This feature layer represents Sustainable Development Goal indicator 3.6.1 'Death Rate due to Road Traffic Accidents per 1,000 Population' for Ireland. The layer was created using data produced by the Road Safety Authority (RSA) as part of the Road Casualties and Collisions in Ireland Report 2013 and pre 2014 Administrative County boundary data (more info) produced by Tailte Éireann. In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the Central Statistics Office (CSO) and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 3 which aims to ensure healthy lives and promote well-being for all at all ages.

Unlock precise, high-quality GIS data covering 3.6M+ verified locations across Canada. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
2. Enhance Navigation & Location-Based Services : Improve real-time route planning, asset tracking, and EV charging station discovery for seamless location-based experiences.
3. Identify Strategic Sites for Business Expansion : Leverage GIS intelligence to select optimal retail sites, franchise locations, and warehouses with precision.
4. Improve Logistics & Address Accuracy : Streamline delivery networks, validate addresses, and optimize courier routes to boost efficiency and customer satisfaction.
5. Support Environmental & Urban Development Initiatives : Utilize GIS insights for disaster preparedness, sustainable city planning, and land-use management.

This dataset is a compilation of ownership rights represented as parcels owned by the State of California, Department of Water Resources. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023.DWR makes no warranties or guarantees —either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements or suggestions should be forwarded to gis@water.ca.gov. This version is considered current as of 5/29/2025.

Indicator 3.6.1The death rate due to road traffic injuries.Methodology:Road accident death rate per 100,000 population by region =(Total road traffic deaths in area X 100,000) / population in the regionNote:The area represents (traffic sections),The number of road traffic fatalities was calculated by collecting the number of fatalities from traffic accidents in Qatar monthly statistics for each year.Data Source:Ministry of Public Health and National Planning Council Calculation.

The MS&R Plan identifies the general location and size of existing and proposed freeways, arterial and collector streets, future rights-of-way, setback requirements, typical intersections and cross sections, and gateway and scenic routes. The City’s Department of Transportation and the Planning and Development Services Department (PDSD) implement the MS&R Plan. The MS&R Plan is considered a Land Use Plan as defined in the Unified Development Code (UDC) Section 3.6, and, therefore, is subject to amendment in accordance with the standard Land Use Plan and Adoption and Amendment Procedures. The MS&R right-of-way lines are used in determining the setback for development through the MS&R Overlay provisions of the UDC. As stated in the current MS&R Plan, page 4, “The purpose of the Major Streets and Routes Plan is to facilitate future street widening, to inform the public which streets are the main thoroughfares, so that land use decisions can be based accordingly, and to reduce the disruption of existing uses on a property. By stipulating the required right-of-way, new development can be located so as to prepare for planned street improvements without demolition of buildings or loss of necessary parking.”PurposeThe major purposes of the Major Streets and Routes Plan are to identify street classifications, the width of public rights-of-way, to designate special routes, and to guide land use decisions. General Plan policies stipulate that planning and developing new transportation facilities be accomplished by identifying rights-of-way in the Major Streets and Routes Plan. The policies also aim to encourage bicycle and pedestrian travel, "minimize disruption of the environment," and "coordinate land use patterns with transportation plans" by using the street classification as a guide to land use decisions.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the Open Data portal and not for regular use in ArcGIS Online and ArcGIS Enterprise.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Update FrequencyAs needed

This dataset is a compilation of survey monuments as points used by the State of California, Department of Water Resources. The associated data is considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023. A survey monument can represent a boundary location or be used a precise location used for mapping and precise engineering measurements. Boundary monuments are the corners of parcels or lines of easements that can only be visualized on the ground by setting markers, or survey monuments. A survey monument is a physical marker that locates a corner or line on the ground. They can be on a line or offset from a line, on, above or below the surface, noticeable or almost invisible. They take many forms but the majority of the monuments set for the state water project are a varying diameter of metal pipe set flush or below the ground with an aluminum cap stamped with a number representing the point.

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.

The Unpublished Digital Geologic-GIS Map of Florissant Fossil Beds National Monument, Colorado, (Root, 1981), Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (root_geology.gdb), a 10.1 ArcMap (.MXD) map document (root_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (flfo_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (flfo_gis_readme.pdf). Please read the flfo_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). Presently, a GRI Google Earth KMZ/KML product doesn't exist for this map. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: National Park Service. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (root_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/flfo/root_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:7,000 and United States National Map Accuracy Standards features are within (horizontally) 3.6 meters or 11.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N. The data is within the area of interest of Florissant Fossil Beds National Monument.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Source:Greenland Mineral Resources Portal (https://www.greenmin.gl/)Geochemical Atlas of Greenland – West and South Greenland (Steenfelt 2001a)

This layer shows the average household size in Lesotho in 2023, in a multiscale map (Country and District). Nationally, the average household size is 3.6 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by 15-year age increments The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

The Watershed Protection District (PDF) is a sensitive area of land that drains to Jordan Lake Reservoir. The reservoir is a drinking water source for thousands of North Carolinians and a potential future drinking water source for Chapel Hill. As part of the NC Division of Water Quality's Water Supply Watershed Development Regulations, land use within this area has strict requirements for density, Resource Conservation Districts, use of toxic materials, and construction standards. The relevant section of the Town's Land Use Management Ordinance (LUMO), Section 3.6.4, describes more about this special area.

OverviewThis layer shows global species richness and rarity patterns for select terrestrial vertebrate and plant groups, based on data availability. Species richness is the number of species ranges estimated to occur in each grid cell. Here richness is expressed as percentile rank values from 1 (low) to 100 (high). Species rarity is the average range-restrictedness of all species expected to occur in each cell. Here rarity is expressed as percentile rank values from 1 (low) to 100 (high). These values are derived from quantile binning as shown on the Half-Earth Map website.Species range maps were used for six terrestrial species groups: amphibians, birds, cacti, conifers, mammals, and reptiles (Wilson et al 2009, Jetz et al 2012, Leslie 2014, Barthlott et al 2015, IUCN 2017, Roll et al 2017). The 'all' taxa attributes gives the richness and rarity patterns of these six species groups combined. The species groups that are included in this layer were determined based on data available based on complete spatial and taxonomic coverage. Vertebrate patterns (amphibians, birds, mammals, and reptiles) are derived from range maps refined by known habitat associations and preferences; cacti and conifers are based on unrefined range maps.The global grid used in this dataset has a grid cell area of ~777 km^2 (approximately 27.75km x 27.75km or 0.25˚ x 0.25˚ at 30˚ latitudes). The grid cells have been clipped to GADM 3.6 political boundaries (Hijmans et al 2018).Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.CitationWhen citing this dataset, please use: Jetz, W., McPherson, J. M., and Guralnick, R. P. (2012). Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology and Evolution 27:151-159. DOI:10.1016/j.tree.2011.09.007.ReferencesBarthlott, W., Burstedde, K., Geffert, J.L., Ibisch, P.I., Korotkoba, N., Miebach, A., Rafiqpoor, M.D., Stein, A., & J. Mutke (2015). Biogeography and biodiversity of cacti. Schumannia, 7.Hijmans, R., Garcia, N., & J. Wieczorek (2018). Global Administrative Areas Database (GADM) Version 3.6. UN: New York, NY, USA.Hurlbert, A.H. & W. Jetz (2007). Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. PNAS, 104(33), 13384-13389.IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed January 2017.Jetz, W., Thomas, G.H., Joy, J.B., Hartmann, K., & A.O. Mooers (2012). The global diversity of birds in space and time. Nature, 491, 444-448.Leslie, A. (2014). Conifers of the World. Unpublished MS.Powers, R.P. & W. Jetz (2019). Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nature Climate Change, 9(4), 323-329.Roll, U., Feldman, A., Novosolov, M., Allison, A., Bauer, A.M., Bernard, R., Bohm, M., Castro-Herrera, F., Chirio, L., Collen, B., & G.R. Colli (2017). The global distribution of tetrapods reveals a need for targeted reptile conservation. Nature Ecology & Evolution, 1(11), 1677-1682.Wilson, D.E., Lacher Jr, T.E., & R.A. Mittermeier eds (2009). Handbook of the mammals of the world, Vols. 1-9. Barcelona: Lynx Edicions.

This dataset represents the cadastral maps created by the Geomatics branch in support of real property acquisitions within the Department of Water Resources. The geographic extent of each map frame was created after using all the spatial attributes available in each map to appropriately georeference it and create the extents from the outer frame of the map. The maps were digitally scanned from the original paper format that were archived after moving to the new resources building. As new maps are created by the branch for real property acquisition services, they will be georeference, attributed and updated into this dataset. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.6, dated September 27, 2023. DWR makes no warranties or guarantees either expressed or implied as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Original internal source projection for this dataset was Teale Albers/NAD83. For copies of data in the original projection, please contact DWR. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov as available and appropriate.

This map plots the Change in Annual Precipitation if Earth’s long-term average temperature reaches specific levels of warming. These Global Warming Levels (GWLs) correspond to global average temperature increases of 1.5, 2, 3, and 4 °C above pre-industrial levels measured from 1851 to 1900. On the Fahrenheit scale, these warming levels are 2.7, 3.6, 5.4, and 7.2 °F. As of the 2020s, global average temperature has already increased around 2 °F above pre-industrial levels.Each layer of the map is style with the same range of data so that the spatial patterns of change can be compared across all scenarios. The projections are derived from downscaled climate models from LOCA2 and STAR-ESDM, and were used in the 5th National Climate Assessment. Click on the layers below to view more detailed descriptions of how the data was generated. The data used in this map is considered in beta release and will be replaced.

streams2: Stream Order 4 and higher in the United States considered by the National Water Model with data fields like: station_id, streamOrder, and ShapeLength. (3.6 thousand total) streams3: All streams in the United States considered by the National Water Model with data fields like: station_id, streamOrder, and ShapeLength. (2.7 million total) midpoint: All streams (not polylines but points) in the United States (converted to Midpoints using ArcGIS Pro) considered by the National Water Model with data fields like: station_id, streamOrder, and ShapeLength. (2.7 million total)

This layer is an estimate of the area within the Chesapeake Bay with a depth of roughly 12 ft (3.6 meters) or less (shallower) below mean high tide. Nautical chart data from the NOAA Office of Coast Survey was downloaded from https://encdirect.noaa.gov/.The Harbor.Depth_Area layer shows areas of different water depths within harbors, based on NOAA’s electronic navigation charts (ENCs). Each area is a shape on the map that tells you the shallowest and deepest points in that spot. It also includes information about how accurate the depth measurements are and where the data came from. The map uses a standard global coordinate system (WGS 84) and covers almost the entire world. You can use this data for mapping, analysis, or to understand where deep and shallow areas are in harbors.These polygons were filtered to include anything greater than 3.6m in depth and removed any vertices that defined deep channels. We then used the Erase tool in ArcPro© (3.2.1) to cut this polygon out of a polygon that covered all the USGS HUC 8 watersheds (https://nas.er.usgs.gov/hucs.aspx) in the Chesapeake Bay.You can view the full details of the Harbor.Depth_Area layer here: Harbor.Depth_Area (ID: 227).

The Köppen-Geiger climate classification divides climates into 5 main climate groups and 30 different classes based on patterns of seasonal precipitation and temperature, and has been used as a way to understand how climate can influence the structure of ecosystems and other natural systems. This multidimensional raster layer provide access to historical and projected Köppen-Geiger classes for the planet using the latest CMIP6 climate models that were downscaled and ensembled at 1-km resolution. Dataset SummaryPhenomenon Mapped: Köppen-Geiger climate classificationCell Size: 1-kilometerPixel Type: 8 bit unsignedCoordinate System: WGS1984Extent: WorldVisible Scale: All scales are visibleSource: gloh2o.org/koppen/Publication Date: March 2024Using the layerThis layer supports visualization and analysis. Using the multidimensional capabilities of ArcGIS Online and ArcGIS Pro, you can access 4 different emissions scenarios, called Shared Socioeconomic Pathways, along with time. Emissions ScenariosSSP1-2.6: One of the most optimistic scenarios, global CO2 emissions are cut severely, but not as fast, reaching net-zero after 2050. Temperatures stabilize around 1.8 degC higher by the end of the century. (Reuters 2021)SSP2-4.5: This is a “middle of the road” scenario. CO2 emissions hover around current levels before starting to fall mid-century, but do not reach net-zero by 2100. Socioeconomic factors follow their historic trends, with no notable shifts. Progress toward sustainability is slow, with development and income growing unevenly. In this scenario, temperatures rise 2.7 degC by the end of the century. (Reuters 2021)SSP3-7.0: On this path, emissions and temperatures rise steadily and CO2 emissions roughly double from current levels by 2100. Countries become more competitive with one another, shifting toward national security and ensuring their own food supplies. By the end of the century, average temperatures have risen by 3.6 degC. (Reuters 2021)SSP5-8.5: This is a future to avoid at all costs. Current CO2 emissions levels roughly double by 2050. The global economy grows quickly, but this growth is fueled by exploiting fossil fuels and energy-intensive lifestyles. By 2100, the average global temperature is a scorching 4.4 degC higher. (Reuters 2021)Time ExtentsSix 30-year average periods were calculated. Each is referred to by the mid-year in the range:1901–1930 (1915)1931–1960 (1945)1961–1990 (1975)1991–2020 (2005)2041–2070 (2055)2071–2099 (2085)Accessing the DimensionsIn ArcGIS Pro, the Multidimension ribbon will activate when the layer is selected. From there you can select the SSP dimension or the Standard Time. In ArcGIS Online, access the Multidimensional information from the righthand menu. Select the SSP and time. Note: To access the pop-up when using Map Viewer, please deactivate the time slider. Otherwise there is a conflict between the time selection in the time slider and the time selection in the Multidimensional information. Source InformationData were download from https://www.gloh2o.org/koppen/The full details are described by Beck et al 2023. Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Scientific Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6

This web map provides the spatial foundation for displaying biological survey points and species observations across Chesapeake Bay segments. Multiple biological survey datasets and species observations across Chesapeake Bay segments from fisheries-independent survey data are included, most of which was collected for the 2020 Tetra Tech report 'Inventory & Evaluation of Environmental and Biological Response Data for Fish Habitat Assessment' (https://www.chesapeakebay.net/what/publications/inventory-evaluation-of-environmental-and-biological-response-data-for-fish). The report only included data up to 2019 and as far back as the 1960’s. However, the data in this map only goes as far back as 1990. Several other layers are included in the map that were not part of the report. These include depth polygons of 1.8 meters and 3.6 meters from mean high tide as well as the polygon layer for the 92-Tidal Segments for the Chesapeake Bay. This map is used within an interactive dashboard, where filters and selections control which points are visible and serves only as a tool for visualizing biological survey points and species observations within the Chesapeake Bay.

The latest global climate models, part of the sixth Coupled Model Intercomparison Program or CMIP6, forecast change comparing a variety of development and emissions scenarios. These scenarios are called the Shared Socioeconomic Pathways, or SSPs. The intent is to model future Earth processes, including what will happen to land cover. A more extreme SSP is scenario SSP3-7.0, which assumes the atmosphere will nearly double its current CO2 levels by 2100. This amount of CO2 would raise the planet's average temperature 3.6 deg C by the end of the century. The Paris Agreement targets an increase of no more than 2.0 deg C. To learn more about this work, read our open access peer-reviewed journal article in Global Ecology and Conservation, Volume 57, January 2025, e03370: Potential 2050 distributions of World Terrestrial Ecosystems from projections of changes in World Climate Regions and Global Land Cover.This layer shows the resultant global distribution of eight land cover types in 2050, at approximately 1km resolution, for scenario SSP5-8.5. To create this global distribution, Esri reduced these eight land cover types from a dataset produced by (Chen et al.) that represents 20 land use and land cover classes based on Plant Functional Types or PFTs. Chen et al. produced their layer to support comparisons with future land cover types generated using CMIP6 protocols. The eight land cover classes are as follows: ForestGrasslandsScrub and ShrubSparsely or not VegetatedSettlementSurface WaterSnow or Ice Esri already grouped PFTs the same way into the same eight types of land cover for World Terrestrial Ecosystems version 1.In addition to this layer, the Living Atlas serves two additional projected future land cover datasets and a baseline dataset for 2015. The future land cover datasets derived from Chen et al. for 2050 are for scenario SSP1-2.6 and scenario SSP5-8.5.Source: https://zenodo.org/records/4584775 obtained on Jun 2, 2023.References: 1. Chen, G., Li, X. & Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Sci Data 9, 125 (2022). https://doi.org/10.1038/s41597-022-01208-6.2. Sayre, Roger, Karagülle, Deniz, Frye, Charlie, Boucher, Timothy, Wolff, Nicholas H., Breyer, Sean, Wright, Dawn, Martin, Madeline, Butler, Kevin, Van Graafeiland, Keith, Touval, Keith, Sotomayor, Leonardo , McGowan, Jennifer , Game, Edward T., Possingham, Hugh. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, v21. DOI: 10.1016/j.gecco.2019.e00860.