Between-population differences in egg composition in Blue Tits (Cyanistes caeruleus)
Bibliographic record
Abstract
Egg production may be influenced by environmental conditions such as local climate or food availability, which may impose physiological constraints on the acquisition and mobilization of egg constituents. We analyzed egg composition of free-ranging female Blue Tits ( Cyanistes caeruleus (L., 1758)) in both deciduous and evergreen oak habitats, which showed large differences in temperature and food availability. We found marked interhabitat differences in yolk mass, shell mass, protein content, and the abundance of linolenic (18:3) and palmitoleic (16:1) fatty acids. A weak but significant decline in total lipid content, as well as 14:0, 16:0, and 18:0 fatty acids, through the laying sequence was also detected. To our knowledge, this is the first evidence of between-population differences in nutrient allocation in eggs for a wild passerine. These differences in egg composition could be viewed as evidence of habitat-specific physiological and nutritional constraints, which in turn may contribute to the contrasting differences in timing of breeding and clutch size that we observed between both habitats. Our results point out the importance of habitat differences in our understanding of the causes and consequences of interhabitat phenotypic variation in breeding traits (timing of egg laying, clutch size) and variation in nestling traits such as growth and development.
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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 | 0.001 | 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".