Sequential Nutrient Uptake by Phytoplankton Maintains High Primary Productivity and Balanced Nutrient Stoichiometry
Bibliographic record
Abstract
Abstract. We hypothesize that phytoplankton have the sequential nutrient uptake strategy in order to maintain nutrient stoichiometry and high primary productivity in the water column. Nutrient limited phytoplankton are capable of taking up the limiting nutrient first and they take up non-limiting nutrients when the limiting nutrient debt has been overcome. We used high resolution continuous vertical profiles of nutrients, nutrient ratios and on-board ship incubation experiments to test this hypothesis in the Strait of Georgia. At the surface in summer, ambient nitrate was depleted with excess phosphate and silicate remaining, and as a result, both N : P and N : Si ratios were low. The two ratios increased to about 10 : 1 and 0.45 : 1, respectively, at 20 m. Time series of vertical profiles showed that the leftover phosphate continued to be removed, resulting in additional phosphorus storage by phytoplankton. There were various shapes of vertical profiles of N : P and at the nutricline it changed quickly in response to mixing events. A field incubation of seawater also demonstrated the sequential uptake of nitrate (the most limiting nutrient) and then phosphate and silicate (the non-limiting nutrients). This sequential uptake strategy allows phytoplankton to acquire additional cellular phosphorus and silicon when they are available and wait for nitrogen to become available through frequent mixing of nitrate (or pulsed regenerated ammonium). Thus, phytoplankton show variability of nutrient stoichiometry and are capable of maintaining high productivity by taking advantage of vigorous mixing regimes. To our knowledge, this is the first study to show the dynamics of continuous vertical profiles of N : P and N : Si ratios and to examine the responses of phytoplankton to nutrients supplied naturally by mixing events. The continuous nutrient profiles provided insight into the in situ dynamics of nutrient stoichiometry in the water column and the transient status of nutrient stoichiometry of phytoplankton in the field.
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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 | 0.001 |
| 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 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".