Skip to main content

Quantifying microbial control of soil organic matter dynamics at macrosystem scales

Abstract

Soil organic matter (SOM) stocks, decomposition and persistence are largely the product of controls that act locally. Yet the controls are shaped and interact at multiple spatiotemporal scales, from which macrosystem patterns in SOM emerge. Theory on SOM turnover recognizes the resulting spatial and temporal conditionality in the effect sizes of controls that play out across macrosystems, and couples them through evolutionary and community assembly processes. For example, climate history shapes plant functional traits, which in turn interact with contemporary climate to influence SOM dynamics. Selection and assembly also shape the functional traits of soil decomposer communities, but it is less clear how in turn these traits influence temporal macrosystem patterns in SOM turnover. Here, we review evidence that establishes the expectation that selection and assembly should generate decomposer communities across macrosystems that have distinct functional effects on SOM dynamics. Representation of this knowledge in soil biogeochemical models affects the magnitude and direction of projected SOM responses under global change. Yet there is high uncertainty and low confidence in these projections. To address these issues, we make the case that a coordinated set of empirical practices are required which necessitate (1) greater use of statistical approaches in biogeochemistry that are suited to causative inference; (2) long-term, macrosystem-scale, observational and experimental networks to reveal conditionality in effect sizes, and embedded correlation, in controls on SOM turnover; and (3) use of multiple measurement grains to capture local- and macroscale variation in controls and outcomes, to avoid obscuring causative understanding through data aggregation. When employed together, along with process-based models to synthesize knowledge and guide further empirical work, we believe these practices will rapidly advance understanding of microbial controls on SOM and improve carbon cycle projections that guide policies on climate adaptation and mitigation.

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

Fig. 1
Fig. 2
Fig. 3

Data availability

No new data were collected for this manuscript, and there is no supplementary material.

References

  1. Abramoff R, Xu XF, Hartman M et al (2018) The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century. Biogeochemistry 137:51–71. https://doi.org/10.1007/s10533-017-0409-7

    Article  Google Scholar 

  2. Abramoff RZ, Torn MS, Georgiou K et al (2019) Soil organic matter temperature sensitivity cannot be directly inferred from spatial gradients. Global Biogeochem Cycles 33:761–776. https://doi.org/10.1029/2018GB006001

    Article  Google Scholar 

  3. Adhikari K, Mishra U, Owens PR et al (2020) Importance and strength of environmental controllers of soil organic carbon changes with scale. Geoderma 375:114472. https://doi.org/10.1016/j.geoderma.2020.114472

    Article  Google Scholar 

  4. Aerts R (1997) Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos 79:439–449

    Article  Google Scholar 

  5. Agren GI, McMurtrie RE, Parton WJ et al (1991) State-of-the-art of models of production decomposition linkages in conifer and grassland ecosystems. Ecol Appl 1:118–138. https://doi.org/10.2307/1941806

    Article  Google Scholar 

  6. Albright MBN, Thompson J, Kroeger ME et al (2020) Differences in substrate use linked to divergent carbon flow during litter decomposition. FEMS Microbiol Ecol 96:fiaa135. https://doi.org/10.1093/femsec/fiaa135

    Article  Google Scholar 

  7. Allison SD, Martiny JBH (2008) Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA 105:115212–211519

    Article  Google Scholar 

  8. Anthony MA, Crowther TW, Maynard DS et al (2020) Distinct assembly processes and microbial communities constrain soil organic carbon formation. One Earth 2:349–360. https://doi.org/10.1016/j.oneear.2020.03.006

    Article  Google Scholar 

  9. Arnqvist G (2020) Mixed models offer no freedom from degrees of freedom. Trends Ecol Evol 35:329–335. https://doi.org/10.1016/j.tree.2019.12.004

    Article  Google Scholar 

  10. Arora VK, Boer GJ, Friedlingstein P et al (2013) Carbon-concentration and carbon-climate feedbacks in CMIP5 Earth System Models. J Clim 26:5289–5314

    Article  Google Scholar 

  11. Averill C, Waring BG, Hawkes CV (2016) Historical precipitation predictably alters the shape and magnitude of microbial functional response to soil moisture. Global Change Biol 22:1957–1964

    Article  Google Scholar 

  12. Ayres E, Steltzer H, Simmons BL et al (2009) Home-field advantage accelerates leaf litter decomposition in forests. Soil Biol Biochem 41:606–610

    Article  Google Scholar 

  13. Bárcenas-Moreno G, Gómez-Brandón M, Rousk J, Bååth E (2009) Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Global Change Biol 15:2950–2957

    Article  Google Scholar 

  14. Baumberger C, Knutti R, Hirsch Hadorn G (2017) Building confidence in climate model projections: an analysis of inferences from fit. WIREs Clim Change 8:e454. https://doi.org/10.1002/wcc.454

    Article  Google Scholar 

  15. Baym M, Lieberman TD, Kelsic ED et al (2016) Spatiotemporal microbial evolution on antibiotic landscapes. Science 353:1147–1151. https://doi.org/10.1126/science.aag0822

    Article  Google Scholar 

  16. Berhe AA, Barnes RT, Six J, Marín-Spiotta E (2018) Role of soil erosion in biogeochemical cycling of essential elements: carbon, nitrogen, and phosphorus. Annu Rev Earth Planet Sci 46:521–548. https://doi.org/10.1146/annurev-earth-082517-010018

    Article  Google Scholar 

  17. Bestelmeyer BT, Ellison AM, Fraser WR et al (2011) Analysis of abrupt transitions in ecological systems. Ecosphere 2:art129. https://doi.org/10.1890/ES11-00216.1

    Article  Google Scholar 

  18. Betancourt M (2018) A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/pdf/1701.02434.pdf

  19. Blankinship JC, Berhe AA, Crow SE et al (2018) Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140:1–13. https://doi.org/10.1007/s10533-018-0478-2

    Article  Google Scholar 

  20. Bolnick DI, Amarasekare P, Araujo MS et al (2011) Why intraspecific trait variation matters in community ecology. Trends Ecol Evol 26:183–192. https://doi.org/10.1016/j.tree.2011.01.009

    Article  Google Scholar 

  21. Bonan GB, Levis S, Kergoat L, Oleson KW (2002) Landscapes as patches of plant functional types: an integrating concept for climate and ecosystem models. Global Biogeochem Cycles 16:5–23

    Article  Google Scholar 

  22. Bonan GB, Hartman MD, Parton WJ, Wieder WR (2013) Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biol 19:957–974. https://doi.org/10.1111/gcb.12031

    Article  Google Scholar 

  23. Bond WJ (1989) The tortoise and the hare—ecology of angiosperm dominance and gymnosperm persistence. Biol J Linnean Soc 36:227–249. https://doi.org/10.1111/j.1095-8312.1989.tb00492.x

    Article  Google Scholar 

  24. Bradford MA, Reynolds JF (2006) Scaling terrestrial biogeochemical processes: contrasting intact and model experimental systems. In: Wu J, Jones B, Li H, Loucks OL (eds) Scaling and uncertainty analysis in ecology: methods and applications. Springer, Amsterdam, pp 107–128

    Google Scholar 

  25. Bradford MA, Warren RJ II, Baldrian P et al (2014) Climate fails to predict wood decomposition at regional scales. Nat Clim Change 4:625–630. https://doi.org/10.1038/nclimate2251

    Article  Google Scholar 

  26. Bradford MA, Berg B, Maynard DS et al (2016a) Understanding the dominant controls on litter decomposition. J Ecol 104:229–238

    Article  Google Scholar 

  27. Bradford MA, Wieder WR, Bonan GB et al (2016b) Managing uncertainty in soil carbon feedbacks to climate change. Nat Clim Change 6:751–758. https://doi.org/10.1038/nclimate3071

    Article  Google Scholar 

  28. Bradford MA, Veen GFC, Bonis A et al (2017) A test of the hierarchical model of litter decomposition. Nat Ecol Evol 1:1836–1845

    Article  Google Scholar 

  29. Bradford MA, McCulley RL, Crowther TW et al (2019) Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat Ecol Evol 3:223–231. https://doi.org/10.1038/s41559-018-0771-4

    Article  Google Scholar 

  30. Buchkowski RW, Bradford MA, Grandy AS et al (2017) Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol Lett 20:231–245

    Article  Google Scholar 

  31. Bünemann EK, Bongiorno G, Bai Z et al (2018) Soil quality—a critical review. Soil Biol Biochem 120:105–125. https://doi.org/10.1016/j.soilbio.2018.01.030

    Article  Google Scholar 

  32. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Meth Res 33:261–304. https://doi.org/10.1177/0049124104268644

    Article  Google Scholar 

  33. Buzzard V, Michaletz ST, Deng Y et al (2019) Continental scale structuring of forest and soil diversity via functional traits. Nat Ecol Evol 3:1298–1308. https://doi.org/10.1038/s41559-019-0954-7

    Article  Google Scholar 

  34. Cash DW, Adger WN, Berkes F et al (2006) Scale and cross-scale dynamics: governance and information in a multilevel world. Ecol Soc 11:art8. https://doi.org/10.5751/ES-01759-110208

    Article  Google Scholar 

  35. Chadwick KD, Brodrick PG, Grant K et al (2020) Integrating airborne remote sensing and field campaigns for ecology and Earth system science. Methods Ecol Evol 11:1492–1508. https://doi.org/10.1111/2041-210X.13463

    Article  Google Scholar 

  36. Clark JS (2010) Individuals and the variation needed for high species diversity in forest trees. Science 327:1129–1132. https://doi.org/10.1126/science.1183506

    Article  Google Scholar 

  37. Clark AT, Ann Turnbull L, Tredennick A et al (2020) Predicting species abundances in a grassland biodiversity experiment: Trade-offs between model complexity and generality. J Ecol 108:774–787. https://doi.org/10.1111/1365-2745.13316

    Article  Google Scholar 

  38. Cline LC, Zak DR (2014) Dispersal limitation structures fungal community assembly in a long-term glacial chronosequence. Environ Microbiol 16:1538–1548

    Article  Google Scholar 

  39. Cornwell WK, Cornelissen JHC, Amatangelo K et al (2008) Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol Lett 11:1065–1071. https://doi.org/10.1111/j.1461-0248.2008.01219.x

    Article  Google Scholar 

  40. Cotrufo MF, Ranalli MG, Haddix ML et al (2019) Soil carbon storage informed by particulate and mineral-associated organic matter. Nat Geosci 12:989–994

    Article  Google Scholar 

  41. Crowther TW, Maynard DS, Crowther TS et al (2014) Untangling the fungal niche: the trait-based approach. Front Microbiol 5:579. https://doi.org/10.3389/fmicb.2014.00579

    Article  Google Scholar 

  42. Crowther TW, Thomas SM, Maynard DS et al (2015) Biotic interactions mediate soil microbial feedbacks to climate change. Proc Natl Acad Sci USA 112:7033–7038

    Article  Google Scholar 

  43. Crowther TW, van den Hoogen J, Wan J et al (2019) The global soil community and its influence on biogeochemistry. Science 365:eaav0550. https://doi.org/10.1126/science.aav0550

    Article  Google Scholar 

  44. Currie WS, Harmon ME, Burke IC et al (2010) Cross-biome transplants of plant litter show decomposition models extend to a broader climatic range but lose predictability at the decadal time scale. Global Change Biol 16:1744–1761

    Article  Google Scholar 

  45. Dacal M, Bradford MA, Plaza C et al (2019) Soil microbial respiration adapts to ambient temperature in global drylands. Nat Ecol Evol 3:232–238. https://doi.org/10.1038/s41559-018-0770-5

    Article  Google Scholar 

  46. Deeks JJ, Higgins JPT, Altman DG (2020) Analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J et al (eds) Cochrane Handbook for Systematic Reviews of Interventions version 6.1. Cochrane

  47. Delgado-Baquerizo M, Eldridge DJ, Maestre FT et al (2017) Climate legacies drive global soil carbon stocks in terrestrial ecosystems. Sci Adv 3:e1602008. https://doi.org/10.1126/sciadv.1602008

    Article  Google Scholar 

  48. Denny M (2017) The fallacy of the average: on the ubiquity, utility and continuing novelty of Jensen’s inequality. J Exp Bot 220:139–146. https://doi.org/10.1242/jeb.140368

    Article  Google Scholar 

  49. Dickie IA, Fukami T, Wilkie JP et al (2012) Do assembly history effects attenuate from species to ecosystem properties? A field test with wood-inhabiting fungi. Ecol Lett 15:133–141. https://doi.org/10.1111/j.1461-0248.2011.01722.x

    Article  Google Scholar 

  50. Dixon Hamil K-A, Iannone BV III, Huang WK et al (2016) Cross-scale contradictions in ecological relationships. Landsc Ecol 31:7–18. https://doi.org/10.1007/s10980-015-0288-z

    Article  Google Scholar 

  51. Doetterl S, Stevens A, Six J et al (2015) Soil carbon storage controlled by interactions between geochemistry and climate. Nat Geosci 8:780–783. https://doi.org/10.1038/ngeo2516

    Article  Google Scholar 

  52. Domeignoz-Horta LA, Pold G, Liu X-JA et al (2020) Microbial diversity drives carbon use efficiency in a model soil. Nat Commun 11:3684. https://doi.org/10.1038/s41467-020-17502-z

    Article  Google Scholar 

  53. Evans SE, Wallenstein MD (2012) Soil microbial community response to drying and rewetting stress: does historical precipitation regime matter? Biogeochemistry 109:101–116

    Article  Google Scholar 

  54. Evans SE, Wallenstein MD (2014) Climate change alters ecological strategies of soil bacteria. Ecol Lett 17:155–164

    Article  Google Scholar 

  55. Evans SE, Wallenstein MD, Burke IC (2014) Is bacterial moisture niche a good predictor of shifts in community composition under long-term drought? Ecology 95:110–122

    Article  Google Scholar 

  56. Faber J, Quadros AF, Zimmer M (2018) A space-for-time approach to study the effects of increasing temperature on leaf litter decomposition under natural conditions. Soil Biol Biochem 123:250–256. https://doi.org/10.1016/j.soilbio.2018.05.010

    Article  Google Scholar 

  57. Fei S, Guo Q, Potter K (2016) Macrosystems ecology: novel methods and new understanding of multi-scale patterns and processes. Landsc Ecol 31:1–6. https://doi.org/10.1007/s10980-015-0315-0

    Article  Google Scholar 

  58. Ferraro PJ, Sanchirico JN, Smith MD (2019) Causal inference in coupled human and natural systems. Proc Natl Acad Sci USA 116:5311–5318. https://doi.org/10.1073/pnas.1805563115

    Article  Google Scholar 

  59. Firn J, McGree JM, Harvey E et al (2019) Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat Ecol Evol 3:400–406. https://doi.org/10.1038/s41559-018-0790-1

    Article  Google Scholar 

  60. Fisher RA, Koven CD (2020) Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems. J Adv Model Earth Syst. https://doi.org/10.1029/2018MS001453

    Article  Google Scholar 

  61. Fisher RA, Muszala S, Verteinstein M et al (2015) Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED). Geosci Model Dev 8:3593–3619

    Article  Google Scholar 

  62. Fisher RA, Koven CD, Anderegg WRL et al (2018) Vegetation demographics in earth system models: a review of progress and priorities. Global Change Biol 24:35–54. https://doi.org/10.1111/gcb.13910

    Article  Google Scholar 

  63. Fitch AA, Lang AK, Whalen ED et al (2020) Fungal community, not substrate quality, drives soil microbial function in northeastern U.S. temperate forests. Front For Global Change 3:569945. https://doi.org/10.3389/ffgc.2020.569945

    Article  Google Scholar 

  64. Friedlingstein P, Cox P, Betts R et al (2006) Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J Clim 19:3337–3353

    Article  Google Scholar 

  65. Fukami T (2015) Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst 46:1–23. https://doi.org/10.1146/annurev-ecolsys-110411-160340

    Article  Google Scholar 

  66. Funk JL, Larson JE, Ames GM et al (2017) Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol Rev Cambridge Philos Soc 92:1156

    Article  Google Scholar 

  67. Geisen S, Hu S, de la Cruz TEE, Veen GFC (2020) Protists as catalyzers of microbial litter breakdown and carbon cycling at different temperature regimes. ISME J 15:618–621. https://doi.org/10.1038/s41396-020-00792-y

    Article  Google Scholar 

  68. Gelman A, Shor B, Bafumi J, Park D (2007) Rich state, poor state, red state, blue state: what’s the matter with Connecticut? Q J Poli Sci 2:345–367

    Article  Google Scholar 

  69. German DP, Chacon S, Allison SD (2011) Substrate concentration and enzyme allocation can affect rates of microbial decomposition. Ecology 92:1471–1480

    Article  Google Scholar 

  70. German DP, Marcelo KRB, Stone MM, Allison SD (2012) The Michaelis-Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Global Change Biol 18:1468–1479

    Article  Google Scholar 

  71. Gholz HL, Wedin DA, Smitherman SM et al (2000) Long-term dynamics of pine and hardwood litter in contrasting environments: toward a global model of decomposition. Global Change Biol 6:751–765

    Article  Google Scholar 

  72. Glassman SI, Weihe C, Li J et al (2018) Decomposition responses to climate depend on microbial community composition. Proc Natl Acad Sci USA 115:11994–11999. https://doi.org/10.1073/pnas.1811269115

    Article  Google Scholar 

  73. Hättenschwiler S, Gasser P (2005) Soil animals alter plant litter diversity effects on decomposition. Proc Natl Acad Sci USA 102:1519–1524

    Article  Google Scholar 

  74. Hawkes CV, Shinada M, Kivlin SN (2020) Historical climate legacies on soil respiration persist despite extreme changes in rainfall. Soil Biol Biochem 143:107752. https://doi.org/10.1016/j.soilbio.2020.107752

    Article  Google Scholar 

  75. Heffernan JB, Soranno PA, Angilletta MJ et al (2014) Macrosystems ecology: understanding ecological patterns and processes at continental scales. Front Ecol Environ 12:5–14. https://doi.org/10.1890/130017

    Article  Google Scholar 

  76. Hochachka PW, Somero GN (2002) Biochemical adaptation: mechanism and process in physiological evolution. Oxford University Press, Inc, New York

    Google Scholar 

  77. Hodapp D, Borer ET, Harpole WS et al (2018) Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilisation. Ecol Lett 21:1364–1371. https://doi.org/10.1111/ele.13102

    Article  Google Scholar 

  78. Hoffman MD, Gelman A (2014) The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res 15:1593–1623

    Google Scholar 

  79. Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81:945–960. https://doi.org/10.1080/01621459.1986.10478354

    Article  Google Scholar 

  80. Isaac NJB, Jarzyna MA, Keil P et al (2020) Data integration for large-scale models of species distributions. Trends Ecol Evol 35:56–67. https://doi.org/10.1016/j.tree.2019.08.006

    Article  Google Scholar 

  81. Jenkinson DS, Rayner JH (1977) The turnover of soil organic matter in some of the Rothamsted classical experiments. Soil Sci 123:298–305

    Article  Google Scholar 

  82. Jian JS, Steele MK, Thomas RQ et al (2018) Constraining estimates of global soil respiration by quantifying sources of variability. Global Change Biol 24:4143–4159. https://doi.org/10.1111/gcb.14301

    Article  Google Scholar 

  83. Jian S, Li J, Wang G et al (2020) Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics. Nat Commun 11:5864. https://doi.org/10.1038/s41467-020-19428-y

    Article  Google Scholar 

  84. Jobbágy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol Appl 10:423–436

    Article  Google Scholar 

  85. Kearney MR, Gillingham PK, Bramer I et al (2020) A method for computing hourly, historical, terrain-corrected microclimate anywhere on earth. Methods Ecol Evol 11:38–43. https://doi.org/10.1111/2041-210X.13330

    Article  Google Scholar 

  86. Keiser AD, Strickland MS, Bradford MA (2014) Disentangling mechanisms underlying functional differences in decomposer communities. J Ecol 102:603–609

    Article  Google Scholar 

  87. Keiser AD, Knoepp JD, Bradford MA (2016) Disturbance decouples biogeochemical cycles across forests of the southeastern US. Ecosystems 19:50–61

    Article  Google Scholar 

  88. Keuskamp JA, Dingemans BJJ, Lehtinen T et al (2013) Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol Evol 4:1070–1075. https://doi.org/10.1111/2041-210X.12097

    Article  Google Scholar 

  89. Knutti R, Sedláček J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Change 3:369–373

    Article  Google Scholar 

  90. Koven CD (2013) Boreal carbon loss due to poleward shift in low-carbon ecosystems. Nat Geosci 6:452–456

    Article  Google Scholar 

  91. Kraft NJB, Adler PB, Godoy O et al (2015) Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol 29:592–599. https://doi.org/10.1111/1365-2435.12345

    Article  Google Scholar 

  92. Kuppler J, Albert CH, Ames GM et al (2020) Global gradients in intraspecific variation in vegetative and floral traits are partially associated with climate and species richness. Global Ecol Biogeogr 29:992–1007. https://doi.org/10.1111/geb.13077

    Article  Google Scholar 

  93. Lancaster LT, Humphreys AM (2020) Global variation in the thermal tolerances of plants. Proc Natl Acad Sci USA 117:13580–13587. https://doi.org/10.1073/pnas.1918162117

    Article  Google Scholar 

  94. Laubmeier AN, Cazelles B, Cuddington K et al (2020) Ecological dynamics: integrating empirical, statistical, and analytical methods. Trends Ecol Evol 35:1090–1099. https://doi.org/10.1016/j.tree.2020.08.006

    Article  Google Scholar 

  95. Lauenroth WK, Sala OE (1992) Long-term forage production of North American shortgrass steppe. Ecol Appl 2:397–403

    Article  Google Scholar 

  96. Lehmann J, Hansel CM, Kaiser C et al (2020) Persistence of soil organic carbon caused by functional complexity. Nat Geosci 13:529–534. https://doi.org/10.1038/s41561-020-0612-3

    Article  Google Scholar 

  97. Lembrechts JJ, Lenoir J (2020) Microclimatic conditions anywhere at any time! Global Change Biol 26:337–339. https://doi.org/10.1111/gcb.14942

    Article  Google Scholar 

  98. Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr (2012) Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology 93:1867–1879

    Article  Google Scholar 

  99. Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73:1943–1967

    Article  Google Scholar 

  100. Loescher H, Ayres E, Duffy P et al (2014) Spatial variation in soil properties among North American ecosystems and guidelines for sampling designs. PLoS ONE. https://doi.org/10.1371/journal.pone.0083216

    Article  Google Scholar 

  101. Luo Z, Wang E, Bryan BA et al (2013) Meta-modeling soil organic carbon sequestration potential and its application at regional scale. Ecol Appl 23:408–420

    Article  Google Scholar 

  102. Lustenhouwer N, Maynard DS, Bradford MA et al (2020) A trait-based understanding of wood decomposition by fungi. Proc Natl Acad Sci USA 117:11551–11558. https://doi.org/10.1073/pnas.1909166117

    Article  Google Scholar 

  103. Mac Nally R, Duncan RP, Thomson JR, Yen JDL (2018) Model selection using information criteria, but is the “best” model any good? J Appl Ecol 55:1441–1444. https://doi.org/10.1111/1365-2664.13060

    Article  Google Scholar 

  104. Malik AA, Martiny JBH, Brodie EL et al (2020) Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J 14:1–9. https://doi.org/10.1038/s41396-019-0510-0

    Article  Google Scholar 

  105. Manski CF (2008) Identification for prediction and decision. Harvard University Press, Cambridge, MA

    Google Scholar 

  106. Martiny JBH, Bohannan BJM, Brown JH et al (2006) Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol 4:102–112

    Article  Google Scholar 

  107. Maynard DS, Crowther TW, Bradford MA (2017) Competitive network determines the direction of the diversity-function relationship. Proc Natl Acad Sci USA 114:11464–11469. https://doi.org/10.1073/pnas.1712211114

    Article  Google Scholar 

  108. Maynard DS, Bradford MA, Covey KR et al (2019) Consistent trade-offs in fungal trait expression across broad spatial scales. Nat Microbiol 4:846–853

    Article  Google Scholar 

  109. McGill BJ (2019) The what, how and why of doing macroecology. Global Ecol Biogeogr 28:6–17. https://doi.org/10.1111/geb.12855

    Article  Google Scholar 

  110. Meyer KM, Schiffers K, Münkemüller T et al (2010) Predicting population and community dynamics: the type of aggregation matters. Basic Appl Ecol 11:563–571. https://doi.org/10.1016/j.baae.2010.08.001

    Article  Google Scholar 

  111. Milcu A, Puga-Freitas R, Ellison AM et al (2018) Genotypic variability enhances the reproducibility of an ecological study. Nat Ecol Evol 2:279–287. https://doi.org/10.1038/s41559-017-0434-x

    Article  Google Scholar 

  112. Moher D (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6:e1000097

    Article  Google Scholar 

  113. Moorcroft P, Hurtt G, Pacala SW (2001) A method for scaling vegetation dynamics: the ecosystem demography model ED. Ecol Monogr 71:557–586

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

    Article  Google Scholar 

  115. Mouquet N, Lagadeuc Y, Devictor V et al (2015) Review: predictive ecology in a changing world. J Appl Ecol 52:1293–1310. https://doi.org/10.1111/1365-2664.12482

    Article  Google Scholar 

  116. Munafò MR, Davey Smith G (2018) Repeating experiments is not enough. Nature 553:399–401

    Article  Google Scholar 

  117. Naeem S (2001) Experimental validity and ecological scale as criteria for evaluating research programs. In: Gardner RH, Kemp WM, Kennedy VS, Petersen JE (eds) Scaling relations in experimental ecology. Columbia University Press, New York, pp 223–250

    Chapter  Google Scholar 

  118. Naylor D, Sadler N, Bhattacharjee A et al (2020) Soil microbiomes under climate change and implications for carbon cycling. Annu Rev Environ Resour 45:29–59. https://doi.org/10.1146/annurev-environ-012320-082720

    Article  Google Scholar 

  119. Neal AL, Bacq-Labreuil A, Zhang X et al (2020) Soil as an extended composite phenotype of the microbial metagenome. Sci Rep 10:10649. https://doi.org/10.1038/s41598-020-67631-0

    Article  Google Scholar 

  120. Nunan N, Schmidt H, Raynaud X (2020) The ecology of heterogeneity: soil bacterial communities and C dynamics. Philos Trans R Soc B 375:20190249. https://doi.org/10.1098/rstb.2019.0249

    Article  Google Scholar 

  121. Oldfield EE, Bradford MA, Wood SA (2019) Global meta-analysis of the relationship between soil organic matter and crop yields. Soil 5:15–32. https://doi.org/10.5194/soil-5-15-2019

    Article  Google Scholar 

  122. Oster E (2019) Unobservable selection and coefficient stability: theory and evidence. J Bus Econ Stat 37:187–204. https://doi.org/10.1080/07350015.2016.1227711

    Article  Google Scholar 

  123. Parton WJ, Schimel DS, Cole CV, Ojima DS (1987) Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci Soc Am J 51:1173–1179

    Article  Google Scholar 

  124. Pavlick R, Drewry DT, Bohn K et al (2013) The Jena Diversity-Dynamic Global Vegetation Model (JeDi- DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs. Biogeosciences 10:4137–4177

    Article  Google Scholar 

  125. Peay KG, Schubert MG, Nguyen NH, Bruns TD (2012) Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol Ecol 21:4122–4136. https://doi.org/10.1111/j.1365-294X.2012.05666.x

    Article  Google Scholar 

  126. Peters DPC, Bestelmeyer BT, Turner MG (2007) Cross–scale interactions and changing pattern–process relationships: consequences for system dynamics. Ecosystems 10:790–796. https://doi.org/10.1007/s10021-007-9055-6

    Article  Google Scholar 

  127. Pioli S, Sarneel J, Thomas HJD et al (2020) Linking plant litter microbial diversity to microhabitat conditions, environmental gradients and litter mass loss: insights from a European study using standard litter bags. Soil Biol Biochem 144:107778. https://doi.org/10.1016/j.soilbio.2020.107778

    Article  Google Scholar 

  128. Poulter B, MacBean N, Hartley A et al (2015) Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci Model Dev 8:2315–2328

    Article  Google Scholar 

  129. Prosser JI (2020) Putting science back into microbial ecology: a question of approach. Philos Trans R Soc B 375:20190240. https://doi.org/10.1098/rstb.2019.0240

    Article  Google Scholar 

  130. Rasmussen C, Heckman K, Wieder WR et al (2018) Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137:297–306. https://doi.org/10.1007/s10533-018-0424-3

    Article  Google Scholar 

  131. Rastetter EB, King AW, Cosby BJ et al (1992) Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecol Appl 2:55–70

    Article  Google Scholar 

  132. Reich PB (2014) The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J Ecol 102:275–301

    Article  Google Scholar 

  133. Rinnan R, Rousk J, Yergeau E et al (2009) Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming. Global Change Biol 15:2615–2625

    Article  Google Scholar 

  134. Robinson WS (1950) Ecological correlations and the behavior of individuals. Am Sociol Rev 15:351–357

    Article  Google Scholar 

  135. Rose KC, Graves RA, Hansen WD et al (2017) Historical foundations and future directions in macrosystems ecology. Ecol Lett 20:147–157. https://doi.org/10.1111/ele.12717

    Article  Google Scholar 

  136. Rousk J, Frey SD, Baath E (2012) Temperature adaptation of bacterial communities in experimentally warmed forest soils. Global Change Biol 18:3252–3258. https://doi.org/10.1111/j.1365-2486.2012.02764.x

    Article  Google Scholar 

  137. Rudgers JA, Chung YA, Maurer GE et al (2018) Climate sensitivity functions and net primary production: a framework for incorporating climate mean and variability. Ecology 99:576–582. https://doi.org/10.1002/ecy.2136

    Article  Google Scholar 

  138. Ruel JJ, Ayres MP (1999) Jensen’s inequality predicts effects of environmental variation. Trends Ecol Evol 14:361–366

    Article  Google Scholar 

  139. Sarkar S (2016) Ecology. In: Zalta EN (ed) The Stanford encyclopedia of philosophy. The Metaphysics Research Lab, Stanford

    Google Scholar 

  140. Schimel JP, Schaeffer SM (2012) Microbial control over carbon cycling in soil. Front Microbiol. https://doi.org/10.3389/fmicb.2012.00348

    Article  Google Scholar 

  141. Schmidt MWI, Torn MS, Abiven S et al (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49

    Article  Google Scholar 

  142. Schmitz OJ (2010) Resolving ecosystem complexity. Princeton University Press, Princeton

    Book  Google Scholar 

  143. Shiffrin RM (2016) Drawing causal inference from Big Data. Proc Natl Acad Sci USA 113:7308–7309. https://doi.org/10.1073/pnas.1608845113

    Article  Google Scholar 

  144. Shiklomanov AN, Cowdery EM, Bahn M et al (2020) Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis. Ecol Appl. https://doi.org/10.1002/eap.2064

    Article  Google Scholar 

  145. Sierra CA, Harmon ME, Thomann E et al (2011) Amplification and dampening of soil respiration by changes in temperature variability. Biogeosciences 8:951–961. https://doi.org/10.5194/bg-8-951-2011

    Article  Google Scholar 

  146. Smith GR, Peay KG (2020) Stepping forward from relevance in mycorrhizal ecology. New Phytol 226:292–294. https://doi.org/10.1111/nph.16432

    Article  Google Scholar 

  147. Soranno PA, Cheruvelil KS, Bissell EG et al (2014) Cross-scale interactions: quantifying multi-scaled cause–effect relationships in macrosystems. Front Ecol Environ 12:65–73. https://doi.org/10.1890/120366

    Article  Google Scholar 

  148. Soranno PA, Wagner T, Collins SM et al (2019) Spatial and temporal variation of ecosystem properties at macroscales. Ecol Lett 22:1587–1598. https://doi.org/10.1111/ele.13346

    Article  Google Scholar 

  149. Sorensen JW, Shade A (2020) Dormancy dynamics and dispersal contribute to soil microbiome resilience. Philos Trans R Soc B 375:20190255. https://doi.org/10.1098/rstb.2019.0255

    Article  Google Scholar 

  150. Spake R, Mori AS, Beckmann M et al (2020) Implications of scale dependence for cross-study syntheses of biodiversity differences. Ecol Lett. https://doi.org/10.1111/ele.13641

    Article  Google Scholar 

  151. Sprugel DG (1989) The relationship of evergreenness, crown architecture, and leaf size. Am Nat 133:465–479

    Article  Google Scholar 

  152. Strickland MS, Keiser AD, Bradford MA (2015) Climate history shapes contemporary leaf litter decomposition. Biogeochemistry 122:165–174. https://doi.org/10.1007/s10533-014-0065-0

    Article  Google Scholar 

  153. Sulman BN, Moore JAM, Abramoff R et al (2018) Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141:109–123

    Article  Google Scholar 

  154. Talbot JM, Bruns TD, Taylor JW et al (2014) Endemism and functional convergence across the North American soil mycobiome. Proc Natl Acad Sci USA 111:6341–6346. https://doi.org/10.1073/pnas.1402584111

    Article  Google Scholar 

  155. Tang J, Riley WJ (2015) Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nature Clim Change 5:56–60

    Article  Google Scholar 

  156. Tarnocai C, Canadell JG, Schuur EAG et al (2009) Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochem Cycles 23:GB2023

    Article  Google Scholar 

  157. Tenney FG, Waksman SA (1929) Composition of natural organic materials and their decomposition in the soil: IV. The nature and rapidity of decomposition of the various organic complexes in different plant materials, under aerobic conditions. Soil Sci 28:55–84

    Article  Google Scholar 

  158. Todd-Brown KEO, Hopkins FM, Kivlin SN et al (2012) A framework for representing microbial decomposition in coupled climate models. Biogeochemistry 109:19–33

    Article  Google Scholar 

  159. Tomczyk NJ, Rosemond AD, Bumpers PM et al (2020) Ignoring temperature variation leads to underestimation of the temperature sensitivity of plant litter decomposition. Ecosphere. https://doi.org/10.1002/ecs2.3050

    Article  Google Scholar 

  160. Transtrum MK, Machta BB, Brown KS et al (2015) Perspective: sloppiness and emergent theories in physics, biology, and beyond. J Chem Phys 143:010901. https://doi.org/10.1063/1.4923066

    Article  Google Scholar 

  161. Tredennick AT, Hooker G, Ellner SP, Adler PB (2021) A practical guide to selecting models for exploration, inference, and prediction in ecology. Ecology. https://doi.org/10.1002/ecy.3336

    Article  Google Scholar 

  162. Urban MC, Tewksbury JJ, Sheldon KS (2012) On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc R Soc B-Biol Sci 279:2072–2080

    Article  Google Scholar 

  163. van der Plas F, Schröder-Georgi T, Weigelt A et al (2020) Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat Ecol Evol 4:1602–1611. https://doi.org/10.1038/s41559-020-01316-9

    Article  Google Scholar 

  164. Vaughan E, Matos M, Ríos S et al (2019) Clay and climate are poor predictors of regional-scale soil carbon storage in the US Caribbean. Geoderma 354:113841

    Article  Google Scholar 

  165. Veen GFC, Fry EL, ten Hooven FC et al (2019) The role of plant litter in driving plant-soil feedbacks. Front Environ Sci 7:168. https://doi.org/10.3389/fenvs.2019.00168

    Article  Google Scholar 

  166. Vervoort JM, Rutting L, Kok K et al (2012) Exploring dimensions, scales, and cross-scale dynamics from the perspectives of change agents in social-ecological systems. Ecol Soc 17:24. https://doi.org/10.5751/ES-05098-170424

    Article  Google Scholar 

  167. von Fromm SF, Hoyt AM, Acquah GE et al (2020) Continental-scale controls on soil organic carbon across sub-Saharan Africa. Soil Discuss Rev. https://doi.org/10.5194/soil-2020-69

  168. Wadoux AMJ-C, Minasny B, McBratney AB (2020) Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth Sci Rev 210:103359. https://doi.org/10.1016/j.earscirev.2020.103359

    Article  Google Scholar 

  169. Wagner T, Fergus CE, Stow CA et al (2016) The statistical power to detect cross-scale interactions at macroscales. Ecosphere 7:e01417. https://doi.org/10.1002/ecs2.1417

    Article  Google Scholar 

  170. Waring B, Adams R, Branco S, Powers JS (2016) Scale-dependent variation in nitrogen cycling and soil fungal communities along gradients of forest composition and age in regenerating tropical dry forests. New Phytol 209:845–854

    Article  Google Scholar 

  171. Waring BG, Sulman BN, Reed S et al (2020) From pools to flow: the PROMISE framework for new insights on soil carbon cycling in a changing world. Global Change Biol. https://doi.org/10.1111/gcb.15365

    Article  Google Scholar 

  172. Wasserstein RL, Schirm AL, Lazar NA (2019) Moving to a world beyond “p < 0.05”. Am Stat 73:1–19. https://doi.org/10.1080/00031305.2019.1583913

    Article  Google Scholar 

  173. Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol 13:e1002128

    Article  Google Scholar 

  174. Wieder WR, Bonan GB, Allison SD (2013) Global soil carbon projections are improved by modelling microbial processes. Nat Clim Change 3:909–912

    Article  Google Scholar 

  175. Wieder WR, Allison SD, Davidson EA et al (2015) Explicitly representing soil microbial processes in Earth system models. Global Biogeochem Cycles 29:1782–1800

    Article  Google Scholar 

  176. Wieder WR, Hartman MD, Sulman BN et al (2018) Carbon cycle confidence and uncertainty: exploring variation among soil biogeochemical models. Global Change Biol 24:1563–1579. https://doi.org/10.1111/gcb.13979

    Article  Google Scholar 

  177. Wieder WR, Sulman BN, Hartman MD et al (2019) Arctic soil governs whether climate change drives global losses or gains in soil carbon. Geophys Res Lett 46:14486–14495. https://doi.org/10.1029/2019GL085543

    Article  Google Scholar 

  178. Wiesmeier M, Urbanski L, Hobley E et al (2019) Soil organic carbon storage as a key function of soils—a review of drivers and indicators at various scales. Geoderma 333:149–162. https://doi.org/10.1016/j.geoderma.2018.07.026

    Article  Google Scholar 

  179. Wilson CH, Gerber S (2020) Insight into biogeochemical models from scale transition theory: a dimensionless, scale-free approach. Ecology. https://doi.org/10.1101/2020.04.13.039818

    Article  Google Scholar 

  180. Wright JP, Sutton-Grier A (2012) Does the leaf economic spectrum hold within local species pools across varying environmental conditions? Funct Ecol 26:1390–1398. https://doi.org/10.1111/1365-2435.12001

    Article  Google Scholar 

  181. Wutzler T, Perez-Priego O, Morris K et al (2020) Soil CO2 efflux errors are lognormally distributed—implications and guidance. Geosci Instrum Method Data Syst 9:239–254. https://doi.org/10.5194/gi-9-239-2020

    Article  Google Scholar 

  182. Xie HW, Romero-Olivares AL, Guindani M, Allison SD (2020) A Bayesian approach to evaluation of soil biogeochemical models. Biogeosciences 17:4043–4057. https://doi.org/10.5194/bg-17-4043-2020

    Article  Google Scholar 

  183. Ye J, Bradford MA, Dacal M et al (2019) Increasing microbial carbon use efficiency with warming predicts soil heterotrophic respiration globally. Glob Change Biol 25:3354–3364. https://doi.org/10.1111/gcb.14738

    Article  Google Scholar 

  184. Zellweger F, De Frenne P, Lenoir J et al (2020) Forest microclimate dynamics drive plant responses to warming. Science 368:772–775. https://doi.org/10.1126/science.aba6880

    Article  Google Scholar 

  185. Zhang H, Goll DS, Wang Y et al (2020) Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon. Global Change Biol 26:2668–2685. https://doi.org/10.1111/gcb.14994

    Article  Google Scholar 

  186. Ziliak ST (2019) How large are your G -Values? Try Gosset’s Guinnessometrics when a little “p” is not enough. Am Stat 73:281–290. https://doi.org/10.1080/00031305.2018.1514325

    Article  Google Scholar 

Download references

Acknowledgement

We thank the Bradford lab group for comments on an earlier version of this manuscript.

Funding

MAB, AP, SAW, WRW, NF, JGR and FJ were supported by the U.S. National Science Foundation’s Macrosystem Biology and NEON-Enabled Science program grants DEB-1926482 and DEB-1926413. WRW was supported by the U.S. Department of Energy under award number BSS DE-SC0016364.

Author information

Affiliations

Authors

Contributions

MAB, SAW and WRW synthesized the ideas presented to address microbial controls on SOM turnover at macrosystem scales. All authors contributed to the ideas presented and then, through the writing of the manuscript, refined their integration.

Corresponding author

Correspondence to Mark A. Bradford.

Ethics declarations

Ethical approval

The authors declare that they have no conflicting or competing interests with respect to the viewpoints presented in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Samantha R. Weintraub.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bradford, M.A., Wood, S.A., Addicott, E.T. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021). https://doi.org/10.1007/s10533-021-00789-5

Download citation

Keywords

  • Forecasting
  • Functional redundancy
  • Jensen’s Inequality
  • Logical inference fallacies
  • Multilevel models
  • Soil carbon