A meta‐analysis of human disturbance effects on glucocorticoid hormones in free‐ranging wild vertebrates
Bibliographic record
Abstract
Free-ranging wild vertebrates need to cope with natural and anthropogenic stressors that cause short and/or long-term behavioural and physiological responses. In areas of high human disturbance, the use of glucocorticoid (GC) hormones as biomarkers to measure stress responses is an increasingly common tool for understanding how animals cope with human disturbance. We conducted a meta-analysis to investigate how human disturbances such as habitat conversion, habitat degradation, and ecotourism influence baseline GC hormones of free-ranging wild vertebrates, and we further test the role of protected areas in reducing the impact of such disturbances on these hormones. A total of 58 studies met the inclusion criteria, providing 152 data points for comparing levels of GC hormones under disturbed and undisturbed conditions. The overall effect size suggests that human disturbance does not cause a consistent increase in levels of GC hormones (Hedges' g = 0.307, 95% CI = -0.062 to 0.677). However, when the data were analysed by disturbance type, living in unprotected areas or in areas with habitat conversion were found to increase GC hormone levels compared to living in protected or undisturbed areas. By contrast, we found no evidence that ecotourism or habitat degradation generates a consistent increase in baseline GC hormone levels. Among taxonomic groups, mammals appeared more sensitive to human disturbance than birds. We advocate the use of GC hormones for inferring major human-caused contributors to the stress levels of free-ranging wild vertebrates - although such information needs to be combined with other measures of stress and interpreted in the context of an organism's life history, behaviour, and history of interactions with human disturbance.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.033 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".