Warming‐Induced Effects on Microbial Communities and Nitrogen Cycling Capacity in Tundra Litter Are Modulated by Herb Abundance and Litter Quality
Bibliographic record
Abstract
ABSTRACT Climate warming is changing tundra vegetation in the Arctic, with implications for plant litter properties. Warming may thus modify bacterial and fungal communities and their nitrogen (N) cycling capacity in the litter layer, which in turn can affect plant N availability. To address potential warming effects, we characterized the responses of bacterial and fungal communities and their genetically encoded capacity for inorganic N‐transformations in the litter layer, as well as 15 N natural abundance in the underlying soil layer as an integrated measure of N processes in the soil, in 16 long‐term alpine and Arctic tundra warming experiments distributed across 12 circumpolar locations. Although abundance, diversity, and composition of microbial communities were structured by the local conditions rather than experimental warming, warming indirectly modified microbial communities and their capacity for N transformations through changes in litter quality. Specifically, experimental warming resulted in stronger connections between the capacity for nitrification, denitrification and N‐fixation in the litter and the δ 15 N signature in the soil. These warming‐induced connections were mainly mediated by increased dominance of herbs but also increased litter mass. These findings suggest accelerated inorganic N cycling in the litter layer with warming, particularly coupled to local abundance of herbs, which can create positive feedback on plant growth as well as ecosystem respiration. Thus, microbial communities in the litter may contribute to an intensification of ongoing vegetation shifts across the tundra biome.
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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".