An experimental test of the causes of small-scale phenotypic differentiation in a population of great tits
Bibliographic record
Abstract
Phenotypic differentiation between populations is thought to occur mainly at spatial scales where gene-flow is restricted and selection regimes differ. However, if gene flow is nonrandom, dispersal may reinforce, rather than counteract, evolutionary differentiation, meaning that differences occurring over small scales might have a genetic basis. The purpose of this study was to determine the cause of differences in mean phenotype between two parts of a population of great tits Parus major, separated by <3 km. We conducted a partial cross-fostering experiment between two contrasting parts of this population to separate genetic and environmental sources of variation, and to test for gene-environment interaction. We found strong environmental effects on nestling size, mass and condition index, with nestlings reared in a low density part of the population being larger, heavier and in better condition, than those in a high density part, irrespective of their origin. In addition, we found smaller, but significant, differences in nestling condition and shape associated with the areas that birds originated from, suggesting the presence of genetic differences between parts of this population. There was no evidence of gene-environment interaction for any character. This experiment is thus consistent with previous analyses suggesting that differences between parts of this population had evolved recently, apparently due to phenotype-dependent dispersal, and indicates that population differentiation can be maintained over small spatial scales despite extensive dispersal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".