You are what you eat: diet-induced chemical crypsis in a coral-feeding reef fish
Bibliographic record
Abstract
The vast majority of research into the mechanisms of camouflage has focused on forms that confound visual perception. However, many organisms primarily interact with their surroundings using chemosensory systems and may have evolved mechanisms to 'blend in' with chemical components of their habitat. One potential mechanism is 'chemical crypsis' via the sequestration of dietary elements, causing a consumer's odour to chemically match that of its prey. Here, we test the potential for chemical crypsis in the coral-feeding filefish, Oxymonacanthus longirostris, by examining olfactory discrimination in obligate coral-dwelling crabs and a predatory cod. The crabs, which inhabit the corals consumed by O. longirostris, were used as a bioassay to determine the effect of coral diet on fish odour. Crabs preferred the odour of filefish fed their preferred coral over the odour of filefish fed a non-preferred coral, suggesting coral-specific dietary elements that influence odour are sequestered. Crabs also exhibited a similar preference for the odour of filefish fed their preferred coral and odour directly from that coral, suggesting a close chemical match. In behavioural trials, predatory cod were less attracted to filefish odour when presented alongside the coral it had been fed on, suggesting diet can reduce detectability. This is, we believe, the first evidence of diet-induced chemical crypsis in a vertebrate.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".