A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat

Watanabe, Shinichi; Izawa, Masako; Kato, Akiko; Ropert‐Coudert, Yan; Naito, Yoshiro

doi:10.1016/j.applanim.2005.01.010

Cited by 126 publications

(135 citation statements)

References 20 publications

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“…Data from tri-axial accelerometers placed on the top of elephants' necks successfully identified elephant behaviors, when combining measures of overall dynamic acceleration with measures of periodicity (for similar examples in cats and birds, see Watanabe et al 2005 andSakamoto et al 2009). When training and test data were from the same individuals (leaveone-out classification), a high overall success rate was achieved (91%).…”

Section: Summary Of Resultsmentioning

confidence: 99%

“…2 shows that walking and swaying behavior show periodic oscillations, while feeding and bathing show little to no such periodicity. Time-series data analyses are useful for examining such periodicity, and have been previously employed to examine accelerometer data (Watanabe et al 2005, Sakamoto et al 2009). We have made extensive use of such time-series analyses in our acoustics research on elephants (e.g.…”

Section: Accelerometer Data Measurementmentioning

confidence: 99%

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Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Soltis¹,

Wilson²,

Douglas‐Hamilton³

et al. 2012

Endang. Species. Res.

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Accelerometers are motion-detection devices that, when attached to animals, are capable of detecting body orientation, overall activity levels, and specific behavior patterns. We deployed accelerometers in order to study the hypothesis that accelerometer output would allow us to distinguish between 4 behavior patterns in 3 adult female African elephants Loxodonta africana at Disney's Animal Kingdom ® , Florida, USA. Tri-axial accelerometer data loggers were attached to the tops of collars worn around the elephants' necks. Behavior was documented on video while the accelerometer output was stored on the data logger. Feeding, bathing, and walking behaviors were recorded in all 3 subjects, while swaying behavior could be recorded in only 1 subject. Data in the 3 physical dimensions (sway, surge, and heave) were analyzed in terms of overall magnitude of movement (dynamic acceleration) and in terms of the periodicity of movement. When classifying accelerometer data of unlabeled origin to the correct behavioral state, overall success ranged from 70 to 91%. Bathing was sometimes confused with feeding and walking, but feeding, walking, and swaying were easily distinguished from each other. These results show that data from accelerometers can distinguish an elephant's behavioral states, and thus may be used to monitor elephant behavior remotely. Such devices could be deployed for a variety of purposes, ranging from monitoring elephant activity in zoos to an early-warning system that could alert the authorities when wild elephants are being illegally hunted. KEY WORDS: Behavior measurement · Accelerometry · Elephant managementResale or republication not permitted without written consent of the publisher OPEN PEN

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Section: Summary Of Resultsmentioning

confidence: 99%

Section: Accelerometer Data Measurementmentioning

confidence: 99%

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Soltis¹,

Wilson²,

Douglas‐Hamilton³

et al. 2012

Endang. Species. Res.

View full text Add to dashboard Cite

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“…For example, if capuchins spend more time being vigilant when researchers are present, at the expense of other activities such as feeding or resting, this may have very important ramifications. Advances in remote sensing, particularly the use of 3D accelerometers to discriminate between activity categories (Yoda et al 2001;Watanabe et al 2005), may allow us to distinguish such finer-scale differences in future studies. Habituation is a key tool for studying the behaviour of a wide range of wild animals in their natural habitat.…”

Section: Discussionmentioning

confidence: 99%

Does watching a monkey change its behaviour? Quantifying observer effects in habituated wild primates using automated radiotelemetry

et al. 2010

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In studies of animal behaviour, researchers have long been concerned that their presence may change the conduct of their study subjects. To minimize observer effects, researchers often habituate their study animals. The premise of this method is that, with sufficient neutral exposure, animals will stop reacting to humans. While numerous studies demonstrate that negative responses to humans decrease over time, the fact that an animal does not flee from or behave aggressively towards observers cannot be taken as evidence that it is not altering its behaviour in other, more subtle ways. Because remotely monitoring the behaviour of wild animals is difficult, it has not been possible to answer the critical question: do habituated animals change their behaviour when researchers are present? Here, we use data from an automated radiotelemetry system that remotely monitored the movement and activity of radiocollared animals to test whether observers affected the behaviour of seven habituated white-faced capuchins, Cebus capucinus. We found no evidence that observers influenced the ranging behaviour or activity patterns of their study subjects. Capuchins did not move faster, stop to rest less frequently, or display higher levels of activity when they were being followed compared to when they were alone. It has been suggested that researchers may embolden habituated study subjects, artificially increasing their relative dominance, but we found no relationship between observer presence and proximity to neighbouring social groups. Although it remains possible that observer effects existed but were too subtle to be detected with the remote sensing technology we used, the results of this study nevertheless provide reassuring evidence that humans can observe habituated wild animals without overly influencing the animals' activity and movement patterns. Ó

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“…Such methods have already been used widely in the study of animal movement, particularly where the goal has been to categorize a set of data inputs into groups. Some examples include categorizing the different behaviors in diving profiles of aquatic animals from time-depth recorders (Schreer and Testa, 1995;Schreer et al, 2001;Thums et al, 2008) and from accelerometry data (Watanabe et al, 2005;Sakamoto et al, 2009;Nielsen et al, 2010;Nathan et al, 2012). Comparable applications exist in studies of human behavior categorized using data from accelerometers in wearable devices and smartphones (Casale et al, 2011;Kwapisz et al, 2011).…”

Section: Development Of Machine Learning Algorithms To Understand Andmentioning

confidence: 99%

How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

et al. 2018

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The rise of the internet coupled with technological innovations such as smartphones have generated massive volumes of geo-referenced data (big data) on human mobility. This has allowed the number of studies of human mobility to rapidly overtake those of animal movement. Today, telemetry studies of animals are also approaching big data status. Here, we review recent advances in studies of human mobility and identify the opportunities they present for advancing our understanding of animal movement. We describe key analytical techniques, potential bottlenecks and a roadmap for progress toward a synthesis of movement patterns of wild animals.

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A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat

Cited by 126 publications

References 20 publications

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Does watching a monkey change its behaviour? Quantifying observer effects in habituated wild primates using automated radiotelemetry

How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

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