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Susanne Fietz, Galina Kobanova, Lyubov Izmest’eva, Andreas Nicklisch, Regional, vertical and seasonal distribution of phytoplankton and photosynthetic pigments in Lake Baikal, Journal of Plankton Research, Volume 27, Issue 8, August 2005, Pages 793–810, https://doi.org/10.1093/plankt/fbi054
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Abstract
A 3-year phytoplankton study was carried out in Lake Baikal (Siberia) as part of the CONTINENT project and in conjunction with a 60-year long monitoring programme by the Irkutsk State University. A combination of microscopy and high performance liquid chromatography (HPLC) pigment analysis was used. All over the lake, the dominant functional group (by biovolume) was the vernal diatom blooms, due to the dominance of endemic Cyclotella species. Chlorophyll a (Chl a) was significantly highest at the Selenga and Barguzin inflows (2.39 ± 0.34 and 2.49 ± 0.18 nmol L–1, mean ± 95% CI, respectively) and higher in the South than in the North (1.43 ± 0.26 and 0.96 ± 0.13 nmol L−1). This variation of Chl a reflected changes in the phytoplankton composition. Diatoms and Chrysophyceae were the major contributors to the total Chl a except in the South (Chlorophyceae) and Selenga Delta (cyanobacterial picoplankton). There were also indications of species composition changes due to enhanced P-loading from the Selenga River. However, canonical analyses indicated that temperature and stratification were the major driving forces for regional distribution patterns and seasonal succession. It seems likely that further global warming will cause a shift in the species and group composition towards small cells at the expense of the large endemic diatom flora.
Received May 2, 2005; accepted in principle July 26, 2005; accepted for publication August 3, 2005; published online August 17, 2005
INTRODUCTION
Lake Baikal is one of the oldest, largest and deepest lake in the world. It has over 569 formally identified algal (planktonic and benthic) species of which 35% are estimated to be endemic (Kozhova and Izmest’eva, 1998). This unique ecosystem has changed very little since regular research began in the early 20th century (Kozhova and Izmest’eva, 1998) but a warming of the air temperature and a decline of the ice cover duration has been reported recently (Shimaraev et al., 2002). Moreover, pollution in the Selenga River and South Basin due to both industrial and domestic discharge was shown (Kozhova and Silow, 1998; Mackay et al., 1998; Beeton, 2002). Yet the effects of global warming or local anthropogenic impacts are poorly known. One group that responds rapidly to such environmental changes is the phytoplankton. However, to quantify the significance of any changes, it is important to determine the variability of populations over space and time.
In Lake Baikal, gradients of temperature, insolation and nutrients are caused by its great length over five degrees of latitude, its rift-generated morphometry as well as its large tributaries and bays (see Kozhov, 1963; Galazyi, 1993; Kozhova and Izmest’eva, 1998). Also, an unusual feature of the lake is the vertical mixing and deep water ventilation driven by small density changes close to the point of maximum density of water (Weiss et al., 1991; Shimaraev et al., 1993; Wuest et al., 2005). This is important for the phytoplankton because results of growth experiments point to temperature as possible cause of community change (Richardson et al., 2000). However, the regional variation of nutrient supply, thermal stratification and ice conditions are also critical factors (Goldman et al., 1996; Genkai-Kato et al., 2002; Mackay et al., 2003). Characteristic of Lake Baikal are the so called Melosira years, which occur every 3 or 4 years (at least in the South Basin). During these years blooms of endemic Bacillariophyceae, such as Aulacoseira baicalensis (formerly Melosira baicalensis) begin to develop in the convective layer under the ice (Kozhov, 1963; Granin et al., 1991, 1999; Kelley, 1997; Kozhova and Izmest’eva, 1998).
Most former studies on Lake Baikal focused on single topics such as nano- and microphytoplankton or on bacteria, primary production or chlorophyll, whereas photosynthetic pigments other than chlorophyll have not been studied. However, pigments have been successfully used to quantify the relative importance of different chemotaxonomic groups in phytoplankton in other freshwater and marine systems (Gieskes et al., 1988; Everitt et al., 1990; Wright et al., 1996; others). Within the multiproxy EU-project CONTINENT (http://continent.gfz-potsdam.de/), a preliminary study in 2001 revealed the potential of an high performance liquid chromatography (HPLC)-aided pigment based approach in Lake Baikal (Fietz and Nicklisch, 2004). In this study, light microscopy was combined with epifluorescence microscopy and HPLC to investigate the distribution of phytoplankton groups and species, including autotrophic picoplankton (APP). In particular three gradients: region, depth and season were studied during three consecutive years (2001–2003) in conjunction with the long-term monitoring of the Scientific Research Institute of Biology at the Irkutsk State University. An attempt was made to evaluate the response of the phytoplankton to global warming or local eutrophication.
METHOD
Sampling
Sampling for the study of regional distribution patterns was conducted in July 2001, 2002 and 2003 during the CONTINENT cruises CON 01–4, CON 01–5, CON 02–8 and CON 03–9 with the Research Vessel ‘Vereshchagin’ (Fig. 1A). In 2001 samples for pigment analyses were taken from 0.5, 5, 10 and 30 m water depth. Samples for phytoplankton counting were taken from 5, 10 and 30 m water depth. In 2002, samples for pigment and phytoplankton analyses were taken from 5, 10 and 30 m and/or in the deep chlorophyll-maxima determined with a submersible fluorometer (FluoroProbe, bbe Moldaenke GmbH, Kiel, Germany). In 2003, only samples for pigment analyses were taken (same depths as in 2002). In 2001, temperature was directly measured in the pigment samples. Temperature in 2002 and 2003 was provided for all sampling stations and depths, where phytoplankton and pigment samples were taken, by the fluorometer and by CTD profiles from Limnological Institute of Irkutsk, Russian Academy of Sciences.
For the study of seasonal dynamics, weekly sampling from May 2002 to June 2003 was carried out at the long-term sampling site of the Scientific Research Institute of Biology, State University Irkutsk (SRIB), located 2.8 km offshore from Bolshye Koti (51°54′ N 105°04′ E, Fig. 1A). Water depth at that site was 800 m. Samples from this latter station were taken with the Research Vessel ‘M.M. Kozhov’ in summer or from the ice in winter and processed at the Biological Station Bolshye Koti.
Phytoplankton qualitative and quantitative determination
Samples for APP (0.2–3 µm; 50 mL) were preserved with formaldehyde (0.7% final concentration) and filtered through black Nuclepore polycarbonate filters (0.2 µm pore size). The filter was placed on a microscope slide, quickly dried and covered with a drop of fluorescence-free immersion oil and a coverslip. Once frozen, preparations were stable for months. APP were counted at 1000× magnification using a Zeiss Axioskop epifluorescence microscope equipped with filters for green (546 nm excitation filter, 580 nm splitter and 590 nm barrier filter) and blue (450–490 nm excitation filter, 510 nm splitter and 520 nm barrier filter) excitation. Eukaryotic APP fluoresced deep red (>665 nm) when excited with blue or green light, whereas cyanobacterial APP fluoresced light red (<665 nm) when excited with green light (Phycobilins). Phycoerythrin and phycocyanin containing cyanobacteria were distinguished by their respective yellow or extreme weak emission at blue light excitation, but this difference was not definite in all stored preparations. Cell counts were converted to biovolume according to their size and geometric form (Fietz and Nicklisch, 2004). During seasonal monitoring APP was counted with a light microscope; in doing so colonies were easily identified, but single cells could be overlooked.
In both years of phytoplankton counts (2001 and 2002), samples from cruises (1–2 L) were concentrated by filtering through Nuclepore polycarbonate filters (2 µm pore size), resuspended in 100 mL, fixed with some drops of Lugol’s solution and stored at room temperature. Samples for seasonal monitoring were not concentrated. Counting and identification was done according to the settling technique (Utermöhl, 1958). The taxonomic composition of algae was established in accordance with ‘The Keys of Freshwater Algae of the USSR’ (see Kozhova and Izmest’eva, 1998; p. 325 for references), with monographs (references in Bourrelly, 1957; Kozhova and Izmest’eva, 1998) and additional keys (references in Topachevsky and Masyuk, 1984; Wasser et al., 1989; Gleser et al., 1992; Kozhova and Izmest’eva, 1998) and supplemented by articles (references in Kozhova and Izmest’eva, 1998; Edlund et al., 1996). Gymnodinium coeruleum (Pyrrophyta) is often cited within Lake Baikal phytoplankton assemblages (Kozhova, 1987; Kozhova and Izmest’eva, 1998; Genkai-Kato et al., 2003) but has been omitted from the phytoplankton counts in this study because it did not contain chloroplasts and, therefore, was counted as protozoan. All species were tentatively grouped into functional associations according to the scheme proposed by Reynolds et al. (Reynolds et al., 2002). We consulted with its authors over the classification of the species we have encountered.
Pigment analysis
Duplicate samples for HPLC-aided pigment determination (1–2.5 L) were filtered through Whatman GF/F-filters with 25 mm diameter, stored in 2 mL reaction vessels, frozen at −25°C and immediately freeze-dried and stored frozen in the dark. The sample temperature during freeze-drying was controlled below −25°C. For 2001 samples, chlorophylls, carotenoids and their derivatives were extracted with 1 mL of a mixture of acetone, methanol and water (80:15:5 by volume, Leavitt et al., 1989) and in 2002 and 2003 with 1 mL of dimethylformamide under dim light at 4°C. No significant difference was found between both solvents. The extraction was done by vibration shaking at a frequency of 2000 min−1 with a supplement of glass beads (0.75–1 mm) over 1.5 h. An IPR solution (ionpairing reagent, 15 g L−1 tetrabutyl ammonium acetate and 77 g L−1 ammonium acetate) was added 1:10. The extract was centrifuged for 20 min at 4°C at 2500 g in a cooled centrifuge (Biofuge Fresco Heraeus Instruments, Hanau, Germany). The separation, identification and quantification of pigments were performed according to Woitke et al. (Woitke et al., 1994) with a Waters HPLC system as described by Fietz and Nicklisch (Fietz and Nicklisch, 2004).
In this study we used chemotaxonomic groups, which may be classes or families, according to the respective pigment compositions. In that way we used ‘Bacillariophyceae + Chrysophyceae’, because both families contain the marker pigments fucoxanthin and Chl c but other families of their class ‘Heterokontophyta’ contain other marker pigments. Also, we used ‘Chlorophyta’, because all phytoplankton families of this class contain the same pigment composition, but we used ‘cyanobacterial picoplankton’ that clearly dominate the Baikalian cyanobacteria because their marker pigments are zeaxanthin and caloxanthin, which are not prominent in filamentous cyanobacteria (Fietz and Nicklisch, 2004).
The contribution of the individual chemotaxonomic groups to total Chl a was calculated according to Fietz and Nicklisch (Fietz and Nicklisch, 2004) by multiple linear regression using specific marker pigments (MP) for the chemotaxonomic groups (Chl a = a·MP1 + b·MP2 + c·MP3). In the first study for the Baikalian summer assemblage 2001 (Fietz and Nicklisch, 2004), it was mentioned that caloxanthin, a zeaxanthin transformation product, possibly coeluted with alloxanthin in some samples. In this study, the correlation between α-carotene and alloxanthin, both contained in Cryptophyta, was significant. Therefore, we assumed that alloxanthin in almost all samples did not coelute with caloxanthin. Few outliers from the significant alloxanthin/α-carotene relationships were omitted in the multiple linear regression calculations of this study.
HPLC-based pigment analysis was provided for the three summer cruises as well as for the intensive monitoring from May 2002 to June 2003. Additional weekly Chl a data were provided from January 2001 to December 2003 by the long-term monitoring program conducted by the SRIB. For these analyses water samples were taken at the above mentioned Bolshye Koti station, filtered through 0.7 µm pore size Nuclepore polycarbonate filters, dried in cold, dark conditions and stored frozen. Extraction was done with 96% acetone. Extracts were centrifuged and the absorbance of the supernatant was measured with a spectrophotometer at 750, 665, 645 and 630 nm. Chl a was calculated according to guidelines given by the SCOR-UNESCO Workgroup (SCOR-UNESCO Workgroup, 1966).
Statistics
Variance analyses, Spearman-Rho and Pearson correlations and linear regressions were calculated with SPSS (SPSS Inc., Chicago, IL, USA) statistical package. Canonical correlation analysis (CCA) was calculated with Statistica (StatSoft Inc., Tulsa, OK, USA). The Simpson index and Shannon-Wiener index were calculated with BioDap (New Brunswick, Canada, cf. Magurran, 2003) using cell abundances.
RESULTS
Regions
The phytoplankton in Lake Baikal included APP, nano- and microphytoplankton. The phytoplankton biovolume, the Chl a and other phytoplankton pigments were distributed very heterogeneously in Lake Baikal, indicating variations of both phytoplankton abundance and composition. Differences were found between the open basins (South, Centre, and North) as well as between the near-shore (Maloe More) and river-inflow (Selenga Delta, Barguzin Bay) sites (Fig. 1A–F).
Distribution of APP, nano- and microphytoplankton
The whole lake average phytoplankton biovolume was 0.61 mm³ L−1 whereof APP comprised only 11% in the North, but 61% in the Selenga Delta (Fig. 1E). Significant differences for the APP biovolume were found in the following order: Selenga Delta > South, Centre > North (Fig. 1E). In contrast, no significant differences were found for the nano- and microphytoplankton between individual regions, either based on biovolumes (Fig. 1C) or cell numbers. Between 9 and 14 nano- and microphytoplankton species were identified at the different sites and depths and the mean reciprocal Simpson diversity index for the whole lake was 3.40 (Shannon-Wiener 1.92) ranging at individual sites from 2.1 to 3.6 (Table I); we did not record significant differences between the regions. On average 53% of the nano- and microphytoplankton biovolume resulted from endemic species.
The dominant functional group (by biovolume) was that of vernal bacillariophycean blooms typical of oligotrophic lakes, due to the dominance of Cyclotella species (Table I). Only at a few sites the dominances changed. In the South (5 m), for example, the dominant functional group was that usually found at the start of summer stratification in oligotrophic conditions (Table I) and in the Selenga Delta the dominant functional groups indicated summer stratification (Table I). Besides Cyclotella species, flagellata, most of them belonging to Chrysophyceae, were numerous across the lake (Table I). The contribution to the total biovolume of these flagellates was nonetheless higher in the South and Selenga Delta than in the North (Table I). A high number of Koliella longiseta (Chlorophyta) was found along with Cyclotella in the South and Centre, as well as a high number of Nitzschia acicularis (Bacillariophyceae) in the Centre (Table I). In the North the contribution of Cyclotella species to the total biovolume was higher than at all other sites unless at Academician Ridge (Table I). At Academician Ridge, Cyclotella cells were very numerous and the biovolume per Cyclotella cell was much higher than at the other sites (their diameter reached 150 µm). Within the Selenga Delta the number of Aulosira sp. (cyanobacteria) cells was the highest; the respective Aulosira species differed morphologically from Aulosira implexa and Aulosira laxa, which up until now were the only Aulosira species described for Lake Baikal (Bondarenko, 1995).
Distribution of phytoplankton pigments
The Chl a concentration was significantly correlated to temperature (r2 = 0.35, P < 0.001) and was significantly lower in the three open basins (South, Centre and North) than at the river inflows (Selenga and Barguzin) (Fig. 1B and D). Within the open basins, the North had a significantly lower Chl a concentration and temperature than the South (Fig. 1B and D). However, significant differences in the Chl a concentrations were found in each basin when the 3 years were compared, particularly in the North (Fig. 2). Similarly, significant interannual differences were found for each of the carotenoids, for Chl b, for Chl c, for the sum of carotenoids and for the carotenoids versus Chl a ratios (data not shown).
Changes in the carotenoid versus Chl a ratios (which varied from 53% in the Maloe More to 98% at Academician Ridge when expressed as weight ratios, Fig. 1F) indicated either changes of the phytoplankton community composition or changes of the physiological state of the cells. The ratios of carotenoids that collected light (such as fucoxanthin, Fig. 3A) versus carotenoids that protected cells against high light (such as diadinoxanthin) was not correlated to the total carotenoid versus Chl a ratio. Changing phytoplankton community composition was, therefore, more likely than light acclimation. Only at Maloe More did the very low carotenoid versus Chl a ratio clearly result from a lowering of protecting carotenoids (data not shown), indicating the low light acclimation of the phytoplankton at that site. That might be due to high content of dissolved coloured organic compounds (humic substances) at this particular site.
Taking a lake average (July 2001, 2002 and 2003), the dominant carotenoids were zeaxanthin, fucoxanthin and lutein. Fucoxanthin (Fig. 3A), Chl c, diadinoxanthin and diatoxanthin concentrations showed no significant differences between the basins although significantly higher values were found at Barguzin Bay and at Academician Ridge. The Chl b (Fig. 3B) and lutein showed significantly higher concentrations in the South than at most of the other sites. Alloxanthin (Fig. 3C) and peridinin did not show significant variations between the open basins and the remaining regions. Violaxanthin concentrations showed significant decreases in the order South > Centre > North (Fig. 3D). Zeaxanthin (Fig. 3E) and β-carotene were significantly highest in the Selenga Delta and the South showed significantly higher concentrations than the North. The 19′butanoyloxyfucoxanthin was not detected in any sample.
Fucoxanthin, Chl b, Alloxanthin, Violaxanthin and Zeaxanthin were used to estimate the composition of the phytoplankton assemblage (Fig. 3F; Table II). The coefficient of determination (r2) was high (0.97; Table II) and the calculated Chl a matched the measured Chl a with a mean error of 6% and a maximum error of 24% in all regions and years. The highest contribution of Bacillariophyceae + Chrysophyceae to the total Chl a was found in the North, Barguzin Bay and at Academician Ridge, whereas the contribution of Chlorophyta was highest in the South (40%) and that of the cyanobacterial APP in the Selenga Delta (30%) (Fig. 3F). The contribution of Eustigmatophyceae was highest at Maloe More (45%). Possibly several Chrysophyceae contributed to the total violaxanthin at Maloe More so that the eustigmatophycean violaxanthin was overestimated at that site. The contribution of Cryptophyta was small (<15%) all over the lake.
. | Molar ratio . | P . | Partial c. . | 95% CI . | r2 . |
---|---|---|---|---|---|
Chl a/fucoxanthin | 1.26 | <0.001 | 0.81 | 0.12 | 0.977 |
Chl a/Chl b | 3.00 | <0.001 | 0.69 | 0.42 | |
Chl a/alloxanthin | 1.62 | <0.001 | 0.37 | 0.54 | |
Chl a/zeaxanthina | 0.61 | <0.001 | 0.82 | 0.06 | |
Chl a/violaxanthinb | 6.49 | <0.001 | 0.84 | 0.57 |
. | Molar ratio . | P . | Partial c. . | 95% CI . | r2 . |
---|---|---|---|---|---|
Chl a/fucoxanthin | 1.26 | <0.001 | 0.81 | 0.12 | 0.977 |
Chl a/Chl b | 3.00 | <0.001 | 0.69 | 0.42 | |
Chl a/alloxanthin | 1.62 | <0.001 | 0.37 | 0.54 | |
Chl a/zeaxanthina | 0.61 | <0.001 | 0.82 | 0.06 | |
Chl a/violaxanthinb | 6.49 | <0.001 | 0.84 | 0.57 |
Ratios and statistics were calculated by multiple linear regression for the complete data set of July 2001, 2002 and 2003.
Cyanobacterial zeaxanthin only (the chlorophycean part has been subtracted using a zeaxanthin/lutein ratio of 5.3%).
Eustigmatophycean violaxanthin only (the chlorophycean part has been subtracted using a violaxanthin/Chl b ratio of 15%).
. | Molar ratio . | P . | Partial c. . | 95% CI . | r2 . |
---|---|---|---|---|---|
Chl a/fucoxanthin | 1.26 | <0.001 | 0.81 | 0.12 | 0.977 |
Chl a/Chl b | 3.00 | <0.001 | 0.69 | 0.42 | |
Chl a/alloxanthin | 1.62 | <0.001 | 0.37 | 0.54 | |
Chl a/zeaxanthina | 0.61 | <0.001 | 0.82 | 0.06 | |
Chl a/violaxanthinb | 6.49 | <0.001 | 0.84 | 0.57 |
. | Molar ratio . | P . | Partial c. . | 95% CI . | r2 . |
---|---|---|---|---|---|
Chl a/fucoxanthin | 1.26 | <0.001 | 0.81 | 0.12 | 0.977 |
Chl a/Chl b | 3.00 | <0.001 | 0.69 | 0.42 | |
Chl a/alloxanthin | 1.62 | <0.001 | 0.37 | 0.54 | |
Chl a/zeaxanthina | 0.61 | <0.001 | 0.82 | 0.06 | |
Chl a/violaxanthinb | 6.49 | <0.001 | 0.84 | 0.57 |
Ratios and statistics were calculated by multiple linear regression for the complete data set of July 2001, 2002 and 2003.
Cyanobacterial zeaxanthin only (the chlorophycean part has been subtracted using a zeaxanthin/lutein ratio of 5.3%).
Eustigmatophycean violaxanthin only (the chlorophycean part has been subtracted using a violaxanthin/Chl b ratio of 15%).
Depth
Temperature and chlorophyll depth profiles from January 2001 to December 2003 near Bolshye Koti indicated that an inverse stratification, with mixing restricted to a shallow layer under the ice occurred regularly in February. Furthermore, every year wind-induced mixing occurred over the upper 250 m from May to June, after ice break up, and again in November (Fig. 4). During spring overturn concentrations over 2 nmol L−1 were found down to 100 m and up to 0.5 nmol L−1 down to 250 m (Fig. 4), while during the autumn overturn the Chl a did not reach depths greater than 200 m.
Mixing conditions varied among the different basins and regions, as high-resolution temperature and conductivity depth profiles showed that in the North mixed conditions prevailed, whereas a weak stratification developed in the South and Selenga Delta (Fig. 5). Stable stratification with a broad epilimnion developed in Barguzin Bay, where the conductivity showed a second maximum at 20–25 m, probably indicating the influence of subsurface water currents induced by the river (Fig. 5).
The Chl a concentration in July was highest in the epilimnion and decreased with depth at the stratified sites, but showed a second maximum at 16 m in the South, and was rather homogeneously distributed in the North and Centre (Fig. 5). The total APP also decreased in the 20–30 m samples compared to the 5 m samples considering the whole lake average, whereas no significant changes could be found for the nano- and microphytoplankton (data not shown). Nonetheless, several groups and species showed distinct relationships with depth. The cyanobacterial APP, for example, was higher at 16 m than above in the Selenga Delta, and, furthermore, Aulacoseira baicalensis formed a deep maximum in 85 m water depth in the South (Table I).
A CCA (Table III) indicated that cyanobacterial APP made the highest contributions when the water column was stratified, with temperature as secondary factor. Chlorophyta dominated in the low latitude regions, probably related to the insolation, as temperature was not of great importance. Bacillariophyceae + Chrysophyceae dominated at the deep water sites, which means in the colder, nutrient-poor open basins rather than in the near-shore or river inflow areas.
. | Regional distribution (canonical loadings) . | . | . | . | Seasonal distribution (canonical loadings) . | . | ||||
---|---|---|---|---|---|---|---|---|---|---|
. | Root 1 . | Root 2 . | Root 3 . | Root 4 . | Root 1 . | Root 2 . | ||||
Set 1, environmental variables | ||||||||||
Latitude | −0.52 | −0.67a | 0.10 | 0.52 | — | — | ||||
Water depth | −0.51 | 0.44 | −0.71a | 0.20 | — | — | ||||
Stratification | 0.99a | −0.05 | −0.15 | −0.05 | −0.73a | −0.68 | ||||
Temperature | 0.53 | −0.47 | −0.20 | −0.68a | −0.74a | 0.67 | ||||
Set 2, phytoplankton group contributionb | ||||||||||
Total Chl a | 0.87a | −0.40 | −0.01 | 0.08 | −0.51 | −0.57a | ||||
Bacillariophyceae + Chrysophyceae | −0.28 | −0.44 | 0.74a | 0.16 | 0.39 | −0.57a | ||||
Chlorophyta | −0.06 | 0.92a | 0.09 | 0.14 | 0.44a | −0.22 | ||||
Cyanobacterial APP | 0.46a | −0.02 | −0.29 | −0.07 | −0.91a | 0.07 | ||||
Eustimatophyceae | 0.16 | −0.12 | −0.30 | −0.84a | −0.33 | 0.38a | ||||
Cryptophyta | −0.62a | −0.10 | −0.50 | −0.02 | 0.16a | −0.04 | ||||
Eigenvalue | 0.51 | 0.27 | 0.10 | 0.03 | 0.75 | 0.20 | ||||
Canonical correlation (r) | 0.72 | 0.52 | 0.31 | 0.18 | 0.86 | 0.45 | ||||
Significance (P) | 0.00 | 0.00 | 0.00 | 0.06 | 0.00 | 0.30 | ||||
Percentage variance, set 1 | 0.45 | 0.22 | 0.14 | 0.19 | 0.54 | 0.46 | ||||
Percentage variance, set 2 | 0.24 | 0.20 | 0.16 | 0.13 | 0.26 | 0.14 | ||||
Redundancy, set 1 | 0.23 | 0.06 | 0.01 | 0.01 | 0.40 | 0.09 | ||||
Redundancy, set 2 | 0.13 | 0.05 | 0.02 | 0.00 | 0.19 | 0.03 |
. | Regional distribution (canonical loadings) . | . | . | . | Seasonal distribution (canonical loadings) . | . | ||||
---|---|---|---|---|---|---|---|---|---|---|
. | Root 1 . | Root 2 . | Root 3 . | Root 4 . | Root 1 . | Root 2 . | ||||
Set 1, environmental variables | ||||||||||
Latitude | −0.52 | −0.67a | 0.10 | 0.52 | — | — | ||||
Water depth | −0.51 | 0.44 | −0.71a | 0.20 | — | — | ||||
Stratification | 0.99a | −0.05 | −0.15 | −0.05 | −0.73a | −0.68 | ||||
Temperature | 0.53 | −0.47 | −0.20 | −0.68a | −0.74a | 0.67 | ||||
Set 2, phytoplankton group contributionb | ||||||||||
Total Chl a | 0.87a | −0.40 | −0.01 | 0.08 | −0.51 | −0.57a | ||||
Bacillariophyceae + Chrysophyceae | −0.28 | −0.44 | 0.74a | 0.16 | 0.39 | −0.57a | ||||
Chlorophyta | −0.06 | 0.92a | 0.09 | 0.14 | 0.44a | −0.22 | ||||
Cyanobacterial APP | 0.46a | −0.02 | −0.29 | −0.07 | −0.91a | 0.07 | ||||
Eustimatophyceae | 0.16 | −0.12 | −0.30 | −0.84a | −0.33 | 0.38a | ||||
Cryptophyta | −0.62a | −0.10 | −0.50 | −0.02 | 0.16a | −0.04 | ||||
Eigenvalue | 0.51 | 0.27 | 0.10 | 0.03 | 0.75 | 0.20 | ||||
Canonical correlation (r) | 0.72 | 0.52 | 0.31 | 0.18 | 0.86 | 0.45 | ||||
Significance (P) | 0.00 | 0.00 | 0.00 | 0.06 | 0.00 | 0.30 | ||||
Percentage variance, set 1 | 0.45 | 0.22 | 0.14 | 0.19 | 0.54 | 0.46 | ||||
Percentage variance, set 2 | 0.24 | 0.20 | 0.16 | 0.13 | 0.26 | 0.14 | ||||
Redundancy, set 1 | 0.23 | 0.06 | 0.01 | 0.01 | 0.40 | 0.09 | ||||
Redundancy, set 2 | 0.13 | 0.05 | 0.02 | 0.00 | 0.19 | 0.03 |
The stratification was determined as 0, mixed and 1, stratified according to the temperature profiles gathered with the CTD and/or submersible fluorometer.
Marks the highest values within a set for each canonical variate.
Respective percentage contribution of each phytoplankton group to the total calculated chlorophyll a (Chl a). See text for calculation of the contributions.
. | Regional distribution (canonical loadings) . | . | . | . | Seasonal distribution (canonical loadings) . | . | ||||
---|---|---|---|---|---|---|---|---|---|---|
. | Root 1 . | Root 2 . | Root 3 . | Root 4 . | Root 1 . | Root 2 . | ||||
Set 1, environmental variables | ||||||||||
Latitude | −0.52 | −0.67a | 0.10 | 0.52 | — | — | ||||
Water depth | −0.51 | 0.44 | −0.71a | 0.20 | — | — | ||||
Stratification | 0.99a | −0.05 | −0.15 | −0.05 | −0.73a | −0.68 | ||||
Temperature | 0.53 | −0.47 | −0.20 | −0.68a | −0.74a | 0.67 | ||||
Set 2, phytoplankton group contributionb | ||||||||||
Total Chl a | 0.87a | −0.40 | −0.01 | 0.08 | −0.51 | −0.57a | ||||
Bacillariophyceae + Chrysophyceae | −0.28 | −0.44 | 0.74a | 0.16 | 0.39 | −0.57a | ||||
Chlorophyta | −0.06 | 0.92a | 0.09 | 0.14 | 0.44a | −0.22 | ||||
Cyanobacterial APP | 0.46a | −0.02 | −0.29 | −0.07 | −0.91a | 0.07 | ||||
Eustimatophyceae | 0.16 | −0.12 | −0.30 | −0.84a | −0.33 | 0.38a | ||||
Cryptophyta | −0.62a | −0.10 | −0.50 | −0.02 | 0.16a | −0.04 | ||||
Eigenvalue | 0.51 | 0.27 | 0.10 | 0.03 | 0.75 | 0.20 | ||||
Canonical correlation (r) | 0.72 | 0.52 | 0.31 | 0.18 | 0.86 | 0.45 | ||||
Significance (P) | 0.00 | 0.00 | 0.00 | 0.06 | 0.00 | 0.30 | ||||
Percentage variance, set 1 | 0.45 | 0.22 | 0.14 | 0.19 | 0.54 | 0.46 | ||||
Percentage variance, set 2 | 0.24 | 0.20 | 0.16 | 0.13 | 0.26 | 0.14 | ||||
Redundancy, set 1 | 0.23 | 0.06 | 0.01 | 0.01 | 0.40 | 0.09 | ||||
Redundancy, set 2 | 0.13 | 0.05 | 0.02 | 0.00 | 0.19 | 0.03 |
. | Regional distribution (canonical loadings) . | . | . | . | Seasonal distribution (canonical loadings) . | . | ||||
---|---|---|---|---|---|---|---|---|---|---|
. | Root 1 . | Root 2 . | Root 3 . | Root 4 . | Root 1 . | Root 2 . | ||||
Set 1, environmental variables | ||||||||||
Latitude | −0.52 | −0.67a | 0.10 | 0.52 | — | — | ||||
Water depth | −0.51 | 0.44 | −0.71a | 0.20 | — | — | ||||
Stratification | 0.99a | −0.05 | −0.15 | −0.05 | −0.73a | −0.68 | ||||
Temperature | 0.53 | −0.47 | −0.20 | −0.68a | −0.74a | 0.67 | ||||
Set 2, phytoplankton group contributionb | ||||||||||
Total Chl a | 0.87a | −0.40 | −0.01 | 0.08 | −0.51 | −0.57a | ||||
Bacillariophyceae + Chrysophyceae | −0.28 | −0.44 | 0.74a | 0.16 | 0.39 | −0.57a | ||||
Chlorophyta | −0.06 | 0.92a | 0.09 | 0.14 | 0.44a | −0.22 | ||||
Cyanobacterial APP | 0.46a | −0.02 | −0.29 | −0.07 | −0.91a | 0.07 | ||||
Eustimatophyceae | 0.16 | −0.12 | −0.30 | −0.84a | −0.33 | 0.38a | ||||
Cryptophyta | −0.62a | −0.10 | −0.50 | −0.02 | 0.16a | −0.04 | ||||
Eigenvalue | 0.51 | 0.27 | 0.10 | 0.03 | 0.75 | 0.20 | ||||
Canonical correlation (r) | 0.72 | 0.52 | 0.31 | 0.18 | 0.86 | 0.45 | ||||
Significance (P) | 0.00 | 0.00 | 0.00 | 0.06 | 0.00 | 0.30 | ||||
Percentage variance, set 1 | 0.45 | 0.22 | 0.14 | 0.19 | 0.54 | 0.46 | ||||
Percentage variance, set 2 | 0.24 | 0.20 | 0.16 | 0.13 | 0.26 | 0.14 | ||||
Redundancy, set 1 | 0.23 | 0.06 | 0.01 | 0.01 | 0.40 | 0.09 | ||||
Redundancy, set 2 | 0.13 | 0.05 | 0.02 | 0.00 | 0.19 | 0.03 |
The stratification was determined as 0, mixed and 1, stratified according to the temperature profiles gathered with the CTD and/or submersible fluorometer.
Marks the highest values within a set for each canonical variate.
Respective percentage contribution of each phytoplankton group to the total calculated chlorophyll a (Chl a). See text for calculation of the contributions.
Seasonal monitoring
Intense monitoring was conducted from May 2002 to June 2003. The year 2002 was warmer than 2001 and 2003 with a mean summer (July – September) temperature at 5 m water depth of 12°C, compared to 8.5°C in 2001 and 8°C in 2003 (cf. Fig. 6A). Moreover, the ice cover was shortest in 2002 (1 month) relative to 2001 (2.5 months) and 2003 (2 months).
Dynamics of APP, nano- and microphytoplankton
The amount of APP was high in July (about 0.7 mm3 L−1) and decreased towards autumn (less than 0.2 mm3 L−1; Fig. 6B). Nevertheless, the presence of APP in February and March (about 0.1 mm3 L−1), when the lake was covered with ice, indicated an important APP formation under the ice. The nano- and microphytoplankton biovolume was highest from mid to end of May (median 2.0 mm3 L−1), decreasing at the end of May and was then low during summer and autumn (median 0.08 mm3 L−1; Fig. 6B). In contrast, the cell numbers then grew again in July and were as high as during spring (Fig. 6B). The diversity index (reciprocal Simpson index) increased from a minimum in mid-May (1.2) to a maximum in mid-June (10.6; Fig. 6B) and was, therefore, highest at the time of the minimum biovolume and cell abundance.
The spring bloom (2002) was dominated by Stephanodiscus meyerii and Aulacoseira baicalensis (Bacillariophyceae), endemic species that fill a niche similar to indicators of vernal blooms usually found in mixed, mesotrophic conditions (Table IV). Towards the beginning of July (2002), the contribution of Bacillariophyceae decreased and Chlorophyta (Koliella sp. and Monoraphidium sp.) became important (Fig. 6B; Table IV). The dominant functional groups changed to mixed, oligotrophic or mesotrophic conditions (Table IV). In July, the large number of chrysophycean flagellates indicated the start of summer stratification, and Bacillariophyceae were rather rare (Fig. 6B; Table IV). Small algae such as Rhodomonas pusilla and Chrysophyceae predominated also during the whole August and September (Table IV). Asterionella formosa reached a mass development (10-fold increase within one week) that was unusual for the Bolshye Koti site (Table IV). Under the ice of the next year (2003) the assemblage was dominated by species preferring cold, mixed and enriched conditions, such as A. formosa (Bacillariophyceae) (Table IV), but shortly before ice break up Synedra acus (Bacillariophyceae) and Gymnodinium baicalensis (Pyrrophyta) became dominant (Table IV). Both indicated conditions of nutrient availability.
Dynamics of phytoplankton pigments
The biovolume maximum did not correspond to the chlorophyll and carotenoid one (Fig. 6B). Shifts towards Chl a rich phytoplankton groups prevailed in summer, while large and Chl a poor cells dominated in winter and spring. Thus, the Chl a versus nano- and microphytoplankton biovolume ratios were <6 nmol mm−3 in spring (May–June) but 22 nmol mm−3 on average in summer (July–September) and 8.5 nmol mm−3 on average under the ice (February–April).
After the ice break up at the end of April 2002, Chl a showed a first maximum in mid-May (2 nmol L−1), decreasing strongly from the end of May to the beginning of June (Fig. 6B). The clear-water phase gave way to another Chl a maximum in mid-July (Fig. 6B) and the sum of carotenoids increased up to 160% of Chl a. Then, the Chl a concentration was high during summer, when temperatures rose to a maximum of 16°C and Secchi depth was less than 10 m (Fig. 6B). From the end of summer to autumn the temperature, Chl a concentration and percentage carotenoids decreased while the Secchi depth increased (Fig. 6B). From February to March 2003 the Chl a concentration increased under the ice and Secchi depth was decreased (Fig. 6B).
The distinct pigment changes mostly reflected variations in the phytoplankton groups. For example, pigments found in Bacillariophyceae, such as fucoxanthin, Chl c and diadinoxanthin, had maxima at the same time as the biovolume, i.e. after ice break up in May (Fig. 6B), but in summer, the Bacillariophyceae biovolume decreased while the related pigments decreased to a lesser extent, marking shifts towards smaller, pigment-rich Bacillariophyceae or towards Chrysophyceae (which also contain those pigments). Thereby microscopic counts confirmed the shift towards Chrysophyceae (flagellates, Table IV). Furthermore, the Chlorophyta biovolume was low in spring 2002 and 2003, while lutein and Chl b were high, and in summer, lutein and Chl b increased faster than the biovolume, suggesting the influence of chlorophycean APP with high pigment versus biovolume ratios (Fig. 6B). Thus, the contribution of Chlorophyta to total Chl a was highest in early summer (Fig. 6B). The summer maxima and the winter decrease of zeaxanthin and β-carotene corresponded to the changes in cyanobacterial APP (Fig. 6B) and the contribution of cyanobacterial picoplankton to the total Chl a was highest during summer (Fig. 6B). The CCA indicated that stratification and temperature favoured the seasonal occurrence of chlorophyta and cyanobacterial APP (Table III).
DISCUSSION
Phytoplankton react quickly to environmental changes and thus can be very useful for assessing changes in aquatic ecosystems. This is especially so for Lake Baikal where monitoring has been carried out for more than 60 years (Kozhov, 1963; Galazyi, 1993; Kozhova and Izmest’eva, 1998; Popovskaya, 2000; Goldman and Jassby, 2001; references therein). However, up until now, few multiparameter studies have been conducted of regional, vertical and seasonal variability. Also, comparison of studies set up to investigate the importance of APP has been difficult because of the different methodologies used in different decades. In this study, the aim was to provide an overview of phytoplankton variability over 3 years by comparing phytoplankton determined by microscopy (to give taxonomic details) and HPLC-aided pigment analysis, which gives rapid quantitative results for large numbers of samples.
Regions
Variability of APP, nano- and microphytoplankton
Median total biovolumes found in this investigation were within the ranges of those reported in earlier studies (Kozhova and Izmest’eva, 1998; Popovskaya, 2000). Former studies also already indicated the importance of APP in the South (Votintsev et al., 1972; Nagata et al., 1994) and Centre (Nakano et al., 2003) as well as at near-shore or river delta stations (Boraas et al., 1991), but only few studies compared the APP distribution in the different regions. The broadest study on APP was recently performed by Belykh and Sorokovikova (Belykh and Sorokovikova, 2003) using epifluorescence scanning electron microscopy. They reported an APP abundance that varied by 2–10 times in different parts of the lake. In 2001 and 2002, the APP contribution to total biovolume and to total Chl a varied considerably being lowest in the North and highest in the Selenga Delta. The CCA showed that latitude is negatively correlated to the cyanobacterial APP contribution, and contrasted, therefore, suggestions of Popovskaya (Popovskaya, 2000) that APP is generally most prominent in the North.
In most marine and freshwater systems the contribution of APP to the total biovolume and production was inversely correlated to increasing trophy (Stockner, 1991; Callieri and Stockner, 2002). This general concept may not apply to Lake Baikal, assuming higher trophy in the deltas and oligotrophic conditions in the pelagic regions of the lake (Callender and Granina, 1997; Genkai-Kato et al., 2002). The CCA indicated that water depth (and thus the distance to the shore, which may be indicative for the trophic state of the regions) was of minor influence for the cyanobacterial APP contribution. In growth experiments with Synechocystis limnetica, it was concluded that temperature is the major driving force (Richardson et al., 2000). In contrast, in situ, temperatures only showed secondary influence but stratification was the dominant factor for increasing cyanobacterial APP contribution. Belykh and Sorokovikova (Belykh and Sorokovikova, 2003) also found the highest APP abundances in the epilimnion during the periods of thermal stratification.
The nano- and microphytoplankton biovolume varied between the basins and also between years, but not significantly. A general increase of the biovolume from North to South, as reviewed by Goldman and Jassby (Goldman and Jassby, 2001), could not be confirmed in 2001 and 2002. It could also not be confirmed that the regions with shallow waters and with river water input, such as the Selenga Delta, showed highest biovolumes (even including the APP), as mentioned in former comparative studies (Bondarenko et al., 1996; Popovskaya, 2000). Therefore, if eutrophication occurs in the Selenga Delta due to anthropogenic or natural impacts (Popovskaya, 2000) it might affect the phytoplankton composition rather than the total biovolume.
Kozhova (Kozhova, 1987) noted for river-estuary regions enrichment by the flora of the tributaries. Possibly, these species do not survive, as a diversity increase could not be found in the delta. However, one sign of changing phytoplankton community was the development of the N2-fixing Aulosira sp., which indicated a possible N limitation in the Selenga Delta. Low N/P ratios (14) were found in the Selenga Delta in preliminary nutrient measurements in July 2003, whereas the ratios in the open basins varied between 25 and 36 (V. Straskrábová and J. Borovec, Ceske Budejovice, Czech Republic, personal communication). Increasing P-load from the tributaries (Callender and Granina, 1997) was supposed to create the N limitation (Goldman and Jassby, 2001).
Variability of phytoplankton pigments
As has been pointed out by Kozhova et al. (Kozhova et al., 1985), it is impossible to delineate a region within the pelagic basins characterized from year to year by constant higher or lower Chl a. In fact, in the North for example, each year showed significantly different Chl a concentrations. While lowest Chl a concentrations in 2001 were found in the Centre, they were found in the North in 2002 and 2003. Nevertheless, the combined data set (July 2001 + 2002 + 2003) showed significant changes for Chl a and the sum of carotenoids, as well as for several marker pigments. For example, significantly higher Chl a concentrations in the South compared to the Centre and North were found. SeaWiFs satellite data analyses at the time of the CONTINENT expeditions (July 2001, 2002 and 2003) confirmed these trends (Heim et al., 2005). Moreover, besides variations among the open basins, significantly higher Chl a concentrations were found at the river inflows. Nutrient enrichment could be assumed to trigger Chl a increase but the CCA suggested a higher correlation with stratification. Stratification was enhanced in the delta because of the warm river water inflow and because the shallow water zone warmed up faster than the deep water basins. The role of eutrophication might, therefore, be secondary up to now for total phytoplankton abundance (which does, however, not exclude the aforementioned expected shift in the species composition if nutrient loading further increase).
Marker pigment analysis of the open basins revealed statistically significant changes in the phytoplankton community that could not be distinguished statistically using phytoplankton counts, illustrating the benefit of fast techniques for large sample sets. For example, marker pigments of the chlorophyta, cyanobacteria and Eustigmatophyceae decreased significantly from South to North. Marker pigments are also potential indicators of varying environmental conditions in regions where total biovolume, Chl a and sum of carotenoids did not show significant differences. For example, Chl a and total carotenoids were both high in the Selenga Delta and Barguzin Bay suggesting similar environmental conditions. Nevertheless, the marker pigments of Bacillariophyceae + Chrysophyceae (e.g. fucoxanthin) were significantly higher in the Barguzin Bay than in the Selenga Delta, whereas the marker pigments of cyanobacterial APP (e.g. zeaxanthin) were significantly lower. Therefore, different environmental conditions are likely at the two sites. For instance, much higher concentrations of humic substances (dissolved coloured organic compounds) were observed in the Barguzin Bay (unpublished data; Heim et al., 2005), which decreased the light availability.
Depth
Regional and daily varying wind, insolation and stratification have been shown to strongly influence the phytoplankton vertical distribution (Bondarenko et al., 1996; Kartushinsky, 1997). Below the ice, the homogeneous layer was due to convective flow-fields (Granin et al., 1991, 1999; Kelley, 1997). The euphotic zone under the clear, snow-free ice in Lake Baikal in 2003 was 1.7 * Secchi depth (Straskrábová et al., 2005) and, therefore, a 15–35 m thick euphotic zone can be assumed. In spring 2001, 2002 and 2003 Chl a concentrations up to 0.5 nmol L−1 reached 250 m and concentrations over 2 nmol L−1 reached 100 m. Thus, cells were obviously transported out of the zone of maximal productivity. In summer, the phytoplankton is concentrated in the upper 25 m but even during stratification Chl a was found down to 100 m in 2001 and 2002. Genkai-Kato et al. (Genkai-Kato et al., 2003) found that cells collected during mixing as well as during stratification in the deep water of Lake Baikal (500 m) were able to photosynthesize when exposed to surface levels of irradiance. They suggested that live Bacillariophyceae, remnants from the spring community, sank out to greater depths during stratification, which was supported by unpublished taxon depth profiles of our long-term monitoring.
However, even during homothermy the Baikalian pelagial is not homogenous. The South is ice-free many weeks before the North and thus is stratified earlier. Warm water inflows from rivers, such as the Selenga and Barguzin, also enhanced the stratification locally. Primary productivity and biomass increased strongly at stratified stations in summer 1990 (Goldman et al., 1996). According to the present data, the higher production was due to chlorophyta and APP, both highly correlated to stratification.
Seasonal monitoring
The spring bloom 2002 was founded on Aulacoseira baicalensis and Stephanodiscus meyerii. Thus, although the spring peak 2002 did not reach the biovolume of real Melosira-years, it was based on two formerly called Melosira species (Kozhova and Kobanova, 2000). However, the dominance of large Bacillariophyceae in spring is not conform with the generally agreed Plankton Ecology Group-model (PEG) for freshwater lakes, which would predict a spring crop of small, fast growing algae such as Cryptophyta and small centric Bacillariophyceae (Sommer et al., 1986). Comparable dominance of large Bacillariophyceae was found in only 5 out of 18 compared lakes, and four of these five were stratifying, temperate, eutrophic lakes or reservoirs not deeper than 34 m (references in Sommer et al., 1986).
Subsequently, a decline of biovolume and Chl a (the ‘clear-water’ phase) was found in June that might be due to intense grazing. Consistent with the PEG-model, edible Cryptophyta became dominant, as well as small Chrysophyceae. Then, in summer the ratios of pigments versus biovolume of the Bacillariophyceae and chlorophyta decreased indicating a shift towards smaller, pigment-rich cells with increasing temperatures. Contrary to the PEG-model, which predicts growth of nonedible algae, an explosive growth of edible algae, such as eukaryotic and cyanobacterial APP took place in Lake Baikal, confirming, however, APP trends reported previously (Moskalenko, 1971; Goman, 1973; Belykh and Sorokovikova, 2003; Popovskaya and Belykh, 2004). According to the CCA the seasonal cyanobacterial APP development might be triggered by temperature and stratification. Furthermore light limitation due to mixing (>100 m) much below the euphotic zone (<40 m) might depress the APP formation at the time of maximal homothermy after ice break up and in autumn. However, during inverse stratification under the ice APP contributed surprisingly large amounts to the total Chl a (c. 9%) as well as to the total primary production (up to 40%, Straskrábová et al., 2005).
High fucoxanthin/biovolume and Chl c/biovolume ratios indicated a summer development of small pigment-rich Bacillariophyceae or Chrysophyceae cells along with the development of picoplanktonic cells. This summer development of small cosmopolitans at Bolshye Koti enhanced the suggestion of the shift within the phytoplankton community attributed to global warming (Popovskaya, 1991, 2000; Mackay et al., 1998; Bondarenko, 1999). However, the summer communities in the open basins were still dominated by endemics (Cyclotella baicalensis, ornata, and minuta) and therefore, we may claim that, until now, the warming or eutrophication possibly affects the nearshore regions but that the open basins still remain unaffected, due to the huge water masses.
The maximum of the nano- and microphytoplankton biovolume found in February/March 2003, when ice cover was 0.8 m thick and almost free from snow, could be due to convection under the ice when solar radiation warmed the near-surface water. Then voluminous, nonmotile Bacillariophyceae can be maintained days or even months near the surface providing cells with enough light for growth (Kelley, 1997; Granin et al., 1999). However, because horizontal currents under the ice can confuse where the cells actually grew, caution must be expressed on the role of clear ice for diatom growth. Asterionella formosa dominated this site for the first time since the beginning of the long-term monitoring at Bolshye Koti. According to the regional distribution A. formosa was localized at Bolshye Koti, as it was not found in abundance elsewhere. It may be that this species is an opportunistic taxa filling a niche where available but never dominating the whole lake. Its mass development was probably a result of multiple asexual reproductions (Kobanova and Izmest’eva, 2003). An alternative may be shifts of surface currents that frequently occur under the ice in Lake Baikal and move phytoplankton horizontally. Moreover, Likhoshway et al. (Likhoshway et al., 1996) showed that A. formosa frequently occur in higher concentrations in thermal bars. Then, its mass appearance at Bolshye Koti indicate that caution must be applied when interpreting the long-term phytoplankton record from one site within Lake Baikal.
CONCLUSION
This investigation of regional, vertical and seasonal distribution patterns has provided a broad overview of the present state of Lake Baikal. The use of a combined methods approach, including microscopy and HPLC-based pigment-analysis, provided further details on natural changes, such as shifts of algal species or group dominances. On the one hand, changes of total biovolume and Chl a revealed impacts of the river inflow and shallow strait regions and, on the other hand, marker pigment changes revealed differences between regions, where biovolumes and Chl a were not significantly different, e.g. between Selenga Delta and Barguzin Bay. Factorization and ordination provided further insights into the driving forces. Temperature and stratification were shown to have major impact on the composition of the phytoplankton community. Therefore, we can expect that a possible long-term warming in the lake would lead to significant changes in the phytoplankton group and species composition towards smaller, pigment-rich cells such as small diatoms or picocyanobacteria. Recently, palaeoecological analysis of preserved markers, such as diatom valves or photosynthetic pigments, is increasingly used to monitor environmental change in response to climate and human activities. Insights into the driving forces will aid interpretation of sediment formation in this ancient lake.
We thank the captains, crews and expedition members of the research vessels ‘Vereshchagin’ and ‘Kozhov’. Thanks are also due to the staff members of the Scientific Research Institute of Biology (State University Irkutsk), especially Ludmila Kraschuk and Elena Pislegina, and of the Limnological Institute Irkutsk (Russian Academy of Science), especially Ruslan Gnatovski and Oleg M. Khlustov, for help in field and to staff members of the Leibniz Institute of Freshwater Ecology and Inland Fisheries (Berlin), especially Helgard Täuscher, Hannah Winkler and Marianne Graupe, for help in laboratory. Special thanks are due to Dr. H. Oberhänsli, for her permanent engagement to the CONTINENT project (EVK2-CT-2000-0057) and support of this study. We thank David Morley, Anson Mackay, Vera Straskrábová, Jan Köhler and especially David Jewson, Tammi Richardson and anonymous reviewers for their critical remarks and helpful comments on this manuscript.
REFERENCES
SCOR-UNESCO Workgroup. (
Author notes
1Leibniz Institute of Freshwater Ecology and Inland Fisheries, Mueggelseedamm 301, 12587 Berlin, Germany, 2Scientific Research Institute of Biology, State University Irkutsk, Lenina 3, 664003 Irkutsk, Russia and 3Institute of Biology, Humboldt University Berlin, Luisenstrasse 53, 10099 Berlin, Germany