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26 April 2024 |
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The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation | | Ethan Lawler
; Kim Whoriskey
; William H. Aeberhard
; Chris Field
; Joanna Mills Flemming
; | | Date: |
12 Mar 2019 | | Abstract: | One of the central interests of animal movement ecology is relating movement
characteristics to behavioural characteristics. The traditional discrete-time
statistical tool for inferring unobserved behaviours from movement data is the
hidden Markov model (HMM). While the HMM is an important and powerful tool,
sometimes it is not flexible enough to appropriately fit the data. Data for
marine animals often exhibit conditional autocorrelation, self-dependence of
the step length process which cannot be explained solely by the behavioural
state, which violates one of the main assumptions of the HMM. Using a grey seal
track as an example, along with multiple simulation scenarios, we motivate and
develop the conditionally autoregressive hidden Markov model (CarHMM), which is
a generalization of the HMM designed specifically to handle conditional
autocorrelation.
In addition to introducing and examining the new CarHMM, we provide
guidelines for all stages of an analysis using either an HMM or CarHMM. These
include guidelines for pre-processing location data to obtain deflection angles
and step lengths, model selection, and model checking. In addition to these
practical guidelines, we link estimated model parameters to biologically
meaningful quantities such as activity budget and residency time. We also
provide interpretations of traditional "foraging" and "transiting" behaviours
in the context of the new CarHMM parameters. | | Source: | arXiv, 1903.4999 | | Services: | Forum | Review | PDF | Favorites | |
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