The decomposition of deadwood is a crucial process for the accumulation and sequestration of soil organic carbon (SOC) in forest ecosystems. However, the response of SOC to different decay classes of deadwood and the underlying mechanisms remain poorly understood. Here, we investigated the dynamics of SOC, soil properties, extracellular enzyme activities, and phospholipid fatty acid biomarkers across five decay classes (ranging from 1 to 5) of Masson pine (Pinus massoniana Lamb.) downed deadwood in a subtropical–temperate ecotone forest in Central China. Our results revealed a nonlinear response pattern of SOC along the deadwood decomposition gradient, with the maximum value at the decay class 4. Soil available nitrogen content, bacterial biomass, fungal biomass, the ratio of fungal-to-bacterial biomass, cellulase, activity and ligninase activity all increased with the intensification of deadwood decay, while soil pH decreased. The increase in SOC content was associated with a direct positive effect of bacteria and both direct and indirect positive effects of fungi by cellulose activity, but ligninase activity showed no significant relationship with SOC content. These findings suggest that cellulose and microbial biomass are key determinants of soil C formation and sequestration during deadwood decomposition. This study highlights the importance of the nonlinear response of SOC to deadwood decay, providing valuable insights for predicting future carbon-climate feedbacks.

Understanding the driving mechanisms of forest changes is of great significance for developing effective adaptation strategies to mitigate the impacts of climate change and human activities on ecosystems. This study used Theil–Sen median trend analysis, Mann–Kendall test, contribution rate decomposition, partial least squares, geodetector and residual analysis to explore the impact of climate change and human activities on the forest coverage area and NDVI of the Altai Mountains. Results show that changes in forest cover are driven by both forest management policies and climate change. Among them, forest management policy is the main factor. However, there are differences in the driving mechanisms in different altitude zones: in the alpine and subalpine zones, the promoting effects of natural death and climate change bring the forest coverage area toward a dynamic balance, while under the combined effects of human activities and climate change, the forest coverage area in the low mountain zones shows an expansion trend. For forest NDVI, the analysis results of the six scenarios show that the joint action of climate change and human activities promotes the growth of forest NDVI in the largest proportion (50.20%); the impact of climate change on forest NDVI is greater than that of human activities, and most of it is a promotion effect (30.28%). Forest degradation is mainly caused by human activities (19.39%), especially in the edge areas of the forest. Among climate factors, precipitation and snowmelt water are the main controlling factors for forest growth. Snowmelt water from March to April is an important water source before the growing season. This study provides the important scientific basis for forest management and strategic planning in the Altai Mountains.

Phenological models are valuable tools for predicting vegetation phenology and investigating the relationships between vegetation dynamics and climate. However, compared to temperate and boreal ecosystems, phenological modeling in alpine regions has received limited attention. In this study, we developed a semi-mechanistic phenological model, the Alpine Growing Season Index (AGSI), which incorporates the differential impacts of daily maximum and minimum air temperatures, as well as the constraints of precipitation and photoperiod, to predict foliar phenology in alpine grasslands on the Qinghai–Tibetan Plateau (QTP). The AGSI model is driven by daily minimum temperature (T_min), daily maximum temperature (T_max), precipitation averaged over the previous month (PA), and daily photoperiod (Photo). Based on the AGSI model, we further assessed the impacts of T_min, T_max, PA, and Photo on modeling accuracy, and identified the predominant climatic controls over foliar phenology across the entire QTP. Results showed that the AGSI model had higher accuracy than other GSI models. The total root mean square error (RMSE) of predicted leaf onset and offset dates, when evaluated using ground observations, was 12.9 ± 5.7 days, representing a reduction of 10.9%–54.1% compared to other models. The inclusion of T_max and PA in the AGSI model improved the total modeling accuracy of leaf onset and offset dates by 20.2%. Overall, PA and T_min showed more critical and extensive constraints on foliar phenology in alpine grasslands. The limiting effect of T_max was also considerable, particularly during July–November. This study provides a simple and effective tool for predicting foliar phenology in alpine grasslands and evaluating the climatic effects on vegetation phenological development in alpine regions.