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Background

Background.Rmd

Motivation

Microbial explicit carbon models include process-based representations of microbial activity in soil carbon models. However, microbial-explicit models are diverse in terms of pool structure, representations of carbon flux dynamics, and environmental drivers, as illustrated in Figure 1.

Figure 1: Conceptrual diagram illustrating the variations in pool strucutre, carbon flux dynamics, and environmental drivers of various popular microbial-explicit carbon models.
Figure 1: Conceptrual diagram illustrating the variations in pool strucutre, carbon flux dynamics, and environmental drivers of various popular microbial-explicit carbon models.


The variations in carbon model structures and dynamics contribute to diverging long-term model projections, making model inter-comparisons difficult. In order to explore, attribute, and better understand the implications of varying the representations of the C dynamics, the Microbial Explicit Model Consortium (MEMC) adopts a uniform pool structure to enable easy model comparison exercises. By implementing MEMC as an R package, we hope to make soil carbon modeling accessible to a wider audience.

Model Structure

MEMC uses the common model structure visualized in Figure 2. In the first version of this package, users have the ability to decide which implementation POM decomposition (1), DOM uptake (2), and microbial biomass decay (3) as indicated in (Figure 3). This flexible framework allows users to isolate the effects of flux dynamics on model behavior.

Figure 2: Conceptual diagram of the soil organic matter pool structure used by MEMC. The pools include particulate organic matter (POM), mineral-associated organic carbon matter (MAOM), dissolved organic carbon matter (DOM), adsorbed phase of DOM (QOM), microbial biomass (MB), POM-degrading enzymes (EP), and MOM-degrading enzymes (EM). External inputs for POM, DOM, and MOM are directly are fed into POM, DOM, and MOM, respectively. The fluxes between the pools are detonated with solid arrows, they include POM decomposition (1), DOM uptake by microbial biomass (2), mineral-associated carbon decomposition, adsorption of DOC, microbial biomass decay (3), and enzyme production used in POM and DOM decomposition and enzyme turn over. The dashed arrows indicate enzymes that affect flux rate.
Figure 2: Conceptual diagram of the soil organic matter pool structure used by MEMC. The pools include particulate organic matter (POM), mineral-associated organic carbon matter (MAOM), dissolved organic carbon matter (DOM), adsorbed phase of DOM (QOM), microbial biomass (MB), POM-degrading enzymes (EP), and MOM-degrading enzymes (EM). External inputs for POM, DOM, and MOM are directly are fed into POM, DOM, and MOM, respectively. The fluxes between the pools are detonated with solid arrows, they include POM decomposition (1), DOM uptake by microbial biomass (2), mineral-associated carbon decomposition, adsorption of DOC, microbial biomass decay (3), and enzyme production used in POM and DOM decomposition and enzyme turn over. The dashed arrows indicate enzymes that affect flux rate.

Model Configurations

The MEMC R package ships with six model pre-configured models (MICMS, MEND, CORPSE, BAMS, COMISSION, and MEMS). All of these model implementations use the uniform pool structure from Figure 2 but use a different combination of C flux dynamics as indicated in Figure 3.

Figure 3: Illustrates the different combinations of soil pool fluxes dynamics for POM decomposition, DOM uptake, and microbial biomass decay supported by MEMC V1. The numbers correspond to the arrows labeled in Figure 2. MM denotes Michaelis–Menten and Reverse MM refers to reverse Michaelis–Menten.
Figure 3: Illustrates the different combinations of soil pool fluxes dynamics for POM decomposition, DOM uptake, and microbial biomass decay supported by MEMC V1. The numbers correspond to the arrows labeled in Figure 2. MM denotes Michaelis–Menten and Reverse MM refers to reverse Michaelis–Menten.

Dynamics

  • MM: Michaelis Menten model, a commonly used equation in biogeochemical modelings which describe the rate of a reaction in response to a change in substrate.
  • Reverse MM: Reverse Michaelis Menten model (RMM), a modified version of Michaelis Menten model, however now the modeled reaction rate changes varies in response to enzyme availability.
  • Linear: a simple linear model (LM) is used.
  • Density Dependent: in the Density Dependent (DD) model the density of the population (the microbial biomass) affects the rate of a given process.
  • ECA: Equilibrium Chemistry Approximation kinetics an alternative representation of biogeochemical processes that is more appropriate over a wide range of substrate availability.