Combined drought and bark beetle attacks deplete non‐structural carbohydrates and promote death of mature pine trees
Bibliographic record
Abstract
How carbohydrate reserves in conifers respond to drought and bark beetle attacks are poorly understood. We investigated changes in carbohydrate reserves and carbon-dependent diterpene defences in ponderosa pine trees that were experimentally subjected to two levels of drought stress (via root trenching) and two types of biotic challenge treatments (pheromone-induced bark beetle attacks or inoculations with crushed beetles that include beetle-associated fungi) for two consecutive years. Our results showed that trenching did not influence carbohydrates, whereas both biotic challenges reduced amounts of starch and sugars of trees. However, only the combined trenched-bark beetle attacked trees depleted carbohydrates and died during the first year of attacks. While live trees contained higher carbohydrates than dying trees, amounts of constitutive and induced diterpenes produced did not vary between live and beetle-attacked dying trees, respectively. Based on these results we propose that reallocation of carbohydrates to diterpenes during the early stages of beetle attacks is limited in drought-stricken trees, and that the combination of biotic and abiotic stress leads to tree death. The process of tree death is subsequently aggravated by beetle girdling of phloem, occlusion of vascular tissue by bark beetle-vectored fungi, and potential exploitation of host carbohydrates by bark beetle symbionts as nutrients.
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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 | 0.000 | 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".