Dissolved inorganic nitrogen (DIN) runoff from Great Barrier Reef (GBR) catchments is a threat to coral reef health. Several initiatives address this threat, including the Australian Government's Reef 2050 Plan. However, environmental decision makers face an unsolved prioritization challenge: determining the exposure of reefs to DIN from individual rivers. Here, we use virtual river tracers embedded within a GBR-wide hydrodynamic model to resolve the spatial and temporal dynamics of 16 individual river plumes during three wet seasons (2011−2013). We then used in-situ DIN observations to calibrate tracer values, allowing us to estimate the contribution of each river to reef-scale DIN exposure during each season. Results indicate that the Burdekin, Fitzroy, Tully and Daintree rivers pose the greatest DIN exposure risk to coral reefs during the three seasons examined. Results were used to demonstrate a decision support framework that combines reef exposure risk with river dominance (threat diversity).

Research that was undertaken to identify and evaluate best practices that can effectively minimise the amount of marine litter in European Seas is presented. Best practices were approached in a novel way that aimed to maximise the impact and added value of the FP7-funded project MARLISCO. The practices served as a tool to enhance the active participation and build capacity in key actors through the implementation of the innovative, dedicatedly-developed decision support tool, DeCyDe-4-Marlisco.This paper also presents the key ‘preference’ attributes of successful marine litter management practices and provides a snapshot of the key characteristics of the recorded best practices as a means of helping the reader, and particularly decision makers, recognise what would make their marine litter management efforts more successful.

A fuzzy decision tree (FDT) based framework was developed to facilitate the selection of suitable oil spill response methods in the Arctic. Hypothetical oil spill cases were developed based on six identified attributes, while the suitability of three spill response methods (mechanical containment and recovery, use of chemical dispersants, and in-situ burning) for each spill case was obtained based on expert judgments. Fuzzy sets were used to address the associated uncertainties, and FDTs were then developed through generating: i) one decision tree for all three response methods (FDT-AP1) and ii) one decision tree for each response method and the development of linear regression models at terminal nodes (FDT-LR). The FDT-LR approach exhibited higher prediction accuracy than the FDT-AP1 approach. A maximum of 100% accurate predictions could be achieved for testing cases using it. On average, 75% of suitable oil spill response methods out of 10,000 performed iterations were predicted correctly.

This paper discusses the combined use of tools from dynamical systems theory and remote sensing techniques and shows how they are effective instruments which may greatly contribute to the decision making protocols of the emergency services for the real-time management of oil spills. This work presents the successful interplay of these techniques for a recent situation, the sinking of the Oleg Naydenov fishing ship that took place in Spain, close to the Canary Islands, in April 2015.