Discriminating grey wolf kill sites using GPS clusters

• Published in: The Journal of Wildlife Management
• Main Authors: Irvine, Courtney C., Cherry, Seth G., Patterson, Brent R.
• Other Authors: Government of Ontario, Parks Canada, Natural Sciences and Engineering Research Council of Canada
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Canada; Canis lupus
• Online Access: http://dx.doi.org/10.1002/jwmg.22163; https://onlinelibrary.wiley.com/doi/pdf/10.1002/jwmg.22163; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/jwmg.22163

Abstract Characteristics of spatio‐temporal clusters of locations from global positioning system (GPS)‐collars have been used to distinguish kill sites of various predators. We deployed GPS collars on 9 grey wolves ( Canis lupus ) in the southwest area of Prince Albert National Park in central Saskatchewan, Canada, and used a GPS location clustering algorithm to identify kill sites of ungulate and other large‐bodied prey during winter, December 2013–March 2017. We used logistic regression in a model‐selection framework to determine if spatio‐temporal and habitat characteristics of grey wolf GPS clusters could be used to reliably identify sites where wolves had killed prey. Global positioning system clusters were more likely to be wolf kill sites when they had a higher number of location fixes, did not begin within 300 m and 30 days of a previous cluster, did not begin within 1 km and 4 days of a previous cluster, began in the evening, had a high percentage of fixes occurring during the day, occurred farther from open habitat, and had both a high number of location fixes and a high percentage of fixes occurring during the day. Our results highlight the limits of using spatio‐temporal clusters with a fix rate of 1/hour to discriminate wolf kill sites in systems dominated by deer ( Odocoileus spp.) because of the associated short handling time with these prey.