Abstract/Summary

Capacity for dispersal is a fundamental fitness component of plant pathogens. Characterization of plant pathogen dispersal is important for understanding how pathogen populations change in time and space. We devised a systematic approach to measure and analyze rain splash-driven dispersal of plant pathogens in field conditions, using the major fungal wheat pathogen Zymoseptoria tritici as a case study. We inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains. Next, we measured disease intensity as counts of fruiting bodies (pycnidia) using automated image analysis. These measurements characterized primary disease gradients, which we used to estimate effective dispersal of the pathogen population. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction confirmed the conclusions drawn from phenotypic data. Consistently with controlled environment studies, we found that the characteristic scale of dispersal is tens of centimeters. We analyzed the data using a spatially-explicit mathematical model that incorporates the spatial extent of the source, rather than assuming a point source, which allows for a more accurate estimation of dispersal kernels. We employed bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on kernel density estimation, enabling more robust inference compared to standard methods. We report the first estimates of dispersal kernels of the pathogen in field conditions. However, repeating the experiment in other environments would lead to more robust conclusions. We put forward advanced methodology that paves the way to further measurements of plant pathogen dispersal in field conditions, which can inform spatially targeted plant disease management.