Diatom Transcriptional and Physiological Responses to Changes in Iron Bioavailability across Ocean Provinces
Bibliographic record
Abstract
Changes in iron (Fe) bioavailability influence diatom physiology and community composition, and thus have a profound impact on primary productivity and ecosystem dynamics. Iron limitation of diatom growth rates has been demonstrated in both oceanic and coastal waters of the Northeast Pacific Ocean and is predicted to become more pervasive in future oceans. However, it is unclear how the strategies utilized by phytoplankton to cope with low Fe bioavailability and resupply differ across these ocean provinces. We investigated the response of diatom communities to variable Fe conditions through incubation experiments performed in the Fe mosaic of the California Upwelling Zone and along a natural Fe gradient in the Northeast Pacific Ocean. Through coupling gene expression of two dominant diatom taxa (Pseudo-nitzschia and Thalassiosira) with biological rate process measurements, we provide an in-depth examination of the physiological and molecular responses associated with varying Fe status. Following Fe enrichment, oceanic diatoms showed distinct differential expression of gene products involved in nitrogen assimilation, photosynthetic carbon fixation, and vitamin production compared to diatoms from low-Fe coastal sites, possibly driven by the chronic nature of Fe stress at the oceanic site. Genes of interest involved in Fe and N metabolism additionally exhibited divergent expression patterns between the two diatom taxa investigated, demonstrating that diverse diatoms may invoke alternative strategies when dealing with identical changes in their environment. We report here several mechanisms used distinctly by coastal or oceanic diatom communities as well as numerous taxa-specific strategies for coping with Fe stress and rearranging nutrient metabolism following Fe enrichment.
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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".