Skip to Main content Skip to Navigation
Journal articles

A new method for indicator species analysis in the framework of multivariate analysis of variance

Abstract : Question In vegetation science, the compositional dissimilarity among two or more groups of plots is usually tested with dissimilarity‐based multivariate analysis of variance (db‐MANOVA), whereas the compositional characterization of the different groups is performed by means of indicator species analysis. Although db‐MANOVA and indicator species analysis are apparently very far from each other, the question we address here is: can we put both approaches under the same methodological umbrella? Methods We will show that for a specific class of dissimilarity measures, the partitioning of variation used in one‐factor db‐MANOVA can be additively decomposed into species‐level values allowing us to identify the species that contribute most to the compositional differences among the groups. The proposed method, for which we provide a simple R function, is illustrated with one small data set on Alpine vegetation sampled along a successional gradient. Conclusion The species that contribute most to the compositional differences among the groups are preferentially concentrated in particular group of plots. Therefore, they can be appropriately called indicator species. This connects multivariate analysis of variance with indicator species analysis.
Document type :
Journal articles
Complete list of metadata

https://hal.sorbonne-universite.fr/hal-03172907
Contributor : Gestionnaire Hal-Su <>
Submitted on : Thursday, March 18, 2021 - 9:42:33 AM
Last modification on : Monday, May 31, 2021 - 1:52:02 PM

File

jvs.13013.pdf
Publication funded by an institution

Identifiers

Citation

Carlo Ricotta, Sandrine Pavoine, Bruno Cerabolini, Valério Pillar. A new method for indicator species analysis in the framework of multivariate analysis of variance. Journal of Vegetation Science, Wiley, 2021, ⟨10.1111/jvs.13013⟩. ⟨hal-03172907⟩

Share

Metrics

Record views

44

Files downloads

10