Forests are complex systems in which subtle variations in terrain can reveal much about plant community structure and interspecific interactions. Despite a wealth of studies focusing on broad-scale environmental gradients, the role of fine-scale topographic nuances often remains underappreciated, particularly in subtropical settings. In our study, we explore how minute differences in microtopography—encompassing local elevation, slope, aspect, terrain position index (TPI), terrain ruggedness index (TRI), and flow direction—affect neighborhood-scale interactions among plants. We established an 11.56-hectare dynamic plot in a subtropical forest at the northern margin of China’s subtropical zone, where both microtopographic factors and neighborhood indices (density, competition, diversity) were systematically measured using 5 m × 5 m quadrats. Parameter estimation and mixed-effects models were employed to examine how microtopography influences plant spatial patterns, growth, and competitive dynamics across various life stages. Our findings demonstrate that aspect and TPI act as key drivers, redistributing light and moisture to shape conspecific clustering, heterospecific competition, and tree growth. Remarkably, sun-facing slopes promoted sapling aggregation yet intensified competitive interactions, while shaded slopes maintained stable moisture conditions that benefited mature tree survival. Moreover, in contrast to broader-scale observations, fine-scale TRI was associated with reduced species richness, highlighting scale-dependent heterogeneity effects. The intensification of plant responses with life stage indicates shifting resource demands, where light is critical during early growth, and water becomes increasingly important for later survival. This study thus advances our multiscale understanding of forest dynamics and underscores the need to integrate fine-scale abiotic and biotic interactions into conservation strategies under global change conditions.

This study focuses on a subtropical evergreen broad-leaved forest in China, utilizing a large permanent plot established in the Yaoluoping National Nature Reserve. By integrating data from a full-stem census and total station surveying, we analyzed the phylogenetic structure of the plant community as a whole and across different life-history stages (saplings, juveniles, and adults) while quantitatively assessing microtopographic variables and an interspecific competition index. The results indicate that the overall community in the Yaoluoping plot exhibited a weakly overdispersed pattern, and key microtopographic factors—including aspect, terrain position index (TPI), terrain ruggedness index (TRI), roughness, and flow direction—significantly influenced the evolution of phylogenetic structure. Distinctions were also observed among saplings, juveniles, and adults in phylogenetic structuring across life-history stages. Specifically, saplings displayed a higher degree of phylogenetic clustering, significantly influenced by density, elevation, TPI, and flow direction—suggesting that environmental filtering predominates at this stage, possibly due to lower environmental tolerance, limited dispersal ability, and conspecific negative density dependence. In contrast, juveniles and adults showed a more dispersed phylogenetic structure, with density, interspecific competition, aspect, TRI, TPI, and roughness significantly correlated with phylogenetic patterns, indicating that competition and niche differentiation become increasingly important as trees mature and establish within the community. Interspecific competition was found to play a crucial role in community structuring: the competition index was generally negatively correlated with the net relatedness index (NRI) and nearest taxon index (NTI) in juveniles and adults, implying that intense competition leads to the exclusion of some species and reduces overall diversity, with the strength and significance of competitive effects differing across stages. This study enhances our understanding of the complex interplay between microtopography and interspecific competition in shaping the phylogenetic structure and diversity of subtropical evergreen broad-leaved forests, elucidates the coupled mechanisms among microtopography, phylogenetic structure, and competition, and provides a scientific basis for forest conservation and management.

Biodiversity maintenance function (BMF) denotes the capacity of ecosystems to sustain genetic, species, ecosystem, and landscape diversity. Assessing the spatial distribution and underlying drivers of BMF at the regional scale is essential for biodiversity management. However, research on the socio-ecological drivers of BMF from a geographical perspective remains scarce. Therefore, this study developed an integrated assessment framework encompassing climatic factors, species richness, vegetation status, ecosystem protection, and anthropogenic disturbance. We analyzed the BMF spatial patterns across Liaoning Province, China, and identified the dominant drivers and their spatial heterogeneity using multi-scale geographically weighted regression and geographical detector. The results show that (1) the eastern/western mountainous regions and Liaohe River estuary are critical BMF zones for prioritized conservation; (2) BMF spatial variation is mainly shaped by precipitation, temperature, slope, and forestland/farmland proportion, with factor interactions amplifying their impacts; (3) drivers show distinct spatial heterogeneity. Specifically, precipitation, slope, and NDVI exert homogeneous effects, whereas elevation, temperature, farmland/wetland proportion, and GDP exhibit pronounced heterogeneity. Natural factors generally exert positive effects, while the farmland/urban proportion tends to exert negative impacts—for example, farmland’s negative influence is stronger in the west, whereas the forestland and temperature exert more positive effects in the east. The results enhance the methodological framework for elucidating the spatial relationships between BMF and drivers, providing a scientific basis for biodiversity conservation and ecosystem management in Liaoning Province and similar regions.

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies.

The 400 mm isohyet is an important geographical boundary in China. Owing to climate change, the spatial position of this precipitation line is shifting, which has significant impacts on agricultural production, population distribution, and ecosystems in China. First, precipitation data from 1950 to 2022 were obtained from the fifth-generation ECMWF atmospheric reanalysis (ERA5). The 400 mm isohyet was extracted from the gridded precipitation field and then resampled using an improved uniform parameterization method. Subsequently, the Self-Organizing Map (SOM) method was employed to classify the 400 mm isohyet. Then, the spatiotemporal trends in precipitation corresponding to different isohyet categories were analyzed. Furthermore, the mechanisms driving the spatiotemporal changes in the isohyet were explored from the perspectives of water vapor transport and atmospheric circulation. Finally, based on the optimized outputs from nine models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the future trends in the migration of the 400 mm isohyet in China during the carbon-neutrality period were simulated. The results show that the 400 mm isohyets from 1950 to 2022 can be classified into three types using the SOM method: the Northeast China wet precipitation pattern (NEWP), the Southwest China dry precipitation pattern (SWDP), and the Northeast dry–Southwest wet precipitation pattern (NED-SWWP). From 1950 to 2022, the annual precipitation in China significantly decreased. The multiyear average precipitation was 808.86 mm, and the three precipitation types ranked as NEWP (829.08 mm) > SWDP (808.70 mm) > NED-SWWP (781.47 mm). In terms of spatial distribution, precipitation decreased in Northeast China, North China, Yunnan, and the southern Qinghai–Tibet Plateau. Meanwhile, precipitation increased significantly in South China and the eastern and northwestern sides of the Qinghai–Tibet Plateau. The 400 mm isohyet types show a clear interdecadal shift around the year 2000, with the dominant pattern changing from the NEWP to NED-SWWP. Changes in water-vapor transport intensity between the two periods were a major factor contributing to the interdecadal shift. In addition, an anomalous anticyclone over East Asia weakened precipitation in Northeast China, and anomalous southwesterlies from the Arabian Sea, together with anomalous southeasterlies converging over the northern Qinghai–Tibet Plateau, increased precipitation in the southwestern segment of China. The multimodel ensemble (MME) method improved the simulation accuracy for the isohyet during the carbon-neutral period. The projections indicate that precipitation in China will increase, and the 400 mm isohyet will continue to migrate westward and northward during this period in China. All annual isohyets during this period are projected to be classified as NEWP. These findings are of practical significance for deepening the understanding of the spatiotemporal distribution and evolution of precipitation in China, and for improving the ability to respond to meteorological disaster risks.