Describing the spatial distribution of species is vital for successful species, habitat, or α-diversity conservation policies and recovery plans; however, geographic species occurrence data are rarely complete and are vulnerable to systematic biases. Species distribution models (SDMs) are commonly used to remediate some incertitude by joining occurrence data with geographic predictors (e.g. environmental, socioeconomic) in order to estimate realized species ranges. While useful, traditional SDMs contain weaknesses such as imperfect detections in the form of omission or commission errors, due in part to spatial and taxonomical biases present in open-access species occurrence databases. The S2Bak, a novel integrative SDM, derives an ameliorated estimation approach by combining presence-only species occurrence data with presence-absence data and building systematic bias-adjustment kernels, resulting in improved predictive power and reduced systematic underestimations in comparison to traditional models. While this approach was previously successful in a Panamanian setting, this project aims to build integrative models for a more widespread European Flora by combining presence-absence datasets available from the European Vegetation Archive (EVA) with presence-only datasets available from the Global Biodiversity Information Facility (GBIF), considering both taxonomical and spatial biases inherent to the databases to adjust species distribution projections accordingly. Overall, this project will provide researchers with an optimized plant biodiversity layer of Europe, the most complete and fine-scaled vegetation biodiversity baseline of the continent to date, which will be a key tool in answering applied ecological questions concerning plant distributions and their susceptibility to anticipated anthropogenic stressors.