Altitudinal distribution patterns of AM fungal assemblages in a Tibetan alpine grassland
Bibliographic record
Abstract
A better understanding of biogeography of Glomeromycota is essential for the conservation of arbuscular mycorrhizal (AM) fungal species and the ecosystem services that they provide worldwide. We examined the spatial dynamics of AM fungi along two slopes (4149 m a.s.l. to the summit at 5033 m a.s.l.) of Mount Mila on the Tibetan Plateau. Our hypothesis was that AM fungal communities at higher elevation would show distinct assemblages with lower diversity in conditions of increasing environmental harshness. A total of 52 operational taxonomic units (OTUs) spanning all four orders were detected and some OTUs were habitat specific. Nearly 30% of the OTUs were new phylotypes, including two family-like clades. Distinct communities of AM fungi were found at the higher elevation, demonstrating potential niche differentiation along the elevation gradient. Elevation patterns of taxon richness/diversity differed between the two transects, decreasing with increasing elevation on the eastern slope and being unimodal (or lacking a pattern) on the western slope. Taken together, our findings provide evidence of a significant spatial structure of AM fungi across the elevation gradient, with the distribution patterns of these fungi regulated simultaneously by the plant communities, soil properties and climatic conditions in this plateau montane ecosystem.
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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 | 0.001 | 0.001 |
| 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".