Data from: Meteorological versus spatial drivers of the spatial synchrony of forest insect pest outbreaks in North America
Bibliographic record
Abstract
Population spatial synchrony has major consequences for the impacts of forest insect pest outbreaks at regional scales. We tested the predictions that the strength and drivers of this synchrony would differ among species according to their dispersal abilities and feeding guild. Using a matrix regression approach, we statistically partitioned the importance of spatial and environmental drivers of outbreak synchrony in six species of phloem-feeding bark beetles and six species of defoliating Lepidoptera in North America. Potential drivers included in the regressions were synchrony of weather conditions and spatial proximity. Overall, model selection operations on the matrix regressions indicated that synchrony in the outbreaks of forest insect pests arises from a combination of spatial drivers such as dispersal and synchrony of weather, also known as Moran effects. Moran effects appeared to be more important in driving the synchrony of bark beetles than defoliators, possibly because weather (e.g., drought) has stronger impacts on bark beetle outbreak dynamics. Nonparametric spatial correlation functions showed that defoliators exhibited stronger synchrony over short distances than bark beetles, possibly because the cyclical nature of defoliator populations allows them to be more easily synchronized. The greater influence of Moran effects on the synchrony of bark beetles compared to defoliators, coupled with climate-change-driven increases in synchrony of weather, may lead to more widespread events of high tree mortality due to bark beetle epidemics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".