Urban agglomerations in plateau regions often face severe landscape fragmentation and cross-boundary ecological pressures, highlighting the need for coordinated eco-logical planning for sustainable urban development. We coupled species–landscape interactions and multi-ecological services to construct sustainable ecological security patterns (ESPs) and establish a collaborative optimization framework. Specifically, we integrated MaxEnt-derived habit suitability with InVEST-based ecosystem services to identify ecological sources (ESs) and analysis the environmental impacts on species distribution. Based on this, we built a multi-factor resistance surface and employed circuit theory to extract ecological corridors (ECs) and critical nodes (pinch points and barrier points). Then, we quantitatively compared two simulated scenarios (barrier points restoration and stepping stone augmentation) to assess the spatial priority of ecological nodes. We identified 48 ESs (26,410.48 km², mainly distributed in Chuxiong, Yuxi, Honghe, and Kunming), 115 ECs (2670.02 km, with a west-dense and east-sparse spatial pattern), 43 pinch points, and 39 barrier points. Scenario simulation shows that repairing 39 barrier nodes increases network connectivity by an average of 33.52% and global network efficiency by 19.44%, whereas adding steeping stones yields improvements of 20.09% and 5.56%, respectively, indicating that barrier-node restoration produces larger contribution in both connectivity and efficiency at the global scale. Leveraging EN construction and scenario simulation, we developed an ESP-based sustainable framework for collaborative optimization in plateau urban agglomerations. The framework specifies agglomeration-specific coordination pathways, which are expected to provide a transferable blueprint for biodiversity conservation, ecosystem optimization, and sustainable development.

The increasing fragmentation of habitats is accelerating biodiversity loss, particularly in high-altitude cities’ ecologically fragile green spaces (GSs). The scientific assessment of the distribution patterns of biodiversity (DPOB) in the Circum-Erhai Lake region (CELR) is of great significance for sustainability. We identified GSs using fractional vegetation coverage. The InVEST habitat quality (HQ) module assessed HQ, while GIS evaluated ecological sensitivity (ES). Subsequently, using HQ and ES as environmental variables, we applied the MaxEnt Model to predict the suitable habitat of key species. Finally, HQ, ES, and species distribution results served as proxies for biodiversity to predict DPOB. Key findings: (1) CELR’s overall biodiversity is relatively high, showing a “high in forests, low along Erhai shores” pattern. (2) Core GSs are scarce (1.36% of total GS area). (3) The GSs with high quality ecosystem dominate in high-altitude forests (70.60%). (4) The GSs suitable for key species (18.87%) are mainly distributed in flat areas on the west bank of Erhai Lake. (5) The GSs of non suitable area cover 219.23 km²(20.19%). Based on the ecosystem and biodiversity levels, we constructed a hierarchical planning framework for GSs around the Erhai Lake area. The findings of this study contribute to identifying priority areas for biodiversity conservation and facilitate the development of sustainable ecological management strategies.

The mountainous regions of southwest China represent one of the world’s most distinctive and sensitive areas. Against the backdrop of rapid urbanization and water conservancy construction, rural landscapes in these regions face challenges such as fragmentation, homogenization, and loss of local distinctiveness. Responding to the initiative of the European Landscape Convention (ELC), this study takes the Longchuan River Basin in Southwest China as a case study, and constructs a rural Landscape Character Assessment (LCA) framework adapted to the multi-level governance system. We established a multi-scale evaluation system covering large scale (county-level), medium scale (township-level), and detailed scale (reservoir area-level). The large scale integrated 6 categories of natural variables, while the medium scale involved 4 categories of natural variables and 4 categories of cultural variables. Using a Principal Component Analysis–Two-Step Clustering coupled method and eCognition software, landscape character types and areas were identified respectively. The results show that 11 landscape character types and 41 landscape character areas were identified at the large scale, and 6 landscape character types and 73 landscape character areas at the medium scale. At the detailed scale, 4 typical reservoir areas were selected for field surveys, which verified the in-depth impact of hydropower construction on landscape characteristics. The study provides a transferable technical pathway and policy recommendations for monitoring and managing rural landscapes in mountainous regions. Supports the long-term sustainability and resilience of rural landscapes in China.