Charophytes are one of the main groups of macrophytes occurring in clean, hard-water lakes. They form large meadows and are therefore an important structural and functional element of the littoral zone. In the literature, there are more and more references to charophytes, not only in the context of their sensitivity as water quality indicators, but also as a significant habitat forming element (as summarized by Schneider et al. 2015). This is reflected e.g. in the alternative stable state theory for shallow lakes (Scheffer 1998). According to Van den Berg et al. (1998), charophytes play a special role in maintaining clean water quality, among others through: increasing the sedimentation from the ambient water, preventing the resuspension of bottom sediments or limiting the development of phytoplankton.
The key role is played by the quantitative contribution of charophytes (which form dense meadows in
Despite the seemingly obvious impact of charophyte biomass on lake ecosystems (especially those with a considerable contribution in charophyte meadows), there is still little data concerning in situ variability of charophyte biomass production and factors affecting it.
Studies carried out so far suggest that charophyte biomass depends both on the species (Królikowska 1997), and on the lake (Kufel, Kufel 2002). There is also much evidence that the percentage contribution of precipitated carbonates is species-specific (Kufel et al. 2013, Pełechaty et al. 2013).
Besides, little is also known about factors affecting the charophyte biomass production and the formation of encrustations. Most works on the subject written so far are based on experimental studies investigating single factors which affect charophyte biomass (e.g. Ray et al. 2003). We know that the content of nutrients (Kufel, Rymuza 2014) and the amounts of calcium and magnesium ions in the water (Asaeda et al. 2014) are among the main environmental factors affecting the amount of biomass and the contribution of carbonates. There is much evidence that similar factors operate in the lacustrine environment, and the amount and precipitation rate of carbonates is site-specific (Pełechaty et al. 2015). This is suggested e.g. by the results obtained by Pukacz et al. (2014a), showing that both the dry weight of charophytes and the amount of carbonate encrustations they produce change with depth.
Learning exactly how the quantitative and qualitative structure of biomass changes in relation to environmental characteristics may be very important both for the current studies concerning the functioning of lake ecosystems, and – as Dittrich & Obst (2004) suggested – also in paleoecological and paleoclimatic research.
The aim of the presented study was to: a) quantify the dry weight and CaCO3 precipitation of two charophyte species in different lake ecosystems, b) determine possible relationships between the environmental characteristics of the investigated lakes and the dry weight and CaCO3 content in DW of the two species. We hypothesized that the amount of dry weight and CaCO3 encrustation is species-specific but the CaCO3 content in DW is lake-specific.
The study was carried out between 20 and 26 July 2012, in seven lakes located in Western Poland. The same high-pressure weather (cloudless or slightly cloudy, high temperatures and light wind) prevailed during the whole study period. The lakes differed in terms of morphology, flow type and the nature of the drainage basin (Table 1). On the one hand, the group of lakes included a shallow, small polymictic lake (Lake Jasne) and on the other, a deep, large lake (Lake Niesłysz). The lakes had different hardness levels (from 3.62°dH in Lake Pierwsze to 12.79°dH in Lake Karskie) and the parameters related to hardness varied in a similar manner. Despite this, as well as a considerable morphometric variety, all the lakes are classified as mesotrophic. This is evidenced by relatively low TP concentrations (from 0.010 to 0.033 mg l-1 in Lake Jasne and Lake Niesłysz, respectively) and TN concentrations (from 1.23 to 1.87 mg l-1 in Lake Złoty Potok and Lake Karskie, respectively). All the lakes had high water transparency (Secchi Depth visibility > 3 m), with Lake Meckie having the highest water clarity (SD = 6.5 m). What these lakes had in common was also well-developed aquatic vegetation, dominated by charophyte meadows. Both the structure of the vegetation and the habitat conditions found in most lakes during the study correspond to the data from previous years (e.g. Pełechaty, Pukacz 2006; Pełechaty et al. 2007).
Characteristics of the studied lakes (based on: Jańczak 1996; Pełechaty, Pukacz 2006; Pełechaty et al. 2007)
Unit | Jasne | Karskie | Malcz | Męckie | Niesłysz | Pierwsze | Złoty Potok | |
---|---|---|---|---|---|---|---|---|
Geographical coordinates | - | 52°17'7"N 15°03'06"E | 52°55'4"N 15°04'8"E | 52°21'1”N 15°13'3"E | 52°22'0"N 15°11'2"E | 52°13'9"N 15°23'8"E | 52°23'11”N 15°09'18"E | 52°13'0"N 15°22'5"E |
Surface area | ha | 15.1 | 150 | 36.2 | 40.9 | 486.2 | 19.3 | 32.8 |
Mean depth | m | 4.3 | 6.2 | 3.4 | 9.2 | 7.8 | 4.7 | 5.9 |
Max depth | m | 9.5 | 17.6 | 7.3 | 22.7 | 34.7 | 10.7 | 13.7 |
Lake type | - | outflow | flow | no flows | flow | no flows | no flows | no flows |
Stratification | - | incomplete | complete | incomplete | complete | complete | incomplete | complete |
SD visibility | m | 3 | 3.1 | 3.8 | 6.5 | 4.2 | 5.4 | 4.3 |
m | 3 | 3 | 3 | 2 | 2 | |||
m | 4 | 3 | 3 | 4 | 4 |
The study concerns two morphologically different
At five out of 15 study sites of
Each species was sampled in five lakes from three different sites (Table 1). In three of the lakes (Jasne, Karskie, Meckie), both species co-occurred. In the four other lakes, only one of these species was found, each of them in two lakes. All the sampling sites for each species within a particular lake were located at the same depth. The sites and depths were determined in the preliminary research on the basis of the community formation: the sites where each species formed dense patches, in good condition and with structure typical of a given lake were selected. The samples were manually harvested from the bottom by diving from the central part of a patch. In addition, before sample harvesting, the percentage cover of the bottom by the dominant species (C.
From each of the plant samples, 10 entire individuals (not damaged) were selected for further measurements. The selected individuals were separately transported to a laboratory in plastic bags.
Prior to charophyte sampling, basic physicochemical parameters of water from above each sampling site (a few cm above charophyte meadow) were measured. The following parameters were measured: visibility (in each lake at one pelagial site, using a Secchi disc), dissolved oxygen concentration and temperature (using an Elmetron CX-401 portable meter), electrolytic conductivity and pH (with a Cyber-Scan 200). Similarly, water samples for laboratory analyses were collected directly from above the sampling sites using an electric pump. The samples were collected in one-liter plastic bottles and kept in a portable refrigerator. First, alkalinity analysis was performed (in a laboratory within 6 hours after the sampling). Then, the samples were kept in a refrigerator (at 4°C) until the remaining chemical analyses were performed.
Immediately after their collection at the lake, the individuals were air-dried for 24 hours using a laboratory ventilation system to avoid decomposition. After that, the plants were dried at 105°C for three hours in an electric drier to determine the dry plant weight (DW). The calcium carbonate content (% CaCO3) was determined by the two-step weight loss on ignition method (Heiri et al. 2001). Powdered samples were first combusted at 550°C for 4 hours and subsequently at 950°C for 2 hours. Carbonate content was calculated by multiplying the mass of CO2 evolved in the second step of the analysis by 1.36. Finally, the CaCO3 content was calculated by multiplying the CO32- content by 1.66. The loss on ignition at 550°C is presumed to represent an organic matter percentage. The whole procedure was performed separately for each of the 10 individuals taken from a single sampling site.
Alkalinity was determined by the titration method with an indicator and color using the visual method against the platinum scale. The total water hardness was determined by the versenate method. In order to determine Ca2+ and Mg2+, a Metrohm ion chromatograph, the 881 Compact IC Pro model (Metrohm, Switzerland) was applied, using columns Metrosep C4 Guard (the guard column) and Metrosep C4 150 (the separating column). Total nitrogen was determined by a TOC-L Shimadzu analyzer with a TNM-L unit using catalytic thermal decomposition and chemiluminescence methods (Shimadzu, Japan). Total phosphorus was determined by the molybdate method with ascorbic acid as a reducer using a Merck Spectroquant® Pharo 100 apparatus (Merck KGaA, Darmstadt, Germany). The same analyses were performed separately for each of the samples.
Statistical analyses were performed using STATISTICA 10.1 (StatSoft Inc., Tusla, OK, USA) software. The normality of distributions of the analyzed variables and the homoscedasticity of the samples were tested with the Shapiro-Wilk and Levene tests, respectively. The conditions were satisfied in both cases, thus one-way ANOVA and the post-hoc Scheffe test were used to compare the means of the variables.
For the interpretation of the obtained results, the Ca/Mg ratio and the saturation index (SI) were calculated. For SI, the formula by Kelts and Hsü (1978) was applied:
Dry weight of
Values of
The CaCO3 content in DW varied unlike the pattern of dry weight (Fig. 2). In all three lakes where both analyzed species occurred, the CaCO3 content in DW of
The range of CaCO3 content in DW was 58.8-70.9% for
Values of CaCO3- in DW of
Mean values of CaCO3- content in DW varied considerably between most lakes, both for
The Principal Component Analysis (PCA) showed clear differences between the investigated lakes in relation to physicochemical parameters (Fig. 3), mainly due to conductivity, alkalinity, total hardness, Ca2+ and Mg2+ (correlated with the first principal component) and oxygen (correlated with the second principal component). These parameters explained over 70% of the observed variance, with r > 0.8. This was also confirmed by additional tests, which indicated statistically significant lake-to-lake differences (ANOVA,
Lake Karskie was most different from the other lakes. The waters of this lake were characterized by the highest values of conductivity, alkalinity, hardness and total nitrogen, whereas the lowest values of these parameters were determined in Lake Pierwsze. Lakes Niesłysz and Złoty Potok also clearly stood out in PCA. Both of them had similar water characteristics, which indicate intermediate conditions between those of Lake Karskie and Lake Pierwsze. The other lakes (Malcz, Jasne and Męckie) were clustered together as a group with similar water characteristics. Their differentiation for the second principal component was mainly related to the values of oxygen and, to a lesser extent, TP concentration.
Significant correlations were found for most of the physicochemical parameters of ambient waters and additional characteristics of the sampling sites with DW and the percentage of CaCO3 (Table 3). No statistically significant correlation
The individuals of
Much greater lake-to-lake differentiation for
Means and standard deviations of the physicochemical water properties significantly differentiating the investigated lakes. Results of ANOVA tests (F and p-values are indicated; degrees of freedom: 6 and 23). Results of post hoc analyses are indicated by means of capital letters.
ANOVA | Jasne (n = 6) | Karskie (n = 6) | Malcz (n = 3) | Męckie (n = 6) | Niesłysz (n = 3) | Pierwsze (n = 3) | Złoty Potok (n = 3) | |
---|---|---|---|---|---|---|---|---|
O2 |
F = 31.4 |
A |
BCD |
A |
BCD |
C |
D |
D |
Alkalinity |
F = 345.8 |
A |
B |
CD |
C |
D |
E |
F |
Conductivity |
F = 803.3 |
A |
B |
C |
C |
A |
D |
A |
Ca2+ |
F = 1512.1 |
A |
B |
C |
C |
D |
E |
A |
Mg2+ |
F = 61.3 |
A |
B |
A |
A |
C |
A |
C |
Hardness |
F = 1433.5 |
A |
C |
B |
B |
A |
D |
E |
Significant correlations between the dry weight and calcium carbonate content of each charophyte species and the habitat characteristics of the study sites. For each characteristic n = 15 (DW and % CaCO3 were represented by mean of 10). The asterisks (*) indicate the significance level (< 0.05, < 0.01, < 0.001, respectively)
Chara globularis | Chara tomentosa | |||
---|---|---|---|---|
Dry weight | % CaCO3 | Dry weight | % CaCO3 | |
Temperature | 0.61* | |||
-0.53* | ||||
Conductivity | -0.63** | |||
Alkalinity | -0.59* | -0.59* | ||
Ca2+ | -0.88*** | |||
Mg2+ | -0.55* | |||
Hardness | -0.75*** | |||
TP | 0.73** | |||
Sampling depth | 0.69** | |||
% bottom cover | 0.72** | |||
No. of species | -0.58* |
The fact that DW at all 3 sites in each lake was similar for a given species indicates that the sampling sites did not differ significantly in terms of habitat conditions. The main factor responsible for this situation was certainly the same sampling depth, and thus the availability of light, which is the main limiting factor for charophytes (Blindow et al. 2002; Pukacz et al. 2014a).
Both species produced considerable amounts of carbonate encrustations, which shows they are an important sediment-forming element in the littoral zone (e.g. Wetzel 1960; Hutchinson 1975). The mean values of CaCO3 content in DW of
Apart from the above-mentioned higher mean values of % CaCO3 in
The obtained results show clearly that the percentage of CaCO3 does not directly reflect the growth (DW) of charophytes. This was indicated by different variability patterns of DW and % CaCO3. It is particularly evident in Lake Męckie where values of % CaCO3 were among the highest for both species, while DW values were relatively low. Therefore, such results suggest that the factors affecting the charophyte growth are different from those affecting the formation of carbonate encrustations. These observations are in line with the results obtained by Pukacz et al. 2015 (unpublished data) who found that the percentage of CaCO3 in DW of
The analyses of water characteristics showed that lake-based habitat differences are much bigger than the site-based ones. The lack of significant differences between sites within the same lake is mostly the consequence of a) intensive water mixing in the littoral zone (Wetzel 2001) and b) similar depth of sampling sites (Pukacz et al. 2014). The high contribution of charophyte vegetation may also be an important factor, which stabilizes the habitat conditions within phytolittoral (e.g. van den Berg et al. 1998; Kufel, Kufel 2002).
The significant lake-to-lake differences based on physicochemical water properties showed a great diversity of typical Chara-lakes and a wide spectrum of habitat conditions in which
The fact that Lake Karskie was most different from the other lakes was probably due to the high fluctuations of the water level (rising and then falling within a few months) observed in the year of the study and the preceding year (according to various sources, from 0.5 to 0.8 m). This caused the leaching of mineral and organic substances from the lake margins, and thus considerable changes in water chemistry. This may also be reflected in the brownish color of the water observed during the study. Changes in Lake Karskie probably influenced the development of charophytes, as indicated by the lowest DW values. During the study, we also observed a considerable decrease of the number of species and the area covered by charophytes in Lake Karskie, as compared to 2009 (Cyrwus 2009). It shows that changes of water chemistry may result in changes of quantitative and qualitative structure of charophyte biomass, which was evidenced, among others, by the results of experimental research by Kufel & Rymuza (2014).
Even though lakes Złoty Potok and Niesłysz differ significantly in terms of their morphometry (Lake Niesłysz is more than 10 times larger and much deeper than Lake Złoty Potok), they had very similar water properties. This is also confirmed by previous studies (Pełechaty et al. 2015). This probably results from the fact that the lakes are adjacent and have almost identical drainage basins, both in terms of hydro-geological properties and the use by man. However, both DW and % CaCO3 of
The significant correlations for most physicochemical parameters of ambient waters and additional characteristics showed that both DW and % CaCO3 are closely related to the habitat conditions. However, the differences in correlation coefficients for
The larger number of correlations found for
The positive correlations found for CaCO3 content in
Most of the results discussed above confirm the significance of charophytes as an important element of lake ecosystems and their functioning. However, due to considerable limitations of the in situ study (e.g. difficulties with sampling and a number of potential external factors affecting the results of the analyses), the results presented herein must be considered with caution. A detailed study, including both a wider range of analyses and elements of experimental research (mesocosm studies), would be desirable.