A systematic review and risk matrix of plastic litter impacts on aquatic wildlife: A case study of the Mekong and Ganges River Basins.

Plastic litter is a pollutant of aquatic environments worldwide, with some of the world's highest litter densities occurring in freshwater ecosystems. Little information about the risk that plastic litter poses to aquatic wildlife is available across the world's most polluted waterways. To help assess the risk to aquatic species where empirical data is lacking, our review presents i) a risk assessment methodology for predicting plastic litter impacts on aquatic wildlife in data poor environments, ii) a case study demonstrating this risk assessment methodology for wildlife across two heavily polluted river basins in Asia, the Mekong and Ganges River Basins; and iii) a broad review summarising common trends in litter interactions and risk to freshwater fish, aquatic birds, cetaceans and raptors. This risk analysis unites a systematic review approach with risk matrices following International Standards Organization's risk assessment criteria, evaluating the risk of plastic entanglement and ingestion and the potential for harm to the animal. In the Mekong and Ganges River Basins, we found that the risk of litter entanglement is higher than litter ingestion. Four species were forecast to be at high risk of entanglement: Ganges River dolphin, Gharial, Mekong giant catfish and Irrawaddy dolphin. The eastern imperial eagle and greater spotted eagle were noted to be at moderate risk of entanglement. Both the Ganges River dolphin and Irrawaddy dolphin were predicted to have a moderate risk of plastic ingestion. Interestingly, cranes, waterfowl and wading birds were deemed at low or negligible risk from plastic litter. This risk matrix methodology can be applied to other waterways and taxa to assess the risk posed by plastic. It can also be readily updated as more information becomes available. This review enables decision makers to bridge a data gap by providing a tool for conservation and management before comprehensive empirical data is available.