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).