Changes in the timing of spring and autumn migration in North American migrant passerines during a period of global warming
Bibliographic record
Abstract
Butler (2003) used first arrival dates (FADs) of 103 migrant birds in northeastern USA and found that both long‐distance migrants (LDMs; wintering south of the USA) and short‐distance migrants (SDMs; wintering in the southern USA) arrived earlier in the second half of the 20th century than they had in the first, consistent with scenarios of global warming; the trend was stronger in SDMs. Using FADs to characterize migration systems can be problematic because they are data from one tail of a distribution, they comprise a mostly male population and they may not correlate well with the balance of the migration period. FADs also provide no information about autumn migration. This paper uses a banding dataset from Long Point Bird Observatory, Ontario, for 14 passerines for a period of global warming (1975–2000), taking these issues into account. The data were filtered to minimize effects of unequal netting effort (147 491 resulting records), and the passage dates then calculated in each season of each year for the 1st, 2nd and 3rd quartiles for regression analysis. Only two of 13 species analysed in the spring showed significantly earlier passage times, although the overall trend was towards earlier spring migration, especially among SDMs. Autumn responses were more prevalent, however, and in some cases more dramatic with six of 13 species showing delayed migration (four SDMs, two LDMs). Two LDMs exhibited earlier autumn migration. Where earlier spring migration occurred, both sexes appeared to contribute to the change. Where delayed migration occurred in autumn, both sexes and both adults and hatch‐year birds appeared to contribute in at least some cases. The spring FAD results are consistent with those of Butler, but when the whole migration is considered, change is far from universal in spring and is in fact more substantial and complex in autumn.
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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".