Linking stream microbial community functional genes to dissolved organic matter and inorganic nutrients
Bibliographic record
Abstract
Abstract There is now increasing evidence for the importance of microbial regulation of biogeochemical cycling in streams. Resource availability shapes microbial community structure, but less is known about how landscape‐mediated availability of nutrients and carbon can control microbial functions in streams. Using comparative metagenomics, we examined the relationship between microbial functional genes and composition of dissolved organic matter (DOM), nutrients, and suspended microbial communities in 11 streams, divided into three groups based on the predominant land cover category (agriculture, forested, or wetland). Using weighted gene co‐occurrence network analysis, we identified clusters of functions related to DOM composition, agricultural land use, and/or wetland and forest land cover. Wetland‐dominated streams were characterized by functions related to nitrogen metabolism and processing of aromatic carbon compounds, with strong positive correlations with dissolved organic carbon concentration and DOM aromaticity. Forested streams were characterized by metabolic functions related to monomer uptake and carbohydrates, such as mannose and fructose metabolism. In agricultural streams, microbial functions were correlated with more labile, protein‐like DOM, PO4, and NO3, likely reflecting functional adaptation to labile DOM and higher nutrient concentrations. Distinct changes in the functional composition and loss of functional diversity of microorganisms became evident when comparing natural to agricultural catchments. Although all streams showed signs of functional redundancy, loss of species richness per function in agricultural catchments suggests that microbial functions in natural catchments may be more resilient to disturbance. Our results provide new insight into microbial community functions involved in nutrient and carbon biogeochemical cycles and their dependence on specific environmental settings.
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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) | 0.000 | 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) | 0.001 | 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".