Tree diversity is not always a strong driver of soil microbial diversity: a 7‐yr‐old diversity experiment with trees
Bibliographic record
Abstract
Abstract Trees provide organic substrates in the form of root exudates, litterfall, and fine root turnover. They modify soil physical properties and support soil biological activities. Therefore, trees are hypothesized to control soil biodiversity in forested areas. We predicted that (1) experimental forest plantations with higher tree alpha‐diversity have greater soil microbial alpha‐diversity and (2) that plantations with more divergent tree community composition would have more divergent soil microbial assemblages (Whitaker's beta‐diversity). We tested these predictions by measuring soil bacteria and fungi in a 7‐yr‐old tree biodiversity experiment. The experimental plantation contained 37 different tree assemblages, which were composed of one to four native species from temperate mixed deciduous forests. Further, there was a gradient of functional diversity nested within each level of species diversity. Soil samples were assessed for bacteria and fungi by amplicon sequencing. Tree alpha‐diversity weakly, but significantly, affected bacterial alpha‐diversity, without affecting fungal alpha‐diversity. Tree community composition was weakly, but significantly, linked to soil bacterial and fungal assemblages. In these 7‐yr‐old experimental plantations, tree diversity was not the most influential driver of soil microbial diversity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".