Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA

Abstract

We assessed spatio-temporal patterns of diversity in deep-sea sediment communities using metabarcoding. We chose a recently developed eukaryotic marker based on the v7 region of the 18S rRNA gene. Our study was performed in a submarine canyon and its adjacent slope in the Northwestern Mediterranean Sea, sampled along a depth gradient at two different seasons. We found a total of 5,569 molecular operational taxonomic units (MOTUs), dominated by Metazoa, Alveolata and Rhizaria. Among metazoans, Nematoda, Arthropoda and Annelida were the most diverse. We found a marked heterogeneity at all scales, with important differences between layers of sediment and significant changes in community composition with zone (canyon vs slope), depth, and season. We compared the information obtained from metabarcoding DNA and RNA and found more total MOTUs and more MOTUs per sample with DNA (ca. 20% and 40% increase, respectively). Both datasets showed overall similar spatial trends, but most groups had higher MOTU richness with the DNA template, while others, such as nematodes, were more diverse in the RNA dataset. We provide metabarcoding protocols and guidelines for biomonitoring of these key communities in order to generate information applicable to management efforts.

A comparative analysis of metabarcoding and morphology‐based identification of benthic communities across different regional seas

“…For instance, the clustering of mOTUs at 97% similarity resulted in a much higher number of taxonomic units than the morphological approach. The higher diversity observed with molecular data has previously been observed (e.g., Dell'Anno, Carugati, Corinaldesi, Riccioni, & Danovaro, ; Guardiola et al., ). While a higher diversity is observed in the molecular data, species assignments at the mOTU level are often currently unachievable.…”

“…The higher diversity observed with molecular data has previously been observed (e.g., Dell'Anno, Carugati, Corinaldesi, Riccioni, & Danovaro, 2015;Guardiola et al, 2016). While a higher diversity is observed in the molecular data, species assignments at the mOTU level are often currently unachievable.…”

Environmental DNA metabarcoding: Transforming how we survey animal and plant communities

“…In applied settings, eDNA metabarcoding of surface sediments has revealed benthic impacts of aquaculture for Atlantic salmon farming on short spatial scales using both eDNA and eRNA (Pawlowski, Esling, Lejzerowicz, Cedhagen, & Wilding, 2014). Guardiola et al (2016) showed through a comparison of eDNA and eRNA that spatial trends in species richness from these two sources were similar, but that eDNA detected higher diversity. Overall, the fate, transport and decomposition of animal and plant eDNA in marine environments are poorly known compared to other environments, and there is pressing need for further studies.…”

Cobble community DNA as a tool to monitor patterns of biodiversity within kelp forest ecosystems

“…Metabarcoding has been successfully applied in a number of studies as a powerful and repeatable method for characterizing biodiversity (Hänfling et al, ; Leray & Knowlton, ). The flexibility of metabarcoding protocols allows the analyses of a wide range of sample types (sediment: Guardiola et al, , substrate: Wangensteen et al, , seawater: Bakker et al, , bulk invertebrate samples: Elbrecht & Leese, , stomach contents: Siegenthaler, Wangensteen, Benvenuto, Campos, & Mariani, and scat: Berry et al, ), to more recent approaches that utilize natural environmental samplers as a tool to survey marine biodiversity such as utilizing the diet profiles of benthic scavengers to assess fish diversity or harnessing water‐filtering sponges to sample environmental DNA (natural sampler DNA—nsDNA; Mariani, Baillie, Colosimo, & Riesgo, ; Siegenthaler, Wangensteen, Soto, et al, ). While metabarcoding of environmental samples has many limitations associated with primer performance, bioinformatic workflows and study design (Elbrecht, Hebert, & Steinke, ), it overcomes many morphology‐based challenges in identifying species at different levels (e.g.…”

“…PISCO’s monitoring approach consists of swath sampling to quantify the density and abundance of the macroalgae, invertebrates and fishes that comprise kelp forest communities. A Venn diagram was used to visualize genus‐level gamma richness of WRMS, Seanet and PISCO data sets using venneuler in R (Wilkinson & Urbanek, ). Further, WRMS and PISCO community assemblage structures were compared via rank abundance (phylum‐level).…”