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Activity Number: 384 - Advances in Animal Movement Modeling
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: JABES-Journal of Agricultural, Biological, and Environmental Statistics
Abstract #326505 Presentation
Title: Bayesian Inference for Multistate Step-and-Turn Animal Movement in Continuous Time
Author(s): Alison Parton* and Paul G Blackwell
Companies: University of Sheffield and University of Sheffield
Keywords: Markov chain Monte Carlo; Continuous-time Markov chain; Data augmentation; Ornstein-Uhlenbeck process

Mechanistic modelling of animal movement is often formulated in discrete time despite problems with scale invariance, such as handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow. This lack of implementation is often excused by a difficulty in interpretation. Here we aim to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths. Movement is defined by a joint bearing and speed process, with parameters dependent on a continuous-time behavioural switching process, creating a flexible class of movement models.

Methodology is presented for Markov chain Monte Carlo inference given irregular and noisy observations, involving augmenting observed locations with a reconstruction of the underlying movement process. This is applied to both real and simulated datasets. We demonstrate the interpretable nature of the continuous-time model, finding clear differences in behaviour over time and insights into short term behaviour that could not have been obtained in discrete time.

Authors who are presenting talks have a * after their name.

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