Local adaptation of timing of reproduction: females are in the driver's seat
Bibliographic record
Abstract
1 Breeding at the right time of the year is crucial for many temperate zone organisms, as any mismatch with their optimal breeding period leads to a reduction in fitness. The roles of the breeding partners in these adaptive decisions have, however, only rarely been studied. 2 In blue tits (Cyanistes caeruleus) that breed in heterogenous Mediterranean habitats, nearby populations show up to 1-month differences in breeding time. This difference is adaptive as the optimal time for breeding varies by the same amount due to habitat characteristics. But which sex is determining this large difference in timing? 3 We show here that the seasonal development of female reproductive activity (yolk precursor production) closely matches the local optimal breeding time in these two highly contrasted landscapes, while previous studies have demonstrated that male gonadal development does not. In accordance with this, quantitative genetic analyses reveal that timing for breeding is determined by the female only, with no evidence for an additive genetic male effect on laying date. 4 Our results strongly suggest that the adaptive inter-population difference in the laying periods has been driven by females. The central role of females in the determination of breeding period needs to be included when studying how micro-evolutionary processes are affected by global 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 | 0.000 | 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) | 0.003 | 0.001 |
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".