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  • 1. Weigand, Nicole Ecological and Physiological Effects of Proximity to Roads in Eastern Box Turtles (Terrapene carolina carolina)

    Master of Science (MS), Ohio University, 2018, Biological Sciences (Arts and Sciences)

    Roads are ubiquitous in the United States, and their ecological effects are conspicuous. Turtles are among the vertebrate taxa most affected by roads because of their low vagility and use of road and road-side habitats. In 2013, Wayne National Forest in southeastern Ohio was bisected by a new highway, affecting a road-naive population of eastern box turtles (Terrapene carolina carolina), a species of concern in Ohio and vulnerable throughout its range. The goal of this study was to evaluate ecological, physiological, and behavioral effects of proximity to this new road in this road-naive population of turtles. We used a control-impact study to evaluate potential ecological and physiological effects of proximity to roads, employing radio-telemetry to assess space use, movement behavior, and habitat selection. We used novel bioassay techniques to analyze indicators of chronic stress (across the prior several months) using corticosterone stored in nail keratin. Overall, we found no significant differences in home range sizes, habitat preferences, or corticosterone concentrations between road-side and control sites. While our work suggests that proximity to roads has limited indirect influence on the ecology and chronic stress responses of eastern box turtles, and that road-naive turtles demonstrated avoidance of a high-traffic highway, the road network likely continues to contribute to population declines through direct mortality, and further inquiry is needed to assess road effects, particularly in the areas of stress endocrinology and impacts on demography.

    Committee: Viorel Popescu (Advisor) Subjects: Animal Sciences; Animals; Biology; Conservation; Ecology; Endocrinology; Wildlife Conservation; Wildlife Management
  • 2. Kil, Siyoen Finding a Targeted Subgroup with Efficacy for Binary Response with Application for Drug Development

    Doctor of Philosophy, The Ohio State University, 2013, Statistics

    Identifying subpopulations that benefit from treatment or whose benefit is enhanced over the population at large can improve healthcare for patients and targeted marketing for drug developers. The goal of subgroup analyses in clinical trials is to quantify such heterogeneity of treatment effects across subpopulations. One way of inferring an enhanced drug effect on the target population is to test the treatment- biomarker interaction. A significant treatment-biomarker interaction indicates that the biomarker is predictive and thus can be used to classify the target population with enhanced efficacy. On the other hand, if a single continuous biomarker is involved, we often try to find a threshold that divides the total population into two subsets, one of which is the target population with meaningful efficacy. Permutation tests have been proposed for both testing interactions and finding a threshold in subgroup analyses for personalized medicine. The focus of this dissertation is to both demonstrate the inadequacy of simple permutation testing in each of these applications and to propose valid alternatives. First, the dissertation precisely explores the validity of permutation-based reference distributions for testing treatment-biomarker interactions in randomized clinical trials with binary biomarkers and outcomes. It turns out that the most prevalent method of permutation does not produce a valid reference distribution as expected. Thus, we propose an atypical method of permutation to test for the existence of an interaction term that is equivalent to the underlying idea of an exact conditional logistic regression. Second, we show that proposed permutation-based methods to find a threshold in fact test an inadequate null hypothesis. Instead of using a permutation distribution, we propose a flexible parametric decision-making procedure that can solve the questions not only of how to test for the existence of a the target population but also of how to find the b (open full item for complete abstract)

    Committee: Kaizar Eloise (Advisor) Subjects: Statistics