Combining Air Sampling and DNA Metabarcoding to Monitor Plant Pathogens
Bibliographic record
Abstract
Monitoring the air for airborne plant pathogens is an increasingly common method for the management of economically important plant diseases. In Alberta, Canada, several commodity clusters, including dry bean, canola, potato, and wheat, currently support air monitoring research programs for airborne pathogens of interest. In this study, we assessed the feasibility of monitoring for these, and more, plant fungal pathogens simultaneously using two different sampler types (cyclone versus rotation impaction) and by metabarcoding the ITS1 region using the Illumina sequencing platform. We collected air samples from four geographically distant sites across Alberta and monitored four crop types in southern Alberta. Overall, we found weak, but statistically significant, effects of geographic location and crop type on the aeromycobiota community composition. A few common taxa, such as Ramularia, Alternaria, and Epicoccum, constituted the vast majority of reads across all samples. Nevertheless, in each sample, we identified many plant pathogens of interest and organisms that previous research has found antagonistic to those pathogens, highlighting the utility of these approaches in understanding the pathobiome. In assessing the real-world implications of read counts, we discovered that they were only weakly correlated with spore counts quantified by qPCR. The two types of samplers collected different community profiles, reinforcing the importance of carefully considering which sampler type to use in monitoring programs. Taken together, our results show promise for the future of monitoring the air pathobiome, although much more work is required to understand the relationship of airborne communities to their in-field impact on disease development. [Formula: see text] Copyright © 2023 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada. This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".