Risk assessment and predator learning in a changing world: understanding the impacts of coral reef degradation
Bibliographic record
Abstract
Habitat degradation is among the top drivers of the loss of global biodiversity. This problem is particularly acute in coral reef system. Here we investigated whether coral degradation influences predator risk assessment and learning for damselfish. When in a live coral environment, Ambon damselfish were able to learn the identity of an unknown predator upon exposure to damselfish alarm cues combined with predator odour and were able to socially transmit this learned recognition to naïve conspecifics. However, in the presence of dead coral water, damselfish failed to learn to recognize the predator through alarm cue conditioning and hence could not transmit the information socially. Unlike alarm cues of Ambon damselfish that appear to be rendered unusable in degraded coral habitats, alarm cues of Nagasaki damselfish remain viable in this same environment. Nagasaki damselfish were able to learn predators through conditioning with alarm cues in degraded habitats and subsequently transmit the information socially to Ambon damselfish. Predator-prey dynamics may be profoundly affected as habitat degradation proceeds; the success of one species that appears to have compromised predation assessment and learning, may find itself reliant on other species that are seemingly unaffected by the same degree of habitat degradation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".