Fatty Acid Composition of Novel Host Jack Pine Do Not Prevent Host Acceptance and Colonization by the Invasive Mountain Pine Beetle and Its Symbiotic Fungus
Bibliographic record
Abstract
Fatty acids are major components of plant lipids and can affect growth and development of insect herbivores. Despite a large literature examining the roles of fatty acids in conifers, relatively few studies have tested the effects of fatty acids on insect herbivores and their microbial symbionts. Particularly, whether fatty acids can affect the suitability of conifers for insect herbivores has never been studied before. Thus, we evaluated if composition of fatty acids impede or facilitate colonization of jack pine (Pinus banksiana) by the invasive mountain pine beetle (Dendroctonus ponderosae) and its symbiotic fungus (Grosmannia clavigera). This is the first study to examine the effects of tree fatty acids on any bark beetle species and its symbiotic fungus. In a novel bioassay, we found that plant tissues (hosts and non-host) amended with synthetic fatty acids at concentrations representative of jack pine were compatible with beetle larvae. Likewise, G. clavigera grew in media amended with lipid fractions or synthetic fatty acids at concentrations present in jack pine. In contrast, fatty acids and lipid composition of a non-host were not suitable for the beetle larvae or the fungus. Apparently, concentrations of individual, rather than total, fatty acids determined the suitability of jack pine. Furthermore, sampling of host and non-host tree species across Canada demonstrated that the composition of jack pine fatty acids was similar to the different populations of beetle's historical hosts. These results demonstrate that fatty acids composition compatible with insect herbivores and their microbial symbionts can be important factor defining host suitability to invasive insects.
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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".