Quantifying the uncertainties in multi-wavelength PAM fluorometry due to innate and irradiance-induced variability of fluorescence spectra

Abstract

Multi-wavelength Chl a fluorometers are increasingly applied to assess phytoplankton photosynthetic capacity and composition, but their usefulness is limited by uncertainties in fluorescence excitation spectra (FES). We investigated this issue using the Phyto-PAM fluorometer to evaluate the effects of innate and irradiance-dependent variations in background (F) and variable (Fv) FES on analysis of three pigment groups (cyanobacteria, chlorophytes and chromophytes). The effects on group-specific estimates of minimum fluorescence (F0), a proxy for biomass, and Fv/Fm, the quantum yield of photochemistry, presented some challenges to the interpretation of group-specific results. F0 estimates usually had a 5–15% margin of error, even when measuring highly uneven mixtures, and applying imperfectly matched calibration FES or stressing samples with photosynthetically active and ultraviolet radiation; errors in Fv/Fm were commonly < 15%. Despite such relatively good accuracy, estimates for F0 and, especially, Fv/Fm are unreliable for groups at low relative abundance, and results can sometimes be reported for groups not actually present. We report margins of error for different levels of relative abundance to inform interpretation of measurements from natural communities and show that F and Fv spectra for some taxa can differ in ways that produce severe errors in F0 and Fv/Fm estimates if used uncritically.

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Availability of data

Data are available from authors on request.

Abbreviations

Chl a :

Chlorophyll a

F 0 :

Dark-adapted minimum fluorescence

F :

Low-light adapted minimum fluorescence

F m :

Maximum fluorescence

F v :

Variable fluorescence

F v/F m :

Maximum quantum yield of photochemistry (dark adapted)

FES:

Fluorescence excitation spectrum/spectra

LED:

Light emitting diode

FP:

Fluoroprobe

NMS:

Non-metric multidimensional scaling

NPQ:

Non-photochemical quenching

P:

PAR only treatment

PA:

PAR + UV-A treatment

PAB:

PAR + UV-A + UV-B treatment

PAM:

Pulse amplitude modulation

PAR:

Photosynthetically active radiation

PC:

Phycocyanin

PE:

Phycoerythrin

PSII:

Photosystem II

RS:

Reference Spectrum/Spectra

UV(R):

Ultraviolet (radiation)

References

  1. Aberle N, Beutler M, Moldaenke C, Wiltshire KH (2006) ‘Spectral fingerprinting’ for specific algal groups on sediments in situ: a new sensor. Arch Hydrobiol 167:575–592

    CAS  Article  Google Scholar 

  2. Acuna AM, Snellenburg JJ, Gwizdala M, Kirilovsky D, van Grondelle R, van Stokkum IHM (2016) Resolving the contribution of the uncoupled phycobilisomes to cyanobacterial pulse-amplitude modulated (PAM) fluorometry signals. Photosynth Res 127:91–102

    CAS  PubMed  Article  Google Scholar 

  3. Beecraft L, Watson SB, Smith REH (2017) Multi-wavelength pulse amplitude modulated fluorometry (phyto-PAM) reveals differential effects of ultraviolet radiation on the photosynthetic physiology of phytoplankton pigment groups. Freshw Biol 62:72–86

    CAS  Article  Google Scholar 

  4. Beecraft L, Watson SB, Smith REH (2019) Innate resistance of PSII efficiency to sunlight stress is not an advantage for cyanobacteria compared to eukaryotic phytoplankton. Aquat Ecol 53:347–364

    CAS  Article  Google Scholar 

  5. Brunet C, Johnsen G, Lavaud J, Roy S (2011) Pigments and photoacclimation processes. In: Roy S, Llewellyn CA, Egeland ES, Johnsen G (eds) Phytoplankton pigments: characterization, chemotaxonomy and applications in oceanography. Cambridge University Press, Cambridge, pp 445–471

    Google Scholar 

  6. Catherine A, Escoffier N, Belhocine A, Nasri AB, Hamlaoui S, Yepremian C, Bernard C, Troussellier M (2012) On the use of the FluoroProbe (R), a phytoplankton quantification method based on fluorescence excitation spectra for large-scale surveys of lakes and reservoirs. Water Res 46:1771–1784

    CAS  PubMed  Article  Google Scholar 

  7. de Mendiburu F (2017) Agricolae: statistical procedures for agricultural research. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  8. Escoffier N, Bernard C, Hamlaoui S, Groleau A, Catherine A (2015) Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state. J Plankton Res 37:233–247

    CAS  Article  Google Scholar 

  9. Falkowski PG, Raven JA (2007) Aquatic photosynthesis, 2nd edn. Princeton University Press, Princeton

    Google Scholar 

  10. Fox J, Weisberg S (2011) An {R} companion to applied regression, 2nd edn. Sage Publications, London

    Google Scholar 

  11. Gaevsky N, Kolmakov V, Anishchenko O, Gorbaneva T (2005) Using DCMU-fluorescence method for the identification of dominant phytoplankton groups. J Appl Phycol 17:483–494

    CAS  Article  Google Scholar 

  12. Garrido M, Cecchi P, Malet N, Bec B, Torre F, Pasqualini V (2019) Evaluation of FluoroProbe® performance for the phytoplankton-based assessment of the ecological status of Mediterranean coastal lagoons. Environ Monit Assess 191:204

    PubMed  Article  CAS  Google Scholar 

  13. Genty B, Briantais JM, Baker NR (1989) The relationship between the quantum yield of photosynthetic electron-transport and quenching of chlorophyll fluorescence. Biochim Biophys Acta 990:87–92

    CAS  Article  Google Scholar 

  14. Goldman EA, Smith EM, Richardson TL (2013) Estimation of chromophoric dissolved organic matter (CDOM) and photosynthetic activity of estuarine phytoplankton using a multiple-fixed-wavelength spectral fluorometer. Water Res 47:1616–1630

    CAS  PubMed  Article  Google Scholar 

  15. Goss R, Jakob T (2010) Regulation and function of xanthophyll cycle-dependent photoprotection in algae. Photosynth Res 106:103–122

    CAS  PubMed  Article  Google Scholar 

  16. Gregor J, Geris R, Marsalek B, Hetesa J, Marvan P (2005) In situ quantification of phytoplankton in reservoirs using a submersible spectrofluorometer. Hydrobiologia 548:141–151

    Article  Google Scholar 

  17. Guillard RR, Lorenzen C (1972) Yellow–green algae with chlorophyllide C. J Phycol 8:10–14

    CAS  Google Scholar 

  18. Harrison JW, Howell ET, Watson SB, Smith REH (2016) Improved estimates of phytoplankton community composition based on in situ spectral fluorescence: use of ordination and field-derived norm spectra for the bbe FluoroProbe. Can J Fish Aquat Sci 73:1472–1482

    Article  Google Scholar 

  19. Harrison JW, Beecraft L, Smith REH (2018) Implications of irradiance exposure and non-photochemical quenching for multi-wavelength (bbe FluoroProbe) fluorometry. J Photochem Photobiol B Biol 189:36–48

    CAS  Article  Google Scholar 

  20. Houliez E, Lizon F, Thyssen M, Artigas LF, Schmitt FG (2012) Spectral fluorometric characterization of haptophyte dynamics using the FluoroProbe: an application in the eastern english channel for monitoring Phaeocystis globosa. J Plankton Res 34:136–151

    Article  Google Scholar 

  21. Huot Y, Babin M (2010) Overview of fluorescence protocols: theory, basic concepts, and practice. In: Suggett DJ, Prášil O, Borowitzka MA (eds) Chlorophyll a fluorescence in aquatic sciences methods and applications. Springer, Dordrecht, New York, p 31

    Google Scholar 

  22. Jakob T, Schreiber U, Kirchesch V, Langner U, Wilhelm C (2005) Estimation of chlorophyll content and daily primary production of the major algal groups by means of multiwavelength-excitation PAM chlorophyll fluorometry: performance and methodological limits. Photosynth Res 83:343–361

    CAS  PubMed  Article  Google Scholar 

  23. Kiefer D (1973) Fluorescence properties of natural phytoplankton populations. Mar Biol 22:263–269

    Article  Google Scholar 

  24. Kirilovsky D (2015) Modulating energy arriving at photochemical reaction centers: orange carotenoid protein-related photoprotection and state transitions. Photosynth Res 126:3–17

    CAS  PubMed  Article  Google Scholar 

  25. Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems. Cambridge University Press, Cambridge

    Google Scholar 

  26. Kolbowski J, Schreiber U (1995) Computer-controlled phytoplankton analyzer based on a 4-wavelengths PAM chlorophyll fluorometer. In: Mathis P (ed) Photosynthesis: from light to biosphere, vol V. Kluwer, Dordrecht, pp 825–828

    Google Scholar 

  27. Kring SA, Figary SE, Boyer GL, Watson SB, Twiss MR (2014) Rapid in situ measures of phytoplankton communities using the bbe FluoroProbe: evaluation of spectral calibration, instrument intercompatibility, and performance range. Can J Fish Aquat Sci 71:1087–1095

    Article  Google Scholar 

  28. Kruskopf M, Flynn KJ (2006) Chlorophyll content and fluorescence responses cannot be used to gauge reliably phytoplankton biomass, nutrient status or growth rate. New Phytol 169(3):525–536

    CAS  PubMed  Article  Google Scholar 

  29. Leboulanger C, Dorigo U, Jacquet S, Le Berre B, Paolini G, Humbert J (2002) Application of a submersible spectrofluorometer for rapid monitoring of freshwater cyanobacterial blooms: a case study. Aquat Microb Ecol 30:83–89

    Article  Google Scholar 

  30. Lorenzen C (1966) A method for the continuous measurement of in vivo chlorophyll concentration. Deep-Sea Res 13:223–227

    Google Scholar 

  31. MacDonald JH (2014) Handbook of biological statistics. Sparky House Publishing, Baltimore

    Google Scholar 

  32. MacIntyre HL, Lawrenz E, Richardson TL (2010) Taxonomic discrimination of phytoplankton by spectral fluorescence. In: Suggett DJ, Prášil O, Borowitzka MA (eds) Chlorophyll a fluorescence in aquatic sciences methods and applications. Springer, Dordrecht, New York, pp 129–169

    Google Scholar 

  33. Maxwell K, Johnson GN (2000) Chlorophyll fluorescence—a practical guide. J Exp Bot 51:659–668

    CAS  Article  Google Scholar 

  34. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2016) Vegan: community ecology package. R package version 2.4-1

  35. Papageorgiou GC, Govindjee (2014) The non-photochemical quenching of the electronically excited state of chlorophyll a in plants: definitions, timelines, viewpoints, open questions. In: Demmig-Adams B, Garab G, Adams W III, Govindjee (eds) Non-photochemical quenching and energy dissipation in plants, algae and cyanobacteria. Springer, Berlin, pp 1–33

    Google Scholar 

  36. Parsons TR, Strickland JDH (1963) Discussion of spectrophotometric determination of marine-plant pigments, with revised equations for ascertaining chlorophylls and carotenoids. J Mar Res 21(3):155–163

    CAS  Google Scholar 

  37. Phinney D, Yentsch C (1985) A novel phytoplankton chlorophyll technique—toward automated-analysis. J Plankton Res 7:633–642

    CAS  Article  Google Scholar 

  38. R Core Team (2017) R: a language and environment for statistical computing. R Core Team, Vienna

    Google Scholar 

  39. Rastogi RP, Sonani RR, Madamwar D (2015) Effects of PAR and UV radiation on the structural and functional integrity of phycocyanin, phycoerythrin and allophycocyanin isolated from the marine cyanobacterium lyngbya sp A09DM. Photochem Photobiol 91:837–844

    CAS  PubMed  Article  Google Scholar 

  40. Rolland A, Rimet F, Jacquet S (2010) A 2-year survey of phytoplankton in the marne reservoir (France): a case study to validate the use of an in situ spectrofluorometer by comparison with algal taxonomy and chlorophyll a measurements. Knowl Manag Aquat Ecosyst 398:02

    Article  Google Scholar 

  41. Schmitt-Jansen M, Altenburger R (2008) Community-level microalgal toxicity assessment by multiwavelength-excitation PAM fluorometry. Aquat Toxicol 86:49–58

    CAS  PubMed  Article  Google Scholar 

  42. Schreiber U (1998) Chlorophyll fluorescence: new instruments for special applications. In: Garab G (ed) Photosynthesis: mechanisms and effects, vol V. Springer, Berlin, pp 4253–4258

    Google Scholar 

  43. Schreiber U, Schliwa U, Bilger W (1986) Continuous recording of photochemical and nonphotochemical chlorophyll fluorescence quenching with a new type of modulation fluorometer. Photosynth Res 10:51–62

    CAS  PubMed  Article  Google Scholar 

  44. Seppala J, Olli K (2008) Multivariate analysis of phytoplankton spectral in vivo fluorescence: estimation of phytoplankton biomass during a mesocosm study in the baltic sea. Mar Ecol Prog Ser 370:69–85

    Article  Google Scholar 

  45. Serra T, Borrego C, Quintana X, Calderer L, Lopez R, Colomer J (2009) Quantification of the effect of nonphotochemical quenching on the determination of in vivo Chl a from phytoplankton along the water column of a freshwater reservoir. Photochem Photobiol 85:321–331

    CAS  PubMed  Article  Google Scholar 

  46. Vuorio K, Lepistoe L, Holopainen A (2007) Intercalibrations of freshwater phytoplankton analyses. Boreal Environ Res 12:561–569

    Google Scholar 

  47. Walz GmbH H (2003) Phytoplankton analyzer PHYTO-PAM and Phyto-Win software V 1.45. In: Systems components and principles of operation. Effeltrich, Germany

    Google Scholar 

  48. Watson SB (1999) Outbreaks of taste/odour causing algal species: theoretical, mechanistic and applied approaches. University of Calgary, Calgary

    Google Scholar 

  49. Yentsch C, Phinney D (1985) Spectral fluorescence—an ataxonomic tool for studying the structure of phytoplankton populations. J Plankton Res 7:617–632

    CAS  Article  Google Scholar 

  50. Yentsch C, Yentsch C (1979) Fluorescence spectral signatures—characterization of phytoplankton populations by the use of excitation and emission-spectra. J Mar Res 37:471–483

    CAS  Google Scholar 

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Acknowledgements

The work was supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (R. Smith), with important additional support from Environment and Climate Change Canada (S. Watson). We thank two anonymous reviewers for their feedback that improved this manuscript.

Funding

Funding for this research was from a Natural Sciences and Engineering Research Council of Canada Discovery Grant (R. Smith), with important additional support from Environment and Climate Change Canada (S. Watson).

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by L. Beecraft. The first draft of the manuscript was written by L. Beecraft, and all authors participated in critical revision of the manuscript. All authors read and approved the final manuscript for submission.

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Correspondence to Laura Beecraft.

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Beecraft, L., Watson, S.B. & Smith, R.E.H. Quantifying the uncertainties in multi-wavelength PAM fluorometry due to innate and irradiance-induced variability of fluorescence spectra. Aquat Ecol 55, 169–186 (2021). https://doi.org/10.1007/s10452-020-09821-6

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Keywords

  • Phyto-PAM
  • Phytoplankton analysis
  • Multi-wavelength fluorometry
  • Variable fluorescence
  • Fluorescence excitation spectra
  • Methodological issues