Cetacean‐mediated vertical nitrogen transport in the oceanic realm
Bibliographic record
Abstract
Abstract In natural systems, animal‐mediated nutrient transport can be a major driver of primary productivity, but the role of marine megafauna such as cetaceans in mediating the transfer and recycling of nutrients has been overlooked. Here, we developed a spatially resolved, stochastic, nutrient‐transport model for cetaceans in the oceanic Gulf of Mexico using species−specific foraging depths, distributions, and diets. An estimated 6.4 × 108 mmol N d−1, or 0.06 mt N yr−1 ind−1, is transported to the surface from depths below 100 m by the 19 cetacean species that occur in the oceanic Gulf of Mexico; 75% of this transport occurs seaward of the continental slope, but the per area transported nitrogen is greater on the continental slope (200–1000 m) than in the ocean basin. Benthos to surface transport comprised 6.0 × 107 mmol N d−1 and was much more common on the continental slope than the open basin. Compared to an existing physical‐biogeochemical model, the transported nutrients add 8% N d−1 to the estimated ammonium concentration above the nutricline and could add 16% N d−1 to the surface ammonium concentration if expelled nutrients remain at the surface. Through feeding on diel vertical migrants, cetaceans retain an additional 2.7 × 107 mmol N d−1 in the surface waters that would otherwise return to depth via downward diel vertical migration. Cetaceans contribute to nutrient movements and recycling in the oceanic Gulf of Mexico, and may provide one of the few allochthonous sources of nutrients for primary producers in oligotrophic ecosystems.
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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 | 0.001 |
| Open science | 0.001 | 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".