Nongenetic inheritance and the evolution of costly female preference
Bibliographic record
Abstract
In species where males provide neither direct benefits nor paternal care, it is typically assumed that female preferences are maintained by indirect selection reflecting genetic benefits to offspring of preferred males. However, it remains unclear whether populations harbour sufficient genetic variation in fitness to support costly female preferences - a problem called the 'lek paradox'. Here, we ask whether indirect selection on female preferences can be maintained by nongenetic inheritance. We construct a general model that can be used to represent either genetic or nongenetic inheritance, depending on the choice of parameter values. Interestingly, we find that costly preference is most likely to evolve and persist when fitness depends on an environmentally induced factor that can be transmitted over a single generation only, such as an environment-dependent paternal effect. Costly preference can also be supported when fitness depends on a highly mutable factor that can persist over multiple generations, such as an epigenetic mark, but the necessary conditions are more restrictive. Our findings show that nongenetic inheritance provides a plausible hypothesis for the maintenance of costly female preferences in species where males provide no direct benefits to females. Nongenetic paternal inheritance of fitness can occur in species lacking conventional forms of paternal care. Indeed, transmission of paternal condition via sperm-borne nongenetic factors may be more likely to evolve than conventional forms of paternal investment because sperm-borne effects are protected from cuckoldry. Our results furnish a novel example of an interaction between genetic and nongenetic inheritance that can lead to otherwise unexpected evolutionary outcomes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".