Fungal Volatiles Can Act as Carbon Sources and Semiochemicals to Mediate Interspecific Interactions Among Bark Beetle-Associated Fungal Symbionts
Bibliographic record
Abstract
Mountain pine beetle (Dendroctonus ponderosae) has killed millions of hectares of pine forests in western North America. Beetle success is dependent upon a community of symbiotic fungi comprised of Grosmannia clavigera, Ophiostoma montium, and Leptographium longiclavatum. Factors regulating the dynamics of this community during pine infection are largely unknown. However, fungal volatile organic compounds (FVOCs) help shape fungal interactions in model and agricultural systems and thus may be important drivers of interactions among bark beetle-associated fungi. We investigated whether FVOCs can mediate interspecific interactions among mountain pine beetle's fungal symbionts by affecting fungal growth and reproduction. Headspace volatiles were collected and identified to determine species-specific volatile profiles. Interspecific effects of volatiles on fungal growth and conidia production were assessed by pairing physically-separated fungal cultures grown either on a carbon-poor or -rich substrate, inside a shared-headspace environment. Fungal VOC profiles differed by species and influenced the growth and/or conidia production of the other species. Further, our results showed that FVOCs can be used as carbon sources for fungi developing on carbon-poor substrates. This is the first report demonstrating that FVOCs can drive interactions among bark beetle fungal symbionts, and thus are important factors in beetle attack success.
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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 | 0.001 | 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".