Impacts of the spatial scale of climate data on the modeled distribution probabilities of invasive tree species throughout the world

Abstract

Species distribution models (SDMs) are powerful tools to predict species distributions, and thus support invasion risk assessments for tree species at the global scale. However, SDMs may produce different species distribution probabilities depending on the spatial scale of climate data included in the model. Hence, we must understand impacts of the climate data scale on the modeled distribution probabilities of invasive tree species (ITS) throughout the world. We used nine ITS from the list of “The 100 of the World's Worst Invasive Alien Species” as our study species, and applied Maxent modeling based on presence and background points to model the distribution probabilities of these ITS across the globe using three climate data scales: 2.5, 5.0 and 10.0′. The average distribution probabilities of presence and background points across the nine focal ITS increased significantly from the 2.5 to the 10.0′ resolution, indicating that coarse climate data scales may increase the distribution probabilities of presence and background points for these focal species. The large gap between different climate data scales resulted in high prediction uncertainty for the distribution probabilities of ITS. We offer two suggestions for decreasing the prediction uncertainty of the distribution probabilities of ITS at the global scale due to the effects of the climate data scale when using SDMs: 1) use 5.0′ resolution as the input to SDMs when using GBIF or other specimen databases; and 2) decrease the gap between 2.5, 5.0 and 10.0′ in the number of presence points of ITS.