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Modelling group movement with behaviour switching in continuous time

Author

Listed:
  • Mu Niu
  • Fay Frost
  • Jordan E. Milner
  • Anna Skarin
  • Paul G. Blackwell

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).

Suggested Citation

  • Mu Niu & Fay Frost & Jordan E. Milner & Anna Skarin & Paul G. Blackwell, 2022. "Modelling group movement with behaviour switching in continuous time," Biometrics, The International Biometric Society, vol. 78(1), pages 286-299, March.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:1:p:286-299
    DOI: 10.1111/biom.13412
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    References listed on IDEAS

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    1. Harris, Keith J. & Blackwell, Paul G., 2013. "Flexible continuous-time modelling for heterogeneous animal movement," Ecological Modelling, Elsevier, vol. 255(C), pages 29-37.
    2. Iain D. Couzin & Jens Krause & Nigel R. Franks & Simon A. Levin, 2005. "Effective leadership and decision-making in animal groups on the move," Nature, Nature, vol. 433(7025), pages 513-516, February.
    3. P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
    4. Mu Niu & Paul G. Blackwell & Anna Skarin, 2016. "Modeling interdependent animal movement in continuous time," Biometrics, The International Biometric Society, vol. 72(2), pages 315-324, June.
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