Abstract Soils store the largest amount of carbon (C) in the biosphere, and the C pool in soil is critical to the global C balance. Numerous microbial models have been developed over the last few decades to represent microbial processes that regulate the responses of soil organic carbon (SOC) to climate change. However, the representation of microbial processes varies, and how microbial processes are incorporated into SOC models has not been well explored. Here, we reviewed 71 microbial models to characterize the microbial processes incorporated into SOC models and analyzed variations in mechanistic complexity. We revealed that (a) four processes (microbial‐mediated decomposition, mineral interaction, microbial necromass recycling, and active and dormant microbial dynamics) are commonly incorporated in microbial models, (b) ∼48% of models simulate only one microbial process (i.e., microbial‐mediated decomposition) and 35% of models simulate two microbial processes: for example, microbial‐mediated decomposition and mineral interaction, (c) more than 80% microbial models use nonlinear equations, such as forward Michaelis‐Menten kinetics, to represent SOC decomposition, (d) the concept of persistence of SOC due to its intrinsic properties has been replaced by organo‐mineral interaction (∼39% of microbial models) that protects SOC from decomposition, and (e) various temperature and moisture modifiers and pH effects have been used to explain the environmental effect on microbial processes. In the future, to realistically incorporate microbial processes into Earth System Models, it is imperative to identify experimental evidence on rate limitation processes and firmly ground model structure on the field and laboratory data. 

Microbial Models for Simulating Soil Carbon Dynamics: A Review

The redox activity of Fe-bearing minerals is coupled with the transformation of organic matter (OM) in redox dynamic environments, but the underlying mechanism remains unclear. In this work, a Fe redox cycling experiment of nontronite (NAu-2), an Fe-rich smectite, was performed via combined abiotic and biotic methods, and the accompanying transformation of humic acid (HA) as a representative OM was investigated. Chemical reduction and subsequent abiotic reoxidation of NAu-2 produced abundant hydroxyl radicals (thereafter termed as ·OH) that effectively transformed the chemical and molecular composition of HA. More importantly, transformed HA served as a more premium electron donor/carbon source to couple with subsequent biological reduction of Fe(III) in reoxidized NAu-2 by Geobacter sulfurreducens, a model Fe-reducing bacterium. Destruction of aromatic structures and formation of carboxylates were mechanisms responsible for transforming HA into an energetically more bioavailable substrate. Relative to unaltered HA, transformed HA increased the extent of the bioreduction by 105%, and Fe(III) reduction was coupled with oxidation and even mineralization of transformed HA, resulting in bleached HA and formation of microbial products and cell debris. ·OH transformation slightly decreased the electron shuttling capacity of HA in bioreduction. Our results provide a mechanistic explanation for rapid OM mineralization driven by Fe redox cycling in redox-fluctuating environments.

Promotion of Humic Acid Transformation by Abiotic and Biotic Fe Redox Cycling in Nontronite.

Soil acidification enhanced soil carbon sequestration through increased mineral protection

Temperature responses of ecosystem respiration

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimulated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change. 

Microbe-iron interactions control lignin decomposition in soil

Research on the manipulation of iron ions and alkalis in Chlorella vulgaris culture

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Microbe-iron interactions control lignin decomposition in soil

Research on the manipulation of iron ions and alkalis in Chlorella vulgaris culture