Species distribution modeling approach for biased citizen science data

Ecological research emphasizes the criticality of comprehending species distribution for the formulation of efficient conservation strategies. The advent of Citizen Science initiatives has transformed the landscape of species occurrence data collection, offering a cost-efficient avenue for monitoring wildlife across diverse spatiotemporal scales. Nevertheless, the absence of standardized sampling protocols within these initiatives poses analytical hurdles, leading to skewed sampling efforts biased towards easily accessible or extensively studied regions. This study analyzes the national butterfly monitoring program in the UK. It uses a point process framework that combines spatial and temporal covariates to examine spatial and temporal trends of butterfly species occurrences. The methodology merges two critical elements: (i) the geographical locations visited by participating volunteers and (ii) the presence records of the species under investigation, utilizing the INLA/inlabru framework. The study highlights the need to reduce sampling bias to improve the accuracy and reliability of species distribution modeling. By combining the benefits of Citizen Science data and robust modeling methodologies, this approach provides a more nuanced understanding of species distribution dynamics. It can thus help strengthen the basis for more effective conservation efforts.