Thesis (Ph.D.)--University of Wyoming, 2009. / Title from PDF title page (viewed on July 6, 2010). Includes bibliographical references.

Thesis (M.A.)--State University of New York at Binghamton, Department of Anthropology, 2008.

Increased demand for animal disease surveillance information has led to the development and refinement of methodologies for qualitative and quantitative surveillance system evaluations to maximize efficiency and efficaciousness. The impetus for this surveillance evaluation project was chronic wasting disease (CWD) and the objectives were to apply both qualitative and quantitative methodologies to examine the components of CWD surveillance in Saskatchewan.<p> A retrospective review of deer pathology and hunter-harvest submissions in Saskatchewan was conducted through the Canadian Cooperative Wildlife Health Centre. Qualitative evaluation methods outlined by Klauke et al (1988) were used and included key stakeholder interviews. A quantitative evaluation, with specific focus on disease detection, was conducted to examine system sensitivity, confidence of disease freedom and to compare system components using methods described by Martin et al (2007). The analysis was conducted using a scenario tree and Monte Carlo simulation.<p> Sampling rates of dead and clinically ill deer were low with a high degree of variability by season, year, location and nature of submissions. Ultimately, variability of submission patterns likely affected when and where diseases were detected. Poor data quality reduced the amount of available data for analysis but quality dramatically improved over time.<p> The surveillance evaluation demonstrated that the current surveillance system places more emphasis on monitoring trends in CWD-positive areas, at the expense of early detection. This is explained mostly by the coupling of disease control efforts and surveillance, in that harvests are heavily focused in CWD-positive areas. The system is not sufficient to detect disease in new areas where the disease prevalence is low, primarily due to low submission rates.<p> The quantitative evaluation found that overall sensitivity of the surveillance system and confidence of disease freedom was highly dependent on detection prevalence and the ongoing risk of disease introduction. Surveillance in the eastern part of Saskatchewan was not adequate from 1997-2006 to detect CWD at 0.5-1% prevalence. However, if risk of CWD introduction over this time period was assumed to be low, it can be concluded that the prevalence in this region was not 5% or higher.<p> A detection goal of 0.5-1% prevalence is an ambitious surveillance goal, especially in areas where the risk of disease introduction is high. The use of more targeted surveillance strategies should be further explored to help better meet surveillance these surveillance objectives.

Increased demand for animal disease surveillance information has led to the development and refinement of methodologies for qualitative and quantitative surveillance system evaluations to maximize efficiency and efficaciousness. The impetus for this surveillance evaluation project was chronic wasting disease (CWD) and the objectives were to apply both qualitative and quantitative methodologies to examine the components of CWD surveillance in Saskatchewan.<p> A retrospective review of deer pathology and hunter-harvest submissions in Saskatchewan was conducted through the Canadian Cooperative Wildlife Health Centre. Qualitative evaluation methods outlined by Klauke et al (1988) were used and included key stakeholder interviews. A quantitative evaluation, with specific focus on disease detection, was conducted to examine system sensitivity, confidence of disease freedom and to compare system components using methods described by Martin et al (2007). The analysis was conducted using a scenario tree and Monte Carlo simulation.<p> Sampling rates of dead and clinically ill deer were low with a high degree of variability by season, year, location and nature of submissions. Ultimately, variability of submission patterns likely affected when and where diseases were detected. Poor data quality reduced the amount of available data for analysis but quality dramatically improved over time.<p> The surveillance evaluation demonstrated that the current surveillance system places more emphasis on monitoring trends in CWD-positive areas, at the expense of early detection. This is explained mostly by the coupling of disease control efforts and surveillance, in that harvests are heavily focused in CWD-positive areas. The system is not sufficient to detect disease in new areas where the disease prevalence is low, primarily due to low submission rates.<p> The quantitative evaluation found that overall sensitivity of the surveillance system and confidence of disease freedom was highly dependent on detection prevalence and the ongoing risk of disease introduction. Surveillance in the eastern part of Saskatchewan was not adequate from 1997-2006 to detect CWD at 0.5-1% prevalence. However, if risk of CWD introduction over this time period was assumed to be low, it can be concluded that the prevalence in this region was not 5% or higher.<p> A detection goal of 0.5-1% prevalence is an ambitious surveillance goal, especially in areas where the risk of disease introduction is high. The use of more targeted surveillance strategies should be further explored to help better meet surveillance these surveillance objectives.

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Jan. 19, 2010). Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Stevens Point, 2008. / Submitted in partial fulfillment of the requirements of the degree Master of Science in Natural Resources (Wildlife), College of Natural Resources. Includes bibliographical references.

Thesis (M. Sc.)--University of Alberta, 2010. / Title from pdf file main screen (viewed on July 23, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Ecology, Department of Biological Sciences, University of Alberta. Includes bibliographical references.

White-tailed deer (Odocoileus virginianus) are important game mammals and potential reservoirs of diseases of domestic livestock, so diseases of deer are of great concern to wildlife managers. In many situations, models can be useful for integrating existing data, understanding disease transmission patterns, and predicting effects on host populations. Individual-based modeling (IBM) has become more commonplace in ecology as a tool to link individual behavior to population dynamics and community interactions, especially for gauging the effects of management actions. Spatially explicit IBMs are especially useful when ecological processes, such as disease transmission, are affected by the spatial composition of the environment. I developed a spatially explicit IBM, DeerLandscapeDisease (DLD), to simulate direct and indirect disease transmission in white-tailed deer. Using data from GPS-collared deer in southern Illinois, I developed methods to identify habitats and times of high contact probability. I parameterized movement models, for use in DLD, using field data from GPS-collared deer in both southern and east-central Illinois. I then used DLD to simulate deer movements and epizootiology in two different landscapes: a predominantly agricultural landscape with fragmented forest patches in east-central Illinois and a landscape dominated by forest in southern Illinois. Behavioral and demographic parameters that could not be estimated from the field data were estimated using published literature of deer ecology. I assumed that bioavailability of infectious pathogens deposited in the environment decreased exponentially. Transmission probabilities were estimated by fitting to published trends in infection prevalence, assuming that infection probability during an encounter was equal for all age classes, so infection prevalence varied with sex- and age-specific behavior. DLD simulations of chronic wasting disease epizootiology demonstrated significant effects of landscape structure, social behavior, and mode of transmission on prevalence, emphasizing the importance of spatial, temporal and behavioral heterogeneity in disease modeling. These results demonstrate the utility of IBMs in incorporating spatio-temporal variables as well as animal behavior when predicting and modeling disease spread.

A difficulty in using diffusion models to predict large-scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). In this work, we derive a homogenization procedure for ecological diffusion, which allows us to determine the impact of small-scale (10-100 m) habitat variability on large-scale (10-100 km) movement, and apply it to models for chronic wasting disease (CWD) in mule deer. CWD is an infectious prion disease that affects members of the Cervidae family. It is a slow-developing, fatal disease, which is rare in the free-ranging deer population of Utah. We first present a simple spatial disease model to illustrate our homogenization procedure and the use of ecological diffusion as a way to connect animal movement with disease spread. Then we develop a more disease-specific sex-structured model for the spread of CWD, incorporating both horizontal and environmental transmission pathways. We apply our homogenization technique to greatly reduce the computational load for a simulation of disease spread from the La Sal Mountains to the Abajo Mountains of Southeast Utah. We use the averaged coefficients from the homogenized model to explore asymptotic invasion speed and critical population size for portions of our study area. Lastly, we describe the estimation of motilities for the disease-specific model from GPS location data, using a continuous-time correlated random walk model.

Wildlife diseases often occur under quantifiable and consistent patterns, which can be understood to statistically predict their occurrence and spread across landscapes. Chronic wasting disease (CWD) is a neurodegenerative disease in the deer family Cervidae caused by a prion, a pathogenic and misfolded variant of a naturally occurring protein. Managing and controlling CWD is imperative for conservation of ecologically and economically important cervid species, but unclear transmission mechanisms within landscapes complicate evidence-based management. Gaps of information in the landscape ecology for CWD are particularly pronounced for areas with recent disease emergence and spread, such as within the CWD cluster in the Mid-Atlantic United States. Thus, I identified current gaps in information and sought to fill neglected areas of research, specifically focusing on landscape determinants for CWD occurrence and spread in the state of Virginia. In chapter 2, I conducted a scoping study that collected and synthesized decades of CWD research and identified trends with respect to statistical and mathematical modeling methods used, connectivity within the CWD research community, and the geographic areas from which studies were performed. In chapter 3, I investigated landscape determinants for CWD in Virginia using remote sensing landscape data and an epidemiological dataset from Virginia Department of Wildlife Resources (DWR) using diverse algorithms and model evaluation techniques. Finally, in chapter 4, I modeled landscape connectivity between confirmed CWD cases to examine potential paths and barriers to CWD spread across landscapes. My results indicate that landscape ecology was rarely incorporated throughout CWD's 50+ year history. I provide evidence that remotely-sensed landscape conditions can be used to predict the likelihood of CWD occurrence and connectivity in Virginia landscapes, suggesting plausible CWD spread. I suggest areas of future work by explicitly identifying gaps in CWD research and diagnostic methods from which models are based, and encourage further consideration of host's ecology in modeling. By integrating remotely-sensed data into my modeling framework, the workflow should be easily adaptable to new study areas or other wildlife diseases. / Master of Science / Understanding why diseases occur in some locations and not others can be a critical challenge for disease ecologists. One disease that has received significant attention from the media and scientific community is chronic wasting disease (CWD), which is caused by a misfolded protein called a prion. Virginia Department of Wildlife Resources (DWR) has identified a stark increase in the number of CWD cases since first discovered in 2009, which threatens white-tailed deer populations and a 500 million dollar industry used for conservation of Virginia wildlife species. Previous research found that CWD does not occur randomly on the landscape, but otherwise little is known about the landscape ecology of CWD. To provide insight on Virginia's CWD outbreak, I assessed methods used to investigate other CWD outbreaks in both space and time. Also, I used landscape data collected from satellites and data from CWD cases in Virginia, and applied statistical tools to identify patterns in the landscape that were linked with CWD cases. My results suggest that landscapes were rarely examined to understand CWD, and instead, researchers focused on understanding how populations will respond to the disease. I also provide evidence that, at least in Virginia, researchers can use satellite information with disease data to predict CWD on the landscape and estimate its spread. This information can be used by wildlife managers to control the disease. For example, disease surveillance can be increased in areas where CWD has been predicted, or herd sizes can be reduced in areas likely to promote disease spread. This information could also be used to tailor wildlife health regulations aimed to minimize the risk of other deer populations acquiring the disease. Ultimately, the landscape plays an important role in CWD, but research on this topic is limited; therefore, additional research is needed to understand and eventually control this disease affecting ecologically and culturally important game species.