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Can Ethograms Be Automatically Generated Using Body Acceleration Data from Free-Ranging Birds?

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/42564

Title: Can Ethograms Be Automatically Generated Using Body Acceleration Data from Free-Ranging Birds?
Authors: Sakamoto, Kentaro Q. Browse this author →KAKEN DB
Sato, Katsufumi Browse this author
Ishizuka, Mayumi Browse this author →KAKEN DB
Watanuki, Yutaka Browse this author →KAKEN DB
Takahashi, Akinori Browse this author
Daunt, Francis Browse this author
Wanless, Sarah Browse this author
Issue Date: 30-Apr-2009
Publisher: Public Library of Science
Journal Title: PLoS One
Volume: 4
Issue: 4
Start Page: e5379
Publisher DOI: 10.1371/journal.pone.0005379
Abstract: An ethogram is a catalogue of discrete behaviors typically employed by a species. Traditionally animal behavior has been recorded by observing study individuals directly. However, this approach is difficult, often impossible, in the case of behaviors which occur in remote areas and/or at great depth or altitude. The recent development of increasingly sophisticated, animal-borne data loggers, has started to overcome this problem. Accelerometers are particularly useful in this respect because they can record the dynamic motion of a body in e.g. flight, walking, or swimming. However, classifying behavior using body acceleration characteristics typically requires prior knowledge of the behavior of free-ranging animals. Here, we demonstrate an automated procedure to categorize behavior from body acceleration, together with the release of a user-friendly computer application, "Ethographer". We evaluated its performance using longitudinal acceleration data collected from a foot-propelled diving seabird, the European shag, Phalacrocorax aristotelis. The time series data were converted into a spectrum by continuous wavelet transformation. Then, each second of the spectrum was categorized into one of 20 behavior groups by unsupervised cluster analysis, using k-means methods. The typical behaviors extracted were characterized by the periodicities of body acceleration. Each categorized behavior was assumed to correspond to when the bird was on land, in flight, on the sea surface, diving and so on. The behaviors classified by the procedures accorded well with those independently defined from depth profiles. Because our approach is performed by unsupervised computation of the data, it has the potential to detect previously unknown types of behavior and unknown sequences of some behaviors.
Rights: http://creativecommons.org/licenses/by/2.5/
Type: article
URI: http://hdl.handle.net/2115/42564
Appears in Collections:獣医学院・獣医学研究院 (Graduate School of Veterinary Medicine / Faculty of Veterinary Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 坂本 健太郎

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