Coral degradation alters predator odour signatures and influences prey learning and survival
Bibliographic record
Abstract
Habitat degradation is a key factor leading to the global loss of biodiversity. This problem is particularly acute in coral reef ecosystems. We investigated whether recognition of predator odours by damselfish was influenced by coral degradation and whether these changes altered survival in the wild. We taught whitespot damselfish to recognize the odour of a predator in the presence of live/healthy coral or dead/degraded coral. Fish were tested for a response to predator odours in environments that matched their conditioning environment or in environments that were mismatched. Next, we taught blue damselfish to recognize the odour of three common reef predators in live and degraded coral environments and then stocked them onto live or degraded patch reefs, where we monitored their subsequent response to predator odour along with their survival. Damselfish learned to recognize predator odours in both coral environments, but the intensity of their antipredator response was much greater when the conditioning and test environments matched. Fish released on degraded coral had about 50% higher survival if they had been trained in the presence of degraded coral rather than live coral. Altering the intensity of antipredator responses could have rather profound consequences on population growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".