Mutualism mediates legume response to microbial climate legacies
Bibliographic record
Abstract
Climate change is altering both soil microbial communities and the ecological context of plant-microbe interactions. Predicting how soil microbes modulate plant resilience to climate change is critical to mitigating the negative effects of climate change on ecosystems and agriculture. Previously, it was demonstrated that heat, drought, and their legacies altered soil microbiomes and potential plant symbionts. In this study, we conducted growth chamber experiments to isolate the microbially-mediated indirect effects of heat and drought on plant performance and symbiosis. In the first experiment, we found that drought and drought-treated microbes, along with their interaction, significantly decreased the biomass of Medicago lupulina plants compared to well-watered microbiomes and conditions. In a second experiment, we then tested how the addition of a well-known microbial mutualist, the rhizobium Sinorhizobium meliloti, affected climate-treated microbiomes’ impact on the M. lupulina. We found that drought-adapted microbiomes negatively impacted legume performance by increasing mortality and reducing leaf number early in life, but that adding rhizobia erased climate treatment effects. Drought can negatively affect legume performance through microbial legacy effects alone, but the addition of rhizobia buffers legumes against climate-mediated variation in the microbiome. In contrast, heat-adapted microbiomes did not differ significantly from control microbiomes in their effects on a legume.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".