Anthropogenic macrolitter
(>0.5 cm) in rivers is of increasing
concern. It has been found to have an adverse effect on riverine ecosystem
health, and the livelihoods of the communities depending on and living
next to these ecosystems. Yet, little is known on how macrolitter
reaches and propagates through these ecosystems. A better understanding
of macrolitter transport dynamics is key in developing effective reduction,
preventive, and cleanup measures. In this study, we analyzed a novel
dataset of citizen science riverbank macrolitter observations in the
Dutch Rhine–Meuse delta, spanning two years of observations
on over 200 unique locations, with the litter categorized into 111
item categories according to the river-OSPAR protocol. With the use
of regression models, we analyzed how much of the variation in the
observations can be explained by hydrometeorology, observer bias,
and location, and how much can instead be explained by temporal trends
and seasonality. The results show that observation bias is very low,
with only a few exceptions, in contrast with the total variance in
the observations. Additionally, the models show that precipitation,
wind speed, and river flow are all important explanatory variables
in litter abundance variability. However, the total number of items
that can significantly be explained by the regression models is 19%
and only six item categories display an R
2
above 0.4. This
suggests that a very substantial part of the variability in macrolitter
abundance is a product of chance, caused by unaccounted (and often
fundamentally unknowable) stochastic processes, rather than being
driven by the deterministic processes studied in our analyses. The
implications of these findings are that for modeling macrolitter movement
through rivers effectively, a probabilistic approach and a strong
uncertainty analysis are fundamental. In turn, point observations
of macrolitter need to be planned to capture short-term variability.