Autoregressive hidden Markov models for high-resolution animal movement data

TL;DR

This paper introduces autoregressive hidden Markov models tailored for high-resolution animal movement data, effectively capturing within-state autocorrelation and improving inference over traditional models.

Contribution

It extends standard hidden Markov models by incorporating autoregressive components and automated model selection, addressing the challenges of high-resolution movement data.

Findings

Autoregressive HMMs outperform basic HMMs with strong autocorrelation.

Model automates autoregressive degree selection using lasso.

Application to tern movement data demonstrates practical utility.

Abstract

New types of high-resolution animal movement data allow for increasingly comprehensive biological inference, but method development to meet the statistical challenges associated with such data is lagging behind. In this contribution, we extend the commonly applied hidden Markov models for step lengths and turning angles to address the specific requirements posed by high-resolution movement data, in particular the very strong within-state correlation induced by the momentum in the movement. The models feature autoregressive components of general order in both the step length and the turning angle variable, with the possibility to automate the selection of the autoregressive degree using a lasso approach. In a simulation study, we identify potential for improved inference when using the new model instead of the commonly applied basic hidden Markov model in cases where there is strong within-state autocorrelation. The practical use of the model is illustrated using high-resolution movement tracks of terns foraging near an anthropogenic structure causing turbulent water flow features.