Compensatory evolution via cryptic genetic variation: Distinct trajectories to phenotypic and fitness recovery
Bibliographic record
Abstract
Abstract Populations are constantly exposed to deleterious alleles, most of which are purged via natural selection. However, deleterious fitness effects of alleles can also be suppressed by compensatory adaptation. Compensatory mutations can act directly to reduce deleterious effects of an allele. Alternatively, compensation may also occur by altering other aspects of an organisms’ phenotype or performance, without suppressing the phenotypic effects of the deleterious allele. Moreover, the origin of allelic variation contributing to compensatory adaptation remains poorly understood. Compensatory evolution driven by mutations that arise during the selective process are well studied. However less is known about the role standing (cryptic) genetic variation plays in compensatory adaptation. To address these questions, we examined evolutionary trajectories of natural populations of Drosophila melanogaster fixed for mutations that disrupt wing morphology, resulting in deleterious effects on several components of fitness. Lineages subjected only to natural selection, evolved modifications to courtship behavior and several life history traits without compensation in wing morphology. Yet, we observed rapid phenotypic compensation of wing morphology under artificial selection, consistent with segregating variation for compensatory alleles. We show that alleles contributing to compensation of wing morphology have deleterious effects on other fitness components. These results demonstrate the potential for multiple independent avenues for rapid compensatory adaptation from standing genetic variation, which ultimately may reveal novel adaptive trajectories.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".