Microbial interactions affect sources of priming induced by cellulose

Blagodatskaya, Еvgenia; Khomyakov, Nikita; Myachina, Olga; Богомолова, И. Н.; Blagodatsky, Sergey; Kuzyakov, Yakov

doi:10.1016/j.soilbio.2014.02.017

Cited by 166 publications

(113 citation statements)

References 63 publications

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“…However, whether changes in litter decomposition will ultimately impact downstream soil-C dynamics is uncertain (Kemmitt et al, 2008;Schimel and Schaeffer, 2012;Cleveland et al, 2014). Priming, the increased loss of soil-C due to the addition of labile substrates, has also been related to changes in microbial community structure (Fontaine et al, 2003;De Graaff et al, 2010;Blagodatskaya et al, 2014), and provides a link between the quantity or quality of more thermodynamically labile inputs and the potential destabilization of soil carbon (Guenet et al, 2012;Wild et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Creamer

Menezes

Krull

et al. 2015

Soil Biology and Biochemistry

197

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Section: Introductionmentioning

confidence: 99%

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Creamer

Menezes

Krull

et al. 2015

Soil Biology and Biochemistry

197

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“…MFTs and with or without N dynamics all better captured the temporal dynamics of both respiration and priming effects measured by Blagodatskaya et al (2014).…”

Section: Performance Of Orchimic Modelmentioning

confidence: 92%

“…Data from soil incubation experiments (Blagodatskaya et al, 2014) were used to optimize the parameters of ORCHIMIC, CENTURY and PRIM using a Bayesian calibration procedure described in Sect. 4.2.…”

Section: Data Description and Model Initial Conditionsmentioning

confidence: 99%

“…In Blagodatskaya et al, (2014), not only were the variables mentioned above measured, also the fraction of FOM derived C in both microbial biomass and DOC was measured, which are both very useful for calibrating parameters related to microbial dynamics. As a brief summary of their incubation experiment, 14 C labelled cellulose was added into soil as powder at a dose of 0.4 g C (kg 10 soil)…”

Section: Data Description and Model Initial Conditionsmentioning

confidence: 99%

“…cheaters. This ORCHIMIC model and two other organic matter decomposition models, CENTURY (based on first order kinetics and representative for the structure of most current global 20 soil carbon models) and PRIM (with FOM accelerating the decomposition rate of SOM) were calibrated to reproduce the observed respiration fluxes from FOM and SOM and their possible interactions from incubation experiments of Blagodatskaya et al (2014). Among the three models, ORCHIMIC was the only one that captured well both the temporal dynamics of the respiratory fluxes and the magnitude of the priming effect observed during the incubation experiment.…”

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ORCHIMIC (v1.0), a microbe-driven model for soil organic matter decomposition designed for large-scale applications

Huang

Guenet

Ciais

et al. 2018

Preprint

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Abstract. The role of soil microorganisms in regulating soil organic matter (SOM) decomposition is of primary importance in the carbon cycle, and in particular in the context of global change. Modelling soil microbial community dynamics to simulate its impact on soil gaseous carbon (C) emissions and nitrogen (N) mineralization at large spatial scales is a recent research field with the potential to improve predictions of SOM responses to global climate change. We here present a SOM 15 model called ORCHIMIC whose input data that are consistent with those of global vegetation models. The model simulates decomposition of SOM by explicitly accounting for enzyme production and distinguishing three different microbial functional groups: fresh organic matter (FOM) specialists, SOM specialists, and generalists, while implicitly also accounting for microbes that do not produce extracellular enzymes, i.e. cheaters. This ORCHIMIC model and two other organic matter decomposition models, CENTURY (based on first order kinetics and representative for the structure of most current global 20 soil carbon models) and PRIM (with FOM accelerating the decomposition rate of SOM) were calibrated to reproduce the observed respiration fluxes from FOM and SOM and their possible interactions from incubation experiments of Blagodatskaya et al. (2014). Among the three models, ORCHIMIC was the only one that captured well both the temporal dynamics of the respiratory fluxes and the magnitude of the priming effect observed during the incubation experiment.ORCHIMIC also reproduced well the temporal dynamics of microbial biomass. We then applied different idealized changes 25 to the model input data, i.e. a 5 K stepwise increase of temperature and/or a doubling of plant litter inputs. Under 5 K warming, ORCHIMIC predicted a 0.002 K -1 decrease in the C use efficiency (defined as the ratio of C allocated to microbial growth to the sum of C allocated to growth and respiration) and a 3 % loss of SOC. Under the double litter input scenario, ORCHIMIC predicted a doubling of microbial biomass, while SOC stock increased by less than 1 % due to the priming effect. This limited increase in SOC stock contrasted with the proportional increase in SOC stock as modelled by the 30 conventional SOC decomposition model (CENTURY), which cannot reproduce the priming effect. If temperature increased by 5 K and litter input is doubled, the model predicted almost the same loss of SOC as when only temperature was increased.These tests suggest that the responses of SOC stock to warming and increasing input may differ a lot from those simulated

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Similar soil microbial community structure across different environments after long‐term succession: evidence from volcanoes of different ages

Zhang

Luo

et al. 2018

J Basic Microbiol

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Soil microbes play critical roles in global biogeochemical cycles, but their succession patterns across long temporal scales have rarely been studied. In this study, soil samples were collected from three volcanoes in Wudalianchi, northeastern China: Laoheishan (LH, approximately 240 years old), Dongjiaodebushan (DJ, 0.45-0.6 million years old), and Nangelaqiushan (NG, 0.8-1.3 million years old). For each volcano, both southern (S) and northern (N) slope aspects were sampled. Soil microbial communities were analyzed using phospholipid fatty acid analysis (PLFA). The results showed that soil properties and microbial biomass changed perceptibly among different volcanoes and different slope aspects. Almost all of the detected soil nutrient contents of LH were lowest, and total microbial biomass of LH was 40 and 36% lower than those of NG and DJ, respectively. LH was significantly different from NG and DJ in soil microbial community structure with a higher relative abundance of fungi and a lower relative abundance of actinomycetes and bacteria. However, for the two ancient volcanoes (NG and DJ), soil microbial community structures were highly similar among different ages and different slope aspects. No difference was detected in any of the measured microbial indices, including richness, evenness, Shannon's diversity, Simpson's diversity and the relative abundance of different microbial groups. The results indicated that while soil microbial biomass may change across different soil environments after long-term succession, soil microbial community structure can remain relatively stable. The results further indicated that soil microbes may show different successional patterns in different stages of succession.

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Microbial interactions affect sources of priming induced by cellulose

Cited by 166 publications

References 63 publications

Microbial community structure mediates response of soil C decomposition to litter addition and warming

Microbial community structure mediates response of soil C decomposition to litter addition and warming

ORCHIMIC (v1.0), a microbe-driven model for soil organic matter decomposition designed for large-scale applications

Similar soil microbial community structure across different environments after long‐term succession: evidence from volcanoes of different ages

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