[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120855-en":3,"doc-seo-120855-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},120855,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Modelling reindeer rut activity using on-animal acoustic recorders and machine learning - Thesis","Sound has long supported wildlife research by linking vocalizations to ecology and behaviour, yet manual processing of long audio recordings remains highly time-consuming. This thesis develops a framework that combines on-animal acoustic recorders with machine learning to automate analysis. Recordings of male reindeer rut vocalizations were collected in Finland using neck-mounted recorders, and convolutional neural networks identified grunts with ~95% accuracy. Neural outputs were then used to model rut activity patterns, showing heavier, older males calling more and daytime activity exceeding nighttime overall.","Modelling reindeer rut activity using on-animal acoustic recorders and machine learning  \nAlexander J. Boucher  \nA Thesis  \nin  \nThe Department  \nof  \nBiology  \nPresented in Partial Fulfillment of the Requirements For the Degree of  \nMaster of Science (Biology)  \nAt Concordia University  \nMontréal, Québec, Canada  \nJuly 2023  \n© Alexander J. Boucher, 2023  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Alexander J. Boucher  \nEntitled: Modelling reindeer rut activity using on-animal acoustic recorders and  \nmachine learning  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Science (Biology)  \nand complies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nSigned by the final examining committee:  \nChair  \n_____________________________________  \nDr. Grant E. Brown  \nExternal Examiner  \n_____________________________________  \nDr. James W. A. Grant  \nExaminer  \n_____________________________________  \nDr. Grant E. Brown  \n  Examiner  \nDr. Eric J. Pedersen  \n  Thesis Supervisor Dr. Robert B. Weladji  \nApproved by  \nDr. Robert B. Weladji, Graduate Program Director  \n  , 2023    \nDr. Pascale Sicotte, Dean of Arts and Science  \nAbstract  \nModelling reindeer rut activity using on-animal acoustic recorders and machine learning  \nAlexander J. Boucher  \nResearchers have been using sound to study the biology of wildlife to understand their ecology and behaviour for decades. By gathering audio from free-ranging species using on-animal recorders, their vocalizations can be used to describe their behaviour and ecology through signal processing. Unfortunately, processing hours of recordings is incredibly time-consuming. By applying machine learning to audio recordings, researchers have used neural networks to decrease the processing time of acoustic data. However, until now, most of this research has focused on analyzing the data of stationary recorders. To show the utility of on-animal recorders in combination with machine learning, we recorded the vocalizations of reindeer (Rangifer tarandus) during their rut at the Kutuharju research station in Kaamanen, Finland. We used vocalizations asan activity index to describe the rut activity of male reindeer. In 2019 and 2020, we placed recorders around the necks of seven reindeer during their rut. We trained convolutional neural networks to identify reindeer grunts, which were then used to classify their vocalizations. Of the networks’vocalization classifications, around 95% of them were correct. With such high metrics, we could reliably explore the males' activity patterns using a neural network. We then analyzed the reindeers’vocalization using generalized additive models. The patterns suggested heavier, older males vocalized more than lighter, younger males and, overall, were more active during the day than night. Overall, on-animal acoustic recorders, in tandem with machine learning, proved to be effective tools, and with more attention, they could prove valuable tools for other researchers.  \nAcknowledgments  \nI want to thank my supervisor, Dr. Robert Weladji, for his continued support. Although my project got off to a rocky start (who knew reindeer would not display rutting behaviour during arut), it finally got off the ground eight months into my thesis. I would also like to thank Dr. Jouko Kumpula and Jukka Siitari at the Natural Resources Institute of Finland (Luke) for their help during the data collection phase of the project, and Mika Tervonen at the Reindeer Herder’s Association in Finland for the management of the herd in Kutuharju. I would also like to thank Tuukka Mäkiranta and Noora Kantola for the fieldwork they conducted. I would also like to thank Dr. Eric Pedersen for his aid in analyzing the data. Finally, this research was funded through support from both NSERC and QCBS.  \nI want to thank Isaac, Emily, Louis, France and ","cbCairWpo1LpfDBz","https://ap.wps.com/l/cbCairWpo1LpfDBz","pdf",1708561,1,76,"English","en",105,"# Introduction\n# Methods\n## Field site and focal individuals\n## Acoustic Recorders and acoustic analysis\n## Machine Learning methodology and process\n## Data analysis\n# Results\n## Machine Learning Performance\n## Rutting Behaviour\n# Discussion\n## On-animal acoustic recorders\n## Machine learning methodology and transfer learning\n## Rut activity\n# Conclusion\n# Appendix\n# References","[{\"question\":\"Why use on-animal acoustic recorders in this study?\",\"answer\":\"They enable audio collection directly from free-ranging reindeer during the rut, so vocalizations can be analyzed without relying solely on stationary recorders.\"},{\"question\":\"How did the researchers apply machine learning to the recordings?\",\"answer\":\"They trained convolutional neural networks to identify reindeer grunts from the acoustic data, then used those classifications to support broader vocalization analysis.\"},{\"question\":\"What patterns were found in male reindeer rut activity?\",\"answer\":\"Using generalized additive models, results indicated that heavier, older males vocalized more, and overall rut activity was stronger during the day than at night.\"}]","Modelling reindeer rut activity using on-animal acoustic recorders and machine learning - 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