Global maps of habitat suitability probability for 1,485 European endemic plant species

Abstract

This dataset includes 1,485 raster files (.gri format) representing global maps of habitat suitability probability for the most widespread European endemic plant species. 272 species are already recorded as naturalized outside Europe and 1,213 species are not yet recorded as naturalized outside Europe but might become so in the future depending on their habitat suitability probabilities. The spatial resolution is 0.4166667° × 0.4166667°. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326). To comprehensively describe the distribution of the species in Europe, we combined occurrence records from six sources: the ‘Global Biodiversity Information Facility’ (GBIF), the ‘European Vegetation Archive’ (EVA), the ‘EU-Forest’ dataset, the ‘Atlas Florae Europaeae’, the ‘Plant Functional Diversity of Grasslands’ network (DIVGRASS) and the digital atlas of the German flora. When several occurrence records from these different sources were duplicated on the same cell, only one occurrence record per species was kept to avoid pseudoreplication. We defined six environmental variables to model and project species expected ranges: annual mean temperature (°C), annual precipitation (mm), precipitation seasonality (yearly coefficient of variation) representing the period 1979-2013 provided by the CHELSA climate database, the percentage of each grid cell with primary land cover based on the Harmonized Global Land Use models, organic carbon content (g per kg) and pH in the first 15 cm of soil from the global gridded soil information database SoilGrids. Environmental variables were aggregated (using the mean value) to the resolution of 0.42° × 0.42°. Six species distribution modelling (SDM) methods including generalized additive models, generalized linear models, generalized boosting trees, maximum entropy, multivariate adaptive regression splines and random forest were used. To combine the predictive capability of the six SDMs, their projections were aggregated into a consensus projection. To ensure the quality of the ensemble SDM, we only kept the projections for which the accuracy estimated by AUC and TSS were higher than 0.8 and 0.6, respectively, and each SDM was weighted proportional to its TSS evaluation. The species distribution modelling workflow was performed within the ‘biomod2’ R platform.