Mutualism Mediates Legume Response to Microbial Climate Legacies
Bibliographic record
Abstract
ABSTRACT Climate change is altering both soil microbial communities and the ecological context of plant–microbe interactions. Heat, drought, and their legacies can alter soil microbiomes and potential plant symbionts, but the direct consequences of these microbial changes on plant performance and plant investment in symbiosis remain underexplored. Predicting how soil microbes modulate plant resilience to heat and drought is critical to mitigating the negative effects of climate change on ecosystems and agriculture. In this proof of concept 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, focused on drought, 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, Sinorhizobium meliloti , affected heat‐ and drought‐treated microbiomes' impact on M. lupulina . We found that drought‐adapted microbiomes negatively impacted legume performance by increasing mortality and reducing branch number, but that adding rhizobia erased differences in plant responses to climate‐treated soils. In contrast, heat‐adapted microbiomes did not differ significantly from control microbiomes in their effects on a legume. Our results suggest microbial legacy effects, mutualist partners, and their interactions are important in mediating plant responses to drought, with some mutualists equalizing plant responses across microbial legacies.
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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 | 0.001 |
| 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".