From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
The behaviour of an animal is closely linked to its internal state. Various metrics can be calculated from activity data. Complex patterns of activity within or between individuals, such as cyclic patterns and synchrony, can inform on the biological functioning, the health status, or the welfare of...
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Peer Community In
2024-11-01
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.489/ |
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| author | van Dixhoorn, Ingrid Aubé, Lydiane van Zyl, Coenraad de Mol, Rudi van der Werf, Joop Lardy, Romain Mialon, Marie-Madeleine van Reenen, Kees Veissier, Isabelle |
| author_facet | van Dixhoorn, Ingrid Aubé, Lydiane van Zyl, Coenraad de Mol, Rudi van der Werf, Joop Lardy, Romain Mialon, Marie-Madeleine van Reenen, Kees Veissier, Isabelle |
| author_sort | van Dixhoorn, Ingrid |
| collection | DOAJ |
| description | The behaviour of an animal is closely linked to its internal state. Various metrics can be calculated from activity data. Complex patterns of activity within or between individuals, such as cyclic patterns and synchrony, can inform on the biological functioning, the health status, or the welfare of an animal. These patterns are now available thanks to sensors that continuously monitor the activity of individual animals over long periods. Data processing and calculations, however, should be clarified and harmonised across studies for the results to be comparable. We present metrics describing activity patterns, we discuss their significance, relevance and limitations for behavioural and welfare studies, and we detail how they can be calculated. Four groups of metrics are distinguished: metrics related to overall activity (e.g., time spent in each activity per unit of time), metrics related to fluctuations around mean activity, metrics related to the cyclicity of activity, and metrics related to the synchrony between animals. Metrics may take statistical approaches (e.g., average and variance) or modelling approaches (e.g., Fourier Transform). Examples are taken essentially from cattle for who individual activity sensors are easily available at present. The calculations, however, can be applied to other species and can be performed on data obtained from sensors as well as visual observations. The present methodological article will help researchers to obtain the most benefit from activity data and will support the decision of which metric can be used to address a given purpose. |
| format | Article |
| id | doaj-art-666dce6373954f85bfc6eb60933bb165 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-666dce6373954f85bfc6eb60933bb1652025-02-07T10:17:17ZengPeer Community InPeer Community Journal2804-38712024-11-01410.24072/pcjournal.48910.24072/pcjournal.489From data on gross activity to the characterization of animal behaviour: which metrics for which purposes? van Dixhoorn, Ingrid0https://orcid.org/0000-0003-3642-2523Aubé, Lydiane 1https://orcid.org/0000-0002-6174-3743van Zyl, Coenraad2https://orcid.org/0009-0005-1360-7471de Mol, Rudi3https://orcid.org/0000-0003-4372-401Xvan der Werf, Joop4https://orcid.org/0000-0003-2341-7338Lardy, Romain 5https://orcid.org/0000-0003-1338-8553Mialon, Marie-Madeleine 6https://orcid.org/0000-0002-2356-4365van Reenen, Kees7https://orcid.org/0000-0003-0356-9350Veissier, Isabelle 8https://orcid.org/0000-0002-8497-5395Wageningen University & Research, Livestock Research, Wageningen, P.O. Box 338, 6700 AH Wageningen, the NetherlandsUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genes-Champanelle, FranceWageningen University & Research, Livestock Research, Wageningen, P.O. Box 338, 6700 AH Wageningen, the NetherlandsWageningen University & Research, Livestock Research, Wageningen, P.O. Box 338, 6700 AH Wageningen, the NetherlandsWageningen University & Research, Livestock Research, Wageningen, P.O. Box 338, 6700 AH Wageningen, the NetherlandsUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genes-Champanelle, FranceUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genes-Champanelle, FranceWageningen University & Research, Livestock Research, Wageningen, P.O. Box 338, 6700 AH Wageningen, the NetherlandsUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genes-Champanelle, FranceThe behaviour of an animal is closely linked to its internal state. Various metrics can be calculated from activity data. Complex patterns of activity within or between individuals, such as cyclic patterns and synchrony, can inform on the biological functioning, the health status, or the welfare of an animal. These patterns are now available thanks to sensors that continuously monitor the activity of individual animals over long periods. Data processing and calculations, however, should be clarified and harmonised across studies for the results to be comparable. We present metrics describing activity patterns, we discuss their significance, relevance and limitations for behavioural and welfare studies, and we detail how they can be calculated. Four groups of metrics are distinguished: metrics related to overall activity (e.g., time spent in each activity per unit of time), metrics related to fluctuations around mean activity, metrics related to the cyclicity of activity, and metrics related to the synchrony between animals. Metrics may take statistical approaches (e.g., average and variance) or modelling approaches (e.g., Fourier Transform). Examples are taken essentially from cattle for who individual activity sensors are easily available at present. The calculations, however, can be applied to other species and can be performed on data obtained from sensors as well as visual observations. The present methodological article will help researchers to obtain the most benefit from activity data and will support the decision of which metric can be used to address a given purpose.https://peercommunityjournal.org/articles/10.24072/pcjournal.489/sensors, time budget, animal welfare, health, activity metrics, cow |
| spellingShingle | van Dixhoorn, Ingrid Aubé, Lydiane van Zyl, Coenraad de Mol, Rudi van der Werf, Joop Lardy, Romain Mialon, Marie-Madeleine van Reenen, Kees Veissier, Isabelle From data on gross activity to the characterization of animal behaviour: which metrics for which purposes? Peer Community Journal sensors, time budget, animal welfare, health, activity metrics, cow |
| title | From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
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| title_full | From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
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| title_fullStr | From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
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| title_full_unstemmed | From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
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| title_short | From data on gross activity to the characterization of animal behaviour: which metrics for which purposes?
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| title_sort | from data on gross activity to the characterization of animal behaviour which metrics for which purposes |
| topic | sensors, time budget, animal welfare, health, activity metrics, cow |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.489/ |
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