Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition

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Authors Ali Soleymani, Frank Pennekamp, Somayeh Dodge, Robert Weibel
Journal/Conference Name Philosophical Transactions of the Royal Society B: Biological Sciences
Paper Category
Paper Abstract Individual behaviour, that is, the reaction of an organism to internal state, conspecifics and individuals of other species as well as the environment, is a crucial building block of their ecology. Modern tracking techniques produce high-frequency observations of spatial positions of animals and accompanying speed and tortuosity measurements. However, inferring behavioural modes from movement trajectories remains a challenge. Changes in behavioural modes occur at different temporal and spatial scales and may take two forms abrupt, representing distinct change points; or continuous, representing smooth transitions between movement modes. The multi-scale nature of these behavioural changes necessitates development of methods that can pinpoint behavioural states across spatial and temporal scales. We propose a novel segmentation method based on the discrete wavelet transform (DWT), where the movement signal is decomposed into low-frequency approximation and high-frequency detail sub-bands to screen for behavioural changes at multiple scales. Approximation sub-bands characterizes broad changes by taking the continuous variations between behavioural modes into account, whereas detail sub-bands are employed to detect abrupt, finer scale change points. We tested the ability of our method to identify behavioural modes in simulated trajectories by comparing it to three state-of-the-art methods from the literature. We further validated the method using an annotated dataset of turkey vultures (Cathartes aura) relating extracted segments to the expert knowledge of migratory vs. non-migratory patterns. Our results show that the proposed DWT segmentation is more versatile than other segmentation methods, as it can be applied to different movement parameters, performs better or equally well on the simulated data, and correctly identifies behavioural modes identified by the experts. It is hence a valuable addition to the toolbox of land managers and conservation practitioners to understand the behavioural patterns expressed by animals in natural and human-dominated landscapes.
Date of publication 2017
Code Programming Language R

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