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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

Community response to pulsed substrate input. (A) Changes in AOO biomass over time. (B) Substrate concentration (M). (C) Nitrite dynamics over time. (D) Production of N2O over time.
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Figure 5: Community response to pulsed substrate input. (A) Changes in AOO biomass over time. (B) Substrate concentration (M). (C) Nitrite dynamics over time. (D) Production of N2O over time.

Mentions: We simulated the response of our imposed simple community (seven AOB guilds; one AOA guild; and three NOB guilds) to pulsed input of substrate over a 9-month period (Figure 5). Over time, and with evenly spaced pulsed events, the evenness of the community declines slightly from 0.76 to 0.58 as one guild, AOB(7), begins to dominate. Pulses of NH3 are drawn down more quickly as the biomass of AOB increases. However, the second pulse of NH3 results in its most rapid drawdown due to a high cumulative biomass and greater diversity of AOO (Figures 5A,B). As NOB biomass increases, NO2 demand increases, and the NO2 is oxidized as rapidly as it is produced (Figure 5C). In the present simulation we did not allow for diffusion, and this resulted in an accumulation of N2O (Figure 5D), nevertheless, the rate at which it is produced reflects the pulses of NH3 into the system. The initial pulse elevates NH3 concentrations from 1 × 10−7 to 5 × 10−6 and results in a five-fold increase in the biomass of AOB(7), a four-fold increase in AOB(5), and a small response in AOB(1). As NH3 is drawn down to lower concentrations (<1 × 10−6 M) AOA briefly become the dominant nitrifiers. While AOA biomass peak when substrate concentrations are low, they are inhibited by subsequent substrate pulses.


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

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

Community response to pulsed substrate input. (A) Changes in AOO biomass over time. (B) Substrate concentration (M). (C) Nitrite dynamics over time. (D) Production of N2O over time.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Community response to pulsed substrate input. (A) Changes in AOO biomass over time. (B) Substrate concentration (M). (C) Nitrite dynamics over time. (D) Production of N2O over time.
Mentions: We simulated the response of our imposed simple community (seven AOB guilds; one AOA guild; and three NOB guilds) to pulsed input of substrate over a 9-month period (Figure 5). Over time, and with evenly spaced pulsed events, the evenness of the community declines slightly from 0.76 to 0.58 as one guild, AOB(7), begins to dominate. Pulses of NH3 are drawn down more quickly as the biomass of AOB increases. However, the second pulse of NH3 results in its most rapid drawdown due to a high cumulative biomass and greater diversity of AOO (Figures 5A,B). As NOB biomass increases, NO2 demand increases, and the NO2 is oxidized as rapidly as it is produced (Figure 5C). In the present simulation we did not allow for diffusion, and this resulted in an accumulation of N2O (Figure 5D), nevertheless, the rate at which it is produced reflects the pulses of NH3 into the system. The initial pulse elevates NH3 concentrations from 1 × 10−7 to 5 × 10−6 and results in a five-fold increase in the biomass of AOB(7), a four-fold increase in AOB(5), and a small response in AOB(1). As NH3 is drawn down to lower concentrations (<1 × 10−6 M) AOA briefly become the dominant nitrifiers. While AOA biomass peak when substrate concentrations are low, they are inhibited by subsequent substrate pulses.

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