Key habitats and breeding zones of threatened golden eagles in Eastern North America identified by multi-level habitat selection study
Bibliographic record
Abstract
Abstract Context: Wildlife surveys are limited by the capacity to collect data over the spatial extent of a population, which is challenging and costly for species of large geographic distribution in remote regions. Multi-level habitat selection models can limit the surveying extent and become tools for conservation management by identifying key areas and habitats. Objectives: We studied habitat selection of the threatened Eastern North American population of golden eagles (Aquila chrysaetos) with a multi-level approach over the population’s distribution to identify key habitats and zones of interest. Methods: Using tracking data of 30 adults and 276 nest coordinates, we modelled habitat selection at three levels: landscape, foraging and nesting. Results: At the landscape level, eagles selected topographical features (i.e., terrain ruggedness, elevation) more strongly than land cover features (forest cover, distance to water; mean difference: 0.98, CI: 0.37), suggesting that topographical features, facilitating flight and movement through the landscape, are more important than land cover, indicative of hunting opportunities. We also found that home range size was 50% smaller and relative probability of selection at all three levels was ~ 25% higher in the polar regions than boreal regions. It suggests that eagles in polar regions travel shorter foraging distances and habitat characteristic is more suitable. Conclusion: Using multi-level models, we identified key habitat characteristics for a threatened population over a large spatial scale. We also identifying areas of interest to target for a variety of life cycle needs.
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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 | 0.000 | 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".