Quantifying fish‐derived nutrient hotspots across reefscapes
Bibliographic record
Abstract
Abstract Animals aggregated on habitat patches can generate nutrient “hotspots” that enhance biogeochemical cycling and primary production, yet the conditions under which such hotspots emerge in continuous reef habitats remain unclear. This study aimed to determine how different scales of reef structural complexity regulate fish‐derived nutrient supply and associated benthic enrichment. We conducted fish surveys and high‐resolution photogrammetry across six reefscapes (~2500 m 2 each) in the Florida Keys, USA. At the 25 m 2 scale, we quantified large‐scale vertical relief and fine‐scale complexity using vector ruggedness (VRM), estimated nitrogen (N) and phosphorus (P) supply from fish bioenergetics models, and measured macroalgal tissue %N and %P. We found that fish‐derived nutrient supply increased with reef vertical relief up to ~2.8 m, beyond which supply rates saturated. VRM was positively related to nutrient supply, particularly in low‐relief areas, indicating scale‐dependent effects. Macroalgal nutrient content was non‐linearly related to supply, with uptake plateauing above ~250 mg N m −2 day −1 and ~35 mg P m −2 day −1 . Nonlinear patterns were driven by high‐relief hotspots, where nutrient supply was several times greater than surrounding reef. These findings show that mesoscale habitat complexity interacts across scales to shape consumer‐driven nutrient supply and benthic enrichment. Identifying thresholds in relief and VRM provides new insight into when and where nutrient hotspots form and offers practical guidance for targeting restoration to reef features most likely to enhance productivity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".