Intralocus Sexual Conflict Diminishes the Benefits of Sexual Selection
Bibliographic record
Abstract
Evolution based on the benefits of acquiring "good genes" in sexual selection is only plausible with the reliable transmission of genetic quality from one generation to the next. Accumulating evidence suggests that sexually antagonistic (SA) genes with opposite effects on Darwinian fitness when expressed in the two different sexes may be common in animals and plants. These SA genes should weaken the potential indirect genetic benefits of sexual selection by reducing the fitness of opposite-sex progeny from high-fitness parents. Here we use hemiclonal analysis in the fruit fly, Drosophila melanogaster, to directly measure the inheritance of fitness across generations, over the entire genome. We show that any potential genetic benefits of sexual selection in this system are not merely weakened, but completely reversed over one generation because high-fitness males produce low-fitness daughters and high-fitness mothers produce low-fitness sons. Moreover, male fitness was not inherited by sons, consistent with both theory and recent evidence connecting this form of SA variation with the X chromosome. This inheritance pattern may help to explain how genetic variation for fitness is sustained despite strong sexual selection, and why the ZW sex chromosome system found in birds and butterflies appears to foster the evolution of extreme secondary sexual characters in males.
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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) | 0.004 | 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".