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Arjun ChakrawalDoktorand



I urval från Stockholms universitets publikationsdatabas

  • Leveraging energy flows to quantify microbial traits in soils

    2021. Arjun Chakrawal, Anke M. Herrmann, Stefano Manzoni. Soil Biology and Biochemistry 155


    Heat dissipation from organic matter decomposition is a well-recognized proxy for microbial activity in soils, but only a few modeling studies have used heat signals to quantify microbial traits such as maximum substrate uptake rate, specific growth rate, mortality rate, and growth efficiency. In this contribution, a hierarchy of coupled mass-energy balance models is proposed to estimate microbial traits encoded in model parameters using heat dissipation and respiration data from glucose induced microbial activity. Moreover, the models are used to explain the observed variability in calorespirometric ratios (CR)-the ratio of heat dissipation to respiration rate. We parametrized four model variants using heat dissipation and respiration rates measured in an isothermal calorimeter during the lag-phase only or during the whole growth-phase. The four variants are referred to as: (i) complex physiological model, (ii) simplified physiological model, (iii) lag-phase model, and (iv) growth-phase model. Model parameters were determined using three combinations of data: A) only the heat dissipation rate, B) only the respiration rate, and C) both heat dissipation and respiration rates. We assumed that the 'best' parameter estimates were those obtained when using all the data (i.e., option C). All model variants were able to fit the observed heat dissipation and respiration rates. The parameters estimated using only heat dissipation data were similar to the 'best' estimates compared to using only respiration rate data, suggesting that the observed heat dissipation rate can be used to constrain microbial models and estimate microbial traits. However, the observed variability in CR was not well captured by some model variants such as the simplified physiological model, in contrast to the lag- and growth-phase model that predicted CR well. This suggests that CR can be used to scrutinize how well metabolic processes are represented in decomposition models.

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  • Quantifying Microbial Metabolism in Soils Using Calorespirometry — A Bioenergetics Perspective

    2020. Arjun Chakrawal (et al.). Soil Biology and Biochemistry 148


    Microbial carbon use efficiency (CUE) measures the partitioning between anabolic and catabolic processes. While most work on CUE has been based on carbon (C) mass flows, the roles of organic C energy contents and microbial energy demand on CUE have been rarely considered. Thus, a bioenergetics perspective could provide new insights on how microorganisms utilize C and ultimately allow evaluating their role in C stabilization in soils. Recently, the calorespirometric ratio (CR)— the ratio of heat dissipation and respiration— has been used to characterize the efficiency of microbial growth in soils. Here, we formulate a coupled mass and energy balance model for microbial growth and provide a generalized relationship between CUE and CR. In the model, we consider two types of organic C in soils: an added substrate (e.g., glucose) and the native soil organic matter (SOM), to also account for priming effects. Furthermore, we consider both aerobic and fermentation metabolic pathways. We use this model as a framework to generalize previous formulations and generate hypotheses on the expected variations in CR as a function of substrate quality, metabolic pathways, and microbial traits (specifically CUE). In turn, the same equations can be used to estimate CUE from measured CR. Our results confirm previous findings on CR and show that without microbial growth, CR depends only on the rates of the different metabolic pathways, while CR is also a function of the growth yields for these metabolic pathways when microbial growth occurs. Under strictly aerobic conditions, CUE increases with increasing CR for substrates with a higher degree of reduction than that of the microbial biomass, while CUE decreases with increasing CR for substrates with a lower degree of reduction than the microbial biomass. When aerobic reactions and fermentation occur simultaneously, the relation between CUE and CR is mediated by (i) the degree of reduction of the substrates, (ii) the rates and growth yields of all metabolic pathways, and (iii) the contribution of SOM priming to microbial growth. Using the proposed framework, calorespirometry can be used to evaluate CUE and the role of different metabolic pathways in soil systems.

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  • Dynamic upscaling of decomposition kinetics for carbon cycling models

    2020. Arjun Chakrawal (et al.). Geoscientific Model Development 13 (3), 1399-1429


    The distribution of organic substrates and microorganisms in soils is spatially heterogeneous at the microscale. Most soil carbon cycling models do not account for this microscale heterogeneity, which may affect predictions of carbon (C) fluxes and stocks. In this study, we hypothesize that the mean respiration rate (R) over bar at the soil core scale (i) is affected by the microscale spatial heterogeneity of substrate and microorganisms and (ii) depends upon the degree of this heterogeneity. To theoretically assess the effect of spatial heterogeneities on (R) over bar, we contrast heterogeneous conditions with isolated patches of substrate and microorganisms versus spatially homogeneous conditions equivalent to those assumed in most soil C models. Moreover, we distinguish between biophysical heterogeneity, defined as the nonuniform spatial distribution of substrate and microorganisms, and full heterogeneity, defined as the nonuniform spatial distribution of substrate quality (or accessibility) in addition to biophysical heterogeneity. Four common formulations for decomposition kinetics (linear, multiplicative, Michaelis-Menten, and inverse Michaelis-Menten) are considered in a coupled substrate-microbial biomass model valid at the microscale. We start with a 2-D domain characterized by a heterogeneous substrate distribution and numerically simulate organic matter dynamics in each cell in the domain. To interpret the mean behavior of this spatially explicit system, we propose an analytical scale transition approach in which microscale heterogeneities affect (R) over bar through the second-order spatial moments (spatial variances and covariances). The model assuming homogeneous conditions was not able to capture the mean behavior of the heterogeneous system because the second-order moments cause (R) over bar to be higher or lower than in the homogeneous system, depending on the sign of these moments. This effect of spatial heterogeneities appears in the upscaled nonlinear decomposition formulations, whereas the upscaled linear decomposition model deviates from homogeneous conditions only when substrate quality is heterogeneous. Thus, this study highlights the inadequacy of applying at the macroscale the same decomposition formulations valid at the microscale and proposes a scale transition approach as a way forward to capture microscale dynamics in core-scale models.

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