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A parsimonious modular approach to building a mechanistic belowground carbon and nitrogen model
Author(s) -
Abramoff Rose Z.,
Davidson Eric A.,
Finzi Adrien C.
Publication year - 2017
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1002/2017jg003796
Subject(s) - cycling , carbon cycle , respiration , nitrogen cycle , environmental science , decomposer , substrate (aquarium) , decomposition , water content , atmospheric sciences , arrhenius equation , biological system , nitrogen , soil science , ecology , chemistry , ecosystem , botany , biology , physics , history , geotechnical engineering , activation energy , archaeology , organic chemistry , engineering
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.