A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland

Abstract

Knowledge of oil-induced impacts from the literature and experts were used to develop a Bayesian network to evaluate the biological consequences of an oil accident in the low-saline Gulf of Finland (GOF). Analysis was carried out for selected groups of organisms. Subnetworks were divided into subgroups according to a predicted response to oil exposure. Two scenario analyses are presented: the most probable and the worst-case accident. The impact of the most probable accident in the GOF is rather small. In most of the groups studied oil-induced long-term effects are evaluated to be minor at least from the perspective of the whole GOF. After the worst-case accident negative effects are more likely. The model predicts that the most vulnerable groups are auks and ducks. Amphipods, gulls and to a lesser extend littoral fishes and seals may show delayed recovery after an accident. Also annual plant species may be susceptible to oil-induced disturbances.