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Microbiome. 2018 Jun 28;6(1):120. doi: 10.1186/s40168-018-0496-2.

Signatures of ecological processes in microbial community time series.

Author information

KU Leuven Department of Microbiology and Immunology, Rega Institute, Laboratory of Molecular Bacteriology, Leuven, Belgium.
Division of Microbial Ecology, Department of Microbiology and Ecosystem Sciences, University of Vienna, Althanstr. 14, 1090, Vienna, Austria.
Département de Mathématiques Informatiques Appliquées, INRA, Jouy-en-Josas, France.
Applied Physics, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
Interuniversity Institute of Bioinformatics in Brussels, ULB/VUB, Triomflaan, 1050, Brussels, Belgium.
KU Leuven Department of Microbiology and Immunology, Rega Institute, Laboratory of Molecular Bacteriology, Leuven, Belgium.
VIB Center for the Biology of Disease, Herestraat 49, 3000, Leuven, Belgium.
Department of Mathematics and Statistics, University of Turku, 20014, Turku, Finland.
Department of Biology, Duke University, Durham, NC, USA.
Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA.
Unité de Chronobiologie Théorique, Faculté des Sciences, Université Libre de Bruxelles, Bvd du Triomphe, 1050, Brussels, Belgium.
CeMM-Reseach Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, 1090, Vienna, Austria.
Department of Medicine 1, Research Laboratory of Infection Biology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
Konrad Lorenz Institute for Evolution and Cognition Research, Martinstr. 12, 4300, Klosterneuburg, Austria.



Growth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.


We implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.


We present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.


Brown noise; Community dynamics; Community models; Neutrality test; Noise types; Pink noise; Time series analysis

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