Pathogenicity and Molecular Phylogeny of Diaporthe eres Complex Causing Twig Blight on Stone Fruit Trees

Abstract

Twig blight symptoms causing progressive dieback in stone fruit orchards have increasingly been associated with species within the Diaporthe eres complex, leading to significant yield losses and reduced plant vigor. Isolates recovered from symptomatic twigs of Prunus spp. were characterized through cultural, morphological, and pathogenicity assays to confirm their role in disease development. Representative isolates produced brown necrotic lesions and rapid twig girdling under controlled inoculation conditions, satisfying Koch’s postulates for virulence assessment. Molecular characterization using ITS, TEF1-α, and β-tubulin gene sequencing revealed clustering within the Diaporthe eres species complex with high bootstrap support. Phylogenetic analyses indicated limited but distinct genetic variation among isolates, suggesting cryptic speciation and host-associated differentiation within orchard environments. Pathogenicity indices varied significantly across isolates, demonstrating differential aggressiveness on peach, plum, and cherry cultivars under greenhouse evaluations. Findings emphasize the epidemiological importance of the Diaporthe eres complex in stone fruit decline and highlight the need for integrated disease management strategies. Continuous monitoring and molecular surveillance are recommended to track pathogen diversity and prevent further spread in commercial orchards. These results provide a refined understanding of pathogen biology within the Diaporthe eres complex and its interaction with perennial woody hosts in temperate fruit production systems. Integrated approaches combining molecular diagnostics, field surveillance, and host resistance screening are essential for sustainable management of twig blight in stone fruit orchards. Future research should focus on population genomics and environmental drivers influencing pathogen distribution, alongside evaluation of eco-friendly disease control measures under diverse agro-climatic conditions. This will strengthen predictive disease modeling frameworks.