- Email: [email protected]
Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy
Environmental and Experimental Botany 73 (2011) 42–48
Contents lists available at ScienceDirect
Environmental and Experimental Botany journal homepage: www.elsevier.com/locate/envexpbot
Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy Luka Drinovec a,∗ , Vesna Flander-Putrle b , Mitja Knez a , Alfred Beran c , Maja Berden-Zrimec a a
Institute of Physical Biology, Toplarniska ulica 19, SI-1000 Ljubljana, Slovenia Marine Biology Station Piran, NIB, Fornaˇce 41, SI-6330 Piran, Slovenia c Istituto Nazionale di Oceanografia e Geofisica Sperimentale, OGS, Department of Biological Oceanography, Via Auguste Piccard 54, I-34151 Trieste, Italy b
a r t i c l e
i n f o
a b s t r a c t
Keywords: Phytoplankton Delayed fluorescence HPLC Pigments CHEMTAX
We present a method for in situ monitoring of phytoplankton composition changes in a marine environment. The method is based on delayed fluorescence excitation spectra analyzed with CHEMTAX software, which is generally used for determination of phytoplankton communities with HPLC pigment data. Delayed fluorescence (DF) is a photosynthetic parameter that can only be measured in living cells. Algal DF excitation spectra are group-specific, based on their composition of photosynthetic pigments. DF excitation spectra of 14 marine algal species from different families were measured with a delayed fluorescence spectrometer. Mixtures were prepared from northern Adriatic algal species representing six taxonomic groups: dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), cryptophytes (Teleaulax sp.), and prymnesiophytes (Isochrysis galbana). The DF excitation spectra (DFS) and HPLC pigment compositions of the mixtures were analyzed with CHEMTAX software. The prediction power of DFS–CHEMTAX method was comparable to HPLC–CHEMTAX. © 2011 Elsevier B.V. All rights reserved.
1. Introduction
hyperbolic decay kinetics during the first seconds, which is sometimes followed by a more or less pronounced peak (Bertsch, 1962; Zrimec et al., 2005; Berden-Zrimec et al., 2010). The main source of DF are back reactions in the photosystem II (PSII) (Rutherford and Inoue, 1984), whereas the photosystem I contributes much less to the DF emission (Jursinic, 1986). In PSII, charge pairs are generated during the illumination with positive charges located on the oxygen evolving complex (OEC) and negative charges on quinone acceptors (QA and QB ). The slow components of DF originate in back reactions between the S2 and S3 states of the OEC and quinones QA and QB (Joliot et al., 1971). The half-times of these reactions in isolated chloroplasts are 1.5 s for QA + S2/3 and 25 s for QB + S2/3 (Rutherford and Inoue, 1984). The major advantage of DF is that it is emitted only from cells that are photosynthetically active; that is, alive. Thus additional signals from dead cell debris do not interfere with the measurements. Long-term DF emission (measured for seconds or minutes) also prevents interference problems with fluorescent backgrounds in natural samples (Istvanovics et al., 2005). Delayed fluorescence excitation spectra (DFS) of algae are based on pigment composition. Chlorophyll a is present in all algal classes; chlorophyll b in chlorophytes and euglenophytes; chlorophyll c in diatoms, chrysophytes, dinoflagellates, and cryptophytes; and phycobiliproteins such as phycoerythrin and phycocyanin in cryptophytes and cyanobacteria, or allophycocyanin in cyanobacteria
Good evaluation protocols are critical for understanding phytoplankton population dynamics. More than one method is usually needed to gain sufficient information for good quality analysis. Although many laboratory and in situ methods are available, they have method-specific disadvantages. Thus, research goals must be defined before the method is selected. When studying changes in phytoplankton composition, fast methods with reasonable resolution of algal taxonomic groups can be used for in situ monitoring. A method that has been used for more than 10 years in routine freshwater phytoplankton dynamics studies is delayed fluorescence spectroscopy (Krause and Gerhardt, 1984; Yacobi et al., 1998; Istvanovics et al., 2005; Greisberger and Teubner, 2007). Delayed fluorescence (DF) is the long-term emission of light from cells triggered by illumination (Strehler and Arnold, 1951; Berden-Zrimec et al., 2010). It has the same emission spectrum as chlorophyll a fluorescence, but occurs with a time delay (from milliseconds to minutes) (Arnold and Davidson, 1954). DF originates from repopulation of excited states of chlorophyll from stored energy after charge separation (Joliot et al., 1971). It has
∗ Corresponding author. Tel.: +386 1 5875470. E-mail addresses: [email protected], [email protected] (L. Drinovec). 0098-8472/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.envexpbot.2010.10.010
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
(Barlow et al., 1993; Larkum, 2003). Chlorophylls affect the action spectra for photosynthesis with absorption in blue (430–470 nm) and red (630–680 nm) part of the spectrum (Strain et al., 1963; Jeffrey, 1963). Carotenoids absorb in 450–550 nm and phycobilins in 500–600 nm spectral regions (Haxo and Blinks, 1950). The resulting action spectra of certain algae are a combination of several absorption bands forming a specific spectral fingerprint. The method and spectra analysis used to date distinguish only four “color groups” of phytoplankton: (i) cyanophytes, (ii) chlorophytes (e.g., Chlorophyceae, Euglenophyceae, and Conjugatophyceae), (iii) chromophytes (e.g., Bacillariophyceae, Chrysophyceae, Dinophyceae, Xanthophyceae, and Haptophyceae) and (iv) cryptophytes (Gerhardt and Bodemer, 2005). This method has been used only in freshwater phytoplankton. In a marine environment, these four classes are not sufficient to monitor phytoplankton changes. Several algal taxonomic groups included in the chromophytes color group often prevail in marine phytoplankton populations and must be detected separately for successful monitoring of phytoplankton composition. Therefore we set out to develop a new protocol for monitoring phytoplankton changes using CHEMTAX analysis in addition to a new DF phytoplankton meter developed at our institute. Analysis of phytoplankton pigments by HPLC and data processing using CHEMTAX has been proposed as an alternative to time-consuming microscopic cell counting to determine taxonomic algal composition (Mackey et al., 1996). The resolution of taxonomic groups by this method is generally coarse (e.g., diatoms, dinoflagellates, cryptophytes, cyanobacteria, chlorophytes, prasinophytes, and haptophytes) compared to direct microscopic counting that can distinguish species within these broader groups. The advantage of CHEMTAX analysis is its speed and cost relative to other methods. On the other hand, a blind analysis can result in very significant errors because particular pigment compositions of certain species differ from the average (Irigoien et al., 2004). HPLC–CHEMTAX has been applied in combination with microscopy in the field in the Antarctic peninsula area (Rodriguez et al., 2002), southern Baltic Sea (Eker-Develi et al., 2008), southeast US estuaries (Lewitus et al., 2005), Belgian coastal zone of the North Sea (Muylaert et al., 2006), and others. It has been shown that the Mackey pigment matrix developed for the open ocean has to be optimized for local environments such as estuarine ecosystems (Lewitus et al., 2005). Because CHEMTAX is a matrix factorization program it can be used with any type of data if the differences in vectors between the taxonomic groups are great enough. The DF action spectrum resembles the absorption spectra of the photosynthetic pigments. Compared to direct absorbance measurements, the DF excitation spectra measure only active pigments and not dissolved pigments, organic substances, and debris. The absorption spectra of various photosynthetic pigments are similar, and so the DFS–CHEMTAX method resolution was expected to be somewhat lower than that of the HPLC–CHEMTAX. On the other hand, the DFS method is an in vivo method less prone to errors at the extraction and pigment-analysis stages. Furthermore, DF measures nano- and pico-plankton, which can be lost during filtration or unaccounted for in the microscopic analysis. The objective of this study was to indentify delayed fluorescence excitation spectra of the main marine algal taxonomic groups and use these spectra for their discrimination. The obtained spectra were analyzed using CHEMTAX deconvolution software to obtain the taxonomic composition of algae in prepared mixtures. The predicting power of DFS–CHEMTAX was compared with the standard HPLC–CHEMTAX approach.
43
2. Methods 2.1. Algal cultures Algal cultures were maintained at the culture facility of the National Institute of Oceanography and Experimental Geophysics, Department of Biological Oceanography (OGS/BiO) in Trieste. All species were isolated from the Gulf of Trieste. The algae were sustained as batch cultures in 100 ml borosilicate Erlenmeyer flasks with 50 ml of culture medium B (Agatha et al., 2004). The culture medium for the diatoms contained an addition of Na2 SiO3 × 9H2 O at a final concentration of 1.07 mol l−1 (Guillard, 1975). All organisms were cultured at 15 ◦ C for a 10:14 h dark:light cycle. The diatoms, Teleaulax sp., and Synechococcus sp. were grown at low irradiance of about 20 E m−2 s−1 , and the remaining species were kept at about 50 E m−2 s−1 . The samples were taken from the batch cultures in their exponential phase of growth. Fourteen species of unicellular algae from nine different families were used for the DFS screening. Two different cultures of the cyanobacteria Synechococcus sp. were used for comparison. The samples were measured in three parallels. 2.2. Mixtures The mixtures were prepared from representatives of the six different taxonomic groups that are most frequent in northern Adriatic Sea phytoplankton: dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), cryptophytes (Teleaulax sp.), and prymnesiophytes (Isochrysis galbana). Aliquots of two or three taxonomic groups were mixed in all combinations to a final volume of 1 ml. Mixtures of all six species were prepared such that one of the species was always predominant (50% of 1 ml) and the other five each represented 10% of the total volume. 2.3. DF excitation spectrometry (DFS) DF excitation spectra were measured with a custom-built delayed fluorescence spectrometer (Berden-Zrimec et al., 2010). DF excitation was performed with a halogen lamp and linear filter, providing monochromatic (25 nm half-width) illumination in the range between 400 and 700 nm with an intensity of 100 mol/m2 s PAR. A 1 ml sample of algal culture in a rectangular cuvette was illuminated for 0.6 s. The delayed fluorescence intensity (DFI) was measured in the interval of 0.4–1 s after the end of illumination. Light was detected with a Perkin Elmer C1393 channel photomultiplier in photon-counting mode. Two electromagnetic shutters were used to shield the photomultiplier from the excitation light. DFI was measured sequentially for 18 excitation wavelengths (430.0, 445.8, 493.2, 508.9, 524.7, 540.5, 556.3, 572.1, 587.9, 603.7, 619.5, 635.3, 651.1, 666.8, 682.6, 698.4, 714.2, and 730.0 nm). 2.4. Pigment composition The qualitative and quantitative analysis of pigments in the samples was performed using the reverse-phase HPLC (High Performance Liquid Chromatography) method (Mantoura and Llewellyn, 1983; Barlow et al., 1993). The pure and mixed culture samples used for DF spectroscopy were filtered through Whatman GF/F filters and immediately frozen. For pure cultures (but only in these cases), all three parallel samples were filtered together through the same filter in order to have enough material for HPLC analysis. A control was made with 1 ml of culture media, filtered and prepared for analysis using the same procedure as for the samples. Frozen samples were extracted in 4 ml of 90% acetone using sonication, and centrifuged for 10 min at 4000 rpm in order to
44
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
remove particles. An aliquot (300 l) of clarified extract was mixed with 300 l 1 mol l−1 ammonium acetate and 500 l of mixture injected in the gradient HPLC system with a 200 l loop. The HPLC system was equipped with a reverse-phase 3 m C18 column (Pecosphere, 35 mm × 4.5 mm, Perkin Elmer). Solvent A consisted of 80% methanol and 20% 1 mol l−1 ammonium acetate and solvent B contained 60% methanol and 40% acetone. A linear gradient from 0% B to 100% B for 10 min was followed by an isocratic hold at 100% B for 6 min. The rinse flow was 1 ml min−1 . Chlorophylls and carotenoids were detected by absorbance at 440 nm using an UV/VIS spectrophotometric detector (Spectra Physics, Model UV2000). Degradation products of chlorophyll a were detected by measuring fluorescence (420/672 nm) with a spectrofluorimetric detector (Spectra Physics, Model FL2000). Data collection and integration were performed using Agilent ChemStation software. 2.5. CHEMTAX data evaluation The taxonomic composition was calculated from the input data (HPLC pigments or DF spectra) using CHEMTAX software (Mackey et al., 1996) running under MATHLAB (MathWorks, Natick, MA). The CHEMTAX software uses two input matrices: (1) containing pigment concentrations in the samples and (2) an initial matrix containing marker pigment concentrations for all algal taxonomic groups accounted for. In the case of this DFS analysis, matrix 1 contained the samples’ DFS spectra and matrix 2 the reference algal group spectra. 3. Results 3.1. Pure cultures The DF spectra of the measured taxonomic groups are presented in Fig. 1. For greater clarity, only one representative each of the diatoms, dinoflagellates, prasinophytes, and cyanobacteria is presented. The DF spectra of measured species from the same taxonomic groups were very similar: (i) dinoflagellates: P. minimum, Alexandrium minutum, Ostreopsis ovata, (ii) diatoms: S. costatum, Chaetoceros socialis, Pseudonitzschia sp., (iii) prasinophytes: Micromonas sp., Pyramimonas sp., (iv) cyanobacteria: two strains of Synechococcus sp. The pigment compositions of the selected algal species are presented in Table 1. DF excitation spectra (Fig. 1) consist of absorption spectra of several photosynthetic pigments. Chlorophyll a contributes most DF emissions and has a peak at 680 nm. Chlorophyll c is present in most taxonomic groups with exception of cyanobacteria (Table 1). It has peaks at 450 and 630 nm (Jeffrey, 1963), the latter being clearly visible in diatoms. Chlorophyll b has peaks at 640 and 470 nm (Beeler SooHoo et al., 1985; Xu et al., 2001) and influences the prasinophytes’ spectrum. There are several peaks in DF spectra that can be
Fig. 1. Normalized delayed fluorescence spectra of representative algal species. Dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), prymnesiophytes (Isochrysis galbana), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), euglenophytes (Eutreptiella sp.), cryptophytes (Teleaulax sp.), and silicoflagellates (Dictyocha speculum).
assigned to the carotenoids, such as the peak at 555 nm in dinoflagellates and 545 nm in diatoms. Although pure peridinin and fucoxanthin have their strongest absorption in the range of 450–500 nm, it has been found that the absorption of carotenoid-chlorophyll a complex can be shifted to longer wavelengths (Haxo and Blinks, 1950; Bautista et al., 1999; Shima et al., 2003). Phycobilins in cyanobacteria and cryptophytes (Barlow et al., 1993; Larkum, 2003; Beutler et al., 2004) strongly affect DF spectra (Gerhardt and Bodemer, 2005), but were not recorded with HPLC analysis. These two taxonomic groups had DF peaks at 560 and 580 nm (Fig. 1). 3.2. Phytoplankton mixtures The algal composition estimates provided by the two methods are presented in Tables 2–4. Examples of good and poor estimates in different mixtures are shown in Fig. 2a–f. The correlation between real and estimated values for taxonomic groups was evaluated with linear regression analysis (Table 5). Some discrepancies were regularly detected with the DFS–CHEMTAX technique. In mixtures containing various combinations of prasinophytes, cryptophytes, and prymnesiophytes, the evaluations were comparable with the real volumetric fractions. When these taxonomic groups were combined with the dinoflagellates, diatoms, or cyanobacteria, they were mostly overrated. On the other hand, dinoflagellates, diatoms, or cyanobacteria were repeatedly underrated. Dinoflagellates usually
Table 1 Pigment composition of single algal species, chosen as representatives for the taxonomic groups. dinoflagellates – P. minimum, diatoms – S. costatum, prymnesiophytes – I. galbana, cyanobacteria – Synechococcus sp., prasinophytes – Micromonas sp., euglenophytes – Eutreptiella sp., cryptophytes – Teleaulax sp., silicoflagellates – Dictyocha speculum; chl a – chlorophyll a, chl b – chlorophyll b, chl c2 – chlorophyll c2, chl c3 – chlorophyll c3, per – peridinin, but – 19 -butanoyloxyfucoxanthin, fuc – fucoxanthin, neo – neoxanthin, hex – 19 -hexanoyloxyfucoxanthin, pra – prasinoxanthin, vio – violaxanthin, diad – diadinoxanthin, ant – antheraxanthin, all – alloxanthin, diat – diatoxanthin, zea – zeaxanthin, lut – lutein, -car – beta,beta-carotene; concentrations in ng ml−1 . chl a Dinoflagellates Diatoms Prymnesiophytes Cyanobacteria Prasinophytes Euglenophytes Cryptophytes Silicoflagellates
chl b
346 142 2858 145 688 633 1098 1230 1016 3 2237
chl c2 134 36 831 1 54 1 163 287
chl c3 13 16 9 4 12 12 1
per
but
212
12
79 10
fuc 123 1427 1 8 13 797
neo
hex
pra
viola
diad
1
91 10 262 1 8 240 1 257
4 68 98
94
61
1
ant
all
diat
zea + lut
5 1 19 1 9 2 211 2
36 47 17
58 14 4 2
-car 14 6 123 34 28 57 80 125
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
45
Table 2 Evaluated volumetric fractions (%) of taxonomic groups in fifteen binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups actually present in each mixture are marked bold and underlined. Their real volumetric fractions were 50%. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
DFS–CHEMTAX dino
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
55 64 20 49 21 1 0 1 5 0 0 0 0 1 4
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
HPLC–CHEMTAX
diat
3 6 4 1 4 3 0 2 4 0 0 0 0 1 5
36 14 9 0 4 54 14 10 17 3 0 13 1 1 1
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
prym 6 5 3 0 3 3 4 4 7 3 0 5 2 1 1
2 14 1 3 6 43 0 3 5 9 14 11 0 2 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cyan
2 2 1 0 1 1 0 2 1 1 1 1 0 1 0
7 7 69 1 14 0 84 0 12 88 0 8 50 45 5
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
pras
3 3 4 1 3 0 2 0 2 2 0 3 1 1 0
0 0 0 46 0 1 0 85 1 0 86 0 46 1 45
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cryp
0 0 0 2 0 2 0 1 1 0 1 0 1 1 2
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0 0 0 0 55 0 2 0 60 0 0 67 2 49 45
dino
0 0 0 0 3 0 2 0 5 0 0 3 2 1 5
73 76 44 40 17 0 0 2 0 1 0 0 3 0 1
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
diat
6 2 1 6 2 0 0 2 0 1 1 0 1 1 1
18 0 0 0 2 36 16 19 24 0 0 8 1 10 9
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
prym 16 0 0 0 4 6 4 2 2 0 0 7 1 8 5
0 20 0 0 0 41 0 0 0 15 8 3 0 0 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cyan
0 3 0 0 0 6 0 0 0 1 2 1 0 0 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0 0 50 0 0 0 80 0 0 83 0 0 48 18 0
pras
0 0 2 0 0 0 1 0 0 1 0 0 1 1 0
1 3 5 60 1 3 1 79 0 1 92 0 48 0 18
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cryp
2 4 2 6 1 2 1 4 0 1 1 0 0 0 2
8 0 1 0 80 20 3 0 76 0 0 89 0 71 72
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
14 0 1 0 6 3 4 0 3 0 0 6 0 8 4
Table 3 Evaluated volumetric fractions (%) of taxonomic groups in six mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups actually present in each mixture are marked bold and underlined. Their real volumetric fractions were 33%. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
16 17 18 19 20 21
DFS–CHEMTAX
21 0 17 2 0 4
HPLC–CHEMTAX
dino
diat
± ± ± ± ± ±
30 15 0 0 2 2
2 1 4 2 0 1
prym
± ± ± ± ± ±
6 2 1 0 3 3
28 16 19 4 16 6
± ± ± ± ± ±
cyan
2 0 2 1 0 0
15 62 8 47 44 15
± ± ± ± ± ±
pras 1 1 2 0 0 2
0 0 53 35 38 30
± ± ± ± ± ±
cryp
0 0 3 2 1 0
6 6 2 11 1 43
± ± ± ± ± ±
1 0 3 4 1 2
dino 62 38 43 31 1 11
± ± ± ± ± ±
2 1 5 6 0 1
diat 16 0 0 0 0 4
± ± ± ± ± ±
prym 3 0 0 0 0 5
13 7 6 0 4 0
± ± ± ± ± ±
1 2 1 0 1 0
cyan 0 54 0 32 48 0
± ± ± ± ± ±
0 3 0 2 2 0
pras 0 1 51 37 47 16
± ± ± ± ± ±
1 2 5 3 2 3
cryp 8 0 0 0 0 69
± ± ± ± ± ±
4 0 0 0 0 3
Table 4 Evaluated volumetric fractions (%) of taxonomic groups in six mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups prevalent in each mixture are marked bold and underlined. Their real volumetric fractions were 50%, whereas other taxonomic groups were present in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
DFS–CHEMTAX dino
22 23 24 25 26 27
24 0 0 0 9 1
± ± ± ± ± ±
HPLC–CHEMTAX diat
2 1 1 0 1 2
14 25 15 2 4 1
± ± ± ± ± ±
prym 7 3 6 3 4 1
11 8 27 7 3 7
± ± ± ± ± ±
cyan
3 1 4 1 1 1
26 26 28 61 13 21
± ± ± ± ± ±
pras 3 3 5 0 3 1
5 17 13 8 60 12
± ± ± ± ± ±
2 2 5 0 1 1
20 23 17 22 11 59
cryp
dino
± ± ± ± ± ±
41 11 13 6 9 3
4 5 1 1 3 1
± ± ± ± ± ±
diat 4 4 1 1 1 1
8 41 23 11 13 12
± ± ± ± ± ±
prym 3 1 3 2 3 3
0 0 7 1 0 0
± ± ± ± ± ±
1 0 1 0 0 0
cyan 10 13 14 45 8 4
± ± ± ± ± ±
1 1 0 1 0 0
pras 9 11 11 11 47 4
± ± ± ± ± ±
2 3 2 1 1 2
cryp 32 25 33 27 22 77
± ± ± ± ± ±
4 1 3 1 4 2
Table 5 Statistical analysis of CHEMTAX evaluation of the volumetric fractions in the mixtures. A linear correlation was calculated between real and evaluated volumetric fraction. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). DFS–CHEMTAX Offset dino diat prym cyan pras cryp
−0.01 0.04 0.03 0.03 0.08 0.00
HPLC–CHEMTAX 2
Slope ± ± ± ± ± ±
0.02 0.01 0.01 0.01 0.01 0.01
0.69 0.45 1.08 0.36 1.19 1.22
± ± ± ± ± ±
0.06 0.05 0.03 0.04 0.05 0.05
r
Offset
0.62 0.48 0.93 0.56 0.87 0.88
0.01 0.04 0.05 −0.01 0.00 0.00
r2
Slope ± ± ± ± ± ±
0.02 0.01 0.01 0.01 0.02 0.01
0.99 0.45 1.50 0.31 1.12 1.13
± ± ± ± ± ±
0.07 0.04 0.04 0.03 0.06 0.07
0.72 0.60 0.94 0.50 0.80 0.77
46
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
lost their signal to prasinophytes. Prasinophytes were detected in more than half of the mixtures in which they were not present (offset 0.08). Similarly, diatoms were detected in one-third of the mixtures in which they were not present (offset 0.04). Some discrepancies were also found in the HPLC–CHEMTAX analysis although we used the appropriate pigment fingerprints. Cyanobacteria were seriously underrated in most mixtures, and in some cases even not detected (slope 0.3). The diatoms were mostly underrated (slope 0.45). Prymnesiophytes were mostly overrated (slope 1.5) or falsely detected in half of the cases (offset 0.05).
4. Discussion Delayed fluorescence spectroscopy in combination with the CHEMTAX method was evaluated as a tool for taxonomic characterization of phytoplankton mixtures. The DFS method was compared to the HPLC–CHEMTAX method using laboratory algal cultures. The measured DF spectra of marine algae resemble the spectra of freshwater taxonomic groups measured by others (Gerhardt and Bodemer, 2005; Istvanovics et al., 2005; Greisberger and Teubner, 2007). The DF intensity was lower at excitation of shorter wavelengths. This contrasts the absorbance spectra of photosynthetic pigments with higher extinction coefficients in the blue part of the spectrum, but agrees with the action spectra of photosynthesis (Haxo and Blinks, 1950). When comparing freshwater and marine algae DF spectra, we observed the greatest similarity in prasinophytes, cryptophytes, and diatoms. The DF spectra of cyanobacteria were not comparable, which is unsurprising due to differences between the cyanobacterial species in the composition of photosynthetic pigments (Gerhardt and Bodemer, 2005). In general, the prediction power of DFS–CHEMTAX was comparable to HPLC–CHEMTAX. The offsets of the correlation curves for both methods were quite similar. The exception was prasinophytes, which were strongly overestimated by DFS. When comparing the slopes, the DFS method was superior for prymnesiophyte detection but inferior for detecting dinoflagellates. In most cases, both methods were able to detect the dominant species in the mixture. The exceptions were cyanobacteria in HPLC–CHEMTAX analysis and dinoflagellates in DFS–CHEMTAX. Discrepancies similar to ours have already been found in other HPLC–CHEMTAX studies (Rodriguez et al., 2002; Eker-Develi et al., 2008). The reason for the discrepancies between HPLC–CHEMTAX and microscopic evaluation may be an incorrect CHEMTAX input matrix (Schluter et al., 2000) and the presence of unaccounted-for organisms (Irigoien et al., 2004) on the one hand, and the problem
47
of microscopic quantification of smaller algae on the other (EkerDeveli et al., 2008; Rodriguez et al., 2002). Our DFS estimation of phytoplankton composition is comparable to the freshwater studies. In the study of phytoplankton dynamics in Lake Balaton (Istvanovics et al., 2005), chlorophytes and cryptophytes were overestimated as well. Similar results were also obtained by Gerhardt and Bodemer (2005). In both cases a custom deconvolution algorithm was used. Although the DFS–CHEMTAX method can be used to monitor changes in marine phytoplankton composition, it is not yet sensitive enough for in situ measurements in the open ocean. Chlorophyll concentrations in the open ocean can be as low as 0.1 g l−1 , which is approximately three orders of magnitude lower than in our laboratory samples. On the other hand, simplicity and speed are the main advantages of the DFS method over HPLC. In addition, the DFS method can identify smaller phytoplankton often overlooked by microscopic identification. To overcome the sensitivity problems of the current systems, DF spectrometer for open-ocean samples was built with a 1.2 l measuring chamber and is currently being tested. 5. Conclusions 1. We identified several pigment absorption bands in delayed fluorescence excitation spectra (DFS) of several marine phytoplankton taxonomic groups with comparison to their HPLC pigment composition. 2. We succeeded in distinguishing six taxonomic groups of marine algae by deconvolution of DFS with CHEMTAX software: (1) dinoflagellates, (2) diatoms, (3) cyanobacteria, (4) prasinophytes (Chlorophyta), (5) cryptophytes, and (6) prymnesiophytes. 3. The results of the DFS–CHEMTAX method were in good agreement with the real compositions of dinoflagellates, prasinophytes, cryptophytes, and prymnesiophytes, but underestimated diatoms and cyanobacteria. 4. In our study the prediction powers of the DFS–CHEMTAX and HPLC–CHEMTAX methods were comparable. 5. DFS measurement is a fast, simple method that does not require any sample manipulation. The main drawback compared to HPLC is lower sensitivity of DF spectrometers. Acknowledgment This study was supported financially by the Slovenian Research Agency (grant #P1-0237).
Fig. 2. (a) Real and evaluated volumetric fractions (%) of taxonomic groups in binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture only prasinophytes and cryptophytes were present in 50% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (b) Real and evaluated volumetric fractions (%) of taxonomic groups in binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture only cyanobacteria and prymnesiophytes were present in 50% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (c) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of three species obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture only dinoflagellates, diatoms and cyanobacteria were present in 33% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (d) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of three species obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture only dinoflagellates, prasinophytes and cryptophytes were present in 33% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (e) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of six species obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture cryptophytes are present in 50% and others in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (f) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of six species obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture cyanobacteria are present in 50% and others in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels.
48
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
References Agatha, S., Strüder-Kypke, M.S., Beran, A., 2004. Morphological and genetic variability in the marine planktonic ciliate Laboea strobila Lohmann, 1908 (Ciliophora, Oligotrichia), with notes on its ontogenesis. J. Euk. Microbiol. 5, 267–282. Arnold, W., Davidson, J.B., 1954. The identity of the fluorescent and delayed light emission spectra in Chlorella. J. Gen. Physiol. 37, 677–684. Barlow, R.G., Mantoura, R.F.C., Gough, M.A., Fileman, T.W., 1993. Pigment signatures of the phytoplankton composition in the Northeastern Atlantic during the 1990 spring bloom. Deep-Sea Res. II-Top. Stud. Oceanogr. 40, 459–477. Bautista, J.A., Connors, R.E., Raju, B.B., Hiller, R.G., Sharples, F.P., Gosztola, D., Wasielewski, M.R., Frank, H.A., 1999. Excited state properties of peridin: observation of a solvent dependence of the lowest excited singlet state lifetime and spectral behavior unique among carotenoids. J. Phys. Chem. B 103 (41), 8751–8758. Beeler SooHoo, J., Kiefer, D.A., Collins, D.J., McDermid, I.S., 1985. In vivo fluorescence excitation and absorption spectra of marine phytoplankton: I. Taxonomic characteristics and responses to photoadaptation. J. Plankton Res. 8, 197–214. Berden-Zrimec, M., Drinovec, L., Zrimec, A., 2010. Delayed fluorescence. In: Sugget, D.J., Prasil, O., Borowitzka, M. (Eds.), Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications. Springer, Dordrecht, pp. 293-309. Bertsch, W.F., 1962. Two photoreactions in photosynthesis: evidence from delayed light emission of Chlorella. Proc. Natl. Acad. Sci. U.S.A. 48, 2000–2004. Beutler, M., Wiltshire, K.H., Reineke, C., Hansen, U.P., 2004. Algorithms and practical fluorescence models of the photosynthetic apparatus of red cyanobacteria and Cryptophyta designed for the fluorescence detection of red cyanobacteria and cryptophytes. Aquat. Microb. Ecol. 35, 115–129. Eker-Develi, E., Berthon, J.-F., van der Linde, D., 2008. Phytoplankton class determination by microscopic and HPLC–CHEMTAX analyses in the southern Baltic Sea. Mar. Ecol., Prog. Ser. 359, 87. Gerhardt, V., Bodemer, U., 2005. Delayed fluorescence excitation spectroscopy—an in vivo method to determine photosynthetically active pigments and phytoplankton composition. ALGAS 33, 4–17. Greisberger, S., Teubner, K., 2007. Does pigment composition reflect phytoplankton community structure in differing temperature and light conditions in a deep alpine lake? An approach using HPLC and delayed fluorescence techniques. J. Phycol. 43, 1108–1119. Guillard, R.R.L., 1975. Culture of phytoplankton for feeding marine invertebrates. In: Smith, W.L., Chanley, M.H. (Eds.), Culture of Marine Invertebrate Animals. Plenum Press, New York, pp. 29–60. Haxo, F.T., Blinks, L.R., 1950. Photosynthetic action spectra of marine algae. J. Gen. Physiol. 33 (4), 389–422. Irigoien, X., Meyer, B., Harris, R., Harbour, D., 2004. Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species. Helgol. Mar. Res. 58, 77–82. Istvanovics, V., Honti, M., Osztoics, A., Shafik, H.M., Padisak, J., Yacobi, Y., Eckert, W., 2005. Continuous monitoring of phytoplankton dynamics in Lake Balaton (Hungary) using on-line delayed fluorescence excitation spectroscopy. Freshw. Biol. 50, 1950–1970. Jeffrey, S.W., 1963. Purification and properties of chlorophyll c from Sargassum flavicans. Biochem. J. 86 (2), 313–318. Joliot, P., Joliot, A., Bouges, B., Barbieri, G., 1971. Studies of system-II photocenters by comparative measurements of luminescence, fluorescence, and oxygen emission. Photochem. Photobiol. 14, 287–305.
Jursinic, P.A., 1986. Delayed fluorescence: current concepts and status. In: Govindjee, Fork, D.C. (Eds.), Light Emission by Plant and Bacteria. Academic Press, New York, pp. 291–328. Krause, H., Gerhardt, V., 1984. Application of delayed fluorescence of phytoplankton in limnology and oceanography. J. Lumin. 31-2, 888–891. Larkum, A.W., 2003. Light-harvesting systems in algae. In: Larkum, A.W., Douglas, S.E., Raven, J.A. (Eds.), Advances in Photosynthesis and Respiration. Photosynthesis in Algae. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 277–304. Lewitus, A.J., White, D.L., Tymowski, R.G., Geesey, M.E., Hymel, S.N., Noble, P.A., 2005. Adapting the CHEMTAX method for assessing phytoplankton taxonomic composition in southeastern U.S. estuaries. Estuaries 28, 160–172. Mackey, M.D., Mackey, D.J., Higgins, H.W., Wright, S.W., 1996. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar. Ecol., Prog. Ser. 144, 265–283. Mantoura, R.F.C., Llewellyn, C.A., 1983. The rapid-determination of algal chlorophyll and carotenoid-pigments and their breakdown products in natural-waters by reverse-phase high-performance liquid-chromatography. Anal. Chim. Acta 151, 297–314. Muylaert, K., Gonzales, R., Franck, M., Lionard, M., 2006. Spatial variation in phytoplankton dynamics in the Belgian coastal zone of the North Sea studied by microscopy, HPLC–CHEMTAX and underway fluorescence recordings. J. Sea Res. 55, 265. Rodriguez, F., Varela, M., Zapata, M., 2002. Phytoplankton assemblages in the Gerlache and Bransfield Straits (Antarctic Peninsula) determined by light microscopy and CHEMTAX analysis of HPLC pigment data. Deep-Sea Res. II 49, 723–747. Rutherford, A.W., Inoue, Y., 1984. Oscillation of delayed luminescence from PSII recombination of S2 QB − and S3 QB − . FEBS Lett. 165, 163–170. Schluter, L., Mohlenberg, F., Havskum, H., Larsen, S., 2000. The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas: testing the influence of light and nutrients on pigment/chlorophyll a ratios. Marine Ecology Progress Series 192, 49–63. Shima, S., Ilagan, R.P., Gillespie, N., Sommer, B.J., Hiller, R.G., Sharples, F.P., Frank, H.A., Birge, R.R., 2003. Two-photon and fluorescence spectroscopy and the effect of environment on the photochemical properties of peridinin in solution and in the peridinin-chlorophyll-protein from Amphidinium carterae. J. Phys. Chem. A 107 (40), 8052–8066. Strain, H.H., Thomas, M.R., Katz, J.J., 1963. Spectral absorption properties of ordinary and fully deuteriated chlorophylls a and b. Biochim. Biophys. Acta 75, 306–311. Strehler, B.L., Arnold, W., 1951. Light production by green plants. J. Gen. Physiol. 34, 809–820. Xu, H., Vavilin, D., Vermaas, W., 2001. Chlorophyll b can serve as the major pigment in functional photosystem II complexes of cyanobacteria. Proc. Natl. Acad. Sci. U.S.A. 98 (24), 14168–14173. Yacobi, Y.Z., Gerhardt, V., Gonen-Zurgil, Y., Sukenik, A., 1998. Delayed fluorescence excitation spectroscopy: a rapid method for qualitative and quantitative assessment of natural population of phytoplankton. Water Res. 32, 2577–2582. Zrimec, A., Drinovec, L., Berden-Zrimec, M., 2005. Influence of chemical and physical factors on long-term delayed fluorescence in Dunaliella tertiolecta. Electromagn. Biol. Med. 24, 309–318.
Contents lists available at ScienceDirect
Environmental and Experimental Botany journal homepage: www.elsevier.com/locate/envexpbot
Discrimination of marine algal taxonomic groups using delayed fluorescence spectroscopy Luka Drinovec a,∗ , Vesna Flander-Putrle b , Mitja Knez a , Alfred Beran c , Maja Berden-Zrimec a a
Institute of Physical Biology, Toplarniska ulica 19, SI-1000 Ljubljana, Slovenia Marine Biology Station Piran, NIB, Fornaˇce 41, SI-6330 Piran, Slovenia c Istituto Nazionale di Oceanografia e Geofisica Sperimentale, OGS, Department of Biological Oceanography, Via Auguste Piccard 54, I-34151 Trieste, Italy b
a r t i c l e
i n f o
a b s t r a c t
Keywords: Phytoplankton Delayed fluorescence HPLC Pigments CHEMTAX
We present a method for in situ monitoring of phytoplankton composition changes in a marine environment. The method is based on delayed fluorescence excitation spectra analyzed with CHEMTAX software, which is generally used for determination of phytoplankton communities with HPLC pigment data. Delayed fluorescence (DF) is a photosynthetic parameter that can only be measured in living cells. Algal DF excitation spectra are group-specific, based on their composition of photosynthetic pigments. DF excitation spectra of 14 marine algal species from different families were measured with a delayed fluorescence spectrometer. Mixtures were prepared from northern Adriatic algal species representing six taxonomic groups: dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), cryptophytes (Teleaulax sp.), and prymnesiophytes (Isochrysis galbana). The DF excitation spectra (DFS) and HPLC pigment compositions of the mixtures were analyzed with CHEMTAX software. The prediction power of DFS–CHEMTAX method was comparable to HPLC–CHEMTAX. © 2011 Elsevier B.V. All rights reserved.
1. Introduction
hyperbolic decay kinetics during the first seconds, which is sometimes followed by a more or less pronounced peak (Bertsch, 1962; Zrimec et al., 2005; Berden-Zrimec et al., 2010). The main source of DF are back reactions in the photosystem II (PSII) (Rutherford and Inoue, 1984), whereas the photosystem I contributes much less to the DF emission (Jursinic, 1986). In PSII, charge pairs are generated during the illumination with positive charges located on the oxygen evolving complex (OEC) and negative charges on quinone acceptors (QA and QB ). The slow components of DF originate in back reactions between the S2 and S3 states of the OEC and quinones QA and QB (Joliot et al., 1971). The half-times of these reactions in isolated chloroplasts are 1.5 s for QA + S2/3 and 25 s for QB + S2/3 (Rutherford and Inoue, 1984). The major advantage of DF is that it is emitted only from cells that are photosynthetically active; that is, alive. Thus additional signals from dead cell debris do not interfere with the measurements. Long-term DF emission (measured for seconds or minutes) also prevents interference problems with fluorescent backgrounds in natural samples (Istvanovics et al., 2005). Delayed fluorescence excitation spectra (DFS) of algae are based on pigment composition. Chlorophyll a is present in all algal classes; chlorophyll b in chlorophytes and euglenophytes; chlorophyll c in diatoms, chrysophytes, dinoflagellates, and cryptophytes; and phycobiliproteins such as phycoerythrin and phycocyanin in cryptophytes and cyanobacteria, or allophycocyanin in cyanobacteria
Good evaluation protocols are critical for understanding phytoplankton population dynamics. More than one method is usually needed to gain sufficient information for good quality analysis. Although many laboratory and in situ methods are available, they have method-specific disadvantages. Thus, research goals must be defined before the method is selected. When studying changes in phytoplankton composition, fast methods with reasonable resolution of algal taxonomic groups can be used for in situ monitoring. A method that has been used for more than 10 years in routine freshwater phytoplankton dynamics studies is delayed fluorescence spectroscopy (Krause and Gerhardt, 1984; Yacobi et al., 1998; Istvanovics et al., 2005; Greisberger and Teubner, 2007). Delayed fluorescence (DF) is the long-term emission of light from cells triggered by illumination (Strehler and Arnold, 1951; Berden-Zrimec et al., 2010). It has the same emission spectrum as chlorophyll a fluorescence, but occurs with a time delay (from milliseconds to minutes) (Arnold and Davidson, 1954). DF originates from repopulation of excited states of chlorophyll from stored energy after charge separation (Joliot et al., 1971). It has
∗ Corresponding author. Tel.: +386 1 5875470. E-mail addresses: [email protected], [email protected] (L. Drinovec). 0098-8472/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.envexpbot.2010.10.010
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
(Barlow et al., 1993; Larkum, 2003). Chlorophylls affect the action spectra for photosynthesis with absorption in blue (430–470 nm) and red (630–680 nm) part of the spectrum (Strain et al., 1963; Jeffrey, 1963). Carotenoids absorb in 450–550 nm and phycobilins in 500–600 nm spectral regions (Haxo and Blinks, 1950). The resulting action spectra of certain algae are a combination of several absorption bands forming a specific spectral fingerprint. The method and spectra analysis used to date distinguish only four “color groups” of phytoplankton: (i) cyanophytes, (ii) chlorophytes (e.g., Chlorophyceae, Euglenophyceae, and Conjugatophyceae), (iii) chromophytes (e.g., Bacillariophyceae, Chrysophyceae, Dinophyceae, Xanthophyceae, and Haptophyceae) and (iv) cryptophytes (Gerhardt and Bodemer, 2005). This method has been used only in freshwater phytoplankton. In a marine environment, these four classes are not sufficient to monitor phytoplankton changes. Several algal taxonomic groups included in the chromophytes color group often prevail in marine phytoplankton populations and must be detected separately for successful monitoring of phytoplankton composition. Therefore we set out to develop a new protocol for monitoring phytoplankton changes using CHEMTAX analysis in addition to a new DF phytoplankton meter developed at our institute. Analysis of phytoplankton pigments by HPLC and data processing using CHEMTAX has been proposed as an alternative to time-consuming microscopic cell counting to determine taxonomic algal composition (Mackey et al., 1996). The resolution of taxonomic groups by this method is generally coarse (e.g., diatoms, dinoflagellates, cryptophytes, cyanobacteria, chlorophytes, prasinophytes, and haptophytes) compared to direct microscopic counting that can distinguish species within these broader groups. The advantage of CHEMTAX analysis is its speed and cost relative to other methods. On the other hand, a blind analysis can result in very significant errors because particular pigment compositions of certain species differ from the average (Irigoien et al., 2004). HPLC–CHEMTAX has been applied in combination with microscopy in the field in the Antarctic peninsula area (Rodriguez et al., 2002), southern Baltic Sea (Eker-Develi et al., 2008), southeast US estuaries (Lewitus et al., 2005), Belgian coastal zone of the North Sea (Muylaert et al., 2006), and others. It has been shown that the Mackey pigment matrix developed for the open ocean has to be optimized for local environments such as estuarine ecosystems (Lewitus et al., 2005). Because CHEMTAX is a matrix factorization program it can be used with any type of data if the differences in vectors between the taxonomic groups are great enough. The DF action spectrum resembles the absorption spectra of the photosynthetic pigments. Compared to direct absorbance measurements, the DF excitation spectra measure only active pigments and not dissolved pigments, organic substances, and debris. The absorption spectra of various photosynthetic pigments are similar, and so the DFS–CHEMTAX method resolution was expected to be somewhat lower than that of the HPLC–CHEMTAX. On the other hand, the DFS method is an in vivo method less prone to errors at the extraction and pigment-analysis stages. Furthermore, DF measures nano- and pico-plankton, which can be lost during filtration or unaccounted for in the microscopic analysis. The objective of this study was to indentify delayed fluorescence excitation spectra of the main marine algal taxonomic groups and use these spectra for their discrimination. The obtained spectra were analyzed using CHEMTAX deconvolution software to obtain the taxonomic composition of algae in prepared mixtures. The predicting power of DFS–CHEMTAX was compared with the standard HPLC–CHEMTAX approach.
43
2. Methods 2.1. Algal cultures Algal cultures were maintained at the culture facility of the National Institute of Oceanography and Experimental Geophysics, Department of Biological Oceanography (OGS/BiO) in Trieste. All species were isolated from the Gulf of Trieste. The algae were sustained as batch cultures in 100 ml borosilicate Erlenmeyer flasks with 50 ml of culture medium B (Agatha et al., 2004). The culture medium for the diatoms contained an addition of Na2 SiO3 × 9H2 O at a final concentration of 1.07 mol l−1 (Guillard, 1975). All organisms were cultured at 15 ◦ C for a 10:14 h dark:light cycle. The diatoms, Teleaulax sp., and Synechococcus sp. were grown at low irradiance of about 20 E m−2 s−1 , and the remaining species were kept at about 50 E m−2 s−1 . The samples were taken from the batch cultures in their exponential phase of growth. Fourteen species of unicellular algae from nine different families were used for the DFS screening. Two different cultures of the cyanobacteria Synechococcus sp. were used for comparison. The samples were measured in three parallels. 2.2. Mixtures The mixtures were prepared from representatives of the six different taxonomic groups that are most frequent in northern Adriatic Sea phytoplankton: dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), cryptophytes (Teleaulax sp.), and prymnesiophytes (Isochrysis galbana). Aliquots of two or three taxonomic groups were mixed in all combinations to a final volume of 1 ml. Mixtures of all six species were prepared such that one of the species was always predominant (50% of 1 ml) and the other five each represented 10% of the total volume. 2.3. DF excitation spectrometry (DFS) DF excitation spectra were measured with a custom-built delayed fluorescence spectrometer (Berden-Zrimec et al., 2010). DF excitation was performed with a halogen lamp and linear filter, providing monochromatic (25 nm half-width) illumination in the range between 400 and 700 nm with an intensity of 100 mol/m2 s PAR. A 1 ml sample of algal culture in a rectangular cuvette was illuminated for 0.6 s. The delayed fluorescence intensity (DFI) was measured in the interval of 0.4–1 s after the end of illumination. Light was detected with a Perkin Elmer C1393 channel photomultiplier in photon-counting mode. Two electromagnetic shutters were used to shield the photomultiplier from the excitation light. DFI was measured sequentially for 18 excitation wavelengths (430.0, 445.8, 493.2, 508.9, 524.7, 540.5, 556.3, 572.1, 587.9, 603.7, 619.5, 635.3, 651.1, 666.8, 682.6, 698.4, 714.2, and 730.0 nm). 2.4. Pigment composition The qualitative and quantitative analysis of pigments in the samples was performed using the reverse-phase HPLC (High Performance Liquid Chromatography) method (Mantoura and Llewellyn, 1983; Barlow et al., 1993). The pure and mixed culture samples used for DF spectroscopy were filtered through Whatman GF/F filters and immediately frozen. For pure cultures (but only in these cases), all three parallel samples were filtered together through the same filter in order to have enough material for HPLC analysis. A control was made with 1 ml of culture media, filtered and prepared for analysis using the same procedure as for the samples. Frozen samples were extracted in 4 ml of 90% acetone using sonication, and centrifuged for 10 min at 4000 rpm in order to
44
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
remove particles. An aliquot (300 l) of clarified extract was mixed with 300 l 1 mol l−1 ammonium acetate and 500 l of mixture injected in the gradient HPLC system with a 200 l loop. The HPLC system was equipped with a reverse-phase 3 m C18 column (Pecosphere, 35 mm × 4.5 mm, Perkin Elmer). Solvent A consisted of 80% methanol and 20% 1 mol l−1 ammonium acetate and solvent B contained 60% methanol and 40% acetone. A linear gradient from 0% B to 100% B for 10 min was followed by an isocratic hold at 100% B for 6 min. The rinse flow was 1 ml min−1 . Chlorophylls and carotenoids were detected by absorbance at 440 nm using an UV/VIS spectrophotometric detector (Spectra Physics, Model UV2000). Degradation products of chlorophyll a were detected by measuring fluorescence (420/672 nm) with a spectrofluorimetric detector (Spectra Physics, Model FL2000). Data collection and integration were performed using Agilent ChemStation software. 2.5. CHEMTAX data evaluation The taxonomic composition was calculated from the input data (HPLC pigments or DF spectra) using CHEMTAX software (Mackey et al., 1996) running under MATHLAB (MathWorks, Natick, MA). The CHEMTAX software uses two input matrices: (1) containing pigment concentrations in the samples and (2) an initial matrix containing marker pigment concentrations for all algal taxonomic groups accounted for. In the case of this DFS analysis, matrix 1 contained the samples’ DFS spectra and matrix 2 the reference algal group spectra. 3. Results 3.1. Pure cultures The DF spectra of the measured taxonomic groups are presented in Fig. 1. For greater clarity, only one representative each of the diatoms, dinoflagellates, prasinophytes, and cyanobacteria is presented. The DF spectra of measured species from the same taxonomic groups were very similar: (i) dinoflagellates: P. minimum, Alexandrium minutum, Ostreopsis ovata, (ii) diatoms: S. costatum, Chaetoceros socialis, Pseudonitzschia sp., (iii) prasinophytes: Micromonas sp., Pyramimonas sp., (iv) cyanobacteria: two strains of Synechococcus sp. The pigment compositions of the selected algal species are presented in Table 1. DF excitation spectra (Fig. 1) consist of absorption spectra of several photosynthetic pigments. Chlorophyll a contributes most DF emissions and has a peak at 680 nm. Chlorophyll c is present in most taxonomic groups with exception of cyanobacteria (Table 1). It has peaks at 450 and 630 nm (Jeffrey, 1963), the latter being clearly visible in diatoms. Chlorophyll b has peaks at 640 and 470 nm (Beeler SooHoo et al., 1985; Xu et al., 2001) and influences the prasinophytes’ spectrum. There are several peaks in DF spectra that can be
Fig. 1. Normalized delayed fluorescence spectra of representative algal species. Dinoflagellates (Prorocentrum minimum), diatoms (Skeletonema costatum), prymnesiophytes (Isochrysis galbana), cyanobacteria (Synechococcus sp.), prasinophytes (Micromonas sp.), euglenophytes (Eutreptiella sp.), cryptophytes (Teleaulax sp.), and silicoflagellates (Dictyocha speculum).
assigned to the carotenoids, such as the peak at 555 nm in dinoflagellates and 545 nm in diatoms. Although pure peridinin and fucoxanthin have their strongest absorption in the range of 450–500 nm, it has been found that the absorption of carotenoid-chlorophyll a complex can be shifted to longer wavelengths (Haxo and Blinks, 1950; Bautista et al., 1999; Shima et al., 2003). Phycobilins in cyanobacteria and cryptophytes (Barlow et al., 1993; Larkum, 2003; Beutler et al., 2004) strongly affect DF spectra (Gerhardt and Bodemer, 2005), but were not recorded with HPLC analysis. These two taxonomic groups had DF peaks at 560 and 580 nm (Fig. 1). 3.2. Phytoplankton mixtures The algal composition estimates provided by the two methods are presented in Tables 2–4. Examples of good and poor estimates in different mixtures are shown in Fig. 2a–f. The correlation between real and estimated values for taxonomic groups was evaluated with linear regression analysis (Table 5). Some discrepancies were regularly detected with the DFS–CHEMTAX technique. In mixtures containing various combinations of prasinophytes, cryptophytes, and prymnesiophytes, the evaluations were comparable with the real volumetric fractions. When these taxonomic groups were combined with the dinoflagellates, diatoms, or cyanobacteria, they were mostly overrated. On the other hand, dinoflagellates, diatoms, or cyanobacteria were repeatedly underrated. Dinoflagellates usually
Table 1 Pigment composition of single algal species, chosen as representatives for the taxonomic groups. dinoflagellates – P. minimum, diatoms – S. costatum, prymnesiophytes – I. galbana, cyanobacteria – Synechococcus sp., prasinophytes – Micromonas sp., euglenophytes – Eutreptiella sp., cryptophytes – Teleaulax sp., silicoflagellates – Dictyocha speculum; chl a – chlorophyll a, chl b – chlorophyll b, chl c2 – chlorophyll c2, chl c3 – chlorophyll c3, per – peridinin, but – 19 -butanoyloxyfucoxanthin, fuc – fucoxanthin, neo – neoxanthin, hex – 19 -hexanoyloxyfucoxanthin, pra – prasinoxanthin, vio – violaxanthin, diad – diadinoxanthin, ant – antheraxanthin, all – alloxanthin, diat – diatoxanthin, zea – zeaxanthin, lut – lutein, -car – beta,beta-carotene; concentrations in ng ml−1 . chl a Dinoflagellates Diatoms Prymnesiophytes Cyanobacteria Prasinophytes Euglenophytes Cryptophytes Silicoflagellates
chl b
346 142 2858 145 688 633 1098 1230 1016 3 2237
chl c2 134 36 831 1 54 1 163 287
chl c3 13 16 9 4 12 12 1
per
but
212
12
79 10
fuc 123 1427 1 8 13 797
neo
hex
pra
viola
diad
1
91 10 262 1 8 240 1 257
4 68 98
94
61
1
ant
all
diat
zea + lut
5 1 19 1 9 2 211 2
36 47 17
58 14 4 2
-car 14 6 123 34 28 57 80 125
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
45
Table 2 Evaluated volumetric fractions (%) of taxonomic groups in fifteen binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups actually present in each mixture are marked bold and underlined. Their real volumetric fractions were 50%. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
DFS–CHEMTAX dino
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
55 64 20 49 21 1 0 1 5 0 0 0 0 1 4
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
HPLC–CHEMTAX
diat
3 6 4 1 4 3 0 2 4 0 0 0 0 1 5
36 14 9 0 4 54 14 10 17 3 0 13 1 1 1
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
prym 6 5 3 0 3 3 4 4 7 3 0 5 2 1 1
2 14 1 3 6 43 0 3 5 9 14 11 0 2 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cyan
2 2 1 0 1 1 0 2 1 1 1 1 0 1 0
7 7 69 1 14 0 84 0 12 88 0 8 50 45 5
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
pras
3 3 4 1 3 0 2 0 2 2 0 3 1 1 0
0 0 0 46 0 1 0 85 1 0 86 0 46 1 45
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cryp
0 0 0 2 0 2 0 1 1 0 1 0 1 1 2
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0 0 0 0 55 0 2 0 60 0 0 67 2 49 45
dino
0 0 0 0 3 0 2 0 5 0 0 3 2 1 5
73 76 44 40 17 0 0 2 0 1 0 0 3 0 1
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
diat
6 2 1 6 2 0 0 2 0 1 1 0 1 1 1
18 0 0 0 2 36 16 19 24 0 0 8 1 10 9
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
prym 16 0 0 0 4 6 4 2 2 0 0 7 1 8 5
0 20 0 0 0 41 0 0 0 15 8 3 0 0 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cyan
0 3 0 0 0 6 0 0 0 1 2 1 0 0 0
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0 0 50 0 0 0 80 0 0 83 0 0 48 18 0
pras
0 0 2 0 0 0 1 0 0 1 0 0 1 1 0
1 3 5 60 1 3 1 79 0 1 92 0 48 0 18
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
cryp
2 4 2 6 1 2 1 4 0 1 1 0 0 0 2
8 0 1 0 80 20 3 0 76 0 0 89 0 71 72
± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
14 0 1 0 6 3 4 0 3 0 0 6 0 8 4
Table 3 Evaluated volumetric fractions (%) of taxonomic groups in six mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups actually present in each mixture are marked bold and underlined. Their real volumetric fractions were 33%. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
16 17 18 19 20 21
DFS–CHEMTAX
21 0 17 2 0 4
HPLC–CHEMTAX
dino
diat
± ± ± ± ± ±
30 15 0 0 2 2
2 1 4 2 0 1
prym
± ± ± ± ± ±
6 2 1 0 3 3
28 16 19 4 16 6
± ± ± ± ± ±
cyan
2 0 2 1 0 0
15 62 8 47 44 15
± ± ± ± ± ±
pras 1 1 2 0 0 2
0 0 53 35 38 30
± ± ± ± ± ±
cryp
0 0 3 2 1 0
6 6 2 11 1 43
± ± ± ± ± ±
1 0 3 4 1 2
dino 62 38 43 31 1 11
± ± ± ± ± ±
2 1 5 6 0 1
diat 16 0 0 0 0 4
± ± ± ± ± ±
prym 3 0 0 0 0 5
13 7 6 0 4 0
± ± ± ± ± ±
1 2 1 0 1 0
cyan 0 54 0 32 48 0
± ± ± ± ± ±
0 3 0 2 2 0
pras 0 1 51 37 47 16
± ± ± ± ± ±
1 2 5 3 2 3
cryp 8 0 0 0 0 69
± ± ± ± ± ±
4 0 0 0 0 3
Table 4 Evaluated volumetric fractions (%) of taxonomic groups in six mixtures obtained by CHEMTAX analysis of DFS and HPLC data. The taxonomic groups prevalent in each mixture are marked bold and underlined. Their real volumetric fractions were 50%, whereas other taxonomic groups were present in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The numbers represent average and standard deviation from three parallels. Left part of the table – evaluations with DFS–CHEMTAX analysis, right – evaluations with HPLC–CHEMTAX analysis. Mixture
DFS–CHEMTAX dino
22 23 24 25 26 27
24 0 0 0 9 1
± ± ± ± ± ±
HPLC–CHEMTAX diat
2 1 1 0 1 2
14 25 15 2 4 1
± ± ± ± ± ±
prym 7 3 6 3 4 1
11 8 27 7 3 7
± ± ± ± ± ±
cyan
3 1 4 1 1 1
26 26 28 61 13 21
± ± ± ± ± ±
pras 3 3 5 0 3 1
5 17 13 8 60 12
± ± ± ± ± ±
2 2 5 0 1 1
20 23 17 22 11 59
cryp
dino
± ± ± ± ± ±
41 11 13 6 9 3
4 5 1 1 3 1
± ± ± ± ± ±
diat 4 4 1 1 1 1
8 41 23 11 13 12
± ± ± ± ± ±
prym 3 1 3 2 3 3
0 0 7 1 0 0
± ± ± ± ± ±
1 0 1 0 0 0
cyan 10 13 14 45 8 4
± ± ± ± ± ±
1 1 0 1 0 0
pras 9 11 11 11 47 4
± ± ± ± ± ±
2 3 2 1 1 2
cryp 32 25 33 27 22 77
± ± ± ± ± ±
4 1 3 1 4 2
Table 5 Statistical analysis of CHEMTAX evaluation of the volumetric fractions in the mixtures. A linear correlation was calculated between real and evaluated volumetric fraction. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). DFS–CHEMTAX Offset dino diat prym cyan pras cryp
−0.01 0.04 0.03 0.03 0.08 0.00
HPLC–CHEMTAX 2
Slope ± ± ± ± ± ±
0.02 0.01 0.01 0.01 0.01 0.01
0.69 0.45 1.08 0.36 1.19 1.22
± ± ± ± ± ±
0.06 0.05 0.03 0.04 0.05 0.05
r
Offset
0.62 0.48 0.93 0.56 0.87 0.88
0.01 0.04 0.05 −0.01 0.00 0.00
r2
Slope ± ± ± ± ± ±
0.02 0.01 0.01 0.01 0.02 0.01
0.99 0.45 1.50 0.31 1.12 1.13
± ± ± ± ± ±
0.07 0.04 0.04 0.03 0.06 0.07
0.72 0.60 0.94 0.50 0.80 0.77
46
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
lost their signal to prasinophytes. Prasinophytes were detected in more than half of the mixtures in which they were not present (offset 0.08). Similarly, diatoms were detected in one-third of the mixtures in which they were not present (offset 0.04). Some discrepancies were also found in the HPLC–CHEMTAX analysis although we used the appropriate pigment fingerprints. Cyanobacteria were seriously underrated in most mixtures, and in some cases even not detected (slope 0.3). The diatoms were mostly underrated (slope 0.45). Prymnesiophytes were mostly overrated (slope 1.5) or falsely detected in half of the cases (offset 0.05).
4. Discussion Delayed fluorescence spectroscopy in combination with the CHEMTAX method was evaluated as a tool for taxonomic characterization of phytoplankton mixtures. The DFS method was compared to the HPLC–CHEMTAX method using laboratory algal cultures. The measured DF spectra of marine algae resemble the spectra of freshwater taxonomic groups measured by others (Gerhardt and Bodemer, 2005; Istvanovics et al., 2005; Greisberger and Teubner, 2007). The DF intensity was lower at excitation of shorter wavelengths. This contrasts the absorbance spectra of photosynthetic pigments with higher extinction coefficients in the blue part of the spectrum, but agrees with the action spectra of photosynthesis (Haxo and Blinks, 1950). When comparing freshwater and marine algae DF spectra, we observed the greatest similarity in prasinophytes, cryptophytes, and diatoms. The DF spectra of cyanobacteria were not comparable, which is unsurprising due to differences between the cyanobacterial species in the composition of photosynthetic pigments (Gerhardt and Bodemer, 2005). In general, the prediction power of DFS–CHEMTAX was comparable to HPLC–CHEMTAX. The offsets of the correlation curves for both methods were quite similar. The exception was prasinophytes, which were strongly overestimated by DFS. When comparing the slopes, the DFS method was superior for prymnesiophyte detection but inferior for detecting dinoflagellates. In most cases, both methods were able to detect the dominant species in the mixture. The exceptions were cyanobacteria in HPLC–CHEMTAX analysis and dinoflagellates in DFS–CHEMTAX. Discrepancies similar to ours have already been found in other HPLC–CHEMTAX studies (Rodriguez et al., 2002; Eker-Develi et al., 2008). The reason for the discrepancies between HPLC–CHEMTAX and microscopic evaluation may be an incorrect CHEMTAX input matrix (Schluter et al., 2000) and the presence of unaccounted-for organisms (Irigoien et al., 2004) on the one hand, and the problem
47
of microscopic quantification of smaller algae on the other (EkerDeveli et al., 2008; Rodriguez et al., 2002). Our DFS estimation of phytoplankton composition is comparable to the freshwater studies. In the study of phytoplankton dynamics in Lake Balaton (Istvanovics et al., 2005), chlorophytes and cryptophytes were overestimated as well. Similar results were also obtained by Gerhardt and Bodemer (2005). In both cases a custom deconvolution algorithm was used. Although the DFS–CHEMTAX method can be used to monitor changes in marine phytoplankton composition, it is not yet sensitive enough for in situ measurements in the open ocean. Chlorophyll concentrations in the open ocean can be as low as 0.1 g l−1 , which is approximately three orders of magnitude lower than in our laboratory samples. On the other hand, simplicity and speed are the main advantages of the DFS method over HPLC. In addition, the DFS method can identify smaller phytoplankton often overlooked by microscopic identification. To overcome the sensitivity problems of the current systems, DF spectrometer for open-ocean samples was built with a 1.2 l measuring chamber and is currently being tested. 5. Conclusions 1. We identified several pigment absorption bands in delayed fluorescence excitation spectra (DFS) of several marine phytoplankton taxonomic groups with comparison to their HPLC pigment composition. 2. We succeeded in distinguishing six taxonomic groups of marine algae by deconvolution of DFS with CHEMTAX software: (1) dinoflagellates, (2) diatoms, (3) cyanobacteria, (4) prasinophytes (Chlorophyta), (5) cryptophytes, and (6) prymnesiophytes. 3. The results of the DFS–CHEMTAX method were in good agreement with the real compositions of dinoflagellates, prasinophytes, cryptophytes, and prymnesiophytes, but underestimated diatoms and cyanobacteria. 4. In our study the prediction powers of the DFS–CHEMTAX and HPLC–CHEMTAX methods were comparable. 5. DFS measurement is a fast, simple method that does not require any sample manipulation. The main drawback compared to HPLC is lower sensitivity of DF spectrometers. Acknowledgment This study was supported financially by the Slovenian Research Agency (grant #P1-0237).
Fig. 2. (a) Real and evaluated volumetric fractions (%) of taxonomic groups in binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture only prasinophytes and cryptophytes were present in 50% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (b) Real and evaluated volumetric fractions (%) of taxonomic groups in binary mixtures obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture only cyanobacteria and prymnesiophytes were present in 50% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (c) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of three species obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture only dinoflagellates, diatoms and cyanobacteria were present in 33% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (d) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of three species obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture only dinoflagellates, prasinophytes and cryptophytes were present in 33% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (e) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of six species obtained by CHEMTAX analysis of DFS and HPLC data—an example of good evaluation. In this mixture cryptophytes are present in 50% and others in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels. (f) Real and evaluated volumetric fractions (%) of taxonomic groups in mixtures of six species obtained by CHEMTAX analysis of DFS and HPLC data—an example of poor evaluation. In this mixture cyanobacteria are present in 50% and others in 10% volumetric fractions. dino (dinoflagellates, P. minimum), diat (diatoms, S. costatum), cyan (cyanobacteria, Synecococcus sp.), pras (prasinophytes, Micromonas sp.), cryp (cryptophytes, Teleaulax sp.), and prym (prymnesiophytes, I. galbana). The evaluated fractions are represented by average and standard deviation from three parallels.
48
L. Drinovec et al. / Environmental and Experimental Botany 73 (2011) 42–48
References Agatha, S., Strüder-Kypke, M.S., Beran, A., 2004. Morphological and genetic variability in the marine planktonic ciliate Laboea strobila Lohmann, 1908 (Ciliophora, Oligotrichia), with notes on its ontogenesis. J. Euk. Microbiol. 5, 267–282. Arnold, W., Davidson, J.B., 1954. The identity of the fluorescent and delayed light emission spectra in Chlorella. J. Gen. Physiol. 37, 677–684. Barlow, R.G., Mantoura, R.F.C., Gough, M.A., Fileman, T.W., 1993. Pigment signatures of the phytoplankton composition in the Northeastern Atlantic during the 1990 spring bloom. Deep-Sea Res. II-Top. Stud. Oceanogr. 40, 459–477. Bautista, J.A., Connors, R.E., Raju, B.B., Hiller, R.G., Sharples, F.P., Gosztola, D., Wasielewski, M.R., Frank, H.A., 1999. Excited state properties of peridin: observation of a solvent dependence of the lowest excited singlet state lifetime and spectral behavior unique among carotenoids. J. Phys. Chem. B 103 (41), 8751–8758. Beeler SooHoo, J., Kiefer, D.A., Collins, D.J., McDermid, I.S., 1985. In vivo fluorescence excitation and absorption spectra of marine phytoplankton: I. Taxonomic characteristics and responses to photoadaptation. J. Plankton Res. 8, 197–214. Berden-Zrimec, M., Drinovec, L., Zrimec, A., 2010. Delayed fluorescence. In: Sugget, D.J., Prasil, O., Borowitzka, M. (Eds.), Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications. Springer, Dordrecht, pp. 293-309. Bertsch, W.F., 1962. Two photoreactions in photosynthesis: evidence from delayed light emission of Chlorella. Proc. Natl. Acad. Sci. U.S.A. 48, 2000–2004. Beutler, M., Wiltshire, K.H., Reineke, C., Hansen, U.P., 2004. Algorithms and practical fluorescence models of the photosynthetic apparatus of red cyanobacteria and Cryptophyta designed for the fluorescence detection of red cyanobacteria and cryptophytes. Aquat. Microb. Ecol. 35, 115–129. Eker-Develi, E., Berthon, J.-F., van der Linde, D., 2008. Phytoplankton class determination by microscopic and HPLC–CHEMTAX analyses in the southern Baltic Sea. Mar. Ecol., Prog. Ser. 359, 87. Gerhardt, V., Bodemer, U., 2005. Delayed fluorescence excitation spectroscopy—an in vivo method to determine photosynthetically active pigments and phytoplankton composition. ALGAS 33, 4–17. Greisberger, S., Teubner, K., 2007. Does pigment composition reflect phytoplankton community structure in differing temperature and light conditions in a deep alpine lake? An approach using HPLC and delayed fluorescence techniques. J. Phycol. 43, 1108–1119. Guillard, R.R.L., 1975. Culture of phytoplankton for feeding marine invertebrates. In: Smith, W.L., Chanley, M.H. (Eds.), Culture of Marine Invertebrate Animals. Plenum Press, New York, pp. 29–60. Haxo, F.T., Blinks, L.R., 1950. Photosynthetic action spectra of marine algae. J. Gen. Physiol. 33 (4), 389–422. Irigoien, X., Meyer, B., Harris, R., Harbour, D., 2004. Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species. Helgol. Mar. Res. 58, 77–82. Istvanovics, V., Honti, M., Osztoics, A., Shafik, H.M., Padisak, J., Yacobi, Y., Eckert, W., 2005. Continuous monitoring of phytoplankton dynamics in Lake Balaton (Hungary) using on-line delayed fluorescence excitation spectroscopy. Freshw. Biol. 50, 1950–1970. Jeffrey, S.W., 1963. Purification and properties of chlorophyll c from Sargassum flavicans. Biochem. J. 86 (2), 313–318. Joliot, P., Joliot, A., Bouges, B., Barbieri, G., 1971. Studies of system-II photocenters by comparative measurements of luminescence, fluorescence, and oxygen emission. Photochem. Photobiol. 14, 287–305.
Jursinic, P.A., 1986. Delayed fluorescence: current concepts and status. In: Govindjee, Fork, D.C. (Eds.), Light Emission by Plant and Bacteria. Academic Press, New York, pp. 291–328. Krause, H., Gerhardt, V., 1984. Application of delayed fluorescence of phytoplankton in limnology and oceanography. J. Lumin. 31-2, 888–891. Larkum, A.W., 2003. Light-harvesting systems in algae. In: Larkum, A.W., Douglas, S.E., Raven, J.A. (Eds.), Advances in Photosynthesis and Respiration. Photosynthesis in Algae. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 277–304. Lewitus, A.J., White, D.L., Tymowski, R.G., Geesey, M.E., Hymel, S.N., Noble, P.A., 2005. Adapting the CHEMTAX method for assessing phytoplankton taxonomic composition in southeastern U.S. estuaries. Estuaries 28, 160–172. Mackey, M.D., Mackey, D.J., Higgins, H.W., Wright, S.W., 1996. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar. Ecol., Prog. Ser. 144, 265–283. Mantoura, R.F.C., Llewellyn, C.A., 1983. The rapid-determination of algal chlorophyll and carotenoid-pigments and their breakdown products in natural-waters by reverse-phase high-performance liquid-chromatography. Anal. Chim. Acta 151, 297–314. Muylaert, K., Gonzales, R., Franck, M., Lionard, M., 2006. Spatial variation in phytoplankton dynamics in the Belgian coastal zone of the North Sea studied by microscopy, HPLC–CHEMTAX and underway fluorescence recordings. J. Sea Res. 55, 265. Rodriguez, F., Varela, M., Zapata, M., 2002. Phytoplankton assemblages in the Gerlache and Bransfield Straits (Antarctic Peninsula) determined by light microscopy and CHEMTAX analysis of HPLC pigment data. Deep-Sea Res. II 49, 723–747. Rutherford, A.W., Inoue, Y., 1984. Oscillation of delayed luminescence from PSII recombination of S2 QB − and S3 QB − . FEBS Lett. 165, 163–170. Schluter, L., Mohlenberg, F., Havskum, H., Larsen, S., 2000. The use of phytoplankton pigments for identifying and quantifying phytoplankton groups in coastal areas: testing the influence of light and nutrients on pigment/chlorophyll a ratios. Marine Ecology Progress Series 192, 49–63. Shima, S., Ilagan, R.P., Gillespie, N., Sommer, B.J., Hiller, R.G., Sharples, F.P., Frank, H.A., Birge, R.R., 2003. Two-photon and fluorescence spectroscopy and the effect of environment on the photochemical properties of peridinin in solution and in the peridinin-chlorophyll-protein from Amphidinium carterae. J. Phys. Chem. A 107 (40), 8052–8066. Strain, H.H., Thomas, M.R., Katz, J.J., 1963. Spectral absorption properties of ordinary and fully deuteriated chlorophylls a and b. Biochim. Biophys. Acta 75, 306–311. Strehler, B.L., Arnold, W., 1951. Light production by green plants. J. Gen. Physiol. 34, 809–820. Xu, H., Vavilin, D., Vermaas, W., 2001. Chlorophyll b can serve as the major pigment in functional photosystem II complexes of cyanobacteria. Proc. Natl. Acad. Sci. U.S.A. 98 (24), 14168–14173. Yacobi, Y.Z., Gerhardt, V., Gonen-Zurgil, Y., Sukenik, A., 1998. Delayed fluorescence excitation spectroscopy: a rapid method for qualitative and quantitative assessment of natural population of phytoplankton. Water Res. 32, 2577–2582. Zrimec, A., Drinovec, L., Berden-Zrimec, M., 2005. Influence of chemical and physical factors on long-term delayed fluorescence in Dunaliella tertiolecta. Electromagn. Biol. Med. 24, 309–318.