A parsimonious modular approach to building a mechanistic belowground carbon and nitrogen model

作者：Abramoff Rose Z; Davidson Eric A; Finzi Adrien C

来源：Journal of Geophysical Research-Biogeosciences, 2017, 122(9): 2418-2434.

DOI：10.1002/2017JG003796

• microbial modeling
• soil decomposition
• microbial physiology
• stoichiometry
• C efflux
• heterotrophic respiration

Soil decomposition models range from simple empirical functions to those that represent physical, chemical, and biological processes. Here we develop a parsimonious, modular C and N cycle model, the Dual Arrhenius Michaelis-Menten-Microbial Carbon and Nitrogen Phyisology (DAMM-MCNiP), that generates testable hypotheses regarding the effect of temperature, moisture, and substrate supply on C and N cycling. We compared this model to DAMM alone and an empirical model of heterotrophic respiration based on Harvard Forest data. We show that while different model structures explain similar amounts of variation in respiration, they differ in their ability to infer processes that affect C flux. We applied DAMM-MCNiP to explain an observed seasonal hysteresis in the relationship between respiration and temperature and show using an exudation simulation that the strength of the priming effect depended on the stoichiometry of the inputs. Low C:N inputs stimulated priming of soil organic matter decomposition, but high C:N inputs were preferentially utilized by microbes as a C source with limited priming. The simplicity of DAMM-MCNiP's simultaneous representations of temperature, moisture, substrate supply, enzyme activity, and microbial growth processes is unique among microbial physiology models and is sufficiently parsimonious that it could be incorporated into larger-scale models of C and N cycling.
Plain Language Summary Microorganisms that grow in the soil, like bacteria and fungi, affect how much carbon resides in the soil and how much is released to the atmosphere as CO2. Mathematical models used to make climate change predictions often struggle to capture the activity of soil microbes in realistic ways. This study uses well-established descriptions of water and temperature effects on soil microbes to predict rate of carbon and nitrogen cycling in the soil. Our new model reproduces the changing relationship between temperature and microbial respiration during the growing season. We also show using a theoretical addition of root secretions that the microbial response depends on the nitrogen content of the added plant material. This model is simple and based on well defined physical and biological properties, and could be developed to model microbial activity at larger scales.

• 出版日期2017-9