Estimating larval production of a broadcast spawner: the influence of density, aggregation, and the fertilization Allee effect
Bibliographic record
Abstract
The effect of fishing on reproduction is typically quantified by computing the effects of changes in the species abundance and age structure on egg production. For broadcast spawners, reproduction also depends on the local spatial distribution of individuals. Broadcast spawners exhibit an Allee effect at low density: a decline in the fertilization of eggs, owing to increased distance between spawners. We present a method for assessing the likely impact of a fishery on broadcast spawners, based on gamete dispersion dynamics and individual spatial distributions. We use an individual-based model to simulate larval production over a range of uncertainties in dispersion characteristics. We illustrate our method for the red sea urchin, Strongylocentrotus franciscanus , fishery in northern California, USA. The density of red sea urchins varied over space (0.1–1.6·m–2), and indices of aggregation were highest at low densities. As gamete dispersion distances increased, larval production exhibited a more linear relationship with density. Average larval production in 1996–1998 was 33.8% of production near the inception of the fishery. After accounting for decreases in mean density, the fertilization Allee effect accounted for 21.7 ± 4.1% of the decrease in larval production, and 45.2 ± 21.7% if sea urchins were not aggregated.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".