A review of genetic approaches to the management of blister rust in white pines
Bibliographic record
Abstract
Summary Since introduced a century ago,Cronartium ribicolahas devastated many populations of North American white pines. However, significant genetic resistance to white pine blister rust occurs naturally and can be exploited. In this review, we discuss the progress and different approaches to breeding for resistance in North American white pines. Three broad categories of resistance are: (1) ontogenetic resistance, (2) R‐gene resistance and (3) partial resistance. Ontogenetic resistance is associated with increased host age and indicated by higher susceptibility to infection in primary needles and young seedlings then in grafts and older trees. R‐gene resistance (major gene resistance) is an example of the classic gene‐for‐gene system common in many rust diseases. R‐gene resistance provides immunity but may not be durable. Host resistance and the corresponding rust virulence which defeats it are well described for sugar pine and western white pine. Host plants with partial resistance are able to retard or tolerate disease development without eliminating the pathogen. Partial resistance is also called slow‐rusting resistance or low‐level resistance and is revealed in seedlings by several responses, including slow‐canker‐growth, difficult‐to‐infect, needle‐shed and bark‐reaction. Most of these seedling responses are presumed to be multigenic; but needle‐shed may be controlled by recessive genes. Long‐term, field trials for verification of screening and selection results are sparse. Although 100% higher survival of selected material over unselected occurs in some trials, mortality is high under conditions of high hazard and heavy inoculum load. In several, long‐term trials, some full‐sib crosses expressed a strong phenotypic resistance that indicates specific combining ability between complimentary parents. These and other observations suggest that we might yet find strong and durable resistance. Study of Eurasian white pines infected by blister rust fungi could help us better understand endemic pathosystems. Different strategies are identified for deploying material selected for either R‐gene or partial resistance. Current research suggests that resistance is more complex than previously modelled, but new molecular techniques offer useful methods for investigating the white pine blister rust pathosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".