Limited spatial rescue potential for coral reefs lost to future climate warming
Bibliographic record
Abstract
Abstract Aim The aim was to determine reef connectivity and future coral cover levels under global scenarios of coral bleaching loss and potential recovery. Location Global coral reefs. Time period Present‐day to 2100. Major taxa studied Scleractinian coral. Methods We used a global coral larval dispersal model that describes population connectivity among reefs at a resolution of ⅙° × ⅙° (c. 18 km × 18 km) cells. To simulate different patterns of bleaching events, we ran three scenarios at different levels of coral reef habitat loss followed by a reseeding of coral larvae from surviving reefs to simulate recovery. Results We found a total of 604 distinct reef networks, but more than half of the world's reef cells are contained in six large coral reef networks (294–5,494 cells), whereas the rest form smaller networks. In the bleaching scenario where previously identified predicted climate refugia were maintained, initial connectivity was largely preserved even when 71% of global coral reef habitat was lost, but the relict reef cells were unable to reseed even 50% of former coral reef habitat because many of the relict reefs are in the same networks as each other. In scenarios where refugia were lost first or with random loss, less of the initial connectivity was maintained, but more widespread reseeding was possible because more reef cells within smaller networks were maintained. Main conclusions Our findings highlight the importance of maintaining functional coral reef habitat outside of predicted climate refugia to sustain connectivity globally, and suggest an important role for “stepping stone” reefs between the climate refugia. Without attention to these issues of habitat loss and connectivity, much of global coral reef habitat might not be reseeded without human intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".