Geographic & climatic distances
We extracted georeferenced occurrence records for all 77 investigated
tree species from GBIF and BIEN using the respective R packagesrgbif (Chamberlain et al. 2023) and BIEN (Maitner
2023). Both datasets have been combined and occurrence records have been
cleaned following the workflow described by Zizka et al. (2019)
using the package CoordinateCleaner [removing: ”capitals”,
”centroids”, ”equal”, ”gbif”, ”institutions”, ”outliers”, ”seas”,
”zeros”, “duplicates”].
For each occurrence record, we extracted the BIOCLIM variables mean
annual temperature (1), total annual precipitation (12), mean
temperature of the warmest quarter (10) and mean temperature of the
coldest quarter (11) from CHELSA (v. 2.1, Karger et al. 2017,
2018). We calculated continentality as the temperature difference
between the warmest quarter and the coldest quarter (BIOCLIM10 –
BIOCLIM11). Similarly, we extracted climate data for the EBG Bayreuth
with longitude of 11.57893 and latitude of 49.94782. We scaled all
geographic and climatic data to the centre of the botanical garden in
Bayreuth [ (x – xBayreuth) / sd(x)]. Thereby, the
calculated geographic and climatic distances represent an environmental
distance to the botanical garden in Bayreuth.
We calculated hypervolumes of each species geographic (longitude &
latitude) and climatic (mean annual temperature, total annual
precipitation & continentality) distribution area. Next, we calculated
the Euclidean distance between the botanical garden in Bayreuth and the
geographic as well as the climatic centroid of each species. Please
note, as the geographic distribution area of species can be disjunct, it
thereby can have multiple possible centroids. If so, we calculated the
mean of the centroids’ distances to the EBG Bayreuth after splitting the
hypervolumes. The calculations of hypervolumes and the splitting were
conducted using the R-package hypervolume (Blonder et al.2023).