Heavily hunted wolves have higher stress and reproductive steroids than wolves with lower hunting pressure
Bibliographic record
Abstract
Summary Human‐caused harassment and mortality (e.g. hunting) affects many aspects of wildlife population dynamics and social structure. Little is known, however, about the social and physiological effects of hunting, which might provide valuable insights into the mechanisms by which wildlife respond to human‐caused mortality. To investigate physiological consequences of hunting, we measured stress and reproductive hormones in hair, which reflect endocrine activity during hair growth. Applying this novel approach, we compared steroid hormone levels in hair of wolves ( Canis lupus ) living in Canada's tundra–taiga ( n = 103) that experience heavy rates of hunting with those in the northern boreal forest ( n = 45) where hunting pressure is substantially lower. The hair samples revealed that progesterone was higher in tundra–taiga wolves, possibly reflecting increased reproductive effort and social disruption in response to human‐related mortality. Tundra–taiga wolves also had higher testosterone and cortisol levels, which may reflect social instability. To control for habitat differences, we also measured cortisol in an out‐group of boreal forest wolves ( n = 30) that were killed as part of a control programme. Cortisol was higher in the boreal out‐group than in our study population from the northern boreal forest. Overall, our findings support the social and physiological consequences of human‐caused mortality. Long‐term implications of altered physiological responses should be considered in management and conservations strategies.
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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) | 0.002 | 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".