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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.


Boxplots of parameter estimates obtained from fitting the HMMM and the DCRWSNOME to 50 simulated tracks
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ece32795-fig-0004: Boxplots of parameter estimates obtained from fitting the HMMM and the DCRWSNOME to 50 simulated tracks

Mentions: We simulated 50 tracks from the HMMM with a set of parameters representative of the grey seal track. The HMMM and DCRWSNOME provided accurate estimates of the model parameters (Figure 4), but the DCRWSNOME had a smaller average (over the parameters) RMSE (0.120 vs. 0.140; Table 4). The RMSEs for individual parameters were similar (within 0.01) between the two models with the exception of θ2, where the RMSE of the DCRWSNOME was smaller by 0.149 (Table 4). The DCRWSNOME additionally had the smallest behavioral state error rate (0.175) which differed from the HMMM and moveHMM by approximately 1.5% (0.189) and 18.7% (0.362), respectively. Finally, the average time needed to fit the DCRWSNOME was 5.10 hr, while moveHMM took 1.2 s and the HMMM took 0.08 s.


A hidden Markov movement model for rapidly identifying behavioral states from animal tracks
Boxplots of parameter estimates obtained from fitting the HMMM and the DCRWSNOME to 50 simulated tracks
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383489&req=5

ece32795-fig-0004: Boxplots of parameter estimates obtained from fitting the HMMM and the DCRWSNOME to 50 simulated tracks
Mentions: We simulated 50 tracks from the HMMM with a set of parameters representative of the grey seal track. The HMMM and DCRWSNOME provided accurate estimates of the model parameters (Figure 4), but the DCRWSNOME had a smaller average (over the parameters) RMSE (0.120 vs. 0.140; Table 4). The RMSEs for individual parameters were similar (within 0.01) between the two models with the exception of θ2, where the RMSE of the DCRWSNOME was smaller by 0.149 (Table 4). The DCRWSNOME additionally had the smallest behavioral state error rate (0.175) which differed from the HMMM and moveHMM by approximately 1.5% (0.189) and 18.7% (0.362), respectively. Finally, the average time needed to fit the DCRWSNOME was 5.10 hr, while moveHMM took 1.2 s and the HMMM took 0.08 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.