RESEARCHER
francesco.nugnes(AT)ipsp.cnr.it
06.4993.27826
publications: Orcid
personal details and activity: People
Curriculum Vitae
Ph.D. in agrobiology and agrochemistry with a dissertation about morpho-bio-molecular characterization of species belonging to Anagrus genus (Hymenoptera: Mymaridae).
Francesco’s research activities focus on themes about biological and integrated control of insects inflicting damages to agricultural and forestry activities from an eco-sustainable perspective. He directly deals with biosystematics and phylogeny of native and invasive pests and their own parasitoids, particularly referring to Chalcidoidea. Specific characterization, realized through a multidisciplinary approach, is the main theme where morphology, morphometry and biology represent the lines of evidence aimed at identification of studied organisms. Biological studies include hosts complex, reproductive modalities, identification and localization of symbiotic organism and their influence on fitness and reproduction, molecular phylogenetic analysis and karyology. Among the eco-sustainable strategies, he also deals with the evaluation of plant ecotypes showing resistance or tolerance phenomena to harmful biotic agents.
Francesco studies embryology of Encarsia species, whose developmental modalities could represent further discriminating elements useful in taxonomy. He is furthermore adapting methodologies, already useful in other taxonomic studies, to systematics of some groups of aphelinids. He is also involved in studies concerning the spreading of pests that are vectors of bacterial and viral pathogens in Mediterranean agroecosystems.
With Phytosanitary services and plants protection organizations (i.e. EPPO) Francesco cooperates in the development of techniques to promptly intercept invasive pests, the implementation and the improvement of monitoring techniques aimed at establishing the spreading areas of the pests, and the planning of potential eradication strategies. In this thematic area, he is involved in the optimization and machine learning of remote sensing systems using electronic traps.