Microbial Communities as Drivers of Arctic Soil Greenhouse Gas Fluxes Under Changes in Herbivory
Bibliographic record
Abstract
Recent work has shown that herbivory can indirectly affect greenhouse gas (GHG) fluxes emitted to our atmosphere by altering soil properties, and therefore the microbial community structure, but such interactions have rarely been quantified explicitly. I sampled grazed and ungrazed Arctic wetland soils from the Yukon-Kuskokwin Delta in western Alaska to examine how herbivory modifies microbial community structure, and linked these compositional shifts with trace gas fluxes through two related studies. First, I extracted DNA from the soil samples and used the QIIME2 sequencing curation pipeline to analyze microbial community structure and diversity across different wetland habitats. Second, I performed a fully factorial microcosm incubation experiment to examine how herbivory-induced shifts in soil temperature, moisture, nutrient content, and microbial community structure might impact GHG fluxes. I found that the differences in mean carbon dioxide and methane fluxes were significant at p<0.05 between different treatment combinations of grazing legacy, soil temperature, and soil moisture. I demonstrate that legacy effects of grazing on microbial communities can modify the relationship between GHG fluxes and environmental drivers.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".