Fertilization limitation of <i>Diadema antillarum</i> on coral reefs in the Florida Keys
Bibliographic record
Abstract
Mass mortality of the sea urchin Diadema antillarum due to disease outbreaks in 1983 and 1991 decimated populations in the Florida Keys, and they have yet to recover. Here, we use a coupled advection-diffusion and fertilization-kinetics model to test the hypothesis that these populations are fertilization limited. We found that fertilization success was ≥ 96% prior to the first disease outbreak, decreased substantially following recurrent disease to 3%, and has since remained low. By investigating the combined effects of physical factors (population spatial extent and current velocity) and sea urchin behavior (aggregation) on density-dependent fertilization success, we show that fertilization success at a given density increases with increasing population spatial extent and decreasing current velocity, and is greater under simulated aggregation behavior of D. antillarum. However, at present population densities, the increase in fertilization success due to aggregation is < 1%, even under the most favorable physical conditions. These results indicate that populations are severely fertilization limited, and that Allee effects at low population density will continue to limit recovery. Our results can serve as a practical guide to managers in the development of coral reef restoration strategies, including the design of a D. antillarum restocking program to obtain reproductively viable populations.
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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 | 0.000 | 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".