Changes in soil fungal community composition depend on functional group and forest disturbance type
Bibliographic record
Abstract
Disturbances have altered community dynamics in boreal forests with unknown consequences for belowground ecological processes. Soil fungi are particularly sensitive to such disturbances; however, the individual response of fungal guilds to different disturbance types is poorly understood. Here, we profiled soil fungal communities in lodgepole pine forests following a bark beetle outbreak, wildfire, clear-cut logging, and salvage-logging. Using Illumina MiSeq to sequence ITS1 and SSU rDNA, we characterized communities of ectomycorrhizal, arbuscular mycorrhizal, saprotrophic, and pathogenic fungi in sites representing each disturbance type paired with intact forests. We also quantified soil fungal biomass by measuring ergosterol. Abiotic disturbances changed the community composition of ectomycorrhizal fungi and shifted the dominance from ectomycorrhizal to saprotrophic fungi compared to intact forests. The disruption of the soil organic layer with disturbances correlated with the decline of ectomycorrhizal and the increase of arbuscular mycorrhizal fungi. Wildfire changed the community composition of pathogenic fungi but did not affect their proportion and diversity. Fungal biomass declined with disturbances that disrupted the forest floor. Our results suggest that the disruption of the forest floor with disturbances, and the changes in C and nutrient dynamics it may promote, structure the fungal community with implications for fungal biomass-C.
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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 | 0.000 |
| 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".