Available spatial data
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Temporal extent
StateEcosystem ServicesSoil CarbonSoilGrids
SoilGrids provides global predictions for standard numeric soil properties (such as organic carbon) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm),
Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret.
StateEcosystem ServicesSoil CarbonGlobal mangrove soil carbon
This dataset contains global mosaics of soil carbon stocks for mangrove forests to 1m and 2m depths produced at 100 m resolution and tiled predictions of soil carbon stocks produced at 30 m resolution
This study developed a machine learning-based model of organic carbon density (OCD) which models OCD as a function of depth (d), an initial estimate of the 0–200 cm organic carbon stock (OCS) from the global SoilGrids 250 m model (Hengl et al 2017), and
a suite of spatially explicit covariate layers.
StateEcosystem ServicesBiomass carbonGlobBiomass
This dataset contains information on above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) for the year 2010. The biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.
GlobalGeoTiffAGB was obtained from growing stock volume (GSV, unit: m3/ha) with a set of Biomass Expansion and Conversion Factors (BCEF) following approaches to extend on ground estimates of wood density and stem-to-total biomass expansion factors to obtain a global raster dataset. The GSV estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT) and auxiliary datasets with multiple estimation procedures.https://doi.pangaea.de/10.1594/PANGAEA.894711
StateEcosystem ServicesBiomass carbonGlobal grassland biomass
This dataset contains information on aboveground live biomass carbon density (g·C·m−2) for global grassland ecosystems from 1982 to 2006
GlobalGeoTiffUsing a worldwide grassland biomass measurements dataset, remote sensing NDVI, and climate reanalyzed data, the study applied regression modelling techniques to quantify spatio-temporal patterns of biomass carbon stock of global grassland ecosystems at 8km resolutionhttps://core.ac.uk/download/pdf/26905257.pdf
StateEcosystem ServicesBiomass carbonSynthetic Global Biomass Carbon Map
An harmonized global map of biomass and soil organic carbon stocks that combine recently-released satellite based data of standing forest biomass with novel estimates for non-forest biomass stocks that are typically neglected.
GlobalGeoTiffCreated by overlaying a global landcover map for the year 2010 with satellite-based maps of landcover-specific aboveground biomass carbon and interpolation where necessary.http://adsabs.harvard.edu/abs/2017AGUFMGC21B0945S
StateEcosystem ServicesFisheriesGlobal Coral Reef Fisheries
The global map of fish catch considers four elements:

the reef productivity
local fishing intensity
international fishing for key target species
management influences
StateEcosystem ServicesFisheriesGlobal Mangrove Fisheries
Our initial global model of mangrove fisheries was built up front a detailed review of hundreds of studies from around the world, and informed by an expert panel. In terms of natural productivity, the most important areas are those with high input of freshwater and nutrients notably focused around estuaries, deltas and lagoons, particularly in the wet tropics. Fishing effort of course is uneven, but centered in areas where high populations live close to mangroves, or where smaller fishing populations may nonetheless have access to urban markets.
StateEcosystem ServicesTourismMapping the global value and distribution of coral reef tourismValue of coral reef tourism per grip cellGlobalhttps://www.sciencedirect.com/science/article/pii/S0308597X17300635
StateEcosystem ServicesBiomassPredicting Global Patterns in Mangrove Forest BiomassMangrove biomass per grid cellGlobalhttps://onlinelibrary.wiley.com/doi/full/10.1111/conl.12060
StateEcosystemsBiodiversity intactnessSpecies abundance based biodiversity intactness (PREDICTS)
Measure based on the PREDICTS database which includes point samples of the abundance of each species within a local community. Samples are collected across varying land use types (as well as other pressures). Models are then produced to examine the impacts on species richness when pressures change. Compositional similarity is calculated by examining how the change in the relative proportions of species within communities change in response to pressures. The multiplication of species richness with compositional similarity provides the intactness.
GlobalASCII Gridhttp://data.nhm.ac.uk/dataset/global-map-of-the-biodiversity-intactness-index-from-newbold-et-al-2016-science
StateEcosystemsBiodiversity intactnessSpecies richness based biodiversity intactness (PREDICTS)
Measure based on the PREDICTS database which includes point samples of the abundance of each species within a local community. Samples are collected across varying land use types (as well as other pressures). Models are then produced to examine the impacts on species richness when pressures change. Compositional similarity is calculated by examining how the change in the relative proportions of species within communities change in response to pressures. The multiplication of species richness with compositional similarity provides the intactness.
GlobalASCII Grid
StateEcosystemsBiodiversity persistence
BILBI-Biogeographic modelling Infrastructure for Large-scale Biodiversity Indicators
Metric of species persistence within biological communities (% species remaining). Measures the beta diversity of plants, vertebrates and invertebrates. Each grid cell is scored according to the proportion of original plant species remaining. The potential effects of climate scenarios on beta-diversity patterns are estimated through space-for-time projection of compositional-turnover models fitted to present-day biological and environmental data. These projections are then combined with downscaled land-use scenarios to estimate the proportion of species expected to persist within any given region. This employs an extension of species-
area modelling designed to work with biologically-scaled environments varying continuously across space and time.
StateEcosystemsLand coverLand cover CCI
Consistent global Land Cover maps at 300 m spatial resolution from 1992 to 2015. The “level 1” legend – also called “global” legend – is composed by 22 classes. The CCI-LC maps are also described by a more detailed legend, called “level 2” or “regional”. This
level 2 legend makes use of more accurate and regional information – where available – to define
more LCCS classifiers and so to reach a higher level of detail in the legend.
These maps are derived from a unique baseline from MERIS (2003 to 2012), and backdating and updating techniques are applied based on 300 m PROBA-V (2013 to 2015), 1 km SPOT-VGT (1999 to 2012), and 1 km AVHRR time series (1992–1999).
StateEcosystemsLand coverGlobal Map of Potential Natural Vegetation
Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-impacted by human activities. PNV is useful for estimating land restoration potential. This map estimates global PNV at 1 Km resolution
GlobalGeoTiffMachine Learning Algorithms (MLA) were applied to estimate PNV using (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions), (2) distribution of forest tree species in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief and lithologic variables, were used as explanatory variables. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated.https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QQHCIK
StateEcosystemsForestIntact Forest Landscapes
An Intact Forest Landscape (IFL) is a seamless mosaic of forest and naturally treeless ecosystems within the zone of current forest extent, which exhibit no remotely detected signs of human activity or habitat fragmentation and is large enough to maintain all native biological diversity, including viable populations of wide-ranging species.
ESRI Shapefile (Polygon)
StateEcosystemsWildernessTerrestrial wildernessall terrestrial wilderness and all terrestrial wilderness over 10,000km2
Global1993 - 2009
ESRI Shapefile (Polygon)
StateEcosystemsCritical habitatCritical habitat screening layer
Critical Habitat is identified based on five criteria that address habitat of significant importance to threatened, endemic, congregatory and migratory species, threatened or unique ecosystems, and key evolutionary processes.
Important biodiversity areas
Key Biodiversity Areas
Key Biodiversity Areas (KBA) are 'sites contributing significantly to the global persistence of biodiversity’, in terrestrial, freshwater and marine ecosystems. Noting that species data are the current basis of KBA identification and ecosystem based criteria are coming
ESRI Shapefile (Polygon)
Important biodiversity areas
Endemic Bird AreasAreas of high bird endemismGlobal1998
ESRI Shapefile (Polygon)
Important biodiversity areas
Biodiversity Hotspots
Biogeographic region that is both a significant reservoir of biodiversity and is threatened with destruction.
ESRI Shapefile (Polygon)
Important biodiversity areas
High Biodiversity Wilderness AreasThe large intact ecosystems of the world that hold significant levels of global biodiversity.Global2004
ESRI Shapefile (Polygon)
StateEcosystemsEcoregionsRESOLVE Ecoregions
The RESOLVE Ecoregions dataset, updated in 2017, offers a depiction of the 846 terrestrial ecoregions that represent our living planet
ESRI Shapefile (Polygon)
StateEcosystemsEcoregionsWWF G200 Ecoregions
WWF’s Global 200 project analyzed global patterns of biodiversity to identify a set of the Earth's terrestrial, freshwater, and marine ecoregions that harbor exceptional biodiversity and are representative of its ecosystems.
ESRI Shapefile (Polygon)
StateEcosystemsWetlandsGlobal Lakes and Wetlands Database
The combination of best available sources for lakes and wetlands on a global scale (1:1 to 1:3 million resolution), and the application of GIS functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.
ESRI Shapefile (Polygon)
StateEcosystemsMarineGlobal Distribution of Tidal Flat Ecosystems
The dataset contains global maps of tidal flat ecosystems produced via a supervised classification of 707,528 Landsat Archive images. Each pixel was classified into tidal flat, permanent water or other with reference to a globally distributed set of training data.
StateEcosystemsMarineGlobal Distribution of Saltmarshes
This dataset displays the extent of our knowledge regarding the distribution of saltmarshes globally, drawing from occurrence data (surveyed and/or remotely sensed).
Global1973 - 2015
ESRI Shapefile (Polygon and point)
StateEcosystemsMarineWorld Atlas of Mangroves
This dataset shows the global distribution of mangroves, and was produced as joint initiatives of the International Tropical Timber Organization (ITTO), International Society for Mangrove Ecosystems (ISME), Food and Agriculture Organization of the United Nations (FAO), UN Environment World Conservation Monitoring Centre (UNEP-WCMC), United Nations Educational, Scientific and Cultural Organization’s Man and the Biosphere Programme (UNESCO-MAB), United Nations University Institute for Water, Environment and Health (UNU-INWEH) and The Nature Conservancy (TNC)
Global 1999-2003
ESRI Shapefile (Polygon)
StateEcosystemsMarineGlobal Distribution of Coral Reefs
This dataset shows the global distribution of coral reefs in tropical and subtropical regions. It is the most comprehensive global dataset of warm-water coral reefs to date, acting as a foundation baseline map for future, more detailed, work.
ESRI Shapefile (Polygon)
StateEcosystemsMarineGlobal Distribution of Cold-water CoralsThis dataset shows of the global distribution of cold-water corals. Global1915-2014
ESRI Shapefile (Polygon and point)
StateEcosystemsMarineGlobal Distribution of Seagrasses
This dataset shows the global distribution of seagrasses, and is composed of two subsets of point and polygon occurrence data.
ESRI Shapefile (Polygon and point)
StateEcosystemsMarineGlobal Distribution of Seamounts and Knolls
This dataset shows the global distribution of seamounts and knolls identified using global bathymetric data at 30 arc-sec resolution. A total of 33,452 seamounts and 138,412 knolls were identified, representing the largest global set of identified seamounts and knolls to date.
ESRI Shapefile (Polygon and point)
StateEcosystemsMarineGlobal Estuary Database
This dataset shows the global distribution of over 1,300 estuaries, including some lagoon systems and fjords.
ESRI Shapefile (Polygon)
HydroLAKES is a database aiming to provide the shoreline polygons of all global lakes with a surface area of at least 10 ha.
ESRI Shapefile (Polygon and point)
StateEcosystemsHabitatsMountains of the WorldFAO preliminary vesionGlobalGeoTiff\\I:\ReceivedPendingData\AUDITED_DATASETS\FAO\Current_Version\FAO_2014\Mountains_Nov2014
StateEcosystemsForestEver-wet tropical forestsThis dataset shows locations of ever-wet forest.Global2010https://www.nature.com/articles/nclimate2351#supplementary-information
StateEcosystemsForestGlobal distribution of Tropical dry forestThis dataset shows locations of tropical dry forest.Global2000https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2699.2005.01424.x
StateEcosystemsForestCloud forestsThis dataset shows locations of cloud forest.Global2004
StateEcosystemsImportant habitatIrreplaceable protected areas
The 137 sites listed in this dataset were selected for being within the 100 sites with highest overall irreplaceability and/or within the 100 most irreplaceable areas for threatened species.
StateEcosystemsMarineGlobal Distribution of Sea Turtle Nesting SitesThis dataset shows the known locations of sea turtle nesting sites, for all seven species.Global1949-1993
ESRI Shapefile (Point)
StateEcosystemsHabitatsWorld riversThe dataset presents 687 rivers associated to 405 Major River Basins.Global2017
ESRI Shapefile (Polyline)
StateEcosystemsMarineGlobal marine wilderness
The Global marine wilderness rasters represent areas within the bottom 10% for each of 15 human stressors to the ocean, and also in the bottom 10% of cumulative human impact.
StateEcosystemsLand use
Fine scale map of land use (primary habitat, secondary habitat, cropland, pasture, and urban)
A global map of 5 land use types at 30s (approx. 1km) resolution for 2005. The data set was generated through the statistical downscaling of the Land-use Harmonisation data set.
StateEcosystemsForestGlobal Dryland ForestFAO map of global dryland forestGlobal2015https://science.sciencemag.org/content/356/6338/635.abstract?ijkey=roFpRp.uF.DG6&keytype=ref&siteid=sci
StateGenesGenetic diversityAnthropocene map of genetic diversityThis dataset shows global terrestrial mammals and amphibians genetic diversity.Global2014http://science.sciencemag.org/content/353/6307/1532
ResponsesGenesPhylogenetic diversityPriority areas for conserving phylogenetic diversityThis dataset shows terrestrial mammal species phylogenetic diversity.Global2017https://datadryad.org/resource/doi:10.5061/dryad.rc416
ResponsesResponsesProtected areasWorld Database on Protected Areas (WDPA)Global protected areasGlobalpresent
ESRI file geodatabase
ResponsesResponsesIndigenous lands Indigenous lands
Understanding the scale, location and nature conservation values of the lands over which Indigenous Peoples exercise traditional rights is central to implementation of several global conservation and climate agreements. However, spatial information on Indigenous lands has never been aggregated globally. Here, using publicly available geospatial resources, we show that Indigenous Peoples manage or have tenure rights over at least ~38 million km2 in 87 countries or politically distinct areas on all inhabited continents. This represents over a quarter of the world’s land surface, and intersects about 40% of all terrestrial protected areas and ecologically intact landscapes (for example, boreal and tropical primary forests, savannas and marshes). Our results add to growing evidence that recognizing Indigenous Peoples’ rights to land, benefit sharing and institutions is essential to meeting local and global conservation goals. The geospatial analysis presented here indicates that collaborative partnerships involving conservation practitioners, Indigenous Peoples and governments would yield significant benefits for conservation of ecologically valuable landscapes, ecosystems and genes for future generations.
ResponsesResponsesRestoration potentialRestoration priorities to achieve the global protected area target
With much of Earth's surface already heavily impacted by humans, there is a need to understand where restoration is required to achieve global conservation goals. Here, we show that at least 1.9 million km2 of land, spanning 190 (27%) terrestrial ecoregions and 114 countries, needs restoration to achieve the current 17% global protected area target (Aichi Target 11). Restoration targeted on lightly modified land could recover up to two‐thirds of the shortfall, which would have an opportunity cost impact on agriculture of at least $205 million per annum (average of $159/km2). However, 64 (9%) ecoregions, located predominately in Southeast Asia, will require the challenging task of restoring areas that are already heavily modified. These results highlight the need for global conservation strategies to recognize the current level of anthropogenic degradation across many ecoregions and balance bigger protected area targets with more specific restoration goals.
ResponsesResponsesRestoration prioritiesMangrove Restoration Potential Map showing Mangrove Restoration Potential Globalhttp://maps.oceanwealth.org/mangrove-restoration/
ResponsesResponsesRestoration OpportunitiesAtlas of Forest and Landscape Restoration OpportunitiesComposite dataset based on potential habitat models, human pressure and land coverGlobalhttp://www.wri.org/applications/maps/flr-atlas/#&init=y
StateSpeciesSpecies distributionThe IUCN Red List of Threatened Species
This dataset contains distribution information on species assessed for The IUCN Red List of Threatened Species™. The maps are developed as part of a comprehensive assessment of global biodiversity in order to highlight taxa threatened with extinction, and thereby promote their conservation. The IUCN Red List of Threatened Species™ contains global assessments for over 96,500 species, of which about two-thirds have spatial data. The spatial data provided are for comprehensively assessed taxonomic groups. Groups that have been comprehensively assessed are those containing >80% species evaluated within the described taxon group.
ESRI Shapefile (Polygon)
Extent of occurrence (EOO) polygons defined as "the area contained within the shortest continuous imaginary boundary which can be drawn to encompass all the known, inferred or projected sites of present occurrence of a taxon, excluding cases of vagrancy". The IUCN Red List EOO Calculator tool, which runs in ArcGIS, (downloadable from the IUCN Red List Spatial Resources page on the IUCN Red List website) calculates EOO using only polygons and points of Presence 1 (Extant) and Origin 1 or 2 (Native and Re-Introduced), and it does so separately for Seasonality codes 1 and 2 (if present) or 1 and 3 (if present), with EOO being taken as the lower of these two values.
Mammals, birds, amphibians, reptiles
StateSpeciesSpecies distributionGlobal Assessment of Reptile Distributions
A database containing distribution information for 10,064 reptile species (~99% of all world terrestrial reptiles species). Will be integrated into Red List.
ESRI Shapefile (Polygon)
Extent of occurrence (EOO) polygons using hull geometries.
GARD 1.1Reptileshttps://datadryad.org/resource/doi:10.5061/dryad.83s7k
StateSpeciesSpecies distributionBIEN (Botanical Information and Ecology Network)
Database aiming to bring together data on plant distribution, abundance and traits for 485,902 species. Heaviliy biased towards the Americas. Range maps are available for about 88,000.
ESRI Shapefile (Polygon)
Maps for most species were produced using Maximum Entropy distribution modeling (Maxent), using the single best combination of settings and thresholding procedures, as determined by an comparison of range modeling algorithms for the purpose of estimating range size
BIEN 4.1.1Plantshttp://bien.nceas.ucsb.edu/bien/biendata/bien-4/
StateSpeciesSpecies distributionBGCI Global Tree Assessment
Red List assessments including point maps suitable for calculating extent of occurrence at the minimum for 26,247 tree species. Will be integrated into Red List.
GlobalOccurrence dataPlantshttps://www.bgci.org/plant-conservation/globaltreeassessment/
StateSpeciesSpecies distributionCIAT crop wild relatives project
This dataset contains information of 1,076 crop wild relatives species from the whole world except Antarctica. Data was gathered from more than 100 data providers, including GBIF. This dataset was assembled as part of the project ‘Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives’, which is supported by the Government of Norway.
GlobalCSVOccurrence dataPlantshttps://www.gbif.org/dataset/07044577-bd82-4089-9f3a-f4a9d2170b2e#description
StateSpeciesSpecies distributionWCMC Bamboo
This dataset contains information on bamboo species distribution in Asia, Africa and the Americas. Information consists in ~1600 (coarse locality information) of which 94 have maps. The dataset also contains information on altitudinal ranges for some species; National/regional administrative localities for all species
StateSpeciesSpecies distributionsPlot Global Vegetation database
Primary data. A dataset containing more than million records with full lists of ~54500 plant species co-occurring in small areas (plots). Heavily biased towards Europe. According to the sPlot Rules, the use of the sPlot data is restricted to author teams led by an sPlot Consortium member - so likely fail FAIR criteria.
GlobalVegetation plot records2.1Plantshttps://www.idiv.de/?id=176&L=0
StateSpeciesSpecies distributionGBIF
The Global Biodiversity Information Facility (GBIF) is an international network and research infrastructure funded by the world’s governments aimed at providing open access to digitized occurrence records of all types of taxa. Data derives from many sources, including everything from museum specimens, research projects, expeditions, citizen scientists etc. Datasets can be downloaded in bulk using the rgbif package in R. It requires a considerable data cleaning work. Includes taxonomic backbone
GlobalOccurrence dataAllhttps://www.gbif.org/
StateSpeciesSpecies distributioneBirdFeeds into GBIF but sampling effort, relative abundance work doesn't contribute. Global
StateSpeciesSpecies distributioniNaturalist
Citizen science effort to gather observations of individual organisms. Feeds into GBIF when research grade (many records still require identification to get to research grade). Other citizen science platforms also feed into GBIF.
GlobalCSVOccurrence dataAllhttps://www.inaturalist.org/
StateSpeciesSpecies distributionBioTime
BioTIME is a comprehensive collection of assemblage time-series in which the abundances of the species that comprise ecological communities have been monitored over a number of years. BioTIME data span the globe and encompass land and seas; they also include freshwater systems. The current version of BioTIME contains over 12 million records, features almost 50 thousand species, covers over 600 thousand distinct geographic locations and is representative of over 20 biomes, occurring over 6 different climatic zones.
StateSpeciesSpecies distributionINBAR/Kew world atlas of bamboos & rattans
This dataset contains distribution information for all 1,642 species of bamboo and all 631 rattan species. The data is available in searchable format via the World Checklist of Selected Plant Families web site at http://apps.kew.org/wesp
GlobalThe geographical information used to generate the maps is primarly derived from the World Checklist of Selected Plant Families. Distributions of species and taxa of lower rank are furnished in two ways: firstly by a generalised statement in narrative form, and secondly as TDWG geographical codes (Brummitt, 2001) expressed to that system's third level.Plantshttps://www.researchgate.net/publication/316623225_World_Atlas_of_Bamboos_and_Rattans
Species population abundance
Living Planet Index (LPI) Database
The ongoing dataset contains records of population abundance trends for 22779 populations of 4295 species of vertebrates. It spans terrestrial, freshwater and marine systems.
The Living Planet Database (LPD) currently holds time-series data for over 20,000 populations of more than 4,200 mammal, bird, fish, reptile and amphibian species from around the world, which are gathered from a variety of sources such as journals, online databases and government reports. Using a method developed by ZSL and WWF, these species population trends are aggregated to produce indices of the state of biodiversity. The rest of our work focusses on expanding the coverage of LPI data to more broadly represent vertebrate biodiversity from all around the globe and disaggregating the index to measure trends in different thematic areas. This includes assessing the changes in different taxonomic groups, looking at species trends at a national or regional level, identifying how different threats affect populations and providing an insight into how conservation intervention can promote species recoveries.
StateEcosystemFunctional RichnessMadingley Model of Functional RichnessA bottom-up biodiversity model based on key abiotic maps and rulesGlobal
StateSpeciesPlant diversityCentres of plant diversity
Global priority schemes: Sites of global botanical importance based on their high plant endemism and species richness. No ongoing update
StateSpeciesPlant diversityImportant Plant AreasOngoing updateGlobal
StateSpeciesPlant diversityUseful Plants Database
Useful plant and fungi species - spatial info being developed at Kew based on existing dataset of use; would like to release e.g. within Plants of the World online (Kew); global; not comprehensive but substantial; links to Aichi target 4
GlobalPlants & Fungi
StateSpeciesPlant diversityGIFT
Plant database - regional floras, PFTs (compiled across diff sources & resolutions) - spatial resolution often country scale [to be completed]
StateSpeciesSpecies distributionExtent of suitable habitat for - mammals, amphibians, birds, reptilesVarious groups have been / are working on ESH; ESH will be integrated into Red ListGlobal
StateSpeciesSpecies richnessSpecies richness ESH
Synthetic map from individual ESH maps. This raster layer represents the number of species of mammals, amphibians and birds whose distributions overlap each ~300 m grid cell. Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together into a single layer with equal weighting.
StateSpeciesRange rarityRange rarity ESH
Synthetic map from individual ESH maps. The range rarity, or range size-rarity, is a ~300 m raster layer based on scores for endemism of all mammals, amphibians and bird species. Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species.
StateSpeciesSpecies richnessThreatened species richness ESH
Synthetic map from individual ESH maps. This raster layer represents the number of threatened species of mammals, amphibians and birds potentially occurring in each ~300m grid cell. Species range data were rasterised at 10 arc-seconds (approximately 300m at the equator) from polygon maps developed for the IUCN Red List (IUCN, 2017; BirdLife international and Handbook of the Birds of the World 2017). Each range was then refined by removing areas of unsuitable land cover (Bontemps et al., 2011) using information on species’ habitat preferences (IUCN, 2017). Areas outside the species’ known altitudinal limits (IUCN, 2017) were also removed using elevation data (Danielson and Gesch, 2011). If species had no habitat preference data available, ranges were refined only by altitude. If altitude limits were missing, then extreme values (for either min or max, or both) outside the global min/max of the elevation dataset were applied. This effectively meant there was no altitude refinement in such cases. This refinement process produced extent of suitable habitat (ESH) maps for each species. These maps were then summed together with equal weighting into a single richness layer.
StateEcosystemsBiodiversity IntactnessMean Species Abundance (MSA)
Mean of abundance of original species in impacted situation relative to abundance in undisturbed/reference situation. Disturbances (pressures) include climate change, atmospheric nitrogen deposition, land use, habitat fragmentation, disturbance by roads and hunting (the latter in tropical regions). MSA data layers are available for each pressure as well as combined.
Global2015, 2050GeoTIFFGLOBIO modelGLOBIO 4.0
plants, terrestrial vertebrates
StateEcosystemsBiodiversity IntactnessMean Species Abundance (MSA)
Mean of abundance of original species in impacted situation relative to abundance in undisturbed/reference situation. Disturbances (pressures) include climate change, land use and eutrophication. MSA data layers are available for each pressure as well as combined.
Global2015, 2050GeoTIFFGLOBIO-Aquatic model
GLOBIO-Aquatic 2.0
macrophytes, macro-invertebrates, fish
PressureSpeciesSpecies distributionGlobal Register of Introduced and Invasive Species (GRIIS)Will have total global coverage by end of 2019; includes some disease spp such as chytrid fungi.Global
PressureThreatFuture pressuresRisk of tree cover loss
This dataset consists in a set of maps of tree cover loss risk at 1 km resolution by 2029, based on a BAU scenario
Global2014–2029GeoTiffThe map was produced using of spatially-explicit global variables related to historical tree cover loss and an empirical modeling technique (the multi-layer perceptron neural network) to combine the covariates of the variables with observed historical tree cover and tree cover loss data from 2000 to 2014 and assess the contribution of each variable to historical tree cover loss. This process yields transition potential surfaces that capture the potential of each pixel to transition from its current state. The authors then used the relationship between the variables and historical tree cover loss, captured in the transition potential surfaces, to generate 15-year projections of potential future tree cover loss for 2014–2029 under a BAU scenario.https://www.mdpi.com/2073-445X/8/1/14/htm
PressureThreatCurrent pressuresGlobal terrestrial human footprint
This datasets consists in a set of maps of estimated human footrpint from 1993 to 2009 at 1 km resolution. The human footprint map measures the cumulative impact of direct pressures on nature from human activities. It includes eight inputs: (1) the extent of built environments, (2) crop land, (3) pasture land, (4) human population density, (5) night-time lights, (6) railways, (7) roads, and (8) navigable waterways.
Available at: https://www.nature.com/articles/sdata201667#ref13
PressureThreatCurrent pressuresLandScanGlobal population distribution data at 1 km resolution, updated annualy.
Global2000 - 2017ESRI grid
The LandScanTM global population distribution models are a multi-layered, dasymetric, spatial modeling approach that is also referred to as a “smart interpolation” technique. In dasymetric mapping, a source layer is converted to a surface and an ancillary data layer is added to the surface with a weighting scheme applied to cells coinciding with identified or derived density level values in the ancillary data. In the LandScanTM models, the typical dasymetric modeling is improved by integrating and employing multiple ancillary or indicator data layers. The modeling process uses sub-national level census counts for each country and primary geospatial input or ancillary datasets, including land cover, roads, slope, urban areas, village locations, and high resolution imagery analysis; all of which are key indicators of population distribution. Based upon the spatial data and the socioeconomic and cultural understanding of an area, cells are preferentially weighted for the possible occurrence of population during a day. Within each country, the population distribution model calculates a “likelihood” coefficient for each cell and applies the coefficients to the census counts, which are employed as control totals for appropriate areas. The total population for that area is then allocated to each cell proportionally to the calculated population coefficient. The resultant population count is an ambient or average day/night population count.
Positional or attribute errors and anomalies are to be anticipated in large volumes of disparate spatial data. The LandScanTM methodology includes a manual verification and modification process to improve the spatial precision and relative magnitude of the population distribution. Imagery analysts identify obvious population distribution errors and create an additional spatial data layer of population likelihood coefficient modifications to correct or mitigate input data anomalies. Output cells are converted to points with an attribute field for the cell modification values. Many modifications are made to urban areas and urban extents. Derived land cover data often do not reveal urban properties such as building densities or building heights that can be readily inferred with visual inspection using high resolution imagery. Manual corrections to the likelihood coefficient file using high resolution imagery are made for each country as time and budget constraints allow.
LandScan 2017™
PressureThreatCurrent pressuresGridded Livestock of the World
A spatial dataset on livestock distribution at 1 km resolution for the reference year 2010 and the following species: cattle, sheep, goats, buffaloes, horses, pigs, chickens and ducks. The individual species datasets are available at global extent and 5 minutes of arc resolution (approx. 10 km at the equator)
Available at: https://www.nature.com/articles/sdata2018227
GLW 3https://dataverse.harvard.edu/dataverse.xhtml?alias=glw_3
PressureThreatCurrent pressuresNASA Cropland extent and watering methodCropland extent and watering method for nominal year 2015 at 30 meter resolutionGlobal
2010 & 2015
Available at: https://lpdaac.usgs.gov/sites/default/files/public/product_documentation/GFSAD30SEACE_User_Guide_v1.pdf
PressureThreatCurrent pressuresCropland and Pasture AreaThis dataset displays croplands and pastures circa 2000 at 10 km resolution.
Available at: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GB002952
PressureThreatCurrent pressuresHansen Global Forest ChangeGobal forest extent and change from 2000 through 2017
Available at: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007GB002952
PressureThreatCurrent pressuresLong term Global Land Change
Global map layers representing net changes in Tree Canopy Cover, Short Vegetation Cover and Bare Ground Cover over the past 35 years (1982-2016)
Global1982 - 2016GeoTiff
Available at: https://www.nature.com/articles/s41586-018-0411-9
PressureThreatCurrent pressuresOpenStreetMap
OpenStreetMap is built by a community of mappers that contribute and maintain data.Likely to be the most up-to-date globally consistent information about roads and railways.
PressureThreatCurrent pressuresgRoads
This dataset combines available public domain roads data from the 1980s to 2010 by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model.
Global1980 - 2010
ESRI file geodatabase
Available at: http://sedac.ciesin.columbia.edu/downloads/docs/groads/groads-v1-methods.pdf
PressureThreatCurrent pressuresGRIP (Global Roads Inventory Project)
The Global Roads Inventory Project (GRIP) dataset was developed to provide a more recent and consistent global roads dataset for use in global environmental and biodiversity assessment models like GLOBIO.
ESRI file geodatabase & ASCII Grid
Collection and harmonization of national road datasets
PressureThreatCurrent pressuresAccessibility to cities
A global layer containing information on accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.
Available at: https://www.nature.com/articles/nature25181
PressureThreatCurrent pressuresNighttime lights
A dataset contaning information on nighttime lights at 500 m resolution. Available from January 2012-present. A proxy for built up areas
Available at: https://www.sciencedirect.com/science/article/pii/S003442571830110X
PressureThreatCurrent pressuresGHS BUILT-UP GRID
These data contain a multitemporal information layer on built-up presence from 1975 to 2015, as derived from Landsat image collections
1975, 1990, 2000 and 2015
Available at: https://publications.europa.eu/en/publication-detail/-/publication/6eedd1fe-e046-11e5-8fea-01aa75ed71a1/language-en
PressureThreatCurrent pressuresGlobal landslide susceptibilityA global landslide susceptibility map
Available at: https://link.springer.com/article/10.1007%2Fs11069-017-2757-y
PressureThreatCurrent pressuresMODIS active fire/burnt area
Information on recorded fires/burnt areas at 500m resolution produced from both the Terra and Aqua MODIS-derived daily surface reflectance inputs
2000 - present
HDF; GeoTiff; ESRI Shapefile (Polygon)
Available at: http://modis-fire.umd.edu/guides.html
PressureThreatCurrent pressuresSoil erosionThis layer shows the risk of erosion around the world, from low to high.
Erosion risk was estimated based on the RUSLE model, adjusted to extend its applicability to a global scale.
PressureThreatCurrent pressuresLand surface runoff
A long-term (1971–2000) average ‘naturalized’ discharge and runoff provided by the WaterGAP model (model version 2.2 as of 2014) at 500 m resolution.
Available at: https://www.sciencedirect.com/science/article/pii/S0022169402002834?via%3Dihub
PressureThreatCurrent pressuresGranD
This dataset contains information on reservoirs with a storage capacity of more than 0.1 km³. The recent version contains 6.862 spatially explicit records of reservoirs with their respected dams and gives information on their storage volume.
ESRI Shapefile (Point)
Available at: http://www.gwsp.org/fileadmin/downloads/GRanD_Technical_Documentation_v1_1.pdf
PressureThreatCurrent pressuresNutrient Application for Major Crop
This dataset contains information on nutrient application rates for major crops at 10Km resolution. Data represents the year 2000 largely.
Available at: https://www.nature.com/articles/nature11420
PressureThreatCurrent pressuresHistorical Irrigation Dataset (HID)
A dataset depicting the extent of area equipped for irrigation (AEI) for 1900 to 2005 in 5 arc-minute resolution.
Global1900 - 2005ASCII Grid
Available at: http://publikationen.ub.uni-frankfurt.de/opus4/frontdoor/index/index/year/2015/docId/37236
PressureThreatCurrent pressuresGlobal Gridded Geographically Based Economic Data
Global gridded Gross Domestic Product (GDP) data in both Market Exchange Rate (MER) and Purchasing Power Parity (PPP).
Global1990 - 2005GeoTiff
PressureThreatCurrent pressuresGridded Population of the World GPWv4Estimates of population density.Global2000 - 2020GeoTiff
PressureThreatCurrent pressuresGHS POPULATION GRID
Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch.
1975, 1990, 2000 and 2015