The aim of this study was to develop a regional landslide stability analysis method considering the combined impact of rainfall and the roots of vegetation in densely vegetated areas. A typical mountainous watershed in the Nanling National Nature Reserve of South China was chosen as the study area. First, the unmanned aerial vehicle (UAV) method was used to obtain surface element information including topography, vegetation, and landslides. Five main plant species were identified. The RipRoot model was then used to calculate the additional cohesion of these five plant species, and the relationship between the root systems of the different plant species and the soil shear strength was subsequently revealed. Finally, the root cohesion was introduced into the stability index mapping model (SINMAP), and the receiver operating characteristic curve (ROC) method was used to calculate the accuracy of slope stability when considering only soil cohesion as well as the composite cohesion of both soil and roots. The results showed significant differences in the root cohesion of different plants in the study area and a significant increase in the calculation accuracy (from 90% to 95.6%) when root cohesion was considered in the landslide stability calculation. These study results not only enrich theoretical studies on the impact of vegetation roots on landslide stability but also provide a scientific support for preventing disasters in mountainous landslide‐prone areas.

Together natural growth, afforestation and forest disturbance, such as felling, contribute to the dynamic nature of forests. Thus to enhance forest management, water resource management and carbon sequestration, the net effect of forest changes on water yield must be better understood. Here, we conduct a global meta-analysis of 496 watersheds over 25 years to investigate the impact of forest complexity and overall changes on water yields. We classify watersheds based on the type of human disturbance, including felling and thinning, afforestation, and absence of external disturbances. We find that the runoff coefficient (ratio of annual water yield in watershed outlet to precipitation) is more sensitive to external disturbances in forests with lower ecosystem complexity compared to forests with higher complexity. In addition, we found forest natural growth may increase runoff and lead to an increased runoff coefficient decades later. Our findings highlight the importance of nature-based forest restoration, especially in regions vulnerable to water shortage.

Climate change and large-scale afforestation characterize the conditions in the Upper Dongjiang River Basin (UDRB), which is one of the most important headwater basins in southern China. It is important to understand whether, and to what extent, the observed runoff change can be attributed to forest and/or climate change. Using process- and relation-based methods, we found precipitation in spring (March–May) decreased notably, while precipitation in summer (June–August) showed an increase from the reference period (1961–1990) to the afforestation period (1991–2010). In comparison, annual averaged potential evapotranspiration did not change much. Both of the methods indicated forest had a positive effect while climate change exerted a negative impact on annual averaged runoff in the UDRB. As a result, the observed annual averaged runoff only showed a little decrease from the reference period to the afforestation period. The climate change impact on monthly averaged runoff basically followed the pattern of precipitation change. Except in July and August, climate change exerted negative or little impact on runoff in most of other months. In comparison, the forest effects on monthly averaged runoff change showed a totally different pattern. Except in May and June, forest exerted positive impact on runoff in other months. As a result, the observed monthly averaged runoff in May and June experienced notable reduction, while those in other months experienced increase or no change. The UDRB provides evidence that additional forest cover would not injure but even increase runoff, especially dry season runoff. The study has important implications for sustainable water management and afforestation in this subtropical region and for similar river basins.

The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m‐resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.

Forest productivity is influenced by various factors, including biodiversity, environmental factors, functional traits, and forest types. However, the relative importance of these factors in determining the productivity of subtropical forests in southern China remains controversial. In this study, we analyzed a dataset of 24 forest plots from four subtropical forest types in the Nanling Mountains with the main goal of identifying and quantifying the relative contribution of the main driving factors of forest productivity in these forests. Generalized linear regression and structural equation modeling were used to examine the relationship between forest biomass productivity (aboveground, belowground and total), biodiversity (taxonomic diversity, phylogenetic diversity and functional diversity), and environmental variables (i.e., physiography and climate). The results indicated that both environmental factors and biodiversity played pivotal roles in explaining the biomass productivity of the Nanling subtropical forests. Environmental factors had the greatest influence on total productivity, while the impacts of different types of biodiversity on various productivity components (aboveground and belowground) varied notably. Taxonomic diversity showed the strongest positive effect on the aboveground and belowground biomass productivity. However, phylogenetic and functional diversity had negative effects on productivity. Furthermore, these relationships also exhibited variations when considering different altitude gradients, with low altitudes generally leading to negative biodiversity–productivity correlations. We contextualized our results regarding the three state-of-the-art theories about biodiversity–productivity relationships (selection probability, niche complementarity, and biomass ratio) and concluded that both selection probability and niche complementarity are the driving mechanisms of productivity in the subtropical forests of the Nanling Mountains. This study offers valuable insights into the functioning and biodiversity mechanisms of subtropical forest ecosystems in southern China.