Leveraging sequential least-cost modelling to uncover multiple introductions: a case study of an invasive wild bee species

Abstract

Context: Invasive species pose a significant threat to biodiversity, creating a need for accurate methods to assess their spread. Although multiple introductions are common, estimates of expansion rates often assume a single introduction site due to limited knowledge of population structure. Objectives: This multidisciplinary study aimed to develop a novel spatio-temporal approach to delineate potential populations without prior knowledge of population structure. We applied this approach to the Sculptured Resin Bee, Europe’s first non-native bee species, providing regional expansion rate estimates for its spread across Europe. Methods: Observation data from 2008 to 2024 were analysed. Based on an environmental suitability map, sequential least-cost modelling was applied in annual time steps, linking each new observation to the nearest known observation via a least-cost path. Populations were delineated by excluding high-cost paths and analysing the connectivity of the remaining paths, and expansion rates were calculated using the distance regression method. Results: We identified two populations, which align with known genetic groups in the area of France, Switzerland and Austria. Our modelling results also indicate two additional populations introduced to Italy and Serbia. Expansion rates ranged from 13.3 km/year to 58.6 km/year and peaked at 89.7 km/year during expansion phases, exhibiting a consistent sigmoidal expansion pattern. Conclusions: Our spatio-temporal approach delineates introduced populations without prior genetic knowledge, improving expansion rate estimation and informing targeted genetic sampling, monitoring, and management efforts of invasive species.