Viruses direct carbon cycling in lake sediments under global change
Bibliographic record
Abstract
Global change is altering the vast amount of carbon cycled by microbes between land and freshwater, but how viruses mediate this process is poorly understood. Here, we show that viruses direct carbon cycling in lake sediments, and these impacts intensify with future changes in water clarity and terrestrial organic matter (tOM) inputs. Using experimental tOM gradients within sediments of a clear and a dark boreal lake, we identified 156 viral operational taxonomic units (vOTUs), of which 21% strongly increased with abundances of key bacteria and archaea, identified via metagenome-assembled genomes (MAGs). MAGs included the most abundant prokaryotes, which were themselves associated with dissolved organic matter (DOM) composition and greenhouse gas (GHG) concentrations. Increased abundances of virus-like particles were separately associated with reduced bacterial metabolism and with shifts in DOM toward amino sugars, likely released by cell lysis rather than higher molecular mass compounds accumulating from reduced tOM degradation. An additional 9.6% of vOTUs harbored auxiliary metabolic genes associated with DOM and GHGs. Taken together, these different effects on host dynamics and metabolism can explain why abundances of vOTUs rather than MAGs were better overall predictors of carbon cycling. Future increases in tOM quantity, but not quality, will change viral composition and function with consequences for DOM pools. Given their importance, viruses must now be explicitly considered in efforts to understand and predict the freshwater carbon cycle and its future under global environmental change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".