Legacy effects of anthropogenic disturbances modulate dynamics in the world's coral reefs
Bibliographic record
Abstract
Rapidly changing conditions alter disturbance patterns, highlighting the need to better understand how the transition from pulse disturbances to more persistent stress will impact ecosystem dynamics. We conducted a global analysis of the impacts of 11 types of disturbances on reef integrity using the rate of change of coral cover as a measure of damage. Then, we evaluated how the magnitude of the damage due to thermal stress, cyclones, and diseases varied among tropical Atlantic and Indo-Pacific reefs and whether the cumulative impact of thermal stress and cyclones was able to modulate the responses of reefs to future events. We found that reef damage largely depends on the condition of a reef before a disturbance, disturbance intensity, and biogeographic region, regardless of the type of disturbance. Changes in coral cover after thermal stress events were largely influenced by the cumulative stress of past disturbances and did not depend on disturbance intensity or initial coral cover, which suggests that an ecological memory is present within coral communities. In contrast, the effect of cyclones (and likely other physical impacts) was primarily modulated by the initial reef condition and did not appear to be influenced by previous impacts. Our findings also underscore that coral reefs can recover if stressful conditions decrease, yet the lack of action to reduce anthropogenic impacts and greenhouse gas emissions continues to trigger reef degradation. We uphold that evidence-based strategies can guide managers to make better decisions to prepare for future disturbances.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".