Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada
Bibliographic record
Abstract
Cereal rust diseases are caused by Puccinia spp. urediniospores that travel by air currents to cause losses in wheat, oat, barley, and rye. In western Canada, inoculum can arrive from the USA through the Great Plains or via a Pacific corridor west of the Rocky Mountains. Moreover, climate and land use are known factors in Puccinia dissemination, but their relative effects remain poorly understood. We investigated aeromycobiota in western Canada by weekly air-sampling over four growing seasons (2015-2018) three mixed-crop sites in British Columbia (BC) and one season (2018) at five sites in southern Alberta (AB). ITS2-based metabarcoding and novel bioinformatic comparative analyses to known, especially curated cereal rust fungal sequences, was used for species identification. The overall aeromycobiota and rust fungal community diversity was higher west than east of the Canadian Rockies. This mountain range delineates climate and land use and also creates a barrier to wind flow to prevent the spread of rusts and other plant pathogens (e.g., Bipolaris, Blumeria). We recovered seven major cereal rusts and revealed their geographic and seasonal dynamics at the eight sampling sites. Forward and reverse trajectory HYSPLIT model simulations predicted the potential sources of rust urediniospores and their pathways of movement modulated by air currents, through which some pronounced changes in the abundance of wheat rust pathogens were explained. This study paves the way for a potential application for pathogen monitoring and disease risk forecasting as part of the design and development of an early-warning system for enhanced biovigilance against crop diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".