A simple nutrient-dependence mechanism for predicting the stoichiometry of marine ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is widely recognized that the stoichiometry of nutrient elements in phytoplankton varies within the ocean. However, there are many conflicting mechanistic explanations for this variability, and it is often ignored in global biogeochemical models and carbon cycle simulations. Here we show that globally distributed particulate P:C varies as a linear function of ambient phosphate concentrations, whereas the N:C varies with ambient nitrate concentrations, but only when nitrate is most scarce. This observation is consistent with the adjustment of the phytoplankton community to local nutrient availability, with greater flexibility of phytoplankton P:C because P is a less abundant cellular component than N. This simple relationship is shown to predict the large-scale, long-term average composition of surface particles throughout large parts of the ocean remarkably well. The relationship implies that most of the observed variation in N:P actually arises from a greater plasticity in the cellular P:C content, relative to N:C, such that as overall macronutrient concentrations decrease, N:P rises. Although other mechanisms are certainly also relevant, this simple relationship can be applied as a first-order basis for predicting organic matter stoichiometry in large-scale biogeochemical models, as illustrated using a simple box model. The results show that including variable P:C makes atmospheric CO2 more sensitive to changes in low latitude export and ocean circulation than a fixed-stoichiometry model. In addition, variable P:C weakens the relationship between preformed phosphate and atmospheric CO2 while implying a more important role for the nitrogen cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it