Animal behavior has important impacts on animal populations and the ecosystem at large, but the impact of such behavior on many ecological phenomena is understudied. For example, behavior drives transmission between wildlife disease hosts. Space use and resource selection determines where hosts will make contact, movement determines how pathogens may spread over the landscape, and other fine-scale behaviors determine the rate of contact and transmission. Spatial and movement data from GPS telemetry are useful for studying the causes and consequences of many behavioral processes. One particular focus of such spatial analyses is the behavioral responses of prey to predation risk. While many studies have highlighted the broad impacts of these antipredator behaviors, few studies have emphasized how predation risk may impact the behavioral drivers of disease transmission. White-tailed deer (Odocoileus virginianus) are an excellent system to study these questions for three reasons. First, deer exhibit a fission-fusion social structure, so contacts are dependent on numerous interacting factors. Second, deer face varying predation risks and respond to these risks with varying strategies including spatial avoidance, foraging, and grouping behavior. Third, deer are host to many important diseases with differing transmission mechanisms. In this dissertation, I had three main objectives; 1) to evaluate the factors that produced variation in deer-to-deer contact, 2) to evaluate multiple behavioral responses of deer to predation risk and, 3) to use these behavioral patterns to make predictions of the relative risk of deer-to-deer contact.In chapter one, I evaluated population variation in contact and tested the impact of variation in contact-related behavior on inferences from social network analysis. I used camera trap recordings of visits and behaviors by deer to scrapes throughout DeSoto National Wildlife Refuge, Nebraska from 2005 and 2006. Based on 2,013 interactions by 169 unique identifiable males and 75 females, I produced social networks based on indirect contact among deer at scrapes, with edges weighted based on the frequency, duration, and types of behaviors. Social networks based on scrape-related behavior were highly connected and dependent upon the frequency, duration, and type of behavior exhibited at scrapes (e.g., scraping, interacting with a scrape or overhanging branch, rub-urinating, grazing) as well as the age of the deer. Including behavior when defining edges did not preserve the network properties of simpler measures (i.e., unweighted networks) confirming that heterogeneity in behaviors that affect transmission probability are important for inferring transmission networks from contact networks. In chapters two through five, I evaluated the behavior of deer using movement data from GPS collars. I captured and collared white-tailed deer (Odocoileus virginianus) at two sites: Shelbyville, IL, and Carbondale, IL from January 2020 to March 2022. I collared a total of 156 deer across both sites, 71 in Shelbyville and 85 in Carbondale. Of these deer, 45 in the Shelbyville sample were female and 26 male, and in Carbondale, 54 deer were female and 31 male. Deer were tracked with remotely-sensed GPS telemetry collars for periods of roughly one year on average, resulting in a total of 1,933,465 GPS locations. In chapter two, I used this GPS data to develop a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection estimates at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, I related encounter probabilities to landscape resources and predicted the relative probability of encounter. Additionally, I further tested the usefulness of this approach by applying this framework to two other systems representing social contact and predator-prey contact respectively. This predicted distribution of encounters was more accurate when predicting novel encounters than a naïve approach or any individual scale alone. In chapter three, I improved estimates of the drivers of movement by developing novel methods for step selection analysis (SSA). To determine the impact of long-term behavior on local selection from SSA, I simulated movement trajectories including bias toward locations simulating different types of long-term behavior. Based on these simulated trajectories, I evaluated the impact of long-term behavior by identifying frequently reused locations based on a three-dimensional kernel density estimate including latitude, longitude, and time of day. Following this, I developed two approaches to account for spatial and temporal patterns of long-term behavior. I then compared estimates of known values of selection from models using these correction methods to previously established methods based on factors such as spatial memory. In chapter four, I applied this method to estimate local-scale step selection of deer in response to sources of risk. Additionally, I evaluated the impact of risk variables on behavioral states using hidden Markov models (HMMs) and determined state-specific estimates of selection. I found that deer avoided human modification but were more likely to change behavioral state in response to mesopredators. Since different sources of risk induce different behavioral responses, it is likely necessary to account for all of these behavioral responses when estimating the impacts of predation risk and its potential consequences. In chapter five, I used inferences from the preceding three chapters to build a mechanistic model of home range selection and movement that can be used to infer contact distributions. This approach could include varying levels of complexity including local-scale step selection, behavioral state transitions, and antipredator response. I ran models with varying levels of complexity and compared the performance of those models to the approach in chapter 2 for predicting contacts. I found that this method could predict contacts accurately even with limited data, but still had difficulty when transferring predictions to new locations.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-3244 |
| Date | 01 August 2024 |
| Creators | Egan, Michael |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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