Identifying Priority Stepping Stone Reefs to Maintain Global Networks of Connected Coral Reefs
Bibliographic record
Abstract
Abstract Conserving coral reef climate refugia is a key conservation strategy to address climate change. Yet, the accelerating impacts of coral bleaching and other anthropogenic pressures can jeopardize refugia persistence. The dispersal of coral larvae can increase long-term reef persistence through metapopulation dynamics if connected reefs can share beneficial adaptations to bleaching and facilitate demographic recovery. Here, we modelled reef connectivity using a network approach to identify present-day coral larval dispersal networks of predicted climate refugia and then determined locations of ‘stepping stone’ reefs that can be used to connect these global networks of refugia. We identify 10 key locations of coral reef stepping stones in Indonesia, Mozambique, Glorioso Islands, and Malaysia that may ensure that 84,564km 2 of refugia could remain connected together even if other (non-refugia) reefs become degraded. Global coral reef conservation efforts should consider prioritizing these stepping stones to build more redundancy and resilience into future-proofing conservation strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".