Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan
Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecologic...
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MDPI AG
2025-07-01
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| author | An Tong Yan Zhou Tao Chen Zihan Qu |
| author_facet | An Tong Yan Zhou Tao Chen Zihan Qu |
| author_sort | An Tong |
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| description | Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km<sup>2</sup>) to 37 (725 km<sup>2</sup>), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective. |
| format | Article |
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| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-53ff2ae04b50406c9201c8a4603f73b82025-08-20T03:36:32ZengMDPI AGRemote Sensing2072-42922025-07-011715254810.3390/rs17152548Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in WuhanAn Tong0Yan Zhou1Tao Chen2Zihan Qu3Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, ChinaDepartment of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, ChinaDepartment of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, ChinaDepartment of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, ChinaUnder the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km<sup>2</sup>) to 37 (725 km<sup>2</sup>), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective.https://www.mdpi.com/2072-4292/17/15/2548ecological security pattern optimizationspatiotemporal evolutionecosystem servicesecological processcomplex network |
| spellingShingle | An Tong Yan Zhou Tao Chen Zihan Qu Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan Remote Sensing ecological security pattern optimization spatiotemporal evolution ecosystem services ecological process complex network |
| title | Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan |
| title_full | Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan |
| title_fullStr | Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan |
| title_full_unstemmed | Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan |
| title_short | Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan |
| title_sort | constructing an ecological spatial network optimization framework from the pattern process function perspective a case study in wuhan |
| topic | ecological security pattern optimization spatiotemporal evolution ecosystem services ecological process complex network |
| url | https://www.mdpi.com/2072-4292/17/15/2548 |
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