Bacteriophage 80a did not increase in number in cultures containing less than about 1.0 x 104 to 1.5 x 104 CFU of Staphylococcus aureus per ml, but bacteriophage replication did occur when the number of bacteria exceeded this density, either initially or as a result of host cell multiplication. The minimum density of an asporogenous strain of Bacillus subtilis required for an increase in the number of bacteriophage SP, cI was about 3 x 104 CFU/ml. The threshold density of Escherichia coli for the multiplication of bacteriophage T4 was about 7 x 103 CFU/ml. In the presence of montmorillonite, bacteriophage T4 did not increase in number until the E. coli population exceeded 104 CFU/ml. The mineralization of glucose was not affected in E. coli cultures inoculated with a low number of bacteriophage T4, but it could not be detected in cultures inoculated with a large number of phage. The numbers of bacteriophage T4 and a bacteriophage that lyses Pseudomonas putida declined rapidly after being added to lake water or sewage. We suggest that bacteriophages do not affect the number or activity of bacteria in environments where the density of the host species is below the host cell threshold of about 104 CFU/ml.

Discriminant analysis of patterns of antibiotic resistance in fecal streptococci was used to differentiate between human and animal sources of fecal pollution in natural waters. A total of 1,435 isolates from 17 samples of cattle, poultry, human, and wild-animal wastes were obtained, and their ability to grow in the presence of four concentrations of five antibiotics (chlortetracycline, halofuginone, oxytetracycline, salinomycin, and streptomycin) was measured. When the resulting antibiotic resistance patterns were analyzed, an average of 74% of the known isolates were correctly classified into one of six possible sources (beef, chicken, dairy, human, turkey, or wild). Ninety-two percent of human isolates were correctly classified. When the isolates were pooled into four possible categories (cattle, human, poultry, and wild), the average rate of correct classification (ARCC) increased to 84%. Human versus animal isolates were correctly classified at an average rate of 95%. Human versus wild isolates had an ARCC of 98%, and cattle versus poultry isolates had an ARCC of 92%. When fecal streptococci that were isolated from surface waters receiving fecal pollution from unknown origins were analyzed, 72% of the isolates from one stream and 68% of the isolates from another were classified as cattle isolates. Because the correct classification rates of these fecal streptococci are much higher than would be expected by chance alone, the use of discriminant analysis appears to hold promise as a method to determine the sources of fecal pollution in natural waters.

The use of antibiotic resistance analysis (ARA) for microbial source tracking requires the generation of a library of isolates collected from known sources in the watershed. The size and composition of the library are critical in determining if it represents the diversity of patterns found in the watershed. This study was performed to determine the size that an ARA library needs to be to be representative of the watersheds for which it will be used and to determine if libraries from different watersheds can be merged to create multiwatershed libraries. Fecal samples from known human, domesticated, and wild animal sources were collected from six Virginia watersheds. From these samples, enterococci were isolated and tested by ARA. Based on cross-validation discriminant analysis, only the largest of the libraries (2,931 isolates) were found to be able to classify nonlibrary isolates as well as library isolates (i.e., were representative). Small libraries tended to have higher average rates of correct classification, but were much less able to correctly classify nonlibrary isolates. A merged multiwatershed library (6,587 isolates) was created and was found to be large enough to be representative of the isolates from the contributing watersheds. When isolates that were collected from the contributing watersheds approximately 1 year later were analyzed with the multiwatershed library, they were classified as well as the isolates in the library, suggesting that the resistance patterns are temporally stable for at least 1 year. The ability to obtain a representative, temporally stable library demonstrates that ARA can be used to identify sources of fecal pollution in natural waters.

Microbial source tracking (MST) uses various approaches to classify fecal-indicator microorganisms to source hosts. Reproducibility, accuracy, and robustness of seven phenotypic and genotypic MST protocols were evaluated by use of Escherichia coli from an eight-host library of known-source isolates and a separate, blinded challenge library. In reproducibility tests, measuring each protocol's ability to reclassify blinded replicates, only one (pulsed-field gel electrophoresis; PFGE) correctly classified all test replicates to host species; three protocols classified 48-62% correctly, and the remaining three classified fewer than 25% correctly. In accuracy tests, measuring each protocol's ability to correctly classify new isolates, ribotyping with EcoRI and PvuII approached 100% correctclassification but only 6% of isolates were classified; four of the other six protocols (antibiotic resistance analysis, PFGE, and two repetitive-element PCR protocols) achieved better than random accuracy rates when 30-100% of challenge isolates were classified. In robustness tests, measuring each protocol's ability to recognize isolates from nonlibrary

A study was conducted of possible reasons for acclimation of microbial communities to the mineralization of organic compounds in lake water and sewage. The acclimation period for the mineralization of 2 ng of p-nitrophenol (PNP) or 2,4-dichlorophenoxyacetic acid per ml of sewage was eliminated when the sewage was incubated for 9 or 16 days, respectively, with no added substrate. The acclimation period for the mineralization of 2 ng but not 200 ng or 2 ,ug of PNP per ml was eliminated when the compound was added to lake water that had been first incubated in the laboratory. Mineralization of PNP by Flavobacterium sp. was detected within 7 h at concentrations of 20 ng/ml to 2 ,ug/ml but only after 25 h at 2 ng/ml. PNP-utilizing organisms began to multiply logarithmically after 1 day in lake water amended with 2 ,ug of PNP per ml, but substrate disappearance was only detected at 8 days, at which time the numbers were approaching 105 cells per ml. The addition of inorganic nutrients reduced the length of the acclimation period from 6 to 3 days in sewage and from 6 days to 1 day in lake water. The prior degradation of natural organic materials in the sewage and lake water had no effect on the acclimation period for the mineralization of PNP, and naturally occurring inhibitors that might delay the mineralization were not present. The length of the acclimation phase for the mineralization of 2 ng of PNP per ml was shortened when the protozoa in sewage were suppressed by eucaryotic inhibitors, but it was unaffected or increased if the inhibitors were added to lake water. We suggest that the time for enzyme induction, mutation, diauxie, and the presence of toxins are not the causes of the acclimation period for PNP mineralization in aquatic environments but rather that the acclimation period largely reflects the time for multiplication of the initially small population of active organisms. That time may be further extended by a limiting supply of inorganic nutrients in lake water or by predation by protozoa in sewage.

As part of a larger microbial source tracking (MST) study, several laboratories used library-based, phenotypic subtyping techniques to analyse fecal samples from known sources (human, sewage, cattle, dogs and gulls) and blinded water samples that were contaminated with the fecal sources. The methods used included antibiotic resistance analysis (ARA) of fecal streptococci, enterococci, fecal coliforms and E. coli; multiple antibiotic resistance (MAR) and Kirby-Bauer antibiotic susceptibility testing of E. coli; and carbon source utilization for fecal streptococci and E. coli. Libraries comprising phenotypic patterns of indicator bacteria isolated from known fecal sources were used to predict the sources of isolates from water samples that had been seeded with fecal material from the same sources as those used to create the libraries. The accuracy of fecal source identification in the water samples was assessed both with and without a cut-off termed the minimum detectable percentage (MDP). The libraries (approximately 300 isolates) were not large enough to avoid the artefact of source-independent grouping, but some important conclusions could still be drawn. Use of a MDP decreased the percentage of false-positive source identifications, and had little effect on the high percentage of true-positives in the most accurate libraries. In general, the methods were more prone to false-positive than to false-negative errors. The most accurate method, with a true-positive rate of 100% and a false-positive rate of 39% when analysed with a MDP, was ARA of fecal streptococci. The internal accuracy of the libraries did not correlate with the accuracy of source prediction in water samples, showing that one should not rely solely on parameters such as the average rate of correct classification of a library to indicate its predictive capabilities.

A study was conducted to determine the reliability and repeatability of antibiotic resistance analysis as a method of identifying the sources of fecal pollution in surface water and groundwater. Four large sets of isolates of fecal streptococci (from 2,635 to 5,990 isolates per set) were obtained from 236 samples of human sewage and septage, cattle and poultry feces, and pristine waters. The patterns of resistance of the isolates to each of four concentrations of up to nine antibiotics were analyzed by discriminant analysis. When isolates were classified individually, the average rate of correct classification (ARCC) into four possible types (human, cattle, poultry, and wild) ranged from 64 to 78%. When the resistance patterns of all isolates from each sample were averaged and the resulting sample-level resistance patterns were classified, the ARCCs were much higher (96 to 100%). These data confirm that there are measurable and consistent differences in the antibiotic resistance patterns of fecal streptococci isolated from various sources of fecal pollution and that antibiotic resistance analysis can be used to classify and identify these sources.

Models based on simple air temperature-water temperature relationships have been useful in highlighting potential threats to coldwater-dependent species such as Brook Trout Salvelinus fontinalis by predicting major losses of habitat and substantial reductions in geographic distribution. However, spatial variability in the relationship between changes in air temperature to changes in water temperature complicates predictions. We directly measured paired summer air and water temperatures over 2 years in a stratified representative sample of watersheds (<1-274 km 2 ) supporting wild Brook Trout throughout Virginia near the southern edge of the species distribution. We used the temperature data to rank streams in terms of two important components of habitat vulnerability: sensitivity (predicted change in water temperature per unit increase in air temperature) and exposure (predicted frequency, magnitude, and duration of threshold water temperatures). Across all sites, sensitivity was substantially lower (median sensitivity = 0.35 • C) than the 0.80 • C assumed in some previous models. Median sensitivity across all sites did not differ between the 2 years *Corresponding author: bradly.a.trumbo@usace.army.mil 1 173 174 TRUMBO ET AL. of the study. In contrast, median exposure was considerably greater in 2010 (a particularly warm summer) than in 2009, but exposure ranks of habitat patches were highly consistent. Variation in sensitivity and exposure among habitat patches was influenced by landscape metrics (percent forested riparian corridor, patch area, and elevation), but considerable unexplained variation in sensitivity and exposure among sites was likely due to local-scale differences in the extent of groundwater influence. Overall, our direct measurement approach identified significantly more Brook Trout habitat patches with low sensitivity and low exposure that may persist under warming air temperatures than did previous large-scale models. Our sensitivity and exposure classification should provide a useful general framework for managers in making investment decisions for protecting and restoring Brook Trout habitat.