Applying ant colony algorithm to identify ecological security patterns in megacities. (July 2019)

• Record Type: Journal Article
• Authors: Peng, Jian; Zhao, Shiquan; Dong, Jianquan; Liu, Yanxu; Meersmans, Jeroen; Li, Huilei; Wu, Jiansheng
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 214
• Page End: 222
• Publication Date: 2019-07
• Subjects: Ant colony algorithm -- Kernel density estimation -- Range of ecological corridors -- Ecological restoration points -- Ecological security patterns -- Urban planning; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.03.017
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Ecological security patterns composed of ecological sources and corridors provide an effective approach to conserving natural ecosystems. Although the direction of ecological corridors has been identified in previous studies, the precise range remains unknown. To address this crucial gap, ant colony algorithm and kernel density estimation were applied to identify the range and restoration points of ecological corridors, which is important for natural conservation and ecological restoration. In this case study of Beijing City, ecological sources were identified based on habitat importance and landscape connectivity. The results showed that, in total 3119.65 km 2 of ecological land had been extracted as ecological sources, which were mainly located in the northern, northwestern and northeastern mountainous areas. The identified key ecological corridor covered an area of 198.86 km 2, with 567.30 km 2 for potential ecological corridors, both connecting the ecological sources. 34 key points were also identified with priority in restoring ecological corridors. Highlights: Habitat importance and landscape connectivity are used to extract ecological sources. Ant colony algorithm was applied to identify spatial range of ecological corridors. Key ecological restoration points were identified using kernel density estimation.