Can ethograms be automatically generated using body acceleration data from free-ranging birds?
Can ethograms be automatically generated using body acceleration data from free-ranging birds?
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DOI：
10.1371/journal.pone.0005379
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发表时间：
2009
期刊：
影响因子：
3.7
通讯作者：
Wanless S
中科院分区：
文献类型：
--
作者：
Sakamoto KQ;Sato K;Ishizuka M;Watanuki Y;Takahashi A;Daunt F;Wanless S
人种图是一个物种通常采用的离散行为的目录。传统上，动物的行为是通过直接观察研究个体来记录的。然而，对于在偏远地区和/或在很深或很高的地方发生的行为，这种方法是困难的，往往是不可能的。最近开发的越来越复杂的动物携带的数据记录器已经开始克服这个问题。加速度计在这方面特别有用，因为它们可以记录物体在飞行、行走或游泳时的动态运动。然而，使用身体加速度特征对行为进行分类通常需要事先了解自由放养动物的行为。在这里，我们演示了一种从身体加速中对行为进行分类的自动化程序，以及一个用户友好的计算机应用程序--“种族记录者”的发布。我们使用从一种脚推进的潜水海鸟--欧洲海鸟Phalacrocorax aristotelis收集的纵向加速度数据来评估它的性能。通过连续小波变换将时间序列数据转换为谱。然后，通过非监督聚类分析，使用k-均值方法，将频谱的每一秒归入20个行为组中的一个。提取的典型行为以身体加速度的周期性为特征。每一种分类的行为都被认为与鸟类在陆地上、飞行中、海面上、潜水等时间相对应。按程序分类的行为与根据深度剖面独立定义的行为符合得很好。因为我们的方法是通过对数据的非监督计算来执行的，所以它有可能检测到以前未知的行为类型和一些行为的未知序列。
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.
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影响因子：
2.1
作者：
WANLESS, S;HARRIS, MP;RUSSELL, AF
通讯作者：
RUSSELL, AF
影响因子：
3.1
作者：
Laich, Agustina Gomez;Wilson, Rory P.;Shepard, Emily L. C.
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Shepard, Emily L. C.
影响因子：
30.8
作者：
Tavazoie, S;Hughes, JD;Church, GM
通讯作者：
Church, GM
影响因子：
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作者：
Grenfell, BT;Bjornstad, ON;Kappey, J
通讯作者：
Kappey, J
影响因子：
2
作者：
WANLESS, S;CORFIELD, T;MORRIS, JA
通讯作者：
MORRIS, JA