Digging into the behaviour of an active hunting predator: arctic fox prey caching events revealed by accelerometry

Abstract

Background Biologging 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. Methods We 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. Results The 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. Conclusions Accelerometry 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.

Fishing behavior in the red fox: Opportunistic‐caching behavior or surplus killing?

“…Therefore, it could be understood that there is a use for and optimization of the resource, at least partially, so that a behavior could be occurring with the aim of obtaining a large amount of necessary resources with little effort, which would fall within the optimal foraging theory (Stephens & Krebs, 1986). A similar behavior has been described for arctic foxes (Vulpes lagopus) that cache large amounts of bird eggs during the reproduction season to feed their pups (Clermont et al, 2021;Giroux et al, 2012). Additionally, the male fox seemed to feed on some eggs from the female carps (e.g., Videos S2 and S3), which would be consistent with a selective consumption of the carcasses and discarding lower quality tissues, behavior previously described for brown bears (Ursus arctos) and gray wolves under high availability of salmon in British Columbia and Alaska (Darimont et al, 2003;Lincoln & Quinn, 2019).…”

A mechanistic model of functional response provides new insights into indirect interactions among arctic tundra prey

“…Foxes were captured using cage traps (Tomahawk Live Trap Company, USA) or Softcatch #1 padded leghold traps (Oneida Victor Inc. Ltd., USA). GPS fix intervals were set to 4 min (360 fixes/day) and the location error was 11 m (Poulin et al, 2021); 30‐s bursts of accelerometry data were collected every 4.5 min at 50 Hz (320 bursts/day; Clermont, Gagné, and Berteaux 2021). We extracted the daily activity budget of foxes from accelerometry data (Clermont, Gagné, & Berteaux, 2021).…”

“…GPS fix intervals were set to 4 min (360 fixes/day) and the location error was 11 m (Poulin et al, 2021); 30‐s bursts of accelerometry data were collected every 4.5 min at 50 Hz (320 bursts/day; Clermont, Gagné, and Berteaux 2021). We extracted the daily activity budget of foxes from accelerometry data (Clermont, Gagné, & Berteaux, 2021). We estimated the proportion of time spent active by subtracting the proportion of time spent resting from 1.…”

Patchy indirect effects: predators contribute to landscape heterogeneity and ecosystem function via localized pathways

“…Alternatively, individual-based data collection methods can be used to estimate landscape-scale patch density predator density is known (e.g., number of latrines per individual * individuals/km 2 ). For instance, biologging devices can be used to identify scent-marking and foraging behaviors (Bidder et al 2020;Clermont et al 2021), which, when combined with location data, may be used to identify and quantify hotspots of predator nutrient deposition. And with knowledge of the rate of nutrient deposition at each biogeochemical hotspot (e.g., g/m 2 /unit time of nitrogen), estimates of the landscape-scale magnitude of predator nutrient deposition via these repeated behaviors can be quantified (Ellis-Soto et al 2021).…”