Enhanced summer warming reduces fungal decomposer diversity and litter mass loss more strongly in dry than in wet tundra
Bibliographic record
Abstract
Many Arctic regions are currently experiencing substantial summer and winter climate changes. Litter decomposition is a fundamental component of ecosystem carbon and nutrient cycles, with fungi being among the primary decomposers. To assess the impacts of seasonal climatic changes on litter fungal communities and their functioning, Betula glandulosa leaf litter was surface-incubated in two adjacent low Arctic sites with contrasting soil moisture regimes: dry shrub heath and wet sedge tundra at Disko Island, Greenland. At both sites, we investigated the impacts of factorial combinations of enhanced summer warming (using open-top chambers; OTCs) and deepened snow (using snow fences) on surface litter mass loss, chemistry and fungal decomposer communities after approximately 1 year. Enhanced summer warming significantly restricted litter mass loss by 32% in the dry and 17% in the wet site. Litter moisture content was significantly reduced by summer warming in the dry, but not in the wet site. Likewise, fungal total abundance and diversity were reduced by OTC warming at the dry site, while comparatively modest warming effects were observed in the wet site. These results suggest that increased evapotranspiration in the OTC plots lowered litter moisture content to the point where fungal decomposition activities became inhibited. In contrast, snow addition enhanced fungal abundance in both sites but did not significantly affect litter mass loss rates. Across sites, control plots only shared 15% of their fungal phylotypes, suggesting strong local controls on fungal decomposer community composition. Nevertheless, fungal community functioning (litter decomposition) was negatively affected by warming in both sites. We conclude that although buried soil organic matter decomposition is widely expected to increase with future summer warming, surface litter decay and nutrient turnover rates in both xeric and relatively moist tundra are likely to be significantly restricted by the evaporative drying associated with warmer air temperatures.
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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".