摘要
Mountains in western China, hosted rich biodiversity and millions of people and inhabitant with vital ecosystem services, had experienced the most serious biodiversity loss with fragile ecological problems. Even though increasing attentions had been paid to this issue, we still lacked efficient methods to assess the change of plant biodiversity at medium/large scale due to the poor data and co-existing multiple habitat types. This study proposed an integrated method combining InVEST-habitat quality model, NPP and landscape pattern indexes to analyze the spatial heterogeneity of plant biodiversity and its spatiotemporal change on raster cell scale. The results indicated that plant biodiversity service was high in Bailongjiang watershed with obvious spatial pattern variations. The land area containing higher plant biodiversity were 3161 km2, which mainly distributed in the National Nature Reserve and forestry area. While the areas with lower plant biodiversity accounted for 37.67% and mainly distributed in the valleys between Zhouqu-Wudu-Wenxian County, the valley of Minjiang in Tanchang County and alpine mountain snow-covered regions. During 1990–2010, plant biodiversity level tended to increase and the higher plant biodiversity area increased from 14.13% to 17.15% due to ecological restoration and afforestation, while plant biodiversity decreased in the area with intensive human activities, such as cultivated land, urban and rural land. The results showed that combining InVEST-habitat quality model, NPP and landscape pattern indexes can effective reveal mountain plant biodiversity change. The study was useful for plant biodiversity conservation policy-making and human activity management for the disaster-impacted mountainous areas in China.
Mountains in western China, hosted rich biodiversity and millions of people and inhabitant with vital ecosystem services, had experienced the most serious biodiversity loss with fragile ecological problems. Even though increasing attentions had been paid to this issue, we still lacked efficient methods to assess the change of plant biodiversity at medium/large scale due to the poor data and co-existing multiple habitat types. This study proposed an integrated method combining InVEST-habitat quality model, NPP and landscape pattern indexes to analyze the spatial heterogeneity of plant biodiversity and its spatiotemporal change on raster cell scale. The results indicated that plant biodiversity service was high in Bailongjiang watershed with obvious spatial pattern variations. The land area containing higher plant biodiversity were 3161 km2, which mainly distributed in the National Nature Reserve and forestry area. While the areas with lower plant biodiversity accounted for 37.67% and mainly distributed in the valleys between Zhouqu-Wudu-Wenxian County, the valley of Minjiang in Tanchang County and alpine mountain snow-covered regions. During 1990–2010, plant biodiversity level tended to increase and the higher plant biodiversity area increased from 14.13% to 17.15% due to ecological restoration and afforestation, while plant biodiversity decreased in the area with intensive human activities, such as cultivated land, urban and rural land. The results showed that combining InVEST-habitat quality model, NPP and landscape pattern indexes can effective reveal mountain plant biodiversity change. The study was useful for plant biodiversity conservation policy-making and human activity management for the disaster-impacted mountainous areas in China.
作者
GONG Jie
XIE Yuchu
CAO Erjia
Huang Qiuyan
LI Hongying
巩杰;谢余初;曹二佳;黄秋燕;李红瑛(Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning Normal University, Nanning 530001, China;Key laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, CAS, Changsha 410125, China)
基金
National Natural Science Foundation of China,No.41771196,No.41761039,No.41271199
