Impact of long-term fertilizer and summer warming treatments on bulk soil and birch rhizosphere microbial communities in mesic arctic tundra
Bibliographic record
Abstract
Recent climate warming in the Arctic is enhancing microbial decomposition of soil organic matter, which may result in globally significant greenhouse gas releases to the atmosphere. To better predict future impacts, bacterial and fungal community structures in both the bulk soil and the rhizosphere of Arctic birch, Betula glandulosa, were determined in control, greenhouse summer warming, and annual factorial nitrogen (N) and phosphate (P) addition treatments twelve years after their establishment. DNA sequence analyses at multiple taxonomic levels consistently indicated substantial bulk soil and rhizosphere microbial community differences among the fertilization treatments but no significant greenhouse effects. These results suggest that climate warming will likely increase the activity rates of soil microbial decomposers but without substantially altering the structure of either the bacterial or fungal communities. Differential abundance testing revealed changes in ectomycorrhizal fungal species of the genus Thelephora in both bulk soil and rhizosphere, with increases in their relative abundance in P and N + P amended plots compared with warming and controls. Because birch is the principal low Arctic ectomycorrhizal host, our results suggest that these fungi may promote this shrub’s competitiveness where tundra soil nutrient availability is enhanced by warming or other means, ultimately contributing to arctic vegetation “greening.”
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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".