Modeling coupled pesticide degradation and organic matter turnover: From gene abundance to process rates

Pagel, Holger; Poll, Christian; Ingwersen, Joachim; Kandeler, Ellen; Streck, Thilo

doi:10.1016/j.soilbio.2016.09.014

Cited by 23 publications

(30 citation statements)

References 97 publications

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“…Monomer threshold concentration for deactivation and reactivation 0.001 0.01 0.001 mg g −1 # According to ranges estimated by Pagel et al (2016). * Based on reported ranges of carbon use efficiencies (Manzoni et al, 2012Geyer et al, 2019).…”

Section: Functional Microbial Groupsmentioning

confidence: 99%

“…+ Predation parameters are poorly constrained, values were set based on reported ranges (Coleman et al, 2017, p. 218;Komarov et al, 2017), initial values were set to lower limits of experimental estimates of soil faunal C budgets (Pausch et al, 2018). § Values of sorption parameters were based on sorption characteristics of small polymers and monomers (Kaiser and Zech, 1997;Vandenbruwane et al, 2007;Fischer et al, 2010;Oren and Chefetz, 2012;Pagel et al, 2014Pagel et al, , 2016. & Pagel et al (2014& Pagel et al ( , 2016.…”

Section: Functional Microbial Groupsmentioning

confidence: 99%

“…§ Values of sorption parameters were based on sorption characteristics of small polymers and monomers (Kaiser and Zech, 1997;Vandenbruwane et al, 2007;Fischer et al, 2010;Oren and Chefetz, 2012;Pagel et al, 2014Pagel et al, , 2016. & Pagel et al (2014& Pagel et al ( , 2016.…”

Section: Functional Microbial Groupsmentioning

confidence: 99%

“…Including measured functional traits of plants as well as soil microorganisms and fauna in biogeochemical modeling is a promising approach to improve predictions of biogeochemical cycling in soil (Fry et al, 2019). Biogeochemical C models increasingly include metabolic and physiological traits as well as life-history strategies to account for microbial regulation of decomposition processes (Garnier et al, 2001;Ingwersen et al, 2008;Neill and Guenet, 2010;Allison, 2012;Bouskill et al, 2012;Pagel et al, 2014Pagel et al, , 2016Perveen et al, 2014;Wang et al, 2014;Le Roux et al, 2016). Including microbial dormancy of microbes in models has been shown to improve the prediction of soil organic C dynamics (He et al, 2015) as did accounting for copiotrophic and oligotrophic microorganisms as physiologically distinct functional groups (Wieder et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

Pagel

Kriesche

Uksa

et al. 2020

Front. Environ. Sci.

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Trait-based models have improved the understanding and prediction of soil organic matter dynamics in terrestrial ecosystems. Microscopic observations and pore scale models are now increasingly used to quantify and elucidate the effects of soil heterogeneity on microbial processes. Combining both approaches provides a promising way to accurately capture spatial microbial-physicochemical interactions and to predict overall system behavior. The present study aims to quantify controls on carbon (C) turnover in soil due to the mm-scale spatial distribution of microbial decomposer communities in soil. A new spatially explicit trait-based model (SpatC) has been developed that captures the combined dynamics of microbes and soil organic matter (SOM) by taking into account microbial life-history traits and SOM accessibility. Samples of spatial distributions of microbes at µm-scale resolution were generated using a spatial statistical model based on Log Gaussian Cox Processes which was originally used to analyze distributions of bacterial cells in soil thin sections. These µm-scale distribution patterns were then aggregated to derive distributions of microorganisms at mm-scale. We performed Monte-Carlo simulations with microbial distributions that differ in mm-scale spatial heterogeneity and functional community composition (oligotrophs, copiotrophs, and copiotrophic cheaters). Our modeling approach revealed that the spatial distribution of soil microorganisms triggers spatiotemporal patterns of C utilization and microbial succession. Only strong spatial clustering of decomposer communities induces a diffusion limitation of the substrate supply on the microhabitat scale, which significantly reduces the total decomposition of C compounds and the overall microbial growth. However, decomposer communities act as functionally redundant microbial guilds with only slight changes in C utilization. The combined statistical and process-based modeling approach derives distribution patterns of microorganisms at the mm-scale from microbial biogeography at microhabitat scale (µm) and quantifies the emergent macroscopic (cm) microbial and C dynamics. Thus, it effectively links observable process dynamics to the spatial control by microbial communities. Our study highlights a powerful approach that can provide further insights into the biological control of soil organic matter turnover.

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Section: Functional Microbial Groupsmentioning

confidence: 99%

Section: Functional Microbial Groupsmentioning

confidence: 99%

Section: Functional Microbial Groupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

Pagel

Kriesche

Uksa

et al. 2020

Front. Environ. Sci.

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“…In these simpler methodologies, the data are used as proxies or biomarkers to constrain the dynamic behavior of a microbial community. While in the past, traditional microbial biomarkers such as cell counts, colony forming units, proteins or polysaccharides were used already as experimental references for the dynamics of microbial species or functional groups in the context of reactive transport simulations (Wirtz 2003;Thullner et al 2004) more recent approaches combined these traditional approaches with marker genes to simulate the response of microorganism to fluctuating growth conditions (Stolpovsky et al 2011) or carbon turnover and pesticide degradation in in a 1-D soil column (Pagel et al 2016). Reed et al (2014) and Louca et al (2016) went further and applied a complex 'gene-centric' approach using marker genes as experimental references, the production of which was linked to biogeochemical reactions in the ocean.…”

mentioning

confidence: 99%

Microbial Controls on the Biogeochemical Dynamics in the Subsurface

Thullner

Regnier

2019

Reviews in Mineralogy and Geochemistry

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3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Gerke

Vogel

Weber

et al. 2022

J. Plant Nutr. Soil Sci.

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A 3-4D soil model represents a logical step forward from one-dimensional soil columns (1D), two-dimensional soil maps (2D), and three-dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3-4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3-4D soil model. A 3-4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3-4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing. This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3-4D soil model is a truly an ambitious, but promisingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Modeling coupled pesticide degradation and organic matter turnover: From gene abundance to process rates

Cited by 23 publications

References 97 publications

Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

Microbial Controls on the Biogeochemical Dynamics in the Subsurface

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

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