This web map was created using data downloaded from iNaturalist. The data layer includes all iNaturalist observations made in Westmoreland, Somerset, and Fayette Counties of the Laurel Highlands. Data was downloaded from the iNaturalist website for each of the three counties, then merged to create the iNaturalist observations layer in this web map. iNaturalist data can be obtained here: https://www.inaturalist.org/observations (under "Filters" in the top right, and "Download" in the bottom right of the popup window)

The iNaturalist Multispecies Range Dataset provides modeled geographic ranges for thousands of species. Dataset contains species polygons, with attributes such as taxon_id, name, scientific_name, and geomodel_version. Ranges are estimated from iNaturalist community observations and updated on a monthly basis. These datasets enable large-scale biodiversity analyses, ecological modeling, and integration with …

iNaturalist aims to help map when and where species occur, while connecting its users to nature. It is described as a " crowdsourced species identification system and an organism occurrence recording tool." It can be used to record personal observations of the natural world, help identifying species and findings, collaborating with others to collect information for a collective project, or to access data collected by other users. These personal encounters add to the available scientific biodiversity data. Interact with the mapping application to find species and where they were seen in the area of interest you identify.

(file works best when opened in ArcGIS Pro, using in the scene viewer will result in a limited experience / for users who only have access to the online viewer, please see this web MAP)This provides user with the tools to view iNaturalist observed VGI data for the hedgehog, shrew, and moles in context with their relations to climate zones and precipitation and air temperature data over the period from 1991 - 2020.

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

This web map shows 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: 12 logical taxonomic groups used by the iNaturalist community are used to symbolize like-observations. 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 GuidesiNaturalist statistics and observationsiNaturalist ForumiNaturalist within the pressRevisions and Layer details:The layer used in this map 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.If you would like to be alerted to potential issues or simply see when this service will update next, please visit our Live Feed Status page!

iNaturalist Hotspots (iNaturalist, California Academy of Sciences, and National Geographic Society, 2022)

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

HOLC grade maps linked to iNaturalist data across six taxonomic clades from (2017-2022)

This Web Map provides an overview of Mountain Lion sightings in Santa Cruz County. The sightings were collected through iNaturalist and show where each puma was spotted.

Community science programs like eBird, iNaturalist, and HerpMapper allow any individuals to record and report their nature observations, creating crowd-sourced datasets that cover large areas. Community science observations are often reviewed and verified by experts, with websites noting which observations have been validated. Preprocessing methods: Merged into one community science dataset for the report. No preprocessing for display in the web map.

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.

Appendix III. Literature used for the revised distribution of species of the Trimeresurus macrops species complex. Locality numbers correspond to those shown on the map in Fig. 1.

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.

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.

This interactive web map contains time-enabled layers that display iNaturalist monarch butterfly sightings from March 1 to December 31, 2023, along with a migration path layer showing spring and fall migration paths. Each butterfly point sighting, along with each section of the migration paths, have popups to explore. Although not necessary, it is recommended that you set the end date of the time slider to 12/31/23 (the last day of monarch data) with a monthly step interval when viewing the time-enabled visualization. Individual layers are also available for each month of iNaturalist monarch sightings to allow users to more easily examine the sightings per month. The map also features a time-enabled layer that displays monthly temperature deviations in the continental U.S. from January 2023 to March 2024, compared to 20th-century averages. This extended timeline, beyond the March to December 2023 period for the monarch observations, offers additional temperature anomaly data for more comprehensive context. The temperature variations are visually represented by a color-changing circle, where each color denotes a different temperature anomaly in degrees Celsius. A [pop-up is also provided for more context. Although a line chart was created in ArcGIS Pro from a .csv file of this data before the map was shared, an error currently prevents its display in the web map, and the line chart creation feature in ArcGIS Online will not allow access to the Date field. The .csv file, sourced from NOAA National Centers for Environmental Information (2024), is accessible through the map’s table icon.

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.

Understanding the flowering phenology of invasive alien plants is essential for predicting their potential impacts on invaded ecosystems and developing effective management strategies. However, achieving this on a global scale poses significant challenges, especially for widespread invasive species. Digital data offers an efficient and scalable solution to studying the flowering phenology of plants across diverse regions and environments. Here, we apply this approach to one of the most problematic groups of invasive plants in coastal areas worldwide, to some taxa in the genus Carpobrotus. We collected geotagged photographs from widely used online platforms (i.e., Google Maps, iNaturalist, and Instagram) at key tourist sites in six countries spanning native (South Africa) and non-native (Argentina, New Zealand, Portugal, Spain, and the United States of America) regions. These records were analysed to document the flowering phenology of Carpobrotus plants in different regions linked to th..., 1. Data collection To study the flowering phenology of Carpobrotus taxa, we selected 29 sites (Table 1 in the associated manuscript) that represent populations from all three genetic clusters identified by Novoa et al. (2023) across both their native and non-native ranges. Overall, we selected locations from different world floristic regions (Liu et al. 2023): the Neotropic (Argentina: cluster Admixed), the Novozealandic (New Zealand: cluster A), the Holarctic (Portugal, Spain, and the United States: clusters A, B, and Admixed), and the African (South Africa: clusters A and C). Within the Holarctic realm, we further distinguished the Southern European (e.g., the Azores, the Portuguese volcanic islands in Macaronesia, and peninsular Spain) from the Californian subregion (western United States). For data collection, we utilized a multi-platform approach including Instagram, iNaturalist, and Google Maps, with a focus on photographs uploaded between 2017 and 2022 (Fig. 1 in the associated m..., # Data from: iEcology as a tool to uncover geographic and genetic influences on the flowering phenology of invasive Carpobrotus taxa

Dataset DOI: 10.5061/dryad.gmsbcc319

Description of the data and file structure

This dataset was compiled to investigate the flowering phenology of invasive Carpobrotus taxa across their global distribution using an iEcology approach. We collected geotagged photographs from three online platforms (Instagram, iNaturalist, and Google Maps) to document the presence, absence, and density of flowers throughout the year. The data collection focused on 29 sites across six countries, encompassing both the native range (South Africa) and non-native invaded regions (Argentina, New Zealand, Portugal, Spain, and USA). Each photographic record was systematically evaluated to determine flowering status, flower density, and floral color characteristics. The dataset represents observations from 2017-2022 and includes records that were us...,

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 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.