Species diversity is a crucial index used to evaluate the stability and complexity of forest ecosystems. Studying the relationship between stand structure and understory herbaceous plants species diversity is useful for managers to formulate the best forest structure optimization method with the goal of improving herbaceous species diversity. In this research, Platycladus orientalis plantations in Beijing were taken as the research object. Pearson’s correlation analysis was used to explore the single-factor correlation between stand structure and understory herbaceous plants species diversity; furthermore, a typical correlation analysis and multiple linear regression were used to explore the multi-factor correlation and analyze the dominant stand structure parameters affecting understory herbaceous plants species diversity. In the range of stand structures studied, the results showed that canopy density was negatively correlated with the Shannon–Wiener index and Simpson index (p < 0.01), and tree density was negatively correlated with the Shannon–Wiener index (p < 0.05). In terms of stand spatial structure, the mingling degree was positively correlated with the Shannon–Wiener index, Simpson index, Margalef richness index and Pielou evenness index (p < 0.05), while the uniform angle was negatively correlated with the Pielou evenness index (p < 0.05). The correlation coefficient of the first group of typical variables in the typical correlation analysis was 0.90 (p < 0.05); from this group of typical variables, it can be concluded that canopy density is the most influential indicator affecting the comprehensive index of understory herbaceous plants species diversity, with a load of −0.690, and the Shannon–Wiener index and Simpson index are the most responsive indicators of changes in the comprehensive index of stand structure, with loads of 0.871 and 0.801, respectively. In the process of the management of Platycladus orientalis plantations under a low altitude, south slope, thin soil layer and hard soil parent material, in order to improve the herbaceous species diversity, the canopy density of the overstory and tree density should be appropriately reduced. Additionally, it is necessary to regulate the horizontal spatial structure of stands. When the trees are randomly distributed and the mingling degree is high, the species diversity of herbs can be increased.

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.

We analyzed and compared the effects of biotic factors (species diversity, structural diversity, and aboveground biomass) and abiotic factors (topography and soil properties) on soil organic carbon in selected mountainous areas of Beijing China. An overall goal is to provide a preliminary scientific basis for biodiversity protection and coordinated development of forest ecosystems and the subsequent carbon balance in this region. Our study ecosystems were coniferous forests, mixed coniferous and broadleaved forests, and broadleaved forests in the western mountainous area of Beijing. We determined both direct and indirect effects of abiotic and biotic factors on soil organic carbon using multiple linear regression and a structural equation model. Results showed that the biotic factors aboveground biomass and structural diversity were the main driving forces of organic carbon accumulation in the soil surface layer (0–10 cm), but species diversity had no significant effect (p > 0.05). Abiotic factors (altitude, total nitrogen, and total potassium) had some influence on soil surface organic carbon but were subordinate to the biotic factors. The biotic factors had no significant effect on soil organic carbon in the subsurface layers (10–20 cm) and (20–30 cm) (p > 0.05), whereas the abiotic factors altitude and total nitrogen played a dominant role in subsurface soil organic carbon accumulation of both layers (p < 0.01). The influences were both direct and indirect influences, but the direct influences played a major role. Our results form an initial reference for afforestation management (Beijing-Tianjin Sandstorm-source Control Project) from the perspective of biodiversity protection and carbon storage enhancement.

Enhancing forest ecological functioning by optimizing stand structure is essential in high-quality, sustainable forests. We examined this in 38 plots (20 m × 20 m) of Pinus tabuliformis forests in the mountainous area of Beijing. We investigated and analyzed the spatial structure and functional characteristics of these plots. Structural equation modeling and response surface methodology were used to identify spatial structural stand factors affecting soil nutrient levels and understory biodiversity. We examined the pathways and strengths of the coupling relationships between structure and function and the ranges and thresholds of changes in these structural factors. Mingling degree, openness degree, competition index, and forest layer index substantially affected the understory herbaceous species diversity and soil nutrient levels. Mingling degree significantly impacted understory biodiversity and soil nutrient levels (direct path coefficient (DPC), 0.48 and 0.70, respectively). Openness degree significantly affected understory species diversity (DPC, 0.37). The competition index and forest layer index exerted less significant direct impacts on these functions; competition influenced herbaceous diversity primarily indirectly. The optimal features were as follows: mingling degree, 0.8; openness degree, 1.1; competition index, 0.3; and forest layer index, 0.5. Average understory herbaceous species diversity and soil nutrient levels are projected to increase by ca. 47.74% and 52.29%, respectively, post optimization. These findings provide a reference for precise regulated stand structures and establish multifunction management optimization objectives in Beijing’s mountainous Pinus tabuliformis forests.

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China.

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.