Precision farming technology, including GPS collars with biologging, has revolutionized remote livestock monitoring in extensive grazing systems. High resolution accelerometry can be used to infer the behavior of an animal. Previous behavioral classification studies using accelerometer data have focused on a few key behaviors and were mostly conducted in controlled situations. Here, we conducted behavioral observations of 38 beef cows (Hereford, Limousine, Charolais, Simmental/NRF/Hereford mix) free-ranging in rugged, forested areas, and fitted with a commercially available virtual fence collar (Nofence) containing a 10Hz tri-axial accelerometer. We used random forest models to calibrate data from the accelerometers on both commonly documented (e.g., feeding, resting, walking) and rarer (e.g., scratching, head butting, self-grooming) behaviors. Our goal was to assess pre-processing decisions including different running mean intervals (smoothing window of 1, 5, or 20 seconds), collar orientation and feature selection (orientation-dependent versus orientation-independent features). We identified the 10 most common behaviors exhibited by the cows. Models based only on orientation-independent features did not perform better than models based on orientation-dependent features, despite variation in how collars were attached (direction and tightness). Using a 20 seconds running mean and orientation-dependent features resulted in the highest model performance (model accuracy: 0.998, precision: 0.991, and recall: 0.989). We also used this model to add 11 rarer behaviors (each< 0.1% of the data; e.g. head butting, throwing head, self-grooming). These rarer behaviors were predicted with less accuracy because they were not observed at all for some individuals, but overall model performance remained high (accuracy, precision, recall >98%). Our study suggests that the accelerometers in the Nofence collars are suitable to identify the most common behaviors of free-ranging cattle. The results of this study could be used in future research for understanding cattle habitat selection in rugged forest ranges, herd dynamics, or responses to stressors such as carnivores, as well as to improve cattle management and welfare.

As wolves recolonize areas of Europe ranging from moderate to high anthropogenic impact, fear of wolves is a recurring source of conflict. Shared tools for evaluating wolf responses to humans, and comparing such responses across their range, can be valuable. Experiments in which humans approach wild wolves can increase our understanding of how wolves respond to humans, facilitating human-wolf coexistence. We have developed the first standardized protocol for evaluating wolf responses to approaching humans using high-resolution GPS data, and tested it on wild wolves. We present a field protocol for experimentally approaching GPS-collared wolves, a descriptive comparison of two statistical methods for detecting a measurable flight response, a tutorial for identifying wolf flight initiation and resettling positions, and an evaluation of the method when reducing GPS positioning frequency. The field protocol, a data collection form, and the tutorial with R code for extracting flight parameters are provided. This protocol will facilitate studies of wolf responses to approaching humans, applicable at a local, national, and international level. Data compiled in a standardized way from multiple study areas can be used to quantify the variation in wolf responses to humans within and between populations, and in relation to predictors such as social status, landscape factors, or human population density, and to establish a baseline distribution of wolf response patterns given a number of known predictors. The variation in wolf responses can be used to assess the degree to which results can be generalized to areas where GPS studies are not feasible, e.g., for predicting the range of likely wolf behaviors, assessing the likelihood of wolf-human encounters, and complementing existing tools for evaluating reports of bold wolves. Showing how wolves respond to human encounters should help demystify the behavior of wild wolves toward humans in their shared habitat.

Humans pose a major mortality risk to wolves. Hence, similar to how prey respond to predators, wolves can be expected to show anti-predator responses to humans. When exposed to a threat, animals may show a fight, flight, freeze or hide response. The type of response and the circumstances (e.g., distance and speed) at which the animal flees are useful parameters to describe the responses of wild animals to approaching humans. Increasing knowledge about behavioral responses of wolves toward humans might improve appropriate management and decrease conflicts related to fear of wolves. We did a pilot study by conducting 21 approach trials on seven GPS-collared wolves in four territories to investigate their responses to experimental human approaches. We found that wolves predominantly showed a flight response (N = 18), in a few cases the wolf did not flee (N = 3), but no wolves were seen or heard during trials. When wolves were downwind of the observer the flight initiation distance was significantly larger than when upwind, consistent with the hypothesis that conditions facilitating early detection would result in an earlier flight. Our hypothesis that early detection would result in less intense flights was not supported, as we found no correlation between flight initiation distances and speed, distance or straightness of the flight. Wolves in more concealed habitat had a shorter flight initiation distance or did not flee at all, suggesting that perceived risk might have been affected by horizontal visibility. Contrary to our expectation, resettling positions were less concealed (larger horizontal visibility) than the wolves’ initial site. Although our small number of study animals and trials does not allow for generalizations, this pilot study illustrates how standardized human approach trials with high-resolution GPS-data can be used to describe wolf responses at a local scale. In continuation, this method can be applied at larger spatial scales to compare wolf flight responses within and between populations and across anthropogenic gradients, thus increasing the knowledge of wolf behavior toward humans, and potentially improving coexistence with wolves across their range.