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.

A macroecological theory of microbial biodiversity


Microorganisms are the most abundant, diverse and functionally important organisms on Earth. Over the past decade, microbial ecologists have produced the largest ever community datasets. However, these data are rarely used to uncover law-like patterns of commonness and rarity, test theories of biodiversity, or explore unifying explanations for the structure of microbial communities. Using a global scale compilation of >20,000 samples from environmental, engineered and host-related ecosystems, we test the power of competing theories to predict distributions of microbial abundance and diversity–abundance scaling laws. We show that these patterns are best explained by the synergistic interaction of stochastic processes that are captured by lognormal dynamics. We demonstrate that lognormal dynamics have predictive power across scales of abundance, a criterion that is essential to biodiversity theory. By understanding the multiplicative and stochastic nature of ecological processes, scientists can better understand the structure and dynamics of Earth’s largest and most diverse ecological systems.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Forms of predicted SADs in rank abundance form, that is, ordered from the most abundant species (Nmax) to the least abundant on the x axis.
Figure 2: Relationships between predicted abundance and observed abundance.
Figure 3: The relationship of model performance to the total number of 16S rRNA reads (N).
Figure 4: Predictions of absolute dominance (the greatest species abundance within an SAD, Nmax) using the dominance scaling relationships of each model (Table 1).


  1. 1

    Brown, J. H., Mehlman, D. W. & Stevens, G. C. Spatial variation in abundance. Ecology 76, 1371–1382 (1995).

    Article  Google Scholar 

  2. 2

    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).

    Google Scholar 

  3. 3

    McGill, B. J. Towards a unification of unified theories of biodiversity. Ecol. Lett. 13, 627–642 (2010).

    Article  Google Scholar 

  4. 4

    Harte, J. Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics (Oxford Univ. Press, 2011).

    Book  Google Scholar 

  5. 5

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

    CAS  Article  Google Scholar 

  6. 6

    Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere.” Proc. Natl Acad. Sci. USA 103, 12115–12120 (2006).

    CAS  Article  Google Scholar 

  7. 7

    Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).

    CAS  Article  Google Scholar 

  8. 8

    Reid, A. & Buckley, M. The Rare Biosphere: A Report from the American Academy of Microbiology (American Academy of Microbiology, 2011).

    Google Scholar 

  9. 9

    McGill, B. J. et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).

    Article  Google Scholar 

  10. 10

    Horner-Devine, M. C., Lage, M., Hughes, J. B. & Bohannan, B. J. M. A taxa–area relationship for bacteria. Nature 432, 750–753 (2004).

    CAS  Article  Google Scholar 

  11. 11

    Putnam, R. Community Ecology (Chapman & Hall, 1993).

    Google Scholar 

  12. 12

    MacArthur, R. On the relative abundance of species. Am. Nat. 94, 25–36 (1960).

    Article  Google Scholar 

  13. 13

    Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).

    CAS  Article  Google Scholar 

  14. 14

    Dunbar, J., Barns, S. M., Ticknor, L. O. & Kuske, C. R. Empirical and theoretical bacterial diversity in four Arizona soils. Appl. Environ. Microbiol. 68, 3035–3045 (2002).

    CAS  Article  Google Scholar 

  15. 15

    Curtis, T. P., Sloan, W. T. & Scannell, J. W. Estimating prokaryotic diversity and its limits. Proc. Natl Acad. Sci. USA 99, 10494–10499 (2002).

    CAS  Article  Google Scholar 

  16. 16

    Bohannan, B. J. M. & Hughes, J. New approaches to analyzing microbial biodiversity data. Curr. Opin. Microbiol. 6, 282–287 (2003).

    CAS  Article  Google Scholar 

  17. 17

    Schloss, P. D. & Handelsman, J. Status of the microbial census. Microbiol. Mol. Biol. Rev. 68, 686–691 (2004).

    Article  Google Scholar 

  18. 18

    Pedrós-Alió, C. & Manrubia, S. The vast unknown microbial biosphere. Proc. Natl Acad. Sci. USA 113, 6585–6587 (2016).

    Article  Google Scholar 

  19. 19

    Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58 (1943).

    Article  Google Scholar 

  20. 20

    Jaynes, E. T. Probability Theory: The Logic of Science (Cambridge Univ. Press, 2003).

    Book  Google Scholar 

  21. 21

    White, E. P., Thibault, K. M. & Xiao, X. Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology 93, 1772–1778 (2012).

    Article  Google Scholar 

  22. 22

    Baldridge, E., Harris, D. J, Xiao, X. & White E. P. An extensive comparison of species-abundance distribution models. PeerJ 4, e2823 (2016).

    Article  Google Scholar 

  23. 23

    McGill, B. Strong and weak tests of macroecological theory. Oikos 102, 679–685 (2003).

    Article  Google Scholar 

  24. 24

    Ulrich, W., Ollik, M. & Ugland, K. I. A meta-analysis of species-abundance distributions. Oikos 119, 1149–1155 (2010).

    Article  Google Scholar 

  25. 25

    Locey, K. J. & White, E. P. How species richness and total abundance constrain the distribution of abundance. Ecol. Lett. 16, 1177–1185 (2013).

    Article  Google Scholar 

  26. 26

    Fierer, N. & Lennon, J. T. The generation and maintenance of diversity in microbial communities. Am. J. Bot. 98, 439–448 (2011).

    Article  Google Scholar 

  27. 27

    Allen, A. P., Li, B. & Charnov, E. L. Population fluctuations, power laws and mixtures of lognormal distributions. Ecol. Lett. 4, 1–3 (2001).

    Article  Google Scholar 

  28. 28

    Harte, J. & Newman, E. Maximum information entropy: a foundation for ecological theory. Trends Ecol. Evol. 29, 384–389 (2014).

    Article  Google Scholar 

  29. 29

    Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth microbiome project: successes and aspirations. BMC Biol. 12, 69 (2014).

    Article  Google Scholar 

  30. 30

    Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    CAS  Article  Google Scholar 

  31. 31

    Meyer, F. et al. The metagenomics RAST server—a public resource for the automatic phylo- genetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Flores, G. E. et al. Microbial community structure of hydrothermal deposits from geochemically different vent fields along the Mid-Atlantic Ridge. Environ. Microbiol. 13, 2158–2171 (2011).

    CAS  Article  Google Scholar 

  33. 33

    Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).

    CAS  Article  Google Scholar 

  34. 34

    Chu, H. et al. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12, 2998–3006 (2010).

    CAS  Article  Google Scholar 

  35. 35

    Fierer, N. et al. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017 (2012).

    CAS  Article  Google Scholar 

  36. 36

    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Amend, A. S., Seifert, K. A., Samson, R. & Bruns, T. D. Indoor fungal composition is geographically patterned and more diverse in temperate zones than in the tropics. Proc. Natl Acad. Sci. USA 107, 13748–13753 (2010).

    CAS  Article  Google Scholar 

  38. 38

    Gans, J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science 309, 1387–1390 (2005).

    CAS  Article  Google Scholar 

  39. 39

    Dumbrell, A. J., Nelson, M., Helgason, T., Dytham, C. & Fitter, A. H. Relative roles of niche and neutral processes in structuring a soil microbial community. ISME J. 4, 337–345 (2010).

    Article  Google Scholar 

  40. 40

    Magurran, A. E. & McGill, B. J. Biological Diversity Frontiers in Measurement and Assessment (Oxford Univ. Press, 2011).

    Google Scholar 

  41. 41

    Williamson, M. & Gaston, K. J. The lognormal distribution is not an appropriate null hypothesis for the species-abundance distribution. J. Anim. Ecol. 74, 409–422 (2005).

    Article  Google Scholar 

  42. 42

    Harte, J., Zillio, T., Conlisk, E. & Smith, A. B. Maximum entropy and the state-variable approach to macroecology. Ecology 89, 2700–2711 (2008).

    CAS  Article  Google Scholar 

  43. 43

    Cohen, J. E. Alternate derivations of a species-abundance relation. Am. Nat. 102, 165–172 (1968).

    Article  Google Scholar 

  44. 44

    Heip, C. H. R., Herman, P. M. J. & Soetaert, K. Indices of diversity and evenness. Oceanis 24, 61–87 (1998).

    Google Scholar 

  45. 45

    Zipf, G. K. Human Behavior and the Principle of Least Effort (Addison-Wesley, 1949).

    Google Scholar 

  46. 46

    Newman, M. E. J. Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46, 323–351 (2005).

    Article  Google Scholar 

  47. 47

    Xiao, X., McGlinn, D. J. & White, E. P. A strong test of the maximum entropy theory of ecology. Am. Nat. 185, 70–80 (2015).

    Article  Google Scholar 

Download references


We thank J. Gilbert and S. Gibbons for providing EMP data and guidance on using it. We also thank the researchers who collected, sequenced and provided metagenomic data on MG-RAST, as well as the individuals who maintain and provide the MG-RAST service. We also acknowledge the researchers who provided the open-source code for conducting some of our analyses. Finally, we thank the HMP Consortium for providing their data on the NIH’s publically accessible DACC server. This work was supported by a National Science Foundation Dimensions of Biodiversity Grant (no. 1442246 to J.T.L. and K.J.L.) and the US Army Research Office (W911NF-14-1-0411 to J.T.L.).

Author information




W.R.S. and K.J.L. conceived, designed and performed the experiments, analysed the data and contributed materials/analysis tools. W.R.S., K.J.L. and J.T.L. wrote the paper.

Corresponding author

Correspondence to Kenneth J. Locey.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–6, Supplementary Tables 1–6. (PDF 4995 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shoemaker, W., Locey, K. & Lennon, J. A macroecological theory of microbial biodiversity. Nat Ecol Evol 1, 0107 (2017).

Download citation

Further reading


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