Modelling Group Dynamic Animal Movement

Roland Langrock, Grant Hopcraft, Paul Blackwell, Victoria Goodall, Ruth King, Mu Niu, Toby Patterson, Martin Pedersen, Anna Skarin and Robert Schick

Universities of St. Andrews, Glasgow, Sheffield, South African Environmental Observation Network, Commonwealth Scientific and Industrial Research Organisation, Technical University of Denmark and Swedish University of Agricultural Sciences.


1. Group dynamic movement is a fundamental aspect of many species’ movements. The need to adequately model individuals’ interactions with other group members has been recognised, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods which can include group dynamics in animal movement models have been lacking. 2. We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the groups’ centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multi-state random walks. 3. While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative. 4. We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively small, our model successfully captures group influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group. 5. As technology continues to develop it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and external factors that can influence an individual’s movement.


behavioural state; hidden Markov model, maximum likelihood; random walk