DOI: 10.5061/dryad.2hf101c
title: Data from: A comparison of techniques for classifying behaviour from accelerometers for two species of seabird

description: 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
 (&lt; 1 sec), can be relatively small (&lt; 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.
description: BLKI_Accelerometer_Part1Accelerometer data for black-legged kittiwakes from Middleton Island, AK, in 2013. Data have been processed to calculate accelerometer-derived metrics and classified to three behavioural classes (on colony, flying, swimming) using six different methods. Data are split into three .csv files.BLKI_Accelerometer_Part2Accelerometer data for black-legged kittiwakes from Middleton Island, AK, in 2013. Data have been processed to calculate accelerometer-derived metrics and classified to three behavioural classes (on colony, flying, swimming) using six different methods. Data are split into three .csv files.BLKI_Accelerometer_Part3Accelerometer data for black-legged kittiwakes from Middleton Island, AK, in 2013. Data have been processed to calculate accelerometer-derived metrics and classified to three behavioural classes (on colony, flying, swimming) using six different methods. Data are split into three .csv files.TBMU_Accelerometer_Part1Accelerometer data for thick-billed murres from Coats Island, NU, in 2018. Data have been processed to calculate accelerometer-derived metrics and classified to four behavioural classes (on colony, diving, flying, swimming) using six different methods. Data are split into four .csv files.TBMU_Accelerometer_Part2Accelerometer data for thick-billed murres from Coats Island, NU, in 2018. Data have been processed to calculate accelerometer-derived metrics and classified to four behavioural classes (on colony, diving, flying, swimming) using six different methods. Data are split into four .csv files.TBMU_Accelerometer_Part3Accelerometer data for thick-billed murres from Coats Island, NU, in 2018. Data have been processed to calculate accelerometer-derived metrics and classified to four behavioural classes (on colony, diving, flying, swimming) using six different methods. Data are split into four .csv files.TBMU_Accelerometer_Part4Accelerometer data for thick-billed murres from Coats Island, NU, in 2018. Data have been processed to calculate accelerometer-derived metrics and classified to four behavioural classes (on colony, diving, flying, swimming) using six different methods. Data are split into four .csv files.TBMU_GPS_DataGPS tracking data used to validate accelerometer-derived behavioural classifications.BLKI_GPS_DataGPS tracking data used to validate accelerometer-derived behavioural classifications.BLKI Accelerometer ScriptsR code to classify behaviour of black-legged kittiwakes from accelerometer dataTBMU Accelerometer ScriptsR code to classify behaviour of thick-billed murres from accelerometer data