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Journal of Applied Phycology

, Volume 31, Issue 5, pp 2941–2955 | Cite as

Development of a novel automated analytical method for viability assessment of phytoplankton used for validation of ballast water treatment systems

  • Kim LundgreenEmail author
  • Lisa Eckford-Soper
  • Knud Ladegaard Pedersen
  • Henrik Holbech
Article

Abstract

To limit the spreading of aquatic invasive species, regulations require ships’ ballast water to be treated before discharge. To validate ballast water treatment system (BWTS) performance, treated water is analyzed for living organisms in different size classes. Quantitative assessment of the size class 10–50 μm (mainly phytoplankton) is carried out using the vital stain method, which requires labor-intensive manual microscope counts of fluorescent (i.e., living) cells. The method is slow, demands specialized personnel, and is challenged by subjectivity and mobile organisms. Using a high-content screening platform (HCS-Platform) and image analysis, we developed an automated, objective and faster quantification method. The automated method neutralized subjectivity by using fixed cell recognition parameters for image analysis. The implementation of membrane filters gently manipulated the organisms into a 2D plane that reduced mobility. Quantifications were performed at different concentrations using monocultures of slow-moving Rhodomonas salina, highly mobile Tetraselmis suecica and natural algae. Results were compared to the standard manual counting procedure. Automated counts of monocultures were comparable to manual counts at low and medium concentration levels. Manual counts of T. suecica at high concentration levels were significantly lower compared to automated counts stressing the challenge to count mobile cells in 3D. Natural algal counts were similar for both counting approaches, but accuracy was challenged by colony forming species and high number of algal species ~ 10 μm. Automated counts were significantly faster than manual counts. In conclusion, the HCS-Platform showed promising results as an alternative quantitative phytoplankton assessment method for BWTS validation.

Keywords

Advanced microscopy Algae Invasive species, ballast water Image analysis Monitoring Ballast water treatment systems 

Notes

Acknowledgements

The work was supported by the Danish Maritime Fund (Project 2016-046) and University of Southern Denmark. We would like to thank Annette Duus and the staff at DHI Ballast Water Center, Denmark for technical support.

Supplementary material

10811_2019_1817_MOESM1_ESM.docx (22 kb)
ESM 1 (DOCX 22 kb)

References

  1. Adams JK, Briski E, Ram JL, Bailey SA (2014) Evaluating the response of freshwater organisms to vital staining. Manag Biol Invasion 5:197–208CrossRefGoogle Scholar
  2. Baek SH, Shin K (2015) A staining method to determine marine microplanktonic organism viability and investigate the efficacy of a ship's ballast water treatment system. J Korea Acad Indust Coop Soc 16:4328–4334Google Scholar
  3. Berek M (1927) Grundlagen der Tiefenwahrnehmung in Mikroskop. Sitzungsber Gesellsch Beförd gesamten Naturwissensch Marburg 62:189–223Google Scholar
  4. Bradie J, Broeg K, Gianoli C, He J, Heitmüller S, Lo Curto A, Nakata A, Role M, Schillak L, Stehouwer P, Vanden Byllaardt J, Veldhuis M, Welschmeyer N, Younnan L, Zaaje A, Bailey S (2018) A shipboard comparison of analytic methods for ballast water compliance monitoring. J Sea Res 133:11–19CrossRefGoogle Scholar
  5. Camoying MG, Yñiguez AT (2016) FlowCAM optimization: attaining good quality images for higher taxonomic classification resolution of natural phytoplankton samples. Limnol Oceanogr Methods 14:305–314CrossRefGoogle Scholar
  6. Casas-Monroy O, Chan P-S, Linley RD, Vanden Byllaardt J, Kydd J, Bailey SA (2016) Comparison of three techniques to evaluate the number of viable phytoplankton cells in ballast water after ultraviolet irradiation treatment. J Appl Phycol 28:2821–2830CrossRefGoogle Scholar
  7. Christaki U, Courties C, Massana R, Catala P, Lebaron P, Gasol JM, Zubkov MV (2011) Optimized routine flow cytometric enumeration of heterotrophic flagellates using SYBR Green I. Limnol Oceanogr Methods 9:329–339CrossRefGoogle Scholar
  8. First MR, Drake LA (2012) Performance of the human “counting machine”: evaluation of manual microscopy for enumerating plankton. J Plankton Res 34:1028–1041CrossRefGoogle Scholar
  9. Garvey M, Moriceau B, Passow U (2007) Applicability of the FDA assay to determine the viability of marine phytoplankton under different environmental conditions. Mar Ecol Prog Ser 352:17–26CrossRefGoogle Scholar
  10. Gerchman Y, Vasker B, Tavasi M, Mishael Y, Kinel-Tahan Y, Yehoshua Y (2017) Effective harvesting of microalgae: comparison of different polymeric flocculants. Bioresour Technol 228:141–146CrossRefGoogle Scholar
  11. Gollasch S, David M, Voigt M, Dragsund E, Hewitt C, Fukuyo Y (2007) Critical review of the IMO international convention on the management of ships’ ballast water and sediments. Harmful Algae 6:585–600CrossRefGoogle Scholar
  12. Gollasch S, Cangelosi A, Peperzak L (2012) Testing of ballast water treatment systems performance regarding organisms below 10 micron in minimum dimension. Final report Prepared for Interreg IVB North Sea Ballast Water Opportunity project 17:1–25Google Scholar
  13. Guillard RRL, Ryther JH (1962) Studies of marine planktonic diatoms: I. Cyclotella nana Hustedt, and Detonula confervacea (Cleve) Gran. Can J Microbiol 8:229–239CrossRefGoogle Scholar
  14. Hu Q, Sommerfeld M, Jarvis E, Ghirardi M, Posewitz M, Seibert M, Darzins A (2008) Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. Plant J 54:621–639CrossRefGoogle Scholar
  15. IMO (2004) Convention BWM/CONF/36 international convention for the control and management of ship’s ballast water and sediments. International Maritime Organization (IMO): [S.l.]. pp 1-36Google Scholar
  16. IMO (2016a) International Maritime Organization Marine Environment Protection Committee, Resolution MEPC.279(70). 2016 Guidelines for approval of ballast water management systems (G8). International Maritime Organization (IMO). London, United Kingdom. pp 1–42Google Scholar
  17. IMO (2016b) Review of the guidelines for approval of ballast water management systems (G8): analysis methods for determining the viability of organisms in the 10 to 50 μm size class. Submitted by Denmark and Norway. IMO PPR 4/7. International Maritime Organization (IMO), London, United Kingdom. pp 1-3Google Scholar
  18. Kruk C, Devercelli M, Huszar VLM, Hernández E, Beamud G, Diaz M, Silva LHS, Segura AM (2017) Classification of Reynolds phytoplankton functional groups using individual traits and machine learning techniques. Freshw Biol 62:1681–1692CrossRefGoogle Scholar
  19. Kydd J, Rajakaruna H, Briski E, Bailey S (2018) Examination of a high resolution laser optical plankton counter and FlowCAM for measuring plankton concentration and size. J Sea Res 133:2–10CrossRefGoogle Scholar
  20. Lertsutthiwong P, Sutti S, Powtongsook S (2009) Optimization of chitosan flocculation for phytoplankton removal in shrimp culture ponds. Aquac Eng 41:188–193CrossRefGoogle Scholar
  21. Liebich V (2013) Invasive plankton, implications of and for ballast water management. PhD dissertation. University of HamburgGoogle Scholar
  22. Liu L, Chu X, Chen P, Xiao Y, Hu J (2016) Effects of water quality on inactivation and repair of Microcystis viridis and Tetraselmis suecica following medium-pressure UV irradiation. Chemosphere 163:209–216CrossRefGoogle Scholar
  23. Lundgreen K, Holbech H, Pedersen KL, Petersen GI, Andreasen RR, George C, Drillet G, Andersen M (2018) UV fluences required for compliance with ballast water discharge standards using two approved methods for algal viability assessment. Mar Pollut Bull 135:1090–1100CrossRefGoogle Scholar
  24. MacIntyre HL, Cullen JJ (2016) Classification of phytoplankton cells as live or dead using the vital stains fluorescein diacetate and 5-chloromethylfluorescein diacetate. J Phycol 52:572–589CrossRefGoogle Scholar
  25. Orenstein EC, Beijbom O (2017) Transfer learning and deep feature extraction for planktonic image data sets. In: 2017 IEEE winter conference on applications of computer vision. IEEE winter conference on applications of computer vision. IEEE, New York, pp 1082–1088CrossRefGoogle Scholar
  26. Outinen O, Lehtiniemi M (2017) Literature review for the indicative ballast water analysis methods. Trafi Research Reports. Finnish Transport Safety Agency (Trafi), Finnish Environment Institute SYKE, Helsinki. Pp 1–55Google Scholar
  27. Peperzak L, Brussaard CPD (2011) Flow cytometric applicability of fluorescent vitality probes on phytoplankton. J Phycol 47:692–702CrossRefGoogle Scholar
  28. Peperzak L, Zetsche E-M, Gollasch S, Artigas LF, Bonato S, Creach V, de Vré P, Dubelaar GBJ, Henneghien J, Hess-Erga O-K, Langelaar R, Larsen A, Maurer BN, Mosselaar A, Reavie ED, Rijkeboer M, Tobiesen A (2018) Comparing flow cytometry and microscopy in the quantification of vital aquatic organisms in ballast water. J Mar Eng Technol:1–10.  https://doi.org/10.1080/20464177.2018.1525806
  29. Reavie ED, Cangelosi AA, Allinger LE (2010) Assessing ballast water treatments: evaluation of viability methods for ambient freshwater microplankton assemblages. J Great Lakes Res 36:540–547CrossRefGoogle Scholar
  30. Romero-Martínez L, van Slooten C, Nebot E, Acevedo-Merino A, Peperzak L (2017) Assessment of imaging-in-flow system (FlowCAM) for systematic ballast water management. Sci Total Environ 603-604:550–561CrossRefGoogle Scholar
  31. Rotman B, Papermaster BW (1966) Membrane properties of living mammalian cells as studied by enzymatic hydrolysis of fluorogenic esters. Proc Natl Acad Sci U S A 55:134–141CrossRefGoogle Scholar
  32. Schulze K, López DA, Tillich UM, Frohme M (2011) A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ. BMC Biotechnol 11:118CrossRefGoogle Scholar
  33. Schulze K, Tillich UM, Dandekar T, Frohme M (2013) PlanktoVision - an automated analysis system for the identification of phytoplankton. BMC Bioinformatics 14. doi:Artn11510.1186/1471-2105-14-115
  34. Steinberg MK, Lemieux EJ, Drake LA (2011) Determining the viability of marine protists using a combination of vital, fluorescent stains. Mar Biol 158:1431–1437CrossRefGoogle Scholar
  35. Steinberg MK, First MR, Lemieux EJ, Drake LA, Nelson BN, Kulis DM, Anderson DM, Welschmeyer NA, Herring PR (2012) Comparison of techniques used to count single-celled viable phytoplankton. J Appl Phycol 24:751–758CrossRefGoogle Scholar
  36. Tang YZ, Dobbs FC (2007) Green autofluorescence in dinoflagellates, diatoms, and other microalgae and its implications for vital staining and morphological studies. Appl Environ Microbiol 73:2306–2313CrossRefGoogle Scholar
  37. Ummalyma SB, Gnansounou E, Sukumaran RK, Sindhu R, Pandey A, Sahoo D (2017) Bioflocculation: an alternative strategy for harvesting of microalgae – an overview. Bioresour Technol 242:227–235CrossRefGoogle Scholar
  38. Utermöhl H (1931) Neue Wege in der quantitativen Erfassung des Plankton.(Mit besonderer Berücksichtigung des Ultraplanktons.). SIL Proceedings, 1922–2010 5 (2):567–596CrossRefGoogle Scholar
  39. van der Star I, Liebich V, Stehouwer PP (2011) The forgotten fraction: the importance of organisms smaller than 10 μm when evaluating ballast water treatment systems. In: Ballast Water Management Systems: Proceedings of the Global R&D Forum on Compliance Monitoring and Enforcement – The Next R&D Challenge and Opportunity. Istanbul, Turkey 2011Google Scholar
  40. van Slooten C, Wijers T, Buma AGJ, Peperzak L (2015) Development and testing of a rapid, sensitive ATP assay to detect living organisms in ballast water. J Appl Phycol 27:2299–2312CrossRefGoogle Scholar
  41. Wright DA, Welschmeyer NA, Peperzak L (2015) Alternative, indirect measures of ballast water treatment efficacy during a shipboard trial: a case study. J Mar Eng Technol 14:1–8CrossRefGoogle Scholar
  42. Zhou Q, Chen W, Zhang H, Peng L, Liu L, Han Z, Wan N, Li L, Song L (2012) A flow cytometer based protocol for quantitative analysis of bloom-forming cyanobacteria (Microcystis) in lake sediments. J Environ Sci 24:1709–1716CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of BiologyUniversity of Southern DenmarkOdense MDenmark

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