Florence Erbs, Mike van der Schaar, Miriam Marmontel, Marina Gaona, Emiliano Ramalho, Michel André
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引用次数: 0
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.
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.