Modeling micro-scale ecological processes and emergent patterns of mountain pine beetle epidemics
Bibliographic record
Abstract
Forest insect outbreaks can impose significant tree mortality across vast forested landscapes. The current epidemic of mountain pine beetle, Dendroctonus ponderosae Hopkins, for example, has led to the mortality of pine trees in western Canada and the U.S. spanning tens of millions of hectares. The ecological processes driving mountain pine beetle outbreaks are governed by multiple feedback mechanisms, thresholds, and external constraints that exist along a spatial continuum from individual insect–tree interactions to landscape level change. These components of mountain pine beetle epidemics need to be explicitly parameterized in modeling efforts that aim to predict where insect disturbance will occur in a forest each year and the amount of tree mortality that will ensue as a result. However, do date, minimal efforts exist that examine how local level interactions between beetles and trees translate into broader patterns of tree mortality, and those that do are limited to relatively local scales. In this study, we present an agent-based model that simulates how tree mortality results from the combination of beetle–tree interactions, beetle-to-beetle communication, tree defense to beetle attack, beetle density dynamics, host tree availability, dispersal behavior, and landscape heterogeneity. Our model is tested using data from an area in central British Columbia, Canada, that is near the center of the current outbreak in that region. The model simulates both overall tree mortality and spatial patterns of tree mortality, producing results that are similar to those observed in aerial surveys of tree health. Moving forward, the computational efficiency of our model demonstrates the capability to be applied to large, regional landscapes when implemented with sufficient computing resources.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".