Spatio-Temporal Variation of Denitrifier and Nitrifier Microbial Communities in Soil, and Influence on Denitrification Rate.

            Nitrous oxide is a potent greenhouse gas that is mediated by the soil microbial processes of denitrification and nitrification.  A thorough understanding of denitrification drivers is necessary to accurately predict nitrous oxide emissions.  To determine whether measurements of soil biology can improve prediction of denitrification activity, stepwise multivariate regression models of denitrification were performed using measured soil characteristics with and without inclusion of the nitrite reductase gene (nirK) abundance and community structure as explanatory factors.  The study was carried out across different seasons (autumn, winter, spring, summer) and topographical positions (summit, backslope, footslope) within a field to capture spatial and temporal variation.  Gene abundance of nitrite reductase gene (nirK) was determined by quantitative polymerase chain reaction (qPCR) and the community structure assessed by terminal restriction length polymorphism (T-RFLP) analysis.  Soil chemical analyses included soil water content, nitrate (NO₃-N), ammonium (NH₄-N), soluble total nitrogen, soluble non-purgeable organic carbon, pH, electrical conductivity, total carbon and nitrogen, mineral fraction carbon and nitrogen, and particulate organic matter carbon and nitrogen.  Potential and basal denitrification rates of soil were assessed by short-term incubations using the acetylene inhibition method.  Multiple regression models that only included topographical variation poorly predicted potential and basal denitrification with R-square of 0.25 to 0.38, whereas models that included spatial variation provided R-square of 0.67 to 0.84.  The predictive power of the potential denitrification model was improved by the addition of nirK abundance but only for the summit position model (R-square improved from 0.83 to 0.88).  Our assessment of denitrifier community structure was not a significant explanatory variable for any of the potential or basal denitrification models.  In summary, nirK abundance may contribute to predicting potential denitrification rates if topographical positions are considered separately.