Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity
Bibliographic record
Abstract
Classic marine ecological paradigms view kelp forests as inherently temperate-boreal phenomena replaced by coral reefs in tropical waters. These paradigms hinge on the notion that tropical surface waters are too warm and nutrient-depleted to support kelp productivity and survival. We present a synthetic oceanographic and ecophysiological model that accurately identifies all known kelp populations and, by using the same criteria, predicts the existence of >23,500 km(2) unexplored submerged (30- to 200-m depth) tropical kelp habitats. Predicted tropical kelp habitats were most probable in regions where bathymetry and upwelling resulted in mixed-layer shoaling above the depth of minimum annual irradiance dose for kelp survival. Using model predictions, we discovered extensive new deep-water Eisenia galapagensis populations in the Galápagos that increased in abundance with increasing depth to >60 m, complete with cold-water flora and fauna of temperate affinities. The predictability of deep-water kelp habitat and the discovery of expansive deep-water Galápagos kelp forests validate the extent of deep-water tropical kelp refugia, with potential implications for regional productivity and biodiversity, tropical food web ecology, and understanding of the resilience of tropical marine systems to climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".