Elevated atmospheric CO<sub>2</sub> alters microbial population structure in a pasture ecosystem
Bibliographic record
Abstract
Summary An increase in concentration of atmospheric CO2 is one major factor influencing global climate change. Among the consequences of such an increase is the stimulation of plant growth and productivity. Below‐ground microbial processes are also likely to be affected indirectly by rising atmospheric CO2 levels, through increased root growth and rhizodeposition rates. Because changes in microbial community composition might have an impact on symbiotic interactions with plants, the response of root nodule symbionts to elevated atmospheric CO2 was investigated. In this study we determined the genetic structure of 120 Rhizobium leguminosarum bv. trifolii isolates from white clover plants exposed to ambient (350 μmol mol−1) or elevated (600 μmol mol−1) atmospheric CO2 concentrations in the Swiss FACE (Free‐Air‐Carbon‐Dioxide‐Enrichment) facility. Polymerase Chain Reaction (PCR) fingerprinting of genomic DNA showed that the isolates from plants grown under elevated CO2 were genetically different from those isolates obtained from plants grown under ambient conditions. Moreover, there was a 17% increase in nodule occupancy under conditions of elevated atmospheric CO2 when strains of R. leguminosarum bv. trifolii isolated from plots exposed to CO2 enrichment were evaluated for their ability to compete for nodulation with those strains isolated from ambient conditions. These results indicate that a shift in the community composition of R. leguminosarum bv. trifolii occurred as a result of an increased atmospheric CO2 concentration, and that elevated atmospheric CO2 affects the competitive ability of root nodule symbionts, most likely leading to a selection of these particular strains to nodulate white clover.
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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".