Abstract
BackgroundRecent advances in sensing technologies have enabled us to attach small loggers to animals in their natural habitat. It allows measurement of the animals’ behavior, along with associated environmental and physiological data and to unravel the adaptive significance of the behavior. However, because animal-borne loggers can now record multi-dimensional (here defined as multimodal) time series information from a variety of sensors, it is becoming increasingly difficult to identify biologically important patterns hidden in the high-dimensional long-term data. In particular, it is important to identify co-occurrences of several behavioral modes recorded by different sensors in order to understand an internal hidden state of an animal because the observed behavioral modes are reflected by the hidden state. This study proposed a method for automatically detecting co-occurrence of behavioral modes that differs between two groups (e.g., males vs. females) from multimodal time-series sensor data. The proposed method first extracted behavioral modes from time-series data (e.g., resting and cruising modes in GPS trajectories or relaxed and stressed modes in heart rates) and then identified two different behavioral modes that were frequently co-occur (e.g., co-occurrence of the cruising mode and relaxed mode). Finally, behavioral modes that differ between the two groups in terms of the frequency of co-occurrence were identified.ResultsWe demonstrated the effectiveness of our method using animal-locomotion data collected from male and female Streaked Shearwaters by showing co-occurrences of locomotion modes and diving behavior recorded by GPS and water-depth sensors. For example, we found that the behavioral mode of high-speed locomotion and that of multiple dives into the sea were highly correlated in male seabirds. In addition, compared to the naive method, the proposed method reduced the computation costs by about 99.9%.ConclusionBecause our method can automatically mine meaningful behavioral modes from multimodal time-series data, it can be potentially applied to analyzing co-occurrences of locomotion modes and behavioral modes from various environmental and physiological data.
Highlights
Recent advances in sensing technologies have enabled us to attach small loggers to animals in their natural habitat
Assuming that behavioral mode 1-1 is identified as the cruising mode and behavioral mode 2-1 is identified as the relaxing mode, this co-occurrence indicates that cruising flights are more likely to contribute to energy saving during movement rather than food searching flights
We developed a computationally efficient method to detect the frequent co-occurrence of behavioral modes by automatically selecting the parameters and features that maximize the usefulness of the obtained co-occurrence of those behavioral modes
Summary
Recent advances in sensing technologies have enabled us to attach small loggers to animals in their natural habitat It allows measurement of the animals’ behavior, along with associated environmental and physiological data and to unravel the adaptive significance of the behavior. The proposed method first extracted behavioral modes from time-series data (e.g., resting and cruising modes in GPS trajectories or relaxed and stressed modes in heart rates) and identified two different behavioral modes that were frequently co-occur (e.g., co-occurrence of the cruising mode and relaxed mode). Sensor data from each modality (such as GPS trajectories and heart rates) comprises several behavioral modes, including resting and cruising movements and relaxed and stressed physiological states, as shown in the hypothetical example (Fig. 1). Assuming that behavioral mode 1-1 is identified as the cruising mode and behavioral mode 2-1 is identified as the relaxing mode, this co-occurrence indicates that cruising flights are more likely to contribute to energy saving during movement rather than food searching flights
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