Data from: 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.本研究检验了通用扩散模型能否捕捉美国境内所有破坏性入侵森林害虫的宏生态模式。研究表明，基于害虫发现日期模拟得到的经典恒定扩散核（dispersal kernel）模型，可解释所有受试害虫分布区面积变异的67.94%，且预测空间分布与实际观测分布的定位精度达68.00%。进一步地，若将扩散过程设定为森林面积与人口密度的函数，则模型对变异的解释率提升至75.60%，定位精度也达到74.30%。上述结果表明，单一通用扩散核模型足以解释多数害虫物种的分布范围与空间分布的绝大多数变异，而作为繁殖体压力（propagule pressure）与生境可入侵性（habitat invasibility）的替代指标——这两类已被充分研究的害虫定殖预测因子——同样应被应用于扩散阶段的预测建模。该模型为新型入侵害虫的预测以及将路径级风险分析拓展至涵盖扩散环节提供了关键支撑。