Comparing models of microbial–substrate interactions and their response to warming

Abstract

Abstract. Recent developments in modelling soil organic carbon decomposition include the explicit incorporation of enzyme and microbial dynamics. A characteristic of these models is a positive feedback between substrate and consumers, which is absent in traditional first-order decay models. With sufficiently large substrate, this feedback allows an unconstrained growth of microbial biomass. We explore mechanisms that curb unrestricted microbial growth by including finite potential sites where enzymes can bind and by allowing microbial scavenging for enzymes. We further developed a model where enzyme synthesis is not scaled to microbial biomass but associated with a respiratory cost and microbial population adjusts enzyme production in order to optimise their growth. We then tested short-and long-term responses of these models to a step increase in temperature and find that these models differ in the long-term when shortterm responses are harmonised. We show that several mechanisms, including substrate limitation, variable production of microbial enzymes, and microbes feeding on extracellular enzymes eliminate oscillations arising from a positive feedback between microbial biomass and depolymerisation. The model where enzyme production is optimised to yield maximum microbial growth shows the strongest reduction in soil organic carbon in response to warming, and the trajectory of soil carbon largely follows that of a first-order decomposition model. Modifications to separate growth and maintenance respiration generally yield short-term differences, but results converge over time because microbial biomass approaches a quasi-equilibrium with the new conditions of carbon supply and temperature.

Rate of warming affects temperature sensitivity of anaerobic peat decomposition and greenhouse gas production

“…We analyzed steady-states of SOC stocks based on a microbial model with forward MichaelisMenten kinetics for depolymerization (FWD) and a traditional firstorder decomposition (FOD) model after German et al (2012) and Sihi, Gerber et al (2016).…”

“…To assess the significance of contrasting warming rates, we parameterized a traditional first order decomposition model and a microbial decomposition model with a forward Michaelis-Menten function to estimate long-term losses in C stocks and global warming potential (German et al, 2012;Sihi, Gerber, Inglett, & Inglett, 2016).…”

Explicitly representing soil microbial processes in Earth system models

“…Here we define “microbial models” as soil biogeochemical models that mathematically couple microbial biomass and C substrate pools. Significant efforts are still required to harmonize microbial models, observations [ Sihi et al , ; Wang et al , , ], and theory, especially relating to physicochemical stabilization of SOM [ Cotrufo et al , ; Grandy et al , ]. These findings highlight the need to refine our understanding of factors controlling soil C, the largest terrestrial C pool on Earth, and its representation in models.…”

Evaluation of soil health in organicvs. conventional farming of basmati rice in North India

“…Marinari et al () indicated that lower SOC concentrations in organic farming systems could be associated with induced priming effect resulting from a considerable portion of labile C species in organic manures [for more details see Kuzyakov ()]. Additionally, Leifeld () indicated that the smaller substrate use efficiency (SUE) of soil heterotrophs in low‐input agroecosystems is likely to reduce SOM storage in the long term [for a detailed explanation of how SUE relates to SOC stocks see Sihi et al ()]. To that end, nutrient depletion in organic systems may stimulate nutrient mining by fostering microbial activity at the expense of reduced efficiency and ultimately increase SOC loss ( Ammann et al, ; Craine et al, ), where the effect can be more pronounced in the future warmer world ( Sihi et al, ).…”