Data from: Predicting the spread of all invasive forest pests in the United States
Hudgins, Emma J.; Liebhold, Andrew M.; Leung, Brian (2018), Data from: Predicting the spread of all invasive forest pests in the United States, Dryad, Dataset, https://doi.org/10.5061/dryad.75265
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.
Contiguous United States