Quantifying the Effects of Predator and Prey Body Size on Sea Star Feeding Behaviors
Bibliographic record
Abstract
Body size plays a crucial role in determining the strength of species interactions, population dynamics, and community structure. We measured how changes in body size affect the trophic relationship between the sea star Pisaster ochraceus and its prey, the mussel Mytilus trossulus. We tested the effects of a wide range of predator and prey sizes on sea stars' prey-size preference, feeding rate, and prey tissue consumption. We found that preferred prey size increased with sea star size. Pisaster consumption rate (mussels consumed per day) and tissue intake rate (grams of tissue consumed per day) also increased with sea star size. Pisaster consumption rate, but not tissue intake rate, decreased with increasing mussel size. Juvenile sea stars preferred the most profitable prey sizes-that is, those that maximized tissue consumed per unit handling time. When adult sea stars were offered larger, more profitable mussels, tissue intake rates (grams per day) tended to increase, although this relationship was not statistically significant. Our results indicate that the Pisaster-Mytilus interaction depends on the sizes of both predator and prey, that predation rates are sensitive to even small changes in body size, and that shifts in size distributions may affect predator energetics and prey numbers differently depending on the factors that limit tissue consumption rates.
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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 | 0.002 |
| 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".