Host Plant Chemistry Affects Fungal Interactions, Which Influence the Production of Volatile Fungal Metabolites
Bibliographic record
Abstract
Mountain pine beetle (MPB) has recently expanded its host range to novel jack pine forests in Alberta. Invasion success of MPB may depend on the outcome of interactions between its primary symbiotic fungus Grosmannia clavigera with other organisms sharing the same host. Among resources-sharing organisms, the pine engraver beetle and its vectored fungus Ophiostoma ips are likely to interact with G. clavigera as the pine engraver beetle and its vectored fungus are one of the major disturbance agents on jack pine. On MPB historical host lodgepole pine, beetle brood production significantly decreased in response to the presence of the pine engraver beetles, likely as a result of competition between two fungal species. We hypothesized that the outcome of these interactions between two fungal species will likely affect MPB development in jack pine trees. However, lodgepole pine and jack pine differ in both primary and secondary phytochemistry. Whether such variation could affect the fungal growth as well as the fungal interaction is unknown. Besides phytochemistry, volatile organic compounds (VOCs) produced by beetle symbiotic fungi can also affect fungal growth and reproduction, and function as infochemicals to mediate fungal interaction. However, not much research has focused on the VOC of phytopathogenic fungi, especially how the presence of one fungal species could affect the VOC profile of a given fungal species. We investigated whether: 1) different concentrations of host nitrogen and monoterpenes affect the growth of and interaction between the beetle symbiotic fungi; 2) the presence of a resource-sharing fungus qualitatively and quantitively affect the VOCs production of a given fungal species. We found that jack pine phloem nitrogen levels did not alter the growth of either fungal species as well as their interactions. In the absence of monoterpenes, O. ips had a positive impact on the growth of G. clavigera suggesting commensalism. However, the presence of monoterpenes either promoted or inhibited the growth of both fungi, and furthermore altered the outcome of species interactions from commensalism to amensalism. These finding show that MPB could still obtain the benefit from its symbiotic fungus on jack pine as fungi can establish on jack pine. However, the benefits could be suppressed on jack pine which is also attacked by the pine engraver beetle and O. ips. As for the VOC production, two fungal species share similar VOCs profile revealing their similar ecological function as phytopathogenic fungi. However, each fungus produced qualitatively and quantitively less VOCs in response to the presence of another fungus. The reduction emission of FVOC shows that phytopathogenic fungi could interact at distance by sensing VOC produced by another fungus. Overall, our study is the first to demonstrate how host phytochemistry shifts fungal interaction from facilitation to no-effect. Another interesting finding is how the presence of potential competitor fungus qualitatively and quantitively affect the VOC production of a given fungal species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".