This project implemented novel approaches to assess the source, age, concentration and mobility of fecal indicator microorganisms within a karst groundwater system. Research was conducted in the well-characterized Blue Hole Spring karst groundwater basin in Versailles, Woodford County, Kentucky. At this site the AC/TC ratio and fecal coliform (FC) bacteria counts were used to delineate sources of fecal inputs and determine relative age of the fecal matter. An aging experiment using indicator bacteria (total coliform (TC) and atypical colonies (AC)), which approximated subsurface conditions, indicated that changes in the AC/TC ratio are likely to be retarded during bacterial transport through karst conduits. Decreases in the AC/TC ratio during the monitoring period appear to be the result of sewage releases. Multiple logistic regression (MLR) modeling was performed to examine correlations between physiochemical parameters and FC concentrations. MLR models using physiochemical parameters correctly predicted “safe for contact” (< 200 cfu/100 mL FC) conditions 65.6% of the time and “unsafe for contact” (> 200 cfu/100 mL FC) conditions 69.2% of the time at Blue Hole Spring. Modeling using other indicators (TC and AC) predicted “safe for contact” conditions 87.5% of the time and “unsafe for contact” conditions 61.5% of the time. A series of tracer tests were performed to compare transport of solute and abiotic particle tracers (rhodamine WT fluorescent dye, bromide and fluorescent bacteria-sized microspheres) and bacteria (15N-enriched wild-type E. coli) within the karst system. The surrogate tracers did not suitably mimic microbial mobility within the basin. Solutes and 15N-enriched E. coli arrived concurrently during storm flow to Blue Hole Spring, whereas microsphere breakthrough corresponded with maximum solute concentrations. The 15Nenriched E. coli exhibited slightly more tailing during storm-flow recession than solute tracers, none of which exhibited remobilization. Microspheres demonstrated remobilization within the conduits that correlated with later increases in discharge related to secondary storm events.

The microbial communities found in soils are inherently heterogeneous and often exhibit spatial variations on a small scale. Becker et al. (2006) investigate this phenomenon and present statistical analyses to support their findings. In this project, alternative statistical methods and models are considered and employed in a re-analysis of the data from Becker. First, parametric nested random effects models are considered as an alternative to the nonparametric semivariogram models and kriging methods employed by Becker to analyze patterns of spatial variation. Second, multiple logistic regression models are employed to investigate factors influencing microbial community structure as an alternative to the simple logistic models used by Becker. Additionally, the microbial community profile data of Becker were unobservable at several points in the spatial grid. The Becker analysis assumes that the data are missing completely at random and as such have relatively little impact on inference. In this re-analysis, this assumption is investigated and it is shown that the pattern of missingness is correlated with both metabolic potential and spatial coordinates and thus provides useful information that was previously ignored by Becker. Multiple imputation methods are employed to incorporate the information present in the missing data pattern and results are compared with those of Becker.