Diatoms’ Diversity in the Assessment of the Impact of Diamond and Oil and Gas Mining on Aquatic Ecosystems of the Central Yakut Plain (Eastern Siberia, Yakutia) Using Bioindication and Statistical Mapping Methods

Abstract

Diamond and oil and gas production carries risks to the aquatic ecosystem. In Eastern Siberia, on the territory of the Central Yakut Plain, mining development of the Yakut diamond-bearing province and Tas-Yuryakh oil and gas condensate field has been underway for several decades. But the problem of studying negative consequences in the region is covered only from the point of view of the impact on terrestrial ecosystems. The purpose of this study was to assess the impact of diamond and oil and gas production on the aquatic ecosystems of the region using the bioindicative properties of diatoms. The work used previously widely tested methods of ecological mapping, JASP, and species–environments relationship analysis. The results of chemical analysis of water showed that in oil and gas production areas, there is no pollution with petroleum products, but the concentration of silicon and zinc is increased. The study identified key pollutants in the Central Yakut Plain and demonstrated the effectiveness of diatoms as bioindicators. Elevated chloride levels were found in diamond mining areas, and increased copper levels were noted in oil production regions. In the diatom flora of the region, 144 species were identified, of which 137 are indicator species. Natural and anthropogenic clusters of environmental factors are identified, characterized by a specific effect on the species composition and taxonomic structure of diatom communities. The results obtained are suitable for assessing the level of anthropogenic impact on aquatic communities of photosynthetic microorganisms in permafrost conditions.

1. Introduction

Eastern Siberia is a region where mining began in the 20th century and its scale is steadily increasing. The Republic of Sakha (Yakutia) has a rich diversity of mineral resources and produces 99 percent of Russia’s diamonds, annually amounting to between 20 and 25% of the world’s output [1]. In the territory of the Central Yakut Plain, the development of alluvial diamond deposits of the Yakut diamond-bearing province has been carried out since 1957, and in 1981, work was opened at the Tas-Yuryakh oil and gas condensate field. Oil and products formed during its extraction pose a great potential threat to biota [2,3]. In addition to the direct danger of oil spills, the formation of a film on the surface of waterbodies, the accumulation of toxic hydrocarbons, etc., oil production is associated with the transformation of landscapes and the disruption of water exchange, which can lead to the destruction of the wetland system and large-scale impacts on a wide range of living organisms. Diamond mining is associated with the same significant transformation of landscapes. The diamond mining process requires large amounts of water for various activities such as washing and processing the mined ore. This often leads to contamination of water sources with sediment, chemicals, and heavy metals. Consequently, this poses a threat to aquatic flora and fauna and degrades the quality of drinking water available to the local population. The negative impact of subsoil development on biota is aggravated in the north and permafrost zone, which is due to the extremely low restoration potential of northern ecosystems. Previous studies in various regions have examined the impact of industrial activities on aquatic ecosystems, showing significant alterations in water quality and biodiversity. Thus, in the polar part of the Krasnoyarsk Territory on 29 May 2020, as a result of an omission in the design and construction of the pile foundation of the oil product storage tank of the Norilsk Nickel company, it was accidentally damaged and leaked with a volume of 21 thousand tons to the natural environment [4]. By October 2020, the main stages of liquidation of the consequences were completed at the accident site: 90% of the spill was collected, and the removal of contaminated soil was completed. However, an analysis of the consequences of this accident showed high concentrations of organic carbon in bottom sediments, and the content of polycyclic aromatic hydrocarbons reached 3764.4 ng/g. The lakes were most affected due to their slow water exchange. According to experts, the large polar lake Pyasino will probably never be restored to its original state. It should be added to this that some of the oil products ended up in the marine ecosystem of the Kara Sea.

A number of authors note a trend towards a decrease in the biodiversity of algal communities for northern waterbodies under the influence of oil and diamond mining [5,6].

This problem has been reflected in a number of scientific publications. But existing studies in the region have primarily focused on terrestrial ecosystems [7], leaving a significant gap in understanding the impact on aquatic ecosystems. Changes in waterbodies can be more dangerous and large-scale, as shown by the first bioindication studies using statistical methods [8].

Diatoms are known as one of the most species-rich groups of aquatic organisms, which, in accordance with the European Union Water Framework Directive [9], are used for the environmental assessment of aquatic ecosystems [10,11,12]. Extensive knowledge of diatom ecology [13] facilitates their successful use as bioindicators [14,15].

The aim of the work was to identify the diversity of diatoms in waterbodies of the Central Yakut Plain and to assess the impact of diamond and oil and gas production on aquatic ecosystems using algal bioindication and statistical mapping methods.

2. Materials and Methods

2.1. Description of Study Area

The research area is located in the northeast of the Asian subcontinent, in Eastern Siberia, on the territory of Yakutia, in the interfluve of the Lena and Vilyuy Rivers and represents the Central Yakut Plain (Figure 1). The plain is flat with heights of up to 400 m, heavily swampy, and dissected by a network of shallow river valleys. Two of the largest rivers, Ulakhan-Botuobuya River and Ochchuguy-Botuobuya River, flow to the north and belong to the Vilyui River basin. And the other two, the Ulakhan-Murbayi and Tustakh Rivers, flow in a southerly direction and are tributaries of the Lena River. Permafrost is found everywhere on the territory. The plain is covered with larch and larch-pine forests interspersed with birch forests and meadow steppes (Figure 2a,c,d). The climate is markedly continental and harsh. The duration of the frost-free period is 30 days; the average January temperature and absolute minimum are −32 °C and −59 °C, respectively; the average July temperature and maximum air temperature are 16 °C and 35 °C, respectively [16]. The height of the snow cover is on average 50 cm. Wind speed is up to 22 m/s. The amount of precipitation for the warm period is 250 mm, and for the cold period it is 60 cm.

Various types of reservoirs were studied (

Table 1. Sampling station, geographical position, and parameters.

No of Station	Waterbody	Sampling Date	Altitude, m a.s.l.	Latitude, N	Longitude, E
1	Puddle	8 September 2022	329	62°30′12.7″	113°45′05.2″
2	Puddle	8 September 2022	349	62°30′04.1″	113°45′05.0″
3	Diamond quarry named after XXIII Party Congress	8 September 2022	400	62°27′10.9″	113°46′00.5″
4	Nameless diamond quarry	8 September 2022	311	62°27′56.4″	113°57′10.6″
5	Lake	3 September 2022	322	61°41′59.2″	113°13′44.4″
6	Water reservoir	5 September 2022	321	61°41′33.0″	113°13′36.8″
7	Swamp	5 September 2022	346	61°30′18.3″	113°39′55.7″
8	Stream	5 September 2022	362	61°30′02.4″	113°42′34.0″
9	Swamp	5 September 2022	370	61°29′05.2″	113°45′33.8″
10	Swamp	5 September 2022	355	61°26′41.1″	113°50′49.3″
11	Stream	4 September 2022	306	61°25′59.8″	113°59′58.2″

), including small streams, puddles, swamps, and a lake. An artificial reservoir (station 6) created in the territory of oil and gas production was also investigated (Figure 2b). The reservoir was filled with water in 2018 by taking water from a neighboring stream, has dimensions of 250 × 400 m, a depth of 8 m, and is used to supply industrial water for production. In addition, two artificial reservoirs formed on the site of quarries that remained at the site of diamond mining were studied (stations 3 and 4). Diamond mining in these quarries was stopped in 1980, after which they filled with water naturally, due to precipitation and surface runoff. The diamond quarry named after XXIII Party Congress (station 3) reaches 300 m in diameter and 120 m in depth (Figure 2e); and a nameless diamond quarry (station 4) is 250 m in diameter and 100 m deep (Figure 2f). All of these waterbodies have not been previously explored in terms of algae diversity and water chemistry.

2.2. Sampling

Sites were selected based on the proximity to industrial activities and reference locations with minimal human impact (Table 1). Sampling was carried out using an Apstein plankton net (SEFAR NITEX fabric, Heiden, Switzerland, mesh diameter 15 µm) during the period from 4 September to 8 September 2022. Samples were preserved by adding neutral formaldehyde to 4% immediately upon collection. Preservation was necessary to prevent biological degradation, ensuring the integrity of chemical and biological analyses. Geographic position and altitude above sea level were determined using a Garmin eTrex GPS navigator (Garmin Ltd., Olathe, KS, USA). Water samples of 2 L were taken from each waterbody for chemical analysis. All samples for further study were transported to the Institute for Biological Problems of Cryolithozone SB RAS (Yakutsk).

2.3. Water Chemistry Analysis

Water temperature was measured during sampling with a Chektemp thermometer (Hanna Instruments, Woonsocket, RI, USA). Chemical analyses of water samples were performed following standard methods [17]. Water color was determined using a photometric method. pH was measured using a potentiometric method. Water salinity was calculated as the sum of ions using the following methods: turbidimetry for sulphate anions; flame spectrophotometry for potassium and sodium cations; mercurimetry for chloride ions; and titration for calcium, magnesium, and bicarbonate ions. A photometric method was applied to determine nutrients’ concentrations. Nessler’s reagent, Griess reagent, salicylic acid, ammonium molybdate, and sulfosalicylic acid were used for the measurement of ammonium ions, nitrite ions, nitrate ions, phosphate ions, and total iron, respectively. A combined reagent composed of ammonium molybdate and ascorbic acid was used to determine total phosphorus content. The content of manganese, nickel, copper, and zinc was determined by atomic absorption spectrometry with electrothermal vaporization. A fluorimetry method with the Fluorat-02-2M device (GC LUMEX; Saint Petersburg, Russia) was used to measure the petrochemicals. Instruments were calibrated immediately before performing analytics using standard solutions. Analytical methods were validated by analyzing standard reference materials and conducting duplicate analyses. Limitations include potential matrix effects, which were mitigated by using internal standards and matrix-matched calibration curves.

2.4. Diatom Analysis

Diatom shells were freed from organic matter by burning with 30% hydrogen peroxide followed by 6 h thermal treatment in a thermostat at 85 °C [18]. The preparations were examined in a JSM-6510 LV (JEOL Ltd.; Tokyo, Japan) scanning electron microscope. Relevant handbooks and papers were used for species determination [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. The modern species names were adopted using algaebase.org (accessed on 24 May 2024) [42].

2.5. Bioindication

Bioindicator analysis was performed according to [43] with species-specific ecological preferences of revealed indicator taxa [44]. The BioDiversity Pro 2.0 program was used for similarity calculation [45].

2.6. Ecological Mapping and JASP

Statistical maps were constructed in the Statistica 12.0 Program, the network analysis was conducted in JASP (Jeffreys’s Amazing Statistics Program), and only significant results were analyzed with the botnet package in R Statistica package of JASP 0.17.1 [46].

2.7. Species–Environments Relationship Analysis

Redundancy discriminant analysis (RDA) was conducted with the CANOCO program for the calculation of biological dominated variables and environment variables’ relationships [47].

3. Results

3.1. Water Chemistry

The water temperature of the studied waterbodies varied from 4.5 to 11.1 °C (

Table 2. Averaged physical and chemical variables of the 11 studied waterbodies of the Central Yakut Plain, September 2022.

Variables/Station	St. 1	St. 2	St. 3	St. 4	St. 5	St. 6	St. 7	St. 8	St. 9	St. 10	St. 11
Water temperature, °C	4.50	10.10	11.10	10.70	8.50	11.10	5.10	6.60	8.30	7.10	6.90
pH	6.25	6.29	8.57	8.39	7.42	7.34	6.11	6.28	5.86	6.23	6.31
TDS, mg L−1	108.26	112.10	1565.91	522.94	107.79	109.39	126.32	77.65	122.69	114.84	71.14
Hardness, mmol. L−1	1.35	1.38	5.98	6.98	1.38	1.42	1.81	1.05	1.76	1.40	0.96
Ca, mg L−1	18.05	19.50	62.12	100.20	19.20	19.24	24.40	14.00	22.44	20.04	13.63
Mg, mg L−1	5.46	4.95	34.99	24.06	5.13	5.59	7.20	4.27	7.78	4.86	3.40
Na, mg L−1	1.52	1.65	422.00	12.70	3.11	2.50	0.81	1.09	0.73	1.78	1.01
K, mg L−1	0.21	0.18	11.80	3.78	0.69	0.66	0.77	0.12	0.73	0.18	0.10
HCO3, mg L−1	74.50	78.70	336.00	226.00	38.48	42.60	58.36	39.80	57.00	82.00	35.00
Cl, mg L−1	3.21	2.57	620.00	90.00	6.14	5.40	25.00	9.02	24.00	1.96	8.79
SO4, mg L−1	5.31	4.55	79.00	66.20	35.04	33.40	9.78	9.35	10.00	4.02	9.21
N-NH4, mg L−1	0.25	0.27	0.57	0.28	0.29	0.28	0.28	0.21	0.30	0.23	0.22
N-NO2, mg L−1	0.07	0.05	0.01	0.02	0.04	0.02	0.06	0.05	0.05	0.09	0.06
N-NO3, mg L−1	0.17	0.20	0.16	0.17	0.19	0.18	0.19	0.19	0.20	0.19	0.19
P-PO4, mg L−1	0.04	0.05	0.04	0.04	0.04	0.04	0.19	0.04	0.23	0.04	0.05
Ptot, mg L−1	0.09	0.11	0.05	0.05	0.05	0.06	0.22	0.07	0.24	0.10	0.08
Porg, mg L−1	0.05	0.06	0.01	0.01	0.01	0.02	0.03	0.03	0.01	0.06	0.03
Fetot, mg L−1	1.11	1.18	0.12	0.24	0.61	0.67	0.75	0.64	0.78	1.24	0.62
Si, mg L−1	1.10	1.40	0.60	1.77	3.42	3.68	1.65	3.72	1.56	1.20	3.94
Pt-Co units	96.00	94.00	55	14	54.00	49	91.00	92.00	88	98	89
Petrochemicals, mg L−1	0.0050	0.0050	0.0050	0.0050	0.0050	0.0050	0.0050	0.0050	0.0050	0.0051	0.0050
Mn, mg L−1	0.0010	0.0010	0.0010	0.0010	0.0010	0.0010	0.7430	0.0010	0.1020	0.0010	0.0010
Ni, mg L−1	0.0841	0.0792	0.0720	0.0747	0.0897	0.0958	0.0274	0.0343	0.0328	0.0888	0.0358
Cu, mg L−1	0.0070	0.0050	0.0130	0.0110	0.0160	0.0170	0.0060	0.0060	0.0060	0.0070	0.0050
Zn, mg L−1	0.0010	0.0010	0.0054	0.0010	0.0182	0.0196	0.0018	0.0010	0.0033	0.0010	0.0010

). Most of the studied waterbodies are characterized by a slightly acidic reaction. The lake (station 5) and water reservoir (station 6) had a neutral reaction, and the abandoned diamond quarries had an alkaline reaction. The waters are fresh, low-mineralized, soft, or very soft in terms of hardness. The exception is the waters of abandoned quarries, which are characterized as hard, fresh with a high content of dissolved salts (Station 4) and moderately hard, brackish, and highly mineralized (station 3). The waters are mostly hydrocarbonate-calcium, with the exception of the quarry (station 3), where the waters had sodium chloride, and the lake (station 5) with sulfate-calcium waters. An increased concentration of ammonium nitrogen was noted in an abandoned quarry (station 3). A reduced concentration of nitrite nitrogen was recorded in artificial reservoirs: quarries and the water reservoir (stations 3, 4, and 6). The content of nitrate nitrogen in the studied waterbodies varied insignificantly. An increased content of mineral and total phosphorus was noted in the swamps (stations 7 and 9). High concentrations of organic phosphorus were detected in puddles (stations 1 and 2) and the swamp (station 10). A high content of total iron was noted in all studied waterbodies, with the exception of abandoned quarries (stations 3 and 4). The maximum silicon content was observed in streams (stations 8 and 11), the water reservoir (station 6), and the lake (station 5). The lowest color index was recorded in the quarry (station 4); a low color index was also typical for the water reservoir (station 6), quarry (station 3), and lake (station 5). In other waterbodies, the water color index is high. The content of petrochemicals in all reservoirs is insignificant. The manganese content in most waterbodies is below the detection threshold, with the exception of swamps (stations 7 and 9). The concentration of zinc is insignificant. The content of nickel and copper is increased in all studied waterbodies.

3.2. Taxonomic Analysis

A total of 144 diatom taxa affiliated with 49 genera, 26 families, and 13 orders were revealed during the study of communities from 11 aquatic habitats of the Central Yakut Plain in September 2022 (Appendix A Figure A1 and Appendix A 

Table A1. Diatom species richness in 11 studied waterbodies of the Central Yakut Plain, September 2022.

Taxa	1	2	3	4	5	6	7	8	9	10	11
Achnanthidium minutissimum (Kützing) Czarnecki	0	1	0	1	0	0	0	0	1	0	0
Amphipleura pellucida (Kützing) Kützing	0	0	0	0	1	0	0	0	0	0	0
Asterionella formosa Hassall	1	0	1	0	0	1	0	0	0	0	0
Aulacoseira ambigua (Grunow) Simonsen	0	0	0	1	0	0	0	0	0	1	0
Aulacoseira alpigena (Grunow) Krammer	1	1	1	0	0	0	0	0	0	1	1
Aulacoseira islandica (O.Müller) Simonsen	1	0	0	1	0	0	0	0	0	0	0
Aulacoseira italica (Ehrenberg) Simonsen	0	0	0	0	1	0	0	0	0	0	0
Aulacoseira pusilla (F.Meister) A.Tuji & A.Houki	1	0	0	0	0	0	0	1	1	1	0
Aulacoseira subarctica (O.Müller) E.Y.Haworth	1	0	0	1	0	0	0	0	0	0	0
Aulacoseira valida (Grunow) Krammer	0	0	0	0	1	0	0	0	0	0	0
Boreozonacola hustedtii Lange-Bertalot, Kulikovskiy & Witkowski	0	0	0	0	0	0	0	1	0	0	0
Brachysira brebissonii R.Ross	0	0	1	1	0	0	0	0	0	0	0
Caloneis silicula var. elliptica (Frenguelli) Frenguelli	0	1	0	0	0	0	0	0	0	0	0
Caloneis placentula Ehrenberg	0	0	0	0	1	0	0	0	0	1	0
Cocconeis lineata Ehrenberg	1	0	0	0	0	0	0	0	0	0	0
Craticula ambigua (Ehrenberg) D.G.Mann	0	0	0	0	0	0	0	1	0	0	0
Cyclostephanos dubius (Hustedt) Round	1	0	0	0	0	0	0	0	0	0	0
Cyclotella distinguenda Hustedt	0	0	0	1	0	0	0	0	1	0	1
Cymbella neogena (Grunow) Krammer	0	0	0	0	1	0	0	0	0	0	0
Cymbella cymbiformis C.Agardh	0	0	1	0	1	0	0	0	0	0	0
Cymbella sp.	1	0	0	0	0	0	0	0	0	0	0
Cymbopleura fluminea (R.M.Patrick & Freese) Lange-Bertalot & Krammer ex Lange-Bertalot & Genkal	0	0	0	0	0	0	0	1	0	0	1
Cymbopleura apiculata Krammer	0	0	0	0	0	0	0	1	0	0	0
Cymbopleura subcuspidata (Krammer) Krammer	0	1	0	0	0	0	0	0	0	0	0
Cymbopleura tynnii (Krammer) Krammer	0	0	0	0	0	0	0	1	0	0	0
Diploneis cf. fontanella Lange-Bertalot	0	0	0	0	0	0	0	1	0	0	0
Diploneis ovalis (Hilse) Cleve	0	0	0	0	0	0	0	1	0	0	0
Discostella stelligera (Cleve & Grunow) Houk & Klee	0	0	0	1	0	0	0	0	0	0	0
Encyonema silesiacum (Bleisch) D.G.Mann	0	0	0	0	1	0	0	0	0	0	0
Encyonema minutum (Hilse) D.G.Mann	0	0	0	0	0	0	0	0	0	0	1
Encyonema cf. leibleinii (C.Agardh) W.J.Silva, R.Jahn, T.A.V.Ludwig & M.Menezes	0	1	0	0	0	0	0	0	0	0	0
Entomoneis ornata (Bailey) Reimer	0	0	0	0	0	1	0	0	0	0	0
Eunotia pectinalis (Kützing) Rabenhorst	0	0	0	0	1	1	0	0	0	0	0
Eunotia bidens Ehrenberg	0	1	0	0	0	0	0	1	0	0	1
Eunotia bilunaris (Ehrenberg) Schaarschmidt	0	0	0	0	0	0	0	0	1	1	1
Eunotia boreotenuis Nörpel-Schempp & Lange-Bertalot	0	0	0	0	0	0	0	0	0	1	0
Eunotia curtagrunowii Nörpel-Schempp & Lange-Bertalot	0	0	0	0	0	0	0	1	0	0	0
Eunotia genuflexa Nörpel-Schempp	0	0	0	0	0	0	0	1	0	0	0
Eunotia mucophila (Lange-Bertalot, Nörpel-Schempp & Alles) Lange-Bertalot	0	0	0	0	0	0	0	0	0	0	1
Eunotia praerupta Ehrenberg	0	0	0	0	0	0	0	1	0	0	0
Eunotia scandiorussica Kulikovskiy, Lange-Bertalot, Genkal & Witkowski	0	0	0	0	0	0	0	0	0	0	1
Eunotia septentrionalis Østrup	0	0	0	0	0	0	0	1	0	1	1
Eunotia superbidens Lange-Bertalot	0	0	0	0	0	0	0	0	0	0	1
Fragilaria radians (Kützing) D.M.Williams & Round	0	0	0	1	0	0	0	0	0	0	0
Fragilaria aequalis Heiberg	0	0	0	0	0	1	0	0	1	0	0
Fragilariforma mesolepta (Rabenhorst) Kharitonov	0	0	0	0	1	1	0	0	0	0	0
Fragilaria rumpens (Kützing) G.W.F.Carlson	1	0	0	0	0	0	0	0	0	0	0
Fragilaris sp.	0	0	0	0	0	1	0	0	1	0	0
Frustulia crassinervia (Brébisson ex W.Smith) Lange-Bertalot & Krammer	0	1	0	0	1	0	0	0	0	0	0
Gomphonema naviculoides W.Smith	0	0	0	0	0	1	0	0	0	0	0
Gomphonema acuminatum Ehrenberg	0	0	0	0	1	0	0	0	0	0	0
Gomphonema angusticephalum E.Reichardt & Lange-Bertalot	1	0	0	0	0	0	0	0	0	0	0
Gomphonema brebissonii Kützing	0	0	0	0	1	0	0	0	0	0	1
Gomphonema micropus Kützing	0	1	0	0	0	0	0	0	0	0	0
Gomphonema pala E.Reichardt	0	0	0	0	1	0	0	0	0	0	0
Gomphonema pelisteriense Levkov, Mitic-Kopanja & E.Reichardt	0	0	0	0	0	0	0	0	0	0	1
Gomphonema truncatum Ehrenberg	0	0	0	0	1	0	0	0	0	0	0
Gomphonema sp.	1	1	0	0	1	0	0	1	0	0	1
Gyrosigma acuminatum (Kützing) Rabenhorst	0	0	1	1	0	0	0	0	0	0	0
Lindavia comta (Kützing) T.Nakov & al.	1	1	0	1	1	0	0	0	0	0	0
Kobayasiella parasubtilissima (H.Kobayasi & T.Nagumo) Lange-Bertalot	0	0	0	0	0	0	1	0	0	0	0
Luticola goeppertiana (Bleisch) D.G.Mann ex Rarick, S.Wu, S.S.Lee & Edlund	0	1	1	1	0	0	0	0	0	0	0
Luticola acidoclinata Lange-Bertalot	0	1	0	0	0	0	0	0	0	0	1
Luticola permuticopsis Kopalová & Van de Vijver	1	1	0	0	0	0	0	0	0	0	0
Luticola sp.	0	0	0	0	0	0	0	0	0	0	1
Mastogloia sp.	0	1	1	0	0	0	0	0	0	0	0
Meridion circulare (Greville) C.Agardh	0	0	0	0	0	0	0	1	0	0	0
Navicula salinicola Hustedt	0	0	0	1	0	0	0	0	0	0	0
Navicula antonii Lange-Bertalot	1	0	0	0	1	0	0	0	0	0	0
Navicula capitatoradiata H.Germain ex Gasse	1	0	0	0	0	1	0	0	0	0	0
Navicula cryptocephala Kützing	1	0	0	0	0	0	0	1	0	0	1
Cymbella lanceolata C.Agardh	0	0	0	0	0	0	0	0	0	0	1
Navicula moenofranconica Lange-Bertalot	1	0	0	0	0	0	0	0	0	0	0
Navicula radiosa Kützing	1	0	1	0	1	0	0	0	0	0	0
Navicula rostellata Kützing	1	0	0	0	0	0	0	0	0	0	0
Navicula semenicula Kulikovskiy, Lange-Bertalot & Metzeltin	0	0	0	1	0	0	0	0	0	0	0
Navicula sp.	0	0	0	0	0	0	0	0	0	0	1
Neidium hercynicum Ant.Mayer	0	0	0	0	0	0	0	1	0	0	0
Neidium affine (Ehrenberg) Pfitzer	0	0	0	0	0	0	0	1	0	0	0
Neidium ampliatum (Ehrenberg) Krammer	0	0	0	0	0	0	0	1	0	0	0
Neidium bisulcatum (Lagerstedt) Cleve	0	0	0	0	0	0	0	1	0	1	0
Neidium longiceps (W.Gregory) R.Ross	0	0	0	0	0	0	0	1	0	0	0
Neidium sp.	0	0	0	0	0	0	0	1	0	0	0
Nitzschia acidoclinata Lange-Bertalot	0	1	0	0	0	0	0	1	0	0	0
Nitzschia alpina Hustedt	1	0	0	0	0	0	0	0	0	0	0
Nitzschia archibaldii Lange-Bertalot	0	0	0	0	0	0	0	1	0	1	1
Nitzschia bacillum Hustedt	1	0	0	0	0	0	0	0	0	0	0
Nitzschia inconspicua Grunow	0	0	0	0	0	0	0	1	0	0	0
Nitzschia perminuta Grunow	0	0	0	0	0	0	0	0	0	1	1
Nitzschia vermicularis (Kützing) Hantzsch	0	0	0	1	0	0	0	0	0	0	0
Nitzschia sp.	0	0	0	0	0	0	0	1	0	0	0
Odontidium mesodon (Ehrenberg) Kützing	0	0	0	0	0	1	0	0	0	0	0
Pantocsekiella sp.	0	0	0	0	0	1	0	0	0	0	0
Pinnularia cf. globiceps var. linearis Krammer	0	0	0	1	0	0	0	0	0	0	0
Pinnularia acrosphaeria W.Smith	0	0	0	0	0	0	0	1	0	0	0
Pinnularia anglica Krammer	0	0	0	0	0	0	0	0	0	0	1
Pinnularia borealis var. scalaris (Ehrenberg) Rabenhorst	0	0	0	0	0	0	0	0	0	0	1
Pinnularia crucifera A.Cleve	0	0	0	0	0	0	1	0	0	1	1
Pinnularia eifeliana (Krammer) Krammer	1	1	0	0	0	0	0	0	0	0	0
Pinnularia flexuosa Cleve	0	0	0	0	0	0	1	0	0	0	0
Pinnularia ilkaschoenfelderae Krammer	0	0	0	0	0	0	0	0	0	0	1
Pinnularia isselana Krammer	0	0	0	0	0	0	0	1	0	0	0
Pinnularia lailaensis Foged	0	0	0	0	0	0	0	0	0	1	0
Pinnularia neomajor Krammer	0	0	0	0	1	0	1	1	0	0	1
Pinnularia obscura Krasske	0	0	0	0	0	0	0	0	0	0	1
Pinnularia ovata Krammer	0	0	0	0	0	0	1	0	0	0	0
Pinnularia pisciculus Ehrenberg	0	0	0	0	1	0	0	0	0	0	1
Pinnularia sinistra Krammer	0	0	0	0	0	0	1	0	0	1	1
Pinnularia spitsbergensis Cleve	0	0	0	0	0	0	1	0	0	0	0
Pinnularia stomatophora (Grunow) Cleve	0	0	0	0	0	0	0	1	0	0	0
Pinnularia subgibba var. sublinearis Krammer	0	0	0	0	0	0	0	0	0	0	1
Pinnularia subrostrata (A.Cleve) A.Cleve	0	0	0	0	0	0	1	0	0	0	0
Pinnularia undula (Schumann) Krammer	0	0	0	0	0	0	0	1	0	0	0
Pinnularia viridis (Nitzsch) Ehrenberg	0	0	0	0	0	0	1	0	0	1	1
Pinnularia sp.	0	0	0	1	0	0	0	0	0	0	0
Placoneis symmetrica (Hustedt) Lange-Bertalot	0	0	0	0	0	0	0	0	0	0	1
Platessa conspicua (Ant.Mayer) Lange-Bertalot	0	0	0	0	1	0	0	0	0	0	0
Psammothidium subatomoides (Hustedt) Bukhtiyarova & Round	0	0	0	1	0	0	0	0	0	0	0
Pseudostaurosira linearis (Pantocsek) E.A.Morales, Buczkó & Ector	1	0	0	0	0	0	0	0	0	0	0
Punctastriata subalpina C.E.Wetzel & Ector	0	0	0	0	0	0	0	0	1	0	0
Reimeria sinuata (W.Gregory) Kociolek & Stoermer	1	0	0	0	0	0	0	0	0	0	0
Rhoicosphenia abbreviata (C.Agardh) Lange-Bertalot	0	0	0	0	0	0	0	0	0	1	0
Epithemia gibba (Ehrenberg) Kützing	0	0	1	1	1	0	0	0	0	0	0
Sellaphora pseudopupula (Krasske) Lange-Bertalot	0	0	0	0	0	0	0	1	0	0	0
Sellaphora parapupula Lange-Bertalot	0	0	0	0	0	0	0	1	1	0	0
Sellaphora laevissima (Kützing) D.G.Mann	0	0	0	0	0	0	0	1	0	0	0
Stauroneis amphicephala Kützing	0	0	0	1	0	0	0	1	0	0	0
Stauroneis agrestis J.B.Petersen	0	1	0	0	0	0	0	0	0	0	0
Stauroneis anceps Ehrenberg	1	0	0	0	0	0	0	1	0	0	0
Stauroneis kuelbsii Lange-Bertalot	0	0	0	0	0	0	0	0	0	0	1
Stauroneis phoenicenteron (Nitzsch) Ehrenberg	0	1	0	0	0	0	0	1	0	1	1
Staurosira venter (Ehrenberg) Cleve & J.D.Möller	1	0	0	0	0	0	0	0	0	0	0
Stenopterobia anceps (F.W.Lewis) Brébisson ex Van Heurck	0	0	0	0	0	0	0	1	0	0	0
Stephanodiscus hantzschii Grunow	0	0	0	1	0	0	0	0	0	0	0
Tabellaria flocculosa (Roth) Kützing	1	0	1	1	0	1	0	1	0	1	0
Ulnaria acus (Kützing) Aboal	0	0	0	1	0	1	0	0	0	1	0
Ulnaria ulna (Nitzsch) Compère	0	0	0	0	1	1	0	1	0	0	0

). Since the present study represents the first data on diatoms in this area, a detailed taxonomic analysis was carried out to characterize the “face” of the identified flora. Appendix A Table A1 shows that only 137 taxa from 144 were defined up to the species level but all others were defined up to the genus level only, reflecting the high potential of diversity of this flora that can be rather enriched by species. In Appendix A Table A1, the distribution of diatom species over the studied community of 11 objects is also given, and no one species was presented in each object as a result of high individuality of the studied communities. The species number in studied communities varied from 9 at station 8 to 34 at 11. The richest order was Naviculales (Appendix A 

Table A2. Richest orders in the communities of studied aquatic habitats of the Central Yakut Plain in September 2022.

Order	St. 1	St. 2	St. 3	St. 4	St. 5	St. 6	St. 7	St. 8	St. 9	St. 10	St. 11
Achnanthales	1	1	0	2	1	0	0	0	1	0	0
Aulacoseirales	4	1	1	3	1	0	0	1	1	3	1
Bacillariales	2	1	0	1	0	0	0	5	0	2	2
Cymbellales	5	4	1	0	8	1	0	6	0	1	8
Eunotiales	0	1	0	0	1	1	0	5	1	3	6
Fragilariales	3	0	0	1	1	4	0	0	3	0	0
Licmophorales	0	0	0	1	1	2	0	1	0	1	0
Mastogloiales	0	1	1	0	0	0	0	0	0	0	0
Naviculales	9	8	4	8	7	1	9	22	1	7	16
Rhabdonematales	2	0	2	1	0	2	0	2	0	1	0
Rhopalodiales	0	0	1	1	1	0	0	0	0	0	0
Stephanodiscales	2	1	0	4	1	1	0	0	1	0	1
Surirellales	0	0	0	0	0	1	0	1	0	0	0
No of Species	28	18	10	22	22	12	9	42	8	18	34
No of Orders	8	8	6	9	9	8	1	8	6	7	6
Sp/Order	3.5	2.3	1.7	2.4	2.4	1.5	9.0	5.3	1.3	2.6	5.7

) that participated in each community and contained 22 species at station 8. The index of species richness per order ranged from 1.3 to 9.0 (averaged 3.4) and was greatest at station 7, where the community was composed of Naviculales species only (Appendix A Table A2). The revealed species were affiliated with 26 families, on average 9.9 species per family, with richest Naviculaceae and Pinnulariaceae at station 11, where 10 species of the last were revealed. The index of the family saturation by species was 2.6 on average and highest at station 7 with a low species number (Appendix A 

Table A3. Richest families in the communities of studied aquatic habitats of the Central Yakut Plain in September 2022.

Family	St. 1	St. 2	St. 3	St. 4	St. 5	St. 6	St. 7	St. 8	St. 9	St. 10	St. 11
Achnanthidiaceae	0	1	0	2	1	0	0	0	1	0	0
Amphipleuraceae	0	1	0	0	2	0	0	0	0	0	0
Aulacoseiraceae	4	1	1	3	2	0	0	1	1	3	1
Bacillariaceae	2	1	0	1	0	0	0	5	0	2	2
Brachysiraceae	0	0	1	1	0	0	0	0	0	0	0
Cocconeidaceae	1	0	0	0	0	0	0	0	0	0	0
Cymbellaceae	1	1	1	0	2	0	0	3	0	0	3
Diadesmidaceae	1	3	1	1	0	0	0	0	0	0	2
Diploneidaceae	0	0	0	0	0	0	0	2	0	0	0
Entomoneidaceae	0	0	0	0	0	1	0	0	0	0	0
Eunotiaceae	0	1	0	0	1	1	0	4	1	3	6
Fragilariaceae	1	0	0	1	1	4	0	0	3	0	0
Gomphonemataceae	4	3	0	0	6	1	0	3	0	0	5
Mastogloiaceae	0	1	1	0	0	0	0	0	0	0	0
Naviculaceae	6	1	2	3	3	1	1	2	0	1	2
Neidiaceae	0	0	0	0	0	0	0	6	0	1	0
Pinnulariaceae	1	1	0	2	2	0	8	5	0	4	10
Rhoicospheniaceae	0	0	0	0	0	0	0	0	0	1	0
Rhopalodiaceae	0	0	1	1	1	0	0	0	0	0	0
Sellaphoraceae	0	0	0	0	0	0	0	3	1	0	0
Stauroneidaceae	1	2	0	1	0	0	0	4	0	1	2
Staurosiraceae	2	0	0	0	0	0	0	0	0	0	0
Stephanodiscaceae	2	1	0	4	1	1	0	0	1	0	1
Surirellaceae	0	0	0	0	0	0	0	1	0	0	0
Tabellariaceae	2	0	2	1	0	2	0	2	0	1	0
Ulnariaceae	0	0	0	1	1	2	0	1	0	1	0
No of Species	28	18	10	22	22	13	9	42	8	18	34
No of Family	13	14	8	13	12	8	1	14	6	10	10
Sp/Family	2.2	1.3	1.3	1.7	1.8	1.6	9.0	3.0	1.3	1.8	3.4

). Genera Pinnularia and Aulacoseira were richest in the studied communities with an average of 12.2 per genus with the prevalence of Pinnularia. The average species per genus index was 1.8 with the maximal value (4.5) at station 7 where species richness was minimal (Appendix A 

Table A4. Richest genera in the communities of studied aquatic habitats of the Central Yakut Plain in September 2022.

Genus	St. 1	St. 2	St. 3	St. 4	St. 5	St. 6	St. 7	St. 8	St. 9	St. 10	St. 11
Achnanthidium	0	1	0	1	0	0	0	0	1	0	0
Amphipleura	0	0	0	0	1	0	0	0	0	0	0
Asterionella	1	0	1	0	0	1	0	0	0	0	0
Aulacoseira	4	1	1	3	2	0	0	1	1	3	1
Boreozonacola	0	0	0	0	0	0	0	1	0	0	0
Brachysira	0	0	1	1	0	0	0	0	0	0	0
Caloneis	0	1	0	0	1	0	0	0	0	1	0
Cocconeis	1	0	0	0	0	0	0	0	0	0	0
Craticula	0	0	0	0	0	0	0	1	0	0	0
Cyclostephanos	1	0	0	0	0	0	0	0	0	0	0
Cyclotella	0	0	0	1	0	0	0	0	1	0	1
Cymbella	1	0	1	0	2	0	0	0	0	0	1
Cymbopleura	0	1	0	0	0	0	0	3	0	0	1
Diploneis	0	0	0	0	0	0	0	2	0	0	0
Discostella	0	0	0	1	0	0	0	0	0	0	0
Encyonema	0	1	0	0	1	0	0	0	0	0	1
Entomoneis	0	0	0	0	0	1	0	0	0	0	0
Epithemia	0	0	1	1	1	0	0	0	0	0	0
Eunotia	0	1	0	0	1	1	0	5	1	3	6
Fragilaria	1	0	0	1	0	2	0	0	2	0	0
Fragilariforma	0	0	0	0	1	1	0	0	0	0	0
Frustulia	0	1	0	0	1	0	0	0	0	0	0
Gomphonema	3	2	0	0	5	1	0	3	0	0	4
Gyrosigma	0	0	1	1	0	0	0	0	0	0	0
Kobayasiella	0	0	0	0	0	0	1	0	0	0	0
Lindavia	1	1	0	1	1	0	0	0	0	0	0
Luticola	1	3	1	1	0	0	0	0	0	0	2
Mastogloia	0	1	1	0	0	0	0	0	0	0	0
Meridion	0	0	0	0	0	0	0	1	0	0	0
Navicula	6	0	1	2	2	1	0	1	0	0	2
Neidium	0	0	0	0	0	0	0	6	0	1	0
Nitzschia	2	1	0	1	0	0	0	5	0	2	2
Odontidium	0	0	0	0	0	1	0	0	0	0	0
Pantocsekiella	0	0	0	0	0	1	0	0	0	0	0
Pinnularia	1	1	0	2	2	0	8	5	0	4	10
Placoneis	0	0	0	0	0	0	0	0	0	0	1
Platessa	0	0	0	0	1	0	0	0	0	0	0
Psammothidium	0	0	0	1	0	0	0	0	0	0	0
Pseudostaurosira	1	0	0	0	0	0	0	0	0	0	0
Punctastriata	0	0	0	0	0	0	0	0	1	0	0
Reimeria	1	0	0	0	0	0	0	0	0	0	0
Rhoicosphenia	0	0	0	0	0	0	0	0	0	1	0
Sellaphora	0	0	0	0	0	0	0	3	1	0	0
Stauroneis	1	2	0	1	0	0	0	3	0	1	2
Staurosira	1	0	0	0	0	0	0	0	0	0	0
Stenopterobia	0	0	0	0	0	0	0	1	0	0	0
Stephanodiscus	0	0	0	1	0	0	0	0	0	0	0
Tabellaria	1	0	1	1	0	1	0	1	0	1	0
Ulnaria	0	0	0	1	1	2	0	1	0	1	0
No of Species	28	18	10	22	22	13	9	42	8	18	34
No of Genus	17	14	10	18	15	11	2	17	7	10	13
Sp/Genus	1.6	1.3	1.0	1.2	1.5	1.2	4.5	2.5	1.1	1.7	2.6

). So, the taxonomical step by step analysis revealed the Pinnularia genus as the most important in the formation of the first-time-studied diversity which flourishes in waterbodies of the Central Yakut Plain.

3.3. Bioindication

A bioindication analysis of the ecological preferences of the identified species in the studied communities was carried out to establish the main preferred conditions of their habitat. Algal species’ ecological preferences in 11 studied waterbodies of the Central Yakut Plain are presented in Appendix A 

Table A5. Algal species ecological preferences in 11 studied waterbodies of the Central Yakut Plain, September 2022.

Taxa	HAB	T	OXY	pH	SAL	Index S	SAP	D	AUT-HET	TRO
Achnanthidium minutissimum (Kützing) Czarnecki	P-B	eterm	st-str	ind	i	0.95	b	es	ate	e
Amphipleura pellucida (Kützing) Kützing	P-B	-	st	alf	i	0.8	b	sp	ate	om
Asterionella formosa Hassall	P	temp	st-str	alf	i	1.35	b	sx	ate	me
Aulacoseira ambigua (Grunow) Simonsen	P	temp	st-str	alf	i	1.7	b-o	sp	ate	om
Aulacoseira alpigena (Grunow) Krammer	P-B	temp	st-str	alf	i	0.8	x-b	sp	ate	ot
Aulacoseira islandica (O.Müller) Simonsen	P-B	cool	st-str	ind	i	2.0	b	es	ate	o-e
Aulacoseira italica (Ehrenberg) Simonsen	P-B	cool	st-str	ind	i	1.45	b	es	ate	me
Aulacoseira pusilla (F.Meister) A.Tuji & A.Houki	P	-	-	alf	i	-	-	-	-	-
Aulacoseira subarctica (O.Müller) E.Y.Haworth	P	temp	st-str	alf	i	1.7	b-o	-	ats	om
Aulacoseira valida (Grunow) Krammer	P-B	-	-	alf	i	1.3	o	es	ate	om
Boreozonacola hustedtii Lange-Bertalot, Kulikovskiy & Witkowski	P-B	-	-	ind	i	-	-	-	-	-
Brachysira brebissonii R.Ross	P-B	temp	st-str	acf	hb	0.4	o	sx	ats	ot
Caloneis silicula var. elliptica (Frenguelli) Frenguelli	-	-	-	-	-	-	-	-	-	-
Caloneis placentula Ehrenberg	-	-	-	-	-	-	-	-	-	-
Cocconeis lineata Ehrenberg	P-B	temp	st-str	alf	i	1.2	b	sx	ate	e
Craticula ambigua (Ehrenberg) D.G.Mann	B	temp	st	alf	i	2.3	b	es	-	me
Cyclostephanos dubius (Hustedt) Round	P-B	temp	st-str	alf	hl	2.0	a	es	ate	e
Cyclotella distinguenda Hustedt	P	-	str	alf	hl	1.3	o	-	-	om
Cymbella neogena (Grunow) Krammer	-	-	-	-	-	-	-	-	-	-
Cymbella cymbiformis C.Agardh	B	temp	st-str	alf	i	2.0	b	sx	ats	om
Cymbella sp.	-	-	-	-	-	-	-	-	-	-
Cymbopleura fluminea (R.M.Patrick & Freese) Lange-Bertalot & Krammer ex Lange-Bertalot & Genkal	-	-	-	-	-	-	-	-	-	-
Cymbopleura apiculata Krammer	B	-	-	ind	-	1.0	o	es	ats	ot
Cymbopleura subcuspidata (Krammer) Krammer	P-B	-	str	acf	i	1.0	o	sx	ats	om
Cymbopleura tynnii (Krammer) Krammer	B	-	-	-	-	-	-	-	-	-
Diploneis cf. fontanella Lange-Bertalot	B	-	-	-	-	-	-	-	-	-
Diploneis ovalis (Hilse) Cleve	B	-	st-str	alf	i	0.9	x-b	-	ate	m
Discostella stelligera (Cleve & Grunow) Houk & Klee	P-B	temp	st-str	ind	i	-	-	-	-	-
Encyonema silesiacum (Bleisch) D.G.Mann	B	temp	st-str	ind	i	-	-	-	-	-
Encyonema minutum (Hilse) D.G.Mann	B	temp	st-str	ind	i	1.5	o-b	sx	ats	-
Encyonema cf. leibleinii (C.Agardh) W.J.Silva, R.Jahn, T.A.V.Ludwig & M.Menezes	P-B	-	str	alb	i	2.4	b-a	sx	-	me
Entomoneis ornata (Bailey) Reimer	B	-	st-str	alf	i	2.0	b	-	hne	-
Eunotia pectinalis (Kützing) Rabenhorst	B	-	st-str	acf	i	0.5	x-o	sx	-	ot
Eunotia bidens Ehrenberg	P-B,aer	cool	st-str	acf	hb	1.0	o	-	-	ot
Eunotia bilunaris (Ehrenberg) Schaarschmidt	B	temp	st-str	acf	i	-	-	-	-	-
Eunotia boreotenuis Nörpel-Schempp & Lange-Bertalot	B	-	-	-	hb	1.0	o	-	-	ot
Eunotia curtagrunowii Nörpel-Schempp & Lange-Bertalot	P-B	-	-	acf	hb	0.4	x-o	-	ats	ot
Eunotia genuflexa Nörpel-Schempp	-	-	-	-	-	-	-	-	-	-
Eunotia mucophila (Lange-Bertalot, Nörpel-Schempp & Alles) Lange-Bertalot	P-B	temp	st-str	acf	hb	-	-	-	-	-
Eunotia praerupta Ehrenberg	P-B	cool	st-str	acf	hb	0.3	x	-	-	-
Eunotia scandiorussica Kulikovskiy, Lange-Bertalot, Genkal & Witkowski	-	-	-	-	-	-	-	-	-	-
Eunotia septentrionalis Østrup	P-B	-	str	acf	hb	1.0	o	-	-	ot
Eunotia superbidens Lange-Bertalot	-	-	-	-	-	-	-	-	-	-
Fragilaria radians (Kützing) D.M.Williams & Round	P-B	warm	st-str	alf	i	-	-	-	-	-
Fragilaria aequalis Heiberg	-	-	-	-	-	-	-	-	-	-
Fragilariforma mesolepta (Rabenhorst) Kharitonov	P-B	-	st-str	alf	i	1.0	o	-	-	ot
Fragilaria rumpens (Kützing) G.W.F.Carlson	P-B	eterm	st-str	ind	i	2.0	b	-	ats	e
Fragilaris sp.	-	-	-	-	-	-	-	-	-	-
Frustulia crassinervia (Brébisson ex W.Smith) Lange-Bertalot & Krammer	B	-	str	acf	hb	0.5	x-o	sx	ats	ot
Gomphonema naviculoides W.Smith	-	-	-	-	-	-	-	-	-	-
Gomphonema acuminatum Ehrenberg	B	temp	st-str	ind	i	0.8	x-b	-	-	-
Gomphonema angusticephalum E.Reichardt & Lange-Bertalot	-	-	-	-	-	-	-	-	-	-
Gomphonema brebissonii Kützing	B	-	st	ind	i	-	-	-	-	m
Gomphonema micropus Kützing	B	temp	st-str	ind	i	1.1	-	-	-	-
Gomphonema pala E.Reichardt	B	-	-	-	-	1.0	o	-	-	-
Gomphonema pelisteriense Levkov, Mitic-Kopanja & E.Reichardt	-	-	-	-	-	-	-	-	-	-
Gomphonema truncatum Ehrenberg	B	temp	st-str	ind	i	2.0	b	-	-	-
Gomphonema sp.	-	-	-	-	-	-	-	-	-	-
Gyrosigma acuminatum (Kützing) Rabenhorst	B	temp	st-str	alf	i	-	-	-	-	-
Lindavia comta (Kützing) T.Nakov & al.	-	-	-	-	-	-	-	-	-	-
Kobayasiella parasubtilissima (H.Kobayasi & T.Nagumo) Lange-Bertalot	B	temp	str	acb	hb	1.5	o-b	-	-	-
Luticola goeppertiana (Bleisch) D.G.Mann ex Rarick, S.Wu, S.S.Lee & Edlund	B	temp	st	ind	i	-	-	-	-	-
Luticola acidoclinata Lange-Bertalot	-	-	-	-	-	-	-	-	-	-
Luticola permuticopsis Kopalová & Van de Vijver	-	-	-	-	-	-	-	-	-	-
Luticola sp.	-	-	-	-	-	-	-	-	-	-
Mastogloia sp.	-	-	-	-	-	-	-	-	-	-
Meridion circulare (Greville) C.Agardh	P-B	temp	st-str	ind	i	-	-	-	-	-
Navicula salinicola Hustedt	B	-	-	-	mh	-	-	-	-	-
Navicula antonii Lange-Bertalot	B	temp	-	alf	oh	-	-	es	-	-
Navicula capitatoradiata H.Germain ex Gasse	P-B	temp	st-str	alf	mh	-	-	-	-	-
Navicula cryptocephala Kützing	P-B	temp	st-str	ind	i	2.4	b-a	-	-	-
Cymbella lanceolata C.Agardh	B	temp	st-str	alf	i	2.5	b-a	es	ate	e
Navicula moenofranconica Lange-Bertalot	-	-	-	-	-	-	-	-	-	-
Navicula radiosa Kützing	B	temp	st-str	ind	i	-	-	sx	-	-
Navicula rostellata Kützing	B	-	st-str	alf	i	0.7	o-x	-	ate	ot
Navicula semenicula Kulikovskiy, Lange-Bertalot & Metzeltin	-	-	-	-	-	-	-	-	-	-
Navicula sp.	-	-	-	-	-	-	-	-	-	-
Neidium hercynicum Ant.Mayer	B	-	-	acf	i	-	-	-	-	-
Neidium affine (Ehrenberg) Pfitzer	B	temp	st-str	ind	i	-	-	-	-	-
Neidium ampliatum (Ehrenberg) Krammer	B	temp	st	ind	i	-	-	-	-	-
Neidium bisulcatum (Lagerstedt) Cleve	B	-	st-str	ind	i	1.0	o	-	-	-
Neidium longiceps (W.Gregory) R.Ross	B	temp	str	ind	i	-	-	-	-	-
Neidium sp.	-	-	-	-	-	-	-	-	-	-
Nitzschia acidoclinata Lange-Bertalot	B	temp	str	ind	hb	3.6	a-b	-	ate	e
Nitzschia alpina Hustedt	P-B	temp	str	acf	i	1.0	o	-	-	-
Nitzschia archibaldii Lange-Bertalot	B	temp	st-str	ind	i	-	-	-	-	-
Nitzschia bacillum Hustedt	B	-	-	alf	i	2.0	b	-	-	-
Nitzschia inconspicua Grunow	B	temp	st-str	alf	hl	-	-	-	-	-
Nitzschia perminuta Grunow	P-B	temp	str	alf	hl	-	-	-	-	-
Nitzschia vermicularis (Kützing) Hantzsch	P-B	temp	st-str	alf	i	-	-	-	-	-
Nitzschia sp.	-	-	-	-	-	-	-	-	-	-
Odontidium mesodon (Ehrenberg) Kützing	B	cool	st-str	ind	hb	0.9	x-b	-	-	-
Pantocsekiella sp.	-	-	-	-	-	-	-	-	-	-
Pinnularia cf. globiceps var. linearis Krammer	-	-	-	-	-	-	-	-	-	-
Pinnularia acrosphaeria W.Smith	B	warm	st-str	ind	i	-	-	-	-	-
Pinnularia anglica Krammer	B	-	-	acf	-	2.3	b	es	-	e
Pinnularia borealis var. scalaris (Ehrenberg) Rabenhorst	B	-	-	-	i	-	-	sx	-	-
Pinnularia crucifera A.Cleve	-	-	-	-	-	-	-	-	-	-
Pinnularia eifeliana (Krammer) Krammer	B	-	-	-	-	1.0	o	-	-	-
Pinnularia flexuosa Cleve	B	-	-	-	i	1.0	o	-	-	m
Pinnularia ilkaschoenfelderae Krammer	-	-	-	-	-	-	-	-	-	-
Pinnularia isselana Krammer	-	-	-	-	-	1.0	o	es	-	ot
Pinnularia lailaensis Foged	-	-	-	-	-	-	-	-	-	-
Pinnularia neomajor Krammer	B	-	-	ind	i	-	-	-	-	-
Pinnularia obscura Krasske	B,aer	-	st-str,aer	ind	i	0.5	x-o	-	ats	ot
Pinnularia ovata Krammer	B	-	-	-	-	-	-	-	-	-
Pinnularia pisciculus Ehrenberg	-	-	-	-	-	-	-	-	-	-
Pinnularia sinistra Krammer	B	-	-	-	-	-	-	-	-	-
Pinnularia spitsbergensis Cleve	B	-	-	ind	hb	-	-	-	ats	ot
Pinnularia stomatophora (Grunow) Cleve	B	-	st-str	acf	i	-	-	-	-	-
Pinnularia subgibba var. sublinearis Krammer	-	-	-	-	-	-	-	-	-	-
Pinnularia subrostrata (A.Cleve) A.Cleve	B	-	-	acf	hb	1.0	o	-	-	-
Pinnularia undula (Schumann) Krammer	B	-	-	ind	i	1.0	o	-	-	-
Pinnularia viridis (Nitzsch) Ehrenberg	P-B	temp	st-str	ind	i	0.9	x-b	-	-	ot
Pinnularia sp.	-	-	-	-	-	-	-	-	-	-
Placoneis symmetrica (Hustedt) Lange-Bertalot	B	-	-	-	hb	2.0	b	-	-	-
Platessa conspicua (Ant.Mayer) Lange-Bertalot	B	temp	st-str	alf	i	-	-	-	-	-
Psammothidium subatomoides (Hustedt) Bukhtiyarova & Round	P-B	temp	str	acf	hb	2.0	b	sx	ats	me
Pseudostaurosira linearis (Pantocsek) E.A.Morales, Buczkó & Ector	-	-	-	-	-	-	-	-	-	-
Punctastriata subalpina C.E.Wetzel & Ector	-	-	-	-	-	-	-	-	-	-
Reimeria sinuata (W.Gregory) Kociolek & Stoermer	P-B, aer	temp	st-str	ind	i	-	-	-	-	-
Rhoicosphenia abbreviata (C.Agardh) Lange-Bertalot	B	temp	st-str	alf	i	1.9	o-a	es	ate	me
Epithemia gibba (Ehrenberg) Kützing	P-B	temp	st-str	alf	i	1.4	x-o	es	ate	om
Sellaphora pseudopupula (Krasske) Lange-Bertalot	-	-	-	-	-	1.9	o-a	-	-	-
Sellaphora parapupula Lange-Bertalot	B	-	st	ind	i	1.0	o	-	ate	m
Sellaphora laevissima (Kützing) D.G.Mann	B	-	st-str	ind	i	2.0	b	-	ats	om
Stauroneis amphicephala Kützing	-	-	-	-	-	-	-	-	-	-
Stauroneis agrestis J.B.Petersen	B	-	aer	ind	i	-	-	-	-	-
Stauroneis anceps Ehrenberg	P-B	temp	st-str	ind	i	1.3	o	sx	ats	om
Stauroneis kuelbsii Lange-Bertalot	-	-	-	-	-	-	-	-	-	-
Stauroneis phoenicenteron (Nitzsch) Ehrenberg	P-B	temp	st-str	ind	i	-	-	-	-	-
Staurosira venter (Ehrenberg) Cleve & J.D.Möller	P-B	temp	st-str	alf	i	1.0	o	-	-	ot
Stenopterobia anceps (F.W.Lewis) Brébisson ex Van Heurck	-	-	-	-	-	-	-	-	-	-
Stephanodiscus hantzschii Grunow	P	temp	st-str	alf	i	-	-	sx	-	-
Tabellaria flocculosa (Roth) Kützing	P-B	eterm	st-str	acf	i	3.0	a	-	-	-
Ulnaria acus (Kützing) Aboal	P-B	warm	st-str	alf	i	1.85	o-a	es	ate	me
Ulnaria ulna (Nitzsch) Compère	P-B	temp	st-str	alf	i	2.4	b-a	es	ate	e

Note: “-”, not found. Abbreviations: habitat (HAB) (P—planktonic; P-B—plankto-benthic; B—benthic); temperature (T) preferences (cool—cool water; temp—temperate; eterm—eurythermic; warm—warm water); oxygenation and water moving (OXY) (aer—aerophiles; str—streaming water; st-str—low streaming water; st—standing, H₂S—sulfides); pH preferences groups (pH) according to F. Hustedt [67] (alb—alkalibiontes; alf—alkaliphiles; ind—indifferent; acf—acidophiles; neu—neutrophiles as a part of indifferents); salinity ecological groups (SAL) according to F. Hustedt [68] (hb—oligohalobes-halophobes; i—oligohalobes-indifferent; hl—halophiles; mh—mesohalobes; oh—oligohalobes of wide spectrum with optimum as indifferent); Index S, species-specific index saprobity according to V. Sládeček [69]; self-purification zone with index of saprobity (SAP) (x/0.0—xenosaprobe; x-o/0.4—xeno-oligosaprobe; o-x/0.6—oligo-xenosaprobe; x-b/0.8—xeno-betamesosaprobe; o/1.0—oligosaprobe; o-b/1.4—oligo-betamesosaprobe; b-o/1.6—beta-oligosaprobe; o-a/1.8—oligo-alphamesosaprobe; b/2.0—betamesosaprobe; b-a/2.4—beta-alphamesosaprobe; a-ο/2.6—alpha-oligosaprobe; a/3.0—alphamesosaprobe; p-a/4.0—poly-alphamesosaprobe; i/>4.0—i-eusaprobe); organic pollution indicators according to T. Watanabe et al. [48] (D): sx—saproxenes; es—eurysaprobes; sp—saprophiles; nitrogen uptake metabolism (AUT-HET) [15]: ats—nitrogen-autotrophic taxa, tolerating very small concentrations of organically bound nitrogen; ate—nitrogen-autotrophic taxa, tolerating elevated concentrations of organically bound nitrogen; hne—facultative nitrogen-heterotrophic taxa, needing periodically elevated concentrations of organically bound nitrogen; indicators of trophic state (TRO) [15] (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic; o-e—oligo to hypereutraphentic).

. We summarized these data according to the main ecological groups and presented them in Figure 3, Figure 4 and Figure 5 and Appendix A 

Table A6. Distribution of species indicator numbers in the aquatic communities of the Central Yakut Plain, September 2022.

Variable	St. 1	St. 2	St. 3	St. 4	St. 5	St. 6	St. 7	St. 8	St. 9	St. 10	St. 11
Habitat
B	5	6	4	3	12	3	7	19	2	6	11
P-B	12	6	4	10	6	5	1	11	1	7	8
P	3	0	1	4	0	1	0	1	2	2	1
Temperature
cool	1	1	0	1	1	1	0	2	0	0	1
temp	13	5	8	10	9	3	2	12	1	8	10
eterm	2	1	1	2	0	1	0	1	1	1	0
warm	0	0	0	2	0	1	0	1	0	1	0
Oxygen
aer	0	1	0	0	0	0	0	0	0	0	0
str	1	4	0	2	1	0	1	3	1	2	3
st-str	15	5	8	13	11	9	1	16	2	10	11
st	0	1	1	1	2	0	0	3	1	0	1
Salinity
hb	0	3	1	2	1	1	3	5	0	2	4
i	17	8	8	13	16	7	3	22	4	11	12
hl	1	0	0	1	0	0	0	1	1	1	2
mh	1	0	0	1	0	1	0	0	0	0	0
pH
acb	0	0	0	0	0	0	1	0	0	0	0
acf	2	3	2	3	2	2	1	7	1	3	5
ind	6	6	2	4	7	1	3	17	2	4	8
alf	11	1	5	9	8	6	0	5	2	6	4
alb	0	1	0	0	0	0	0	0	0	0	0
Watanabe
sx	4	3	4	3	4	2	0	1	0	0	2
es	3	1	1	4	5	2	0	4	1	2	2
sp	1	1	1	1	1	0	0	0	0	2	1
Autotrophy-Heterotrophy
ats	3	2	2	3	2	0	1	4	0	0	2
ate	6	3	3	5	5	3	0	4	2	4	2
hne	0	0	0	0	0	1	0	0	0	0	0
Trophy
ot	3	3	2	1	3	2	2	5	0	4	5
om	2	1	2	4	4	0	0	2	1	1	1
m	0	0	0	0	1	0	1	2	1	0	1
me	1	1	1	2	1	2	0	1	0	2	0
e	3	2	0	1	1	1	0	2	1	0	2
o-e	1	0	0	1	0	0	0	0	0	0	0
Class of Water Quality
Class 1	0	1	1	1	2	1	0	2	0	0	1
Class 2	8	6	3	3	7	3	4	9	3	3	6
Class 3	5	0	1	5	3	3	0	5	0	0	4
Class 4	1	1	0	1	0	1	0	1	0	1	0
Class 5	0	0	0	0	0	0	0	1	0	0	0

Note: “0”, not found. Abbreviations: habitat (Hab) (P—planktonic; P-B—plankto-benthic; B—benthic); temperature (T) preferences (cool—cool water; temp—temperate; eterm—eurythermic; warm—warm water); oxygenation and water moving (Oxy) (aer—aerophiles; str—streaming water; st-str—low streaming water; st—standing; H₂S—sulfides); pH preferences groups (pH) according to F. Hustedt [67] (alb—alkalibiontes; alf—alkaliphiles, ind—indifferent; acf—acidophiles; neu—neutrophiles as a part of indifferents); salinity ecological groups (Sal) according to F. Hustedt [68] (hb—oligohalobes-halophobes; i—oligohalobes-indifferent; hl—halophiles; mh—mesohalobes); Index S, species-specific index saprobity according to V. Sládeček [69]; organic pollution indicators according to T. Watanabe et al. [48] (D): sx—saproxenes; es—eurysaprobes; sp—saprophiles; nitrogen uptake metabolism (Aut-Het) [15]: ats—nitrogen-autotrophic taxa, tolerating very small concentrations of organically bound nitrogen; ate—nitrogen-autotrophic taxa, tolerating elevated concentrations of organically bound nitrogen; hne—facultative nitrogen-heterotrophic taxa, needing periodically elevated concentrations of organically bound nitrogen; trophic state indicators (Tro) [15] (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic; o-e—oligo to hypereutraphentic).

. We divided the data of the studied stations into three parts affiliated with the spatial distribution of the points of sampling.

Figure 3a shows the dynamic of habitat preferences by diatom species in groups of indicators, of which plankto-benthic species prevailed at stations 1–6 but stations 7–11 were enriched mostly by benthic species. Despite the general prevalence of moderate temperature indicators, the communities at stations 7–11 are enriched with eurythermal and heat-loving species (Figure 3b). In general, the indicators show an average oxygen saturation of waters with a predominance of st-str species; however, communities at stations 7–11 contain a larger number of species associated with flowing waters (str) with a high oxygen content (Figure 3c). Among the indicators of salinity, indifferent inhabitants of fresh waters prevailed (Figure 3d).

Alkaliphile indicators predominated at stations 1–6, but at stations 7–11, there were more pH-indifferents and acidophiles (Figure 4a). Indicators for the Watanabe system, supported only by diatom species, showed a weak organic load at stations 1–6 with a predominance of saproxenes, but there was an enrichment in organics at stations 7–11 and a predominance of eurysaprobes and saprophiles (Figure 4b). All studied communities were composed of autotrophic species, but at stations 9 and 10, there were more nitrogen-autotrophic taxa, tolerating elevated concentrations of organically bound nitrogen (ate), and at station 6, there were even facultative nitrogen-heterotrophic taxa, needing periodically elevated concentrations of organically identified bound nitrogen (hne), which indicates the suppression of photosynthesis functions in this water reservoir (Figure 4c). Indicators of trophic status in the studied communities covered the entire spectrum from oligotrophic to eutrophic species with an average predominance of mesotrophic species in all communities (Figure 4d).

The dynamics of indicators with species-specific saprobity indices (Appendix A Table A5) were distributed within the boundaries of water quality classes and colored in Figure 5 in the colors of the European Monitoring System from green to red. In general, water quality indicators of classes 2 and 3 prevailed in all studied waterbodies.

3.4. Ecological Mapping

The spatial distribution of environmental (Figure 6) and biological (Figure 7) indicators, in the form of statistical maps, was made to identify similar influencing environmental factors on communities of the Central Yakut Plain. Environmental statistical maps show that there are warmer waters with higher pH in the north of the study area (Figure 6a,b), chlorides are associated with diamond mining (Figure 6c), water color and nitrate saturation are greater in the southern swampy area (Figure 6d,e), and copper concentrations are increased in oil production areas (Figure 6f).

Statistical maps constructed for the most important biological indicators demonstrate a more subtle response to natural and anthropogenic impacts (Figure 7). Thus, the number of species was greatest in the communities of the marshy, undisturbed southern part of the studied territory (Figure 7a). Eurythermic species, indicators of warm waters, were concentrated in communities accumulating runoff from the drainage basin after diamond mining enterprises (Figure 7b). Here, as well as in the oil production area, alcaliphilic species indicated the increased alkalinity of water (Figure 7c). Indicators of high salinity, mesohalobes, as well as indicators of eutrophic waters (o-e), pointed to a diamond mining area (Figure 7d,e). Here, as well as in the richest community in the southern area of the swamps, water quality class 4 indicators were found (Figure 7f).

3.5. Species–Environments Relationship Analysis

To identify which chemical or biological indicators are most important for analysis, we plotted similarity graphs using two different methods. The comparison was based on the data in Table 2 as well as biological data from Appendix A Table A6. At the first stage, similarity trees were built for all data, both biological and environmental (Figure 8). Then, similarity trees were built separately for environmental (Figure 9) and biological indicators (Figure 10), then the results were compared.

As a result of the comparison, it is clear that according to the composition of biological indicators, a separate cluster is identified with stations 7 and 9, where chlorides are increased in the oil production area, but station 3 with the maximum concentration of chlorides is not included in it, which indicates the association of biological indicators more with factors in the areas of oil production. At the same time, the similarity trees for the sum of indicators and for environmental indicators turn out to be similar, which indicates a significant influence of the values of chemical indicators on the statistical calculation of data similarity with the selection of stations 3 and 4, where diamond mining is carried out.

Similar operations carried out within the framework of JASP (Jeffrey’s Amazing Statistics Program) showed that plots of similarities for total bioindicators and chemical (Figure 11a) and environmental indicators only (Figure 11b) strictly differentiate data on diamond mining stations and oil production stations from reference unaffected stations.

However, the similarity of biological indicators of communities shows a more detailed picture of influences (Figure 12). It can be seen that all reference sites are grouped into two clusters, 1 and 2, depending on their position on the ground and their relationship to a particular drainage basin. Cluster 3 included all stations where oil and diamond mining enterprises are located, as well as station 1, located several hundred meters down the stream from the main diamond pipe.

Thus, performing a similarity analysis is more productive in the JASP program but constructing Bray–Curtis trees is also possible based on the sum of all chemical and biological data (or only chemical data) to identify the individuality of the studied stations. But for analyzing the intensity of anthropogenic impact, the best results can be obtained by JASP based on bioindication data.

Redundance discriminant analysis (RDA) for the species number, bioindicator groups, and environmental variables’ results can be seen on the triplot (Figure 13a). The calculation was based on data in Table 2 and Appendix A Table A6.

Environmental indicators were divided into two packages, one of which (upper left and right quadrants) can be called “natural”; it stimulates species richness, oligotrophic species, acidophiles, indicators of clean water of the 2nd quality class, and is represented by forms of nitrogen, phosphorus, silicon, and increased water color. The second package (lower left quadrant) consists of indicators of pollution with oil, metals, chlorides, and other ions, which can be called “anthropogenic”, which stimulates the development of indicators of salinization, eutrophication, and organic water pollution of quality class 4. Thus, the analysis showed an answer adequate to the question posed—which indicators are the indicator “face” of the studied area of the industrial region.

Figure 13b presents the results of RDA calculations for the same environmental indicators from Table 2, taxonomic data, and our calculated indices of species richness of higher taxa (Appendix A Table A1, Table A2, Table A3 and Table A4). According to the degree of influence on higher taxa, environmental indicators were also divided into two packages, natural and anthropogenic, as in Figure 13a. The natural package of environmental indicators stimulated the species richness of higher taxa, and the anthropogenic package stimulated the number of large taxa. This can serve as a significant indicator in determining the degree of susceptibility of natural flora to anthropogenic influence.

4. Discussion

As previously shown, the direct impacts of oil and gas development on biodiversity and ecosystem services include habitat loss and fragmentation, pollution, changes in water resources and flow regimes, and changes in the structure of freshwater communities [3,49], especially under the climatic fluctuation of ultraviolet radiation and the temperature range [50]. The effects of stressors on freshwater ecosystems are often combined and their impacts have a synergistic effect, leading to irreversible or difficult complex consequences, the counteraction of which is expensive in many situations [49,50]. Toxic oil suspended from oil spills, as well as wastewater, can affect all levels of biological organization from the organism to the ecosystem or landscape, and at the community level, impacts cause changes in species composition and diversity or alter the primary productivity of benthic algae. In addition to direct impacts, indirect threats from oil exploration and production can also exacerbate negative impacts on ecosystem functioning and biodiversity loss regardless of continent or climate zone as in Brasilia, Mississippi, or Polar Ural [51,52,53].

Regarding the impact of diamond mining operations, the literature is sparse because the locations of such industries are rare [7]. For example, there is information that in the river at the diamond deposits of the Arkhangelsk region, the concentration of silicon has increased, which the authors attribute to the pollution of river waters with saponite suspension associated with the mining process. In addition, the average concentration of dissolved oxygen decreased [54].

Therefore, assessing the impact of both types of enterprises, even those located close to each other, becomes highly important not only for recommendations on reducing the impact but also in methodological terms for the formation of monitoring indicators in oil and diamond mining areas. First of all, we provided a theoretical basis for the indicators that form aquatic communities in permafrost conditions. Our studies of aquatic communities in the Arctic [55,56] gave us the basis to assume that the high individuality of algal complexes in permafrost conditions is associated with the effects of climate. Accordingly, assessing the impact on such communities should be approached as objects of island biota. This assumption is based on the following reasoning.

To assess the spatial isolation and individuality of aquatic communities on a continent, ecosystem type is important because taxa in lotic systems exhibit stochastic distributions due to physical disturbances, unlike taxa in standing aquatic systems [57]. For continental diatom floras, the law of the square of the radius of area operates and as spatial scale increases, stochastic factors tend to become more dominant in structuring biological communities [58]. In the case of assessing the individuality of diatom diversity in Arctic waterbodies or the same located in the permafrost zone, we identified the influence of harsh climatic conditions on the process of community formation as a separation factor, which allows us to consider individual waterbodies as island biotas [55,56]. Thus, the biota of individual waterbodies in permafrost conditions can be considered as island-like. At the same time, homogeneous processes in the flora are higher on the islands, and disruption of the structure of diatom communities decreases as isolation increases. So, as the islands’ diatom communities are forming under the combined effect of environmental filtering, especially climatic variables, and dispersal limitation [59], we can assess the entire diatom communities as island diversity.

While it is understood that assessing the impact of oil and diamond mining operations is necessary, the methods used to do so fall in two directions. The first includes direct chemical analyses, as in our case, leading to an assessment of the concentrations of some elements directly at the sampling point and not taking into account the synergistic effect and, especially, the impact on the surrounding landscape. In our case, an increase in the concentrations of silicon and zinc, but not oil, is visible at the points of oil production. Mining activities often result in the contamination of natural waters with toxic heavy metals throughout the world [60]. And even long after the end of mining (in the case of abandoned quarries), heavy metals can enter natural waterbodies from mining waste through physical erosion and geochemical processes [1,61,62,63]. Increased concentrations of chlorides, TDS, sodium and bicarbonates are also visible at locations associated with diamond mining. However, direct water analysis cannot provide an environmental assessment for other natural elements.

The second strand of assessments includes elements of landscape, community, species richness, and beta diversity [59]. At the same time, the overwhelming influence of the spread of oil is noted, leading to the defragmentation of elements of terrestrial ecosystems and a decrease in diversity indicators in aquatic ones. In our case, a decrease in the number of species in oil and diamond mining areas was also noted, but only if unaffected landscapes were included in the assessments, since there is a priori no criterion for how many species there were before and after the impact.

Our efforts to include the bioindicator properties of aquatic communities in assessments, undertaken earlier [8], showed the effectiveness of this approach, since the assessments included not only indicators of the total diversity of communities but also indicator values of species in various indication systems [64]. In our case, nine indicator systems were used to assess nine environmental variables. The use of diatoms as bioindicators can inform environmental monitoring and policy-making in regions affected by industrial activities.

The studied flora contained 137 indicator species out of 144 identified, therefore, the use of bioindication methods was conducted on a very thorough basis. It turned out that the stations in the studied area of the flattened landscape belong to three different drainage basins, and this is noticeable in the distribution of species among indicator groups. Thus, the northern and western areas where the enterprises were located were distinguished by the predominance of plankto-benthic species, while in the reference areas of the southern part, the species preferred benthic habitats. In all studied communities, indicators of moderately oxygenated waters noticeably predominated. The reference stations in the southern part, according to the composition of indicators, were less saline and less alkaline, as well as more saturated with organic substances, but their trophic content was lower, and the class of water quality in terms of organic pollution was higher. All communities were composed of autotrophic species, and only at the oil production station were indicator species of mixotrophic nutrition found, which indicates a probable impact of photosynthesis.

With such an abundance of material and its diversity, it was necessary to apply statistical methods to identify general trends in the distribution of both chemical and biological indicators. Statistical maps showed that increased temperature and pH are characteristic of northern areas, and color and nitrates are characteristic of southern areas. Using the maps, specific indicators of the impact of diamond mining, including chlorides, and oil production, including copper, were identified.

Statistical comparison of chemical and biological data packages using the Bray–Curtis method showed that the similarity tree of the overall composition of indicators reveals the same clusters as the chemical tree, indicating the impact of diamond mining. However, a tree constructed only for biological indicators clearly identifies communities of stations under the anthropogenic influence of oil production. The construction of JASP rafts turned out to be more effective, where separate clusters were identified for reference sites, as well as one common cluster for sites under the anthropogenic influence of oil production and diamond mining.

Redundance discriminant analysis identified two packages of environmental indicators, which correlate with so-called “natural” indicators, which stimulate species richness in oligotrophic environments, and “anthropogenic” indicators, which stimulate the development of indicators of salinization, eutrophication, and organic pollution.

A comparison of the list of species first identified for the Central Yakut Plain with relatively closely located diatom floras of Yakutia [8,55,56] emphasized the high individuality of this flora. RDA analysis, performed for chemical indicators of the environment as independent and indicators of the species richness and species richness of higher taxa, also identified two clusters, natural and anthropogenic, and showed that the natural package of environmental indicators stimulated the species richness of higher taxa and the anthropogenic package stimulated the number of large taxes. These calculations can be used as a significant indicator in determining the degree of susceptibility of natural flora to anthropogenic impact [50,65,66]. Thus, we were able to include biodiversity indicators in the analysis of the combined impact of diamond mining and oil production enterprises on aquatic communities of the Central Yakut Plain in Eastern Siberia, located in the permafrost zone.

This study’s findings are limited by the short sampling period, which may not capture seasonal variations. Future research should focus on long-term monitoring to capture temporal changes and expand the geographical scope to include comparative studies in different regions.

5. Conclusions

Thus, for the previously unstudied flora of 11 reservoirs of the Central Yakut Plain, 144 species of diatoms were identified. Chemical analysis of water samples showed an increase in the concentration of silicon and zinc at oil and gas production sites, but a possible increase in the content of petroleum products was not noted here. The waterbodies in the territory of diamond mining were characterized by increased concentrations of chlorides, TDS, sodium, and bicarbonates.

The high number of indicator species in the studied flora created a reliable basis for bioindication. As a result, a difference was identified between reference stations and subsoil development sites, which manifested itself in the type of habitats and the composition of salinity and pH indicators. Using statistical mapping methods, the main abiotic factors of diamond mining (chlorides) and oil and gas mining (copper) areas were determined. Two clusters were identified, which should be characterized as natural and anthropogenic, differing in the taxonomic structure of the flora.

The studied area of the Central Yakut Lowland is a unique testing ground for studying the combined impact of diamond mining and oil and gas subsoil development on aquatic ecosystems. Particular interest in this region is also associated with the distribution of permafrost here. It is obvious that continued research of aquatic ecosystems is required, taking into account the control and probable anthropogenic transformations and the increased risk of the impact of global climate change on permafrost landscapes. Future studies should include long-term monitoring to capture seasonal and interannual variations, expand the geographical scope to different industrial regions, and investigate the combined effects of climate change and industrial activities on aquatic ecosystems. Additionally, further development of bioindicator systems tailored to specific pollutants and ecosystem types will enhance the precision and applicability of monitoring programs.