| 基于生态系统服务权衡与复杂网络理论的粤港澳大湾区生态网络优化 |
| 摘要点击 776 全文点击 7 投稿时间：2025-06-03 修订日期：2025-09-04 |
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| 中文关键词 生态系统服务权衡 有序加权平均模型（OWA） 生态网络 鲁棒性 复杂网络优化 粤港澳大湾区（GBA） |
| 英文关键词 trade-offs in ecosystem services ordered weighted averaging（OWA） model ecological network robustness complex network optimization Guangdong-Hong Kong-Macao Greater Bay Area（GBA） |
| DOI 10.13227/j.hjkx.202506010 |
| 作者 | 单位 | E-mail | | 李相逸 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | lixiangyi@szu.edu.cn | | 王正午 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | | | 洪武扬 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | | | 张立 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | zhangliszu@szu.edu.cn | | 陈宇 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | | | 李金鑫 | 深圳大学建筑与城市规划学院, 亚热带建筑与城市科学全国重点实验室, 深圳 518060 | |
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| 中文摘要 |
| 构建生态系统服务协同发挥与稳定有效的生态网络是城市群可持续发展的重要空间途径，具有重要的理论与现实意义. 既有研究较少考虑城市群生态系统服务功能权衡对生态源地选择的影响，及针对复杂网络理论开展生态网络拓扑结构优化方法有待深入. 以超大型城市群——粤港澳大湾区为例，基于有序加权平均（OWA）模型考虑生态系统服务权衡选择协同发挥最佳的生态源地，采用最小成本路径与电路理论构建生态网络，基于复杂网络理论，依据生态网络拓扑与鲁棒性分析优化生态网络. 结果表明：①将生态系统服务权衡纳入到生态源地选择过程，能有效地识别关键生态系统服务协同发挥的生态斑块. 基于OWA模型的多权衡情景对比，最佳情景（情景3）具有最高的生态系统服务平均保护效率（1.942）和相对较低的决策风险（0.2），有效提升了生态源地选择的综合效益. ②共识别278个生态源地，分布于北部连绵山体森林生态屏障区域；700条生态廊道共6 680.627 km，与132个生态夹点和283处生态障碍点区域共同构成了一体化的生态网络. ③基于复杂网络理论的优化策略，通过对生态网络的节点与边进行空间增补，显著提升了网络的整体连通性和稳定性. 新增的32个生态源地与91条生态廊道主要集中于大湾区东部及北部等生态基底较好的区域及原有廊道连接相对薄弱的地带. 将生态系统服务权衡与复杂网络理论相结合，为超大城市群构建功能协同发挥和有效稳定的生态网络提供了新视角，为以粤港澳大湾区为代表的超大城市群应对高强度人类活动干扰下城镇发展与生态保护的空间冲突问题提供理论和实践参考. |
| 英文摘要 |
| Constructing an ecological network that promotes the synergistic delivery of ecosystem services and ensures stability and effectiveness is a crucial spatial approach for the sustainable development of urban agglomerations， possessing significant theoretical and practical implications. Existing research has seldom considered the impact of trade-offs in urban agglomeration ecosystem services on the selection of ecological sources， and in-depth exploration is needed for methods optimizing ecological network topology based on complex network theory. This study takes the Guangdong-Hong Kong-Macao Greater Bay Area （GBA）， an ultra-large urban agglomeration， as a case study. It employs the ordered weighted averaging （OWA） model， considering ecosystem service trade-offs， to select ecological sources that best achieve synergistic delivery. The ecological network is constructed using the least-cost path and circuit theory and then optimized based on complex network theory， informed by topological and robustness analyses. The results indicate that： ① Incorporating ecosystem service trade-offs into the ecological source selection process effectively identified ecological patches that promote the synergistic delivery of key ecosystem services. Among multiple trade-off scenarios evaluated using the OWA model， the optimal scenario （Scenario 3） demonstrated the highest average protection efficiency for ecosystem services （1.942） and a relatively low decision-making risk （0.2）， significantly enhancing the overall benefits of ecological source selection. ② A total of 278 ecological sources were identified， predominantly distributed in the northern continuous mountainous forest ecological barrier zone. Together with 700 ecological corridors （totaling 6 680.627 km）， 132 ecological pinch points， and 283 ecological obstacle areas， they constituted an integrated ecological network. ③ Optimization strategies derived from complex network theory， involving the spatial supplementation of network nodes and edges， markedly improved the overall connectivity and stability of the ecological network. The 32 new ecological sources and 91 new ecological corridors were primarily concentrated in regions of the eastern and northern GBA with favorable ecological conditions and in areas where existing corridor connectivity was relatively weak. By integrating ecosystem service trade-offs with complex network theory， this study offers a novel perspective for establishing ecological networks that are both functionally synergistic and robustly stable in ultra-large urban agglomerations. It provides theoretical and practical references for addressing spatial conflicts between urban development and ecological conservation under intense human activity， particularly for mega-urban agglomerations exemplified by the GBA. |
