XIX Ocean Optics Conference

Phytoplankton Spectral Absorption and Scattering for Optical Groups Discrimination Using Complete and Reduced Spectral Information

Emanuele Organelli1, Luca Massi1, Caterina Nuccio1, Fabiola Fani1, Luigi Lazzara2

1Laboratorio di Ecologia e Fisiologia vegetale, Dipartimento di Biologia Vegetale, Università di

Firenze, via Micheli 1 – 50121 Firenze, Italia 2Laboratorio di Ecologia marina, Dipartimento di Biologia Evoluzionistica, Università di Firenze, via

Romana 17 – 50125 Firenze, Italia

1. INTRODUCTION In the last years increasingly frequent events of marine Harmful Algal Blooms (HABs) and the necessity to detect them rapidly, especially for public health and human activities protection (Cullen et al., 1997), represented a problem whose scientific community turned special attention. Optical detection of marine phytoplankton by means of ocean colour remote sensed data represents the more actual possibility to rapidly observe these phytoplanktonic phenomena. Ocean colour, which is determined by optical properties of suspended and solute water components, can be described according to Gordon et al. (1975) and Morel & Prieur (1977) by the back-scattering (bb(λ)) to absorption (a(λ)) ratio such as:

))()(()()( λλλλ bb babR +≈

phytoplankton being the main component which determines these optical properties in case 1 waters (Morel, 1988). Absorption is an important property that can carry useful information about phytoplankton (Sathyendranath & Platt, 2007) because its spectral shape is strictly associated to pigments composition (Sathyendranath et al., 1987; Hoepffner & Sathyendranath, 1991; 1993) and varies with respect to the modality of pigments packaging into the cells (package effect) (Morel & Bricaud, 1981; Kirk, 1994; Bricaud et al., 2004). So a different taxonomical composition of the phytoplankton assemblages gives absorption spectra with different spectral shapes. Johnsen et al. (1994) defined a method allowing to classify absorption spectra of 10 classes and about 31 species of bloom-forming phytoplankton grown in laboratory, thanks to a stepwise discriminant analysis. Millie et al. (1997) have introduced a technique based on Spectral Angle Mapper and fourth-derivative analysis to discriminate, on the basis of the spectral similarity (similarity index), absorption spectra of bloom-forming Gymnodinium breve from those of phytoplankton species belonging to different algal classes and from hypothetical mixed spectra, reconstructed scaling G. breve contribution with respect to another algal species. Analysis of absorption spectral similarity has been used also to discriminate and quantify the presence of G. breve in natural mixed populations. Kirkpatrick et al. (2000) and Craig et al. (2006) compared its reference absorption spectrum with those of natural populations obtained, respectively, from water sampling and from an inversion model deriving phytoplankton absorption from remote sensing reflectance spectra. They found strong correlation between obtained spectral similarity and G. breve

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fraction of chlorophyll a biomass. Organelli et al. (2007) highlight the possibility to discriminate five different algal groups correlating the concentration of the groups marker pigments, obtained by HPLC, with similarity indices computed between reference absorption and back-scattering spectra and the ones of natural populations collected in case I waters of the Mediterranean Sea. In this work we have evaluated through a spectral analysis (Millie et al., 1997) the possibility to optically discriminate and quantify the presence of different species in hypothetical mixed assemblages. This possibility has been tested utilizing both 316 data absorption spectra obtained with QFT (Yentsch, 1962; Mitchell & Kiefer, 1988), and ac-9 absorption spectra (9 data) to mimic those achievable from remote sensors. In addition to absorption we introduced in this experiment also the spectral information associated to scattering. 2. METHODS Absorption and scattering measurements were carried out on 5 mono-specific cultures of 5 different marine algal classes (Table 1). All species were grown in f/2 medium (Guillard, 1975; 1983) at 22 °C under light/dark cycle of 12 h: 12 h with about 50 µmol quanta m-2 s-1 (LI-1000 with a Licor LI192 SA quantum sensor).

Table I. Species and classes of laboratory mono-specific cultures.

Classes Species Abbr. Bacillariophyceae Phaeodactylum tricornutum Phaeo

Cryptophyceae Cryptomonas sp. Crypto Cyanobacteria Synechocystis sp. Syn

Prasinophyceae Tetraselmis sp. Tetra Raphidophyceae Heterosigma akashiwo Het

Measurements of absorption coefficients were carried out both with a spectroradiometer Licor LI1800UW equipped with a LI1800-12S integrating-sphere and by a WetLabs ac-9. Scattering coefficients were measured only by means of Wetlabs ac-9. Both instruments operate from 400 to 715 nm. Licor scans 1 nm spectra and provides a complete spectral information with 316 entry data, while Wetlabs ac-9 scans nine bands (412, 440, 488, 510, 555, 630, 650, 676, 715 nm) supplying spectra with reduced spectral information (9 entry data against 316). For analyses of complete absorption spectra, aliquots from 2 to 10 mL were filtered under low vacuum (about 30 KPa) on Whatman GF/F glass-fiber filters (Ø 25 mm) and directly frozen (-80 °C). Particulate absorption spectra were measured according to Massi et al. (1997) and Transmittance-Reflectance method described in Tassan & Ferrari (1995; 2002). Detritus absorption spectra were then measured on the same filters after pigments extraction in 5 mL of absolute methanol at 4 °C during 24 h (Kishino et al., 1985). Phytoplankton absorption spectra were obtained subtracting the detritus spectra from the total particulate ones. Spectra were corrected for the path-length amplification factor (β) according to Bricaud & Stramski (1990). The reference absorption spectra of each species of cultured phytoplankton were obtained from the mean values of three samples. Wetlabs ac-9 measurements on culture suspension have been carried out before and after filtration on filter Millipack 0.22 μm to measure absorption (a) and attenuation (c) coefficients of the cultured

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phytoplankton. Scattering coefficient (b) was computed subtracting absorption coefficient from the attenuation one. Chlorophyll a concentration was determined by spectro-photometric analysis (Shimazdu UV25PC) on methanol extract according to Jeffrey & Haxo (1968) and Porra et al. (1989) only for chlorophyll b- containing phytoplankton. Similarity between spectra of different species was computed using Spectral Angle Mapper (SAM) algorithm (Sohn & Rebello, 2002). SAM is a technique which allows to evaluate the similarity between two different spectra computing the cosine of the angle between two spectral vectors. Similarity index (SI) can be calculated according to Millie et al. (1997) and Kirkpatrick et al. (2000) as:

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⎥⎥⎦

⎤

⎢⎢⎣

⎡

×⋅

−=π

cb

cb

AAAA

SIarccos*2

1

where Ab and Ac are two mean-normalized spectra. In the developed routine for each mean-normalized spectrum a normalized-ratio transform (Millie et al., 1997) and then its fourth derivative have been computed. In reduced spectra with only 9 bands the computation of the normalized-ratio transform has been modified respect to Millie et al. (1997); this modification allows to greatly improve the discrimination capacity comparing each band both with previous and subsequent one. Besides we proposed a new method to improve the single information obtained by absorption and scattering coefficients. We joined them in a single vector trying to increase the optical discrimination capacity of the different phytoplankton groups. Hypothetically mixed spectra were reconstructed to evaluate the magnitude of the spectral signature of each optical group in a mixed assemblage. In the first instance, in this experiment the 5 cultured phytoplankton species identify 5 different optical groups. Since chlorophyll a values resulted different among groups, each spectrum has been multiplied by a factor that homogenizes the optical contribution with respect to chlorophyll a concentration. Hypothetical mixed spectra (atot) were reconstructed scaling each spectral group concentration at a time from 0 to 1 by 0.2 steps. Simultaneously the concentration of the sum of other four spectral groups change from 1 (individual contribution of 0.25) to 0 with 4 decreasing steps of 0.05 each, such as:

⎟⎟⎠

⎞⎜⎜⎝

⎛++⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛= ∑

=i

i

toti

toti

n

tottot x

chlchl

axchl

chlax

chlchl

aa **20.0

*.......**20.0

***20.0

* 22

21

11

1

where ai is the spectrum of one species; x is the species concentration such as x1+x2+….+xi=1. Every mixed spectrum was normalized for its spectral mean.

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Similarity indices for absorption, scattering and single vector were then computed, using the algorithm above-mentioned, between each mixed spectrum and the reference spectra of each phytoplanktonic species characterizing optical groups. Regressions between obtained similarity index and hypothetical specific contribution of each phytoplankton species were computed to evaluate the quantitative response of the similarity index with respect to the different optical contribution of a species in a mixed assemblage. 3. RESULTS AND DISCUSSIONS Complete absorption spectra (Fig. 1 left) generally present differentiated spectral shapes. Particularly these spectra clearly emphasize absorption peaks and shoulder of present pigments. From these properties it is possible to highlight similarities as in the case of P. tricornutum and H. akashiwo containing similar major pigments sets, or differences between all phytoplankton species with different sets of pigments. Reduced absorption spectra reconstruct only the main features of the spectra (Fig. 1 centre) allowing at least differentiation of the main absorption peaks of the major pigments. Scattering coefficient spectra (Fig. 1 right) show an high differentiation of their shapes. Moreover scattering spectra differentiate phytoplankton species such as H. akashiwo from P. tricornutum in spite of similar pigmentary composition. Pair wise computation of similarity index by means of SAM algorithm (Tables II-V) allows to evaluate the efficiency of optically discriminating the two species to whom spectra belong. Low SI indicate that tested spectra have different spectral shapes and so higher possibility to be optically discriminated. Similarity indices computed on complete absorption spectra (Table II) show generally a low value between species with different sets of pigments. In this cases SI vary in a narrow range, up to 0.37 (Cryptomonas sp. vs P. tricornutum), while testing of the spectra of species with similar pigments (P. tricornutum vs H. akashiwo) shows an higher similarity (SI=0.50) (Table II).

Table II. SI computed between complete absorption spectra.

Species Phaeo Tetra Syn Het Crypto Phaeo 1 0.23 0.05 0.50 0.37 Tetra 0.23 1 0.32 0.17 0.22 Syn 0.05 0.32 1 0.003 0.10 Het 0.50 0.17 0.003 1 0.24 Crypto 0.37 0.22 0.10 0.24 1

SI computed using reduced absorption spectra are generally higher (Table III) up to 0.56 in species with different major pigments (Table III) and for spectra of species with similar major pigments, as for P. tricornutum and H. akashiwo, SI further increase reaching 0.95. The results of these trials generally show that SI computed on the absorption spectra allows a good spectral discrimination between species characterized by the presence of different major pigments. This capacity is lower as similarity in the sets of pigments increases.

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400 450 500 550 600 650 700

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Tetraselmis sp.

Synechocystis sp.

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Cryptomonas sp.

Tetraselmis sp. Tetraselmis sp.

Synechocystis sp. Synechocystis sp.

H. akashiwo

Cryptomonas sp. Cryptomonas sp.

Fig. 1. Spectra of: complete absorption, 316 data, (left column); reduced absorption, ac-9 data,

(central column); scattering, ac-9 data, (right column). The use of a reduced optical information can allow to obtain a discrimination of the spectral shapes of different species but, as SI values show (Tables II-III), with a lower efficiency than for complete spectra. Similarity indices obtained pair-wise comparing scattering spectra (Table IV) show values ranging from 0.06 to 0.58. Higher SI is found comparing Cryptomonas sp. with Synechocystis sp.. Similarity indices of H. akashiwo respect to P. tricornutum result lower (Table IV) than those obtained using

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complete or reduced absorption spectra (respectively Table II and Table III). Scattering provides lower indices also for Tetraselmis sp. respect to Synechocystis sp. (Tables II-IV) and for Cryptomonas sp. respect to H. akashiwo (Tables III-IV).

Table III. SI computed between reduced absorption spectra.

Species Phaeo Tetra Syn Het Crypto Phaeo 1 0.47 0.24 0.95 0.52 Tetra 0.47 1 0.47 0.50 0.04 Syn 0.24 0.47 1 0.24 0.01 Het 0.95 0.50 0.24 1 0.49 Crypto 0.52 0.04 0.01 0.49 1

Table IV. SI computed between reduced scattering spectra.

Species Phaeo Tetra Syn Het Crypto Phaeo 1 0.40 0.31 0.11 0.22 Tetra 0.40 1 0.19 0.53 0.06 Syn 0.31 0.19 1 0.45 0.58 Het 0.11 0.53 0.45 1 0.33 Crypto 0.22 0.06 0.58 0.33 1

Even scattering allows to discriminate the spectral shapes of two different species, in some cases also better than complete and reduced absorption (Tables II-IV). Spectral feature of scattering coefficient is mainly due to cellular dimensions and form (Bricaud & Morel, 1986; Wyatt & Jackson, 1989; Stramski et al., 2001; Vaillancourt et al., 2004). Differently to spectral absorption, scattering does not strongly depend on pigment composition and makes it possible to discriminate species as H. akashiwo and P. tricornutum with similar major pigments but with different shape (Table IV). The use of scattering to discriminate different algal species can be important to implement the information brought by absorption. So spectral discrimination can be carried on considering not only pigments composition, but also cell size and form. As reduced absorption, SI computed using single vectors (Table V) too, have a reduced range of variation with lower values around 0.2 and a generally higher similarity than that obtained with absorption and scattering. SI is high (SI= 0.89) between P. tricornutum and H. akashiwo. Generally the joining of reduced absorption and scattering spectra in single vectors does not allow to increase the discrimination efficiency of the single optical properties.

Table V. SI computed between reduced single vector.

Species Phaeo Tetra Syn Het Crypto Phaeo 1 0.53 0.38 0.89 0.58 Tetra 0.53 1 0.50 0.56 0.21 Syn 0.38 0.50 1 0.36 0.20 Het 0.89 0.56 0.36 1 0.57 Crypto 0.58 0.21 0.20 0.57 1

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Spectra of hypothetically mixed assemblages have been reconstructed to evaluate the capability to quantify concentration of the cells belonging to a species by their optical contribution. A pair-wise analysis of similarity between the reference spectrum of the studied species and the spectra of mixed assemblages, while its concentration is scaled from 0 to 1, has been carried out. Regressions of similarity indices so computed with respect to concentration has been calculated (Fig. 2). The aim of this experiment is to evaluate the efficiency of quantitative optical discrimination of the cells belonging to a species with respect to a variable optical contribution of the cells of other 4 species in a mixed assemblage.

0.0 0.2 0.4 0.6 0.8 1.0

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P. tricornutum Tetraselmis sp. Synechocystis sp. Cryptomonas sp.H. akashiwo

D

Fig. 2. Quantitative relationships between SI and concentration for A) complete absorption; B) reduced absorption; C) reduced scattering; D) reduced absorption and scattering vector.

Determination coefficients (R2), regression coefficients (b) and Y-intercepts (a) are the parameters used to analyze the goodness of computed regressions (Table VI). High value of R2 and regression coefficients near to 1, mean linear trends of SI respect to concentration and high efficiency in determining two different concentrations. Y-intercepts represents the smallest SI value that can be detected when the concentration of a species is 0; such low values mean a higher capability to discriminate different species at low concentrations. SI computed on complete absorption spectra vary linearly according to concentration in a wide range from about 0.2 to 1 (Fig. 2A). R2 are always higher than 0.94. Value of b coefficients and Y-intercepts (Table VI) show a generally high efficiency discriminating a species by its optical contribution even at low concentration.

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SI computed on reduced absorption spectra show a linear trend with concentration (Fig. 2B) with R2

always higher than 0.93 (Table VI). Y-intercepts are generally higher than those obtained with complete absorption, and in particular for P. tricornutum, Tetraselmis sp. and H. akashiwo. They vary from 0.26 to 0.76. So calculated SI vary according to concentration in a closer range than complete absorption, and the regression coefficients ranges from 0.22 to 0.78.

Table VI. Linear regression parameters of the relationships between SI and concentration.

Absorption spectra N b a R2

P. tricornutum 6 0.6285 0.3638 0.99 Tetraselmis sp. 6 0.6233 0.3487 0.99 Synechocystis sp. 6 0.8428 0.0948 0.94 H. akashiwo 6 0.7481 0.2475 0.99 Cryptomonas sp. 6 0.6480 0.3094 0.98 AC9 Absorption spectra N b a R2

P. tricornutum 6 0.3047 0.7256 0.93 Tetraselmis sp. 6 0.4643 0.5278 0.99 Synechocystis sp. 6 0.7877 0.2637 0.98 H. akashiwo 6 0.2203 0.7631 0.98 Cryptomonas sp. 6 0.7306 0.2734 0.99 AC9 scattering spectra N b a R2

P. tricornutum 6 0.4677 0.6065 0.82 Tetraselmis sp. 6 1.0027 0.0311 0.99 Synechocystis sp. 6 0.9092 0.2489 0.79 H. akashiwo 6 0.8281 -0.1073 0.66 Cryptomonas sp. 6 0.6523 0.1829 0.57

AC9 Vector a and b N b a R2

P. tricornutum 6 0.2815 0.7453 0.94 Tetraselmis sp. 6 0.4167 0.562 0.99 Synechocystis sp. 6 0.6359 0.4093 0.97 H. akashiwo 6 0.2137 0.7584 0.94 Cryptomonas sp. 6 0.5939 0.4051 0.99

Differently from absorption, SI computed using scattering are not distributed uniformly around the regression line (Fig. 2C) and often show a non linear trend. Except for Tetraselmis sp. whose regression (R2= 0.99; b= 1; a= 0.03) is the best result obtained, other regressions have low R2 (Table VI). In particular for H. akashiwo and Cryptomonas sp., although regression coefficients and Y-intercepts are generally good, SI show a non monotonic trend (Fig. 2C). A linear trend is obtained between similarity indices computed using absorption and scattering spectra joined in single vectors (Fig. 2D). Determination coefficients (Table VI) are higher than 0.94 and in fact SI are uniformly distributed around the regression line. Regression coefficients are lower than 0.63, SI vary according to concentration from 0.4 to 1. Y-intercepts show higher values ranging from 0.41 to about 0.75, with the highest one reached by the two species with similar major pigments. Single vector shows a significant reduction of discrimination capability with respect to reduced absorption for Cryptomonas sp. and Synechocystis sp.. To evaluate the efficiency of quantitative discrimination of a species vs. another one , complete absorption results the optical property that can allow to obtain a linear trend of similarity indices with

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respect to concentration and showing, above all, an high capability to discriminate the contribution of a species in a mixed assemblage also at low concentrations. The reduced spectral information decreases the efficiency of a quantitative discrimination of species in mixed assemblages, particularly at low concentration and especially in the case of phytoplankton cells with similar major pigments. Although scattering allows to obtain a good discrimination between species (Table IV), when a quantitative reconstruction is done, it does not always determine a linear regression between SI and concentration of the species and so it is not recommended to use for a quantitative purpose. Only for Tetraselmis sp. SI obtained from scattering spectra gives good quantitative relationship with concentration. Quantitative relationships obtained by single vector are generally similar or little worse compared to those obtained with reduced absorption.

4. CONCLUDING REMARKS Comparison of complete absorption spectra of phytoplankton by means of the SI (Millie et al., 1997) allows to discriminate a phytoplankton species when it is mixed with another one and to reconstruct its quantitative proportion, confirming what some authors already pointed out (Millie et al., 1997; Kirkpatrick et al., 2000; Craig et al., 2006). Moreover our results highlight the possibility to quantitative reconstruct a mixed assemblage of 5 phytoplankton species. For natural assemblages this possibility has been evaluated by Organelli et al. (2007). Reduced absorption spectra, transformed with the proposed routine, give a discrimination possibility slightly lower than complete absorption, in particular at low concentrations and above all in the case of phytoplankton cells with similar major pigments sets. Scattering spectra of the tested phytoplankton species, show spectral shapes well differentiated by the pair wise test, even in the case of species with similar major pigments contents. On the contrary scattering spectra give poor results in quantitative reconstruction, and they are not recommended for this purpose. In this experiment the discrimination conducted on absorption base, allows to characterize 4 groups depending on their pigmentary sets, in spite of the 5 species belonging to 5 different taxonomic classes. H. akashiwo and P. tricornutum are joined in a unique group characterized by the presence of chlorophyll a, chlorophylls c, and fucoxanthin as major pigments. So in some cases pigmentary composition cannot coincide with the taxonomic one. Instead, by means of scattering spectra, we have characterized 5 optical groups because the discrimination capability is based on cellular size and form. Keeping in mind these considerations joining the two information should give the best performance. On the contrary our results do not confirm this hypothesis. Performances of single joined vector are similar or a bit worse than those of reduced absorption, even if Organelli et al. (2007) highlighted that this joining, for case I waters natural assemblages, allows to obtain better results than from the absorption and back-scattering alone. So the opportunity to use absorption and scattering simultaneously should be furtherly evaluated.

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Finally this preliminary test highlights the possibility to extend the use of optical quantitative discrimination of phytoplankton groups also in assemblages formed by more than two species. For this purpose, also the optical properties measured by remote sensors which use a reduced spectral resolution, as in this work, could be used. 5. REFERENCES Bricaud A., Claustre H., Ras J., Oubelkheir K., 2004. Natural variability of phytoplanktonic absorption

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Millie D.F., Schofield O.M., Kirkpatrick G.J., Johnsen G., Tester P.A., Vinyard B.T., 1997. Detection of harmful algal blooms using photopigments and absorption signatures: A case study of the Florida red tide dinoflagellate, Gymnodinium breve. Limnol. Oceanogr., 42 (5, part 2): 1240-1251.

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