Data analysis
We quantified both functional (i.e., biomass based) and structural
resistance and recovery of plant communities to examine their responses
to our experimental treatments. We focused on absolute biomass changes
as measures of functional resistance and recovery because they are
directly relevant for agriculture and animal husbandry. Biomass
resistance (B rst) was determined as the difference in AGB between
each of the drought years (2015-2017) and the year before the drought
(2014) (Pfisterer & Schmid, 2002). Biomass recovery (B rc) was
determined as the difference in AGB between the post-drought year (2018)
and each of the drought years (2015-2017) (Jasper & Frank, 2010).
Structural resistance (S rst) was defined as the Bray-Curtis
similarity (Bray & Curtis, 1957) between communities in the pre-drought
year and each of the drought years, which was calculated as:Srst =
2Σi min(AGBi ,pre-drought,AGBi ,drought)/(ΣiAGBi ,pre-drought+
ΣiAGBi ,drought),
where AGBi ,pre-drought andAGBi ,drought are the AGB of
species i in the community before and during drought,
respectively. S rst equals 1 if there is no structural difference
between drought and pre-drought communities and equals 0 when drought
and pre-drought communities have no species in common. Structural
recovery (S rc) was measured as the Bray-Curtis dissimilarity
(Bray & Curtis, 1957) between communities in each of the drought years
and the post-drought year, which was calculated as: Src =
1-2Σi min(AGBi ,post-drought,AGBi ,drought)/(ΣiAGBi ,post-drought+
ΣiAGBi ,drought),
where AGBi ,postdrought is the AGB
of species i in the community after drought. Maximum recovery
values approach 1 (i.e., when post-drought AGB is substantially greater
than AGB during the drought), whereas values <<1
reflect low recovery. We also calculated Srst and Src of
dominant species (hereafter dominant structural resistance and dominant
structural recovery, respectively) using the above formulas by
considering only the five most common species (L. chinensis ,S. baicalensis , C. squarrosa , C. duriuscula andB. scorzonerifolium ), whose summed AGB accounted for 79.2% of
community AGB across treatments and sampling years. To determine the
role of species asynchrony in regulating community stability, we
calculated species asynchrony as , where is the variance of community
AGB and is the standard deviation of the AGB of the i th species
in a community (Loreau & de Mazancourt, 2008). Species asynchrony was
calculated separately for resistance and recovery by using data from the
years relevant to the calculation of resistance and recovery,
respectively.
Linear mixed-effects models were used to assess the effects of block, N,
mowing, and their interactions on soil pH, inorganic nitrogen
concentration, community AGB, species richness, biomass resistance,
biomass recovery, structural resistance, and structural recovery; year
was included as a random effect in the models. Two-way ANOVA was used to
test the effects of block, N and mowing on species asynchrony. One-way
ANOVAs with Duncan’s multiple range tests were used to evaluate
differences among treatments. Relationships between community
resistance/recovery and their potential abiotic and biotic drivers were
explored with bivariate regressions. Bivariate regressions were also
used to test for potential trade-offs among stability properties. Based
on the bivariate relationships, we constructed a prioristructural equation models (SEMs) to understand the direct and indirect
effects of abiotic and biotic factors on community biomass resistance,
biomass recovery, structural resistant and structural recovery (Fig.
S2). Overall fit of the SEM was evaluated using the chi-square test (the
model has a good fit when 0.05 < P ≤ 1.00 for
χ2 test) and Akaike information criteria (AIC; lower
AIC indicating a better fit); final models were obtained by eliminating
non-significant pathways and state variables based on regression weight
estimates.
The data on community AGB and biomass recovery were ln-transformed to
meet the assumptions of normality. SEM analyses were performed using
AMOS 18.0 (Amos Development Co., Greene, Maine, USA). The remaining
statistical analyses were conducted using SPSS 13.0 (SPSS, Inc.,
Chicago, IL, USA).
RESULTS
Nitrogen addition reduced soil pH and increased inorganic nitrogen
concentration, while mowing had no significant effects on these two
variables (Table 1, Fig. S3). The three-year (2015-2017) drought
significantly reduced species richness and community AGB (all P< 0.01) across treatments when compared to 2014, the year
before the drought (Fig. S4). Nitrogen enrichment increased community
AGB, but had no effect on species richness; mowing resulted in decreased
community AGB, but increased species richness (Tabe 1; Fig. S4).
As the most dominant species, L. chinensis accounted for
approximately 46.5% of plant AGB, which was much greater than the
contribution of any other species (none exceeded 12.3%). Nitrogen
enrichment consistently increased the AGB of L. chinensis (allP < 0.05), while mowing consistently reduced the AGB ofL. chinensis , across the five years of the experiment (allP < 0.001, Table S1; Fig. S5). The treatment effects on
the summed AGB of the five dominant species mirrored those for L.
chinensis (Table S1; Fig. S5).
Nitrogen enrichment significantly reduced community biomass resistance
(Fig. 1a), but increased community biomass recovery (Fig. 1b). Mowing
marginally significantly increased community biomass resistance, but did
not affect community biomass recovery (Fig. 1a,b). Mowing, however,
mitigated the negative effect of nitrogen enrichment on biomass
resistance (significant nitrogen × mowing term; Table 1; Fig. 1a). For
structural stability, nitrogen enrichment had no discernable effect on
community structural resistance or recovery, whereas mowing
significantly decreased community structural resistance, and increased
community structural recovery (Table 1; Fig. 1c,d). Nitrogen enrichment,
but not mowing, decreased species asynchrony for both resistance and
recovery (Table 1).
Nitrogen enrichment decreased biomass resistance of L. chinensisbut increased its biomass recovery (Table 1; Fig. 2a,b), paralleling its
effect on community-level biomass resistance and recovery. By contrast,
mowing increased biomass resistance of L. chinensis but decreased
its biomass recovery (Table 1; Fig. 2a,b). Mowing, but not nitrogen
addition, reduced dominant structural resistance; both nitrogen addition
and mowing had a positive effect on dominant structural recovery (Table
1; Fig. 2c,d).
Structural equation modelling (SEM) revealed that nitrogen deposition
decreased community biomass resistance by reducing biomass resistance of
the most dominant species L. chinensiss and, in turn, its
positive effect on species asynchrony (Fig. 3a). Mowing promoted
community biomass resistance by increasing
biomass resistance
of L. chinensis and resultant
species asynchrony (Fig. 3a). Nitrogen deposition had no detectable
effect on community structural resistance, as its positive effect
through decreasing L. chinensis biomass resistance was offset by
its negative effect through decreasing species asynchrony and dominant
structural resistance (Fig. 3a). Mowing decreased community structural
resistance mainly through reducing dominant structural resistance, and
this negative effect was partially offset by increased L.
chinensis biomass resistance which increased dominant structural
resistance (Fig. 3a).
Nitrogen
addition promoted community biomass recovery by increasing biomass
recovery of L . chinensis and a host of other less dominant
species (the latter captured as decreasing species asynchrony in the
SEM; Fig. 3b). However, mowing showed no significant effect on community
biomass recovery, as its negative effect through decreasing biomass
recovery of L. chinensis was largely offset by its positive
effect through increased dominant structural recovery (Fig. 3b).
Whereas nitrogen deposition had
little effect on community structural recovery, mowing promoted
community structural recovery mainly by increasing dominant structural
recovery (Fig. 3b).
Bivariate
regressions revealed that community biomass resistance and recovery were
significantly positively associated with their structural resistance and
recovery, respectively (Fig. S6a, b). SEMs also revealed a positive
effect of community structural recovery on biomass recovery, but no
significant relationship between community biomass and structural
resistance (Fig. 3).