Statistical analyses
Analyses of the above trait values were performed in four steps with R
3.5.2 (R core team 2018).
First, we tested for divergent trait evolution in plants descending from
dry, control and wet manipulated plots in the central sites SA and M.
For each trait separately, linear mixed models were calculated with
climate change manipulation (dry, control, wet), site (SA, M), the five
greenhouse watering levels, and their interactions as fixed factors, as
well as genotype as random factor. Some traits were transformed prior to
analyses to meet homoscedasticity (sqrt: stomata density, height,
reproductive allocation, seed number; log: leaf number at flowering,
vegetative biomass). Significance
was assessed with Chi-square tests in the package car (Fox & Weisberg
2011) and posthoc tests identified contrasting climate manipulations
using the package ‘multcomp’ (Hothorn et al. 2008) with P-values
corrected for false discovery rate (FDR) sensu Benjamini &
Hochberg (1995). For germination fraction (binary) we used a
corresponding glm with logit link-function and quasibinomial error
structure.
Second, we tested for clinal trends in traits across the rainfall
gradient, including only plants descending from control plots in all
four sites. We calculated linear mixed models per trait with site and
greenhouse water level as fixed factors, and genotype as random factor
(transformations as above). Posthoc tests with FDR-correction as above
identified contrasting sites. Germination fraction was analyzed with a
binomial glm as above, using only site as main factor.
Third, we applied selection analyses for trait responses to low
and high irrigation in the greenhouse. They assessed adaptivity of
traits without environmental factors that may correlate with water
availability under natural conditions (Mitchell-Olds & Schmitt 2006; De
Frenne et al. 2013). We ran selection analyses for all traits
showing either rapid evolution (first step) or clines with rainfall
(second step). We included all plants from sites with climate
manipulation, computed genotype mean trait values separately across low
watering (15ml, 20ml) and high watering (50ml, 90ml), followed by
normalization (zero mean, 1 SD) per population (SA and M) and watering
level. Similarly, relative fitness was computed per population for high
and low watering. We fitted generalized least squares models (gls,rms package (Harrell 2019)), with relative fitness as the
dependent variable and trait value, water availability (high, low) and
their interaction as predictors. A significant trait value × water
interaction indicated contrasting directional selection on that trait
contingent on water availability (Lande & Arnold 1983), computed using
type III sums of squares (Anova, car package (Fox & Weisberg
2019)).
Fourth, we tested whether climate manipulations favored genotypes with
higher plasticity. Plasticity was quantified for the above traits using
the Coefficient of Variation (CV) across the five individuals (i.e.
water levels) per genotype in the greenhouse. CV is s a standardized
parameter that allows comparing plasticity across traits of different
units and scales (Houle 1992;
Acasuso-Rivero et al .
2019). With these CV-values per genotype, we calculated two-way ANOVAs
and FDR-post hoc tests separately for each trait, including the factors
site (SA, M) and climate change treatment (dry, control, wet).