Individual timing consistency across long‐distance songbird migrations
Bibliographic record
Abstract
), a neotropical migratory songbird that travels between breeding sites throughout eastern North America and nonbreeding sites in Brazil. Migration timing data were collected for 295 different individual purple martins over 9 years using light-level geolocators deployed at breeding sites across the range. We used linear mixed-effect models to examine the influence of the rank order of individual departure dates in one season on the rank order of four subsequent migration events while controlling for the effects of breeding latitude, sex, and age. Overall, we found evidence for consistent individual timing that can extend across 8 months and 12,000-24,000 km of migration. Individual rank order of migration timing in purple martins was generally conserved across migrations with consistent timings between fall departure dates from, and spring arrival dates to the breeding site the following year (0.28 ± 0.03, 95% CI 0.22-0.34), as well as at a finer scale across fall migration (0.33 ± 0.05, 95% CI 0.23-0.43), over the stationary nonbreeding period (0.39 ± 0.04, 95% CI 0.31-0.47), and across spring migration (0.03 ± 0.001, 95% CI 0.028-0.032). These results demonstrate that purple martins exhibit consistency in individual migration timing throughout the annual cycle that is likely driven by inherent individual circannual schedules. We additionally found that migration distance played a significant role, as the consistency of individual timing lessened over longer distances. Understanding how individual birds time migrations and if individuals are consistent between events can provide insight into how birds respond to shifts in their environment with climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".