A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks

TL;DR

This paper introduces the Hidden Markov Movement Model (HMMM), a fast and accurate method for identifying animal behavioral states from high-precision movement data, improving upon existing models with rapid likelihood estimation.

Contribution

The authors developed the HMMM using maximum likelihood and TMB, providing a computationally efficient alternative to Bayesian models for high-accuracy animal tracking data.

Findings

HMMM accurately identifies behavioral states across multiple species.

HMMM is faster than Bayesian state-space models like DCRWS.

HMMM performs well on both real and simulated data.

Abstract

1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common. 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. 3. We compared the HMMM to a modified version of the DCRWS for highly accurate tracks, the DCRWSnome, and to a common HMM for animal tracks fitted with the R package moveHMM. We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. 4. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation for highly accurate tracking data. It additionally provides a groundwork for development of more complex modelling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.