Migration patterns and changes in population biology associated with the worldwide spread of the oilseed rape pathogen <i>Leptosphaeria maculans</i>
Bibliographic record
Abstract
Pathogen introductions into novel areas can lead to the emergence of new fungal diseases of plants. Understanding the origin, introduction pathways, possible changes in reproductive system and population size of fungal pathogens is essential in devising an integrated strategy for the control of these diseases. We used minisatellite markers to infer the worldwide invasion history of the fungal plant pathogen Leptosphaeria maculans, which causes stem canker (blackleg) of oilseed and vegetable brassicas. Clustering analyses partitioned genotypes into distinct populations corresponding to major geographic regions, along with two differentiated populations in Western Canada. Comparison of invasion scenarios using Approximate Bayesian Computation suggested an origin of the pathogen in the USA, the region where epidemics were first recorded, and independent introductions from there over the last few decades into Eastern Canada (Ontario), Europe and Australia. The population in Western Canada appeared to be founded from a source in Ontario and the population in Chile resulted from an admixture between multiple sources. A bottleneck was inferred for the introduction into Western Canada but not into Europe, Ontario or Australia. Clonality appeared high in Western Canada, possibly because environmental conditions there were less conducive to sexual reproduction. Leptosphaeria maculans is a model invasive pathogen with contrasting features in different regions: shallow population structure, high genetic variability and regular sexual recombination in some regions, by comparison with reduced genetic variability, high rates of asexual multiplication, strong population structure or admixture in others.
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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) | 0.000 | 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) | 0.001 | 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".