Fine-tuning of seasonal timing of breeding is regulated downstream in the underlying neuro-endocrine system in a small songbird
Bibliographic record
Abstract
The timing of breeding is under selection in wild populations as a result of climate change, and understanding the underlying physiological processes mediating this timing provides insight into the potential rate of adaptation. Current knowledge on this variation in physiology is, however, mostly limited to males. We assessed whether individual differences in the timing of breeding in females are reflected in differences in candidate gene expression and, if so, whether these differences occur in the upstream (hypothalamus) or downstream (ovary and liver) parts of the neuroendocrine system. We used 72 female great tits from two generations of lines artificially selected for early and late egg laying, which were housed in climate-controlled aviaries and went through two breeding cycles within 1 year. In the first breeding season we obtained individual egg-laying dates, while in the second breeding season, using the same individuals, we sampled several tissues at three time points based on the timing of the first breeding attempt. For each tissue, mRNA expression levels were measured using qPCR for a set of candidate genes associated with the timing of reproduction and subsequently analysed for differences between generations, time points and individual timing of breeding. We found differences in gene expression between generations in all tissues, with the most pronounced differences in the hypothalamus. Differences between time points, and early- and late-laying females, were found exclusively in the ovary and liver. Altogether, we show that fine-tuning of the seasonal timing of breeding, and thereby the opportunity for adaptation in the neuroendocrine system, is regulated mostly downstream in the neuro-endocrine system.
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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 | 0.000 |
| 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".