Managing forest understory has a significant impact on soil greenhouse gas (GHG) emissions and the ecosystem’s capacity for carbon sequestration. However, its specific impacts and mechanisms within hickory (Carya cathayensis Sarg) forests remain unclear. The objective of this study was to examine the effects of different understory vegetation treatments on hickory stands with similar growth history, site conditions, and slopes: Cinnamomum chekiangense (Cinnamomum chekiangense Nakai) and strip-sown ryegrass (Lolium perenne L.) (CR1), Cinnamomum chekiangense and scattered ryegrass (CR2), Torreya grandis (Torreya grandis’ ‘Merrillii’ Hu) and strip-sown wild rapeseed (Brassica napus L.) (TW1), Torreya grandis and scattered wild rapeseed (TW2), and removal of understory vegetation (CK). Twenty experimental plots were established at the Lin’an Forestry Carbon Sink Pioneer Base, and after 12 months of monitoring, the responses of GHG emissions, vegetation, and soil organic carbon (SOC) sequestration were analyzed, revealing the differences in ecosystem carbon sequestration capacity. Compared to CK, CR1, CR2, TW1, and TW2 increased the global warming potential (GWP) by approximately 26%, 55%, 26%, and 16%, respectively. The SOC increased by approximately 76%, 102%, 51%, and 32%, respectively, while the vegetation carbon sink increased by approximately 30%, 27%, 53%, and 62%, respectively. In summary, ecosystem carbon sequestration increased by approximately 109%, 98%, 95%, and 92%, respectively. The findings indicate that managing understory vegetation in pecan forests significantly enhances ecosystem carbon sequestration but also increases soil GHG emissions. To enhance future research priorities, it is essential to focus on increasing carbon sequestration in hickory forests and managing soil GHG emissions through effective and rational understory vegetation management.

Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer rates on soil greenhouse gas (GHG) emissions, vegetation and soil organic carbon (SOC) sinks, and to illustrate the differences in agroforestry systems’ carbon sinks through a one-year positioning test across 12 plots, applying different compound fertilizer application rates (0 (CK), 400 (A1), 800 (A2), and 1600 (A3) kg ha⁻¹). The study demonstrated that, after fertilization, the total GHG emissions of A1 decreased by 4.41%, whereas A2 and A3 increased their total GHG emissions by 17.13% and 72.23%, respectively. The vegetation carbon sequestration of A1, A2, and A3 increased by 18.04%, 26.75%, and 28.65%, respectively, and the soil organic carbon sequestration rose by 32.57%, 42.27% and 43.29%, respectively. To sum up, in contrast with CK, the ecosystem carbon sequestration climbed by 54.41%, 51.67%, and 0.90%, respectively. Our study suggests that rational fertilization can improve the carbon sink of the ecosystem and effectively ameliorate climate change.

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.

Forest management, especially understory vegetation conversion, significantly affects soil greenhouse gas (GHG) emissions and soil C and N pools. However, it remains unclear what effect renovating understory vegetation has on GHG emissions and soil C and N pools in plantations. This study investigates the impact of renovating understory vegetation on these factors in Chinese hickory (Carya cathayensis Sarg) plantation forests. Different understory renovation modes were used in a 12-month field experiment: a safflower camellia (SC) (Camellia chekiangoleosa Hu) planting density of 600 plants ha⁻¹ and wild rape (WR) (Brassica napus L.) strip sowing (UM1); SC 600 plants ha⁻¹ and WR scatter sowing (UM2); SC 1200 plants ha⁻¹ and WR strip sowing (UM3); SC 1200 plants ha⁻¹ and WR scatter sowing (UM4); and removal of the understory vegetation layer (CK). The results showed that understory vegetation modification significantly increased soil CO₂ and emission fluxes and decreased soil CH₄ uptake fluxes (p < 0.01). The understory vegetation transformation significantly improved soil labile carbon and labile nitrogen pools (p < 0.01). This study proposes that understory vegetation conversion can bolster soil carbon sinks, preserve soil fertility, and advance sustainable development of Chinese hickory plantation forests.

Mangrove ecosystems rank among the most productive on Earth. Conducting research on the biomass prediction model of mangroves, as well as achieving simple and efficient estimations of the biomass of mangrove plant organs and the overall biomass, is of utmost significance for evaluating the productivity of the mangrove ecosystem and offering guidance for the future planning, restoration, and management of mangroves. This study examines the biomass distribution characteristics of Kandelia obovata at the northern edge of its range in China and develops models for estimating the biomass of its various components and individual trees. The findings provide valuable references for accurately assessing the biomass of Kandelia obovata plantations in Zhejiang Province. We measured the biomass of different components (branches, leaves, roots) using the harvest method and employed independent variables, including basal diameter (D), tree height (H), diameter squared (D²), the product of diameter squared and height (D²H), and the product of basal diameter and height (DH). Dependent variables included the leaf, branch, root, and total biomass. We developed linear, quadratic, and power function regression equations, selecting the optimal models based on the coefficient of determination (R²), significance of regression, root mean square error (RMSE), and Akaike Information Criterion (AIC). The total biomass ranged from 0.100 to 0.925 Mg ha⁻¹, while the carbon stocks ranged from 0.038 to 0.377 Mg C ha⁻¹. Results indicated that branch biomass accounted for the highest proportion (47.44%~68.35%), while leaf biomass (8.61%~27.83%) and root biomass (23.04%~25.64%) were relatively lower. Similarly, branch carbon storage constituted the highest proportion (52.68%~77.79%), with leaf (8.70%~29.36%) and root carbon storage (13.51%~20.55%) being lower. The optimal model exhibited R² values ranging from 0.594 to 0.921 and significant F-tests (p < 0.001). Single variables D, D², and combined variables D²H and DH provided the best fits. Basal diameter (D) and tree height (H) effectively predict the biomass of Kandelia obovata across different ages, with combined variables DH and D²H enhancing model accuracy. The biomass estimation model for total biomass is: W_Total = 0.0584(DH)^1.3918 (R² = 0.908, F = 2459.87, RMSE = 0.448). This model serves as a reliable tool for estimating the biomass of Kandelia obovata mangroves at the northern edge of its distribution in China.

As an important means of near-natural management in secondary forests, thinning effectively enhances the growth status of forest trees, and increases the overall forest yield and carbon sequestration capacity of the forest floor through rational stand regulation. This study focused on subtropical secondary broadleaf forests in the mountainous regions of western Zhejiang, China, and involved three treatment groups: a high-intensity thinning group (HT) (retaining trees with a diameter greater than 8 cm and removing shrub-layer vegetation), a low-intensity thinning group (LT) (retaining trees with a diameter greater than 5 cm and removing shrub-layer vegetation), and a control plot (no thinning treatment) (CK). The study aimed to investigate the effects of different thinning intensities on forest carbon stocks, soil respiration, and soil environmental factors, as well as to assess how thinning modifications impact the carbon sequestration capacity of the forest floor. The results illustrate the following: (1) The biomass growth and carbon sequestration capacity of individual trees under HT were significantly greater compared to other treatment groups. Conversely, carbon stock growth in the arboreal layer was higher under LT than under other treatments. (2) Thinning operations increased soil carbon stocks and improved soil fertility. (3) High-intensity (HT) and low-intensity thinning (LT) increased the soil moisture in both the surface and subsurface layers, with HT showing the highest levels. (4) One year after thinning, the soil organic matter (SOM) increased significantly, rising by 30.86% in HT and 21.84% in LT compared to CK. (5) In comparison to LT and CK, HT showed a significant increase in soil respiration rates, which led to higher CO₂ and N₂O emissions from the soil while also suppressing CH₄ uptake. The study concludes that LT can enhance carbon sequestration in both the tree and soil layers, improve soil fertility, and reduce greenhouse gas emissions. This supports the sustainable development of forest ecosystems and effective carbon management.