Large-diameter trees and deadwood correspond with belowground ectomycorrhizal fungal richness
Bibliographic record
Abstract
Abstract Background Large-diameter trees have an outsized influence on aboveground forest dynamics, composition, and structure. Although their influence on aboveground processes is well studied, their role in shaping belowground fungal communities is largely unknown. We sought to test if (i) fungal community spatial structure matched aboveground forest structure; (ii) fungal functional guilds exhibited differential associations to aboveground trees, snags, and deadwood; and (iii) that large-diameter trees and snags have a larger influence on fungal community richness than smaller-diameter trees. We used MiSeq sequencing of fungal communities collected from soils in a spatially intensive survey in a portion of Cedar Breaks National Monument, Utah, USA. We used random forest models to explore the spatial structure of fungal communities as they relate to explicitly mapped trees and deadwood distributed across 1.15 ha of a 15.32-ha mapped subalpine forest. Results We found 6,177 fungal amplicon sequence variants across 117 sequenced samples. Tree diameter, deadwood presence, and tree species identity explained more than twice as much variation (38.7% vs. 10.4%) for ectomycorrhizal composition and diversity than for the total or saprotrophic fungal communities. Species identity and distance to the nearest large-diameter tree (≥ 40.2 cm) were better predictors of fungal richness than were the identity and distance to the nearest tree. Soil nutrients, topography, and tree species differentially influenced the composition and diversity of each fungal guild. Locally rare tree species had an outsized influence on fungal community richness. Conclusions These results highlight that fungal guilds are differentially associated with the location, size, and species of aboveground trees. Large-diameter trees are implicated as drivers of belowground fungal diversity, particularly for ectomycorrhizal fungi.
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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 | 0.000 | 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) | 0.002 | 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".