Assessing functional diversity: the influence of the number of the functional traits

Type Article
Date 2020-03
Language English
Author(s) Legras Gaëlle1, Loiseau Nicolas2, 3, Gaertner Jean-Claude4, Poggiale J-C.5, Gaertner-Mazouni N.1
Affiliation(s) 1 : Univ Polynesie Francaise, IFREMER,ILM,IRD,UMR 241 EIO, Tahiti, French Polynesie, France
2 : Univ Montpellier, MARBEC, CNRS, IFREMER, IRD, Montpellier, France
3 : Univ Grenoble Alpes, Univ,CNRS,Savoie Mont Blanc,LECA, Laboratoire d'Ecol Alpine, F-38000 Grenoble, France
4 : Institut de Rech pour le Developpement, IRD, UMR 241, EIO UPF,IFREMER,ILM Labex Corail, BP 6570, Tahiti, French Polynesie, France
5 : Univ Toulon & Var, Aix Marseille Univ,Mediterranean Inst Oceanog, CNRS,INSU,IRD, F-13288 Marseille, France
Source Theoretical Ecology (1874-1738) (Springer Science and Business Media LLC), 2020-03 , Vol. 13 , N. 1 , P. 117-126
DOI 10.1007/s12080-019-00433-x
WOS© Times Cited 30
Keyword(s) Functional traits, Dissimilarity metric, Functional diversity, Index sensitivity, Trend analysis
Abstract

The impact of the variation of the number of functional traits on functional diversity assessment is still poorly known. Although the covariation between these two parameters may be desirable in some situations (e.g. if adding functional traits provides relevant new functional information), it may also result from mathematical artefacts and lead to misinterpretation of the results obtained. Here, we have tested the behaviour of a set of nine indices widely used for assessing the three main components of functional diversity (i.e. functional richness, evenness and divergence), according to the variation in the number of functional traits. We found that the number of functional traits may strongly impact the values of most of the indices considered, whatever the functional information they contain. The FRic, TOP and n-hypervolume indices that have been developed to characterize the functional richness component appeared to be highly sensitive to the variation in the number of traits considered. Regarding functional divergence, most of the indices considered (i.e. Q, FDis and FSpe) also showed a high degree of sensitivity to the number of traits considered. In contrast, we found that indices used to compute functional evenness (FEve and Ru), as well as one of the indices related to functional divergence (FDiv), are weakly influenced by the variation in the number of traits. All these results suggest that interpretation of most of the functional diversity indices considered cannot only be based on their values as they are, but requires taking into account the way in which they have been computed.

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