Data from: Multidimensional environmental influences on timing of breeding in a tree swallow population facing climate change
Bibliographic record
Abstract
Most phenological traits are extremely sensitive to current climate change and advances in the timing of important life-history events have been observed in many species. In birds, phenotypic plasticity in response to temperature is thought to be the main mechanism underlying yearly adjustment in the timing of breeding. However, other factors could be important and interact to affect the levels of plastic responses between and/or within-individuals. Here we use long-term individual-based data on Tree swallow (Tachycineta bicolor) to identify the spatial and environmental drivers affecting plasticity in laying date and to assess their importance at both population and individual levels. We found that laying date has advanced by 4.2 days over 10 years, and that it was mainly influenced by latitude and an interaction between spring temperature and breeder density. Analyses of individual plasticity showed that increases in temperature, but not in breeder density, resulted in within-individual advances in laying date. Our results suggest that females can adjust their laying date as a function of temperature, but that this adjustment will be partly constrained in habitats with lower breeder densities. Such potential constraint is especially worrying for the broad array of species already declining as a result of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".