Figure legends
Figure 1 Three structure
equation models (SEM) with different assumptions on direct and indirect
relationships between environmental factors and topsoil
Δ14C are compared based on the available datasets for
alpine grasslands provided by Chen et al. (2021). (a) Model-A
includes direct effect of plant carbon input and indirect effect of
precipitation on Δ14C. (b) Model-B adds direct effect
of precipitation on Δ14C. (c) Model-C adds direct
effect of precipitation and mineral protection and indirect effect of
plant carbon input on Δ14C. Fit indices, including
degree of freedom (DF ), Chi-squareis
(χ2 ), probability level (P ), Akaike
information criterion (AIC ), comparative fit index (CFI ),
goodness of fit (GFI ), root mean square residual (RMR ) and
root mean square error of approximation (RMSEA ), are listed on
the left panel of each model. Similar to Chen et al. (2021),
precipitation is the first principal component (PC1) of mean annual
precipitation (MAP), precipitation of the wettest month (PWM), and
precipitation of the wettest quarter (PWQ); Plant C input is the PC1 of
plant carbon input in topsoil, normalized difference vegetation index
(NDVI), enhanced vegetation index (EVI) and leaf area index (LAI);
mineral protection is the PC1 of molar ratios of dithionite- and
oxalate-extractable Fe/Al oxides to SOC (Fed +
Ald and Feo + Alo), and
the molar ratios of exchangeable Ca2+(Caexe) and Mg2+(Mgexe) to SOC. Logarithm transformation is performed
for the four variables of mineral protection before principal component
analysis (PCA).