A field-based investigation of behavioural interactions between invasive green crab ( <i>Carcinus maenas</i> ), rock crab ( <i>Cancer irroratus</i> ), and American lobster ( <i>Homarus americanus</i> ) in southern Newfoundland
Bibliographic record
Abstract
Marine species invasions pose a global threat to native biodiversity and commercial fisheries. The European green crab ( Carcinus maenas ) is one of the most successful marine invaders worldwide and has, in the last decade, invaded the southern and western coastal waters of the island of Newfoundland, Newfoundland and Labrador (NL), Canada. Impacts of green crab on the American lobster ( Homarus americanus ), which are native to Newfoundland, are not well understood, particularly for interactions around deployed fishing gear. Declines in lobster catch rates in invaded systems (i.e., Placentia Bay, NL), have prompted concerns among lobster fishers that green crab are interfering with lobster catch. Here, we conducted a field experiment in a recently-invaded bay (2013) in which we deployed lobster traps pre-stocked with green crab, native rock crab ( Cancer irroratus ) (a procedural control), or empty (control). We compared catch per unit effort across each category, and used underwater cameras to directly observe trap performance in situ . In addition, we used SCUBA surveys to determine the correlation between ambient density of lobster and green crab in the ecosystem and the catch processes of lobster in traps. We found: (1) Regardless of the species of crab stocked, crab presence reduced the total number of lobster that attempted to enter the trap, and also reduced entry success rate, (2) lobster consumed green crab, rock crab and other lobster inside traps and (3) there was a positive association between lobster catch and ambient lobster density. Our results suggest that while there was a relationship between in-trap crab density and trap catch rates, it was not linked to the non-native/native status of the crab species.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".