Linking symbioses with intrinsic chemical defences in conifers: Fungal‐mediated resistance against an invasive pathogen
Bibliographic record
Abstract
Abstract Invasive pathogens threaten the sustainability of forest ecosystems globally. Trees possess intrinsic pathogen defence mechanisms, including major gene resistance (MGR) and quantitative disease resistance (QDR). Plant‐symbiotic fungi can enhance tree defences, generating far‐reaching ecosystem impacts. However, the specific contributions of various fungal guilds to this symbiont‐mediated resistance remain unclear. In this study, we inoculated six Pinus monticola seedling families exhibiting resistance (MGR or QDR) or high susceptibility to the introduced invasive pathogen Cronartium ribicola, the causative agent of white pine blister rust (WPBR), with endophytic and ectomycorrhizal fungi, either alone or in combination (symbiont treatments), in an open‐air greenhouse study. Over a period of 25 months, we monitored the growth, foliar terpene defences, and disease progression in trees before and after C. ribicola infection and in trees inoculated with the symbionts but never inoculated with C. ribicola. We observed enhanced inducible host defences and evidence of defensive priming in response to symbiont treatments. In WPBR‐free control treatments, differences in pine defences coincided with an increased seedling growth rate. For WPBR‐infected seedlings, symbiont treatments reduced disease symptoms in seedlings with QDR and to a lesser extent in susceptible families, but not in those with MGR. Furthermore, disease symptoms correlated with variations in terpene composition. Synthesis and applications: Interactions with fungal symbionts should be considered when breeding native trees for resistance against invasive pathogens. Our study underscores the capacity of endophytic and ectomycorrhizal fungi to enhance tree growth and defence while also reducing disease symptoms. We also show that fungi can induce long‐lasting changes in conifer foliar terpenes, suggesting potential applications in tree protection from invasive pathogens.
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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 | 0.000 | 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 | 0.000 |
| 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".