A comparison of techniques for classifying behavior from accelerometers for two species of seabird

• Published in: Ecology and Evolution
• Main Authors: Allison Patterson, Hugh Grant Gilchrist, Lorraine Chivers, Scott Hatch, Kyle Elliott
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: accelerometer; animal behavior; behavioral classification; movement ecology; Rissa tridactyla; seabird tracking; Ecology; QH540-549.5; rissa tridactyla; Uria lomvia; uria
• Online Access: https://doi.org/10.1002/ece3.4740; https://doaj.org/article/245e0fcf9c2848009b565c0460d9c445

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 ...