Circadian Patterns of Flight in a Long-Distance Migratory Songbird
Bibliographic record
Abstract
Migration is a common strategy in many different animal taxa, including birds. Most migratory songbirds migrate mainly at night (i.e., nocturnal migrants), with some species migrating primarily during daytime (i.e., diurnal migrants). Some birds can even combine nocturnal and diurnal flights, but these tend to be associated with barrier crossings (e.g., long overwater flights). Whether diurnally migrating songbirds also incorporate night flights into other periods of their spring migrations despite a lack of migration barriers has not been investigated. Further, we lack a precise understanding of the influence of specific environmental conditions on day or night flight behaviour. We investigated circadian patterns of flight in purple martins (Progne subis), a long-distance migratory songbird that journeys between nonbreeding areas in South America and breeding sites in eastern North America. We used direct-tracking (GPS loggers) of individual spring migrations to quantify the daily timing of flights, flight speed, flight distance, and altitude as well as whether these factors were associated with temperature. Birds were tagged with GPS loggers in Winnipeg, Canada, which collected location and altitude every two hours during spring migration. We used Movebank to align location data with average local temperature. We found that martins migrated mainly during daylight, but surprisingly, 15% of flights overland occurred during the night. The average speed for daytime and nighttime flights was similar. We found that neither temperature nor timing of flights influenced flight speed and distance travelled, but flights at higher altitudes were associated with longer flight distance and faster speeds. Our results suggest that a species classified as a diurnal migrant routinely makes night flights overland that are not associated with crossing known migratory barriers. Further studies could increase the sample size of birds and the number of usable GPS fixes, which would allow us to track birds as close to their breeding grounds in temperate areas as possible.
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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".