Plankton biodiversity and species co-occurrence based on environmental DNA – a multiple marker study

By

Sildever, Sirje
Laas, Peeter
Kolesova, Natalja
Lips, Inga
Lips, Urmas
Nagai, Satoshi

Type

Article

Date of Publication

2021-11-3

Original Publication

Metabarcoding and metagenomics

Volume

5

Pages

1--23

Subjects

Bacteria , Baltic Sea , metabarcoding , Phytoplankton , rRNA genes , Seasons , Zooplankton

Contributed by

Pensoft Publishers

Language

English

Published by

Bulgaria Pensoft Publishers 2017-

Identifiers

DOI: https://doi.org/10.3897/mbmg.5.72371

 

Abstract

Metabarcoding in combination with high-throughput sequencing (HTS) allows simultaneous detection of multiple taxa by targeting single or several taxonomically informative gene regions from environmental DNA samples. In this study, a multiple-marker HTS approach was applied to investigate the plankton diversity and seasonal succession in the Baltic Sea from winter to autumn. Four different markers targeting the 16S, 18S, and 28S ribosomal RNA genes were employed, including a marker for more efficient dinoflagellate detection. Typical seasonal changes were observed in phyto- and bacterioplankton communities. In phytoplankton, the appearance patterns of selected common, dominant, or harmful species followed the patterns also confirmed based on 20 years of phytoplankton monitoring data. In the case of zooplankton, both macro- and microzooplankton species were detected. However, no seasonal patterns were detected in their appearance. In total, 15 and 2 new zoo- and phytoplankton species were detected from the Baltic Sea. HTS approach was especially useful for detecting microzooplankton species as well as for investigating the co-occurrence and potential interactions of different taxa. The results of this study further exemplify the efficiency of metabarcoding for biodiversity monitoring and the advantage of employing multiple markers through the detection of species not identifiable based on a single marker survey and/or by traditional morphology-based methods.