Sperm storage reduces sperm and embryo quality in animals
Bibliographic record
Abstract
Abstract In many animals, sperm are stored for extended periods either in the reproductive tracts of males before ejaculation, or of females after copulation. Sperm storage reduces the risk of sperm limitation in both sexes and avoids the costs of female re-mating. However, sperm storage can lead to post-meiotic sperm senescence, i.e. within-sperm-age-dependent deterioration, potentially impacting conceived offspring and lowering male and female fitness. Yet, the extent and magnitude of such deterioration and the variables modulating it during sperm storage are not well understood. Using a meta-analysis across humans (115 studies) and non-human animals (56 studies from 30 species), we investigate how in-vivo sperm storage affects sperm quality, fertilisation success, and offspring quality. In humans, sperm storage leads to greater sperm oxidative stress and DNA damage, and reduces sperm viability and motility. In other animals, sperm performance and embryo quality decline. We identify the duration of sperm storage, the design used for sampling individuals, and the sex of the individual storing sperm as potentially important moderators of the effects of sperm storage. These findings have key biomedical implications, including optimising the timing of ejaculation and fertilisation in fertility clinics or captive breeding programs. Overall, our results reveal the mechanisms that cause post-meiotic sperm senescence, the fitness consequences of sperm storage, and provide evolutionary insights into sex-specific adaptations that potentially mitigate the detrimental effects of sperm storage.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".