Arbuscular mycorrhizal fungal communities with contrasting life-history traits and trait diversity influence host nutrient acquisition
Bibliographic record
Abstract
Abstract Life-history traits differ substantially among arbuscular mycorrhizal (AM) fungal families, potentially affecting hyphal nutrient acquisition efficiency, host nutrition, and thereby plant health and ecosystem function. Despite these implications, AM fungal community life-history strategies and community trait diversity effects on host nutrient acquisition are poorly understood. To address this knowledge gap, we grew Sudan grass (Sorghum sudanense) with AM fungal communities representing contrasting life-history traits and trait diversity: either 1) five species in the AM family Gigasporaceae, representing competitor traits, 2) five species in the family Glomeraceae, representing ruderal traits, or 3) a mixed-family community combining all ten AM species. After 12 weeks, we measured above and below ground plant biomass and the uptake and concentration of 12 nutrients in aboveground biomass. Overall, AM fungal colonization increased host nutrition, biomass, and foliar 15nitrogen enrichment compared to the uncolonized control. We observed the largest effects between the mixed-family community and the single-family communities for plant tissue quality, especially plant phosphorus (P), and in colonization rates. The mixed community increased plant P 1.2 and 1.3 times more than Glomeraceae and Gigasporaceae communities. However, this higher P did not translate to the greatest gains in plant biomass. Between the single-family communities, the Glomeraceae community generally outperformed the Gigasporaceae community in host nutrition and plant growth, increasing plant P concentrations 1.1 times more than the Gigasporaceae community. These findings demonstrate that AM fungal community trait composition established at the family level affects plant nutrition and that AM family diversity increases colonization and plant tissue quality.
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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) | 0.000 | 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".