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The application of continuous‐time Markov chain models in the analysis of choice flume experiments

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  • Michael A. Spence
  • Evalyne W. Muiruri
  • David L. Maxwell
  • Scott Davis
  • Dave Sheahan

Abstract

An inhomogeneous continuous‐time Markov chain model is proposed to quantify animal preference and avoidance behaviour in a choice experiment. We develop and apply our model to a choice flume experiment designed to assess the preference or avoidance responses of sea bass (Dicentrarchus labrax) exposed to chlorinated seawater. Due to observed fluctuations in chlorine levels, a stochastic process was applied to describe and account for uncertainty in chlorine concentrations. A hierarchical model was implemented to account for differences between eight experimental runs and use Bayesian methods to quantify preference/avoidance after accounting for observed shoaling behaviour. The application of our method not only overcomes the need to track individuals during an experiment but also circumvents temporal autocorrelation and any violations of independence. Our model therefore surpasses current methods in choice chamber studies, incorporating variability in the environment and group‐level dynamics to yield results that scale and generalise to the real‐world.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:4:p:1103-1123
    DOI: 10.1111/rssc.12510
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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. 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.
    3. 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.
    4. Ann E. McKellar & Roland Langrock & Jeffrey R. Walters & Dylan C. Kesler, 2015. "Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(1), pages 148-157.
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