Unraveling the multifaceted effects of climatic factors on mountain pine beetle and its interaction with fungal symbionts
Bibliographic record
Abstract
Abstract Mountain pine beetles (MPBs) pose a substantial threat to North American pine forests, causing extensive tree mortality over large areas. Their tree‐killing ability is closely linked to mass aggregation on host trees triggered via pheromones and dependence on their symbiotic fungi. However, the influence of a changing climate on the biology of MPBs and their co‐evolved interactions with their fungal symbionts remains uncertain. To investigate this, male and female pairs of beetles were introduced into freshly cut logs from lodgepole pine trees and placed in controlled climate chambers with manipulated environmental conditions, including two levels of CO2 (ambient vs. 1000 ppm), O3 (ambient vs. 100 ppb) and humidity (33% vs. 65%). The beetle‐infested logs were left in these chambers for 1 month and then returned to ambient conditions until brood emergence. Emerging broods were collected for further analysis. Additionally, three species of fungal symbionts (Grosmannia clavigera, Ophiostoma montium and Leptographium longiclavatum) were subjected to the same CO2, O3 and humidity conditions for 5 days. Lower humidity promoted MPB reproduction and fungal growth. Elevated CO2 accelerated larval growth and emergence while improving brood pheromone production. Elevated O3 had a negative impact on MPB reproduction and brood fitness while improving its immune responses to an entomopathogenic fungus (Beauveria bassiana). It also inhibited fungal growth and reproduction, whereas elevated CO2 had varied (positive or negative) effects on fungal growth and ergosterol (proxy to fungal mass) production depending on the fungal species. Together, these findings suggest that climate change can potentially alter the interactions between MPBs and their fungal symbionts, highlighting the importance of understanding how climate change affects forest pests and their symbiotic relationships to develop effective management strategies in the future.
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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".