Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques

IF 3.9 2区 环境科学与生态学 Q1 ECOLOGY Remote Sensing in Ecology and Conservation Pub Date : 2024-10-31 DOI:10.1002/rse2.418
Florence Erbs, Mike van der Schaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André
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Abstract

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.
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基于人工智能的被动声学技术揭示亚马逊海牛关键栖息地
对于许多濒危物种来说,由于视觉可探测性低和行为难以捉摸,传统的视觉调查在收集关键信息方面受到限制,从而阻碍了合理保护战略的制定。被动声学可以经济有效地获取陆地和水下的长期数据。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。我们将被动声学与深度学习模型相结合,开发出一种方法,在巴西亚马逊洪泛平原连续两个汛期监测神秘的亚马逊海牛。随后,我们根据发声频率和时间特征研究了栖息地使用背景下的发声行为参数。卷积神经网络模型成功地检测到了亚马逊海牛的发声,训练数据的平均精度为 0.98。在精确度(范围:0.83-1.00)和召回率(范围:0.97-1.00)方面,每年都取得了相似的分类效果。利用该模型,我们对 2021 年和 2022 年共计 226 天的海牛声学存在进行了评估。在这两年中,海牛的叫声一直都能被探测到,2021 年海牛叫声的日出现率高达 94%,在海牛出现高峰期,每天的探测时间长达 11 小时。海牛叫声的特点是强调频率高、重复率高,大多以快速序列发出。这种发声行为强烈表明雌海牛与幼海牛之间存在交流。我们将被动声学监测与深度学习模型相结合,并扩大了时间监测范围,提高了物种可探测性,证明该方法可用于根据季节性识别海牛的核心栖息地。这种综合方法是一种可靠、经济、可扩展的生态监测技术,可纳入长期、标准化的水生物种调查方案。它可以极大地促进对亚马逊淡水系统等难以进入地区的监测,这些地区正面临着水电建设增加所带来的直接威胁。
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来源期刊
Remote Sensing in Ecology and Conservation
Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
9.80
自引率
5.50%
发文量
69
审稿时长
18 weeks
期刊介绍: emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.
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