Projecting the global spread of xylella fastidiosa under climate change using maxent modeling

Abstract

Xylella fastidiosa, a virulent plant pathogen native to the Americas, presents considerable risks to economically valuable crops and ornamental flora. It is a highly virulent bacterium that causes the most critical plant infections. Many regions around the world such as the European Union countries posed the strongest constraints to prevent the introduction and spread of Xylella fastidiosa, including obligatory surveillance, and removal measures for new outbreaks. This research utilizes Geographic Information Systems (GIS) and maximum entropy modeling (Maxent) to forecast the worldwide dissemination of Xylella fastidiosa across different climate change scenarios. We gathered occurrence data from various sources, yielding 113 distinct sites, and employed 19 bioclimatic variables from the WorldClim database to ascertain four principal factors—precipitation seasonality, precipitation of the driest month, mean temperature of the warmest quarter, and minimum temperature of the coldest month—that affect habitat suitability. The Maxent model exhibited superior performance, with an Area Under the Curve (AUC) of 0.91 and a True Skill Statistic (TSS) of 0.66, signifying its efficacy in forecasting suitable environments. Current distribution maps indicate high-risk areas predominantly in subtropical and tropical regions, particularly in the Americas and Mediterranean Europe. Forecasts for 2050 and 2070 based on Representative Concentration Pathways (RCP) suggest a significant expansion of these high-risk areas, implying that climate change may intensify the proliferation of this pathogen especially under elevated emissions scenarios. These findings highlight the critical necessity for proactive management techniques to alleviate the dangers associated with Xylella fastidiosa, protecting global agricultural systems and biodiversity.