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TECHNICAL REPORTS

Surface Water Quality

Assessment of the 16S-23S rDNA Intergenic Spacer Region in Enterococcus spp. for Microbial Source Tracking

^a Dep. of Crop and Soil Environmental Sciences, 330 Smyth Hall, Virginia Polytechnic Inst. and State Univ., Blacksburg, VA 24061
^b Dep. of Biology, Roanoke College, Salem, VA 24153

^* Corresponding author (chagedor{at}vt.edu).

Received for publication December 21, 2006. A new library-based microbial source tracking (MST) approach intended for initial application in the coastal waters of Virginia was evaluated. Host-origin isolates of Enterococcus spp. were collected from beaches and the surrounding tidewater region of Virginia and used to construct a library based on the pattern of DNA band lengths produced by the amplification of the 16S-23S rDNA intergenic spacer (IGS) region, and subsequent digestion with MboI. Initial results from small host-origin libraries (64 and 200 total isolates) with discriminant analysis (DA) and logistic regression (LR) yielded high average rates of correct classification (ARCC) for a four-source classification split (birds, dogs, sewage, and wildlife), with ARCCs ranging from 83 to 100%. However, the poor results obtained when classification was attempted on a non-library validation set (VS, ARCCs of 47 and 48%, respectively, using DA and LR) demonstrated that a library of 200 isolates was insufficient to adequately represent the diversity of the enterococci in the sampled region. An increase in the library size to 1029 total isolates was accompanied by a reduction in the ARCC of the library to 42.7% with DA and 45.7% with LR, plus similarly poor results obtained from the VS. The low correct classification rates generated by the larger known-source library were unsuitable for field application. Many reported MST methods have been based on results obtained using small host-origin libraries without external validation. Our results indicate that such an approach can be very misleading, and that larger libraries and external validation is essential for the confirmation of preliminary results.

Abbreviations: ARCC, average rate of correct classification • DA, discriminant analysis • IGS, intergenic spacer • LR, logistic regression • MST, microbial source tracking • RCC, rate of correct classification • VS, validation set

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