Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

Abstract

Background Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. Methods We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. Results HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. Conclusions The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Integrating immersion withGPSdata improves behavioural classification for wandering albatrosses and shows scavenging behind fishing vessels mirrors natural foraging

“…HMMs are typically used to identify behavioural states from animal movement based on step length and turning angle between subsequent locations, with short‐medium step lengths and high turning angles considered to represent foraging (McClintock & Michelot, 2018; Conners et al., 2021). The addition of other data provided by the concurrent deployment of additional sensors may provide increased power to resolve behavioural states (Dean et al., 2013; McClintock & Michelot, 2018; Clark, Handby, et al., 2020).…”

Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean

“…Whereas analyses that identify discrete behaviors (e.g. expectation-maximization binary clustering, EMbC; hidden Markov movement models) work well for high-resolution data with temporal resolutions ≤ 5 min (Mendez et al 2017, Conners et al 2021, FPT is meaningful for analyses where behaviors related to larger-scale processes, and where tracking data sampled at coarser scales (e.g. 15 min) are of interest.…”

Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

“…A combination of model selection criteria (AIC), pseudoresidual plots and visual validation from decoded behavioral states overlaid onto pseudo-tracks (i.e., biological interpretability; Conners et al, 2021) were used to determine the best-fit model (number of states, inclusion of covariates) from a suite of candidate models. Several baseline models (no covariates) were first fit to determine the number of behavioral states included (up to a maximum of 5).…”