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A hidden Markov movement model for rapidly identifying behavioral states from animal tracks

View Article: PubMed Central - PubMed

ABSTRACT

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 are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, 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. The HMMM was compared 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. 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 and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.

No MeSH data available.


Behavioral states as obtained by fitting the HMMM (panel a), DCRWSNOME (panel b), and moveHMM (panel c) models to the blue shark track. Different behavioral states are indicated by grey (state 1) and blue (state 2)
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ece32795-fig-0003: Behavioral states as obtained by fitting the HMMM (panel a), DCRWSNOME (panel b), and moveHMM (panel c) models to the blue shark track. Different behavioral states are indicated by grey (state 1) and blue (state 2)

Mentions: All three models identified two states from the blue shark track, although half of the switching probabilities estimated by moveHMM differed greatly from those estimated by the HMMM and DCRWSNOME (Table 3), and this led to different state sequences (Figure 3). Specifically, all three models estimated a high probability of remaining in state 1, α1,1, but moveHMM estimated a low probability of switching from state 2 to state 1, α2,1, while the DCRWSNOME and HMMM estimated a high α2,1. The switching probabilities of the HMMM and DCRWSNOME therefore led to state sequences containing long stretches of state 1 interspersed with short (length 1 or 2) stretches of state 2. By contrast, moveHMM estimated a state sequence with longer stretches of both behavioral states. While the DCRWSNOME took 1.7 hr to fit to the blue shark track, moveHMM took 1.2 s, and the HMMM took 0.02 s.


A hidden Markov movement model for rapidly identifying behavioral states from animal tracks
Behavioral states as obtained by fitting the HMMM (panel a), DCRWSNOME (panel b), and moveHMM (panel c) models to the blue shark track. Different behavioral states are indicated by grey (state 1) and blue (state 2)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5383489&req=5

ece32795-fig-0003: Behavioral states as obtained by fitting the HMMM (panel a), DCRWSNOME (panel b), and moveHMM (panel c) models to the blue shark track. Different behavioral states are indicated by grey (state 1) and blue (state 2)
Mentions: All three models identified two states from the blue shark track, although half of the switching probabilities estimated by moveHMM differed greatly from those estimated by the HMMM and DCRWSNOME (Table 3), and this led to different state sequences (Figure 3). Specifically, all three models estimated a high probability of remaining in state 1, α1,1, but moveHMM estimated a low probability of switching from state 2 to state 1, α2,1, while the DCRWSNOME and HMMM estimated a high α2,1. The switching probabilities of the HMMM and DCRWSNOME therefore led to state sequences containing long stretches of state 1 interspersed with short (length 1 or 2) stretches of state 2. By contrast, moveHMM estimated a state sequence with longer stretches of both behavioral states. While the DCRWSNOME took 1.7 hr to fit to the blue shark track, moveHMM took 1.2 s, and the HMMM took 0.02 s.

View Article: PubMed Central - PubMed

ABSTRACT

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 are now becoming more common. We developed a new hidden Markov model (HMM) for identifying behavioral states from animal tracks with negligible error, 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. The HMMM was compared 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. 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 and have highly accurate tracking data. It additionally provides a groundwork for development of more complex modeling of animal movement with TMB. To facilitate its uptake, we make it available through the R package swim.

No MeSH data available.