Statistical analysis
Statistical analyses were performed using R software (version 3.5.2, R
Foundation for Statistical Computing, Vienna, Austria). Regression
analysis were performed, using the mean values of traits for each
species, in order to examine the relationships between key variables of
interest among the 18 C4 grasses. Particularly, we
investigated the relationships of leaf structural and anatomical traits
associated with gm and photosynthetic C-gain and
Kleaf and transpirational water-loss with habitat MAP
and MAT. One-way ANOVA with posthoc Tukey’s test was used to examine
differences in leaf-level physiological, structural, anatomical and
biochemical traits among the 18 diverse C4 grasses
(Refer Table S2, S3 and Pathare et al ., 2020). For the one-way
ANOVA, values of P ≤ 0.05 were considered to be statistically
significant. Results of one-way ANOVA for traits used in the current
study are given in Table S2 and S3 in current manuscript and in Pathareet al ., 2020. In addition, we used a principal component analysis
(PCA) to identify the major axes of variation among the important
leaf-level traits associated with gm and
Kleaf (Table 1). The R package FACTOMINER (Le et
al. , 2008) was used to perform PCA. All traits were scaled during the
analysis. The first three principal components (PCs) had eigenvalues
> 1 (Table S4) and were retained according to Kaiser’s rule
(Kaiser, 1960). For each trait, factor loadings > 0.5 in
absolute value were considered important.