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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.


Related in: MedlinePlus

Schematic representation of the model. Model abbreviations. DOM, dissolved organic matter; DON, dissolved organic nitrogen; AOB/AOA, ammonia-oxidizing bacteria/archaea; NOB, nitrite-oxidizing bacteria.
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Figure 1: Schematic representation of the model. Model abbreviations. DOM, dissolved organic matter; DON, dissolved organic nitrogen; AOB/AOA, ammonia-oxidizing bacteria/archaea; NOB, nitrite-oxidizing bacteria.

Mentions: MicroTrait-N resolves intra-functional group diversity of the nitrifier populations (AOB, AOA, NOB) by parameterizing multiple guilds spanning a range in the trait-space (Figure 1). Although this nitrifier model will be integrated in an ecosystem model that allows for a wide range of interactions (Tang et al., submitted), we focus here on resolving nitrifier diversity in a competitive environment across a range of conditions, including pH, O2, substrate type (NH3 or urea), and temperature. Our approach is general enough that it can be applied to nitrifier populations in freshwater and aquatic environments and flexible enough to be used within soil pores. The model is written in Matlab (Matlab R2011b, Natick, MA, USA).


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

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

Schematic representation of the model. Model abbreviations. DOM, dissolved organic matter; DON, dissolved organic nitrogen; AOB/AOA, ammonia-oxidizing bacteria/archaea; NOB, nitrite-oxidizing bacteria.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Schematic representation of the model. Model abbreviations. DOM, dissolved organic matter; DON, dissolved organic nitrogen; AOB/AOA, ammonia-oxidizing bacteria/archaea; NOB, nitrite-oxidizing bacteria.
Mentions: MicroTrait-N resolves intra-functional group diversity of the nitrifier populations (AOB, AOA, NOB) by parameterizing multiple guilds spanning a range in the trait-space (Figure 1). Although this nitrifier model will be integrated in an ecosystem model that allows for a wide range of interactions (Tang et al., submitted), we focus here on resolving nitrifier diversity in a competitive environment across a range of conditions, including pH, O2, substrate type (NH3 or urea), and temperature. Our approach is general enough that it can be applied to nitrifier populations in freshwater and aquatic environments and flexible enough to be used within soil pores. The model is written in Matlab (Matlab R2011b, Natick, MA, USA).

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