Coral identity underpins architectural complexity on Caribbean reefs
Bibliographic record
Abstract
The architectural complexity of ecosystems can greatly influence their capacity to support biodiversity and deliver ecosystem services. Understanding the components underlying this complexity can aid the development of effective strategies for ecosystem conservation. Caribbean coral reefs support and protect millions of livelihoods, but recent anthropogenic change is shifting communities toward reefs dominated by stress-resistant coral species, which are often less architecturally complex. With the regionwide decline in reef fish abundance, it is becoming increasingly important to understand changes in coral reef community structure and function. We quantify the influence of coral composition, diversity, and morpho-functional traits on the architectural complexity of reefs across 91 sites at Cozumel, Mexico. Although reef architectural complexity increases with coral cover and species richness, it is highest on sites that are low in taxonomic evenness and dominated by morpho-functionally important, reef-building coral genera, particularly Montastraea. Sites with similar coral community composition also tend to occur on reefs with very similar architectural complexity, suggesting that reef structure tends to be determined by the same key species across sites. Our findings provide support for prioritizing and protecting particular reef types, especially those dominated by key reef-building corals, in order to enhance reef complexity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".