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A chemo-ecologists’ practical guide to compositional data analysis

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An Erratum to this article was published on 08 December 2016

Abstract

Compositional data are commonly used in chemical ecology to describe the biological role of chemical compounds in communication, defense or other behavioral modifications. Statistical analyses of compositional data, however, are challenging due to several constraints (e.g., constant sum constraint). We use an ontogenetic series of defensive gland secretions from larvae, three nymphal stages and adults of the oribatid model species Archegozetes longisetosus as a typical chemo-ecological data set to prepare a practical guide for compositional data analyses in chemical ecology. We compare various common and less common statistical and ordination methods to depict small quantitative and/or qualitative differences in compositional datasets: principal component analysis (PCA), non-metric multidimensional scaling (NMDS), multivariate statistical tests (Anderson’s permutational multivariate analyses of variance = PERMANOVA; permutational analyses of multivariate dispersions = PERMDIPS), linear discriminant analysis (LDA), the data mining algorithm Random Forests, bipartite network analysis and dynamic range boxes (dynRB). We summarize which methods are suitable for different research questions and how data needs to be structured and pre-processed. Network analyses and dynamic range boxes are promising tools for analyzing compositional data beyond the “classical” methods and provide additional information.

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Acknowledgements

Adrian Brückner is supported by a PhD scholarship from the German National Academic Foundation (Studienstiftung des deutschen Volkes). We thank Klaus Birkhofer (Lund University), Robert R. Junker (University of Salzburg) and Nico Blüthgen (TU Darmstadt) for discussing PERMANOVA/PERMDISP, dynamic range boxes and the network approach with us. We further thank Lukas Kauling for experimental assistance and carrying out a preliminary experiment. This study was partly funded by the German Research Foundation (DFG, HE 4593/5-1).

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  1. Ecological Networks, Technische Universität Darmstadt, Schnittspahnstraße 3, 64287, Darmstadt, Germany

    Adrian Brückner & Michael Heethoff

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  1. Adrian Brückner
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Correspondence to Michael Heethoff.

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Handling Editor: Marko Rohlfs.

An erratum to this article is available at http://dx.doi.org/10.1007/s00049-016-0228-7.

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Brückner, A., Heethoff, M. A chemo-ecologists’ practical guide to compositional data analysis. Chemoecology 27, 33–46 (2017). https://doi.org/10.1007/s00049-016-0227-8

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