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).