Metatranscriptomics reveals declines in ice cover influence winter viral community activity
Bibliographic record
Abstract
Freshwater lakes are sentinels of environmental change, and climate change-driven declines in ice cover have been shown to disrupt aquatic communities and jeopardize ecosystem services. Viruses shape microbial communities and regulate biogeochemical cycles by acting as top-down controls, yet there is relatively little known about how declining ice cover will influence viral community activity. Lake Erie is a critical freshwater ecosystem and serves as a model system to assess how ice cover extent will affect winter limnology. We surveyed size selected surface water metatranscriptomes for conserved viral hallmark genes as a proxy for active virus populations and compared activity profiles between ice-covered and ice-free conditions from two contrasting winters. Active virus communities were present in both conditions, spanning diverse phylogenetic clades of bacteriophage ( Caudovirales ), giant viruses ( Nucleocytoviricota ), and RNA viruses ( Orthornavirae ). However, viral activity was significantly shaped by the extent of ice cover. Notably, viral richness and relative transcript abundance in the surface waters were reduced under ice relative to the ice-free conditions. Correlations with microbial community metrics suggest the differences in viral communities are at least in part driven by the decreased winter diatom bloom associated with declines in ice cover. Overall, our data suggest viral community activity is influenced by ice cover extent, and viruses may serve as sentinels of environmental disturbance and ecosystem response(s) to climate change.
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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".