Reefscape proxies for the conservation of Caribbean coral reef biodiversity
Bibliographic record
Abstract
The explanatory value of four hypotheses for geographic variation in total species richness and species richness was evaluated per family in coral and fish communities in the North Sector of the Mesoamerican Barrier Reef System (NS-MBRS). The four hypotheses emphasize different reefscape attributes that are important for coral and fish: reef area (RA), live coral cover (LCC), habitat complexity (HC), and coral richness itself and for fish. For both coral and fish communities, we estimated the total number of species and number of species per family on 11 coral reefs along a 400-km section of NS-MBRS. Hard coral cover and HC were quantified using line and chain transects, respectively, and RA was estimated using Landsat TM images and a geographic information system. We used multiple regression and canonical redundancy analysis to study the fish-environment and coral-environment relationships. The three reefscape features (RA, LCC, and HC) in combination were much stronger explanatory variables for the observed biogeographic patterns of fish and coral biodiversity than they were singly. Coral and fish species richness were strongly correlated. Indicators of functional diversity (fish trophic groups and coral morphofunctional groups) followed the same biogeographic patterns as species richness. Reefscape attributes (RA, LCC, and HC) were shown to be good proxies for critical coral reef biodiversity values. This means that simple reefscape attributes can be used to predict more complex biodiversity values of different reef areas. Such predictions can provide an invaluable guide for regional biodiversity assessments, the extrapolation of these results to unsurveyed areas, and guidance for ecoregionalization within large reef tracts where data are sparse.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".