Abstract：
AbstractAutonomous sensors provide opportunities to observe organisms across spatial and temporal scales that humans cannot directly observe. By processing large data streams from autonomous sensors with deep learning methods, researchers can make novel and important natural history discoveries. In this study, we combine automated acoustic monitoring with deep learning models to observe breeding-associated activity in the endangered Sierra Nevada yellow-legged frog (Rana sierrae), a behavior that current surveys do not measure. By deploying inexpensive hydrophones and developing a deep learning model to recognize breeding-associated vocalizations, we discover three undocumented R. sierrae vocalization types and find an unexpected temporal pattern of nocturnal breeding-associated vocal activity. This study exemplifies how the combination of autonomous sensor data and deep learning can shed new light on species\' natural history, especially during times or in locations where human observation is limited or impossible.
摘要：
自主传感器提供了在人类无法直接观察的空间和时间尺度上观察生物的机会。通过使用深度学习方法处理来自自主传感器的大数据流，研究人员可以做出新颖而重要的自然历史发现。在这项研究中,我们将自动声学监测与深度学习模型相结合，以观察濒临灭绝的内华达山脉黄腿蛙（Ranasierrae）的繁殖相关活动，当前调查无法衡量的行为。通过部署廉价的水听器并开发深度学习模型来识别与繁殖相关的发声，我们发现了三种未记录的R.sierrae发声类型，并发现了夜间繁殖相关的发声活动的意外时间模式。这项研究证明了自主传感器数据和深度学习的结合如何为物种自然史提供新的启示。特别是在人类观察有限或不可能的时间或地点。
