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Data on measuring splash dispersal of a major wheat pathogen in the field

Citation

Karisto, Petteri; Suffert, Frédéric; Mikaberidze, Alexey (2021), Data on measuring splash dispersal of a major wheat pathogen in the field, Dryad, Dataset, https://doi.org/10.5061/dryad.kkwh70s5r

Abstract

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 reisolated pathogen strains with strain-specific PCR 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 with 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 for further measurements of plant pathogen dispersal in field conditions, which can inform spatially targeted plant disease management. 

Methods

The data was collected by analysing scanned images of infected leaves, and analysed using Python. The raw data and results of the analysis are included in the deposited package. Source code for the analysis is deposited separately.

Usage Notes

Readme file included in the data package

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: PZ00P3_161453