Otsuka Ryoma, Sugiyama Hibiki, Mizutani Yuichi, Yoda Ken, Maekawa Takuya
Graduate School of Information Science and Technology The University of Osaka Suita Osaka Japan.
Graduate School of Environmental Studies Nagoya University Nagoya Aichi Japan.
Ecol Evol. 2025 Aug 6;15(8):e71832. doi: 10.1002/ece3.71832. eCollection 2025 Aug.
Audio playback experiments in the natural environment have been a powerful tool in animal behaviour and ecology, revealing causal relationships between animal movements/behaviours and audio stimuli. However, traditional audio playback experiments could only be performed in limited locations and/or situations where direct observation and/or video recording by human observers or installation of automated devices, such as camera traps, were possible. To overcome the limitation, we designed an autonomous audio playback system on bio-loggers in the natural environment. In this system, an on-board machine learning model estimates animals' behavioural state (e.g., flying or not) in real time using data from a low-power accelerometer. If the target behaviour (e.g., flying) is detected and other predefined criteria are met, the logger activates high-cost sensors, including a video camera, and plays audio from a built-in speaker. The logger can record fine-scale behavioural data before, during, and after the playback using multiple modalities (e.g., acceleration, GPS, and video). To examine the validity of the system, we performed field experiments targeting freely ranging black-tailed gulls () in Japan. The real-time behaviour recognition using acceleration data demonstrated high accuracy in the field experiments (macro F1-score = 0.91). The playback experiments were performed almost perfectly as we intended when birds were flying outside the colony (46 playback events were collected from eight birds), except for several failures due to hardware malfunctions. Using three response indicators (based on acceleration, GPS, and video data), Bayesian statistical modelling and causal inference analysis showed that several birds clearly responded to the audio stimuli, but to both predator call and noise sound. Despite some remaining practical challenges, the results demonstrated a successful proof of concept for the proposed audio playback system on bio-loggers. By removing the location constraints of traditional playback experiments, the system allows a variety of playback experiments to be tested in various situations. In the future, the system can be extended to stimulate other sensor modalities (e.g., magnetic sensors), expanding the possibilities for intervention methods in the wild environment.
在自然环境中进行的音频回放实验一直是研究动物行为和生态学的有力工具，它揭示了动物运动/行为与音频刺激之间的因果关系。然而，传统的音频回放实验只能在有限的地点和/或情况下进行，即人类观察者能够直接观察和/或视频记录，或者可以安装自动设备（如相机陷阱）的地方。为了克服这一限制，我们设计了一种在自然环境中基于生物记录器的自主音频回放系统。在这个系统中，一个机载机器学习模型利用来自低功耗加速度计的数据实时估计动物的行为状态（例如，是否在飞行）。如果检测到目标行为（例如，飞行）并满足其他预定义标准，记录器就会激活高成本传感器，包括摄像机，并通过内置扬声器播放音频。记录器可以使用多种模式（例如，加速度、全球定位系统和视频）在回放前、回放期间和回放后记录精细尺度的行为数据。为了检验该系统的有效性，我们针对日本自由放养的黑尾鸥（ ）进行了野外实验。在野外实验中，利用加速度数据进行的实时行为识别显示出了很高的准确率（宏F1分数 = 0.91）。当鸟类在群体外飞行时（从八只鸟身上收集了46次回放事件），回放实验几乎完全按照我们的预期进行，只是由于硬件故障出现了几次失败。使用三个响应指标（基于加速度、全球定位系统和视频数据），贝叶斯统计建模和因果推断分析表明，几只鸟对音频刺激有明显反应，但对捕食者叫声和噪声都有反应。尽管仍存在一些实际挑战，但结果证明了所提出的基于生物记录器的音频回放系统概念验证的成功。通过消除传统回放实验的地点限制，该系统允许在各种情况下测试各种回放实验。未来，该系统可以扩展到刺激其他传感器模式（例如，磁传感器），从而扩大野外环境中干预方法的可能性。
