A Trans-Hemispheric Migratory Songbird Does Not Advance Spring Schedules or Increase Migration Rate in Response to Record-Setting Temperatures at Breeding Sites
Bibliographic record
Abstract
The decline of long distance migratory songbirds has been linked to an increasing mismatch between spring arrival date and timing of food availability caused by climate change. It is unclear to what extent individuals can adjust migration timing or en route rate in response to annual variation in temperature at breeding sites. We tracked the ca. 7300 km spring migration of 52 purple martins Progne subis from the Amazon basin to two breeding sites in eastern North America. Spring 2012 was the warmest on record in eastern North America, but contrary to predictions, this did not result in earlier departure, faster migration, or earlier arrival at breeding areas compared with earlier years. Temperatures and rainfall in the Amazon basin at the time of departure were not higher in 2012, and conditions along migration routes did not give consistent signals of a warmer spring at the breeding site. Once in North America, individuals likely had limited opportunity to speed up their migration because this final portion of the journey was already very rapid (570 km/d; 4-5 d in duration). Migration timing over the entire journey was best predicted by breeding latitude and sex and was not sensitive to ecological cues (temperature and rainfall amount) at departure from South American overwintering sites or en route, in contrast to recent studies of other songbirds. Our results provide the first direct evidence for a mismatch between higher spring temperatures at breeding sites and departure schedules of individual songbirds, and suggest phenotypic responses to short-term climatic warming may be limited for some species. Further direct-tracking data with greater geographic and temporal scope is needed to test for individual plasticity in response to temperature and rainfall along migratory routes for this, and other, species.
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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 | 0.000 | 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) | 0.002 | 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".