Unveiling Distribution Patterns of Freshwater Phytoplankton by a Next Generation Sequencing Based Approach

Abstract

The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.

Seasonality and environmental control of freshwater SAR11 (LD12) in a temperate lake (Lake Erken, Sweden)

“…If no such hit was found, the sequence was classified as 'no match' (Andersson et al 2010). Denoising the 454 sequences using Amplicon Noise as previously described (Eiler et al 2013), did not affect the calculated LD12/Bacteria 454-read ratio (see Fig S1 in the Supplement)…”

Phosphorus resource heterogeneity in microbial food webs

“…Functional groups. We separated sequences into 3 functional groups based on 16S rRNA sequence identity: bacteria (non-cyanobacterial), cyanobacteria, and eukaryotic algae (classified via choloroplasts, see Eiler et al 2013). We determined 16S rRNA sequence identity using the Ribosomal Database Project's 16S rRNA reference sequences and taxonomy version 7 (Cole et al 2009).…”

Bioassessment of a Drinking Water Reservoir Using Plankton: High Throughput Sequencing vs. Traditional Morphological Method

“…Vasselon et al (2017) [23] compared HTS and morphological water quality index values in 33 river sites in Island Mayotte and found that the water quality status was congruent between the two methods. Eiler et al (2013) [29] reported that DNA-sequencing-derived phytoplankton composition differed significantly among lakes with different trophic status, showing that DNA sequencing could resolve phytoplankton assemblages at a level relevant for ecosystem management. …”

“…The method is faster in terms of sampling processing, and may become cheaper as the technology improves, and more importantly with the ability to provide more reliable and richer biological information than the traditional morphology-based method [22][23][24]. Different plankton assemblages as bioindicators were characterized using DNA sequencing including bacterioplankton [18,[24][25][26], and phytoplankton [27][28][29][30][31][32][33]. Baird and Hajibabaei (2012) [17] envisioned a new paradigm (i.e., Biomonitoring 2.0) in ecosystem assessment based on a HTS platform, though a complete paradigm shift may require more research.…”