Divergent climate impacts despite similar response to temperature in a widespread aerial insectivore
Bibliographic record
Abstract
Climate change is shifting when animals breed 1,2 , but it is still not clear why some populations keep pace with warming while others fall behind 3,4 . Differences could arise from variation in sensitivity to temperature 3 or constraints on the ability to respond to temperature. Without knowing whether populations differ in sensitivity—or in their ability to act on that sensitivity—we cannot identify which are most at risk. Using 1,555 population-years from 123 populations of tree swallows ( Tachycineta bicolor ), we show that populations have similar sensitivity to local temperature, advancing breeding by about one day per degree of warming. However, northern populations face tighter time constraints and greater exposure to recent warming. Northern populations have advanced laying dates the most, but still experience stronger selection for earlier breeding, especially in warm years; they have also declined most in breeding abundance. These findings show that vulnerability to climate change can arise not just from different sensitivity to warming, but from when and where populations can respond effectively. By disentangling sensitivity from timing constraints, our results support a general mechanism by which even uniformly responsive species can show uneven impacts of climate change across their ranges.
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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) | 0.001 | 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".