Reduced heterozygosity impairs sperm quality in endangered mammals
Bibliographic record
Abstract
Inbreeding causes increases in homozygosity and is commonly associated with reductions in fertility and embryogenesis. Although the mechanisms underlying such effects are unknown, recent work has suggested that inbred males may suffer impaired ejaculate quality, thus providing a functional explanation for reductions in reproductive function in inbred populations. However, the relationship between inbreeding and sperm quality remains controversial, particularly in wild populations where the level of inbreeding is typically estimated using neutral molecular markers. Such markers are thought to reflect genome-wide levels of heterozygosity only under restricted conditions, and rarely in outbred populations. Here we employ a comparative approach that takes account of these criticisms and evaluates the evidence linking inbreeding to reductions in sperm quality in 20 mammal species. We focus on sperm abnormalities and sperm motility, which are key determinants of male fertility in many species. We show that species with reduced mean heterozygosity have impaired ejaculated quality, although subsequent analyses revealed that these effects were confined to endangered populations. Our findings therefore support the notion that inbreeding can severely impair sperm quality while concomitantly addressing criticisms surrounding the use of heterozygosity estimates to estimate the level of inbreeding.
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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 | 0.001 | 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".