Marine Biology

, 163:149 | Cite as

Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy

  • David Abad
  • Aitor Albaina
  • Mikel Aguirre
  • Aitor Laza-Martínez
  • Ibon Uriarte
  • Arantza Iriarte
  • Fernando Villate
  • Andone Estonba
Original paper

Abstract

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.

Keywords

Phytoplankton Copy Number Variation Plankton Community Representative Sequence Taxonomic Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

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.

Funding

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.

Ethical approval

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).

Supplementary material

227_2016_2920_MOESM1_ESM.pdf (571 kb)
Supplementary material 1 (PDF 571 kb)

References

  1. Albaina A, Villate F, Uriarte I (2009) Zooplankton communities in two contrasting Basque estuaries (1999–2001): reporting changes associated with ecosystem health. J Plankton Res 31:739–752CrossRefGoogle Scholar
  2. Albaina A, Aguirre M, Abad D, Santos M, Estonba A (2016a) 18S rRNA V9 metabarcoding for diet characterization: a critical evaluation with two sympatric zooplanktivorous fish species. Ecol Evol 6:1809–1824. doi: 10.1002/ece3.1986 CrossRefGoogle Scholar
  3. 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
  4. Aljanabi SM, Martinez I (1997) Universal and rapid salt extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res 25:4692–4693CrossRefGoogle Scholar
  5. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410CrossRefGoogle Scholar
  6. Amend AS, Seifert KA, Bruns TD (2010) Quantifying microbial communities with 454 pyrosequencing: does read abundance count? Mol Ecol 19:5555–5565CrossRefGoogle Scholar
  7. Amorim Visco J, Apothoz-Perret-Gentil L, Cordonier A, Esling P, Pillet L, Pawlowski J (2015) Environmental monitoring: inferring diatom index from next-generation sequencing data. Environ Sci Technol 49:7597–7605CrossRefGoogle Scholar
  8. Aravena G, Villate F, Uriarte I, Iriarte A, Ibáñez B (2009) Response of Acartia populations to environmental variability and effects of invasive congenerics in the estuary of Bilbao, Bay of Biscay. Est Coast Shelf Sci 83:621–628CrossRefGoogle Scholar
  9. 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
  10. Baird DJ, Hajibabaei M (2012) Biomonitoring 2.0: a new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol Ecol 21:2039–2044CrossRefGoogle Scholar
  11. 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
  12. Borja A, Muxika I, Franco J (2006) Long-term recovery of soft-bottom benthos following urban and industrial sewage treatment in the Nervión estuary (southern Bay of Biscay). Mar Ecol Prog Ser 313:43–55CrossRefGoogle Scholar
  13. Borja A, Elliott M, Carstensen J, Heiskanen AS, van de Bund W (2011) Marine management—towards an integrated implementation of the European Marine Strategy Framework and the Water Framework Directives. Mar Pollut Bull 60:2175–2186CrossRefGoogle Scholar
  14. Bourlat SJ, Borja A, Gilbert J, Taylor MI, Davies N, Weisberg SB, Griffith JF, Lettierih T et al (2013) Genomics in marine monitoring: new opportunities for assessing marine health status. Mar Pollut Bull 74(1):19–31CrossRefGoogle Scholar
  15. Bricker SB, Ferreira JG, Simas T (2003) An integrated methodology for assessment of estuarine trophic status. Ecol Modell 169:39–60CrossRefGoogle Scholar
  16. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336. doi: 10.1038/nmeth.f.303 CrossRefGoogle Scholar
  17. Chen G, Hare MP (2008) Cryptic ecological diversification of a planktonic estuarine copepod, Acartia tonsa. Mol Ecol 17:1451–1468CrossRefGoogle Scholar
  18. Comtet T, Sandionigi A, Viard F, Casiraghi M (2015) DNA (meta)barcoding of biological invasions: a powerful tool to elucidate invasion processes and help managing aliens. Biol Invasions 17:905–922CrossRefGoogle Scholar
  19. Cowart DA, Pinheiro M, Mouchel O, Maguer M, Grall J, Miné J, Arnaud-Haond S (2015) Metabarcoding is powerful yet still blind: a comparative analysis of morphological and molecular surveys of seagrass communities. PLoS One 10:e0117562CrossRefGoogle Scholar
  20. de Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, Lara E, Berney C et al (2015) Eukaryotic plankton diversity in the sunlit ocean. Science 348(6237):1261605. doi: 10.1126/science.1261605 CrossRefGoogle Scholar
  21. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461CrossRefGoogle Scholar
  22. Edgar RC, Haas BJ, Clememte JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200CrossRefGoogle Scholar
  23. 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
  24. Eiler A, Drakare S, Bertilsson S, Pernthaler J, Peura S, Rofner C, Simek K, Yang Y, Znachor P, Lindström ES (2013) Unveiling distribution patterns of freshwater phytoplankton by a next generation sequencing based approach. PLoS One 8:e53516CrossRefGoogle Scholar
  25. Ferreira JG, Andersen JH, Borja A, Bricker SB, Camp J, Cardoso da Silva M, Garcés E, Heiskaneng AS et al (2011) Overview of eutrophication indicators to assess environmental status within the European Marine Strategy Framework Directive. Estuar Coast Shelf Sci 93:117–131CrossRefGoogle Scholar
  26. Gaudy R, Boucher J (1983) Relation between respiration, excretion (ammonia and inorganic phosphorus) and activity of amylase and trypsin in different species of pelagic copepods from an Indian Ocean equatorial area. Mar Biol 75:37–45CrossRefGoogle Scholar
  27. Gilbert JA, Steele JA, Caporaso JG, Steinbrük L, Reeder J, Temperton B, Huse S, McHardy AC et al (2012) Defining seasonal marine microbial community dynamics. ISME J 6:298–308CrossRefGoogle Scholar
  28. Godhe A, Asplund ME, Härnström K, Saravanan V, Tyagi A, Karunasagar I (2008) Quantification of diatom and dinoflagellate biomasses in coastal marine seawater samples by real-time PCR. Appl Environ Microbiol 74:7174–7182CrossRefGoogle Scholar
  29. Gonzalez JM, Portillo MC, Belda-Ferre P, Mira A (2012) Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities. PLoS One 7(1):e29973. doi: 10.1371/journal.pone.0029973 CrossRefGoogle Scholar
  30. Herlemann DP, Labrenz M, Jurgens K, Bertilsson S, Waniek JJ, Andersson AF (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579CrossRefGoogle Scholar
  31. Hirai J, Kuriyama M, Ichikawa T, Hidaka K, Tsuda A (2015) A metagenetic approach for revealing community structure of marine planktonic copepods. Mol Ecol Res 15:68–80CrossRefGoogle Scholar
  32. 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
  33. 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
  34. Kelly RP, Port JA, Yamahara KM, Martone RG, Lowell N, Thomsen PF, Mach ME, Bennett M et al (2014) Environmental monitoring. Harnessing DNA to improve environmental management. Science 344:1455–1456CrossRefGoogle Scholar
  35. Kembel SW, Wu M, Eisen JA, Green JL (2012) Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comput Biol 8(10):e1002743. doi: 10.1371/journal.pcbi.1002743 CrossRefGoogle Scholar
  36. Laakmann S, Gerdts G, Erler R, Knebelsberger T, Martínez Arbizu P, Raupach MJ (2013) Comparison of molecular species identification for North Sea calanoid copepods (Crustacea) using proteome fingerprints and DNA sequences. Mol Ecol Res 13:862–876CrossRefGoogle Scholar
  37. Lindeque PK, Hay SJ, Heath MR, Ingvarsdottir A, Rasmussen J, Smerdon GR, Waniek JJ (2006) Integrating conventional microscopy and molecular analysis to analyse the abundance and distribution of four Calanus congeners in the North Atlantic. J Plankton Res 28:221–238CrossRefGoogle Scholar
  38. Lindeque PK, Parry HE, Harmer RA, Somerfield PJ, Atkinson A (2013) Next generation sequencing reveals the hidden diversity of zooplankton assemblages. PLoS One 8(11):e81327CrossRefGoogle Scholar
  39. Logares R, Audic S, Bass D, Bittner L, Boutte C, Christen R, Claverie JM, Decelle J et al (2014) Patterns of rare and abundant marine microbial eukaryotes. Curr Biol 24:813–821CrossRefGoogle Scholar
  40. Mahé F, Mayor J, Bunge J, Chi J, Siemensmeyer T, Stoeck T, Wahl B, Paprotka T et al (2015) Comparing high-throughput platforms for sequencing the V4 region of SSU-rDNA in Environmental Microbial Eukaryotic Diversity surveys. J Eukaryot Microbiol 62:338–345CrossRefGoogle Scholar
  41. Massana R, Gober A, Audic S, Bass D, Bittner L, Boutte C, Chambouvet A, Christen R et al (2015) Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ Microbiol 17:4035–4049. doi: 10.1111/1462-2920.12955 CrossRefGoogle Scholar
  42. 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
  43. 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
  44. Pochon X, Bott NJ, Smith KF, Wood SA (2013) Evaluating detection limits of next-generation sequencing for the surveillance and monitoring of international marine pests. PLoS One 8:e73935CrossRefGoogle Scholar
  45. Prokopowich CD, Gregory TR, Crease TJ (2003) The correlation between rDNA copy number and genome size in eukaryotes. Genome 46:48–50CrossRefGoogle Scholar
  46. Quail M, Smith ME, Coupland P et al (2012) A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and Illumina MiSeq sequencers. BMC Genom 13:341CrossRefGoogle Scholar
  47. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:590–596CrossRefGoogle Scholar
  48. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  49. Roh C, Villatte F, Kim B-G, Schmid RD (2006) Comparative study of methods for extraction and purification of environmental DNA from soil and sludge samples. Appl Biochem Biotechnol 134:97–112CrossRefGoogle Scholar
  50. Romari K, Vaulot D (2004) Composition and temporal variability of picoeukaryote communities at a coastal site of the English channel from 18SrDNA sequences. Limnol Oceanogr 49:784–798CrossRefGoogle Scholar
  51. Smith D (2012) fastq-barcode.pl. [Software]. https://gist.github.com/dansmith01/3920169
  52. Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner HW, Richards TA (2010) Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol 19(Suppl 1):21–31CrossRefGoogle Scholar
  53. Stoeck T, Breiner HW, Filker S, Ostermaier V, Kammerlander B, Sonntag B (2014) A morphogenetic survey on ciliate plankton from a mountain lake pinpoints the necessity of lineage-specific barcode markers in microbial ecology. Environ Microbiol 1:430–444CrossRefGoogle Scholar
  54. Sun C, Zhao Y, Li H, Dong Y, MacIsaac HJ, Zhan A (2015) Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities. Aquat Biol 24:9–15CrossRefGoogle Scholar
  55. Taylor AH, Allen JI, Clark PA (2002) Extraction of a weak climatic signal by an ecosystem. Nature 416:629–632CrossRefGoogle Scholar
  56. 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
  57. Uriarte I, Villate F (2004) Differences in the abundance and distribution of copepods in two estuaries of the Basque coast (Bay of Biscay) in relation to pollution. J Plankton Res 27(9):863–974CrossRefGoogle Scholar
  58. Uriarte I, Villate F, Iriarte A, Duque J, Ameztoy I (2014) Seasonal and axial variations of net water circulation and turnover in the estuary of Bilbao. Estuar Coast Shelf Sci 150:312–324CrossRefGoogle Scholar
  59. Uriarte I, Villate F, Iriarte A (2015) Zooplankton recolonization of the inner estuary of Bilbao: influence of pollution abatement, climate and non-indigenous species. J Plankton Res. doi: 10.1093/plankt/fbv060 Google Scholar
  60. 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
  61. Villate F, Iriarte A, Uriarte I, Intxausti L, de la Sota A (2013) Dissolved oxygen in the rehabilitation phase of an estuary: influence of sewage pollution abatement and hydro-climatic factors. Mar Pollut Bull 70(1–2):234–246CrossRefGoogle Scholar
  62. Ward BA, Dutkiewicz SA, Jahn O, Follows MJ (2012) A size-structured food-web model of the global ocean. Limnol Oceanogr 57:1877–1891CrossRefGoogle Scholar
  63. 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
  64. Zaiko A, Martinez JL, Schmidt-Petersend J, Ribicic D, Samuiloviene A, Garcia-Vazquez E (2015b) Metabarcoding approach for the ballast water surveillance—an advantageous solution or an awkward challenge? Mar Pollut Bull 92(1–2):25–34CrossRefGoogle Scholar
  65. Zaiko A, Samuiloviene A, Ardura A, Garcia-Vazquez E (2015c) Metabarcoding approach for nonindigenous species surveillance in marine coastal waters. Mar Pollut Bull 100(1):53–59. doi: 10.1016/j.marpolbul.2015.09.030 CrossRefGoogle Scholar
  66. Zhan A, Hulak M, Sylvester F, Huang X, Adebayo AA, Abbott CL, Adamowicz SJ, Heath DD et al (2013) High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol Evol 4:558–565CrossRefGoogle Scholar
  67. Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30:614–620CrossRefGoogle Scholar
  68. Zhu F, Massana R, Not F, Marie D, Vaulot D (2005) Mapping of picoeukaryotes in marine ecosystems with a quantitative PCR of the 18S rRNA gene. FEMS Microbiol Ecol 52(1):79–92CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Genomic Resources Group, Department of Genetics, Physical Anthropology and Animal PhysiologyUniversity of the Basque Country, UPV/EHULeioaSpain
  2. 2.Environmental Studies Centre (CEA)Vitoria-Gasteiz City CouncilVitoria-GasteizSpain
  3. 3.Phytoplankton Group, Department of Plant Biology and EcologyUniversity of the Basque Country, UPV/EHULeioaSpain
  4. 4.Zooplankton Group, Laboratory of Ecology of Zooplankton, Department of Plant Biology and EcologyUniversity of the Basque Country, UPV/EHUGasteizSpain
  5. 5.Zooplankton Group, Laboratory of Ecology of Zooplankton, Department of Plant Biology and EcologyUniversity of the Basque Country, UPV/EHULeioaSpain

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