We investigated the potential for a variety of environmental reservoirs to harbor or contribute fecal indicator bacteria (FIB), DNA markers of human fecal contamination, and human pathogens to a freshwater lake. We hypothesized that submerged aquatic vegetation (SAV), sediments, and stormwater act as reservoirs and/or provide inputs of FIB and human pathogens to this inland water. Analysis included microbial source tracking (MST) markers of sewage contamination (Enterococcus faecium esp gene, human-associated Bacteroides HF183, and human polyomaviruses), pathogens (Salmonella, Cryptosporidium, Giardia, and enteric viruses), and FIB (fecal coliforms, Escherichia coli, and enterococci). Bayesian analysis was used to assess relationships among microbial and physicochemical variables. FIB in the water were correlated with concentrations in SAV and sediment. Furthermore, the correlation of antecedent rainfall and major rain events with FIB concentrations and detection of human markers and pathogens points toward multiple reservoirs for microbial contaminants in this system. Although pathogens and human-source markers were detected in 55% and 21% of samples, respectively, markers rarely coincided with pathogen detection. Bayesian analysis revealed that low concentrations (<45 CFU × 100 ml-1) of fecal coliforms were associated with 93% probability that pathogens would not be detected; furthermore the Bayes net model showed associations between elevated temperature and rainfall with fecal coliform and enterococci concentrations, but not E. coli. These data indicate that many under-studied matrices (e.g. SAV, sediment, stormwater) are important reservoirs for FIB and potentially human pathogens and demonstrate the usefulness of Bayes net analysis for water quality assessment.
Bibliographical noteFunding Information:
This project was funded by the Environmental Protection Commission of Hillsborough County via the Pollution Recovery Fund. We would also like to thank David Glicksberg and Orfilio Ramos of the Hillsborough County Public Works Department for assistance in determining peak flow rates at stormwater outfalls.
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- Bayesian analysis
- Fecal indicator bacteria
- Microbial source tracking
- Water quality