Biosurveillance of Invasive Southern Corn Rust: Insights Into Recent Migration Patterns and Virulence Variation
Bibliographic record
Abstract
Emerging pathogen races spreading via long-distance migration increasingly threaten global agricultural ecosystems. Understanding how pathogens migrate and adapt to new hosts via virulence evolution is crucial for developing strategies to mitigate future crop damage. Here we performed biosurveillance of Puccinia polysora, a global fungal pathogen causing southern corn rust (SCR), across China, Thailand and the Philippines. By analysing 193 field transcriptomic data, we detected both epidemic and endemic lineages co-circulating in each country and elucidated the crucial role of host selection in driving the diversification of endemic lineages. Gene flow assessments and trajectory tracking indicated that the SCR infection source in northern China is likely of domestic origin and pathogen migration from the Philippines/Thailand into China is restricted to Hainan, coastal Guangdong and southern Yunnan. We detected country-specific variants in 32 effector genes, with AvrRppC exhibiting the strongest positive selection. A phylogenetically distinct Luzon Island lineage (Philippines), carrying a novel AvrRppC allele capable of overcoming RppC-mediated resistance and represents a potentially invasive threat. Finally, we reviewed the global migration history of P. polysora in light of our findings. Our work represents the first step toward establishing an international surveillance network for P. polysora and emphasised a comprehensive control strategy integrating local governance and invasion prevention of international races.
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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".