How to account for behavioral states in step-selection analysis: a model comparison

Abstract

Step-selection models are widely used to study animals’ fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, often depend on the animal’s unobserved behavioral states, such as resting or foraging. As this is ignored in standard (integrated) step-selection analyses (SSA, iSSA), different approaches have emerged to account for such states in the analysis. The performance of these approaches and the consequences of ignoring the states in step-selection analysis, however, have rarely been quantified. We evaluate the recent idea of combining iSSAs with hidden Markov models (HMMs), which allows for a joint estimation of the unobserved behavioral states and the associated state-dependent habitat selection. Besides theoretical considerations, we use an extensive simulation study and a case study on fine-scale interactions of simultaneously tracked bank voles (Myodes glareolus) to compare this HMM-iSSA empirically to both the standard and a widely used classification-based iSSA (i.e., a two-step approach based on a separate prior state classification). Moreover, to facilitate its use, we implemented the basic HMM-iSSA approach in the R package HMMiSSA available on GitHub.

Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations

“…Another alternative method of incorporating temporal dynamics into animal movement models is through state-switching models such as a hidden Markov model (HMM) (Langrock et al, 2012; Mc-Clintock et al, 2012). In HMMs, behaviours such as foraging, resting and transiting are represented as states with different movement parameters, and when combined with SSFs (HMM-SSF/HMM-iSSA), different habitat selection parameters (Picardi et al, 2022; Beumer et al, 2023; Klappstein et al, 2023; Pohle et al, 2024). These models can easily incorporate temporal dynamics as the transition matrix governing state-switching can depend on time, although in HMMs the states are discrete, whereas real behaviour changes may be gradual and continuous, which may affect predictions in some cases.…”

“…Step selection functions (SSFs) are particularly advantageous as they can be used to simulate trajectories (Signer et al, 2017; Potts & Börger, 2023; Signer et al, 2023), are straightforward to parameterise, and can incorporate temporal dynamics (Ager et al, 2003; Forester et al, 2009; Tsalyuk et al, 2019; Richter et al, 2020; Klappstein et al, 2024). An SSF combines a movement and a external selection kernel, can take a range of forms (Munden et al, 2021; Klappstein et al, 2022; Beumer et al, 2023; Eisaguirre et al, 2024; Pohle et al, 2024), and can accommodate a wide range of covariates including habitat, linear features, distance-to-feature variables, proximity to other animals (Potts et al, 2022; Ellison et al, 2024) and representations of previous space use (Schlägel & Lewis, 2014; Oliveira-Santos et al, 2016; Rheault et al, 2021).…”