Nonpoint sources of pollution that contribute fecal bacteria to surface waters have proven difficult to identify. Knowledge of pollution sources could aid in restoration of the water quality, reduce the amounts of nutrients leaving watersheds, and reduce the danger of infectious disease resulting from exposure to contaminated waters. Patterns of antibiotic resistance in fecal streptococci were analyzed by discriminant and cluster analysis and used to identify sources of fecal pollution in a rural Virginia watershed. A database consisting of patterns from 7,058 fecal streptococcus isolates was first established from known human, livestock, and wildlife sources in Montgomery County, Va. Correct fecal streptococcus source identification averaged 87% for the entire database and ranged from 84% for deer isolates to 93% for human isolates. To field test the method and the database, a watershed improvement project (Page Brook) in Clarke County, Va., was initiated in 1996. Comparison of 892 known-source isolates from that watershed against the database resulted in an average correct classification rate of 88%. Combining all animal isolates increased correct classification rates to ≥95% for separations between animal and human sources. Stream samples from three collection sites were highly contaminated, and fecal streptococci from these sites were classified as being predominantly from cattle (>78% of isolates), with small proportions from waterfowl, deer, and unidentified sources (≈7% each). Based on these results, cattle access to the stream was restricted by installation of fencing and in-pasture watering stations. Fecal coliforms were reduced at the three sites by an average of 94%, from prefencing average populations of 15,900 per 100 ml to postfencing average populations of 960 per 100 ml. After fencing, <45% of fecal streptococcus isolates were classified as being from cattle. These results demonstrate that antibiotic resistance profiles in fecal streptococci can be used to reliably determine sources of fecal pollution, and water quality improvements can occur when efforts to address the identified sources are made.
Antibiotic resistance analysis (ARA) was used to determine if enterococci of human origin were present in a stream (Spout Run) that passes through a rural nonsewered community (Millwood, VA). Millwood consists of 82 homes, all served by individual septic systems, and Spout Run drains a 5,800-ha karst topography watershed that contains large populations of livestock and wildlife. Periodic monitoring by state regulatory officials had resulted in Spout Run being placed on the Virginia impaired stream list and Millwood being categorized as an at-risk community. Stream samples were collected monthly and analyzed for fecal coliforms and enterococci (May 1999-May 2000); ARA was performed on enterococci stream isolates on a quarterly basis. All 117 stream samples were positive for fecal coliforms, and 32% exceeded the Virginia recreational water standard (1,000 fecal coliforms/100 mL). A library of 1,174 known source Enterococcus isolate antibiotic resistance profiles was constructed, and yielded correct classification rates of 94.6% for 203 human isolates, 93.7% for 734 livestock isolates, and 87.8% for 237 wildlife isolates. Antibiotic resistance analysis of 2,012 enterococcal isolates recovered from stream samples indicated isolates of human origin appeared throughout the stream as it passed through Millwood, with a yearly average of approximately 10% human, 40% wildlife, and 50% livestock. There were no human origin isolates in samples collected upstream from Millwood, and the percent human origin isolates declined downstream from Millwood. While a human signature was found in Spout Run, it was small compared with the proportion of isolates from livestock and wildlife.
Aims: Carbon source utilization profiles as a phenotypic fingerprinting methodology for determining sources of faecal pollution in water were evaluated. Methods and Results: Three hundred and sixty-five Enterococcus isolates were collected from known faecal sources in four different geographical regions and were identified to species with the commercial Biolog system. Discriminant analysis (DA) was used to identify the substrate-containing wells that best classified the 365 isolates by source. By using 30 of the 95 wells for the analysis, the average rate of correct classification (ARCC) by source was 92AE7% for a human vs non-human two-way classification when isolates from all regions were combined into one library. Corresponding ARCCs for other classification schemes were 81AE9% for a four-way classification of human vs livestock vs wildlife vs domestic pets, and 85AE7% for a three-way classification without human isolates. When three individual libraries were made based on classification of sources within Enterococcus species, the ARCC was 95AE3% for the Ent. faecalis library, 95AE8% for the Ent. gallinarum library and 94AE7% for the Ent. mundtii library. Thirty Enterococcus isolates (unknown sources) were obtained from each of three stream sites where a specific source of pollution was apparent; 90AE0% of the isolates from a human-suspected source were classified as human, 86AE6% were classified as livestock from a livestock-suspected site, and 93AE3% were classified as wildlife from a wildlife-suspected site. Conclusions: Phenotypic fingerprinting with carbon source utilization profiles provided levels of correct classification by sources from an Enterococcus library that were in the upper range of those reported in the literature. ARCCs for three Enterococcus species-specific libraries were very high and may be the best approach for further developing this concept and methodology. Significance and Impact of the Study: The results, based on a modest Enterococcus library and a preliminary field validation test, demonstrated the potential for carbon source utilization profiles to be employed as a phenotypic method for determining sources of faecal pollution in water.
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