Gene flow does not prevent personality and morphological differentiation between two blue tit populations
Bibliographic record
Abstract
Abstract Understanding the causes and consequences of population phenotypic divergence is a central goal in ecology and evolution. Phenotypic divergence among populations can result from genetic divergence, phenotypic plasticity or a combination of the two. However, few studies have deciphered these mechanisms for populations geographically close and connected by gene flow, especially in the case of personality traits. In this study, we used a common garden experiment to explore the genetic basis of the phenotypic divergence observed between two blue tit (Cyanistes caeruleus) populations inhabiting contrasting habitats separated by 25 km, for two personality traits (exploration speed and handling aggression), one physiological trait (heart rate during restraint) and two morphological traits (tarsus length and body mass). Blue tit nestlings were removed from their population and raised in a common garden for up to 5 years. We then compared adult phenotypes between the two populations, as well as trait‐specific Qst and Fst. Our results revealed differences between populations similar to those found in the wild, suggesting a genetic divergence for all traits. Qst–Fst comparisons revealed that the trait divergences likely result from dissimilar selection patterns rather than from genetic drift. Our study is one of the first to report a Qst–Fst comparison for personality traits and adds to the growing body of evidence that population genetic divergence is possible at a small scale for a variety of traits including behavioural 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".