Details

Autor(en) / Beteiligte

• Ricotta, Carlo
• Pavoine, Sandrine
• Cerabolini, Bruno E. L.
• Pillar, Valério D.
• Botta‐Dukát, Zoltán

Titel

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

Ist Teil von

• Journal of vegetation science, 2021-03, Vol.32 (2), p.n/a

Ort / Verlag

Hoboken: Wiley Subscription Services, Inc

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Wiley Blackwell Single Titles

Beschreibungen/Notizen

• 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. In vegetation science, the compositional dissimilarity among groups of plots is usually tested with dissimilarity‐based multivariate analysis of variance (db‐MANOVA). Here, we show that the partitioning of variation used in one‐factor db‐MANOVA can be decomposed into species‐level values. 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.