Three species of Phytophthora, P. cambivora, P. citricola and P. cactorum, were found to be associated with a recent outbreak of ink disease causing high mortality of chestnut trees in central Italy. Phytophthora cambivora was isolated from 11·6% of the soil samples taken around symptomatic trees, and was mainly associated with heavily diseased trees. It was the most aggressive species to Castanea sativa, but survived poorly in the soil. Phytophthora citricola and P. cactorum showed a limited ability to induce disease on chestnut, but could be recovered from soil during most of the year. A fourth species, P. gonapodyides, was recovered only from mud of stream beds within the chestnut stands. Involvement of these species in the development of disease is discussed.

Distribution and gradient analysis of Ink disease caused by Phytophthora cambivora were studied over a 2 years period in a chestnut forest in Italy. Ink disease incidence, severity and tree mortality increased during the period over the studied chestnut forest. Disease descriptors and landform datasets were analysed by Geographic Information System software and displayed as a multilayer thematic map. Indices of dispersion and empirical dispersion models were used to study spatial distribution of disease in the investigated area. Semivariograms were used to interpolate spatial data and to map Ink disease. A logical spatial dependence of the studied variables was found. Ink disease occurred preferentially along natural drainages routes where multicyclic inoculum build up was likely to occur. A negative relationship was found between Ink disease incidence, severity and tree mortality with respect to distance from the drainage.

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Ink disease of sweet chestnut (Castanea sativa) caused by the oomycetes Phytophthora cinnamomi and P. x cambivora is the limiting factor for chestnut cultivation in several European regions. The objective of this study was to explore how the spatial landscape heterogeneity affects the distribution pattern of ink disease over a large chestnut area in Central Italy using an approach that combined remote sensing, ground truthing activities and GIS. A multivariate model was developed that explained a large proportion of the variance of the impact of the disease using the density of roads and drainage networks as predictor variables. The association of these landscape elements, specifically with ink disease foci, also provides practical tool to improve the accuracy of monitoring of this disease and the preparation of risk maps.

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