The quest for good genes: Epigamic traits, fitness, MHC and multilocus heterozygosity in the guppy
Bibliographic record
Abstract
Abstract The ‘good genes’ hypothesis for the evolution of male secondary sexual traits poses that female preferences for such traits are driven by indirect genetic benefits. However, support for the hypothesis remains ambiguous, and, in particular, the genetic basis for the benefits has rarely been investigated. Here, we use seminatural populations of Trinidadian guppies to investigate whether sexually selected traits (orange, black and iridescent colouration, gonopodium length and body size) predict fitness measured as the number of grandoffspring, a metric that integrates across fitness components and sexes. Furthermore, we tested whether two potential sources of genetic benefits—major histocompatibility complex (MHC) genotypes and multilocus heterozygosity (MLH)—are significant predictors of fitness and of the size of sexually selected traits. We found a significant, nonlinear effect of the area of black pigmentation and male body size on the number of grandoffspring, suggesting stabilizing selection on black area, and nonlinear selection favouring small body size. MLH was heritable (h2 = 0.14) and significantly predicted the number of grandoffspring, indicating the potential for genetic benefits based on heterozygosity. We also found support for local heterozygosity effects, which may reflect a noneven distribution of genetic load across the genome. MHC genotype was not significantly associated with any tested fitness component, or with the load of Gyrodactylus parasites. Neither MHC nor MLH was significant predictor of sexually selected traits. Overall, our results highlight the role of heterozygosity in determining fitness, but do not provide support for male sexually selected traits being indicators of genetic quality.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".