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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

Simulations of AOO diversity and activity across a pH gradient. Community evenness values are given above the stacked bars. (A) Community diversity (proportion of total biomass) predictions using mean trait values. (B) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using mean trait values. (C) Community diversity (proportion of total biomass) predictions using Monte Carlo simulations of multiple AOO analogs (n = 5 analogs per guild). (D) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild).
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Figure 2: Simulations of AOO diversity and activity across a pH gradient. Community evenness values are given above the stacked bars. (A) Community diversity (proportion of total biomass) predictions using mean trait values. (B) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using mean trait values. (C) Community diversity (proportion of total biomass) predictions using Monte Carlo simulations of multiple AOO analogs (n = 5 analogs per guild). (D) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild).

Mentions: We simulated a pH gradient from approximately neutral (pH = 7.8) to acidic (pH = 4.5) conditions and recorded diversity and activity (NH3 oxidation rate and N2O production). During the hydrolysis reaction of NH3, the ratio NH4:NH3 increased hyperbolically as pH decreased. Thus, at pH < 5, the extremely low [NH3] encouraged the growth of oligotrophic ammonia oxidizers. Both baseline (i.e., fixed trait values, Figures 2A,B) and MC (Figures 2C,D) approaches showed a decline in AOB community evenness with decreasing pH. The highest evenness values are predicted around neutral values where AOB guilds 7 [AOB(7)] and 4 [AOB(4)] dominate. As pH decreases, community diversity declines until the AOA guild dominates. Although both simulations had similar trends in diversity, the multiple analog experiments (Figures 2C,D) predicted more variability in community diversity, as evidenced by more variable evenness values. Predicted nitrifier activity (as indicated by NH3 oxidation rates and N2O production) also declined with decreasing pH from a maximum NH3 oxidation rate of 1.9 M N day−1 to less than 0.1 M N day−1. Predicted N2O production was linearly related to NH3 oxidation (data not shown, r = 0.98, p = 0.001, slope = 0.94) indicating the AOB and NOB reactions were coupled regardless of the pH and N2O was primarily by hydroxylamine decomposition.


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

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

Simulations of AOO diversity and activity across a pH gradient. Community evenness values are given above the stacked bars. (A) Community diversity (proportion of total biomass) predictions using mean trait values. (B) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using mean trait values. (C) Community diversity (proportion of total biomass) predictions using Monte Carlo simulations of multiple AOO analogs (n = 5 analogs per guild). (D) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Simulations of AOO diversity and activity across a pH gradient. Community evenness values are given above the stacked bars. (A) Community diversity (proportion of total biomass) predictions using mean trait values. (B) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using mean trait values. (C) Community diversity (proportion of total biomass) predictions using Monte Carlo simulations of multiple AOO analogs (n = 5 analogs per guild). (D) Simulated nitrifier activity (NH3 oxidation, NO2 production, N2O production) using Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild).
Mentions: We simulated a pH gradient from approximately neutral (pH = 7.8) to acidic (pH = 4.5) conditions and recorded diversity and activity (NH3 oxidation rate and N2O production). During the hydrolysis reaction of NH3, the ratio NH4:NH3 increased hyperbolically as pH decreased. Thus, at pH < 5, the extremely low [NH3] encouraged the growth of oligotrophic ammonia oxidizers. Both baseline (i.e., fixed trait values, Figures 2A,B) and MC (Figures 2C,D) approaches showed a decline in AOB community evenness with decreasing pH. The highest evenness values are predicted around neutral values where AOB guilds 7 [AOB(7)] and 4 [AOB(4)] dominate. As pH decreases, community diversity declines until the AOA guild dominates. Although both simulations had similar trends in diversity, the multiple analog experiments (Figures 2C,D) predicted more variability in community diversity, as evidenced by more variable evenness values. Predicted nitrifier activity (as indicated by NH3 oxidation rates and N2O production) also declined with decreasing pH from a maximum NH3 oxidation rate of 1.9 M N day−1 to less than 0.1 M N day−1. Predicted N2O production was linearly related to NH3 oxidation (data not shown, r = 0.98, p = 0.001, slope = 0.94) indicating the AOB and NOB reactions were coupled regardless of the pH and N2O was primarily by hydroxylamine decomposition.

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