Date Thesis Awarded

4-2015

Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)

Department

Biology

Advisor

Matthias Leu

Committee Members

Oliver Kerscher

Brent Kaup

Abstract

Tick-borne diseases are on the rise, causing concern for human health as ticks continue to expand both in range and numbers. This study sought to assess the prevalence of two tick-borne diseases on the Virginia and Middle Peninsula and to identify the variables that explain their distributions. The two disease-causing bacteria, Ehrlichia chaffeensis and Rickettsia parkeri, are both transmitted by the lone star tick, the most common human-biting tick in the study area and in the southeastern United States. Nymph ticks were collected at 122 random sites in southeastern Virginia and DNA was extracted from the pooled ticks from each site. Bacterial DNA was detected using both endpoint PCR and Taqman qPCR to compare sensitivities of the assays. The two methods detected E. chaffeensis at 48.4% and 52.3% of sites, respectively, while R. parkeri was not detected at any sites based on Taqman PCR. We developed two logistic regressions models, based on each PCR method, which were spatially applied to develop spatial models of probability of E. chaffeensis presence, to determine biotic and abiotic variables explaining heterogeneity in disease occurrence. We selected candidate sets of models based on the information-theoretic approach. The variables identified for both sets of models were linked to white-tailed deer browsing and space use, while tick counts and time of year were not included in the final candidate models. These results suggest that the white-tailed deer, the primary host of lone star ticks and the only known reservoir host of E. chaffeensis, is the most important predictor of E. chaffeensis distribution.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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