Non-native species invasions are causing worldwide ecosystem degradation and economic loss, with average global economic costs exceeding 27 billion dollars per year over the past five decades. More urgently, both the number of non-native species and their impacts are projected to increase over the coming decades. For example, approximately an additional 1,500 non-native species are likely to establish in North America by 2050. Furthermore, the economic costs of biological invasions are predicted to increase threefold per decade. Government agencies, conservation organizations, and private citizens have spent significant resources to mitigate the impacts of species invasions, but the outcomes are far from satisfactory. One main reason is that we still do not have a holistic and predictive understanding of species invasion across scales. This project will compile an open-access, cross-scale database of species invasion centered around the datasets collected by the National Ecological Observatory Network (NEON). This database will be analyzed using advanced statistical methods to test theory on the relative roles propagule pressure, abiotic variables, and biotic variables on invasions for multiple taxonomic groups (plants, birds, and beetles) across spatial scales. Model results will be disseminated by building an online interactive application that can dynamically present and forecast risks of invaders at all NEON sites. This application will be updated automatically with new d