Relationships of leaf structural and anatomical traits
Across all the C4 grasses, gm was
positively correlated with leaf-level traits like IVD
(R 2 = 0.18, P = 0.07, Fig. 1a), leaf
thickness (R 2 = 0.45, P <0.01,
Fig. 1b) and average VED (R 2 = 0.44, P< 0.01, Fig. 1c). Similarly, Anet was
positively correlated with IVD (R 2 = 0.46,P = 0.01, Fig. 2a), leaf thickness (R 2 =
0.15, P = 0.1, Fig. 2b) and average VED
(R 2 = 0.22, P = 0.05, Fig. 2c). In our
previous study on these C4 grasses (Pathare et
al ., 2020), we showed that gm also scaled positively
with Smes (R 2 = 0.63, P< 0.001, Fig. S5a), SDada(R 2 = 0.47, P = 0.01, Fig. S5c), SR
(R 2 = 0.26, P = 0.04, Fig. S5d) and
Anet (R 2 = 0.26, P =
0.03, Fig. S5g), but showed no relationship with MCW,
SDaba and gsw (Fig. S5b,e and f). Here,
we further investigated the relationship of traits related to
gm like SDada, SR and
Smes with traits related to Kleaf like
IVD, leaf thickness and VED. Particularly, SDadapositively correlated with IVD (R 2 = 0.45,P = 0.01, Fig. 3a), leaf thickness (R 2 =
0.36, P < 0.01, Fig. 3b) and average VED
(R 2 = 0.38, P < 0.01, Fig.
3c). Similarly, SR positively correlated with IVD
(R 2 = 0.55, P < 0.01, Fig.
4a), leaf thickness (R 2 = 0.45, P< 0.01, Fig. 4b) and average VED (R 2= 0.53, P < 0.001, Fig. 4c). Whereas,
Smes positively correlated with leaf thickness
(R 2 = 0.56, P < 0.001, Fig.
5b) and average VED (R 2 = 0.45, P< 0.01, Fig. 5c).