The Loss of Beneficial Thermal Priming on Global Coral Reefs
Bibliographic record
Abstract
Warm-season marine heatwaves (MHWs) have greatly increased in frequency, severity, and extent over the last few decades, driving more frequent and severe coral bleaching episodes. Given the grave near-term threat to coral reefs imposed by MHWs, it is important to assess the mechanisms by which corals may acquire higher thermal tolerance. Recent field and laboratory studies have demonstrated that exposure to sublethal heat stress, known as "priming," can reduce bleaching susceptibility during a subsequent MHW. Little is known, however, about how often priming conditions occur, and how effective those conditions may be at protecting coral reefs. We employed a global historical coral bleaching database and a high-resolution sea surface temperature dataset to assess the frequency of priming and examine its effect on coral bleaching sensitivity on a global scale. The analysis showed that coral reefs in parts of the western to central tropical Pacific experienced priming on average over twice a decade and had a higher likelihood of priming protection. Mixed-effects regression models indicated that priming conditions could mitigate coral bleaching response by up to 12% in advance of a moderate MHW. However, the protective effect of priming decreased, and even became harmful, with more severe MHWs. We detected spatial variations in priming frequency that could provide insight for conservation planning and explain some variations in bleaching sensitivity to MHWs. Even so, our findings suggest that thermal priming will not be sufficient to protect most coral reefs from MHWs in the future, without substantial efforts to mitigate climate 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".