From genomes to forest management – tackling invasive<i>Phytophthora</i>species in the era of genomics
Bibliographic record
Abstract
Species of Phytophthora pose one of the most serious biosecurity threats to forest ecosystems worldwide. Despite management efforts and increased awareness of forest pathogens, there is continued introduction and spread of Phytophthora species. Uncertainty about the center of origin for many of the invasive species hampers disease control efforts. Additionally, the management efforts are often made impossible either by the vast host range or the extreme susceptibility of naïve hosts. In this review, we discuss how genomics has shed light on the extent of spread and destruction caused by invasive Phytophthora species, and how approaches leveraged by genomics can be applied to enhance the management of these invasive forest pathogens. Four case studies, Phytophthora ramorum, Phytophthora lateralis, Phytophthora cinnamomi, and Phytophthora pluvialis are used to illustrate how genomics can be applied to forest management. We urge researchers, governmental research institutes, private companies, and citizens to collaborate in order to stop the spread of invasive Phytophthora species. To accomplish this, we see the following themes as critical parts of resolving this forest health crisis: i) integration of DNA-based pathogen detection into forest inventory programs; ii) development of practical and affordable DNA-based diagnostic methods; iii) sequence hosts as models for resistance gene identification; iv) prediction of pathogen impact based on genomic data; and v) increase collaborative projects and outreach to raise awareness of forest 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".