Unravelling the universal spatial properties of coral reefs
Bibliographic record
Abstract
Abstract Coral reefs are under rapid decline due to human pressures such as climate change. Achieving the Kunming-Montreal Global Biodiversity Framework goals, which include restoring 30% of degraded habitats like coral reefs by 2030, requires a comprehensive understanding of their extent and structure, which has been hitherto lacking. We address this limitation based on the unprecedented canonical inventory of coral reefs extracted from the Allen Coral Atlas of shallow-water tropical reefs. We identified a total of 1,579,772 individual reefs globally, extending over a total of 52,423 km 2 of ocean area with mean and median sizes of 3.32 ha and 0.3 ha, respectively. We unravelled three universal laws that are common to all coral reef provinces: the size-frequency distribution, the inter-reef distance distribution and the area-perimeter relation, which follow power laws with an exponent of 1.8, 2.33 and 1.26, respectively. We demonstrate that coral reefs develop universal fractal patterns characterised by a perimeter fractal dimension of D P = 1.3 and a surface fractal dimension of D A = 1.6. Our analysis shows that coral reefs display intricate fractal-like geometries and exhibit universal macroecological patterns, largely independent of their geographical location. The universality of the observed patterns suggests that these features possibly stem from the highly conserved interactions of biological, physical, and chemical processes. Over geological scales, these processes lead to reef landscape patterns common among all provinces, providing new information relevant to reef growth modelling.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".