Dynamic shifts and a drastic decline in reported landings for southern Gulf of St. Lawrence commercial clam fisheries over the past two decades
Bibliographic record
Abstract
Clams constitute an important socioeconomic and Indigenous resource in the southern Gulf of St. Lawrence (sGSL); however, detailed analyses of commercial clam fisheries are outdated. I provide a synthesis of sGSL clam landings from 2003 to 2022. Three species comprised >99% of landings: Mya arenaria, Mercenaria mercenaria, and Spisula solidissima. Annual landings mostly came from Prince Edward Island (75 ± 7%; mean ± standard deviation), followed by New Brunswick (23 ± 6%) and Nova Scotia (2 ± 2%). For the sGSL as a whole, the three species contributed equally to landings from 2003 to 2020, but Mya arenaria dominated landings from 2021 to 2022. This trend was not consistent for individual provinces: province-specific fluctuations in species composition and a contemporary shift from multi-species to single-species harvests were evident. Overall, landings and their associated value sharply declined by 80% and 74%, respectively, over the time series. The number of catch records (i.e., active licenses and Supplement B records) also declined by 80%, suggesting progressively fewer people entering clam fisheries. Annual catch records were a strong predictor of annual landings, and declines in landings per catch record (proxy of CPUE) were apparent. This analysis ultimately suggests a dwindling Canadian fishery. Understanding the proximate causes of fishery declines, how to address them, and determining whether such declines reflect population trends, should be prioritized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".