Abstract
We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through space taken by an animal. By selecting a set of appropriate movement parameters, we develop a method to assess movement behavioral states, reflected by changes in the movement parameters. The two fundamental tasks of our study are segmentation and clustering. By segmentation, we mean the partitioning of the trajectory into segments, which are homogeneous in terms of their movement parameters. By clustering, we mean grouping similar segments together according to their estimated movement parameters. The proposed method is evaluated using field observations (done by humans) of movement behavior. We found that on average, our method agreed with the observational data (ground truth) at a level of 80.75% ± 5.9% (SE).
Highlights
Animal movement analysis is being revolutionized by the increasing positional accuracy and temporal frequency of tracking devices, such as ARGOS tags, RFID (Radio Frequency IDentification) tags, Geotags, and GNSS (Global Navigation Satellite System) tags [1]
By doing the behavioral change point analysis (BCPA) for various moving window sizes and inspecting their diagnostic plots, we concluded that 30 consecutive sampling points was a feasible moving window size for our dataset
We demonstrate how changes in movement behavior can be inferred from the tracks of individual animals, and how the movement behavior of individual animals varies over time
Summary
Animal movement analysis is being revolutionized by the increasing positional accuracy and temporal frequency of tracking devices, such as ARGOS tags, RFID (Radio Frequency IDentification) tags, Geotags, and GNSS (Global Navigation Satellite System) tags [1]. There are different approaches for distinguishing movement behaviors from animal movement paths, including statistical modelling, data mining techniques, mixtures of random walk models, and movement-derived parameters [3,4,5,9,14,15,16]. Gurarie et al [12] group behavioral movement analysis methods into four categories: (1) metricbased, (2) classification and segmentation, (3) phenomenological time series analysis and (4) mechanistic movement modelling. They compare the categories in terms of complexity of the results and the intrinsic differences in the output, using one method from each category. Movement parameters include mean squared displacement [17], first passage time [18], (multi-scale) straightness index [14] and fractal dimension [19]
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