The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose ( Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. We equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. One such example is the caching behaviour of the arctic fox ( Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators.
Biologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago.
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