Evaluating the Effects of Wildfires on Microbial Communities and Gene Expression Involved in Nitrogen Cycling in Arctic Tundra Soils
Bibliographic record
Abstract
Wildfires in the Arctic tundra are increasing in frequency in response to global warming.Under this changing fire regime, important components of permafrost ecosystems, such as soils and their microbial communities, are disturbed by the burning of vegetation, organic soils, and their nutrients. Previous studies of wildfires have documented significant changes in nutrient availability following wildfires, including an increase in inorganic nitrogen (N), which is a key nutrient component of soil organisms. Microbes are the main drivers of transformation of soil N, which is ultimately taken up by both microbes and plants as a nutrient source. However, due to the historical low occurrence of wildfires in tundra ecosystems, the effects of fire on microbial communities involved in N-cycling in tundra soils are underexplored. Peat plateau tundra soils from the Yukon Kuskokwim Delta, Alaska, with documented fire history from 1972 and 2015 were sampled to evaluate the postfire repercussions on soil microbial communities and gene expression involved in N cycling through 16S rRNA amplicon sequencing and metatranscriptomic analyses. A decrease in microbial species richness and changes in community composition were observed in the humic horizon 7- and 50-years post-fire. In mineral soils, total N stocks and dissolved N increased post-fire. However, no changes in N stocks or N-related gene expression were observed between burned and unburned soils in the humic horizon, indicating that despite changes in microbial community composition, ecological function is restored to promote nutrient recovery in tundra soils.
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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 | 0.001 | 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 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".