Synechococcus growth in the ocean may depend on the lysis of heterotrophic bacteria
Bibliographic record
Abstract
In experiments designed primarily to investigate viral lysis, we found that the presence of viruses had a positive effect on the growth of Synechococcus. A Landry-Hassett-type stepwise dilution experiment conducted during a Synechococcus bloom in the Gulf of Mexico used both (i) 0.2-µm filtered seawater in which the abundance of bacteria and grazers were reduced but the majority of viruses were retained, and (ii) ultrafiltered (30 000 MW cutoff) virus-free seawater in which the abundance of viruses, bacteria and grazers were reduced. High growth rates and frequency of dividing cells (FDCs) were recorded in 0.2-µm filtered treatments while growth was inhibited in incubations with a high proportion of virus-free ultrafiltered water. In two subsequent experiments using Mediterranean Sea populations, a two-point dilution approach in which viral abundance was reduced by 80–90% yielded similar results, and showed that Synechococcus only grew well in the presence of viruses, bacteria and grazers. In four further Mediterranean experiments viruses removed via ultrafiltration were added back, either untreated, or inactivated by a heat treatment. Growth rates and FDCs were higher in the presence of untreated viruses than with viruses inactivated by heat, suggesting that it was not organic matter in the virus-size fraction but rather the presence of infectious viruses which sustained growth. While Synechococcus was also infected by viruses during these experiments, our data imply that growth of Synechococcus may depend upon viral lysis of heterotrophic bacteria. This finding is consistent with the view that nutrient cycling by viral lysis of heterotrophic bacteria may control phytoplankton growth and ecosystem scale carbon production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".