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A new method for indicator species analysis in the framework of multivariate analysis of variance
Author(s) -
Ricotta Carlo,
Pavoine Sandrine,
Cerabolini Bruno E. L.,
Pillar Valério D.
Publication year - 2021
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.1111/jvs.13013
Subject(s) - multivariate analysis of variance , multivariate statistics , multivariate analysis , variance (accounting) , analysis of variance , indicator species , set (abstract data type) , indicator value , gradient analysis , statistics , compositional data , ecology , mathematics , biology , ordination , computer science , habitat , accounting , business , programming language
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. Results 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 groups of plots. Therefore, they can be appropriately called indicator species. This connects multivariate analysis of variance with indicator species analysis.