Recent climate change strongly impacted the population dynamic of a North American insect pest species
Bibliographic record
Abstract
Climate change is redefining the dynamics of forest ecosystems globally, particularly through its impact on forest pest populations such as the spruce budworm (SBW, Choristoneura fumiferana [Clem.]), a major defoliator in North American boreal forests. This study investigates the shifts in the population dynamics of spruce budworm across its range in response to recent climate change. We used a process-based, temperature-dependent ecophysiological model combined with the ERA5 reanalysis to assess changes in SBW phenology, reproduction rate, winter survival and population growth rates from 1950 to 2022 across North America. Our findings demonstrate a pronounced northward expansion of suitable climate conditions for SBW, accompanied by earlier phenological events and increased reproduction rates in northern regions. Conversely, the southern parts of its range are experiencing increased winter mortality due to warmer temperatures. This study highlights the significant impact of elevated temperatures, particularly during critical developmental windows such as spring and summer, which are pivotal for spruce budworm survival and reproduction. Additionally, our results reveal that the observed shifts in pest dynamics are more strongly driven by climate change than by changes in landscape composition and structure. We estimated that suitable growth rates have shifted northward by over 68 km on average, but this shift reached more than 200 km in the easternmost portions of its range. Climate-induced shift in suitable conditions for SBW underscores the need for adaptive forest management strategies that consider the rapid ecological changes and the potential for increased forest vulnerability due to climatic and biotic stressors. This study provides vital insights that can inform adaptive management ensuring the sustainability of forest ecosystems in the face of ongoing climate change.
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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 | 0.001 | 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".