Using a behaviour discovery curve to predict optimal observation time

Lea, Stephen E. G.; Leaver, Lisa A.; Jule, Kristen

doi:10.1163/156853909x447775

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Indeed, a previous study aiming to establish the ethogram of red pandas (Ailurus fulgens fulgens) and using a similar model showed that, if the total number of observation hours exceeds the number of animals observed, and if the degree of behavioral idiosyncrasy in the population is relatively low, the number of behaviors observed per hour increases at a more rapid rate when observing several animals within an observation time than when observing one single animal for the same length of time. In other words, it is better to observe more individuals in any given observation period than one individual for a long period of time (Jule, Lea, & Leaver, 2009). Our study met these two requirements.…”

Section: Discussionmentioning

confidence: 74%

Behavior Systems Approach to Object Play: Stone Handling Repertoire as a Measure of Propensity for Complex Foraging and Percussive Tool Use in the Genus Macaca

Pelletier¹,

Kaufmann²,

Mohak³

et al. 2017

ABC

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Section: Discussionmentioning

confidence: 74%

Behavior Systems Approach to Object Play: Stone Handling Repertoire as a Measure of Propensity for Complex Foraging and Percussive Tool Use in the Genus Macaca

Pelletier¹,

Kaufmann²,

Mohak³

et al. 2017

ABC

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“…Even when animals are being observed directly, it is hard to be certain that all possible relevant behaviors have been witnessed [169], especially when extrapolating behavior in captivity to behavior in the wild. As accelerometry has matured, researchers have developed special software tools to address this obstacle and reduce the time and labor necessary for direct observation [164,170].…”

Section: Data Filters For Separating Gravitational Acceleration From mentioning

confidence: 99%

Observing the unwatchable through acceleration logging of animal behavior

et al. 2013

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Behavior is an important mechanism of evolution and it is paid for through energy expenditure. Nevertheless, field biologists can rarely observe animals for more than a fraction of their daily activities and attempts to quantify behavior for modeling ecological processes often exclude cryptic yet important behavioral events. Over the past few years, an explosion of research on remote monitoring of animal behavior using acceleration sensors has smashed the decades-old limits of observational studies. Animal-attached accelerometers measure the change in velocity of the body over time and can quantify fine-scale movements and body postures unlimited by visibility, observer bias, or the scale of space use. Pioneered more than a decade ago, application of accelerometers as a remote monitoring tool has recently surged thanks to the development of more accessible hardware and software. It has been applied to more than 120 species of animals to date. Accelerometer measurements are typically collected in three dimensions of movement at very high resolution (>10 Hz), and have so far been applied towards two main objectives. First, the patterns of accelerometer waveforms can be used to deduce specific behaviors through animal movement and body posture. Second, the variation in accelerometer waveform measurements has been shown to correlate with energy expenditure, opening up a suite of scientific questions in species notoriously difficult to observe in the wild. To date, studies of wild aquatic species outnumber wild terrestrial species and analyses of social behaviors are particularly few in number. Researchers of domestic and captive species also tend to report methodology more thoroughly than those studying species in the wild. There are substantial challenges to getting the most out of accelerometers, including validation, calibration, and the management and analysis of large quantities of data. In this review, we illustrate how accelerometers work, provide an overview of the ecological questions that have employed accelerometry, and highlight the emerging best practices for data acquisition and analysis. This tool offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and, across scientific disciplines, it improves understanding of the role of behavioral mechanisms in ecological and evolutionary processes. AbstractResumen: El comportamiento es un mecanismo importante de la evolución y que se paga a través del gasto de energía. Sin embargo, los biólogos de campo raramente observan los animales durante más de una fracción de sus actividades y los intentos de cuantificar el comportamiento para el modelado de los procesos ecológicos a menudo excluyen eventos crípticos pero importantes. En los últimos años se produjeron avances importantes en el monitoreo remoto del comportamiento de los animales, utilizando sensores de telemétro de aceleración (acelerómetros) que empujan los límites tradicionales de los estudios observacionales. Acelerómetros uni...

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