Plant–Soil Microbial Interaction: Differential Adaptations of Beneficial vs. Pathogenic Bacterial and Fungal Communities to Climate-Induced Drought
Bibliographic record
Abstract
Climate change and the increasing frequency and severity of drought events pose significant challenges for sustainable agriculture worldwide. Soil microorganisms, both beneficial and pathogenic, play a crucial role in mediating plant–environment interactions and shaping the overall functioning of agroecosystems. This review summarizes current knowledge on the adaptive mechanisms used by different groups of plant-beneficial soil microorganisms—rhizobacteria and arbuscular mycorrhizal fungi (AMF)—as well as phytopathogenic bacteria and fungi, in response to drought. The review focuses on identifying the commonalities and differences in the survival strategies of these groups of beneficial and pathogenic soil microorganisms under drought conditions. Additionally, it reviews and compares the plant defence mechanisms under drought conditions facilitated by rhizobacteria and AMF. Special attention is given to the genetic exchange between beneficial and pathogenic soil microorganisms through horizontal gene transfer (HGT), which allows them to exchange traits. It is observed that drought may favor enhanced genetic exchange and the spread of pathogenic traits in the soil microbiome. This review will be useful for a wide range of readers to better understand the dynamics of the soil microbiome under climate change and to apply this knowledge to sustainable agricultural practices.
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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 | 0.001 | 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".