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Trait-based representation of biological nitrification: model development, testing, and predicted community composition.

Bouskill NJ, Tang J, Riley WJ, Brodie EL - Front Microbiol (2012)

Bottom Line: The model predicted that transient N(2)O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N(2)O by AOB.When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH(3) oxidation and N(2)O production.We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH(3) oxidation rates and the relative ratio of AOA:AOB biomass.

View Article: PubMed Central - PubMed

Affiliation: Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory Berkeley, CA, USA.

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 (NH(3)) oxidation rates, and nitrous oxide (N(2)O) 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 N(2)O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N(2)O by AOB. However, cumulative N(2)O 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 NH(3) oxidation and N(2)O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH(3) 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.

No MeSH data available.


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Relative magnitude of guild parameters.
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FA1: Relative magnitude of guild parameters.

Mentions: N2O is produced by AOO via two distinct pathways: (1) decomposition of the hydroxylamine intermediate and (2) the likely more significant mechanism of NO2 detoxification (Figure A1 in Appendix; Frame and Casciotti, 2010; Kool et al., 2011; Stein and Klotz, 2011). Under the first pathway, N2O production is modeled as a linearly related fraction of hydroxylamine decomposition (Frame and Casciotti, 2010). The second pathway simulates the detoxification of accumulated NO2 as the two steps of nitrification become uncoupled. This decoupling can occur because NOB have a lower affinity for O2 than the AOB; therefore as O2 is consumed during nitrification (or in low O2 environments), the two reactions may become spatially or temporally uncoupled. NO2 toxicity stimulates a detoxification pathway converting NO2 to N2O via NO. This detoxification pathway is potentially the more significant mechanism by which AOB produce N2O. AOA have recently been shown to produce N2O (Santoro et al., 2011), although the mechanism has not yet been elucidated. Therefore, in the present version of the model we predict AOA N2O production using the same relationships as for AOB.


Trait-based representation of biological nitrification: model development, testing, and predicted community composition.

Bouskill NJ, Tang J, Riley WJ, Brodie EL - Front Microbiol (2012)

Relative magnitude of guild parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475126&req=5

FA1: Relative magnitude of guild parameters.
Mentions: N2O is produced by AOO via two distinct pathways: (1) decomposition of the hydroxylamine intermediate and (2) the likely more significant mechanism of NO2 detoxification (Figure A1 in Appendix; Frame and Casciotti, 2010; Kool et al., 2011; Stein and Klotz, 2011). Under the first pathway, N2O production is modeled as a linearly related fraction of hydroxylamine decomposition (Frame and Casciotti, 2010). The second pathway simulates the detoxification of accumulated NO2 as the two steps of nitrification become uncoupled. This decoupling can occur because NOB have a lower affinity for O2 than the AOB; therefore as O2 is consumed during nitrification (or in low O2 environments), the two reactions may become spatially or temporally uncoupled. NO2 toxicity stimulates a detoxification pathway converting NO2 to N2O via NO. This detoxification pathway is potentially the more significant mechanism by which AOB produce N2O. AOA have recently been shown to produce N2O (Santoro et al., 2011), although the mechanism has not yet been elucidated. Therefore, in the present version of the model we predict AOA N2O production using the same relationships as for AOB.

Bottom Line: The model predicted that transient N(2)O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N(2)O by AOB.When the reactions uncouple, the AOB become unstable and biomass declines rapidly, resulting in decreased NH(3) oxidation and N(2)O production.We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH(3) oxidation rates and the relative ratio of AOA:AOB biomass.

View Article: PubMed Central - PubMed

Affiliation: Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory Berkeley, CA, USA.

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 (NH(3)) oxidation rates, and nitrous oxide (N(2)O) 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 N(2)O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N(2)O by AOB. However, cumulative N(2)O 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 NH(3) oxidation and N(2)O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH(3) 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.

No MeSH data available.


Related in: MedlinePlus