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作 者:梁红 达良俊[2] 宋坤[2] 周春玲 高志文 王凯 LIANG Hong;DA Liangjun;SONG Kun;ZHOU Chunling;GAO Zhiwen;WANG Kai(School of Landscape Architecture and Forestry,Qingdao Agricultural University,Qingdao Shandong 266109,China;School of Ecological and Environmental Sciences,East China Normal University,Shanghai 200241,China;State Key Laboratory of Urban and Regional Ecology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085,China)
机构地区:[1]青岛农业大学园林与林学院,山东青岛266109 [2]华东师范大学生态与环境科学学院,上海200241 [3]中国科学院生态环境研究中心城市与区域生态国家重点实验室,北京100085
出 处:《华东师范大学学报(自然科学版)》2022年第3期17-26,共10页Journal of East China Normal University(Natural Science)
基 金:国家自然科学基金(31770468);国家自然科学基金青年基金(31800448);国家科技基础性工作专项(2015FY210200-4);上海市城市化生态过程与生态恢复重点实验室开放基金(SHUES2018A03);青岛农业大学高层次人才科研基金(6631118009,6631119001)。
摘 要:为探究生境片断化对城市残存植被物种丰富度的影响,以经典的岛屿生物地理学理论为基础,选取了青岛市区内崂山余脉的13个丘陵为岛屿,以崂山为大陆,除斑块面积、距离种源远近外,增加斑块隔离度和人为干扰强度2个影响因子,通过CCA分析量化影响因子与物种组成的关系,以其影响因素的矢量线段长短赋予各影响因子权重,形成岛屿化综合指数(Isolated Island Index,III),探讨其与物种丰富度的关系.结果表明,城市中片断化森林斑块的物种丰富度与岛屿化综合指数的关系随着种类性质的变化而不同.乡土物种丰富度与岛屿化综合指数有显著关系,外来物种无此关系;鸟类传播和风力传播物种的丰富度与岛屿化综合指数有显著关系,重力传播物种无此关系.线性函数是最佳的拟合模型.Based on the classical theory of island biogeography,thirteen remnant forest patches of fragmented urban habitats were chosen as islands,and the Lao Mountain as the mainland.Then,canonical correspondence analysis(CCA)was used to analyze the relationship between species composition and four factors—namely,patch area,distance to species pool,human disturbance intensity,and isolation degree.The lengths of vectors in CCA biplots were used as weights for each influencing factor to calculate the overall sum,named the isolated island index(III);an isolated island index evaluation system of forest patches in fragmented urban habitats was subsequently designed.The results indicated that the relationship between III and richness was significantly negative for native species,but not significant for alien species richness.There was a significant relationship found for bird dispersal and wind dispersal species.The linear function was determined to be the best simulating model.
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