Advertisement

Hydrobiologia

, Volume 818, Issue 1, pp 71–86 | Cite as

Spatial and temporal dynamics of a freshwater eukaryotic plankton community revealed via 18S rRNA gene metabarcoding

  • A. Banerji
  • M. Bagley
  • M. Elk
  • E. Pilgrim
  • J. Martinson
  • J. Santo Domingo
Primary Research Paper

Abstract

DNA metabarcoding is a sophisticated molecular tool that can enhance biological surveys of freshwater plankton communities by providing broader taxonomic coverage and, for certain groups, higher taxonomic resolution compared to morphological methods. We conducted 18S rRNA gene metabarcoding analyses on 214 water samples collected over a four-month period from multiple sites within a freshwater reservoir. We detected 1,314 unique operational taxonomic units that included various metazoans, protists, chlorophytes, and fungi. Alpha diversity differed among sites, suggesting local habitat variation linked to differing species responses. Strong temporal variation was detected at both daily and monthly scales. Diversity and relative abundance patterns for several protist groups (including dinoflagellates, ciliates, and cryptophytes) differed from arthropods (e.g., cladocerans and copepods), a traditional focus of plankton surveys. This suggests that the protists respond to different environmental dimensions and may therefore provide additional information regarding ecosystem status. Comparison of the sequence-based population survey data to conventional-based data revealed similar trends for taxa that were ranked among the most abundant in both approaches, although some groups were missing in each data set. These results highlight the potential benefit of supplementing conventional biological survey approaches with metabarcoding to obtain a more comprehensive understanding of freshwater plankton community structure and dynamics.

Keywords

Aquatic arthropods Biological survey Community dynamics Protists 

Notes

Acknowledgements

We gratefully acknowledge Joel Allen, Mia Varner, Dana Macke, Kit Daniels, and Armah de la Cruz for their role in collecting, transporting and processing the lake water samples used in this study and Chris Nietch and Jade Young for their roles in providing the USACE zooplankton data. The U.S. Environmental Protection Agency, through its Office of Research and Development, partially funded and participated in the research described herein. Any opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the agency; therefore, no official endorsement should be inferred. Any mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Supplementary material

10750_2018_3593_MOESM1_ESM.tif (306 kb)
Supplementary material 1 (TIFF 306 kb) Fig. S1. Rarefaction curve.

References

  1. Anderson, M. J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26: 32–46.Google Scholar
  2. Bachy, C., J. R. Dolan, P. López-García, P. Deschamps & David Moreira, 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. The ISME Journal 7: 244–255.CrossRefPubMedGoogle Scholar
  3. Bista, I., G. R. Carvalho, K. Walsh, M. Seymour, M. Hajibabae, D. Lallias, M. Christmas & S. Creer, 2017. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nature Communications 8: 14087.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Blaise, C. & J. F. Férard, 2005. Small-Scale Freshwater Toxicity Investigations, Vol. 1. Springer, Berlin: 1–68.CrossRefGoogle Scholar
  5. Blinn, D. W. & D. B. Herbst, 2003. Use of diatoms and soft algae as indicators of environmental determinants in the Lahontan Basin, USA. Annual Report for California State Water Resources Board Contract Agreement 704558.01.CT766.Google Scholar
  6. Bradley, I. M., A. J. Pinto & J. S. Guest, 2016. Design and evaluation of Illumina MiSeq-compatible, 18S rRNA gene-specific primers for improved characterization of mixed phototrophic communities. Applied & Environmental Microbiology 82: 5878–5891.CrossRefGoogle Scholar
  7. Bucklin, A., P. K. Lindeque, N. Rodriguez-Ezpeleta, A. Albaina, & M. Lehtiniemi, 2016. Metabarcoding of marine zooplankton: prospects, progress and pitfalls. Journal of Plankton Research 38: 393–400.CrossRefGoogle Scholar
  8. Buskey, E. J., 1997. Behavioral components of feeding selectivity of the heterotrophic dinoflagellate Protoperidinium pellucidum. Marine Ecology Progress Series 153: 77–89.CrossRefGoogle Scholar
  9. Camacho, C., G. Coulouris, V. Avagyan, N. Ma, J. Papadopoulos, K. Bealer, & T. L. Madden, 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Carpenter, S. R., N. F. Caraco, D. L. Correll, R. W. Howarth, A. N. Sharpley & V. H. Smith, 1998. Nonpoint pollution of surface waters with phosphorous and nitrogen. Ecological Applications 8: 559–568.CrossRefGoogle Scholar
  11. Chan, A., L.-P. Chiang, H. C. Hapuarachchi, C.-H. Tan, S.-C. Pang, R. Lee, K.-S. Lee, L.-C. Ng & S.-G. Lam-Phua, 2014. DNA barcoding: complementing morphological identification of mosquito species in Singapore. Parasites & Vectors 7: 569.CrossRefGoogle Scholar
  12. Chang, F., J. J. Pagano, B. S. Crimmins, M. S. Milligan, X. Xia, P. K. Hopke, & T. M. Holsen, 2012. Temporal trends of polychlorinated biphenyls and organochlorine pesticides in Great Lakes fish, 1999–2009. Science of The Total Environment 439: 284–290.CrossRefPubMedGoogle Scholar
  13. Chen, K., J. Allen & J. Lu, 2017. Community structures of phytoplankton with emphasis on toxic cyanobacteria in an Ohio inland lake during bloom season. Journal of Water Resources and Protection 9: 1299–1318.CrossRefGoogle Scholar
  14. Creer, S., V. G. Fonseca, D. L. Porazinska, R. M. Giblin-Davis, W. Sung, D. M. Power, M. Packer, G. R. Carvalho, M. L. Blaxter, P. J. Lambshead & W. K. Thomas, 2010. Ultrasequencing of the meiofaunal biosphere: practice, pitfalls and promises. Molecular Ecology 19: 4–20.CrossRefPubMedGoogle Scholar
  15. Debroas, D., I. Domaizon, J. F. Humbert, L. Jardillier, C. Lepère, A. Oudart & N. Taib, 2017. Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data. FEMS Microbiology Ecology.  https://doi.org/10.1093/femsec/fix023.PubMedGoogle Scholar
  16. Diaz, R. J., 2001. Overview of hypoxia around the world. Journal of Environmental Quality 30: 275–281.CrossRefPubMedGoogle Scholar
  17. Edsall, T. A., M. T. Bur, O. T. Gorman & J. S. Schaeffer, 2005. Burrowing mayflies as indicators of ecosystem health: status of populations in western Lake Erie, Saginaw Bay and Green Bay. Aquatic Ecosystem Health & Management 8: 107–116.CrossRefGoogle Scholar
  18. Edgar, R. C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460–2461.CrossRefPubMedGoogle Scholar
  19. Edlund, M. B., J. M. Ramstack, D. R. Engstrom, J. E. Elias, & B. M. Lafrancois, 2011. Biomonitoring using diatoms and paleolimnology in the western Great Lakes national parks. Natural Resource Technical Report NPS/GLKN/NRTR—2011/447. National Park Service, Fort Collins, Colorado.Google Scholar
  20. Emilson, C. E., D. G. Thompson, L. A. Venier, T. M. Porter, T. Swystun, D. Chartrand, S. Capell, & M. Hajibabaei. 2017. DNA metabarcoding and morphological macroinvertebrate metrics reveal the same changes in boreal watersheds across an environmental gradient. Scientific Reports 7, Article number: 12777.Google Scholar
  21. Fenchel, T., 2008. The microbial loop—25 years later. Journal of Experimental Marine Biology and Ecology 366: 99–103.CrossRefGoogle Scholar
  22. Finlay, B. J., 1981. Oxygen availability and seasonal migrations of ciliated protozoa in a freshwater lake. Microbiology 123: 173–178.CrossRefGoogle Scholar
  23. Gihring, T. M., S. J. Green & C. W. Schadt, 2012. Massively parallel rRNA gene sequencing exacerbates the potential for biased community diversity comparisons due to variable library sizes. Environmental Microbiology 14: 285–290.CrossRefPubMedGoogle Scholar
  24. Hadziavdic, K., K. Lekang, A. Lanzen, I. Jonassen, E. M. Thompson & C. Troedsson, 2014. Characterization of the 18S rRNA gene for designing universal eukaryote specific primers. PLoS ONE 9: e87624.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Hänfling, B., H. L. Lawson, D. S. Read, C. Hahn, J. Li, P. Nichols, R. C. Blackman, A. Oliver & I. J. Winfield, 2016. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Molecular Ecology 25: 3101–3119.CrossRefPubMedGoogle Scholar
  26. Harrell, F. E., Jr., C. Dupont, & others. 2017. Hmisc: Harrell Miscellaneous. R package version 4.0-3. https://CRAN.R-project.org/package=Hmisc.
  27. Havel, J. E. & J. B. Shurin, 2004. Mechanisms, effects, and scales of dispersal in freshwater zooplankton. Limnology and Oceanography 49: 1229–1238.CrossRefGoogle Scholar
  28. Hayes, N. M., B. R. Deemer, J. R. Corman, N. R. Razavi, & K. E. Strock, 2017. Key differences between lakes and reservoirs modify climate signals: A case for a new conceptual model. Limnology and Oceanography Letters 2: 47–62.CrossRefGoogle Scholar
  29. Holdren, C., W. Jones, & J. Taggart, 2001. Managing lakes and reservoirs. North American Lake Management Society and Terrene Institute, in cooperation with Office of Water, Assessment and Watershed Protection Division, Madison, WI.Google Scholar
  30. Hopkins, G. W. & R. P. Freckleton, 2002. Declines in the numbers of amateur and professional taxonomists: implications for conservation. Animal Conservation Forum 5: 245–249.CrossRefGoogle Scholar
  31. Indiana Department of Environmental Management. 2016. Indiana’s 2016 consolidated assessment and listing methodology (CALM). http://www.in.gov/idem/nps/files/ir_2016_report_apndx_l_attch_1.pdf. Accessed 19 July 2017.
  32. Jeong, H. J., Y. D. Yoo, S. T. Kim & N. S. Kang, 2004. Feeding by the heterotrophic dinoflagellate Protoperidinium bipes on the diatom Skeletonema costatum. Inter-Research Aquatic Microbial Ecology 36: 171–179.CrossRefGoogle Scholar
  33. Jørgensen, S. E., F.-L. Xu & R. Costanza, 2016. Handbook of ecological indicators for assessment of ecosystem health, 2nd ed. CRC Press Taylor & Francis Group, New York.Google Scholar
  34. Kammerlander, B., H.-W. Breiner, S. Filker, R. Sommaruga, B. Sonntag & T. Stoeck, 2015. High diversity of protistan plankton communities in remote high mountain lakes in the European Alps and the Himalayan mountains. FEMS Microbiology Ecology 91(4): fiv010.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Kans, J., 2017. Entrez Direct: E-utilities on the UNIX Command Line. https://www.ncbi.nlm.nih.gov/books/NBK179288/?report=reader#!po=0.574713. Accessed 12 July 2017.
  36. Kapoor, V., M. Elk, X. Li, C. A. Impellitteri & J. W. Santo Domingo, 2016. Effects of Cr(III) and Cr(VI) on nitrification inhibition as determined by SOUR, function-specific gene expression and 16S rRNA sequence analysis of wastewater nitrifying enrichments. Chemosphere 147: 361–367.CrossRefPubMedGoogle Scholar
  37. Karr, J. R., 1981. Assessment of biotic integrity using fish communities. Fisheries 6: 21–27.CrossRefGoogle Scholar
  38. Keck, F., V. Vasselon, K. Tapolczai, F. Rimet, & A. Bouchez, 2017. Freshwater biomonitoring in the Information Age. Frontiers in Ecology and the Environment 15: 266–274.CrossRefGoogle Scholar
  39. Kelly, R. P., J. A. Port, K. M. Yamahara & L. B. Crowder, 2014. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9: e86175.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Kentucky Department for Environmental Protection, Division of Water, 2016. Standard operating procedures. http://water.ky.gov/Pages/SurfaceWaterSOP.aspx.
  41. Ko, H.-L., Y.-T. Wang, T.-S. Chiu, M.-A. Lee, M.-Y. Leu, K.-Z. Chang, W.-Y. Chen & K.-T. Shao, 2013. Evaluating the accuracy of morphological identification of larval fishes by applying DNA barcoding. PLoS ONE 8: e53451.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Kumar, S., G. Stecher & K. Tamura, 2016. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Molecular Biology and Evolution 33: 1870–1874.CrossRefPubMedGoogle Scholar
  43. Leray, M. & N. Knowlton, 2016. Censusing marine eukaryotic diversity in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences 371: 20150331.CrossRefGoogle Scholar
  44. Lepère, C., I. Doimazon, M. Hugoni, A. Vellet & D. Debroas, 2016. Diversity and dynamics of active small microbial eukaryotes in the anoxic zone of a freshwater meromictic lake (Pavin, France). Frontiers in Microbiology 7: 130.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Leung, B., D. M. Lodge, D. Finnoff, J. F. Shogren, M. A. Lewis & G. Lamberti, 2002. An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proceedings of the Royal Society B—Biological Sciences 269: 2407–2413.CrossRefPubMedCentralGoogle Scholar
  46. Lovell, D., V. Pawlowsky-Glahn, J. J. Egozcue, S. Marguerat & J. Bähler, 2015. Proportionality: a valid alternative to correlation for relative data. PLOS Computational Biology.  https://doi.org/10.1371/journal.pcbi.1004075.PubMedPubMedCentralGoogle Scholar
  47. Lynch, A. J., S. J. Cooke, A. M. Deines, S. D. Bower, D. B. Bunnell, I. G. Cowx, V. M. Nguyen, J. Nohner, K. Phouthavong, B. Riley, M. W. Rogers, W. W. Taylor, W. Woelmer, S.-J. Youn & T. D. Beard Jr., 2016. The social, economic, and environmental importance of inland fish and fisheries. Environmental Reviews 24: 115–121.CrossRefGoogle Scholar
  48. Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal 17: 10–12.CrossRefGoogle Scholar
  49. McCune, B., & M. J. Mefford, 2016. PC-ORD: multivariate analysis of ecological data; Version 7.02.Google Scholar
  50. McMahon, G. F., & M. C. Farmer, 2004. Reallocation of federal multipurpose reservoirs: principles, policy and practice. Proceedings of the 2003 Georgia Water Resources Conference, held April 23–24, 2003, at the University of Georgia. Kathryn J. Hatcher, editor, Institute of Ecology, The University of Georgia, Athens, Georgia.Google Scholar
  51. Merchant, S. S., S. E. Prochnik, O. Vallon, E. H. Harris, S. J. Karpowicz, G. B. Witman, A. Terry, A. Salamov, L. K. Fritz-Laylin, L. Maréchal-Drouard, W. F. Marshall, L.-H. Qu, D. R. Nelson, A. A. Sanderfoot, M. H. Spalding, V. V. Kapitonov, Q. Ren, P. Ferris, E. Lindquist, H. Shapiro, S. M. Lucas, J. Grimwood, J. Schmutz, P. Cardol, H. Cerutti, G. Chanfreau, C.-L. Chen, V. Cognat, M. T. Croft, R.l Dent, S. Dutcher, E. Fernández, H. Fukuzawa, D. González-Ballester, D. González-Halphen, A. Hallmann, M. Hanikenne, M. Hippler, W. Inwood, K. Jabbari, M. Kalanon, R. Kuras, P. A. Lefebvre, S. D. Lemaire, A. V. Lobanov, M. Lohr, A. Manuell, I. Meier, L. Mets, M. Mittag, T. Mittelmeier, J. V. Moroney, J. Moseley, C. Napoli, A. M. Nedelcu, K. Niyogi, S. V. Novoselov, I. T. Paulsen, G. Pazour, S. Purton, J.-P. Ral, D. M. Riaño-Pachón, W. Riekhof, L. Rymarquis, M. Schroda, D. Stern, J. Umen, R. Willows, N. Wilson, S. Lana Zimmer, J. Allmer, J. Balk, K. Bisova, C.-J. Chen, M. Elias, K. Gendler, C. Hauser, M. Rose Lamb, H. Ledford, J. C. Long, J. Minagawa, M. D. Page, J. Pan, W. Pootakham, S. Roje, A. Rose, E. Stahlberg, A. M. Terauchi, P. Yang, S. Ball, C. Bowler, C. L. Dieckmann, V. N. Gladyshev, P. Green, R. Jorgensen, S. Mayfield, B. Mueller-Roeber, S. Rajamani, R. T. Sayre, P. Brokstein, I. Dubchak, D. Goodstein, L. Hornick, Y. W. Huang, J. Jhaveri, Y. Luo, D. Martínez, W. Chi, A. Ngau, B.Otillar, A. Poliakov, A. Porter, L. Szajkowski, G. Werner, K. Zhou, I. V. Grigoriev, D. S. Rokhsar, & A. R. Grossman, 2007. The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318: 245–250.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Naigaga, I., H. Kaiser, W. J. Muller, L. Ojok, D. Mbabazi, G. Magezi, & E. Muhumuza, 2011. Fish as bioindicators in aquatic environmental pollution assessment: a case study in Lake Victoria wetlands, Uganda. Physics and Chemistry of the Earth 36: 918–928.CrossRefGoogle Scholar
  53. Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, E. Szoecs, & H. Wagner, 2017. vegan: Community Ecology Package. R package version 2.4-3. https://CRAN.R-project.org/package=vegan.
  54. Pace, N. R., D. A. Stahl, D. J. Lane, & G. J. Olsen, 1986. The analysis of natural microbial populations by ribosomal RNA sequences. In Advances in Microbial Ecology (pp. 1–55). Springer, USA.Google Scholar
  55. Patz, J. A., P. Daszak, G. M. Tabor, A. A. Aguirre, M. Pearl, J. Epstein, N. D. Wolfe, A. M. Kilpatrick, J. Foufopoulos, D. Molyneux, D. J. Bradley & Members of the Working Group on Land Use Change Disease Emergence, 2004. Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence. Environmental Health Perspectives 112: 1092–1098.CrossRefPubMedPubMedCentralGoogle Scholar
  56. Pawlowski, J., F. Lejzerowicz, L. Apotheloz-Perret-Gentil, J. Visco & P. Esling, 2016. Protist metabarcoding and environmental biomonitoring: time for change. European Journal of Protistology 55: 12–25.CrossRefPubMedGoogle Scholar
  57. Piredda, R., M. P. Tomasino, A. M. D’Erchia, C. Manzari, G. Pesole, M. Montresor, W. H. C. F. Kooistra, D. Sarno & A. Zingone, 2017. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiology Ecology.  https://doi.org/10.1093/femsec/fiw200.PubMedGoogle Scholar
  58. Pochon, X., S. A. Wood, N. B. Keeley, F. Lejzerowicz, P. Esling, J. Drew & J. Pawlowski, 2015. Accurate assessment of the impact of salmon farming on benthic sediment enrichment using foraminiferal metabarcoding. Marine Pollution Bulletin 100: 370–382.CrossRefPubMedGoogle Scholar
  59. Pruesse, E., J. Peplies, & F. O. Glöckner, 2012. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28: 1823–1829. (Online alignment tool and search engine accessed via https://www.arb-silva.de/aligner/). Accessed 3 June 2017.
  60. Rimet, F., V. Vasselon, B. A. Keszte & A. Bouchez, 2018. Do we similarly assess diversity with microscopy and high-throughput sequencing? Case of microalgae in lakes. Organisms Diversity & Evolution 8: 1–12.Google Scholar
  61. Quast, C., E. Pruesse, P. Yilmaz, J. Gerken, T. Schweer, P. Yarza, J. Peplies & F. O. Glöckner, 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41: D590–D596.CrossRefPubMedGoogle Scholar
  62. R Core Team, 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
  63. Rees, H. C., B. C. Maddison, D. J. Middleditch, J. R. M. Patmore & K. C. Gough, 2014. The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. Journal of Applied Ecology 51: 1450–1459.CrossRefGoogle Scholar
  64. Roller, B. R. K., S. F. Stoddard & T. M. Schmidt, 2016. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nature microbiology 1(11): 16160.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Schmidt, T. M., E. F. DeLong & N. R. Pace, 1991. Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. Journal of Bacteriology 173: 4371–4378.CrossRefPubMedPubMedCentralGoogle Scholar
  66. Schulz, R., 2004. Field studies on exposure, effects, and risk mitigation of aquatic nonpoint-source insecticide pollution. Journal of Environmental Quality 33: 419–448.CrossRefPubMedGoogle Scholar
  67. Sherr, E. B. & B. F. Sherr, 2002. Significance of predation by protists in aquatic microbial food webs. Antonie van Leeuwenhoek 81: 293–308.CrossRefPubMedGoogle Scholar
  68. Sherr, E. B. & B. F. Sherr, 2007. Heterotrophic dinoflagellates: a significant component of microzooplankton biomass and major grazers of diatoms in the sea. Marine Ecology Progress Series 352: 187–197.CrossRefGoogle Scholar
  69. Siciliano, A., R. Gesuele & M. Guida, 2015. How Daphnia (Cladocera) assays may be used as bioindicators of health effects. Journal of Biodiversity & Endangered Species S1: 005.Google Scholar
  70. Simon, M., P. López-García, P. Deschamps, D. Moreira, G. Restoux, P. Bertolino & L. Jardillie, 2015. Marked seasonality and high spatial variability of protist communities in shallow freshwater systems. The ISME Journal 9: 1941–1953.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Smayda, T. J., 2008. Complexity in the eutrophication-harmful algal bloom relationship, with comment on the importance of grazing. Harmful Algae 8: 140–151.CrossRefGoogle Scholar
  72. Sogin, M. L., H. G. Morrison, J. A. Huber, D. M. Welch, S. M. Huse, P. R. Neal, J. M. Arrieta & G. J. Herndl, 2006. Microbial diversity in the deep sea and the underexplored ‘‘rare biosphere’’. Proceeding of the National Academy of Sciences USA 103: 12115–12120.CrossRefGoogle Scholar
  73. Stoddard, S. F., B. J. Smith, R. Hein, B. R. K. Roller, & T. M. Schmidt, 2014. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Research 43: D593–D598.  https://doi.org/10.1093/nar/gku1201 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Tautz, D., P. Arctander, A. Minelli, R. H. Thomas & A. P. Vogler, 2003. A plea for DNA taxonomy. Trends in Ecology & Evolution 18: 70–74.CrossRefGoogle Scholar
  75. Techtmann, S. M., M. Zhuang, P. Campo, E. Holder, M. Elk, T. C. Hazen, R. Conmy & J. W. Santo Domingo, 2017. Corexit 9500 enhances oil biodegradation and changes active bacterial community structure of oil-enriched microcosms. Applied and Environmental Microbiology 83: e03462-16.CrossRefPubMedPubMedCentralGoogle Scholar
  76. Trebitz, A. S., J. C. Hoffman, J. A. Darling, E. M. Pilgrim, J. R. Kelly, E. A. Brown, W. L. Chadderton, S. P. Egan, E. K. Grey, S. A. Hashsham, K. E. Klymus, A. R. Mahon, J. L. Ram, M. T. Schultz, C. A. Stepien & J. C. Schardt, 2017. Early detection monitoring for aquatic non-indigenous species: optimizing surveillance, incorporating advanced technologies, and identifying research needs. Journal of Environmental Management 202: 299–310.CrossRefPubMedGoogle Scholar
  77. USEPA, 2016. National Lakes Assessment 2012: A Collaborative Survey of Lakes in the United States. EPA 841-R-16-113. U.S. Environmental Protection Agency, Washington, DC. https://nationallakesassessment.epa.gov/.
  78. USEPA, 2017. Indicators used in the national aquatic resource surveys. https://www.epa.gov/national-aquatic-resource-surveys/indicators-used-national-aquatic-resource-surveys. Accessed 25 June 2017.
  79. Valentini, A., P. Taberlet, C. Miaud, R. Civade, J. Herder, P. F. Thomsen, E. Bellemain, A. Besnard, E. Coissac, F. Boyer, C. Gaboriaud, P. Jean, N. Poulet, N. Roset, G. H. Copp, P. Geniez, D. Pont, C. Argillier, J. M. Baudoin, T. Peroux, A. J. Crivelli, A. Olivier, M. Acqueberge, M. Le Brun, P. R. Møller, E. Willerslev & T. Dejean, 2016. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology 25: 929–942.CrossRefPubMedGoogle Scholar
  80. Vasselon, V., F. Rimet, K. Tapolczai & A. Bouchez, 2017. Assessing ecological status with diatoms DNA metabarcoding: scaling-up on a WFD monitoring network (Mayotte island, France). Ecological Indicators 82: 1–12.CrossRefGoogle Scholar
  81. Wagner, C. & R. Adrian, 2011. Consequences of changes in thermal regime for plankton diversity and trait composition in a polymictic lake: a matter of temporal scale. Freshwater Biology 56: 1949–1961.CrossRefGoogle Scholar
  82. Walker, P. J. & J. R. Winton, 2010. Emerging viral diseases of fish and shrimp. Veterinary Research 41: 51–75.CrossRefPubMedPubMedCentralGoogle Scholar
  83. White, M. M. & I. A. McLaren, 2000. Copepod development rates in relation to genome size and 18S rDNA copy number. Genome 43: 750–755.CrossRefPubMedGoogle Scholar
  84. Wilde, S. B., J. R. Johansen, H. D. Wilde, P. Jiang, B. A. Bartelme & R. S. Haynie, 2014. Aetokthonos hydrillicola gen. et sp. nov.: Epiphytic cyanobacteria on invasive aquatic plants implicated in Avian Vacuolar Myelinopathy. Phytotaxa 181: 243–260.CrossRefGoogle Scholar
  85. Woese, C. R. & G. E. Fox, 1977. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proceedings of the National Academy of Sciences of the United States of America 74: 5088–5090.CrossRefPubMedPubMedCentralGoogle Scholar
  86. Wurzbacher, C., A. Fuchs & K. Attermeyer, 2017. Shifts among Eukaryota, Bacteria, and Archaea define the vertical organization of a lake sediment. Microbiome 5: 41.CrossRefPubMedPubMedCentralGoogle Scholar
  87. Xu, F.-L., S. Tao, R. W. Dawson, P.-G. Li & J. Cao, 2001. Lake ecosystem health assessment: indicators and methods. Water Research 35: 3157–3167.CrossRefPubMedGoogle Scholar
  88. Yang, J., X. Zhang, Y. Xie, C. Song, Y. Zhang, H. Yu, & G. Allen Burton, 2017. Zooplankton community profiling in a eutrophic freshwater ecosystem-Lake Tai Basin by DNA metabarcoding. Scientific Reports 7: 1773.CrossRefPubMedPubMedCentralGoogle Scholar
  89. Yarza, P., P. Yilmaz, E. Pruesse, F. O. Glöckner, W. Ludwig, K. H. Schleifer, W. B. Whitman, J. Euzéby, R. Amann & R. Rosselló-Móra, 2014. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nature Reviews Microbiology 12: 635–645.CrossRefPubMedGoogle Scholar
  90. Yi, Z., C. Berney, H. Hartikainen, S. Mahamdallie, M. Gardner, J. Boenigk, T. Cavalier-Smith & D. Bass, 2017. High-throughput sequencing of microbial eukaryotes in Lake Baikal reveals ecologically differentiated communities and novel evolutionary radiations. FEMS Microbiology Ecology.  https://doi.org/10.1093/femsec/fix073.PubMedGoogle Scholar
  91. Yilmaz, P., L. W. Parfrey, P. Yarza, J. Gerken, E. Pruesse, C. Quast, T. Schweer, J. Peplies, W. Ludwig & F. O. Glöckner, 2014. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Research 42: D643–D648.CrossRefPubMedGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply  2018

Authors and Affiliations

  • A. Banerji
    • 1
  • M. Bagley
    • 1
  • M. Elk
    • 1
  • E. Pilgrim
    • 1
  • J. Martinson
    • 1
  • J. Santo Domingo
    • 1
  1. 1.US Environmental Protection AgencyCincinnatiUSA

Personalised recommendations