Data from: The fading of fear effects due to coral degradation is modulated by community composition
Bibliographic record
Abstract
An increasing number of coral reefs throughout the world have become degraded as a result of climate change. This degradation has resulted in a significant decline in local biodiversity. Studies have shown that some fishes (non-responders) within these altered habitats are not able to adequately access olfactory cues, specifically chemical alarm cues that are crucial in mediating predation risk. We propose that the inability to access this crucial information is a potential mechanism for increased mortality of these species under natural conditions. However, we posit that the presence of certain key species (responders that are unaffected by degradation) may buffer the handicap of non-responders by providing an alternate source of information. To explore this hypothesis, we investigated if a high ratio of responders to non-responders could mitigate the impact of information loss for the affected species. Using mesocosms, we manipulated the ratio of two damselfish species, Pomacentrus chrysurus (responder) and P. moluccensis (non-responders), to determine if community composition can be predictive of information transfer about predation threats in nearby non-responder individuals. Our results indicate that the magnitude of fear effects seen in P. moluccensis exposed to a predator was proportional to the number of P. chrysurus present and consumed by the predator in the community. This indicates that P. chrysurus became the only functional source of predation-related information for P. moluccensis in degraded habitats. Our study provides evidence that the presence of non-affected species in the community provides a potential mechanism allowing increased resilience by affected species, therefore providing another example of the way biodiversity affects ecological resilience of species in changing ecosystems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.040 |
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".