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Environmental and stand conditions related to Fistulina hepatica heart rot attack on Castanea sativa
Author(s) -
Regué Adrià,
Bassié Ludovic,
deMiguel Sergio,
Colinas Carlos
Publication year - 2019
Publication title -
forest pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.535
H-Index - 49
eISSN - 1439-0329
pISSN - 1437-4781
DOI - 10.1111/efp.12517
Subject(s) - hepatica , biology , mediterranean climate , fungus , forestry , botany , ecology , geography , zoology , fasciola hepatica , helminths
Castanea sativa forests in north‐eastern Spain have historically been healthy and have been good producers of timber and non‐wood forest products. In the last quarter of a century, Fistulina hepatica has gained relevance in this ecosystem and is believed to be responsible for Chestnut Red Stain (CRS), which inflicts heart rot in trees and causes severe economic losses. The fungus grows in the heartwood and produces no external symptoms that would enable early detection. With the objective of developing an inexpensive method to detect heart rot and to discover possible relationships between the severity of F. hepatica attack and the silvicultural management or site characteristics of chestnut stands, 38 pure C. sativa plots were selected, sampled and analysed for the presence of the fungus. DNA was extracted from the heartwood samples, and PCR was used to amplify target sequences with specific F. hepatica primers for non‐homologous ITS1 and ITS2 regions. Correlation analysis was used to inspect the multiple interactions among the environmental and management variables examined, and a predictive model was designed for forest managers to estimate the risk of infection. Both analyses revealed that stress variables were related to CRS severity. This result is especially relevant since temperatures are expected to rise in coming decades throughout the northern rim of the Mediterranean region, where chestnut is an important species. This study provides two tools for better managing chestnut forests in the coming years: an inexpensive and fast tool for diagnosis and a predictive model to estimate the risk of infection.

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