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Article

Diversity of the Summer Phytoplankton of 43 Waterbodies in Bulgaria and Its Potential for Water Quality Assessment

by
Maya P. Stoyneva-Gärtner
1,
Jean-Pierre Descy
2,
Blagoy A. Uzunov
1,*,
Peter Miladinov
3,
Katerina Stefanova
4,
Mariana Radkova
4 and
Georg Gärtner
5
1
Department of Botany, Faculty of Biology, Sofia University, 8 Blvd. Dragan Zankov, 1164 Sofia, Bulgaria
2
Unité d’Océanographie Chimique, Université de Liège, Sart Tilman, 4000 Liège, Belgium
3
Department of Library and Information Studies, Faculty of Philosophy, Sofia University, 125 Blvd. Tzarigradsko Shousse, 1113 Sofia, Bulgaria
4
AgroBioInstitute, Bulgarian Agricultural Academy, 8 Blvd. Dragan Zankov, 1164 Sofia, Bulgaria
5
Institut für Botanik, Universität Innsbruck, Sternwartestrasse 15, 6020 Innsbruck, Austria
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(4), 472; https://doi.org/10.3390/d15040472
Submission received: 30 January 2023 / Revised: 17 March 2023 / Accepted: 18 March 2023 / Published: 23 March 2023
(This article belongs to the Special Issue Aquatic Organisms Diversity and Bio-Indication of Water Resources—2nd Edition)
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Abstract

:
The general awareness of the threats on biodiversity and water quality raised the number of studies that use phytoplankton in assessment procedures. Since most metrics require obtaining mean values, this paper presents data that may help speed up field work and find indicators for a rapid water quality assessment based on single samplings, allowing simultaneous work on many sites. The phytoplankton from 43 Bulgarian waterbodies collected during three summer campaigns (2018, 2019, 2021) at sites selected after drone observations was studied by conventional light microscopy (LM) and an HPLC analysis of marker pigments. Our results allowed us to recommend drones and the HPLC application as reliable methods in rapid water quality assessments. In total, 787 algae from seven phyla (53 alien, new for Bulgaria) were identified. Chlorophyta was the taxonomically richest group, but Cyanoprokaryota dominated the biomass in most sites. New PCR data obtained on anatoxin and microcystin producers confirmed the genetic diversity of Cuspidothrix and Microcystis and provided three new species for the country’s toxic species, first identified by LM. A statistical analysis revealed significant correlations of certain algal phyla and classes with different environmental variables, and their species are considered promising for future search of bioindicators. This is especially valid for the class Eustigmatophyceae, which, as of yet, has been almost neglected in water assessment procedures and indices.

1. Introduction

Since mankind has existed, water has been one of the most important and precious resources of our planet. It is commonly recognized that the lifestyle, agriculture and industry of the modern society, experienced during the last century, led to climate changes and nutrient enrichment of waters, which, in turn, caused a considerable impact on the aquatic habitats. These changes provoked the interest of the scientific community with an increasing intensity of studies on all characteristics of water regarding its use, united by the term “water quality”, and its assessment and management [1,2,3,4,5]. Since the end of the 19th century, they have been related with the inhabitants of aquatic biotopes and their potential role in bioindication (for historical details see [6]). Although today, different approaches serve to assess water quality, the use of primary producers with a short life cycle, such as phytoplankters, has a long and worldwide-known tradition [6,7,8,9,10,11]. The methodological tools applied involve certain indicator species or different functional groups, but also the total composition and indices based on diversity, sensitive to the number of species or to their quantitative role [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Over the years, the research has also focused on so-called algal blooms, toxic compounds and their producers with increasing number of records [4,24,25]. Phytoplankton, with its different characteristics, has been used in the methods aiming at the assessment of the ecological status in the European Union’s Water Framework Directive (WFD) [26]. However, most of the proposed metrics require obtaining the mean values from more than one sampling per year or season [27], which is not applicable in single, “snapshot” samplings, when numerous waterbodies are investigated in a short time.
At the same time, since the end of the 20th century, there has been an increasing general awareness of the importance of biodiversity and its threats [28,29], which intermingles with different problems related with water quality. Numerous comparative studies have revealed a significant among-lake variation, that has not completely been explainable by available environmental data. This may suggest the influence of unmeasured drivers of the phytoplankton community and the recognition of the fact that current phytoplankton structure in a certain waterbody represents a biological response to previous environmental conditions [30,31,32,33,34,35]. Moreover, waterbodies normally contain a bulk of rare species, which keep such ecological memory and can become dominants in changed conditions [36]. Considering all these aspects, the comparison of phytoplankton data from different geographic regions and the types of waterbodies with taxonomically well-defined taxa can possibly lead to novel successful combinations of tools based on phytoplankton in order to outline reliable indicator species in rapid but effective methods for water quality assessment even in cases of single samplings.
The present study provides new data on the summer phytoplankton in 43 standing waterbodies of Bulgaria, a country with more than 10,000 wetlands, most of which are still poorly studied [37]. The work was done within the framework of three complementary projects, oriented towards harmful algal blooms of cyanoprokaryotes/cyanobacteria, which produce different toxic compounds (cyanotoxins) in relation to public health and national security in the country. Some data have been published and demonstrated the broad distribution of different cyanotoxins and their producers (i.e., microcystins, anatoxins, saxitoxins, cylindrospermopsin, microviridins) at the studied sites [38,39,40,41,42,43,44]. In the present paper, they are completed with new data on anatoxin and microcystin producers in the country. The simultaneous application and comparison of the results from the conventional light microscopic (LM) work and HPLC marker pigment analysis demonstrate the similarity in the results obtained on the relative algal contribution to the phytoplankton biomass [38,40,41,42]. These allow us to encourage a broader application of HPLC in the methodology of a fast water quality assessment in order to avoid the time- and effort-consuming counting of the phytoplankton by relevant experts. In addition, concerning the improvement of the sampling methodology, it has to be noted that all the results we obtained over these three years prove the usefulness of its application in the studies of biodiversity and water quality assessment with modern remote vehicles, drones. They ensure a fast orientation for the selection of sampling sites, which allows us to save time and efforts but also fuel for vehicles (cars, boats) during field studies [38,45]. Our results demonstrate the great biodiversity of the phytoplankton in all waterbodies but also its variability from site to site, with more than half of the species found in a single waterbody. This great diversity, on one hand, shows the phytoplankton sensitivity to water quality, but on the other hand, it hinders the consideration of certain indicator species for its rapid assessment. Therefore, we provide statistical data that demonstrate a more specific distribution of three phyla and four classes according to the environmental variables such as altitude, water conductivity, water hardness, and chlorophyll a concentration as a robust measurement for the trophic status [46,47], which can serve as a grounded basis for future work for bioindicator selection. Eustigmatophyceae is considered one such promising group, almost neglected in accepted phytoplankton metrics.

2. Materials and Methods

2.1. Sampling Sites

The study is based on phytoplankton samples from 43 selected waterbodies in Bulgaria (35 inland and 8 coastal) collected during three summer sampling campaigns in June 2018, August 2019 and August 2021 (Table 1). Detailed descriptions of the type, morphology, hydrology, history of development, physicochemical parameters, biota, use, conservation status and protection measures with references to previous studies and publications are available in the Database of the Inventory of Bulgarian wetlands, IBW [37]. Therefore, the identification numbers of the studied waterbodies are provided in Table 1. In order to help the reader, here, we provide some important notes: (i) three general categories of surface waterbodies were studied such as natural lakes, large reservoirs (>100 ha) and small reservoirs (<100 ha), the latter quite widespread and commonly known in Bulgaria as microreservoirs; (ii) in addition to the core group of coastal lakes and reservoirs, which have been studied for years due to their global conservational importance [37,48,49], a set of 20 small reservoirs of local importance, used mainly for irrigation and as fishponds, was sampled for the first time; (iii) the sampled waterbodies were situated from the sea level up to 1550 m a.s.l. and were selected in accordance mainly to their use by people (for drinking water, irrigation, fishing and fish-farming, recreation) and potential threat from harmful algal blooms; (iv) the sampling in the summer of 2020 was impossible due to the restrictions caused by the COVID-19 pandemic; (v) different months for sampling were chosen because of the different meteorological conditions in the years 2018, 2019 and 2021, with extremely high temperatures and dryness in April–May 2018 and strong rains in May–July 2019 [40].
The sampling was preceded by a drone sent to observe in real time the whole water area of each waterbody (Figure 1) and to identify the sites with algal blooms [38,39,40,41,42,43,44,45]. In cases of visible water homogeneity, the sites from our previous studies were repeated, or new sites were selected in cases of waterbodies sampled for the first time. Two types of drones (each supplied by a photo camera) were used: DJI Mavic Pro, Model: M1P GL200A (SZ DJI Technology Co., LTD, Shenzhen, China) in 2018 and DJI Mavic 2 Enterprise Dual Pro (DJI Technology Co, LTD, Shenzhen, China) in 2019, 2021 because the latter had the ability to measure the surface water temperature [38,39,40,41,42,43,44,45].
The sampling was conducted from inflatable boats and by motorboats in the large reservoirs. Aquameter AM-200 and Aquaprobe AP-2000 from Aquaread’s water-monitoring instruments, 2012 Aquaread Ltd., were applied for in situ measurement of the coordinates and the altitude of each site, as well as the water temperature, pH, water hardness (expressed by total dissolved solids), oxygen concentration, chlorophyll a and conductivity (Table 1). Total nitrogen (TN) and total phosphorus (TP) were measured ex situ with an Aqualytic AL410 photometer from AQUALYTIC®, Dortmund, Germany—Table 1.

2.2. Algal Identification and Counting by Light Microscopy

At each site, a water sample was collected for algal determination and counting by light microscopy (LM). The samples were taken from the surface layer (0–50 cm) in a volume of 0.5 L in case of visible blooms and of 1–1.5 L in cases of bright color of the water. The samples were immediately fixed with 2–4% formalin and transported to the lab, where they were sedimented to 30 mL for at least 48 h [38,39,40,41,42,43].
The taxonomic LM work was performed twice for all samples: (i) almost immediately after the collection on a Motic BA microscope with a Moticam 2000 camera, supported by the Motic Images 2 Plus software program; (ii) some months later, all samples were processed in a repetitive and comparative way on a Motic B1 microscopes supplied by a Moticam 2.0 MP camera with the Motic Images 3 Plus software program. Here, we note that the identification and counting was done by the same person (MPSG), which ensured the consistency of the LM data.
The algal identification was done on nonpermanent slides under 100× magnification with the application of immersion oil and was based on the standard European taxonomic literature ([52,53,54,55,56], etc.) consulted with recent data from AlgaeBase [57]. With the lack of general consensus on common algal classification system, the phytoplankton composition was represented in the following main phyla: Cyanoprokaryota (blue-green algae), Chlorophyta, Streptophyta, Pyrrhophyta, Euglenophyta, Cryptophyta and Ochrophyta (yellow-brown algae), the last subdivided in the following classes: Bacillariophyceae (diatoms), Chrysophyceae (golden algae), Synurophyceae (silica-scaled chrysophytes), Xanthophyceae (yellow-green algae), Eustigmatophyceae and Raphidophyceae [58].
Algae were counted on a Thoma blood-counting chamber, with a minimum of four iterations for each sample with the cell taken as the main counting unit and a further estimation of the biomass [10,38,39,40,41,42]. The relative abundance of the species was expressed according to the following modification of the Starmach scale [59] in comparison with species’ contribution to the biomass [60]: “rare species” were those seen as single specimens in the whole microscopic slide (<0.5% of the biomass), “occasional species” those represented by up to five specimens (<5% of the biomass), “common, or abundant species” those seen with 6 to 30 specimens in a slide (5–20% of the biomass), whereas dominants and subdominants were evaluated among the most numerous species which contributed to >20 and >25% of the biomass, respectfully.

2.3. Analysis of Phytoplankton Marker Pigments

For the estimation of the general phytoplankton composition and relative phytoplankton biomass, HPLC was applied for marker pigment analysis following the standard operational procedure SOP5 described by [61]. Phytoplankton samples in a volume of 0.5–1 L were filtered at the earliest possibility after collection through 0.45 cellulose filters Whatman NC45 ST/Sterile EO (Merck KGaA, Darmstadt, Germany). Pigments were extracted by two 15 min sonications in ice, separated by an overnight stay in darkness, at a temperature of 4 °C and the final application of 90% acetone. Afterwards, the samples were transported to the lab in plastic tubes in a box with dry ice. During this transportation, only 1 of 70 samples, the tube from the reservoir Sopot, was destroyed and, therefore, pigment data for this reservoir are not provided in the paper.
The pigment analysis was performed on a Waters HPLC system equipped with a photodiode array detector. Pigment concentrations were determined from calibration with chlorophyll and carotenoid standards (DHI, Denmark), and CHEMTAX was used for the calculation of the contribution of the main phytoplankton groups [38,40,41,42,61,62,63,64]. The initial table of pigments, applied as a matrix, is provided in [38].
The chlorophyll a, measured by HPLC, was compared with its field measurement and used as an expression of total algal biomass for the assessment of the trophic status according to the OECD System [46] and of the ecological status according to the intercalibrations related with the WFD [47].

2.4. Molecular-Genetic Analysis

  • Molecular-genetic analysis for the identification of anatoxin producers
Anatoxin-A (ATX) and its analogues, anatoxins (ATXs), are alkaloid neurotoxins released by more than 40 species of Cyanoprokaryota [65,66]. They are produced by eight ATX synthetase genes (ana genes) [67], among which anaB-anaG genes are common for different producing genera [68]. Therefore, the anaC gene was selected for the amplification by the set of the following primer sequences, F-ATGGTCAGAGGTTTTACAAG and R-CGACTCTTAATCATGCGATC [69], of the material extracted from the samples collected in 2021 in order to complete our data, obtained after the analysis of the samples from 2018 and 2019 [43].
DNA was extracted from the field samples through filtration performed on 0.45 cellulose filters Whatman NC45 ST/Sterile EO (Merck KGaA, Darmstadt, Germany). The extracted DNA was amplified following the procedure MyTaqHS Mix (Bioline), which included the 12.5 μL Tag Mix, 10 pmol (1 μL) primers (both straight and inverted) and 50 ng total DNA. A specified program was used for the incubation of the reaction mixtures in a QB-96 Thermal Cycler: 35 cycles of denaturation (each 10 s at 95 °C), annealing at 55 °C for 30 s, an extension for 30 s at 72 °C, followed by a final extension for 5 min at 72 °C.
GeneJET™ Thermo Scientific and Clone JET PCR kits (Thermo Fisher Scientific, Waltham, MA, USA) were used for the purification and cloning of the anaC PCR products, and the recombinant sequences were sent to Macrogen Inc. (Seoul, Republic of Korea) for Sanger sequencing with the same pJET primers. All resulting data were manually edited and initially analyzed using the Vector NTI 11.5 (Thermo Scientific) software package. The Mega 6.0. program [70], a BLAST [71] search in the National Centre for Biotechnology Information (NCBI) GenBank database [72] and the neighbor-joining method with 1000 bootstrap values were used for organizing the anaC sequences in a phylogenetic tree. The obtained sequences were deposited in the NCBI GenBank [72] under the accession numbers OQ3119995–OQ3200013 and OQ355032.
  • Molecular-genetic analysis for the identification of microcystin producers
Microcystins are the best-known and most-studied cyanotoxins, produced by Cyanoprokaryota, considered as being the most widely spread toxins in freshwaters [25,73]. In this study, the amplification of the mcyA gene from the microcystin synthetase mcyA-J gene cluster [74] was applied to the samples from 2018 in order to complete our earlier investigations, in which the mcyB and mcyE genes were used [39,40,42]. The amplified region was 510 bp long, described from the toxic strains M. aeruginosa UWOCCPCC 7806 and M. aeruginosa UWOCCPCC 7820 [75]. The amplification was accomplished by the set of forward primer mcyA-102F-CGATGAACAAATCGGGCAATGGCA and reverse primer u-620R-TGCAAGTTTCGCACATCTCCAAGG following [76,77].
A specified manufacturer program was used for the incubation of the reaction mixtures in a QB-96 Thermal Cycler starting with the denaturation at 95 °C for 3 min, followed by 35 cycles of denaturation (each 30 s at 95 °C) and 30 s of annealing at 52 °C, an extension at 72 °C for 30 s with a final extension step lasting 5 min at 72 °C. The cloning and further steps coincided with those described above for anatoxin. The obtained sequences were deposited in the NCBI GenBank database [72] under the accession numbers OM525685-OM525722, and ON075818-ON075819.

2.5. Statistical Analysis

The statistical analysis was conducted using the records of all identified species organized by main taxonomic groups (phyla and classes) and their abundance (rare, occasional, abundant, subdominant and dominant species) in the studied waterbodies. All records were encoded for use by statistical software. Data processing was done by the cross-disciplinary tool SPSS version 19, developed by IBM [78] using descriptives, frequencies and crosstabs, which aimed to prove the relations between the taxonomic groups and environmental parameters.
The environmental parameters were grouped in the following categories regarding the water quality in accordance with their distribution in drinking and natural waters [4,37,79]: (i) water hardness: 0–4 °dh—very soft water, 4–8 °dh—soft water, 8–12 °dh—middle hard water, 12–18 °dh—rather hard water, 18–30 °dh—hard water, >30 °dh—very hard water, considering that 1 °dh = TDS/10; (ii) CN: <10 µS cm−1 (distilled water, uncontaminated freshwater), <800–10 µS cm−1 (drinking water), 800–2000 µS cm−1 (water for irrigation and freshwater streams), >2000 µS cm−1 (industrial and wastewater); (iii) pH: >6—acid water, 6–7—neutral water, >7—alkaline water; (iv) TN: <0.3 mg L−1, 0.4–7 mg L−1, 7–10 mg L−1, >10 mg L−1; (v) TP: <10 µg L−1—oligotrophic, 10–35 µg L−1—mesotrophic, 35–100 µg L−1—eutrophic, >100 µg L−1—hypertrophic; (vi) chlorophyll a: <1.5 µg L−1—oligotrophic, 1.5–10 µg L−1—mesotrophic, 10–25 µg L−1—eutrophic and >25 µg L−1—hypertrophic waters. In addition, the altitude was considered, classified after [37], as follows: 0–200 m a.s.l.—lowland, 200–500 m a.s.l.—plain, 500–1000 m a.s.l.—kettle, and >1000 m a.s.l.—mountain waterbodies.
The statistical error was estimated by Pearson chi-square values and the correlations were determined according to a comparative analysis in crosstabs [80]. The strength of the relations between two discrete variables was measured by Cramér’s V, with a value between 0 and +1, as an effective size measurement for the chi-square test of independence [78,80].
On the basis of the statistical tests, graphs were created using Microsoft®® Excel®® from Microsoft 365 MSO (Version 2212 Build 16.0.15928.20196) 64-bit.

3. Results

3.1. Total Biodiversity of the Phytoplankton

The total biodiversity of the phytoplankton comprised 787 species from seven phyla (Figure 2a). Green algae were represented by the highest number of species (330) with a predominance of taxa from the phylum Chlorophyta (292) and less from the second green phylum—Streptophyta (38). Cyanoprokaryota, represented with 160 species, occupied the second place in the total taxonomic structure, followed by Ochrophyta, Euglenophyta, Pyrrhophyta and Cryptophyta (Figure 2a). Among Ochrophyta (169 taxa), diatoms (class Bacillariophyceae) were the most diverse (119), while all other classes of this large phylum (Chrysophyceae, Synurophyceae, Xanthophyceae, Eustigmatophyceae, Raphidophyceae) contained much less species (Figure 2b).
In almost all waterbodies, chlorophytes were the main contributors to the biodiversity, followed by cyanoprokaryotes (Figure 3). An exception was the phytoplankton of the small mountain reservoir Beglika, in which algae from these two phyla were not found by conventional LM. Cyanoprokaryotes were not found by LM in two other waterbodies—in the large reservoir Suedinenie and in the small reservoir Krapets (Figure 3).
The average number of species per waterbody was 45, about half of which (20) were green algae (18 chlorophytes and 2 streptophytes), while the other phyla contributed to the phytoplankton with eight to one species on average (Figure 4a).
Most of the algal taxa (421, or 53%) were found in a single waterbody and the number of species found in more than five waterbodies was much lower—63, or 8%. The same trend was valid for the species from each of the recorded phyla (Figure 4b). The most widely spread algae belonged to chlorophytes: Tetraedron minimum (25 sites), followed by Coelastrum astroideum (17), Nephrochlamys subsolitaria (14), Golenkinia radiata and Oocystis lacustris (each in 13 sites), Monactinus simplex and Tetradesmus lagerheimii (Syn. Scenedesmus acuminatus) (each in 12 sites). The most spread cyanoprokaryote was Planktolyngbya limnetica (14 sites), followed by Microcystis wesenbergii (12 sites), Microcystis aeruginosa and Raphidiopsis raciborskii (each in 11 sites), Aphanizomenon klebahnii and Coelomoron pusillum (each in 10 sites). The most widespread species from other taxonomic groups in descending order of findings were the streptophyte Cosmarium neodepressum var. planctonicum and the pyrrhophyte Parvodinium elpatiewskyi (each found in 12 sites), followed by the ochrophytes Lindavia comta (11 sites) and Aulacoseira granulata (10 sites), as well as the euglenophyte Trachelomonas volvocina (10 sites).
Altogether, 79 algae were identified as dominants, codominants or subdominants (Table 2). Among them the most significant was Cyanoprokaryota (33 species of which dominated/codominated in 24 waterbodies and were subdominants in 17), followed by Ochrophyta (14, mainly diatoms) and Chlorophyta (13 taxa), Pyrrhophyta (8 taxa), Euglenophyta (7 species), Streptophyta and Cryptophyta (each with 3 taxa).
According to the available Bulgarian algological literature, out of all 787 species, at least 53 (7%), recorded for first time in the country, can be considered alien. Most of them were observed as rare species in a small number of waterbodies. The exceptions were: (i) the tropical cyanoprokaryotes Raphidiopsis acuminato-crispa and R. gangetica, which codominated in the small inland reservoir Mechka together with R. raciborskii, found earlier in the country [81,82,83,84,85,86,87,88,89,90,91]; (ii) the North-Asian cyanoprokaryote Aphanizomenon yezoense, described as being from Japan [92] but currently spread also in Northern and Central Europe [57], which dominated in the small reservoir Studena and was subdominant in the coastal natural lake Durankulak; (iii) the chlorophyte Tetrallantos lagerheimii, described as being from Sweden [93] but afterwards recorded on different continents except the Antarctic [57] and currently found as dominant in the small inland reservoir Hadzhidimovo (Figure 5). Regarding the non-native, allochthonous species, we would like to note that during this study, the invasive R. raciborskii (Figure 5) was found as abundant in 11 waterbodies, where in 9 of them, it was recorded for the first time (i.e., Byalata Prust, Kaynaka, Eleshnitsa, Malka Smolnitsa, Mechka, Preselka, Shabla, Tsonevo and Uzungeren).

3.2. Phytoplankton Structure according to the Marker Pigment Composition

In the general phytoplankton composition, based on pigment structure (Figure 6), the average relative contribution of the taxonomic groups to the biomass was as follows: cyanoprokaryotes—42%, green algae—10%, ochrophytes—25%, pyrrhophytes—2%, euglenophytes—12%, and cryptophytes—9%.
The values of chlorophyll a, measured by HPLC, ranged significantly from 0.199 µg L−1 (Tsonevo) to 765 µg L−1 (Izvornik 2)—Figure 7. As far as single values can be relied upon, considering the boundary values from the OECD [46] and WFD [47], chlorophyll a concentrations indicated the oligotrophic status of seven waterbodies (Beglika, Byalata Prust-Mezek, Dospat, Krapets, Koprinka, Shiroka Polyana, Shumensko Ezero, Tsonevo). Thirteen waterbodies had a mesotrophic status (Ablanitsa, Al. Stamboliyski, Birgo, Dubnitsa, Eleshnitsa, Ezerets, Golyam Beglik, Hadzhidimovo, Mechka, Shabla, Shilkovtsi, Studena and Zhrebchevo). Eight were eutrophic (Aheloy, Chetiridesette Izvora, Durankulak, Fisek, Plachidol 2, Satovcha 2, Suedinenie, Yunets), and thirteen waterbodies were hypertrophic (Burgasko Ezero, Duvanli, Hadzhi Yani, Izvornik 2, Mandra, Mogila, Poroy, Preselka, Kriva Reka, Malka Smolnitsa, Nikolovo, Sinyata Reka and Uzungeren), where strong cyanoblooms were detected (Figure 7).

3.3. Algal Blooms and Toxic Species

According to the drone observations, supported by conventional LM studies and the HPLC analysis of marker pigments, during the three summers of investigation, blooms of cyanoprokaryotes occurred in the microreservoirs Birgo, Duvanli, Izvornik 2, Malka Smolnitsa, Mechka, Mogila, Nikolovo, Plachidol 2, Poroy, Preselka, Sinyata Reka, and Studena, in the large reservoir Mandra, as well as in the coastal lakes Burgasko Ezero and Durankulak (Figure 3, Figure 6 and Figure 7 and details in [38,39,40,41,42,43,44]). A relatively high contribution of cyanoprokaryotes to the biomass was detected in the reservoirs Krapets and Dospat (Figure 6), for which chlorophyll a data clearly showed a lack of blooms and a low trophic state (Figure 7). Similar was the case of the large mesotrophic reservoir Shilkovtsi, in which blooms were not seen and the relatively high contribution of cyanoprokaryotes was explained by the identification of marker pigments of Synechococcus type T1, typical for picoplankters which cannot be detected by conventional LM [42].
Most detected blooms, despite their different intensity, supported the development of microcystin-, anatoxin- and microviridin-producing species and of the different cyanotoxins (Table 3) with proved the natural water cytotoxicity [94] and demonstrated the effects of low cylindrospermopsin doses on the gastrointestinal human cells [95]. It has to be noted that toxic cyanoprokaryotes were also found in waterbodies without blooms at the moment of sampling, such as in Ezerets, Koprinka, Uzungeren and Zhrebchevo (Table 3 and Figure 6 and Figure 7). Up to now, in the studied waterbodies, nodularins and their main producer, Nodularia, have not been found despite the conducted targeted microscopic, chemical and molecular-genetic analyses [39]. Although cylindrospermopsin was detected in Bulgarian waterbodies [38,96], the molecular-genetic studies also revealed that the identified Raphidiopsis raciborskii, Raphidiopsis mediterranea and Chrysosporum bergii in our study did not contain the cyrJ gene responsible for its production [44].
Currently, by combining LM data and molecular-genetic studies based on anaC gene with 24 newly obtained sequences, the presence of anatoxin producing Cuspidothrix in the 2021 summer samples from Durankulak, Mechka, Nikolovo, Studena and Yunets was proved (Figure 8). A comparison of these results with our data from 2018 and 2019 (Figure 8 and [43]) demonstrated the presence of toxic Cuspidothrix issatschenkoi in the samples from Durankulak, Nikolovo and Yunets, and suggested once more the potential toxicity of Cuspidothrix elenkinii (found in 2019 in Koprinka [43] and in 2021 in Yunets) and of Cuspidothrix tropicalis (found in 2018 in Burgasko Ezero, in 2019 in Sinyata Reka [43] and in 2021 in Studena). In Mechka, rarely, morphologically peculiar young nonheterocytous and sterile trihomes of Cuspidothrix were found. Due to a lack of reproductive and resting cells, akinetes, their morphological determination was unreliable. Molecular-genetic data separated the sequences from Mechka from all other identified Cuspidothrix strains. In this small reservoir, three different Raphidiopsis species (R. acuminato-crispa, R. gangetica, R. raciborskii) codominated and, considering the close phylogenetic position of both genera Cuspidothrix and Raphidiopsis (for details see [43]), a further analysis of more genes is needed for a clarification of the strains isolated from Mechka. The coincidence with sequences of Aphanizomenon sp. in the constructed phylogenetic tree was explained in detail in [43] as caused by the taxonomic separation of the genus Cuspidothrix and of its type species Cuspidothrix issatschenkoi, in particular, from the genus Aphanizomenon [52].
During the PCR amplification of the mcyA gene, responsible for the microcystin synthesis, 47 sequences were obtained, 9 of which showed a 100% homology with strains in NCBI [72] and 38 had a 99% homology with them. Molecular-genetic studies based on mcyA gene outlined two clusters and four subclusters in the 2018 summer samples (Figure 9), which, in combination with the LM observations, confirmed the presence of microcystin-producing Microcystis as follows: Microcystis aeruginosa and Microcystis novacekii in Mandra (cluster I), Microcytis botrys in Poroy (subcluster I of cluster II), Microcystis aeruginosa in Poroy, Mandra and Durankulak (subcluster II of cluster II), Microcystis aeruginosa in Poroy, Burgasko Ezero and Mandra (subcluster III of cluster II), Microcysis novacekii in Burgasko Ezero, Microcystis botrys in Durankulak, where Microcystis aeruginosa also occurred (subcluster IV of cluster II).

3.4. Algal Groups and Environmental Variables—Results from Statistical Analysis

In the conducted statistical SPSS analysis [78], the species from each algal group found at certain environmental conditions were expressed as a percentage from all species of the relevant group. The first results from the data processing by the SPSS tool and the application of Cramer’s V evaluation [80] of 1996 records of all algal taxa and their abundance showed different but insignificant correlations. Therefore, we decided to exclude all rare species, the presence of which in the waterbodies was considered as nonrepresentative due to their finding in single specimens and in single sites. The resulting correlations obtained in this way were of moderate significance (0.2 < effect size field) except those with pH, which showed low confidence. Most probably, the lack of strong significance in this case was due to the targeted sampling in mostly eutrophic and hypertrophic waters with an alkaline character. The results presented below concern only taxonomic groups that were significantly correlated with other environmental parameters (TN, TP, trophic status, water hardness, conductivity and altitude). They were obtained after conducting the SPSS analysis based on the common, abundant, subdominant and dominant species from all algal groups with the subsequent exclusion of classes and phyla that showed correlations of low confidence.
The significant negative correlations were found between four taxonomic groups and the exact chlorophyll a values, considered as a proxy of the trophic status; the occurrence of species from Euglenophyta, Streptophyta and Eustigmatophyceae increased with the rising trophic status, whereas Chrysophyceae demonstrated a clear preference for a lower trophicity (Figure 10).
The occurrence of all main algal groups was correlated with the TN concentrations, and the SPSS analysis revealed the preference of most of the identified species for high water quality conditions with TN below 7 mg L−1 [4], with Eustigmatophyceae in particular concentrated in waters with a TN range of 0.4–7 mg L−1, and only Cyanoprokaryota, Euglenophyta and Bacillariophyta were spread in waters with TN values ranging between 7 and 10 mg L−1 (Figure 11).
According to the SPSS analysis, the identified taxonomic groups were significantly reverse-correlated with different concentrations of the other important nutrient, TP, except Eustigmatophyceae (Figure 12).
Although most species from all groups were found in lowland and plain waterbodies (0–500 m a.s.l.), the distribution of the following taxonomic groups was more specific according to the altitude location: xanthophyceans and eustigmatophyceaens were spread only in the lowland waterbodies (0–200 m a.s.l.), while pyrrhophytes and streptophytes occurred in all altitude groups but had a preference for lowland and plain waterbodies (Figure 13).
Regarding water conductivity, we have to note that during the field studies we did not measure values below <10 µS cm−1 (typical for distilled water, Table 1), and the SPSS analysis conducted for the three other conductivity categories allowed us to reveal nine taxonomic groups that showed a significant reverse relationship with this parameter, among which Synurophyceae could be outlined as related with waters of lower conductivity (Figure 14). In this way, only species of Bacillariophyceae and Eustigmatophyceae found in this study could be excluded from the search for potential indicators, as independent from the conductivity of the water.
Considering water hardness, the SPSS analysis revealed that the spread of the species of four phyla and three classes was significantly correlated with this variable (Figure 15). The number of species of most algal groups increased with the rise of water hardness, but only Cryptophyta showed a preference for very soft water and Eustigmatophyceae to very hard water (Figure 15).

4. Discussion

Results from the present study demonstrated a high phytoplankton diversity in the sampled waterbodies, which comprised 787 species from seven phyla with a clear predominance of the green algae with 330 species, or 42% from all identified taxa. The second taxonomically rich group was Cyanoprokaryota, represented by 160 species. All data obtained by the LM and HPLC studies indicated the generally high contribution of blue-green algae in the summer phytoplankton of the studied waterbodies, especially in eutrophic and hypertrophic ones. A comparison of the results from the LM observations on algal abundance and dominance with the HPLC data (Table 2, Figure 6 and Figure 7) once more demonstrated the reliability of the application of the HPLC analysis of marker pigments in rapid phytoplankton characterization for water quality assessment [38,42,61,62].
Although the use of dominants for indicative purposes has long been debated, focusing on them is supported by the fact that their dynamics is important for the community stability, and they enhance the evaluation of resources availability [34,96,97,98]. In this study, blue-green algae dominated by 33 species in 60% of the sampled water bodies (Table 2). These data are consistent with the well-known summer dominance of cyanoprokaryotes in nutrient-rich waters (e.g., [11,18,99]). If such dominance in small, shallow, lowland and plain waterbodies can be taken as a normal seasonal event, finding the heterocytous cyanoprokaryote Dolichospermum planctonicum as a dominant in the highest (among the studied sites) large oligotrophic mountain reservoir Golyam Beglik (Table 2) can be considered as alarming for the potential decrease of its water quality. This finding is in accordance with previous observations on the enlarged spread of blue-green algae, and of their potentially toxic species in particular in our mountain reservoirs [100,101,102].
The phytoplankton quantitative structure revealed by the application of the HPLC marker pigment analysis combined with the use of chlorophyll a values as a proxy for trophic status showed that by contrast with the summer dominance of cyanoprokaryotes in nutrient-rich waters, green and most yellow-brown, pyrrhophyte, or euglenophyte algae dominated in the oligo- to mesotrophic waterbodies (Table 2, Figure 6 and Figure 7). Since the water quality in such waters is traditionally considered as being better, we support the use of a lack of cyanoprokaryote dominants to rapidly indicate nonproblematic water quality in the case of single, snapshot samplings. In addition, we confirm our earlier opinion [10] that in water quality assessment and relevant ecological status of the waterbodies both autochthonous and allochthonous species have to be taken into account. This comes from our current results that 53, or 7% of the recorded species were alien, newly recorded in the country. Although most of them were rare, found in single specimens, a few occurred in dominant phytoplankton complexes: the green Tetrallantos lagerheimii and the cyanoprokaryotes Aphanizomenon yezoense, Raphidiopsis acuminato-crispa and R. gangetica. Since the last three species belong to well-known cyanotoxin-producing genera [66,73], their abundant development can be problematic, ensuring their future spread in the country, as it was earlier shown for the invasive Raphidiopsis raciborskii [89,90,91] and is supported by the newly obtained data from this study on the increases of its spread and abundance in the country.
The combined LM and molecular-genetic data provided here are in accordance with our previous results on the high genetic diversity of Microcystis in Bulgarian waterbodies [39,40,42]. They prove its toxicity, as suggested by us earlier for the species Microcystis novacekii in addition to the well-known toxicity of Microcystis aeruginosa [39,40,42]. With the current phylogenetic tree, based on the PCR amplification of the mcyA gene, we are the first to provide for Bulgaria genetic data on the presence of potentially toxic Microcystis botrys, identified also by LM in Durankulak and Poroy, and we genetically confirmed our earlier LM finding of Microcystis novacekii in Mandra and Burgasko Ezero [39]. The current PCR data, based on the anaC gene amplification from the 2021 summer phytoplankton samples confirmed the presence and relatively broad spread of three potentially toxic Cuspidothrix species in our waterbodies (mainly C. issatschenkoi, but also C. elenkinii and C. tropicalis) recorded in 2018 and 2019 [43]. They also indicated this finding in four more waterbodies (Durankulak, Nikolovo, Studena and Yunets) and revealed a yet unidentified Cuspidothrix sequence in the small reservoir Mechka. The diversity and wide spread of numerous toxigenic cyanoprokaryote strains has already been stressed as alarming for Bulgarian waterbodies and their water quality ([37,38,49,85,87,88,89,100,101], among others).
On one hand, the high phytoplankton biodiversity associated with the great variability from site to site (reaching 198 species in Durankulak) showed the phytoplankton sensitivity to water quality, but on the other hand, it complicated the identification of indicator species for its assessment. In order to try to identify taxa that reflected particular environmental parameters, we conducted an SPSS statistical analysis [78,80]. After obtaining the first results based on 1996 records of all taxa and their relative abundance, we had to exclude all rare species, which occurred in single specimens in a single waterbody. In this way, it was possible to demonstrate different responses of the algae from different groups to the environmental variables such as nutrients (TP, TN) and chlorophyll a as proxy of the trophic status, water hardness and conductivity, and altitude as well. After the exclusion of some groups whose correlations were statistically insignificant, we outlined that Chrysophyceae showed a preference for a lower trophic status, Bacillariophyceae were indifferent to the water conductivity and occurred in waters of high TN, Cryptophyta preferred more soft water, Eustigmatophyceae were indifferent to the water conductivity but were significantly correlated with the increased trophic status, TP, water hardness and lowland waterbodies, and Euglenophyta preferred waters of higher trophicity and TN concentration. These results may encourage further search for bioindicators from these taxonomic groups, and this is especially valid for Eustigmatophyceae, which showed significant correlations with most variables but up to now was almost neglected in water quality assessments. Most species of this group found in this study were recorded earlier by us as commonly occurring, with an increasing abundance in the summer periods in the coastal lake Durankulak during its ongoing eutrophication [103,104]. Although they never dominated, we believe that their increasing records and recent outlining by the SPSS analysis will sharpen the attention of phytoplanktonologists to this group.
Last, but not least, all study results strongly supported our earlier opinion about the successful application of remote vehicles in the studies of water quality based on phytoplankton diversity and its blooms in particular [38,45]. The usage of drones allowed us to quickly choose the representative sampling sites, and thus save time, efforts and fuel during the sampling process. Therefore, we strongly recommend the application of this method in future field studies related to rapid water quality assessments.

Author Contributions

Conceptualization, M.P.S.-G.; methodology, M.P.S.-G., G.G., J.-P.D., P.M., K.S. and M.R.; formal analysis, P.M.; investigation, M.P.S.-G., J.-P.D., B.A.U., K.S. and M.R.; resources, B.A.U. and M.P.S.-G.; writing—original draft preparation, M.P.S.-G.; writing—review and editing, M.P.S.-G., G.G., J.-P.D. and B.A.U.; visualization, B.A.U., P.M., M.P.S.-G., J.-P.D., K.S. and M.R.; supervision, M.P.S.-G.; project administration, B.A.U.; funding acquisition, B.A.U. and M.P.S.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the SCIENTIFIC RESEARCH FUND of the Bulgarian Ministry of Education through projects with the following grant numbers DN-13/9—15.12.2017, KP-06-OPR03/18—19.12.2018, and KP-06-OPR06/2—18.12.2018. Sampling in 2018 and 2021 was financed by the project DN-13/9—15.12.2017, whereas the sampling in 2019 was financed by KP-06-OPR03/18—19.12.2018 for 16 sites, and by KP-06-OPR06/2—18.12.2018 for 11 sites.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data sequences from this study have been deposited in the NCBI database with the following numbers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Delpla, I.; Jung, A.-V.; Baures, E.; Clement, M.; Thomas, O. Impacts of climate change on surface water quality in relation to drinking water production. Environ. Int. 2009, 35, 1225–1233. [Google Scholar] [CrossRef]
  2. Whitehead, P.G.; Wilby, R.L.; Battabee, R.W.; Kernan, M.; Wade, A.J. A review of the potential impacts of climate change on surface water quality. Hydrol. Sci. J. 2009, 54, 101–123. [Google Scholar] [CrossRef] [Green Version]
  3. Ahmed, T.; Zounemat-Kermani, M.; Scholz, M. Climate Change, Water Quality and Water-Related Challenges: A Review with Focus on Pakistan. Int. J. Environ. Res. Public Health 2020, 17, 8518. [Google Scholar] [CrossRef] [PubMed]
  4. World Health Organization (WHO). Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First and Second Addenda. 2022. Available online: https://www.who.int/publications/i/item/9789240045064 (accessed on 10 January 2023).
  5. Zeppernick, B.N.; Wilhelm, S.W.; Bullerjahn, G.S.; Paerl, H. Climate change and the aquatic continuum: A cyanobacterial comeback story. Environ. Microbiol. Rep. 2023, 15, 3–12. [Google Scholar] [CrossRef]
  6. Willén, E. Phytoplankton in Water Quality Assessment—An Indicator Concept. In Hydrological and Limnological Aspects in Lake Monitoring; Heinonen, P., Ziglio, G., Van der Beken, A., Eds.; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2000. [Google Scholar]
  7. Zhang, Y.; Gao, W.; Li, Y.; Jiang, Y.; Chen, X.; Yao, Y.; Messyasz, B.; Yin, K.; He, W.; Chen, Y. Characteristics of the Phytoplankton Community Structure and Water Quality Evaluation in Autumn in the Huaihe River (China). Int. J. Environ. Res. Public Health 2021, 18, 12092. [Google Scholar] [CrossRef]
  8. Cellamare, M.; Morin, S.; Coste, M.; Haury, J. Ecological assessment of French Atlantic lakes based on phytoplankton, phytobenthos and macrophytes. Environ. Monit. Assess 2012, 184, 4685–4708. [Google Scholar] [CrossRef]
  9. Ptachnik, R.; Solimini, A.; Bretum, P. Performance of a new phytoplankton composition metric along a eutrophication gradient in Nordic lakes. Hydrobiologia 2009, 633, 75–82. [Google Scholar] [CrossRef]
  10. Stoyneva, M.; Traykov, I.; Tosheva, A.; Uzunov, B.; Zidarova, R.; Descy, J.-P. Comparison of ecological state/potential assessment of 19 Bulgarian water bodies based on macrophytes and phytoplankton (2011–2012). Biotechnol. Biotechnol. Equip. 2015, 29 (Suppl. 1), S33–S38. [Google Scholar] [CrossRef] [Green Version]
  11. Bellinger, E.G.; Sigee, D.C. Freshwater Algae. Identification, Enumeration and Use as Bioindicators, 2nd ed.; Wiley Blackwell: Singapore, 2015. [Google Scholar]
  12. Huszar, V.L.M.; Silva, L.H.S.; Domingos, P.; Marinho, M.M.; Melo, S. Phytoplankton species composition is more sensitive than OECD criteria to the trophic status of three Brazilian lakes. Hydrobiologia 1998, 129, 59–71. [Google Scholar] [CrossRef]
  13. Huszar, V.L.M.; Carraco, N. The relationship between phytoplankton composition and physical-chemical variables: A comparison of taxonomic and morphological-functional groups approaches in six temperate lakes. Freshw. Biol. 1998, 40, 1–18. [Google Scholar]
  14. Reynolds, C.S.; Huszar, V.; Kruk, C.; Naselli-Flores, L.; Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 2002, 24, 417–428. [Google Scholar] [CrossRef]
  15. Salmaso, N.; Naselli-Flores, L.; Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 2015, 60, 603–619. [Google Scholar] [CrossRef] [Green Version]
  16. Padisák, J.; Grigorszky, I.; Borics, G.; Soróczki-Pintér, É. Use of phytoplankton assemblages for monitoring ecological status of lakes within the Water Framework Directive: The assemblage index. Hydrobiologia 2006, 553, 1–14. [Google Scholar] [CrossRef]
  17. Kruk, C.; Peeters, E.; Van Nes, E.H.; Huszar, V.L.M.; Costa, L.S.; Scheffer, M. Phytoplankton community composition can be predicted best in terms of morphological groups. Limnol. Oceanogr. 2011, 56, 110–118. [Google Scholar] [CrossRef]
  18. Mischke, U.; Riedmüller, U.; Hoehn, E.; Schönfelder, I.; Nixdorf, B. Description of the German System for Phytoplankton-Based Assessment of Lakes for Implementation of the EU Water Framework Directive (WFD); Gewässereport 10, Aktuelle Reihe 2/2008; Univ. Cottbus, Lehrstuhl Gewässerschutz: Freiburg, Germany, 2008; pp. 117–146. [Google Scholar]
  19. Phillips, G.; Skjelbred, B.; Morabito, G.; Carvalho, L.; Lyche Solheim, A.; Andersen, T.; Mischke, U.; de Hoyos, C.; Borics, G. WISER Deliverable D3.1-1: Report on Phytoplankton Composition Metrics, Including a Common Metric Approach for Use in Intercalibration by All GIGs. 2010. Available online: http://www.wiser.eu/results/deliverables/ (accessed on 6 March 2023).
  20. Phillips, G.; Lyche Solheim, A.; Skjelbred, B.; Mischke, U.; Drakare, S.; Free, G.; Järvinen, M.; de Hoyos, C.; Morabito, G.; Poikane, S.; et al. A phytoplankton trophic index to assess the status of lakes for the Water Framework Directive. Hydrobiologia 2013, 704, 75–95. [Google Scholar] [CrossRef] [Green Version]
  21. Järvinen, M.; Drakare, S.; Free, G.; Lyche Solheim, A.; Phillips, G.; Skjelbred, B.; Mischke, U.; Ott, I.; Poikane, S.; Søndergaard, M.; et al. Phytoplankton indicator taxa for reference conditions in Northern and Central European lowland lakes. Hydrobiologia 2013, 704, 97–113. [Google Scholar] [CrossRef]
  22. Laplace-Treyture, C.; Feret, T. Performance of the Phytoplankton Index for Lakes (IPLAC): A multimetric phytoplankton index to assess the ecological status of water bodies in France. Ecol. Indic. 2016, 69, 686–698. [Google Scholar] [CrossRef] [Green Version]
  23. Gebler, D.; Kolada, A.; Pasztaleniec, A.; Szoszkiewicz, K. Modelling of ecological status of Polish lakes using deep learning techniques. Environ. Sci. Pollut. Res. 2020, 28, 5383–5397. [Google Scholar] [CrossRef]
  24. Evangelista, V.; Barsanti, L.; Frassanito, A.M.; Passarelli, V.; Gualtieri, P. (Eds.) Algal Toxins: Nature, Occurrence, Effect and Detection; NATO Advanced Study Institute on Sensor Systems for Biological Threats: The Algal Toxins Case; Springer: Dordrecht, The Netherlands, 2007. [Google Scholar]
  25. Svirčev, Z.; Lalić, D.; Savić, G.B.; Tokodi, N.; Backovic, D.D.; Chen, L.; Meriluoto, J.; Codd, G.A. Global geographical and historical overview of cyanotoxin distribution and cyanobacterial poisonings. Arch. Toxicol. 2019, 93, 2429–2481. [Google Scholar] [CrossRef]
  26. Lyche-Solheim, A.; Feld, C.K.; Birk, S.; Phillips, G.; Carvalho, L.; Morabito, G.; Mischke, U.; Willby, N.; Søndergaard, M.; Hellsten, S.; et al. Ecological status assessment of European lakes: A comparison of metrics for phytoplankton, macrophytes, benthic invertebrates and fish. Hydrobiologia 2013, 704, 57–74. [Google Scholar] [CrossRef] [Green Version]
  27. Mischke, U.; Thackeray, S.; Dunbar, M.; McDonald, C.; Carvalho, L.; de Hoyos, C.; Jarvinen, M.; Laplace-Treyture, C.; Morabito, G.; Skjelbred, B.; et al. WISER Deliverable D3.1-4: Guidance Document on Sampling, Analysis and Counting Standards for Phytoplankton in Lakes. 2012. Available online: https://nora.nerc.ac.uk/id/eprint/17466/ (accessed on 28 February 2023).
  28. CBD: The Convention on Biological Diversity. Available online: https://www.cbd.int/convention/text (accessed on 20 January 2023).
  29. Ohtani, S. How Is People’s Awareness of “Biodiversity” Measured? Using Sentiment Analysis and LDA Topic Modeling in the Twitter Discourse Space from 2010 to 2020. SN Comput. Sci. 2022, 3, 371. [Google Scholar] [CrossRef] [PubMed]
  30. Madgwick, G.; Jones, I.D.; Thackeray, S.J.; Elliott, J.A.; Miller, H.J. Phytoplankton communities and antecedent conditions: High resolution sampling in Esthwaite Water. Freshw. Biol. 2006, 51, 1798–1810. [Google Scholar] [CrossRef]
  31. Thackeray, S.; Nõges, P.; Dunbar, M.; Dudley, J.B.; Skjelbred, B.; Morabito, G.; Carvalho, L.; Phillips, G.; Mischke, U. WISER Deliverable D3.1-3: Uncertainty in Lake Phytoplankton Metrics. 2010. Available online: http://www.wiser.eu/download/D3.1-3.pdf (accessed on 27 February 2023).
  32. Thackeray, S.; Nõges, P.; Dunbar, M.; Dudley, J.B.; Skjelbred, B.; Morabito, G.; Carvalho, L.; Phillips, G.; Mischke, U.; Catalan, J.; et al. Quantifying uncertainties in biologicallybased water quality assessment: A pan-European analysis of lake phytoplankton community metrics. Ecol. Indic. 2013, 29, 34–47. [Google Scholar] [CrossRef] [Green Version]
  33. Carvalho, L.; Poikane, S.; Lyche Solheim, A.; Phillips, G.; Borics, G.; Catalan, J.; De Hoyos, C.; Drakare, S.; Dudley, B.; Järvinen, M.; et al. Strength and uncertainty of phytoplankton metrics for assessing eutrophication impacts in lakes. Hydrobiologia 2013, 704, 127–140. [Google Scholar] [CrossRef] [Green Version]
  34. Maileht, K. Phytoplankton as Ecological Indicator of Lakes. Ph.D. Thesis, Estonian University of Life Sciences, Tartu, Estonia, 2021. [Google Scholar]
  35. Khalighi, M.; Sommeria-Klein, G.; Gonze, D.; Faust, K.; Lahti, L. Quantifying the impact of ecological memory on the dynamics of interacting communities. PLoS Comput. Biol. 2022, 18, e1009396. [Google Scholar] [CrossRef]
  36. Padisák, J. Seasonal Succession of Phytoplankton in a Large Shallow Lake (Balaton, Hungary)—A Dynamic Approach to Ecological Memory, Its Possible Role and Mechanisms. J. Ecol. 1992, 80, 217–230. [Google Scholar] [CrossRef]
  37. Michev, T.; Stoyneva, M. (Eds.) Inventory of Bulgarian Wetlands and Their Biodiversity; Elsi-M: Sofia, Bulgaria, 2007. [Google Scholar]
  38. Stoyneva-Gärtner, M.P.; Uzunov, B.A.; Descy, J.-P.; Gärtner, G.; Draganova, P.H.; Borisova, C.I.; Pavlova, V.; Mitreva, M. Pilot application of drone observations and pigment marker detection by HPLC in the studies of CyanoHABs in Bulgarian inland waters. Mar. Freshw. Res. 2019, 71, 606–616. [Google Scholar] [CrossRef]
  39. Radkova, M.; Stefanova, K.; Uzunov, B.; Gärtner, G.; Stoyneva-Gärtner, M. Morphological and Molecular Identification of Microcystin-Producing Cyanobacteria in Nine Shallow Bulgarian Water Bodies. Toxins 2020, 12, 39. [Google Scholar] [CrossRef] [Green Version]
  40. Stoyneva-Gärtner, M.; Stefanova, K.; Descy, J.-P.; Uzunov, B.; Radkova, M.; Pavlova, V.; Mitreva, M.; Gärtner, G. Microcystis aeruginosa and M. wesenbergii were the primary planktonic microcystin producers in several Bulgarian waterbodies (August 2019). Appl. Sci. 2021, 11, 357. [Google Scholar] [CrossRef]
  41. Uzunov, B.; Stefanova, K.; Radkova, M.; Descy, J.-P.; Gärtner, G.; Stoyneva-Gärtner, M. First Report on Microcystis as a Potential Microviridin Producer in Bulgarian Waterbodies. Toxins 2021, 13, 448. [Google Scholar] [CrossRef]
  42. Uzunov, B.; Stefanova, K.; Radkova, M.; Descy, J.-P.; Gärtner, G.; Stoyneva-Gärtner, M. Microcystis species and their toxigenic strains in phytoplankton of ten Bulgarian wetlands (August 2019). Botanica 2021, 27, 77–94. [Google Scholar] [CrossRef]
  43. Stoyneva-Gärtner, M.; Stefanova, K.; Uzunov, B.; Radkova, M.; Gärtner, G. Cuspidothrix Is the First Genetically Proved Anatoxin A Producer in Bulgarian Lakes and Reservoirs. Toxins 2022, 14, 778. [Google Scholar] [CrossRef] [PubMed]
  44. Stefanova, K.; Radkova, M.; Uzunov, B.; Gärtner, G.; Stoyneva-Gärtner, M. Pilot search for cylindrospermopsin-producers in nine shallow Bulgarian waterbodies reveals nontoxic strains of Raphidiopsis raciborskii, R. mediterranea and Chrysosporum bergii. Biotechnol. Biotechnol. Equip. 2020, 34, 384–394. [Google Scholar] [CrossRef]
  45. Valskys, V.; Gulbinas, Z.; Stoyneva-Gärtner, M.; Uzunov, B.; Skorupskas, R.; Karosienė, J.; Kasperovičienė, J.; Rašomavičius, V.; Uogintas, D.; Audzijonytė, A.; et al. Remote sensing in environmental studies: Advantages and challenges. Ann. Sof. Univ. 2022, 106, 31–44. [Google Scholar]
  46. OECD. Eutrophication of Waters—Monitoring, Assessment and Control; Organization for Economic Cooperation and Development: Paris, France, 1982. [Google Scholar]
  47. Poikāne, S.; Alves, M.H.; Argillier, C.; van den Berg, M.; Buzzi, F.; Hoehn, E.; de Hoyos, C.; Karottki, I.; Laplace-Treyture, C.; Solheim, A.L.; et al. Defining chlorophyll-a reference conditions in European lakes. Environ. Manag. 2010, 45, 1286–1298. [Google Scholar] [CrossRef] [Green Version]
  48. Descy, J.-P.; Stoyneva-Gärtner, M.P.; Uzunov, B.A.; Dimitrova, P.H.; Pavlova, V.T.; Gärtner, G. Studies on cyanoprokaryotes of the water bodies along the Bulgarian Black Sea Coast (1890–2017): A review, with special reference to new, rare and harmful taxa. Acta Zool. Bulgar. 2018, 11, 43–52. Available online: https://acta-zoologica-bulgarica.eu/supplement-11-2018/ (accessed on 10 December 2022).
  49. Stoyneva-Gärtner, M.P.; Descy, J.-P.; Latli, A.; Uzunov, B.; Pavlova, V.; Bratanova, Z.l.; Babica, P.; Maršálek, B.; Meriluoto, J.; Spoof, L. Assessment of cyanoprokaryote blooms and of cyanotoxins in Bulgaria in a 15-years period (2000–2015). Adv. Oceanogr. Limnol. 2017, 8, 131–152. [Google Scholar] [CrossRef] [Green Version]
  50. Google Earth. Available online: https:/?earth.google.com (accessed on 25 January 2023).
  51. Ginkgo Maps—Free Digital Maps. Available online: https://www.ginogomaps.com (accessed on 25 January 2023).
  52. Komárek, J. Cyanoprokaryota. In 3rd Part: Heterocytous Genera; Büdel, B., Krienitz, L., Gärtner, G., Schagerl, M., Eds.; Süßwasserflora von Mitteleuropa; Elsevier, Spektrum Akad. Verl.: Heidelberg, Germany, 2014; Volume 19. [Google Scholar]
  53. Komárek, J.; Anagnostidis, K. Cyanoprokaryota. 1. Teil: Chroococcales. In Süßwasserflora von Mitteleuropa. Bd. 19/1; Ettl, H., Gärtner, G., Heynig, G., Mollenhauer, D., Eds.; Gustav Fischer: Jena, Germany; Stuttgart, Germany; Lübeck Germany, 1999. [Google Scholar]
  54. Komárek, J.; Anagnostidis, K. Cyanoprokaryota. 2. Teil: Oscillatoriales. In Süßwasserflora von Mitteleuropa. Bd. 19/2; Büdel, B., Gärtner, G., Krienitz, L., Schagerl, M., Eds.; Elsevier, Spektrum Akad. Verl.: Heidelberg, Germany; München, Germany, 2005. [Google Scholar]
  55. Komárek, J.; Fott, B. Chlorophyceae (Grünalgen). Ordnung: Chlorococcales. In Das Phytoplankton des Süßwassers, 7/1; Schweizerbart’sche Verlagsbuchhandlung: Stuttgart, Germany, 1983; pp. 1–1044. [Google Scholar]
  56. Moestrup, Ø.; Calado, A.J. Süßwasserflora von Mitteleuropa, Bd. 6—Freshwater Flora of Central Europe, Vol. 6: Dinophyceae; Springer Spektrum: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
  57. Guiry, M.D.; Guiry, G.M. AlgaeBase. Available online: http://www.algaebase.org/ (accessed on 26 December 2022).
  58. Stoyneva-Gärtner, M.; Uzunov, B. Bases of Systematics of Algae and Fungi; JAMG Books: Sofia, Bulgaria, 2017. [Google Scholar]
  59. Starmach, K. Metody Badania Planktonu; PWRiL: Warszawa, Poland, 1955; pp. 1–135. [Google Scholar]
  60. Stoyneva, M.P. Planktic green algae of Bulgarian coastal wetlands. Hydrobiologia 2000, 438, 25–41. [Google Scholar] [CrossRef]
  61. Descy, J.P. SOP5: Estimation of cyanobacteria biomass by marker pigment analysis. In Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis; Meriluoto, J., Spoof, L., Codd, J., Eds.; John Wiley & Sons, Ltd.: Chichester, UK, 2017; pp. 343–349. [Google Scholar]
  62. Sarmento, H.; Descy, J.-P. Use of marker pigments and functional groups for assessing the status of phytoplankton assemblages in lakes. J. Appl. Phycol. 2008, 20, 1001–1011. [Google Scholar] [CrossRef]
  63. Mackey, M.D.; Mackey, D.J.; Higgins, H.W.; Wright, S.W. CHEMTAX—A program for estimating class abundances from chemical markers: Application to HPLC measurements of phytoplankton. Mar. Ecol. Prog. Ser. 1996, 144, 265–283. [Google Scholar] [CrossRef] [Green Version]
  64. Wright, S.W.; Jeffrey, S.W. Pigment markers for phytoplankton production. In Marine Organic Matter: Biomarkers, Isotopes and DNA; Volkman, J.K., Ed.; The Handbook of Environmental Chemistry; Springer: Berlin/Heidelberg, Germany, 2006; Volume 2N, pp. 71–104. [Google Scholar]
  65. Christensen, V.G.; Khan, E. Freshwater neurotoxins and concerns for human, animal, and ecosystem health: A review of anatoxin-a and saxitoxin. Sci. Total Environ. 2020, 736, 139515. [Google Scholar] [CrossRef] [PubMed]
  66. World Health Organization (WHO). Cyanobacterial Toxins: Anatoxin-a and Analogues. Background Document for Development of WHO Guidelines for Drinking-Water Quality and Guidelines for Safe Recreational Water Environments; World Health Organization: Geneva, Switzerland, 2020.
  67. Legrand, B.; Lesobre, J.; Colombet, J.; Latour, D.; Sabart, M. Molecular tools to detect anatoxin-a genes in aquatic ecosystems: Toward a new nested PCR-based method. Harmful Algae 2016, 58, 16–22. [Google Scholar] [CrossRef] [PubMed]
  68. Ballot, A.; Scherer, P.I.; Wood, S.A. Variability in the anatoxin gene clusters of Cuspidothrix issatschenkoi from Germany, New Zealand, China and Japan. PLoS ONE 2018, 13, e0200774. [Google Scholar] [CrossRef]
  69. Rantala-Ylinen, A.; Känä, S.; Wang, H.; Rouhiainen, L.; Wahlsten, M.; Rizzi, E.; Berg, K.; Gugger, M.; Sivonen, K. Anatoxin-a synthetase gene cluster of the cyanobacterium Anabaena sp. strain 37 and molecular methods to detect potential producers. Appl. Environ. Microbiol. 2011, 77, 7271–7278. [Google Scholar] [CrossRef] [Green Version]
  70. Tamura, K.; Stecher, G.; Peterson, D.; Filipski, A.; Kumar, S. MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Mol. Biol. Evol. 2013, 30, 2725–2729. [Google Scholar] [CrossRef] [Green Version]
  71. Basic Local Alignment Search Tool (BLAST). Available online: https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 24 January 2023).
  72. NCBI: National Centre for Biotechnology Information. Available online: https://www.ncbi.nlm.nih.gov/ (accessed on 12 January 2023).
  73. World Health Organization (WHO). Cyanobacterial Toxins: Microcystins. Background Document for Development of WHO Guidelines for Drinking-Water Quality and Guidelines for Safe Recreational Water Environments; WHO: Geneva, Switzerland, 2020.
  74. Humbert, J.-F. Molecular tools for the detection of toxigenic Cyanobacteria in natural ecosystems. In Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis; Meriluoto, J., Spoof, L., Codd, J., Eds.; John Wiley & Sons, Ltd.: Chichester, UK, 2017; pp. 280–283. [Google Scholar]
  75. Mbukwa, E.; Boussiba, S.; Wepener, V.; Leu, S.; Kaye, Y.; Msagati, T.A.M.; Mamba, B.B. PCR amplification and DNA sequence of mcyA gene: The distribution profile of a toxigenic Microcystis aeruginosa in the Hartbeespoort Dam, South Africa. J. Water Health 2013, 11, 563–572. [Google Scholar] [CrossRef] [PubMed]
  76. Mbukwa, E.; Msagati, T.A.M.; Mamba, B.B.; Boussiba, S.; Wepener, V.; Leu, S.; Kaye, Y. Toxic Microcystis novacekii T20-3 from Phakalane Ponds, Botswana: PCR Amplifications of Microcystin Synthetase (mcy) Genes, Extraction and LC-ESI-MS Identification of Microcystins. J. Environ. Anal. Toxicol. 2015, S7, 010. [Google Scholar] [CrossRef] [Green Version]
  77. Lee, J.; Choi, J.; Fatka, M.; Swanner, E.; Ikuma, K.; Liang, X.; Leung, T.; Howe, A. Improved detection of mcyA genes and their phylogenetic origins in harmful algal blooms. Water Res. 2020, 176, 115730. [Google Scholar] [CrossRef] [PubMed]
  78. Statistical Analysis Software Programs in Biomedical Research. Available online: https://www.labome.com/method/Statistical-Analysis-Software-Programs-in-Biomedical-Research.html (accessed on 26 January 2023).
  79. Ward, M.H.; Jones, R.R.; Brender, J.D.; de Kok, T.M.; Weyer, P.J.; Nolan, B.T.; Villanueva, C.M.; van Breda, S.G. Drinking Water Nitrate and Human Health: An Updated Review. Int. J. Environ. Res. Public Health 2018, 15, 1557. [Google Scholar] [CrossRef] [Green Version]
  80. Haralampiev, K. SPSS for Advanced; University Press, St. Kliment Ohridski: Sofia, Bulgaria, 2007. [Google Scholar]
  81. Stoyneva, M. Algal flora of the Danube River (Bulgarian sector) and adjoined water basins. V. Algal flora of the water bodies adjacent to the Lake of Srebarna. Ann. Univ. Sof. 1995, 88, 5–19. [Google Scholar]
  82. Stoyneva, M.P. Development of the phytoplankton of the shallow Srebarna Lake (North-Eastern Bulgaria) across the trophic gradient. Hydrobiologia 1998, 369/370, 259–367. [Google Scholar] [CrossRef]
  83. Stoyneva, M. Algae. In Biodiversity of the Srebarna Biosphere Reserve. Checklist and Bibliography; Michev, T.M., Georgiev, B.B., Petrova, A.V., Stoyneva, M.P., Eds.; Co-Publ. Context & Pensoft: Sofia, Bulgaria, 1998; pp. 10–371. [Google Scholar]
  84. Stoyneva, M.P. Steady-state phytoplankton assemblages in shallow Bulgarian wetlands. Hydrobiologia 2003, 502, 169–176. [Google Scholar] [CrossRef]
  85. Stoyneva, M.P. Contribution to the Studies of the Biodiversity of Hydro- and Aerobiontic Prokaryotic and Eukaryotic Algae in Bulgaria. Ph.D. Thesis, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria, 2014. [Google Scholar]
  86. Stoyanov, P.; Teneva, I.; Mladenov, R.; Belkinova, D. Diversity and Ecology of the Phytoplankton of Filamentous Blue-Green Algae (Cyanoprokaryota, Nostocales) in Bulgarian Standing Waters. Ecol. Balk 2013, 5, 1–6. [Google Scholar]
  87. Dimitrova, R.E.; Nenova, E.P.; Uzunov, B.A.; Shihiniova, M.D.; Stoyneva, M.P. Phytoplankton composition of Vaya Lake (2004–2006). Bulg. J. Agric. Sci. 2014, 20 (Suppl. 1), 165–172. [Google Scholar]
  88. Pavlova, V.; Stoyneva, M.; Georgieva, V.; Donchev, D.; Spoof, L.; Meriluoto, J.; Bratanova, Z.; Karadjova, I. New records of microcystins in some Bulgarian water bodies of health and conservational importance. J. Water Resour. Prot. 2014, 6, 446–453. [Google Scholar] [CrossRef] [Green Version]
  89. Stoyneva, M.P. Allochthonous planktonic algae recorded during the last 25 years in Bulgaria and their possible dispersal agents. Hydrobiologia 2016, 764, 53–64. [Google Scholar] [CrossRef]
  90. Kokociński, M.; Akçaalan, R.; Salmaso, N.; Stoyneva-Gärtner, M.P.; Sukenik, A. Expansion of alien and invasive cyanobacteria. In Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis; Meriluoto, J., Spoof, L., Codd, J., Eds.; John Wiley & Sons, Ltd.: Chichester, UK, 2017; pp. 28–39. [Google Scholar]
  91. Dochin, K. The dominance of invasive algae Raphidiopsis raciborskii in lowland reservoirs in Bulgaria. BJAS 2022, 28, 158–165. [Google Scholar]
  92. Watanabe, M. Studies on the planktonic blue-green algae 3. Some Aphanizomenon species in Hokkaido, northern Japan. Bull. Natl. Sci. Mus. Nat. Sci. Ser. B Bot. 1991, 17, 141–150. [Google Scholar]
  93. Teiling, E. Schwedische Planktonalgen. II. Tetrallantos, eine neue Gattung der Protococcoideen. Svenka Bot. Tidskr. Upps. 1916, 10, 59–66. [Google Scholar]
  94. Tasinov, O.B.; Vankova, D.G.; Nazifova-Tasinova, N.F.; Pasheva, M.G.; Kiselova, Y.D.; Sokrateva, T.D.; Ivanov, D.L.; Uzunov, B.A.; Stoyneva-Gärtner, M.P.; Ivanova, D.G. Cytotoxicity of water from five Bulgarian wetlands contaminated by toxigenic cyanobacteria and cyanotoxins. Bulg. Chem. Commun. 2020, 52, 257–262. [Google Scholar]
  95. Chichova, M.; Tasinov, O.; Shkodrova, M.; Mishonova, M.; Sazdova, I.; Ilieva, B.; Doncheva-Stoimenova, D.; Kiselova-Kaneva, Y.; Raikova, N.; Uzunov, B.; et al. New Data on Cylindrospermopsin Toxicity. Toxins 2021, 13, 41. [Google Scholar] [CrossRef]
  96. Teneva, I.; Belkinova, D.; Mladenov, R.; Stoyanov, P.; Moten, D.; Basheva, D.; Kazakov, S.; Dzhambazov, B. Phytoplankton composition with an emphasis of Cyanobacteria and their toxins as an indicator for the ecological status of Lake Vaya (Bulgaria)—Part of the Via Pontica migration route. BDJ 2020, 8, e57507. [Google Scholar] [CrossRef] [PubMed]
  97. Maileht, K.; Nõges, T.; Nõges, P.; Ott, I.; Mischke, U.; Carvalho, L.; Dudley, B. Water colour, phosphorus and alkalinity are the major determinants of the dominant phytoplankton species in European lakes. Hydrobiologia 2013, 704, 115–126. [Google Scholar] [CrossRef]
  98. Nõges, P.; van de Bund, W.; Cardoso, A.C.; Solimini, A.G.; Heiskanen, A.-S. Assessment of the ecological status of European surface waters: A work in progress. Hydrobiologia 2009, 633, 197–211. [Google Scholar] [CrossRef]
  99. Reynolds, C.S. The Ecology of Phytoplankton; Cambridge University Press: Cambridge, UK, 2006. [Google Scholar]
  100. Dochin, K.T.; Stoyneva, M.P. Effect of long-term cage fish-farming on the phytoplankton biodiversity in two large Bulgarian reservoirs. Ber. Naturwiss.-Med. Ver. Innsbr. 2014, 99, 49–66. [Google Scholar]
  101. Dochin, K.T.; Stoyneva, M.P. Phytoplankton of the Dospat Reservoir (Rhodopi Mts, Bulgaria)—Indicator of negative trend in reservoir development due to long-term cage fish farming. Ann. Sof. Univ. 2016, 99, 47–60. [Google Scholar]
  102. Teneva, I.; Mladenov, R.; Belkinova, D.; Dimitrova-Dyulgerova, I.; Dzhambazov, B. Phytoplankton community of the drinking water supply reservoir Borovitsa (South Bulgaria) with an emphasis on cyanotoxins and water quality. Open Life Sci. 2010, 5, 231–239. [Google Scholar] [CrossRef]
  103. Stoyneva, M.P. Algological studies of Bulgarian coastal wetlands. I. Species composition of the phytoplankton of Durankulak and Shabla-Ezeretz lakes. Ann. Univ. Sof. 2000, 91, 27–48. [Google Scholar]
  104. Stoyneva, M. Algological studies of Bulgarian coastal wetlands. II. Quantitative structure of the phytoplankton of Durankulak and Shabla-Ezeretz lakes. Ann. Univ. Sof. 2002, 92, 91–109. [Google Scholar]
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Figure 1. Map of Bulgaria (modified after [50,51]) with locations of the studied waterbodies and indication of their type. The waterbodies are represented by numbers that follow those in Table 1.
Figure 1. Map of Bulgaria (modified after [50,51]) with locations of the studied waterbodies and indication of their type. The waterbodies are represented by numbers that follow those in Table 1.
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Figure 2. Total biodiversity (expressed as number of species) of the summer phytoplankton of 43 Bulgarian waterbodies (abbreviations follow those in Table 1): (a) Biodiversity in the main taxonomic phyla: Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta and Crypto—Cryptophyta; (b) biodiversity in different classes of the phylum Ochrophyta: Bacillario—Bacillariophyceae, Chryso—Chrysophyceae, Synuro—Synurophyceae, Xantho—Xanthophyceae, Eustigmato—Eustigmatophyceae and Raphido—Raphidophyceae.
Figure 2. Total biodiversity (expressed as number of species) of the summer phytoplankton of 43 Bulgarian waterbodies (abbreviations follow those in Table 1): (a) Biodiversity in the main taxonomic phyla: Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta and Crypto—Cryptophyta; (b) biodiversity in different classes of the phylum Ochrophyta: Bacillario—Bacillariophyceae, Chryso—Chrysophyceae, Synuro—Synurophyceae, Xantho—Xanthophyceae, Eustigmato—Eustigmatophyceae and Raphido—Raphidophyceae.
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Figure 3. Total number of species in comparison with number of species in the main taxonomic phyla in the summer phytoplankton of 43 Bulgarian waterbodies (abbreviations follow those in Table 1): Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta and Crypto—Cryptophyta.
Figure 3. Total number of species in comparison with number of species in the main taxonomic phyla in the summer phytoplankton of 43 Bulgarian waterbodies (abbreviations follow those in Table 1): Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta and Crypto—Cryptophyta.
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Figure 4. (a) Average number of species—total and in the main taxonomic phyla—in the summer phytoplankton of 43 Bulgarian waterbodies; (b) distribution of species from main phyla by number of waterbodies (1, 2, 3, 4, 5, and 6–25) in which they were found. For better visibility, the number of species found in more than 5 waterbodies (6 to 25) is summarized in a common group. Legend: Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta, and Crypto—Cryptophyta.
Figure 4. (a) Average number of species—total and in the main taxonomic phyla—in the summer phytoplankton of 43 Bulgarian waterbodies; (b) distribution of species from main phyla by number of waterbodies (1, 2, 3, 4, 5, and 6–25) in which they were found. For better visibility, the number of species found in more than 5 waterbodies (6 to 25) is summarized in a common group. Legend: Cyano—Cyanoprokaryota, Chloro—Chlorophyta, Strepto—Streptophyta, Pyrrho—Pyrrhophyta, Eugleno—Euglenophyta, Ochro—Ochrophyta, and Crypto—Cryptophyta.
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Figure 5. Alien phytoplankters in Bulgarian waterbodies: (a) Raphidiopsis raciborskii (straight trichome, thick green arrow points to its akinete, thin green arrow points to the heterocyst) and Raphidiopsis gangetica (white arrow points to its coiled trichome) in the inland microreservoir Mechka; (b) coiled trichomes of Raphidiopsis gangetica (thin green arrow points to the typical rounded heterocyst); (c) Raphidiopsis acuminato-crispa coiled around the straight trichome of Raciborskii raciborskii in Mechka (arrow indicates its pointed heterocyst); (d) Raphidiopsis acuminato-crispa in Mechka (thin green arrow points to the heterocyst, thick green arrow points to the akinete); (e) Aphanizomenon yezoense in the coastal lake Durankulak—aggregation of trichomes in a fascicle (long white arrow points to the akinete, short white arrow points to the long transparent apical cell, and green arrow points one of the heterocysts in the fascicle); (f) Tetrallantos lagerheimii—coenobium of bent cells from the small inland microreservoir Hadzhidimovo, black scale—5 µm, relevant to all figures.
Figure 5. Alien phytoplankters in Bulgarian waterbodies: (a) Raphidiopsis raciborskii (straight trichome, thick green arrow points to its akinete, thin green arrow points to the heterocyst) and Raphidiopsis gangetica (white arrow points to its coiled trichome) in the inland microreservoir Mechka; (b) coiled trichomes of Raphidiopsis gangetica (thin green arrow points to the typical rounded heterocyst); (c) Raphidiopsis acuminato-crispa coiled around the straight trichome of Raciborskii raciborskii in Mechka (arrow indicates its pointed heterocyst); (d) Raphidiopsis acuminato-crispa in Mechka (thin green arrow points to the heterocyst, thick green arrow points to the akinete); (e) Aphanizomenon yezoense in the coastal lake Durankulak—aggregation of trichomes in a fascicle (long white arrow points to the akinete, short white arrow points to the long transparent apical cell, and green arrow points one of the heterocysts in the fascicle); (f) Tetrallantos lagerheimii—coenobium of bent cells from the small inland microreservoir Hadzhidimovo, black scale—5 µm, relevant to all figures.
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Figure 6. Relative contribution of main taxonomic groups to the phytoplankton biomass (calculated through the chlorophyll a concentration) according to the HPLC analysis of marker pigment composition in the studied Bulgarian waterbodies. Legend: cyano—cyanoprokaryotes, green—algae from both phyla Chlorophyta and Streptophyta, pyrrho—pyrrhophytes, eugleno—euglenophytes, ochro—ochrophytes, crypto—cryptophytes. Abbreviations of the names of the waterbodies are in accordance with Table 1.
Figure 6. Relative contribution of main taxonomic groups to the phytoplankton biomass (calculated through the chlorophyll a concentration) according to the HPLC analysis of marker pigment composition in the studied Bulgarian waterbodies. Legend: cyano—cyanoprokaryotes, green—algae from both phyla Chlorophyta and Streptophyta, pyrrho—pyrrhophytes, eugleno—euglenophytes, ochro—ochrophytes, crypto—cryptophytes. Abbreviations of the names of the waterbodies are in accordance with Table 1.
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Figure 7. Chlorophyll a content (µg L−1) according to the HPLC analysis of marker pigment composition in the studied waterbodies. Green line indicates the upper border of oligotrophic waters (<1.5 µg L−1), yellow line shows the upper border of mesotrophic waters (1.5–10 µg L−1), and the red line indicates the upper border of eutrophic waters (10–25 µg L−1), above which waters were hypertrophic., The abbreviations of the names of the waterbodies are in accordance with Table 1. Asterisks indicate that the real value of chl a in Izvornik 2 was 765 µg L−1.
Figure 7. Chlorophyll a content (µg L−1) according to the HPLC analysis of marker pigment composition in the studied waterbodies. Green line indicates the upper border of oligotrophic waters (<1.5 µg L−1), yellow line shows the upper border of mesotrophic waters (1.5–10 µg L−1), and the red line indicates the upper border of eutrophic waters (10–25 µg L−1), above which waters were hypertrophic., The abbreviations of the names of the waterbodies are in accordance with Table 1. Asterisks indicate that the real value of chl a in Izvornik 2 was 765 µg L−1.
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Figure 8. Neighbor-joining phylogenetic tree constructed after processing the samples from the 2021 summer phytoplankton using nucleotide sequences from five library samples indicated in blue and their closest sequences retrieved after a BLAST search [71] of the NCBI database [72]. Bootstrap values are shown at the branch points (percentage of 1000 trials) and an outgroup represented by Aphanizomenon gracile DC-1 and Anabaena sp. WA102. The 24 newly obtained nucleotides sequences are indicated by the abbreviated name of the waterbody, year of sampling and the relevant accession number in NCBI [72]: OQ3119995-OQ3200013. After the abbreviation, the number of the isolated sequence and after the slash, the year of the collection of the sample, are indicated. For the identical sequences obtained during this study, only one NCBI-derived accession number is provided in each cluster: OQ320003 for the Yunets clones 5 and 6, OQ320010 for the Studena clones 6 and 7, and OQ320013 for the Mechka clones 5 and 6. The following abbreviations are used for the waterbodies: Blu—Sinyata Reka (translated from its Bulgarian name as Blue River), DRE—Durankulak, Duv—Duvanli, FSH—Studena (due to the synonymous name Fishera), Kop—Koprinka, MCK—Mechka, Pla—Plachidol 2, Por—Poroy, Vai—Burgasko Ezero (from the synonymous name Vaya) and YNT—Yunets.
Figure 8. Neighbor-joining phylogenetic tree constructed after processing the samples from the 2021 summer phytoplankton using nucleotide sequences from five library samples indicated in blue and their closest sequences retrieved after a BLAST search [71] of the NCBI database [72]. Bootstrap values are shown at the branch points (percentage of 1000 trials) and an outgroup represented by Aphanizomenon gracile DC-1 and Anabaena sp. WA102. The 24 newly obtained nucleotides sequences are indicated by the abbreviated name of the waterbody, year of sampling and the relevant accession number in NCBI [72]: OQ3119995-OQ3200013. After the abbreviation, the number of the isolated sequence and after the slash, the year of the collection of the sample, are indicated. For the identical sequences obtained during this study, only one NCBI-derived accession number is provided in each cluster: OQ320003 for the Yunets clones 5 and 6, OQ320010 for the Studena clones 6 and 7, and OQ320013 for the Mechka clones 5 and 6. The following abbreviations are used for the waterbodies: Blu—Sinyata Reka (translated from its Bulgarian name as Blue River), DRE—Durankulak, Duv—Duvanli, FSH—Studena (due to the synonymous name Fishera), Kop—Koprinka, MCK—Mechka, Pla—Plachidol 2, Por—Poroy, Vai—Burgasko Ezero (from the synonymous name Vaya) and YNT—Yunets.
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Figure 9. Neighbor-joining phylogenetic tree based on nucleotides sequences from ten library samples and closest sequences retrieved after a BLAST [71] search in the NCBI database [72] with indication of their accession number. Bootstrap values are shown at branch points (percentage of 1000 trials). Legend for the abbreviations of the waterbodies: Man—Mandra; Dur—Durankulak; Vai—Burgasko Ezero (Vaya); Por—Poroy; Blu—Sinyata Reka (Blue River) with Arabic numerals after the abbreviation, indicating the exact site of sampling and number of the sequence. For the identical sequences obtained during this study, only one accession number received from NCBI [72] is provided in each cluster or subcluster: (i) OM525686 is representing the sequences from the reservoir Sinyata Reka and site 4 of lake Durankulak (Blu 1 and Dur (1) 4); (ii) OM525702 is relevant for the sequences obtained from sites 1 and 3 of the reservoir Mandra, Poroy (Man (1) 4, Man (3) 6 and Por 1); (iii) OM525709 is for sites 1–2 from Lake Burgasko Ezero (i.e., Vai (1–2) 4, and Vai (1–2) 9) and for two sequences from the reservoir Poroy (Por 2, Poroy 5); (iv) OM525721 represents the sequences from sites 3 and 4 of Lake Durankulak (Dur (3) 3 and Dur (4) 1).
Figure 9. Neighbor-joining phylogenetic tree based on nucleotides sequences from ten library samples and closest sequences retrieved after a BLAST [71] search in the NCBI database [72] with indication of their accession number. Bootstrap values are shown at branch points (percentage of 1000 trials). Legend for the abbreviations of the waterbodies: Man—Mandra; Dur—Durankulak; Vai—Burgasko Ezero (Vaya); Por—Poroy; Blu—Sinyata Reka (Blue River) with Arabic numerals after the abbreviation, indicating the exact site of sampling and number of the sequence. For the identical sequences obtained during this study, only one accession number received from NCBI [72] is provided in each cluster or subcluster: (i) OM525686 is representing the sequences from the reservoir Sinyata Reka and site 4 of lake Durankulak (Blu 1 and Dur (1) 4); (ii) OM525702 is relevant for the sequences obtained from sites 1 and 3 of the reservoir Mandra, Poroy (Man (1) 4, Man (3) 6 and Por 1); (iii) OM525709 is for sites 1–2 from Lake Burgasko Ezero (i.e., Vai (1–2) 4, and Vai (1–2) 9) and for two sequences from the reservoir Poroy (Por 2, Poroy 5); (iv) OM525721 represents the sequences from sites 3 and 4 of Lake Durankulak (Dur (3) 3 and Dur (4) 1).
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Figure 10. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different chlorophyll a concentration (µg L−1) as an expression of the trophic state [37].
Figure 10. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different chlorophyll a concentration (µg L−1) as an expression of the trophic state [37].
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Figure 11. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different concentrations of total nitrogen [4,37].
Figure 11. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different concentrations of total nitrogen [4,37].
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Figure 12. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different concentrations of total phosphorus (µg L−1).
Figure 12. Cumulative species number in main taxonomic groups, expressed as a percentage of the total within the relevant phyla and classes (SP%), in waterbodies with different concentrations of total phosphorus (µg L−1).
Diversity 15 00472 g012
Diversity 15 00472 g013 550
Figure 13. Cumulative species number expressed as percentage of the total within the phyla and classes (SP%), in different waterbodies according to the altitude (m a.s.l.), classified after [37].
Figure 13. Cumulative species number expressed as percentage of the total within the phyla and classes (SP%), in different waterbodies according to the altitude (m a.s.l.), classified after [37].
Diversity 15 00472 g013
Diversity 15 00472 g014 550
Figure 14. Cumulative species number expressed as a percentage of the total within the phyla and classes (SP%), in waterbodies of different conductivity values (S m−1), classified after [37].
Figure 14. Cumulative species number expressed as a percentage of the total within the phyla and classes (SP%), in waterbodies of different conductivity values (S m−1), classified after [37].
Diversity 15 00472 g014
Diversity 15 00472 g015 550
Figure 15. Cumulative species number expressed as a percentage of the total within the phyla and classes (SP%), in different waterbodies according to the water hardness (°dh), classified after [37].
Figure 15. Cumulative species number expressed as a percentage of the total within the phyla and classes (SP%), in different waterbodies according to the water hardness (°dh), classified after [37].
Diversity 15 00472 g015
Table
Table 1. Sampling sites in Bulgarian waterbodies and their environmental parameters during summer sampling campaigns in 2018, 2019 and 2021. Legend: WBN—name of the waterbody, IBW—identification number in the Inventory of Bulgarian Wetlands [37], Abbr—abbreviation of the name, Type—type of waterbody: M (small reservoir/”microreservoir”, <100 ha), R (large reservoir, >100 ha) and L (natural lake), Alt—altitude above the sea level (m), WT—water temperature (°C), CN—conductivity (S m−1), TDS—total dissolved solids (µg L−1), DO—oxygen concentration (mg L−1), TP—total phosphorus (µg L−1), TN—total nitrogen (mg L−1). Waterbodies are presented according to their geographical location in counterclockwise order, starting from South-Western Bulgaria. Asterisks indicate the waterbodies which were sampled for the first time.
Table 1. Sampling sites in Bulgarian waterbodies and their environmental parameters during summer sampling campaigns in 2018, 2019 and 2021. Legend: WBN—name of the waterbody, IBW—identification number in the Inventory of Bulgarian Wetlands [37], Abbr—abbreviation of the name, Type—type of waterbody: M (small reservoir/”microreservoir”, <100 ha), R (large reservoir, >100 ha) and L (natural lake), Alt—altitude above the sea level (m), WT—water temperature (°C), CN—conductivity (S m−1), TDS—total dissolved solids (µg L−1), DO—oxygen concentration (mg L−1), TP—total phosphorus (µg L−1), TN—total nitrogen (mg L−1). Waterbodies are presented according to their geographical location in counterclockwise order, starting from South-Western Bulgaria. Asterisks indicate the waterbodies which were sampled for the first time.
WBN and IBWAbbrTypeYearAltLatitudeLongitudeWTpHCNTDSDOTPTN
1* HadzhidimovoHdzM202115641°29.893323°50.189029.19.530019217.000.10.1
2* Dubnitsa (IBW3698)DbnM202160041°33.850023°50.750025.29.62461599.210.10.1
3* Ablanitsa (IBW6013)AblM202168241°32.859423°55.586927.28.82421578.541.00.5
4* Satovcha 2 (IBW1197)StvM2021101741°36.822223°58.144627.48.702721769.000.50.1
5Dospat (IBW3155)DspR2021121441°39.149524°89.559625.99.981528.730.10.5
2021121241°39.149324°89.591825.69.583528.700.30.5
6Golyam Beglik (IBW1314) GBgR2021154041°48.892724°07.872522.09.199638.921.51.0
7Shiroka Polyana (IBW3144) SPlR2021155041°46.177624°08.820125.38.966428.700.50.5
8Beglika (IBW3141) BglM2021153541°49.796324°07.819621.79.12421579.111.00.8
9* Chetiridesette Izvora 24642°00.5510
(IBW1523)CIzM202124°56.281928.77.54022638.661.00.5
10* Mechka (IBW1584)MckM202131941°55.897025°06.159527.19.02411548.501.51.0
11* Byalata Prust-MezekBPMM202116741°45.108026°05.240329.78.52911889.372.01.0
12* Birgo (Shtit) BrgM202121541°49.774326°22.188927.38.05943858.751.51.8
13* Studena (Fishera) 28241°54.2136 1
(IBW2421)StdM202126°24.596429.39.06524233.351.00.3
14* Mogila (Kaynaka) 16642°29.8310
(IBW2626)MglM202126°36.104329.29.568244215.754.01.0
15* Hadzhi Yani (Lozenets) 1242°12.0333
(IBW2893)HYnM202127°47.300026.17.57514888.421.50.8
16Mandra (IBW1720)MndR20181242°24.0643′27°26.1120′25.98.36494216.813.03.0
2018 42°24.0670′27°19.1310′26.28.26634615.896.04.0
2018 42°26.1420′27°26.5860′24.98.56394157.914.03.3
2019 42°24.0295′27°19.1194′25.887.96764367.930.70.5
2019 42°25.9303′27°26.7652′27.28.55783757.871.51.8
2021 42°24.237027°19.1205′27.39.05133339.327.04.0
2021 42°25.928227°26.7675′27.39.051333310.707.54.0
17Uzungeren (IBW0710)UznL2018742°26.1782′27°27.1998′25.98.11458 93517.835.02.8
2019 42°26.1551′27°27.2235′27.68.51748 1132 9.700.40.3
2021 42°26.1532′27°27.2214′28.19.0185201200011.215.54.0
18Burgasko Ezero (Vaya) (IBW0191)BEzL2018042°30.5940′27°22.0750′26.99.72588168212.51135.4
2018 42°28.4540′ 27°25.4820′28.288.9118376811.94113.7
2018 42°29.1850′27°26.5310′23.79.510246657.01124.6
2019 42°30.5940′27°22.0750′27.99.24901707.690.50.3
2021 42°30.7934′27°24.2425′26.69.0442128731.26125.3.
19Poroy (IBW3038)PorM20184142°43.0190′27°37.3160′25.108.37624959.451.02.8
2019 42°43.3403′27°37.5255′27.58.16444167.600.10.3
2021 42°43.4683′27°36.8757′26.19.079251411.682.11.5
20Aheloy (IBW3032)AhlM201814442°42.8230′27°30.9740′25.48.56143998.9214.1
21* YunetsYntM20217942°55.6700′27°45.3074′27.48.596576511.002.51.8
22Tsonevo (IBW3022)TsnR20197543°01.8055′27°24.3965′24.88.83552318.200.10.1
2021 43°01.8278′27°24.3954′26.68.041727210.650.10.1
23Eleshnitsa (IBW3023)ElsM20194443°00.3344′27°28. 0744′26.78.45323476.780.10.3
24Ezerets (IBW0233)EzrL2018043°35.2770′28°33.2290′26.48.41084109.940.55.3
2019643°35.2681′28°33.2096′25.98.61669 1739 8.580.10.1
25Shabla (IBW0219)ShbL2018043°33.8180′28°34.1860′27.18.510877069.970.15.1
2019 43°33.8212′28°34.8204′25.98.7184211969.640.11.0
26Durankulak (IBW0216)DrnL2018443°40.3240′28°32.0470′24.038.511117227.35212.8
2018 43°40.3340′28°32.0220′24.78.210947117.79204.0
2018 43°40.5300′28°32.9930′24.68.510756986.19243.9
2018 43°40.6950′28°32.6000′26.58.510877069.60203.2
2019 43°40.0006′29°32.6166′26.58.99746317.860.30.7
2019 43°40.5355′28°33.0806′26.78.910486806.040.30.6
2021 43°40.6935′28°32.6000′25.59.0296073610.70144.5
2021 43°40.5300′28°33.0826′25.59.0300819527.40112.0
27* Plachidol 2 (IBW5073)PlcM201922043°33.3504′27°52.6338′24.69.012257939.130.20.4
28* Malka Smolnitsa (IBW3107)MSmM201921143°36.2606′27°44.5367′25.29.17554907.050.60.6
29* Preselka (IBW3078)PrsM201928143°25.3767′27°16.6214′24.19.013828210.050.62.8
30* Izvornik 2 (IBW3082)IzvM201925543°27.3838′ 27°21.1110′24.59.438925313.269.04.8
31* Fisek (IBW2674)FskM201918243°18.8453′26°44. 3765′27.28.76903977.520.20.1
32* Shumensko Ezero (IBW2754)SEzM201915243°14.8140′26°57.5675′25.28.54714456.320.20.5
33* Kriva Reka (IBW3071)KRkM201913343°22.6573′27°10.9807′23.78.46624286.241.09.0
34Suedinenie (IBW2642)SdnR201913343°20.0734′26°33.6368′28.17.67394816.770.10.3
35* Nikolovo (IBW3176) NklM20218943°50.976826°05.179626.09.82156140011.88112.0
36Shilkovtsi (Iovkovtsi) (IBW2105)ShlR201941042°55.2320′25°47.6743′27.28.97464797.480.030.1
37Koprinka (IBW2062)KprR201945042°37.0172′25°19.4795′27.28.22501637.210.10.2
38Zhrebchevo (IBW2545)ZhrR201925342°36.6024′25°51.2345′27.67.7358 233 8.010.10.2
39Al. Stamboliyski (IBW2056)AStR201919043°07.0000′25°07.3936′29.48.96704339.821.43.5
40Krapets (IBW2000)KrpM201941043°04.0316′24°52.3835′28.78.38705647.740.11.0
41Sopot (IBW1437)SptR201937640°00.7017′24°52.6045′29.08.37794903.440.10.1
42* Duvanli (IBW1483)DvnM201925042°23.1851′24°43.1000′26.38.840502917.090.10.3
43Sinyata Reka (IBW1890)SRkM201831742°28.1480′24°42.217027.49.74703059.36254.8
2018 42°28.1473′24°42.217526.79.44683069.21274.3
2019 42°28.1518′24°42.0159′28.210.449031714.761.00.2
Table
Table 2. Dominants, codominants and subdominants in the summer phytoplankton of 43 waterbodies in Bulgaria. The samples obtained in different years are indicated in brackets after the relevant name.
Table 2. Dominants, codominants and subdominants in the summer phytoplankton of 43 waterbodies in Bulgaria. The samples obtained in different years are indicated in brackets after the relevant name.
SpeciesAbundanceWaterbody
Cyanoprokaryota
Anabaenopsis elenkinii + Cuspidothrix issatschenkoiCodominantsMogila
Aphanizomenon klebahniiDominant/SubdominantMandra (2019), Poroy (2019, 2021)/Hadzhi Yani
Aphanizomenon yezoense + Sphaerospermopsis aphanizomenoidesCodominantsStudena
Chrysosporum minor + Raphidiopsis mediterraneaCodominantsPlachidol 2
Chrysosporum ovalisporumDominantShabla (2019)
Dolichospermum compactumDominantIzvornik 2
Dolichospermum perturbatum + Planktothrix isothrixCodominantsBurgasko Ezero (2018)
Dolichospermum planctonicumDominant/CodominantGolyam Beglik/Ablanitsa
Dolichospermum scheremetieviaeDominantYunets
Limnothrix redekeiDominantPreselka
Limnothrix mirabilisCodominantPoroy (2018)
Microcystis wesenbergiiDominantKriva Reka, Nikolovo, Sinya Reka (2018)
Planktothrix isothrix + Planktothrix suspensaCodominantsBurgasko Ezero (2019)
Pseudanabaena limneticaCodominant/SubdominantDuvanli, Malka Smolnitsa/Preselka
Raphidiopsis raciborskiiCodominant/SubdominantMalka Smolnitsa/Byalata Prust, Poroy (2018), Preselka
Raphidiopsis raciborskii + R. acuminato-crispa + R. gangeticaCodominantsMechka
Romeria simplexCodominantDuvanli
Sphaerospermopsis aphanizomenoidesDominantBurgasko Ezero (2021)
Sphaerospermopsis torques-reginaeDominantSinyata Reka (2019)
Anabaenopsis milleriSubdominantIzvornik 2
Aphanizomenon yezoense + Microcystis aeruginosa+Pseudanabaena mucicola + Synechocystis endobioticaSubdominantsDurankulak (2021)
Aphanocapsa delicatissimaSubdominantShumensko Ezero
Aphanocapsa holsaticaSubdominantDurankulak (2018), Hadzhi Yani
Coelomoron pusillumSubdominantKriva Reka
Dolichospermum perturbatumSubdominantIzvornik 2
Microcystis aeruginosaSubdominantMandra (2021)
Microcystis sp. (separate cells)SubdominantDuvanli
Oscillatoria cf. simplicissimaSubdominantBurgasko Ezero (2021)
Planktolyngbya limneticaSubdominantEleshnitsa
Pseudanabaena mucicolaSubdominantNikolovo
Raphidiopsis raciborskii + Pseudanabaena limneticaSubdominantsShabla (2019)
Chlorophyta
Binuclearia lauterborniiDominant/SubdominantSopot, Tsonevo (2019)/Durankulak (2019), Uzungeren (2018)
Gloeocystis sp. DominantEzerets (2018), Shabla (2018)
Monactinus simplexDominantHadzhi Yani
Oocystis sp.DominantAl. Stamboliyski, Zhrebchevo
Siderocystopsis pseudoblongaCodominantShilkovtsi
Coelastrum astroideum + Tetrallantos lagerheimii SubdominantsHadzhidimovo
Didymocystis inconspicua + Pediastrum duplexSubdominantsPoroy (2018)
Elakatothrix lacustrisSubdominantAl. Stamboliyski
Golenkinia radiataSubdominantPlachidol 2
Hariotina polychordaSubdominantSuedinenie
Lauterborniella appendiculata + Lobocystis sp.SubdominantsDurankulak (2019)
Scenedesmus ellipticusSubdominantAheloy
Streptophyta
Cosmarium neodepressum var. planctonicumDominantFisek
Closterium acerosumSubdominantUzungeren (2018)
Cosmarium phaseolus var. elevatumSubdominantDubnitsa
Pyrrhophyta
Ceratium rhomvoidesDominantAl. Stamboliyski
Peridinium volzii var. cinctiformeDominantSuedinenie
Parvodinium elpatiewskyiDominant/SubdominantBirgo, Dubnitsa/Satovcha 2
Parvodinium cunningtoniiCodominantAblanitsa
Parvodinium umbonatumDominantHadzhidimovo
Sphaerodinium polonicumDominantDuvanli, Eleshnitsa (2019)
Ceratium furcoidesSubdominantMandra (2018), Suedinenie
Parvodinium goslavienseSubdominantMechka, Mogila
Euglenophyta
Euglena adhaerensDominantUzungeren (2021)
Euglenaria clavataDominantSatovcha 2
Phacus rotundusCodominantHadzhi Yani
Discoplastis spathirhynchaSubdominantKriva Reka
Euglena sp.SubdominantUzungeren (2018)
Trachelomonas hispidaSubdominantBirgo
Trachelomonas intermediaSubdominantSatovcha 2
Ochrophyta
Bacillariophyceae
Asterionella formosaCodominantDospat
Coscinodiscus sp.Dominant/SubdominantDurankulak (2021)/Mandra (2021), Poroy (2021)
Ctenophora pulchellaDominantShumensko Ezero
Cymbella cf. cymbiformisDominantMandra (2021), Tsonevo (2018)
Fragilaria crotonensisCodominantShilkovtsi
Lindavia comtaDominantBeglika, Chetiridesette Izvora
Nitzschia acicularisDominantUzungeren (2018)
Ulnaria acusDominantEzerets (2019)
Ulnaria ulnaCodominantEleshnitsa (2021)
Stephanocyclus meneghinianusSubdominantKoprinka
Chrysophyceae
Dinobryon bavaricumDominant/CodominantShiroka Polyana/Eleshnitsa (2021)
Dinobryon korschikoviiDominantSopot
Synurophyceae
Mallomonas akrokomosCodominantDospat
Xanthophyceae
Nephrodiella cf. acutaDominantUzungeren (2019)
Cryptophyta
Cryptomonas erosaDominant/SubdominantKoprinka/Durankulak (2021)
Cryptomonas cf. ovataSubdominantShabla (2018)
Cryptomonas sp.SubdominantUzungeren (2019)
Table
Table 3. Toxins and toxin-producing cyanoprokaryotes in the considered Bulgarian waterbodies, sampled in 2018 and 2019. Legend: CPS—cylindrospermopsin; MC—microcystin, followed by the exact type (LR, RR or YR), MV—microviridin, followed by the letter indicating the specific type (A, B, C, etc.), SXT—saxitoxins; (?)—supposed toxicity based on a comparison of newly obtained genetic sequences with light microscopic data. Waterbodies are arranged by years in alphabetical order.
Table 3. Toxins and toxin-producing cyanoprokaryotes in the considered Bulgarian waterbodies, sampled in 2018 and 2019. Legend: CPS—cylindrospermopsin; MC—microcystin, followed by the exact type (LR, RR or YR), MV—microviridin, followed by the letter indicating the specific type (A, B, C, etc.), SXT—saxitoxins; (?)—supposed toxicity based on a comparison of newly obtained genetic sequences with light microscopic data. Waterbodies are arranged by years in alphabetical order.
Waterbody/YearToxinsSpeciesReference
2018
Burgasko Ezero CPSMicrocystis aeruginosa, Microcystis wesenbergii, Microcystis novacekii; Cuspidothrix issatschenkoi, Cuspidothrix tropicalis[38,39,43]
DurankulakMC-LR, MC-RR, MC-YR, SXTMicrocystis aeruginosa,
Microcystis wesenbergii		[38,39]
Mandra Microcystis novacekii[39]
Poroy Microcystis novacekii; Cuspidothrix issatschenkoi, Cuspidothrix tropicalis[39,43]
Sinyata Reka MC-LR,
MC-RR		Microcystis wesenbergii; Cuspidothrix issatschenkoi, Cuspidothrix tropicalis[38,39,43]
2019
Burgasko EzeroMV-CBJMicrocystis aeruginosa, Microcystis wesenbergii, Cuspidothrix elenkinii, Cuspidothrix issatschenkoi, Cuspidothrix tropicalis[40,41,43]
DurankulakMC-LR,
MV-CBJ		Microcystis aeruginosa[40,41,42]
Duvanli Microcystis aeruginosa, Microcystis viridis (?), Cuspidothrix issatschenkoi[42,43]
Ezerets Microcystis aeruginosa, Microcystis viridis (?)[42]
Izvornik 2 Microcystis wesenbergii[40]
Koprinka Microcystis aeruginosa, Microcystis viridis (?), Microcystis wesenbergii, Cuspidothrix elenkinii[42,43]
Malka Smolnitsa Microcystis aeruginosa, Microcystis viridis (?)[42]
MandraMC-LR, MV-CBJMicrocystis aeruginosa[40,41]
Plachidol 2 Cuspidothrix issatschenkoi[43]
Preselka Microcystis aeruginosa, Microcystis viridis[42]
PoroyMV-A, MV/MC19Microcystis aeruginosa, Microcystis viridis, Microcystis wesenbergii[40,41]
Sinyata RekaMV-B/CMicrocystis aeruginosa, Microcystis wesenbergii, Cuspidothrix tropicalis[40,41,43]
Uzungeren Microcystis aeruginosa[40]
Zhrebchevo Microcystis aeruginosa, Microcystis viridis (?), Microcystis wesenbergii[42]
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Stoyneva-Gärtner, M.P.; Descy, J.-P.; Uzunov, B.A.; Miladinov, P.; Stefanova, K.; Radkova, M.; Gärtner, G. Diversity of the Summer Phytoplankton of 43 Waterbodies in Bulgaria and Its Potential for Water Quality Assessment. Diversity 2023, 15, 472. https://doi.org/10.3390/d15040472

AMA Style

Stoyneva-Gärtner MP, Descy J-P, Uzunov BA, Miladinov P, Stefanova K, Radkova M, Gärtner G. Diversity of the Summer Phytoplankton of 43 Waterbodies in Bulgaria and Its Potential for Water Quality Assessment. Diversity. 2023; 15(4):472. https://doi.org/10.3390/d15040472

Chicago/Turabian Style

Stoyneva-Gärtner, Maya P., Jean-Pierre Descy, Blagoy A. Uzunov, Peter Miladinov, Katerina Stefanova, Mariana Radkova, and Georg Gärtner. 2023. "Diversity of the Summer Phytoplankton of 43 Waterbodies in Bulgaria and Its Potential for Water Quality Assessment" Diversity 15, no. 4: 472. https://doi.org/10.3390/d15040472

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