4.7 Article

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

期刊

BIOGEOSCIENCES
卷 13, 期 4, 页码 887-902

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/bg-13-887-2016

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资金

  1. National Science Foundation
  2. US Department of Homeland Security
  3. US Department of Agriculture through NSF [EF-0832858]
  4. University of Tennessee, Knoxville
  5. EPSRC [EP/I004165/1] Funding Source: UKRI
  6. Direct For Biological Sciences [1300426] Funding Source: National Science Foundation
  7. Engineering and Physical Sciences Research Council [EP/I004165/1] Funding Source: researchfish

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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 CO2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO2 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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