Multivariate Bayesian analysis to predict invasiveness of Phytophthora pathogens

Global concerns are many for the invasive impacts of Phytophthora pathogens on native vegetation, agriculture, nurseries, and urban parks and gardens. We compiled a database of 32 traits on 204 species of Phytophthora including data on each species' taxonomy (clade and subclade), historical knowledge (years since first described), impacted ecosystems, microenvironments inhabited, dispersal mode, physiology, and morphology. Drawing from approximately 11,394 unique host, pathogen, and country plant disease records from GenBank and other sources, we calculated potential invasiveness of 103 better studied species from cluster relationships. We used the species data to create a Bayesian network model predicting the degree and probability of invasiveness of individual Phytophthora species. Model calibration testing resulted in Phytophthora species, and the general modeling approach can be used for other pests and pathogens, to advise land and resource managers to thwart potential invasions before they occur or intensify.

Automated summary

We compiled a database of 32 traits on 204 species of Phytophthora and calculated the potential invasiveness of 103 better studied species. Using a Bayesian network model, we predicted the degree and probability of invasiveness of individual Phytophthora species. This modeling approach can be used for other pests and pathogens to advise land and resource managers in preventing potential invasions.