Mountain pine beetle: an example of a climate-driven eruptive insect impacting conifer forest ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change is altering the survival and reproductive capacity of plant-feeding insects in multiple ecosystems worldwide, in some cases creating conditions highly suitable for population eruptions. Forest ecosystems are particularly sensitive to climate change as their vulnerability is manifested, in part, as an upsurge in natural disturbances such as native insect outbreaks. The mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae), is a phloem-feeding bark beetle indigenous to western North America that attacks most species of pine including its major hosts, lodgepole pine and ponderosa pine. Adult mass aggregation, mediated by pheromones, helps the beetle to overcome tree defenses eventually killing the tree. Recent outbreaks of this insect have caused extensive pine mortality and have affected millions of hectares of forested area in western North America. Climate is a major driver of these outbreaks. In this review, we describe the direct influences of various climate-related factors on MPB development, outbreak behavior, and range expansion and their indirect impact on MPB epidemiology via influences on host trees and MPB-associated fungi. We also underscore the ecological and economic consequences of the recent, unprecedented MPB outbreak. Of serious concern currently is whether climate change will facilitate rapid establishment and spread of MPB in naïve pine forests. MPB will likely adapt quickly to new thermal environments under climate change given its short generation time; however, uncertainties and gaps in our understanding of MPB population dynamics (e.g., trophic interactions) in newly invaded habitats preclude an accurate assessment of outbreak potential and spread at this time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.003 | 0.001 |
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 it