Flexible hidden Markov models for behaviour-dependent habitat selection.

ABSTRACT:

Background

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 habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF.

Methods

We build on this recent methodological work to make the HMM-SSF approach more efficient and general.

SUBMITTER: Klappstein NJ 

PROVIDER: S-EPMC10239607 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature