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Abstract: Bayesian networks (BNs) are often praised on their easy to update -characteristic. This is commonly understood as either updating the conditional probability tables when new data or knowledge appears or updating our prior knowledge by setting some of the variables to a "known" state. In addition to that, BNs are relatively easy to update in a sense that the structures can be modified to answer different research questions. In many cases, selected nodes and their defined mutual dependencies can be detached from the original model and linked to another BN as such. It is also possible to integrate whole BNs as submodels to larger entities – metamodels, which are useful e.g. for policy analysis where alternative management actions affecting different parts of the system should be evaluated and compared. We present a cross-disciplinary BN for analyzing the ecosystem risks caused by the increasing oil transport in the Gulf of Finland (GoF), the North-Eastern Baltic Sea. This integrative metamodel consists of several submodels for e.g. the areal probabilities for tanker accidents and the following oil leaks, the likely oil combating efficiency and the oil induced biological consequences for the selected groups of organisms. Our model enables ranking management actions in the light of uncertainty arising from both the submodels and the alternative future scenarios for the amount of oil transported in the GoF. We describe the process of building the metamodel and discuss the central challenges related to the process.