Response of the Cyanobacteria Plankton Community to Anthropogenic Impact in Small Lakes of Urbanized Territory in the Permafrost Zone of Northeast Asia (Eastern Siberia, Yakutia)
1
Institute of Evolution, University of Haifa, Mount Carmel, 199 Abba Khoushi Ave., Haifa 3498838, Israel
2
Institute for Biological Problems of Cryolithozone Siberian Branch of Russian Academy of Science (IBPC SB RAS), Lenin Av., 41, 677980 Yakutsk, Russia
*
Author to whom correspondence should be addressed.
Water 2024, 16(19), 2834; https://doi.org/10.3390/w16192834
Submission received: 6 September 2024
/
Revised: 30 September 2024
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Accepted: 4 October 2024
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Published: 6 October 2024
(This article belongs to the Special Issue Aquatic Ecosystem: Problems and Benefits—2nd Edition)
Abstract
:In the conditions of growing anthropogenic pressure, aquatic ecosystems all over the world are subject to transformation, expressed in the growth of eutrophication, increase in acidity, changes in water exchange, etc. In the region of Eastern Siberia we studied, located in Yakutia in the middle reaches of the Lena River basin, there is a significant population growth accompanied by advancements in agriculture and public utilities. The region is rich in small lakes, which have been under pressure from human activities for the past few decades. The studied region is located in the permafrost zone and is characterized by severe climatic conditions, cold long winters, short hot summers, and a short ice-free period on reservoirs. We studied 17 lakes of various genesis, with varying degrees of anthropogenic pressure, located in the largest city of the region, small villages, and at different distances from them. Previous studies have established that cyanobacteria constitute the phytoplankton main group in these lakes during the summer period. Therefore, we selected them as the focus for our bioindication analysis. An integrated assessment of the bioindication properties of cyanobacteria, along with chemical water parameters, was undertaken using statistical mapping methods, JASP, and Redundancy Analysis (RDA). This analysis revealed the impact of urbanized areas, characterized by a decrease in pH, runoff of nitrogen compounds, and an increase in organic matter. Despite the cryolithozone harsh conditions, in small lakes of urbanized areas, cyanobacteria exhibit their competitive advantages within the plankton community. The prospect of continuing our work is associated with the need to determine the risk of cyanoHAB development since potentially toxic cyanobacteria have a mass development in a number of lakes.
1. Introduction
The development of urban economy, agricultural, and industrial production has an increasing impact on aquatic ecosystems around the world. This is associated with the acceleration of the rates of primary production, the process of eutrophication, acidification of water bodies, changes in water exchange, etc. Eastern Siberia is not one of the densely populated regions of the planet. However, due to the implementation of large projects for the extraction of fossil resources, and other economic or social reasons, in certain parts of the region, due to internal migration processes, local population growth is noted.
The area we studied is located in the middle reaches of the Lena River basin. The population here is growing rapidly and has increased by 43% over the past 20 years, currently amounting to 420.8 thousand residents [1]. The largest city in the region, Yakutsk, is the largest city in the world located in a permafrost zone. With the development of agriculture and public utilities in the studied area, the degree of stress on water bodies associated with human activity is steadily increasing.
The lakes within the city of Yakutsk, which is the most densely populated area of the studied region, have been under anthropogenic pressure for several decades, which has been confirmed by a number of studies based on the study of the chemical composition of water [2,3,4,5,6,7,8,9,10,11,12,13,14] and bioindicative properties of lake inhabitants [7,9,14,15,16,17,18,19]. But the question of how great the human impact on the lakes of this area is in small villages, where the population density is much lower, but agricultural and livestock enterprises are actively functioning, still remains unanswered.
The species composition of phytoplankton in lakes located in the middle reaches of the Lena River basin in Central Yakutia has been studied by various authors since the second half of the last century [15,20,21,22,23]. According to long-term observations, cyanobacteria are found in the lakes of the region from June to September, and their maximum biomass occurs in the second half of July and early August. In summer, cyanobacteria dominate the plankton of the lakes of the region not only in terms of numbers but also in terms of species, forming the basis of the plankton communities of these reservoirs in most lakes. The total dominance of cyanobacteria in the summer phytoplankton of these lakes was the reason for our choice of this group of photosynthetic organisms as an object of bioindication.
The aim of the work was to identify the diversity of cyanobacteria in small lakes of Central Yakutia and to assess the anthropogenic impact of urbanized areas on aquatic ecosystems in the permafrost zone using bioindication and statistical mapping methods.
2. Materials and Methods
2.1. Site Description
The study area is situated in the northeastern part of the Asian subcontinent, specifically in Eastern Siberia’s Yakutia region, within the middle reaches of the Lena River basin, characterized by continuous permafrost (Figure 1). The climate is sharply continental with long, severe winters and short, hot summers. Yakutia records the coldest temperatures in the entire Earth’s Northern Hemisphere. The duration of the frost-free period for the study area reaches 90 days [24]. The ice-free period on the region’s water bodies and, consequently, the growing season, is limited to 120–125 days [25]. The area we studied is the most densely populated part of Yakutia, with 42% of its population living here [1]. The largest city in the region, Yakutsk, is the largest city in the world located in a permafrost zone. With the development of agriculture and public utilities in the research area, the degree of pressure on water bodies associated with human activity is steadily increasing
The study area is rich in small lakes. For our work, 17 different types of lakes were selected (Figure 1, Table 1), differing in the degree of anthropogenic load: located in the largest city of the region—Yakutsk, near small villages, and at a distance from populated areas. The lakes also differed in size and origin. Some lakes are located on the floodplain terrace of the Lena River and are river lakes (oxbow lakes) representing channels separated from the river. Other lakes are of thermokarst origin; their basins were formed as a result of thawing of underground ice of permafrost (Table 1, Figure 2).
2.2. Sampling
All samples including plankton and water ones were taken from surface layer (0–20 cm) of water column between 2 and 4 August 2023. Sampling was carried out using an Apstein plankton net (Sefar AG, Heiden, Switzerland) (SEFAR NITEX fabric, mesh diameter 15 µm) in early August. Samples were preserved immediately upon collection with 4% neutral formaldehyde solution. Geographic position and altitude above sea level were determined using a Garmin eTrex GPS navigator (Garmin Ltd., Olathe, KS, USA). Water samples of 2 L were taken from each water body for chemical analysis and transported to the Institute for Biological Problems of Cryolithozone SB RAS (Yakutsk, Russia) for further studies. Water temperature was measured with Chektemp electronic thermometer (Hanna Instruments, Woonsocket, RI, USA).
2.3. Water Chemistry Analysis
Chemical analyses of water samples were performed following standard methods [26]. Water color was determined using a photometric method. pH was measured using a potentiometric method. Water salinity was calculated as the sum of ions using the following methods: turbidimetry for sulphate anions; flame spectrophotometry for potassium and sodium cations; mercurimetry for chloride ions; and titration for calcium, magnesium, and bicarbonate ions. A photometric method was applied to determine nutrient concentrations. Nessler’s reagent, Griess reagent, salicylic acid, ammonium molybdate, and sulfosalicylic acid were used for the measurement of ammonium ion, nitrite ion, nitrate ion, phosphate ion, and total iron, respectively. A combined reagent composed of ammonium molybdate and ascorbic acid was used to determine total phosphorus content.
2.4. Algological Analysis
Olympus BH-2 light microscope (Olympus, Tokyo, Japan) was used for phytoplankton sample analysis. Relevant handbooks and papers were used for cyanobacteria species determining [27,28,29]. The modern species names were adopted using algaebase.org (accessed on 20 July 2024) [30]. Cyanobacteria cell abundance estimation was performed visually using a 6-score system and then was unified to cell number of each species as a percentage according to the aligning scheme by S. Barinova [31].
2.5. Bioindication and Statistics
Bioindicator analysis and Index WESI calculation were performed according to [32] with species-specific ecological preferences of revealed indicator taxa [33]. The BioDiversity Pro 2.0 program was used for similarity calculation [34]. Pearson coefficients calculation was conducted in the composition of cyanobacterial community analysis [35].
2.6. Ecological Mapping and JASP
Statistical maps were constructed in Statistica 12.0 Program, as well as the network analysis in JASP 0.16.4.0 (Jeffreys’s Amazing Statistics Program), significant only, conducted using the botnet package in R Statistica package of [36].
2.7. Species-Environments Relationships Analysis
Redundancy Discriminant analysis (RDA) was conducted with CANOCO program for calculation of biological-dominated variables and environment variables relationships [37].
3. Results
3.1. Physico-Chemical Parameters
During sampling, lake water in the surface layer was well-warmed (Table 2). Furthermore, all lakes showed a higher water pH value.
Most lakes have fresh water, with the exception of Lake Ochchuguy-Matta, which contains brackish water. Thermokarst lakes Balyktakh and Mayya had a high color index. The COD index was high in all water bodies, reaching maxima values in thermokarst lakes. The concentration of ammonium ion was high in all lakes, with maxima values characteristic of lakes Unnamed 1, Diring, Beloye, and minima values for lakes Churapcha, Arylakh, and Unnamed 2. The nitrite content fluctuated within a wide range and reached maxima values in Lake Ytyk-Kyuyol. The nitrate content was also high, reaching a maximum in Lake Ytyk-Kyuyol. The maximum concentration of phosphates was noted for lakes Mayya, Khomustakh, Prokhladnoe, and total phosphorus—for lakes Ochchuguy-Matta, and Unnamed 3. The content of total iron was high in all lakes, reaching maxima values in Lake Diring.
3.2. Composition of Cyanobacterial Community and Dominant Species
A total of 44 cyanobacterial species belonging to 22 different genera were identified within the plankton of the lakes. The highest diversity of cyanobacteria species was found in lakes Ytyk-Kyuyol, Balyktakh, and Arylakh (Appendix A). The fewest number of species was observed in lakes Unnamed 2, Prokhladnoye, and Khomustakh. The species Microcystis flos-aquae (Wittrock) Kirchner was found in all the studied lakes. The species Microcystis wesenbergii (Komárek) Komárek ex Komárek (found in 14 lakes), Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault (in 13 lakes), and Snowella lacustris (Chodat) Komárek & Hindák (in 11 lakes) were also widespread in these water bodies (Appendix A). The total number of cyanobacteria species was correlated with the sum of abundance scores (Table 3) with the Pearson coefficient = 0.88 (p < 0.0001).
Cyanobacteria in most of the studied lakes accounted for up to 98% of the species composition of phytoplankton. The only exceptions were Lake Mayya and Lake Usun-Kyuyol, where the proportion of cyanobacteria species in the plankton community was 70% and 50%, respectively. In the plankton of most lakes, two representatives of the genus Microcystis predominated in abundance: M. aeruginosa (Kützing) Kützing and M. flos-aquae, accounting for 80–100% of the cell abundance of all cyanobacteria in the sample (Appendix A).
In some lakes, together with Microcystis species, representatives of other genera were co-dominant: Dolichospermum sigmoideum (Nygaard) Wacklin, L.Hoffmann & Komárek (Beloye Lake), Anabaenopsis elenkinii V.V.Miller (Maralayy Lake), and Aphanizomenon flos-aquae (Diring Lake), reaching 10–40% of the cyanobacteria abundance. And in four of the studied lakes (Mayya, Usun-Kyuyol, Temiye, and Unnamed 3 lakes), Microcystis species were not among the dominants and representatives of other genera that predominated in abundance: Aphanizomenon, Dolichospermum, and Planktothrix (Appendix A). For most lakes, water blooms were noted during sampling in the form of observed accumulations of cyanobacteria in the near-surface layer. The exceptions were Mayya, Unnamed 1, Prokhladnoye, and Usun-Kyuyol lakes, where we did not notice any signs of water blooming visually.
3.3. Bioindication
The ecological preferences of the identified species indicate optimal conditions for their development; bioindication is based on this principle. Figure 3 and Figure 4 show the percentage distribution of the abundance of organisms in the cyanobacterial indicator groups in each of the surveyed lakes. It is evident that the species inhabiting the lakes were mainly planktonic-benthic inhabitants (Figure 3a), surviving in waters with low or moderate oxygen saturation, the communities of which were also enriched with aerophiles (Figure 3b). The indicator groups covered a wide range of water pH (Figure 3c). Alkalibionts prevailed in many lakes, but a noticeable number of acidophiles was also found in lakes 6–9 and 16, located near or under the influence of populated areas. The indicators of water molecularity belonged to only two groups—indifferent and halophiles (Figure 3d), which made up to 70% in lakes 7 and 16.
Indicator species showing the trophic state of the lakes belonged to five groups (Figure 4a), among which the most represented group is eutraphents, especially in lakes 3 and 14, making up to 90% of the abundance of communities. In all the surveyed lakes, indicator species belonging to the 3rd class of water quality prevailed (Figure 4b). At the same time, this indicates an average saturation of the lake water with dissolved organic matter and a high self-purifying capacity of their ecosystems.
Calculated Index saprobity S reflects Class 3 of water quality in most of the studied lakes (Table 3) that is consistent with the percentage distribution of indicators in Figure 4b.
Index WESI, calculated on the basis of the rank of Index saprobity S and rank of nitrate-nitrogen concentrations that can help to reveal toxic influence on the species in community is presented in Table 3. There is no one value of WESI that is below 1, therefore, cyanobacteria species do not fill any toxic influence of their photosynthesis.
3.4. Comparative Statistics
In order to identify the most similar indicator species in composition and abundance among the studied lakes, a tree of similarity was constructed (Figure 5). It is evident that the composition of indicator groups in the lakes represents a difficult to cluster set, that is, it indicates a fairly high similarity (more than 70%) in the species composition and abundance of cyanobacteria in the studied lakes.
Then we tried to strengthen the similarity analysis by dividing the data into environmental and biological. The JASP plot showed that chemical variables are clearly clustered into 3 groups (Figure 6). Cluster 1 unites karst lakes on the left and right banks. Cluster 2 includes lakes of different origin and located on different banks of the river but each of them is under the anthropogenic influence of the city of Yakutsk or villages. This group also includes brackish lake 11. The remaining lakes of different origin and position relative to the riverbed are united by chemical variables in cluster 3. Comparison of the average values of the variables in each cluster indicates that the water in cluster 1 is the least saturated with salts, then comes cluster 3 and the highest average TDS in the lakes of cluster 2 (Table 2). Comparing the bioindication data, one can see an increase in the average number of species and the sum of abundance scores in the series of clusters 1–3–2, and in the opposite direction an increase in the average saprobity index S (Table 3).
The JASP plot for bioindicator parameters in 17 lakes (Figure 7), divided by us according to the principle of location on the left or right bank of the Lena River, showed that the lake communities are quite similar and are grouped into only two clusters. Cluster 1 includes lakes either remote from the riverbed on the right bank, or those on the left, but outside the city of Yakutsk. Cluster 2 unites the remaining lakes, including those located near the city of Yakutsk and brackish lake 11. It should be noted that in the lakes of cluster 1, the number of species and the abundance of cyanobacteria in the communities are on average lower than in cluster 2 (Table 3). That is, the lakes of cluster 1 can be called relatively pure water; cluster 2—under anthropogenic influence.
Thus, comparative statistics on the one hand show a high similarity of lake parameters but upon detailed analysis it finds differences, which, both in chemical data and in the composition of indicator species, show subtle differences that allow us to point out cleaner lakes, as well as those that are under the influence of factors not typical for a given landscape.
3.5. Statistical Mapping
Our previous experience has shown that statistical mapping of lake parameters on the landscape helps to reveal hidden properties of their ecosystems not noticeable in the tabular data. For this purpose, we constructed such maps for environmental (Figure 8) and biological (Figure 9) indicators in 17 studied lakes. The distribution of lake altitude confirms the adequacy of the approach, since the gradient is about one hundred meters and, therefore, the studied lakes are located on a low floodplain of a wide, full-flowing river (Figure 8a).
Water temperature in August is higher in the northeastern part of the landscape (Figure 8b). The pH of the river water is lower than in the lakes on both banks (Figure 8c). The influx of chlorides occurs within the city of Yakutsk (Figure 8d). Nitrite-nitrogen is usually higher where there is decomposition of dissolved organic matter (Figure 8e), which is visible in the lakes of the city and the warm zone. The presence of ammonia in the water is usually associated with the influx of fresh organic pollutants, which is associated on the map with certain places in the city of Yakutsk and villages (Figure 8f).
The number of cyanobacterial species in the communities of 17 lakes, as well as their abundance, were distributed unevenly and were higher in the lakes located along the Lena River beds (Figure 9a,b). As indicated in the bioindicator histograms (Figure 4b), most indicator species belonged to water quality Class 3. However, their distribution was also uneven, with a predominance in the lakes near Yakutsk and brackish lakes on the right bank (Figure 9c). The distribution of eutrophication indicator species was associated with populated areas (Figure 9d). We also constructed an abundance map of two Microcystis species, previously identified here as producers of toxic microcystin [41] (Figure 9e). Indicators of acidification were noted within the city of Yakutsk (Figure 9f).
3.6. Biological and Environmental Variables Relationships
An RDA plot was constructed for the major above-mentioned environmental variables as independent and major biological variables revealed earlier in the analysis as dependent. Figure 10 shows two groups of environmental variables grouped together. The group that combined TDS, nitrites, pH, and chlorides can be defined as positively influenced organic pollution (as Index S), acidification, and microcystin toxin production. These variables were mostly associated with lake numbers 11, 15, 7, and 4. The second group included water temperature only increasing which positively influenced cyanobacteria species number and abundance in the lake communities as well as increasing in eutraphentic indicators. For these variables, responsible ecosystems are lake numbers 12, 14, and 17.
4. Discussion
All the studied lakes experience anthropogenic load to varying degrees, which is manifested in the influx of organic matter and biogenic substances from the catchment area, and consequently, a high concentration of nitrogen and phosphorus compounds, high color, and COD, due to which the trophicity of the lakes is increased.
The flora of the studied lakes was characterized by a relatively high biodiversity of cyanobacteria, which may be caused by anthropogenic load on water bodies. A study conducted on the scale of the Eurasian continent found that the species diversity and proportion of cyanobacteria species in phytoplankton increases under the influence of anthropogenic eutrophication [42]. Our results of the JASP plot for bioindicator indicators confirmed that lakes within the city of Yakutsk and located directly near villages on the right bank of the Lena River are characterized by a large number of species and an abundance of cyanobacteria in communities, which indicates the anthropogenic influence of urbanized areas.
According to long-term observations, cyanobacteria are found in the lakes of the region from June to September [21,22]. Available data on the plankton communities of Lake Ytyk-Kyuyol for the second half of the 20th century indicate that the phenomenon of cyanobacterial bloom was typical in the region earlier [15]. However, the composition of the dominant species differed from the modern one; there were no representatives of the genus Microcystis among them. These were species from the genera Dolichospermum, Aphanizomenon, and Trichodesmium. However, in some other lakes within the city of Yakutsk, mass development of Microcystis flos-aquae, which provokes cyanobacterial blooms, has been noted since the 1960s [20]. The results of the work of L. Kopyrina et al., based on long-term observations of the species composition of nine thermokarst lakes located in the middle section of the Lena River, showed that cyanobacteria dominated only in the lakes of the most urbanized and densely populated area we studied, while in the lakes of adjacent areas, Chlorophyta and Bacillariophyta species dominated [23].
The lower pH value of water in lakes located near the Lena River bed, as compared to lakes on both banks, noted thanks to statistical mapping, may be associated with the influence of the river since it is known that the pH level for the rivers of the region is lower than in the studied lakes [43]. However, the acidification indicators within the city of Yakutsk that we noted may indicate ongoing processes of anthropogenic acidification of lake ecosystems in the most urbanized areas of the region. The influx of nitrite and ammonium nitrogen increases not only in the city of Yakutsk but also in the villages on the right bank, which indicates a high anthropogenic load. This is confirmed by the distribution of eutrophication indicator species, which also showed a connection with populated areas.
The abundance of potentially toxigenic Microcystis species is positively related to pH in the studied lakes. It is known that high pH can favor the selection of Microcystis in competition with other phytoplankton species, due to the higher tolerance of representatives of this genus of cyanobacteria to alkaline environmental conditions [44]. There are also reports on the possibility of the selection of toxic strains of Microcystis over non-toxic ones under elevated pH conditions [45].
Redundancy analysis results showed a positive correlation between the abundance of Microcystis not only with the pH value but also with the water temperature, its salinity, and the content of nitrogen compounds in it. In conditions of well-warmed water bodies, cyanobacteria gain an advantage over eukaryotic phytoplankton since their vegetation rates are optimized at relatively high temperatures. There is evidence that as the vegetation rates of eukaryotic organisms’ level off and decrease, the proliferation rate of cyanobacteria reaches its optimum and remains high even when the environmental temperature exceeds 25 °C [46]. Despite the harsh climatic conditions of the study area, the short duration of the growing season, and the fact that the period of good warming of the waters here is very limited and is observed only at its peak [41], planktonic cyanobacterial communities in them are actively developing and successfully competing with eukaryotic plankton.
It is interesting that in relation to the level of vegetation of cyanobacteria and the temperature of their environment, researchers have noted positive feedback when planktonic cyanobacteria, due to the intense absorption of light by their photosynthetic pigments, can locally increase the temperature of the water. Thus, according to remote sensing data obtained by Ibelings et al. [47], the temperature of the surface water layer in cyanobacterial blooms in Lake IJsselmeer, Netherlands, was higher than in the surface water layer outside the blooms. Increased salinity of the reservoir can also have a regulating effect on the taxonomic structure of planktonic communities, in which an advantage can be gained by a number of cyanobacterial species. Thus, some species from the genera noted in the studied lakes, such as Anabaena, Anabaenopsis, and Microcystis, are quite resistant to increased salinity and can successfully compete in the community with eukaryotic freshwater phytoplankton [47].
For example, the growth rate of Microcystis strains remains unchanged with increasing water salinity from 0 g L−1 to 10 g L−1 [48]. The high stability of cyanobacterial blooms is confirmed by reports of observations of this phenomenon in brackish waters of the Baltic, Caspian Seas, and San Francisco Bay, as well as in Lake Ponchartrain (United States) and other water bodies and regions around the world [48]. Nitrogen availability is also a known regulator of the structure of cyanobacterial communities since it is generally believed that when nitrogen enters, non-diazotrophic species, which include representatives of Microcystis, displace slow-growing taxa capable of nitrogen fixation [49].
Most of the studied water bodies are thermokarst lakes, which are the result of permafrost landscape formation, when local disturbances of ice-rich permafrost give rise to subsidence landforms in which such water bodies are formed over time. These water bodies, as a rule, have no runoff and are characterized by slow water exchange, which also contributes to the development of cyanobacterial bloom. As noted earlier by H.W. Paerl and V.J. Paul [46], the conditions when water exchange is reduced and water residence time increases, its nutrient load will be captured and cycled by receiving water bodies, eventually promoting cyanobacterial bloom potentials. In addition, due to the small size of the studied lakes, the wind–wave phenomena in them are extremely limited. Such reduced wind-mixing also increases the risks of mass development of cyanobacteria.
In our previous study, the first data on the distribution of cyanobacterial toxins were obtained and molecular genetic detection of cyanotoxin producers in the plankton of some lakes in this region was carried out for the first time [50]. The main producers of microcystins were identified as two species: Microcystis aeruginosa and M. flos-aquae. And during year-round observations carried out on Lake Ytyk-Kyuyol, the presence of intracellular and extracellular MCs in the lake ice was recorded [41]. In our current study, the results of statistical mapping showed that a number of lakes located remotely from the Lena River on its right bank (Ochchuguy-Matta, Balyktakh, Maralayy, Diring, Churapcha) are characterized by a high abundance of these two toxigenic species.
It should be noted that residents of villages located near these and similar lakes in the region, due to their remoteness from the Lena River and the lack of other available water sources, traditionally use lake water for drinking water supply in the winter, for which purpose the local population harvests ice. Thus, in the region, there is a previously unassessed risk of the negative impact of MCs on the health of local residents. In this regard, water quality control, as well as year-round monitoring studies on the lakes of the region, are also acquiring practical value.
To assess the anthropogenic impact on aquatic ecosystems, researchers often calculate indices based on chemical indicators or roughen the estimates by dividing the identified species into functional groups [51,52]. By comparing chemical indicators and the area of lakes over a large region, a significant temperature value was revealed for small lakes, as in our case, which led to changes in their ecosystems [53]. The integrated bioindication method we used is a more subtle tool that shows the connections between species diversity and not only water quality but also landscape and climate that are not quantifiable by other simple methods.
5. Conclusions
An integrated assessment of the bioindicator properties of cyanobacteria and chemical indicators of water allowed us to estimate the degree of anthropogenic impact on the ecosystem of small shallow lakes in an urbanized area in the permafrost area. It was shown that even in the harsh climatic conditions of the cryolithozone, a combination of anthropogenic load with slow water exchange and good heating of small lakes allows cyanobacteria to use their competitive advantages to dominate phytoplankton. At the same time, a number of cyanobacteria species not only find an optimal habitat here at the peak of the short vegetation season of the cryolithozone but are also able to form the most suitable conditions for development. In many lakes in the region, potentially toxigenic cyanobacteria are developing en masse, so one of the important areas of future research is to identify the degree of risk of cyanoHAB for animals and humans.
Author Contributions
Conceptualization, S.B. and V.A.G.; methodology, S.B. and V.A.G.; software, S.B.; validation, S.B. and V.A.G.; formal analysis, V.A.G. and O.I.G.; investigation, V.A.G. and O.I.G.; resources, V.A.G.; data curation, S.B. and V.A.G.; writing—original draft preparation, S.B. and V.A.G.; writing—review and editing, S.B., V.A.G. and O.I.G.; visualization, S.B.; supervision, V.A.G.; project administration, V.A.G.; funding acquisition, V.A.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research was carried out within the state assignment of the Ministry of Science and Higher Education of the Russian Federation (theme No. FWRS-2021-0023, reg. No. AAAA-A21-121012190038-0; theme No. FWRS-2021-0026, reg. No. AAAA-A21-121012190036-6), (theme No. 121051100099-5).
Data Availability Statement
All data in the article is in the public domain and can be used provided the article is cited.
Acknowledgments
We are grateful to the Israeli Ministry of Aliyah and Integration for partial support of this work.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Table A1.
Species list of cyanobacteria of 17 studied lakes and species occurrence on a six-point scale, August 2023.
Table A1.
Species list of cyanobacteria of 17 studied lakes and species occurrence on a six-point scale, August 2023.
| Species | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Anabaena bornetiana Collins | 1 | ||||||||||||||||
| Anabaena cylindrica Lemmermann | 1 | ||||||||||||||||
| Anabaenopsis elenkinii V.V.Miller | 3 | ||||||||||||||||
| Anabaenopsis tanganyikae (G.S.West) V.V.Miller | 2 | ||||||||||||||||
| Anagnostidinema amphibium (Gomont) Strunecký, Bohunická, J.R.Johansen & Komárek | 1 | 1 | 1 | ||||||||||||||
| Anagnostidinema tenue (Anisimova) Strunecky & al. | 1 | 1 | 1 | 1 | |||||||||||||
| Anathece clathrata (West & G.S.West) Komárek, Kaštovský & Jezberová | 1 | 1 | |||||||||||||||
| Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault | 6 | 1 | 2 | 3 | 2 | 2 | 2 | 6 | 1 | 1 | 2 | 3 | 2 | ||||
| Aphanocapsa delicatissima West & G.S.West | 1 | 1 | 1 | ||||||||||||||
| Aphanocapsa holsatica (Lemmermann) G.Cronberg & Komárek | 1 | 1 | |||||||||||||||
| Arthrospira jenneri Stizenberger ex Gomont | 1 | ||||||||||||||||
| Chroococcus turgidus (Kützing) Nägeli | 1 | 1 | 1 | ||||||||||||||
| Coelosphaerium aerugineum Lemmermann | 1 | ||||||||||||||||
| Dolichospermum affine (Lemmermann) Wacklin, L.Hoffmann & Komárek | 4 | 2 | 1 | 1 | |||||||||||||
| Dolichospermum circinale (Rabenhorst ex Bornet & Flahault) Wacklin, Hoffmann & Komárek | 1 | ||||||||||||||||
| Dolichospermum crassum (Lemmermann) P.Wacklin, L.Hoffmann & J.Komárek | 1 | 1 | 2 | 1 | |||||||||||||
| Dolichospermum flos-aquae (Bornet & Flahault) P.Wacklin, L.Hoffmann & Komárek | 3 | 2 | 3 | ||||||||||||||
| Dolichospermum lemmermannii (Richter) P.Wacklin, L.Hoffmann & J.Komárek | 2 | ||||||||||||||||
| Dolichospermum sigmoideum (Nygaard) Wacklin, L.Hoffmann & Komárek | 3 | 1 | 2 | ||||||||||||||
| Dolichospermum smithii (Komárek) Wacklin, L.Hoffmann & Komárek | 2 | 1 | 1 | 6 | 1 | 1 | 1 | 1 | |||||||||
| Dolichospermum spiroides (Klebahn) Wacklin, L.Hoffmann & Komárek | 1 | 1 | |||||||||||||||
| Dolichospermum viguieri (Denis & Frémy) Wacklin, L.Hoffmann & Komárek | 2 | 1 | 1 | 1 | |||||||||||||
| Kamptonema chlorinum (Kützing ex Gomont) Strunecký, Komárek & J.Smarda | 1 | ||||||||||||||||
| Limnothrix planctonica (Wołoszyńska) Meffert | 1 | ||||||||||||||||
| Merismopedia glauca (Ehrenberg) Kützing | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||
| Merismopedia tranquilla (Ehrenberg) Trevisan | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||
| Microcrocis irregularis (Lagerheim) Geitler | 1 | ||||||||||||||||
| Microcystis aeruginosa (Kützing) Kützing | 2 | 3 | 4 | 6 | 3 | 3 | 3 | 2 | 3 | ||||||||
| Microcystis flos-aquae (Wittrock) Kirchner | 2 | 3 | 4 | 4 | 2 | 4 | 1 | 3 | 5 | 2 | 5 | 4 | 5 | 3 | 4 | 4 | 6 |
| Microcystis ichthyoblabe (G.Kunze) Kützing | 4 | 3 | |||||||||||||||
| Microcystis wesenbergii (Komárek) Komárek ex Komárek | 4 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 3 | 3 | 1 | 3 | 2 | 2 | |||
| Oscillatoria ornata Kützing ex Gomont | 1 | ||||||||||||||||
| Oscillatoria rupicola (Hansgirg) Hansgirg ex Forti | 1 | 1 | 1 | 1 | |||||||||||||
| Oscillatoria tenuis C.Agardh ex Gomont | 1 | 1 | 1 | 1 | |||||||||||||
| Phormidium breve (Kützing ex Gomont) Anagnostidis & Komárek | 1 | ||||||||||||||||
| Phormidium chalybeum (Mertens ex Gomont) Anagnostidis & Komárek | 1 | ||||||||||||||||
| Phormidium corium Gomont | 1 | 1 | |||||||||||||||
| Phormidium inundatum Kützing ex Gomont | 1 | ||||||||||||||||
| Planktolyngbya contorta (Lemmermann) Anagnostidis & Komárek | 1 | ||||||||||||||||
| Planktolyngbya limnetica (Lemmermann) Komárková-Legnerová & Cronberg | 1 | ||||||||||||||||
| Planktothrix agardhii (Gomont) Anagnostidis & Komárek | 1 | 1 | 1 | 4 | |||||||||||||
| Rhabdogloea smithii (Chodat & F.Chodat) Komárek | 1 | 1 | 1 | ||||||||||||||
| Snowella lacustris (Chodat) Komárek & Hindák | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | ||||||
| Woronichinia naegeliana (Unger) Elenkin | 2 | 2 | 1 | 1 | |||||||||||||
| Total number of species | 7 | 8 | 4 | 14 | 11 | 5 | 6 | 11 | 11 | 12 | 9 | 14 | 14 | 8 | 8 | 8 | 11 |
Appendix B
Table A2.
Cyanobacteria species ecological preferences in 17 studied lakes, August 2023.
| Species | Hab | OXY | HAL | pH | pH rank | TRO | Index S | SAP |
|---|---|---|---|---|---|---|---|---|
| Anabaena bornetiana Collins | ||||||||
| Anabaena cylindrica Lemmermann | P-B,S | aer | e | 1.7 | b-o | |||
| Anabaenopsis elenkinii V.V.Miller | P-B | st | me | 1.5 | o-b | |||
| Anabaenopsis tanganyikae (G.S.West) V.V.Miller | ||||||||
| Anagnostidinema amphibium (Gomont) Strunecký, Bohunická, J.R.Johansen & Komárek | P-B,S | st-str,H2S | hl | alf | 4.9–8.0 | m | 2.6 | a-o |
| Anagnostidinema tenue (Anisimova) Strunecky & al. | ||||||||
| Anathece clathrata (West & G.S.West) Komárek, Kaštovský & Jezberová | P-B | hl | me | 1.8 | o-a | |||
| Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault | P-B | hl | alb | 7.0–8.2 | m | 1.95 | o-a | |
| Aphanocapsa delicatissima West & G.S.West | P-B | i | 7.6 | m | ||||
| Aphanocapsa holsatica (Lemmermann) G.Cronberg & Komárek | P-B | i | 6.8–8.0 | me | 1.4 | o-b | ||
| Arthrospira jenneri Stizenberger ex Gomont | P-B | st | 4.7–9.0 | m | 3.7 | b-p | ||
| Chroococcus turgidus (Kützing) Nägeli | P-B,S | aer | hl | alf | 8.1 | e | 0.8 | x-b |
| Coelosphaerium aerugineum Lemmermann | P | me | ||||||
| Dolichospermum affine (Lemmermann) Wacklin, L.Hoffmann & Komárek | P-B | 7.0–8.2 | om | 0.5 | x-o | |||
| Dolichospermum circinale (Rabenhorst ex Bornet & Flahault) Wacklin, Hoffmann & Komárek | P-B | i | om | |||||
| Dolichospermum crassum (Lemmermann) P.Wacklin, L.Hoffmann & J.Komárek | P | e | ||||||
| Dolichospermum flos-aquae (Bornet & Flahault) P.Wacklin, L.Hoffmann & Komárek | P-B | st | i | alb | e | |||
| Dolichospermum lemmermannii (Richter) P.Wacklin, L.Hoffmann & J.Komárek | P | i | e | |||||
| Dolichospermum sigmoideum (Nygaard) Wacklin, L.Hoffmann & Komárek | P | i | e | 1.7 | b-o | |||
| Dolichospermum smithii (Komárek) Wacklin, L.Hoffmann & Komárek | P | |||||||
| Dolichospermum spiroides (Klebahn) Wacklin, L.Hoffmann & Komárek | P-B | st-str | i | e | 1.3 | o | ||
| Dolichospermum viguieri (Denis & Frémy) Wacklin, L.Hoffmann & Komárek | P | e | 2.0 | b | ||||
| Kamptonema chlorinum (Kützing ex Gomont) Strunecký, Komárek & J.Smarda | P-B,S | st-str,H2S | 5.8–7.3 | 3.8 | b-p | |||
| Limnothrix planctonica (Wołoszyńska) Meffert | P | i | ot | 1.0 | o | |||
| Merismopedia glauca (Ehrenberg) Kützing | P-B | i | ind | 7.9–11 | e | |||
| Merismopedia tranquilla (Ehrenberg) Trevisan | P-B | i | ind | 8.1–8.9 | 2.3 | b | ||
| Microcrocis irregularis (Lagerheim) Geitler | P | i | 1.5 | o-b | ||||
| Microcystis aeruginosa (Kützing) Kützing | P-B | hl | acf | 6.0–7.8 | me | 2.2 | b | |
| Microcystis flos-aquae (Wittrock) Kirchner | P-B | i | 6.6–7.7 | e | 1.6 | b-o | ||
| Microcystis ichthyoblabe (G.Kunze) Kützing | P | i | e | |||||
| Microcystis wesenbergii (Komárek) Komárek ex Komárek | P-B | 2.3 | b | |||||
| Oscillatoria ornata Kützing ex Gomont | P-B,S | st-str | i | me | 1.5 | o-b | ||
| Oscillatoria rupicola (Hansgirg) Hansgirg ex Forti | P-B,S | aer | me | 2.7 | a-o | |||
| Oscillatoria tenuis C.Agardh ex Gomont | P-B,S | st-str | hl | |||||
| Phormidium breve (Kützing ex Gomont) Anagnostidis & Komárek | P-B,S | st,aer | alb | 8.2 | 3.1 | a | ||
| Phormidium chalybeum (Mertens ex Gomont) Anagnostidis & Komárek | P-B,S | st-str | 7.0 | e | 3.3 | a | ||
| Phormidium corium Gomont | B,S | st-str | m | 1.3 | o | |||
| Phormidium inundatum Kützing ex Gomont | B,S | aer | ot | 0.1 | x | |||
| Planktolyngbya contorta (Lemmermann) Anagnostidis & Komárek | P-B | alf | 7.6 | |||||
| Planktolyngbya limnetica (Lemmermann) Komárková-Legnerová & Cronberg | P-B,S | st-str | hl | alf | 7.9–8.1 | me | 1.8 | o-a |
| Planktothrix agardhii (Gomont) Anagnostidis & Komárek | P-B | st | hl | |||||
| Rhabdogloea smithii (Chodat & F.Chodat) Komárek | P | st | alf | 7.8–9.6 | 2.0 | b | ||
| Snowella lacustris (Chodat) Komárek & Hindák | P | i | alb | 8.1 | me | 1.6 | b-o | |
| Woronichinia naegeliana (Unger) Elenkin | P | st | e | 1.8 | o-a |
Note: “-”, not found. Abbreviations: Habitat (Hab) (P—planktonic, P-B—plankto-benthic, B—benthic, S—soil); oxygenation and water moving (Oxy) (aer—aerophiles, st-str—low streaming water, st—standing, H2S—sulfides); pH preference groups (pH) according to Hustedt (1957) [38] (alb—alkalibiontes; alf—alkaliphiles, ind—indifferent; acf—acidophiles; salinity ecological groups (Sal) according to Hustedt (1938–1939) [39] (i—oligohalobes-indifferent, hl—halophiles; Index S, species-specific index saprobity according to Sládeček (1986) [54]; self-purification zone with index of saprobity (Sap) (x/0.0—xenosaprobe; x-o/0.4—xeno-oligosaprobe; x-b/0.8—xeno-betamesosaprobe; o/1.0—oligosaprobe; o-b/1.4—oligo-betamesosaprobe; b-o/1.6—beta-oligosaprobe; o-a/1.8—oligo-alphamesosaprobe; b/2.0—betamesosaprobe; b-p/2.4—beta-polysaprobe; a-o/2.6—alpha-oligosaprobe; a/3.0—alphamesosaprobe; trophic state indicators (Tro) (Van Dam et al., 1994) [40]: (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic).
Appendix C
Table A3.
Distribution of abundance scores in the ecological groups over 17 studied lakes.
| Ecological Group | 1-Ma | 2-Un | 3-Un | 4-Yt | 5-Te | 6-Pr | 7-Kh | 8-Be | 9-So | 10-Us | 11-Oc | 12-Ba | 13-Ar | 14-Un | 15-Ma | 16-Dr | 17-Ch |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Habitat | |||||||||||||||||
| B | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| P-B | 13 | 12 | 5 | 16 | 14 | 8 | 13 | 15 | 16 | 15 | 13 | 14 | 14 | 12 | 15 | 13 | 14 |
| P | 3 | 2 | 6 | 6 | 10 | 4 | 0 | 4 | 2 | 5 | 2 | 7 | 5 | 3 | 0 | 4 | 3 |
| Oxygen | |||||||||||||||||
| aer | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 0 | 1 |
| st-str | 0 | 1 | 0 | 2 | 1 | 0 | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 0 | 0 | 1 | 1 |
| st | 3 | 0 | 1 | 4 | 2 | 1 | 2 | 1 | 0 | 0 | 1 | 1 | 2 | 7 | 3 | 0 | 0 |
| Salinity | |||||||||||||||||
| i | 7 | 0 | 10 | 9 | 6 | 5 | 3 | 8 | 7 | 8 | 6 | 13 | 9 | 8 | 5 | 4 | 9 |
| hl | 6 | 0 | 1 | 5 | 4 | 4 | 9 | 7 | 7 | 8 | 6 | 1 | 2 | 4 | 4 | 7 | 3 |
| Water pH | |||||||||||||||||
| acf | 0 | 2 | 0 | 3 | 0 | 4 | 6 | 3 | 3 | 0 | 3 | 0 | 0 | 0 | 2 | 3 | 0 |
| ind | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 0 | 2 |
| alf | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 0 | 0 | 1 | 0 |
| alb | 10 | 2 | 2 | 4 | 5 | 0 | 4 | 2 | 3 | 9 | 1 | 1 | 2 | 4 | 2 | 3 | 3 |
| Trophic state | |||||||||||||||||
| ot | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| om | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| m | 6 | 1 | 0 | 0 | 5 | 0 | 3 | 2 | 2 | 6 | 2 | 1 | 2 | 0 | 2 | 4 | 2 |
| me | 2 | 3 | 2 | 0 | 4 | 4 | 6 | 3 | 4 | 2 | 4 | 3 | 3 | 1 | 5 | 3 | 2 |
| e | 5 | 4 | 8 | 0 | 4 | 7 | 3 | 7 | 8 | 7 | 6 | 12 | 8 | 7 | 5 | 7 | 8 |
| Water Quality Class | |||||||||||||||||
| Class 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Class 2 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 3 | 1 | 1 | 0 | 1 | 1 | 0 | 3 | 0 | 0 |
| Class 3 | 10 | 11 | 6 | 17 | 12 | 11 | 10 | 15 | 13 | 13 | 14 | 11 | 15 | 6 | 11 | 14 | 12 |
| Class 4 | 1 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 |
Note: Abbreviation of the lake names as in Table 1. “0”, not found. Ecological group abbreviations: Habitat (Hab) (P—planktonic, P-B—plankto-benthic, B—benthic); oxygenation and streaming (Oxy) (st—standing water, str—streaming water, st-str—low streaming water, aer—aerophiles); pH preference groups (pH) according to Hustedt (1957) [38] (alb—alkalibiontes; alf—alkaliphiles, ind—indifferent; acf—acidophiles); salinity ecological groups (Sal) according to Hustedt (1938–1939) [39] (i—oligohalobes-indifferent, hl—halophiles); trophic state indicators (Tro) (Van Dam et al., 1994) [40]: (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic); Class of water quality was calculated on the basis of Index saprobity S as in Appendix B according [32].
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Figure 1.
Map with red dots indicating the sampling stations on the studied lakes numbered according to Table 1 and world map with a green point showing the geographic location of the study area. Pink colored areas are the city of Yakutsk and suburbs territory. Gray colored areas are other settlements. White dots with black outline show the villages. Yellow lines show the federal roads. Black lines show the local roads. Blue arrow shows the Lena River’s flow direction.
Figure 1.
Map with red dots indicating the sampling stations on the studied lakes numbered according to Table 1 and world map with a green point showing the geographic location of the study area. Pink colored areas are the city of Yakutsk and suburbs territory. Gray colored areas are other settlements. White dots with black outline show the villages. Yellow lines show the federal roads. Black lines show the local roads. Blue arrow shows the Lena River’s flow direction.
Figure 2.
View of some of the lakes explored: (a) Balyktakh Lake, (b) Diring Lake, (c) Microcystis sp. flakes on the water surface, Unnamed 2 Lake.
Figure 2.
View of some of the lakes explored: (a) Balyktakh Lake, (b) Diring Lake, (c) Microcystis sp. flakes on the water surface, Unnamed 2 Lake.
Figure 3.
Bioindication group preferences of habitat (a), oxygen (b), water pH (c), and salinity (d) distribution over 17 studied lakes. Lake numbering is the same as in Table 1. Ecological group abbreviations: Habitat (P—planktonic, P-B—plankto-benthic, B—benthic); oxygenation and streaming: Oxygen (st—standing water, str—streaming water, st-str—low streaming water, aer—aerophiles); pH preference groups (Water pH) according to Hustedt (1957) [38] (alb—alkalibiontes; alf—alkaliphiles, ind—indifferent; acf—acidophiles); salinity ecological groups (Salinity) according to Hustedt (1938–1939) [39] (i—oligohalobes-indifferent, hl—halophiles).
Figure 3.
Bioindication group preferences of habitat (a), oxygen (b), water pH (c), and salinity (d) distribution over 17 studied lakes. Lake numbering is the same as in Table 1. Ecological group abbreviations: Habitat (P—planktonic, P-B—plankto-benthic, B—benthic); oxygenation and streaming: Oxygen (st—standing water, str—streaming water, st-str—low streaming water, aer—aerophiles); pH preference groups (Water pH) according to Hustedt (1957) [38] (alb—alkalibiontes; alf—alkaliphiles, ind—indifferent; acf—acidophiles); salinity ecological groups (Salinity) according to Hustedt (1938–1939) [39] (i—oligohalobes-indifferent, hl—halophiles).
Figure 4.
Bioindication groups of trophic state (a) and water quality class (b) distribution over 17 studied lakes. Lake numbering is the same as in Table 1. Ecological group abbreviations: trophic state indicators (trophic state) (Van Dam et al., 1994) [40]: (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic); Class of water quality was calculated on the basis of Index saprobity S as in Appendix B according to S. Barinova [32].
Figure 4.
Bioindication groups of trophic state (a) and water quality class (b) distribution over 17 studied lakes. Lake numbering is the same as in Table 1. Ecological group abbreviations: trophic state indicators (trophic state) (Van Dam et al., 1994) [40]: (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic); Class of water quality was calculated on the basis of Index saprobity S as in Appendix B according to S. Barinova [32].
Figure 5.
Bray–Curtis similarity tree for bioindication groups of 17 studied lakes. Abbreviations of the lakes are the same as in Table 1.
Figure 5.
Bray–Curtis similarity tree for bioindication groups of 17 studied lakes. Abbreviations of the lakes are the same as in Table 1.
Figure 6.
JASP correlation plot for chemical variables of 17 studied lakes. The lake’s type of origin and its position on the riverbeds are highlighted by different colors. Abbreviations of the lakes are the same as in Table 1. The strongest links are shown by the thickest lines. Positive correlations are shown in blue lines and negative ones in red. Clusters 1–3 are outlined by dashed lines of different colors.
Figure 6.
JASP correlation plot for chemical variables of 17 studied lakes. The lake’s type of origin and its position on the riverbeds are highlighted by different colors. Abbreviations of the lakes are the same as in Table 1. The strongest links are shown by the thickest lines. Positive correlations are shown in blue lines and negative ones in red. Clusters 1–3 are outlined by dashed lines of different colors.
Figure 7.
JASP correlation plot for bioindicator variables of 17 studied lakes. Abbreviations of the lakes are the same as in Table 1. The lake’s position on the riverbeds is highlighted by different colors. The strongest links are shown by the thickest lines. Positive correlations are shown in blue lines and negative ones in red. Clusters 1 and 2 are outlined by dashed lines of different colors.
Figure 7.
JASP correlation plot for bioindicator variables of 17 studied lakes. Abbreviations of the lakes are the same as in Table 1. The lake’s position on the riverbeds is highlighted by different colors. The strongest links are shown by the thickest lines. Positive correlations are shown in blue lines and negative ones in red. Clusters 1 and 2 are outlined by dashed lines of different colors.
Figure 8.
Statistical maps of environmental variables: altitude (a), water temperature (b), water pH (c), chlorides (d), nitrite-nitrogen (e), and ammonia (f) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Figure 8.
Statistical maps of environmental variables: altitude (a), water temperature (b), water pH (c), chlorides (d), nitrite-nitrogen (e), and ammonia (f) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Figure 9.
Statistical maps of biological variables: the species number (a), sum of abundance scores (b), and bioindicator variables: Class 3 of water quality (c), eutraphentic species (d), sum of scores of Microcystis aeruginosa and M. flos-aquae (e), acidophilic indicators’ (f) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Figure 9.
Statistical maps of biological variables: the species number (a), sum of abundance scores (b), and bioindicator variables: Class 3 of water quality (c), eutraphentic species (d), sum of scores of Microcystis aeruginosa and M. flos-aquae (e), acidophilic indicators’ (f) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Figure 10.
RDA plot for dependent biological variables: the species number, sum of abundance scores, eutraphentic species indicators, sum of scores of Microcystis aeruginosa and M. flos-aquae (e), acidophilic indicators’ (acf) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Figure 10.
RDA plot for dependent biological variables: the species number, sum of abundance scores, eutraphentic species indicators, sum of scores of Microcystis aeruginosa and M. flos-aquae (e), acidophilic indicators’ (acf) distribution in the 17 studied lakes of the Central Yakut Plain (Eastern Siberia, Yakutia), August 2023.
Table 1.
Brief characterization of the studied lakes.
| No of Station | Code | Lake Name | Altitude Above Sea Level, m | Water Surface Area, km2 | Latitude, N | Longitude, E | Type of Origin |
|---|---|---|---|---|---|---|---|
| 1 | 1-Ma | Mayya | 155 | 1772.7 | 61°44′0.20″ | 130°14′56.78″ | C |
| 2 | 2-Un | Unnamed 1 | 148 | 72.6 | 61°51′31.26″ | 130°6′28.22″ | C |
| 3 | 3-Un | Unnamed 2 | 113 | 76.7 | 61°54′46.08″ | 129°36′50.26″ | C |
| 4 | 4-Yt | Ytyk-Kyuyol | 108 | 790.3 | 62°1′22.02″ | 129°36′59.0″ | R |
| 5 | 5-Te | Temiye | 219 | 483.1 | 62°2′48.93″ | 129°28′15.31″ | C |
| 6 | 6-Pr | Prokhladnoye | 204 | 61.5 | 62°7′53.82″ | 129°29′13.02″ | C |
| 7 | 7-Kh | Khomustakh | 204 | 90.7 | 62°6′34.55″ | 129°31′36.12″ | C |
| 8 | 8-Be | Beloye | 104 | 612.1 | 62°5′13.53″ | 129°44′12.88″ | R |
| 9 | 9-So | Solyonoe | 102 | 252.4 | 62°7′11.72″ | 129°46′11.85″ | R |
| 10 | 10-Us | Usun-Kyuyol | 31 | 126.7 | 62°12′11.55″ | 129°50′9.98″ | R |
| 11 | 11-Oc | Ochchuguy-Matta | 147 | 980.9 | 62°21′6.48″ | 130°38′17.43″ | C |
| 12 | 12-Ba | Balyktakh | 142 | 4349.5 | 62°15′12.89″ | 130°42′46.50″ | C |
| 13 | 13-Ar | Arylakh | 169 | 2845.9 | 62°9′21.34″ | 130°55′24.57″ | C |
| 14 | 14-Un | Unnamed 3 | 177 | 147.1 | 62°8′47.69″ | 131°9′17.29″ | C |
| 15 | 15-Ma | Maralayy | 203 | 357.5 | 61°59′17.36″ | 131°54′7.50″ | C |
| 16 | 16-Dr | Diring | 198 | 399 | 61°58′38.52″ | 132°10′17.26″ | C |
| 17 | 17-Ch | Churapcha | 185 | 3427.7 | 61°59′59.54″ | 132°27′9.39″ | C |
Note: Classification of lakes by their type of origin: (R) oxbow lakes and (C) thermokarst lakes.
Table 2.
Averaged physical and chemical variables of the 17 studied lakes, August 2023.
| Variables/Lake | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pH | 8.91 | 9.32 | 8.08 | 8.74 | 8.08 | 9.23 | 9.07 | 8.61 | 8.55 | 8.33 | 9.27 | 9.12 | 9.33 | 9.27 | 9.31 | 9.18 | 9.54 |
| Water temperature, °C | 18.2 | 20.2 | 19.6 | 21.6 | 22.1 | 20.1 | 19.1 | 21.2 | 21.0 | 20.6 | 25.1 | 27.5 | 22.7 | 25.7 | 21.3 | 22.8 | 25.7 |
| TDS, mg L−1 | 408.5 | 802.1 | 209.4 | 448.6 | 214.4 | 798.4 | 844.9 | 872.5 | 784.4 | 328.4 | 1235.1 | 418.6 | 272.8 | 246.2 | 933.1 | 378.5 | 382.6 |
| Hardness, mg L−1 | 4.1 | 9.0 | 2.0 | 2.7 | 2.4 | 9.5 | 9.9 | 6.2 | 5.8 | 3.0 | 12.4 | 4.6 | 3.2 | 3.0 | 8.7 | 4.2 | 4.2 |
| Ca2+, mg L−1 | 26.7 | 23.9 | 26.5 | - | 31.5 | 29.3 | 36.5 | 43.7 | 32.1 | 34.5 | 18.2 | 25.5 | 25.5 | 29.5 | 23.9 | 33.7 | 27.1 |
| Mg2+, mg L−1 | 33.5 | 94.4 | 8.8 | - | 10.2 | 97.9 | 97.6 | 48.4 | 51.5 | 15.9 | 139.9 | 40.3 | 23.1 | 18.7 | 90.7 | 30.7 | 34.3 |
| Na+, mg L−1 | 30.4 | 47.4 | 19.4 | - | 9.1 | 32.0 | 23.2 | 150.8 | 123.6 | 35.2 | 94.2 | 19.1 | 10.6 | 5.6 | 104.8 | 17.3 | 25.3 |
| K+, mg L−1 | 13.5 | 5.2 | 2.0 | - | 4.0 | 33.2 | 53.0 | 30.4 | 17.2 | 4.3 | 23.8 | 8.9 | 6.0 | 4.9 | 13.2 | 7.7 | 5.3 |
| HCO3-, mg L−1 | 185.0 | 546.7 | 103.7 | - | 122.0 | 320.0 | 350.0 | 144.8 | 300.0 | 152.6 | 746.0 | 285.0 | 150.6 | 148.0 | 400.0 | 226.5 | 187.5 |
| Cl-, mg L−1 | 31.9 | 48.0 | 28.7 | - | 17.6 | 76.0 | 74.7 | 224.9 | 184.0 | 51.0 | 63.0 | 20.7 | 17.6 | 17.6 | 100.5 | 19.1 | 24.7 |
| SO42-, mg L−1 | 87.5 | 36.5 | 20.4 | - | 20.0 | 210.0 | 210.0 | 229.5 | 76.0 | 35.0 | 150.0 | 19.0 | 39.5 | 22.0 | 200.0 | 43.5 | 78.5 |
| N-NH4, mg L−1 | 0.61 | 0.90 | 0.31 | 0.41 | 0.67 | 0.75 | 0.61 | 0.87 | 0.40 | 0.53 | 0.60 | 0.67 | 0.27 | 0.52 | 0.58 | 0.89 | 0.26 |
| N-NO2, mg L−1 | 0.06 | 0.04 | 0.07 | 0.11 | 0.08 | 0.07 | 0.09 | 0.07 | 0.08 | 0.04 | 0.10 | 0.06 | 0.07 | 0.08 | 0.09 | 0.09 | 0.10 |
| N-NO3, mg L−1 | 0.24 | 0.31 | 0.26 | 0.90 | 0.27 | 0.45 | 0.56 | 0.20 | 0.24 | 0.16 | 0.68 | 0.44 | 0.18 | 0.37 | 0.51 | 0.29 | 0.26 |
| P-PO4, mg L−1 | 0.31 | 0.01 | 0.01 | 0.15 | 0.01 | 0.26 | 0.30 | 0.01 | 0.01 | 0.00 | 0.03 | 0.01 | 0.01 | 0.07 | 0.02 | 0.02 | 0.02 |
| P tot, mg L−1 | 0.60 | 0.20 | 0.07 | 0.46 | 0.20 | 0.40 | 0.40 | 0.21 | 0.18 | 0.11 | 0.70 | 0.16 | 0.18 | 0.70 | 0.36 | 0.43 | 0.36 |
| P org, mg L−1 | 0.29 | 0.19 | 0.06 | - | 0.19 | 0.14 | 0.10 | 0.20 | 0.17 | 0.11 | 0.68 | 0.15 | 0.17 | 0.64 | 0.34 | 0.41 | 0.34 |
| Fe tot, mg L−1 | 1.17 | 0.80 | 0.80 | 0.58 | 1.23 | 1.00 | 1.05 | 0.93 | 1.12 | 0.95 | 0.94 | 0.90 | 0.92 | 1.30 | 0.92 | 1.30 | 1.14 |
| Si-SiO2, mg L−1 | 1.41 | 1.09 | 0.68 | 1.31 | 1.34 | 2.16 | 1.93 | 0.80 | 1.25 | 0.78 | 1.37 | 0.80 | 0.81 | 2.45 | 1.16 | 1.94 | 1.63 |
| Color, Pt/Co grad. | 115 | 107 | 100 | 87.17 | 95 | 90 | 85 | 100 | 95 | 85 | 90 | 120 | 110 | 107 | 90 | 95 | 97 |
| COD, mg O L−1 | 83.3 | 83.0 | 82.7 | 47.2 | 81.8 | 80.9 | 79.2 | 82.8 | 81.6 | 64.2 | 78.7 | 83.8 | 83.2 | 83.0 | 79.5 | 81.8 | 82.0 |
| C org, mg L−1 | 31.2 | 31.1 | 31.0 | - | 30.7 | 30.3 | 29.7 | 31.1 | 30.6 | 24.1 | 29.5 | 31.4 | 31.2 | 31.1 | 29.8 | 30.7 | 30.8 |
| Diss. Org., mg L−1 | 62.5 | 62.3 | 62.0 | - | 61.4 | 60.7 | 59.4 | 62.1 | 61.2 | 48.2 | 59.0 | 62.9 | 62.4 | 62.3 | 59.6 | 61.4 | 61.5 |
Note: Lake numbering is the same as in Table 1. “-”, not determined.
Table 3.
Values of the cyanobacteria species number, sum of abundance scores, and calculated Index saprobity S and Index WESI in the 17 studied lakes, August 2023. Lake numbers are the same as in Table 1.
Table 3.
Values of the cyanobacteria species number, sum of abundance scores, and calculated Index saprobity S and Index WESI in the 17 studied lakes, August 2023. Lake numbers are the same as in Table 1.
| Variables/Lake | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No. of species | 7 | 8 | 4 | 14 | 11 | 5 | 6 | 11 | 11 | 12 | 9 | 14 | 14 | 8 | 8 | 8 | 11 |
| Sum of scores | 16 | 14 | 11 | 21 | 23 | 11 | 13 | 20 | 18 | 20 | 16 | 22 | 19 | 15 | 18 | 17 | 18 |
| Index S | 1.95 | 2.00 | 1.60 | 1.94 | 1.50 | 1.88 | 2.15 | 1.65 | 1.80 | 1.82 | 1.95 | 1.85 | 1.90 | 1.83 | 1.75 | 2.00 | 1.90 |
| Index WESI | 1.33 | 1.33 | 1.33 | 1.00 | 1.00 | 1.33 | 1.25 | 2.00 | 1.33 | 2.00 | 1.00 | 1.33 | 2.00 | 1.33 | 1.00 | 1.33 | 1.33 |
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Barinova, S.; Gabyshev, V.A.; Gabysheva, O.I. Response of the Cyanobacteria Plankton Community to Anthropogenic Impact in Small Lakes of Urbanized Territory in the Permafrost Zone of Northeast Asia (Eastern Siberia, Yakutia). Water 2024, 16, 2834. https://doi.org/10.3390/w16192834
AMA Style
Barinova S, Gabyshev VA, Gabysheva OI. Response of the Cyanobacteria Plankton Community to Anthropogenic Impact in Small Lakes of Urbanized Territory in the Permafrost Zone of Northeast Asia (Eastern Siberia, Yakutia). Water. 2024; 16(19):2834. https://doi.org/10.3390/w16192834
Chicago/Turabian StyleBarinova, Sophia, Viktor A. Gabyshev, and Olga I. Gabysheva. 2024. "Response of the Cyanobacteria Plankton Community to Anthropogenic Impact in Small Lakes of Urbanized Territory in the Permafrost Zone of Northeast Asia (Eastern Siberia, Yakutia)" Water 16, no. 19: 2834. https://doi.org/10.3390/w16192834
APA StyleBarinova, S., Gabyshev, V. A., & Gabysheva, O. I. (2024). Response of the Cyanobacteria Plankton Community to Anthropogenic Impact in Small Lakes of Urbanized Territory in the Permafrost Zone of Northeast Asia (Eastern Siberia, Yakutia). Water, 16(19), 2834. https://doi.org/10.3390/w16192834
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