Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy
Metabarcoding is becoming an increasingly valuable alternative approach to biodiversity assessment, due to the combination of extreme sensitivity and potential for the highest taxonomic resolution in a cost- and time-effective methodology. To evaluate the capacity of metabarcoding for estuarine plankton monitoring, a comparison between the results obtained with this approach were compared with those based on traditional taxonomic analysis (microscopy). Database incompleteness, one of the main limitations of metabarcoding, was somewhat overcome by the addition of DNA sequences for local species, which increased the taxonomic assignment success from 23.7 to 50.5 %. When the communities were studied along with environmental variables, similar spatial and temporal trends of taxonomic diversity were observed for metabarcoding and microscopic studies of zooplankton, but not for phytoplankton. This is most likely attributable to the lack of representative sequences for phytoplankton species in current databases. In addition, there was high correspondence in community composition when comparing abundances estimated from metabarcoding and microscopy, suggesting semiquantitative potential for metabarcoding. Furthermore, metabarcoding allowed the detection and identification of two non-indigenous species (NIS) found in the study area at abundances hardly detectable by microscopy. Overall, our results indicate that metabarcoding is a powerful approach with excellent possibilities for use in plankton monitoring, early detection of NIS and plankton biodiversity shifts.
KeywordsPhytoplankton Copy Number Variation Plankton Community Representative Sequence Taxonomic Resolution
The authors thank Ann Bucklin (University of Connecticut) for her comments on the manuscript. The authors also thank the SGIker (UPV/EHU) for the technical and human support provided and the Hydrometeorology Service of the Regional Council of Bizkaia for the precipitation data.
AA and DA work was supported by a contract with the Euskampus Foundation (Euskampus Fundazioa) and a Basque Government doctoral fellowship (UPV/EHU “ZabaldUz” program), respectively. This study was funded by a Basque Government Grant “Grupo de Investigación Consolidado del Sistema Universitario Vasco” to support activities of the Genomic Resources Research Group (IT-558-10), the Phytoplankton (IT-699-13) and Zooplankton (IT-778-13) Ecology Groups. Sampling was also partly financed by the Euskampus Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
Archiving of data
Metabarcoding data (quality-filtered, chimera-free merged reads) are available at Qiita repository (https://qiita.ucsd.edu/; ID 10518).
- Albaina A, Uriarte I, Aguirre M, Abad D, Iriarte A, Villate F, Estonba A. (2016b) Insights on the origin of invasive copepods colonizing Basque estuaries; a DNA barcoding approach. Mar Biodivers Rec (in press)Google Scholar
- Bachy C, Dolan JR, López-García P, Deschamps P, Moreira D (2013) Accuracy of protist diversity assessments: morphology compared with cloning and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous tintinnid ciliates as a case study. ISME J 7(2):244–255CrossRefGoogle Scholar
- Båmstedt U (1986) Chemical composition and energy content. In: Corner EDS, O’Hara SCM (eds) The biological chemistry of marine copepods. Clarendon, Oxford, pp 1–58Google Scholar
- Edler L, Elbrächter M. (2010) The Utermöhl method for quantitative phytoplankton analysis. In: Microscopic and molecular methods for quantitative phytoplankton analysis. IOC Manuals and GuidesGoogle Scholar
- Jeffrey SW, Mantoura RFC (1997) Development of pigment methods for oceanography: SCOR-supported working groups and objectives. In: Jeffrey SW et al (eds) Phytoplankton pigments in oceanography: guidelines to modern methods. Monographs on oceanographic methodology, vol 10. pp 19–36Google Scholar
- Joshi NA, Fass JN (2011) Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. https://github.com/najoshi/sickle
- Navas-Molina JA, Peralta-Sánchez JM, González A, McMurdie PJ, Vázquez-Baeza Y, Xu Z, Ursell LK, Lauber C, Zhou H, Song SJ, Huntley J, Ackermann GL, Berg-Lyons D, Holmes S, Caporaso JG, Knight R (2013) Advancing our understanding of the human microbiome using QIIME. Methods Enzymol 531:371–444CrossRefGoogle Scholar
- Olenina I, Hajdu S, Edler L, Andersson A, Wasmund N, Busch S, Göbel J, Gromisz S et al (2006) Biovolumes and size-classes of phytoplankton in the Baltic Sea. In: HELCOM Baltic sea environment proceedings no.106, Helsinki, Finland, p 144Google Scholar
- R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
- Smith D (2012) fastq-barcode.pl. [Software]. https://gist.github.com/dansmith01/3920169
- ter Braak CJF, Smilauer P (2002) CANOCO Reference Manual and CanoDraw for Windows User’s Guide: Software for Canonical Community Ordination (Version 4.5). Microcomputer Power, IthacaGoogle Scholar
- Villate F, Uriarte I, Irigoien X, Beaugrand G, Cotano U (2004) Zooplankton communities. In: Borja A, Collins M (eds) Oceanography and marine environment of the Basque country. Elsevier Oceanography Series, vol 70. pp 395–423Google Scholar
- Zaiko A, Martinez JL, Ardura A, Clusa L, Borrell YJ, Samuiloviene A, Roca A, Garcia-Vazquez E (2015a) Detecting nuisance species using NGST: methodology shortcomings and possible application in ballast water monitoring. Mar Environ Res 112:64–72. doi: 10.1016/j.marenvres.2015.07.002 CrossRefGoogle Scholar