We report the development and application of a method using domestic dogs (Canis familiaris Linnaeus, 1758) to systematically locate wildlife scat over large remote areas. Detection dogs are chosen for their strong object orientation, high play drive, and willingness to strive for a reward. Dogs were trained to detect grizzly bear (Ursus arctos Linnaeus, 1758) and black bear (Ursus americanus Pallas, 1780) scats over a 5200-km 2 area of the Yellowhead Ecosystem, Alberta, Canada. DNA from scat provided the species and (for grizzly bears only) sex and individual identities of the animal at each location. Concentrations of fecal cortisol and progesterone metabolites from these same grizzly bear scats provided indices of physiological stress and reproductive activity (in females), respectively. Black and grizzly bears were most concentrated in the northern portion of the multiuse study area, where food is most abundant yet poaching-related mortality appears to be heaviest. Physiologic stress was also lowest and female reproductive activity correspondingly highest for grizzly bears in the north. The scat-based distributions corresponded to concurrently collected hair-snag data in 1999 and global positioning system radiotelemetry data (of grizzly bears) in 1999 and 2001. Results suggest that the scat dog detection methodology provides a promising tool for addressing a variety of management and research questions in the wildlife sciences.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.