# _forests_
## 3 Results
### Influencing Factors on Subsoil \(\tau\)  
GLMM analysis showed that there was a significant negative correlation between climate variables (MAT, MAP, and RH) and subsoil \(\tau\) (\(p\) < 0.001, Figure 3A-C and Table 2). Vegetation variables (GPP, NDVI, and EVI) were also significantly correlated with subsoil \(\tau\), leading to a negative impact on subsoil \(\tau\) (\(p\) < 0.001, Figure 3D-F). Subsoil \(\tau\) increased with the increase in C:N, SMC, and SMN (\(p\) < 0.001, Figure 3G-I).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Slope** & **95\% CI** & **R\({}^{2}\) (m/c)** \\ \hline MAT & \(-\)0.016 *** & \((-\)0.020, \(-\)0.012) & 0.10/0.12 \\ Log (MAP) & \(-\)0.366 *** & \((-\)0.468, \(-\)0.265) & 0.07/0.07 \\ Log (RH) & \(-\)0.560 *** & \((-\)0.740, \(-\)0.460) & 0.12/0.13 \\ Log (GPP) & \(-\)0.370 *** & \((-\)0.475, \(-\)0.265) & 0.07/0.07 \\ Log (EVI) & \(-\)0.770 *** & \((-\)1.076, \(-\)0.463) & 0.04/0.04 \\ Log (NDVI) & \(-\)0.817 *** & \((-\)1.130, \(-\)0.503) & 0.04/0.04 \\ Log (Soil C:N) & 0.672 *** & \((0.615, 0.730)\) & 0.44/0.56 \\ Log (SMC) & 0.509 *** & \((0.386, 0.632)\) & 0.09/0.12 \\ Log (SMN) & 0.516 *** & \((0.361, 0.672)\) & 0.07/0.10 \\ \hline \hline \end{tabular} Note: R\({}^{2}\) (m/c): variance of fixed effects explanations (R\({}^{2}\) marginal)/variance of fixed and random effects explanations (R\({}^{2}\) conditional); 95\% CI; 95\% confidence interval. *** \(p\) < 0.001.  
\end{table}
Table 2: Statistics of GLMM relating climatic, vegetation, and soil variables with \(\tau\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**df** & **SS** & **MS** & **F Value** & \(p\) \\ \hline Forest types & 4 & 15.9 & 3.96 & 3.67 & \(<\)0.01 \\ Climatic zones & 3 & 55.4 & 18.45 & 17.09 & \(<\)0.001 \\ Forest types: Climatic zones & 10 & 33.1 & 3.31 & 3.07 & \(<\)0.001 \\ \hline \hline \end{tabular} Note: SS—sum of squares; MS—mean square; df—degree of freedom.  
\end{table}
Table 1: Two-way ANOVA of forest types, climatic zones, and their interaction effect on subsoil \(\tau\).  
Figure 2: Subsoil \(\tau\) boxplots for each forest type (**A**); climate zone (**B**); forest origin (**C**); and forest age (**D**). Lowercase letters (a, b, c) indicate significant differences (\(p\) < 0.05). Blue points indicate the average. SubT—subtropical; Temp—temperate; Trop—tropical.  
In China's terrestrial ecosystem, climate, forest types, forest origins, vegetation, and soil variables explained 37% of the variations in subsoil \(\tau\), as indicated by the SEM (Figure 4). Regardless of forest types, the SEM showed that soil property was the most important variable, exerting a directly positive impact on subsoil \(\tau\), followed by vegetation, climate, and forest origins, which all have negative effects on subsoil \(\tau\). However, the relationship between forest types and subsoil C turnover time was not significant. Climate has a significantly positive effect on vegetation and forest types, and vegetation variables caused exerted a positive influence on soil variables and forest types, but exhibited a negative effect on forest origins.  
SEM models were also established for different forest types and climatic zones (Figures A5-A13). Although the SEM results of different forest types showed that the main controlling factors were soil properties (Figures A5-A9), the subsoil \(\tau\) of different climatic zones was driven by different environmental factors (Figures A10-A13). Climate was the main driving factor for temperate forests, and soil property was the primary influencing factor for boreal, tropical, and subtropical forests.  
Figure 3: The variation of C turnover time (\(\tau\), naturally log-transformed) according to climatic, soil, and vegetation variables, etc., in the sample mean (**A–I**) are constant. The fitted lines were determined by GLMM, with stand types as the random factor. The parameters are shown in Table 2.