Unraveling complexity in climate change effects on beneficial plant–microbe interactions: mechanisms, resilience, and future directions
Bibliographic record
Abstract
Plant microbiomes have the potential to mitigate the impacts of climate change, yet both the complexity of climate change and the complexity of plant-microbe interactions make applications and future predictions challenging. Here, we embrace this complexity, reviewing how different aspects of climate change influence beneficial plant-microbe interactions and how advances in theory, tools, and applications may improve understanding and predictability of climate change effects on plants, microbiomes, and their roles within ecosystems. New advances include consideration of (1) interactions among climate stressors, such as more variable precipitation regimes combined with warmer mean temperature; (2) mechanisms that promote the stability of microbiome functions; (3) legacies of stress affecting the functionality of microbial communities under future stress; and (4) temporally repeated plant-microbe interactions or feedbacks. We also identify key gaps in each of these areas and spotlight the need for more research bridging molecular biology and ecology to develop a more mechanistic understanding of how climate change shapes beneficial microbe-plant interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".