Aquamaps. Abstract coming soon.<br><br>See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>.

Aquamaps. Abstract coming soon.<br><br>See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>.

This raster layer represents the number of threatened species of mammals, amphibians and birds potentially occurring in each ~300 m grid cell. <br> 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. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent threatened terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

Aquamaps. Abstract coming soon.<br><br>See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>.

<span style="font-size:0.95em"><span style="font-size:1.1em"><b>Description</b></span> 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 overall species richness and 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. http://www.iucnredlist.org), the Global Assessment of Reptile Distributions (GARD) (Roll et al. (2017), Version 1.5, https://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. http://www.iucnredlist.org), Botanic Gardens Conservation International (BGCI) (https://www.bgci.org/) and the Global Biodiversity Information Facility (GBIF) (https://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. 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). This refinement process produced Areas of Habitat (AOH) maps for each species (Brooks et al. 2019).<br><br>Each grid cell of the species’ AOH was then scored for range-size rarity (the proportion of the species’ AOH the cell represents; i.e. size of grid cell/AOH). The total score for each cell was calculated by summing scores across all the species whose AOH intersected with it. 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 range rarity score). <br><br>NatureMap is currently soliciting feedback on the validity of this map at the national and sub-national levels. Please submit any comments via the <a href="https://explorer.naturemap.earth/" target="_blank">Nature Map Explorer</a>.<br><br><b>References</b><br></<span>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. <a href="https://doi.org/10.1016/j.tree.2019.06.009" target="_blank">doi.org/10.1016/j.tree.2019.06.009</a>. <br><br>BGCI (2019). ThreatSearch online database. <a href="www.bgci.org/threat_search.php" target="_blank">www.bgci.org/threat_search.php</a>. <br><br>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. <br><br>Jung, M. et al. (in prep). A global map of species terrestrial habitat types.<br><br>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. <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12861" target="_blank">doi/10.1111/2041-210X.12861</a>.<br><br>Roll, U. et al. (2017), The global distribution of tetrapods reveals a need for targeted reptile conservation, Nature Ecology & Evolution, 1: 1677–1682, <a href="https://doi.org/10.1038/s41559-017-0332-2" target="_blank">doi.org/10.1038/s41559-017-0332-2</a>.</span>

This raster layer represents the number of species of mammals, amphibians and birds whose distributions overlap each ~300m grid cell. <br> 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. <br> The three taxonomic groups included are those that have been comprehensively assessed for the IUCN Red List, and are used as a proxy to represent terrestrial biodiversity. Thus, users should be aware of the taxonomic bias and that marine areas have been removed from analysis. Similarly, areas of permanent snow and ice have values of zero due to a lack of corresponding IUCN habitat types. <br> <br><b>References:</b> <br> Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G. and Zülkhe, M. (2013). Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In: Proceedings of the ESA Living Planet Symposium, September 2013 (pp. 9-13). <br> Danielson, J.J., and Gesch, D.B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011–1073, 26 p. <a href="https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf" target="_blank">https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf</a> IUCN (2017). <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-3.</a> <br><br> <b>Attribution:</b><br>IUCN 2017. <a href="http://www.iucnredlist.org" target="_blank">The IUCN Red List of Threatened Species. Version 2017-1</a>.<br> <a href="http://www.iucnredlist.org/info/terms-of-use" target="_blank">Terms and Conditions</a> <br>Note that commercial use is not permitted.

This dataset shows a distribution of wetland that covers the tropics and sub tropics (40° N to 60° S; 180° E to -180° W), excluding small islands. It was mapped in 231 meters spatial resolution by combining a hydrological model and annual time series of satellite-derived estimates of soil moisture to represent water flow and surface wetness that are then combined with geomorphological data.<br><br>The Global Wetlands Map is produced by the Sustainable Wetlands Adaptation and Mitigation Program (SWAMP), a collaborative effort between the Center for International Forestry Research (<a href=""https://www.cifor.org"">CIFOR</a>) and the <a href=""http://www.fs.fed.us"">United States Forest Service</a>, supported by the United States Agency for International Development (<a href=""https://www.usaid.gov"">USAID</a>) and the CGIAR Research Program on Forests, Trees and Agroforestry (<a href=""http://foreststreesagroforestry.org"">FTA</a>).<br/><br>Term of Use: The information contained in this dataset is the exclusive property of Center for International Forestry Research (CIFOR) and any respective copyright owners. This work is protected under international copyright treaties and convention (<a href=""https://creativecommons.org/licenses/by/4.0/"">CC BY 4.0</a>). All uses for commercial purposes require the written permission of the <a href=""https://www.cifor.org/"">Center for International Forestry Research (CIFOR)</a><br/>.

Raster of Active Fires frequency per square kilometer for the period 01/01/2019 - 25/09/2019, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This raster layer was produced by GRID-Geneva. Data accessed in October 2019, at https://modis.gsfc.nasa.gov/data/dataprod/mod14.php. <br> <br> The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. <br> <br> Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.

The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008). This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure. An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>

Raster of Active Fires frequency per square kilometer, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. This raster layer was produced by GRID-Geneva. Data accessed in December 2018, at <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod14.php" target="_blank">https://modis.gsfc.nasa.gov/data/dataprod/mod14.php</a>. The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.