Ectomycorrhizal fungal hyphae communities vary more along a pH and nitrogen gradient than between decayed wood and mineral soil microsites
Bibliographic record
Abstract
Ectomycorrhizal (ECM) fungal community composition is structured by soil properties, but specialization for woody microsites by ECM fungi is equivocal. Because fungal mycelia explore the substrate and colonize nutrient patches, studies targeting ECM fungal hyphae may reveal niche preferences. Moreover, studying the distribution and composition of ECM fungal hyphal communities contributes to our understanding of nutrient cycling in forest soils. We used next-generation sequencing to determine whether the composition of forest floor fungal communities present as hyphae differed among three microsite types: decayed wood, mineral soil adjacent to intact logs, or control mineral soil of mature spruce forests in British Columbia. The microsites were located in three blocks that were separated by 1 km and varied in elevation. Across the site, the ECM fungal lineage /amphinema–tylospora was the most operational taxonomic unit (OTU)-rich group, while the saprotrophic order Mortierellales was also dominant. ECM fungal species differed among microsites. For example, ECM fungal OTUs identified as Tylospora fibrillosa and Russula curtipes were more frequent in decayed wood as compared with control mineral soil. However, ECM fungal communities were more strongly structured by block characteristics, and we conclude there is no distinct group of ECM fungi specializing in the soil microsites examined in this forest.
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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 | 0.001 |
| 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) | 0.001 | 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".