Description Usage Format Details References
Raster brick with predictive variables to fit species distribution models for the European territory at ~20km resolution and reference system WGS84 (units are degrees of latitude and longitude, EPSG is 4326). The variables are:
bio1: annual mean temperature (ºC x 10).
bio2: mean diurnal temperature range, computed as mean of monthly (max temp - min temp).
bio3: isothermality, computed as (bio2/bio7)*100.
bio4: temperature seasonality (standard deviation of monthly averages * 100).
bio5: maximum temperature of the warmest month.
bio6: minimum temperature of the coldest month.
bio7: temperature annual range, computed as (bio5-bio6.
bio8: mean temperature of the wettest quarter.
bio9: mean temperature of the driest quarter.
bio10: mean temperature of the warmest quarter.
bio11: mean temperature of the coldest quarter.
bio12: annual precipitation (mm.).
bio13: precipitation of wettest month.
bio14: precipitation of driest month.
bio15: precipitation seasonality (standard deviation of monthly averages * 100).
bio16: precipitation of wettest quarter.
bio17: precipitation of driest quarter.
bio18: precipitation of warmest quarter.
bio19: precipitation of coldest quarter.
human_footprint: intensity of human presence and activities in the range [0, 100] (see details).
landcover_veg_bare: percentage of bare cover.
landcover_veg_herb: percentage of herbaceous cover.
landcover_veg_tree: percentage of tree cover.
ndvi_average: average normalized vegetation index.
ndvi_minimum: minimum normalized vegetation index.
ndvi_maximum: maximum normalized vegetation index.
sun_rad_average: yearly average of potential solar radiation, in Wh/(m^2)/day.
sun_rad_maximum: maximum potential solar radiation.
sun_rad_minimum: minimum potential solar radiation.
sun_rad_range: yearly range of solar radiation.
topo_slope: topographic slope (º).
topo_diversity: relative measure in the range [0, 100] of the topographic complexity within a given cell (see details).
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Raster brick with 31 layers.
Bioclim variables (bioXX) were downloaded from the Worldclim v1.4 dataset (Hijmans et al. 2005, https://www.worldclim.org/version1).
The human_footprint map was downloaded from SEDAC's Last of the Wild dataset, version v1 (Sanderson et al. 2002, https://sedac.ciesin.columbia.edu/data/collection/wildareas-v1).
The variables landcover_veg_XXX were downloaded form the Vegetation Continuous Fields dataset https://modis.gsfc.nasa.gov/data/dataprod/mod44.php. The ndvi_X variables were downloaded from the Blue Marble dataset COMPLETE THIS.
The variables sun_rad_X were generated with the r.sun function (Hofierka 2002) of the GRASS GIS software from the SRTM digital elevation model (Jarvis et al. 2008 https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/). See further details at https://grass.osgeo.org/grass76/manuals/r.sun.html.
The variable topo_slope was generated from the same digital elevation model with the r.slope.aspect function of the GRASS GIS software (Hofierkaet al. 2009).
The topo_diversity map was computed by reclassifying elevation, slope and aspect maps at 1km resolution into 10 classes each, and summing the number of classes of each map within 20 km around each cell. Results are aggregated at ~20km in the final map.
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.
Sanderson, E.W., Jaiteh, M., Levy, M.A., Redford, K.H., Wannebo, A.V., Woolmer, G. (2002) The Human Footprint and the Last of the Wild: The human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not, BioScience, Volume 52, Issue 10, October 2002, Pages 891–904, https://doi.org/10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2
Hofierka, J., Suri, M. (2002): The solar radiation model for Open source GIS: implementation and applications. International GRASS users conference in Trento, Italy, September 2002.
Jarvis, A., H.I. Reuter, A. Nelson, E. Guevara, 2008, Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org).
Hofierka, J., Mitasova, H., Neteler, M., 2009. Geomorphometry in GRASS GIS. In: Hengl, T. and Reuter, H.I. (Eds), Geomorphometry: Concepts, Software, Applications. Developments in Soil Science, vol. 33, Elsevier, 387-410 pp, http://www.geomorphometry.org.
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