The high level of spatial and temporal heterogeneity in nitrogen cycling processes hinders our ability to develop an ecosystem-wide understanding of this cycle. Determining nitrogen cycling “hotspots” has proved to be very complicated. In the southern Appalachians nitrogen cycling varies along elevation gradients. This study examines how the incorporation of spatial “hotspots” of soil moisture, carbon, nutrients, and soil texture can better explain ecosystem nitrogen cycling? Sites (80 m x 80 m) distributed over an elevation gradient (788 m-1389 m), and vegetation (mixed oak hardwood, northern hardwood, cove hardwood, mixed oak-pine) gradient were sampled monthly for estimates of soil moisture using a non-destructive technique based on electromagnetic induction (EMI). The sites were sampled quarterly for assessments of soil nutrient and carbon content (extractable NH4+, acid extractable PO43+, total C, and total N) using near infrared reflectance spectroscopy (NIRS). Clay content was determined on georeferenced soil cores (0-20 cm) and collected in June of 2011. As indicators of potential nitrogen cycling rates, we conducted laboratory estimates of denitrification (0-5, 5-10, and 10-20 cm) and nitrification (0-5 and 5-20 cm) in November 2010 and March 2011. Stepwise multivariate regression analysis with minimum corrected Akaike Information Criterion (AICc) was used to determine the most appropriate model of predictors for nitrogen cycling processes.
Results/Conclusions
Total C and N were highest at the soil surface (0-5 cm depth; p<0.0001) on all sites and was significantly greater at the high elevation site (p<0.05). Potential nitrification and denitrification rates were higher in March compared to November and for both processes higher at 0-5 cm soils than at deeper depths (p<0.05). Potential nitrification rates were low with the exception of the high elevation northern hardwood site in March (P<0.05). Potential denitrification rates were highest in the northern hardwood and cove hardwood sites (P<0.05). Overall, NIRS data was able to explain 38-69% of the potential denitrification rates (p<0.01) and 88-98% of potential nitrification variability (P<0.01). EMI data was significantly related to soil moisture, explaining 20%-52% of the variability. Multivariate regression combined with AICc analysis, revealed that conductivity and clay content explained 55% (0-5 cm) and 75% (10-20 cm) of the potential denitrification variability (p<0.05). Extractable phosphate and conductivity was found to explain 67% of the potential denitrification variability at 5-10 cm depth (p<0.05). This study showed the potential of geophysical tools in developing an ecosystem level understanding of the nitrogen cycle.