Data from: Liking the good guys: amplifying local adaptation via the evolution of condition-dependent mate choice
Bibliographic record
Abstract
Local adaptation can be strengthened through a diversity of mechanisms that reduce gene flow between contrasting environments. Recent work revealed that mate choice could enhance local adaptation when females preferentially mate with locally adapted males and that such female preferences readily evolve, but the opposing effects of recombination, migration, and costs of female preferences remain relatively unexplored. To investigate these effects, we develop a two-patch model with two genes, one influencing an ecological trait and one influencing female preferences, where both male signals and female preferences are allowed to depend on the match between an individual's ecological trait and the local environment (condition). Because trait variation is limited when migration is rare and the benefits of preferential mating are short-lived when migration is frequent, we find that female preferences for males in high condition spread most rapidly with intermediate levels of migration. Surprisingly, we find that preferences for locally adapted males spread fastest with higher recombination rates, which contrasts with earlier studies. This is because a stronger preference allele for locally adapted males can only get uncoupled from maladapted ecological alleles following migration through recombination. The effects of migration and recombination depend strongly on the condition of the males being chosen by females, but only weakly on the condition of the females doing the choosing, except when it comes to the costs of preference. While costs always impede the spread of female preferences for locally adapted males, the impact is substantially lessened if costs are borne primarily by females in poor condition. The abundance of empirical examples of condition-dependent mate choice combined with our theoretical results suggests that the evolution of mate choice could commonly facilitate local adaptation in nature.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.066 |
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".