Global pattern and controls of soil microbial metabolic quotient

Xu, Xiaofeng; Schimel, Joshua P.; Janssens, Ivan A.; Song, Xiaoning; Song, Chen; Yu, Guirui; Sinsabaugh, Robert L.; Tang, Diandong; Zhang, Xiaochun; Thornton, P. E.

doi:10.1002/ecm.1258

Cited by 130 publications

(185 citation statements)

References 63 publications

(161 reference statements)

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“…POC‐cellulose is on average smaller than POC‐lignin (11% vs. 16% of SOC) since it is consumed faster, but POC‐lignin has a much larger variability across ecosystems, which is related to the composition of the litter with grasslands having a much smaller fraction of POC‐lignin when compared to forests or shrubs. Microbial biomass is simulated to be on the range 1.0–3.1% of SOC (Figure b) consistent with observations published in several articles (Wang et al, ; Xu et al, , , ). However, the microbial nitrogen and phosphorus fraction of SOM tend to be underestimated at low values of NPP (Figure S3).…”

Section: Resultssupporting

confidence: 87%

“…The fractions of mineral‐associated organic carbon (MOC) and particulate organic carbon (POC) subdivided in POC‐lignin (POC‐Lig) and POC‐cellulose/hemicellulose (POC‐Cel) and dissolved organic carbon (DOC) are shown. (b) Boxplot representation of the simulated variability in the ratio between microbial biomass and SOC compared with observations reported by Wang et al (; OBS 1), Xu et al (; OBS 2), and Xu et al (; OBS 3). (c) Boxplot representation of the simulated variability of the ratio between DOC and SOC compared with observations reported by Wang et al ().…”

Section: Resultsmentioning

confidence: 99%

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A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

Fatichi

Manzoni

et al. 2019

Global Biogeochemical Cycles

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Present gaps in the representation of key soil biogeochemical processes such as the partitioning of soil organic carbon among functional components, microbial biomass and diversity, and the coupling of carbon and nutrient cycles present a challenge to improving the reliability of projected soil carbon dynamics. We introduce a new soil biogeochemistry module linked with a well‐tested terrestrial biosphere model T&C. The module explicitly distinguishes functional soil organic carbon components. Extracellular enzymes and microbial pools are differentiated based on the functional roles of bacteria, saprotrophic, and mycorrhizal fungi. Soil macrofauna is also represented. The model resolves the cycles of nitrogen, phosphorus, and potassium. Model simulations for 20 sites compared favorably with global patterns of litter and soil stoichiometry, microbial and macrofaunal biomass relations with soil organic carbon, soil respiration, and nutrient mineralization rates. Long‐term responses to bare fallow and nitrogen addition experiments were also in agreement with observations. Some discrepancies between predictions and observations are appreciable in the response to litter manipulation. Upon successful model reproduction of observed general trends, we assessed patterns associated with the carbon cycle that were challenging to address empirically. Despite large site‐to‐site variability, fine root, fungal, bacteria, and macrofaunal respiration account for 33%, 40%, 24%, and 3% on average of total belowground respiration, respectively. Simulated root exudation and carbon export to mycorrhizal fungi represent on average about 13% of plant net primary productivity. These results offer mechanistic and general estimates of microbial biomass and its contribution to respiration fluxes and to soil organic matter dynamics.

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Section: Resultssupporting

confidence: 87%

Section: Resultsmentioning

confidence: 99%

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

Fatichi

Manzoni

et al. 2019

Global Biogeochemical Cycles

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“…Xu et al . () presented a global regression model for specific respiration rate as a function of soil temperature. From their model, based on > 2400 observations, we calculated an apparent temperature sensitivity of 0.0675°C −1 (apparent E a 0.456 eV) for specific microbial respiration ( R h / B h ).…”

Section: Discussionmentioning

confidence: 99%

“…This CUE h meta‐analysis is supported by a complementary global meta‐analysis of specific microbial respiration ( R h / B h ) in soils by Xu et al . (). They calculated a mean respiration‐based biomass turnover ( B h / R h ) of 23–25 d, implying that the ratio R h : (μ + R h ), equivalent to 1 − CUE, is c .…”

Section: Introductionmentioning

confidence: 97%

Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production

Sinsabaugh

Moorhead

et al. 2017

New Phytologist

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The carbon use efficiency of plants (CUE ) and microorganisms (CUE ) determines rates of biomass turnover and soil carbon sequestration. We evaluated the hypothesis that CUE and CUE counterbalance at a large scale, stabilizing microbial growth (μ) as a fraction of gross primary production (GPP). Collating data from published studies, we correlated annual CUE , estimated from satellite imagery, with locally determined soil CUE for 100 globally distributed sites. Ecosystem CUE , the ratio of net ecosystem production (NEP) to GPP, was estimated for each site using published models. At the ecosystem scale, CUE and CUE were inversely related. At the global scale, the apparent temperature sensitivity of CUE with respect to mean annual temperature (MAT) was similar for organic and mineral soils (0.029°C ). CUE and CUE were inversely related to MAT, with apparent sensitivities of -0.009 and -0.032°C , respectively. These trends constrain the ratio μ : GPP (= (CUE × CUE )/(1 - CUE )) with respect to MAT by counterbalancing the apparent temperature sensitivities of the component processes. At the ecosystem scale, the counterbalance is effected by modulating soil organic matter stocks. The results suggest that a μ : GPP value of c. 0.13 is a homeostatic steady state for ecosystem carbon fluxes at a large scale.

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“…The large uncertainties in modeled terrestrial carbon processes [Friedlingstein et al, 2006;Good et al, 2013;Arora et al, 2013] have been attributed to a number of mechanisms, including the lack of important soil carbon processes [Todd-Brown et al, 2014;Xu et al, 2013Xu et al, , 2014Xu et al, , 2017, nutrient limitations [Thornton et al, 2009;Yang et al, 2014], and the coarse representation of permafrost dynamics in the arctic [Schuur et al, 2015]. The algorithm responsible for vegetation carbon allocation is another key cause for the ESM uncertainty [Friedlingstein et al, 2006;Ise et al, 2010;Weng and Luo, 2011;De Kauwe et al, 2014] due to large differences in carbon residence time for aboveground and belowground vegetation biomass.…”

Section: Vegetation Carbon Density In Esms 2282mentioning

confidence: 99%

Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data

et al. 2017

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Earth system models (ESMs) have been widely used for projecting global vegetation carbon dynamics, yet how well ESMs performed for simulating vegetation carbon density remains untested. We compiled observational data of vegetation carbon density from literature and existing data sets to evaluate nine ESMs at site, biome, latitude, and global scales. Three variables—root (including fine and coarse roots), total vegetation carbon density, and the root:total vegetation carbon ratios (R/T ratios), were chosen for ESM evaluation. ESM models performed well in simulating the spatial distribution of carbon densities in root (r = 0.71) and total vegetation (r = 0.62). However, ESM models had significant biases in simulating absolute carbon densities in root and total vegetation biomass across the majority of land ecosystems, especially in tropical and arctic ecosystems. Particularly, ESMs significantly overestimated carbon density in root (183%) and total vegetation biomass (167%) in climate zones of 10°S–10°N. Substantial discrepancies between modeled and observed R/T ratios were found: the R/T ratios from ESMs were relatively constant, approximately 0.2 across all ecosystems, along latitudinal gradients, and in tropic, temperate, and arctic climatic zones, which was significantly different from the observed large variations in the R/T ratios (0.1–0.8). There were substantial inconsistencies between ESM‐derived carbon density in root and total vegetation biomass and the R/T ratio at multiple scales, indicating urgent needs for model improvements on carbon allocation algorithms and more intensive field campaigns targeting carbon density in all key vegetation components.

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Global pattern and controls of soil microbial metabolic quotient

Cited by 130 publications

References 63 publications

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check

Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production

Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data

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