Zhe Zhang

Rapid urbanization has intensified ecosystem service supply–demand mismatches, severely constraining regional sustainable development. Ecological security patterns represent a critical strategy to address these challenges, yet existing studies predominantly focus on natural supply while neglecting human ecological demands. This study develops an integrated framework combining XGBoost machine learning and ecological network theory to assess and optimize ecological security patterns from a supply–demand perspective, using the Chengdu–Chongqing Urban Agglomeration in the upper Yangtze River Basin as a case study. Results reveal a distinct spatial pattern of “high supply in peripheries and high demand in urban cores”. Ecological demand sources and corridors have experienced explosive growth, expanding significantly faster than supply components. While the ecological supply–demand network has become more structurally complex with improved structural connectivity overall, the supply–demand balance has progressively deteriorated. Targeted optimization through supplementing key ecological sources and refining network structure enhanced connectivity by 136.46% and improved supply–demand balance by 35.4%. A “three zones, four corridors, six nodes” optimization scheme is proposed. This research provides valuable insights for mitigating ecological conflicts in rapidly urbanizing regions and informs evidence-based decision-making for territorial ecological restoration and public policy design to balance conservation and socioeconomic development.

Constructing ecological networks (ENs) is an effective measure to mitigate the conflict between urban development and ecological conservation. However, existing simulating methods lack adequate consideration of human ecological demands and the spatial scale differences between these demands and natural ecological processes. This might lead to issues such as incomplete ecological process cycles or structural mismatches being overlooked during ENs simulations. To address these gaps, this study proposed an urban multi-scale nested ENs simulating framework that integrates human ecological demands with natural ecological processes. The framework first simulated an ENs focused on natural ecological process cycles at a global scale (GS). Then, it simulated an ENs centered on human ecological needs within the core urban areas at local scale (LS). Finally, it nested these multi-scale ENs by using cross-scale ecological supply sources as connecting points. This framework was applied to simulate spatio-temporal pattern changes in ENs of Jinan City, a core city in downstream of the Yellow River in China, aiming to mitigate cross-scale ecological conflicts between human–nature interactions under the background of urbanization. The study’s findings revealed that the area of demand sources increased by 8.56 times over 20 years. the area of cross-scale supply sources decreased by 15 km² relative to 2000, and the deterioration in connectivity was more pronounced in GS compared to LS, with a decline of approximately 13.8%. These changes indicate the presence of incomplete ecological process cycles and structural mismatches across the multi-scale boundaries within the study area, which have been worsening annually. We recommend optimizing Jinan City’s multi-scale ecological network through three key strategies: rectifying internal structural mismatches, protecting core ecological areas, and aligning regional ecological demands. Implementing these strategies could significantly improve the network structure, reduce cross-scale mismatches, and enhance ecological connectivity by about 9%. This study highlights the importance of addressing structural mismatches and promoting complete ecological cycles in urban multi-scale ENs simulating, providing valuable insights for formulating urban multi-scale ecological conservation and restoration policies.

Urbanization has significantly impacted ecological connectivity, making the optimization of ecological networks (ENs) crucial. However, many existing strategies focus on overall network structure and overlook the spatial concentration of local ecological processes flow (EPF), limiting the effectiveness of ecological planning. This study proposes a novel EN optimization framework based on urban–rural gradient spatial zoning to enhance connectivity from the perspective of EPF. The framework divides areas outside the core urban zone (CUZ) into the urban fringe zone (UFZ), urban–rural interface zone (UIZ), and natural rural zone (NRZ), applying tailored optimization strategies in each zone. These strategies include increasing corridor redundancy, reducing corridor resistance, and expanding corridor width to alleviate EPF concentration. Using Jinan, a mega-city in China’s Yellow River Basin, as a case study, this study simulated EN changes over 20 years and validated the framework’s effectiveness. Optimization validation showed that increasing ecological land in low-flow corridors to 65% in the UIZ and expanding NRZ corridors to 5 km improved connectivity by 6.3%, addressing seven pinch points and three barrier points. This study highlights the importance of optimizing ENs via urban–rural zoning to support sustainable development and ecological protection policies.

With the rapid development of my country’s economy and the advancement of urban-rural integration, the urban population has increased sharply, people’s basic survival problems have been solved, and more and more attention has been paid to landscapes and landscape water. The sources of water pollution were described, the effects and characteristics of water restoration methods were analyzed and compared, and suggestions were made for landscape water restoration.Immobilization technology has great advantages in ecological restoration of landscape water bodies, with strong environmental adaptability and high tolerance to environmental conditions.