More than Moran: coupling statistical and simulation models to understand how defoliation spread and weather variation drive insect outbreak dynamics
Bibliographic record
Abstract
Understanding the processes that underlie species fluctuations is crucial to the development of efficient management strategies for outbreaks of destructive forest pests. Yet, the role of biotic and abiotic factors as well as their interactions in synchronizing outbreaks is not understood, despite many empirical and theoretical studies of species fluctuations. Here, we use a combined statistical–simulation model to investigate how defoliation spread and autocorrelated weather affect outbreaks of a major defoliator of North American boreal forest, the spruce budworm (Choristoneura fumiferana (Clemens)). We modelled the regional dynamics of spruce budworm and based the model on data collected from spatiotemporal aerial surveys of defoliation from 1968–2015 in Quebec, Canada. The effects of weather on local forest stand defoliation and mortality transitions, along with defoliation spread probability and distance, were estimated statistically. Simulations were run with these estimates to identify the effects of spatiotemporal weather autocorrelation on synchronicity of outbreaks. Defoliation spread together with weather variables was found to best fit the observed outbreak size. Simulation models suggest that positive temporal autocorrelation in weather promotes outbreaks, indicating that a series of suitable years could encourage outbreaks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".