Selective Feeding by a Predatory Sea Star Across a Depth Gradient in Northern Patagonia, Chile
Bibliographic record
Abstract
Sea stars often function as keystone predators in food webs of intertidal and subtidal communities, especially in temperate and sub-polar regions. In South America the sea starCosmasterias luridais distributed along both the Atlantic and Pacific coasts of Patagonia and is one of the most conspicuous and abundant benthic predators in the shallow subtidal zone (<25 m). Its feeding strategy and prey selection are, however, still poorly known. This study describes the feeding behavior ofC. luridaat a site in the Seno del Reloncaví (Chile), assessing its abundance, size and prey selection in the field relative to observed prey abundance and size along a bathymetric gradient. We hypothesized thatC. luridais a generalist predator, feeding on suitable prey according to their availability. However, we found that this predator only consumed a limited number (7 of 48) of potential prey species, primarily the slipper limpetsCrepipatellaspp. and the musselsAulacomya aterandMytilus chilensis. Electivity analysis revealed a clear preference for one mussel (A. ater) but not the other (M. chilensis) as well as depth-dependent selectivity for the slipper limpets, which changed from avoidance to preference with increasing depth. Sea star densities varied with depth, peaking between depths of 5 and 10 m, but the size of sea stars and the size of their prey did not vary significantly along a depth gradient. No significant correlations were found with the most commonly selected prey. These results would indicate that while this predator may be a generalist–opportunist, its feeding behavior is context-dependent and its high selectivity for certain species suggests that this sea star plays a key role structuring subtidal benthic communities in Patagonia.
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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 | 0.001 | 0.001 |
| 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".