Using prior information to build probabilistic invasive species risk assessments

Abstract

Understanding why some introduced species become naturalized and invasive whereas others do not is a major focus of invasion ecology. Invasive species risk assessments address this same question, but are not typically based on the results from recent ecological studies. Applying results from the ecological literature to risk assessment is difficult, in part because there are no general explanations of invasion likelihood across taxa. Most ecological studies are also specific to a particular region and it is unclear whether outcomes in one region will necessarily apply to another. Here we show how a hierarchical Bayesian statistical framework can make better use of ecological studies for applied risk assessments. We focus on three key opportunities afforded by these models: (1) the ability to leverage information from one region to form prior expectations for other regions about which little is known, (2) the ability to quantify uncertainty of predictions, and (3) flexibility to incorporate within-group heterogeneities in probabilities of naturalization. We illustrate these principles using a case study where we predict the probability of plant taxa naturalizing in New Zealand and Australia, showing how prior information can be particularly valuable when data are limited. As more studies document invasion patterns around the world, a framework that can formally incorporate prior information will help link the accumulating data on species introductions to risk assessments.