Metabarcoding reveals high diversity of benthic foraminifera linked to water masses circulation at coastal Svalbard.

Arctic marine biodiversity is undergoing rapid changes due to global warming and modifications of oceanic water masses circulation. These changes have been demonstrated in the case of mega- and macrofauna, but much less is known about their impact on the biodiversity of smaller size organisms, such as foraminifera that represent a main component of meiofauna in the Arctic. Several studies analyzed the distribution and diversity of Arctic foraminifera. However, all these studies are based exclusively on the morphological identification of specimens sorted from sediment samples. Here, we present the first assessment of Arctic foraminifera diversity based on metabarcoding of sediment DNA samples collected in fjords and open sea areas in the Svalbard Archipelago. We obtained a total of 5,968,786 reads that represented 1384 amplicon sequence variants (ASVs). More than half of the ASVs (51.7%) could not be assigned to any group in the reference database suggesting a high genetic novelty of Svalbard foraminifera. The sieved and unsieved samples resolved comparable communities, sharing 1023 ASVs, comprising over 97% of reads. Our analyses show that the foraminiferal assemblage differs between the localities, with communities distinctly separated between fjord and open sea stations. Each locality was characterized by a specific assemblage, with only a small overlap in the case of open sea areas. Our study demonstrates a clear pattern of the influence of water masses on the structure of foraminiferal communities. The stations situated on the western coast of Svalbard that are strongly influenced by warm and salty Atlantic water (AW) are characterized by much higher diversity than stations in the northern and eastern part, where the impact of AW is less pronounced. This high diversity and specificity of Svalbard foraminifera associated with water mass distribution indicate that the foraminiferal metabarcoding data can be very useful for inferring present and past environmental conditions in the Arctic.