The spread of invasive species and the sustainability of shellfish resources: shiftingpopulations of the European green crab threaten prime oyster habitats in Prince Edward Island, Canada
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.European green crabs (Carcinus maenas) were introduced to Prince Edward Island, eastern Canada, just over a decade ago. Since then, preliminary observations suggest that the species continue to spread and grow I numbers that may become detrimental to a variety of shellfish resources. To document their potential spread and impact on oyster habitats, we carried out trapping surveys in several estuaries during 2008, 2009, and 2010, and conducted a series of predator–prey (crab-–oyster) manipulations. The trapping surveys confirmed that there is an ongoing spread of green crabs into new suitable areas, while rapidly increasing in numbers in most others. Aiming to facilitate potential mitigation strategies, we ran a series of field inclusions to identify both the size of oysters most vulnerable to green crab predation, and the crab sizes most detrimental to oysters. Specifically, we measured the predation rates of small, medium, and large green crabs feeding on small, medium, large, and extra large oysters. In general, almost no predation occurred on extra-large oysters, but large green crabs preyed more heavily on all other oyster sizes. As expected, small oysters were most vulnerable to predation by virtually every size of green crab. Overall, our results suggest that the outcomes of green crab–oyster predator–prey interactions are heavily dependent on both oyster size and crab size with oysters reaching a partial size refuge from green crabs at ~35 mm. We discuss these results in the light of the oyster industry and the further spread and growth of green crabs in the region.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".