Unraveling the Environmental Drivers of <i>Phyllachora maydis</i> Spore Release and Dispersal Using Spore Trapping and qPCR
Bibliographic record
Abstract
Phyllachora maydis, the causal agent of tar spot of corn, is an emerging disease in the United States and Canada. This study aimed to improve our understanding of P. maydis spore release and dispersal by utilizing spore trapping and quantitative PCR to assess the relationship between spore capture and environmental conditions. Burkard and rotating-arm air samplers were deployed in Michigan corn fields with natural disease pressure from 2021 to 2023. Correlation analysis and mixed-effects logistic regression were applied to examine the impact of environmental factors on spore capture. Through an exhaustive screening of candidate logistic regression models, results indicated that spore quantity is significantly negatively correlated with daily summaries of minimum temperature ( P < 0.05, τ = −0.24), mean temperature ( P < 0.05, τ = −0.25), maximum precipitation rate ( P < 0.05, τ = −0.33), and durations of temperature between 16.6 to 23°C and relative humidity over 85% ( P < 0.001, τ = −0.27). Logistic regression models frequently incorporated temperature and humidity predictors, and the best performing model used daily averages of mean temperature and maximum humidity to discriminate between the presence and absence of spore detection, achieving a balanced accuracy of 85%. Across all site-years, spore traps did not detect P. maydis spores prior to the visible detection of tar spot symptoms but did detect spores before tar spot incidence reached 100%. Through this study, the environmental drivers of spore release and dispersal were described to fill current knowledge gaps in the tar spot disease cycle. Additionally, a methodology for the capture and molecular quantification of airborne P. maydis spores is described, which will benefit future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".