Unexpectedly high coral heat tolerance at thermal refugia
Bibliographic record
Abstract
Abstract Marine heatwaves and mass bleaching have led to global declines in coral reefs. Corals can adapt, yet, to what extent local variations in thermal stress regimes influence heat tolerance and adaptive potential remains uncertain. Here we identify persistent local-scale thermal refugia and hotspots among the reefs of a remote Pacific archipelago, based on 36 years of satellite-sensed temperatures. Theory suggests that hotspots should promote coral heat tolerance through acclimatisation and directional selection. While historic patterns of mass bleaching and marine heatwaves align with this expectation, we find a contrasting pattern for a single species, Acropora digitifera , exposed to a marine heatwave experiment. Higher heat tolerance at thermal refugia (+0.7 °C-weeks) and correlations with other traits suggest that non-thermal selective pressures may also influence heat tolerance. We also uncover widespread heat tolerance variability, indicating climate adaptation potential. Compared to the least-tolerant 10% of the A. digitifera population, the most-tolerant 10% could withstand an additional heat stress of 5.2 and 4.1 °C-weeks for thermal refugia and hotspots, respectively. Despite expectations, local-scale thermal refugia can harbour higher heat tolerance, and mass bleaching patterns do not necessarily predict species responses. This has important implications for designing climate-smart initiatives to tackle global-scale adaptive management problems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".