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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 the activity and diversity of AOB communities in high-latitude ecosystems. (A) Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild) across the different sites. Each guild is represented by a distinct color. Subtle differences in the shade of that color demarcate the different analogs/guild. A box outlines the boundaries of each guild’s biomass. Evenness statistic given above the bar plots. (B) NH3 oxidation rates from just simulated and observed data. (C) Predicted rates of N2O production and measured NH3 concentrations. Error bars are the result of multiple simulations (n = 3). BS, Black Spruce; BB, Bog Birch; RF, Rich Fen; EF, Emergent Fen; TG, Tussock Grassland.
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Figure 6: Simulations of the activity and diversity of AOB communities in high-latitude ecosystems. (A) Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild) across the different sites. Each guild is represented by a distinct color. Subtle differences in the shade of that color demarcate the different analogs/guild. A box outlines the boundaries of each guild’s biomass. Evenness statistic given above the bar plots. (B) NH3 oxidation rates from just simulated and observed data. (C) Predicted rates of N2O production and measured NH3 concentrations. Error bars are the result of multiple simulations (n = 3). BS, Black Spruce; BB, Bog Birch; RF, Rich Fen; EF, Emergent Fen; TG, Tussock Grassland.

Mentions: The dataset presented by Petersen et al. (2012) examined AOO community diversity across five-plant community types characteristic of the interior of Alaska. These soils were characterized by high substrate concentrations (range = 7.3 × 10−3 to 0.1 M NH3) and low pH (4.3–4.8). These observations therefore provide a comparison to our earlier examination of a pH gradient with a fixed substrate concentration. The model predicted that, in contrast to our previous predictions at low pH and NH3 substrate levels (Figure 2), bacteria dominated the AOO community at these sites (Figure 6A). Using mean values for traits, the Black Spruce and Bog Birch sites were dominated by AOB(7) and AOB(3) in the case of the Bog Birch site. The Tussock Grassland, Emergent Fen, and Rich Fen also showed lower evenness and were generally dominated by one guild [AOB(1)] accounting for approximately 90% of the total AOB biomass. The AOA guild was never a significant component of the community diversity under these conditions (data not shown). Within-guild diversity was represented using MC simulations that stochastically assigned traits to multiple analogs of each guild. The community composition that emerged when using this approach was different than when traits were represented by their mean values. For example, the AOA became more prominent in the MC simulations, although they were still only a relatively small proportion (2–4%) of the Fen communities and Tussock grassland (Figure 6A).


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 the activity and diversity of AOB communities in high-latitude ecosystems. (A) Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild) across the different sites. Each guild is represented by a distinct color. Subtle differences in the shade of that color demarcate the different analogs/guild. A box outlines the boundaries of each guild’s biomass. Evenness statistic given above the bar plots. (B) NH3 oxidation rates from just simulated and observed data. (C) Predicted rates of N2O production and measured NH3 concentrations. Error bars are the result of multiple simulations (n = 3). BS, Black Spruce; BB, Bog Birch; RF, Rich Fen; EF, Emergent Fen; TG, Tussock Grassland.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Simulations of the activity and diversity of AOB communities in high-latitude ecosystems. (A) Monte Carlo simulations of multiple AOB analogs (n = 5 analogs per guild) across the different sites. Each guild is represented by a distinct color. Subtle differences in the shade of that color demarcate the different analogs/guild. A box outlines the boundaries of each guild’s biomass. Evenness statistic given above the bar plots. (B) NH3 oxidation rates from just simulated and observed data. (C) Predicted rates of N2O production and measured NH3 concentrations. Error bars are the result of multiple simulations (n = 3). BS, Black Spruce; BB, Bog Birch; RF, Rich Fen; EF, Emergent Fen; TG, Tussock Grassland.
Mentions: The dataset presented by Petersen et al. (2012) examined AOO community diversity across five-plant community types characteristic of the interior of Alaska. These soils were characterized by high substrate concentrations (range = 7.3 × 10−3 to 0.1 M NH3) and low pH (4.3–4.8). These observations therefore provide a comparison to our earlier examination of a pH gradient with a fixed substrate concentration. The model predicted that, in contrast to our previous predictions at low pH and NH3 substrate levels (Figure 2), bacteria dominated the AOO community at these sites (Figure 6A). Using mean values for traits, the Black Spruce and Bog Birch sites were dominated by AOB(7) and AOB(3) in the case of the Bog Birch site. The Tussock Grassland, Emergent Fen, and Rich Fen also showed lower evenness and were generally dominated by one guild [AOB(1)] accounting for approximately 90% of the total AOB biomass. The AOA guild was never a significant component of the community diversity under these conditions (data not shown). Within-guild diversity was represented using MC simulations that stochastically assigned traits to multiple analogs of each guild. The community composition that emerged when using this approach was different than when traits were represented by their mean values. For example, the AOA became more prominent in the MC simulations, although they were still only a relatively small proportion (2–4%) of the Fen communities and Tussock grassland (Figure 6A).

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