Territory surveillance and prey management: Wolves keep track of space and time

• Published in: Ecology and evolution 2017-10, Vol.7 (20), p.8388-8405
• Main Authors: Schlägel, Ulrike E., Merrill, Evelyn H., Lewis, Mark A.
• Format: Article
• Language: English
• Subjects: Animal behavior; animal movement; Cognition; Cognitive ability; Delay; Environmental information; GPS data; landscape of fear; Memory; movement ecology; Original Research; predator–prey; Prey; Random walk; Scent marking behavior; spatial memory; step selection; Surveillance; territoriality; Territory; Time dependence; time since last visit; Wolves
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.3176

Identifying behavioral mechanisms that underlie observed movement patterns is difficult when animals employ sophisticated cognitive‐based strategies. Such strategies may arise when timing of return visits is important, for instance to allow for resource renewal or territorial patrolling. We fitted spatially explicit random‐walk models to GPS movement data of six wolves (Canis lupus; Linnaeus, 1758) from Alberta, Canada to investigate the importance of the following: (1) territorial surveillance likely related to renewal of scent marks along territorial edges, to reduce intraspecific risk among packs, and (2) delay in return to recently hunted areas, which may be related to anti‐predator responses of prey under varying prey densities. The movement models incorporated the spatiotemporal variable “time since last visit,” which acts as a wolf's memory index of its travel history and is integrated into the movement decision along with its position in relation to territory boundaries and information on local prey densities. We used a model selection framework to test hypotheses about the combined importance of these variables in wolf movement strategies. Time‐dependent movement for territory surveillance was supported by all wolf movement tracks. Wolves generally avoided territory edges, but this avoidance was reduced as time since last visit increased. Time‐dependent prey management was weak except in one wolf. This wolf selected locations with longer time since last visit and lower prey density, which led to a longer delay in revisiting high prey density sites. Our study shows that we can use spatially explicit random walks to identify behavioral strategies that merge environmental information and explicit spatiotemporal information on past movements (i.e., “when” and “where”) to make movement decisions. The approach allows us to better understand cognition‐based movement in relation to dynamic environments and resources. To better understand cognitive‐based movement behavior of free‐ranging animals, we analyzed GPS data of wolves (Canis lupus) with statistical yet mechanistic models. We found that individual travel history in form of the time since last visiting a location influenced avoidance behavior at territory edges, but we could detect only a weak effect of time since last visit on hunting behavior. Our method is applicable to many other situations where timing of visits is important, for example, when animals forage for resources that require time to r