干旱
环境科学
中国
构造盆地
鉴定(生物学)
蚁群优化算法
流域
蚂蚁
生态学
水资源管理
地理
计算机科学
地质学
算法
地图学
地貌学
生物
考古
作者
Jinghu Pan,Jia Liang,Chengcheng Zhao
标识
DOI:10.1016/j.ecolind.2023.110588
摘要
Optimization of ecological security patterns (ESP) is an important way to implement land space restoration and maintain regional sustainable development. How to comprehensively identify regional ESP while maintaining the balanced development of each ecosystem service is the key issue in the identification and construction of ESP at present. Using data on land use, meteorology, vegetation biomass and socio-economic statistics, the quality of six typical ecosystem services in the Shule River basin (SRB) in 2010 and 2020 were calculated for food supply, water conservation, carbon fixation and oxygen release, habitat quality, soil conservation and culture and leisure. The ordered weighted averaging (OWA) model was introduced to identify the priority conservation areas of ecosystem services as ecological source, and the minimum cumulative resistance (MCR) model was used to extract ecological corridors and ecological nodes. Based on the ant colony algorithm model to identify the optimal paths of corridors in the study area, identify the spatial scope of ecological corridors, potential corridors and key recovery points, and construct the optimization model of basin ESP. The results show that the spatial and temporal differences of ecosystem services in the SRB is significant, showing the characteristics of overall dispersion and small regional concentration. 76 ecological source patches with a total area of 2.29 × 104 km2; 54 ecological corridors with a total length of 3161.87 km; and 36 ecological nodes in the study area were identified. Overall, the basin ESP shows that the network is complete but there is still room for optimization, and local problems are prominent. The total area of key ecological corridors extracted is 4.21 × 104 km2, and there are 4 key recovery points. Finally, the optimization model of the ESP of the SRB of "two corridors, two belts, and three areas" was determined.
科研通智能强力驱动
Strongly Powered by AbleSci AI