Interactions between below‐ground traits and rhizosheath fungal and bacterial communities for phosphorus acquisition
Bibliographic record
Abstract
Abstract Plant–soil–microbe interactions play a central role in plant nutrient acquisition and thus ecosystem functioning and nutrient availability in agroecosystems. Adjustments in root morphology, root exudation and associations with micro‐organisms such as arbuscular mycorrhizal fungi are common for phosphorus acquisition. Yet how plant below‐ground functional traits interact with microbial communities for P acquisition remains largely unknown, limiting our understanding of phosphorus availability in agroecosystems. Interactions between below‐ground functional traits and rhizosheath soil microbial communities for P acquisition were investigated across eight herbaceous species with contrasting root traits. Root morphological and physiological traits involved in P acquisition were quantified simultaneously with PLFA (phospholipid fatty acid) and NLFA (neutral lipid fatty acid) microbial bioindicators. Multiple correlations were observed between root morphology, root exudates and rhizosheath fungal and bacterial communities. Root exudates and in particular release of malate and malonate were strongly linked with indicators of Gram‐negative bacteria, which were correlated with changes in rhizosheath soil P concentration and plant P content. Our results suggest that root exudation of carboxylates may play an important role in plant–soil–microbe interactions for P acquisition, underlining their likely role in shaping microbial communities. Incorporating these interactions in biogeochemical models would lead to better predicting power and understanding of P cycling and ecosystem functioning. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.000 | 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".