Topography drives migratory flight altitude of golden eagles: implications for on‐shore wind energy development
Bibliographic record
Abstract
Summary Wind power is a fast‐growing industry with broad potential to impact volant wildlife. Flight altitude is a key determinant of the risk to wildlife from modern horizontal‐axis wind turbines, which typically have a rotor‐swept zone of 50–150 m above the ground. We used altitudinal GPS data collected from golden eagles Aquila chrysaetos tracked using satellite telemetry to evaluate the potential impacts of wind turbines on eagles and other raptors along migratory routes. Eagle movements during migration were classified as local (1–5 km h−1) or migratory (>10 km h−1) and were characterized based on the type of terrain over which each bird was flying, and the bird's distance from wind resources preferred for energy development. Birds engaged in local movements turned more frequently and flew at lower altitude than during active migration. This flight behaviour potentially exposes them to greater risk of collision with turbines than when engaged in longer‐distance movements. Eagles flew at relatively lower altitude over steep slopes and cliffs (sites where orographic lift can develop) than over flats and gentle slopes (sites where thermal lift is more likely). Eagles predominantly flew near to wind resources preferred by energy developers, and locally moving eagles flew closer to those wind resources with greater frequency than eagles in active migration. Synthesis and applications. Our research outlines the general effects of topography on raptor flight altitude and demonstrates how topography can interact with raptor migration behaviour to drive a potential human–wildlife conflict resulting from wind energy development. Management of risk to migratory species from industrial‐scale wind turbines should consider the behavioural differences between both locally moving and actively migrating individuals. Additionally, risk assessment for wind energy–wildlife interactions should incorporate the consequences of topography on the flight altitude of potentially impacted wildlife.
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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 | 0.001 |
| 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 | 0.001 | 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".