Do follicles matter? Testing the effect of follicles on hair cortisol levels
Bibliographic record
Abstract
Abstract Cortisol concentrations in hair are used increasingly as a biomarker of long-term stress in free-ranging wildlife. Cortisol is believed to be integrated into hair primarily during its active growth phase, typically occurring over weeks to months or longer periods, depending on latitude. Cortisol concentrations in hair thus reflect the activity of the hypothalamic–pituitary–adrenal axis over this time. However, local, independent cortisol secretion within the skin, which includes hair follicles, may also contribute to cortisol levels in growing hair. Methodological differences between studies include the measurement of cortisol in only the hair shaft (i.e. follicle absent, as with shaved hair) versus the whole hair (i.e. follicle present, as with plucked hair). If the concentration of cortisol in the follicle is high enough to influence the overall hair cortisol concentration (HCC), this could confound comparisons between studies using different types of hair samples (hair shafts vs. whole hair) and collection methods. Here, we test the hypothesis that cortisol present in follicles influences HCC. We compared HCC in paired subsamples of hair with and without follicles from 30 free-ranging Scandinavian brown bears (Ursus arctos) and observed significantly greater HCC in samples with follicles present. The effect of follicles remained significant also with sex and age of sampled bears taken into account in a linear mixed model. Finally, we provide an overview of collection methods and types of hair samples used for HCC analysis in 77 studies dealing with stress in wild mammal species. Our findings highlight the need to unify methods of hair collection and preparation to allow for valid comparisons, and to optimize labour input in ecophysiological studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".