Characterizing variability in marine protist communities via ARISA fingerprints — a method evaluation

• Published in: Limnology and Oceanography: Methods
• Main Authors: Kilias, Estelle, Wolf, Christian, Metfies, Katja
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Association for the Sciences of Limnology and Oceanography 2015
• Subjects: Arctic; Arctic Ocean; Southern Ocean
• Online Access: https://epic.awi.de/id/eprint/37476/; https://epic.awi.de/id/eprint/37476/1/Kilias2015.pdf; https://hdl.handle.net/10013/epic.45125; https://hdl.handle.net/10013/epic.45125.d001

It is important to characterize and understand the diversity of marine protists because of their relevance for ecosystem functioning. In the era of molecular science, diversity studies have received renewed attention. High-throughput, cost-intensive next generation sequencing provides deep insight in protist diversity but limits the volume of studied samples. Protist observations with high spatiotemporal resolution, therefore, require a quick and cost-effective tool to channelize the large sample volume and help select representatives for diversity studies. In this study, we evaluated the validity of “Automated Ribosomal Intergenic Spacer Analysis” (ARISA) as a means of estimating variability in marine protist communities. The evaluation was based on statistical correlation of ARISA data and 454-pyrosequencing data from samples collected in the Southern Ocean and Arctic Ocean. Here, we provide evidence that differences in ARISA profiles reflect taxon-specific differences observed in 454-pyrosequencing data sets. Calculated similarity indices for the ARISA profiles and 454- pyrosequencing data of 27 marine protist samples revealed strong agreements between the results of both methods regarding the extent of variability among protist communities. We suggest that ARISA might become an important tool for surveillance of differences in marine protist communities with high spatiotemporal resolution. Furthermore, it might serve as a preselection tool to identify representative samples in large data sets.