Structure of local adaptation across the landscape: flowering time and fitness in Mexican maize (Zea mays L. subsp. mays) landraces
Bibliographic record
Abstract
In crop centers of origin and diversity, often biotic and abiotic conditions vary across the landscape creating the possibility for local adaptation of crops, whereby local landraces perform better than non-local ones under local conditions. By studying patterns of local adaptation we can better understand the degree of adaptation of landraces, phenotypic mechanisms driving that adaptation, and the plastic responses of adapted populations to environmental change. Studying these basic processes in crop centers of origin and diversity improves basic understanding of adaptive evolution and provides insight for existing farming systems encountering climate change. Using maize landraces collected and reciprocally transplanted in the field in two years along an elevational gradient in Chiapas, Mexico, we aimed to understand their degree of local adaptation, the distribution of adaptive diversity within elevations, and how landraces compared to improved varieties in their responses to environmental variation. We found some patterns consistent with local adaptation among the landraces, although the degree of adaptation differed across measures of fitness components and years. Flowering time variables showed more variability within elevations than total fitness estimates or fitness components did. Improved varieties, like low elevation landraces, were not well-adapted to conditions at higher elevations, although they did possess some beneficial traits. These data reaffirmed experimentally the local adaptation of landraces and their difficulty in reproducing under novel conditions, and indicated the importance of landraces for high productivity (especially in middle and high elevation systems).
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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 | 0.000 |
| 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".