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.

Understanding how microbiomes influence the systems they inhabit


Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

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

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1: Diagram of microbial–ecosystem linkages.
Fig. 2: A conceptual map of the intersection between microbial (vertical) and ecosystem (horizontal) ecology.


  1. Graham, E. B. et al. Do we need to understand microbial communities to predict ecosystem function? A comparison of statistical models of nitrogen cycling processes. Soil Biol. Biochem. 68, 279–282 (2014).

    Article  CAS  Google Scholar 

  2. Graham, E. B. et al. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front. Microbiol. 7, 214 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. Rocca, J. D. et al. Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed. ISME J. 9, 1693–1699 (2015).

    Article  PubMed  Google Scholar 

  4. Bier, R. L. et al. How are we forging conceptual, analytical, and mechanistic links between microbial community structure and ecosystem process? FEMS Microbiol. Ecol. 91, fiv113 (2015).

    Article  PubMed  Google Scholar 

  5. Falkowski, P. G., Fenchel, T. & DeLong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1038 (2008).

    Article  CAS  PubMed  Google Scholar 

  6. Felip, M., Pace, M. L. & Cole, J. J. Regulation of planktonic bacterial growth rates: the effects of temperature and resources. Microb. Ecol. 31, 15–28 (1996).

    Article  CAS  PubMed  Google Scholar 

  7. Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Microbiol. Rev. 9, 119–130 (2011).

    Article  CAS  Google Scholar 

  8. Comte, J., Fauteux, L. & del Giorgio, P. A. Links between metabolic plasticity and functional redundancy in freshwater bacterioplankton communities. Front. Microbiol. 4, 112 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Adams, H. E., Crump, B. C. & Kling, G. W. Metacommunity dynamics of bacteria in an arctic lake: the impact of species sorting and mass effects on bacterial production and biogeography. Front. Microbiol. 5, 82 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Schimel, J. P., Bennett, J. & Fierer, N. in Biological Diversity and Function in Soils (eds Bardgett, R. D. et al.) 171–188 (Cambridge Univ. Press, Cambridge, 2005).

  11. Schimel, J. P. in Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences Vol. 113 (eds Chapin III, F. S. & Körner, C.) 239–254 (Springer, 1995).

  12. Lenhart, K. et al. Evidence for methane production by saprotrophic fungi. Nat. Commun. 3, 1046 (2012).

    Article  PubMed  Google Scholar 

  13. Hagstroem, A., Azam, F., Berg, C. & Zweifel, U. L. Isolates as models to study bacterial ecophysiology and biogeochemistry. Aquat. Microb. Ecol. 80, 15–27 (2017).

    Article  Google Scholar 

  14. Larkin, A. A. & Martiny, A. Microdiversity shapes the traits, niche space and biogeography of microbial taxa. Environ. Microbiol. Rep. 9, 55–70 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. German, D. P., Marcelo, K. R. B., Stone, M. M. & Allison, S. D. The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob. Change Biol. 18, 1468–1479 (2012).

    Article  Google Scholar 

  16. Hall, E. K., Dzialowski, A. R., Stoxen, S. M. & Cotner, J. B. The effect of temperature on the coupling between phosphorus and growth in lacustrine bacterioplankton communities. Limnol. Oceanogr. 54, 880–889 (2009).

    Article  CAS  Google Scholar 

  17. Manzoni, S., Jackson, R. B., Trofymow, J. A. & Porporato, A. The global stoichiometry of litter nitrogen mineralization. Science 321, 684–686 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Elser, J. J., Chrzanowski, T. H., Sterner, R. W., Schampel, J. H. & Foster, D. K. Elemental ratios and the uptake and release of nutrients by phytoplankton and bacteria in three lakes of the canadian shield. Microb. Ecol. 29, 145–162 (1995).

    Article  CAS  PubMed  Google Scholar 

  19. Goldman, J. C., Caron, D. A. & Dennett, M. R. Regulation of gross growth efficiency and ammonium regeneration in bacteria by substrate C:N ratio. Limnol. Oceanogr. 32, 1239–1252 (1987).

    Article  CAS  Google Scholar 

  20. Jensen, B. D., Wise, K. E. & Odegard, G. M. Simulation of the elastic and ultimate tensile properties of diamond, graphene, carbon nanotubes, and amorphous carbon using a revised ReaxFF parameterization. J. Phys. Chem. A 119, 9710–9721 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Salt, G. W. A comment on the use of the term emergent properties. Am. Nat. 113, 145–148 (1979).

    Article  Google Scholar 

  22. Konopka, A. What is microbial community ecology? ISME J. 3, 1223–12230 (2009).

    Article  PubMed  Google Scholar 

  23. Battin, T., Kaplan, L. A., Newbold, L., Cheng, X. & Hansen, C. Effects of current velocity on the nascent architecture of stream microbial biofilms. Appl. Environ. Microbiol. 69, 5443–5452 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).

    Article  PubMed  Google Scholar 

  25. Ruiz‐González, C., Niño‐García, J. P., Lapierre, J. F. & del Giorgio, P. A. The quality of organic matter shapes the functional biogeography of bacterioplankton across boreal freshwater ecosystems. Glob. Ecol. Biogeogr. 24, 1487–1498 (2015).

    Article  Google Scholar 

  26. Fierer, N., A. Barberán, A. & Laughlin, D. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front. Microbiol. 5, 614 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Judd, C. R., Koyama, A., Simmons, M. P., Brewer, P. & von Fischer, J. C. Co-variation in methanotroph community composition and activity in three temperate grassland soils. Soil Biol. Biochem. 95, 78–86 (2016).

    Article  CAS  Google Scholar 

  28. Grilli, J., Barabas, 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 

  29. Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  33. Lennon, J. T., Aanderud, Z. A., Lehmkuhl, B. K. & Schoolmaster, D. R. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology 93, 1867–1879 (2012).

    Article  PubMed  Google Scholar 

  34. Treseder, K. K. & Lennon, J. T. Fungal traits that drive ecosystem dynamics. Microbiol. Molec. Biol. Rev. 79, 243–262 (2015).

    Article  CAS  Google Scholar 

  35. Czechowska, K., Johnson, D. R. & van der Meer, J. R. Use of flow cytometric methods for single-cell analysis in environmental microbiology. Curr. Opin. Microbiol. 11, 205–212 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Galand, P. E. et al. Contrasting activity patterns determined by BrdU incorporation in bacterial ribotypes from the Arctic Ocean in winter. Front. Microbiol. 4, 118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Biller, S. J., Berube, P. M., Lindell, D. & Chisholm, S. W. Procholorcoccus: the structure and function of collective diversity. Nat. Rev. Microbiol. 13, 13–27 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Wagner, M. Single cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging. Annu. Rev. Microbiol. 63, 411–429 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Pester, M., Bittner, N., Deevong, P., Wagner, M. & Loy, A. A ‘rare biosphere’ microorganism drives sulfate reduction in a peatland. ISME J. 4, 1591–1602 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Behrens, S., Kappler, A. & Obst, M. Linking environmental processes to the in situ functioning of microorganisms by high-resolution secondary ion mass spectrometry (NanoSIMS) and scanning transmission X-ray microscopy (STXM). Env. Microbiol. 14, 2851–2869 (2012).

    Article  CAS  Google Scholar 

  43. Norland, S., Fagerbakke, K. & Heldel, M. Light element analysis of individual bacteria by X-ray microanalysis. Appl. Environ. Microbiol. 61, 1357–1362 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Wilbanks, E. G. Microscale sulfur cycling in the phototrophic pink berry consortia of the Sippewissett Salt Marsh. Environ. Microbiol. 16, 3398–3415 (2014).

  45. Hall, E. K. et al. Linking microbial and ecosystem ecology using ecological stoichiometry: a synthesis of conceptual and empirical approaches. Ecosystems 14, 261–273 (2011).

    Article  Google Scholar 

Download references


This work is a product of the Next Generation of Ecosystem Indicators Working Group, supported by the USGS John Wesley Powell Center for Synthesis and Analysis. Development of this manuscript was supported by NSF DEB IOS #1456959, awarded to E.K.H., and was prepared in part by E.K.H. as writer in residence at the Wolverine Farm Publick House and Press. C. Pepe-Ranney and A. Peralta provided valuable feedback on previous versions of this manuscript. This paper is dedicated to D. Nemergut, an integral part of our working group who passed away during the preparation of this manuscript.

Author information

Authors and Affiliations



All listed authors have contributed to the conceptualization, writing and preparation of the manuscript.

Corresponding author

Correspondence to Ed K. Hall.

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hall, E.K., Bernhardt, E.S., Bier, R.L. et al. Understanding how microbiomes influence the systems they inhabit. Nat Microbiol 3, 977–982 (2018).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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