1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present the development of those models usually requires direct observation of the target animals.

2. The goal of this study was to infer behaviour of wild, free roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations.

3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output.

4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82) , SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 

5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the outputs credibility. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.

The data was recorded with UHF-GPS collars (“1C-light” and “1C-heavy”, E-obs GmbH, Munich). The recording settings were: 3 axes, Sampling Frequency: 33.33Hz, Sampling Duration: 3,3sec, 110 measurements per axis.

The data set contains the raw acceleration measurements with corresponding timestamps (UTC) and corresponding behaviour the individual performed during each measurement interval.

The observation data set was created during the Master Thesis of Lisa Giese 2016. "Validation of Tri-axial Acceleration Data to identify behavioral modes of captive red foxes (Vulpes vulpes)" (University Potsdam, Germany)  

The data set contains the raw e-obs output that is an arbitrary value. It could be recalculated to g-values but not without prior calibration of each tag. Unfortunately, we do not have the calibration values and can therefore not share them.