Responses to abiotic and biotic factors
All data analysis was conducted using R version 3.5.3 (R Core Team
2019). To quantify plot-level abiotic conditions we ran a Principal
Components Analysis (PCA) including canopy closure, litter cover, soil
pH, and phosphorous, ammonium, nitrate, and potassium content (chosen as
the most important soil nutrients for plant growth). The first PC axis
(PC1) explained 54.2% of the variation in measured abiotic conditions
and was mainly loaded with canopy closure, litter cover and soil
macronutrients (phosphorous and nitrate). PC2 explained 18.5% of the
abiotic variation and was mainly associated with soil pH (see Figure
S3).
To assess how demographic rates correlate with abiotic conditions and
plant-plant interactions, we modelled emergence, survival, seed
production, and population growth rate separately per species using
mixed-effects models. Emergence was modelled as the number of seeds that
emerged, accounting for the number of seeds that were sown into each
replicate, and survival was modelled as a binary outcome of whether a
focal plant survived to produce at least one seed (viable or inviable).
Due to low emergence fractions and hence insufficient focal plants to
model survival and seed production, Podolepis lessonii was only
assessed for emergence responses, whereas the remaining eight species
were modelled for all demographic rates.
Prior to modelling, we log-transformed total neighbour abundance to
improve linearity with vital rates and standardised all continuous
explanatory variables to a mean of 0 and standard deviation of 1. Block
and plot (nested in block) were included as random effects in all
models. An observation-level random effect (OLRE) of sub-plot ID was
nested within plot for emergence models to assist with potential
overdispersion for all species, except for the G. rosea model
which was not over-dispersed and including the OLRE led to convergence
issues.
Emergence and survival were modelled using binomial errors and
logit-link functions with the “glmer” function from the ‘lme4’ package
(Bates et al. 2015). Seed production was modelled with negative
binomial error distributions and log link functions using the
“glmmTMB” function from the ‘glmmTMB’ package (Brooks et al.2017). Population growth rate was log-transformed and modelled with a
normal error distribution using the “lmer” function from the ‘lme4’
package.
To allow for quadratic responses to the environment, we initially
modelled each vital rate with main and quadratic terms for PC1 (soil
fertility, canopy closure and litter cover), PC2 (soil pH) and neighbour
abundance. Where quadratic terms were significant (see Table S1), they
were retained in all subsequent models. Emergence was modelled in
response to PC1 and PC2 only (because it was measured before the
watering and neighbour treatments were implemented). Survival, seed
production, and population growth were modelled in response to PC1, PC2,
watering treatment (dry, ambient, or wet) and neighbour abundance (for
survival and seed production) or presence (for population growth). The
presence of Cuscuta campestris , a parasitic invasive annual
plant, was included as a covariate as it appeared to impact host plant
performance. To allow for interactions between factors, we included all
pairwise interactions between watering treatment, PC1 and neighbour
abundance in a full model, and then removed non-significant interaction
terms to create the final models from which results are reported.
Results figures were plotted in base R or using the “ggplot” function
in the package ‘ggplot2’ (Wickham 2016) and tables were built using the
“kable” function in kableExtra (Zhu 2021). We calculated marginal and
conditional pseudo-R2 values using the
“r.squaredGLMM” function in the ‘MuMIn’ package (Bartoń 2022) to
estimate the proportion of variance explained by fixed effects and
combined fixed and random effects (Nakagawa & Schielzeth 2013). All
data and R code to reproduce our results are available at
https://github.com/acatling/Perenjori_watering_exp.