Use of bio-loggers to characterize red fox behavior with implications for studies of magnetic alignment responses in free-roaming animals

Abstract

Spontaneous magnetic alignment (SMA), in which animals position their body axis in fixed alignments relative to magnetic field lines, has been shown in several classes of vertebrates and invertebrates. Although these responses appear to be widespread, the functional significance and sensory mechanism(s) underlying SMA remain unclear. An intriguing example comes from observations of wild red foxes (Vulpes vulpes) that show a ~fourfold increase in hunting success when predatory ‘mousing’ attacks are directed toward magnetic north-northeast. This form of SMA is proposed to receive input from a photoreceptor-based magnetoreception mechanism perceived as a ‘visual pattern’ and used as a targeting system to increase the accuracy of mousing attempts targeting hidden prey. However, similar to previous observational studies of magnetic orientation in vertebrates, direct evidence for the use of magnetic cues, and field-based experiments designed to characterize the biophysical mechanisms of SMA are lacking. Here, we develop a new approach for studies of SMA using triaxial accelerometer and magnetometer bio-loggers attached to semidomesticated red foxes. Accelerometer data were recorded from 415 ground-truth events of three behaviors exhibited by an adult red fox. A 5-nearest neighbor classifier was developed for behavioral analysis and performed with an accuracy of 95.7% across all three behaviors. To evaluate the generalizability of the classifier, data from a second fox were tested yielding an accuracy of 66.7%, suggesting the classifier can extract behaviors across multiple foxes. A similar classification approach was used to identify the fox’s magnetic alignment using two 8-way classifiers with differing underlying assumptions to distinguish magnetic headings in eight equally spaced 45° sectors. The magnetic heading classifiers performed with 90.0 and 74.2% accuracy, suggesting a realistic performance range for a classifier based on an independent set of training events equal in size to our sample. We report the development of ‘magnetic ethograms’ in which the behavior and magnetic alignment of foxes can be accurately extracted from raw sensor data. These techniques provide the basis for future studies of SMA where direct observation is not necessary and may allow for more sophisticated experimental designs aimed to characterize the sensory mechanisms mediating SMA behavior.