Do male secondary sexual characters signal ejaculate quality? A meta‐analysis
Bibliographic record
Abstract
There are two reasons why researchers are interested in the phenotypic relationship between the expression of male secondary sexual characters (SSCs) and 'ejaculate quality' (defined as sperm/ejaculate traits that are widely assumed to increase female fertility and/or sperm competitiveness). First, if the relationship is positive then females could gain a direct benefit by choosing more attractive males for fertility assurance reasons ('the phenotype-linked fertility' hypothesis). Second, there is much interest in the direction of the correlation between traits favoured by pre-copulatory sexual selection (i.e. affecting mating success) and those favoured by post-copulatory sexual selection (i.e. increasing sperm competitiveness). If the relationship is negative this could lead to the two forms of selection counteracting each other. Theory predicts that the direction of the relationship could be either positive or negative depending on the underlying genetic variance and covariance in each trait, the extent of variation among males in condition (resources available to allocate to reproductive traits), and variation among males in the cost or rate of mating. We conducted a meta-analysis to determine the average relationship between the expression of behavioural and morphological male secondary sexual characters and four assays of ejaculate quality (sperm number, viability, swimming speed and size). Regardless of how the data were partitioned the mean relationship was consistently positive, but always statistically non-significant. The only exception was that secondary sexual character expression was weakly but significantly positively correlated with sperm viability (r = 0.07, P < 0.05). There was no significant difference in the strength or direction of the relationship between behavioural and morphological SSCs, nor among relationships using the four ejaculate quality assays. The implications of our findings are discussed.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.033 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".