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Complexity–stability trade-off in empirical microbial ecosystems

Nature Ecology & Evolution volume 6, pages 693–700 (2022)Cite this article

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Abstract

May’s stability theory, which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge with inconsistent results. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks. Here we introduce a computational framework for estimating an ecosystem’s complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites and sponge-associated microbial communities from different geographical locations, we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity.

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Fig. 1: Observing complexity–stability patterns in natural microbial communities without network reconstruction.
Fig. 2: Dissimilarity–overlap analysis of cross-sectional data uncovers the predetermined connectance of the underlying GLV dynamics.
Fig. 3: DOCs across real-world microbial communities from diverse ecological environments.
Fig. 4: Complexity–stability patterns observed in real microbial ecosystems.

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Data availability

All the data analysed in this research are publicly and freely available online.

Human Microbiome Project22: https://hmpdacc.org/

Sponge Microbiome Project41: https://qiita.ucsd.edu/study/description/10793

Code availability

All MATLAB codes used in this research are available at: https://github.com/yogev-yonatan/Microbiome-complexity-stability.

References

  1. May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).

    Article  CAS  PubMed  Google Scholar 

  2. May, R. M. & Mac Arthur, R. H. Niche overlap as a function of environmental variability. Proc. Natl Acad. Sci. USA 69, 1109–1113 (1972).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. May, R. M. Stability and Complexity in Model Ecosystems (Princeton Univ. Press, 2019).

  4. Sinha, S. Complexity vs. stability in small-world networks. Phys. A 346, 147–153 (2005).

    Article  Google Scholar 

  5. Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).

    Article  PubMed  Google Scholar 

  6. Mougi, A. & Kondoh, M. Diversity of interaction types and ecological community stability. Science 337, 349–351 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Allesina, S. & Tang, S. Stability criteria for complex ecosystems. Nature 483, 205–208 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Allesina, S. & Tang, S. The stability–complexity relationship at age 40: a random matrix perspective. Popul. Ecol. 57, 63–75 (2015).

    Article  Google Scholar 

  9. Qian, J. J. & Akçay, E. The balance of interaction types determines the assembly and stability of ecological communities. Nat. Ecol. Evol. 4, 356–365 (2020).

    Article  PubMed  Google Scholar 

  10. Landi, P., Minoarivelo, H. O., Brännström, Å., Hui, C. & Dieckmann, U. in Systems Analysis Approach for Complex Global Challenges (eds Mensah, P. et al.) 209–248 (Springer, 2018).

  11. Townsend, S. E., Haydon, D. T. & Matthews, L. On the generality of stability–complexity relationships in Lotka–Volterra ecosystems. J. Theor. Biol. 267, 243–251 (2010).

    Article  PubMed  Google Scholar 

  12. Pimm, S. L., Lawton, J. H. & Cohen, J. E. Food web patterns and their consequences. Nature 350, 669–674 (1991).

    Article  Google Scholar 

  13. Yodzis, P. The stability of real ecosystems. Nature 289, 674–676 (1981).

    Article  Google Scholar 

  14. Winemiller, K. O. Must connectance decrease with species richness? Am. Naturalist 134, 960–968 (1989).

    Article  Google Scholar 

  15. Warren, P. H. Variation in food-web structure: the determinants of connectance. Am. Nat. 136, 689–700 (1990).

    Article  Google Scholar 

  16. de Ruiter, P. C., Neutel, A.-M. & Moore, J. C. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science 269, 1257–1260 (1995).

    Article  PubMed  Google Scholar 

  17. Schmid-Araya, J. M. et al. Connectance in stream food webs. J. Anim. Ecol. 71, 1056–1062 (2002).

    Article  Google Scholar 

  18. Neutel, A.-M. et al. Reconciling complexity with stability in naturally assembling food webs. Nature 449, 599–602 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. James, A. et al. Constructing random matrices to represent real ecosystems. Am. Nat. 185, 680–692 (2015).

    Article  PubMed  Google Scholar 

  20. Jacquet, C. et al. No complexity–stability relationship in empirical ecosystems. Nat. Commun. 7, 12573 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Thompson, L. R. et al. A communal catalogue reveals earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207 (2012).

    Article  CAS  Google Scholar 

  23. Fricker, A. M., Podlesny, D. & Fricke, W. F. What is new and relevant for sequencing-based microbiome research? A mini-review. J. Adv. Res. 19, 105–112 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sander, E. L., Wootton, J. T. & Allesina, S. Ecological network inference from long-term presence-absence data. Sci. Rep. 7, 7154 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Steinway, S. N., Biggs, M. B., Loughran Jr, T. P., Papin, J. A. & Albert, R. Inference of network dynamics and metabolic interactions in the gut microbiome. PLoS Comput. Biol. 11, e1004338 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bucci, V. et al. Mdsine: microbial dynamical systems inference engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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  PubMed  PubMed Central  Google Scholar 

  28. 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  PubMed  PubMed Central  Google Scholar 

  29. Gerber, G. K., Onderdonk, A. B. & Bry, L. Inferring dynamic signatures of microbes in complex host ecosystems. PLoS Comput. Biol. 8, e1002624 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cao, H.-T., Gibson, T. E., Bashan, A. & Liu, Y.-Y. Inferring human microbial dynamics from temporal metagenomics data: pitfalls and lessons. BioEssays 39, 1600188 (2017).

    Article  Google Scholar 

  31. David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Buffie, C. G. et al. Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to clostridium difficile-induced colitis. Infect. Immun. 80, 62–73 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dohlman, A. B. & Shen, X. Mapping the microbial interactome: statistical and experimental approaches for microbiome network inference. Exp. Biol. Med. 244, 445–458 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jiang, D. et al. Microbiome multi-omics network analysis: statistical considerations, limitations, and opportunities. Front. Genet. 10, 995 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Faust, K. Open challenges for microbial network construction and analysis. ISME J. 15, 3111–3118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bashan, A. et al. Universality of human microbial dynamics. Nature 534, 259–262 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Vila, J. C., Liu, Y.-Y. & Sanchez, A. Dissimilarity–overlap analysis of replicate enrichment communities. ISME J. 14, 2505–2513 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Moitinho-Silva, L. et al. The sponge microbiome project. Gigascience 6, gix077 (2017).

    Article  PubMed Central  Google Scholar 

  42. Swierts, T., Cleary, D. & de Voogd, N. Prokaryotic communities of Indo-Pacific giant barrel sponges are more strongly influenced by geography than host phylogeny. FEMS Microbiol. Ecol. 94, fiy194 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  43. Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).

    Article  Google Scholar 

  44. Suweis, S., Grilli, J., Banavar, J. R., Allesina, S. & Maritan, A. Effect of localization on the stability of mutualistic ecological networks. Nat. Commun. 6, 10179 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Grilli, J., Barabás, G., Michalska-Smith, M. J. & Allesina, S. Higher-order interactions stabilize dynamics in competitive network models. Nature 548, 210–213 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Butler, S. & O'Dwyer, J. P. Stability criteria for complex microbial communities. Nat. Commun. 9, 2970 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Allesina, S. & Grilli, J. in Theoretical Ecology: Concepts and Applications (eds McCann, K. & Gellner, G.) Ch. 6 (Oxford Univ. Press, 2020).

  48. Jayant, P. & Shnerb, N. M. How temporal environmental stochasticity affects species richness: destabilization neutralization and the storage effect. J. Theor. Biol. 539, 111053 (2022).

    Article  Google Scholar 

  49. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Gajer, P. et al. Temporal dynamics of the human vaginal microbiota. Sci. Transl. Med. 4, 132ra52–132ra52 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Van der Maaten, L. & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  52. Bunin, G. Ecological communities with Lotka-Volterra dynamics. Phys. Rev. E 95, 042414 (2017).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank D. Vaknin Ben Porath for reviewing the manuscript. A.B. thanks the German-Israeli Foundation for Scientific Research and Development (grant number I-1523-500.15/2021), the Israel Science Foundation (grant number 1258/21) and the Azrieli Foundation for supporting this research.

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Author notes

  1. These authors contributed equally: Yogev Yonatan and Guy Amit.

Authors and Affiliations

  1. Physics Department, Bar-Ilan University, Ramat-Gan, Israel

    Yogev Yonatan, Guy Amit & Amir Bashan

  2. Department of Plant Pathology and Microbiology, The Hebrew University of Jerusalem, Rehovot, Israel

    Jonathan Friedman

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  1. Yogev Yonatan
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  2. Guy Amit
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Contributions

A.B. conceived and supervised the project. Y.Y. and G.A. developed the methodology, performed numerical simulations and analysed the real data. Y.Y., G.A., J.F. and A.B. analysed the results. Y.Y., G.A. and A.B. wrote the manuscript. Y.Y., G.A., J.F. and A.B. edited the manuscript.

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Correspondence to Amir Bashan.

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Nature Ecology & Evolution thanks Akshit Goyal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary discussion, analytical derivations and Figs. 1–27.

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Yonatan, Y., Amit, G., Friedman, J. et al. Complexity–stability trade-off in empirical microbial ecosystems. Nat Ecol Evol 6, 693–700 (2022). https://doi.org/10.1038/s41559-022-01745-8

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