Animal-movement

Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

Bayesian inference for multistate ‘step and turn’ animal movement in continuous time

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

Inferring animal movement & behaviour in continuous time from irregular & noisy GPS observations

Inferring animal movement & behaviour in continuous time from irregular & noisy GPS observations

Inference for continuous-time movement

Bayesian inference for continuous-time step-and-turn movement models with noisy observations. PhD thesis: 2014-2018. Paul Blackwell (supervisor) This PhD thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the `instantaneous’ parameters associated with a continuous-time model.