Climate Change Drives Changes in the Size and Composition of Fungal Communities Along the Soil–Seedling Continuum of <i>Schima superba</i>
Bibliographic record
Abstract
Plant microbiomes have a major influence on forest structure and functions, as well as tree fitness and evolution. However, a comprehensive understanding of variations in fungi along the soil-plant continuum, particularly within tree seedlings, under global warming is lacking. Here, we investigated the dynamics of fungal communities across different compartments (including bulk soil and rhizosphere soil) and plant organs (including the endosphere of roots, stems and leaves) of Schima superba seedlings exposed to experimental warming and drought using AccuITS absolute quantitative sequencing. Our results revealed that warming and drought significantly reduced the number of specific fungal amplicon sequence variants (ASVs) in the bulk soil and rhizosphere soil, respectively. Variations in fungal communities were mainly explained by compartments and plant organs, with the composition of endophytic fungal communities within leaves (primarily attributed to species gain or loss) being most influenced by climate change. Moreover, warming significantly reduced the migration of Ascomycota, soil saprotrophs, wood saprotrophs and yeasts from the bulk soil to the rhizosphere soil but increased that of plant pathogens from the roots to the stems. Drought significantly decreased the absolute abundances of Chytridiomycota, Glomeromycota and Rozellomycota, as well as the migration of ectomycorrhizal fungi from the bulk soil to the rhizosphere soil but increased that of plant pathogens. Warming could indirectly reduce leaf area by increasing the diversity of leaf pathogens. These findings have potential implications for enhancing the resilience and functioning of natural forest ecosystems under climate change through the manipulation of plant microbiomes, as demonstrated in agroecosystems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".