Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale

Abstract

Pseudomonas syringae is a pathogenic bacterium that poses a significant threat to global agriculture, necessitating a deeper understanding of its ecological dynamics in the context of global warming. This study investigates the current and projected future distribution of P. syringae, focusing on the climatic factors that influence its spread. To achieve this, we employed Maximum Entropy (MaxEnt) modeling based on Geographic Information Systems (GIS) to analyze species occurrence records alongside relevant climate data. The MaxEnt model was calibrated using 75 % of the occurrence data, with the remaining 25 % reserved for validation. The model's performance was meticulously assessed utilizing the area under the curve (AUC) and true skill statistics (TSS), resulting in an AUC score of 0.92, indicating excellent predictive capability. Our analysis identified key climatic parameters—temperature, precipitation, and humidity—that significantly affect the presence of P. syringae. Notably, our findings project an expansion of the bacterium's geographic range in the coming decades, with optimal conditions shifting toward the poles. This research underscores the significant influence of climate change on the distribution of P. syringae and provides valuable insights for developing targeted disease management strategies. The anticipated increase in bacterial infections in crops highlights the urgent need for proactive measures to mitigate these effects.

Keywords

• Climate change
• Disease management
• GIS
• Maxent modeling
• Pseudomonas syringae