A previous version of this article is available at http://arxiv.org/abs/1308.5850.
Modelling group dynamic animal movement†
Article first published online: 24 JAN 2014
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 5, Issue 2, pages 190–199, February 2014
How to Cite
Langrock, R., Hopcraft, J. G. C., Blackwell, P. G., Goodall, V., King, R., Niu, M., Patterson, T. A., Pedersen, M. W., Skarin, A., Schick, R. S. (2014), Modelling group dynamic animal movement. Methods in Ecology and Evolution, 5: 190–199. doi: 10.1111/2041-210X.12155
- Issue published online: 13 FEB 2014
- Article first published online: 24 JAN 2014
- Accepted manuscript online: 23 DEC 2013 02:45AM EST
- Manuscript Accepted: 11 DEC 2013
- Manuscript Received: 1 MAR 2013
- Engineering and Physical Sciences Research Council. Grant Number: EP/F069766/1
- US Office of Naval Research. Grant Number: N00014-12-1-0286
- EPSRC/NERC. Grant Number: EP/1000917/1
- Lord Kelvin Adam Smith Fellowship
- behavioural state;
- hidden Markov model;
- maximum likelihood;
- random walk
- Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, 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.
- We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's 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 multistate random walks.
- 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.
- 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 weak, 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.
- 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.