Classification of forest communities
The 135 plots harboured a total of 332 vascular plant species, with
species richness values ranging from two to 71. We classified the
different forest communities according to their plant species
composition. We log transformed and scaled the relevé data via the
Hellinger method prior to data analysis
(Legendre
and Gallagher 2001). We calculated dissimilarities in species
composition using the Bray-Curtis Index and created hierarchical cluster
dendrograms using option “ward.D2” for Ward clustering within the R
package Vegan
(Murtagh
and Legendre 2014). To find the optimal numbers of clusters, we used the
clustering method of the R package NbClust
(Charrad
et al. 2014); we chose “NULL” for distance, “ward.D2” as method, and
“kl” as index.
This resulted in four clusters of forest plant communities that clearly
differ in species composition, due to environmental differences, such as
soil chemistry, altitude and slope, as well as tree species richness
(Table S1, Fig. S2). Therefore, we tested our hypotheses with both the
entire dataset and these four forest types separately.