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Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in ‘Daily Diary’ tags / James S. Walker; Mark W. Jones; Robert S. Laramee; Mark D. Holton; Emily LC Shepard; Hannah J. Williams; D. Michael Scantlebury; Nikki, J. Marks; Elizabeth A. Magowan; Iain E. Maguire; Owen R. Bidder; Agustina Di Virgilio; Rory P. Wilson
Movement Ecology, Volume: 3, Issue: 1
Swansea University Author: Holton, Mark
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BackgroundSmart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realis...
|Published in:||Movement Ecology|
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BackgroundSmart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.DescriptionThis work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.ConclusionsFramework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.
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