Central New York is located in one of the snowiest regions in the United States, with the city of Syracuse, New York the snowiest metropolis in the nation. Snowfall in the region generally begins in mid-November and lasts until late-March. Snow accumulation occurs from a multitude of conditions: frontal systems, mid-latitude cyclones, Nor'easters, and most notably lake-effect storms. Lake effect snowfall (LES) is a difficult parameter to forecast due to the isolated and highly variable nature of the storm. Consequently, studies have attempted to determine changes in snowfall for lake-effect dominated regions. Annual snowfall patterns are of particular concern as seasonal snowfall totals are vital for water resources, winter businesses, agriculture, government and state agencies, and much more. Through the use of snowfall, temperature, precipitation, and location data from the National Weather Service's Cooperative Observer Program (COOP), spatial and temporal changes in snowfall for Central New York were determined. In order to determine climatic changes in snowfall, statistical analyses were performed (i.e. least squares estimation, correlations, principal component analyses, etc.) and spatial maps analyzed. Once snowfall trends were determined, factors influencing the trends were examined. Long-term snowfall trends for CNY were positive for original stations (~0.46 +/- 0.20 in. yr-1) and homogenously filtered stations (0.23 +/- 0.20 in. yr-1). However, snowfall trends for shorter time-increments within the long-term period were not consistent, as positive, negative, and neutral trends were calculated. Regional differences in snowfall trends were observed for CNY as typical lake-effect areas (northern counties, the Tug Hill Plateau and the Southern Hills) experienced larger snowfall trends than areas less dominated by LES. Typical lake-effect months (December - February) experienced the greatest snowfall trend in CNY compared to other winter months. The influence of teleconnections on seasonal snowfall in CNY was not pronounced; however, there was a slight significant (5%) correlation (< 0.35) with the Atlantic Multidecadal Oscillation. It was not clear if changes in air temperature or changes in precipitation were the cause of variations in snowfall trends. It was also inconclusive if the elevation or distance from Lake Ontario resulted in increased snowfall trends. Results from this study will aid in seasonal snowfall forecasts in CNY, which can be used to predict future snowfall. Even though the study area is regionally specific, the methods may be applied to other lake effect dominated areas to determine temporal and spatial variations in snowfall. This study will enhance climatologists and operational forecasters' awareness and understanding of snowfall, especially lake effect snowfall in CNY.

The convergence of three young scientific disciplines (ecology, geospatial sciences, and remote sensing) has generated unique advancements in wildlife research by connecting ecological data with remote sensing data through the application of geospatial techniques. Ecological datasets may contain spatial and sampling biases. By using geospatial techniques, datasets may be useful in revealing landscape scale (e.g., statewide) trends for wildlife populations, such as population recovery and human-wildlife interactions. Specifically, black bear populations across North America vary greatly in their degree of distribution stability. The black bear population in Michigan may be considered stable or secure, whereas the population in Missouri is currently recolonizing. The focus of the research in this dissertation is to examine the ecological and anthropogenic impacts 1) on human-black bear interactions in Michigan (see Chapter 2) and 2) on black bear presence in Missouri (see Chapter 3), through the use of black bear reports provided by the public to the state wildlife agencies. By using generalized linear modeling (GLM) and maximum entropy (MaxEnt), I developed spatial distribution models of probability of occurrence/presence for the 2 study areas (Michigan and Missouri). For the Missouri study, I quantified the spatiotemporal shifts in the probability of bear presence statewide. The results from my statewide studies corroborate previous local-scale research based on rigorous data collection. Overall, human-black bear interactions (e.g., wildlife sightings, conflicts), while very dynamic, appear greatest in forested and rural areas where the preferred habitat for black bears (i.e., forest) intersects with low density anthropogenic activities. As both human and black bear populations continue to expand, it is reasonable to expect human-black bear interactions to spatiotemporally increase across both study areas. The results from my studies provide wildlife managers with information critical to management decisions such as harvest regulations and habitat conservation actions across the landscape and through time. The ability to detect and monitor ecological changes through the use of geospatial techniques can lead to insights about the stressors and drivers of population-level change, further facilitating the development of proactive causeocused management strategies.