ArticleMovement ecology2023
Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable.
Article in Movement ecology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- From electrocardiography to energetics: methods, constraints, and practical considerations for heart rate biologging in small animals.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2026Review
- Sweat the small stuff: A review of the use of accelerometers to estimate energy expenditure in wild animals.The Journal of animal ecology · 2025Review
- Herbivorous fish feeding dynamics and energy expenditure on a coral reef: Insights from stereo-video and AI-driven 3D tracking.Ecology and evolution · 2024Article
- Correction: Combining accelerometry with allometry for estimating daily energy expenditure in joules when in-lab calibration is unavailable.Movement ecology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 5 countries.
Funding
Abstract
backgroundAll behaviour requires energy, and measuring energy expenditure in standard units (joules) is key to linking behaviour to ecological processes. Animal-borne accelerometers are commonly used to infer proxies of energy expenditure, termed 'dynamic body acceleration' (DBA). However, converting acceleration proxies (m/s
methodsWe used past allometric work quantifying energy expenditure during resting and locomotion as a function of body mass to calibrate DBA. We used the resulting 'power calibration equation' to estimate daily energy expenditure (DEE) using two models: (1) locomotion data-based linear calibration applied to the waking period, and Kleiber's law applied to the sleeping period (ACTIWAKE), and (2) locomotion and resting data-based linear calibration applied to the 24-h period (ACTIREST24). Since both models require locomotion speed information, we developed an algorithm to estimate speed from accelerometer, gyroscope, and behavioural annotation data. We applied these methods to estimate DEE in free-ranging meerkats (Suricata suricatta), and compared model estimates with published DEE measurements made using doubly labelled water (DLW) on the same meerkat population.
resultsACTIWAKE's DEE estimates did not differ significantly from DLW (t(19) = - 1.25; P = 0.22), while ACTIREST24's estimates did (t(19) = - 2.38; P = 0.028). Both models underestimated DEE compared to DLW: ACTIWAKE by 14% and ACTIREST by 26%. The inter-individual spread in model estimates of DEE (s.d. 1-2% of mean) was lower than that in DLW (s.d. 33% of mean).
conclusionsWe found that linear locomotion-based calibration applied to the waking period, and a 'flat' resting metabolic rate applied to the sleeping period can provide realistic joule estimates of DEE in terrestrial mammals. The underestimation and lower spread in model estimates compared to DLW likely arise because the accelerometer only captures movement-related energy expenditure, whereas DLW is an integrated measure. Our study offers new tools to incorporate body mass (through allometry), and changes in behavioural time budgets and intra-behaviour changes in intensity (through DBA) in acceleration-based field assessments of daily energy expenditure.
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