Metagenomics-informed soil biogeochemical models projected less carbon loss in tropical soils in response to climate warming

Abstract A major challenge of quantifying feedback between microbial communities and climate is the vast diversity of microbial communities and the intricacy of soil biogeochemical processes they mediate. We overcome this challenge by simplifying the representation of diverse enzyme functions from metagenomics data. We developed a dynamic allocation scheme for enzyme functional classes (EFCs) based on the premise that microbial communities act to maximize acquisition of limiting resources while minimizing energy expenditure for acquiring unlimited resources. We incorporated this scheme into a biogeochemical model to explicitly represent microbial functional diversity and simulate responses of microbially-mediated soil biogeochemical processes to varying environmental and nutrient conditions. Representing microbial functional diversity and environmental acclimation improved predictions of the stoichiometry of microbial biomass and mitigated the sensitivity of soil organic carbon to warming in nutrient-deficient regions. Our results indicate the importance of microbial functional diversity and environmental acclimation for projecting climate feedbacks of nutrient-limited soils.

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PROFILING THE FUNCTIONAL DIVERSITY OF MICROBIAL COMMUNITIES TO UNDERSTAND THE DEVELOPMENT AND MAINTENANCE OF THE EDWARDS AQUIFER ECOSYSTEM

10.1130/abs/2018am-322716 ◽

2018 ◽

Author(s):

Annette Summers Engel ◽

◽

Benjamin Hutchins ◽

Weston Nowlin ◽

Benjamin F. Schwartz

Keyword(s):

Microbial Communities ◽

Functional Diversity ◽

Edwards Aquifer

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Soil aggregate size-dependent relationships between microbial functional diversity and multifunctionality

Soil Biology and Biochemistry ◽

10.1016/j.soilbio.2021.108143 ◽

2021 ◽

Vol 154 ◽

pp. 108143

Author(s):

Shun Han ◽

Manuel Delgado-Baquerizo ◽

Xuesong Luo ◽

Yurong Liu ◽

Joy D. Van Nostrand ◽

...

Keyword(s):

Functional Diversity ◽

Aggregate Size ◽

Soil Aggregate ◽

Microbial Functional Diversity ◽

Size Dependent

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Enzyme activities and microbial functional diversity in metal(loid) contaminated soils near to a copper smelter

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.146423 ◽

2021 ◽

pp. 146423

Author(s):

Humberto Aponte ◽

Pedro Mondaca ◽

Christian Santander ◽

Sebastián Meier ◽

Jorge Paolini ◽

...

Keyword(s):

Functional Diversity ◽

Enzyme Activities ◽

Contaminated Soils ◽

Copper Smelter ◽

Microbial Functional Diversity

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Collection of a Bacterial Community Reconstructed from Marine Metagenomes Derived from Jinhae Bay, South Korea

Data ◽

10.3390/data6050044 ◽

2021 ◽

Vol 6 (5) ◽

pp. 44

Author(s):

Jae-Hyun Lim ◽

Il-Nam Kim

Keyword(s):

Microbial Community ◽

South Korea ◽

Microbial Communities ◽

Marine Bacteria ◽

Sampling Site ◽

Community Change ◽

Rrna Gene ◽

Water Column Stratification ◽

Metagenomics Data ◽

Jinhae Bay

Marine bacteria are known to play significant roles in marine biogeochemical cycles regarding the decomposition of organic matter. Despite the increasing attention paid to the study of marine bacteria, research has been too limited to fully elucidate the complex interaction between marine bacterial communities and environmental variables. Jinhae Bay, the study area in this work, is the most anthropogenically eutrophied coastal bay in South Korea, and while its physical and biogeochemical characteristics are well described, less is known about the associated changes in microbial communities. In the present study, we reconstructed a metagenomics data based on the 16S rRNA gene to investigate temporal and vertical changes in microbial communities at three depths (surface, middle, and bottom) during a seven-month period from June to December 2016 at one sampling site (J1) in Jinhae Bay. Of all the bacterial data, Proteobacteria, Bacteroidetes, and Cyanobacteria were predominant from June to November, whereas Firmicutes were predominant in December, especially at the middle and bottom depths. These results show that the composition of the microbial community is strongly associated with temporal changes. Furthermore, the community compositions were markedly different between the surface, middle, and bottom depths in summer, when water column stratification and bottom water hypoxia (low dissolved oxygen level) were strongly developed. Metagenomics data contribute to improving our understanding of important relationships between environmental characteristics and microbial community change in eutrophication-induced and deoxygenated coastal areas.

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Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Nature Communications ◽

10.1038/s41467-021-22452-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kuang-Yu Chang ◽

William J. Riley ◽

Sara H. Knox ◽

Robert B. Jackson ◽

Gavin McNicol ◽

...

Keyword(s):

Temperature Sensitivity ◽

Spatial Scales ◽

Biogeochemical Model ◽

Spatial And Temporal Variability ◽

Biogeochemical Models ◽

Model Parameterization ◽

Machine Learning Model ◽

Meta Analyses ◽

The Relationship ◽

Global Carbon

AbstractWetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

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