Soil fungal communities vary more with soil characteristics than tree diversity at a local scale
Bibliographic record
Abstract
Soil fungal communities vary spatially due to factors including variations in plant diversity and soil characteristics; however, the relative influences of these factors on composition and therefore function remain unclear. Small-scale variation in fungal communities may drive local variation in nutrient cycling and decomposition and may respond more to local factors compared with large climatic variations. Clarifying the roles of these factors can improve our predictions of soil fungal community and biogeochemical cycling responses to anthropogenic changes. Therefore, we examined relationships among abiotic and biotic factors and soil fungal communities associated with sapling and mature trees in a mixed-hardwood woodland. We also compared community composition and fungal enzymatic activity. Fungal community composition was most associated with spatial heterogeneity of soil characteristics, while sapling and mature tree species identity were poor predictors of community composition. Further, most of the compositional variation was unexplained by measured variables, suggesting stochasticity and other environmental characteristics may drive spatial variation in these communities. Additionally, enzymatic activity did not clearly correlate with fungal community composition. Overall, soil fungal communities and enzymatic activity adjacent to trees in this woodland are most likely influenced by soil characteristics and not plant species identity.
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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 | 0.001 | 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) | 0.001 | 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".