Standing genetic variation in host preference for mutualist microbial symbionts
Bibliographic record
Abstract
Many models of mutualisms show that mutualisms are unstable if hosts lack mechanisms enabling preferential associations with mutualistic symbiotic partners over exploitative partners. Despite the theoretical importance of mutualism-stabilizing mechanisms, we have little empirical evidence to infer their evolutionary dynamics in response to exploitation by non-beneficial partners. Using a model mutualism-the interaction between legumes and nitrogen-fixing soil symbionts-we tested for quantitative genetic variation in plant responses to mutualistic and exploitative symbiotic rhizobia in controlled greenhouse conditions. We found significant broad-sense heritability in a legume host's preferential association with mutualistic over exploitative symbionts and selection to reduce frequency of associations with exploitative partners. We failed to detect evidence that selection will favour the loss of mutualism-stabilizing mechanisms in the absence of exploitation, as we found no evidence for a fitness cost to the host trait or indirect selection on genetically correlated traits. Our results show that genetic variation in the ability to preferentially reduce associations with an exploitative partner exists within mutualisms and is under selection, indicating that micro-evolutionary responses in mutualism-stabilizing traits in the face of rapidly evolving mutualistic and exploitative symbiotic bacteria can occur in natural host 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".