Abstract
Genetic and genomic monitoring of wildlife can reveal insights from individuals to populations and from genes to behavior. Genetic and genomic methodologies can aid in evaluating how population abundance, demographics, behavior, and genes change over time and in relation to changing environments, policies, and management (Sauer and Knutson, 2008; Allendorf et al., 2022). Traditionally, wildlife science has employed genetic markers, such as mitochondrial DNA and nuclear microsatellites, to investigate these topics, but recently the discipline has seen a transition to using high throughput genomic sequencing technologies to monitor and address questions about wildlife populations (Barbosa et al., 2020). The gray wolf (Canis lupus) provides a unique opportunity to employ genetic and genomic methodologies to investigate population genetics, demographics, and behavior as wolves have been variably managed and harvested throughout their range (Mech and Boitani 2003). Due to strong public interest in charismatic carnivores and mandated harvest reporting for wolves throughout much of their range in the western United States, thousands of samples have been collected that can be used with genetic and genomic techniques to inform management decisions. Furthermore, wolves share approximately 99.9% of their genome with domestic dogs (C. familiaris) (Wayne and Ostrander 2007), allowing researchers to leverage genome-wide association studies to identify genes associated with traits of interest and expand research to wild populations of wolves. As cooperatively breeding carnivores, changes in both external pressures and population genetic composition have the potential to impact the social composition and behavior of individuals in wolf groups.
In this dissertation, I developed and implemented genetic and genomic techniques in two populations of wolves to investigate population genetics and evaluate the genomic underpinnings of dispersal behavior. In Chapter 1, I used a 30-year dataset of genetic samples from Alaska, USA, to evaluate genetic diversity and connectivity between Denali National Park and Preserve and Yukon-Charley Rivers National Preserve which are separated by a region with recurrent human-caused mortality. In Chapter 2, I transitioned from using microsatellites to using genomic data by optimizing a putatively neutral microhaplotype panel for use with non-invasively collected wolf fecal samples, maintaining the ability to identify and match individuals across tissue and fecal sample sources and draw familial inferences. In Chapter 3, I leverage the unique ability of genomic data to capture putatively adaptive loci through the development of a GT-seq panel covering candidate genes associated with physical traits and behavioral traits. Finally, I combine these two genomic datasets in Chapter 4 to identify candidate loci potentially influencing dispersal in wolves.