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..