Programming code for article: Predicting energy expenditure from dynamic body acceleration in a large boreal ungulate species

Version 1.1
Kirchner, Theresa M; Devineau, Olivier; Thompson, Daniel P; Thiel, Alexandra; Chimienti, Marianna; Evans, Alina L; Crouse, John; Eriksen, Ane, 2025, "Programming code for article: Predicting energy expenditure from dynamic body acceleration in a large boreal ungulate species", https://doi.org/10.18710/3EB2DM, DataverseNO, V1
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Description
These files contain code to prepare data for and fit a dynamic generalized additive model to predict energy heart rate and energy expenditure from dynamic body acceleration in moose (Alces alces). In this study, we collected heart rate data using implanted loggers and accelerometer data using tri-axial accelerometer collars. To quantify heart rate measurement accuracy, we manually validated heart rates calculated by the loggers by manually calculating heart rate from raw ECG data. The dynamic generalized additive model can be used to predict heart rate from tri-axial accelerometer data. In this study, we calculated energy expenditure from heart rate using a previously published equation. (2025-11-09)
Subject Medicine, Health and Life Sciences
Keyword Moose, Alces alces, Cervid, Heart rate, ODBA, Accelerometer, Biologging, Dynamic generalized additive model, Alaska
Related Publication Kirchner, T. M., Devineau, O., Thompson, D. P., Thiel, A., Chimienti, M., Evans, A. L., Crouse, J. & Eriksen, A. (2025). Predicting energy expenditure from dynamic body acceleration in a large boreal ungulate species. Animal Biotelemetry, 14(1), 4. https://doi.org/10.1186/s40317-025-00440-2doi: 10.1186/s40317-025-00440-2
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00README.txt
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Published Nov 12, 2025
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MD5: 1b308a0bf8ce633ec197c888352fd213
README file
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1_hr.prep.R
R Syntax - 8.2 KB
Published Nov 12, 2025
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MD5: 3da9a28b237125dc23df834a54db97e1
Time drift correction of HR logger measurements
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2_hr.qc.R
R Syntax - 13.7 KB
Published Nov 12, 2025
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MD5: 8d216d940d8f850882ceef151c705c1a
Manual validation of HR logger measurements
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3_acc.prep.R
R Syntax - 7.5 KB
Published Nov 12, 2025
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MD5: c65364fe42d51ee3e733a75217ce6170
Prepare accelerometer data to predict behavior using random forest model (Kirchner et al. 2023)
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4_hr.acc.prep.R
R Syntax - 3.3 KB
Published Nov 12, 2025
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MD5: 952f91918bb5729986bc4258d7baab7c
Join heart rate and ODBA and behavioral data by timestamps
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5_filter.inspect.data.R
R Syntax - 5.0 KB
Published Nov 12, 2025
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MD5: c248bfdfa494fe15c98a01b95046f964
Create final dataset (combine data from all individuals and filter)
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6_model.R
R Syntax - 8.4 KB
Published Nov 12, 2025
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MD5: aa7aaaea2509e5c3930fbb6d3c41b2b8
Run and inspect dynamic generalized additive model of moose heart rate
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7_EE.R
R Syntax - 12.5 KB
Published Nov 12, 2025
2 Downloads
MD5: 9f60d4c36f6b683864be986f50680366
Calculate energy expenditure from heart rate
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8_dataflow.R
R Syntax - 7.0 KB
Published Nov 12, 2025
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MD5: 705e1f46b36f29684e22b859c05ac226
Plot ODBA, heart rate and energy expenditure
Preview "8_dataflow.R"
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