Clermont Jeanne, Woodward-Gagné Sasha, Berteaux Dominique
Canada Research Chair On Northern Biodiversity, Université du Québec À Rimouski, 300 Allée des Ursulines, Rimouski, QC, G5L 3A1, Canada.
Center for Northern Studies, Quebec, Canada.
Mov Ecol. 2021 Nov 27;9(1):58. doi: 10.1186/s40462-021-00295-1.
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.
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.
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.
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.
生物记录技术如今能够详细记录动物的活动，从而以几年前难以想象的方式为行为生态学提供信息。特别是，将全球定位系统（GPS）和加速度计相结合，可以在空间上明确追踪各种行为，包括大型陆地哺乳动物捕食者的捕食事件。具体而言，识别由猎物处理产生的位置集群有助于高效定位捕杀事件。然而，对于猎物处理时间较短的小型捕食者来说，通过技术识别捕食事件仍未得到解决。我们认为，当特定的觅食行为产生诊断性加速度模式时，就会出现一条有前景的途径。一个这样的例子是北极狐（Vulpes lagopus）的贮藏行为，北极狐是一种活跃的捕猎性捕食者，在靠近鸟类栖息地生活时强烈依赖食物贮藏。
我们为来自加拿大努纳武特地区拜洛特岛的16只北极狐配备了GPS和加速度计，获得了23个狐狸夏季的活动数据。加速度计以50赫兹的频率记录三轴加速度，同时我们获取了狐狸行为的同步视频记录样本。测试了多种监督机器学习算法，以将加速度计数据分类为4种行为：静止、奔跑、行走和挖掘，后者与食物贮藏相关。最后，我们评估了狐狸挖掘行为与大雪雁（Anser caerulescens antlanticus）筑巢的时空一致性，以在一个由自上而下的营养相互作用主导的著名研究系统中检验我们行为分类的生态相关性。
随机森林模型产生了最佳的行为分类，每种行为的准确率超过96%。总体而言，北极狐静止时间占49%，奔跑时间占34%，行走时间占9%，挖掘时间占8%。挖掘的概率随着鹅巢密度的增加而增加，这一结果在鹅卵孵化期和育雏期均成立。
加速度计与GPS相结合使我们能够在空间和时间上追踪一种小型活跃捕猎性捕食者的关键觅食行为，了解北极脊椎动物群落中捕食风险的时空分布。我们的研究为评估陆地捕食者的觅食行为开辟了新的可能性，这是解开构建许多捕食者 - 猎物相互作用和营养网络的微妙机制的关键一步。
