Molecular underpinnings and biogeochemical consequences of enhanced diatom growth in a warming Southern Ocean
Bibliographic record
Abstract
Abstract The Southern Ocean (SO) harbours some of the most intense phytoplankton blooms on Earth. Changes in temperature and iron availability are expected to alter the intensity of SO phytoplankton blooms, but little is known about how environmental change will influence community composition and downstream biogeochemical processes. We performed experimental manipulations on surface ocean microbial communities from McMurdo Sound in the Ross Sea, with and without iron addition, at −0.5 °C, 3 °C, and 6 °C. We then examined nutrient uptake patterns as well as the growth and molecular responses of two dominant diatoms, Fragilariopsis and Pseudo-nitzschia , to these conditions. We found that nitrate uptake and primary productivity were elevated at increased temperature in the absence of iron addition, and were even greater at high temperature with added iron. Pseudo-nitzschia became more abundant under increased temperature without added iron, while Fragilariopsis required additional iron to benefit from warming. We attribute the apparent advantage Pseudo-nitzschia shows under warming to upregulation of iron-conserving photosynthetic processes, utilization of iron-economic nitrogen assimilation mechanisms, and increased iron uptake and storage. These data identify important molecular and physiological differences between dominant diatom groups and add to the growing body of evidence for Pseudo-nitzschia ’s increasingly important role in warming SO ecosystems. This study also suggests that temperature-driven shifts in SO phytoplankton assemblages may increase utilization of the vast pool of excess nutrients in iron-limited SO surface waters, and thereby influence global nutrient distributions and carbon cycle. Significance Statement Phytoplankton assemblages contribute to the Southern Ocean’s ability to absorb atmospheric CO 2 , form the base of marine food webs, and shape the global distribution of macronutrients. Anthropogenic climate change is altering the SO environment, yet we do not fully understand how resident phytoplankton will react to this change. By comparing the responses of two prominent SO diatom groups to changes in temperature and iron in a natural community, we find that one group, Pseudo-nitzschia , grows better under warmer low-iron conditions by managing cellular iron demand and efficiently increasing photosynthetic capacity. This ability to grow and draw down nutrients in the face of warming, regardless of iron availability, may have major implications for ocean ecosystems and global nutrient and carbon cycles.
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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".