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The urgent need for large-scale spatiotemporal assessments of biodiversity in the face of rapid environmental change prompts technological advancements in species identification and bio-monitoring such as metabarcoding. The high-throughput DNA sequencing of bulk samples offers many advantages over traditional morphological identification for describing community composition. Our objective was to evaluate the applicability of metabarcoding to identify species in taxonomically complex samples, evaluate biodiversity trends across broad geographical and temporal scales, scan for non-indigenous species, and facilitate cross-study comparisons. (B) Location: Marine and freshwater ports along Canadian coastlines (Pacific, Arctic, Atlantic) and the Great Lakes. (B) Methods: We used metabarcoding of bulk zooplankton samples to identify species and profile biodiversity across habitats and seasons in busy commercial ports. A taxonomic assignment approach circumventing sequence clustering was implemented to provide increased resolution and accuracy compared to pre-clustering. (B) Results: Taxonomic classification of over seven million sequences identified organisms spanning around 400 metazoan families and complement previous surveys based on morphological identification. Metabarcoding revealed over 30 orders that were previously not reported, while certain taxonomic groups were underrepresented due to depauperate reference databases. Despite the limitations of assigning metabarcoding data to the species level, zooplankton communities were distinct among coastlines and significantly divergent between marine, freshwater, and estuarine habitats even at the family level. Furthermore, biodiversity varied substantially across two seasons reaching a beta diversity of 0.9 in a sub-Arctic port exposed to high vessel traffic. (B) Main Conclusions: Metabarcoding data offers a powerful and sensitive approach to conduct large-scale biodiversity surveys and allows comparability across studies when rooted in taxonomy. We highlight ways of overcoming current limitations of metabarcoding for identifying species and assessing biodiversity, which has important implications for detecting organisms at low abundance such as endangered species and early invaders. Our study conveys pertinent and timely considerations for future large-scale monitoring surveys in relation to environmental change. |