This layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases.Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived.Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the monthSpatial Reference: WGS 1984 (WKID 4326)Area Covered: WorldAttribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available). iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade ObservationsOnly Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements:Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality.Note about Location PrivacyTo protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.)Additional iNaturalist ResourcesiNaturalist Guides iNaturalist statistics and observations iNaturalist Forum iNaturalist within the press Spatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

iNaturalist Taxonomy with vernacular names based on https://www.inaturalist.org/taxa/inaturalist-taxonomy.dwca.zip

iNaturalist is a mobile application used for capturing flora, and fauna observations by naturalists and community scientists. This dataset includes clustered user observations (hotspots) for the Chicago Wilderness area from 2011-2020.

This layer contains the latest collection of research-grade species observations contributed by iNaturalist users through the iNaturalist social network app and website. These Open Data observations can be used by the GIS community to better understand biodiversity, sustainability, migration patterns, invasive and threatened species distributions, and climate change adaptations, among many other use cases.Consumption Best PracticesDue to the high volume of observations, the service limits individual point visibility to only draw at the largest scales, using multi-scale H3 hexbins to summarize predominant observations at smaller scales.Small subsets of iNaturalist observations (128,000) can be copied from the service for use in analysis, data enrichment, or other visualizations. For larger iNaturalist archive requests or for access to iNaturalist Project datasets, use the iNaturalist website, or the iNaturalist AWS S3 Open Data extract, from which this service was derived.Source: iNaturalist AWS S3 Open DataUpdate Frequency: Monthly, end of the monthSpatial Reference: WGS 1984 (WKID 4326)Area Covered: WorldAttribute InformationTaxonomy: Each observation contains its taxonomic hierarchy (Kingdom, Phylum, Class, Order, Family, Genus, Species), as well as its Scientific Name and Common Name (where available). iNaturalist Taxon Category: Observations are symbolized according to 12 unique taxonomic groups used by the iNaturalist community. User Information: All observations are credited to the iNaturalist User ID, User Login, and User Name (where provided)Media and Licenses: Direct URL links are provided to one original-resolution image from the iNaturalist observation. Creative Commons licensing also indicates the sharing and attribution of any photographic media associated with a user observation.Dates: Observations include an Observed on Date and a Modified on Date provided by iNaturalist. In addition, these date fields were added to simplify the filtering and visualization of observations by year or month:Observed on Month (integer)Observed on Year (integer)Note about Research Grade ObservationsOnly Verifiable and Research Grade observations are included in this service. An observation is Verifiable if it meets these requirements:Has a dateIs georeferenced (has lat/lon coordinates)Has photographs or soundsIsn’t of a captive or cultivated organismIn addition, a Verifiable observation moves from "Needs ID" to "Research Grade" in iNaturalist when at least 2 species-level identifications (and 2/3 of all suggested identifications) are in agreement. See here for more information on how iNaturalist assesses data quality.Note about Location PrivacyTo protect the livelihood of endangered or threatened species, the X/Y locations of some iNaturalist observations are automatically obscured to a random location in a 400 square-kilometer grid cell. Similarly, users can choose to obscure the location of their observations in the iNaturalist app settings for personal privacy reasons. The result is that you may see dense, blocky aggregations of observations as you navigate around the map – or observations that appear in unusual places (e.g., an endangered coastal plant that has been relocated out in the ocean.)Additional iNaturalist ResourcesiNaturalist Guides iNaturalist statistics and observations iNaturalist Forum iNaturalist within the press Spatial Filtering options and examplesRevisionsJuly 10, 2024: Beta release of the iNaturalist Observations Live Feed service.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Any changes or deletions made to user observations through the iNaturalist app or website will not be reflected in this service until the next monthly update.

Plant and animal checklists, with conservation status information, are fundamental for conservation management. Historical field data, more recent data of digital origin and data-sharing platforms provide useful sources for collating species locality data. However, different biodiversity datasets have different formats and inconsistent naming systems. Additionally, most digital data sources do not provide an easy option for download by protected area. Further, data-entry-ready software is not readily available for conservation organization staff with limited technical skills to collate these heterogeneous data and create distribution maps and checklists for protected areas. The insights presented here are the outcome of conceptualizing a biodiversity information system for South African National Parks. We recognize that a fundamental requirement for achieving better standardization, sharing and use of biodiversity data for conservation is capacity building, internet connectivity, national institutional data management support and collaboration. We focus on some of the issues that need to be considered for capacity building, data standardization and data support. We outline the need for using taxonomic backbones and standardizing biodiversity data and the utility of data from the Global Biodiversity Information Facility and other available sources in this process. Additionally, we make recommendations for the fields needed in relational databases for collating species data that can be used to inform conservation decisions and outline steps that can be taken to enable easier collation of biodiversity data, using South Africa as a case study.

In the face of rapidly declining biodiversity and the increasing fragmentation of habitats, identifying and prioritizing conservation areas have become crucial challenges for environmental sustainability. This study seeks to address these challenges by leveraging the power of citizen science data from iNaturalist and integrating it with GIS technology to assess conservation priorities in Campbell River, British Columbia. By integrating species occurrence data, conservation status, and cultural value, we have used GIS tools to assess conservation priority land parcels visually. Species occurrence data from iNaturalist Meticulous collection and validation of data emphasizes research-grade observations to reduce identification errors and ensure reliability. We integrated species conservation status from CDC-iMap and cultural value from IMPRESS and applied a tiered scoring system to quantify Species Importance Scores (IV). Through GIS analysis, the spatial visualization of species distribution can be realized and the corresponding land parcel Importance Score (LPIS) calculation can be obtained by summing up each land parcel based on IV. The results demonstrate significant differences in species importance across land cover types, identify several higher-value conservation land parcels in the Campbell River region, and highlight key conservation values that emphasize certain types of land cover habitat. The results showed that the riparian area along the Elk Falls Provincial Park and nearby urban and coastal areas of Campbell River tend to contain the highest conservation value. We also discussed potential limitations, mainly caused by the species occurrence data selectivity bias, and species identification accuracy. This approach would guide species and biodiversity conservation and land management planning in the Campbell River region.

This layer shows the occurences of invasive species relevant to Luxembourg from the 1st of January 2000 onwards in their original resolution as grid squares. Data come from the national database Recorder-Lux, and from the international platforms Ornitho.lu, GBIF.org and Inaturalist.org. They are accessed through the biodiversity map portal mdata.mnhn.lu.

Reason for Selection Native grasslands and savannas are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands and savannas have significantly declined from their historical extent. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland and savanna birds are experiencing significant declines and are currently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential habitat. Input Data

Texas Ecological Mapping Systems: statewide raster, accessed 12-2023
Oklahoma Ecological Systems Map: download the raster, accessed 12-2023
Protected Areas Database of the United States (PAD-US): PAD-US 3.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement; PAD-US 4.0 national geodatabase - Combined Proclamation Marine Fee Designation Easement
National Land Cover Database (NLCD): 2021 Land Cover, 2021 U.S. Forest Service (USFS) Tree Canopy Cover, 2013 Land Cover, and 2013 USFS Tree Canopy Cover
2020 LANDFIRE Biophysical Settings (BPS) [LF 2.2.0]
Southeast Blueprint 2024 landscape condition indicator
Southeast Blueprint 2024 extent

Known grasslands

Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University in Oct 2020 (available on request by emailing rua_mordecai@fws.gov); this is an improved version of the Known Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO) layer
Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailing rua_mordecai@fws.gov).
Grassland polygons from the Catawba Indian Nation, provided by Aaron Baumgardner, Natural Resources Director, in July 2023 (for more information email rua_mordecai@fws.gov)
Grassland polygons from two iNaturalist projects in Texas: erwin-park-prairie-restoration-area, stella-rowan-prairie
Southeastern Grasslands Institute polygons from selected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: 
    allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades, folder-six-glades

Mapping Steps

Combine all known grasslands polygons and convert to raster, assigning them a value of 7.
From the 2021 and 2013 NLCD landcover, create rasters that only include classes likely to have grasslands and savannas. The classes included are based on NLCD classes that overlap known grassland and savanna polygons. Any class that covered >1% of known grasslands and savannas is included: 31 Barren Land, 41 Deciduous Forest, 42 Evergreen Forest, 43 Mixed Forest, 52 Scrub/Shrub, 71 Grassland/Herbaceous, 81 Pasture/Hay.
For those 2021 and 2013 selected landcover rasters, remove forest with ≥ 60% canopy cover using NLCD USFS Tree Canopy Cover for the corresponding year. This results in potential grassland and savanna rasters for 2021 and 2013.
Make a single potential grassland and savanna raster that only includes pixels that are potential grasslands and savannas in both 2013 and 2021. This removes temporary grasslands and savannas that result from clearcuts.
From the Texas and Oklahoma ecological systems maps, extract classes that predict areas invaded by mesquite, a non-native tree that spreads aggressively in the grasslands and savannas of the Southwest and disrupts natural ecosystems through its heavy water consumption. For Oklahoma, this is VegName = 'Ruderal Mesquite Shrubland'. For Texas, this is CommonName = 'Native Invasive: Mesquite Shrubland'. Combine these and use them to remove areas that are no longer grassland and savanna due to mesquite invasion. The resulting layer represents potential grasslands.
To identify potential grasslands and savannas in natural landscapes, use values 5 and 6 from the landscape condition indicator. Assign a value of 3 to any potential grassland pixel that receives a landscape condition score of 5 or 6. Assign all other potential grassland pixels a value of 2.
To identify likely grasslands and savannas, overlay the potential grasslands and savannas raster with select polygons from PAD-US 4.0. To pull out types of protected lands that commonly manage grasslands and savannas, we used GAP status, designation type, manager name, and easement holder. We also identified a number of protected areas directly by name that had important areas of grassland and savanna but weren’t captured by the other rules.
    GAP status (GAP_sts) 1 or 2: Gap status 1 and 2 refer to areas managed for biodiversity that are not subject to extractive uses like logging and mining. GAP status 2 is technically intended to encompass areas where disturbance events are suppressed, but in practice, most protected areas in the Southeast that are actively managing grasslands and savannas are classified as GAP status 2.
    Designation type (Des_Tp) of ‘NWR’, ‘MIL’, ‘NF’, or ‘NG’ (i.e. National Wildlife Refuge, military installation, National Forest, or National Grassland)
    Manager name (Mang_Name) of ‘RWD’ (i.e. Regional Water District)
    Local manager name (Loc_Mang) of 'Ducks Unlimited (Wetlands America Trust)'
    Easement holder (EsmtHldr) of 'Tall Timbers Research Station & Land Conservancy'
    Unit name (Unit_Nm) of ‘Point Washington State Forest’, ‘Pine Log State Forest’, ‘M. C. Davis - Seven Runs Creek Conservation Easement’, ‘Nokuse Plantation Conservation Easements’, ‘Tate's Hell State Forest’, ‘Box-R Wildlife Management Area’, ‘Aucilla Wildlife Management Area’, ‘Snipe Island Unit’, ‘Big Bend Wildlife Management Area’, ‘Goethe State Forest’, ‘Amelia Wildlife Management Area’, ‘Powhatan Wildlife Management Area’, ‘Cumberland State Forest’, ‘Appomattox-Buckingham State Forest’, ‘Haw River State Park’, ‘R. Wayne Bailey - Caswell Game Land’, ‘Medoc Mountain State Park’, ‘Embro Game Land’, ‘Dupont State Forest’, ‘Hanging Rock State Park’, ‘Bladen Lakes State Forest’, ‘Whitehall Plantation Game Land’, ‘Suggs Mill Pond Game Land’, ‘Bushy Lake State Natural Area’, ‘Pondberry Bay Plant Conservation Preserve’, ‘Green Swamp Game Land’, ‘Holly Shelter Game Land’, ‘Chowan Swamp Game Land’, ‘Brookgreen Gardens’, ‘Cary State Forest’, ‘Suwannee Ridge Mitigation Park Wildlife and Environmental Area’, ‘Adams-Alapha Ag & Conservation Easement’, ‘Twin Rivers State Forest’, ‘Chattahoochee Fall Line Wildlife Management Area’, ‘Enon Plantation’, or ‘Georgia-Alabama Land Trust Easement #214’, ‘Covington Wildlife Management Area’, ’ Magnolia Branch Wildlife Reserve’, ‘Little River State Forest’, or ‘Susan Turner Plantation’, or have the local name (Loc_Nm) 'Sandhills Game Land', 'Blackwater River State Forest', 'Three Lakes Wildlife Management Area', 'Herky Huffman/Bull Creek Wildlife Management

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

This is classification dataset of photographs from Flirck and iNaturalist applied to buffer 500 meters of main cours rivers Minho, Lima and Cávado in Minho Region (NW Portugal), in between the years 2018- 2019 for cultural ecosystem services studies. Four categories assigned as follows: i) landscape; ii) historical heritage; iii) recreation and river beaches; iv) biodiversity.

This dataset consists of the historical range limits and poleward-most extent of recent extralimital occurrence of 82 potential marine species’ range extensions around Australia (NSW, QLD, SA, TAS, VIC and WA) revealed by an assessment of citizen scientists observations from Redmap, Reef Life Survey, and iNaturalist from 2013–2022.

The purpose of this project was to assess evidence for climate-driven marine species range extensions with data provided by Australian citizen scientists. While there are at least 198 documented range shifts of marine species in the scientific literature, there are large gaps in our ability to formally monitor changes in species distributions both geographically and across taxonomic groups that citizen scientists (beachcombers, divers, fishers, snorkelers, etc.) can help resolve. This assessment had two main components: Establish historical poleward range limits for each of 200 target species tracked by Redmap (up to the year 2012); and assess the evidence for extralimital occurrence of each species in the subsequent decade across three citizen science databases (Redmap, Reef Life Survey, iNaturalist). Confidence of species range extension occurring was qualitatively estimated (high, medium, or low) based on species traits (e.g., mobility, detectability) and strength of evidence provided by citizen science data (e.g., evidence of overwintering, multi-year detections). These results provide an early indication of species and regions where more focused monitoring or research effort may be warranted. These findings were developed into report cards for dissemination to both demonstrate the value of citizen science and engage with the public on climate change and marine biodiversity, using their own information.

Methods: This assessment had two main components: 1) Establish historical poleward range limits up to the year 2012 for each of 200 target species; and 2) assess the evidence for extralimital occurrence of each species in the subsequent decade across three citizen science databases (Redmap, Reef Life Survey, iNaturalist). Confidence of species range extension occurring was qualitatively estimated (high, medium, or low) based on species traits (e.g., mobility, detectability) and strength of evidence provided by citizen science data (e.g., evidence of overwintering, multi-year detections). These methods were a modified version of the Robinson et al. (2015) framework developed through a workshop process in November 2021.

The assessment target species list consisted mainly of species on the Redmap (Range Extension Database and Mapping project) target list and a select opportunistic additions, totalling 200 species.

Species historical poleward range limits were established up to the year 2012 from both distributional references such as Australian Faunal Directory (AFD, ABRS 2009) and Australian National Expert Fish Distributions (https://researchdata.edu.au/australian-national-fish-expert-distributions/671428) and a review of available raw occurrence data (Atlas of Living Australia, pre-2013 citizen science observations of target species, etc.) to document the poleward-most known occurrences of each target species. Species for which there was uncertainty regarding taxonomy or historical distribution limits that would have a bearing on assessment results were excluded from the assessment. A 20 km buffer was added on to range limits to reduce artifact from range limits established to low precision.

Citizen science observations of target species up to February 2022 were accessed from iNaturalist Australasian Fishes project, Redmap, and Reef Life Survey (via the Australian Ocean Data Network). iNaturalist records underwent additional verification by species experts, and as Reef Life Survey data does not formally have photographic evidence associated with it, these data were only used in a corroboratory role for species for which extralimital observations from the other citizen science databases were available.

Confidence of potential range extensions evidence by extralimital observations was estimated qualitatively based on detections across multiple years and for non-highly-mobile species, evidence of overwintering (during the coldest months of the year on either coast). Detectability, due to rarity, small size and/or cryptic colouration/behaviour was also taken into consideration.

To summarise the extent of each assessed range extension, the assessed historical range limit and the most out-of-range observation were identified for each species and collated in this data set. Locations represent latitudes, or on the south coast, longitudes.

Limitations of the data: These data only represent the latitudes (or longitudes on the south coast) of species range limits (as of 2012) and recent extralimital observations rather than precise coordinates.

These results only reflect the assessment of 200 target species and not an exhaustive list of marine species range extensions to the present or those reflected in the citizen science databases.

Not all assessed extralimital observations indicate a species is undergoing a geographical range extension (see confidence estimates)

Format of the data: The dataset consists of a table with 82 instances of potential marine species range extensions noted by the latitude (or longitude, on the south coast) of historical range limits and extent of recent observations.

Data dictionary: - #: Corresponds to alphabetical ordered species, and the numbers on the preview map. - Species name: Species scientific name. - State: state(s) along which potential range extension occurred (to distinguish disparate extensions of the same species, e.g. on both east and west coasts). - Confidence in range extension: qualitative estimate that out-of-range observations represent an ongoing range extension produced by the assessment. - Historical distribution limit: latitude (or longitude on the south coast) of the species’ poleward known distributional limit as of 2012. - New extent: latitude (or longitude on the south coast) of new poleward-most extent provided by citizen science observations of the species from 2013-2022. - Distance (km): latitudinal (or longitudinal, insofar as inferred to be occurring along the south coast) distance between historical distribution limit and new extent, in kilometres. - Notes: “adults” indicates range extensions of adult life stages only (i.e., into areas where juveniles were previously known to occur).

eAtlas Processing: The original data were provided as a csv file with a png map and preview image (jpg). No modifications to the underlying data were performed and the data package are provided as submitted.

Location of the data: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2021-2022-NESP-MaC-1\1.30_Climate-driven-species-redistribution

Research grade observations of selected species in the wild in LA County.Downloaded from iNaturalist on 10/15/2020

This layer shows the occurrences of plant species that are protected by the Grand-Ducal Regulation of 8 January 2010 concerning the full and partial protection of certain species of wild flora. Occurrences from the 1st Jjanuary 2000 onwards are shown on the map with their original resolution as grid squares. Data come from the national database Recorder-Lux and from the international platforms GBIF.org and Inaturalist.org. They are accessed through the biodiversity map portal mdata.mnhn.lu.

This layer shows the occurrences of plant species that are protected by theGrand-Ducal Regulation of 8 January 2010 concerning the full and partial protection of certain species of wild flora. Occurrences from the 1st of January 2000 onwards are shown on the map with their original resolution as points. Data come from the national database Recorder-Lux and from the international platforms GBIF.org and Inaturalist.org. They are accessed through the biodiversity map portal mdata.mnhn.lu.

This public dataset is a summary of the information contained in the Barred Owl Observations Database (BDOWDB, [ds8], secured). The BDOWDB provides point locations for barred owls, Strix hybrids, and unknown Strix. It is a secured layer because it includes references to sensitive spotted owl locations. This summary layer provides all of the PLSS Sections in which barred owls have been reported to CDFW and the first and last years that owls were reported in each section. Inquiries regarding this layer or the BDOWDB can be directed to the database manager. Most of the barred owl data reported to CDFW was collected incidentally during spotted owl surveys conducted on private and government timberlands; therefore, it may not capture the full extent of the current barred owl range in California. Additional data sources include citizen science applications such as eBird and iNaturalist and only verifiable observations are used. Barred owl presence has been attributed to northern spotted owl (Strix occidentalis caurina) population declines and threaten California spotted owls (Strix occidentalis occidentalis). Additional information on barred owls in California and submitting data can be found on CDFW's Barred Owl Threat webpage: https://wildlife.ca.gov/Conservation/Birds/Barred-Owl-Threat.

Joshua tree is a visually distinctive plant found in California''s Mojave Desert and adjacent areas. The unique silhouette and tall stature of Joshua tree relative to typical surrounding vegetation make it one of the most recognizable native plants of California deserts. There are two species of Joshua tree in California, western Joshua Tree (Yucca brevifolia) and eastern Joshua tree (Yucca jaegeriana). Eastern Joshua tree (Yucca brevifolia ssp. jaegeriana) distribution is represented in the data incidentally, but the primary purpose of this dataset is to illustrate the distribution of western Joshua tree. Western Joshua tree is distributed in discontinuous populations in the Mojave Desert and in a portion of the Great Basin Desert. Western Joshua tree is often noted as being abundant near the borders of the Mojave Desert in transition zones. No attempt was made to map Joshua tree distribution outside of California, and therefore the data are limited to geographic areas within California. CDFW possesses vegetation maps that cover a large portion of the California deserts where Joshua tree generally occurs. CDFWs Vegetation Classification and Mapping Program (VegCAMP) uses a combination of aerial imagery and fieldwork to delineate polygons with similar vegetation and to categorize the polygons into vegetation types. In 2013, an effort was made to create a vegetation map that covers a large portion of the California deserts. The vegetation data from this project includes percent absolute cover of Joshua tree and in some instances only Joshua tree presence and absence data. Western Joshua tree and eastern Joshua tree were lumped together as one species in these vegetation maps. A rigorous accuracy assessment of Joshua tree woodland vegetation alliance was performed using field collected data and it was determined to be mapped with approximately 95 percent accuracy. This means that approximately 95 percent of field-verified, polygons mapped as Joshua tree woodland alliance were mapped correctly. While Joshua tree woodland alliance requires even cover of Joshua tree at greater than or equal to 1 percent, the vegetation dataset has polygons recorded with less than 1 percent cover of Joshua tree as well as simple presence and absence data. The CDFW used Joshua tree polygons from vegetation mapping combined with additional point data from other sources including herbarium records, Calflora, and iNaturalist to create the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree. CDFW reviewed publicly available point observations that appeared to be geographic outliers to ensure that incorrectly mapped and erroneous observations did not substantially expand the presumed range of the species. In a limited region, hand digitized points were used where obvious Joshua tree occurrences that had not been mapped elsewhere were present on aerial photographs. Creating a range map with incomplete presence data can sometimes be misleading because the absence of data does not necessarily mean the absence of the species. Some of the observations used to produce the range map may also be old, particularly if they are based on herbarium records, and trees may no longer be present in some locations. Additionally, different buffer distances around data points can yield wildly different results for occupied areas. To create the the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree, CDFW buffered presence locations, but did not use a specific buffer value, and instead used the data described above in a geographic information system exercise to extend the range polygons to closely follow known occurrence boundaries while eliminating small gaps between them.

This layer shows the occurrences of bird species (except for sensitive data) that are protected by the Grand-Ducal Regulation of 9 January 2009 concerning the integral and partial protection of certain animal species of wild fauna. Occurrences from the 1st of January 2010 onwards are shown on the map with their original resolution as grid squares. Data come from the national database Recorder-Lux, and from the international platforms Ornitho.lu, GBIF.org and Inaturalist.org. They are accessed through the biodiversity map portal mdata.mnhn.lu.

Pool Visit data includes a reference to its VPAtlas Mapped Pool data. Pool Visit data is information used toverify the status of a vernal pool. It includes geoLocation information, landscape, size and depth, water inletand outlet, disturbances, surrounding habitat, hydro-period, and the identification of indicator species.Vernal Pool Visit data may also include photos of the pool and its surroundings and photos of indicatorspecies, as well as links to iNaturalist species observations.VPAtlas Pool Visit data does not itself confirm the presence of a vernal pool. VPAtlas Visits are reviewed byAdministrative staff to determine and assign a status (Confirmed, Probable, Potential, Duplicate,Eliminated).VPAtlas Pool Visit data may also include VPAtlas Review data. VPAtlas Reviews are conducted by VPAtlasadministrators - biologists who determine the status of each Visited pool according to the informationincluded in each Visit. Reviews include the assignment of a pool’s status, provide a coded reason for thatstatus value, may include QA notes, may assign the Visit’s geoLocation to the Mapped Pool’s geoLocation,records the QA person and date, and assigns a unique ID to the Review.

The dataset provides background data (occurrence records based on herbarium specimens and human observations) for mapping the distribution of alien plants in Kyrgyzstan. Besides personal observations, the main sources for the dataset were herbarium collections (FRU, LE, MW, TASH), citizen science resources (iNaturalist, Plantarium) and floristic literature.

All records have been taxonomically evaluated by the authors. Georeferences are provided ad hoc on the basis of historical and current maps when missing in the original source.

The current dataset is developing and therefore incomplete. At present it includes a set of 25 species published in Contributions 1-3 (Biodiversity Data Journal). Other species will be added with further publications.

The extent of Arundo donax (common names: Arundo, or giant reed) was digitized using a combination of imagery classification and visual interpretation of Nearmap imagery from April 2022. A total of 80 observations of Arundo locations from the community science website iNaturalist were used to ensure that as much Arundo as possible was correctly mapped. A one ha (100 x 100 m) grid was overlaid on the resulting products and the change in cover between the two time periods was calculated for each grid cell, as shown in the "Arundo change hectare" layerThis item supports the 2022 StoryMap Habitat Changes in the Tijuana River Valley, which can be found here: https://storymaps.arcgis.com/collections/bf7945934d4e4dd1a9259fde1e1f6e22