Data from: A comparison of techniques for classifying behaviour from accelerometers for two species of seabird

The behavior of many wild animals remains a mystery, as it is difficult to
quantify behaviour of species that cannot be easily followed throughout
their daily or seasonal movements. Accelerometers can solve some of these
mysteries, as they collect activity data at a high temporal resolution
(< 1 sec), can be relatively small (< 1 g) so they minimally
disrupt behavior, and are increasingly capable of recording data for long
periods. Nonetheless, there is a need for increased validation of methods
to classify animal behaviour from accelerometers to promote widespread
adoption of this technology in ecology. We assessed the accuracy of six
different behavioral assignment methods for two species of seabird,
thick-billed murres (Uria lomvia) and black-legged kittiwakes (Rissa
tridactyla). We identified three behaviors using tri-axial accelerometers:
standing, swimming and flying, after classifying diving using a pressure
sensor for murres. We evaluated six classification methods relative to
independent classifications from concurrent GPS tracking data. We used
four variables for classification: depth, wing beat frequency, pitch and
dynamic acceleration. Average accuracy for all methods was greater than
98% for murres, and 89% and 93% for kittiwakes during incubation and chick
rearing, respectively. Variable selection showed that classification
accuracy did not improve with more than two (kittiwakes) or three (murres)
variables. We conclude that simple methods of behavioral classification
can be as accurate for classifying basic behaviors as more complex
approaches, and that identifying suitable accelerometer metrics is more
important than using a particular classification method when the objective
is to develop a daily activity or energy budget. Highly accurate daily
activity budgets can be generated from accelerometer data using a multiple
methods and a small number of accelerometer metrics; therefore,
identifying a suitable behavioral classification method should not be a
barrier to using accelerometers in studies of seabird behavior and
ecology.