Population‐level variation in host plant response to multiple microbial mutualists
Bibliographic record
Abstract
PREMISE: Multipartite mutualisms are widespread in nature, but population-level variation in these interactions is rarely quantified. In the model multipartite mutualism between legumes, arbuscular mycorrhizal (AM) fungi and rhizobia bacteria, host responses to microbial partners are expected to be synergistic because the nutrients provided by each microbe colimit plant growth, but tests of this prediction have not been done in multiple host populations. METHODS: To test whether plant response to associations with AM fungi and rhizobia varies among host populations and whether synergistic responses to microbial mutualists are common, we grew 34 Medicago truncatula populations in a factorial experiment that manipulated the presence or absence of each mutualist. RESULTS: Plant growth increased in response to each mutualist, but there were no synergistic effects. Instead, plant response to inoculation with AM fungi was an order of magnitude higher than with rhizobia. Plant response to AM fungi varied among populations, whereas responses to rhizobia were relatively uniform. There was a positive correlation between plant host response to each mutualist but no correlation between AM fungal colonization and rhizobia nodulation of plant roots. CONCLUSIONS: The greater population divergence in host response to AM fungi relative to rhizobia, weak correlation in host response to each microbial mutualist, and the absence of a correlation between measures of AM fungal and rhizobia performance suggests that each plant-microbe mutualism evolved independently among M. truncatula populations.
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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 | 0.000 |
| 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".