The effect of near‐bed turbulence on sperm dilution and fertilization success of broadcast‐spawning bivalves
Bibliographic record
Abstract
Lay Abstract Many marine and some freshwater bottom‐dwelling (benthic) invertebrates broadcast their gametes into the water column where fertilization occurs. The relatively slow swimming speed and rapid dilution of sperm by water currents is thought to limit fertilization, even though species that reproduce this way can be extremely successful. We examined how water velocity and bottom roughness affected the fertilization success of zebra and quagga mussels (Dreissena polymorpha and D. bugensis) in a laboratory flow chamber and in Lake Erie. Our results demonstrate that velocity gradients dilute the concentration of sperm to levels that can lead to sperm limitation. We also found that the strength and pattern of the turbulence in the water flow near the bottom had a strong effect on fertilization. Bottom roughness that led to the ejection of fluid away from the bottom—such as what occurs downstream of a patch of mussels—contributed to higher fertilization success than bottom roughness that led to skimming flows or sweeps of flow toward the bed. Bottom roughness needs to be considered in biological and other transport processes occurring near the bottom. Biologically, dreissenid mussels are the first freshwater benthic organisms that have been shown to be sperm limited. Ecologically, the presence of their shells in the flow chamber and on the lakebed in Lake Erie created sufficient roughness to affect fertilization success. In other words, the mussels had changed the physical environment in a way that favored their reproduction. This new observation helps to explain why dreissenid mussels are successful invaders of freshwater ecosystems.
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".