Increase in the extent of mass coral bleaching over the past half-century, based on an updated global database
Bibliographic record
Abstract
The recurrence of mass coral bleaching and associated coral mortality in the past few decades have raised questions about the future of coral reef ecosystems. Although coral bleaching is well studied, our understanding of the spatial extent of bleaching events continues to be limited by geographical biases in data collection. To address this gap, we updated a previous observational database and spatially modelled the probability of past bleaching occurrence. First, an existing raw observational database was updated to cover the 1963-2017 period using searches of the academic and grey literature and outreach to coral reef monitoring organizations. Then, in order to provide spatially-explicit global coverage, we employed indicator kriging to spatially model the probability of bleaching occurrence each year from 1985 through 2017 at 0.05° x 0.05° lat-long resolution. The updated raw database has 37,774 observations, including 22,650 positive bleaching reports, three times that in the previous version. The spatial interpolation suggests that 71% of the world's coral reefs likely (>66% probability) experienced bleaching at least once during the 1985 and 2017 period. The mean probability of bleaching across all reefs globally was 29-45% in the most severe bleaching years of 1998, 2005, 2010 and 2016. Modelled bleaching probabilities were positively related with annual maximum Degree Heating Weeks (DHW), a measure of thermal stress, across all years (p<0.001), and in each global bleaching event (p<0.01). In addition, the annual maximum DHW of reef cells that very likely (>90% probability) experienced bleaching increased over time at three times the rate of all reef cells, suggesting a possible increase in reef thermal tolerance. The raw and spatially interpolated databases can be used by other researchers to enhance real-time predictions, calibrate models for future projections, and assess the change in coral reef response to thermal stress over time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".