Imaging with spatio-temporal modelling to characterize the dynamics of plant-pathogen lesions

Abstract

Abstract Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.

Plant phenotyping relevance

画像ベースの病斑計測と時空間数理モデルを統合し、病斑拡大の定量的パラメータを推定する手法が研究の中心であるため。

Code and data availability

The paper states that the plant images and trained Random Forest classifiers used for lesion segmentation are available in an open dataverse, but the supplied blocks do not include the dataverse citation details or any public URL (only the bioRxiv DOI is allowed). The asset is paper-specific and public per the text, so

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