Metabarcoding reveals strong spatial structure and temporal turnover of zooplankton communities among marine and freshwater ports

Abstract Aim The urgent need for large‐scale spatio‐temporal assessments of biodiversity in the face of rapid environmental change prompts technological advancements in species identification and biomonitoring 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 and facilitate cross‐study comparisons. Location Marine and freshwater ports along Canadian coastlines (Pacific, Arctic and Atlantic) and the Great Lakes. 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. Results Taxonomic classification of over seven million sequences identified organisms spanning around 400 metazoan families and complements previous surveys based on morphological identification. Metabarcoding revealed over 30 orders that were previously not reported, while certain taxonomic groups were underrepresented because of depauperate reference databases. Despite the limitations of assigning metabarcoding data to the species level, zooplankton communities were distinct among coastlines and significantly divergent among 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. Main Conclusions Metabarcoding 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 relationship to environmental change.