A glimmer of hope – ash genotypes with increased resistance to ash dieback pathogen show cross‐resistance to emerald ash borer
Bibliographic record
Abstract
Plants rely on cross-resistance traits to defend against multiple, phylogenetically distinct enemies. These traits are often the result of long co-evolutionary histories. Biological invasions can force naïve plants to cope with novel, coincident pests, and pathogens. For example, European ash (Fraxinus excelsior) is substantially threatened by the emerald ash borer (EAB), Agrilus planipennis, a wood-boring beetle, and the ash dieback (ADB) pathogen, Hymenoscyphus fraxineus. Yet, plant cross-resistance traits against novel enemies are poorly explored and it is unknown whether naïve ash trees can defend against novel enemy complexes via cross-resistance mechanisms. To gain mechanistic insights, we quantified EAB performance on grafted replicates of ash genotypes varying in ADB resistance and characterized ash phloem chemistry with targeted and untargeted metabolomics. Emerald ash borer performed better on ADB-susceptible than on ADB-resistant genotypes. Moreover, changes in EAB performance aligned with differences in phloem chemical profiles between ADB-susceptible and ADB-resistant genotypes. We show that intraspecific variation in phloem chemistry in European ash can confer increased cross-resistance to invasive antagonists from different taxonomic kingdoms. Our study suggests that promotion of ADB-resistant ash genotypes may simultaneously help to control the ADB disease and reduce EAB-caused ash losses, which may be critical for the long-term stability of this keystone tree species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".