Quantifying behavior and life‐history events of an Arctic ungulate from year‐long continuous accelerometer data
Bibliographic record
Abstract
Abstract Bio‐logging technology is now the golden standard for assessing how individual animals change their movement and behavior over time and space. Three‐dimensional accelerometer data, in particular, can provide extremely detailed information on individuals' activity and energetics associated with critical life‐history events, such as reproduction and mortality. Applications, where accelerometer data have been recorded over sufficiently long periods of time to quantify how individuals modulate their activities when facing seasonality, environmental constraints, and how this might affect life‐history events, remain rare, however. We collected high‐resolution accelerometer data, over an entire year, from seven muskox females ( Ovibos moschatus ) with different reproductive statuses moving in the high‐Artic. Individual‐specific hidden Markov models (HMMs) were built based on overall dynamic body acceleration (ODBA) and pitch. Snow depth was included as a dependent structure to incorporate the dominant environmental constraint on muskox activity. We used GPS and vaginal implant transmitter data to further clarify the behavioral partition and to validate calving and mortality events. We detected lower ODBA recordings during periods with increased snow depth, suggesting that snow influences animal velocity and movement‐related (energetic) costs. Time budgets and behavioral switching showed clear seasonal patterns, with distinct signatures depending on individuals' survival and reproductive status. Individuals that ultimately died drastically reduced time spent foraging/searching for food during winter, between February and May when snow depth is highest, while increasing time spent transiting/being highly active. This pattern could indicate failure to acquire sufficient food resources. Overall, individuals that survived the Arctic year spent greater amounts of time foraging yet with high individual variability in time spent foraging and transiting. Individuals that gave birth showed marked behavioral shifts at parturition times with a clear reduction in foraging behavior and increased activity. We show how long‐term high‐resolution accelerometer data analyzed within HMM frameworks can successfully be used to detect environmental‐dependent behavioral changes with implications for life‐history events. Such information opens up opportunities to study life‐history events in more detail and will facilitate integration of data at both individual and population levels, which is critical for management and conservation of species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".