MHC heterozygosity may increase subordinate but not alpha male siring success in white-faced capuchin monkeys ( <i>Cebus imitator</i> )
Bibliographic record
Abstract
Abstract The genes of the major histocompatibility complex (MHC) are vital to vertebrate immunity and may influence mate choice in several species. The extent to which the MHC influences female mate choice in primates remains poorly understood, and studies of MHC-based mate choice in platyrrhines are especially rare. White-faced capuchin monkeys ( Cebus imitator ) reside in multimale-multifemale groups where alpha males sire most of the offspring. In this study, we investigated the roles of social dominance, relatedness, and MHC genotypes in determining which mating pairs produced offspring in wild white-faced capuchins in the Sector Santa Rosa (SSR), Área de Conservación Guanacaste, Costa Rica. We find that males in this population do not differ significantly in MHC metrics based on their social status or siring success. Using mixed conditional logit models and generalized linear models, we find that alpha males that are distantly related to reproducing females are significantly more likely to sire offspring while MHC metrics do not predict the probability of siring offspring, or becoming an alpha male. However, we do find some evidence that subordinate males heterozygous at MHC loci sire significantly more offspring than homozygous subordinates. Further, one-sided binomial simulations reveal that offspring are more frequently heterozygous at MHC loci than expected given the gene pool. We conclude that in this population with limited genomic variation, females may preferentially mate with MHC-diverse subordinate males when related to the alpha, leading to increased probabilities of MHC-diverse offspring.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".