Statistical analysis
To detect the effects of the shrub on herbaceous plants in response to
increased precipitation and N enrichment, statistical analyses were
conducted at three levels (species, groups of species, and community).
For the RII at the community level, linear mixed effect models
were carried out to detect the effect of watering and N
fertilization
on RII biomass and richness of the whole herbaceous community.
Watering and N fertilization treatments were used as fixed variables,
and block as a random variable.
At the species level, to assess the relationships between functional
traits (height and LDMC) and species RII in all treatments, we
conducted a redundancy analysis (RDA) using mean species RII in
each of the nine treatments as dependent variable and the two functional
traits (height and LDMC) as independent (constraining) variables. We
excluded four species that occurred in fewer than three of the 72 plots
(three watering levels × three N fertilization levels × four
replications × two types of quadrats). The automatic stepwise model
building method (using permutation testing with 999 steps) was used to
evaluate whether the independent variable was a significant predictor. A
cluster analysis (Ward’s method; Murtagh & Legendre, 2014) was then
conducted to separate species into groups with similar responses to the
effect of the shrub and functional trait values, according to the two
rows of species RDA scores.
At the group level, we pooled species biomass values within groups and
calculated the biomass in open patches and the RII biomass of
each cluster group in each replicate of each of the nine treatments.
Then, general linear models were performed to assess the effect of
watering and N fertilization treatments on biomass in the open plots andRII biomass of each cluster group. We also used a one-way ANOVA
model for testing the differences in mean height and LDMC among cluster
groups of species. Species traits were not weighed by species abundances
since species have been sampled in different patches within the
community and abundances could vary among patches. Additionally, our
goal was not to assess the community-level functional diversity but the
effect of divergence in species functional traits within communities on
the responses of herbaceous species to the dominant neighbour along
environmental treatments.
All analyses were performed using
the R software, v.3.5.1 (R Core Team, 2014). One-sample t -tests
were conducted to test the significance of RII values from zero.
Biomass data were log transformed since they were not normally
distributed. We used the lme4 and lmerTest packages to conduct linear
mixed effect model (Kuznetsova et al. , 2017), and the vegan
package to conduct the RDA (Dixon et al. , 2003).