Stoichiometric N:P Ratios, Temperature, and Iron Impact Carbon and Nitrogen Uptake by Ross Sea Microbial Communities
Bibliographic record
Abstract
Abstract Phytoplankton growth in the seasonally productive Southern Ocean is typically limited by iron (Fe). In the next century, however, Fe inputs and temperature are predicted to increase. Dissolved concentrations of nitrogen (N) and phosphorus (P) may also change due to physical and biological drivers, altering the stoichiometric N:P ratio of the water column. Two separate experiments were conducted to study how these predicted changes will impact nutrient utilization by two natural microbial communities from the Ross Sea, Antarctica. The first investigation focused on a Terra Nova Bay community amended with Fe and grown under a wide range of seven different N:P ratios. Building on the results of the first study, a McMurdo Sound community was incubated under a factorial design at high, medium (ambient), and low N:P ratios, with and without Fe addition, and at ambient versus elevated temperature. In both experiments absolute uptake rates of bicarbonate, nitrate, and amino acids by two size fractions (0.7–5.0 μm and > 5.0 μm) of microorganisms were measured using stable isotopes. For Terra Nova Bay microorganisms, significant differences in nutrient uptake rates occurred when the N:P ratio was elevated, and nitrate uptake rates significantly increased early in the experiment but were not different at the end. For McMurdo Sound microorganisms, changing the N:P supply ratio did not have a clear effect, while temperature elevation and/or Fe addition significantly increased nutrient uptake. Results indicate that changing the dissolved N:P ratio can potentially alter nutrient uptake rates; however, the impact of temperature and Fe are greater.
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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 | 0.001 | 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".