A distance-based framework for measuring functional diversity from multiple traits

被引:3196
作者
Laliberte, Etienne [1 ]
Legendre, Pierre [2 ]
机构
[1] Univ Canterbury, Sch Forestry, Christchurch 8140, New Zealand
[2] Univ Montreal, Dept Sci Biol, Montreal, PQ H3C 3J7, Canada
关键词
functional composition; functional dispersion; functional divergence; functional diversity; functional evenness; functional identity; functional richness; functional trait; multivariate dispersion; GENERAL COEFFICIENT; SIMILARITY; BIODIVERSITY;
D O I
10.1890/08-2244.1
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
A new framework for measuring functional diversity (FD) from multiple traits has recently been proposed. This framework was mostly limited to quantitative traits without missing value and to situations in which there ire more Species than traits, although the authors had suggested a way to extend their framework to other trait types. I-lie main purpose of this note is 10 further develop this suggestion. We describe I highly flexible distance-based framework to measure different Facets of FD in multidimensional trait space from any distance Or dissimilarity measure, any number of traits, and from different trait types (i.e., quantitative, semi-quantitative, and qualitative). This new approach allows for missing trait values and the weighting of individual traits. We also present a new multidimensional FD index, called functional dispersion (FDis), which is closely related to Rao's quadratic entropy. FDis is the multivariate analogue of the weighted mean absolute deviation (MAD), in which the weights are species relative abundances. For unweighted presence-absence data, FDis can be used for a formal statistical test of differences in FD. We provide the "FD" R language package to easily implement our distance-based FD framework.
引用
收藏
页码:299 / 305
页数:7
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