A novel GIS-based tool for predicting coastal litter accumulation and optimising coastal cleanup actions

Abstract Effective site selection is a key component of maximising debris removal during coastal cleanup actions. We tested a GIS-based predictive model to identify marine litter hotspots in Lofoten, Norway based on shoreline gradient and shape. Litter density was recorded at 27 randomly selected locations with 5 transects sampled in each. Shoreline gradient was a limiting factor to litter accumulation when >35%. The curvature of the coastline correlated differently with litter density at different spatial scales. The greatest litter concentrations were in small coves located on larger headlands. A parsimonious model scoring sites on a scale of 1–5 based on shoreline slope and shape had the highest validation success. Sites unlikely to have high litter concentrations were successfully identified and could be avoided. The accuracy of hotspot identifications was more variable, and presumably more parameters influencing litter deposition, such as shoreline aspect relative to prevailing winds, should be incorporated.