Habitat quality (HQ) is of great significance to regional sustainable development, which is a key link for regional ecological security and human welfare. Assessing the temporal and spatial evolution of HQ caused by land-use change could provide a scientific basis for regional ecological protection and land management. Here, based on the ArcGIS platform and the InVEST model, this study quantified the spatial and temporal evolutions of land use, landscape patterns, and HQ from 1985 to 2015, in Taihu Lake Basin (TLB). Hotspot analysis tools were used to identify the spatial agglomeration and evolution characteristics of HQ in TLB. The results showed that, (1) the land use and landscape pattern in TLB experienced dramatic change process during 1985–2015, with the dominating conversion being from farmland to construction land, which led to an increase in landscape heterogeneity and fragmentation. (2) The HQ was generally reduced in the past 30 years. In particular, the decline of HQ was extremely severe in the peripheral area of cities and roads, due to urban sprawl. (3) Regarding the spatial distribution of HQ, the northern and eastern areas were generally higher, while the western and southern areas were generally lower. The hotspots areas were mainly located in the southwestern mountain area and west-central lake area, while the cold spots areas were mainly located in urban areas in the north and the east. (4) The area and degree of habitat degradation were both increased significantly due to land-use change, and the degradation of the Taihu Lake was highlighted. Strengthening the ecological environment management and reducing the threat of urban expansion to the HQ is urgently required. This study could help understand HQ of the TLB and provide a scientific basis for decision-makers.

Regional clustering has become a distinctive feature of the homestay industry in terms of spatial distribution in many countries. However, identifying and quantifying the advantageous locations suitable for the agglomeration of the homestay industry at the micro-scale is still in the exploratory stage. Therefore, with the support of multi-source data and the spatial entropy weight superposition method, drawing on concepts from physics such as gravitational and magnetic fields, and based on relevant location theories, the environmental field strength and its quantitative model are proposed to evaluate micro-locations suitable for homestay development. Finally, a case study was conducted in Wuyuan, China. The results show that the homestay industry is mainly distributed along traffic lines and rivers, which is greatly affected by tourism resources and rural settlements, and the degree of agglomeration is gradually increasing; The environmental field strength is closely related to the accessibility of infrastructure and the development of tourism resources, and the overall structure tends to be networked, polycentric and “core-periphery”; there is a positive linear correlation between the distribution of homestay industry and environmental field strength. This study provides a basis for optimising decision-making related to the sustainable planning and site-selection of tourism destinations and the homestay industry.

Understanding the trade-offs and synergies among ecosystem services is fundamental to regional ecological management, yet systematic assessments that integrate multidimensional coordination dynamics with nonlinear driving mechanisms remain limited. Taking the Poyang Lake Basin as the study area, this research evaluates six key ecosystem services, including food production, water yield, carbon storage, soil conservation, habitat quality and net primary productivity, from 2000 to 2020 using the InVEST model, the RUSLE equation, and the CASA model. In contrast to previous studies that focus on pairwise service comparisons or static assessments, this study introduces a mechanical equilibrium model within a principal component analysis framework to measure the coordination degree of ecosystem service provision across multiple dimensions, and further employs the XGBoost-SHAP machine learning approach to disentangle the nonlinear effects and threshold characteristics of natural and anthropogenic drivers. The results reveal that the basin maintains a basic coordination state overall, yet exhibits a pronounced core-periphery spatial pattern alongside a three-phase adaptive trajectory of coordination, diffusion, and restabilization over time. Rainfall variability emerges as the predominant driver, exhibiting a U-shaped nonlinear relationship with two critical thresholds, while vegetation dynamics, economic growth, and land use change display distinct marginal effects and interactive influences with topographic conditions. The integration of mechanical equilibrium modeling with interpretable machine learning enables a coherent analytical chain from coordination state identification to causal attribution. These findings provide both theoretical contributions to ecosystem service interaction research and practical implications for differentiated watershed management and ecological conservation strategies.