Phd in ENVIRONMENTAL AND EVOLUTIONARY BIOLOGY

Thesis title: Bridging Knowledge Gaps in Coastal Sand Dune Insect Communities through Large-Scale Approaches: Robotic Tools, Diversity Patterns, and Conservation

Coastal sand dunes are ecologically significant yet understudied ecosystems, hosting a diverse insect community that remains largely neglected. This PhD thesis bridges critical knowledge gaps by integrating large-scale field surveys, molecular techniques, conservation methodologies, and automation technologies to assess the biodiversity of coastal sand dune insect communities. A comprehensive sampling campaign was conducted across Italian coastal dunes, collecting more than 33,000 arthropod specimens from four habitat types. Special focus was given to Diptera, which dominated the dataset, revealing high species richness and substantial knowledge gaps in taxonomic databases. The DNA megabarcoding approach enabled molecular clustering and species delimitation, allowing a multi-level analysis of barcode repositories aimed at assessing their taxonomic completeness and identifying biases affecting small-bodied insects. A cost-effective and scalable workflow was employed to process multiple hyperdiverse taxa, combining accessible sampling methods, optimized laboratory protocols, and advanced bioinformatics pipelines. Functional diversity analyses provided insights into the ecological roles of Diptera within coastal ecosystems. This thesis also explored conservation applications, testing the feasibility of using Key Biodiversity Area (KBA) criteria for neglected insect groups with fragmented spatial data and unresolved taxonomy. We identified ecologically important coastal sites that remain unprotected. Moreover, technological advancements were leveraged to improve biodiversity research efficiency. A robotic system, EntoSieve, was developed for automated insect size-sorting, optimizing sample processing for molecular studies and enhancing biodiversity assessments. Artificial intelligence was applied to improve insect measurement techniques, supporting high-throughput taxonomic and functional analyses. By combining taxonomic, ecological, and technological approaches, this research advances biodiversity assessments of hyperdiverse but neglected insect communities in urgent need of increased conservation efforts. The integration of robotics, AI, and molecular techniques marks a step forward in overcoming long-standing analytical challenges in entomology, paving the way for more comprehensive and efficient biodiversity monitoring.