The effect of urbanization on meiofauna in sandy beaches of the southern North Sea

Abstract

The effect of urbanization on sandy beach meiofauna is poorly understood. To understand how intertidal meiofauna communities will respond to increasing urbanization pressures, their relationship to urbanization factors were investigated using environmental DNA metabarcoding with a COI marker. Eleven beaches were sampled in the southern North Sea between Scheveningen and Zandvoort and categorized based on a paper by Gonzalez et al. (2014) to determine their degree of urbanization. Generalized linear mixed modelling and nonmetric dimensional scaling statistical methods were used to assess alpha and beta diversity of the meiofauna communities at sampling location. The results showed that the lower intertidal zone were overwhelmingly dominated by polychaeta, followed by chromadorea and hydrozoan while in the upper intertidal zone the most abundant taxa were clitellata, chromadorea, and polychaeta. The urbanization factors showed no impact on OTU richness in the lower intertidal meiofauna, though the GLMM model showed a negative effect on OTU richness in the upper intertidal meiofauna associated with vehicle traffic, buildings on the sand, and poor dune condition. The nonmetric multidimensional scaling results presented a variety of factors that affected community composition. In the lower intertidal zone these factors were SQM, beach slope, dune condition, and UI, and SQM, beach slope, vehicle traffic, and beach cleaning. A GDM model revealed that geographic distance and beach slope greatly influenced the meiofauna compositional turnover, and that dune condition, vehicle traffic, and beach cleaning also had a smaller impact on the meiofauna beta diversity. These results suggest that beach meiofauna communities are influenced by urbanization and warrants further studies to unravel these links., We emphasize the importance of study design that is appropriate for your research question and recommend using a combination of methods to avoid biases.