The right choice: predation pressure drives shell selection decisions in the hermit crab <i>Calcinus</i> <i>californiensis</i>
Bibliographic record
Abstract
Several prey species use refuges to avoid predation. Prey need to abandon and shift between refuges. However, during such shifting, prey can be vulnerable to predators. We hypothesize that predator presence may induce prey to make mistakes in choosing their refuge. We tested this by inducing the hermit crab Calcinus californiensis Bouvier, 1898 to shift to a new empty gastropod shell (three different species: Columbella Lamarck, 1799, Nerita scabricosta Lamarck, 1822, and Stramonita biserialis (Blainville, 1832)) in the absence and presence of Eriphia squamata Stimpson, 1860, which is an efficient shell-crushing natural crab predator. We expected that when a predator was present, hermit crabs would (i) inspect fewer shells and (or) (ii) change to a shell that is either too heavy to allow escape or unfit in size to accommodate the hermit crab. Although the first prediction was met, the second prediction was supported only when S. biserialis shells were used. Thus, in the presence of a predator, hermit crabs prioritize escaping by selecting lighter shells, which would allow the crab to move faster. We conclude that predator presence may induce prey to make mistakes in refuge selection, suggesting that this has severe consequences in future predatory events.
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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 | 0.001 | 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".