|SHREWSBURY, LIA - Washington State University|
|CARPENTER-BOGGS, LYNNE - Washington State University|
|Reardon, Catherine - Kate|
Submitted to: Soil Biology and Biochemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/10/2016
Publication Date: 9/1/2016
Publication URL: http://handle.nal.usda.gov/10113/5495559
Citation: Shrewsbury, L., Smith, J.L., Huggins, D.R., Carpenter-Boggs, L., Reardon, C.L. 2016. Denitrifier abundance has a greater influence on denitrification rates at larger landscape scales but is a lesser driver than environmental variables. Soil Biology and Biochemistry. 103:221-231. dx.doi.org/10.1016/j.soilbio.2016.08.016.
Interpretive Summary: Nitrous oxide is a potent greenhouse gas that is a byproduct of soil microbial processes of denitrification and nitrification. In order to predict and estimate nitrous oxide (N2O) emissions from a landscape, it is important to understand the biological factors driving denitrification. This study examined whether the ability to estimate N2O production across a topographically diverse landscape could be improved by quantifying the abundance (or amount) of microbes involved in denitrification and nitrification. Several physical and chemical soil characteristics analyzed over the four seasons were used as variables to predict potential (maximum rate) and basal (resting rate) denitrification rates. The variables included gravimetric water content, pH, electrical conductivity (estimate of salinity), and concentrations of nitrate (NO3-N), ammonium (NH4-N), total nitrogen, total soluble nitrogen, total carbon and soluble non-purgeable organic carbon (NPOC, a measurement of soil organic carbon). Stepwise multivariate regression models revealed that the abundance of the denitrifier community (based on quantification of the nirS nitrite reductase gene) contributed to predictive estimates of potential denitrification activity; however measurements of soil moisture, carbon and nitrogen availability were more useful. The study also gives evidence that greater landscape variation results in a stronger correlation between denitrification rates and denitrifier abundance, indicating that measurements of the denitrifier population are likely important and useful predictors of denitrification rates at larger scales. This work will benefit the general scientific community and modelers in developing methods to predict N2O emissions in topographically diverse landscapes.
Technical Abstract: 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 and manage nitrous oxide emissions. However, studies disagree on the utility of quantifying the denitrifier community to predict denitrification rates. This study examines the influence of nitrite reductase gene (nirK and nirS) abundance on denitrification rates at the field scale of a topographically diverse region. The study was carried out across different seasons (autumn, winter, spring, summer) and topographic positions (shoulder and backslope) within a field cropped to spring wheat. A footslope cropped to winter wheat was included in certain analyses to introduce additional variation at a broader scale. We measured denitrification enzyme activity (DEA) and basal denitrification (BD), nirK and nirS abundance, bacterial and archaeal ammonia monooxygenase gene (amoA) abundance, and soil environmental and chemical characteristics which included gravimetric water content, pH, electrical conductivity, and concentrations of nitrate (NO3-N), ammonium (NH4-N), total nitrogen, total soluble nitrogen, total carbon and soluble non-purgeable organic carbon (NPOC). Stepwise multivariate regression models of DEA and BD were performed using the measured soil characteristics and denitrifier abundance as explanatory factors to determine whether the denitrifier population size influenced prediction of denitrification activity. We found nirS abundance to be a significant explanatory variable (P < 0.05) of DEA rates when footslope samples were included in the analysis, explaining 9% and 16% of variation depending upon whether soil gravimetric water content or soluble NPOC was accepted as the primary explanatory variable, respectively. However, when only shoulder and backslope samples were included in the analysis, nirS abundance was only a significant explanatory variable of DEA rates when soluble NPOC was the primary explanatory variable. Models that included soluble NPOC, soluble total N, and nirS population size consistently produced robust predictive models of DEA at the two different field scales of with and without footslope inclusion (R2 = 0.53 and 0.70 compared to our greatest-R2 models of R2 = 0.61 and 0.79, with and without footslope inclusion, respectively). The nirK abundance was insignificant in the stepwise multivariate regression models of DEA, although it was significantly correlated to DEA (r = 0.35, P = 0.003) when footslope samples were included in the analysis. The nirK+nirS abundance was found to be a significant explanatory variable of BD rates (explaining 5% of variance), but total soluble nitrogen concentration explained more variance (43%). Although we found that denitrifier abundance can be a significant predictor of denitrification rates at the field scale, we conclude that measurements of soil moisture, carbon and nitrogen availability are more useful. Additionally, our study gives evidence that greater landscape variation results in a stronger correlation between denitrification rates and denitrifier abundance, indicating that measurements of the denitrifier population are likely important and useful predictors of denitrification rates at larger scales.