Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning

• Published in: Ecology and Evolution
• Main Authors: Boucher, Alexander J., Weladji, Robert B., Holand, Øystein, Kumpula, Jouko
• Other Authors: 4100110810, Luonnonvarakeskus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley-Blackwell
• Subjects: convolutional neural network; machine learning; on-animal acoustic recorder; Rangifer tarandus; reindeer; rutting behaviour; Kaamanen
• Online Access: https://jukuri.luke.fi/handle/10024/555142; https://doi.org/10.1002/ece3.11479

For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study at the Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting reindeer were recorded during their mating season. By attaching recorders to seven male reindeer during the rutting periods of 2019 and 2020, we trained convolutional neural networks to distinguish reindeer grunts with a 95% accuracy rate. This high level of accuracy allowed us to examine the reindeers' grunting behaviour, revealing patterns indicating that older, heavier males vocalized more compared to their younger, lighter counterparts. The success of this study underscores the potential of on-animal acoustic recorders coupled with machine learning techniques as powerful tools for wildlife research, hinting at their broader applications with further advancement and optimization.