Seasonal Patterns in Microbial Community Composition in Denitrifying Bioreactors Treating Subsurface Agricultural Drainage.

Denitrifying bioreactors, consisting of water flow control structures and a woodchip-filled trench, are a promising approach for removing nitrate from agricultural subsurface or tile drainage systems. To better understand the seasonal dynamics and the ecological drivers of the microbial communities responsible for denitrification in these bioreactors, we employed microbial community "fingerprinting" techniques in a time-series examination of three denitrifying bioreactors over 2 years, looking at bacteria, fungi, and the denitrifier functional group responsible for the final step of complete denitrification. Our analysis revealed that microbial community composition responds to depth and seasonal variation in moisture content and inundation of the bioreactor media, as well as temperature. Using a geostatistical analysis approach, we observed recurring temporal patterns in bacterial and denitrifying bacterial community composition in these bioreactors, consistent with annual cycling. The fungal communities were more stable, having longer temporal autocorrelations, and did not show significant annual cycling. These results suggest a recurring seasonal cycle in the denitrifying bioreactor microbial community, likely due to seasonal variation in moisture content.

Spatial variation in the bacterial and denitrifying bacterial community in a biofilter treating subsurface agricultural drainage.

Denitrifying biofilters can remove agricultural nitrates from subsurface drainage, reducing nitrate pollution that contributes to coastal hypoxic zones. The performance and reliability of natural and engineered systems dependent upon microbially mediated processes, such as the denitrifying biofilters, can be affected by the spatial structure of their microbial communities. Furthermore, our understanding of the relationship between microbial community composition and function is influenced by the spatial distribution of samples.In this study we characterized the spatial structure of bacterial communities in a denitrifying biofilter in central Illinois. Bacterial communities were assessed using automated ribosomal intergenic spacer analysis for bacteria and terminal restriction fragment length polymorphism of nosZ for denitrifying bacteria.Non-metric multidimensional scaling and analysis of similarity (ANOSIM) analyses indicated that bacteria showed statistically significant spatial structure by depth and transect,while denitrifying bacteria did not exhibit significant spatial structure. For determination of spatial patterns, we developed a package of automated functions for the R statistical environment that allows directional analysis of microbial community composition data using either ANOSIM or Mantel statistics.Applying this package to the biofilter data, the flow path correlation range for the bacterial community was 6.4 m at the shallower, periodically in undated depth and 10.7 m at the deeper, continually submerged depth. These spatial structures suggest a strong influence of hydrology on the microbial community composition in these denitrifying biofilters. Understanding such spatial structure can also guide optimal sample collection strategies for microbial community analyses.