Accelerated microbial turnover but constant growth efficiency with warming in soil

Hagerty, Shannon B.; Groenigen, Kees Jan van; Allison, Steven D.; Hungate, Bruce A.; Schwartz, Egbert; Koch, George; Kolka, Randall K.; Dijkstra, Paul

doi:10.1038/nclimate2361

Cited by 287 publications

(223 citation statements)

References 38 publications

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“…Although microbial CUE of higher quality substrates is unresponsive to temperature (Frey et al, 2013), the pre-incubated litter contained more low-quality substrates (Table 1) and therefore should have lower CUE. Alternatively, recycling of microbial biomass results in high microbial CUE (Schimel and Weintraub, 2003;Manzoni et al, 2012), and increased microbial turnover has been observed with warming (Hagerty et al, 2014). The greater proportion of lipid-derived C in the preincubated litter (Table 1) was either derived from the selective enrichment of plant-derived lipids (e.g.…”

Section: Discussionmentioning

confidence: 99%

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Creamer

Menezes

Krull

et al. 2015

Soil Biology and Biochemistry

205

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Section: Discussionmentioning

confidence: 99%

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Creamer

Menezes

Krull

et al. 2015

Soil Biology and Biochemistry

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“…3a); therefore the simulated equilibrium soil carbon by model B increases with warming if the warmed soil temperature is above 0 • C. Figure 3b shows that T x for model B decreases with an increase in b or x. When the turnover rate of microbial biomass is not sensitive to soil temperature (b = 0) and x = 0.016 • C −1 as the default value for model B, T x is about 35 • C. For b = 0.063, as estimated by Hagerty et al (2014), T x < 0 • C; therefore the equilibrium soil carbon pool size as simulated by model B always increases with soil warming for most terrestrial ecosystems, irrespective of the value of x.…”

Section: Response Of Soil Carbon To Warmingmentioning

confidence: 98%

“…For example, there has been debate about the temperature sensitivities of microbial biomass turnover rate and microbial growth efficiency (Frey et al, 2013;Hargety et al, 2014), and the simulated sensitivity of soil carbon to warming (Hagerty et al, 2014). Regardless of the temperature sensitivity of microbial growth efficiency, model A always simulates a decrease in the equilibrium soil carbon under warming, whereas model B can simulate an increase or a decrease in the equilibrium soil carbon under warming, depending on the temperature sensitivities of microbial growth efficiency and turnover rate.…”

Section: Discussionmentioning

confidence: 99%

“…Predictions of future soil carbon change by these nonlinear models can differ significantly from conventional linear models (Fontaine et al, 2007;Wieder et al, 2013). For example, conventional linear soil carbon models predict that soil carbon will decrease with increased temperature, all else being equal (Jenkinson et al, 1991), whereas the nonlinear models predict that the soil carbon can decrease or increase, depending on the temperature sensitivity of microbial growth efficiency and turnover rates (Frey et al, 2013;Hagerty et al, 2014;Li et al, 2014). However, the nonlinear models have yet to be validated against field measurements as extensively as the conventional linear soil carbon models (Wieder et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Based on the work of Allison et al (2010) and Hagerty et al (2014), we model the temperature dependence of parameters as…”

Section: Parameter Valuesmentioning

confidence: 99%

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Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

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Abstract.A number of nonlinear microbial models of soil carbon decomposition have been developed. Some of them have been applied globally but have yet to be shown to realistically represent soil carbon dynamics in the field. A thorough analysis of their key differences is needed to inform future model developments. Here we compare two nonlinear microbial models of soil carbon decomposition: one based on reverse Michaelis-Menten kinetics (model A) and the other on regular Michaelis-Menten kinetics (model B). Using analytic approximations and numerical solutions, we find that the oscillatory responses of carbon pools to a small perturbation in their initial pool sizes dampen faster in model A than in model B. Soil warming always decreases carbon storage in model A, but in model B it predominantly decreases carbon storage in cool regions and increases carbon storage in warm regions. For both models, the CO 2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO 2 efflux (F max ) decreases with an increase in soil temperature in both models. However, the sensitivity of F max to the increased amount of carbon input increases with soil temperature in model A but decreases monotonically with an increase in soil temperature in model B. These differences in the responses to soil warming and carbon input between the two nonlinear models can be used to discern which model is more realistic when compared to results from field or laboratory experiments. These insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.

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Explicitly representing soil microbial processes in Earth system models

Wieder

Allison

Davidson

et al. 2015

Global Biogeochemical Cycles

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Microbes influence soil organic matter decomposition and the long‐term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro‐scale process‐level understanding and measurements to macro‐scale models used to make decadal‐ to century‐long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial‐explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial‐explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model‐data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well‐curated repositories; and (3) the application of scaling methods to integrate microbial‐explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.

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Accelerated microbial turnover but constant growth efficiency with warming in soil

Cited by 287 publications

References 38 publications

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Responses of two nonlinear microbial models to warming and increased carbon input

Explicitly representing soil microbial processes in Earth system models

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