Distribution and Population Dynamics of Cryptomonads in a Chinese Lake with Three Basins Varying in Their Trophic State

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Phycological Research 2014 doi: 10.1111/pre.12079

Distribution and population dynamics of cryptomonads in a Chinese lake with three basins varying in their trophic state

Shuang Xia,1,2 Yingyin Cheng,3 Huan Zhu,1 Guoxiang Liu1* and Zhengyu Hu3 1Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, P. R. China, 2College of Life Sciences, South-central University for Nationalities, Wuhan, 430074, P. R. China, and 3State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, P. R. China

taxonomic group of protists (Barlow & Kugrens 2002; SUMMARY Novarino 2003). Recent research modified the taxonomic system of cryptophyta by combined datasets of light and Cryptomonads are unicellular algae that are important primary electron microscopical and molecular data, and provided producers in various aquatic ecosystems. However, their ecological importance was often neglected owing to their brittle- several morphological characters to distinguish some ness. Their population in freshwater was thought to be cryptomonad species under light microscopy, which made the regulated mainly by grazing pressure, and the effects of lake identification more rational (e.g. Hoef-Emden & Melkonian trophy were less important. In this study, the cryptomonad 2003; Hoef-Emden 2007). Thus, ecological investigation of species in three basins of Lake Donghu, a shallow lake in cryptomonads at species level is now feasible. China were identified, and their distribution, seasonal dynam- Previous ecological studies on cryptomonads have mainly ics, and relationships with several environmental factors were focused on the identification, distribution and seasonal investigated. Eight cryptomonads were identified at species dynamics (e.g. Barlow & Kugrens 2002; Barone & level by morphological examination; species belonging to the Naselli-Flores 2003; Menezes & Novarino 2003; Tolotti et al. genera Komma and Cryptomonas were most common. 2003; Cerino & Zingone 2006). These algae may form blooms Cryptomonads displayed inconsistent distribution and population dynamics among the three basins of different trophic in marine (Andreoli et al. 1986; Dame et al. 2000), fresh- status. They were the dominant species in the eutrophic water (Nishijima 1990), and brackish (Laza-Martínez 2012) basin, while their proportion was lower in the hypertrophic ecosystems. In freshwater environments, cryptomonads are basin and in the mesotrophic basin. The biomass of often present in constant but relatively low populations cryptomonads was highest in the hypertrophic region. As a throughout the year, and sudden increases in population are whole, cryptomonads kept low biomass during winter, while common (Novarino 2003). However, the underlying factors rapid waxing and waning of their population was observed in that affect their populations remain obscure. Previous studies the other seasons. Cryptomonads species exhibit distinct sea- revealed they were optimal or near-optimal food organisms for sonal trends. Canonical correspondence analysis revealed zooplankton (Stemberger & Gilbert 1985; Sarnelle 1993; water temperature and dissolved total nitrogen were the most Tirok & Gaedke 2007), and their population was mainly important factors that affected the composition of the cryptomonads community. Spearman correlation analysis regulated by grazing pressure (Willén 1992; Barone & demonstrated that the biomass of cryptomonads was posi- Naselli-Flores 2003) in freshwater habitats. But undoubtedly, tively correlated with pH value, dissolved total nitrogen and the effects of lake trophy need to be investigated. dissolved organic carbon. In conclusion, lake trophy is a In the current study, cryptomonads species in three basins crucial factor affecting the total cryptomonads population. of different trophic status of Lake Donghu, a shallow lake in China were identified. Their distribution, seasonal dynamics, Key words: dissolved organic carbon, dissolved total nitrogen, and relationships with environmental factors were also exam- environmental factors, phytoplankton, seasonal trend. ined. The effects of lake trophy on cryptomonads population were discussed.

MATERIALS AND METHODS INTRODUCTION Cryptomonads comprise autotrophic and heterotrophic ﬂagel- Study site and sample collection lates with unique morphological features (Menezes & Novarino Limnological investigations were performed in Lake Donghu, 2003). These microalgae are important primary producers in which is a shallow lake with an average depth of 2.2 m. It is both fresh water and marine habitats. They contribute greatly located on the alluvial plain of the middle basin of Yangtze to the aquatic food web and many species are cosmopolitan in River, City of Wuhan, Hubei province, China. The lake has a their distribution (Reynolds 1978; Klaveness 1988a,b; Gillot 1990; Barlow & Kugrens 2002). However, cryptomonads may be underrepresented in preserved samples due to their brit- tleness, and their ecological importance was often neglected. *To whom correspondence should be addressed. Moreover, by virtue of their unicellularity and small size, Email: [email protected] proper identiﬁcation cannot be accomplished by light micros- Communicating editor: M. Hoppenrath copy, and they were assumed to be a small and obscure Received 21 January 2014; accepted 28 September 2014.

© 2014 Japanese Society of Phycology 2 S. Xia et al. total surface area of 32 km2 and is composed of several basins Hoef-Emden and Melkonian (2003), Javornický (2003), and separated by artificial dykes. The main basin of Lake Donghu Kugrens and Clay (2003). At least 30 live cells were observed (the Guozhenghu Basin) is eutrophic, while the Shuiguohu to identify each cryptomonad species. Basin is hypertrophic and the Niuchaohu Basin is mesotrophic The 1.2 L water samples were allowed to settle for 24 h, (Qiu et al. 2001; Zhou et al. 2002; Zhao et al. 2003). siphoned, and concentrated to 30 mL. Then, 0.1 mL of the In the present study, seven sampling sites were set in three concentrated water samples was placed in a counting basins of the lake. Five sites were in the main basin (the chamber, and the cells were counted under an optical micro- Guozhenghu Basin), and one site each in the Shuiguohu Basin scope (CX21, OLYMPUS, Tokyo, Japan) at 400 × magnifica- and the Niuchaohu Basin. The locations of sampling sites tion. At least 300 phytoplankton cells were counted. Mean were as follows: 1# (30°33′ 13.73″ N, 114°20′ 53.81″ E); cell volume (μm3/cell) was calculated using appropriate geo- 2# (30°32′ 46.77″ N, 114°21′ 57.58″ E); 3# (30°32′ metric configurations after measuring 50 cells of each taxon 46.86″ N, 114°23′ 42.84″ E); 4# (30°33′ 42.45″ N, with a calibrated ocular micrometer (Hillebrand et al. 1999; 114°24′ 14.57″ E); 5# (30°34′ 05.86″ N, 114°23′ 23.61″ Domingues et al. 2005). Volume values were converted to E); 6# (30°34′ 34.88″ N, 114°22′ 37.71″ E); 7# (30°35′ biomass assuming that 1 μm3 is equivalent to 1 pg (Eker & 05.35″ N, 114°22′ 46.07″ E). The map of Lake Donghu and Kideys 2003). sampling sites are shown in Appendix S1 in supporting information. Phytoplankton samples for qualitative identification were collected by plankton nets (10 μm mesh size), and Statistical analysis brought back to the lab alive. For quantitative analysis, phy- Data of biomass, cell density, DTN and DTP were log2 (x+1) toplankton samples of surface water (0–50 cm deep) and transformed. Spearman correlation analysis was performed bottom water (0–50 cm above the sediment surface) were using the SPSS 16.0 package (SPSS, Chicago, IL, USA). collected monthly from July 2010 to June 2012 using Canonical correspondence analysis (CCA), a multivariate 600 mL plastic bottles. Surface and bottom water samples direct gradient analysis technique, was used to elucidate the from the same site were mixed and fixed with Lugol’s iodine relationships between biological assemblages of species and solution to obtain a final concentration of 1%. In addition, their environment. Ordination analysis was performed using 100 mL water samples were obtained for environmental CANOCO 4.5 (Ter Braak & Šmilauer 2002). The significance parameter measurements. of environmental variables to explain the variance of cryptomonad data in the CCA was tested using Monte Carlo simulations with 499 permutations (Ter Braak & Šmilauer Measurements of environmental parameters 2002). Water temperature was measured directly using a mercury thermometer, and pH value was recorded using a pH sensor RESULTS (HI98103, Hanna Instruments, TX, USA). Water samples were filtered through precleaned 0.45 μm pore size Whatman GF/C glass-fiber filters. Dissolved total Environmental parameters nitrogen (DTN) and dissolved total phosphorus (DTP) were The annual average values of the environmental parameters measured using potassium peroxydisulfate oxidation and deg- and phytoplankton biomass of the three investigated basins of radation methods (State Environmental Protection Bureau Lake Donghu are shown in Figure 1. Annual average values of 2002), respectively. A spectrometer (UV-1700 PharmaSpec, phytoplankton biomass, DTN and DTP in the Shuiguohu Basin Shimadzu, Kyoto, Japan) was used to record the optical were highest, while the above three values were lowest in the density (OD) value. Dissolved inorganic carbon (DIC) and Niuchaohu Basin (Fig. 1a,c). Annual average values of DOC dissolved organic carbon (DOC) were measured using a cata- and DIC did not display obvious spatial trends among the lytic combustion analyzer (Multi N/C 2100TOC, Analytic Jena three basins (Fig. 1b). Monthly variation of the abiotic vari- AG, Eisfeld, Germany). These environmental parameters were ables for the three basins is shown in Figure 2. Water tem- measured within 24 h after sampling. perature ranged from 1.5 to 34.5°C, and pH values were above 7.0 during the whole monitoring period. DTP and DTN had dramatic change in the Shuiguohu Basin, and DIC and Species identification and calculation DOC showed similar trends in the three basins (Fig. 2). Living cells and cells fixed with Lugol’s solution were observed using differential interference contrast and phase contrast Cryptomonads identification and under a light microscope (Leica DM5000B). Micrographs population dynamics were captured using a digital camera (Leica DFC320, Wetzlar, Germany). When observing live cells, low-melting agarose was The cryptomonads, as a group, were present in all the samples added to prevent cells from swimming. In particular, we con- examined. The identified species were: Komma caudata Hill sidered six principal characteristics used in the taxonomy of (Fig. 3a,b), Cryptomonas marssonii (Skuja) emend. Hoef- these species, namely, cell size, cell shape, cell colour, pres- Emden & Melkonian (Fig. 3c,d), C. obovata Skuja (Fig. 3e), ence and direction of a furrow, number and shape of chloro- C. pyrenoidifera (Geitler) emend. Hoef-Emden & Melko- plast, and arrangement of large ejectosomes. Identification nian (Fig. 3f), C. platyuris Skuja (Fig. 3g,h), C. curvata mainly followed the procedure of Huber-Pestalozzi (1968), (Ehrenberg) emend. Hoef-Emden & Melkonian (Fig. 3i,j), Hill (1991), Novarino et al. (1994), Clay et al. (1999), C. tenuis Pascher (Fig. 3k) and Plagioselmis nannoplanctica

During the monitoring period of 2 years, the cryptomonads population showed distinct seasonality in the three basins examined; the biomass of cryptomonads was minimal during winter in the three basins, and rapid population changes were observed in the other seasons (Figs 4,5). In the Shuiguohu Basin, cryptomonads contributed to the phytoplankton biomass most in summer. In the Guozhenghu Basin, cryptomonads became the dominant species of the phytoplankton community in warm and hot months, and they appeared most signiﬁcant in spring. In the Niuchaohu Basin, cryptomonads were present in constant but relatively low population throughout the year. Rapid growth in the cryptomonads population was observed once in the Niuchaohu Basin (May 2011). In the Shuiguohu Basin, Cryptomonas always accounted for more than 90% of the biomass of cryptomonads (Fig. 6a). Except for that in May 2011, when Pandorina sp. bloomed there, K. caudata was the only observed Cryptomonad. In the Guozhenghu Basin, where the cryptomonads had the highest contribution to the phytoplankton biomass, Komma was the richest in cell density among the cryptomonads genera, and Cryptomonas accounted for the highest proportion of the biomass of cryptomonads. Komma was observed throughout the year, and dominated the cryptomonads community during winter. By contrast, Cryptomonas was dominant during spring and autumn. Chroomonas spp. and Plagioselmis spp. mostly proliferated during summer and autumn (Fig. 6b) In the Shuiguohu Basin, different seasonal dynamics were observed. Cryptomonas marssonii peaked in spring and autumn, and K. caudata mostly proliferated in summer and spring; C. curvata bloomed twice during summer (July 2010 and June 2011). Other cryptomonad species were occasion- ally observed. The monthly cell density of cryptomonad species in the Shuiguohu Basin are shown in Appendix S2 in supporting information. While in the Guozhenghu Basin, the more common species, K. caudata, C. marssonii, C. obovata and C. pyrenoidifera had similar seasonal dynamics, and they mostly proliferated in spring and autumn. Cryptomonas marssonii bloomed twice in site 2# (September 2011 and May 2012) and once in site 7# (February 2012) in the Guozhenghu Basin. The monthly cell density of cryptomonad species in the Guozhenghu Basin are shown in Appendix S3 in supporting information.

Fig. 1. Annual average values of environmental parameters and phytoplankton biomass in three basins of Lake Donghu. (a) dissolved total nitrogen (DTN) and dissolved total phosphorus (DTP); Cryptomonads and environmental factors (b) dissolved organic carbon (DOC) and dissolved inorganic carbon The influence of environmental variables on the cryptomonads (DIC); (c) phytoplankton biomass. SGH: the Shuiguohu Basin; species is illustrated in Figure 7. In the ordination biplots, arrows GZH: the Guozhenghu Basin; NCH: the Niuchaohu Basin. represent environmental variables and triangles depict cryptomonads species. The eigen value (λ) for CCA axis 1 (λ: 0.132) and CCA axis 2 (λ: 0.039) explained 84.4% of the variance in (Skuja) Novarino, Lucas & Morrall (Fig. 3l). Their morphologi- the species data. The summary statistics for the first four axes of cal characteristics used to identify species are listed in Canonical correspondence analysis are shown in Appendix S4 in Table 1. Chroomonas spp. and Cryptomonas spp. were also supporting information. CCA axis 1 was mainly negatively corre- observed, but they cannot be identified at species level under lated with T (r =−0.881, P = 0.001), and DTP (r =−0.3205, light microscopy. The more common species were K. caudata P = 0.041), and positively correlated with DIC (r = 0.4215, and C. marssonii. P = 0.023). CCA axis 2 was mainly positively correlated with The Shuiguohu Basin held the largest biomass of DTN (r = 0.5455, P = 0.009), and negatively correlated with pH cryptomonads, while cryptomonads contributed most to the (r =−0.4200, P = 0.015). All the selected environmental vari- phytoplankton biomass in the Guozhenghu Basin. ables explained 99.0% of the total variability in species data.

Fig. 2. Monthly variation of the abiotic variables, water temperature (T), pH, dissolved total nitrogen (DTN) and dissolved total phosphorus (DTP), dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC)for the three basins. See Figure 2 for other information. cryptomonads species were scattered sparsely on the ordination dynamics of the cryptomonads species. DOC promoted biplot. The two significant environmental variables screened by C. marssonii, which contributed greatly to the phytop- automatic forward selection of CCA were T (F = 18.46, lankton biomass in the Guozhenghu Basin; DTP promoted P = 0.002) and DTN (F = 5.69, P = 0.004). The test for signifi- P. nannoplanctica; and C. curvata have large demands for DTP cance of all canonical axes by Monte Carlo simulation showed and DTN. that all canonical axes were significant (F = 6.201, P = 0.002, During the monitoring period, the biomass of crypto- 499 permutations under the reduced model, three replicates for monads was correlated significantly positively with pH value, each parameter). DTN and DOC (P < 0.01) (Table 2). Correlation analysis also Tendencies revealed by CCA indicated that nutrients were showed that phytoplankton biomass had significant positive decisive factors that influence the temporal and spatial correlations with the biomass of cryptomonads.

Fig. 3. Differential interference contrast micrographs of cryptomonads. (a,b) Ventro-lateral view of Komma caudata, showing the typical comma shaped cell, a refractive body (R), a blue-green chloroplast (C), a prominent pyrenoid (P) and a ventrally curved tail (arrow). (c,d) Ventro-lateral view of Cryptomonas marssonii, showing the sigmoid cell shape, a large number of starch grains (S), two unequal ﬂagella (F) and a dorsally curved tail (arrow). (e) Ventro-lateral view of C. obovata, showing the typical obovoid cell shape, a lobed appearance at the cell subapex (arrow) and rows of ejectoisomes (E) located near the furrow. (f) Dorsal view of C. pyrenoidifera, showing the ellipse cell shape and two prominent pyrenoids (P). (g) Ventral view of C. platyuris, showing the slightly twisted cell shape, two Corps de Maupas (M) and rows of ejectosomes (E). (h) Lateral view of C. platyuris, showing the dorsal-ventrally compressed cell shape. (i) Ventro-lateral view of C. curvata, showing the rostrate antetior (arrow), and large ejectosomes (E) located near the furrow. (j) Ventral view of C. curvata, showing the numerous pyrenoids (P). (k) Ventral view of C. tenuis, showing the ellipsoid cell shape, and the arrangement of starch grains near the edge of chloroplast. (l) Lateral view of Plagioselmis nannoplanctica, showing the brownish chloroplast and the acute tail (arrow). (a,c,e,f,g,h,I,k,l) living cells. (b,d,j) cells preserved with Lugol’s solution. Scale bars = 10 μm.

Table 1. Morphological characteristics used to identify cryptomoand species

Species Cell length (μm) Cell shape Colour Furrow Chloroplast number Ejectosomes

Kom 8–12 Comma shaped Blue-green Narrow, straight, short 1, with a prominent pyrenoid 2 rows Cma 16–33 Sigmoid Olive green Wide, straight, long 2, without pyrenoids 2 rows Cob 18–26 Obovate Olive green Wide, straight, long 2, without pyrenoids 4–5 rows Cpy 15–27 Ellipsoid Olive green Narrow, straight, short 2, with a prominent pyrenoid each 2 rows Cpl 33–55 Flattened, twisted Olive green Wide, curved, long 2, without pyrenoids 6–8 rows Ccu 35–48 Sigmoid Olive green Wide, curved, long 2, with 3–4 pyrenoids each 8–10 rows Cte 8–14 Oblong Olive green Narrow, straight, short 2, without pyrenoids 2 rows Pla 8–15 Comma shaped Brownish red Narrow, straight, short 1, with a prominent pyrenoid 2 rows

Fig. 5. Seasonal trends CB and PCB in three basins of Lake Donghu. See Figure 2 for other information.

DISCUSSION At least 12 cryptomonad morphotypes were observed in our samples collected from Lake Donghu, but only eight species were identified by light microscopy by virtue of their distinct morphological characters. There are still many cryptomonad species that cannot be properly identified under light microcopy. In order to get a more comprehensive understanding of the ecological habits of these flagellated microalgae, a combined means including light and electron microscopical and molecular data urgently need to be developed. In addition, the identified cryptomonads in the present study are all common species reported in other parts of the world, which highlighted their eurytopicity. Previous studies revealed that cryptomonads often domi- nate the phytoplankton community in cool and under-ice water (Barlow & Kugrens 2002; Vance et al. 2013). The present study showed they were also dominant in a shallow lake in the subtropical region of China. It is widely believed that resource availability, water motion and temperature largely contribute to set the specific composition of the phytoplankton assemblages, the shape and size of the organisms involved and their seasonal succession (Naselli-Flores et al. 2007; Naselli-Flores & Barone 2011). Hence, we inferred two Fig. 4. Monthly trends of the biomass of cryptomonads (CB; main abiotic factors for the domination of cryptomnads in the shading) and percentage of cryptomonads in total phytoplankton Guozhenghu Basin. On one hand, in eutrophic and hyper- biomass (PCB; line) in three basins of Lake Donghu. trophic environments, light availability is a major force shaping phytoplankton assemblages (Naselli-Flores et al. 2007). The water body in the Guozhenghu Basin was very turbid, with a secchi depth of approximately 40 cm (Yan et al.

© 2014 Japanese Society of Phycology Cryptomonads and lake trophy 7 1.000 0.061 − 0.084 0.574** 0.183* 0.598** 0.095 0.148 1.000 0.119 − − − ); DOC, dissolved organic 1 − 0.145 0.278** 0.136 1.000 0.215** 0.028 − − 1.000 0.02 0.208** 0.324** 0.227** 0.118 0.022 0.056 0.015 0.233** − − ). 1 − 1.000 0.327** 0.403** 0.262** 0.656** 0.039 0.026 0.081 − − ); DIC, dissolved inorganic carbon (mg L 1 − 1.000 0.340** 0.137 0.404** 0.016 0.809** 0.091 −

Fig. 6. Monthly trends of the biomass of cryptomonads in the 1.000 0.438** 0.375** 0.049 0.301** 0.261** 0.455** 0.175* Shuiguohu Basin and the Guozhenghu Basin of Lake Donghu. − Cry = Cryptomnas; Chr = Chroomonas; Kom = Komma; Pla = Plagioselmis; (a) the Shuiguohu Basin; (b) the Guozhenghu Basin. ); CB, total biomass of cryptomonads (mg L 1 − 0.203* 0.055 0.190* 0.182* 0.069 0.199* 0.236** 0.016 0.415** 0.256** 1.000 0.320** 0.569** 0.270** 0.052 0.470** − − − ); DTP, dissolved total phosphorus (mg L 1 − sp. (mg L 0.017 1.000 0.272** 0.357** 0.490** 0.327** 0.170* 0.361** 0.179* 0.577** − Chroomonas 0.159* 0.324** 0.205* 0.330** 0.233** 0.272** 0.122 0.201* 0.042 0.306** 0.316** 0.162* − − ); Chr, biomass of 0.008 1 − − 156) between environmental parameters and phytoplankton biomass of Lake Donghu = n 0.042 −

Fig. 7. Ordination biplot of cryptomonads species assemblages and environmental variables obtained by Canonical correspond- 0.100 − ence analysis in Lake Donghu. Ccu: Cryptomonas curvata; Chr: Chroomonas sp.; Cma: Cryptomonas marssonii; Cpy: Cryptomonas pyrenoidifera; Cob: Cryptomonas obovata; Kom: komma caudata 0.01. T, water temperature (°C); DTN, dissolved total nitrogen (mg L <

Pla: Plagioselmis nannoplanctica. P ); PB, total phytoplankton biomass (mg L 1 −

1997; Mei et al. 2007), and cryptomonads could tolerate low Spearman correlation coefﬁcients ( 0.05; ** T pH DTN DTP DOC DIC PB Kom Chr Cma Cpy Ccu Cob Pla CB irradiance (Morgan & Kalff 1975; 1979; Pithart 1999). On < P the other hand, cryptomonads were reported to favour alkaline * Ccu Chr Cma Cpy Cob Pla CB PB Kom DTP 1.000 0.168* 0.124 pHDTN 1.000 1.000 0.338** 0.221** 0.494** 0.221** T 1.000 0.323** 0.035 0.224** 0.256** Table 2. DOCDIC 1.000 0.277** 0.125 1.000 0.105 0.081 0.069 0.116 0.156 0.144 0.384** 0.268** 0.050 0.074 0.083 0.209** conditions and most prominent in lakes with pH values above carbon (mg L

7.5 (Kugrens & Clay 2003). In the Guozhenghu Basin of Lake cryptomonads was most significant in summer. And nutrients Donghu, the pH values were always above 7.0. played a relatively more important role in regulating the The proportions of the cryptomonads in phytoplankton in the cryptomonads population in the other seasons. Shuiguohu Basin and the Niuchaohu Basin of Lake Donghu were lower than that in the Guozhenghu Basin. We infer this was due to the fact that the Niuchaohu Basin is in a sparsely populated ACKNOWLEDGMENTS district, and the pollution from human activities is relatively low. This work was financially supported by the National Natural In the Niuchaohu Basin, the phytoplankton community was Science Foundation of China (Grant no. 30970501, and mainly dominated by diatoms and chrysophytes. The 31400185). Special thanks to Dr. Qi Zhang, Dr. Kongxian Zhu, mesotrophic habitat was not favourable for cryptomonads Mr. Xigong Yuan and Ms. Yue Zheng for their help in sample (Reynolds et al. 2002). By contrast, the Shuiguohu Basin was collection and environmental parameter measurements. located in a densely populated district, and the pollution from human activities is the most severe among all basins of Lake Donghu. In addition, the water region is narrow, almost static and REFERENCES semi-enclosed. This hypertrophic water body promoted several Andreoli, C., Tolomio, C., Rascio, N. and Talarico, R. 1986. Some pollution-resistant species, such as Microcystis, Oscillatoria, observations on a Cryptophyceae responsible for a winter red Pandorina, Lepocinclis, and Phacus. Cryptomonads were not bloom. Nuovo G. Bot. Ital. 120: 70–1. dominant species because of their relatively small size, although Antia, N. J. and Chorney, V. 1968. 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