Analysis of central place foraging behaviour of wolves using hidden Markov models

The foraging movement behaviour of grey wolves (Canis lupus) is unique in summer, when the focus of breeding wolves is on taking care of new-born offspring. In this study, we analysed the movement tracks of nine radio-collared wolves during their pup-rearing season. The wolves lived in the boreal zone in Finland. Our interest was in analysing the foraging trips of wolves from the den site, which serves as the central place of the pack in summer. Based on the information on spatial relocations and time of the day, the movement tracks of the collared wolves were split into segments using hidden Markov models (HMM). Those segments were considered to be produced by different movement behaviour modes that were not observed. We first split the movement tracks of the wolves into separate foraging trips using a two-state HMM and further extracted the different movement modes with a four-state HMM. The modes were interpreted as rest, moderate activity, homing to the den site and fast movement for other purposes, such as leaving the den. Our analysis showed that, for most of the individuals, the movement during homing was fastest and most persistent. This research highlights the foraging behaviour of wolves during the pup-rearing season, which has gained less attention than the nomadic behaviour outside of this season. Our study showed how cyclic foraging trips can be divided into behavioural phases using HMMs, and how these behaviours appear in different times of the day. These results have potential uses, for instance, when studying the habitat requirements and usage or assessing the risk of human-wildlife conflicts.

Automated summary

The study analyzed the foraging behavior of wolves in the boreal zone in Finland during the pup-rearing season, using hidden Markov models to divide the movement tracks into different behavioral modes. The results showed that the movement speed was fastest and most persistent during homing for most individuals.