Oak seedling microbiome assembly under climate warming and drought
Bibliographic record
Abstract
Despite that climate change is currently one of the most pervasive challenges, its effects on the plant-associated microbiome is still poorly studied. The aim of this study was to evaluate the impact of the independent and combinatory effect of climate warming and drought on the microbiome assembly of oak from seed to seedling. In a multifactorial experimental set up, acorns were subjected to different temperatures (15 °C, 20 °C, and 25 °C) and soil moisture levels (drought (15%) and control (60%)) from germination until the seedling stage, after which the bacterial and fungal communities associated to the rhizosphere and phyllosphere were characterized by amplicon sequencing and qPCR. The results showed a stronger effect of temperature on fungal than on bacterial diversity and the effect was more pronounced in the phyllosphere. Under drought condition, temperature had a significantly negative effect on phyllosphere fungal diversity. In the rhizosphere, temperature had a significant effect on the fungal community composition which was primarily caused by species turnover. Regardless of temperature, Actinobacteriota was significantly enriched in drought, a group of bacteria known to increase plant drought tolerance. This study provides new insights into the effect of climate change on the plant microbiome in natural 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 | 0.000 | 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) | 0.001 | 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".