Resilience of Arctic mycorrhizal fungal communities after wildfire facilitated by resprouting shrubs
Bibliographic record
Abstract
Climate-induced changes in the tundra fire regime are expected to alter shrub abundance and distribution across the Arctic. However, little is known about how fire may indirectly impact shrub performance by altering mycorrhizal symbionts. We used molecular tools, including ARISA and fungal ITS sequencing, to characterize the mycorrhizal communities on resprouting Betula nana shrubs across a fire-severity gradient after the largest tundra fire recorded in the Alaskan Arctic (July-October 2007). Fire effects on the components of fungal composition were dependant on the scale of taxonomic resolution. Variation in fungal community composition was correlated with fire severity. Fungal richness and relative abundance of dominant taxa declined with increased fire severity. Yet, in contrast to temperate and boreal regions with frequent wildfires, mycorrhizal fungi on resprouting shrubs in tundra were not strongly differentiated into fire-specialists and fire-sensitive fungi. Instead, dominant fungi, including taxa characteristic of late successional stages, were present regardless of fire severity. It is likely that the resprouting life history strategy of tundra shrubs confers resilience of dominant mycorrhizal fungi to fire disturbance by maintaining an inoculum source on the landscape after fire. Based on these results, we suggest that resprouting shrubs may facilitate post-fire vegetation regeneration and potentially the expansion of trees and shrubs under predicted scenarios of increased warming and fire disturbance in Arctic tundra.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".