Graduate Thesis Or Dissertation
 

Drug resistance, source, and environmental factors that influence fecal coliform levels of Tillamook Bay

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  • In order to determine the source of bacteria in Tillamook Bay, Oregon, water samples were collected monthly for six months during the rainy season from October 1975 through March 1976 from the bay and its tributaries, the Kilchis, Trask, Tillamook, and Wilson Rivers. Fecal coliform levels of these samples were determined and the 1,917 bacteria isolated were tested for their resistance patterns to chloramphenicol (Cm), streptomycin (Sm), ampicillin (Am), tetracycline (Tc), chlortetracycline (Ct), oxytetracycline (Ot), neomycin (Nm), nitrofurazone (Ni), nalidixic acid (Na), sulfathiazole (Su), kanamycin (Km), and procaine penicillin G (Pe). The fecal coliform count per 100 ml of bay water ranged from 3.6 to 42.0. The counts for Tillamook River ranged from 13.5 to 112.0, Trask River from 0.0 to 132.0, Wilson River from 8.5 to 105.0, and Kilchis River from 0.5 to 13.9. The rise and fall of fecal coliform levels were characteristic of the sampling date and each sampling station showed its characteristic maximum and minimum levels. The 1,917 fecal coliform isolates showed 176 different resistance patterns to the 12 antibiotics tested. None of the patterns, however, was characteristic of any specific sampling site. The fecal coliform counts of the bay were statistically compared to 135 independent variables that included the fecal coliform counts of tributaries, temperature, river flow data, tide information, antibiotic use data, and the antibiotic resistance patterns. Bay fecal coliform levels were highly correlated with the fecal coliform counts of tributaries especially those of the Trask and Wilson Rivers, degree of resistance to antibiotics, recreational activities, and precipitation. Negative correlation existed between bay fecal coliform count and the ambient temperature. two potentially useful linear regression models to predict bay fecal coliform level were developed using a computerized stepwise multiple linear regression program.
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