Benjamin Dupuis, Akiko Kato, Olivia Hicks, Danuta M Wisniewska, Coline Marciau, Frederic Angelier, Yan Ropert-Coudert, Marianna Chimienti
{"title":"利用深度数据估算阿德利企鹅能量摄入和消耗的创新方法。","authors":"Benjamin Dupuis, Akiko Kato, Olivia Hicks, Danuta M Wisniewska, Coline Marciau, Frederic Angelier, Yan Ropert-Coudert, Marianna Chimienti","doi":"10.1242/jeb.249201","DOIUrl":null,"url":null,"abstract":"<p><p>Energy governs species' life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and biologging techniques, to estimate both energy expended and acquired on 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R2=0.68, RMSE=344.67). Movement metrics derived from the RF algorithm deployed on depth data were able to accurately (accuracy=0.82) detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately (R2=0.81) compared to historical proxies like number of undulations (R2=0.51) or dive bottom duration (R2=0.31). The proposed framework is accurate, reliable, and simple to implement on data from biologging technology widely-used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals' energetics.</p>","PeriodicalId":15786,"journal":{"name":"Journal of Experimental Biology","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Innovative use of depth data to estimate energy intake and expenditure in Adélie penguins.\",\"authors\":\"Benjamin Dupuis, Akiko Kato, Olivia Hicks, Danuta M Wisniewska, Coline Marciau, Frederic Angelier, Yan Ropert-Coudert, Marianna Chimienti\",\"doi\":\"10.1242/jeb.249201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Energy governs species' life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and biologging techniques, to estimate both energy expended and acquired on 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R2=0.68, RMSE=344.67). Movement metrics derived from the RF algorithm deployed on depth data were able to accurately (accuracy=0.82) detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately (R2=0.81) compared to historical proxies like number of undulations (R2=0.51) or dive bottom duration (R2=0.31). The proposed framework is accurate, reliable, and simple to implement on data from biologging technology widely-used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals' energetics.</p>\",\"PeriodicalId\":15786,\"journal\":{\"name\":\"Journal of Experimental Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1242/jeb.249201\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1242/jeb.249201","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Innovative use of depth data to estimate energy intake and expenditure in Adélie penguins.
Energy governs species' life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and biologging techniques, to estimate both energy expended and acquired on 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R2=0.68, RMSE=344.67). Movement metrics derived from the RF algorithm deployed on depth data were able to accurately (accuracy=0.82) detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately (R2=0.81) compared to historical proxies like number of undulations (R2=0.51) or dive bottom duration (R2=0.31). The proposed framework is accurate, reliable, and simple to implement on data from biologging technology widely-used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals' energetics.
期刊介绍:
Journal of Experimental Biology is the leading primary research journal in comparative physiology and publishes papers on the form and function of living organisms at all levels of biological organisation, from the molecular and subcellular to the integrated whole animal.