2.4 Statistical analysis
Because the C, N, P concentrations and N and P stock of soil and plant at each sampling plot were average at the stand level, the data were statistically summarized. One-way analyses of variance (ANOVA) were performed to identify the effects of forest stand on various ecosystem components (vegetation biomass, soil, and microbial biomass) C, N, and P concentrations, stoichiometric ratio, microorganism quotient, and stocks. All data were expressed as mean ± standard error. All analysis was carried out by R programming language version 4.0 (R core team) based on tukey post hoc test (P < 0.05). Further, we used linear relationships to evaluate the relationship between soil and microbial biomass C, N, and P concentrations and their stoichiometry. The responses of soil microbial biomass C, N, and P concentrations and their stoichiometry are defined as dependent on soil C, N, and P and their stoichiometry, defined as the independent variable. We performed structural equation modeling (SEM) with AMOS 21.0 (AMOS IBM, USA) to identify the relationship between vegetation biomass, soil, and microbial biomass variables’ effects on ecosystem N and P stocks. Furthermore, in SEM, standardized path coefficients were applied to demonstrate the correlation between factors. The root means the square error of approximation (RMSEA) and the model χ² test was used to evaluate the model fitness. The model fit was considered to be acceptable when RMSEA was close to zero, and the χ² test was not significant (P > 0.05) (Schermelleh-Engel et al., 2003).