For many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic methods need to be developed, tested and applied. Combining passive acoustics with deep learning models, we developed a method to monitor the secretive Amazonian manatee over two consecutive flooded seasons in the Brazilian Amazon floodplains. Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. Similar classification performance in terms of precision (range: 0.83–1.00) and recall (range: 0.97–1.00) was achieved for each year. Using this model, we evaluated manatee acoustic presence over a total of 226 days comprising recording periods in 2021 and 2022. Manatee vocalizations were consistently detected during both years, reaching 94% daily temporal occurrence in 2021, and up to 11 h a day with detections during peak presence. Manatee calls were characterized by a high emphasized frequency and high repetition rate, being mostly produced in rapid sequences. This vocal behavior strongly indicates an exchange between females and their calves. Combining passive acoustic monitoring with deep learning models, and extending temporal monitoring and increasing species detectability, we demonstrated that the approach can be used to identify manatee core habitats according to seasonality. The combined method represents a reliable, cost‐effective, scalable ecological monitoring technique that can be integrated into long‐term, standardized survey protocols of aquatic species. It can considerably benefit the monitoring of inaccessible regions, such as the Amazonian freshwater systems, which are facing immediate threats from increased hydropower construction.

中文翻译：

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基于人工智能的被动声学技术揭示了亚马逊海牛的重要栖息地


对于许多处于危险中的物种来说，与低视觉可探测性和难以捉摸的行为相关的监测挑战限制了使用传统的视觉调查来收集关键信息，阻碍了健全保护策略的制定。被动声学可以经济高效地获取陆地和水下长期数据。然而，要从大型数据集中提取有价值的信息，需要开发、测试和应用自动方法。将被动声学与深度学习模型相结合，我们开发了一种方法，可以在巴西亚马逊洪泛区连续两个洪水季节监测神秘的亚马逊海牛。随后，我们研究了在栖息地使用背景下基于发声频率和时间特征的声音行为参数。卷积神经网络模型成功检测到亚马逊海牛的发声，训练数据的平均精度为 0.98。每年在精确率（范围：0.83-1.00）和召回率（范围：0.97-1.00）方面都实现了类似的分类性能。使用这个模型，我们评估了海牛在总共 226 天内的声学存在，包括 2021 年和 2022 年的记录期。这两年一直检测到海牛的发声，在 2021 年达到 94% 的每日时间发生率，在高峰期每天检测到长达 11 小时。海牛叫声的特点是高强调频率和高重复率，主要以快速序列产生。这种发声行为强烈表明雌性与幼崽之间的交流。 将被动声学监测与深度学习模型相结合，并扩展时间监测并提高物种可检测性，我们证明了该方法可用于根据季节性识别海牛核心栖息地。这种组合方法代表了一种可靠、经济高效、可扩展的生态监测技术，可以整合到水生物种的长期标准化调查方案中。它可以极大地有利于监测无法进入的地区，例如亚马逊淡水系统，这些地区正面临水电建设增加的直接威胁。