Western Veeries use an eastern shortest-distance pathway: New insights to migration routes and phenology using light-level geolocators
Bibliographic record
Abstract
We investigated movements of a western population of Veeries (Catharus fuscescens) breeding in the Okanagan region of British Columbia, Canada, in 2013–2014 using light-level geolocators. We tracked 9 individuals and incorporated a state-space Kalman filter model approach to estimate movement parameters. During migration, Veeries traversed the Rocky Mountains, Great Plains, Gulf of Mexico, and Caribbean Sea with stopovers generally closer to the shorter orthodromic (great circle) route than a loxodromic (straight line) route between breeding and first wintering grounds, particularly on fall migration. Birds initially settled in the south-central portion of the Amazon basin in Brazil at sites that were 666 ± 299 km apart, suggesting low migratory connectivity. Intra-tropical movements were observed for 8 of 9 (88.9%) birds, with second wintering sites an average of 1,447 ± 472 km to the northwest (initial bearing x̄ = 316 ± 16°). Veeries typically followed a pattern of loop migration at the Gulf of Mexico, with more birds using the Yucatan Peninsula to stop and reorient toward destinations on spring migration (n = 7) vs. fall migration (n = 2). Western Veeries follow a presumed ancestral (eastern) migration route, but this route is also the shortest (great circle) route between breeding and wintering grounds, even though this route was only ~100 km shorter than the straight line route. Eight Veeries (88.9%) underwent a post-breeding, pre-migratory movement up to 628 km (x̄ = 263 ± 152 km) away from breeding territories, possibly to molt. We encourage researchers utilizing light-level geolocators to apply similar state-space modeling approaches to reduce the influence of observers and erroneous location estimates on analysis and interpretation of geolocator data.
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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 | 0.001 | 0.001 |
| 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".