Genomic adaptation of marine phytoplankton populations regulates phosphate uptake
Bibliographic record
Abstract
Abstract In this study, we combined “reciprocal transplant experiments,” cell‐sorting, and metagenomics to understand how phytoplankton adapt to differences in phosphate availability and the implications for nutrient uptake rates. Reciprocal transplant experiments were conducted on six stations ranging from cold, nutrient‐rich water in the Labrador Sea to warm, extremely P‐deplete water in the Sargasso Sea. In most cases, the direct impact of environmental conditions and likely P availability was the strongest control on phosphate uptake. However, especially the transplant experiments between the northern and southern stations revealed that there are situations where changes in community composition and functional genes have an important effect on uptake rates. Phytoplankton lineages responded uniquely to changing environmental conditions. The picoeukaryotic phytoplankton P uptake response was strongly regulated by the phosphate concentration, whereas the effect of community composition was larger for Prochlorococcus and Synechococcus. In support, we found a tight negative relationship between ambient phosphate concentration and the frequency of P acquisition genes in both Prochlorococcus and Synechococcus, and such differences in genome content could be linked to lineage‐specific shifts in uptake rates. Linking genes with ocean biogeochemistry is a major scientific and technical challenge and most studies rely on correlations between genotypes and environmental conditions. However, our study demonstrates how reciprocal transplant experiments are a possible tool for understanding the relative role of environmental condition vs. plankton diversity in regulating important open ocean ecosystem processes.
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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".