Combining morphological and metabarcoding approaches reveals the freshwater eukaryotic phytoplankton community

Shouliang Huo,Chunzi Ma,Zhuoshi He,Hanxiao Zhang,Xiaochuang Li,Beidou Xi

doi:10.1186/s12302-020-00321-w

Abstract

BackgroundPhytoplankton diversity can be difficult to ascertain from morphological analyses, because of the existence of cryptic species and pico- and concealed phytoplankton. In-depth sequencing and metabarcoding can reveal microbial diversity, and identify novel diversity. However, there has been little comparison of metabarcoding and morphological datasets derived from the same samples, and metabarcoding studies covering total eukaryotic phytoplankton diversity are rare. In this study, the variable V7 region of the 18S rDNA gene was employed to explore eukaryotic phytoplankton diversity in 11 Chinese freshwater environments, and further compared with the dataset obtained through morphological identification.ResultsAnnotation by the evolutionary placement algorithm (EPA) rather than alignment with the SILVA database improved the taxonomic resolution, with 346 of 524 phytoplankton operational taxonomic units (OTUs) being assigned to the genus or species level. The number of unassigned OTUs was greatly reduced from 259 to 178 OTUs by using the EPA in place of the SILVA database. Metabarcoding detected 3.5 times more OTUs than the number of morphospecies revealed by morphological identification; furthermore, the number of species and the Shannon–Wiener index inferred from the two methods were correlated. A total of 34 genera were identified via both methods, while 31 and 123 genera were detected solely in the morphological or metabarcoding dataset, respectively.ConclusionThe dbRDA plot showed distinct separation of the phytoplankton communities between lakes and reservoirs according to the metabarcoding dataset. The same pattern was obtained on the basis of 10 environmental variables in the PCO ordination plot, while the separation of the populations based on morphological data was poor. However, 30 morphospecies contributed 70% of the community difference between lakes and reservoirs in the morphological dataset, while 11 morphospecies were not found by metabarcoding. Considering the limitations of each of the two methods, their combination could substantially improve phytoplankton community assessment.

Highlights

Phytoplankton diversity can be difficult to ascertain from morphological analyses, because of the existence of cryptic species and pico- and concealed phytoplankton
The average T and Dissolved oxygen (DO) values showed no difference between the lakes and reservoirs within the ranges of 19.7 to 23.3 °C and 7.2 to 13.6 mg/L, respectively, with the exception of a relatively lower T of 9.9 °C in the Changtan Reservoir
Good coverage (≥ 0.992) of operational taxonomic units (OTUs) richness was observed for each sample (Additional file 1: Table S3), indicating that the number of phytoplankton reads was approaching saturation in most samples and that the performance of the 18S ribosomal DNA (rDNA) primers selected for revealing phytoplankton diversity in the environment was good

Summary

Introduction

Phytoplankton diversity can be difficult to ascertain from morphological analyses, because of the existence of cryptic species and pico- and concealed phytoplankton. There has been little comparison of metabarcoding and morphological datasets derived from the same samples, and metabarcoding studies covering total eukaryotic phytoplankton diversity are rare. Phytoplankton have traditionally been described and characterized based on morphological characteristics Their correct identification is often difficult or impossible, especially for cryptic species complexes, species with distinct sexual dimorphism, or species with multiple life stages [1, 2]. Molecular approaches can detect cryptic species, and have been treated as one of the most important approaches for the taxonomic revision of phytoplankton [6]. Next-generation sequencing (NGS) technologies produce a large sequence dataset consisting of molecular markers, making them a promising tool for understanding microbial diversity in ecosystems. NGS allows automated sample handling and involves standard laboratory protocols, which increases the potential for comparisons between different research studies and facilitates largescale in-depth monitoring programs and investigations [8]

Methods

Results

Discussion

Conclusion