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The application of continuous‐time Markov chain models in the analysis of choice flume experiments
Author(s) -
Spence Michael A.,
Muiruri Evalyne W.,
Maxwell David L.,
Davis Scott,
Sheahan Dave
Publication year - 2021
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12510
Subject(s) - markov chain , flume , preference , autoregressive model , econometrics , bayesian probability , autocorrelation , markov chain monte carlo , computer science , sea bass , statistics , mathematics , fish <actinopterygii> , fishery , biology , flow (mathematics) , geometry
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.