Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Metabolic complexity drives divergence in microbial communities

Nature Ecology & Evolution volume 8, pages 1493–1504 (2024)Cite this article

Subjects

Abstract

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Microbial communities may diverge in environments with increasing metabolic complexity.
Fig. 2: Divergence of microbial communities increases in environments of increasing metabolic complexity.
Fig. 3: Divergence dynamically correlates with diversity.
Fig. 4: Endemic taxa are enriched and unevenly distributed in complex conditions.
Fig. 5: Trophic resource transformations reproduce divergence-complexity effect with CRM simulations.

Similar content being viewed by others

Data availability

All raw 16S sequencing data and associated metadata generated for this study can be accessed with the NCBI BioProject accession PRJNA1074799 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1074799). All processed data used in our analyses can be found on GitHub at https://github.com/segrelab/MetabolicComplexityDivergence. Source data are provided with this paper.

Code availability

All analysis and simulation code can be found on GitHub at https://github.com/segrelab/MetabolicComplexityDivergence.

References

  1. Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Bowman, K. A., Broussard, E. K. & Surawicz, C. M. Fecal microbiota transplantation: current clinical efficacy and future prospects. Clin. Exp. Gastroenterol. 8, 285–291 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. Averill, C. et al. Defending Earth’s terrestrial microbiome. Nat. Microbiol. 7, 1717–1725 (2022).

    CAS  PubMed  Google Scholar 

  4. Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).

    CAS  PubMed  Google Scholar 

  5. Silverstein, M. R., Segrè, D. & Bhatnagar, J. M. Environmental microbiome engineering for the mitigation of climate change. Glob. Change Biol. https://doi.org/10.1111/gcb.16609 (2023).

  6. Martiny, J. B. H. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).

    CAS  PubMed  Google Scholar 

  7. Martiny, J. B. H., Eisen, J. A., Penn, K., Allison, S. D. & Horner-Devine, M. C. Drivers of bacterial β-diversity depend on spatial scale. Proc. Natl Acad. Sci. USA 108, 7850–7854 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535.e6 (2019).

    CAS  PubMed  Google Scholar 

  11. Bittleston, L. S., Gralka, M., Leventhal, G. E., Mizrahi, I. & Cordero, O. X. Context-dependent dynamics lead to the assembly of functionally distinct microbial communities. Nat. Commun. 11, 1440 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Čaušević, S., Tackmann, J., Sentchilo, V., von Mering, C. & van der Meer, J. R. Reproducible propagation of species-rich soil bacterial communities suggests robust underlying deterministic principles of community formation. mSystems 7, e00160–22 (2022).

    PubMed  PubMed Central  Google Scholar 

  13. Rivett, D. W. & Bell, T. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat. Microbiol. 3, 767–772 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Datta, M. S., Sliwerska, E., Gore, J., Polz, M. F. & Cordero, O. X. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat. Commun. 7, 11965 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Estrela, S. et al. Functional attractors in microbial community assembly. Cell Syst. 13, 29–42.e7 (2022).

    CAS  PubMed  Google Scholar 

  16. Grilli, J. Macroecological laws describe variation and diversity in microbial communities. Nat. Commun. 11, 4743 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Lawson, C. E. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17, 725–741 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Dal Bello, M., Lee, H., Goyal, A. & Gore, J. Resource–diversity relationships in bacterial communities reflect the network structure of microbial metabolism. Nat. Ecol. Evol. 5, 1424–1434 (2021).

    PubMed  Google Scholar 

  19. Marsland, R. et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Marsland, R., Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Marsland, R., Cui, W., Goldford, J. & Mehta, P. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Sposito, G. The Chemistry of Soils (Oxford Univ. Press, 2016).

  25. Atiwesh, G., Parrish, C. C., Banoub, J. & Le, T. T. Lignin degradation by microorganisms: a review. Biotechnol. Prog. 38, e3226 (2022).

    CAS  PubMed  Google Scholar 

  26. Weng, C., Peng, X. & Han, Y. Depolymerization and conversion of lignin to value-added bioproducts by microbial and enzymatic catalysis. Biotechnol. Biofuels 14, 84 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu, Z., Lei, P., Zhai, R., Wen, Z. & Jin, M. Recent advances in lignin valorization with bacterial cultures: microorganisms, metabolic pathways and bio-products. Biotechnol. Biofuels 12, 32 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Iram, A., Berenjian, A. & Demirci, A. A review on the utilization of lignin as a fermentation substrate to produce lignin-modifying enzymes and other value-added products. Molecules 26, 2960 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Li, K. et al. Investigating lignin-derived monomers and oligomers in low-molecular-weight fractions separated from depolymerized black liquor retentate by membrane filtration. Molecules 26, 2887 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Gilkes, N. R., Kilburn, D. G., Miller, R. C. & Warren, R. A. J. Bacterial cellulases. Bioresour. Technol. 36, 21–35 (1991).

    CAS  Google Scholar 

  31. Bhardwaj, N., Kumar, B., Agrawal, K. & Verma, P. Current perspective on production and applications of microbial cellulases: a review. Bioresour. Bioprocess. 8, 95 (2021).

    PubMed  PubMed Central  Google Scholar 

  32. Jones, D. L. Organic acids in the rhizosphere—a critical review. Plant Soil 205, 25–44 (1998).

    CAS  Google Scholar 

  33. Fischer, Z., Blažka, P. & Dubis, L. Respiration rates of organic soil depending on changes of moisture and aeration. Open J. Soil Sci. 7, 101–110 (2017).

    CAS  Google Scholar 

  34. Ramonell, K. M. et al. Influence of atmospheric oxygen on leaf structure and starch deposition in Arabidopsis thaliana: low oxygen effects on leaf development in Arabidopsis. Plant Cell Environ. 24, 419–428 (2001).

    CAS  PubMed  Google Scholar 

  35. Petersen, S. O., Nielsen, T. H., Frostegård, Å. & Olesen, T. O2 uptake, C metabolism and denitrification associated with manure hot-spots. Soil Biol. Biochem. 28, 341–349 (1996).

    CAS  Google Scholar 

  36. Sierra, J. & Renault, P. Oxygen consumption by soil microorganisms as affected by oxygen and carbon dioxide levels. Appl. Soil Ecol. 2, 175–184 (1995).

    Google Scholar 

  37. Parisutham, V., Chandran, S.-P., Mukhopadhyay, A., Lee, S. K. & Keasling, J. D. Intracellular cellobiose metabolism and its applications in lignocellulose-based biorefineries. Bioresour. Technol. 239, 496–506 (2017).

    CAS  PubMed  Google Scholar 

  38. Blount, Z. D., Borland, C. Z. & Lenski, R. E. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl Acad. Sci. USA 105, 7899–7906 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).

    CAS  PubMed  Google Scholar 

  40. Goyal, A., Bittleston, L. S., Leventhal, G. E., Lu, L. & Cordero, O. X. Interactions between strains govern the eco-evolutionary dynamics of microbial communities. eLife 11, e74987 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Dragosits, M. & Mattanovich, D. Adaptive laboratory evolution—principles and applications for biotechnology. Microb. Cell Fact. 12, 64 (2013).

    PubMed  PubMed Central  Google Scholar 

  42. Debray, R. et al. Priority effects in microbiome assembly. Nat. Rev. Microbiol. 20, 109–121 (2022).

    CAS  PubMed  Google Scholar 

  43. Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife 10, e65948 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).

    PubMed  Google Scholar 

  45. Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl Acad. Sci. USA 105, 11512–11519 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Louca, S. et al. High taxonomic variability despite stable functional structure across microbial communities. Nat. Ecol. Evol. 1, 0015 (2017).

    Google Scholar 

  47. Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. eLife 8, e49720 (2019).

    PubMed  PubMed Central  Google Scholar 

  48. Johnson, D. R., Goldschmidt, F., Lilja, E. E. & Ackermann, M. Metabolic specialization and the assembly of microbial communities. ISME J. 6, 1985–1991 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Berlemont, R. & Martiny, A. C. Phylogenetic distribution of potential cellulases in bacteria. Appl. Environ. Microbiol. 79, 1545–1554 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Kost, C., Patil, K. R., Friedman, J., Garcia, S. L. & Ralser, M. Metabolic exchanges are ubiquitous in natural microbial communities. Nat. Microbiol. 8, 2244–2252 (2023).

    CAS  PubMed  Google Scholar 

  51. Speck, E. L. & Freese, E. Control of metabolite secretion in Bacillus subtilis. J. Gen. Microbiol. 78, 261–275 (1973).

    CAS  PubMed  Google Scholar 

  52. Hannya, A., Nishimura, T., Matsushita, I., Tsubota, J. & Kawata, Y. Efficient production and secretion of oxaloacetate from Halomonas sp. KM-1 under aerobic conditions. AMB Express 7, 209 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Beg, Q. K. et al. Detection of transcriptional triggers in the dynamics of microbial growth: application to the respiratorily versatile bacterium Shewanella oneidensis. Nucleic Acids Res. 40, 7132–7149 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Fernández-Veledo, S. & Vendrell, J. Gut microbiota-derived succinate: friend or foe in human metabolic diseases? Rev. Endocr. Metab. Disord. 20, 439–447 (2019).

    PubMed  PubMed Central  Google Scholar 

  55. Balado, M. et al. Secreted citrate serves as iron carrier for the marine pathogen Photobacterium damselae subsp damselae. Front. Cell. Infect. Microbiol. 7, 361 (2017).

    PubMed  PubMed Central  Google Scholar 

  56. Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).

    PubMed  PubMed Central  Google Scholar 

  57. Tiedje, J., Sexstone, A., Parkin, T. & Revsbech, N. Anaerobic processes in soil. Plant Soil 76, 197–212 (1984).

    CAS  Google Scholar 

  58. Fritts, R. K., McCully, A. L. & McKinlay, J. B. Extracellular metabolism sets the table for microbial cross-feeding. Microbiol. Mol. Biol. Rev. 85, e00135–20 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Amarnath, K. et al. Stress-induced metabolic exchanges between complementary bacterial types underly a dynamic mechanism of inter-species stress resistance. Nat. Commun. 14, 3165 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Al-Ani, A. et al. Oxygenation in cell culture: critical parameters for reproducibility are routinely not reported. PLoS ONE 13, e0204269 (2018).

    PubMed  PubMed Central  Google Scholar 

  62. Rocca, J. D. et al. The microbiome stress project: toward a global meta-analysis of environmental stressors and their effects on microbial communities. Front. Microbiol. 9, 3272 (2019).

    PubMed  PubMed Central  Google Scholar 

  63. Mandakovic, D. et al. Structure and co-occurrence patterns in microbial communities under acute environmental stress reveal ecological factors fostering resilience. Sci. Rep. 8, 5875 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. Dal Co, A., van Vliet, S., Kiviet, D. J., Schlegel, S. & Ackermann, M. Short-range interactions govern the dynamics and functions of microbial communities. Nat. Ecol. Evol. 4, 366–375 (2020).

    PubMed  Google Scholar 

  65. Carthew, R. W. Gene regulation and cellular metabolism: an essential partnership. Trends Genet. 37, 389–400 (2021).

    CAS  PubMed  Google Scholar 

  66. Ramin, K. I. & Allison, S. D. Bacterial tradeoffs in growth rate and extracellular enzymes. Front. Microbiol. 10, 2956 (2019).

    PubMed  PubMed Central  Google Scholar 

  67. Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).

    CAS  PubMed  Google Scholar 

  68. Stokes, J. M., Lopatkin, A. J., Lobritz, M. A. & Collins, J. J. Bacterial metabolism and antibiotic efficacy. Cell Metab. 30, 251–259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. van den Berg, N. I. et al. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat. Ecol. Evol. 6, 855–865 (2022).

    PubMed  PubMed Central  Google Scholar 

  70. Sánchez, Á. et al. Directed evolution of microbial communities. Annu. Rev. Biophys. 50, 323–341 (2021).

    PubMed  PubMed Central  Google Scholar 

  71. M9 minimal medium (modified). Cold Spring Harb. Protoc. pdb.rec12296 (2010).

  72. Liu, J., Li, J., Feng, L., Cao, H. & Cui, Z. An improved method for extracting bacteria from soil for high molecular weight DNA recovery and BAC library construction. J. Microbiol. 48, 728–733 (2010).

    CAS  PubMed  Google Scholar 

  73. Syuhada, N. H. et al. Strong and widespread cycloheximide resistance in Stichococcus-like eukaryotic algal taxa. Sci. Rep. 12, 1080 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Google Scholar 

  76. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).

    PubMed  PubMed Central  Google Scholar 

  78. Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

    PubMed  PubMed Central  Google Scholar 

  79. Quinn, T. P., Erb, I., Richardson, M. F. & Crowley, T. M. Understanding sequencing data as compositions: an outlook and review. Bioinformatics 34, 2870–2878 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

Download references

Acknowledgements

We thank M. Dal Bello, A. Goyal, M. Gralka, Z. Werbin, J. Zhang, K. Herbst, J. Goldford, H. Scott, S. Bald and the members of the Bhatnagar and Segrè labs for their guidance and insight. Additionally, we thank C. Vietorisz for assistance with soil community sampling. M.R.S. was supported by a synthetic biology NIH-funded predoctoral training fellowship (T32GM130546), a bioinformatics NIH-funded predoctoral training fellowship (T32GM100842), the Biological Design Center Kilachand Multicellular Design Program Graduate Fellowship and the Hariri Graduate Student Fellows Program. J.M.B. acknowledges support from DOE DE-SC0020403 and DOE DE-SC0012704. D.S. acknowledges support from the National Institutes of Health (National Institute of General Medical Sciences, award R01GM121950; National Institute on Aging, award number UH2AG064704), the US Department of Energy, Office of Science, Office of Biological & Environmental Research through the Microbial Community Analysis and Functional Evaluation in Soils SFA Program (m-CAFEs) under contract number DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory, the National Science Foundation (grants 1457695, NSFOCE-BSF 1635070 and NSF-BSF 2246707; and the NSF Center for Chemical Currencies of a Microbial Planet, publication #044) and the Human Frontiers Science Program (RGP0020/2016 and RGP0060/2021). Figures were created with BioRender.com.

Author information

Authors and Affiliations

  1. Bioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, MA, USA

    Michael R. Silverstein, Jennifer M. Bhatnagar & Daniel Segrè

  2. Biological Design Center, Boston University, Boston, MA, USA

    Michael R. Silverstein & Daniel Segrè

  3. Department of Biology, Boston University, Boston, MA, USA

    Jennifer M. Bhatnagar & Daniel Segrè

  4. Department of Biomedical Engineering and Department of Physics, Boston University, Boston, MA, USA

    Daniel Segrè

Authors
  1. Michael R. Silverstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer M. Bhatnagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Segrè
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.R.S., J.M.B. and D.S. conceived the study. M.R.S. acquired samples, conducted the experiments, performed the simulations and analysed the data. M.R.S., J.M.B. and D.S. interpreted results and wrote the manuscript.

Corresponding author

Correspondence to Daniel Segrè.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Microbial communities stabilize.

Each point is the mean distance to a community’s previous timepoint within each condition and the shaded region represents the 95% confidence interval. On day 3, communities are substantially different from their initial state and then continue to change in composition, but by approximately the same amount.

Source data

Extended Data Fig. 2 Divergence of a subset of communities which are available for all days.

A reproduction of Fig. 1d–f, but excluding all samples from communities HF1P and HF3H, which are missing data for days 3, 6, and 9 (Extended Data Table 1). a, Divergence of communities within each condition over time from day 3 onwards. Points on each line represent the mean divergence and the shaded region represents the 95% confidence interval for pairwise distances between all four included communities within each condition. b, Distribution of divergence for the final time point. Boxes are bound by the interquartile range, divided by the median, and whiskers extend to a maximum of 1.5 times the interquartile range. c, Divergence-complexity effect by condition type for all time points (one-sided Wald Test on effect (slope) > 0, p-values shown in figure for each day and condition type.

Source data

Extended Data Fig. 3 Divergence-complexity effect at Family level.

A reproduction of divergence-complexity effect calculation in Fig. 2f except computing divergence at the Family level instead of at the ASV level (one-sided Wald Test on effect (slope) > 0, p-values shown in figure for each day and condition type). Unlike our results at the ASV level, the divergence-complexity effect for our mixed-metabolite conditions at the Family level was only significant for day 33. The lack of significance at other points may be related to their reduced sample size (for days 3, 6, and 9; see Extended Data Table 1), but may in principle reflect additional effects emerging upon taxonomic coarse graining.

Source data

Extended Data Fig. 4 Family composition over time.

Each subplot shows the Family composition over time for a microcosm. Colors for each Family are defined by Phylum where blue represents Actinobacteria, yellow represents Bacteroidetes, green represents Firmicutes, and red represents Proteobacteria. Each column of subplots pertains to one source community and each row pertains to a condition (increasing in complexity from top to bottom, with single- and then mixed-metabolite conditions).

Source data

Extended Data Fig. 5 Microcosm replicates cluster.

a, The final state at day 33 of two replicates for community HF1P and three replicates for all other communities (see Extended Data Table 1) projected separately for each condition using MDS. b, The distribution of distances across communities (N=1,066 pairwise distances) and between replicates within the same community (N=142 pairwise distances). Each violin outlines the kernel density estimate and contains a box which is bound by the interquartile range with an open circle at the media and whiskers that extend up to 1.5 times the interquartile range. Communities are colored by their source and squares represent the replicate used in the main text. Independent of condition, communities from the same replicate are more similar to each other than communities from separate replicates.

Source data

Extended Data Fig. 6 Divergence and diversity on a subset of communities which are available for all days.

A reproduction of Fig. 3, but excluding all samples from communities HF1P and HF3H, which are missing data for days 3, 6, and 9 (Extended Data Table 1). a, The slope of the relationship between community alpha diversity and divergence (black) and the mean community alpha diversity (red) over time. Shaded areas around each regression line represents the 95% confidence interval. b, The data underlying the relationship in a over time. Each point is the diversity of a community in a condition (x-axis) and the mean divergence of that community from all others within a condition (y-axis).

Source data

Extended Data Fig. 7 Consumer resource parameterizations.

D Matrices for trophic (a) and random (b) resource transformations. Each matrix column defines which resources are generated from a given input resource. a, Trophic resource transformations were parameterized by defining resource types (T0, T1, T2, and T3; red lines) which mostly transform into resources of the subsequent type with some self-renewal. b, Random resource transformations were parameterized by defining any resource to transform into any other resource with uniform probability. The colorbar indicates log10 resource transformation rate for a and b. C matrices for trophic (c) and random (d) consumer preferences. Each matrix row defines which resources can be utilized by each consumer. c, Trophic consumer preferences were parameterized by defining consumer families (F0, F1, F2, and G; blue lines) which consume a total of 35 resources of their associated type (for example, F0 consumers utilize T0 resources) and a common resource, T3. G consumers (generalists) consume resources of any type. d, Random consumer preferences were parameterized by defining consumers to utilize any resource with uniform probability. e, Seven initial environmental conditions were defined with 20 resources sampled from (1) T0, (2) T1, (3) T2, (4) T3, (5) T3+T2, (6) T3+T2+T1, and (7) T3+T2+T1+T0. f, Six source communities were defined by sampling 200 consumers.

Source data

Extended Data Table 1 Availability of each sample
Full size table
Extended Data Table 2 Consumer-resource model parameters
Full size table

Supplementary information

Supplementary Information

Supplementary text, Fig. 1 and Table 1.

Reporting Summary

Peer Review File

Supplementary Data 1

Source data for Supplementary Fig. 1.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1/Table 1

Statistical source data.

Source Data Extended Data Fig. 2/Table 2

Statistical source data.

Source Data Extended Data Fig. 3/Table 3

Statistical source data.

Source Data Extended Data Fig. 4/Table 4

Statistical source data.

Source Data Extended Data Fig. 5/Table 5

Statistical source data.

Source Data Extended Data Fig. 6/Table 6

Statistical source data.

Source Data Extended Data Fig. 7/Table 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Silverstein, M.R., Bhatnagar, J.M. & Segrè, D. Metabolic complexity drives divergence in microbial communities. Nat Ecol Evol 8, 1493–1504 (2024). https://doi.org/10.1038/s41559-024-02440-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-024-02440-6

Search

Advanced search

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