The fitness consequences of genetic variation in wild populations of mice
Bibliographic record
Abstract
Adaptive evolution can occur when genetic change affects traits subject to natural selection. Although selection is a deterministic process, adaptation can be difficult to predict in finite populations because the functional connections between genotype, phenotype, and fitness are complex. Here, we make these connections using a combination of field and laboratory experiments. We conduct a large-scale manipulative field experiment with wild populations of deer mice in distinct habitats to directly estimate natural selection on pigmentation traits and next test whether this selection drives changes in allele frequency at an underlying pigment locus. We find that divergent cryptic phenotypes are repeatedly favoured in each habitat, leaving footprints of selection in the Agouti gene. Next, using transgenic experiments in Mus , we functionally test one of the Agouti mutations associated with survival, a Serine deletion in exon 2, and find that it causes lighter coat colour via changes in its protein binding properties. Finally, we show significant change in the frequency of this mutation in our field experiment. Together, our findings demonstrate how a sequence variant alters phenotype and show the ensuing ecological consequences that drive changes in population allele frequency, thereby revealing the full process of evolution by natural selection.
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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 | 0.000 | 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 | 0.001 |
| 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".