Springtime phytoplankton responses to light and iron availability along the western Antarctic Peninsula
Bibliographic record
Abstract
Abstract Light and iron availability are intertwined in controlling Southern Ocean primary production because several photosynthetic proteins require iron. Changes in light and iron availability can also affect phytoplankton species composition, which impacts nutrient cycling, carbon drawdown, and food web structure. To investigate the interactive effects of light and iron on phytoplankton growth, photosynthesis, photoacclimation strategy, micronutrient stress‐induced protein expression, and species composition, we conducted five bioassay experiments during spring in waters along the western Antarctic Peninsula with four treatments: low light (LL) or high light (HL) combined with or without iron addition. This region has rarely been studied in spring. We found that light limits growth while iron does not, despite overall low iron concentrations. Our results demonstrate that phytoplankton were LL acclimated in situ but photosynthetically optimized for higher light than they were experiencing, likely due to a highly dynamic light regime. Expression patterns of micronutrient stress‐induced proteins were consistent with iron stress in off‐shelf regions, but remarkably this iron stress did not result in lower carbon fixation and growth rates. Notably, manganese drawdown was highest under elevated light, suggesting a possible role in managing HL, although high flavodoxin expression indicated that Phaeocystis antarctica may not have been manganese‐limited. Although light and iron treatments did not impact species composition, high methionine synthase indicated that diatoms could have experienced stress induced by low vitamin B12, potentially contributing to P. antarctica's general dominance throughout the experiments. Our results indicate that P. antarctica may be better adapted to spring conditions than diatoms.
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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 | 0.001 | 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".