School of Biological Sciences and Centre for Applied Conservation Science, University of Adelaide, Adelaide, SA 5005, Australia
School of Biological Sciences and Centre for Applied Conservation Science, University of Adelaide, Adelaide, SA 5005, Australia.
J Exp Biol. 2018 Nov 29;221(Pt 23):jeb184085. doi: 10.1242/jeb.184085.
Accelerometers are a valuable tool for studying animal behaviour and physiology where direct observation is unfeasible. However, giving biological meaning to multivariate acceleration data is challenging. Here, we describe a method that reliably classifies a large number of behaviours using tri-axial accelerometer data collected at the low sampling frequency of 1 Hz, using the dingo () as an example. We used out-of-sample validation to compare the predictive performance of four commonly used classification models (random forest, -nearest neighbour, support vector machine, and naïve Bayes). We tested the importance of predictor variable selection and moving window size for the classification of each behaviour and overall model performance. Random forests produced the highest out-of-sample classification accuracy, with our best-performing model predicting 14 behaviours with a mean accuracy of 87%. We also investigated the relationship between overall dynamic body acceleration (ODBA) and the activity level of each behaviour, given the increasing use of ODBA in ecophysiology as a proxy for energy expenditure. ODBA values for our four 'high activity' behaviours were significantly greater than all other behaviours, with an overall positive trend between ODBA and intensity of movement. We show that a random forest model of relatively low complexity can mitigate some major challenges associated with establishing meaningful ecological conclusions from acceleration data. Our approach has broad applicability to free-ranging terrestrial quadrupeds of comparable size. Our use of a low sampling frequency shows potential for deploying accelerometers over extended time periods, enabling the capture of invaluable behavioural and physiological data across different ontogenies.
加速度计是研究动物行为和生理学的一种有价值的工具，在这些研究中直接观察是不可行的。然而，将多维加速度数据赋予生物学意义是具有挑战性的。在这里，我们以澳大利亚野犬（）为例，描述了一种使用三轴加速度计数据在 1Hz 的低采样频率下可靠地对大量行为进行分类的方法。我们使用样本外验证来比较四种常用分类模型（随机森林、-最近邻、支持向量机和朴素贝叶斯）的预测性能。我们测试了预测变量选择和移动窗口大小对每种行为和整体模型性能分类的重要性。随机森林产生了最高的样本外分类准确性，我们表现最好的模型预测了 14 种行为，平均准确率为 87%。我们还研究了整体动态体加速度（ODBA）与每种行为活动水平之间的关系，因为 ODBA 在生态生理学中作为能量消耗的替代指标的使用越来越多。我们四种“高活动”行为的 ODBA 值明显大于其他所有行为，ODBA 值与运动强度之间呈整体正相关趋势。我们表明，一个相对简单的随机森林模型可以减轻从加速度数据中得出有意义的生态结论所面临的一些主要挑战。我们的方法广泛适用于体型相当的自由活动的陆生四足动物。我们使用的低采样频率显示出在较长时间内部署加速度计的潜力，从而能够在不同的个体发育阶段捕获宝贵的行为和生理数据。
