Antibiotic resistance analysis (ARA) of Enterococci was used to determine sources of fecal contamination in three District of Columbia waterways: Rock Creek, the Anacostia River, and the Potomac River. These three waterways were identified as exceeding water quality standards set for fecal coliform levels and were designated by the District of Columbia to the Environmental Protection Agency's 303 (d) impaired waters list. A library profile of 1,806 enterococcus isolates from known sources was built based on antibiotic resistance patterns from thirty concentrations of nine antibiotics. These sources included human, cattle, chicken, horse, goat, sheep, deer, raccoon, muskrat, goose, seagull, coyote, duck, wild turkey, dog, and cat. Antibiotic profiles were characterized for 24 unknown enterococci isolates on each of 198 samples (38 samples from the Potomac River, 79 samples from the Anacostia River, and 81 samples from Rock Creek) collected periodically from July 2002 through April 2003. Two major storm events were also sampled during this period. These isolate profiles were compared to the known source library using logistic regression. Three dominant sources of fecal pollution were detected in the Potomac River: livestock (30%), human (29%), and wildlife (22%). Three dominant signatures were also detected in Rock Creek: horse (26%), human (26%), and wildlife (24%). Human was the only dominant source detected in the Anacostia River, averaging 43% over the sampling period. The results of this study indicate that human is a substantial contributor to the fecal contamination problems, especially in the Anacostia River, but there are significant agricultural and wildlife contributions as well. Significant and predictable seasonal variations were also detected, indicating the influence of precipitation on source distributions. The results of this study will aid the Metropolitan Washington D.C. Council of Governments in making important management decisions to help improve the water quality in and around the Washington D.C. area. Expanding the limits of ARA was also an integral part of this research. Three new and even controversial analytical techniques were run on the data collected from this project in an attempt to improve confidence and provide direction to the results of this study. The first was a comparison of the more commonly used statistical analysis model discriminate analysis (DA) with logistic regression (LR). No significant difference was found between the output of the two models for the known source libraries, therefore no suggestion could be made in favor of one model over the other. Another analytical test of the data was the introduction of a standard requiring isolates to meet a minimum of 80% similarity to the known source profiles where it was classified. With the 80% cutoff, between 41% and 44% of the isolates could not be classified to any source and were placed in an unknown category. Based on the remaining isolates, source distributions were recalculated and were not statistically different than those calculated with no restriction for isolate similarity for matching. The last major test of the data was the analysis of the library for representativeness via pulled sample cross validation and the exclusion of all duplicate patterns from the known source library. These analyses did not confirm the representativeness of the databases, but results were further analyzed based on the implications these analyses have on library based methods. / Master of Science

Antibiotic resistance analysis (ARA) was used to determine sources of fecal pollution in the Blackwater River in South-central Virginia. The Department of Environmental Quality designated six segments as impaired due to high fecal coliform concentrations with non-point source (NPS) agriculture the suspected source of impairment. The Blackwater River watershed encompasses 72,000 ha of dairy, beef, and intensive production agriculture, abundant wildlife populations and many homes with onsite septic systems. A library of antibiotic resistance profiles based on 30 concentrations of 9 antibiotics was developed for 1,451 enterococci isolates from human, cattle, chicken, horse, goat, sheep, deer, raccoon, muskrat, goose, duck, coyote, and wild turkey fecal samples. Each isolate was classified as human, wildlife or livestock. Correct classification rates were 82.3% for human, 86.2% for livestock and 87.4% for wildlife isolates when profiles were analyzed with discriminant analysis. Profiles were also determined for 48 isolates from 128 stream samples collected periodically from August 1999 thru April 2001 and compared to the known sources using discriminate analysis. A human signature was found at each site at least once during the year, ranging from 0.0% to 85.0% of the sample isolates. The livestock signature varied from 2.3% to 100% over sites and months, and the wildlife signature varied from 0.0% to 79.5%. The results indicate that both humans and wildlife contribute to fecal pollution in addition to the suspected source, livestock, and reducing fecal pollution will require consideration of all three sources. The results from this research are being used to develop a total maximum daily load (TMDL) project allocations for fecal coliforms in the Blackwater River. Isolates identified by ARA were also profiled using the Biolog metabolic identification system. A library of metabolic profiles was constructed from known source isolates. Stream isolates were identified by Biolog and the metabolic profile was compared to the Biolog library. Of ten stream isolates identified by ARA as human, the Biolog library identified one as human, four as livestock, and five as wildlife. Of ten isolates identified by ARA as livestock, the Biolog library identified seven as livestock and three as wildlife. Of ten isolates identified by ARA as wildlife, one was identified as human, three as livestock and six as wildlife. The overall correct classification of Blackwater isolates in the Biolog library was 14 of 30 isolates, or 47%. Although the Biolog library was constructed with some isolates from the Blackwater basin, there may not be enough isolates in the Biolog library to adequately represent the variability shown by the Blackwater isolates, resulting in lower than expected correct classifications. In spite of these results, Biolog remains promising as one of several tools with potential as a bacterial source tracking method. / Master of Science

Bacterial source tracking (BST) methods often involve the use of phenotypic or genotypic fingerprinting techniques to compare indicator bacteria such as Escherichia coli isolated from unknown sources against a library of fingerprints from indicator bacteria found in the feces of various known source animals. The predictive capability of a library is based in part on how well the library isolates reflect the true population diversity of indicator bacteria that can potentially impact a water body. The purpose of this study was to compare the behavior of E. coli population structures in the feces of humans, beef cattle and horses across different parameters. Ribotyping and antibiotic resistance analysis were used to "fingerprint", or subtype E. coli isolates. Significantly greater diversity was observed in the E. coli population of horses compared to the human or beef cattle sampled. Subtype sharing between individuals from all host categories was infrequent, therefore the majority of E. coli subtypes were sampled from a single individual. The dominant E. coli populations of nine individuals (three per host source category) were monitored over time, which demonstrated that E. coli subtypes within a host individual vary on a monthly time frame, and an increase in the frequency of subtype sharing was noted between individuals within the same source group over time. The E. coli population of a single human that had just finished antibiotic treatment was studied on a daily basis for one month. The loss of an E. coli subtype with high antibiotic resistance was observed over time, however there was a single dominant E. coli subtype that was present at every sampling event during the entire month. Geographic distinctiveness of E. coli populations was investigated by sampling four herds located in different geographical regions. We observed that E. coli populations are not geographically distinct, but are somewhat individual-specific, as most E. coli isolates had a subtype that was found in a single individual. This study defines factors that should be considered when constructing a successful BST library, and suggests that E. coli may not be the appropriate indicator organism for BST.

Title from PDF of title page. / Document formatted into pages; contains 117 pages. / Thesis (M.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Bacterial source tracking (BST) methods often involve the use of phenotypic or genotypic fingerprinting techniques to compare indicator bacteria such as Escherichia coli isolated from unknown sources against a library of fingerprints from indicator bacteria found in the feces of various known source animals. The predictive capability of a library is based in part on how well the library isolates reflect the true population diversity of indicator bacteria that can potentially impact a water body. The purpose of this study was to compare the behavior of E. coli population structures in the feces of humans, beef cattle and horses across different parameters. Ribotyping and antibiotic resistance analysis were used to "fingerprint", or subtype E. coli isolates. Significantly greater diversity was observed in the E. coli population of horses compared to the human or beef cattle sampled. / ABSTRACT: Subtype sharing between individuals from all host categories was infrequent, therefore the majority of E. coli subtypes were sampled from a single individual. The dominant E. coli populations of nine individuals (three per host source category) were monitored over time, which demonstrated that E. coli subtypes within a host individual vary on a monthly time frame, and an increase in the frequency of subtype sharing was noted between individuals within the same source group over time. The E. coli population of a single human that had just finished antibiotic treatment was studied on a daily basis for one month. The loss of an E. coli subtype with high antibiotic resistance was observed over time, however there was a single dominant E. coli subtype that was present at every sampling event during the entire month. Geographic distinctiveness of E. coli populations was investigated by sampling four herds located in different geographical regions. We observed that E. / ABSTRACT: coli populations are not geographically distinct, but are somewhat individual-specific, as most E. coli isolates had a subtype that was found in a single individual. This study defines factors that should be considered when constructing a successful BST library, and suggests that E. coli may not be the appropriate indicator organism for BST. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Prince William County, located in the rapidly developing Northern Virginia region, contains watersheds of mixed rural and urban/suburban uses. The project goal was to monitor and evaluate 21 stream locations, over 13 months, in the Occoquan Basin identified as impaired due to high E. coli densities. One site on each of eight streams, two sites on each of five streams, and three sites on the remaining stream were chosen for E. coli monitoring and microbial source tracking (MST). MST was performed using antibiotic resistance analysis (ARA) and fluorometric analysis. Escherichia coli was chosen as the indicator bacterium for purposes of comparison with previous project data and because a large body of evidence supports its use in freshwater systems. This study involved the only known MST project to incorporate data from five or more consecutive years. A total of 2854 environmental isolates were collected for analysis with ARA. These isolates were classified using a known source library (KSL) that consisted of 1003 unique resistance patterns. The resistance patterns of the KSL came from known fecal sources (human, pets, livestock, wildlife) in Prince William County. The KSL included isolates from previous years but was also updated with fresh isolates. The accuracy of the KSL was assessed through the use of a challenge set. The challenge set was classified against the KSL using discriminant analysis, verified by logistic regression. The average rate of correct classification was 93% for discriminant analysis and 96% for logistic regression. Results indicated that multiple sources of contamination were present at all sampling locations and that the major source(s) (human, pets, livestock, wildlife) of contamination were generally related to the land-use patterns and human activities at each location. Although no major or minor human signatures were found, all but two locations had either pet or livestock as the major signature, suggesting that human-related activities are playing a key role in contamination of the streams. Pets were the single most frequent major signature and wildlife was the most common minor signature. Fluorometric analysis was used to corroborate human-derived contamination. Fluorometric analysis has the ability to detect the presence of optical brighteners, synthetic compounds added to such household items as laundry detergent, dishwashing detergent and other washing agents. Despite having an undesirably high rate of false negatives (negative fluorometry readings not supported by ARA), fluorometric analysis maintained a low rate of false positives (positive fluorometry readings not supported by ARA) and continued to demonstrate its potential for source tracking. This project represented one of the first attempts at applying a full suite of performance criteria now recommended by the source tracking community for all MST projects. Such concepts as experimental design, toolbox approach, minimum detectable percentage, quantification, accuracy, specificity, robustness, range of applicability, and practicality were successfully incorporated. These performance criteria have in effect set a new standard to which all subsequent MST projects should adhere. / Master of Science

The microbial source tracking (MST) methods of antibiotic resistance analysis (ARA) and fluorometry (to detect optical brighteners in detergents) were used in the summers of 2004 and 2005 to determine the origins of fecal pollution at beaches with a past history of, or the potential for, high enterococci counts and posted advisories. At Hilton and Anderson beaches, ARA and fluorometry in the summer of 2004 detected substantial human-origin pollution in locations producing consistently high counts of Enterococcus spp. Investigations by municipal officials led to the fluorometric detection and subsequent repair of sewage infrastructure problems at both beaches. The success of these mitigation efforts was confirmed during the summer of 2005 using ARA and fluorometry, with the results cross-validated by pulsed-field gel electrophoresis (PFGE). Results at other beaches indicated that birds and/or wildlife were largely responsible for elevated enterococci levels during 2004 and 2005. The application of fluorometry proved difficult in opens waters due to high levels of dilution, but showed potential for use in storm drains. An additional study developed and tested a new library-based MST approach based on the pattern of DNA band lengths produced by the amplification of the 16S-23S rDNA intergenic spacer region, and subsequent digestion using the restriction endonuclease MboI. Initial results from small known-source libraries yielded high average rates of correct classification (ARCC). However, an increase in the library size was accompanied by a reduction in the ARCC of the library and the method was deemed unsuccessful, and unsuitable for field application. A final study focused on the potential for classification bias with disproportionate source category sizes using discriminant analysis (DA), logistic regression (LR), and k-nearest neighbor (K-NN) statistical classification algorithms. Findings indicated that DA was the most robust algorithm for use with source category imbalance when measuring both correct and incorrect classification rates. Conversely k-NN was identified as the most sensitive algorithm to imbalances with the greatest levels of distortion obtained from the highest k values. Conclusions of this project include: 1) application of a validation set, as well as a minimum detectable percentage to known-source libraries aids in accurately assessing the classification power of the library and reducing the false positive identification of contributing fecal sources; 2) the validation of MST results using multiple methods is recommended for field applications; 3) fluorometry displayed potential for detecting optical brighteners as indicators of sewage leaks in storm drains; 4) the digestion of the 16S-23S rDNA intergenic spacer region of Enterococcus spp. using MboI does not provided suitable discriminatory power for use as an MST method; and 5) DA was the least, and k-NN the most, sensitive algorithm to imbalances in the size of source categories in a known-source library. / Ph. D.

Fecal pollution of surface water is a significant environmental health issue. Indicator organisms are used to monitor microbial water quality, but often their presence does not coincide with the presence of pathogens. Bacterial source tracking is a term describing methods to determine the origin of fecal pollution based on bacterial traits. The objective of this research is to evaluate the use of 2 bacterial source tracking techniques, antibiotic resistance analysis (ARA) and ribotyping, to determine the sources of bacteria isolated from Sinking Creek. Based on the results of this study, ARA and ribotyping are not useful techniques for identifying sources of fecal pollution in Sinking Creek. ARA classification rates were low, and ribotype pattern generation success was 37%. The results of this study bring into question the reliability and reproducibility of these 2 source tracking methods for routine use in small watersheds.