FIGURE 4. Effects of simulated extreme climate events on
seedling survival. (A) Survival rates of seedlings assigned to
indicated short-term extreme climate events starting at indicated ages;
(B) survival rates of seedlings that survived short-term extreme climate
events subjected to indicated long-term extreme climate events after
growth for 120 days in ‘conducive’ conditions. n = 6 (replicate
pots with 30 initially sown seeds), data are mean and standard error
(s.e.). See Table S3 for the complete statistical details (Linear
mixed-effects models). Very few seedlings in the control (‘conducive’)
group died from the start to the end of the experiment, so only their
final survival rate is shown.
Germination rates of seeds buried in the field (2 cm deep) were
relatively high (more than 60%) in the following May and July. Seeds
buried at lower elevation (warmer condition) generally showed higher
frequencies of germination than those buried at higher elevation (colder
condition; Table S4; Figure S5). No viable seeds remained in July,
because if they did not germinate, seeds rotted in the soil. Most seeds
and seedlings had rotted in September, and only a small proportion of
the seedlings survived to the end of September if they did not break out
of the soil (Figure S5).
All transplanted seedlings were still living a week after
transplantation, suggesting that the establishment of seedlings in the
field was not inhibited by transplantation. However, two months later
few seedlings survived possibly due to constraints from surrounding
environments (Table S5). More specifically, in late September survival
rates of seedlings transplanted within vegetation and in bare ground
micro-habitats were similar at high elevation, but at low elevation the
seedlings transplanted into bare ground had higher survival rates than
those transplanted into vegetation. Surprisingly, no seedlings survived
until mid-November in any of the micro-habitats used in the experiment.
Finally, surrounding vegetation also imposed certain allelopathic
constraints on seed germination and seedling growth (Table S6; Figure
S6).
Beneficiary plants generally reduced the cushions’ nutrient contents and
two stable isotope ratios (δ13C and
δ15N), although not all effects were significant
(Table S7, Figure S7). Beneficiary plants also reduced the specific leaf
area (SLA) and leaf dry matter content (LDMC) of individual cushions in
all study populations, and these effects were independent of the
dominating beneficiary species (Table S8; Figure S8A, C). However, when
beneficiary plants’ cover increased, both SLA and LDMC significantly
increased (Figure S8B, D).
Beneficiary plant cover and cushion flower production were not
correlated in the CM1 population, weakly negatively correlated (P= 0.118) in the PJ4 population and strongly negatively correlated in the
other populations (Figure 5A-H). These findings clearly indicate that
beneficiary species can significantly reduce the cushion plant’s flower
production. A significant negative correlation between beneficiary cover
and fruit production was also detected in the PY population (Figure 5),
but this relationship was neutral in the PJ3, CM1, CM2 and YL
populations, and weakly negative in the others (PJ1, PJ2 and PJ4). Thus,
beneficiary plants seem to generally have slightly negative effects on
the cushion plant’s fruit production.
Moreover, beneficiary plant cover also positively correlated with the
mortality of individual cushions in all study populations (Figure 5I-K),
indicating that increases in this variable can promote the mortality of
cushion plants and accelerate their exclusion.