Dynamic interplay: disentangling the temporal variability of fish effects on coral recruitment
Bibliographic record
Abstract
Ecosystems around the world are continuously undergoing recovery from anthropogenic disturbances like climate change, overexploitation, and habitat destruction. Coral reefs are a prime example of a threatened ecosystem and coral recruitment is a critical component of reef recovery from disturbances. Reef fishes structure this recruitment by directly consuming macroalgae and coral recruits or by indirectly altering the substrate to facilitate coral settlement (e.g., grazing scars). However, how these direct and indirect mechanisms vary through time remains largely unknown. Here, we quantified coral recruitment on settlement tiles with divots that mimic grazing scars and caging treatments to exclude or allow fish feeding over 3 years at Palmyra Atoll in the Pacific Ocean. We found that the positive and negative effects of fishes on coral recruitment varies through time. After 3 years, both grazing scars and fish grazing no longer predicted coral recruitment, suggesting that the role of fishes decreases over time. Our results emphasize that reef fish populations are important in promoting initial coral recovery after disturbances. However, over time, factors like the environment may become more important. Future work should continue to explore how the strength and direction of top-down control by consumers varies through time across multiple ecosystems.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".