Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins

Research output: Contribution to journalArticlepeer-review

Standard Standard

Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins. / Dupuis, Benjamin; Kato, Akiko; Hicks, Olivia et al.
In: Journal of Experimental Biology, 12.10.2024.

Research output: Contribution to journalArticlepeer-review

HarvardHarvard

Dupuis, B, Kato, A, Hicks, O, Wisniewska, DM, Marciau, C, Angelier, F, Ropert-Coudert, Y & Chimienti, M 2024, 'Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins', Journal of Experimental Biology.

APA

Dupuis, B., Kato, A., Hicks, O., Wisniewska, D. M., Marciau, C., Angelier, F., Ropert-Coudert, Y., & Chimienti, M. (in press). Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins. Journal of Experimental Biology.

CBE

Dupuis B, Kato A, Hicks O, Wisniewska DM, Marciau C, Angelier F, Ropert-Coudert Y, Chimienti M. 2024. Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins. Journal of Experimental Biology.

MLA

Dupuis, Benjamin et al. "Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins". Journal of Experimental Biology. 2024.

VancouverVancouver

Dupuis B, Kato A, Hicks O, Wisniewska DM, Marciau C, Angelier F et al. Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins. Journal of Experimental Biology. 2024 Oct 12.

Author

Dupuis, Benjamin ; Kato, Akiko ; Hicks, Olivia et al. / Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins. In: Journal of Experimental Biology. 2024.

RIS

TY - JOUR

T1 - Innovative Use of Depth Data to Estimate Energy Intake and Expenditure in Adélie Penguins

AU - Dupuis, Benjamin

AU - Kato, Akiko

AU - Hicks, Olivia

AU - Wisniewska, Danuta M.

AU - Marciau, Coline

AU - Angelier, Frederic

AU - Ropert-Coudert, Yan

AU - Chimienti, Marianna

PY - 2024/10/12

Y1 - 2024/10/12

N2 - 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 subsurface 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.

AB - 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 subsurface 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.

M3 - Article

JO - Journal of Experimental Biology

JF - Journal of Experimental Biology

SN - 0022-0949

ER -