Effects of habitat complexity on cannibalism rates in European green crabs (<i>Carcinus maenas</i> Linnaeus, 1758)
Bibliographic record
Abstract
Abstract The habitat in which predator–prey interactions take place may have a profound influence on the outcome of those interactions. Cannibalism is an intriguing form of predation whereby foraging by predators may contribute to the regulation of their own populations.This is particularly interesting in the case of invasive species, like the widely distributed European green crab (Carcinus maenas). This study explores how habitat complexity influences cannibalism rates in green crab populations of Prince Edward Island, Atlantic Canada. Both laboratory and field experiments were conducted to measure feeding rates by individual adult green crabs on a standard number of smaller conspecifics. In the laboratory, experimental treatments mimicked unstructured to increasingly structured habitats: water, sandy bottom, oyster shells, mussel shells, oyster shells with sandy bottom and mussel shells with sandy bottom. In those trials, adult green crabs consumed several times more juveniles on unstructured habitats than on the most structured ones, with a gradual decrease in predation rates across increasingly complex habitats. Field inclusion experiments used the same approach and were conducted in sandy bottoms, sandy bottoms with a layer of oyster shells and sandy bottoms with a layer of mussel shells. These trials showed similar patterns of decreasing feeding rates across increasingly complex habitats, but differences among treatments were not significant. These results support the idea that complex habitats have the potential to mediate predator–prey interactions, including adult–juvenile cannibalism in green crabs.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".