Climate change has already reshaped North American forest pest dynamics: Insights from multidecadal process-based modelling
Bibliographic record
Abstract
Abstract Ongoing anthropogenic climate change has the potential to modify the population dynamics of forest pest insects by shifting the distribution of suitable climate conditions for their development. We used processed-based temperature-driven physiological models to assess the impact of changing climate conditions between 1951 and 2022 across North America on eight important forest pest insects, namely western spruce budworm, eastern spruce budworm, spongy moth, hemlock woolly adelgid, mountain pine beetle, southern pine beetle, spruce beetle and emerald ash borer. Our analyses revealed substantial changes in climate suitability resulting in pronounced northward and elevational shifts for most pest species although the magnitude and spatial patterns of these shifts varied both geographically and among species. We also showed that shifts in highly suitable conditions were more important at the colder edge (either northern or upper elevation) than at the warmer boundaries for several species, either driven by the Arctic amplification or elevation-dependant warming. Our results indicated that both the total area and the host tree biomass exposed to highly suitable climate conditions have increased for many pest species over the last decades, further exposing ecosystems to elevated risk. Our analyses also identified areas (e.g., western Canada) that are increasingly exposed to overlapping, potentially cumulative and interacting, biological disturbances. We also showed that climate change has already contributed to increasing the climatic suitability and geographic spread of exotic forest pest species in North America. Changes in climate suitability over the past seven decades across North America likely represent early signals of continued and potentially accelerating shifts under ongoing anthropogenic climate forcing. In this context, efforts to limit the expansion of pest populations under future climate change scenarios will be key to mitigating their cultural, ecological and economic impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".