The UN Biodiversity Lab is an online platform that allows policymakers and other partners to access global data layers, upload and manipulate their own datasets, and query multiple datasets to provide key information on the Aichi Biodiversity Targets and nature-based Sustainable Development Goals.

The core mission of the UN Biodiversity Lab is three-fold: to build spatial literacy to enable better decisions, to use spatial data as a vehicle for improved transparency and accountability, and to apply insights from spatial data across sectors to deliver on the Convention on Biological Diversity and the 2030 Agenda for Sustainable Development.

Gridded datasets used in Jones et al. (2021) paper 'Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems'. Details of how datasets were made and underlying sources are provided in Jones et al. (2021) Supplementary Information. Datasets included: - H_2010_spam_V2r0_42c: crop commodity diversity (Shannon's diversity index) at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c: crop commodity richness at 10x10km resolution, based on SPAM 2010 V2 physical area maps - sr_2010_spam_v2r0_42c_maj22: locations of cropland with at least 22 crop commodities (1) versus cropland with <22 crop commodities at 10x10km resolution, based on SPAM 2010 V2 physical area maps - Livestock_8_shannons_LSU: livestock diversity (Shannon's diversity index) calculated from population numbers converted to standard livestock units at 1x1km resolution, based on Global Livestock of the World v3 - Fish_srichness raster: freshwater fish species richness per major river basin, based on Tedesco et al (2017) - CropPasture_2000_bool: locations where cropland and pasture co-exist (1) versus locations where either cropland OR pasture exist (0), at 10x10km resolution, based on cropland and pasture maps for the year 2000 available from EarthStat - esa2015_natag_1km_pc: percentage of natural or semi-natural vegetation within a 1x1km window around cropped pixels, based on European Space Agency Climate Change Initiative (ESA-CCI) land cover maps for 2015 Not uploaded (no post-processing so data can be accessed at source): - potential soil biodiversity index (see https://esdac.jrc.ec.europa.eu/content/global-soil-biodiversity-atlas) - tree cover on agricultural land (see Zomer et al. 2016 and https://apps.worldagroforestry.org/global-tree-cover/index.html)

A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks have dominated research describing biodiversity change along longitudinal gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to specific stream ecosystems and biomes, and these frameworks have placed less emphasis on general mechanisms driving biodiversity patterns. Rethinking biodiversity patterns and processes in stream ecosystems with a focus on the overarching mechanisms common across ecosystems will provide a more holistic understanding of why biodiversity patterns vary along river networks. In this study, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: dispersal, speciation, niche selection, and ecological drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test established stream ecology frameworks and compared them to the TEC framework. Local diversity increased and β-diversity decreased moving downstream from the headwaters, consistent with the river continuum concept and the small but mighty framework of mountain stream biodiversity. Local diversity was also structured by distance below upstream lakes, where diversity increased with distance below upstream lakes, in support of the serial discontinuity concept. Despite some support for the biodiversity patterns predicted from the stream ecology frameworks, no single framework was fully supported, suggesting “context dependence”. By framing our results under the TEC, we found species diversity was structured by niche selection, where local diversity was highest in environmentally favorable sites. Local diversity was also highest in sites with small community sizes, countering predicted effects of ecological drift. Moreover, higher β-diversity in the headwaters was influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites exhibit higher community variation. Taken together our results suggest that combining system specific ecological frameworks with the TEC provides a powerful approach for inferring the mechanisms driving biodiversity patterns and provides a path toward generalization of biodiversity research across ecosystems. Methods Study Area The study area was located in the Sierra Nevada Mountains of eastern California (USA) and encompasses portions of Inyo National Forest and Sequoia-Kings Canyon National Park. Over the ice-free seasons (June-September), we sampled five distinct lake-stream networks, where each network was within a spatially distinct catchment and were treated as independent replicate systems (Fig. 3). The Kern (n=24) and Bubbs (n=26) networks were sampled in 2011, the Evolution (n=21) and Cascades (n=11) networks in 2018, and Rock Creek (n=36) in 2019. For each lake-stream network, streams were sampled throughout the network along a spatial gradient from headwaters downstream as well as along a spatial gradient downstream from lakes. Because the spatial distances of the river networks and the distance separating lakes naturally vary among networks as well as backcountry sampling constraints, the number of sites sampled along the distance from headwaters gradient varied (n=11 to n=36) and the downstream lake gradient varied (n=1 to n=9). This field system and the data collected naturally provide spatial gradients relevant to test stream ecology theories. In addition, this data is ideal for testing TEC processes because of the naturally varying gradients of community size, connectivity, and environmental heterogeneity present in our sampling design. Field Methods At each sampling location, we established transects in riffle sections of streams. At five equally spaced points along transects we measured stream depth and current velocity at mid-depth using a portable flow meter (Marsh-McBirney Flow Mate 2000). We then calculated stream discharge as the sum of the product of average depth x current velocity x width/5 over all transect points (Gordon et al. 2010; Herbst et al. 2018). A calibrated YSI multiparameter device was placed above transects to measure temperature, dissolved oxygen, conductivity, and pH. Benthic chlorophyll data was collected by scrubbing the entire surface area of three randomly selected cobble sized rocks (64-255 mm) of benthic algae (periphyton) with a toothbrush for 60 seconds (Herbst and Cooper 2010). Chlorophyll measurements were taken using a handheld fluorometer (Turner Designs Aquafluor), which measures raw fluorescence units. Florescent measurements were calibrated to chlorophyll concentration using a known concentration of Rhodamine. We standardized chlorophyll measurements by accounting for both the surface area of rocks and volume of water used to remove algae. Eight to twelve macroinvertebrate samples at each site were collected using a D-frame kick net (250 mm mesh, 30cm opening, 0.09m2 sample area) in riffle habitats, depending on the density of macroinvertebrate samples collected. We took samples by placing the net on the streambed, then turning and brushing all substrate by hand in the sampling area (30cm x 30cm) immediately above the net, with dislodged invertebrates being carried by currents into the net. All macroinvertebrate samples were preserved in 75% ethanol within 48 hours of sampling. Samples were sorted, identified, and counted in the laboratory. Taxa were identified to the finest taxonomic level possible, usually to genus or species for insects (excluding Chironomidae) and order or class for non-insects (Merritt, Cummins, and Berg 2019). The replicate samples taken at each site were pooled together and divided by the number of replicates and the area sampled to determine the density of invertebrate communities. Spatial Data Stream distance measurements were taken using the R package “riverdist”, which utilizes data from the USGS National Hydrological Dataset Flowline in order to determine pairwise distances from sampling sites along the river network (Tyers 2020). We determined distance below upstream lakes, with the closest upstream lake location being the outlet of the lake determined by the USGS Watershed Boundary Dataset. For sites where multiple upstream lakes were draining into streams, we defined the upstream lake as the closest upstream lake to sites that was also along the mainstem of the flowline. We determined distance from headwaters as the streamwise distance from sites to the uppermost portion (headwaters) of the mainstem of streams, where the headwaters of streams was determined by the endpoint (beginning) of the flowline in the USGS NHD Flowline Dataset (U.S. Geological Survey 2016). In cases where multiple headwater stream reaches corresponded to downstream sites, we defined the headwaters as the particular reach that accounted for the most discharge which was determined using USGS Flowline Dataset. Upstream lake area and perimeter measurements were determined using the USGS Watershed Boundary Dataset. Land-cover proportions were computed using the 2016 USGS National Land Cover Database (Jin et al. 2019).

To support local to international actions on climate change mitigation and biodiversity conservation, this spatial dataset prioritizes forestlands for preservation in the Western United States. The need for joint climate change mitigation and biodiversity conservation has led to efforts to protect 30% of land area by 2030 (30x30) and 50% by 2050 (50x50). A crucial aspects of these efforts is prioritizing lands for new protection so they best achieve climate and biodiversity goals. We developed and applied a quantitative forest preservation priority ranking (PPR) system that incorporated existing geospatial datasets related to forest carbon, biodiversity, and future vulnerabilities to climate change across the Western United States. Specifically, the forest PPR system incorporated estimates of (1) current forest carbon stocks, (2) near-term forest carbon accumulation, (3) terrestrial vertebrate species richness by taxa, (4) tree species richness, and (5) near-term forest vulnerability to increasing mortality rates from drought or fire. Input datasets were re-gridded to a common 1 x 1 km (1 km2) spatial resolution and reflect contemporary (2000-2020) and near-future (2020-2050) forest conditions, with near-future conditions derived using land surface simulations from the Community Land Model (CLM 4.5). We applied the forest PPR system such that each patch of forest (i.e., a 1 km2 grid cell) was ranked relative to others in its ecoregion based on metrics of carbon and/or biodiversity both with and without considering future vulnerabilities (i.e., six scenarios). We assessed the extent of forestlands that are currently protected (GAP 1 or 2; IUCN Ia-VI) and then identified the highest-ranked unprotected forestlands that could be preserved to meet the 30x30 and 50x50 targets using each prioritization scenario. This spatial dataset thus includes the locations of forestlands that could be strategically preserved to meet the 30x30 and 50x50 targets as prioritized using six scenarios. Each raster is provided at 1 km2 resolution in an Albers Equal Area Projection (EPSG 9822) and covers forestlands that occur across the 11 contiguous western states (i.e., Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). Raster files are in GeoTiff format. These spatial data were produced as part of Law et al. (2021) and support cross-scale efforts to preserve forests for climate change mitigation and biodiversity conservation.

This data includes the remote sensing monitoring and simulation spatial dataset of the 30 meter carbon storage ecosystem service capacity in the 2000 Songnen Plain Biodiversity Protection and Flood Regulation Ecological Functional Zone, which can provide data support for the ecological carbon fixation project in the Songnen Plain Biodiversity Protection and Flood Regulation Ecological Functional Zone.

The Biodiversity GIS (BGIS) unit is responsible for the management of the South African National Biodiversity Institute's (SANBI) spatial biodiversity planning information. The unit is also responsible for the implementation of the Cape Action Plan for People and Environment's (C.A.P.E.) spatial information management objectives. The BGIS unit's primary objective is to provide easy access to spatial biodiversity planning information thereby facilitating its use in biodiversity planning and decision-making across the landscape. More information on this dataset can be found in the Freshwater Metadatabase - BFE_81 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_81).

The full name of this spatial dataset is Biodiversity Areas (BioClass). The dataset is designed, owned and managed by Coillte. Biodiversity areas are Coillte sites managed primarily for nature. Coillte first began mapping biodiversity areas on its estate in 2001, when freelance ecologists began to identify sites of ecological value on the Coillte estate, using Coillte's inventory information and the local knowledge of forest managers. The biodiversity areas were first mapped on Coillte inventory maps, which were the 3rd Editions of the Ordnance Survey maps, scale 6":1 mile. After the field work was completed, the maps were digitised. Subsequently, the maps were transferred to ITM projection. In 2017/2018, Coillte's biodiversity areas were reclassified using a procedure called BioClass, which summarises key features of ecological interest and provides a high-level estimate of what habitats may develop, but this can change significantly over time. BioClass rank is based on an assessment of defined ecological parameters, which achieve consistency in how habitats on Coillte sites are assessed. Other attributes in the dataset are more subjective, e.g. Target Habitat. The BioClass data is a summary of ecological information designed to inform forest managers at a high level about the location, type and overall significance of habitats on the Coillte estate. It should not be interpreted as a detailed guide for site managers. Coillte’s biodiversity areas vary widely in terms of their ecological value and management requirements. Some biodiversity areas contain habitats of high nature conservation value that are in excellent condition, while others show potential to develop into valuable habitats over time The maps and data on Coillte’s biodiversity areas are updated over time. Currently, Coillte is working to increase the area of the estate managed from nature from the current 20% to 30%. The new biodiversity areas are to be mapped by the end of 2025. REFERENCES Coillte. (2020). BioClass: Our approach to biodiversity. Coillte CGA, Newtownmountkennedy. Add link on website Smith, G.S., O'Sullivan, A. & Fuller, J. (in prep.). The BioClass system: an evidence-based methodology for assessing and conserving biodiversity in forest management. VERSION NO: 1.0 ISSUE DATE: 25/04/24

The intrinsic characteristics of islands make them unique for studying ecological and evolutionary dynamics. The Mediterranean Basin, a biodiversity hotspot, is rich in islands, hosting a significant global biodiversity proportion. Despite extensive research, a comprehensive spatial dataset for these islands is lacking. This study presents the first comprehensive spatial dataset of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research on economic, environmental, and social issues. The MEDIS spatial dataset offers detailed information on 36 geographic, climatic, ecological, and land-use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, paleogeography, road networks, and geological information, providing a multifaceted view of each island's characteristics. The study encompasses 2212 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain varies, with datasets like CHELSA-BIOCLIM+ and EU-DEM providing high-resolution climatic and terrain data. The spatial dataset incorporates various datasets, each with its own timeframes, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum. While not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic, or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids’ coordinates.

To support local to regional climate change mitigation and adaptation actions, this spatial dataset prioritizes forestlands for preservation across Oregon, United States. The urgent need for climate change mitigation and adaptation actions has led to efforts to protect 30% of land area by 2030 (30x30) and 50% by 2050 (50x50). A key aspect of these efforts is strategically prioritizing lands for new protection, so they most effectively protect climate and biodiversity. Oregon has among the most carbon-rich forests on the planet, yet only about 10% of it's forests are currently protected, which is lower than any other state in the western United States. We therefore developed and applied a quantitative forest preservation priority ranking system that incorporated existing statewide spatial datasets related to forest carbon, biodiversity, and climate change resilience. Specifically, this approach utilized estimates of (1) tree aboveground carbon stocks, (2) tree, amphibian, bird, mammal, and reptile species richness, and (3) climate change resilience derived from metrics of topoclimatic diversity and landscape connectivity. Input datasets reflect contemporary (2000-2020) forest conditions and were re-gridded to a common 30 m x 30 m spatial resolution. Each forest patch (i.e., a 30 x 30 m grid cell) was ranked relative to others in its ecoregion based on carbon, biodiversity, and/or resilience metrics (i.e., four prioritization scenarios). The extent of currently protected (GAP 1 or 2; IUCN Ia-VI) forestlands was determined for each ecoregion and then the highest-ranked unprotected forestlands were identified that could be preserved to meet the 30x30 and 50x50 targets using each prioritization scenario. This spatial dataset therefore identifies the locations of forestlands that could be strategically preserved to meet the 30x30 and 50x50 targets as prioritized using each of the four scenarios. Each raster covers forestlands across Oregon at 30 m x 30 m spatial resolution and is provided in GeoTiff format using an Albers Equal Area projection. These spatial data were produced by Law et al. (2022) and support efforts to preserve Oregon's forests for climate change mitigation and adaptation.

This EnviroAtlas dataset contains species richness metrics based on habitat models generated by the U.S. Geological Survey (USGS) National Gap Analysis Project (GAP). Ecosystem services, i.e., services provided to humans from ecological systems have become a key issue of this century in resource management, conservation planning, and environmental decision analysis. Mapping and quantifying ecosystem services have become strategic national interests for integrating ecology with economics to help understand the effects of human policies and actions and their subsequent impacts on both ecosystem function and human well-being. Some aspects of biodiversity are valued by humans in varied ways, and thus are important to include in any assessment that seeks to identify and quantify the benefits of ecosystems to humans. Some biodiversity metrics clearly reflect ecosystem services (e.g., abundance and diversity of harvestable species), whereas others may reflect indirect and difficult to quantify relationships to services (e.g., relevance of species diversity to ecosystem resilience, cultural and aesthetic values). Wildlife habitat has been modeled at broad spatial scales and can be used to map a number of biodiversity metrics. We map 24 biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for terrestrial vertebrate species. Metrics include all species richness, taxa specific species richness and other lists identifying species of conservation concern, climate vulnerabilities, etc. This dataset was produced by a joint effort of New Mexico State University, US EPA, and USGS to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The rapid erosion of biodiversity is among the biggest challenges human society is facing. Concurrently, major efforts are in place to quantify changes in biodiversity, to understand the consequences for ecosystem functioning and human wellbeing, and to develop sustainable management strategies. Based on comprehensive bibliometric analyses covering 134,321 publications, we report systematic spatial biases in biodiversity-related research. Research is dominated by wealthy countries, while major research deficits occur in regions with disproportionately high biodiversity as well as a high share of threatened species. Similarly, core scientists, who were assessed through their publication impact, work primarily in North America and Europe. Though they mainly exchange and collaborate across locations of these two continents, the connectivity among them has increased with time. Finally, biodiversity-related research has primarily focused on terrestrial systems, plants, and the species level, and is frequently conducted in Europe and Asia by researchers affiliated with European and North American institutions. The distinct spatial imbalances in biodiversity research, as demonstrated here, must be filled, research capacity built, particularly in the Global South, and spatially-explicit biodiversity data bases improved, curated and shared.

This spatial dataset (polygons) identifies 67 sites of significance for biodiversity conservation in the Northern Territory: 42 considered to be of International conservation significance and 25 sites of National conservation significance. Twenty nine sites are in the Arid Centre, 12 in the Savanna Region and 26 in the Top End.

Data: Fine-Scale Models of Bee Species Diversity and Habitat in New York StateMark A. Buckner, Erin L. White, Timothy G. Howard, Matthew D. Schlesinger, Bryan N. Danforth

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

California Nature Conserved Areas Explorer The Conserved Areas Explorer is a web application enabling users to investigate a synthesis of the best available data representing lands and coastal waters of California that are durably protected and managed to support functional ecosystems, both intact and restored, and the species that rely on them. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or group of parcels, such that the spatial features of CPAD correspond to ownership boundaries. • The California Conservation Easement Database (CCED), also managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. • The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks , and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1. CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Conserved Areas Map Layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the Conserved Areas Map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6. Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7. These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Conserved Areas Explorer were GAP coded at the level of Superunits intersected by designation boundaries, the coarsest unit of analysis. By adopting this coarser analytical unit, the Conserved Areas Explorer maintains a greater level of user responsiveness, avoiding the need to maintain and display hundreds of thousands of additional parcel records, which in most cases would only reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.Marine Data • The Conserved Areas Explorer displays the network of 124 Marine Protected Areas (MPAs) along coastal waters and the shoreline of California. There are several categories of MPAs, some permitting varying levels of commercial and recreational fishing and waterfowl hunting, while roughly half of all MPAs do not permit any harvest. These data include all of California's marine protected areas (MPAs) as defined January 1, 2019. This dataset reflects the Department of Fish and Wildlife's best representation of marine protected areas based upon current California Code of Regulations, Title 14, Section 632: Natural Resources, Division 1: FGC- DFG. This dataset is not intended for navigational use or defining legal boundaries.Tracking Conserved AreasThe total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Conserved Areas Explorer is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. These terrestrial data sources are derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Conserved Areas Explorer, please use this link to initiate a review. The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.

Terrestrial and Freshwater Data
• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries.

• The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity.
Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership.

•The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such,
boundaries represented within it may not align with other data sources.

• Numerous datasets representing designated boundaries for entities such as
National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas,
and others, were downloaded from publicly available sources, typically
hosted by the managing agency.

Methodology
1.CPAD and CCED represent the most accurate location and ownership information for
parcels in California which contribute to the preservation of open space
and cultural and biological resources.

2. Superunits are collections of parcels (Holdings) within CPAD which share a name,
manager, and access policy. Most Superunits are also managed with a
generally consistent strategy for biodiversity conservation. Examples of
Superunits include Yosemite National Park, Giant Sequoia National
Monument, and Anza-Borrego Desert State Park.

3. Some Superunits, such as those owned and managed by the Bureau of Land
Management, U.S. Forest Service, or National Park Service , are
intersected by one or more designations, each of which may have a
distinct management emphasis with regards to biodiversity. Examples of
such designations are Wilderness Areas, Wild and Scenic Rivers, or
National Monuments.

4. CPAD Superunits and CCED easements were
intersected with all designation boundary files to create the operative
spatial units for conservation analysis, henceforth 'Conservation
Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit.

5. Each Conservation Unit was intersected with the PAD-US dataset in order to
determine the management emphasis with respect to biodiversity, i.e.,
the GAP code. Because PAD-US is national in scope and not specifically
parcel aligned with California assessors' surveys, a direct spatial
extraction of GAP codes from PAD-US would leave tens of thousands of GAP
code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single
GAP code was uniformly assigned that code. Additionally, the total area
of GAP codes 1 and 2 were summed for the remaining uncoded Conservation
Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2.

6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded,
either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset.

7.These uncoded Conservation Units were then broken down into their
constituent, finer resolution Holdings, which were then analyzed
according to the above workflow.

8. Areas remaining uncoded following the two-step process of coding at the Superunit and
then Holding levels were assigned a GAP code of 4. This is consistent
with the definition of GAP Code 4: areas unknown to have a biodiversity
management focus.

9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved
Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.

10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved.

Tracking Conserved Areas
The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing
improvements in our understanding of existing biodiversity conservation
efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data.

CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels, such that the spatial features of CPAD correspond to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. • The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1. CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6. Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7. These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.Tracking Conserved AreasThe total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review. The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

The datasets and code presented in this Data in Brief article are related to the research article entitled "Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets". The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes. Resources in this dataset:Resource Title: Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants - Mendeley dataset. File Name: Web Page, url: https://data.mendeley.com/datasets/2jxj4k32m2/1 Khoury, Colin K.; Amariles, Daniel; Soto, Jonatan; Diaz, Maria Victoria; Sotelo, Steven; Sosa, Chrystian C.; Ramírez-Villegas , Julian; Achicanoy, Harold; Castañeda-Álvarez , Nora P.; León, Blanca; Wiersema, John H. (2018), Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants, Mendeley Data, v1. http://dx.doi.org/10.17632/2jxj4k32m2.1 The datasets presented here are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets” (Khoury et al., 2019). The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.