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09 21, 2020;3 (1): 520.

A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition.

Elsa Abs, Hélène Leman, Régis Ferrière
Author Information
  1. Elsa Abs: Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, 92697, USA. eabs@uci.edu. ORCID
  2. Hélène Leman: Numed Inria team, UMPA UMR 5669, Ecole Normale Supérieure, Lyon, 69364, France. helene.leman@inria.fr.
  3. Régis Ferrière: Interdisciplinary Center for Interdisciplinary Global Environmental Studies (iGLOBES), CNRS, Ecole Normale Supérieure, Paris Sciences & Lettres University, University of Arizona, Tucson, AZ, 85721, USA. regisf@email.arizona.edu.
PMID: 32958833 DOI: 10.1038/s42003-020-01198-4

Abstract

The decomposition of soil organic matter (SOM) is a critical process in global terrestrial ecosystems. SOM decomposition is driven by micro-organisms that cooperate by secreting costly extracellular (exo-)enzymes. This raises a fundamental puzzle: the stability of microbial decomposition in spite of its evolutionary vulnerability to "cheaters"-mutant strains that reap the benefits of cooperation while paying a lower cost. Resolving this puzzle requires a multi-scale eco-evolutionary model that captures the spatio-temporal dynamics of molecule-molecule, molecule-cell, and cell-cell interactions. The analysis of such a model reveals local extinctions, microbial dispersal, and limited soil diffusivity as key factors of the evolutionary stability of microbial decomposition. At the scale of whole-ecosystem function, soil diffusivity influences the evolution of exo-enzyme production, which feeds back to the average SOM decomposition rate and stock. Microbial adaptive evolution may thus be an important factor in the response of soil carbon fluxes to global environmental change.

References

  1. Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive eartha92s biogeochemical cycles. Science 320, 1034–1039 (2008). [PMID: 18497287]
  2. Ratledge, C. (ed.) In Biochemistry of Microbial Degradation 89–141 (Kluwer Academic Publishers, Boston, MA, 1994).
  3. Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. Elife 2, e01169 (2013). [PMID: 24347543]
  4. Driscoll, W. W. & Pepper, J. W. Theory for the evolution of diffusible external goods. Evolution: Int. J. Org. Evolution 64, 2682–2687 (2010). [DOI: 10.1111/j.1558-5646.2010.01002.x]
  5. Velicer, G. J. Social strife in the microbial world. Trends Microbiol. 11, 330–337 (2003). [PMID: 12875817]
  6. West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nat. Rev. Microbiol. 4, 597 (2006). [PMID: 16845430]
  7. Nowak, M. A. Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006). [PMID: 17158317]
  8. Buckling, A. et al. Siderophore-mediated cooperation and virulence in pseudomonas aeruginosa. FEMS Microbiol. Ecol. 62, 135–141 (2007). [PMID: 17919300]
  9. Cordero, O. X., Ventouras, L.-A., DeLong, E. F. & Polz, M. F. Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations. Proc. Natl Acad. Sci. 109, 20059–20064 (2012).
  10. Griffin, A. S., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430, 1024 (2004). [PMID: 15329720]
  11. Julou, T. et al. Cell-cell contacts confine public goods diffusion inside pseudomonas aeruginosa clonal microcolonies. Proc. Natl Acad. Sci. 110, 12577–12582 (2013). [PMID: 23858453]
  12. Rainey, P. B. & Rainey, K. Evolution of cooperation and conflict in experimental bacterial populations. Nature 425, 72 (2003). [PMID: 12955142]
  13. LeGac, M. & Doebeli, M. Environmental viscosity does not affect the evolution of cooperation during experimental evolution of colicigenic bacteria. Evol.: Int. J. Org. Evol. 64, 522–533 (2010). [DOI: 10.1111/j.1558-5646.2009.00814.x]
  14. Koch, A. L. The Macroeconomics of Bacterial Growth (Special Publications of the Society for General Microbiology) (Academic Press, 1985).
  15. Schimel, J. P. & Weintraub, M. N. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol. Biochem. 35, 549–563 (2003). [DOI: 10.1016/S0038-0717(03)00015-4]
  16. Sinsabaugh, R. L. & Moorhead, D. L. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorus control of litter decomposition. Soil Biol. Biochem. 26, 1305–1311 (1994). [DOI: 10.1016/0038-0717(94)90211-9]
  17. Allison, S. D. Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments. Ecol. Lett. 8, 626–635 (2005). [DOI: 10.1111/j.1461-0248.2005.00756.x]
  18. Folse, H. J. & Allison, S. D. Cooperation, competition, and coalitions in enzyme-producing microbes: social evolution and nutrient depolymerization rates. Front. Microbiol. 3, 338 (2012). [PMID: 23060866]
  19. Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014). [PMID: 24628731]
  20. Kaiser, C., Franklin, O., Richter, A. & Dieckmann, U. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nat. Commun. 6, 8960 (2015). [PMID: 26621582]
  21. Tisdall, J. M. & Oades, J. M. Organic matter and water-stable aggregates in soils. J. Soil Sci. 33, 141–163 (1982). [DOI: 10.1111/j.1365-2389.1982.tb01755.x]
  22. Vetter, Y. A., Deming, J. W., Jumars, P. A. & Krieger-Brockett, B. B. A predictive model of bacterial foraging by means of freely released extracellular enzymes. Microb. Ecol. 36, 75–92 (1998). [PMID: 9622567]
  23. Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. J. Evolut. Biol. 27, 1869–1877 (2014). [DOI: 10.1111/jeb.12437]
  24. Ferriere, R., Bronstein, J. L., Rinaldi, S., Law, R. & Gauduchon, M. Cheating and the evolutionary stability of mutualisms. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 269, 773–780 (2002).
  25. Lee, W., van Baalen, M. & Jansen, V. A. A. Siderophore production and the evolution of investment in a public good: an adaptive dynamics approach to kin selection. J. Theor. Biol. 388, 61–71 (2016). [PMID: 26471069]
  26. Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define fitness for general ecological scenarios? Trends Ecol. Evol.7, 198–202 (1992). [PMID: 21236007]
  27. Ferriere, R. & Gatto, M. Lyapunov exponents and the mathematics of invasion in oscillatory or chaotic populations. Theor. Popul. Biol. 48, 126–171 (1995). [DOI: 10.1006/tpbi.1995.1024]
  28. Abs, E. & Ferrière, R. In Biogeochemical Cycles: Ecological Drivers and Environmental Impact (American Geophysical Union, 2020).
  29. Wieder, W. R. et al. Explicitly representing soil microbial processes in earth system models. Glob. Biogeochem. Cycles 29, 1782–1800 (2015). [DOI: 10.1002/2015GB005188]
  30. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165 (2006). [PMID: 16525463]
  31. Alster, C. J., Baas, P., Wallenstein, M. D., Johnson, N. G. & von Fischer, J. C. Temperature sensitivity as a microbial trait using parameters from macromolecular rate theory. Front. Microbiol. 7, 1821 (2016). [PMID: 27909429]
  32. Trivedi, P. et al. Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships. ISME J. 10, 2593 (2016). [PMID: 27168143]
  33. Comins, H. N., Hamilton, W. D. & May, R. M. Evolutionarily stable dispersal strategies. J. Theor. Biol. 82, 205–230 (1980). [PMID: 7374178]
  34. Friedenberg, N. A. Experimental evolution of dispersal in spatiotemporally variable microcosms. Ecol. Lett. 6, 953–959 (2003). [DOI: 10.1046/j.1461-0248.2003.00524.x]
  35. Gandon, S. & Michalakis, Y. Evolutionarily stable dispersal rate in a metapopulation with extinctions and kin competition. J. Theor. Biol. 199, 275–290 (1999). [PMID: 10433892]
  36. Olivieri, I., Michalakis, Y. & Gouyon, P.-H. Metapopulation genetics and the evolution of dispersal. Am. Naturalist 146, 202–228 (1995). [DOI: 10.1086/285795]
  37. Champagnat, N., Ferrière, R. & Méléard, S. Unifying evolutionary dynamics: from individual stochastic processes to macroscopic models. Theor. Popul. Biol. 69, 297–321 (2006). [PMID: 16460772]
  38. Metz, J. A. J., Geritz, S. A. H., Meszéna, G., Jacobs, F. J. A. & Van Heerwaarden, J. S. In Stochastic and Spatial Structures of Dynamical Systems (eds Strien, S.J. van & Lunel, S.M. Verduyn) (North-Holland, Amsterdam, 1995).
  39. Ferriere, R. Spatial structure and viability of small populations. Revue d’Ecologie-La Terre et la Vie 7, 135–138 (2000).
  40. Ferriere, R. & Legendre, S. Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory. Philos. Trans. R. Soc. B: Biol. Sci. 368, 20120081 (2013). [DOI: 10.1098/rstb.2012.0081]
  41. Parvinen, K. Evolutionary suicide. Acta Biotheoretica 53, 241–264 (2005). [PMID: 16329010]
  42. Champagnat, N. et al. Evolution of discrete populations and the canonical diffusion of adaptive dynamics. Ann. Appl. Probab. 17, 102–155 (2007). [DOI: 10.1214/105051606000000628]
  43. Ross-Gillespie, A., Gardner, A., West, S. A. & Griffin, A. S. Frequency dependence and cooperation: theory and a test with bacteria. Am. Naturalist 170, 331–342 (2007). [DOI: 10.1086/519860]
  44. West, S. A. & Buckling, A. Cooperation, virulence and siderophore production in bacterial parasites. Proc. R. Soc. Lond. B: Biol. Sci. 270, 37–44 (2003).
  45. Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012). [PMID: 22642621]
  46. Homyak, P. M. et al. Effects of altered dry season length and plant inputs on soluble soil carbon. Ecology 99, 2348–2362 (2018). [PMID: 30047578]
  47. Zhang, X. et al. Assessing five evolving microbial enzyme models against field measurements from a semiarid savannah-what are the mechanisms of soil respiration pulses? Geophys. Res. Lett. 41, 6428–6434 (2014). [DOI: 10.1002/2014GL061399]
  48. Allison, S. D. & Goulden, M. L. Consequences of drought tolerance traits for microbial decomposition in the dement model. Soil Biol. Biochem. 107, 104–113 (2017). [DOI: 10.1016/j.soilbio.2017.01.001]
  49. Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2019).
  50. Melbourne, B. A. & Chesson, P. The scale transition: scaling up population dynamics with field data. Ecology 87, 1478–1488 (2006). [PMID: 16869424]
  51. Chakrawal, A. et al. Dynamic upscaling of decomposition kinetics for carbon cycling models. Geosci. Model Dev. 13, 1399–1429 (2020).
  52. Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909 (2013). [DOI: 10.1038/nclimate1951]
  53. Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336 (2010). [DOI: 10.1038/ngeo846]
  54. Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE 9, e87217 (2014). [PMID: 24489873]
  55. Kierzek, A. M. Stocks: stochastic kinetic simulations of biochemical systems with gillespie algorithm. Bioinformatics 18, 470–481 (2002). [PMID: 11934747]
  56. Fournier, N. & Méléard, S. A microscopic probabilistic description of a locally regulated population and macroscopic approximations. Ann. Appl. Probab. 14, 1880–1919 (2004). [DOI: 10.1214/105051604000000882]
  57. 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). [DOI: 10.1111/j.1365-2486.2011.02615.x]
  58. Hagerty, S. B. et al. Accelerated microbial turnover but constant growth efficiency with warming in soil. Nat. Clim. Change 4, 903 (2014). [DOI: 10.1038/nclimate2361]
  59. Geritz, S. A. H. et al. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12, 35–57 (1998). [DOI: 10.1023/A]
  60. Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns in belowground communities. Ecol. Lett. 12, 1238–1249 (2009). [PMID: 19674041]
  61. Abs. elsaabs/IBMAbsLemFer: First release of IBMAbsLemFer. https://doi.org/10.5281/zenodo.3892054 (2020).

MeSH Term

Adaptation, Physiological
Bacteria
Biological Evolution
Ecosystem
Models, Biological
Soil
Soil Microbiology

Chemicals

Soil
Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
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Word Cloud

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