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Flexible continuous-time modelling for heterogeneous animal movement

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  • Harris, Keith J.
  • Blackwell, Paul G.

Abstract

We describe a flexible class of continuous-time models for animal movement, allowing movement behaviour to depend on location in terms of a discrete set of regions and also on an underlying behavioural state. We demonstrate the ability of these models to represent complex behaviour and spatial heterogeneity, as found in real movement studies, while retaining tractability and the conceptual advantages of a continuous-time formulation. We discuss the relationship between the models defined here and a range of important applications, both when movement behaviour is the main focus and when it is essentially a nuisance process, for example in spatially explicit capture–recapture.

Suggested Citation

  • Harris, Keith J. & Blackwell, Paul G., 2013. "Flexible continuous-time modelling for heterogeneous animal movement," Ecological Modelling, Elsevier, vol. 255(C), pages 29-37.
    • Handle: RePEc:eee:ecomod:v:255:y:2013:i:c:p:29-37
    DOI: 10.1016/j.ecolmodel.2013.01.020
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    References listed on IDEAS

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    1. Brockwell, P. J. & Hyndman, R. J., 1992. "On continuous-time threshold autoregression," International Journal of Forecasting, Elsevier, vol. 8(2), pages 157-173, October.
    2. P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
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    Cited by:

    1. 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.
    2. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    3. A. Parton & P. G. Blackwell, 2017. "Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 373-392, September.
    4. Michael A. Spence & Evalyne W. Muiruri & David L. Maxwell & Scott Davis & Dave Sheahan, 2021. "The application of continuous‐time Markov chain models in the analysis of choice flume experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1103-1123, August.
    5. Toby A. Patterson & Alison Parton & Roland Langrock & Paul G. Blackwell & Len Thomas & Ruth King, 2017. "Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 399-438, October.
    6. James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.

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