Sweat the small stuff: A review of the use of accelerometers to estimate energy expenditure in wild animals
Bibliographic record
Abstract
Dynamic body acceleration (DBA), a measure of work based on Newtonian biomechanics, is a metric often used to estimate daily energy expenditure (DEE) with accelerometers, but studies validating DBA in wild animals have shown mixed results. I review all studies using accelerometry to measure energy expenditure in free-living (wild or farmed) animals, focussing on those that calibrate DBA against doubly labelled water or heart rate as the 'gold standard' for DEE. Most (~90%) energetics studies using DBA focus on endotherms, even though DBA may work better for ectotherms. In nearly all studies of endotherms, average DBA increased linearly with DEE, but the intercept of the DBA-DEE relationship was not constant across contexts-even within the same species. DBA-DEE relationships were stronger with mass-specific DEE and slightly stronger with vectorial DBA than overall DBA. In a case study of six seabird species with similar activity modes and physiologies, DBA-DEE slopes varied with activity, but were consistent across species for flight, implying that activity-specific slopes might be applied across species in some cases where sufficient sampling occurs within similar taxa. I offer recommendations for the use of DBA to estimate DEE. I explore the potential for a general physiological model across species, without species-specific calibrations, and note where the DBA-DEE relationship breaks down and where we need more data. DBA, used under appropriate conditions, is an index of energy use in many endotherms, and I encourage its use with more ecological questions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".