Winter intensity shapes overwintering energy gain and use in bark beetles under range expansion
Bibliographic record
Abstract
The mountain pine beetle (Dendroctonus ponderosae) is an eruptive bark beetle that overwinters as a freeze-avoidant larvae under the bark of pine hosts. In recent years, D. ponderosae has undergone a climate change-driven range expansion into previously unsuitable habitats with historically more severe winter conditions. Dendroctonus ponderosae overwinters in a non-feeding dormant phase, and energy use is important to post-overwintering fitness. Little is known about how D. ponderosae balances energy supply and demand during overwintering. We quantified shifts in energy reserve (supply) and Complex I activity (as an index of demand) in D. ponderosae during natural overwintering and simulated early winter onset. We collected D. ponderosae larvae from infested lodgepole pine in the autumn (October), winter (January) and spring (April), and sampled a portion of these animals. During autumn and winter, another set of larvae were subjected to either mild overwintering conditions at 6°C or an experimental cold stress of stepwise decreases in temperature to test how an early onset of cold conditions influences the energetic status of overwintering individuals. Dendroctonus ponderosae larvae exposed to natural winter conditions accumulated lipids and proteins early in overwintering, which were then available for later use. Early exposure to cold stress in the autumn before full winter acclimatization, however, depleted energy reserves. These findings suggest that the timing and regulation of seasonal acclimatization in D. ponderosae have important implications for energy use that can influence subsequent fitness, and thus warming of the overwintering period may facilitate early winter feeding and enhance energy gain of D. ponderosae larvae, which could further exacerbate the spread and impact of this pest.
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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".