Spatial analysis facilitates invasive species risk assessment

• We use spatial statistics to predict regional-scale invasion risks.
• Ensemble of different species distribution models enhances model performance.
• Multi-threshold approach on invasion probability reduces model uncertainty.
• Regional hotspot analysis can help to prioritize management activities.
• Variables representing corridors are important for the prediction of invasion.

Regional scale quantitative invasion risk analyses are needed to allow early detection and rapid response in order to effectively control the spread of exotic invasion. Most of the current invasion risk analyses are qualitative and ad hoc based. In this study, we used a spatial statistics based framework to assess the invasion risks of hemlock woolly adelgid (Adelges tsugae) with the following major steps: (1) invasion probability was first predicted by two widely used spatial statistics tools, maximum entropy (Maxent) and Mahalanobis distance (MD), based on known adelgid infestation locations and a set of environmental and anthropogenic related factors; (2) an ensemble of the above two models and a multi-threshold approach were employed to reduce prediction uncertainties; and (3) a spatial hotspot analysis were applied to enhance invasion prevention and management decision making. Among the factors investigated, variables representing corridors (e.g., trails and railroads) that are inadvertently spreading adelgid were important for the prediction of adelgid invasion. Large portion of the hemlock forests in the study area had a high adelgid invasion probability. The hotspot analysis based on the ensemble model showed three major clustered areas with high adelgid infestation probability. Our study demonstrated the feasibility of regional-scale quantitative invasion risk assessment with the application of a spatial statistics based framework, which can be used for effective and proactive invasion prevention and management.