Bayesian inference for continuous time animal movement based on steps and turns

TL;DR

This paper introduces a Bayesian inference method for continuous-time animal movement models that combine stochastic processes for speed and bearing, using MCMC and data augmentation to analyze irregular GPS data.

Contribution

It develops a novel Bayesian inference approach for continuous-time animal movement models that effectively handle irregular observations and measurement error.

Findings

Successfully applied to reindeer movement data

Demonstrates improved inference over discrete-time models

Provides a flexible framework for movement analysis

Abstract

Although animal locations gained via GPS, etc. are typically observed on a discrete time scale, movement models formulated in continuous time are preferable in order to avoid the struggles experienced in discrete time when faced with irregular observations or the prospect of comparing analyses on different time scales. A class of models able to emulate a range of movement ideas are defined by representing movement as a combination of stochastic processes describing both speed and bearing. A method for Bayesian inference for such models is described through the use of a Markov chain Monte Carlo approach. Such inference relies on an augmentation of the animal's locations in discrete time that have been observed with error, with a more detailed movement path gained via simulation techniques. Analysis on real data on an individual reindeer (Rangifer tarandus) illustrates the presented methods.