPhillips Alaska Inc.For Brownian Bridge Movement Models - Conducted dynamic Brownian Bridge Movement Models (dBBMM) to delineate movmeents on seasonal herd ranges. dBBMM models were run using the move package for r using the following methods.1. Locations within 30 days of first collaring were removed from the analysis. 2. Selected females from PTT and GPS collars during the date range July 1 2012–June 30 2017 and individuals having more than 30 locations per season.3. ran a dBBMMM model for each individual during each season using 1 km pixels. 4. Calculate the 95% isopleth for each individual.5. Overlap all 95% isopleths and calculate the proportion of animals using (as defined by 95% isopleth) each pixel. Value shown is proportion times 1000. Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30). For Kernel Density Estimates - Conducted Kernel Density Estimation (KDE) to delineate seasonal herd ranges. Kernels were run using the ks package for r and the plugin bandwidth estimator. 1. Locations within 30 days of first collaring were removed from the analysis. 2. The mean latitiude and longitude for each animal was calculated for each day.3. A KDE utilization distribution was calculated for Julian day of the season (all years combined). 4. The daily KDE uds were averaged across the season. This method accounts for individual's movements during the seasons without the overfitting that results from using autocorrelated lcoations from individuals.Seasons used: Winter (Dec 1-Apr 15); Spring (Apr 16-May 31); Calving (June 1-15); postcalving (June 16-30); Mosquito (July 1-15); Oestrid Fly (July 16-Aug 7); late summer (August 8-Sept 15); Fall Migration (Sept 16-Nov 30).

Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

An animal's home-range can be expected to encompass the resources it requires for surviving or reproducing. Thus, animals inhabiting a heterogeneous landscape, where resource patches vary in size, shape and distribution, will naturally have home-ranges of varied sizes, so that each home-range encompasses a minimum required amount of a resource. Home-range size can be estimated from telemetry data, and often key resources, or proxies for them such as the areas of important habitat types, can be mapped. We propose a new method, Resource-Area-Dependence Analysis (RADA), which uses a sample of tracked animals and a categorical map to i) infer in which map categories important resources are accessible, ii) within which home range cores they are found, and iii) estimate the mean minimum areas of these map categories required for such resource provision. We provide three examples of applying RADA to datasets of radio-tracked animals from southern England: 15 red squirrels Sciurus vulgaris, 17 gray squirrels S. carolinensis and 114 common buzzards Buteo buteo. The analyses showed that each red squirrel required a mean (95% CL) of 0.48 ha (0.24-0.97) of pine wood within the outermost home-range, each gray squirrel needed 0.34 ha (0.11-1.12) ha of mature deciduous woodland and 0.035-0.046 ha of wheat, also within the outermost home-range, while each buzzard required 0.54 ha (0.35-0.82) of rough ground close to the home-range center and 14 ha (11-17) of meadow within an intermediate core, with 52% of them also relying on 0.41 ha (0.29-0.59) of suburban land near the home-range center. RADA thus provides a useful tool to infer key animal resource requirements during studies of animal movement and habitat use.

Designates boundaries to establish extent of livestock distribution and management within the allotment. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Features represent element occurrences of significant natural communities (ecological communities), as recorded in the New York Natural Heritage Program's Biodiversity Database (Biotics). An element occurrence is one natural community type at one location. Examples of community types include deep emergent marsh, red maple-hardwood swamp, dwarf shrub bog, hemlock-northern hardwood forest, and tidal creek. Natural community occurrences in this dataset are considered significant from a statewide perspective. NY Natural Heritage documents all occurrences of community types that are rare in New York State. For more common community types, NY Natural Heritage documents occurrences where the community at that location is ranked as being of excellent or good quality, by meeting specific, documented criteria for size, undisturbed and intact condition, and quality of the surrounding landscape. A natural community is an assemblage of interacting plant and animal populations that share a common environment; the particular assemblage of plant and animal species occurs across the landscape in areas with similar environmental conditions. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats, ecosystems, and natural areas. NY Natural Heritage tracks locations of significant natural communities because they serve as habitat for a wide range of plants and animals, both rare and common; and because community occurrences in good condition support intact ecological processes and provide ecological value and services.

The Qld Wildlife API is being replaced

Please note that the Qld Wildlife API will soon be replaced with a new API. The new API is currently planned to be released in May 2025. Please come back to this webpage for updates. If you have any questions or feedback, please email wildnet@des.qld.gov.au

This API accesses data from QLD Government's WildNet database that has been approved for public release. There are a number of functions that retrieve species names, profiles, notes, statuses, images, species survey locations and project information.

Please see https://apps.des.qld.gov.au/species for more information on using the API functions.

Data can be retrieved in 3 different formats by adding the format variable to the end of the url (e.g. &f=xml). The default format is json if the f (format) variable is omitted.
E.g.
- JSON: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=json
- XML: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=xml
- CSV: https://apps.des.qld.gov.au/species/?op=getkingdomnames&f=csv

When spatial locations are returned, GeoJSON or KML will be used when requesting the json and xml formats.

Species profile search can be used to locate species information (by name or a taxonomy search). It uses the Get species by ID function to display species profiles with images and maps and uses the Get surveys by species function for downloading data.

Biomaps provides a map interface to display the WildNet records approved for publication with other spatial layers (such as cadastre, protected areas, vegetation and biodiversity value mapping). A range of WildNet species list reports based on all WildNet records and other environmental reports can be requested for properties and drawn areas etc.

WetlandMaps provides a map interface to display WildNet records approved for publication with other spatial layers (such as wetland mapping).

The Queensland Globe can be used to access WildNet records approved release and access summarised WildNet data in 10x10km grids.

Other WildNet products are made available via the Queensland Government Open Data Portal.

The resources listed below are the service endpoints for each of the operations (or functions) available.

Available variables
f: Format - Setting the 'f' variable will determine the format of the response. There are 4 possible options; json, xml, kml and csv. Json is the default if 'f' is not set. If the output is spatial, GeoJson will be return for 'f=json' and KML will be returned for 'f=xml' or 'f=kml'.
projids: Project Ids - Comma separated list of project ids. Use Get projects to access project IDs.
projtitle: Project Title - A title (full or partial) that is used as a search string to search for a project or projects.
proj: Include Project Details - This indicates if the project details are to be included in the output. The default is true.
org: Organisation ID - An ID that is associated with an organisation. Use Get organisations to access organisation IDs.
bbox: Bounding Box - A bounding box that defines a geographical area. Specified as top left, bottom right, e.g. latitude,longitude,latitude,longitude.
circle: Circle - A circle (buffered point) that defines a geographical area. It is specified as a centroid and a radius (metres), e.g. latitude,longitude,distance.
pagecount: Page Count - The number of records to return on a page.
pageindex: Page Index - The page index to return.
p: Location Precision - The distance in metres that indicates the accuracy of the records location.
min: Minimum Start Date - The earliest date for a record to be returned.
max: Maximum Start Date - The latest date for a record to be returned.
kingdom: Kingdom - A kingdom's common name.
class: Class - A class scientific name.
classes: Classes - A comma separated list of class scientific names.
family: Family - A scientific family name.
species: Species Name - A scientific species name.
taxonid: Taxon ID - A unique id that identifies a particular species. Use Species search to access taxonids for particular species.

This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326). Information on a total of 65,125 endemic species including 27,294 globally threatened endemic species (55% threatened plant species, 45% threatened animal species) was extracted from the IUCN Red List. The categories of threatened species used in the analyses included vulnerable (VU), endangered (EN), critically endangered (CR), extinct in the wild (EW) and globally extinct (EX). We calculated the proportion of globally threatened endemic species among the total number of assessed endemic species per country (Chamberlain et al., 2020). Associated environmental socioeconomic regional correlates included: 1) Cropland: The proportion of each country covered by crops (including food, fibre and fodder crops and pasture grasses) was determined based on a FAO global map with a resolution of 5 arc-minutes (von Velthuizen et al., 2007); 2) HANPP: The proportion of net primary production appropriated by humans (HANPP) by harvesting or burning biomass and by converting natural ecosystems to managed lands with lower productivity was derived for the year 2010 from Krausmann et al. (2013); 3) Delta HANPP: We also computed the increase in HANPP over the period 1962-2010 (Krausmann et al., 2013); 4) per area GDP: The per area gross domestic product (GDP, in international $) was obtained by calculating the median value over each country of all 5 arcmin cells of a recently gridded GDP dataset (Kummu et al., 2018); 5) Human Footprint (HFP): The global terrestrial human footprint (HFP) is an index integrating the influence of built environments, population density, electric infrastructure, croplands, pasture lands, roads, railways, and navigable waterways on the environment based on remotely-sensed and bottom-up survey information (Venter et al., 2016). We extracted from a 1 km resolution HFP map the median value over each country in 2009; 6) Delta HFP: We also calculated the increase in median HFP over the period 1993-2009 (Venter et al., 2016); 7) Invasive alien plants: The richness of invasive alien vascular plant species recorded in each country was compiled by Essl et al. (2019); 8) Invasive alien animals: The richness of invasive alien animal species was derived from the Global Register of Introduced and Invasive Species database (http://griis.org/ accessed on 27-6-2018); 9) Delta temperature: Based on decadal climate maps produced by the IPCC over the last century with a 0.5° resolution, we calculated the median of the change in annual mean temperature (in °C) between 1901-1910 and 1981-1990 (Mitchell & Jones, 2005); 10) Delta rainfall: The same for annual precipitation (in mm); 11) Velocity temperature: We also calculated the median velocity of climate change based on the formula from Hamann et al. (2015) to evaluate the distance (in °) over which a species must migrate over the surface of the earth to maintain constant temperature conditions; 12) Velocity rainfall: The same for precipitation; 13) Roadless areas: The median area of a roadless fragment (in km²) was calculated from the global map of roadless areas published by Ibisch et al. (2016); 14) Wilderness areas: The proportion of wildlands (categories ‘wild woodlands' and ‘wild treeless and barren lands') was calculated from the anthropogenic biome map of Ellis et al. (2010); 15) Protected areas: The proportion of protected areas was estimated from the IUCN's shapefile of World Database on Protected Areas (https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas); 16) Conservation spending: The mean annual conservation spending of each country (in international $) was taken from Waldron et al. (2017) to quantify investment to mitigate biodiversity loss; 17) Completeness of biodiversity information: We used data on the estimated percentage completeness of species records in GBIF, as assessed through comparison with independent estimates of native richness. Inventory effort indices available for vertebrates (Meyer et al., 2015) and vascular plants (Meyer et al., 2016) were merged into a single metric based upon an average weighted by estimated native species richness.

This dataset contains a collation of marine habitat and species biotope records created during contracts commissioned by Natural England; collected by Defra and associated bodies/agencies; or provided by third parties that have allowed their data to be republished under the Open Government Licence (OGL). There are two datasets available for download: 'Marine Habitats and Species Open Data' and 'Marine Designated Site Features Open Data'. The dataset 'Marine Habitats Species Open Data' comprises eight sub-datasets: three point datasets and five polygonal. These represent all publicly available datasets of marine habitats and species held by Natural England. The dataset 'Marine Designated Site Features Open Data' is a subset of the habitat and species data, which shows habitats and species (feature) data only within the site in which they are legally designated. The datasets comprises 6 sub-datasets: one point dataset and five polygonal. Both datasets are provided as an ESRI File Geodatabase (GDB) and as an OGC GeoPackage (GPKG). Additionally, 'Marine Designated Site Features Open Data' is provided with an ESRI structured layer file (LYR). All dataset geometry has been validated using the ESRI validation method. It has not been validated using the Open Geospatial Consortium (OGC) validation method and therefore may not comply with the OGC specification. These datasets are available under the Open Government Licence (OGL).

All analysis and visualization can be reproduced using the R programming language. The code used in the analysis is found in the GitHub repository for this paper: https://github.com/The-Frederickson-Lab/ant-legume-range

This dataset consists of the home ranges and satellite tracks taken from eleven dugongs and ten green turtles.

Methods:

Fast-acquisition satellite telemetry was used to track eleven dugongs and ten green turtles at two geographically distinct foraging locations in Queensland, Australia to evaluate the inter- and intra-species spatial relationships and assess the efficacy of existing protection zones. Home-range analysis and bathymetric modeling were used to determine spatial use and compared with existing protection areas using GIS.

Raw, unfiltered tracking data were collected using fast acquisition GPS satellite transmitters attached to six dugongs (three females and three males) and four adult female green sea turtles near Mabuiag Island, Torres Strait, Australia in July 2009 and September 2010, and five dugongs (four females and one male) and six female green sea turtles (five adults and one prepubescent) in Shoalwater Bay, Australia in June/July 2012. The dugongs were captured using the dermal hold fast technique in Torres Strait and the standard rodeo technique in Shoalwater Bay. At both locations, the dugongs were fitted with Telonics Gen 4 GPS/ARGOS marine units attached to a 3 m tether linked to a padded tailstock harness.

The green turtles were captured using the standard rodeo technique, brought to Mabuiag Island (Torres Strait) or MacDonald Point (Shoalwater Bay), and fitted with one of four types of satellite transmitters (Sirtrack F4G 291A, Wildlife Computers SPLASH10 BF-273A and Splash10 BF-273C, or SMRU SRDL 9000x). Each transmitter was attached to the carapace using the methods described in Shimada et al. (2012). Each turtle was released from shore the day after capture.

Dugong units were programmed to collect a GPS position hourly; turtle units every 30 minutes. All units were programmed with a five minute repeat in case a signal was not received when the animal surfaced.

Home-ranges were calculated for each animal using data from the entire period in which they were tracked and were calculated using fixed kernel density estimation with bandwidths selected by likelihood cross-validation (CVh). Kernel densities and bandwidths were calculated using the Geospatial Modelling Environment (GME), an extension to ArcGIS, with a resolution of 50 m.

For a more detailed description of the methods see Gredzens(2014).

Format:

This dataset consists of shapefiles for the satellite tracks (lines and points) for the 21 animals as well as shapefiles for the calculated home ranges.

References:

Gredzens C, Marsh H, Fuentes MMPB, Limpus CJ, Shimada T, et al. (2014) Satellite Tracking of Sympatric Marine Megafauna Can Inform the Biological Basis for Species Co-Management. PLoS ONE 9(6): e98944. doi:10.1371/journal.pone.0098944

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\1.2_GBR-Turtles-dugong-monitoing

Change log: 2024-05-29 - Added interactive map of the resource link to Layer id: ea_nerp:TS_QLD_NERP-1-2-2-1_JCU_Turtle-dugong-tracking_2009-2012

This national dataset contains geographic range data for 488 Species at risk based on NatureServe data, SAR recovery strategies, Environment Canada resources and COSEWIC status reports.

This dataset is managed by Environment Information Australia and is updated when new listings take effect, or a significant number of distributions have been added or updated.The data contains generalised versions of species distributions that have been modelledfor department use. This data should not be used for quantitative analysisdue to the level of generalisationapplied.This vector product contains distribution models for Species of National Environmental Significance (those listed as threatened or migratory)and some EPBC Act listed marineand cetacean species. The distributions are at a national scale and are indicative only. They are not intended for use at a local or regional scale and should not be used for absolute area calculations. The primary purpose of the data is to narrow the list of species or species habitat that might reasonably occur in an area of interest when undertaking a spatial search for protected matters under the EPBC Act. Data is stored by map_id(SpeciesProfiles and Threats database (SPRAT) taxon_id)for the current scientific name at the time of mapping) and includes non-overlapping presence rank categories which can beone of:1 - Species or species habitat may occur within area2 - Species or species habitat is likelyto occur within areaRegion Codes:ACT Australian Capital TerritoryNSW New South WalesNT Northern TerritoryQLD QueenslandSA South AustraliaTAS TasmaniaVIC VictoriaWA Western AustraliaACI Ashmore and Cartier IslandsCKI Cocos (Keeling) IslandsCI Christmas IslandCSI Coral Sea IslandsJBT Jervis Bay TerritoryNFI Norfolk IslandHMI Heard and McDonald IslandsAAT Australian Antarctic TerritoryCMA Commonwealth Marine AreaField descriptions:LISTED_TAXON_ID Taxonomic identification number in Species Profile and Threats Database (SPRAT)MAP_TAXON_ID Taxonomic identification number of associated GIS data within the departmentSCIENTIFIC_NAME Scientific name at the time of listing under the EPBC Act. Other names (synonyms) are linked in SPRAT via the other_ids attributeVERNACULAR_NAME The species common name recorded in SPRATTHREATENED_STATUS EPBC Act listed threatened status (Critically Endangered, Endangered, Vulnerable or Conservation Dependent) on the extract_date. Extinct species are not included. Note the status may have changed since that date. Check SPRAT for current status.MIGRATORY_STATUS Indicates if the taxon is listed as a migratory species under the EPBC Act.MARINE Indicates if the taxon is listed as a marine species under the EPBC Act at the extract dateCETACEAN Indicates if the taxon is a cetacean for the purposes of the EPBC Act at the extract datePRESENCE_RANK Code to indicate species presence: 2-species or species habitat likely to occur , 1-species or species habitat may occur.PRESENCE_CATEGORY Description of presence rankEXTRACT_DATE The date the spatial data and status was extracted from SPRATTAXON_GROUP Taxonomic Group (Birds, Fishes, Flora, Frogs, Reptiles, Mammals, Other-animals)TAXON_FAMILY Taxonomic FamilyTAXON_ORDER Taxonomic OrderTAXON_CLASS Taxonomic ClassTAXON_PHYLUM Taxonomic PhylumTAXON_KINGDOM Taxonomic KingdomOTHER_IDS Taxonomic identification number(s) of SPRAT synonyms (associated or old records)CELL_SIZE Resolution – 0.01 degree (approximately 1km) or 0.1 degree (approximately 10km) for sensitive speciesREGIONS Indicative region of occurrence (State or Territory, Commonwealth External Territories, Ocean Area or None for Migratory species). See list of codes in regions tableATTRIBUTION Citation for data use: Species of National Environmental Significance

This sub-surface hydrology dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. This dataset includes water quality index values for sub-surface hydrology and also maps surface and sub-surface irrigation zones in the Jazira region. Evidence suggests that wells have been dug over the millennia to extract potable groundwater for human and animal consumption. It is feasible that groundwater could also have been extracted to irrigate gardens. Derived from 1:500,000 maps produced for following report: Food and Agriculture Organization (FAO), United Nations. Etude des Ressources en Eaux Souterraines de la Jezireh Syrienne. Rome: FAO, 1966.Sub-surface hydrology map was copied to mylar and scanned to create a polygon coverage. Attribute information includes water quality index values with a range of 0 to 6 with the latter value corresponding to high quality. Subsequently, each polygon was labelled and attributed with the water quality index values. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-06-30 and migrated to Edinburgh DataShare on 2017-02-21.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Number of Features: 36,569,286Source: USDA Natural Resources Conservation ServicePublication Date: December 2021Data from the gSSURGO database was used to create this layer.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied the first value in the table was selected.Percent AlfisolsPercent AndisolsPercent AridisolsPercent EntisolsPercent GelisolsPercent HistosolsPercent InceptisolsPercent MollisolsPercent SpodosolsPercent UltisolsPercent VertisolsSoil Order - Dominant ConditionEsri Popup StringThis field contains a text string calculated by Esri that is used to create a basic pop-up using some of the more popular SSURGO attributes.Map Unit KeyThe Mapunit key field is found

While resource selection varies according to the scale and context of study, gathering data representative of multiple scales and contexts can be challenging especially when a species is small, elusive, and threatened. We explore resource selection in a small, nocturnal, threatened species—the greater bilby (Macrotis lagotis)—to test (a) which resources best predict bilby occupancy, and (b) whether responses are sex-specific and/or vary over time. We tracked a total of 20 bilbies and examined within home range resource selection over multiple seasons in a large (110ha) fenced sanctuary in temperate Australia. We tested a set of plausible models for bilby resource selection, showing that food biomass (terrestrial and subterranean invertebrates, and subterranean plants) and soil textures (% sand, clay and silt) best predicted bilby resource selection for all sampling periods. Selection was also sex-specific; female resource use, relative to males, was more closely linked to the location o..., Data includes terrestrial invertebrate, and subterranean invertebrate and plant biomass data collected over four seasons (summer 2020, winter 2020, spring 2020, and summer 2021). We have also provided the R code used for generating the interpolation rasters and the necessary shapefiles (generated in QGIS) and rasters for this interpolation. The R code used for construction of resource selection functions and for generating 'Figure 4' in the main paper is also provided. The GPS data associated with this analysis can be requested from the corresponding author upon reasonable request., , # Data from: Digging deeper: habitat selection within the home ranges of a threatened marsupial

This README file lists the supplementary datasets, shapefiles, rasters, and R code used in the associated paper. It also provides a brief description of how each file was used in generating the results, and definitions for any abbreviated variables within datasets. Note: dates are all in the format ‘date-month-year’.Â

Description of the data and file structure

Shapefiles.zip (and auxiliary files):

Sanctuary fenceline.shp

• Sanctuary fenceline.qpj
• Sanctuary fenceline.prj
• Sanctuary fenceline.dbf
• Sanctuary fenceline.cpg
• Sanctuary fenceline.shx

water sources.shp

• water sources.shx
• water sources.prj
• water sources.dbf
• water sources.cpg

Roads.shp

• Roads.shx
• Roads.prj
• Roads.dbf
• Roads.cpg

Individual Minimum Convex Polygons (MCPs):

Minimum convex polygons (MCPs) were generated for individual bilbies tracked in each season u...