Data from: Territory surveillance and prey management: wolves keep track of space and time

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.

当动物采用基于复杂认知的策略时，解析观测到的运动模式背后的行为机制颇具挑战。这类策略往往在回访时机至关重要时演化形成，例如为了实现资源更新或开展领地巡逻。我们针对加拿大阿尔伯塔省的6只灰狼（Canis lupus；林奈，1758）的GPS运动数据，拟合了空间显式随机游走模型（spatially explicit random-walk models），以探究以下两类机制的重要性：（1）与领地边缘气味标记更新相关的领地监视行为，以降低狼群间的种内竞争风险；（2）延迟回访近期狩猎区域的行为，该行为可能与不同猎物密度下猎物的反捕食响应有关。该运动模型纳入了时空变量「上次到访以来的时间」，该变量可作为灰狼旅行历史的记忆指数，并与灰狼相对于领地边界的位置、局域猎物密度信息一同被整合进运动决策过程。我们采用模型选择框架，针对这些变量在灰狼运动策略中的联合重要性检验相关假说。所有灰狼的运动轨迹均支持与领地监视相关的时间依赖性运动模式。灰狼通常会规避领地边缘，但随着上次到访以来的时间延长，这种规避行为会有所减弱。除一只灰狼外，其余个体的时间依赖性猎物管理行为均不显著。该灰狼偏好选择上次到访以来时间更长、猎物密度更低的区域，这使得其回访高猎物密度位点的延迟时间更长。本研究表明，我们可通过空间显式随机游走模型，识别出融合环境信息与过往运动的显式时空信息（即「何时」与「何地」）的行为策略，以此指导运动决策。该方法有助于我们更好地理解与动态环境和资源相关的基于认知的运动模式。