Defence syndromes in lodgepole – whitebark pine ecosystems relate to degree of historical exposure to mountain pine beetles
Bibliographic record
Abstract
Warming climate is allowing tree-killing bark beetles to expand their ranges and access naïve and semi-naïve conifers. Conifers respond to attack using complex mixtures of chemical defences that can impede beetle success, but beetles exploit some compounds for host location and communication. Outcomes of changing relationships will depend on concentrations and compositions of multiple host compounds, which are largely unknown. We analysed constitutive and induced chemistries of Dendroctonus ponderosae's primary historical host, Pinus contorta, and Pinus albicaulis, a high-elevation species whose encounters with this beetle are transitioning from intermittent to continuous. We quantified multiple classes of terpenes, phenolics, carbohydrates and minerals. Pinus contorta had higher constitutive allocation to, and generally stronger inducibility of, compounds that resist these beetle-fungal complexes. Pinus albicaulis contained higher proportions of specific monoterpenes that enhance pheromone communication, and lower induction of pheromone inhibitors. Induced P. contorta increased insecticidal and fungicidal compounds simultaneously, whereas P. albicaulis responses against these agents were inverse. Induced terpene accumulation was accompanied by decreased non-structural carbohydrates, primarily sugars, in P. contorta, but not P. albicaulis, which contained primarily starches. These results show some host species with continuous exposure to bark beetles have more thoroughly integrated defence syndromes than less-continuously exposed host species.
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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 | 0.001 | 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) | 0.000 | 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".