Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition
Abstract
Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an “organism” in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait) focused on nitrification (MicroTrait-N) that represents the ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) using traits related to enzyme kinetics and physiological properties.We used this model to predict nitrifier diversity, ammonia (NH3) oxidation rates, and nitrous oxide (N2O) production across pH, temperature, and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over 6 month simulations) is maximized in a system where the relationship between AOB and NOB is maintained.When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. Wemore »
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division. Ecology Dept.
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division. Climate Science Dept.
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1628074
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Microbiology
- Additional Journal Information:
- Journal Volume: 3; Journal ID: ISSN 1664-302X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Microbiology; nitrogen cycle; models; biological; geochemistry; mathematical modeling; nitrification
Citation Formats
Bouskill, Nicholas J., Tang, Jinyun, Riley, William J., and Brodie, Eoin L. Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition. United States: N. p., 2012.
Web. doi:10.3389/fmicb.2012.00364.
Bouskill, Nicholas J., Tang, Jinyun, Riley, William J., & Brodie, Eoin L. Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition. United States. https://doi.org/10.3389/fmicb.2012.00364
Bouskill, Nicholas J., Tang, Jinyun, Riley, William J., and Brodie, Eoin L. Sun .
"Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition". United States. https://doi.org/10.3389/fmicb.2012.00364. https://www.osti.gov/servlets/purl/1628074.
@article{osti_1628074,
title = {Trait-Based Representation of Biological Nitrification: Model Development, Testing, and Predicted Community Composition},
author = {Bouskill, Nicholas J. and Tang, Jinyun and Riley, William J. and Brodie, Eoin L.},
abstractNote = {Trait-based microbial models show clear promise as tools to represent the diversity and activity of microorganisms across ecosystem gradients. These models parameterize specific traits that determine the relative fitness of an “organism” in a given environment, and represent the complexity of biological systems across temporal and spatial scales. In this study we introduce a microbial community trait-based modeling framework (MicroTrait) focused on nitrification (MicroTrait-N) that represents the ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) using traits related to enzyme kinetics and physiological properties.We used this model to predict nitrifier diversity, ammonia (NH3) oxidation rates, and nitrous oxide (N2O) production across pH, temperature, and substrate gradients. Predicted nitrifier diversity was predominantly determined by temperature and substrate availability, the latter was strongly influenced by pH. The model predicted that transient N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O production (over 6 month simulations) is maximized in a system where the relationship between AOB and NOB is maintained.When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 oxidation rates and the relative ratio of AOA:AOB biomass. The predicted community structure and activity indicate (a) parameterization of a small number of traits may be sufficient to broadly characterize nitrifying community structure and (b) changing decadal trends in climate and edaphic conditions could impact nitrification rates in ways that are not captured by extant biogeochemical models.},
doi = {10.3389/fmicb.2012.00364},
journal = {Frontiers in Microbiology},
number = ,
volume = 3,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2012},
month = {Sun Jan 01 00:00:00 EST 2012}
}
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