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New ecological and physiological dimensions of beech bark disease development in aftermath forests

Cale, Jonathan A., Teale, Stephen A., Johnston, Mariann T., Boyer, Gregory L., Perri, Katherine A., Castello, John D.
Forest ecology and management 2015 v.336 pp. 99-108
Cryptococcus fagisuga, Fagus grandifolia, Neonectria, Neonectria ditissima, bark, biomarkers, biotic factors, case-control studies, catechin, disease models, forests, fungi, isorhamnetin, phosphorus, population growth, scale insects, trees, New York
The ecology of northeastern North American forests is significantly altered by the effects of beech bark disease (BBD) on American beech (Fagus grandifolia). The conventional understanding is that BBD involves two native pathogenic fungi, Neonectria ditissima and N. faginata, which are preceded by Cryptococcus fagisuga, a non-native, invasive scale insect. Described in the early 20th century, this model of disease development describes the sequence of insect colonization at the expanding front of this invasive disease complex but lacks supporting data in long-affected (aftermath) forests. A clearer understanding of factors governing BBD development in aftermath forests is needed to develop sound, targeted management strategies. We established a case-control study to determine how Neonectria infections and C. fagisuga infestations are driven by nutritional, physiological, and entomological factors of the previous year. Findings suggest that N. ditissima infection is predisposed by the native scale insect Xylococculus betulae and low bark levels of phosphorus and the phenolic isorhamnetin while C. fagisuga and low bark levels of phosphorus and the phenolic catechin predispose trees to N. faginata infection. C. fagisuga population growth was best predicted by insect density the previous year, but was variously correlated with bark chemistry factors. We conclude that the classic BBD development model does not adequately predict disease development in aftermath forests of New York, and propose separate modified models for N. ditissima and N. faginata that include distinct abiotic and biotic factors. An improved understanding of the ecological interactions among the organisms involved and the physiological basis of infection have implications for future disease development and resistance biomarkers. We suggest that BBD management in aftermath forests should avoid single-factor strategies in favor of multi-factor approaches targeting specific pathosystems.