Eight Years of Citizen Science Reveal Taxonomic Confusion Patterns and Dynamic Participation in the Monitoring of Vespa velutina in Spain

The yellow‐legged hornet ( Vespa velutina nigrithorax ) is a successful invasive species in Europe, where its monitoring is partially reliant on citizen science (CS) initiatives. While CS is a powerful tool for early detection, taxonomic confusion and fluctuating participant engagement can limit its effectiveness. We conducted an 8‐year study (2016–2024) using data from the VespApp platform in Spain to evaluate the drivers of reporting precision and abundance. We analysed 1329 valid reports to detect misidentification patterns and the ecological, spatial, and temporal factors influencing data quality through generalised linear mixed‐effects models (GLMM). Our results show an overall validation precision of 29.30%, with a marked difference between nest reports (65.40%) and individual insect observations (18.50%). Taxonomic confusion was concentrated in native taxa, particularly within the Scoliidae and other Vespidae families. GLMM revealed that reporting precision was significantly driven by temporal variables (month) and stage of the invasion. In contrast, report abundance was influenced by month and platform source, with mobile app submission being significantly more frequent than web‐based reports. However, user engagement was characterised by a high proportion of transient participants, with a low retention rate of only 8.11% of participants submitting multiple reports. We conclude that while public identification skills evolve and improve over time, participation does not follow a cumulative trend, likely due to the novelty decrease as the invasion becomes established. These findings place the emphasis on the need for iterative and proactive outreach strategies and the integration of mobile technologies to sustain public engagement in long‐term invasive species monitoring programmes.