[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119123-en":3,"doc-seo-119123-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119123,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Modelling reindeer rut activity using on-animal acoustic recorders and machine learning","Sound has long been used to investigate wildlife biology to clarify ecology and behaviour. On-animal recorders enable audio capture from freely moving individuals, while signal processing and machine learning can accelerate interpretation of large audio datasets. Existing work often emphasizes stationary recording devices, leaving the benefits of on-animal recording underexplored. This study used on-animal recorders at a reindeer research station in Finland to capture rutting vocalizations during mating seasons in 2019–2020 and trained convolutional neural networks for accurate classification.","Received: 17 October 2023 | Revised: 24 April 2024 | Accepted: 13 May 2024  \nDOI: 10. 1002/ece3 .11479  \nR E S E A RC H A RT I C L E  \nModelling reindeer rut activity using on-animal acoustic recorders and machine learning  \nAlexander J. Boucher1 | Robert B. Weladji1  | Øystein Holand2 | Jouko Kumpula3  \n1Department of Biology, Concordia University, Montreal, Quebec, Canada 2Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway  \n3 Natural Resources Institute of Finland (Luke), Reindeer Research Station, Helsinki, Finland  \nCorrespondence  \nRobert B. Weladji, Department of Biology, Concordia University, 7141 Sherbrooke St. West, Montreal, QC H4B 1R6, Canada. Email: [robert.weladji@concordia.ca](robert.weladji@concordia.ca)  \nFunding information  \nNatural Sciences and Engineering Research Council of Canada, Grant/Award Number: 327505; NordForsk, Grant/  \nAward Number: 76915  \nAbstract  \nFor decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study atthe Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting reindeer were recorded during their mating season. By attaching recorders to seven male reindeer during the rutting periods of 2019 and 2020, we trained convolutional neural networks to distinguish reindeer grunts with a 95% accuracy rate. This high level of accuracy allowed us to examine the reindeers' grunting behaviour, revealing patterns indicating that older, heavier males vocalized more compared to their younger, lighter counterparts. The success of this study underscores the potential of on-animal acoustic recorders coupled with machine learning techniques as powerful tools for wildlife research, hinting at their broader applications with further advancement and optimization.  \nK E Y WO R D S  \nconvolutional neural network, machine learning, on-animal acoustic recorder, Rangifer tarandus, reindeer, rutting behaviour  \nTA X ON OMY C L A S S I F I C AT I ON  \nApplied ecology, Behavioural ecology, Life history ecology, Population ecology, Theorectical ecology, Zoology  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.  \n2 of 14  \n|  \nBOUCHER et al.  \n1 | INTRODUCTION  \nAcoustic signals facilitate communication and social interaction in animals, and are used for danger avoidance, peer recognition, social learning and mating (Bradbury & Vehrencamp, 2011) . Bioacoustics research is advancing our understanding of animal behaviours via the sounds they produce, despite the complexity of interpreting these signals and the early stage of development in this field. Scholars are making headway by tying this information to behavioural ecology, leading to insights into animal occupancy, behaviour and ecology despite technological and methodological challenges (Blumstein et al., 2011; Charlton et al., 2007; Enari et al., 2019; Garcia et al., 2013; Stowell et al., 2017; Studd et al., 2021) .  \nBioacous","cbCaivXxb7OGBgo4","https://ap.wps.com/l/cbCaivXxb7OGBgo4","pdf",2730688,1,14,"English","en",105,"# Introduction\n## Acoustic signals and bioacoustics research\n## Limitations of stationary autonomous recording units (ARUs)\n## Adoption and challenges of on-animal acoustic recorders\n# Study design and machine-learning approach\n## Field recordings during the rutting seasons\n## Training convolutional neural networks for call classification","[{\"question\":\"Why are on-animal acoustic recorders useful for studying wildlife behaviour?\",\"answer\":\"They capture audio directly from freely moving individuals, enabling researchers to analyze vocalizations and infer behaviour and ecosystem dynamics beyond what stationary units can record.\"},{\"question\":\"What machine-learning method was used to analyze reindeer vocalizations?\",\"answer\":\"Convolutional neural networks were trained to distinguish reindeer grunts from audio recorded by on-animal devices.\"},{\"question\":\"What did the model reveal about reindeer during the mating season?\",\"answer\":\"The classified grunting behaviour showed patterns suggesting older, heavier males vocalized more than younger, lighter males.\"}]","Modelling reindeer rut activity using on-animal acoustic recorders and machine learning | 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are on-animal acoustic recorders useful for studying wildlife behaviour?","Question",{"text":75,"@type":76},"They capture audio directly from freely moving individuals, enabling researchers to analyze vocalizations and infer behaviour and ecosystem dynamics beyond what stationary units can record.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine-learning method was used to analyze reindeer vocalizations?",{"text":80,"@type":76},"Convolutional neural networks were trained to distinguish reindeer grunts from audio recorded by on-animal devices.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the model reveal about reindeer during the mating season?",{"text":84,"@type":76},"The classified grunting behaviour showed patterns suggesting older, heavier males vocalized more than younger, lighter 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