Original Research ARTICLE
Trait-based representation of biological nitrification: model development, testing, and predicted community composition
- 1Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- 2Climate Science Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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 (NH3) oxidation rates, and nitrous oxide (N2O) 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 N2O production rates are maximized by a decoupling of the AOB and NOB communities, resulting in an accumulation and detoxification of nitrite to N2O by AOB. However, cumulative N2O 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 NH3 oxidation and N2O production. We evaluated this model against site level chemical datasets from the interior of Alaska and accurately simulated NH3 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.
Keywords: nitrogen cycle, models, biological, geochemistry, mathematical modeling, nitrification
Citation: Bouskill NJ, Tang J, Riley WJ and Brodie EL (2012) Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Front. Microbio. 3:364. doi: 10.3389/fmicb.2012.00364
Received: 03 July 2012; Paper pending published: 16 August 2012;
Accepted: 25 September 2012; Published online: 18 October 2012.
Edited by:Bess B. Ward, Princeton University, USA
Reviewed by:J. Michael Beman, University of California, Merced, USA
Daniel Laughlin, University of Waikato, New Zealand
Copyright: © 2012 Bouskill, Tang, Riley and Brodie. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Nicholas J. Bouskill, Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. e-mail: firstname.lastname@example.org