RedOx Layer Model: A Tool for Analysis of the Water Column Oxic/Anoxic Interface Processes

Yakushev, E. V.

doi:10.1007/698_2012_145

Cited by 7 publications

(14 citation statements)

References 58 publications

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“…1 make any statement about what the reaction rates Rr should be, as this requires additional biological reasoning. Conventional models either require a priori knowledge of reaction kinetics and/or microbial population dynamics to predict the rates Rr or estimate unknown kinetic parameters and/or unknown rates by fitting the model to measured geochemical depth profiles (3,10,20,43,(45)(46)(47)(48)(49). Here we circumvent these requirements by assuming that microbial communities eventually occupy every available metabolic niche and become infinitely efficient at gathering available substrates (assumption iv).…”

Section: A Framework For Spatial Metabolic Flux Analysismentioning

confidence: 99%

Circumventing kinetics in biogeochemical modeling

Louca

Scranton

Taylor

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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SignificancePredicting biochemical processes driven by microbes in the environment remains challenging, because the "kinetic" parameters conventionally used to predict reaction rates are usually poorly known. Here we mathematically show that in poorly mixed systems, such as stagnant waters, bulk biochemical reaction rates can become limited by the slow transport of substrates across space and essentially independent of kinetic parameters. We demonstrate our arguments for a large and heavily studied ocean basin, where we accurately predict the microbially driven fluxes of various substances, across a depth range of hundreds of meters. Our work opens up avenues for predicting ecosystem processes without knowledge of kinetic parameters and without laborious chemical profile measurements.

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Section: A Framework For Spatial Metabolic Flux Analysismentioning

confidence: 99%

Circumventing kinetics in biogeochemical modeling

Louca

Scranton

Taylor

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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“…In Cariaco Basin analogous parameter estimates are lacking, and previous studies simply assumed an exponent of p = 1 Taylor et al, 2001;Ho et al, 2004;Li et al, 2012b;Samodurov et al, 2013;Taylor et al, 2018). A value of p = 1 is also frequently assumed in other systems (Zopfi et al, 2001;Yakushev et al, 2007;Yakushev, 2013), although some studies instead assumed p = 2 (Fennel and Boss, 2003;Reed et al, 2014).…”

Section: Estimating Diffusivity Over Space and Timementioning

confidence: 99%

Microbial metabolite fluxes in a model marine anoxic ecosystem

Louca

Astor

Doebeli

et al. 2019

Preprint

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conceived the project, wrote the computer code, performed the analyses and wrote a first draft of the manuscript. M.I.S., G.T.T. and Y.M.A. were some of the coordinators of the CARIACO Ocean Time Series program. All authors helped interpret the results, and contributed to the writing of the manuscript. AbstractPermanently anoxic regions in the ocean are widespread, and exhibit unique microbial metabolic activity exerting substantial influence on global elemental cycles and climate. Reconstructing microbial metabolic activity rates in these regions has been challenging, due to the technical difficulty of direct rate measurements. In Cariaco Basin, which is the largest permanently anoxic marine basin and an important model system for geobiology, long-term monitoring has yielded time series for the concentrations of biologically important compounds; however the underlying metabolite fluxes remain poorly quantified. Here we present a computational approach for reconstructing vertical fluxes and in situ net production/consumption rates from chemical concentration data, based on a 1-dimensional time-dependent diffusive transport model that includes adaptive penalization of overfitting. We use this approach to estimate spatiotemporally resolved fluxes of oxygen, nitrate, hydrogen sulfide, ammonium, methane and phosphate within the sub-euphotic Cariaco Basin water column (depths 150-900 m, years 2001-2014), and to identify hotspots of microbial chemolithotrophic activity. Predictions of the fitted models are in excellent agreement with the data, and substantially expand our knowledge of the geobiology in Cariaco Basin. In particular, we find that the diffusivity, and consequently fluxes of major reductants such as hydrogen sulfide and methane, are about two orders of magnitude greater than previously estimated, thus resolving a long standing apparent conundrum between electron donor fluxes and measured dark carbon assimilation rates.Abbreviations: ILTM, inverse linear transport modeling; DCA, dark carbon assimilation

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“…In Cariaco Basin, analogous parameter estimates are lacking, and previous studies simply assumed an exponent of p = 1 (Ho et al, 2004;Li et al, 2012b;Samodurov et al, 2013;Scranton et al, 1987;Taylor et al, 2001Taylor et al, , 2018. A value of p = 1 is also frequently assumed in other systems (Yakushev, 2013;Yakushev et al, 2007;Zopfi, Ferdelman, Jorgensen, Teske, & Thamdrup, 2001), although some studies instead assumed p = 2 (Fennel & Boss, 2003;Reed et al, 2014). The factor α is usually chosen roughly based on estimates from other marine systems (Ho et al, 2004;Li et al, 2012b;Samodurov et al, 2013;Taylor et al, 2018).…”

Section: Estimating Diffusivity Over Space and Timementioning

confidence: 99%

Microbial metabolite fluxes in a model marine anoxic ecosystem

et al. 2019

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Permanently anoxic regions in the ocean are widespread and exhibit unique microbial metabolic activity exerting substantial influence on global elemental cycles and climate. Reconstructing microbial metabolic activity rates in these regions has been challenging, due to the technical difficulty of direct rate measurements. In Cariaco Basin, which is the largest permanently anoxic marine basin and an important model system for geobiology, long‐term monitoring has yielded time series for the concentrations of biologically important compounds; however, the underlying metabolite fluxes remain poorly quantified. Here, we present a computational approach for reconstructing vertical fluxes and in situ net production/consumption rates from chemical concentration data, based on a 1‐dimensional time‐dependent diffusive transport model that includes adaptive penalization of overfitting. We use this approach to estimate spatiotemporally resolved fluxes of oxygen, nitrate, hydrogen sulfide, ammonium, methane, and phosphate within the sub‐euphotic Cariaco Basin water column (depths 150–900 m, years 2001–2014) and to identify hotspots of microbial chemolithotrophic activity. Predictions of the fitted models are in excellent agreement with the data and substantially expand our knowledge of the geobiology in Cariaco Basin. In particular, we find that the diffusivity, and consequently fluxes of major reductants such as hydrogen sulfide, and methane, is about two orders of magnitude greater than previously estimated, thus resolving a long‐standing apparent conundrum between electron donor fluxes and measured dark carbon assimilation rates.

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RedOx Layer Model: A Tool for Analysis of the Water Column Oxic/Anoxic Interface Processes

Cited by 7 publications

References 58 publications

Circumventing kinetics in biogeochemical modeling

Circumventing kinetics in biogeochemical modeling

Microbial metabolite fluxes in a model marine anoxic ecosystem

Microbial metabolite fluxes in a model marine anoxic ecosystem

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