Testing and modelling the effects of climate on the incidence of the emergent nut rot agent of chestnut Gnomoniopsis castanea

Abstract

Gnomoniopsis castanea is an emerging fungal pathogen causing nut rot of sweet chestnut, Castanea sativa. This study was aimed at testing and modelling the effects of climate on disease incidence. Up to 120 ripe nuts were collected in 2011 from trees in each of 12 sites located in the northwest of Italy. The incidence of G. castanea in each site was expressed as the number of infected nuts out of the total number of nuts sampled (%), determined by combining the results of morphological identification of isolates obtained from nuts, and their typing through a newly developed taxon-specific molecular assay. Disease incidence ranged from 20 to 93%, depending on site. Geostatistical analyses revealed that, despite the clustering of sites (P < 0Á05), disease incidence was not spatially autocorrelated (P > 0Á05). This finding suggests that the disease is influenced by site-dependent factors whose scale (c. 7Á5-15Á6 km) is consistent with the climate variability throughout the sampling region. Multivariate analyses on maximum, mean and minimum temperatures and on rainfall showed that warmer temperatures were associated with higher levels of disease incidence. The temperatures of months before nut harvesting were selected as predictors for partial least squares regression (PLSR) models (GnoMods) of G. castanea incidence. External validation on data collected either on sites or in years not used for model fitting showed the good predictive abilities of the GnoMods (Spearman's q obs/pred > 0Á72, P < 0Á05). The above findings support a relationship between climate and incidence of G. castanea, providing statistical tools to forecast disease incidence at site level.

HRM analysis provides insights on the reproduction mode and the population structure of Gnomoniopsis castaneae in Europe

“…Putative colonies of G. castaneae were identified by examining macro‐ and micromorphological features including the aspect of mycelium, acervuli, the shape and size of conidia, and by molecular identification through the specific primer set developed by Lione et al . (). A total of 132 isolates were obtained (Table S1).…”

“…The European chestnut (Castanea sativa) has faced several diseases and pests in recent centuries, including ink disease caused by Phytophthora spp., blight caused by Cryphonectria parasitica and, more recently, the Asian gall wasp Dryocosmus kuriphilus (Gonthier & Ferracini, 2014). In this complex phytosanitary situation, fungi causing nut rot at pre-and/or post-harvest have also played a role, resulting in yield and economic losses (Lione et al, 2015). The ascomycete Gnomoniopsis castaneae (Visentin et al, 2012;Tamietti, 2016) is an emergent nut rot agent in several areas of Europe, including Italy, France and Switzerland (Visentin et al, 2012;Maresi et al, 2013;Dennert et al, 2015;Pasche et al, 2016).…”

“…Airborne infections may occur through flowers, as experimentally documented (Visentin et al, 2012). Disease incidence is related to warm temperatures in the months preceding the chestnut harvest rather than to rainfall (Lione et al, 2015); the disease is randomly distributed within orchards (Lione & Gonthier, 2016). With the exception of this, very little is known about the infection biology and epidemiology of this pathogen, and even less about its population genetics.…”

Environmental Factors Driving the Recovery of Bay Laurels from Phytophthora ramorum Infections: An Application of Numerical Ecology to Citizen Science

“…Models predicting spread rates of emerging pathogens in forest ecosystems are on the rise, as documented in the last few years for Heterobasidion irregulare and Gnomoniopsis castaneae, just to cite two relevant examples other than P. ramorum [11,16,18,19,108]. Notwithstanding this increased effort, unraveling factors favorable to recovery may help to improve the modelling of disease dynamics and the prediction of related economic losses, as well as to plan more effective mitigation strategies under different climatic or geographic scenarios.…”

“…The interest of forest pathologists in unraveling environmental factors driving plant diseases has been amplified in the last decades by the onset of relevant epidemics caused by emerging pathogens such as Phytophthora ramorum Werres, De Cock and Man in't Veld in Western North America, Heterobasidion irregulare Garbelotto and Otrosina, Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz and Hosoya, and Gnomoniopsis castaneae G. Tamietti in Europe, just to cite a few relevant examples [3][4][5][6][7][8][9][10][11][12]. The main environmental drivers underlying the success of such novel epidemics have often been identified through a numerical ecology approach, based on computational and multivariate statistical techniques suitable to deal with complex ecological datasets [10,11,[13][14][15][16][17][18][19][20][21][22].…”