Causal drivers of climate‐mediated coral reef regime shifts
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Climate‐induced coral bleaching events are a leading threat to coral reef ecosystems and can result in coral–macroalgal regime shifts that are difficult to reverse. It is unclear how different factors causally influence regime shift or recovery trajectories after a bleaching event. Here, we use structural causal modeling (SCM) and its application of directed acyclic graphs (DAGs) to determine how key factors affect regime shift versus recovery potential across coral reefs in Seychelles, which were severely impacted by bleaching events in 1998 and 2016. Our causal models reveal additional causal drivers of regime shifts, including initial macroalgal cover, wave exposure, and branching coral cover. We also find that reduced depth and structural complexity and increased nutrients increase the likelihood of regime shifting. Further, we use a DAG‐informed predictive model to show how recovering reefs are expected to change after a recent 2016 bleaching event, suggesting that three out of 12 recovering reefs are expected to regime shift given their predisturbance conditions. Collectively, our results provide the first causally grounded analysis of how different factors influence postbleaching regime shift versus recovery potential on coral reefs. More broadly, SCM stands apart from previous observational analysis and provides a strong framework for causal inference across other observational ecological studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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 it