Data analysis
For each species and container, we calculated the allocation to
generative reproduction (RA) as the number of flowering ramets divided
by the number of all ramets (over all individuals present in each
container). For tussock species, we also calculated the RA for each
founding individual plant in a container. The RA of species and
individuals was calculated as the mean RA over the years (i.e. 2012,
2013, 2014 and 2015). To express whether the reproductive allocation of
species and individuals was stable or highly variable over the years, we
calculated the stability of RA as its standard deviation divided by its
mean. Lower stability of RA under the interannual drought regime
indicates increasing interannual variation in RA, as predicted by the
resource switching hypothesis. Lower stability under the permanent
drought regime indicates that species invest more in flowering after
accumulating resources over years, as predicted by the resource budget
hypothesis.
To estimate interspecific RA synchrony we used correlation among species
RA over years and calculated it for each container as RA synchrony
=\(\sum_{i}^{n}{\left[r\left(RA_{i},\overline{RA_{j}}\right)\right]/n},\ \)where
the value r is the correlation coefficient between \(RA_{i}\) of
species i and the mean RA of the remaining species j in
the container \(\overline{RA_{j}}\) and n is the number of
species in the community. The synchrony index reaches a maximum of 1
when species are perfectly synchronised in RA and a minimum of -1 when
species are perfectly asynchronised. It equals 0 if species RA are
temporally independent. Accordingly, we also calculated intraspecific RA
synchrony, where \(RA_{i}\) is the RA of species individual i and\(\overline{RA_{j}}\) is the mean RA of the remaining individuals of the
given species j in the container, and n is the number of
individuals of the given species in the container.
The effects of hydrological regime and dominant species removal on
species mean RA, its stability and temporal shift were analysed using
Bayesian mixed linear models. We also tested the effects of these
treatments on RA synchrony among all and subordinate species (see
Supporting information S3 for methodological details). Treatments were
included as fixed variables and blocks as a random variable in the
models. We described the effects of the model variables from the
parameter estimates as the 89% highest density interval (HDI) (because
the 95% HDI is unstable when the effective sample size is less than
10,000), the probability of direction (PD), and the region of practical
equivalence (ROPE) (Makowski et al. 2019). To visualise the pairwise
Pearson correlations of RA between species under different treatments,
we used the chord diagram. All data analysis was performed using R
Statistical Software (version 4.3.3; R Core Team, 2024).