Coral reef recovery in a remote and large marine protected area is resilient to cascading trophic interactions
Bibliographic record
Abstract
Large marine protected areas (MPAs) will likely multiply worldwide to address accelerating biodiversity loss. Coral reefs are an especially at-risk ecosystem, but our understanding of whether and how large MPAs will benefit reef recovery from disturbances remains limited. Here, we evaluate how fishes influence coral reef recovery and if there are cascading trophic interactions in the fish community that influence recovery. We combine i) a meta-analysis of previous research evaluating how fishes affect coral reef succession, ii) field surveys of sharks, fishes, and benthic cover, and iii) a 2.5-year coral recruitment experiment of caged, uncaged, and partially caged settlement tiles in a remote and large MPA, in the Chagos Archipelago. In both our meta-analysis and field experiment, we found variable effects of fishes on coral recruitment with no overall significant effect, but strong control by fishes on the developing benthic communities, where fishes promote crustose coralline algae and reduce macroalgae. Within the Chagos, fishes had a positive effect on corals in protected microhabitats but a negative effect on exposed surfaces, leading to an overall neutral effect from fishes. Although mesopredatory fish abundance was negatively correlated with shark abundance, these top-down effects did not cascade down the food web through herbivores and corallivores to coral recruitment and benthic succession. Thus, our results suggest that within this large MPA, herbivores are important in limiting algae during recovery and there are compensatory responses to the loss of benthic feeding fishes and sharks, with implications for coral reef persistence in the face of global change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".