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Statistically learning the functional landscape of microbial communities

Nature Ecology & Evolution volume 7, pages 1823–1833 (2023) Cite this article

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Abstract

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes—analogues to fitness landscapes—that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

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Fig. 1: Statistically learning community-function landscapes.
The alternative text for this image may have been generated using AI.
Fig. 2: Community function is predictable from species presence/absence in empirical datasets.
The alternative text for this image may have been generated using AI.
Fig. 3: Empirical community-function landscapes are not rugged.
The alternative text for this image may have been generated using AI.
Fig. 4: Ecological models indicate both optimism and caution for inferring community-function landscapes.
The alternative text for this image may have been generated using AI.

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Data availability

Data analysed here are either available from the original studies or in the following repository: https://github.com/abbyskwara2/regression_on_landscapes.

Code availability

Code to run all analyses presented in this paper is available in the following repository: https://github.com/abbyskwara2/regression_on_landscapes.

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Acknowledgements

We thank S. Allesina and the members of the Center for the Physics of Evolving Systems at the University of Chicago for useful discussions. We thank J. Softcheck for assistance with the experiments. S.K., K.G., M.Y., and M.T. acknowledge funding from the National Science Foundation (EF-2025293, MCB-2117477, PHY-2310746). S.K. acknowledges funding from the National Institutes of Health (NIH R01GM151538). A. Sanchez acknowledges support from the Spanish Ministry of Science and Innovation under project PID2021-125478NA-100.

Author information

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Abigail Skwara

  2. Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL, USA

    Karna Gowda, Mahmoud Yousef & Seppe Kuehn

  3. Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA

    Karna Gowda, Mahmoud Yousef & Seppe Kuehn

  4. Department of Microbial Biotechnology, National Center for Biotechnology (CNB-CSIC), Madrid, Spain

    Juan Diaz-Colunga & Alvaro Sanchez

  5. Department of Pathology, University of Chicago, Chicago, IL, USA

    Arjun S. Raman

  6. Duchossois Family Institute, University of Chicago, Chicago, IL, USA

    Arjun S. Raman

  7. Department of Physics, Washington University in St. Louis, St. Louis, MO, USA

    Mikhail Tikhonov

Authors
  1. Abigail Skwara
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Contributions

S.K., M.T., K.G., M.Y., A. Skwara, A. Sanchez and A.S.R. conceptualized the study. A. Skwara, K.G., M.Y., M.T. and S.K. developed the methodology. A. Skwara, K.G., M.Y., M.T. and S.K. designed and conducted formal analysis. J.D.-C. and A. Sanchez designed experiments and collected experimental data, and J.D.-C. assisted with data analysis and visualization. K.G., S.K., M.T. and A. Skwara wrote the paper. S.K. and M.T. supervised the project and acquired funding.

Corresponding authors

Correspondence to Mikhail Tikhonov or Seppe Kuehn.

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Nature Ecology & Evolution thanks Elle Barnes, Daniel Amor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Residuals obtained from second-order regressions.

Data from (A) Clark et al.22, (B) Langenheder et al.27, (C) Sanchez-Gorostiaga et al.21, (D) Diaz-Colunga et al.28, (E) data from the Sanchez lab, and (F) Kehe et al.29.

Extended Data Fig. 2 Residuals obtained from second-order regressions as a function of community richness.

Data from (A) Clark et al.22, (B) Langenheder et al.27, (C) Sanchez-Gorostiaga et al.21, (D) Diaz-Colunga et al.28, (E) data from the Sanchez lab, and (F) Kehe et al.29.

Extended Data Fig. 3 Model predictions from second-order regression fits with all data in-sample and experimental replicates included.

Data from (A) Clark et al.22, (B) Diaz-Colunga et al.28, (C) data from the Sanchez lab, and (D) Kehe et al.29. Black points correspond to the mean observed value for each distinct experimental community, and are identical to the points shown in Extended Data Fig. S1. Gray points correspond to values for distinct experimental replicates. The datasets here include only datasets for which experimental replicates were measured.

Extended Data Fig. 4 Model predictions from second-order regression fits with all data in-sample.

Data from (A) Clark et al.22, (B) Langenheder et al.27, (C) Sanchez-Gorostiaga et al.21, (D) Diaz-Colunga et al.28, (E) data from the Sanchez lab, and (F) Kehe et al.29.

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Skwara, A., Gowda, K., Yousef, M. et al. Statistically learning the functional landscape of microbial communities. Nat Ecol Evol 7, 1823–1833 (2023). https://doi.org/10.1038/s41559-023-02197-4

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