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ABSTRACT Biological invasions have become an important threat to global ecological security and forest health, and exploring the environmental driving mechanisms of invasive species is important for prevention and control. Bursaphelenchus xylophilus (Steiner and Buhrer, 1934), as a highly destructive invasive species, has its distribution and spread driven by a combination of various environmental factors. The study systematically evaluated the habitat suitability and key driving factors of B. xylophilus in the current period by applying an ensemble model and an optimized parameter‐based geographical detector. The results indicate that bioclimatic, vegetation indices, topographical features, and human activities are key environmental factors influencing the distribution of B. xylophilus, with highly suitable areas primarily located in southern, northern, and northeastern China. Meanwhile, the synergistic interaction between slope and population density (PD) significantly enhanced the suitability of B. xylophilus distribution, while the interaction between normalized difference vegetation index (NDVI) and global human influence index (GHII) exhibited a nonlinear weakening effect. Additionally, the habitat suitability of B. xylophilus increased with the expansion of isothermality, mean temperature of the wettest quarter, precipitation of the driest month, global human footprint, GHII, and PD, while it gradually decreased with the increase of UV‐B seasonality and NDVI. This study thoroughly explored the mechanisms by which various environmental factors influence the habitat suitability of B. xylophilus, revealing the complexity of regional driving factors. The findings not only provide theoretical support for predicting the ecological suitability of B. xylophilus but also offer scientific evidence for comprehensively analyzing the key factors affecting its distribution. While most studies focus on a single species distribution model, this study further analyzed the drivers of environmental factors by incorporating a geographic detector.

Evaluating Ecological Environment Quality (EEQ) is crucial for sustainable development, particularly in arid and semi-arid regions with particular environmental and socioeconomic characteristics. This study introduces a Particular Remote Sensing Ecological Index (PRSEI), incorporating the Sand Index (SI) and Turbidity Index (TI) based on the traditional Remote Sensing Ecological Index (RSEI), to better reflect the EEQ in the Beijing-Tianjin Sand Source Region (BTSSR) from 2000 to 2020. The Theil-Sen median, Mann–Kendall test, and Hurst index were applied to explore the trend of PRSEI, and the optimal parameters-based geographical detector model evaluated the impact of influencing factors and their interactions. The results in this study indicate that (1) PRSEI and RSEI demonstrated high consistency (R2 = 0.972), with PRSEI outperforming RSEI in capturing regional ecological differences. (2) The EEQ of BTSSR was significantly improved during the study period, and PRSEI showed a strong uptrend in most areas, 61.771% of the areas were strongly improved, 26.154% were light improved, and 11.821% were seriously degraded. The future development trend is mainly based on the “Up-Up” model, which indicates that the EEQ of BTSSR is generally improved. The mean q-value for the single-factor detection was 0.382, while for the interactive detection, it was 0.612, highlighting the significant role of factors’ interaction in PRSEI variation. In general, this study serves as a scientific foundation for advancing ecological conservation and fostering sustainable socio-economic development in arid and semi-arid regions, offering critical insights to support targeted strategies and informed decision-making.

Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen’s slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, 93.08 % of the land in the research region exhibited a declining LDI trend, 3.95 % showed no change, and only 2.96 % showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.

The Green View Index (GVI) is utilized to evaluate urban street value and ecosystem services and to gauge public perceptions of street greening. This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. The results indicate that: (1) In Yuzhong District, 58.9% of streets have a GVI within a low to moderate range, suggesting room for improvement. Higher GVI levels are generally associated with elevated Digital Elevation Models (DEM), while slope, aspect, and terrain undulation have relatively minor overall impacts on GVI. (2) The GVI is highest in the western regions and lowest in the eastern regions, with streets along the riversides exhibiting lower GVI levels. (3) GWR analysis reveals that road type and NDVI significantly influence the GVI. Higher DEM values promote increased GVI, whereas high road density suppresses it. (4) The interaction between influencing factors drives the differentiated distribution of GVI within the study area. The interaction effects between Road type, NDVI, and DEM are particularly notable among these.

Lake basins are one of the most significant areas of human–land interaction. It is essential for the region’s ecological protection and high-quality development to assess their ecological vulnerability (EV) and analyze the key driving factors of EV. Considering the characteristics of the lake basin, we chose 17 indicators to evaluate the EV of the Nansi Lake Basin based on the “sensitivity-resilience-pressure” (SRP) model. Then, spatial principal component analysis (SPCA) and a transfer matrix were used to analyze the spatial-temporal variation characteristics of the EV. Moreover, the optimal parameters-based geographical detector (OPGD) was applied to investigate the factors influencing the spatial heterogeneity of the EV. The results indicated that the EV of the Nansi Lake Basin was characterized by a circling spatial structure, with low values distributed in the Nansi Lake and its surrounding areas, as well as high values concentrated in the northwest. The EV of the Nansi Lake Basin decreased from 2010 to 2020, indicating that the overall ecological pressure in the Nansi Lake Basin decreased. Climatic factors, land use type, and habitat quality were the primary factors that influenced the spatial heterogeneity of the EV in the basin. Our findings can serve as policy guidelines for ecological management and the sustainable development of the Nansi Lake Basin and also contribute to the EV assessment of lake basins.

Rapid urban development and human activities have led to drastic changes in land use, resulting to heightened ecological pressures and risks to ecosystems, especially in cities along the lower reaches of the Yellow River (CLRYR), China. However, the landscape pattern and associated ecological risks in the CLRYR in the past twenty years remain unclear. In this study, we employed the land use data to identify the primary landscape types and their transformations, providing an in-depth analysis of the prevailing landscape pattern and the landscape ecological risk (LER) in the CLRYR. Additionally, we explored the spatial distribution of LER and investigated the underlying driving forces behind these changes. The results reveal that: (1) Cropland is the main landscape type in the CLRYR region; however, the area of cropland decreases with the transition to impervious. (2) Due to human activities, landscape fragmentation and diversity have gradually increased in CLRYR, while aggregation has gradually decreased, until there was some improvement between 2015 and 2020. (3) The LER in the CLRYR region exhibits instability, with values of 0.1761, 0.1751, 0.1760, 0.1773, and 0.1751 displaying a fluctuating downward trend. Directional distribution analysis indicates a movement of the LER center of gravity towards the mouth of the Yellow River, accompanied by an increasing dispersion pattern. Analysis of driving forces suggests that natural factors hold greater explanatory power compared to social factors. Moreover, interaction detection results reveal that the combined effect of any two factors surpasses that of a single factor. The findings offer a theoretical foundation for enhancing planning policies aimed at striking a balance between environmental preservation and social advancement within the CLRYR region.

The oasis serves as the central component of the arid ecosystem and plays a crucial role in supporting human activities. However, the ecological environment in the oasis region is fragile, and even a minor alteration in land use (LU) can significantly impact the stability of the ecosystem. Therefore, it is imperative to undertake comprehensive research on the spatio–temporal patterns of LU change in the oasis, reveal its driving factors, and predict future development. This is crucial for devising scientifically and logically sound land management strategies, upholding the equilibrium between humans and land in arid areas, and attaining sustainable development of the regional ecology and economy. This study focuses on the Weigan–Kuqa River Delta Oasis in China as the research area, analyzes the changes in LU in the oasis from 2010 to 2022 using various methods such as transition matrix, dynamic degree, intensity analysis, and center of gravity shift. The study also investigates the factors influencing these changes using the optimal parameters–based geographical detector (OPGD). Additionally, it predicts the future trends in LU development under four different scenarios using the mixed–cell cellular automata (MCCA), and illustrates distribution characteristics by combining Moran’s I index and hotspot analysis. The results suggest that: (1) Between 2010 and 2022, the LU in the oasis changed rapidly, with consistent increase in the amount of construction land, arable land, and garden land, while the amount of forest-grassland and unused land decreased overall. (2) Population density played a leading role in the changes, but soil type also had a significant impact. Over the course of time, the influence of roads and transportation has progressively increased. (3) Compared with 2022, the acreage of arable land, garden land, and construction land increases under the four future scenarios: natural development scenario (NDS), economic development scenario (EDS), cropland development scenario (CDS), and ecological protection scenario (EPS). However, the acreage of forest–grassland and unused land decrease. From a spatial perspective, large towns, the downstream of alluvial fans, and the central oasis are key areas where the distribution of hot spots and sub–hot spots of each LU type varies significantly among the four scenarios. The EPS provides a certain level of protection for forest-grassland areas and water bodies, making it the most appropriate development model for oases. These findings have the potential to offer valuable academic guidance for oasis land resource management and are crucial for achieving coordinated development at regional level.

The “Forest Village” model utilizes forests as a central medium, leveraging rural forest resources as a key asset for rural revitalization. Taking 1140 national forest villages in Southwest China as the research object, the spatial distribution characteristics of national forest villages in southwest China were analyzed from two dimensions, nature and village nature, using ArcGIS 10.8 tools with watersheds as the research unit. The two dimensions of nature and society and their influencing factors were identified by using a combination of methods such as spatial autocorrelation, the closest neighbor index, the standard deviation ellipse, kernel density analysis and OPGD. The results revealed the following: (1) It is sparsely in the west and densely in the east, featuring four high—density cores that radiate outward to the surrounding areas. (2) The most significant among these factors are socio—economic ones, such as GDP density and population density, which demonstrate the notable impact of human disturbances on rural distribution. (3) Among the natural factors, topography and climate exert the most significant influence. Among the remaining factors, the densities of the river network and road network are strongly influenced by urban development, showing a high degree of alignment with the distribution of other social factors.

The Dabie Mountain area in Anhui Province is an essential ecological security barrier and a critical protected area in East China. It is very important to assess its ecological environment quality and identify its key driving forces. Five indicators, including Greenness, Wetness, Dryness, Heat, and Biological Richness, were used to construct an improved remote sensing ecological Index (IRSEI) to assess ecological environment quality. The weights of the five indicators were determined by coupling the analytic hierarchy process (AHP) and the entropy weight method (EWM). The optimal parameters-based geographical detector (OPGD) was used to recognize driving factors. The main conclusions were as follows: (1) the overall rank of ecological environment quality was mainly good and excellent. The ecological quality of forest land was excellent, that of farmland was good, and that of built-up areas was poor. (2) The change in ecological environment quality was mainly stable from 2000 to 2020. The ecological quality of some forests and farmlands improved, with a deteriorating trend in the built-up areas. (3) The Moran’s Index of ecological quality ranged from 0.77 to 0.85, indicating high spatial agglomeration. (4) The OPGD indicated that the DEM had the most explanatory power for ecological quality, and the interactive relationship between the DEM and population density had the most significant impact. (5) In comparison to the conventional remote sensing ecological Index (RSEI), the IRSEI exhibited higher congruence with observed circumstances and improved ecological interpretability.