Patterson Allison, Gilchrist Hugh Grant, Chivers Lorraine, Hatch Scott, Elliott Kyle
Department of Natural Resource Sciences McGill University Ste Anne-de-Bellevue Quebec Canada.
Environment and Climate Change Canada National Wildlife Research Centre Ottawa Ontario Canada.
Ecol Evol. 2019 Feb 21;9(6):3030-3045. doi: 10.1002/ece3.4740. eCollection 2019 Mar.
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 () and black-legged kittiwakes (). 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.
许多野生动物的行为仍是个谜，因为要对那些在日常或季节性活动中难以轻易追踪的物种的行为进行量化很困难。加速度计可以解开其中一些谜团，因为它们能以高时间分辨率（<1秒）收集活动数据，体积相对较小（<1克），从而对行为的干扰降至最低，并且越来越能够长时间记录数据。尽管如此，仍需要加强对从加速度计数据分类动物行为方法的验证，以促进该技术在生态学中的广泛应用。我们评估了针对两种海鸟——厚嘴海鸦（）和黑脚三趾鸥（）的六种不同行为分类方法的准确性。在使用压力传感器对海鸦的潜水行为进行分类后，我们利用三轴加速度计识别出三种行为：站立、游泳和飞行。我们相对于来自同步GPS跟踪数据的独立分类评估了六种分类方法。我们使用四个变量进行分类：深度、翅膀拍动频率、俯仰和动态加速度。对于海鸦，所有方法的平均准确率均超过98%，对于三趾鸥，在孵化期和育雏期的准确率分别为89%和93%。变量选择表明，使用超过两个（三趾鸥）或三个（海鸦）变量时，分类准确率并未提高。我们得出结论，在对基本行为进行分类时，简单的行为分类方法可以和更复杂的方法一样准确，并且当目标是制定每日活动或能量预算时，识别合适的加速度计指标比使用特定的分类方法更重要。使用多种方法和少量加速度计指标，可从加速度计数据生成高度准确的每日活动预算；因此，在海鸟行为和生态学研究中，识别合适的行为分类方法不应成为使用加速度计的障碍。
