Abstract
Accurate assessment of species rarity and conservation status requires an approach that integrates data-driven models with established ecological knowledge. In this study, we applied multispecies occupancy (MSO) and latent factor multispecies occupancy (LFMSO) models to estimate the occurrence of 133 Odonata species in South Korea. Using the model outputs, we implemented the Rabinowitz rarity framework to conduct data-based rarity assessments, which were then compared with known ecological information, including geographic ranges, habitat preferences, regional Red List statuses, and citizen science observations. Our findings reveal both alignments and discrepancies between these data-driven rarity assessments and traditional ecological knowledge. For example, species classified as near threatened (NT) or vulnerable (VU) on the regional Red List generally corresponded with high-rarity classifications based on the Rabinowitz framework. However, significant inconsistencies were identified, particularly for certain lentic Odonata species traditionally considered common. These results suggest that spatial biases in field surveys, combined with limited access to data on legally protected species, can impede accurate rarity assessments. These findings underscore the need for standardized survey protocols and improved data-sharing policies for sensitive species to reduce biases and enhance the reliability of rarity assessments. This is essential for effective conservation planning and biodiversity management in freshwater ecosystems.