Highly variable taxa-specific coral bleaching responses to thermal stresses
Bibliographic record
Abstract
Complex histories of chronic and acute sea surface temperature (SST) stresses are expected to trigger taxon- and location-specific responses that will ultimately lead to novel coral communities. The 2016 El Niño-Southern Oscillation provided an opportunity to examine large-scale and recent environmental histories on emerging patterns in 226 coral communities distributed across 12 countries from East Africa to Fiji. Six main coral communities were identified that largely varied across a gradient of Acropora to massive Porites dominance. Bleaching intensity was taxon-specific and was associated with complex interactions among the 20 environmental variables that we examined. Coral community structure was better aligned with the historical temperature patterns between 1985 and 2015 than the 2016 extreme temperature event. Additionally, bleaching responses observed during 2016 differed from historical reports during past warm years. Consequently, coral communities present in 2016 are likely to have been reorganized by both long-term community change and acclimation mechanisms. For example, less disturbed sites with cooler baseline temperatures, higher mean historical SST background variability, and infrequent extreme warm temperature stresses were associated with Acropora-dominated communities, while more disturbed sites with lower historical SST background variability and frequent acute warm stress were dominated by stress-resistant massive Porites corals. Overall, the combination of taxon-specific responses, community-level reorganization over time, geographic variation, and multiple environmental stressors suggest complex responses and a diversity of future coral communities that can help contextualize management priorities and activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".