Mechanistic models predicting influence of climate change on epidemics of a fungal pathogen that infects a pest insect /

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Bibliographic Details
Author / Creator:Kyle, Colin Hector, author.
Ann Arbor : ProQuest Dissertations & Theses, 2015
Description:1 electronic resource (102 pages)
Format: E-Resource Dissertations
Local Note:School code: 0330
URL for this record:
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Other authors / contributors:University of Chicago. degree granting institution.
Notes:Advisors: Greg Dwyer Committee members: Stefano Allesina; Sarah Cobey; Rao Kotamarthi; Mei Wang; Timothy Wootton.
Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Summary:Our planet's climate is currently changing at an unprecedented rate. The changes in climate will have profound impacts on some ecosystems, and predicting where and to what degree theses changes will manifest is a pressing concern. In my dissertation, I quantified how weather and population density influences epidemics of an environmentally sensitive pathogen and made quantitative predictions about how climate change will alter the transmission dynamics of this disease. I utilize the fungal pathogen, Entomophaga maimaiga, which infects and helps manage the invasive gypsy moth, a highly destructive, defoliating pest insect. I collected observational and experimental data during field epidemics of these organisms for three years in Michigan, USA. Using these data, I estimated parameters to mechanistic, epidemiological models of population-level transmission. Because E. maimaiga exhibits both weather- and density-dependent transmission mechanisms, I constructed models of varying complexity that included different combinations of population and environmental variables. After conducting model selection tests, I simulated my best fitting models to evaluate how changes in population density and climate influenced epidemics of this pathogen. By linking my models with output from a high resolution (12km2), regional climate change model, I made predictions about how epidemic sizes could change across the twenty-first century. From my results, I concluded that climate change will on average reduce epidemic sizes in the pathogen by 12.7% of the population. Decreases in transmission will likely be due to higher temperatures accelerating pathogen decay, and increasingly variable precipitation conditions that disrupt timing of spore germination. Comparing disease models with and without density-dependent transmission, I found that using only daily weather conditions underpredicted the negative consequences of climate change by 46.7%. My results will help managers prepare for coming changes to this important host-pathogen system and demonstrate how critical it is to include ecological mechanisms when making predictions about biotic responses to climate change.