Multimarker Metabarcoding of Prokaryotic and Eukaryotic Communities in Contaminated UK Estuaries

Abstract

Anthropogenic metal contamination, particularly from historic mining, has led to persistent copper enrichment in estuarine sediments of southwestern England. Although this contamination is well documented, its ecological consequences remain less understood, especially in microbial and meiofaunal communities, which can offer early indicators of pollution. This thesis aimed to determine (i) whether a multi-marker metabarcoding strategy (16S, ITS, 18S, 28S, COI) can better detect pollution-induced changes compared with single- marker approaches (ii) which taxa respond most consistently to elevated metal levels and (iii) whether porewater copper more accurately predicts benthic community shifts than sediment copper concentrations. Field sampling encompassed 12- and 34-site datasets across major estuaries, covering porewater Cu concentrations from 4.6 to over 400 µg/L. Sediment DNA was extracted in triplicate at each site. Amplicon sequencing of multiple markers profiled bacterial, archaeal and eukaryotic assemblages. Statistical analyses linked community data to copper levels (porewater and sediment), revealing thresholds for compositional turnover. Bacterial and archaeal communities underwent threshold-like shifts near 20 µg/L porewater Cu, with archaea showed greater sensitivity in more uniform site subsets. Eukaryotic assemblages, particularly nematodes and alveolates, exhibited marked changes at heavily contaminated sites, corroborating previous morphological observations. Multi-primer metabarcoding captured a broader range of taxa than single markers alone but remained limited by primer biases and incomplete reference databases. These results refine pollution threshold estimates and highlight porewater copper as a strong predictor of community disruption. Archaea emerged as potential bioindicators, responding more sharply to contamination than bacteria. The multi-marker approach significantly improves ecological resolution, underscoring the importance of integrating morphological and molecular data. Moving forward, enhancing primer sets, expanding databases and applying functional assays (e.g. metatranscriptomics) will further strengthen the use of metabarcoding in environmental management and remediation efforts.