Linkages between changes in plant and mycorrhizal fungal community composition at high versus low elevation in alpine ecosystems
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMF) play an important role in maintaining plant diversity and productivity in grassland ecosystems. However, very few studies have investigated how AMF and plant communities co-vary between contrasting environments in natural ecosystems. Intensive sampling (50 soil samples) was conducted in natural open grasslands at both 3570 and 4556 m on Mount Segrila on the Southeast Tibetan Plateau. We used 454-pyrosequencing to investigate soil AMF communities and to explore relationships between AMF diversity and plant richness, productivity and community composition. AMF diversity was negatively correlated with plant richness at 3570 m but positively at 4556 m. Differences in AMF community composition between elevations were attributable to plant community composition, soil pH and available phosphorus concentration. The AMF community was more phylogenetically clustered at the higher elevation than the lower elevation. However, greater phylogenetic clustering (under dispersion) of AMF communities at the two elevations was positively correlated with above-ground biomass. Our results indicate that plant community composition and environmental filtering are the primary drivers structuring the AMF community. Phylogenetic relatedness may be important in explaining the function of AMF communities in alpine ecosystems.
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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 | 0.000 |
| 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".