Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning
Abstract For 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...
Published in: | Ecology and Evolution |
---|---|
Main Authors: | , , , |
Other Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2024
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1002/ece3.11479 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.11479 |
id |
crwiley:10.1002/ece3.11479 |
---|---|
record_format |
openpolar |
spelling |
crwiley:10.1002/ece3.11479 2024-09-15T18:15:44+00:00 Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning Boucher, Alexander J. Weladji, Robert B. Holand, Øystein Kumpula, Jouko Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada NordForsk 2024 http://dx.doi.org/10.1002/ece3.11479 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.11479 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Ecology and Evolution volume 14, issue 6 ISSN 2045-7758 2045-7758 journal-article 2024 crwiley https://doi.org/10.1002/ece3.11479 2024-08-01T04:19:38Z Abstract For 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 at the 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. Article in Journal/Newspaper Kaamanen Wiley Online Library Ecology and Evolution 14 6 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Abstract For 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 at the 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. |
author2 |
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada NordForsk |
format |
Article in Journal/Newspaper |
author |
Boucher, Alexander J. Weladji, Robert B. Holand, Øystein Kumpula, Jouko |
spellingShingle |
Boucher, Alexander J. Weladji, Robert B. Holand, Øystein Kumpula, Jouko Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
author_facet |
Boucher, Alexander J. Weladji, Robert B. Holand, Øystein Kumpula, Jouko |
author_sort |
Boucher, Alexander J. |
title |
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
title_short |
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
title_full |
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
title_fullStr |
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
title_full_unstemmed |
Modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
title_sort |
modelling reindeer rut activity using on‐animal acoustic recorders and machine learning |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/ece3.11479 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.11479 |
genre |
Kaamanen |
genre_facet |
Kaamanen |
op_source |
Ecology and Evolution volume 14, issue 6 ISSN 2045-7758 2045-7758 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ece3.11479 |
container_title |
Ecology and Evolution |
container_volume |
14 |
container_issue |
6 |
_version_ |
1810453680766320640 |