A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome


Understanding when and why new species are recruited into microbial communities is a formidable problem with implications for managing microbial systems, for instance by helping us better understand whether a probiotic or pathogen would be expected to colonize a human microbiome. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example, species that are closely related may competitively exclude each other. However, several recent human microbiome studies have observed closely related bacteria being recruited into microbial communities in short succession, suggesting that microbial community assembly is historically contingent, but competitive exclusion of close relatives may not be important. To address this, we developed a mathematical model that describes the order in which new species are detected in microbial communities over time within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species recruitment is more likely if a close relative was previously detected), overdispersion (recruitment is more likely if a close relative has not been previously detected), and the neutral model (recruitment likelihood is not related to phylogenetic relationships among species). We applied our model to longitudinal human microbiome data, and found that for the individuals we analyzed, the human microbiome generally follows the underdispersion (i.e., nepotism) hypothesis. Exceptions were oral communities and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

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Fig. 1: Phylodiversity accumulation and model fitting in the female feces dataset [25].
Fig. 2: Dispersion parameter (D) estimates for “moving pictures” [25] datasets.
Fig. 3: Empirical phylodiversity accumulation in the infant gut microbiome [26].
Fig. 4: Dispersion parameter (D) estimates in the infant gut, preformula, and during formula use.

Data availability

R code and data to replicate our analysis, or to perform a similar analysis on other data, are available on GitHub, at https://github.com/darcyj/pd_model.


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The authors thank D.R. Nemergut for her help and support, and also thank P. Sommers, E. Gendron, A. Solon, E. Pruesse, A. Armstrong, C. Martin, K. Hazleton, and S. Sauce for many helpful discussions. Funding was provided by an NSF grant for studying microbial community assembly following disturbance (DEB-1258160) and by a NIH NLM Computational Biology training grant (5 T15 LM009451-12). The funding bodies had no role in study design, analysis, interpretation, or in the preparation of this paper.

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Correspondence to John L. Darcy.

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Darcy, J.L., Washburne, A.D., Robeson, M.S. et al. A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome. ISME J 14, 1359–1368 (2020). https://doi.org/10.1038/s41396-020-0613-7

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