Increased precipitation, rather than warming, exerts a strong influence on arbuscular mycorrhizal fungal community in a semiarid steppe ecosystem
Bibliographic record
Abstract
Knowing the responses of arbuscular mycorrhizal (AM) fungi to warming and increased precipitation are critical for understanding how biodiversity is maintained and how the ecosystem functions under global climate-change scenarios in natural ecosystems. In this study, AM fungal communities were examined in a 6 year experiment with warming and increased precipitation, in a semiarid steppe in northern China. Only the increased precipitation, regardless of warming, significantly increased AM fungal extra-radical hyphal density, compared with the control treatment. AM fungal spore density was significantly increased by the combination of warming and increased precipitation, and increased precipitation-only treatments, but not by warming alone. A total of 36 operational taxonomic units (OTUs) of AM fungi were recovered by 454 pyrosequencing of 18S rDNA. Only increased precipitation, regardless of warming, significantly decreased AM fungal OTU richness and Shannon diversity index, and yet significantly increased AM fungal Bray–Curtis dissimilarity index, compared with the control treatment. AM fungal community composition was significantly affected by increased precipitation via water availability, but not by warming. Our findings demonstrated that the AM fungal community responded more strongly to water availability than to warming in the semiarid steppe 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 | 0.000 | 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".