Abstract
Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C–CO2 and 14C–CO2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20–71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.
Similar content being viewed by others
Log in or create a free account to read this content
Gain free access to this article, as well as selected content from this journal and more on nature.com
or
References
Allison SD, Wallenstein MD, Bradford MA . (2010). Soil-carbon response to warming dependent on microbial physiology. Nat Geosci 3: 336–340.
Anderson TH, Domsch KH . (1985a). Determination of ecophysiological maintenance carbon requirements of soil microorganisms in a dormant state. Biol Fert Soils 1: 81–89.
Anderson TH, Domsch KH . (1985b). Maintenance carbon requirements of actively-metabolizing microbial populations under in situ conditions. Soil Biol Biochem 17: 197–203.
Batstone DJ, Pind PF, Angelidaki I . (2003). Kinetics of thermophilic, anaerobic oxidation of straight and branched chain butyrate and valerate. Biotechnol Bioeng 84: 195–204.
Blagodatskaya E, Kuzyakov Y . (2013). Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biol Biochem 67: 192–211.
Bradford MA . (2013). Thermal adaptation of decomposer communities in warming soils. Front Microbiol 4: Article 333.
Bradford MA, Davies CA, Frey SD, Maddox TR, Melillo JM, Mohan JE et al. (2008). Thermal adaptation of soil microbial respiration to elevated temperature. Ecol Lett 11: 1316–1327.
Brockett BF, Prescott CE, Grayston SJ . (2012). Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol Biochem 44: 9–20.
Caldwell BA . (2005). Enzyme activities as a component of soil biodiversity: A review. Pedobiologia 49: 637–644.
Colores GM, Schmidt SK, Fisk MC . (1996). Estimating the biomass of microbial functional groups using rates of growth-related soil respiration. Soil Biol Biochem 28: 1569–1577.
Currie WS . (2007). Modeling the dynamics of stable-isotope ratios for ecosystem biogeochemistry. In: Michener R, Lajtha K (eds) Stable Isotopes in Ecology and Environmental Science. Blackwell Publishing: Malden, MA, USA, pp 450–479.
Davidson EA, Samanta S, Caramori SS, Savage K . (2012). The Dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Global Change Biol 18: 371–384.
Dawson CW, Abrahart RJ, See LM . (2007). HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Software 22: 1034–1052.
Devevre OC, Horwath WR . (2000). Decomposition of rice straw and microbial carbon use efficiency under different soil temperatures and moistures. Soil Biol Biochem 32: 1773–1785.
Devore JL . (2008) Probability and Statistics for Engineering and the Sciences 7th edn Brooks/Cole Cengage Learning: Florence, KY, USA.
Drake J, Darby B, Giasson M-A, Kramer M, Phillips R, Finzi A . (2013). Stoichiometry constrains microbial response to root exudation-insights from a model and a field experiment in a temperate forest. Biogeosciences 10: 821–838.
Duan QY, Sorooshian S, Gupta V . (1992). Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28: 1015–1031.
Falkowski PG, Fenchel T, Delong EF . (2008). The microbial engines that drive Earth's biogeochemical cycles. Science 320: 1034–1039.
Frey S, Drijber R, Smith H, Melillo J . (2008). Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol Biochem 40: 2904–2907.
Frey SD, Lee J, Melillo JM, Six J . (2013). The temperature response of soil microbial efficiency and its feedback to climate. Nat Clim Change 3: 395–398.
Giraudoux P . (2013). R Package 'pgirmess': Data analysis in ecology, 1.5.8 edn.
Haddix ML, Plante AF, Conant RT, Six J, Steinweg JM, Magrini-Bair K et al. (2011). The role of soil characteristics on temperature sensitivity of soil organic matter. Soil Sci Soc Am J 75: 56–68.
Hanson PJ, Edwards NT, Garten CT, Andrews JA . (2000). Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry 48: 115–146.
Hanson PJ, Swanston CW, Garten C, Todd D, Trumbore SE . (2005). Reconciling change in Oi-horizon carbon-14 with mass loss for an oak forest. Soil Sci Soc Am J 69: 1492–1502.
Hartley IP, Ineson P . (2008). Substrate quality and the temperature sensitivity of soil organic matter decomposition. Soil Biol Biochem 40: 1567–1574.
Jagadamma S, Steinweg JM, Mayes MA, Post WM . (2014a). Substrate quality alters microbial mineralization of added substrate and soil organic carbon. Biogeosci Discuss 11: 4451–4482.
Jagadamma S, Steinweg JM, Mayes MA, Wang G, Post WM . (2014b). Decomposition of added and native organic carbon from physically separated fractions of diverse soils. Biol Fert Soils 50: 613–621.
Jones SE, Lennon JT . (2010). Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci 107: 5881–5886.
Kaiser C, Franklin O, Dieckmann U, Richter A . (2014). Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol Lett 17: 680–690.
Lawrence CR, Neff JC, Schimel JP . (2009). Does adding microbial mechanisms of decomposition improve soil organic matter models? A comparison of four models using data from a pulsed rewetting experiment. Soil Biol Biochem 41: 1923–1934.
Lennon JT, Jones SE . (2011). Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol 9: 119–130.
Li J, Ziegler SE, Lane CS, Billings SA . (2013). Legacies of native climate regime govern responses of boreal soil microbes to litter stoichiometry and temperature. Soil Biol Biochem 66: 204–213.
Manzoni S, Jackson RB, Trofymow JA, Porporato A . (2008). The global stoichiometry of litter nitrogen mineralization. Science 321: 684–686.
Manzoni S, Schaeffer SM, Katul G, Porporato A, Schimel JP . (2014). A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils. Soil Biol Biochem 73: 69–83.
Manzoni S, Taylor P, Richter A, Porporato A, Ågren GI . (2012). Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol 196: 79–91.
Mayes MA, Heal KR, Brandt CC, Phillips JR, Jardine PM . (2012). Relation between soil order and sorption of dissolved organic carbon in temperate subsoils. Soil Sci Soc Am J 76: 1027–1037.
McDaniel P, Wilson M . (2007). Physical and chemical characteristics of ash-influenced soils of Inland Northwest forests. In: Page-Dumroese D, Miller R, Mital J, McDaniel P, Miller D (eds) Volcanic-Ash-Derived Forest Soils of the Inland Northwest: Properties and Implications for Management and Restoration. U.S. Department of Agriculture: Coeur d'Alene, ID, USA, pp 31–45.
McDonald JH . (2009) Handbook of Biological Statistics vol. 2. Sparky House Publishing: Baltimore, MD, USA.
Melillo JM, Butler S, Johnson J, Mohan J, Steudler P, Lux H et al. (2011). Soil warming, carbon–nitrogen interactions, and forest carbon budgets. Proc Natl Acad Sci USA 108: 9508–9512.
Moorhead DL, Lashermes G, Sinsabaugh RL . (2012). A theoretical model of C-and N-acquiring exoenzyme activities, which balances microbial demands during decomposition. Soil Biol Biochem 53: 133–141.
Nanzyo M . (2002). Unique properties of volcanic ash soils. Global Environ Res-Eng Ed 6: 99–112.
Parton WJ, Hanson PJ, Swanston C, Torn M, Trumbore SE, Riley W et al. (2010). ForCent model development and testing using the Enriched Background Isotope Study experiment. J Geophys Res Biogeosci 115: G04001.
Paul EA, Morris SJ, Böhm S . (2001). The determination of soil C pool sizes and turnover rates: biophysical fractionation and tracers. In: Lal R, Kimble JM, Follett RF, Stewart BA (eds) Assessment Methods for Soil Carbon. Lewis Publishers: Boca Raton, USA, pp 193–206.
Raich J, Schlesinger W . (1992). The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus B 44: 81–99.
Sala OE, Jackson RB, Mooney HA, Howarth RW (eds). (2000) Methods in Ecosystem Science. Springer-Verlag: NY, USA, p 421.
Schimel JP . (2013). Microbes and global carbon. Nat Clim Change 3: 867–868.
Schimel JP, Schaeffer SM . (2012). Microbial control over carbon cycling in soil. Front Microbiol 3: Article 348.
Schimel JP, Weintraub MN . (2003). The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol Biochem 35: 549–563.
Sierra J, Brisson N, Ripoche D, Déqué M . (2010). Modelling the impact of thermal adaptation of soil microorganisms and crop system on the dynamics of organic matter in a tropical soil under a climate change scenario. Ecol Model 221: 2850–2858.
Sinsabaugh RL, Manzoni S, Moorhead DL, Richter A . (2013). Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol Lett 16: 930–939.
Steinweg JM, Plante AF, Conant RT, Paul EA, Tanaka DL . (2008). Patterns of substrate utilization during long-term incubations at different temperatures. Soil Biol Biochem 40: 2722–2728.
Stolpovsky K, Martinez-Lavanchy P, Heipieper HJ, Van Cappellen P, Thullner M . (2011). Incorporating dormancy in dynamic microbial community models. Ecol Model 222: 3092–3102.
Swarbreck SM, Sudderth EA St, Clair SB, Salve R, Castanha C, Torn MS et al. (2011). Linking leaf transcript levels to whole plant analyses provides mechanistic insights to the impact of warming and altered water availability in an annual grass. Global Change Biol 17: 1577–1594.
Tang JY, Riley WJ . (2013). A total quasi-steady-state formulation of substrate uptake kinetics in complex networks and an example application to microbial litter decomposition. Biogeosciences 10: 8329–8351.
Thiet RK, Frey SD, Six J . (2006). Do growth yield efficiencies differ between soil microbial communities differing in fungal: bacterial ratios? Reality check and methodological issues. Soil Biol Biochem 38: 837–844.
Todd-Brown KE, Hopkins FM, Kivlin SN, Talbot JM, Allison SD . (2012). A framework for representing microbial decomposition in coupled climate models. Biogeochemistry 109: 19–33.
Treseder KK, Balser TC, Bradford MA, Brodie EL, Dubinsky EA, Eviner VT et al. (2012). Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109: 7–18.
Tucker CL, Bell J, Pendall E, Ogle K . (2013). Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Global Change Biol 19: 252–263.
Van de Werf H, Verstraete W . (1987). Estimation of active soil microbial biomass by mathematical analysis of respiration curves: calibration of the test procedure. Soil Biol Biochem 19: 261–265.
Wang G, Barber ME, Chen S, Wu JQ . (2014a). SWAT modeling with uncertainty and cluster analyses of tillage impacts on hydrological processes. Stochastic Environ Res Risk Assess 28: 225–238.
Wang G, Chen S . (2012). A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil. Geoderma 170: 206–216.
Wang G, Chen S . (2013). Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Model uncertainty. Ecol Model 253: 97–106.
Wang G, Mayes MA, Gu L, Schadt CW . (2014b). Representation of dormant and active microbial dynamics for ecosystem modeling. PLoS One 9: e89252.
Wang G, Post WM . (2013). A note on the reverse Michaelis–Menten kinetics. Soil Biol Biochem 57: 946–949.
Wang G, Post WM, Mayes MA . (2013). Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol Appl 23: 255–272.
Wang G, Post WM, Mayes MA, Frerichs JT, Jagadamma S . (2012). Parameter estimation for models of ligninolytic and cellulolytic enzyme kinetics. Soil Biol Biochem 48: 28–38.
Wang G, Xia J, Chen J . (2009). Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: a case study of the Chaobai River basin in northern China. Water Resour Res 45: W00A11.
Weedon JT, Aerts R, Kowalchuk GA, van Logtestijn R, Andringa D, van Bodegom PM . (2013). Temperature sensitivity of peatland C and N cycling: Does substrate supply play a role? Soil Biol Biochem 61: 109–120.
Wieder W, Grandy A, Kallenbach C, Bonan G . (2014). Integrating microbial physiology and physiochemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. Biogeosci Discuss 11: 1147–1185.
Wieder WR, Bonan GB, Allison SD . (2013). Global soil carbon projections are improved by modelling microbial processes. Nat Clim Change 3: 909–912.
Xu X, Thornton PE, Post WM . (2013). A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Global Ecol Biogeogr 22: 737–749.
Yapo PO, Gupta HV, Sorooshian S . (1998). Multi-objective global optimization for hydrologic models. J Hydrol 204: 83–97.
Zhou J, Xue K, Xie J, Deng Y, Wu L, Cheng X et al. (2012). Microbial mediation of carbon-cycle feedbacks to climate warming. Nat Clim Change 2: 106–110.
Acknowledgements
This research was funded by the Laboratory Directed Research and Development Program of the Oak Ridge National Laboratory (ORNL) and by the US Department of Energy (DOE) Biological and Environmental Research (BER) program. ORNL is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. The authors appreciate the insightful reviews of Dr Paul Hanson on earlier drafts of the manuscript. We also thank the two anonymous reviewers for their constructive comments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no conflict of interest.
Additional information
Disclaimer
The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
Supplementary Information accompanies this paper on The ISME Journal website
Supplementary information
Rights and permissions
About this article
Cite this article
Wang, G., Jagadamma, S., Mayes, M. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J 9, 226–237 (2015). https://doi.org/10.1038/ismej.2014.120
Received:
Revised:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/ismej.2014.120
This article is cited by
-
How to adequately represent biological processes in modeling multifunctionality of arable soils
Biology and Fertility of Soils (2024)
-
Biotic Interactions in Soil are Underestimated Drivers of Microbial Carbon Use Efficiency
Current Microbiology (2023)
-
Life and death in the soil microbiome: how ecological processes influence biogeochemistry
Nature Reviews Microbiology (2022)
-
Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool
The ISME Journal (2021)
-
Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria
Microbiome (2020)