Zooplankton Community Profiling in a Eutrophic Freshwater Ecosystem-Lake Tai Basin by DNA Metabarcoding

Abstract

Communities of zooplankton, a critical portion of aquatic ecosystems, can be adversely affected by contamination resulting from human activities. Understanding the influence of environmental change on zooplankton communities under field-conditions is hindered by traditional labor-intensive approaches that are prone to taxonomic and enumeration mistakes. Here, metabarcoding of cytochrome c oxidase I (COI) region of mitochondrial DNA was used to characterize the genetic diversity of zooplankton. The species composition of zooplankton communities determined by metabarcoding was consistent with the results based on the traditional morphological approach. The spatial distribution of common species (frequency of occurrence >10 samples) by metabarcoding exhibited good agreement with morphological data. Furthermore, metabarcoding can clearly distinguish the composition of the zooplankton community between lake and river ecosystems. In general, rotifers were more abundant in riverine environments than lakes and reservoirs. Finally, the sequence read number of different taxonomic groups using metabarcoding was positively correlated with the zooplankton biomass inferred by density and body length of zooplankton. Overall, the utility of metabarcoding for taxonomic profiling of zooplankton communities was validated by the morphology-based method on a large ecological scale. Metabarcoding of COI could be a powerful and efficient biomonitoring tool to protect local aquatic ecosystems.

DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient

“…As reference databases continue to grow, we can expect the proportion of high confidence taxonomic assignments to improve. Additionally, supplementing existing reference datasets with CO1 barcode sequences obtained from locally-collected morphologically identified specimens can substantially improve taxonomic assignment success 17 .…”

Scaling up: A guide to high‐throughput genomic approaches for biodiversity analysis

“…This study showed that freshwater invertebrate biodiversity obtained from metabarcoding the CO1 BR5 region was positively correlated with stream condition gradients (Emilson et al., ). Additionally, DNA metabarcoding compares favourably to traditional methods for zooplankton and diatom community profiling by alleviating the taxonomic impediment—the bottleneck implicated in traditional morphology‐based specimen identification (Ji et al., ; Yang et al., ; Zimmermann, Glöckner, Jahn, Enke, & Gemeinholzer, ). Although fieldwork is still a costly and time‐consuming endeavour, once samples are subsampled into tubes or plates, this method becomes highly scalable and amenable to parallelization and automation (Box 2).…”

“…A key question for biodiversity analyses is how metabarcoding compares with traditional methods for community profiling. Despite differences in the exact taxa recovered using traditional methods and DNA metabarcoding (Hajibabaei et al, 2011), recent studies have found that metabarcoding of insects, birds, diatoms and zooplankton tends to recover more taxa than traditional methods, provide a finer level of resolution and can similarly be used as a DNA-based biological indicator (Ji et al, 2013;Pawlowski, Esling, Lejzerowicz, Cedhagen, & Wilding, 2014;Sweeney et al, 2011;Yang et al, 2017). Results from studies focusing on plants and animals have been reviewed in Deiner et al (2017) and also found that DNA metabarcoding provided either complimentary T A B L E 2 A list of the commonly used markers for DNA metabarcoding, databases, and tools for various taxonomic groups.…”

Metabarcoding analysis of the stomach contents of the Antarctic Toothfish (Dissostichus mawsoni) collected in the Antarctic Ocean

“…Previous studies showed that the proportion of fish species as prey items of D. mawsoni was between 77% and 86% in %F ( Fenaughty, Stevens & Hanchet, 2003 ), between 58% and 62% in % W ( Petrov & Tatarnikov, 2011 ), and between 75% and 95% in % IRI ( Stevens et al, 2014 ). Although it is well known that numbers of contigs generated by NGS represent a rough estimate of the biomass of each species ( Saitoh et al, 2016 ; Yang et al, 2017 ), there is no currently accepted relationship between traditional measurements and molecular quantification. Because morphological analysis primarily depends on morphological traits of species, it is difficult to quantify the mass of mostly digested stomach contents.…”