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).