Dear MARMAM community, We're excited to announce our new paper is out! We combined 2 years of passive acoustic monitoring to detect Amazonian manatee vocalizations using deep learning and we characterized the vocal repertoire of wild Amazonian manatees for the first time (abstract below). Erbs, F., van der Schaar, M., Marmontel, M., Gaona, M., Ramalho, E. and André, M. (2024), Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.418. The paper is open access, you can find it here: https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.418 Call for collaboration: We would like to build on these results to extend the PAM of manatees in the Amazon and contribute to the identification of priority areas for conservation. The objective is to use the current automatic classification model to quickly detect manatee in other datasets from various locations in the Amazon (ideally representing different habitat types). If you have underwater recordings from locations that could be interesting to explore for manatee presence, please get in touch with us, we will be happy to discuss collaboration. Call for pictures: In addition, we are looking for pictures of Amazonian manatees in the wild to get the word out about the dangers they're facing and how research can contribute to their conservation. The pictures will be used on posts (from short posts on social media to longer blog posts) to illustrate the species in its habitat and put in context the research using acoustics as a tool for detecting their presence. We will ensure that proper attribution is given to the photographer/institution. You can reach out to us at florence.erbs@upc.edu or michel.andre@upc.edu. Thank you very much, Florence *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. -- [image: UPC Icon] *Florence Erbs* Laboratory of Applied Bioacoustics (LAB) Rambla Exposició, 24 | 08800 Vilanova i la Geltrú Barcelona, Spain Office +34 93 896 72 27 florence.erbs@upc.edu lab.upc.es <http://www.lab.upc.es/>, listentothedeep.com <http://www.listentothedeep.com/>