Role of virus‐mediated lysis in spatiotemporal dynamics of prokaryotic communities in river–estuary–coastal ecosystems
Bibliographic record
Abstract
Abstract Viral lysis accounts for approximately 50% of prokaryotic mortality, significantly influencing the diversity, composition, and succession of prokaryotic communities. Despite its importance, the specific influence of viral lysis on seasonal dynamics within aquatic ecosystems remains poorly understood. In this study, we investigated the seasonal dynamics of prokaryotes in the river–estuary–coastal ecosystem surrounding Xiamen Island and explored the environmental factors and virus‐mediated cell lysis driving microbial seasonal successions across spatiotemporal scales. A taxon‐specific cell lysis was applied to evaluate the contribution of viral lysis to seasonal variations in prokaryotes. Our findings revealed distinct spatiotemporal successions within the prokaryotic community structure, where temporal‐related factors, spatial‐related factors, and virus‐mediated cell lysis contribute comparably to the seasonal variation of prokaryotes. The viral lysis controls on prokaryotic structures were determined by a significant negative correlation between the total microbial community and the cell lysis index (CLI) from amplicon sequence variant (ASV) to order levels. Viral lytic shaping on prokaryotic communities was more pronounced in the estuary–coastal compared to the river region, with similar seasonal variations noted. Specific ASVs, such as ASV3 (Nitrosopumilales), ASV2 (Synechococcales), ASV16 (Nitrosopumilales), and ASV17 (Oceanospirillales) were significantly correlated with CLI, highlighting the pivotal role of viral lysis in their seasonal succession. This study highlights the intricate interplay between microbial populations and viral lysis across spatiotemporal scales, enhancing our understanding of how top‐down (virus‐mediated cell lysis) and bottom‐up (environmental factors) controls drive the seasonal variations in prokaryotic communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".