Year of Award
2023
Document Type
Dissertation
Degree Type
Doctor of Philosophy (PhD)
Degree Name
Fish and Wildlife Biology
Department or School/College
W.A. Franke College of Forestry and Conservation
Committee Co-chair
Gordon Luikart, William E. Holben
Commitee Members
Brian K. Hand, Mark Hebblewhite, Kelly Proffitt, Douglas Raiford, Michael Schwartz
Keywords
Cervus canadensis, Greater Yellowstone Ecosystem, Host-microbiome, machine-learning, non-invasive, predation
Publisher
University of Montana
Abstract
This work develops and applies novel methods for metagenomic and landscape genomic (LSG) investigation of wildlife populations, specifically in Rocky Mountain elk (Cervus canadensis) in the Greater Yellowstone Ecosystem (GYE). Host fecal microbiomes facilitate and reflect interactions between the host and its environment. We conducted metagenomic surveys and developed a machinelearning classifier for microbiomes that provided new insights into elk-microbiome biogeography, and the impact of fecal microbiomes on host health and the ecology of elk-wolf (Canis lupus) predation. In the microbiome study, we found strong evidence for multiple management-relevant factors structuring elk microbiomes including body fat percentage, sex, age, and elk location. The use of non-invasive fecal sampling for wildlife monitoring requires microbiome stability across time in ambient conditions after defecation. Using a longitudinal experiment and linear mixed models, we confirmed the elk microbiome is stable under varying field conditions and fecal sample age up to 14 days. These findings support the value of the non-invasive fecal microbiomes in augmenting wildlife monitoring techniques. Applying these methods to living and wolf-killed elk microbiomes from the GYE, we found strongly associating bacteria involved in short-chain fatty-acid (SCFA) metabolism. This suggests a link between elk condition and predation vulnerability facilitated by microbial mediators. Landscape connectivity corridors and barriers to gene flow are crucial to planning and management of wildlife populations. Using LSG methods, we identified gene flow corridors, partial barriers, and population structure to guide sexspecific disease interventions in the GYE. Different corridors of gene flow for mitochondrial DNA (mtDNA) and biparentally inherited nuclear DNA (nDNA) were identified. For nDNA, a combined resistance surface of elevation and landcover performed better than geographic (Euclidean) distance in describing genetic patterns and models approximate previously published GPS-based long-distance elk migration routes. Our results suggest interventions limiting female elk dispersal along low-elevation corridors may slow spread of maternally transmitted diseases like brucellosis. This research demonstrates usefulness of host-associated microbiomes and LSG assessments for understanding and monitoring animal health, links to predator-prey interactions, and landscape connectivity. These findings help address difficult problems in applied conservation while the novel microbiome and LSG approaches should be useful in species and systems beyond elk and the GYE.
Recommended Citation
Pannoni, Samuel B., "PREDICTING GENE FLOW CORRIDORS AND WILDLIFE HEALTH USING LANDSCAPE GENOMICS AND NON-INVASIVE METAGENOMIC MONITORING: INVESTIGATION OF ELK (CERVUS CANADENSIS) IN THE GREATER YELLOWSTONE ECOSYSTEM" (2023). Graduate Student Theses, Dissertations, & Professional Papers. 12232.
https://scholarworks.umt.edu/etd/12232
© Copyright 2023 Samuel B. Pannoni