Stress responses to repeated captures in a wild ungulate
Bibliographic record
Abstract
While capture-mark-recapture studies provide essential individual-level data in ecology, repeated captures and handling may impact animal welfare and cause scientific bias. Evaluating the consequences of invasive methodologies should be an integral part of any study involving capture of live animals. We investigated short- and long-term stress responses to repeated captures within a winter on the physiology, behaviour, and reproductive success of female Svalbard reindeer (Rangifer tarandus platyrhynchus). Short-term responses were evaluated using serum concentrations of glucocorticoids and catecholamines during handling, and post-release recovery times in heart rate and activity levels. Repeated captures were associated with an increase in measured catecholamines and glucocorticoids, except cortisone, and delayed recovery in heart rate but not activity. Four months later, in summer, individuals captured repeatedly in winter exhibited a small increase in behavioural response to human disturbance and had a lower probability of being observed with a calf, compared to animals not captured, or captured only once. Our findings imply that single annual capture events have no significant negative consequences for Svalbard reindeer, but repeated captures within a season may impact offspring survival in the same year. Such unanticipated side effects highlight the importance of addressing multiple indicators of animal responses to repeated captures.
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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 | 0.000 | 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".