  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.
