The Impact of Phloem Nutrients on Overwintering Mountain Pine Beetles and Their Fungal Symbionts
Bibliographic record
Abstract
In the low nutrient environment of conifer bark, subcortical beetles often carry symbiotic fungi that concentrate nutrients in host tissues. Although bark beetles are known to benefit from these symbioses, whether this is because they survive better in nutrient-rich phloem is unknown. After manipulating phloem nutrition by fertilizing lodgepole pine trees (Pinus contorta Douglas var. latifolia), we found bolts from fertilized trees to contain more living individuals, and especially more pupae and teneral adults than bolts from unfertilized trees at our southern site. At our northern site, we found that a larger proportion of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae built pupal chambers in bolts from fertilized trees than in bolts from unfertilized trees. The symbiotic fungi of the mountain pine beetle also responded to fertilization. Two mutualistic fungi of bark beetles, Grosmannia clavigera (Rob.-Jeffr. & R. W. Davidson) Zipfel, Z. W. de Beer, & M. J. Wingf. and Leptographium longiclavatum Lee, S., J. J. Kim, & C. Breuil, doubled the nitrogen concentrations near the point of infection in the phloem of fertilized trees. These fungi were less capable of concentrating nitrogen in unfertilized trees. Thus, the fungal symbionts of mountain pine beetle enhance phloem nutrition and likely mediate the beneficial effects of fertilization on the survival and development of mountain pine beetle larvae.
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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".