The global terrestrial habitat type map is a spatial-explicit dataset produced through an intersection of currently best available data on land cover, climate and human pressures.
A particular novelty of this map is that the separation of habitat classes follows the IUCN habitat classification system to allow for instance an easy refinement of species distributions to an area of suitable habitat or assist in IUCN redlist assessments.
To create the habitat type map we mainly used land-cover data from the Copernicus land-cover product (Buchhorn et al. 2019) as well as the most recent data for the world's climatic zones based on the global Köppen-geiger climate classification system (Beck et al. 2018) and the distribution of biomes (Dinerstein et al. 2017).
In addition, we relied on ancillary datasets describing the global distribution of mountains (Sayre et al. 2018) and wetlands (Lehner and Döll 2004) as well as currently best available global datasets on human pressures such as data from the ‘Human impact on Forest' layer.
To construct the global map of IUCN habitat types all datasets were then intersected in Google Earth Engine using sequential ternary statements so that each habitat type can be reproducibly mapped to a single grid cell.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Buchhorn, M. et al. (2019) Copernicus Global Land Service: Land Cover 100m: epoch 2015: Globe. Dataset of the global component of the Copernicus Land Monitoring Service. doi.org/10.5281/zenodo.3243509.
Beck HE, et al (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214. doi.org/10.1038/sdata.2018.214.
Dinerstein E, et al (2017) An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. Bioscience 67:534–545. doi.org/10.1093/biosci/bix014.
Lehner B & Döll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. J Hydrol 296:1–22. doi.org/10.1016/j.jhydrol.2004.03.028.
Sayre R, et al (2018) A New High-Resolution Map of World Mountains and an Online Tool for Visualizing and Comparing Characterizations of Global Mountain Distributions. Mt Res Dev 38:240–249. doi.org/10.1659/MRD-JOURNAL-D-17-00107.1.
Predicted distribution of UN FAO Land Cover Classification System (LCCS) at 250 m spatial resolution using a global compilation of land cover and land use reference data and Ensemble Machine Learning. The legend of the maps was purposively selected to match the most up-to-date land cover map of the world at 100 m resolution (https://lcviewer.vito.be/about).
For each of the 17 classes we also provide a probability and model error map.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
A global map containing information on forest management, using the following categories: 1) forest without any signs of human impact, 2) forest with signs of human impact, including clear cuts, logging, and built-up roads; 3) replanted forest and forest - rotation period longer than 20 years; 4) woody plantations - rotation period of maximum 15 years; 5) oil palm plantations; and 6) agroforestry, including fruit tree plantations, tree shelter-belts, and individual trees on pastures.
This information has been produced based on more than 100,000 observations collected through a citizen science campaign using the Geo-Wiki Platform. Machine learning techniques were applied in combination with Proba-V 100m images to generate the final map.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
This layer represents number of species of amphibians, birds, mammals, reptiles and a representative set of plant taxa whose distribution overlaps in each 10 km cell.
Species ranges were rasterised at 1 km resolution from polygon maps from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. www.iucnredlist.org), the Global Assessment of Reptile Distributions (GARD) (Roll et al. (2017), Version 1.5, datadryad.org/stash/dataset/doi:10.5061/dryad.83s7k) and the Botanical Information and Ecology Network (BIEN) database (Enquist et al. 2019 and Maitner et al. 2017, version 4.1. http://bien.nceas.ucsb.edu/bien/biendata/).
Additional vascular plant species ranges were created from point data from the IUCN Red List, Botanic Gardens Conservation International (BGCI) (www.bgci.org) and the Global Biodiversity Information Facility (GBIF) (www.gbif.org).
Species range maps were refined, when possible, by removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits obtained from the IUCN Red List and collated from the scientific literature.
Habitats distributions were obtained from the global map of terrestrial habitat types (Jung et al. in prep), while altitudinal data was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) (USGS) and Global 30 Arc-Second Elevation (GTOPO30).
For species without habitat preference information, such as those with modelled ranges, anthropogenic land use classes from the map of terrestrial habitat types were used to remove potentially unsuitable areas within their ranges. This refinement process produced Area of Habitat (AOH) maps for each species (Brooks et al. 2019)
Ten representative sets of species were then chosen to limit the geographic bias in the plant data. For each set, the species richness was calculated based on AOH presence in each cell of a 10km raster, and scaled between 0 and 1. The average scores from these maps was used to create the final map of relative richness.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Brooks, T. M. et al. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution 34:977–986. doi.org/10.1016/j.tree.2019.06.009.
Enquist, B.J. et al. (in prep.). Botanical big data shows that plant diversity in the New World is driven by climatic-linked differences in evolutionary rates and biotic exclusion.
Global 30 Arc-Second Elevation (GTOPO30) Digital Object Identifier (DOI) number: /10.5066/F7DF6PQS
Jung, M. et al. (in prep). A global map of species terrestrial habitat types.
Maitner, B.S. et al. (2017). The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution; 9:373–379. doi/10.1111/2041-210X.12861.
Roll, U. et al. (2017), The global distribution of tetrapods reveals a need for targeted reptile conservation, Nature Ecology & Evolution, 1: 1677–1682, doi.org/10.1038/s41559-017-0332-2.
Rarity-weighted richness is a measure that combines endemism and species richness of amphibians, birds, mammals, reptiles and a representative set of plant taxa in each 10 km cell. This index lowers the contribution of wide ranging species to the overall species richness and thus highlights the areas that have a relatively high proportion of narrow‐range species.
Species ranges were rasterised at 1 km resolution from polygon maps from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. www.iucnredlist.org), the Global Assessment of Reptile Distributions (GARD) (Roll et al. (2017), Version 1.5, datadryad.org/stash/dataset/doi:10.5061/dryad.83s7k) and the Botanical Information and Ecology Network (BIEN) database (Enquist et al. 2019 and Maitner et al. 2017, version 4.1. http://bien.nceas.ucsb.edu/bien/biendata/).
Additional vascular plant species ranges were created from point data from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. www.iucnredlist.org), Botanic Gardens Conservation International (BGCI) (www.bgci.org) and the Global Biodiversity Information Facility (GBIF) (www.gbif.org).
Species range maps were refined, when possible, by removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits obtained from the IUCN Red List and collated from the scientific literature.
Habitat distributions were obtained from the global map of terrestrial habitat types (Jung et al. in prep), while altitudinal data was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) (USGS) and Global 30 Arc-Second Elevation (GTOPO30).
For species without habitat preference information, such as those with modelled ranges, anthropogenic land use classes from the map of terrestrial habitat types were used to remove potentially unsuitable areas within their ranges. This refinement process produced Area of Habitat (AOH) maps for each species (Brooks et al. 2019).
Each grid cell of the species’ AOH was then scored by the proportion of the species’ AOH the cell represents (i.e., AOH in grid cell/AOH). The rarity-weighted richness score for each cell was then calculated by summing scores across all species.
To limit geographic bias, due to the plant data being incomplete, AOH maps were split into 10 representative sets. For each set, the rarity-weighted richness was calculated based on AOH at 10km resolution, and scaled between 0 and 1. The average score from these maps was then used to create the final map of rarity-weighted richness. Higher values occur in cells with more species that have smaller ranges (i.e. both the number of species and the degree to which their ranges are restricted contribute to the rarity-weighted richness score).
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Brooks, T. M. et al. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution 34:977–986. doi.org/10.1016/j.tree.2019.06.009.
Enquist, B.J. et al. (in prep.). Botanical big data shows that plant diversity in the New World is driven by climatic-linked differences in evolutionary rates and biotic exclusion.
Global 30 Arc-Second Elevation (GTOPO30) Digital Object Identifier (DOI) number: /10.5066/F7DF6PQS
Jung, M. et al. (in prep). A global map of species terrestrial habitat types.
Maitner, B.S. et al. (2017). The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution; 9:373–379. doi/10.1111/2041-210X.12861.
Roll, U. et al. (2017), The global distribution of tetrapods reveals a need for targeted reptile conservation, Nature Ecology & Evolution, 1: 1677–1682, doi.org/10.1038/s41559-017-0332-2.
This layer represents number of threatened species of amphibians, birds, mammals, reptiles and a representative set of plant taxa whose distribution overlaps in each 10 km cell.
Conservation status data was used to select a subset of threatened species, based on data from the IUCN Red List and the ThreatSearch online database (BGCI 2019). Given that extinction risk data is not available for all species considered in the analyses, users should be aware of the taxonomic bias of the layer.
Species ranges were rasterised at 1 km resolution from polygon maps from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. www.iucnredlist.org), the Global Assessment of Reptile Distributions (GARD) (Roll et al. (2017), Version 1.5, datadryad.org/stash/dataset/doi:10.5061/dryad.83s7k) and the Botanical Information and Ecology Network (BIEN) database (Enquist et al. 2019 and Maitner et al. 2017, version 4.1. http://bien.nceas.ucsb.edu/bien/biendata/).
Additional vascular plant species ranges were created from point data from the IUCN Red List (IUCN Red List of Threatened Species (2019) Version 2019.2. www.iucnredlist.org), Botanic Gardens Conservation International (BGCI) (www.bgci.org) and the Global Biodiversity Information Facility (GBIF) (www.gbif.org).
Species range maps were refined, when possible, by removing unsuitable areas using information on species' habitat preferences and species' known altitudinal limits obtained from the IUCN Red List and collated from the scientific literature.
Habitats distributions were obtained from the global map of terrestrial habitat types (Jung et al. in prep), while altitudinal data was obtained from the Global Multi-resolution Terrain Elevation Data (GMTED2010) (USGS) and Global 30 Arc-Second Elevation (GTOPO30).
For species without habitat preference information, such as those with modelled ranges, anthropogenic land use classes from the map of terrestrial habitat types were used to remove potentially unsuitable areas within their ranges. This refinement process produced Area of Habitat (AOH) maps for each species (Brooks et al. 2019)
Ten representative sets of species were then chosen to limit the geographic bias in the plant data. For each set, the species richness was calculated based on AOH presence in each cell of a 10km raster, and scaled between 0 and 1. The average scores from these maps was used to create the final map of relative richness for threatened species.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Brooks, T. M. et al. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution 34:977–986. doi.org/10.1016/j.tree.2019.06.009.
BGCI (2019). ThreatSearch online database. www.bgci.org/threat_search.php.
Enquist, B.J. et al. (in prep.). Botanical big data shows that plant diversity in the New World is driven by climatic-linked differences in evolutionary rates and biotic exclusion.
Global 30 Arc-Second Elevation (GTOPO30) Digital Object Identifier (DOI) number: /10.5066/F7DF6PQS
Jung, M. et al. (in prep). A global map of species terrestrial habitat types.
Maitner, B.S. et al. (2017). The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution; 9:373–379. doi/10.1111/2041-210X.12861.
Roll, U. et al. (2017), The global distribution of tetrapods reveals a need for targeted reptile conservation, Nature Ecology & Evolution, 1: 1677–1682, doi.org/10.1038/s41559-017-0332-2.
This dataset provides a spatially explicit estimation of above- and below-ground terrestrial biomass carbon storage.
The map was produced by combining the most reliable publicly-available datasets on biomass carbon. These datasets were selected based on a literature review of existing datasets on biomass carbon in terrestrial ecosystems, and evaluated against a criteria based on resolution, accuracy, biomass definition and reference date.
The selected datasets were then overlaid with the Copernicus land cover dataset (Buchhorn et al. 2019) assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell's land cover type. GlobBiomass AGB map by Santoro and Cartus (2018) is taken as a base biomass map.
For Africa, the Bouvet et al. (2018) dataset provided above-ground biomass “open forest” and “shrubland” land cover classes. The “herbaceous vegetation” and “moss and lichen” classes in the Copernicus land cover dataset were used to extract grassland biomass from the Xia et al (2014) dataset. Spawn et al. (2017) was used for the “cropland” and “bare/sparse vegetation” land cover classes.
The layer on human impacts on forests, produced by the Nature Map project, was used to refine carbon biomass values for tree plantations by averaging above-ground biomass value for 2010 and 2017 (Santoro et al., 2018, 2019).
Below-ground biomass was added by using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories. The values of the resulting map (in tonnes of dry organic matter per hectare) were multiplied by 0.5 to convert to carbon, following the guidelines established in the IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (Penman et al. 2003).
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Bouvet, A. et al. (2018). An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sensing of Environment 206: 156–173. doi.org/10.1016/j.rse.2017.12.030.
Buchhorn, M. et al. (2019) Copernicus Global Land Service: Land Cover 100m: epoch 2015: Globe. Dataset of the global component of the Copernicus Land Monitoring Service. doi.org/10.5281/zenodo.3243509.
Penman J et al. (2003) Good practice guidance for land use, land-use change and forestry. Kanagawa Prefecture: Institute for Global Environmental Strategies.
Santoro M. et al. (2018). GlobBiomass - global datasets of forest biomass. PANGAEA, doi.org/10.1594/PANGAEA.894711.
Santoro, M. and Cartus, O. (2019): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1. Centre for Environmental Data Analysis, 02 December 2019. doi.org/10.5285/bedc59f37c9545c981a839eb552e4084.
Spawn, S.A. et al. (2017) New Global Biomass Map for the Year 2010. (2017) American Geophysical Union. New Orleans, LA.
Xia, J. et al. (2014). Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sensing 6: 1783-1802.
This global dataset provides information on the amount of soil organic carbon that is vulnerable to degradation by human impact on land. Vulnerability was estimated up to 2050 to inform efforts related to the 2050 Vision for Biodiversity of the Convention on Biological Diversity.
Following a literature review on available datasets, this map was derived from a reliable, publicly-available and global dataset on soil organic carbon (Hengl and Wheeler, 2018). Vulnerable carbon stocks were estimated following the Tier 1 approach on IPCCC Guidelines for National Greenhouse Gas Inventories.
Soils were initially classified into mineral or organic soils given their different rates if decomposition. Organic soils were defined as those with a probability equal or higher than 5% in the Predicted USDA soils orders dataset by Hengl and Nauman (2019).
The remaining land surface was considered mineral soil. The Copernicus land cover dataset (Buchhorn et al. 2019) was reclassified into IPCC Land Use categories and overlapped with IPCC Climate Zones. Furthermore, mineral soils associated with forests were classified by their degree of management according to the Human Impact on Forest layer.
Based on these conditions, each pixel was assigned a change (for mineral soils) or an emission factor (for organic soils) according to the IPCC Guidelines mentioned above. These allowed estimating the amount of soil organic carbon that would be vulnerable to human impact in the above mentioned period.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Hengl, T. and Wheeler, I. (2018) Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. Available at: https://zenodo.org/record/2536040#.XkKA9jH7RPY
Hengl, T., and T. Nauman (2019). Predicted USDA soil orders at 250 m (probabilities) (Version v0.1). http://doi.org/10.5281/zenodo.2658183
Buchhorn, M. et al. (2019) Copernicus Global Land Service: Land Cover 100m: epoch 2015: Globe. Dataset of the global component of the Copernicus Land Monitoring Service. doi.org/10.5281/zenodo.3243509.
This map shows the total terrestrial carbon density (above and belowground biomass, plus soil organic carbon) vulnerable to human impact. It is the result of the sum of the vulnerable soil organic carbon and biomass carbon density layer
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
This dataset consists of a globally normalized map (from 0 to 1) of ranked pixels for their relative importance in delivering clean water to downstream beneficiaries.
Water pollution is estimated based on the Human Footprint on Water Quality (HFWQ) index. The HFWQ is a measure of the extent to which water runoff is drawn from contaminating human land uses, both point (urban, roads, mining, oil and gas) and non-point (unprotected cropland, unprotected pasture) sources.
The HFWQ is calculated relative to all runoff by cumulating the downstream runoff from polluting and non-polluting land uses and expressing the former runoff as a proportion of the total runoff. This is calculated by assigning an associated pollution (or dilution) intensity fraction to each land use class to reflect spatially variable eco-efficient management or pollution risk intensities.
To calculate the realized water service, first the potential water provisioning service is calculated for each cell as the volume of clean (i.e. 100-HFWQ) water available from upstream. The volume of water is calculated as downstream cumulated water balance, based on (rainfall + fog + snowmelt) - actual evapotranspiration.
The realised water services depends on the intensity of downstream use measured as the normalised area of irrigation, number of people and number of dams. The realized water service is thus the product of the normalised potential service and the normalised downstream beneficiaries.
Realised service is high where the prevailing climate and land use generate high volumes of clean water which can be used (and reused) by large numbers of downstream users. The greater the downstream population, number of dams and actual water available, the greater the service provided.
If there is plenty of water, but no people or dams, then there is no realised service. In this way, not all water provides a direct service, only that water that is accessed and used. Untapped water services are considered to be the difference between potential and realised water services.
All analyses were carried out using the WaterWorld (Mulligan 2013) and Co$ting Nature (Mulligan et al. 2010) ecosystem services assessment tools (www.policysupport.org).
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Mulligan, M. (2013) WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally. Hydrology research 44, 5; 748-769.
Mulligan, M.A. Guerry, K. Arkema, K. Bagstad and F. Villa (2010) Capturing and quantifying the flow of ecosystem services in Silvestri S., Kershaw F., (eds.). Framing the flow: Innovative Approaches to Understand, Protect and Value Ecosystem Services Across Linked Habitats. UN Environment Programme World Conservation Monitoring Centre, Cambridge, UK. ISBN 978-92-807-3065-4.
This dataset provides a spatially explicit measure of broad scale patterns of cumulative human pressures on three components of terrestrial nature: biodiversity, above and below ground carbon stocks and water provisioning services.
The biodiversity pressure map, known as the Human Footprint (HF), shows the human footprint on the landscape in 2009 (Venter et al., 2016a).
Pressures to carbon stocks were estimated based on active fires and climate change risk. Fire risk was assessed using the number and intensity of active fires for a 5 year period (2013-2018) using MODIS active fire data.
Climate risk was estimated based on the difference between average annual future temperature for the reference year 2050 and mean annual historic temperature (1970 – present) (Karger et al. 2017).
The final pressure component, that on water provisioning, was derived from the top five weighted water security risk factors obtained from Vörösmarty et al. (2010) and aggregated water basins using hydro basin level 5 from HydroBASINS (Lehner and Grill, 2013).
Finally, these three layers were then combined into one single layer to illustrate broad scale patterns of cumulative human pressures in order to ascertain where the intrinsic values of nature are most at risk.
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Karger, D.N. et al. (2017) Climatologies at high resolution for the earth's land surface areas. Scientific Data 4, 170122. doi.org/10.1038/sdata.2017.122.
Lehner, B. and Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.
Venter, O. et al. (2016). Global terrestrial Human Footprint maps for 1993 and 2009. Scientific Data, 3, 160067. doi.org/10.1038/sdata.2016.67.
Vörösmarty, C. J. et al. (2010). Global threats to human water security and river biodiversity. Nature, 467(7315), 555–561. doi.org/10.1038/nature09440.
This layer is the result of a global joint optimization of valuable biodiversity and carbon storage. For this analysis we collated distribution data for over 170000 terrestrial vertebrate and plant species globally. These data were then refined by removing unsuitable areas (Brooks et al. 2019) using information on species' habitat preferences and species' known altitudinal limits or, where unknown, by removing anthropogenically modified land.
Given existing biases in taxonomic coverage for plant species, we calculated for the analysis in total 10 representative sets of species, containing approximately 10% of species of each taxonomic group. For carbon we used the combined amount of above-ground and below-ground biomass carbon density and vulnerable soil organic carbon density.
We then determined those areas globally that contributed the most to biodiversity protection and carbon storage if up to 10, 20, … 100% of the Earth were to be protected. Specifically we minimized the shortfall so that the greatest number of species extinctions are prevented (Fastre et al. 2020) and the largest amount of tonnes carbon.
We then solved this problem to a single optimal solution using an Integer Linear Programming (ILP) approach (Hanson et al. 2019) and determined for each 10km grid cell the fraction of land that needs to be protected. Finally, across representative sets all solutions were then ranked from 0 (highest) to 100 (lowest).
We are currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the feedback form on the relevant layer.
Brooks, T. M. et al. (2019). Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List. Trends in Ecology & Evolution 34:977–986. doi.org/10.1016/j.tree.2019.06.009.
Hanson JO, Schuster R, Strimas‐Mackey M, Bennett JR (2019) Optimality in prioritizing conservation projects. Methods Ecol Evol 2041–210X.13264. doi.org/10.1111/2041-210X.13264.
Fastre C, Mogg S, Jung M, Visconti P (2019) Targeted expansion of Protected Areas to maximise the persistence of terrestrial mammals. bioRxiv 3124:1–19. doi.org/10.1101/608992v2.