Current spring warming as a driver of selection on reproductive timing in a wild passerine
Bibliographic record
Abstract
Evolutionary adaptation as a response to climate change is expected for fitness-related traits affected by climate and exhibiting genetic variance. Although the relationship between warmer spring temperature and earlier timing of reproduction is well documented, quantifications and predictions of the impact of global warming on natural selection acting on phenology in wild populations remain rare. If global warming affects fitness in a similar way across individuals within a population, or if fitness consequences are independent of phenotypic variation in key-adaptive traits, then no evolutionary response is expected for these traits. Here, we quantified the selection pressures acting on laying date during a 24-year monitoring of blue tits in southern Mediterranean France, a hot spot of climate warming. We explored the temporal fluctuation in annual selection gradients and we determined its temperature-related drivers. We first investigated the month-specific warming since 1970 in our study site and tested its influence on selection pressures, using a model averaging approach. Then, we quantified the selection strength associated with temperature anomalies experienced by the blue tit population. We found that natural selection acting on laying date significantly fluctuated both in magnitude and in sign across years. After identifying a significant warming in spring and summer, we showed that warmer daily maximum temperatures in April were significantly associated with stronger selection pressures for reproductive timing. Our results indicated an increase in the strength of selection by 46% for every +1°C anomaly. Our results confirm the general assumption that recent climate change translates into strong selection favouring earlier breeders in passerine birds. Our findings also suggest that differences in fitness among individuals varying in their breeding phenology increase with climate warming. Such climate-driven influence on the strength of directional selection acting on laying date could favour an adaptive response in this trait, since it is heritable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".