Multiple Mutualism Effects generate synergistic selection and strengthen fitness alignment in the interaction between legumes, rhizobia and mycorrhizal fungi
Bibliographic record
Abstract
Nearly all organisms participate in multiple mutualisms, and complementarity within these complex interactions can result in synergistic fitness effects. However, it remains largely untested how multiple mutualisms impact eco-evolutionary dynamics in interacting species. We tested how multiple microbial mutualists-N-fixing bacteria and mycorrrhizal fungi-affected selection and heritability of traits in their shared host plant (Medicago truncatula), as well as fitness alignment between partners. Our results demonstrate for the first time that multiple mutualisms synergistically affect the selection and heritability of host traits and enhance fitness alignment between mutualists. Specifically, we found interaction with multiple microbial symbionts doubled the strength of natural selection on a plant architectural trait, resulted in 2- to 3-fold higher heritability of plant reproductive success, and more than doubled fitness alignment between N-fixing bacteria and plants. These findings show synergism generated by multiple mutualisms extends to key components of microevolutionary change, emphasising the importance of multiple mutualism effects on evolutionary trajectories.
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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 | 0.001 |
| 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 | 0.001 |
| 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".