Predation behaviour of Cancer irroratus and Carcinus maenas during conspecific and heterospecific challenges
Bibliographic record
Abstract
We investigated the predation behaviour of rock crab Cancer irroratus and green crab Carcinus maenas in laboratory experiments in autumn 2006 and spring 2007 during various conspecific and heterospecific challenges. The number of prey eaten by a focal crab during a given challenge was recorded for both crab species using competition treatments (solitary crab, 2 conspecifics or 2 heterospecifics) crossed with 2 different prey densities (4 or 30 mussels) and 3 different temperatures (5, 12 or 20C). To validate laboratory results, complementary field experiments were carried out in aquaculture leases in Prince Edward Island, Canada, in 2007, during which crab stomach contents from identical competition treatments were studied and mussel socks were surveyed monthly for crab abundance. In the laboratory, predation rates of both crab species generally increased with temperature and mussel density, and were not affected by the presence of a heterospecific regardless of the season. During autumn 2006, the Temperature Mussel density interaction influenced the predation rate of rock crab while only temperature affected the predation rate of green crab. During spring 2007, the predation rate of green crab varied again according to temperature whereas the predation rate of rock crab was affected by the Temperature Mussel density Competition interaction. In the field, blue mussels Mytilus edulis were the most abundant food item observed in stomach contents. The competition treatments did not affect the stomach contents. Both crab species displayed different abundance patterns and seemed to avoid each other on mussel socks. Overall, our results suggest that the 2 crab species can potentially coexist.
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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 | 0.000 |
| 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) | 0.001 | 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".