Study on the Plant Community for Waterfront Green Space of Sand River in Chengdu City
Using quadrat method,the plant community species composition,Shannon-Wiener plant diversity index,community similarity coefficient (Jaccard's coefficient) and frequentness of 32 quadrats in 8 attractions of waterfront green space along the coastal of Sand fiver in Chengdu city was analyzed.Results showed that there were 109 plant species in the 32 quadrats,belonging to 88 genera of 54 families; among which there were 49 arbor species,36 bush species and 24 ground cover species.The distribution frequentness of 109 plants species was mainly 10％～30％(48 species,accounting for 44.04％ of the total); and the frequentness difference of arbor,bush,and ground cover plants was not obvious.However,number of native plant species was decreasing; ground covers plant species was fewer than that of arbor and shrubs,aquatic plants were even less,resulted in tedious water plant landscape.The highest Shannon-Wiener diversity index of arbor,bush,and ground cover plants was in Beihuningcui attraction on the upper reach.Shannon-Wiener diversity index of arbor and ground covers plants was the lowest in the Kejixiuyuan attraction in city center; of bush plants was the lowest in Shahekeka attraction.Beihuningcui and Xinlüshuinian were both on the upstream of Sand river; and the Jaccard's coefficient of the two attractions was the highest.While Beihuningcui and Mashiyanyun were in the middle section of Sand fiver had the lowest Jaccard's coefficient.Application frequentness of native plants in waterfront green space was high,but did not affect the landscape and ecological function of waterfront green space.It was suggested that the species and number of water plants and native plants as well as the landscape richness should be increased,species and number of native plants should be reserved and increased so as to keep the balance of diversity of native species and the stability of ecological community.
waterfront green spaceplant communityspecies diversityChengdu city
NETL
NSTL
万方数据
维普
