Genotype-environment interaction and the maintenance of genetic variation: an empirical study of Lobelia inflata (Campanulaceae)
Bibliographic record
Abstract
Genetic variation in natural populations often occurs at levels suggesting the action of processes such as frequency-dependent selection, heterozygote advantage, and variable selection. However, the maintenance of genetic variation in fitness-related traits remains incompletely explained. Obligately self-fertilizing populations of Lobelia inflata(Campanulaceae L.) are characterized by high levels of genetic variation, strongly implying balancing selection. L. inflataoffers an exceptional opportunity for an empirical test of genotype-environment interaction (GxE) as a variance-maintaining mechanism under fluctuating selection: L. inflatais monocarpic and reproduces only by seed, facilitating assessment of lifetime fitness; genome-wide homozygosity precludes some mechanisms of balancing selection, and microsatellites are, in effect, genotypic lineage markers. Here, we find support for the temporal GxE hypothesis using a manipulated space-for-time approach across four environments: a field environment, an outdoor experimental plot and two differing growth chamber environments. High genetic variance was confirmed: 83 field-collected individuals consisted of 45 distinct microsatellite lineages with on average 4.5 alleles per locus. Rank-order fitness, measured as lifetime fruit production in sixteen replicated multilocus genotypes, changed significantly across environments. Phenotypic differences among microsatellite lineages were detected. Although results support the GxE hypothesis in principle, evaluation of the effect size of this mechanism and fitness effects of life-history traits will require long-term study of fluctuating selection on labelled genotypes in the field.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".