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As a crucial forest resource in southern China and a significant economic forest species for forestry production, moso bamboo has a notable influence on carbon stocks across the entire bamboo forest ecosystem. Studying the impact of different management measures on carbon stocks in moso bamboo forests and soil carbon stocks can assist bamboo forest operators in incorporating the carbon sequestration capacity of bamboo into forest production and management decisions, which can contribute to achieving carbon sequestration, emission reduction, and sustainable development in the decision-making processes of forest production and management. In this study, we utilized a randomized block design to investigate the changes in moso bamboo forests’ carbon stocks and soil carbon stocks under different management measures across three intensities: high-intensity intensive management (HT), moderate-intensity intensive management (MT), and regular management (CK). Additionally, we employed meta-analysis methods to enhance the accuracy of our conclusions. The experimental results showed that MT increased the carbon storage in moso bamboo forests by 19.86%, which was significantly different from CK (p < 0.05), while there was no significant difference between the HT group and the MT and CK groups. For soil carbon stocks, in the 10–30 m and 0–50 m soil layers, HT decreased soil carbon storage by 29.89% and 22.38%, while MT increased soil carbon storage by 64.15% and 31.02%, respectively. Both HT and MT were significantly different from CK (p < 0.05). However, for the soil layers of 0–10 m and 30–50 m, there was no significant difference between the treatments within the experimental group. The results of the meta-analysis indicate that, compared to traditional regular management, intensive management, especially high-intensity intensive management, can significantly increase the carbon storage in bamboo forests (p < 0.05). However, it will significantly reduce soil carbon storage (p < 0.05). Moreover, a significant difference in soil carbon storage is observed only within the 0–20 cm soil layer group. Therefore, from the perspective of the long-term ecological benefits of bamboo forest management, the selection of management measures should prioritize reasonable and moderate-intensity intensive management. Additionally, adopting appropriate and moderate-intensity fertilization, ploughing, and other management methods is recommended to enhance the productivity of moso bamboo forests while concurrently protecting the natural environment and improving the carbon sequestration capacity of moso bamboo forests.

Forests play a crucial role as the primary sink for greenhouse gases, and forest mortality significantly impacts the carbon sequestration capacity of forest ecosystems. A single type of forest mortality model has been developed, and its model variables are incomplete, leading to significant bias in mortality prediction. To address this limitation, this study harnessed data collected from 773 permanent plots situated in Zhejiang Province, China, spanning a period from 2009 to 2019. The primary objectives were to pinpoint the key variables influencing forest mortality and to construct forest mortality prediction models utilizing both traditional regression methods and machine learning techniques, ultimately aiming to provide a theoretical basis for forest management practices and future predictions. Four basic linear regression models were used in this study: Linear Regression (LR), Akaike Information Criterion (AIC) Stepwise Regression, Ridge Regression, and Lasso Regression. Four machine learning models, Gradient Boosting Regression (GBR), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), were used to model stand mortality. Mortality was used as the dependent variable, and environmental factors such as topographic factors, soil composition, stand characteristics, and climatic variables were used as independent variables. The findings unveiled that soil and stand-related factors exerted significant effects on the mortality rate, whereas terrain-related and climate factors did not exhibit statistical significance. The Random Forest model established by using stand age, tree height, ADBH, crown cover, humus layer thickness, and the biodiversity index has the highest fitting statistics such as R² and Mean Squared Error, indicating that it has a good fitting and prediction effect, which effectively predicts mortality at the stand level, and is a valuable tool for predicting changes in forest ecosystems, with practical value in estimating tree mortality to enhance forest management and planning.

Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems.