Describing the behaviors of free-living animals is broadly useful for ecological and physiological research, but obtaining accurate records for difficult-to-observe species presents a considerable challenge. Tri-axial accelerometers are increasingly used for this purpose by exploiting behavioral observations from accelerometer-carrying animals to predict behaviors of unobserved conspecifics. We developed a modeling approach to predict behaviors of wolverines from collar-mounted accelerometers using Support Vector Machines. By applying a temporal smoothing function and setting a lower threshold for a-posteriori prediction probabilities, we improve the predictive performance of our model and simultaneously create a framework for explicitly accounting for behaviors unknown to the model, a problem that remains largely unaddressed in similar studies. We demonstrate that such an approach can achieve a model-averaged accuracy of 98.3%, with high predictive performance for the behaviors resting, running, scanning, tearing at food, and transferring items with the mouth, a behavior typically associated with caching food among captive wolverines. To illustrate the utility of this approach, we apply this model to a sample of seven free-living wolverines in Arctic Alaska.

描述自由活动动物的行为对生态和生理研究具有广泛的意义，但是要获得难以观察到的物种的准确记录则是一个巨大的挑战。通过利用携带加速度计的动物的行为观察来预测未观察到的同种动物的行为，三轴加速度计被越来越多地用于此目的。我们使用支持向量机开发了一种建模方法，用于从安装在衣领上的加速度计预测金刚狼的行为。通过应用时间平滑函数并为后验预测概率设置较低的阈值，我们改善了模型的预测性能，同时创建了一个框架以明确说明模型未知的行为，这一问题在类似研究中仍未解决。我们证明了这种方法可以达到98.3％的模型平均准确率，对于休息，奔跑，扫描，撕裂食物以及用嘴转移物品等行为具有较高的预测性能，这种行为通常与在圈养者之间缓存食物有关金刚狼。为了说明此方法的实用性，我们将此模型应用于北极阿拉斯加的七个自由生活的金刚狼样本。