Bark Beetles Utilize Ophiostomatoid Fungi to Circumvent Host Tree Defenses
Bibliographic record
Abstract
Bark beetles maintain symbiotic associations with a diversity of microbial organisms, including ophiostomatoid fungi. Studies have frequently reported the role of ophiostomatoid fungi in bark beetle biology, but how fungal symbionts interact with host chemical defenses over time is needed. We first investigated how inoculations by three fungal symbionts of mountain pine beetle affect the terpene chemistry of live lodgepole pine trees. We then conducted a complimentary laboratory experiment specifically measuring the host metabolite degradation by fungi and collected the fungal organic volatiles following inoculations with the same fungal species on lodgepole pine logs. In both experiments, we analyzed the infected tissues for their terpene chemistry. Additionally, we conducted an olfactometer assay to determine whether adult beetles respond to the volatile organic chemicals emitted from each of the three fungal species. We found that all fungi upregulated terpenes as early as two weeks after inoculations. Similarly, oxygenated monoterpene concentrations also increased by several folds (only in logs). A large majority of beetles tested showed a strong attraction to two fungal species, whereas the other fungus repelled the beetles. Together this study shows that fungal symbionts can alter host defense chemistry, assist beetles in overcoming metabolite toxicity, and provide possible chemical cues for bark beetle attraction.
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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".