Date of Award

Spring 1-1-2013

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department

Ecology & Evolutionary Biology

First Advisor

Pieter T. J. Johnson

Second Advisor

Valerie J. McKenzie

Third Advisor

Carol A. Wessman

Abstract

Understanding the factors that constrain species distributions is a long-standing goal of ecology, although many studies involve only free-living species. Studies of disease occurrence and spread often require broader knowledge of distributional overlap for free-living and parasitic species, emphasizing the importance of determining the distributional constraints on parasites. Flatworm trematodes in the genus Alaria are a broadly distributed group of parasites with wildlife and human health implications. Using a 10-year survey of 624 ponds across the United States, we evaluated the relative roles of climate, geology, and land cover for Alaria occurrence using species distribution modeling (Maxent). We also conducted a step-wise parameterization of Maxent and a sampling bias control method, which may be useful for improving the functionality of Maxent. From among 26 considered models simulations, we identified the primary Alaria occurrence areas that included western and mid-western US with a low probability of predicted occurrence in the central and southern US. The best-fitting Alaria model (mean test AUC:0.829 ± 0.070 SD; average of 10 ensemble models) is comprised of 9 variables including climate, geology, and land cover. Bootstrapping with 20 replicates was found to be the best Maxent method because it maximized mean test AUC and decreased mean standard deviation. Geology was the most important variable explaining 24% of the variation followed by precipitation in the wettest month (15%) and mean temperature of the driest quarter (14%). Land cover was not a substantial explanation of Alaria occurrence (7%). Geology is likely mediating through its effect on water quality and pH, while climatic variables may affect the composition of Alaria hosts. Our results may help inform predictions of infection risk in wildlife and humans.

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