Recruitment overfishing in a bivalve mollusc fishery: hard clams (<i>Mercenaria mercenaria</i>) in North Carolina
Bibliographic record
Abstract
Because of their high fecundity, marine invertebrate fisheries are rarely considered at risk to recruitment overfishing. This presumption can be criticized on population theoretic grounds and conflicts with growing evidence of recruitment limitation in a variety of marine invertebrate populations. Sampling in 11 years spanning a 24-year period from 1978 to 2001 reveals that hard clam (Mercenaria mercenaria (L.)) recruitment declined significantly by 6572% within the fishing grounds of central North Carolina. This 24-year period began when high demand and prices drove increased clamming effort. Accordingly, landings grew rapidly 5-fold, a yield that was not sustained and subsequently fell by over 50% from 1983 to 2000. Fishery-independent sampling repeated identically in three representative habitats demonstrates declines of 17, 79, and 95% in hard clam density and of 24, 46, and 83% in spawning stock biomass during the 18+ years of 19801997. Small-scale experiments and measurements in depleted habitats show no compensatory enhancement of hard clam recruitment with local reduction in adult density. Consequently, the hard clam in North Carolina serves as perhaps the most compelling example of unsustainable fishing mortality leading to recruitment overfishing in a bivalve mollusc stock. Spawner sanctuaries could serve to restore and protect spawning stock biomass in this and other invertebrate fisheries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".