Modelling group dynamic animal movement

• Published in: Methods in Ecology and Evolution
• Main Authors: Langrock, Roland, Hopcraft, Grant, Blackwell, Paul, Goodall, Victoria, King, Ruth, Niu, Mu, Patterson, Toby, Pedersen, Martin, Skarin, Anna, Schick, Robert Schilling
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2014
• Subjects: Behavioural state; Hidden Markov model; Maximum likelihood; Random walk; Rangifer tarandus
• Online Access: https://risweb.st-andrews.ac.uk/portal/en/researchoutput/modelling-group-dynamic-animal-movement(9f6085ff-0270-4eea-9f6c-621a13690d2d).html; https://doi.org/10.1111/2041-210X.12155; https://research-repository.st-andrews.ac.uk/bitstream/10023/4555/1/1308.5850v1.pdf

1). 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. 2). 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. 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 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. 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 ...