Diseases vectored by mosquitoes cause millions of deaths each year. In modern times Florida*s disease risk has been reduced due to efforts to lessen the prevalence of mosquitoes through habitat modification of non-adults. With emerging diseases (i.e. Dengue and Chikunguya) encroaching into Florida from the Caribbean, this traditional approach may not be enough. Alternatively, we can better understand the ecology of how disease works in an ecosystem. One possible way is through the Dilution Effect, which states that the more species that are in a system the lower the chance for zoonosis. This project models mosquito diversity across regions, land use, and vegetation height in South-Central Florida, for the purpose of identifying predictors that indicate a higher disease risk using information theory (AICc). The plains and coastal regions as well as the developed areas have a relatively higher risk of disease. Florida is a fire maintained habitat, but has been fire suppressed for the last century. Archbold Biological Station (ABS) has used prescribed fires since the early 1980s to try and restore a more natural system. This has created a mosaic of different fire histories. Fire affects the structures that mosquitoes rest under during the day (they are vulnerable to desiccation during the day and hide in darker/shady places), therefore there is a high likelihood that fire will have some effect on mosquito assemblages. This project used model selection to determine the most plausible set of predictors that describe the effect of fire on mosquito assemblages at ABS, using information theory (AICc). In general, time of season accounted for the largest proportion of the variation in the data and TSF had negligible effect on adult mosquito assemblages measured as abundance, speices richness, and Jost D.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-1658 |
| Date | 01 January 2015 |
| Creators | Debevec, Caitlyn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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