Genetic Structure and Asymmetric Migration of Wheat Stripe Rust Pathogen in Western Epidemic Areas of China
Bibliographic record
Abstract
Puccinia striiformis f. sp. tritici causes severe global epidemics of wheat stripe rust primarily by airborne urediniospores. Understanding long-distance migration patterns of P. striiformis f. sp. tritici is critical for disease forecasting and management. Although the western epidemic areas in China have been considered as the source of inoculum spread eastward across the country, migration pathways among different populations within the western epidemic areas are poorly understood. In this study, we investigated the population genetics of 200 P. striiformis f. sp. tritici isolates from western epidemic areas using amplified fragment length polymorphism and simple sequence repeat markers. A coalescent approach was also used to calculate the migration rates among populations. Data analyses with both marker systems indicated high genetic diversity in each regional population. The Mantel test revealed significant positive correlation between genetic and geographic distances. Both discriminant analysis of principal components and STRUCTURE analysis supported moderate population structure shaped by seasonality and geography. The calculated migration rates indicated the presence of asymmetric migration between major populations in western epidemic areas, with greater migration rates from high elevation, oversummering areas to low elevation, overwintering areas. Sichuan Basin, one of the low elevation, overwintering areas, sampled in both fall and spring, was inferred as a recipient in fall but a donor in spring. Migration among P. striiformis f. sp. tritici populations may be partly attributable to terrace farming and prevailing wind direction in different seasons. Our study provides a better understanding of fine-scale population structure and the interregional migration pattern of P. striiformis f. sp. tritici in northwestern China and will inform future rust management.
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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) | 0.000 | 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".