Replication Data for: Simple yet effective: historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland

Species distribution models are scarcely applicable to invasive species because of their breaking of the models’ assumptions. So far, few mechanistic, semi-mechanistic or statistical solutions like dispersal constraints or propagule limitation have been applied. We evaluated a novel quasi-semi-mechanistic approach for regional scale models, using historical proximity variables (HPV) representing a state of the population in a given moment in the past. Our aim was to test the effects of addition of HPV sets of different minimal recentness, information capacity and the total number of variables on the quality of the species distribution model for Heracleum mantegazzianum on 116000 km2 in Poland. As environmental predictors, we used fragments of 103 1×1 km, world- wide, free-access rasters from WorldGrids.org. Single and ensemble models were computed using BIOMOD2 package 3.1.47 working in R environment 3.1.0. The addition of HPV improved the quality of single and ensemble models from poor to good and excellent. The quality was the highest for the variants with HPVs based on the distance form the most recent past occurrences. It was mostly affected by the algorithm type, but all HPV traits (minimal recentness, information capacity, model type or the number of the time periods) were significantly important determinants. The addition of HPVs improved the quality of current projections, raising the occurrence probability in regions where the species had occurred before. We conclude that HPV addition enables semi-realistic estimation of the rate of spread and can be applied to the short-term forecasting of invasive or declining species, which also break equal-dispersal probability assumptions.

物种分布模型（Species Distribution Models）难以适配入侵物种，因为入侵物种的扩散过程会打破该类模型的预设假设条件。迄今为止，仅有少量基于机制、半机制或统计的解决方案（如扩散约束、繁殖体限制）得到了应用。本研究针对区域尺度物种分布模型，评估了一种全新的准半机制建模方法，即使用历史邻近变量（Historical Proximity Variables, HPV）来表征物种种群在过去特定时刻的状态。本研究的目标为：针对波兰境内11.6万平方千米区域内的大豕草（Heracleum mantegazzianum），测试添加不同最小时效性、信息容量及变量总数的HPV数据集对物种分布模型性能的影响。作为环境预测变量，本研究使用了来自WorldGrids.org的103幅全球免费获取的1×1千米分辨率栅格数据片段。本研究借助运行于R 3.1.0环境中的BIOMOD2 3.1.47版本包，构建了单一模型与集成模型。添加HPV数据集可将单一模型与集成模型的性能从较差提升至良好甚至优秀水平。基于距最新历史发生点距离构建的HPV数据集对应的模型性能最优。模型性能主要受算法类型影响，但所有HPV特征（最小时效性、信息容量、模型类型及时期数量）均为影响性能的重要显著因子。添加HPV数据集可提升当前预测的性能，提高该物种历史发生区域的物种出现概率。综上，添加HPV数据集可实现半现实的扩散速率估算，能够应用于同样打破均等扩散概率假设的入侵物种或衰退物种种群的短期预测。