Variation in phenotypic plasticity and selection patterns in blue tit breeding time: between‐ and within‐population comparisons
Bibliographic record
Abstract
1. Phenotypic plasticity, the response of individual phenotypes to their environment, can allow organisms to cope with spatio-temporal variation in environmental conditions. Recent studies have shown that variation exists among individuals in their capacity to adjust their traits to environmental changes and that this individual plasticity can be under strong selection. Yet, little is known on the extent and ultimate causes of variation between populations and individuals in plasticity patterns. 2. In passerines, timing of breeding is a key life-history trait strongly related to fitness and is known to vary with the environment, but few studies have investigated the within-species variation in individual plasticity. 3. Here, we studied between- and within-population variation in breeding time, phenotypic plasticity and selection patterns for this trait in four Mediterranean populations of blue tits (Cyanistes caeruleus) breeding in habitats varying in structure and quality. 4. Although there was no significant warming over the course of the study, we found evidence for earlier onset of breeding in warmer years in all populations, with reduced plasticity in the less predictable environment. In two of four populations, there was significant inter-individual variation in plasticity for laying date. Interestingly, selection for earlier laying date was significant only in populations where there was no inter-individual differences in plasticity. 5. Our results show that generalization of plasticity patterns among populations of the same species might be challenging even at a small spatial scale and that the amount of within-individual variation in phenotypic plasticity may be linked to selective pressures acting on these phenotypic traits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".