Elevated pCO2 increases sperm limitation and risk of polyspermy in the red sea urchin Strongylocentrotus franciscanus
Bibliographic record
Abstract
Anthropogenic carbon dioxide (CO2) emissions and the resultant acidification of surface ocean waters are predicted to have far-reaching consequences for biological processes in the marine environment. For example, because changes in pH and pCO2 can alter sperm performance, ocean acidification may be accompanied by reductions in the success of fertilization in marine broadcast spawners. Several studies have attempted to determine the effects of elevated pCO2 on marine invertebrate fertilization success, albeit with differing results. These conflicts may stem from the use of inappropriate sperm–egg contact times and, in several cases, the lack of measurements over a range of sperm concentrations extending from sperm-limited conditions to polyspermy scenarios. In our study, we used biologically realistic sperm–egg contact times and a full range of sperm concentrations to assess the effect of elevated pCO2 on fertilization in the broadcast spawning sea urchin, Strongylocentrotus franciscanus. Fertilization experiments were carried out in seawater bubbled with CO2 to 400 (control), 800, and 1800 ppm. Using a fertilization kinetics model, we estimate that elevated pCO2 levels both increased sperm limitation and reduced the efficiency of fast blocks to polyspermy. Thus, elevated pCO2 decreased the range of sperm concentrations over which high fertilization success was likely. Given the inherent difficulties in achieving high fertilization success in broadcast spawners, raised pCO2 levels are likely to exacerbate low fertilization success in low-density populations or in areas with high water turbulence.
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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 | 0.000 |
| 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 | 0.001 | 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".