Undergraduate Honors Theses

Thesis Defended

Spring 2019

Document Type


Type of Thesis

Departmental Honors


Ecology and Evolutionary Biology

First Advisor

Dr. Andrew Martin


Just as food and water are resources for organisms, so is space. The way animals make efficient use of space may be based on resource availability and genetic relatedness among individuals and has the potential to help inform how humans could use space more efficiently. It is unclear if/when animals are using space randomly or according to patterns in spatial organization. My study characterized six prairie dog colonies in Boulder and Gunnison, Colorado, to model spatial dynamics with GPS (global positioning system) data complemented by images of full colonies collected by drones. Spatial burrow data were analyzed for non- random distribution of burrows and clustering, and it was assessed whether there are differences in data collected via GPS or drones. GPS has large spatial error (5-15m), while drones have none, and GPS data take 2-5 times longer to collect. Five out of six colonies had burrows with non-random distributions and exhibited significant levels of clustering distinct from random distributions that display equal levels of clustering and hyper-dispersion (i.e. intentional grouping or dispersion). Clusters were identified with the DBSCAN (density-based spatial clustering of applications with noise) algorithm in R software, and the suitability of this approach for use in future studies on genetic relatedness was evaluated. Network models created based on the spatial burrow data demonstrate how networks can be used to analyze prairie-dog spatial dynamics. In addition, this study served to explore network analysis and develop a protocol for implementation in future studies based on spatial data from individual prairie dogs. Drone surveys are less costly than ground GPS data collection, and both methods yielded similar results. These results inform how prairie dogs use space and provide a foundation to study how spatial organization relates to genetic relatedness and resource availability.