Three experiments were conducted to determine the effect of energy, protein, sex, and time on serum T4 and T3 concentrations. All sampling periods occurred at 28-day intervals. In the first experiment, (March-February) 7 adult bucks were placed on 2 feed levels, ad libitum or 25% restricted. Feed consumption of ad libitum deer was highest (P≤0.05) from June-October, fell in November, and remained low through March. Body weights of both groups were highest (P≤0.05) from September-October; lowest from March-April. Serum T4 was highest (P≤0.05) in May and July, and lowest in November. From November-February, restricted deer had lower T4 concentrations (P<0.01) than did ad libitum deer. Serum T3 was highest from May-August; lowest in November. Ad libitum had higher T3 concentrations (P<0.01) than the restricted animals. The second experiment compared the effects of energy and protein on body weight, and serum T4 and T3 of 24 fawns (12 male) from October-May. Feed intake fell gradually to low levels maintained from January-March, then increased slightly. Body weight gain was initially rapid (P<0.01), minimal from November-March, and slow through May. Serum T4 was highest in late April; lowest in October and February. Maximum serum T3 concentrations occurred in April; lowest values in February. Females had higher T4 and T3 values than did males. The third experiment involved 1 adult buck. Blood samples were drawn every 2 hours for a 24-hour period via a jugular catheter. Serum T4 and T3 concentrations were highest from 1600-2000 hours (EST), lowest at 1000 hours. / M.S.

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.

Pellet group surveys were conducted on 21 transects in February-March, 1985, and January-March, 1986, to estimate relative deer densities on eleven study areas on the Jefferson National Forest, southwestern Virginia. Habitat data were collected on the same transects in July-September, 1985. Additional habitat information was measured from aerial photographs and USDA Forest Service compartment maps. These data were used to develop eleven multiple linear regression models and one pattern recognition (PATREC) model for predicting deer winter habitat quality, based on the assumption that relative density of deer is a good indicator of habitat quality. The densities of evergreen broad-leaved shrubs and"Nonforage" shrubs, basal area, mean distance to a field, and percent slope were among the most important variables selected in the regression model building process. Six variables were selected for use in the PATREC model: mean tree diameter, oak basal area, basal area of"Other Winter Forage" tree species, density of"Nonforage" shrubs, mean distance to a gated gravel road, and mean canopy closure. Spearman's rank correlations were used to compare the model outputs with estimated pellet group densities. All of the models had correlation coefficients ≥ 0.60, four had correlation coefficients > 0.80. The models need to be validated, i.e. tested with independent data from areas outside the study sites. These tests will help refine the models and assess their effectiveness in other regions of the southern Appalachian Mountains. / Master of Science

no abstract provided by author / Master of Science

A habitat suitability index (HSI) model for white-tailed deer (Odocoileus virginianus) was tested to determine the relationship between habitat quality predicted by the model and habitat quality suggested by the condition of 1.5 year-old bucks on Quantico Marine Corps Base, Virginia. Additionally, new models were developed that predict the response of habitat variables important to a variety of species to succession and management. Habitat quality predicted by the white-tailed deer HSI model for 11 different deer management units was not strongly correlated with body weight (Spearman's r = -0.40, f = 0.221, n = 11), beam diameter (rs = 0.06, f = 0.851, n = 11), beam length (rs = 0.37, f = 0.265, n = 11), and number of points (rs = -0.24, f = 0.473, n = 11). The area within each management unit with HSI > 0.5 was weakly correlated (rs = 0.48, P = 0.13) with beam diameter and beam length. We attempted to model the response of vegetation to succession and management. The strength of the relationship between habitat changes and stand age (succession) varied depending on the variable and cover type being modeled. R2adj values were highest on average for habitat parameters associated with overstory trees, including basal area, dbh, density, and height. R2adj values were low (R2a~ < 0.5) and regressions nonsignificant (f > 0.10) for models associated with shrubs and herbaceous vegetation. In general, the response of habitat parameters was most predictable in loblolly-shortleaf pine plantations that were hand planted and not subject to the same variation associated with naturally regenerated stands. / Master of Science

Master of Science

The mixed oak-pine cover type was evaluated as white-tailed deer range on four study areas by measuring dry matter production of key forages and determining their nutrient composition. Composite diets containing plant species which represented the major portions of each seasonal diet as indicated by food habits studies, were mixed for the summer, fall and winter seasons. For the spring flush and spring seasons, individual key forages were analysed. All samples were assayed for soluble carbohydrates, lignin, phosphorus, gross energy, proximate composition, and in vitro dry matter digestibility. Digestible energy production in kcal/ha/day was calculated seasonally for key forages. The ratios of digestible energy available in key forages to that required by the estimated deer herd were 3.01, 5.94, 0.96, 2.14, and 1.23, for the spring flush, spring, summer, fall, and winter, respectively. These ratios indicate the potential of the study areas to support the estimated population density of 1 deer per 16.4 ha. The mixed oak-pine cover type appears to be adequate to support the estimated deer herd if 50 percent of the key forages are consumed seasonally but inadequate if only 25 percent are used. During all seasons, forage protein appeared to be adequate and phosphorus was possibly lower than that required for optimal animal performance. / Master of Science

I examined the efficacy of Quality Deer Management (QDM) on Amelia Springs hunt club in Amelia County, Virginia, during 2003-2006. I examined home range dynamics of male white-tailed deer (Odocoileus virginianus), deer/hunter interactions, and aspects of population dynamics. I also developed a new rocket net method to capture deer using a remote video system that was more efficient than traditional methods. I monitored 20 deer; 50% died due to hunting and 15% to natural mortality. The emigration rate for juvenile males was 46%, dispersal distance averaged 6.4 km. I used Home Range Extension (HRE) in ArcView to generate annual home ranges (adaptive-kernel) for 16 male deer; I also generated annual and seasonal home ranges using MCP. Annual and seasonal home ranges (MCP) of adult males were larger than those of juveniles. Adult male annual home ranges averaged 2.5 km2 and juveniles 0.9 km2. Seasonal home ranges of adult males were 1.6 km2 and 1.3 km2 during non-hunting and hunting seasons respectively. Juvenile non-hunting and hunting season home ranges were 0.6 km2 and 0.8 km2 respectively. I detected no differences in day/night movements of male deer during the hunting season; however, deer appeared to avoid areas that were hunted based on hunter GPS locations and deer locations during the hunting season. Frequency of deer movement increased during October-November. Population estimates based on remote camera mark-recapture averaged 60 antlered males for the 3-year survey period. Using population reconstruction, the minimum buck:doe ratio was 1:1.8. Estimated density of antlered males was 4.1/km2, in Amelia County, and 5.0/km2 for Amelia Springs. Deer harvested on Amelia Springs, compared to deer harvested on other hunt clubs in Amelia County, were larger. Antler diameters averaged 32.6mm on Amelia Springs versus 26.9mm for other Amelia county hunt clubs, average age at harvest for 2+ males was higher on Amelia Springs (2.4) than other Amelia county hunt clubs (2.2), and dressed body weights averaged 11.2kg heavier (46.2 kg versus 35 kg) on Amelia Springs. QDM on Amelia Springs appears to be successful based on the results. While bigger bucks existed on Amelia Springs, hunters failed to encounter them. Hunters likely would increase buck sightings during the hunting season by becoming more mobile. Expectations of the size of animal (antlers) Amelia Springs can produce should be adjusted to reflect what is possible based on the habitat. The harvest program in place should be continued at the current level for continued success using QDM. / Master of Science

Nutritional quality and availability is thought to regulate geographic patterns of variability in animal body size due to phenotypic plasticity. The purpose of this study is to determine how vegetation quality, abundance and population density influence white-tailed deer (Odocoileus virginianus) body size on a subregional spatial scale at Fort Hood, Texas. Harvest and census records are used to test the hypothesis that white-tailed deer exhibit phenotypic plasticity (e.g. larger body size) in response to differences in vegetation quality and availability. Results from these analyses suggest that forage quality and abundance alone is not a main driver of white-tailed deer body size. Analysis of deer population density (generally) resulted in an inverse relationship with body size. Areas with high quality forage and low population density support larger deer while areas with low quality forage and high density support smaller than average deer. The few exceptions occur in areas exhibiting poor quality forage and low population density or high forage quality and high density. Results from this study suggest that continued overcrowding of deer within isolated areas may eventually lead to efficiency phenotypic conditions producing smaller sized deer. These results could prove useful in interpreting deer population responses to harvest management. For successful local management of deer, studies examining the combined influence of habitat variables (such as forage quality, abundance and population density) on deer health offer managers valuable information needed to establish annual harvest goals and understand deer-habitat relationships relative to carrying capacity.

This study utilized geographic information systems, remote sensing, and infrared-triggered cameras to assess white-tailed deer habitat in Denton County, Texas. Denton County is experiencing tremendous growth in both population and development. Despite their presence here historically, white-tailed deer were all but extirpated by the beginning of the 20th century, and there are no data available which support their presence in Denton County again until the 1980's. This study attempts to equate the increase in white-tailed deer population to Denton County's transformation from an agricultural to an urban economy and lifestyle. Eighteen sites were chosen throughout the county to research the following metrics: geology, soils, landcover, landscape ecology, streams, shorelines, land use, population, roads, structures, and census methods.