Using tri-axial accelerometers to identify wild polar bear behaviors


Tri-axial accelerometers have been used to remotely identify the behaviors of a wide range of taxa. Assigning behaviors to accelerometer data often involves the use of captive animals or surrogate species, as their accelerometer signatures are generally assumed to be similar to those of their wild counterparts. However, this has rarely been tested. Validated accelerometer data are needed for polar bears Ursus maritimus to understand how habitat conditions may in fluence behavior and energy demands. We used accelerometer and water conductivity data to remotely distinguish 10 polar bear behaviors. We calibrated accelerometer and conductivity data collected from collars with behaviors observed from video-recorded captive polar bears and brown bears U. arctos, and with video from camera collars deployed on free-ranging polar bears on sea ice and on land. We used random forest models to predict behaviors and found strong ability to discriminate the most common wild polar bear behaviors using a combination of accelerometer and conductivity sensor data from captive or wild polar bears. In contrast, models using data from captive brown bears failed to reliably distinguish most active behaviors in wild polar bears. Our ability to discriminate behavior was greatest when speciesand habitat-specific data from wild individuals were used to train models. Data from captive individuals may be suitable for calibrating accelerometers, but may provide reduced ability to discriminate some behaviors. The accelerometer calibrations developed here provide a method to quantify polar bear behaviors to evaluate the impacts of declines in Arctic sea ice.

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@inproceedings{Pagano2017UsingTA, title={Using tri-axial accelerometers to identify wild polar bear behaviors}, author={Angela M. Pagano and Melanie A Owen and Sfren Jensen and Jasmine V Ware and Charles T. Robbins and George M Durner and Todd C. Atwood and Martyn E. Obbard and Kevin R. Middel and Gregory W. Thiemann and Terri M Williams}, year={2017} }