Data from: Assessing the effects of quantitative host resistance on the life-history traits of sporulating parasites with growing lesions
Leclerc, Melen, French National Institute for Agricultural Research
Clément, Julie, French National Centre for Scientific Research
Andrivon, Didier, French National Institute for Agricultural Research
Hamelin, Frédéric, French National Institute for Agricultural Research
Published Sep 23, 2019 on Dryad.
Cite this dataset
Leclerc, Melen; Clément, Julie; Andrivon, Didier; Hamelin, Frédéric (2019). Data from: Assessing the effects of quantitative host resistance on the life-history traits of sporulating parasites with growing lesions [Dataset]. Dryad. https://doi.org/10.5061/dryad.g108557
Assessing life-history traits of parasites on resistant hosts is crucial in evolutionary ecology. In the particular case of sporulating pathogens with growing lesions, phenotyping is difficult because one needs to disentangle properly pathogen spread from sporulation. By considering Phytophthora infestans on potato, we use mathematical modelling to tackle this issue and refine the assessment pathogen response to quantitative host resistance. We elaborate a parsimonious leaf-scale model by convolving a lesion growth model and a sporulation function, after a latency period. This model is fitted to data obtained on two isolates inoculated on three cultivars with contrasted resistance level. Our results confirm a significant host-pathogen interaction on the various estimated traits, and a reduction of both pathogen spread and spore production, induced by host resistance. Most interestingly, we highlight that quantitative resistance also changes the sporulation function, whose mode is significantly time-lagged.This alteration of the infectious period distribution on resistant hosts may have strong impacts on the dynamics of parasite populations, and should be considered when assessing the durability of disease control tactics based on plant resistance management. This inter-disciplinary work also supports the relevance of mechanistic models for analysing phenotypic data of plant-pathogen interactions.
Phenotypic data used for the study. Columns names are similar to the notations used in the manuscript (See Table 1).
R script for fitting lesion growth and sporulation models to destructive phenotypic data.