Phylogenetically related fast-growing trees and their root traits affect soil microbial communities
Bibliographic record
Abstract
The soil microbiome plays a key role in tree growth and nutrient cycling. Although hybrid poplar clones are phylogenetically related, their influence on the soil microbiome may differ due to phenotypic differences, especially their root mean diameter or mass density (RMD). This influence remains relatively unexplored, limiting our understanding of how soil microbial communities contribute to the growth strategies of fast-growing trees. Our objective was to determine how phylogenetically related trees and their root traits influence soil microbial diversity and composition by studying five hybrid poplar clones growing in a plantation located in New Liskeard, Ontario, Canada. We collected soil cores at depths of 0–20 cm (topsoil) and 20–40 cm (subsoil) and analyzed fine root traits and soil bacterial and fungal communities. We found that phylogenetically related hybrid poplars influenced the soil microbiome by shaping the composition of microbial communities. Differences between clones were evident in the relative abundance of soil ectomycorrhizal fungi and Actinobacteriota in both topsoil and subsoil. The increase in relative abundance of ectomycorrhizal fungi was driven by high RMD and root length density (RLD), i.e., high fine root mass and length per unit soil volume. Fine roots with high RLD led to a higher relative abundance of Actinobacteriota in the topsoil, while recalcitrant fine roots (high lignin/nitrogen ratio) promoted their abundance in the subsoil. Thus, root traits are key factors in determining the effects of trees on soil microbiome. The soil microbiome, together with root traits, could be an important aspect to consider in tree selection.
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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 | 0.000 | 0.000 |
| 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".