The remarkable morphological diversity of leaf shape in sweet potato (<i>Ipomoea batatas</i>): the influence of genetics, environment, and G×E
Bibliographic record
Abstract
Leaf shape, a spectacularly diverse plant trait, varies across taxonomic levels, geography and in response to environmental differences. However, comprehensive intraspecific analyses of leaf shape variation across variable environments is surprisingly absent. Here, we performed a multilevel analysis of leaf shape using diverse accessions of sweet potato (Ipomoea batatas), and uncovered the role of genetics, environment, and G×E on this important trait. We examined leaf shape using a variety of morphometric analyses, and complement this with a transcriptomic survey to identify gene expression changes associated with shape variation. Additionally, we examined the role of genetics and environment on leaf shape by performing field studies in two geographically separate common gardens. We showed that extensive leaf shape variation exists within I. batatas, and identified promising candidate genes associated with this variation. Interestingly, when considering traditional measures, we found that genetic factors are largely responsible for most of leaf shape variation, but that the environment is highly influential when using more quantitative measures via leaf outlines. This extensive and multilevel examination of leaf shape shows an important role of genetics underlying a potentially important agronomic trait, and highlights that the environment can be a strong influence when using more quantitative measures of leaf shape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".