论文部分内容阅读
植物功能多样性(Functional diversity)是将植物功能性状与群落结构相结合而推导的整合参数,为人们揭示植物适应策略、植物性状间关系以及植物性状与功能间关系等提供了一种可选择的新技术手段.然而,至今人们还不清楚功能多样性参数与所选取性状数量的关系,即它是否存在性状数量的依赖性?这些信息对正确使用功能多样性参数至关重要.本文利用中国东部森林样带9个典型森林生态系统的规范化测定的366个乔木物种和34种功能性状,对7个广泛使用的功能多样性参数的数量依赖性进行了检验,它们分别是功能丰富度参数(functional attribute diversity,FAD;modified FAD,MFAD;convex hull hyper-volume,FRic)、功能均匀度参数(functional evenness,FEve)、功能离散度参数(functional divergence,FDiv;functional dispersion,FDis;quadratic entropy,RaoQ).分析结果表明:功能丰富度参数均随着所选择性状数量增加而增加,但功能离散度的各个参数对性状数量变化表现出不一致趋势.整体而言,虽然FAD、MFAD、FRic、RaoQ随着性状数量变化而变化,但它们是可预测的,可比较的.实验结果证明所选择的性状数量强烈地影响功能多样性参数,大部分参数随着性状数量变化是可预测的,证实了功能性状参数是具有重要潜力的技术手段(特定地点研究和不同研究间的比较),因此在使用过程中需要高度重视性状数量选择的影响.“,”Analysis of functional diversity, based on plant traits and community structure, provides a promising ap- proach for exploration of the adaptive strategies of plants and the relationship between plant traits and ecosystem functioning. However, it is unclear how the number of plant traits included influences functional diversity, and whether or not there are quantitatively dependent traits. This information is fundamental to the correct use of func- tional diversity metrics. Here, we measured 34 traits of 366 plant species in nine forests from the tropical to boreal zones in China. These traits were used to calculate seven functional diversity metrics: functional richness (func- tional attribute diversity (FAD), modified FAD (MFAD), convex hull hypervolume (FRic)), functional evenness (FEve), and functional divergence (functional divergence (FDiv), functional dispersion (FDis), quadratic entropy (RaoQ)). Functional richness metrics increased with an increase in trait number, whereas the relationships between the trait divergence indexes (FDiv and FDis) and trait number were inconsistent. Four of the seven functional diversity in- dexes (FAD, MFAD, FRic, and RaoQ) were comparable with those in previous studies, showing predictable trends with a change in trait number. We verified our hypothesis that the number of traits strongly influences functional diversity. The relationships between these predictable functional diversity metrics and the number of traits facilitated the development of a standard protocol to enhance comparability across different studies. These findings can support integration of functional diversity index data from different studies at the site to the regional scale, and they focus attention on the influence of quantitative selection of traits on functional diversity analysis.