Characterising menotactic behaviours in movement data using hidden Markov models

Abstract

1. Movement is the primary means by which animals obtain resources and avoid hazards. Most movement exhibits directional bias that is related to environmental features (taxis), such as the location of food patches, predators, ocean currents, or wind. Numerous behaviours with directional bias can be characterized by maintaining orientation at an angle relative to the environmental stimuli (menotaxis), including navigation relative to sunlight or magnetic fields and energy-conserving flight across wind.However, no statistical methods exist to flexibly classify and characterise such directional bias.2. We propose a biased correlated random walk model that can identify menotactic behaviours by predicting turning angle as a trade-off between directional persistence and directional bias relative to environmental stimuli without making a priori assumptions about the angle of bias. We apply the model within the framework of a multi-state hidden Markov model (HMM) and describe methods to remedy information loss associated with coarse environmental data to improve the classification and parameterization of directional bias.3. Using simulation studies, we illustrate how our method more accurately classifies behavioural states compared to conventional correlated random walk HMMs that do not incorporate directional bias. We illustrate the application of these methods by identifying cross wind olfactory foraging and drifting behaviour mediated by wind-driven sea ice drift in polar bears (Ursus maritimus) from movement data collected by satellite telemetry.4. The extensions we propose can be readily applied to movement data to identify and characterize behaviours with directional bias toward any angle, and open up new avenues to investigate more mechanistic relationships between animal movement and the environment.

Drivers of polar bear behavior, and the possible effects of prey availability on foraging strategy

“…To avoid interpolating large gaps, we segmented the location data into separate bouts whenever missing locations spanned more than 6 h (i.e., we interpolated a maximum of three missing locations). To remove data-sparse bouts, we removed segments spanning less than 24 h or those with fewer than eight locations (as in Togunov et al, 2021). Any missing locations in the remaining bouts were interpolated using the R Package crawl (Johnson et al, 2008;Johnson and London, 2018).…”

“…We extracted two variables from the tracking data: step length l t ∈ (0, ∞) (the distance between consecutive locations) and turning angle ϕ t ∈ (−π, π] (change in bearing between consecutive steps; Langrock et al, 2012;McClintock and Michelot, 2018;Togunov et al, 2021). Following Togunov et al (2021), we assumed step lengths followed a gamma distribution:…”

“…Recent advances in data acquisition (Nathan et al, 2022) and analytical methods (e.g., McClintock and Michelot, 2018; Togunov et al, 2021) have enabled the identification of more intricate behaviors and research at an increasingly large scale and resolution. Pagano et al (2017) described the use of accelerometers to identify up to ten fine-scale behaviors, and Pagano et al (2020) used a combination of accelerometer data and conductivity sensors to identify resting, walking, and swimming.…”

“…Unfortunately, most existing telemetry datasets do not lend themselves to many of the newer analytical methods because they lack necessary auxiliary data (i.e., they only estimate tag location). However, Togunov et al (2021) described the use of location data in combination with wind to identify up to three behavioral states, a method readily applicable to most existing polar bear movement data.…”

Smoothing splines of apex predator movement: Functional modeling strategies for exploring animal behavior and social interactions

“…As in recent developments in standard continuous time models, there are opportunities to improve the fit of the model by accounting for geographic features/barriers, social encounters, atmospheric conditions, etc. (Togunov et al, 2021). Random walk schematics have shown great potential improving the modeling of animal movement, and these methods should be adapted to the FDA paradigm.…”