Predicting the spread of all invasive forest pests in the United States.
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.
Biology Department, McGill University, Montreal, QC, Canada.
Emma J
EJ
Hudgins
Northern Research Station, USDA Forest Service, Morgantown, WV, USA.
Andrew M
AM
Liebhold
Biology Department, McGill University, Montreal, QC, Canada.
Brian
B
Leung
D063147:Animal Distribution; D000818:Animals; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D065928:Forests; D005658:Fungi / Q000502:physiology*; D007313:Insecta / Q000502:physiology*; D058865:Introduced Species; D008925:Mites / Q000502:physiology*; D008954:Models, Biological; D011157:Population Dynamics; D014481:United States
Dispersal kernel; habitat invasibility; macroecology; propagule pressure; spatially explicit