Open Access
Add to BibSonomyAbstract
We investigated phytoplankton absorption properties of Lake Taihu, in the spring and summer of 2005 and 2006, and for 17 days studied laboratory cultures of Scenedesmus obliquus (chlorophyta) and Microcystis aeruginosa (cyanophyta) to determine the effect of phytoplankton community composition and cell size on the absorption properties. There were significant seasonal differences in phytoplankton community composition and absorption coefficients. In spring, the phytoplankton community was dominated by chlorophyta with large cells, whereas in summer was dominated by cyanophyta with small cells. Phytoplankton absorption coefficients increased significantly from spring to summer, with the increase in chlorophyll a (Chla) concentration. In addition, Chla-specific absorption coefficients increased with the phytoplankton community succession from chlorophyta to cyanophyta. In culture, the cells density of S. obliquus was generally lower than that of M. aeruginosa, and Chla concentrations of S. obliquus were significantly higher than those of M. aeruginosa. Correspondingly, the Chla-specific absorption coefficients of S. obliquus were significantly lower than those of M. aeruginosa. Significant exponential correlations were found between absorption and Chla-specific absorption coefficients and Chla concentration for S. obliquus and M. aeruginosa. In addition, we developed a model to predict absorption and Chla-specific absorption coefficients using Chla concentration and cell size when data from two species was grouped together. Field and experimental results both showed that the Chla-specific absorption coefficients of cyanophyta were significantly higher than those of chlorophyta. The variability in specific absorption can attributed to phytoplankton community composition, cell size and pigment composition. As phytoplankton community composition changed significantly with season in the lake, and as variation in the cell sizes and accessory pigments of the phytoplankton community influenced the Chla-specific absorption coefficient, these factors may be considered explicitly in future improvements to bio-optical algorithms to more accurately estimate Chla concentration, primary production and phytoplankton community composition.
©2012 Optical Society of America
1. Introduction
Ocean color remote sensing combined with bio-optical model has been widely used to estimate phytoplankton chlorophyll a (Chla) concentration and primary production in ocean, coastal, and lake ecosystems [1–3]. However, the increase of estimation precision of Chla concentration and primary production using bio-optical models, requires that we continue to improve our understanding of how phytoplankton physiology changes in response to phytoplankton community and impacts photosynthetic processes [4,5].
Phytoplankton absorption and specific absorption coefficients are the key parameters of bio-optical models for characterizing regional maps of Chla concentration and primary production using remote sensing data [3,6]. However, these two parameters are also the main sources of error and uncertainty in such models. Therefore, efforts have been made to understand how phytoplankton species composition and environmental factors can affect phytoplankton absorption and specific absorption coefficients [1,7–9].
Theoretical and experimental studies show that changes in cell size and in internal pigment composition significantly affect the magnitude and spectral shape of phytoplankton absorption and specific absorption coefficients [1,8,10–13]. Although there are many studies of the variation in phytoplankton specific absorption from field and experimental observations in the ocean waters, there are relatively few studies focused on eutrophic shallow lakes [14–16]. In particular, field and experimental evidence of the effects of phytoplankton community composition on the specific absorption coefficient is lacking for eutrophic shallow lakes. It is generally admitted that the average cell size increases from oligotrophic to eutrophic waters. Therefore, phytoplankton specific absorption in the ocean waters cannot simply used in the lake waters due to the great difference in phytoplankton community and cell size between the ocean and lake waters.
In many eutrophic shallow lakes, cyanobacteria dominate the phytoplankton community, and algal blooms frequently occur; and seasonal succession in phytoplankton community composition has also been observed in these lakes [17]. In the transition from spring to summer, top–down control and low temperatures cause the well-known clear-water phase, when the phytoplankton community is dominated by Chlorophyta with large cells [18,19]. In summer, top-down control disappears and temperature increases, and the phytoplankton community becomes dominated by cyanobacteria with small cells [18,19]. In eutrophic, shallow, Lake Taihu in China, some 85% of the summer phytoplankton biomass is comprised of cyanobacteria, particularly the harmful genus Microcystis [17].
Here, we investigate the effects of phytoplankton community composition and cell size on absorption properties in a eutropic, shallow, lake. We conducted spring and summer field observations of different phytoplankton communities, and conducted laboratory culture experiments on the two dominant species, Scenedesmus obliqnu (chlorophyta) and harmful Microcystis aeruginosa (cyanobacteria), which belonged to different cell size categories. Our specific objectives were to: (1) assess the effect of phytoplankton community composition and cell size on the absorption properties of a eutrophic shallow lake, (2) obtain the specific absorption coefficients of chlorophyta and harmful cyanobacteria, and (3) parameterize phytoplankton absorption and Chla-specific absorption coefficients using cell size and Chla concentration. These specific absorption coefficients and parameterization models will be useful for future studies of regional and seasonal bio-optical models of Chla concentration, primary production estimation, and harmful algal bloom detection.
2. Materials and methods
2.1 Field study
Sampling sites
Lake Taihu (30°55′40″-31°32′58″N, 119°52′32″-120°36′10″E) is a large, eutrophic, shallow lake in China, with frequent cyanobacteria algal blooms in the summer, and for which remote sensing has been used to monitor its water quality and algal blooms [2,16,20].
We conducted 24 field programs in the lake in the middle every month in 2005 and 2006. In each program, samples were taken from 8 sites in the northern part of the lake, where cyanobacteria algal blooms have occurred every summer since the 1990’s (Fig. 1 ). Surface, middle and bottom water samples were collected, and mixed in 2 L acid-washed bottles and kept on ice at 4 °C while in the field. The samples were transported to a laboratory at the Taihu Lake Laboratory Ecosystem Research Station (TLLER) located in the littoral region of Meiliang Bay. In the laboratory, samples were stored at −20 °C, and all measurements were conducted within 2 weeks. A total of 192 samples were collected in the 24 field programs (24 field programs × 8 sites per program).
Phytoplankton community
Phytoplankton samples were fixed with Lugol’s iodine solution in situ and allowed to settle for 48 hours prior to counting under the microscope at × 40 magnification. Phytoplankton species were identified according to Hu et al. [21]. Algal biovolumes were calculated from cell density and size measurements. The conversion to biomass assumed that 1 mm3 of volume was equivalent to 1 mg of fresh-weight biomass.
Phytoplankton absorption coefficients
Phytoplankton absorption coefficients were only measured in April (spring) and July (summer) in 2005 and 2006. We used the quantitative filter technique (QFT) [22] to determine the absorption coefficients of phytoplankton. Water samples (50–200 ml) were filtered onto a 47 mm diameter Whatman fiberglass GF/F filter under low vacuum pressure. The maximum optical density of the particulate matter on the filters was kept below 0.4 AU. Absorption spectra were recorded every 1-nm from 350 to 800 nm, using the transmittance (T) method and a Shimadzu UV-2401PC UV-Vis recording spectrophotometer. Measurements were stopped at 350 nm because of a sharp decrease in the signal-to-noise ratio, due to the high absorption of the GF/F filters below 350 nm, and possibly also because of potential artifacts in the particulate matter absorption coefficients attributable to mycosporine-like amino acids (MAAs) [23,24]. A blank filter wetted with filtered sample water was used as a reference. All spectra were set to zero at 750 nm to minimize differences between the sample and the reference filter.
The QFT method assumes that all light scattered by the filter reaches the spectrophotometer detector. However, the detector has a limited aperture size, such that some scattered light will inevitably be lost. To minimize this loss, we placed the filter as close as possible to the detector. Measured optical densities of the particulate matter were corrected for the increase in path length caused by multiple scattering in the glass-fiber filter, using the equation of Cleveland & Weidemann [25]:
where ODs(λ) and ODf(λ) are the corrected and measured optical densities of the particulate matter, respectively.The absorption coefficients of the particulate matter ap(λ) were calculated as follows [25]:
where 2.303 is the factor used to convert base 10 to the natural logarithm, S is the filter clearance area (m2), and V is the filtered volume (m3).After each measurement of the optical densities of the total particles, the filter was soaked in methanol for 4 hours to dissolve the phytoplankton, and then rinsed with filtered water. As the phytoplankton had been dissolved, all remaining particles on the filter were non-phytoplankton particles. The absorption spectra of the filter were then measured again to obtain the optical densities of the non-phytoplankton particles. The absorption coefficients of non-phytoplankton particles ad(λ) were determined similarly, using Eqs. (1) and (2).
The phytoplankton absorption coefficient aph(λ) was calculated as follows:
2.2 Culture experiment
Organisms and growth conditions
A laboratory experiment was conducted from 16 December, 2011 to 1 January, 2012 to determine the effect of phytoplankton community composition and cell size on absorption properties. S. obliquus and M. aeruginosa were obtained from the Freshwater Algae Culture Collection of Institute of Hydrobiology, Chinese Academy of Sciences. The samples were cultivated in BG11 medium [26] at 25°C with continuous light irradiance of 40 μmol/(m2·s) on a 12 h light/12 h dark cycle. Visible irradiance was measured with a Li-Cor 192SA quantum sensor. All experiments were conducted with exponential growing cells after 2 week of cultivation. Three parallel samples of S. obliquus and M. aeruginosa were taken every 1 day during 17 days culture experiment for pigment analyses and absorption coefficient measurement.
Microscopic observation of phytoplankton cell size and number
After being exposed to light for 1 h every day, 20 ml pure algae sample was collected into a 0.1 ml phytoplankton counting chamber. Cells were measured and counted with an Olympus U-TVO.63XC microscope at × 40 magnification. Cell size measurements were taken from 30 randomly selected photographs using the software Image Pro Express 6.0 (Media Cybernetics Inc., Bethesda, MD, USA).
Phytoplankton absorption coefficients
Pure cultures of S. obliquus and M. aeruginosa (10–20 ml) were filtered through a 25-mm diameter Whatman fiberglass GF/F filter under low vacuum pressure. The maximum optical density of the particulate matter on the filters was kept below 0.4 AU. Absorption spectra were recorded every 1-nm from 350 to 800 nm, using the transmittance-reflectance (T-R) method, and a Shimadzu UV-2550PC UV-Vis recording spectrophotometer equipped with an integrating sphere type ISR-240A, with the sample placed at a port outside the sphere. A blank filter wetted with filtered sample water was used as a reference. All spectra were set to zero at 750 nm to minimize differences between the sample and the reference filter.
Measured ODf(λ) values were corrected for the increase in path length caused by multiple scattering in the glass-fiber filter, using the equation of Tassan and Ferrari [27]:
where ODs(λ) and ODf(λ) are the corrected and measured optical densities of the particulate matter, respectively.The absorption coefficients of particles were calculated as described above in the field study. Due to the pure phytoplankton in the culture experiment, phytoplankton absorption coefficients were equal to the particle absorption coefficients.
2.3 Pigment analysis and specific absorption coefficients
Samples for measurement of Chla were filtered through GF/F filters (Whatman). Chla was extracted with ethanol (90%) at 80 °C, and analyzed spectrophotometrically at 665 nm and 750 nm using a Shimadzu UV-2401PC (or UV-2550PC) UV-Vis spectrophotometer with a correction for phaeopigments (Pa). All Chla concentration referred to the sum of Chla and Pa concentrations to account for absorption by Chla as well as Pa.
The freeze-thaw procedure and method described by Sarada et al. [28] was used to extract the phycocyanin (PC) concentration of the culture of M. aeruginosa. The biomass of the 20 ml sample was centrifuged (10000 g, 15 min) and the collected cells were mixed with 5 ml of a 0.05M phosphate buffer solution, pH 6.8, and homogenized. The samples were subjected to nine cycles of freezing (−20 °C) and thawing (room temperature, 20 °C). Purification of the samples was carried out by centrifugation at 10000g for 60 min. PC concentrations were spectrophotometrically derived using the equations of Bennett and Bogorad [29].
where CPC, OD620 and OD652 are PC concentration, the optical densities at 620 and 652 nm, respectively.Chla-specific absorption a*ph(λ) was calculated as
where a*ph(λ), aph(λ), and CChla represent the phytoplankton specific absorption (m2/mgChla), the absorption coefficient (m−1), and Chla concentration (μg/L), respectively.For magnitude comparisons among the two communities of chlorophyta and cyanophyta, phytoplankton specific absorption spectra were normalized using the mean Chla-specific absorption coefficient computed between 400 and 700 nm.
where a*ph(PAR) is the mean Chla-specific absorption coefficient between 400 and 700 nm (m2/mgChla) and λ is the wavelength.2.4 Statistical analyses
Statistical analyses, including mean values and linear and non-linear fitting, were performed with SPSS 17.0 software (Statistical Program for Social Sciences). For phytoplankton community composition and other variables, the seasonal means as well as the standard deviations and ranges were calculated by pooling the data for 8 sites in 2005 and 2006 using SPSS 17.0 software. Seasonal and species differences in parameters were assessed with one-way analysis of variance (ANOVA) (p < 0.05). Regression analyses were used to examine the relationships among cell density, absorption, specific absorption and Chla concentration. Significance was reported when p < 0.05.
3. Results
3.1 Field study
Phytoplankton community composition in spring and summer
Monthly variation of the phytoplankton community composition in 2005 and 2006, is presented as the percentage of the total density of phytoplankton cells in seven categories (cyanophyta, chlorophyta, bacillariophyta, cryptophyta, euglenophyta, chrysophyta and others). Four categories (cyanophyta, chlorophyta, bacillariophyta and cryptophyta) accounted for more than 99.5% of the total cell density (Fig. 2 ). Overall, chlorophyta is the dominant species in the spring with the highest percentage in April but cyanophyta is the dominant species in the summer and autumn with the highest percentage in July (Fig. 2), which is consistent to the previous study [19].
Fig. 2 Monthly variation of phytoplankton community composition in Lake Taihu (data pooled for 8 sites in 2005 and 2006).
In April in the spring, the composition of phytoplankton was dominated by chlorophyta, accounting for 94.44% of the total density (Fig. 2). In contrast, in July in summer, the phytoplankton was dominated by cyanophyta, which contributed 96.33% of the total density (Fig. 2). There was a significant seasonal succession of the phytoplankton community composition from spring to summer in Lake Taihu.
Chla concentrations in spring and summer
Phytoplankton Chla concentrations varied widely across the two seasons, with a minimum of 4.9 μg/L recorded in the spring of 2006 at site TL8, and a maximum of 147.5 μg/L recorded in the summer of 2005 at site TL8. Chla concentrations at each of the 8 sites in spring and summer of each year are shown in Fig. 3 . The highest Chla concentration appeared at site TL8 in summer in both 2005 and 2006. Chla concentrations were significantly higher in the summer than in the spring based on pooled data from both years (one-way ANOVA, p<0.005), and the mean value of 67.5 ± 35.6 μg/L in summer was 2.05 times higher than the mean value of 32.9 ± 21.5 μg/L in the spring. The high Chla concentrations in Lake Taihu in summer represented the appearance of algal blooms.
The mean ratios of Chla concentration to cell density in the spring and summer were 5.54 μg/(L·106 cell) and 2.14 μg/(L·106 cell), respectively, demonstrating that the dominant chlorophyta cells in spring were larger than the dominant cyanophyta cells in summer.
Absorption and specific absorption coefficients
Phytoplankton absorption coefficient spectra, aph(λ), revealed two distinct peaks, one at the blue wavelength (approximately 440 nm) and one at the red wavelengths (approximately 675 nm) in the visible range, typical of the absorption peaks of Chla (Fig. 4 ). The mean aph(440) and aph(675) values were 1.29 ± 1.03 (range: 0.12-4.78 m−1) and 0.77 ± 0.59 m−1 (range: 0.07-2.41 m−1) respectively; these values varied by approximately 39.8 and 34.4 times respectively. The aph(440) and aph(675) values were significantly higher in the summer than in the spring (one-way ANOVA, p<0.01), corresponding to the increase in Chla concentration (Fig. 5 ).
The spectral characteristics of the Chla-specific absorption coefficients were similar to those of the phytoplankton absorption coefficients, with two distinct peaks at approximately 440 nm and 675 nm (Figs. 4 and 5). The mean a*ph(440) and a*ph(675) values were 0.0266 ± 0.0158 m2/mgChla (range: 0.010-0.090 m2/mgChla) and 0.0153 ± 0.0092 m2/mg Chla (range: 0.010-0.050 m2/mgChla), respectively, which were consistent to the previous studies in Lake Taihu [15,16], and very close to the value of 0.026 ± 0.008 m2/mgChla and 0.018 ± 0.005 m2/mgChla observed in the similar highly eutrophic lake (Lake Kasumigaura, Japan) [30]. In addition, Chla-specific absorption coefficients were close to those of microphytoplankton and nanophytoplankton, but significantly lower than those of ultraplankton and picoplankton observed in the ocean waters [31]. In the eutrophic lake waters, phytoplankton community is mostly dominated by microphytoplankton and nanophytoplankton. Therefore, the low Chla-specific absorption coefficient was easily understanding due to the large cell size. The aph(440) and aph(675) values were higher, but not significantly so, in the summer than in the spring. The mean a*ph(PAR) value in summer, 0.0159 ± 0.0105 m2/mgChla, was significantly higher than that in the spring, 0.0108 ± 0.0020 m2/mgChla (one-way ANOVA, p<0.05).
In addition to the difference in magnitude of the Chla-specific absorption coefficient between the two seasons, the spectral slope of Chla-specific absorption coefficient differed slightly by season. In vivo absorption of Chla revealed peaks at 440 and 675 nm; various photosynthetic accessory and photoprotective pigments also absorbed in the regions 400–550 nm and 600–650 nm, depending on community type and acclimation status. Based on our four surveys, Chla-specific absorption spectra often had shoulders between 470 and 490 nm (Fig. 6 ) caused by various carotenoid accessory pigments. However, a marked peak in the Chla-specific absorption spectra was observed at approximately 625 nm in summer, due to the phycocyanin of cyanobacteria, which was consistent to the previous studies in the eutrophic lake waters with cyanobacteria [16,30]. The ratio of a*ph(625)/a*ph(675) indicates a concentration of phycocyanin of 0.69 ± 0.07 in summer, which was significantly higher than the value of 0.45 ± 0.12 in spring (one-way ANOVA, p<0.01).
3.2 Experimental study
Cell size, density and pigment concentration
The S. obliquus cells were significantly larger than the M. aeruginosa cells (Fig. 7 ), mean cell sizes were 7.53 ± 0.96 μm and 3.09 ± 0.45 μm respectively (one-way ANOVA, p<0.001) based on 30 random samples. Therefore, S. obliquus and M. aeruginosa belonged to nanoplankton and ultraplankton from the cell size classification [31].
The S. obliquus cell density ranged from 0.25 ± 0.13 × 106 to 2.43 ± 0.49 × 106 cells/L, with a mean of 1.49 ± 0.76 × 106 cells/L. For M. aeruginosa, cell density ranged from 0.46 ± 0.24 × 106 to 7.01 ± 0.26 × 106 cell/L, with a mean of 3.94 ± 2.25 × 106 cell/L (Fig. 8(a) ). Cell density of S. obliquus was markedly lower than that of M. aeruginosa.
Fig. 8 Increases in cell volume and phytoplankton Chla concentration during the 17 day growth of S. obliquus and M. aeruginosa in laboratory culture (data pooled for three parallel samples).
Correspondingly, S. obliquus Chla concentration ranged from 134.4 ± 2.9 to 974.9 ± 66.0 μg/L, with a mean of 638.6 ± 280.5 μg/L; for M. aeruginosa, Chla concentration ranged from 54.8 ± 8.2 to 574.0 ± 14.6 μg/L, with a mean of 276.2 ± 167.4 μg/L (Fig. 8(b)). The Chla concentrations in S. obliquus were significantly higher than those of M. aeruginosa (one-way ANOVA, p<0.01), which was consistent with the significantly smaller cell sizes of S. obliquus relative to M. aeruginosa (one-way ANOVA, p<0.001). In addition, for M. aeruginosa, PC concentration ranged from 70.5 ± 17.2 to 1543.1 ± 87.9 μg/L, with a mean of 477.9 ± 444.9 μg/L.
Absorption and specific absorption coefficients
During exponential growth, there were differences in the absorption and Chla-specific absorption coefficients between the 2 phytoplankton species (Fig. 9 ). Although the magnitudes of the absorption coefficients of S. obliquus were not significantly different from those of M. aeruginosa, the magnitudes of the Chla-specific absorption coefficients of S. obliquus were significantly lower than those of M. aeruginosa (one-way ANOVA, p<0.001) (Fig. 9, Fig. 10 ), consistent with the results from the field (Fig. 6). The magnitudes of Chla-specific absorption coefficients of S. obliquus we now report are similar to the values for cultured chlorophyta reported by Mao et al. [32]. In addition, the cell sizes of S. obliquus and M. aeruginosa were 7.53 ± 0.96 μm and 3.09 ± 0.45 μm, which fell the size range of nanoplankton (between 5 and 20 μm) and ultraplankton (between 2 and 5 μm). Therefore, the mean Chla-specific absorption coefficients of S. obliquus and M. aeruginosa were very close to those of nanoplankton and ultraplankton [31].
Fig. 9 Variation in spectral absorption and Chla-specific absorption coefficients during the growth of S. obliquus (a: absorption, c: specific absorption) and M. aeruginosa (b: absorption, d: specific absorption).
The mean a*ph(PAR) of 0.0199 ± 0.0016 m2/mgChla for M. aeruginosa was significantly higher than that of S. obliquus (0.0106 ± 0.0011 m2/mgChla) (one-way ANOVA, p<0.001). The similar field and lab values for a*ph(PAR) confirm the absolute dominance of S. obliquus in the spring, and of M. aeruginosa in the summer, and reflect a high partition accuracy of phytoplankton absorption coefficients from the total particle absorption coefficients in the field samples.
The spectral shape of the absorption and Chla-specific absorption coefficients are similar to each other because the Chla-specific absorption coefficient was normalized to the phytoplankton Chla concentration of the absorption coefficient. However, there were some differences in spectral shape between S. obliquus and M. aeruginosa (Fig. 9, Fig. 10). For S. obliquus culture, the Chla-specific absorption spectra had shoulders between 470 and 490 nm (Fig. 10), similar to the spring field results (Fig. 6). For M. aeruginosa culture, the Chla-specific absorption spectra had a marked peak at approximately 625 nm (Fig. 10), which was similar to the summer field results (Fig. 6). The peaks at approximately 480 and 625 for the experimental results were higher than those of the field results, which may reflect the interference of the Chla-specific absorption coefficients of the field samples by non-phytoplankton particles.
For S. obliquus and M. aeruginosa in our study, absorption and Chla-specific absorption coefficients were exponentially correlated with Chla concentration when examined individually (Fig. 11 ). The fitting coefficients between absorption, Chla-specific absorption coefficients and Chla concentration were species-specific with a higher exponential slope for absorption coefficient for M. aeruginosa than for S. obliquus (Figs. 11(a) and 11(b)) but lower exponential slope for Chla-specific absorption coefficient for M. aeruginosa than for S. obliquus (Figs. 11(c) and 11(d)). When data from two species was grouped together, significant low correlations were found between Chla concentration and absorption, Chla-specific absorption coefficients compared to individual species. This observation helps to explain the wide range of variability between Chla concentration and absorption coefficient observed in the different waters [13,15,16,33,34], where individual members of the phytoplankton community variously contribute to the bulk signals. In addition, this helps to explain why Chla concentration is not a universal predictor of the magnitude absorption coefficient in situ. Thus, we developed empirical models (Eqs. (8)-(11)) to predict absorption and Chla-specific absorption coefficients using cell size and Chla concentration when data from two species was grouped together. The determination coefficients were significant higher than those using Chla concentration (one-way ANOVA, p<0.05).
Fig. 11 Correlations between absorption and Chla-specific absorption coefficients and Chla concentration of S. obliquus (a, c) and M. aeruginosa (b, d).
Where aph(675), aph(440), aph*(675), aph*(440), D and CChla are absorption coefficients at 675 nm, 440 nm, Chla-specific absorption coefficient at 675 nm, 440 nm, cell size and Chla concentration, respectively.
4. Discussion
Variation in the value and spectral shape of Chla-specific absorption coefficients is caused by variation in the package effect, and in the relative abundance of Chla and accessory pigments [5,11,13,31]. Variation in the package effect is mainly caused by changes in Chla concentration, cell size, shape or morphology [1,10]. Therefore, we now separately discuss the effects of cell size and accessory pigment on Chla-specific absorption coefficients, in sections 4.1 and 4.2 respectively. In addition, the potential applications of specific absorption coefficients in phytoplankton community and cell size remote sensing retrieval are also presented.
4.1 Variation in specific absorption due cell size
The distribution of cell sizes in the phytoplankton community is a major biological factor governing the functioning of pelagic food-webs, and regulating the photosynthetic efficiency of carbon fixation and primary production [3,35]. For example, microphytoplankton (primarily diatoms) are considered to be responsible for new primary production (meaning that which is nitrate-based), and for contributing substantially to carbon export [36]. Thus, accounting for phytoplankton community structure is crucial to our understanding and modeling of biogeochemical processes and cycles [37,38].
Likewise, the species composition and size distribution of phytoplankton can affect the optical properties of surface waters, which are important factors affecting seasonal and regional variation in bio-optical properties and in ocean optical and ecosystem models [31,32,38]. It is possible to map size distribution of phytoplankton, and distinguish the phytoplankton community by using hyperspectral remote sensing. Therefore, color remote sensing has been applied in recent years to measure the size distribution of phytoplankton and distinguish the phytoplankton community, enhancing data collection over the simple estimation of chlorophyll a concentration [39–43].
Our field and experimental results showed that the size distribution of phytoplankton, due to differences in community composition, had a significant effect on the magnitude of specific absorption coefficients of phytoplankton, which would affect the remote sensing reflectance. Meanwhile, Matsuoka et al. [34] found a significant difference in Chla-specific absorption coefficient from spring to summer, autumn due to the package effect. Consistent with previous studies [8,13,44], we found that Chla-specific absorption coefficients decreased with increasing cell size. For example in our experiment, the mean a*ph(PAR) value decreased significantly from 0.0199 ± 0.0016 m2/mgChla to 0.0106 ± 0.0011 m2/mgChla as cell size increased significantly from 3.09 ± 0.45 μm of M. aeruginosa to 7.53 ± 0.96 μm of S. obliquus. According to the phytoplankton size classification, S. obliquus and M. aeruginosa are classified as nanoplankton (cell size between 5 and 20 μm) and ultraplankton (cell size between 2 and 5 μm), respectively. Correspondingly, the spectral absorption coefficients of S. obliquus and M. aeruginosa were consistent with the values of nanoplankton and ultraplankton reported by Ciotti et al. [31].
In Lake Taihu, the phytoplankton community was increasingly dominated by cyanobacteria with small cell sizes from spring to summer, with the increase of temperature and the disappearance of top–down control [19]. Although we did not measure chlorophyta and cyanophyta cell size, the mean ratios of Chla concentration to cell density in spring and summer were 5.54 μg/(L·106 cell) and 2.14 μg/(L·106 cell), respectively, indicating that the cell size of chlorophyta in the spring was larger than that of cyanophyta in the summer. Correspondingly, the mean a*ph(PAR) increased from 0.0108 ± 0.0020 m2/mgChla to 0.0159 ± 0.0105 m2/mgChla with the succession in the phytoplankton community. Likewise, Stæhr et al. [44] found a significantly negative correlation between a*ph(PAR) and cell size in oceanic, coastal, and estuarine waters. Our experiments confirmed that Chla-specific absorption coefficients decreased with increasing cell size. Theoretically, it is expected that smaller phytoplankton result in less packaging effects and higher Chla-specific absorption coefficients than do larger phytoplankton [8,13].
To evaluate this relationship, we use a*ph(675) as an indicator of the packing effect, because absorption by accessory pigments at 675 nm is minimal [7]. Values of a*ph(675) for unpackaged pigments range from 0.02 to 0.03 m2/mgChla or more [7,45,46]. The a*ph(675) values of S. obliquus and M. aeruginosa were 0.0147 ± 0.0022 and 0.0207 ± 0.0032 m2/mgChla, respectively, indicating that the packing effect of M. aeruginosa (which has smaller cell sizes) was less than that of S. obliquus, a result consistent with the predictions of Stramski et al. and Matsuoka et al. [7,9].
4.2 Variation in specific absorption due to accessory pigments
We found spectral shape differences in the Chla-specific absorption coefficients of different phytoplankton communities in the field and experiment studies (Fig. 6, Fig. 10). Spectral shape was partly attributed to the accessory pigments. For the spring field samples of S. obliquus, Chla-specific absorption spectra often had shoulders between 470 and 490 nm, caused by various carotenoid accessory pigments. For the summer field samples and laboratory culture of M. aeruginosa, a marked peak in the Chla-specific absorption spectra was observed at approximately 625 nm (Fig. 6, Figs. 9(b) and 9(d)). This peak is due to the appearance of phycocyanin, a pigment present primarily in cyanobacteria. For laboratory culture of M. aeruginosa, the increase of PC concentration and a significant positive correlation found between PC concentration and aph(620) proved the appearance of phycocyanin (Fig. 12 ). Accordingly, a local reflectance minimum at 625 nm has often been used to monitor harmful cyanobacterial blooms in eutrophic lakes [47,48].
Fig. 12 Increases in PC concentration during the 17 day growth of M. aeruginosa in laboratory culture (a) and exponential correlation between PC concentration and phytoplankton absorption coefficient at 625 nm (data pooled for three parallel samples).
In addition to differences in cell size and pigment composition due to differences in the phytoplankton community, environmental conditions (including nutrient concentrations, light and temperature) also affect cell size, accessory pigment concentration, and the ratio of chlorophyll a to accessory pigment [49]. These environmental factors further affect the magnitude and spectral shape of Chla-specific absorption coefficients [7,50]. For example, Bouman et al. [49] found that temperature was significantly and negatively correlated to cell diameter in a diverse group of oceanic provinces, suggesting that temperature is an effective indicator of phytoplankton community structure. Temperature, as an independent variable, could explain 43 and 49% of the variance in aph*(443) and aph*(676) [7–9,49]. Our previous experiment showed Chla-specific coefficients of M. aeruginosa and S. obliqnus increased with increasing irradiation intensity [51]. In addition, at photo-inhibiting light intensities, phytoplankton increased their cellular concentrations of photoprotective carotenoid pigments [8,52]. This variation in cell size and pigment composition with changing temperature and light intensity can lead to variation in Chla-specific absorption coefficients. Different species of phytoplankton, exhibiting a wide range of cell sizes and accessory pigments, often coexist in the aquatic environment, jointly affecting the magnitude and spectral shape of the Chla-specific absorption coefficient.
4.3 Remote sensing application of absorption and Chla-specific absorption coefficients
Phytoplankton absorption and Chla-specific absorption spectra can be estimated from the remotely sensed reflectance [31,53]. Therefore, the magnitude and spectral shape differences of the Chla-specific absorption coefficient in different phytoplankton species have two aspects remote sensing applications. On the one hand, due to the fact that cell size had a significant effect on the magnitude of the Chla-specific absorption coefficient, a size parameter reflecting the composition of picoplankton, ultraplankton, nanoplankton and microplankton presented by Ciotti et al. [31] could be used to retrieve phytoplankton cell size [38,39,42]. On the other hand, the characteristic absorption peaks due to the unique pigment of different phytoplankton species in the Chla-specific absorption coefficient were widely used to monitor phytoplankton community [43,47,48]. In recent years, many studies showed that harmful cyanobacteria algal bloom could be easily detected and the abundance of phytoplankton species could be analyzed using multi-spectral satellite imagery through PC absorption peak around 625 nm [47,54]. The ability of satellite remote sensing to provide information on phytoplankton size and community composition will improve our understanding of ecosystem evolution and biogeochemical processes, which are directly related to the distribution of phytoplankton size and community. More work is needed to monitor cyanobacteria algal bloom in Lake Taihu in summer from remote sensing due to the absolute dominance of Microcystis through PC absorption peak in the future.
5. Conclusions
Our field and experimental data show that phytoplankton community composition and cell size play an important role in determining the Chla-specific absorption coefficient, and may therefore affect remote sensing reflectance and estimates of phytoplankton pigment concentrations and primary production. In Lake Taihu, the significant seasonal succession of the phytoplankton community suggests that season-specific bio-optical models coupling the Chla-specific absorption coefficient should be developed and validated for the estimation of phytoplankton pigment concentration and primary production. Overall, our analysis showed that different species-specific cell sizes caused the magnitude difference in the observed Chla-specific absorption coefficients; differences in spectral shape may be due to the accessory pigment. Although laboratory experiments cannot replicate the lake environment, the results from the pure algae culture experiment suggest that the absorption spectra of different species exhibit different spectral characteristics, and that the changes in species composition can significantly change the absorption characteristics. Chla concentration and cell size can jointly be used to predict phytoplankton absorption and Chla-specific absorption coefficients. Our results indicate that the spectral decomposition can be used to identify species from the absorption spectra, using as a reference of standard absorption spectra of known species in a database. However, the phytoplankton community was also distinguishable when species were relatively abundant, or had unique pigment compositions, such as during cyanobacterial blooms.
Acknowledgments
This study was jointly funded by the Knowledge Innovation Project of the Chinese Academy of Sciences (KZCX2-YW-QN312), the National Natural Science Foundation of China (grants 40971252 and 40825004), the Major Projects on Control and Rectification of Water Body Pollution (2011ZX07101-010), and Ministry of Industry and Information Technology of China (MIITC-2010ZX03006-006).
References and links
1. J. T. O. Kirk, Light and Photosynthesis in Aquatic Ecosystems, 2nd ed. (Cambridge University Press, 1994).
2. Y. L. Zhang, M. L. Liu, B. Q. Qin, H. J. van der Woerd, J. S. Li, and Y. L. Li, “Modeling remote-sensing reflectance and retrieving chlorophyll-a concentration in extremely turbid Case-2 waters (Lake Taihu, China),” IEEE Trans. Geosci. Rem. Sens. 47(7), 1937–1948 (2009). [CrossRef]
3. J. Uitz, H. Claustre, B. Gentili, and D. Stramski, “Phytoplankton class-specific primary production in the world’s oceans: seasonal and interannual variability from satellite observations,” Global Biogeochem. Cycles 24(3), GB3016 (2010), doi:. [CrossRef]
4. G. Dall’Olmo and A. A. Gitelson, “Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results,” Appl. Opt. 44(3), 412–422 (2005). [CrossRef] [PubMed]
5. J. Uitz, Y. Huot, F. Bruyant, M. Babin, and H. Claustre, “Relating phytoplankton photophysiological properties to community structure on large scales,” Limnol. Oceanogr. 53(2), 614–630 (2008). [CrossRef]
6. H. Loisel, B. Lubac, D. Dessailly, L. Duforet-Gaurier, and V. Vantrepotte, “Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach,” Opt. Express 18(20), 20949–20959 (2010). [CrossRef] [PubMed]
7. D. Stramski, A. Sciandra, and H. Claustre, “Effects of temperature, nitrogen, and light limitation on the optical properties of the marine diatom Thalassiosira pseudonana,” Limnol. Oceanogr. 47(2), 392–403 (2002). [CrossRef]
8. T. Fujiki and S. Taguchi, “Variability in chlorophyll a specific absorption coefficient in marine phytoplankton as a function of cell size and irradiance,” J. Plankton Res. 24(9), 859–874 (2002). [CrossRef]
9. A. Matsuoka, P. Larouche, M. Poulin, W. Vincent, and H. Hattori, “Phytoplankton community adaptation to changing light levels in the southern Beaufort Sea, Canadian Arctic,” Estuar. Coast. Shelf Sci. 82(3), 537–546 (2009). [CrossRef]
10. A. Morel and A. Bricaud, “Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton,” Deep-Sea Res. 28(11), 1375–1393 (1981). [CrossRef]
11. S. Sathyendranath, L. Lazzara, and L. Prieur, “Variations in the spectral values of specific absorption of phytoplankton,” Limnol. Oceanogr. 32(2), 403–415 (1987). [CrossRef]
12. E. Millán-Núñez and R. Millán-Núñez, “Specific absorption coefficient and phytoplankton community structure in the Southern Region of the California Current during January 2002,” J. Oceanogr. 66(5), 719–730 (2010). [CrossRef]
13. A. Bricaud, H. Claustre, J. Ras, and K. Oubelkheir, “Natural variability of phytoplanktonic absorption in oceanic waters: influence of the size structure of algal populations,” J. Geophys. Res. 109(C1), C11010 (2004). [CrossRef]
14. J. Toepel, U. Langner, and C. Wilhelm, “Combination of flow cytometry and single cell absorption spectroscopy to study the phytoplankton structure and to calculate the chl a specific absorption coefficients at the taxon level,” J. Phycol. 41(6), 1099–1109 (2005). [CrossRef]
15. C. F. Le, Y. M. Li, Y. Zha, and D. Y. Sun, “Specific absorption coefficient and the phytoplankton package effect in Lake Taihu, China,” Hydrobiologia 619(1), 27–37 (2009). [CrossRef]
16. Y. L. Zhang, L. Q. Feng, J. S. Li, L. C. Luo, Y. Yin, M. L. Liu, and Y. L. Li, “Seasonal–spatial variation and remote sensing of phytoplankton absorption in Lake Taihu, a large eutrophic and shallow lake in China,” J. Plankton Res. 32(7), 1023–1037 (2010). [CrossRef]
17. Y. W. Chen, B. Q. Qin, K. Teubner, and M. T. Dokulil, “Long-term dynamics of phytoplankton assemblages: microcystis-domination in Lake Taihu, a large shallow lake in China,” J. Plankton Res. 25(4), 445–453 (2003). [CrossRef]
18. W. Lampert, W. Fleckner, H. Rai, and B. E. Taylor, “Phytoplankton control by grazing zooplankton: A study on the spring clear-water phase,” Limnol. Oceanogr. 31(3), 478–490 (1986). [CrossRef]
19. Z. Ke, P. Xie, and L. Guo, “Controlling factors of spring–summer phytoplankton succession in Lake Taihu (Meiliang Bay, China),” Hydrobiologia 607(1), 41–49 (2008). [CrossRef]
20. C. M. Hu, Z. P. Lee, R. H. Ma, K. Yu, D. Q. Li, and S. L. Shang, “Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China,” J. Geophys. Res. 115(C4), C04002 (2010), doi:. [CrossRef]
21. H. J. Hu, R. Y. Li, Y. X. Wei, H. Zhu, J. Chen, and Z. Shi, Freshwater algae in China (Shanghai Science and Technology Press, Shanghai (in Chinese), 1980).
22. B. G. Mitchell, “Algorithms for determining the absorption coefficient of aquatic particulates using the quantitative filter technique (QFT),” Proc. SPIE 1302, 137–148 (1990). [CrossRef]
23. H. Sosik, “Storage of marine particulate samples for light-absorption measurements,” Limnol. Oceanogr. 44(4), 1139–1141 (1999). [CrossRef]
24. I. Laurion, F. Blouin, and S. Roy, “The quantitative filter technique for measuring phytoplankton absorption: interference by MAAs in the UV waveband,” Limnol. Oceanogr. Methods 1(1), 1–9 (2003).
25. J. S. Cleveland and A. D. Weidemann, “Quantifying absorption by aquatic particles: a multiple scattering correction for glass-fiber filters,” Limnol. Oceanogr. 38(6), 1321–1327 (1993). [CrossRef]
26. R. Y. Stanier, R. Kunisawa, M. Mandel, and G. Cohen-Bazire, “Purification and properties of unicellular blue-green algae (order Chroococcales),” Bacteriol. Rev. 35(2), 171–205 (1971). [PubMed]
27. S. Tassan and G. M. Ferrari, “A sensitivity analysis of the ‘transmittance-reflectance’ method for measuring light absorption by aquatic particles,” J. Plankton Res. 24(8), 757–774 (2002). [CrossRef]
28. R. Sarada, M. G. Pillai, and G. A. Ravishankar, “Phycocyanin from Spirulina sp.: influence of processing of biomass on phycocyanin yield, analysis of efficacy of extraction methods and stability studies on phycocyanin,” Process Biochem. 34(8), 795–801 (1999). [CrossRef]
29. A. Bennett and L. Bogorad, “Complementary chromatic adaptation in a filamentous blue-green alga,” J. Cell Biol. 58(2), 419–435 (1973). [CrossRef] [PubMed]
30. K. Yoshimura, N. Zaitsu, Y. Sekimura, B. Matsushita, T. Fukushima, and A. Imai, “Parameterization of chlorophyll a-specific absorption coefficients and effects of their variations in a highly eutrophic lake: a case study at Lake Kasumigaura, Japan,” Hydrobiologia (to be published), doi: (2012). [CrossRef]
31. A. Ciotti, M. Lewis, and J. Cullen, “Assessment of the relationship between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient,” Limnol. Oceanogr. 47(2), 404–417 (2002). [CrossRef]
32. Z. H. Mao, V. Stuart, D. L. Pan, J. Y. Chen, F. Gong, H. Q. Huang, and Q. K. Zhu, “Effects of phytoplankton species composition on absorption spectra and modelled hyperspectral reflectance,” Ecol. Inform. 5(5), 359–366 (2010). [CrossRef]
33. M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108(C7), 3211–3230 (2003), doi: . [CrossRef]
34. A. Matsuoka, V. Hill, Y. Huot, M. Babin, and A. Bricaud, “Seasonal variability in the light absorption properties of western Arctic waters: parameterization of the individual components of absorption for ocean color applications,” J. Geophys. Res. 116(C2), C02007 (2011), doi: . [CrossRef]
35. L. Guidi, L. Stemmann, G. A. Jackson, F. Ibanez, H. Claustre, L. Legendre, M. Picheral, and G. Gorsky, “Effects of phytoplankton community on production, size, and export of large aggregates: a world–ocean analysis,” Limnol. Oceanogr. 54(6), 1951–1963 (2009). [CrossRef]
36. T. L. Richardson and G. A. Jackson, “Small phytoplankton and carbon export from the surface ocean,” Science 315(5813), 838–840 (2007). [CrossRef] [PubMed]
37. A. Fujiwara, T. Hirawake, K. Suzuki, and S. I. Saitoh, “Remote sensing of size structure of phytoplankton communities using optical properties of the Chukchi and Bering Sea shelf region,” Biogeosciences 8(12), 3567–3580 (2011). [CrossRef]
38. C. B. Mouw, J. A. Yoder, and S. C. Doney, “Impact of phytoplankton community size on a linked global ocean optical and ecosystem model,” J. Mar. Syst. 89(1), 61–75 (2012). [CrossRef]
39. A. M. Ciotti and A. Bricaud, “Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water-leaving radiances at SeaWiFS channels in a continental shelf region off Brazil,” Limnol. Oceanogr. Methods 4(1), 237–253 (2006). [CrossRef]
40. A. Bracher, M. Vountas, T. Dinter, J. P. Burrows, R. Röttgers, and I. Peeken, “Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data,” Biogeosciences 6(5), 751–764 (2009). [CrossRef]
41. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Global variability of phytoplankton functional types from space: assessment via the particle size distribution,” Biogeosciences 7(10), 3239–3257 (2010). [CrossRef]
42. C. B. Mouw and J. A. Yoder, “Optical determination of phytoplankton size composition from global SeaWiFS imagery,” J. Geophys. Res. 115(C12), C12018 (2010), doi: . [CrossRef]
43. X. J. Pan, A. Mannino, H. G. Marshall, K. C. Filippino, and M. R. Mulholland, “Remote sensing of phytoplankton community composition along the northeast coast of the United States,” Remote Sens. Environ. 115(12), 3731–3747 (2011). [CrossRef]
44. P. A. Stæhr, S. Markager, and K. Sand-Jensen, “Pigment specific in vivo light absorption of phytoplankton from estuarine, coastal and oceanic waters,” Mar. Ecol. Prog. Ser. 275, 115–128 (2004). [CrossRef]
45. G. Johnsen, B. B. Prézelin, and R. V. M. Jovine, “Fluorescence excitation spectra and light utilization in two red tide dinoflagellates,” Limnol. Oceanogr. 42(5, Part 2), 1166–1177 (1997). [CrossRef]
46. T. A. Moisan and B. G. Mitchell, “Photophysiological acclimation of Phaeocystis Antarctica Karsten under light limitation,” Limnol. Oceanogr. 44(2), 247–258 (1999). [CrossRef]
47. S. G. H. Simis, S. W. M. Peters, and H. J. Gons, “Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water,” Limnol. Oceanogr. 50(1), 237–245 (2005). [CrossRef]
48. T. Kutser, L. Metsamaa, N. Strömbeck, and E. Vahtmäe, “Monitoring cyanobacterial blooms by satellite remote sensing,” Estuar. Coast. Shelf Sci. 67(1–2), 303–312 (2006). [CrossRef]
49. H. A. Bouman, T. Platt, S. Sathyendranath, W. K. W. Li, V. Stuart, C. Fuentes-Yaco, H. Maass, E. P. W. Horne, O. Ulloa, V. Lutz, and M. Kyewalyanga, “Temperature as indicator of optical properties and community structure of marine phytoplankton: implications for remote sensing,” Mar. Ecol. Prog. Ser. 258, 19–30 (2003). [CrossRef]
50. S. Chee Yew Leong and S. Taguchi, “Optical characteristics of the harmful dinoflagellate Alexandrium tamarense in response to different nitrogen sources,” Harmful Algae 4(2), 211–219 (2005). [CrossRef]
51. Y. Yin, Y. L. Zhang, M. Z. Wang, X. H. Liu, and Z. Q. Shi, “Effects of different irradiation intensity on the growth and absorption properties of Microcystis aeruginosa and Scenedesmus obliqnus,” J. Lake Sci. (to be published).
52. P. A. Stæhr, P. Henriksen, and S. Markager, “Photoacclimation of four marine phytoplankton species to irradiance and nutrient availability,” Mar. Ecol. Prog. Ser. 238, 47–59 (2002). [CrossRef]
53. Z. P. Lee and K. L. Carder, “Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance,” Remote Sens. Environ. 89(3), 361–368 (2004). [CrossRef]
54. P. D. Hunter, A. N. Tyler, L. Carvalho, G. A. Codd, and S. C. Maberly, “Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes,” Remote Sens. Environ. 114(11), 2705–2718 (2010). [CrossRef]
