Multiple extreme climatic events strengthen selection for earlier breeding in a wild passerine
Bibliographic record
Abstract
Global climate warming results in an increase in mean temperatures and in the frequency of extreme climatic events (ECEs), which could both strongly impact ecosystems and populations. Most studies assessing the impact of global warming on ecosystems have focused on warming trends while neglecting ECEs. In particular, the effects of multiple ECEs on fitness, and their consequences for selection, are still missing. Here we explored the effects of daily extreme rainfalls, as well as the occurrence of extremely hot and cold days, on clutch size and laying date in a wild blue tit population ( Cyanistes caeruleus ) monitored over 25 years. During the nestling phase (8–15 days old), the number of fledglings in a brood was negatively correlated with extremely hot days. The presence of extremely hot days between days 8 and 15 was also associated with an increase in the strength of selection acting on laying date, independently of mean temperature trends during the same period: when 10% of broods in the population experienced this type of ECE, selection for earlier breeding increased by 39%. Our results represent a unique quantification of the impact of multiple ECEs on the fitness landscape and emphasize their role as climatic drivers of selection. This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".