Summer and winter space use and home range characteristics of Golden Eagles (<i>Aquila chrysaetos</i>) in eastern North America
Bibliographic record
Abstract
Movement behavior and its relationship to habitat provide critical information toward understanding the effects of changing environments on birds. The eastern North American population of Golden Eagles (Aquila chrysaetos) is a genetically distinct and small population of conservation concern. To evaluate the potential responses of this population to changing landscapes, we calculated the home range and core area sizes of 52 eagles of 6 age–sex classes during the summer and winter seasons. Variability in range size was related to variation in topography and open cover, and to age and sex. In summer, eagle ranges that were smaller had higher proportions of ridge tops and open cover and had greater topographic roughness than did larger ranges. In winter, smaller ranges had higher proportions of ridge tops, hillsides and cliffs, and open cover than did larger ranges. All age and sex classes responded similarly to topography and open cover in both seasons. Not surprisingly, adult eagles occupied the smallest ranges in both seasons. Young birds used larger ranges than adults, and subadults in summer used the largest ranges (>9,000 km2). Eastern adult home ranges in summer were 2–10 times larger than those reported for other populations in any season. Golden Eagles in eastern North America may need to compensate for generally lower-quality habitat in the region by using larger ranges that support access to adequate quantities of resources (prey, updrafts, and nesting, perching, and roosting sites) associated with open cover and diverse topography. Our results suggest that climate change–induced afforestation on the breeding grounds and ongoing land cover change from timber harvest and energy development on the wintering grounds may affect the amount of suitable habitat for Golden Eagles in eastern North America.
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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) | 0.001 | 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".