Quantifying long-term stress in brown bears with the hair cortisol concentration: a biomarker that may be confounded by rapid changes in response to capture and handling
Bibliographic record
Abstract
The measurement of cortisol in hair is becoming important in studying the role of stress in the life history, health and ecology of wild mammals. The hair cortisol concentration (HCC) is generally believed to be a reliable indicator of long-term stress that can reflect frequent or prolonged activation of the hypothalamic-pituitary-adrenal axis over weeks to months through passive diffusion from the blood supply to the follicular cells that produce the hair. Diffusion of cortisol from tissues surrounding the follicle and glandular secretions (sebum and sweat) that coat the growing hair may also affect the HCC, but the extent of these effects is thought to be minimal. In this study, we report on a range of factors that are associated with, and possibly influence, cortisol concentrations in the hair of free-ranging brown bears (Ursus arctos). Through two levels of analyses that differed in sample sizes and availability of predictor variables, we identified the presence or absence of capture, restraint and handling, as well as different methods of capture, as significant factors that appeared to influence HCC in a time frame that was too short (minutes to hours) to be explained by passive diffusion from the blood supply alone. Furthermore, our results suggest that HCC was altered after hair growth had ceased and blood supply to the hair follicle was terminated. However, we also confirmed that HCC was inversely associated with brown bear body condition and was, therefore, responsive to diminished food availability/quality and possibly other long-term stressors that affect body condition. Collectively, our findings emphasize the importance of further elucidating the mechanisms of cortisol accumulation in hair and the influence of long- and short-term stressors on these mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".