Shell Traits of a Marine Mussel Mediate Predation Selectivity by Crabs and Sea Stars
Bibliographic record
Abstract
Crabs and sea stars are known to preferentially select mussels with morphological traits that diminish the predators' searching or handling times. I compared two distinct morphotype of mussels (Mytilus trossulus; Gould, 1850) from Howe Sound, British Columbia, Canada, by dissection and measurement. Then, I experimentally offered mussels of the two morphotypes on the same patch to crabs (Cancer productus; Randall, 1839) and sea stars (Pisaster ochraceus; Brandt, 1835) to quantify the extent to which these predators select prey based on morphological features. Sea stars preferentially consumed mussels with gaps in shell closures, although these mussels also had larger adductor muscles compared with mussels rejected. Gaps at the shell margin presumably allowed sea stars easier access between shell valves to insert their stomachs and begin digestion. Small crabs preferentially consumed mussels with thin shells, which are easier to crush, whereas, large crabs consumed more thick-shelled mussels, possibly because these mussels were larger and offered greater energetic return. However, overall, crabs and sea stars did not exhibit strong preferences for smaller or larger mussel prey. These findings indicate that morphological features of mussels are important in prey selection by crab and sea star predators. Predator selectivity could cause a trade-off in defense strategies in mussels, and ultimately mediate indirect effects between these predators in the rocky intertidal community.
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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) | 0.002 | 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".