CONCLUSIONS
We here showed that functional traits of successful grassland species
differed from those of less successful ones, but that the pattern
depended on the success metric and the spatial scale it applies to. Low
root-tissue density was the only trait that characterized successful
species at every spatial scale, from being abundant in German grassland
plots to being widely naturalized around the world. We showed that
belowground traits are at least as important as the aboveground traits
in explaining species success at the different spatial scales. The
variation in importance and the sometimes-opposing directions of the
effects of traits on species success at different spatial scales can
explain why trait variation is maintained. Our study shows that, for
Central European grassland species, variation in success is related not
only to aboveground traits, but also to belowground traits.
ACKNOWLEDGEMENTS
We thank Vanessa Pasqualetto for weighing the seeds, Otmar Ficht,
Maximilian Fuchs and Heinz Vahlenkamp for help setting up the
experiments, Beate Rüter, Ekaterina Mamonova, Huy Manh Nguyen, Simon
Gommel, Maximilian Rometsch and Anika Schick for help measuring the
plant traits. We also thank the managers of the three Biodiversity
Exploratories, Konstanz Wells, Swen Renner, Kirsten Reichel-Jung, Sonja
Gockel, Kerstin Wiesner, Katrin Lorenzen, Andreas Hemp, Martin Gorke and
Miriam Teuscher, and all former managers for their work in maintaining
the plot and project infrastructure; Christiane Fischer for giving
support through the central office, Andreas Ostrowski for managing the
central data base, and Markus Fischer, Eduard Linsenmair, Dominik
Hessenmöller, Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef
Schulze, Wolfgang W. Weisser and the late Elisabeth Kalko for their role
in setting up the Biodiversity Exploratories project. The work has been
(partly) funded by the DFG Priority Program 1374
”Infrastructure-Biodiversity-Exploratories” (DFG-Refno.). Field work
permits were issued by the responsible state environmental offices of
Baden-Württemberg, Thüringen, and Brandenburg. We acknowledge funding
from the German Research Foundation (DFG, grants KL 1866/12-1 and
264740629 to MvK and 323522591 to MR).
Figure 1 Estimates of trait effects on different success
metrics of German grassland species from generalized linear models. On
the y-axis are the nine traits used as predictors, with a linear term
(white rows) and a quadratic (non-linear) term (grey rows) for each
trait. The errors bars around the estimates are standard errors. Red
points and orange points indicate significant (p < 0.05) and
marginally significant (p < 0.10) negative model estimates
respectively. Blue points and purple points indicate significant (p
< 0.05) and marginally significant (p < 0.10)
positive estimates respectively, and grey points indicate
non-significant estimates (p > 0.10). The spatial scale of
the success metric decreases from left to right. GloNAF: number of
regions in which a species is naturalized (number of species, N=242);
Euro+Med: number of regions in Europe and the Mediterranean basin in
which a species is native (N=238); FloraWeb: number of grid cells in
Germany in which a species is present (N=236); GPs Frequency: number of
grassland grid plots in which a species is present (N=209); EPs
Frequency: number of grassland experimental plots in which a species is
present (N=240); GPs Abundance: mean species cover in grassland grid
plots in which the species is present (N=209); EPs Abundance: mean
species cover in grassland experimental plots in which a species is
present (N=240). Delta R² was calculated according to