A general predictive model for the spread of United States forest pests
Emma Hudgins, Andrew Liebhold, Brian Leung
MCGILL UNIVERSITY

Invasive forest pest species pose a serious threat to the biodiversity and economy of the United States, and the level of devastation they have caused continues to rise. The spread of these pests must be forecasted in order to take appropriate management actions. Existing models of American pest spread are highly idiosyncratic and often require detailed data. This limits their application to novel invaders and to species possessing sparse data. 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 60.73% overlap on average 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 67.13% locational overlap. These results indicated that a single general dispersal kernel (GDK) model was sufficient to predict the majority of variation in extent and locational distribution across pest species and that proxies of propagule pressure (human population density) and habitat invasibility (forested land area) – well studied predictors of establishment – should also be applied to the dispersal stage. This model can be used to forecast novel invaders and to extend pathway-level risk analyses to include spread. This framework’s integrative approach allows for the most accurate forecasts of pest spread possible with the data available, thereby facilitating rapid and effective responses to pest invasions.