Introduction to Invasive Species
This use case (UC) covers Invasive Alien Species. Together with the Endangered Species UC, it will populate and demonstrate the 'Dynamics and Threats from and for Species of Policy Concern' part of the Biodiversity Digital Twin. At present, the Endangered Species UC has not yet been developed.
This UC aims at quantifying the levels of invasion (i.e., the number of naturalized alien species) in different terrestrial habitat types across Europe under baseline conditions and various future climate and land-use change scenarios. The primary outcome of the use case will be spatially explicit and habitat-explicit projections of current and future invasion levels that can be updated as new current species distributions and/or scenario projections of environmental data become available. The UC will first explore naturalized alien plant species because of abundant vegetation and floristic data, which allow mapping species to habitat types. This will allow producing more refined estimates of invasion levels that are more informative for management and policy-making.
At the core of the Invasive Alien Species Digital Twin (IAS-DT) is joint species distribution modeling (JSDM). In the JSDM framework, the invasion level is calculated by modeling the presence of multiple co-occurring alien species and then summing species occurrences at a chosen spatial resolution. Compared to single-species or stacked species distribution models, JSDMs can better estimate the effects of environmental factors on species distributions because the information across many species is pooled together. Additionally, by leveraging the residual information, JSDMs can better predict species co-occurrence patterns and the overall assemblage composition. Such an approach is, therefore, advantageous to the previously employed approach to modeling the levels of plant invasion in Europe that involved first aggregating species occurrence data in calculating the level of invasion and then modeling the latter as a function of environmental predictors.