For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.

中文翻译：

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使用生物记录和机器学习方法监视时空上的犬科动物气味标记。

对于犬科动物而言，气味标记在领土，社会动态和繁殖方面起着至关重要的作用。然而，部分由于人类依赖视觉作为我们的主要感觉方式，因此缺乏易处理的方法阻碍了嗅觉交流的研究。在这项研究中，我们利用一种强大的生物记录方法，将加速度计与GPS记录器配合使用，以监视和描述时空中的气味标记事件。我们对家养的狗进行了验证实验，并通过视频与新颖的生物记录方法同时监控了它们。我们在31只狗（19只雄性和12只雌性）的骨盆上安装了一个加速度计，通过监视设备方向的变化来检测小腿抬高和下蹲姿势。然后，我们采用了这项技术来描述3只监护犬在加利福尼亚保护牲畜免遭土狼掠夺时的气味标记活动，并提供了该技术的示例用例。在验证期间，该算法正确分类了加速度计读数的92％。高性能的部分原因在于加速度计数据中原型抬高腿姿势的明显特征。准确性没有随狗的体重，年龄和性别而变化，因此该方法可广泛应用于各种犬科动物的形态。我们还使用对每个人进行训练的模型来检测其他人的气味标记，以模拟使用人工代用品进行模型训练。我们观察到狗对之间的体重相似度与预测的整体准确性之间没有关系，尽管模型在同一个人上进行训练和测试时表现最佳。我们讨论了如何将运动生态学领域的现有方法扩展到使用这种令人兴奋的新数据类型。本文是利用现代技术和机器学习在这一领域的力量，为开辟新的研究途径迈出的重要的第一步。