Reducing Measurement Error in Riverbank Litter Sampling

Accurate and reliable monitoring data are essential to design effective litter reduction and mitigation strategies within riverine systems. A common method to gather data is riverbank litter sampling, where observers manually collect and categorize items >0.5 cm. While monitoring efforts are scaling up to meet growing demand for data, quantification of measurement error has not yet been undertaken for riverbank litter sampling methods to date, which is key data needed to design improved monitoring strategies. We quantified measurement error in riverbank litter sampling conducted along lowland rivers with sparsely vegetated banks. Interobserver variability was substantial (mean coefficient of variation 22.4%) and was unaffected by litter concentration or sample area size. Item size and color significantly influenced recovery: smaller, transparent, and black items were most commonly missed. Repeated observations reduced uncertainty, with mean recovery rates rising from 67% to 87% between one and two observations. We recommend two ways to reduce these uncertainties and improve reliability in monitoring protocols: (1) repeated observations, and (2) addressing biases for small items through additional measurement steps or correction factors. Incorporating these suggestions can contribute to reducing measurement error, improving long-term litter assessments and enhancing evidence-based decision-making in litter management.