Complex effects of assortative mating on adaptation to environmental change in the presence of sex-specific selection
Bibliographic record
Abstract
Previous theory suggests that fast adaptation of body size and phenology to climate warming could be facilitated by assortative mating. We here test whether this still holds when natural selection is sex-specific and assortative mating is driven by different mechanisms. We model a trait with identical distribution for females and males, but different optima across sexes and time. Predictions derived from the infinitesimal model of inheritance are confirmed by individual-based simulations. We find that female maladaptation depends on a sexual-conflict mismatch in a constant environment, and an adaptive lag generated by the environmental change. By strengthening the effect of natural selection on females, assortative mating reduces the sexual-conflict mismatch compared to random mating. However, it can either increase or decrease the adaptive lag, depending on the relative strength of natural selection on females versus males. Conditions under which assortative mating is beneficial depend on whether the environmental change and the sexual conflict displace the mean phenotype in the same direction, the strength of the sexual conflict and the assortment, and the mechanism that drives assortative mating. Associated with sex-specific selection, assortative mating does not always facilitate adaptation to environmental change, and its effects depend on how it is modeled.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".