Abstract
BackgroundBiologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. 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.MethodsWe equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. 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. 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.ResultsThe random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods.ConclusionsAccelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator–prey interactions and trophic networks.
Highlights
Biologging allows detailed recording of animal movement, informing behavioural ecology in ways unthinkable just a few years ago
Behavioural classification of accelerometry data The random forest model yielded the greatest average accuracy, precision and recall values compared to other algorithms, and it provided a good classification of the 4 behaviours, with accuracies > 96% (Table 2)
It yielded by far the greatest precision for digging (92.5%, compared values are identified with an asterisk in Table 2) and the fewest number of false positives for this behaviour, which was required to address our second objective
Summary
Biologging allows detailed recording of animal movement, informing behavioural ecology in ways unthinkable just a few years ago. In large terrestrial mammals, killing events of large prey can be identified through the clusters of GPS locations resulting from prey handling, which includes prey consumption and sometimes food caching [13,14,15]. This approach, which often necessitates field confirmation of kills, works for large predators, it depends on long prey handling times (and large prey sizes) [12, 16] matched with adequate GPS fix frequency [13, 14]
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