Trait‐based assembly of arbuscular mycorrhizal fungal communities determines soil carbon formation and retention
Bibliographic record
Abstract
Summary Fungi are crucial for soil organic carbon (SOC) formation, especially for the more persistent mineral‐associated organic C (MAOC) pool. Yet, evidence for this often overlooks arbuscular mycorrhizal fungi (AMF) communities and how their composition and traits impact SOC accumulation. We grew sudangrass with AMF communities representing different traits conserved at the family level: competitors, from the Gigasporaceae family; ruderals, from the Glomeraceae family; or both families combined. We labeled sudangrass with 13C‐CO2 to assess AMF contributions to SOC, impacts on SOC priming, and fungal biomass persistence in MAOC. Single‐family AMF communities decreased total SOC by 13.8%, likely due to fungal priming. Despite net SOC losses, all AMF communities contributed fungal C to soil but only the Glomeraceae community initially contributed to MAOC. After a month of decomposition, both the Glomeraceae and mixed‐family communities contributed to MAOC formation. Plant phosphorus uptake, but not hyphal chemistry, was positively related to AMF soil C and MAOC accumulation. Arbuscular mycorrhizal fungi contribution to MAOC is dependent on the specific traits of the AMF community and related to phosphorus uptake. These findings provide insight into how variations in AMF community composition and traits, and thus processes like environmental filtering of AMF, may impact SOC accumulation.
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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) | 0.000 | 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".