Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator

Abstract

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.