Integrating information from geolocators, weather radar, and citizen science to uncover a key stopover area of an aerial insectivore
Bibliographic record
Abstract
Determining the distribution of stopover and overwintering areas of migratory animals is essential for understanding population dynamics and building predictive models. Tree Swallows (Tachycineta bicolor) are small songbirds that breed across North America. Data from Doppler weather radar and eBird indicate that Tree Swallow numbers increase throughout October and November in southeastern Louisiana, but then decrease during December. We thus hypothesized that southeastern Louisiana is a stopover area used by Tree Swallows during fall migration before they move to farther overwintering areas. We tested this hypothesis by attaching light-logging geolocators to Tree Swallows at five breeding sites spanning the species' breeding range from British Columbia to Nova Scotia, and then tracking their fall migration routes, stopover sites, and wintering locations. Of 38 individuals that returned in the following breeding season, 11 birds from three breeding sites (Saskatchewan, Wisconsin, and Ontario) used southeastern Louisiana as a stopover site. Arrival date and duration of stay closely matched observations from both eBird and radar data. From Louisiana, most Tree Swallows continued their migration to one of three wintering sites: peninsular Florida, the Bahamas, or the Yucatán Peninsula, whereas two birds remained until spring within 200 km of the stopover area. Our results (1) suggest that southeastern Louisiana is an extended stopover site for Tree Swallows that originate from a wide geographic range on the breeding grounds; and (2) demonstrate how geolocators, combined with other sources of movement information, reveal habitat use throughout the annual cycle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".