Özvarol, Y. (2026). A critical review of benthic macrolitter monitoring methods: Biases, comparability, and decision-support framework. Marine Pollution Bulletin, 233, 120154.

Özvarol Y.

MARINE POLLUTION BULLETIN THE INTERNATIONAL JOURNAL FOR MARINE ENVIRONMENTALISTS, SCIENTISTS, ENGINEERS, ADMINISTRATORS, POLITICIANS AND LAWYERS, vol.233, pp.1-15, 2026 (SCI-Expanded, Scopus)

Abstract

The seafloor is a major sink for marine litter, yet monitoring remains challenging due to technical and meth odological constraints. A wide range of survey techniques exists, but differences in spatial coverage, detection capability, and reporting practices hinder cross-study comparability. This review evaluates principal approaches for benthic macrolitter monitoring based on 60 peer-reviewed studies published between 2000 and 2025. Reviewed techniques include bottom trawls, grabs, diver surveys, towed cameras, remotely operated vehicles, autonomous underwater vehicles, acoustic methods, and AI-assisted image analysis. The synthesis reveals clear trade-offs: diver surveys provide high-resolution observations but are spatially limited, whereas trawl-based methods offer broader coverage but suffer from gear selectivity and substrate constraints. Imaging platforms balance spatial coverage and detection detail, though performance depends on environmental conditions. Bias-frequency analysis showed that reporting inconsistency (87%), gear selectivity (80%), and detection bias (75%) were the most frequent limitations. Only 23% of studies used comparable reporting units, constraining data integration and indicating that litter densities are often method-dependent. Based on these findings, the study develops an objective-driven decision-support framework consisting of a decision matrix and a decision tree linking monitoring objectives, habitat characteristics, environmental conditions, and logistical constraints to survey techniques. The framework supports method selection for baseline assessments, long-term monitoring, ecological impact studies, and hotspot identification. The results highlight the need for standardized reporting, cross-method calibration, and open-access training datasets for automated image analysis. By integrating cross-method evi dence, semi-quantitative synthesis, and decision-support tools, this review provides a practical foundation for improving benthic marine litter monitoring.