Temporal and spatial propagule deposition patterns of the emerging fungal pathogen of chestnut Gnomoniopsis castaneae in orchards of north-western Italy

Two chestnut (Castanea sativa) orchards of north-western Italy were sampled with passive spore traps 35 times over 24 months. Samples were analysed through a newly developed quantitative PCR assay to quantify propagule loads of the emerging fungal pathogen Gnomoniopsis castaneae. Average propagule deposition patterns were assessed along with temporal and climatic variables, including sampling month and season, temperatures, relative humidity, precipitations, and wind. Machine learning algorithms combining information theory, fractal analysis, unbiased recursive partitioning, ordinary least squares and logistic regressions, were used to model propagule deposition patterns. The trained models were validated on independent data gathered from 24 samplings conducted in a third chestnut orchard during the same timeframe. Results showed that propagule deposition rate (DR) was variable within and among sites, with a site average ranging from 173 to 765 spores ?m ?h. Propagule deposition was observed across all seasons, although the DR dropped substantially during wintertime (p < 0.05). Mean, maximum, and minimum temperatures, the growing degree days at 0 and 5? thresholds, and wind gust were all positively correlated (p < 0.05) with DR of G. castaneae. The trained models were all significant (p < 0.05), as well as their validation (p < 0.05). Fluctuations of propagule deposition throughout the year were consistent among sites and proved to be driven by temperatures. Wind gust was associated with the overall amount of propagules deposited at site level. In future, the increase in temperatures and strong winds as a result of climate change may boost the spread of G. castaneae.