生态网络
栖息地
环境资源管理
景观连通性
生物多样性
计算机科学
碎片(计算)
景观生态学
航程(航空)
生态学
地理
环境科学
生态系统
生物扩散
人口
工程类
生物
人口学
社会学
航空航天工程
操作系统
作者
Zhou Shen,Haiwei Yin,Fanhua Kong,Wei Wu,Hui Sun,Jie Su,Shiqi Tian
标识
DOI:10.1016/j.landurbplan.2024.105079
摘要
While insightful, ecological networks (ENs) incorporated in landscape planning and management may not provide a sufficient reference for maintaining biodiversity without explicit species information and coordinated actions, particularly in urban agglomeration areas. To address this gap, we conducted a study in Southern Jiangsu to refine the habitat ranges of 25 target species within the current ecological spaces by integrating explicit species information and diversified environmental variables using the MaxEnt model. Subsequently, an eco-space network and a habitat network were established for the region with identified connectivity hotspots as strategic areas for optimizing ENs. To assess the potential impacts of divergent actions during implementation, we developed four decision-making criteria from global and local views to simulate multiple optimization scenarios. These scenarios were quantitatively evaluated based on connectivity performance using cohesive metrics derived from complex network theory. Our findings demonstrated that incorporating species distribution and their interactions with the environment into ENs establishment can strengthen biodiversity conservation planning schemes within a limited range of regional ecological spaces. Furthermore, it highlighted shortcomings such as lack of focus on landscape priorities, discrepancies in corridors, and underestimation of landscape fragmentation caused by solely relying on an eco-space network-based approach. During optimization efforts, our results revealed that adopting a criterion focused on maximizing the number of strategic areas while adhering to a total area threshold can lead to a more connected and effective habitat network strategy when coordinating individual cities from a global view. This study provides valuable insights into prioritized landscapes and offers practical contributions toward mitigating biodiversity loss and improving urban sustainability.
科研通智能强力驱动
Strongly Powered by AbleSci AI