Data analyses
We applied a landscape ecological conception (Pickett & Cadenasso,
1995) to analyze the spatial distributions of current and historical
individual cushions within communities.
For this, we used Fragstats4.2
software (McGarigal & Ene, 2013) to calculate the following six
landscape metrics for cushion patches (McGarigal & Ene, 2013; Lustig et
al., 2017; Wang et al., 2014): the total area of cushion patches (CA),
the edge density (ED), the mean cushion patch area (Area_ Mn), the
percent of core area of landscape (CPLAND), the total patch number (NP),
and sixth the percentage of like adjacencies (PLADJ). See Text S13 for
the details.
To illustrate the abundance of beneficiary species and successional
processes in different successional stages (represented by the PJ1 to
PJ3 communities) of cushion-dominated communities, we generated stacked
graphs at species level for each community.
Since population ID includes information on the study site, including
elevation and potential relevant micro-climates, we applied one-way
ANOVA to assess the differences in population density and productivity
between populations, with population density and productivity as
dependent variables and population ID as an independent variable. Tukey
HSD tests were applied to assess the significance of differences between
populations.
Linear mixed-effects models were applied to assess the following
effects. 1) The effects of simulated climate events on seed germination
and subsequent seedling survival, with temperature, light availability,
water availability and their potential interactions as fixed effects and
pot replicate as a random effect. 2) The germination and viability of
seeds buried in the natural field, with elevation, checking time and
their interaction as fixed effects and tea bag replicate as a random
effect. 3) The survival of seedlings transplanted in the natural field,
with elevation, micro-habitat and their interaction as fixed effects and
plot replicate as a random factor. 4) The effects of simulated extreme
climate events on seedling survival, with climate treatment, seedling
age and their interaction as fixed effects and pot replicate as a random
effect. 5) The effects of beneficiary species on nutrient contents (and
stable isotope ratios) of cushion leaves, with beneficiary cover ratio,
leaf source (beneficiary-covered or beneficiary-free) and their
interaction as fixed effects and sample replicate as a random effect. 6)
The effects of beneficiary species on the physiological traits (SLA and
LDMC) of individual cushions, with elevation, beneficiary cover ratio,
dominating beneficiary species (Kobresia pygmaea orSaussurea leontodontoides ) and their interactions as fixed
effects and sample replicate as a random effect. 7) The effects of
allelopathical materials on the seed germination and seedling survival,
with allelopthical source (aboveground or belowground), extracting mode
(aqueous or ethanol), elevation, concentration and their interactions as
fixed effects and sample replicate as a random effect. Nutrient content
and physiological trait values were square root-transformed to meet
assumptions of parametric statistics, while seed germination, viability
and seedling survival data were standardized between 0 and 1 by the
formula
(X -Xmin )/(Xmax -Xmin ),
where X is the relevant value of seed germination or seedling
survival. The significance of each contrast (Rosenthal & Rosnow, 2010)
was assessed using type-I analysis of variance with Satterthwaite’s
method for all linear mixed-effects models.
To assess effects of beneficiary species on the performance (including
flowering, fruiting, surface death ratio and physiological status) ofA. polytichoides cushions, we calculated Pearson correlation
coefficients.
R v.4.1.1 (R. C. Team, 2021) was used for all the above analyses, the
lme4 packages was used for the linear mixed-effects modeling (Bates et
al., 2015), the ggplot2 package (Wickham, 2016) was used to plot all
reported figures and the layout was designed with Adobe Illustrator
2021.