ArticleEcology2026
Modeling stratified dispersal in forest pests: A case study of the mountain pine beetle in Alberta.
Article in Ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Forest pests pose critical threats to forest ecosystems worldwide, yet accurately predicting their spatial spread remains challenging due to complex dispersal behaviors, weather effects, and the inherent difficulty of tracking small organisms across large landscapes. These challenges have resulted in divergent estimates of typical dispersal distances across studies. Here, we use high-quality data from helicopter and field-crew surveys to parameterize dispersal kernels for the mountain pine beetle, a destructive pest that has recently expanded its range into Alberta, Canada. We find that fat-tailed kernels-those which allow for a small number of long-distance dispersal events-consistently provide the best fit to these data. Specifically, the radially symmetric Student's t-distribution with parameters
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Registered trials
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