Principal component analysis
A PCA, using MAP and leaf-level structural and anatomical traits associated with gm and Kleaf, was performed. The first two major axes (PC1 and PC2) along with the average position of 18 C4 grasses in PC1-PC2 space are shown in Fig. 1. The, first four axes with eigenvalues and scores are shown in Table S4. PC1 explained about 54.5 % of the total variation in the C4 grasses. PC1 scaled positively with Anet/E, Anet, gmax-ada, gm, SDada, Smes, SR, Narea, IVD, leaf thickness and average VED but negatively with Kleaf, total VLA and BSias. Thus, PC1 delineated the C4grasses into those which show traits associated with greater gm and hence photosynthetic C-gain (higher score on PC1) from those which show traits associated with greater Kleaf and water-loss (lower score on PC1). PC2 explained about 15 % of the total variation and scaled positively with BSCW and Smes but negatively with BS area ratio, BSIAS and Anet. Together, the first two major axes explained about 70% of the total variation observed in the C4 grasses. PC3 explained 9% of total variation and scaled positively only with BS area ratio and negatively with Anet/E. PC4 explained 7.67% of total variation and scaled positively with SDada..