We generated this dataframe to model EU habitat richness at continental scale as a function of geographical, climate and anthropogenic variables (please, see Material and Method section in the published paper version for all the details). We found geographical variables were by far the most strongly correlated with habitat richness, followed by climate. However, anthropogenic variables gained importance when consindering their interactions, with important implications for conservation planning.

We used the distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network (i.e., Article 17 Habitat Directive - 2015 dataset - III report of Habitat distribution - Environmental European Agency (EEA)), to calculate habitat richness for the 10 km x 10 km EU grid map. We calculated EU habitat richness by summing-up the number of habitat type reported in each cell of the above mentioned map provided by the EEA . We then investigated how environmental variables (please, see Material and Method section in the published paper version for all the details) affect habitat richness, using generalized linear models, generalized additive models and boosted regression trees.