The evolution of age-specific choosiness when mating
Bibliographic record
Abstract
Mate choice is a crucial element of many processes in evolutionary biology. Empirical research has shown that mating preference and choosiness often change with age. Understanding the evolutionary causes of patterns of age-specific choosiness is challenging because different mechanisms can give rise to the same pattern. Instead of focusing on the optimal age-specific choosiness strategy given fitness trade-offs, we approach this question from a more general standpoint and ask how the strength of selection on choosiness changes with the age at which it is expressed. We show that the strength of selection on a modifier of choosiness at a given age depends on the relative contribution of this age class to the pool of offspring but does not depend directly on the strength of selection on fitness components at the age affected by the modifier. We illustrate our results by contrasting two life histories from the literature. We further show how mutation-selection balance at the choosiness locus can shape age-specific choosiness. Our results provide new insights for understanding the evolution of choosiness throughout life, with implications for understanding the evolution of mate choice and reproductive isolation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.020 |
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".