Improvements in GPS tracking technologies have resulted in the collection of high resolution movement datasets for a range of wildlife species. In combination with new high resolution remote sensing products, researchers now have the ability to ask complex questions regarding animal movement in heterogeneous landscapes. However, there currently exists a dearth of analytical approaches to combine movement data with environmental variables. Developing methods to examine wildlife movement-environment interactions are particularly relevant given our unprecedented access to high resolution data; however, the analytical and technical challenges of integrating two disparate data types have yet to be effectively overcome. In the analyses presented in this thesis, I examine current approaches for linking wildlife movement to the physical environment, and introduce a data-driven method for examining wildlife movement-environment interactions. The first analysis consists of a review of existing tools in wildlife movement analysis, specifically tools supported within R statistical software, to highlight any existing methodological opportunities and limitations associated with relating movement to landscape features. The review highlights R’s strengths as an integrated toolbox for exploratory analyses, and the current lack of applications for linking high density telemetry datasets with environmental variables. AdehabitatLT was the most functional package available, offering the greatest variety of analysis options. Due to the comprehensive nature of adehabitatLT, I recommend that future method development be implemented through its package specific framework. Extending the first analysis, the second portion of this research introduces a data-driven method, based in semivariogram modelling, for quantifying wildlife movement patterns relative to linear features. The semivariogram-based method is applied to grizzly bear telemetry data to quantify how grizzly bear movement patterns change in relation to roads. The semivariogram-based method demonstrated that the bears’ spatial scale of response ranged from 35 m- 90 m from roads but varied by age, sex, and season. Applying the scales of response to link near-road movement patterns to survival and mortality, revealed that bears that were killed displayed less-risk adverse movements near roads than bears that survived (i.e., longer step lengths and more day light movements around roads). Given this pattern, my data suggest a minimum vegetation buffer of 90 m to serve as screening cover along roadsides to help mitigate the effects of roads on grizzly bear populations in west-central Alberta, Canada. Through the development of data-driven methods in wildlife movement analysis, I can realize the full potential of high resolution telemetry datasets. Data-driven methods reduce the subjectivity within movement analyses, providing more relevant measures of wildlife response to environment. The semivariogram-based method can identify definitive zones of influence around linear disturbance features in any wildlife system, thereby, providing managers with spatially explicit, data-driven insights to reduce impacts on wildlife in multi-use landscapes. / Graduate

Mesocarnivores, including bobcats (Lynx rufus) and coyotes (Canis latrans), fill a unique ecological role throughout much of North America, where they were once influenced by larger predators, but are now the top predators where large carnivores have been extirpated. Their adaptability, generalist traits, and ability to coexist with humans to a greater extent than many species makes them an important subject for current predator research. In addition, their recent population recovery in the case of bobcats and historical range expansion in the case of coyotes make their study timely given a potential increase in their influence on their prey and environments. I investigated how bobcats and coyotes in southern and central Illinois respond in their spatial behavior to factors in their environment like human modification and resources, including a local pulsed resource, white-tailed deer (Odocoileus virginianus) fawns. These spatial behaviors include home-ranging behaviors, habitat selection, and movement, which can be extremely informative in estimating how mesocarnivores respond to landscape heterogeneity. I found that individual variation, which is understudied in much of current spatial research, played a powerful role in all of these behaviors. Bobcats and coyotes used different strategies to respond to human modification in their home ranges, with bobcats broadly expanding their home range with increases in human modification, and clearly selecting for or avoiding these features on the landscape. Meanwhile, coyotes did not expand their home ranges with human modification, but instead displayed temporal and spatial complexity in their functional responses to human modification. These differences in response revealed a gradient in spatial behaviors animals can use to exist in anthropogenic environments, influenced by a species’ behavioral plasticity. I also found that while bobcat and coyote targeting of fawns during fawns’ most vulnerable period was weakly supported at the population-level, there was a substantial amount of individual variation in fawn exploitation. These results provided evidence that there were some specialist individuals that may contribute much more to fawn predation than others, which was somewhat influenced by habitat type. Overall, I found important interspecies and interindividual variation in mesocarnivore spatial behaviors. My study demonstrates how mesocarnivores respond to habitat and prey resources and risks associated with human development. Using this information, I present a framework for predicting how species may respond to changes in their environments, as well as provide further insight into how mesocarnivores may affect ungulate recruitment.