Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities

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 app...

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Main Authors: Holger Pagel, Björn Kriesche, Marie Uksa, Christian Poll, Ellen Kandeler, Volker Schmidt, Thilo Streck
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-01-01
Series:Frontiers in Environmental Science
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fenvs.2020.00002/full
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spelling doaj-e142386673a94dd8b9d680e4cbd3724a2020-11-24T22:07:34ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2020-01-01810.3389/fenvs.2020.00002498113Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer CommunitiesHolger Pagel0Björn Kriesche1Marie Uksa2Christian Poll3Ellen Kandeler4Volker Schmidt5Thilo Streck6Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, GermanyInstitute of Stochastics, Ulm University, Ulm, GermanySoil Biology, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, GermanySoil Biology, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, GermanySoil Biology, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, GermanyInstitute of Stochastics, Ulm University, Ulm, GermanyBiogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, GermanyTrait-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.https://www.frontiersin.org/article/10.3389/fenvs.2020.00002/fullmicrobial functional groupstrait-based modelmicrobial biogeographyupscalingsoil organic matter cyclinglog gaussian cox process point pattern
collection DOAJ
language English
format Article
sources DOAJ
author Holger Pagel
Björn Kriesche
Marie Uksa
Christian Poll
Ellen Kandeler
Volker Schmidt
Thilo Streck
spellingShingle Holger Pagel
Björn Kriesche
Marie Uksa
Christian Poll
Ellen Kandeler
Volker Schmidt
Thilo Streck
Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
Frontiers in Environmental Science
microbial functional groups
trait-based model
microbial biogeography
upscaling
soil organic matter cycling
log gaussian cox process point pattern
author_facet Holger Pagel
Björn Kriesche
Marie Uksa
Christian Poll
Ellen Kandeler
Volker Schmidt
Thilo Streck
author_sort Holger Pagel
title Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
title_short Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
title_full Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
title_fullStr Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
title_full_unstemmed Spatial Control of Carbon Dynamics in Soil by Microbial Decomposer Communities
title_sort spatial control of carbon dynamics in soil by microbial decomposer communities
publisher Frontiers Media S.A.
series Frontiers in Environmental Science
issn 2296-665X
publishDate 2020-01-01
description 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.
topic microbial functional groups
trait-based model
microbial biogeography
upscaling
soil organic matter cycling
log gaussian cox process point pattern
url https://www.frontiersin.org/article/10.3389/fenvs.2020.00002/full
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