2022
DOI: 10.1101/2022.11.30.518554
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Flexible hidden Markov models for behaviour-dependent habitat selection

Abstract: There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on … Show more

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Cited by 15 publications
(40 citation statements)
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“…The use of segmentation-clustering algorithm such as segclust2d or hidden-Markov models on the time-series coefficients obtained by time-varying HSA will allow one to do this, and to estimate the duration and frequencies of such modes. This could complement very recent works developing behavioral-mode detection approaches based on step-selection functions (Prima et al 2022; Klappstein, Thomas, and Michelot 2022).…”
Section: Discussionsupporting
confidence: 55%
“…The use of segmentation-clustering algorithm such as segclust2d or hidden-Markov models on the time-series coefficients obtained by time-varying HSA will allow one to do this, and to estimate the duration and frequencies of such modes. This could complement very recent works developing behavioral-mode detection approaches based on step-selection functions (Prima et al 2022; Klappstein, Thomas, and Michelot 2022).…”
Section: Discussionsupporting
confidence: 55%
“…These states allow for a mixture of movement types (long and directed vs. short and tortuous), which in turn induces correlation between step lengths and turn angles (Hodel and Fieberg 2022). Building on these two approaches, one might next consider fitting a state-switching step-selection function (Klappstein et al 2023, Pohle et al 2023), though the software for fitting and simulating from this class of models is less well developed.…”
Section: Discussionmentioning
confidence: 99%
“…Integrated step-selection analyses (ISSAs) (Avgar et al 2016, Fieberg et al 2021) and hidden Markov models (HMMs) (Langrock et al 2012, McClintock et al 2012), in particular, are extremely popular for analyzing wildlife telemetry data due to the availability of open-source software () for implementing them (McClintock and Michelot 2018, Signer et al 2019). These approaches, as well as recently developed methods that make it possible to combine the two frameworks (Klappstein et al 2023, Pohle et al 2023), allow researchers to fit rich models to telemetry data in which movements may vary spatially and temporally as a function of environmental features (e.g., landcover types, distance to roads) and latent (unobserved) behavioral states (e.g., that may be associated with whether the individual has recently fed).…”
Section: Introductionmentioning
confidence: 99%
“…The approach proposed by Nicosia et al (2017) would facilitate accounting for uncertainties in the HMM state decoding, but it would likewise prove difficult to incorporate the different sampling strategies investigated in this paper. This approach was recently extended by Klappstein et al (2023) to account for covariate influence on state transition probabilities, which may provide further insights into the interplay between behavioural state switching and habitat selection. With increasing availability of high‐resolution tracking data (sub‐minute to sub‐second resolution), SSFs may also be modified to define a ‘step’ as the straight‐line movement between turning points instead of regular positions determined by arbitrary GPS fix rates (Munden et al, 2021).…”
Section: Discussionmentioning
confidence: 99%