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:

Bacterial diversification through geological time

Abstract

Numerous studies have estimated plant and animal diversification dynamics; however, no comparable rigorous estimates exist for bacteria—the most ancient and widespread form of life on Earth. Here, we analyse phylogenies comprising up to 448,112 bacterial lineages to reconstruct global bacterial diversification dynamics. To handle such large phylogenies, we developed methods based on the statistical properties of infinitely large trees. We further analysed sequencing data from 60 environmental studies to determine the fraction of extant bacterial diversity missing from the phylogenies—a crucial parameter for estimating speciation and extinction rates. We estimate that there are about 1.4–1.9 million extant bacterial lineages when lineages are defined by 99% similarity in the 16S ribosomal RNA gene, and that bacterial diversity has been continuously increasing over the past 1 billion years (Gyr). Recent bacterial extinction rates are estimated at 0.03–0.05 per lineage per million years (lineage–1 Myr–1), and are only slightly below estimated recent bacterial speciation rates. Most bacterial lineages ever to have inhabited this planet are estimated to be extinct. Our findings disprove the notion that bacteria are unlikely to go extinct, and provide a valuable perspective on the evolutionary history of a domain of life with a sparse and cryptic fossil record.

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

Access options

Buy this article

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

Fig. 1: Non-parametric methods capture complex diversification scenarios.
Fig. 2: Bacterial, cyanobacterial and bird diversification dynamics through time.
Fig. 3: Estimated recent speciation, extinction and diversification rates.

Similar content being viewed by others

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Fischer, W. W., Hemp, J. & Johnson, J. E. Evolution of oxygenic photosynthesis. Annu. Rev. Earth Planet. Sci. 44, 647–683 (2016).

    Article  CAS  Google Scholar 

  3. Raup, D. M. & Sepkoski, J. J. Mass extinctions in the marine fossil record. Science 215, 1501–1503 (1982).

    Article  CAS  PubMed  Google Scholar 

  4. Signor, P. W. Biodiversity in geological time. Am. Zool. 34, 23–32 (1994).

    Article  Google Scholar 

  5. McElwain, J. C. & Punyasena, S. W. Mass extinction events and the plant fossil record. Trends Ecol. Evol. 22, 548–557 (2007).

    Article  PubMed  Google Scholar 

  6. Nee, S., May, R. M. & Harvey, P. H. The reconstructed evolutionary process. Phil. Trans. R. Soc. Lond. B 344, 305–311 (1994).

    Article  CAS  Google Scholar 

  7. Nee, S., Holmes, E. C., May, R. M. & Harvey, P. H. Extinction rates can be estimated from molecular phylogenies. Phil. Trans. R. Soc. Lond. B 344, 77–82 (1994).

    Article  CAS  Google Scholar 

  8. Sanderson, M. J. & Donoghue, M. J. Reconstructing shifts in diversification rates on phylogenetic trees. Trends Ecol. Evol. 11, 15–20 (1996).

    Article  CAS  PubMed  Google Scholar 

  9. Morlon, H. Phylogenetic approaches for studying diversification. Ecol. Lett. 17, 508–525 (2014).

    Article  PubMed  Google Scholar 

  10. Morlon, H., Kemps, B. D., Plotkin, J. B. & Brisson, D. Explosive radiation of a bacterial species group. Evolution 66, 2577–2586 (2012).

    Article  PubMed  Google Scholar 

  11. Lorén, J. G., Farfán, M. & Fusté, M. C. Molecular phylogenetics and temporal diversification in the genus Aeromonas based on the sequences of five housekeeping genes. PLoS. ONE 9, 1–15 (2014).

    Article  CAS  Google Scholar 

  12. Lebreton, F. et al. Tracing the Enterococci from paleozoic origins to the hospital. Cell 169, 849–861 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gubry-Rangin, C. et al. Coupling of diversification and pH adaptation during the evolution of terrestrial Thaumarchaeota. Proc. Natl Acad. Sci. USA 112, 9370–9375 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Marin, J., Battistuzzi, F. U., Brown, A. C. & Hedges, S. B. The timetree of prokaryotes: new insights into their evolution and speciation. Mol. Biol. Evol. 34, 437–446 (2017).

    CAS  PubMed  Google Scholar 

  15. Stadler, T. On incomplete sampling under birth–death models and connections to the sampling-based coalescent. J. Theor. Biol. 261, 58–66 (2009).

    Article  PubMed  Google Scholar 

  16. Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Schopf, J. W. Disparate rates, differing fates: tempo and mode of evolution changed from the Precambrian to the Phanerozoic. Proc. Natl Acad. Sci. USA 91, 6735–6742 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dykhuizen, D. E. Santa rosalia revisited: why are there so many species of bacteria? Antonie Van Leeuwenhoek 73, 25–33 (1998).

    Article  CAS  PubMed  Google Scholar 

  19. Butterfield, N. Macroevolution and macroecology through deep time. Palaeontology 50, 41–55 (2007).

    Article  Google Scholar 

  20. Schopf, J. W. et al. Sulfur-cycling fossil bacteria from the 1.8-Ga duck creek formation provide promising evidence of evolution’s null hypothesis. Proc. Natl Acad. Sci. USA 112, 2087–2092 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Weinbauer, M. G. & Rassoulzadegan, F. Extinction of microbes: evidence and potential consequences. Endang. Species Res. 3, 205–215 (2007).

    Article  Google Scholar 

  22. Höhna, S., Stadler, T., Ronquist, F. & Britton, T. Inferring speciation and extinction rates under different sampling schemes. Mol. Biol. Evol. 28, 2577–2589 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Stackebrandt, E. & Ebers, J. Taxonomic parameters revisited: tarnished gold standards. Microbiol. Today 33, 152–155 (2006).

    Google Scholar 

  24. Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Sanmartn, I. & Meseguer, A. S. Extinction in phylogenetics and biogeography: from timetrees to patterns of biotic assemblage. Front. Genet. 7, 35 (2016).

    Google Scholar 

  26. Stadler, T. Mammalian phylogeny reveals recent diversification rate shifts. Proc. Natl Acad. Sci. USA 108, 6187–6192 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Silvestro, D., Schnitzler, J. & Zizka, G. A Bayesian framework to estimate diversification rates and their variation through time and space. BMC Evol. Biol. 11, 311 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Stadler, T. How can we improve accuracy of macroevolutionary rate estimates? Syst. Biol. 62, 321–329 (2013).

    Article  PubMed  Google Scholar 

  29. Marshall, C. R. Five palaeobiological laws needed to understand the evolution of the living biota. Nat. Ecol. Evol. 1, 165 (2017).

    Article  PubMed  Google Scholar 

  30. Schloss, P. D., Girard, R. A., Martin, T., Edwards, J. & Thrash, J. C. Status of the archaeal and bacterial census: an update. mBio 7, e00201-16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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 

  33. Glöckner, F. O. et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J. Biotechnol. 261, 169–176 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Krebs, C. J. Ecological Methodology (Benjamin Cummings, San Francisco, CA, 1999).

  35. Zanne, A. E. et al. Three keys to the radiation of angiosperms into freezing environments. Nature 505, 89–99 (2014).

    Article  CAS  Google Scholar 

  36. Jetz, W. et al. Global distribution and conservation of evolutionary distinctness in birds. Curr. Biol. 24, 919–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Welch, R. A. et al. Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli. Proc. Natl Acad. Sci. USA 99, 17020–17024 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shapiro, B. J. & Polz, M. F. Ordering microbial diversity into ecologically and genetically cohesive units. Trends Microbiol. 22, 235–247 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Xie, S., Pancost, R. D., Yin, H., Wang, H. & Evershed, R. P. Two episodes of microbial change coupled with Permo/Triassic faunal mass extinction. Nature 434, 494–497 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Gibbons, S. M. et al. Evidence for a persistent microbial seed bank throughout the global ocean. Proc. Natl Acad. Sci. USA 110, 4651–4655 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Kim, M., Oh, H.-S., Park, S.-C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int. J. Syst. Evol. Microbiol. 64, 346–351 (2014).

    Article  PubMed  Google Scholar 

  43. Straub, T. J. & Zhaxybayeva, O. A null model for microbial diversification. Proc. Natl Acad. Sci. USA 114, E5414–E5423 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Butterfield, N. J. Proterozoic photosynthesis—a critical review. Palaeontology 58, 953–972 (2015).

    Article  Google Scholar 

  46. Brocks, J. J. & Banfield, J. Unravelling ancient microbial history with community proteogenomics and lipid geochemistry. Nat. Rev. Microbiol. 7, 601–609 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  52. Li, W., Fu, L., Niu, B., Wu, S. & Wooley, J. Ultrafast clustering algorithms for metagenomic sequence analysis. Brief. Bioinform. 13, 656–668 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rasmussen, B., Fletcher, I. R., Brocks, J. J. & Kilburn, M. R. Reassessing the first appearance of eukaryotes and cyanobacteria. Nature 455, 1101–1104 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Shih, P. M., Hemp, J., Ward, L. M., Matzke, N. J. & Fischer, W. W. Crown group Oxyphotobacteria postdate the rise of oxygen. Geobiology 15, 19–29 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Parfrey, L. W., Lahr, D. J. G., Knoll, A. H. & Katz, L. A. Estimating the timing of early eukaryotic diversification with multigene molecular clocks. Proc. Natl Acad. Sci. USA 108, 13624–13629 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Brocks, J. J. et al. Biomarker evidence for green and purple sulphur bacteria in a stratified palaeoproterozoic sea. Nature 437, 866–870 (2005).

    Article  CAS  PubMed  Google Scholar 

  59. Walter, M., Buick, R. & Dunlop, J. Stromatolites 3,400–3,500 Myr old from the North Pole area, Western Australia. Nature 284, 443–445 (1980).

    Article  Google Scholar 

  60. Ryder, G., Koeberl, C. & Mojzsis, S. J. in Origin of the Earth and Moon (eds Canup, R. & Kevin Righter, K.) 475–492 (Univ. Arizona Press, Tucson, AZ, 2000).

  61. Dykhuizen, D. Species numbers in bacteria. Proc. Calif. Acad. Sci. 56, 62–71 (2005).

    PubMed  PubMed Central  Google Scholar 

  62. Fraser, C., Alm, E. J., Polz, M. F., Spratt, B. G. & Hanage, W. P. The bacterial species challenge: making sense of genetic and ecological diversity. Science 323, 741–746 (2009).

    Article  CAS  PubMed  Google Scholar 

  63. Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).

    Article  PubMed  Google Scholar 

  66. May, R. M. How many species inhabit the earth? Sci. Am. 267, 42–49 (1992).

    Article  Google Scholar 

  67. Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. & Worm, B. How many species are there on Earth and in the ocean? PLoS Biol. 9, e1001127 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Eastman, J. M., Harmon, L. J., Tank, D. C. & Paradis, E. Congruification: support for time scaling large phylogenetic trees. Methods Ecol. Evol. 4, 688–691 (2013).

    Article  Google Scholar 

  69. Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2017).

    Article  CAS  Google Scholar 

  70. Smith, S. A., Beaulieu, J. M. & Donoghue, M. J. An uncorrelated relaxed-clock analysis suggests an earlier origin for flowering plants. Proc. Natl Acad. Sci. USA 107, 5897–5902 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Kuo, C.-H. & Ochman, H. Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria. Biol. Direct 4, 35 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Day, W. H. E. Optimal algorithms for comparing trees with labeled leaves. J. Classif. 2, 7–28 (1985).

    Article  Google Scholar 

  73. Borg, I., Groenen, P. J. F. & Mair, P. Applied Multidimensional Scaling (Springer, Berlin, 2013).

  74. Ricklefs, R. E. Estimating diversification rates from phylogenetic information. Trends Ecol. Evol. 22, 601–610 (2007).

    Article  PubMed  Google Scholar 

  75. Ochman, H. & Wilson, A. Evolution in bacteria: evidence for a universal substitution rate in cellular genomes. J. Mol. Evol. 26, 74–86 (1987).

    Article  CAS  PubMed  Google Scholar 

  76. Moran, N. A., Munson, M. A., Baumann, P. & Ishikawa, H. A molecular clock in endosymbiotic bacteria is calibrated using the insect hosts. Proc. R. Soc. Lond. B 253, 167–171 (1993).

    Article  Google Scholar 

  77. Scrucca, L. Model-based SIR for dimension reduction. Comput. Stat. Data Anal. 55, 3010–3026 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

We thank D. H. Parks for providing the 16S rRNA sequences from MAGs48. S.L. was supported by an NSERC grant and a postdoctoral fellowship from the Biodiversity Research Centre, University of British Columbia. M.W.P., M.D. and L.W.P. were supported by NSERC Discovery Grants. P.M.S. was supported by The Branco Weiss Fellowship – Society in Science. W.W.F. acknowledges support from the Simons Collaboration on the Origins of Life and NASA Exobiology award number NNX16AJ57G.

Author information

Authors and Affiliations

Authors

Contributions

S.L., L.W.P. and M.D. conceived the project. S.L. developed the mathematical methods, performed the diversification analyses and wrote the first draft of the manuscript. P.M.S. performed the molecular clock analyses of the BEAST trees, provided the cyanobacterial multigene tree and contributed to the development of the project ideas. All authors helped to interpret the results, advised on methodological improvements and contributed to writing the manuscript.

Corresponding author

Correspondence to Stilianos Louca.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary methods, figures and tables.

Reporting Summary

Supplementary file 1

Accession numbers and summaries of amplicon sequencing data used to recover de novo OTUs.

Supplementary file 2

Accession numbers and summaries of sequencing data used from the Earth Microbiome Project.

Supplementary file 3

R code used for analysing diversification dynamics, including required input files.

Supplementary file 4

Time trees and undated phylogenetic trees constructed in this study.

Supplementary file 5

Taxonomic classifications of de novo OTUs.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Louca, S., Shih, P.M., Pennell, M.W. et al. Bacterial diversification through geological time. Nat Ecol Evol 2, 1458–1467 (2018). https://doi.org/10.1038/s41559-018-0625-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-018-0625-0

This article is cited by

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