Planning conservation corridors in mountain areas based on integrated conservation planning models: A case study for a giant panda in the Qionglai Mountains
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With the accelerated urbanization, human activities pose serious threats to species because of fragmentation. Planning conservation corridors between habitats could improve species mobility in order to address the human disturbance. However, there are a limited number of studies that focus on assessing the effect of human activities on species movement in mountain areas as well as how to integrate different conservation models relating to conservation corridors identification. In our study, we modified the resistance model and took land use data as input parameters to quantify the impacts of human activities in mountain areas. Meanwhile, different conservation planning models, including Circuit model and Least Cost Path (LCP) algorithm, were integrated. Moreover, our approach was applied to identify giant panda corridors in Miarro nature reserve, Caopo nature reserve, and Wolong nature reserve. The results suggested that the impacts of human activities were limited in valley regions due to topography by resistance model. Secondly, Circuit model demonstrated that the conservation corridors for giant panda could not be identified between the Miarro and the Caopo nature reserves. Additionally, more detailed corridors between habitats were planned by the LCP algorithm. Furthermore, we also identified bottlenecks for migration in each corridor, indicating that human activities’ interference was the primary cause. Our approach not only could connect habitats for conservation in mountain areas but also found out that the corridor could not be identified between habitats.
KeywordsCorridors Human activities Mountain areas Giant panda
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This study was funded by National Nature Science Foundation of China (Grant No. 41701114), the West Light Foundation of The Chinese Academy of Sciences (Grant No. Y7R280080), and the Research on Conservation and Breeding of Plant Diversity in Giant Panda National Park (Grant No. 2018SZDZX0035). We are grateful to the supports in field survey data from The Nature Conservancy (TNC).
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