Ecology Letters (2017) 20, 426-435

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Emma J. Hudgins, Andrew M. Liebhold and Brian Leung (2017)
Predicting the spread of all invasive forest pests in the United States
Ecology Letters 20 (4), 426-435
Abstract: We tested whether a general spread model could capture macroecological patterns across all damaging invasive forest pests in the United States. We showed that a common constant dispersal kernel model, simulated from the discovery date, explained 67.94% of the variation in range size across all pests, and had 68.00% locational accuracy between predicted and observed locational distributions. Further, by making dispersal a function of forest area and human population density, variation explained increased to 75.60%, with 74.30% accuracy. These results indicated that a single general dispersal kernel model was sufficient to predict the majority of variation in extent and locational distribution across pest species and that proxies of propagule pressure and habitat invasibility – well-studied predictors of establishment – should also be applied to the dispersal stage. This model provides a key element to forecast novel invaders and to extend pathway-level risk analyses to include spread.
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Database assignments for author(s): Andrew M. Liebhold

Research topic(s) for pests/diseases/weeds:
population dynamics/ epidemiology
new introduction of pest


Pest and/or beneficial records:

Beneficial Pest/Disease/Weed Crop/Product Country Quarant.