An analysis of selection on a colour polymorphism in the northern leopard frog
Bibliographic record
Abstract
In this study, we investigated the role of selection in the maintenance of a dorsal colour polymorphism in natural populations of the northern leopard frog, Rana pipiens. We determined genetic structure both spatially and temporally from a suite of putatively neutral molecular markers and tested whether or not the colour locus exhibited patterns of genetic variation that differed from those of the neutral loci. Spatial genetic structure at the colour locus was indistinguishable from structure at neutral loci [95% confidence intervals of F(ST) (neutral) = (0.07, 0.35), F(ST) (colour locus) = 0.114]. In the temporal analysis, we found that the variance among populations in the change in allele frequency at the colour locus (equal to 0.004) lies within the 95% confidence intervals for the variance among populations in changes in allele frequencies at neutral loci. In light of our inability to show evidence for the selective maintenance of the colour polymorphism, we used computer simulations to infer the power of our spatial and temporal techniques to detect selection. The computer simulations showed that although the strength of selection (s) would need to be relatively strong to have been detected by the temporal approach (s = 0.1-0.4, depending on the model tested), the spatial analysis would have detected all but weak selection (s = 0.01-0.04, depending on the model tested). This study illustrates the importance of using a locus comparison approach to detect evidence for selective maintenance before conducting studies to measure the selective mechanisms maintaining a polymorphism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".