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森林入侵害虫的宏观生态格局。研究结果显示，基于发现日期模拟的常规恒定扩散核（dispersal kernel）模型可解释所有害虫分布范围变异的67.94%，其预测分布与实测分布的空间匹配精度达68.00%。进一步而言，若将扩散速率设定为森林面积与人口密度的函数，模型对变异的解释力提升至75.60%，空间匹配精度达74.30%。上述结果表明，单一通用扩散核模型足以预测多数害虫物种的分布范围与空间分布变异；而作为被广泛研究的定殖预测因子，繁殖体压力（propagule pressure）与生境可入侵性（habitat invasibility）的替代指标，同样可应用于扩散阶段的预测。该模型可为新型入侵物种的预警预测以及将扩散过程纳入路径级风险分析提供核心支撑要素。