Predicting the spread of all invasive forest pests in the United States

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Authors Emma J. Hudgins, Andrew M. Liebhlod, Brian Leung
Journal/Conference Name Ecology Letters
Paper Category , ,
Paper 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.
Date of publication 2017
Code Programming Language R

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