Modelling group movement with behaviour switching in continuous time

Niu, M., Frost, F., Milner, J. E., Skarin, A. and Blackwell, P. G. (2022) Modelling group movement with behaviour switching in continuous time. Biometrics, 78(1), pp. 286-299. (doi: 10.1111/biom.13412) (PMID:33270218)

Abstract

This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi-domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between ‘following’ and ‘independent’. The ‘following’ movement is modelled through a linear stochastic differential equation, while the ‘independent’ movement is modelled as Brownian motion. The movement of the leading point is modelled either as an Ornstein-Uhlenbeck (OU) process or as Brownian motion (BM), which makes the whole system a higher-dimensional Ornstein-Uhlenbeck process, possibly an intrinsic non-stationary version. An inhomogeneous Kalman filter Markov chain Monte Carlo algorithm is developed to estimate the diffusion and switching parameters and the behaviour states of each individual at a given time point. The method successfully recovers the true behavioural states in simulated data sets , and is also applied to model a group of simultaneously tracked reindeer (Rangifer tarandus).

• Item Type: Articles
• Additional Information: FF is supported by the Centre for Advanced Biological Modelling at the University of Sheffield, funded by the Leverhulme Trust, award number DS-2014-081. JEM is supported by a studentship from the Engineering and Physical Sciences Research Council.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Niu, Dr Mu
• Authors: Niu, M., Frost, F., Milner, J. E., Skarin, A., and Blackwell, P. G.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Biometrics
• Publisher: Wiley
• ISSN: 0006-341X
• ISSN (Online): 1541-0420
• Published Online: 03 December 2020
• Copyright Holders: Copyright © 2020 John Wiley & Sons
• First Published: First published in Biometrics 78(1): 286-299
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher