Effect of viral infection on sinking rates of Heterosigma akashiwo and its implications for bloom termination
Bibliographic record
Abstract
Viruses play many important roles in the ecology of marine microbes, including that of mortality agents of phytoplankton.The widespread abundance of viruses in sediments, coupled with observations that some flagellated algae lose motility when infected, prompted this investigation into the fate of infected cells and the propagation of infection throughout a population in situ.We used 2 unrelated viruses that infect the bloom-forming alga Heterosigma akashiwo as model systems.Settling columns were used to determine the impact of viral infection on the vertical movement of H. akashiwo.Within 24 h following infection with HaRNAV 263 or H. akashiwo virus strain OIs1, 20.9 ± 5.8 and 12.2 ± 1.9% of the populations were sinking at rates of 1.25 and 0.73 m d -1 , respectively.By 48 h post-infection approximately 50% of cells infected with either virus had lysed.As sinking cells would encounter decreasing levels of light in the natural environment, we incubated infected cultures in complete darkness and compared the length of the lytic cycle to that of cultures incubated under a L:D cycle.Again, approximately 50% of the population lysed by 48 h, regardless of the light conditions or type of virus, and lysis of the entire population was complete by 144 h.Based on these results, we propose that, depending on the characteristics of the water column, there are 3 potential fates for viruses from cells infected in situ.In water with a deep mixed layer (> 8 m), lysis of infected cells would occur within the mixed layer and the infection would be propagated within the euphotic zone.If the water is relatively deep (100s of meters), and the mixed layer shallow (< 8 m), cell lysis and viral release would occur below the mixed layer, where viral propagation would not occur.Finally, in shallow waters (10s of meters) lysis of infected cells would occur at the sediment surface, resulting in the accumulation of a high abundance of viruses.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".