Vulnerability and behavioural avoidance of Golden Eagles near wind farms during the breeding season
Bibliographic record
Abstract
Wind energy presents a novel stressor to wildlife, sometimes resulting in habitat loss and reduced reproductive output. Vulnerability index is a powerful metric to estimate the susceptibility to harm from stressors. We aim to assess the regional-level exposure, sensitivity and vulnerability to wind farms of breeding Golden Eagles ( Aquila chrysaetos ). Our study focused on the Gaspé Peninsula (Québec, Canada), a region hosting a vulnerable population of Golden Eagles and 26 wind farms built in the last 25 years. Using nests monitoring and over 13 years of satellite tracking of eight breeding eagles, we show that eagles' exposure to wind farms increases with nest proximity, resulting in higher vulnerability for eagles nesting closer (≤20 km), especially during chick-rearing and post-fledging. Eagles nesting ≤20 km avoided wind farms, using them less than other similar areas, resulting in unused areas in the home range. Our sensitivity and vulnerability assessments revealed that wind farms are positioned in moderate to highly suitable habitats, indicating functional habitat loss. We monitored breeding before, during and after wind farm development, but reproductive outcomes did not significantly vary with development phases. However, overall breeding success in the region appeared to decline over the last two decades, possibly due to a combination of factors. Our study presents the behavioural effects of wind farms on breeding birds of prey while identifying areas most sensitive and nests most vulnerable to wind development in the region. This research underscores the need for pre-construction assessments and continued breeding monitoring for long-term assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".