Population density effects on gamete traits and fertilisation dynamics under varying sperm environments in mussels
Bibliographic record
Abstract
Gamete traits can vary widely among species, populations and individuals, influencing fertilisation dynamics and overall reproductive fitness. Sexual selection can play an important role in determining the evolution of gamete traits with local environmental conditions determining the strength and direction of sexual selection. Here, we test for signatures of post-mating selection on gamete traits in relation to population density, and possible interactive effects of population density and sperm concentration on sperm motility and fertilisation rates among natural populations of mussels. Our study shows that males from high-density populations produce smaller sperm compared with males from low-density populations, but we detected no effect of population density on egg size. Our results also reveal that females from low-density populations tended to exhibit lower fertilisation rates across a range of sperm concentrations, although this became less important as sperm concentration increased. Variances in fertilisation success were higher for females than males and the effect of gamete compatibility between males and females increases as sperm concentrations increase. These results suggest that local population density can influence gamete traits and fertilisation dynamics but also highlight the importance of phenotypic plasticity in governing sperm-egg interactions in a highly dynamic selective environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".