Data from: Interannual consistency of migration phenology is season- and breeding region-specific in North American Golden Eagles
Bibliographic record
Abstract
Avian migrants can adjust the time they depart for migration and arrive at their destination (i.e. phenology) based on environmental conditions, the period of the annual cycle, and the distance of migration. Our study shows that interannual consistency (an indicator of the strength of adjustments) in migration schedule of Golden Eagles (Aquila chrysaetos) in North America was greatest in boreal spring migration and the breeding regions of eastern Canada, suggesting that migration schedule is partly environmentally driven. Using multi-year GPS tracks of 83 adults breeding in three spatially distant regions (Alaska, northeast Canada, and southeast Canada), we quantified the interannual consistency of migration timing with variations within individuals tracked across multiple years and among-individuals and repeatability (r) of migration schedule, duration, and wintering latitude by breeding regions and seasons. By comparing regions and seasons, we found that consistency was highest (r > 0.85) for the schedule of the boreal spring migration in eastern Canada while Alaska had the lowest value (r < 0.15). Since seasonal consistency of migration schedule was only detected in eastern Canada, we conclude that seasonal features are not the main constraint on consistency of migration schedule. While regional differences in consistency were not related to differences in migratory distances, they could be the result of genetic or habitat differences. We also found that warmer temperatures than the decadal average at the region of departure delayed the start of boreal spring migration by ~10 days and advanced boreal autumn migration by ~20 days. It suggests that warmer temperatures would reduce residence time on breeding grounds, which is contrary to expectations and trends found in other studies. Wide variations in migratory strategies across a species distribution can add to the lists of challenges for conservation yet such variations can give migrants the capacity to acclimate to environmental changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".