Latitudinal patterns in ocean C:N:P reflect phytoplankton acclimation and macromolecular composition
Bibliographic record
Abstract
The proportions of carbon (C), nitrogen (N), and phosphorus (P) in surface ocean particulate matter deviate greatly from the canonical Redfield Ratio (C:N:P = 106:16:1) in space and time with significant implications for global carbon storage as this matter reaches the deep ocean. Recent work has revealed clear latitudinal patterns in C:N:P, yet the relative importance of ecological, physiological, or biochemical processes in creating these patterns is unclear. We present high-resolution, concurrent measurements of particulate C:N:P, macromolecular composition, environmental conditions, and plankton community composition from a transect spanning a subtropical-subpolar boundary, the North Pacific Transition Zone. We find that the summed contribution of macromolecules to particulate C, N, and P is consistent with, and provides interpretation for, particulate C:N:P patterns. A decline in particulate C:N from the subtropical to subpolar North Pacific largely reflects an increase in the relative contribution of protein compared to carbohydrate and lipid, whereas variation in C:P and N:P correspond to shifts in protein relative to polyphosphate, DNA, and RNA. Possible causes for the corresponding trends in C:N and macromolecular composition include physiological responses and changes in community structure of phytoplankton, which represented approximately 1/3 rd of particulate C across the transect. Comparison with culture experiments and an allocation-based model of phytoplankton macromolecular composition suggest that physiological acclimation to changing nutrient supply is the most likely explanation for the latitudinal trend in C:N, offering both a mechanistic interpretation and biochemical basis for large-scale patterns in C:N:P.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".