Outbreak of <i>Phytophthora abietivora</i> in a Québec Forest Nursery: Emergence of a New <i>Phytophthora</i> Tree Pathogen?
Bibliographic record
Abstract
ABSTRACT Tree nurseries play a key role in the Canadian economy and reforestation efforts, producing over 600 million seedlings annually. Despite rigorous management practices, nurseries are not exempt from pathogen outbreaks, which can be devastating on many levels. In October 2022, the public forest nursery of St‐Modeste (Canada) noted an unusually high mortality rate among their 2‐year‐old balsam fir ( Abies balsamea ) seedlings. Phytophthora abietivora , recently identified as responsible for the Phytophthora root rot (PRR) in Christmas tree plantations, was suspected to be the causative agent of the outbreak. The objectives of this study were to identify the pathogen(s) responsible for the outbreak in the nursery and determine its pathogenicity and transmissibility to other seedlings. After the isolation of the pathogen and molecular detection, it was confirmed that the epidemic was caused by P. abietivora . The pathogen was not only found on healthy‐looking balsam seedlings, but also on many other tree species grown in the nursery showing no above‐ground PRR symptoms, such as spruce seedlings. The strain isolated in the nursery proved to be highly infectious to Fraser fir seedlings, and results were exacerbated by artificial flooding of seedlings. More worryingly, the disease could be transmitted to susceptible recipient seedlings from asymptomatic donor seedlings. The pathogen could be detected in soil and roots from both donor and recipient seedlings. Together, these findings indicate the first report of an outbreak of P. abietivora on balsam fir seedlings under nursery conditions. Efforts must be increased to minimise economic losses and to manage future outbreaks better in order to protect Christmas trees and forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".