Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model (MIMICS-CN)

Abstract

Abstract. Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon-nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; 6 litter types, 10 years of observations, 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R2 = 0.63, N: R2 = 0.29) results that are comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R2 = 0.67, N: R2 = 0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbial-explicit model but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios.

Preferential substrate use decreases priming effects in contrasting treeline soils

“…Microbial substrate use explained particularly the negative priming effects in this study (Fig. 3, Table 4), thus supporting the central role of microbes in the soil C cycle (Kuzyakov 2010;Cortufo et al 2013;Classen et al 2015;Liang et al 2017;Kyker-Snowman et al 2020). However, we did not observe that substrate use was highest when the C:N composition of the substrate matched that of the receiving microbial community, and the interaction term between substrate C:N and microbial biomass C:N was not a significant predictor of priming effects (Table 3).…”

A holistic framework integrating plant-microbe-mineral regulation of soil bioavailable nitrogen

“…In the twentieth century, researchers theorized that the inherent chemical recalcitrance of carbon (C) to decomposition controlled SOC turnover, but evidence from the last two or more decades reveals that microbes can degrade even the most complex molecules (Gleixner et al 2001 , 2002 ; Rasse et al 2006 ) and that, in the context of overall soil organic matter (SOM) dynamics, recalcitrance only temporarily controls microbial SOC processing rates. Instead, SOC persistence largely emerges from constraints that the soil mineral matrix imposes on microbial access to substrates (Kleber et al 2011 ; Schimel and Schaeffer 2012 ) and SOC dynamics are better predicted by biological and physical controls on C transfer between different SOC pools (Six et al 2006 ; Grandy and Neff 2008 ), motivating several recent soil C cycling models to explicitly incorporate soil physical fractions (Sulman et al 2014 ; Wieder et al 2015 ; Abramoff et al 2018 ; Kyker-Snowman et al 2019 ). The fate of ON similarly relies on how associations with minerals regulate access to N-containing molecules (Lavallee et al 2020 ) which are in turn regulated by biologically mediated chemical and physical processes that have yet to be integrated into the soil N paradigm (Darrouzet-Nardi and Weintraub 2014 ).…”

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution

“…Our analysis of the parametric uncertainty in the model projections shows the direct opportunities for additional observational data pertaining to microbial properties and SOC persistence to further refine MIMICS parameter estimates. Further potential also exists to generate more complex, fine scale estimates and projections of SOC dynamics by adapting the methods from this study to similarly enhance the spatial application scale and parameterization of the CN-coupled 70 and soil depth resolved 71 versions of the MIMICS model.…”

“…The MIcrobial-Mineral Carbon Stabilization (MIMICS) model has been widely used for estimation of soil C across diverse ecosystems and has been found to perform well across ecosystem gradients and at global scales 22 , 26 , 43 , 46 , 70 , 79 . Here we use a version of the model that calculates equilibrium SOC stocks based on the mean annual values for net primary productivity, soil temperature, litter lignin:N ratio and the soil clay content.…”