Statistical analyses
We first combined EAB presence data obtained from historical publications with the two sets of field collected data (the field surveys from 2003-2019, and the common garden field study from 2012-2013), then used a multinomial logistic regression (MLR) model to analyze the relationships between degrees of EAB infestation (as ordinal dependent variable) and host tree species, host tree age, DBH, habitat, latitude, and altitude (as independent variables) collected from different regions using the software JMP Pro 10.0.0 (SAS Institute 2012). Four variables, including latitude, altitude, DBH (continuous), and host tree age (discrete) were binned to create categories using data conversions presented in Table S3, respectively, to maintain consistency of data processing. Host tree species, habitat and bark splits were set as nominal variables, while host tree age, DBH, latitude, altitude, canopy index and degree of infestation were set as ordinal variables. One-way analysis of variance (ANOVA), followed by Tukey’s means separation test, was used to evaluate effects of ash tree species and habitat (as independent variables) on level of EAB infestation (as ordinal dependent variable) in different regions using SPSS 21.0 (SPSS Institute 2012). In the Oleaceae common garden experiment, ANOVA followed by Tukey’s means separation test was used to evaluate effects of ash tree species (as independent variable) on canopy index, bark splits and percentage of infested trees (as dependent variable). When determining the trend of EAB infestation level in relation to host tree age, host tree size, and host tree geographic distribution mentioned above, number of sample plots by age, DBH, and latitudinal and altitudinal distribution of ash species were plotted by EAB infestation level using OriginPro 2018 (OriginLab 2018).