Cold-air damming (CAD) is a prevalent Mid-Atlantic United States weather phenomenon that occurs when cold, dense air is dammed alongside the eastern slopes of the Appalachian Mountains. Lower-than-normal maximum temperatures, increased and prolonged cloud cover, and precipitation that produces hazardous impacts are common features of this weather event, which are well known for presenting difficulties to both human forecasters and weather prediction models. This study explores CAD events between 2007 and 2016 archived in a Blacksburg National Weather Service ‘bust’ database – instances when forecasters erred by at least 8°F (4.4°C) on either maximum or minimum daily air temperature. The database includes the temperature error within Model Output Statistics (MOS) guidance in association with these forecast ‘busts.’ During the 10-year study period, MOS guidance produced warm-biased maximum temperatures and cold-biased minimum temperatures for most of the problematic CAD events, suggesting MOS guidance tended to underestimate the strength of CAD in these cases, seeming to struggle with weaker CAD events. During CAD erosion, MOS tended to prematurely erode CAD scenarios at night and predicted them to persist for too long during the day. Hourly surface meteorological and synoptic atmosphere composites during these ‘busted’ CAD events failed to reveal obvious differences from what is expected for central Appalachian CAD. However, a comparison to well-forecast classic cold-season CAD events suggest that busted cases of this same type of CAD may be drier than is typical. As the atmospheric patterns associated with busted CAD events are typical of the phenomenon, but a bit weaker or more marginal, forecast errors appear to stem from subtle model errors rather than forecaster error. It is possible that the models may inadequately characterize low-level moisture, but further research is needed to isolate the source of model forecast error. Nonetheless, the results of this research serve as guidance for operational forecasters as they consider model guidance during weak CAD events. / Master of Science

White-tailed deer (Odocoileus virginianus) are a highly charismatic game species with considerable ecological and economic impacts across most of their range. In the Central Appalachian Mountains, deer are a keystone species in forested ecosystems. Regionally, populations vary in herd growth or decline. These fluctuations are important in that they often drive many aspects of population management and regulation, which are dependent on herd demographics. Some key population vital rates allowing better understanding of these changes in white-tailed deer herds are survival, cause-specific mortality, home-range variation, both broad and fine-scale resource selection, and ultimately population growth trends in response to changes in both population vital rates and hunter harvest regulations. In this study, I address each of these concepts within a deer population in Bath County, Virginia, that has presumably been in overall decline since the early 1990's. From June-September, 2019-2020, I monitored survival and cause specific mortality of 57 neonate white-tailed deer until 12 weeks of age. Fawn and adult female survival was 0.310 (95% CI = 0.210-0.475) and 0.871 (95%CI=0.790-0.961) respectively. During the study, I observed a total of 37 fawn mortalities and identified the cause of death using field evidence and through analyzing genetics from residual predator salvia recovered on deer carcasses. Mortalities included 28 predation events and 9 deaths from other causes (e.g., abandonment, malnutrition, or disease). Black bears accounted for 48.6% of all mortality and 64.2% of known predations within our study. My top model identified elevation as a significant predictor of fawn survival, with mortality risk increasing 20% for every 100m increase in elevation. My model using observed vital rates predicted an increasing population of λ = 1.10 (interquartile range, IQR 1.06-1.14). The population was predicted to increase by 2% with a 10% increase in doe harvest (λ = 1.02, IQR = 0.97-1.06) but declined by 7% at 20% harvest (λ = 0.93, IQR = 0.89-0.96). I found that fawning home ranges of females that successfully reared fawns to the end of the season had significantly larger home ranges than those that were unsuccessful at higher elevations. Fawning home ranges for females with fawns increased approximately 71ha in size for every 100m increase in mean home range elevation, whereas seasonal home ranges of females without fawns decreased approximately 1.5 ha for every 100m increase in mean home range elevation. Deer selected fawn-rearing areas nearer to forested edges, open habitats, and at higher elevations, while they avoided areas near disturbed and mature forests. Within the fawn rearing area, females selected locations closer to disturbed forest, open habitats, and forested edge, while avoiding mature forest habitats, and higher elevations. Females selected birth sites with higher levels of visual obstruction. Using a step-selection method for real-time resource selection across biological seasons, we found that female deer selected for open areas during the fawning, breeding, early gestational, and late gestational seasons. During the fall breeding season, females avoided forested edge, but selected for areas at higher elevations. During early gestational seasons females selected disturbed habitats and areas at higher elevations while again avoiding forested edge. Overall, my work highlights variations in population dynamics of white-tailed deer in areas of the Central Appalachian Mountains that are primarily characterized by poor habitat quality and provides novel insights into fine-scale spatial ecology of female deer across biological seasons within the region. Ultimately, while the deer population in our study was not predicted to be in decline, this work supports predation risk as being a significant factor associated with habitat quality. / Doctor of Philosophy / White-tailed deer (Odocoileus virginianus) are a game species with extensive ecological and economic impacts across most of their range. In the central Appalachian Mountains, many populations across the region vary in terms of herd size growth, stability, or decline. These fluctuations are important in that they often drive many aspects of population management and regulation, which are dependent on the status of herd demographics. Some key population vital rates that aid us in better understanding these changes in white-tailed deer herds are survival and cause-specific mortality, home-range variation in association with habitat quality and the ability to successfully reproduce, population trends under hypothetical management scenarios, and resource selection of various habitats that are available across the landscape. In this study, I address each of these concepts within a deer population in Bath County, Virginia, which has presumably been in decline since the early 1990's. As expected, fawn survival was lower than previously reported from other study areas of the Central Appalachians Mountains. Predation was the leading cause of fawn mortality, with black bears being responsible for most predation events. Fawn mortality risk was significantly associated with elevation - where fawns at higher elevations were those at greater risk. Surprisingly, the deer population in Bath County was projected to be increasing under current conditions and was also projected to be stable-to-increasing even under some hypothetical scenarios which would negatively impact population growth (i.e., 10% increase in female harvest or 17% reduction in fawn survival). Fawning home ranges of collared females which successfully reared known fawns to the end of the biological season were significantly influenced by elevation; such that females with fawns had home ranges that increased in size with increasing elevation, whereas females without fawns had home ranges which decreased slightly in size with increasing elevation. At birth sites, females selected locations characterized by higher levels of visual obstruction compared to randomly sampled areas. Of the habitat types analyzed, both selection or avoidance of specific habitats varied across both biological season and spatial scale. Ultimately, while I found that some deer populations associated with poor quality habitats in the Central Appalachians may not be in decline, deer were likely influenced greatly by habitat quality – especially pertaining to predation risk – throughout Bath County.