Environmental variation impacts trait expression and selection in the legume–rhizobium symbiosis
Bibliographic record
Abstract
PREMISE: The ecological outcomes of mutualism are well known to shift across abiotic or biotic environments, but few studies have addressed how different environments impact evolutionary responses, including the intensity of selection on and the expression of genetic variance in key mutualism-related traits. METHODS: We planted 30 maternal lines of the legume Medicago lupulina in four field common gardens and compared our measures of selection on and genetic variance in nodulation, a key trait reflecting legume investment in the symbiosis, with those from a previous greenhouse experiment using the same 30 M. lupulina lines. RESULTS: We found that both the mean and genetic variance for nodulation were much greater in the greenhouse than in the field and that the form of selection on nodulation significantly differed across environments. We also found significant genotype-by-environment (G × E) effects for fitness-related traits that were generated by differences in the rank order of plant lines among environments. CONCLUSIONS: Overall, our results suggest that the expression of genotypic variation and selection on nodulation differ across environments. In the field, significant rank-order changes for plant fitness potentially help maintain genetic variation in natural populations, even in the face of directional or stabilizing selection.
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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 | 0.001 | 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".