Evaluation of Biodiversity at the County Level Using DPSIR Model:A Case Study on Pilot Biodiversity Survey Counties in Jiangsu Province
Currently,there is still a relative lack of research on the multidimensional assessment of biodiversity,the influence of multiple drivers,and long-term dynamic simulation and prediction.Given its advantages in systematic and multidimensional analysis,DPSIR(Drive,Pressure,Status,Impact and Response)framework has become an important method for comprehensive international biodiversity assessment.However,the construction and weighting of indicators at the county level still face challenges due to the lack of data.In this paper we examines data from the biodiversity baseline surveys conducted in 21 biodiversity pilot survey counties in Jiangsu province from 2017 to 2019,focusing on biological,socio-economic development,and environmental data,to explore the construction and weighting of DPSIR indicators suitable for county-level biodiversity assessment.The study recorded 4,324 species(excluding phytoplankton and zooplankton),with terrestrial vascular plants being the most abundant and reptiles the least.The DPSIR comprehensive index values for 6 counties in Yili mountain area,coastal wetlands and Ninzheng mountain area were higher than those of other counties.The weights of the five factors in the model are as follows:Impact,State,Response,Driver and Pressure.The five high-weight indicators identified by the DPSIR model are the percentage of natural reserve area,the ratio of forest,urbanization rate and the number of invasive alien species.Our results suggest that the key future efforts for biodiversity in Jiangsu province should focus on protecting forest and wetland habitats,controlling the invasion of non-native species,and mitigating the impacts of urbanization.Our research provides methodological references for scientific assessment,conservation,and management of county-level biodiversity in China.
Convention on Biological Diversity(CBD)DPSIR modelComprehensive biodiversity evaluation indexCounty scale
NETL
NSTL
万方数据
