Decision-science navigates trade-offs between environmental and socio-economic objectives for marine debris mitigation

Context Marine litter is a growing global problem that impacts biodiversity and human societies alike. South-east Asia suffers significant impacts due to high biodiversity, dense human populations, and large volumes of plastics entering the marine environment, primarily through rivers. Aims Drawing on decision-theory principles, Structured Decision Making (SDM) can improve site selection for marine debris management by identifying the best options to reduce plastic exposure to species, ecosystems, and human populations in the marine and coastal environment, as well as an overall reduction of drifting plastic debris in the open ocean. Methods We combine an SDM framework with a plastic transport model and quantify benefits for environmental and social objectives across 542 locations covering 683 rivers along the coasts of south-east Asia in the biodiversity hotspot of the Coral Triangle. We modelled and quantified metrics for the reduction in volume and flow of plastics to all downstream coral reefs, key biodiversity areas, marine protected areas, and coastal communities. Key results No location is the best option across all objectives, but the multiple metrics help to navigate trade-offs across specific objectives. Despite 95% of all plastic debris remaining in circulation in the seascape after 2 months, several rivers contribute not only large volumes of plastic debris to the overall marine pollution but also large volumes of pollution downstream. Conclusions The increasing pollution of the marine environment with plastic debris can only be stopped by regulating and reducing the production of plastic products. However, as long as plastic debris is still circulating in the environment, the identification of these locations where the removal of plastic pollution will deliver the best outcomes for a set of important objectives will remain an important mitigation measure. The proposed framework effectively facilitates understanding existing trade-offs and can easily be adapted to include additional metrics or objectives. Using this framework enables decision-makers to develop a tailor-made prioritisation process for clean-up interventions in their unique socio-ecological contexts. Implications This new decision-science approach for identifying efficient spatial management strategies for plastic clean-up is transferable to any geography and has the capacity to enhance local-to-global plastic management.