Local adaptation of timing of reproduction: females are in the driver's seat
Why this work is in the frame
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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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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 | 0.000 | 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) | 0.000 | 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 it