XLI CICLO | Di Lorenzo Noemi

My research focuses on advancing biodiversity studies and conservation planning by integrating functional diversity with traditional taxonomic approaches. A key aspect of my work is examining the functional roles of insects, with a special emphasis on Diptera. I have also contributed to the development of deep learning-based methods for the automated identification of insects and the estimation of their length, volume, and biomass, offering valuable insights for biodiversity monitoring and conservation efforts.

The project aims to shift the paradigm inbiodiversity studies and conservation  planning by integrating functional diversity with traditional taxonomy to improve protected area management. It evaluates whether combining taxonomic and functional metrics can moreaccurately identify ecologically significant areas and support more effective conservation decisions. While species richness, abundance,
and biomass have long been key conservation indicators, these variables alone provide a limited view of ecological phenomena, as they are influenced by spatial scale, taxonomic group, and sampling methods. This is particularly evident for insects, a hyper-diverse and underexplored taxon, with conservation efforts focused mainly on charismatic groups. The Appennino Lucano Val d’Agri Lagonegrese National Park is an ideal model system for exploring environmental and land-use gradients. The project also aims for extensive taxonomic coverage through multi-taxon sampling, with species identification based on Large-scale Integrative Taxonomy. The goal is to develop a replicable methodology for integrating taxonomic and functional metrics into adaptive zoning and management strategies for protected areas, providing a comprehensive tool for biodiversity conservation in rapidly changing environments.

In pre-print:
Di Marco, M., Gabrieli, M., Marcolin, L., Di Lorenzo, N., Cerretti, P., & Santini, L. (2025). Two forms, two functions: Functional strategies of parasitoid bristle flies and their larvae. bioRxiv.

Shirali, H., Ascenzi, A., Wührl, L., Beyer, N., Di Lorenzo, N., Vaccarella, E., Klug, N., Meier, R., Cerretti, P., & Pylatiuk, C. (2025). Deep learning-based methods for automated estimation of insect length, volume, and biomass. bioRxiv