# _forests_
## 5 Conclusions  
SOC turnover time and its influencing factors were estimated based on field observations in forest ecosystems across China. The robust conclusions include: firstly, the subsoil \(\tau\) (0.2-1 m) varied from 2.3 to 896.2 years, with an average of 72.4 \(\pm\) 68.6 years, and it differed significantly among different forest types (\(p\) = 0.01), 82.9 \(\pm\) 68.7 years for DBF, 75.3 \(\pm\) 78.6 years for DNF, 59.9 \(\pm\) 40.7 years for EBF, 77.6 \(\pm\) 60.8 years for ENF, 71.3 \(\pm\) 80.9 years for NBF, as well as climate zones (\(p\) < 0.001), 89.5 \(\pm\) 68.6 years for boreal, 60.1 \(\pm\) 41.0 years for subtropical, 92.2 \(\pm\) 96.8 years for temperate, and 49.7 \(\pm\) 45.6 years for tropical. The subsoil \(\tau\) in natural and mature forests was slower than that in plantations and young forests. Secondly, the GLMM showed that there was a significant negative correlation between subsoil \(\tau\) and MAT and MAP, indicating the sensitivity to climate change. Thirdly, the results of the SEM model indicated that soil variables (SMN, SMC, and C:N) were the most important controlling environmental factors of forest subsoil \(\tau\) change on a national scale, which was different from the previous perception that climate was the main dominant factor for soil \(\tau\). Finally, the dominant driver differed with climate zones (boreal, temperate, subtropical, tropical), which could improve our understanding of subsoil \(\tau\). This study further emphasizes the importance of soil variables, particularly for soil microbial properties, on subsoil \(\tau\). Conceptualization, P.Y. and X.T. ; methodology, Y.S. and J.L. ; software, X.Z. and Y.D. ; validation, P.Y., M.D. and S.F. ; formal analysis, C.C., Y.H. and S.G.; investigation, Z.L. ; data curation, X.T. ; writing--original draft preparation, P.Y. ; writing--review and editing, P.Y., X.T. and Y.S. All authors have read and agreed to the published version of the manuscript. This study was supported by the Fundamental Research Funds of CAF (CAFYBB2018 MA002), the National Natural Science Foundation of China (31800365), the State Key Development Program of the National \"Thitteenth Five-Year\" plan of China (2018 YED0600105), the Second Tibetan Plateau Scientific Expedition and Research Program (2019 QZKK0307); the Sichuan Science and Technology Program (2021Y0377); the State Key Laboratory of Geohazard Prevention and Geowenwinnent Protection Independent Research Project (SKLGP2018Z004 and SKLGP2021K024), the Fundamental Research Funds for the Platform of the International Center for Bamboo and Rattan (1632019011), and the Everest Scientific Research Program, Chengdu University of Technology (80000-202 DF11410). We extend our thanks to [PERSON] for her great efforts to develop the primary database of soil organic C stock. A summary of the survey data is available from the corresponding author ([PERSON]) upon request. We have no conflict of interest regarding this work. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Slope** & **95\% CI** & **R2 (m/c)** \\ \hline MAT & \(-0.016\) *** & (\(-0.020\), \(-0.012\)) & 0.10/0.23 \\ Log (MAP) & \(-0.367\) *** & (\(-0.469\), \(-0.266\)) & 0.07/0.07 \\ Log (RH) & \(-0.601\) *** & (\(-0.739\), \(-0.461\)) & 0.12/0.12 \\ Log (GPP) & \(-0.370\) *** & (\(-0.475\), \(-0.266\)) & 0.07/0.07 \\ Log (EVI) & \(-0.769\) *** & (\(-1.107\), \(-0.463\)) & 0.04/0.04 \\ Log (NDVI) & \(-0.816\) *** & (\(-1.130\), \(-0.503\)) & 0.04/0.04 \\ Log (Soll C-N) & \(0.675\) *** & (0.628, 0.732) & 0.44/0.69 \\ Log (SMC) & \(0.512\) *** & (0.389, 0.635) & 0.10/0.12 \\ Log (SMN) & \(0.521\) *** & (0.365, 0.676) & 0.06/0.06 \\ \hline \end{tabular} Note: R2 (m/c): variance of fixed effects explanations (R2 marginal)/variance of fixed and random effects explanations (R2 conditional); 95\% CI; 95\% confidence interval. ***\(p<0.001\).  
\end{table} 
Table 10: Statistics of CLMM relating environmental variables with subsoil \(\tau\), treating the states as a random effect.