Characterization of Fungal Communities Associated to Willow SRIC Plantations in the Canadian Prairies Ecozone Using PCR-Based Molecular Methods
Bibliographic record
Abstract
Willow (Salix spp.), a major source of biomass and renewable fiber production, is one of the best choices for short-rotation intensive culture (SRIC) in Canada. Since fungal communities play important roles in the plant’s health status, it is vital to understand their interactions with willows and their roles in the sustainability of SRIC. In this study, fungal diversity of the above-ground organs (stem/leaf) of healthy and diseased willow plants in western Canadian Prairies were assessed using cultural and PCR-denaturing gradient gel electrophoresis (DGGE) techniques. Comparison of the mycoprofiles within established plantations vs. newly introduced cuttings revealed differences in the fungal communities. Ascomycota were mainly isolated, followed by Basidiomicota and Zygomycota. Willow genotypes seem have an influence on the abundance of fungal pathogens and disease severity; among them Charlie (Salix alba x gladfelteri) and SV1 (S. eriocephala) cultivars demonstrated superior performances. Photosynthesis measurements and biomass compositions confirmed these findings. Potentially pathogenic fungi (Dothioraceae, Diaporthaceae, Glomeraceae, and Pleosporaceae) dominated in diseased or symptomatic willows, whereas potentially beneficial fungi (Coniochaetaceae, Hypoceraceae, Nectriaceae, Trichocomaceae, and Agaricaceae) prevailed in healthy plants. In-vivo and greenhouse assays showed that inoculation with potentially pathogenic fungi induced leaf necrosis, anthracnose and open cankers. However, suppression of the latter was still possible using fungal antagonists. Hence, assessment of stem/bark and leaf fungal communities with respect to willow genotypes, cuttings origin, and SRIC location, is useful for the design of an effective management strategy to increase the productivity of the SRIC-biomass systems.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".