The Nitrogen Adaptation Strategy of the Wild Teosinte Ancestor of Modern Maize, <i>Zea mays</i> subsp. <i>parviglumis</i>
Bibliographic record
Abstract
ABSTRACT Examination of wild ancestors can identify which traits have been altered by selection as possible targets for genetic improvement. We investigated the whole plant response to low nitrogen (LN), especially below ground, by the wild ancestor of modern maize (Zea mays L.), Balsas teosinte (Zea mays subsp. parviglumis H. H. Iltis & Doebley). Teosinte responded to LN by reducing the shoot N concentration and increasing the root:shoot biomass ratio. The lengths of individual crown roots and the total lateral root length increased, compensated by reduced crown root number. Low N caused a decrease in total root hair (RH) length and increased expression of high affinity nitrate transporters. To facilitate future mapping studies, these results were compared to a modern inbred (‘W22’) used as the parent in a modern maize × teosinte population and extensively employed in maize domestication studies. The adaptations to LN in teosinte and W22 were surprisingly conserved, but the strategies employed were often different. To reduce total RH length, teosinte reduced RH density whereas W22 reduced average RH length. To achieve reduced shoot biomass in response to LN, teosinte reduced tiller number and hence leaf number whereas W22 reduced average leaf size. Since tiller crown roots initiate from stem tissue, teosinte used tiller plasticity to reduce crown root number whereas modern maize reduced crown root number independently of tillering. We discuss the implications of these results for maize domestication.
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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 | 0.000 | 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) | 0.000 | 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".