Evolutionary history constrains microbial traits across environmental variation


Organisms influence ecosystems, from element cycling to disturbance regimes, to trophic interactions and to energy partitioning. Microorganisms are part of this influence, and understanding their ecology in nature requires studying the traits of these organisms quantitatively in their natural habitats—a challenging task, but one which new approaches now make possible. Here, we show that growth rate and carbon assimilation rate of soil microorganisms are influenced more by evolutionary history than by climate, even across a broad climatic gradient spanning major temperate life zones, from mixed conifer forest to high-desert grassland. Most of the explained variation (~50% to ~90%) in growth rate and carbon assimilation rate was attributable to differences among taxonomic groups, indicating a strong influence of evolutionary history, and taxonomic groupings were more predictive for organisms responding to resource addition. With added carbon and nitrogen substrates, differences among taxonomic groups explained approximately eightfold more variance in growth rate than did differences in ecosystem type. Taxon-specific growth and carbon assimilation rates were highly intercorrelated across the four ecosystems, constrained by the taxonomic identity of the organisms, such that plasticity driven by environment was limited across ecosystems varying in temperature, precipitation and dominant vegetation. Taken together, our results suggest that, similar to multicellular life, the traits of prokaryotes in their natural habitats are constrained by evolutionary history to a greater degree than environmental variation.

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Fig. 1: Schematic of the ecosystems sampled along the C. Hart Merriam elevation gradient near Flagstaff, northern AZ.

Victor O. Leshyk, Center for Ecosystem Science and Society, Northern Arizona University

Fig. 2: Variation in microbial community composition across the ecosystems studied.
Fig. 3: Variance in potential growth rate and carbon assimilation rate attributable to taxonomy and ecosystem.
Fig. 4: Example taxa representative of variation in trait values in response to substrate addition and across ecosystems.
Fig. 5: Comparison of pairwise inter-ecosystem similarity in microbial traits (correlations of taxon-specific trait values between ecosystems) and dissimilarity in environment (Gower’s distance considering elevation, precipitation and temperature).

Data availability

The data supporting this manuscript are available in the NCBI short-read archive under accession no. PRJNA521534.


  1. 1.

    Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).

    Article  Google Scholar 

  2. 2.

    Verheijen, L. M. et al. Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model. Glob. Change Biol. 21, 3074–3086 (2015).

    Article  Google Scholar 

  3. 3.

    Krause, S. et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front. Microbiol. 5, 251 (2014).

    Article  Google Scholar 

  4. 4.

    Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Tripathi, A. et al. Are microbiome studies ready for hypothesis-driven research? Curr. Opin. Microbiol. 44, 61–69 (2018).

    Article  Google Scholar 

  6. 6.

    Martiny, A. C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Ho, A., Di Lonardo, D. P. & Bodelier, P. L. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol. Ecol. 93, fix006 (2017).

    Article  Google Scholar 

  8. 8.

    Green, J. L., Bohannan, B. J. & Whitaker, R. J. Microbial biogeography: from taxonomy to traits. Science 320, 1039–1043 (2008).

    CAS  Article  Google Scholar 

  9. 9.

    Philippot, L. et al. The ecological coherence of high bacterial taxonomic ranks. Nat. Rev. Microbiol. 8, 523 (2010).

    CAS  Article  Google Scholar 

  10. 10.

    McGill, B. J. Exploring predictions of abundance from body mass using hierarchical comparative approaches. Am. Nat. 172, 88–101 (2008).

    Article  Google Scholar 

  11. 11.

    Neyret, M. et al. Examining variation in the leaf mass per area of dominant species across two contrasting tropical gradients in light of community assembly. Ecol. Evol. 6, 5674–5689 (2016).

    Article  Google Scholar 

  12. 12.

    Messier, J., McGill, B. J. & Lechowicz, M. J. How do traits vary across ecological scales? A case for trait‐based ecology. Ecol. Lett. 13, 838–848 (2010).

    Article  Google Scholar 

  13. 13.

    Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).

    Article  Google Scholar 

  14. 14.

    McGill, B. J. et al. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).

    Article  Google Scholar 

  15. 15.

    Martiny, J. B. et al. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).

    Article  Google Scholar 

  16. 16.

    Gogarten, J. P. & Townsend, J. P. Horizontal gene transfer, genome innovation and evolution. Nat. Rev. Microbiol. 3, 679 (2005).

    CAS  Article  Google Scholar 

  17. 17.

    Popa, O., Landan, G. & Dagan, T. Phylogenomic networks reveal limited phylogenetic range of lateral gene transfer by transduction. ISME J. 11, 543 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Lenski, R. E. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 11, 2181 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Blankinship, J. C. et al. Response of terrestrial CH4 uptake to interactive changes in precipitation and temperature along a climatic gradient. Ecosystem 13, 1157–1170 (2010).

    CAS  Article  Google Scholar 

  20. 20.

    Liu, X. J. A. et al. Labile carbon input determines the direction and magnitude of the priming effect. Appl. Soil Ecol. 109, 7–13 (2017).

    Article  Google Scholar 

  21. 21.

    Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Jones, D. L., Nguyen, C. & Finlay, R. D. Carbon flow in the rhizosphere: carbon trading at the soil–root interface. Plant Soil 321, 5–33 (2009).

    CAS  Article  Google Scholar 

  23. 23.

    Austin, A. T. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004).

    Article  Google Scholar 

  24. 24.

    Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).

    CAS  Article  Google Scholar 

  25. 25.

    Albert, C. H. et al. A multi‐trait approach reveals the structure and the relative importance of intra‐vs. interspecific variability in plant traits. Funct. Ecol. 24, 1192–1201 (2010).

    Article  Google Scholar 

  26. 26.

    Jiang, Y. et al. Interspecific and intraspecific variation in functional traits of subtropical evergreen and deciduous broadleaved mixed forests in Karst topography, Guilin, Southwest China. Trop. Conserv. Sci. 9 9, https://doi.org/10.1177/1940082916680211 (2016).

    Article  Google Scholar 

  27. 27.

    Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635 (2014).

    CAS  Article  Google Scholar 

  28. 28.

    Morrissey, E. M. et al. Phylogenetic organization of bacterial activity. ISME J. 10, 2016 (2016).

    Article  Google Scholar 

  29. 29.

    Goberna, M. & Verdú, M. Predicting microbial traits with phylogenies. ISME J. 10, 959 (2016).

    Article  Google Scholar 

  30. 30.

    Vilà‐Cabrera, A., Martínez‐Vilalta, J. & Retana, J. Functional trait variation along environmental gradients in temperate and Mediterranean trees. Glob. Ecol. Biogeogr. 24, 1377–1389 (2015).

    Article  Google Scholar 

  31. 31.

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

    CAS  Article  Google Scholar 

  32. 32.

    Morrissey, E. M. et al. Taxonomic patterns in the nitrogen assimilation of soil prokaryotes. Environ. Microbiol. 20, 1112–1119 (2018).

    CAS  Article  Google Scholar 

  33. 33.

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621 (2012).

    CAS  Article  Google Scholar 

  34. 34.

    Berry, D. et al. Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Appl. Environ. Microbiol. 77, 7846–7849 (2011).

    CAS  Article  Google Scholar 

  35. 35.

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

    CAS  Article  Google Scholar 

  36. 36.

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

    CAS  Article  Google Scholar 

  37. 37.

    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

    CAS  Article  Google Scholar 

  38. 38.

    Wang, Q. et al. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  Article  Google Scholar 

  39. 39.

    Bokulich, N. A. et al. (2013). Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57 (2013).

    CAS  Article  Google Scholar 

  40. 40.

    Morrissey, E. M. et al. (2017). Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 11, 1890 (2017).

    Article  Google Scholar 

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This research was supported by grants from the Department of Energy’s Biological Systems Science Division (No. DE-SC0016207), Program in Genomic Science and the National Science Foundation (Nos. DEB-1645596 and DEB-1241094). Work at Lawrence Livermore National Laboratory (LLNL) was funded by the Department of Energy through the Genome Sciences Program under contract Nos. SCW1424 and SCW1590, and performed under the auspices of LLNL under Contract No. DE-AC52-07NA27344.

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E.M.M., E.S. and B.A.H. developed the core ideas explored in this manuscript. R.L.M., M.H. and X.-J.A.L. carried out the soil manipulation and participated in sample analysis. R.L.M. and M.H. performed the sequencing and initial bioinformatics analysis. E.M.M., B.J.K. and K.A. performed the data analysis. K.H., J.P-R., S.J.B., B.A.H. and P.D. provided guidance on data analysis and interpretation. E.M.M. drafted the manuscript and all authors contributed equally to writing, editing and revision.

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Correspondence to Ember M. Morrissey.

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Morrissey, E.M., Mau, R.L., Hayer, M. et al. Evolutionary history constrains microbial traits across environmental variation. Nat Ecol Evol 3, 1064–1069 (2019). https://doi.org/10.1038/s41559-019-0918-y

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