Diversity in virulence and metalaxyl-m sensitivity of <i>Phytophthora rubi</i> isolates has implications for raspberry root rot and wilting complex management
Bibliographic record
Abstract
Raspberry root rot and wilting complex (RRWC), primarily caused by Phytophthora rubi, is the most important disease for raspberry producers in Canada. There is currently little to no information on phenotypic diversity or sensitivity to metalaxyl-m of P. rubi isolates affecting raspberries in Canada. This study was conducted to understand the diversity in virulence and sensitivity to metalaxyl-m of P. rubi populations collected from diverse raspberry cultivars, fields, and locations during 2018–2020. All isolates of P. rubi (n = 24) were pathogenic on ‘Chemainus’ raspberry and showed significant variation (P < 0.05) in days to first foliar symptom development (DFSD) and progress in wilting index, as indicated by relative areas under disease progress curves (rAUDPC). Principal component analyses identified that root rot index, rAUDPC, DFSD, and percentage reduction in root length and weight were important variables in assessing pathogen virulence. Metalaxyl-m sensitivity was assessed using a radial growth assay and effective concentrations that inhibited 50% of mycelial growth (EC50) were determined for 44 isolates. The EC50 values ranged from 0.035–4.71 µg mL−1 with a mean value of 0.793 µg mL−1. About 61% of the isolates were very sensitive (EC50 values <0.20 µg mL−1), 25% of isolates were considered sensitive (EC50 values of 0.21–1.18 µg mL−1), and 14% of isolates showed reduced sensitivity to metalaxyl-m (EC50 value range of 1.19–4.71 µg mL−1). Knowledge of pathogen phenotypic diversity will be useful for raspberry breeders in screening genotypes against RRWC and the results of the fungicide sensitivity study will be useful in developing management strategies for RRWC.
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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 | 0.001 | 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) | 0.001 | 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".