Abstract

Over the last decade, opportunistic data collected through citizen science (CS)
programs have proven to be a valuable and cost-effective resource for monitoring wildlife populations. However, the absence of standardized sampling protocols in these initiatives introduces analytical challenges, as data collection is often influenced by observer preferences, accessibility, and areas of public interest. In contrast, standardized surveys are carefully structured to minimize these biases but provide much lower spatiotemporal coverage. Integrating these two valuable data sources could enhance the accuracy and quality of species distribution predictions. Yet, reliably combining them remains challenging due to differences in study design, spatial and temporal coverage, and potential sampling biases. In this work, we introduce a new approach to integrating opportunistic citizen science (CS) observations with data from planned surveys while accounting for observational errors inherent in both sources. The proposed integrated model is fitted using inlabru to jointly analyse the geographical locations where a species has been reported by participating volunteers and the detection/non-detection data from robust designed surveys. By integrating CS data with survey data using robust modelling techniques, we can enhance our understanding of species distributions.