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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.

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.

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.

Urban flooding is caused by multiple factors, which seriously restricts the sustainable development of society. Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters. Although the effects of various factors on urban flooding have been extensively evaluated, few studies consider both interregional flood connection and interactions between driving factors. In this study, driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector (OPGD). An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding. Furthermore, interregional flood volume connection was analyzed based on simulation results. Subsequently, driving force of urban flooding factors and interactions between them were quantified using the OPGD model. Taking Haidian Island in Hainan Province, China as an example, the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area. The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force (0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force (0.968). In addition, the interactions between driving factors have bivariable or nonlinear enhancement effects. The interactions between two factors strengthen the influence of each factor on urban flooding. This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.

Urban vitality is an essential reference for formulating urban planning and urban development policies, while the Modifiable Areal Unit Problem (MAUP) is a crucial issue that affects the accurate assessment of urban vitality and the understanding of its driving factors. Currently, MAUP in urban vitality studies has not received sufficient attention, and there are limited investigations on whether MAUP significantly affects the spatial autocorrelation of urban vitality. Moreover, existing studies have mainly focused on the impact of individual factors on urban vitality, with minor consideration of the interactions between factors and their effects on urban vitality at different spatial scales. This study takes the Pearl River Delta Core (PRDC) region as the research area. It used the Optimal Parameters-based Geographic Detector (OPGD) model and spatial autocorrelation to analyze the impact of the MAUP on urban vitality and its Built Environment (BE) factors. The results indicated that (1) the five types of BE driving factors, including work, living, rest, traffic, and Points of Interest (POI) diversity, were spatially sensitive and exhibited scale threshold effects. (2) The zoning schemes demonstrated the ability to alter the explanatory power (q-value) of driving factors and their trends. The spatial granularity and aggregation level of the grid-based zoning scheme, NPP-VIIRS nighttime light zoning scheme, and land surface temperature zoning scheme exhibited a logarithmic relationship, where spatial autocorrelation levels decreased with increasing aggregation level. (3) Compared to single-factor effects, the interactive effects of BE factors exhibited a higher q-value for urban vitality. Additionally, the q-values for factor interactions were more stable in multiple spatial scales than those q-values for individual factors. This study provides decision-making references for urban planners and policymakers to accurately assess urban vitality and find balanced, improved paths to enhance it.

Achieving the coordination and symbiosis of cold chain logistics and green finance is notably critical for promoting regional green and sustainable development. However, The existing research on the coupling coordination relationship between cold chain logistics and green finance, as well as its driving factors, remains limited and lacks in-depth analysis. This study portrays the coupling coordination degree (CCD) from the perspectives of measurement, spatial patterns, and driving factors in China with multi-source data and the optimal parameters-based geographical detector. Results show that the CCD in China demonstrates an overall increasing trend of fluctuations, along with obvious regional differences. The spatial distribution of the CCD demonstrates a positive correlation, characterized by H-H and L-L clustering. The spatial pattern of the CCD is high in the eastern, southern regions and low in the western, northern regions, this gap is gradually narrowing between the east and west, south and north gap is widening. This spatial pattern is marked by infrastructure, economic factors, human capital, energy intensity, technological factors, and natural factors. Notably, the interactive impact among human capital, financial markets, and digital intelligence technology contributes to further integration, with the impact of individual factors ranging from 7.11 to 632.79%. It offers valuable implications for policymakers and logistics companies for sustainable development, and contributes empirical insights to emerging countries.

Exploring the built environment's impact on subway station ridership can aid in developing effective built environment update strategies. Previous studies lack an analysis of the impact of the interaction effects of built environment explanatory variables on subway station ridership. Beijing is divided into three zones with different buffer scales, and 18 built environment explanatory variables are selected as independent variables based on the ‘7D’ dimension of the built environment, and these are calculated based on a hypothetical circular scale range centered on a subway station. The inbound ridership of subway stations during morning peak hours, outbound ridership of subway stations during morning peak hours, inbound ridership of subway stations during evening peak hours, and outbound ridership of subway stations during evening peak hours are taken as dependent variables. For different dependent variables, the optimal parameters-based geographic detector (OPGD) model determines the recommended scale combination of the built environment around subway stations for three zones. Moreover, the impact of single explanatory variables and the interaction of explanatory variables on subway ridership were explored at the recommended scale combination based on the OPGD model. The results show that: (1) the recommended circular buffer radius combinations for the inbound ridership of subway stations during morning peak hours, outbound ridership of subway stations during morning peak hours, inbound ridership of subway stations during evening peak hours, and outbound ridership of subway stations during evening peak hours are 800–800–2000 m, 800–1000–2000 m, 800–1000–2000 m, and 800–800–2000 m, respectively. (2) The density of apartment facilities and the density of office facilities are the variables that contribute substantially to the ridership of Beijing subway stations. (3) The interaction between multiple explanatory variables has a much stronger contribution to ridership than single factors. In particular, the contribution of the explanatory variables to inbound ridership during the morning peak and outbound ridership during the evening peak increased significantly. Based on the analysis results, targeted built environment updating strategies are provided from the perspective of supply–demand balance, which can provide an important decision-making basis for updating the built environment around subway stations. In addition, it can also provide a theoretical basis for delineating the scope of transit-oriented development (TOD) in Beijing.

The clarification of community assembly mechanisms in benthic macroinvertebrates and their respective contributions to the development of beta diversity is a fundamental concern in aquatic ecology. Nonetheless, the intrinsic complexity of community alterations and their non-linear reactions to gradients of explanatory variables present considerable obstacles to measuring the determinants of beta diversity. Fifty sampling points were set up along the major rivers of the Yellow River Irrigation Area (YRIA), the Central Arid Zone (CAZ), and the Southern Mountainous Area (SMA) in Ningxia in April, July, and October 2023. The findings demonstrate that the optimal parameter-based geographical detector (OPGD) model identified a 3000 m circular buffer as the spatial scale at which landscape structure most significantly influences water quality. A degradation in water quality presumably results in diminished differences in species composition among communities. The Sørensen index was determined to be more appropriate for this investigation, and the total beta diversity of the communities was relatively high (βSOR ≥ 0.82), with no identifiable nested spatial patterns detected. Except in the YRIA, environmental variability contributed more significantly to the variance in beta diversity than spatial factors, and deterministic mechanisms dominated the community assembly of benthic macroinvertebrates across all three months. To improve biodiversity and aquatic ecosystem health, the study region should optimize its landscape structure by reducing the amount of bare land and increasing the percentage of forest land within buffer zones. Additionally, a multi-site conservation strategy should be put into place.

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth.

● From 2005 to 2020, GEP in the Chaobai River's upper reaches increased by 58%. ● GEP changes in the Chaobai River's upper reaches exhibited spatial differentiation. ● POP, GDP, and LD were the main driving force factors. ● The interactions between different factors had higher impact than single factor. The Chaobai River Basin, which is a crucial ecological barrier and primary water source area within the Beijing-Tianjin-Hebei region, possesses substantial ecological significance. The gross ecosystem product (GEP) in the Chaobai River Basin is a reflection of ecosystem conditions and quantifies nature's contributions to humanity, which provides a basis for basin ecosystem service management and decision-making. This study investigated the spatiotemporal evolution of GEP in the upper Chaobai River Basin and explored the driving factors influencing GEP spatial differentiation. Ecosystem patterns from 2005 to 2020 were analyzed, and GEP was calculated for 2005, 2010, 2015, and 2020. The driving factors influencing GEP spatial differentiation were identified using the optimal parameter-based geographical detector (OPGD) model. The key findings are as follows: (1) From 2005 to 2020, the main ecosystem types were forest, grassland, and agriculture. Urban areas experienced significant changes, and conversions mainly occurred among urban, water, grassland and agricultural ecosystems. (2) Temporally, the GEP in the basin increased from 2005 to 2020, with regulation services dominating. At the county (district) scale, GEP exhibited a north-west-high and south-east-low pattern, showing spatial differences between per-unit-area GEP and county (district) GEP, while the spatial variations in per capita GEP and county (district) GEP were similar. (3) Differences in the spatial distribution of GEP were influenced by regional natural geographical and socioeconomic factors. Among these factors, gross domestic product, population density, and land-use degree density contributed significantly. Interactions among different driving forces noticeably impacted GEP spatial differentiation. These findings underscore the necessity of incorporating factors such as population density and the intensity of land-use development into ecosystem management decision-making processes in the upper reaches of the Chaobai River Basin. Future policies should be devised to regulate human activities, thereby ensuring the stability and enhancement of GEP.