Modelling Invasive Insect Spread with Citizen Science Data: A Dynamic Occupancy-Detection Model

Yufan Zheng , Elle Saber, Bernd Gruber, Helen Nahrung, Angus Carnegie, Richard P Duncan

Human-mediated biological invasions are accelerating worldwide, but detecting early incursions and tracking their spread remains difficult. Opportunistic citizen science platforms, such as iNaturalist and eBird, generate large volumes of presence-only records, offering new opportunities to detect and monitor invasive species. However, these data come with uneven sampling effort, strong spatial and taxon biases, and lack explicit non-detections, complicating efforts to infer species absences. To address these challenges, we develop a dynamic occupancy-detection model (DODM) that estimates colonisation, local extinction, and occupancy dynamics for invasive species using presence-only data. The model infers detection probability as a function of background sampling effort from related taxa, enabling separation of non-detection from true absence. This detection process, combined with a temporal correlation structure and spatial dependency, is used to infer spatiotemporal changes in species occupancy using a hierarchical Bayesian model that captures uncertainty in how invasions unfold over space and time. Using iNaturalist observations, we apply this model to infer detection probability, occupancy trajectories, dynamic colonisation and extinction probabilities for eight recently introduced insect species in eleven locations worldwide. Our results show that a well-structured DODM can unlock dynamic information from presence-only data, offering a flexible and generalizable framework for monitoring and risk mapping biological invasions.