Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Community structure follows simple assembly rules in microbial microcosms


Microorganisms typically form diverse communities of interacting species, whose activities have tremendous impact on the plants, animals and humans they associate with. The ability to predict the structure of these complex communities is crucial to understanding and managing them. Here, we propose a simple, qualitative assembly rule that predicts community structure from the outcomes of competitions between small sets of species, and experimentally assess its predictive power using synthetic microbial communities composed of up to eight soil bacterial species. Nearly all competitions resulted in a unique, stable community, whose composition was independent of the initial species fractions. Survival in three-species competitions was predicted by the pairwise outcomes with an accuracy of ~90%. Obtaining a similar level of accuracy in competitions between sets of seven or all eight species required incorporating additional information regarding the outcomes of the three-species competitions. Our results demonstrate experimentally the ability of a simple bottom-up approach to predict community structure. Such an approach is key for anticipating the response of communities to changing environments, designing interventions to steer existing communities to more desirable states and, ultimately, rationally designing communities de novo.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: A bottom-up approach to predicting community composition from qualitative competitive outcomes.
Figure 2: Pairwise competitions resulted in stable coexistence or competitive exclusion.
Figure 3: Observed and predicted outcomes of trio competitions.
Figure 4: Survival in trio competitions is well predicted by pairwise outcomes.
Figure 5: Predicting survival in more diverse competition required incorporating the outcomes of the trio competitions.


  1. Falkowski, P., Fenchel, T. & Delong, E. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).

    Article  CAS  Google Scholar 

  2. Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).

    Article  CAS  Google Scholar 

  3. Flint, H. J., Scott, K. P., Louis, P. & Duncan, S. H. The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9, 577–589 (2012).

    Article  CAS  Google Scholar 

  4. Matsuoka, K. & Kanai, T. The gut microbiota and inflammatory bowel disease. Semin. Immunopathol. 37, 47–55 (2015).

    Article  CAS  Google Scholar 

  5. Widder, S. et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 10, 2557–2568 (2016).

    Article  Google Scholar 

  6. Großkopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).

    Article  Google Scholar 

  7. Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).

    Article  CAS  Google Scholar 

  8. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    Article  CAS  Google Scholar 

  9. Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).

    Article  CAS  Google Scholar 

  10. Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).

    Article  Google Scholar 

  11. Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity. Methods Ecol. Evol. 6, 895–906 (2015).

    Article  Google Scholar 

  12. Billick, I. & Case, T. J. Higher order interactions in ecological communities: What are they and how can they be detected? Ecology 75, 1530–1543 (1994).

    Article  Google Scholar 

  13. Momeni, B. & Shou, W. The validity of pairwise models in predicting community dynamics. Preprint at bioRxiv (2016).

  14. Wootton, J. T. The nature and consequences of indirect effects in ecological communities. Annu. Rev. Ecol. Syst. 25, 443–466 (1994).

    Article  Google Scholar 

  15. Kelsic, E. D., Zhao, J., Vetsigian, K. & Kishony, R. Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature 521, 516–519 (2015).

    Article  CAS  Google Scholar 

  16. Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).

    Article  Google Scholar 

  17. Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).

    Article  Google Scholar 

  18. Vandermeer, J. H. The competitive structure of communities: an experimental approach with protozoa. Ecology 50, 362–371 (1969).

    Article  Google Scholar 

  19. Dormann, C. F. & Roxburgh, S. H. Experimental evidence rejects pairwise modelling approach to coexistence in plant communities. Proc. R. Soc. B 272, 1279–1285 (2005).

    Article  Google Scholar 

  20. Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).

    Article  CAS  Google Scholar 

  21. Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).

    Article  Google Scholar 

  22. Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).

    Article  CAS  Google Scholar 

  23. Case, T. J. & Bender, E. A. Testing for higher order interactions. Am. Nat. 118, 920–929 (1981).

    Article  Google Scholar 

  24. Zeeman, M. L. Hopf bifurcations in competitive three-dimensional Lotka–Volterra systems. Dyn. Stab. Syst. 8, 189–216 (1993).

    Google Scholar 

  25. Celiker, H. & Gore, J. Clustering in community structure across replicate ecosystems following a long-term bacterial evolution experiment. Nat. Commun. 5, 4643 (2014).

    Article  CAS  Google Scholar 

  26. Concepción-Acevedo, J., Weiss, H. N., Chaudhry, W. N. & Levin, B. R. Malthusian parameters as estimators of the fitness of microbes: a cautionary tale about the low side of high throughput. PLoS ONE 10, e0126915 (2015).

    Article  Google Scholar 

  27. Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).

    Article  Google Scholar 

  28. Wright, E. S. & Vetsigian, K. H. Inhibitory interactions promote frequent bistability among competing bacteria. Nat. Commun. 7, 11274 (2016).

    Article  CAS  Google Scholar 

  29. Armstrong, R. A. & McGehee, R. Competitive exclusion. Am. Nat. 115, 151–170 (1980).

    Article  Google Scholar 

  30. Huisman, J. & Weissing, F. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).

    Article  Google Scholar 

  31. Yurtsev, E. A., Conwill, A. & Gore, J. Oscillatory dynamics in a bacterial cross-protection mutualism. Proc. Natl Acad. Sci. USA 113, 6236–6241 (2016).

    Article  CAS  Google Scholar 

  32. Kerr, B., Riley, M. A., Feldman, M. W. & Bohannan, B. J. Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors. Nature 418, 171–174 (2002).

    Article  CAS  Google Scholar 

  33. Allesina, S. & Levine, J. A competitive network theory of species diversity. Proc. Natl Acad. Sci. USA 108, 5638–5642 (2011).

    Article  CAS  Google Scholar 

Download references


We thank A. Perez-Escudero, N. Vega, E. Yurtsev and members of the Gore Laboratory for critical discussions and comments on the manuscript. This work was supported by the Defense Advanced Research Projects Agency’s Biological Robustness in Complex Settings programme, an National Institutes of Health New Innovator Award (NIH DP2), an National Science Foundation CAREER Award, a Sloan Research Fellowship, the Pew Scholars Program and the Allen Investigator Program.

Author information

Authors and Affiliations



J.F. and J.G. designed the study. J.F. and L.M.H. performed the experiments and analysis. J.F., L.M.H. and J.G. wrote the manuscript.

Corresponding authors

Correspondence to Jonathan Friedman or Jeff Gore.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–8 and Supplementary Tables 1–3. (PDF 2112 kb)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Friedman, J., Higgins, L. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol 1, 0109 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing