Peng Li

Accurately identifying critical soil erosion source zones and sediment transport pathways within watersheds represents a key challenge in current soil erosion control and watershed management. To address this challenge, an integrated "RUSLE-IC" analytical framework was developed in this study, coupling the Revised Universal Soil Loss Equation (RUSLE) with an improved Sediment Connectivity Index (IC) to systematically quantify the spatial correspondence between erosion and transport processes. In this study, we utilized high-resolution UAV remote sensing techniques in a typical red soil hilly watershed in southern China. Based on calculated soil erosion modulus, and the sediment connectivity index was revised by integrating topography, vegetation, and road-ditch-pond artificial network elements. Additionally, the four-quadrant method was utilized to analyze the spatial matching patterns between erosion modulus and connectivity levels, thus accurately identifying the critical “source-pathway-sink” zones for sediment transport within the watershed. The results indicated that: (1) The predominant soil erosion level in the watershed was slight (75.43%), but the light erosion level, which accounted for only 23.50% of the area, contributed 60.03% of the total soil loss in the watershed, demonstrating highly spatially concentrated erosion. (2) The IC was significantly enhanced by the effects of road and ditch-pond networks, which combined to contribute 58.82% of the total IC enhancement effect. Meanwhile, a distance-threshold decay in the effect was observed, where the IC values decreased significantly with increasing distance within a 40-meter buffer zone (R²>0.91), but the effect weakened beyond that range. (3) The coupled erosion-connectivity analysis revealed that high erosion–high connectivity (HE-HC) zones, which comprising only 6.34% of the watershed area, contributed 25.76% of sediment yield and were identified as priority areas for management. And low erosion–high connectivity (LE-HC) zones (11.81%), predominantly associated with roads, ditches, and ponds, were characterized as potential high-efficiency sediment delivery pathways. Generally, (4) sediment transport in small watersheds was more sensitive to soil erosion source areas than to high-connectivity zones. The effectiveness of the proposed framework for identifying soil erosion and sediment delivery hotspots under complex surface conditions was validated, providing a scientific basis for implementing targeted soil and water conservation measures based on coordinated regulation of “source areas and transport pathways.”

The distribution pattern and influencing factors of specific species diversity play a crucial role in decision-making for biodiversity conservation. Identifying suitable regional habitat indicators to assess specific species diversity patterns is a global focus topic. A total of 112 sample plots were surveyed to investigate the relationship between Asteraceae species diversity and topography, soil nutrients, and stand factors, using a Structural Equation Model (SEM). Additionally, the Maxent model was utilized to predict the distribution pattern of Asteraceae species diversity in response to specific habitat factors. The findings revealed that soil nutrients, topography, and canopy closure had different impacts on Asteraceae species diversity, with soil nutrients showing the highest relative coefficient, followed by topography and canopy closure. The elevation and slope gradient were identified as direct and indirect influences on Asteraceae species diversity. The contribution rate of potential environmental variables on the Asteraceae species diversity was ranked as follows: STN (29.7%) > SOM (28.5%) > slope (8.5%) > Ele (8.1%). Asteraceae species diversity was found to be abundant in the locations with SOM (>27 g/kg), STN (>1.8 g/kg), Ele (165–333 m), and slopes (5–12 degrees). Soil nutrient content serves as a key indicator for assessing the abundance of Asteraceae species diversity and should be considered in biodiversity conservation.

Soil quality is a very important indicator used to assess ecosystem restoration states in terms of vegetation recovery and establishment. Evaluating the soil quality of different vegetation restoration types in mountainous areas of Beijing and identifying their influencing factors would provide a scientific basis and be helpful for vegetation restoration in the future. Six vegetation types (or communities), including Platycladus orientalis (L.) Franco pure forest (POP), Pinus tabulaeformis Carr. pure forest (PTP), Platycladus orientalis–Pinus tabulaeformis mixed forest (PPM), Platycladus orientalis coniferous and broadleaved mixed forest (POCB), Pinus tabulaeformis coniferous and broadleaved mixed forest (PTCB), deciduous broadleaved mixed forest (DBMF), and one area of non-afforested land (NF), with similar stand conditions were selected and fourteen factors of soil physical and chemical characteristics were measured and used to establish a total data set (TDS), while a minimum data set (MDS) was obtained by using the principal component analysis (PCA) and Pearson correlation analysis methods. Two scoring methods, linear (L) and non-linear (NL), were used to calculate the soil quality index (SQI), and the key factors influencing soil quality by vegetation were identified by a general linear model (GLM), PCA, and correlation analysis. The results showed that: (1) The screened MDS indicators which showed good relationships with the SQIs in the study areas were total nitrogen (TN), sand content, total potassium (TK), pH, and available water capacity (AWC). The SQI–NLM method has better applicability. (2) The contribution rates of vegetation to different soil factors accounted for 28.644% (TN), 21.398% (sand content), 24.551% (TK), 16.075% (pH), and 9.332% (AWC). (3) TN showed a positive relationship with all vegetation types; the content of TN in PTCB and DBMF was obviously larger than in the other types in the 0–10 cm layer; PPM, PTCB, and POCB affected the sand content, which showed negative correlativity; and DBMF showed positive correlativity with AWC. The mechanism of how different species affect TN, sand content, and AWC should be focused on and taken into consideration in further studies.