A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Issue 6 (21st February 2019) - Patterson, Allison; Gilchrist, Hugh Grant; Chivers, Lorraine; Hatch, Scott; Elliott, Kyle
Abstract: The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior 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 s), 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 behavior 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 >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 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. Abstract : The behavior of many wild animals remains a mystery, as it is difficult to generate ethograms for animals that are impossible to follow. We assessed the accuracy of different behavioral assignment methods for two species of seabird using data from tri‐axial accelerometers. Highly accurate daily activity budgets can be generated from accelerometer data using a wide range of methods and only a small number of input variables; therefore, identifying a suitable behavioral classification method should not be a barrier to greater adoption of accelerometers in studies of animal behavior and wildlife ecology.