View of Water microbiota and physico–chemical parameters in relation with fish di-versity along the Danube River (Romanian–Bulgarian sector)

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Water microbiota and physico–chemical parameters in relation with fish diversi- ty along the Danube River (Romanian–Bulgarian sector)

Ivan Mădălina-Andreea¹, Carmen Curuțiu¹, Jawdhari Abdulhusein², Jujea Valentin³, Crăciun Nicolai⁴ and Pop Cristian-Emilian⁵*

1Department of Microbiology, Faculty of Biology, University of Bucharest, 1–3 Aleea Portocalelor Str., 60101 Buchar- est, Romania;

2Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91–95 Splaiul Independenței Str., 050095, Bucharest, Romania;

3Department of Geographic Information Systems, Faculty of Geography, University of Bucharest, 1 Nicolae Bălcescu Str., 010041, Bucharest, Romania;

4Zoology Section, Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Bucharest, 91–95 Splaiul Independenței Str., 050095, Bucharest, Romania;

5Department of Organic Chemistry, Biochemistry and Catalysis, Faculty of Chemistry, University of Bucharest, 90 Panduri Str., 050663, Bucharest, Romania;

*[email protected]

Abstract

Water and water habitats are essential to life and to the wellness of the communities that rely on them; as the Danube River is subject to human impact and vice versa, periodic surveys are required to determine the status of its pollution levels and current ichthyofauna diversity. In this work we used known microbiological indicators as well as physico–chemical ones to assess the ecotox- icological status of the Danube River from eleven locations partially forming the ―Romanian–Bulgarian Danube Sector‖. Most of the microbial communities found, and their densities, could be explained by point and diffuse sources scattered along the sector such as discharged household and farm wastewater, as well as nearby agricultural areas where fertilizers have been used and then leaked in the water stream during rainfalls. However, microbial data on non-point sources that lead to diffuse pollution of surface waters could be linked to such parameters and furthermore shown a slight correlation with the current status of fish communities which we sur- veyed.

Keywords: Danube; ecotoxicology; ichthyofauna; pollution indicators; wastewater; water microbiology

1. Introduction.

The Danube River has been subject to many pollution sources over the years, studies revealing sediments (Gati et al 2016; Begy et al 2018) being able to highlight historical modifications of the major impacts over it, including agriculture fertilizers, pesticides and insecticides, as well as dis- charge of wastewater and other anthropogenic activities (Michałowicz 2014). Although many studies have focused on the sediments and zoobenthos as traditional indicators of pollution and fish habitat preferences factors (Lamouroux et al 1999; Jiang et al 2018; Jacquin et al 2020) data regarding connections between water microbiota and ichthyofauna diversity of the Danube River remains scarce. As in all natural habitats, different trophic communities are linked together tightly and although some may seem unrelated, the causality effects are always present. Current studies between gut microbiota and health in humans have raised the same questions in the aquatic ecotoxicology field, where a strong link between gut microbiota and fish health began to take shape (Round &

Mazmanian 2009; Sylvain et al 2016; Xiong et al 2019). No matter the species, the gastrointestinal tract is the primary site of interaction between the host immune system and microorganisms, both symbiotic and pathogenic, resulting in the wellness and fitness of the specimen.

The fish intestinal tract is considered to be the main portal for pathogens (Roeselers et al 2011;

de Bruijin et al 2018) and as environmental and ecological factors shape the intestinal tract microbiota (Sullam et al 2012; Wong & Rawls 2012) a correlation between water microbiota and fish diver-

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sity is thus consequential. Another factor worth mentioning is that small variations in water pH are also a factor in fish communication and social interaction patterns (Kleinhappel et al 2019), having important effects on the growth and reproduction of the fish assemblages (Blanck et al 2007; Tesfay et al 2019; Huang et al 2019) and as well as on bacterial community composition (Muniz 1990; Syl- vain et al 2016).

Variations in water microbiome patterns have been reported (Payne et al 2017; Ren et al 2019) as well as their distinct potential functions of nitrogen and sulfur metabolisms that lead to changes in water quality and sediment properties. The variation of bacterial communities, which regulate the core biogeochemical processes such as carbon and nitrogen metabolisms in aquatic ecosystems indi- rectly influences ichthyofauna (Cotner & Biddanda 2002). Nitrogen fixing bacteria that convert ni- trite into the much safer for fish nitrate have a crucial role in fish habitats but also in their physiology, recent studies revealing a symbiotic relationship between fish gills and several ammonia oxidiz- ing and denitrifying bacteria (Van Kessel et al 2016). However, not all bacteria act in a beneficial or even near symbiotic manner with fish specimens or fish communities. Coliforms, which are general- ly considered harmless water pollution indicators, can affect fish gills health (Fonseca et al 2016) as well as transfer drug resistance to pathogens with detrimental consequences for both humans and fish (Grabow & Prozesky 1973; Sanyal & Banerjee 2013; Marinescu et al 2015; Fonseca et al 2016). Therefore we aimed to assay the current status of the water microbiota and fish diversity along the Danube River (Romanian–Bulgarian sector) in order to observe any relations and potential causality.

2. Material and Method

2.2. Sampling sites and methodological approach.

Along the Romanian–Bulgarian Danube sector, 11 locations, which were important from an anthropogenic impact point of view, were chosen measuring a total River length of 401 km (Figure 1), from which two are the confluence of the Danube River with its tributaries (locations 5 and 6).

Water physical and chemical parameters such as general hardness (GH), carbonate hardness (KH), redox, conductivity, temperature, as well as pH, carbon dioxide, dissolved oxygen and oxygen satu- ration were measured in situ using a portable multi-parameter analyzer (HQ40D Hach-Lange, UK).

All physico–chemical samplings took place on a 8 days expedition in September 2020, around 10- 11 a.m. hours, as the period of the day just like the temperature, may influence water oxygen satura- tion levels.

Ichthyofauna sampling was performed with the use of the non-lethal electrofishing devices (SAMUS 725 MP and SUM Electrofisher, Poland), then calipers (Adoric, China) and an 0.01 g pre- cision range digital scale (Kern, Germany) were used to measure the total length and weight of the captured specimens; the measurements were performed in triplicates with the median value being used as the final value. All field equipments were verified and calibrated according to the manufac- turer's instructions and specifications prior to use. Sterile 120 mL containers and sterile brown 50 mL Falcon tubes were used to collect surface water. Samples corresponding to the 11 locations (S1–

S11) were stored and transported at 4°C for microbiological analysis.

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Figure 1. Satellite image of the Danube Romanian–Bulgarian sector, 11 locations (1-Garla Mare; 2-Salcia; 3-Bogdan Secian; 4-Dobrina; 5-Jiu Corridor; 6-Olt Confluence; 7-Belene; 8-Vardim; 9-Iantra; 10-Batin; 11-Kosui) measuring a total River length of 401 km.

2.3. Water physico-chemical analysis.

Apart from the in situ water probing, chemical analysis colorimetric kits (Merck Aquaquant, Darmstadt, Germany) were used to evaluate the levels of ammonium, nitrogen dioxide, nitrate, phosphate, silicon dioxide, iron and cooper. The robustness and efficiency of the Merck colorimetric tests being based on the reaction between two chemicals (reagent A and reagent B) with a certain amount of water sample pipetted in the designated test tubes, accordingly to each kit specifications.

2.4. Water microbiology analysis.

Water samples were filtrated through sterile 0.45 µm white gridded cellulose membranes with the use of a filtration unit (Merck Eazy-Fit, Darmstadt, Germany), 100 mL volumes were used to perform the assays. The cellulose membranes were then placed on Petri plates with chromogenic agar media (CCA) and on Petri plates with Slanetz Bartley media (SB). Confirmation of SB media results was performed with the use of bile esculin azide agar, 2 hours after incubation at 44°C, accordingly to ISO 7899-2 guidelines (ISO 7899-2:2000). The esculin from the bile esculin azide media is hydrolyzed by the enterococci and Esculetin, the end metabolite, bonds with ferric ions to form a noticeable brown to black compound which diffuses into the medium. Accompanying Gram- positive and Gram-negative flora is inhibited by the azide anion and bile salts, thus reaching a selec- tivity for enterococci.

Modified Postgate media was used to determine sulphate reducing bacteria (SRB). SRB are anaerobic microorganisms that use sulphate as a terminal electron acceptor, playing a key role in the sulfur and carbon cycles. The determination of SRB densities was performed with the use of serial dilutions. In brief, 9 tubes with Postgate media were used per sample as in: 3 tubes containing 1 mL of sample, 3 tubes with a 10^-1dilution and another 3 tube with a 10^-2dilution. All 99 test tubes were incubated for 7 days at 37°C and then checked for the presence of ferrous sulphate, a black precipi-

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tate that indicates the presence of SRB, afterwards the microorganism number (MO) and most prob- able number (MPN) were calculated.

For total heterotrophs determinations, 1 mL of sample as well as 10^-1, 10^-2 and 10^-3 dilutions from each sample were placed in Petri dishes and agar-agar media heated at 45°C was poured over the sample and then homogenized. The sample containing media was left to cool and was then incubated for 48h at 22°C, after incubation the colonies were counted.

2.5. Ichthyofauna diversity determination.

Electrofishing was performed in linear 500 m transects, upstream and downstream over shore, as well as over the main water body, with a focus on accurate identification of the inhabitant fish species. Electrically numbed specimens were collected with telescopic pond nets and then classi- fied, weighted and measured, before gently being released back into the water stream. Composition, diversity, abundance and the degree of dominance were noted as in catch per unit (Hubert & Fabri- zio 2007).

2.6. Geographic information system (GIS) mapping.

Geographic information system (GIS) integrates both hardware and software as well as data for capturing and analyzing all referenced geographic information, allowing the user to visualize and interpret data in many ways. It can be described as a database management system which usual- ly presents the data to the user in an interactive graphical way that can be queried and analyzed.

Thus, a correlation between relationships, patterns and trends is made possible in the form of maps, reports, or graphs. Data collection for the 11 locations was performed using a dedicated GPS device (Garmin, U.S.A), the coordinates were processed and inserted in the GIS freeware software Qgis and Saga, having as a base map the freeware source from Bing search engine, the measurement er- ror of the location being less than one meter.

3. Results and Discussion 3.1. Water analysis.

Physico–chemical and microbiological results from the 11 locations (Tables 1, 2) reveal the quality of water in the sector of interest, with variations linked to the anthropogenic activities.

Although the microbiological, physical and chemical parameters were found to be in what are considered normal ranges (Kavka et al 2006; Makori et al 2017; Nyanti et al 2018), locations 3 (Bogdan Secian), 7 (Belene), 8 (Vardim) and 11 (Kosui) were the most affected from a microbiological point of view, as they are subject to wastewater impact from the residential settlements and from the Bulgarian penitentiary located on Belene Island.

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Table 1 Table 1. Physico-chemical measurements.

Location Physical parameters Chemical parameters Parameter Value Unit

measure Parameter Value Unit measure

Garla Mare (S1)

Conductivity 204 µS cm^-1 KH = 8 dKH pH 8.07 -log(H⁺) GH = 10 dGH Redox -60.60 mV CO₂ = < 15 mg L^-1 Temperature 11.8 °C NH₄ = < 0.05 mg L^-1

Dissolved

Oxygen 9.24 mg L^-1 NO2 = 0.25 mg L^-1 Oxygen satu-

ration 83.50 % NO₃ = 25 mg L^-1 PO₄ = < 0.05 mg L^-1 SiO2 = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Salcia (S2)

Conductivity 225 µS cm^-1 KH = 8 dKH pH 7.91 -log(H⁺) GH = 10 dGH Redox -51.80 mV CO₂ = < 15 mg L^-1 Temperature 13.9 °C NH₄ = < 0.05 mg L^-1

Dissolved

ration 83.40 % NO₃ = 25 mg L^-1 PO4 = < 0.05 mg L^-1 SiO2 = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Bogdan Se- cian (S3)

Conductivity 324 µS cm^-1 KH = 8 dKH pH 8.09 -log(H⁺) GH = 10 dGH Redox -61.70 mV CO₂ = 15 mg L^-1 Temperature 12.5 °C NH4 = < 0.05 mg L^-1

Dissolved

Oxygen 9.33 mg L^-1 NO2 = 0.25 mg L^-1

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Oxygen satu-

ration 86.10 % NO3 = 40 mg L^-1 PO₄ = < 0.05 mg L^-1 SiO₂ = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Dobrina (S4)

Conductivity 256 µS cm^-1 KH = 8 dKH pH 8.06 -log(H⁺) GH = 10 dGH Redox -60.50 mV CO2 = 15 mg L^-1 Temperature 13.4 °C NH₄ = < 0.05 mg L^-1

Dissolved

Oxygen 9.02 mg L^-1 NO₂ = 0.25 mg L^-1 Oxygen satu-

Jiu Corridor (S5)

Conductivity 207 µS cm^-1 KH = 4.5 dKH pH 8.36 -log(H⁺) GH = > 7 dGH Redox -76.60 mV CO₂ = <15 mg L^-1 Temperature 11.0 °C NH₄ = < 0.05 mg L^-1

Dissolved

Olt Conflu- ence (S6)

Conductivity 251 251 µS

cm^-1 KH = 8 dKH

pH 8.17 -log(H⁺) GH = > 14 dGH Redox -65.90 mV CO₂ = 20 mg L^-1 Temperature 10.8 °C NH4 = < 0.05 mg L^-1

Dissolved

Oxygen 10.14 mg L^-1 NO₂ = 0.5 mg L^-1 Oxygen satu- 89.80 % NO₃ = 40 mg L^-1

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ration

PO4 = < 0.02 mg L^-1 SiO₂ = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Belene (S7)

Conductivity 255 µS cm^-1 KH = 8 dKH pH 8.16 -log(H⁺) GH = >10 dGH Redox -65.50 mV CO2 = 15 mg L^-1 Temperature 11.4 °C NH4 = < 0.05 mg L^-1

Dissolved

ration 87.20 % NO3 = 40.00 mg L^-1 PO₄ = < 0.02 mg L^-1 SiO₂ = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Vardim (S8)

Conductivity 227 µS cm^-1 KH = 10 dKH pH 8.18 -log(H⁺) GH = > 14 dGH Redox -65.60 mV CO2 = 30 mg L^-1 Temperature 11.2 °C NH₄ = < 0.05 mg L^-1

Dissolved

ration 87.70 % NO3 = 40 mg L^-1 PO₄ = < 0.02 mg L^-1 SiO₂ = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Iantra (S9)

Conductivity 262 µS cm^-1 KH = 6 dKH pH 8.22 -log(H⁺) GH = > 10 dGH Redox -68.70 mV CO₂ = < 15 mg L^-1 Temperature 10.6 °C NH₄ = < 0.05 mg L^-1

Dissolved

ration 89.40 % NO₃ = 40.00 mg L^-1 PO₄ = < 0.02 mg L^-1

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SiO2 = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Batin (S10)

Conductivity 243 µS cm^-1 KH = 10 dKH pH 8.15 -log(H⁺) GH = > 14 dGH Redox -65.00 mV CO₂ = < 15 mg L^-1 Temperature 11.2 °C NH₄ = < 0.05 mg L^-1

Dissolved

ration 93.80 % NO₃ = 40 mg L^-1 PO4 = < 0.02 mg L^-1 SiO2 = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

Kosui (S11)

Conductivity 253 µS cm^-1 KH = 10 dKH pH 8.15 -log(H⁺) GH = > 14 dGH Redox -65.00 mV CO₂ = 15 mg L^-1 Temperature 11.7 °C NH4 = < 0.05 mg L^-1

Dissolved

ration 88.40 88.40% NO3 = 50 mg L^-1 PO4 = < 0.02 mg L^-1 SiO₂ = > 6.0 mg L^-1 Fe = < 0.02 mg L^-1 Cu = < 0.1 mg L^-1

*dKH = degree of Carbonate Hardnessș, *dGH = degree of General Harness.

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Escherichia coli and other coliforms were found in significant densities, represented as colony forming units (CFU), in S3, S7, S8 and S10, all locations being residential and agricultural areas. Without a strong relation between water physico-chemical parameters and coliforms as well as fecal coliform concentrations, we suggest that the high E. coli and other coliforms densities are due to both human and animal fecal dejec- tion and especially a consequence of the practice of manure spreading as agricultural fertilizer, which even- tually ends up in the main water stream during rainfall.

The Enterococcus bacteria, a known indicator of the presence of fecal matter in water, were found most prominent in S7 followed by S11 and S3. High density of Enterrococcus in S7 can be directly linked with the presence of Belene penitentiary located on Belene Island. The lack of evidence of Enterrococus in S8 suggests that the contamination in location 8 is not due to fecal matter, however, further investigations should be made regarding this topic.

Heterotrophic bacteria represent the totality of bacteria that require organic nutrients for growth, thus it cannot be used as an indicator of fecal contamination. Heterotrophic plate count (HPC) technique includes a wide range of bacterial genera, which may include primary and secondary pathogens concluded as total via- ble count (TVC). Some bacteria enumerated by HPC method may also include opportunistic pathogens such as Klebsiella, Aeromonas and Pseudomonas, thus the HPC is considered to be a health-based water parameter test (Payment et al 1994). The HPC test revealed high concentrations in S10, S8 and S7 with the smallest densities found in S4 and S3, highlighting S7 as a common point of high bacterial density.

Although SRB prefer oxygen-deficient environments, samples collected from the surface water, where oxygen saturations were high, shown representative variations, as in: S1, S4, S7, S9-11, with a high density in S11. Anaerobic iron corrosive and key microbiological indicators, SRB (which also generate the smell of rotten egg of hydrogen sulfide) can also be linked with immersed metals suffering biocorrosion (Videla &

Herrera 2005; Marangoni et al 2013).

Table 2. Microbiological parameters of water samples (CFU/100 mL).

Sample Coliforms Escherichia

coli Enterococ-

cus TVC at

22^oC SRB

S1 2.3*10² 7.3 2 8.5x10³ 1

S2 1.4x10² 1.7*10 2 3.3*10³ 0

S3 4*10² 3*10² 8 7*10² 0

S4 1.5*10² 1.1*10 1 6*10² 1

S5 1.5*10² 3.8*10 0 1*10³ 0

S6 1.6*10² 2.4*10 2 3.2*10³ 0

S7 3.4*10² 6.7*10 1.7*10² 2.9*10⁴ 1

S8 6.2*10 2.5*10² 0 7.4*10⁴ 0

S9 1.5*10² 1.4*10 0 1.2*10⁴ 1

S10 6.1*10² 5.9*10² 5 8.3*10⁴ 2

S11 7.5*10 7.2*10 1.5*10 2.2*10³ 2.5*10

3.2. Ichthyofauna diversity.

3.2.1. Garla Mare Station.

Habitats are maintained at this station by the offshore islands where there is wild vegetation, especially willows that are partially submerged, providing good spawning places for most phytophilic fish species.

The total number of fish species observed at this station was 20 species on shore water (Figure 3) and 12 species in open water (Figure 4). Species of community interest found were Pelecus cultratus and Aspius aspius.

The dominant fish species at Garla Mare station were Alburnus alburnus, Carassius gibelio and Neo- gobius fluviatillis, with only one individual observed per species: Blicca bjoerkna, Carassius carassius and Leuciscus idus.

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0.0%

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35.0%

40.0%

Garla Mare Relative Abundance

Figure 3. Garla Mare relative abundance . Identified fish species in shore water.

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30.0%

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50.0%

60.0%

Garla Mare Relative Abundance

Figure 4. Garla Mare relative abundance . Identified fish species in open water.

3.2.2. Salcia Station.

For shore water study 5 species were observed (Figure 5) and 8 species were observed in the open water study (Figure 6). No species of community interest was identified. The dominant fish species were: Al- burnus alburnus, Carassius gibelio and Neogobius fluviatilis. Species with only one individual observed was Proterorhinus semilunaris.

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0.0%

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45.0%

Salcia Relative Abundance

Figure 5. Salcia relative abundance. Identified fish species in shore water.

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50.0%

60.0%

Salcia Relative Abundance

Figure 6. Salcia relative abundance. Identified fish species in open water.

3.2.3. Bogdan Secian Station.

The total number of fish species observed at this station was 4 species on shore water (Figure 7) and 6 species in open water (Figure 8), no species of community interest were found. The dominant fish species at Bogdan Secian station were Alburnus alburnus and Vimba vimba in equal numbers, followed by Neogobius fluviatilis. The less frequent species for this station was Neogobius gymnotrachelus with only two specimens and Neogobius melanostomus with three specimens.

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45,0%

Neogobius melanostomus

Neogobius fluviatilis Neogobius gymnotrachelus

Abramis sapa

Bogdan Secian Relative Abundance Figure 7. Bogdan Secian relative abundance. Identified fish species in shore water.

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Bogdan Secian Relative Abundance

Figure 8. Bogdan Secian relative abundance. Identified fish species in open water.

3.2.4. Dobrina Station.

For Dobrina station 4 species were identified in shore water (Figure 9) and 11 species in open water (Figure 10), with 1 species of community interest, Aspius aspius. The dominant fish species were Alburnus alburnus and Neogobius fluviatilis, less frequent species were Blicca bjoerkna and Esox lucius.

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30.0%

35.0%

40.0%

45.0%

Neogobius melanostomusNeogobius gymnotrachelus Dobrina Relative Abundance

Figure 9. Dobrina relative abundance. Identified fish species in shore water.

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10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Dobrina Relative Abundance

Figure 10. Dobrina relative abundance. Identified fish species in open water.

3.2.5. Jiu corridor.

At this confluence point of the Danube with Jiu River, 11 species were found on shore water (Figure 11) and 11 species in open water (Figure 12), with 2 species of community interest: Barbus barbus and Sa- banejewia aurata balcanica. The dominant fish species were: Neogobius fluviatilis and Pseudorasbora par- va. The less frequent species for this station were: Abramis brama, Sander lucioperca, Leuciscus idus and Sabanejewia aurata balcanica with only one observed individual per species.

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Jiu Corridor Relative Abundance

Figure 11. Jiu Corridor relative abundance. Identified fish species in shore water.

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5.00%

10.00%

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25.00%

30.00%

Jiu Corridor Relative Abundance

Figure 12. Jiu Corridor relative abundance. Identified fish species in open water.

3.2.6. Olt confluence.

The widely spread and invasive species Alburnus alburnus, a lacustrine and fluvial cyprinid, was found to be to dominant at this location both on shore and in the open water. For the shore study a total of 6 species were found and 22 species were identified in the open water study, with 5 species of community interest:

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Aspius aspius, Barbus barbus, Eudontomyzon mariae, Rhodeus amarus and Sabanejewia aurata romanica.

The less frequent species for this station were: Proterorhinus marmoratus, Barbus barbus, Eudontomyzon mariae, Pseudorasbora parva, Sabanejewia aurata romanica, Sander lucioperca, Silurus glanis, Syngna- thus abaster and Vimba vimba with only one observed individual per species.

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Olt Confluence Relative Abundance

Figure 13. Olt Confluence relative abundance. Identified fish species in shore water.

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Alburnus alburnus Aspius aspius Barbus barbus Blicca bjoerkna Carassius gibelio Chondrostoma nasus Esox lucius Eudontomyzon mariae Leuciscus idus Neogobius fluviatilis Neogobius… Neogobius melanostomus Perca fluviatilis Pseudorasbora parva Rhodeus amarus Rutilus rutilus Sabanejewia aurata… Sander lucioperca Silurus glanis Squalius cephalus Syngnathus abaster Vimba vimba Olt Confluence Relative Abundance

Figure 14. Olt Confluence relative abundance. Identified fish species in open water.

3.2.7. Belene Station.

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As one of the most polluted locations from a microbiological point of view, at Belene station 4 species were identified on shore water (Figure 15) and 18 species in open water (Figure 16), with 4 species of community interest: Cobitis taenia, Misgurnus fossilis, Aspius aspius and Sabanejewia aurata romanica. The dominant fish species at Belene Station were: Alburnus alburnus, Neogobius fluviatilis and Rutilus rutilus.

The less frequent species for this station with only one observed specimen per species were Misgurnus fossi- lis, Sander lucioperca, Blicca bjoerkna, Lepomis gibbosus, Pseudorasbora parva. For Silurus glanis, 2 spe- cimens were observed and 3 were identified as Cobitis taenia.

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Cobitis taenia Carassius gibelio Belene Relative Abundance

Figure 15. Belene relative abundance. Identified fish species in shore water.

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Alburnus alburnus Aspius aspius Blicca bjoerkna Carassius gibelio Esox lucius Leuciscus idus Lepomis gibbosus Neogobius fluviatilis Neogobius gymnotrachelus Neogobius melanostomus Perca fluviatilis Pseudorasbora parva Rutilus rutilus Sabanejewia aurata… Sander lucioperca Silurus glanis Squalius cephalus Vimba vimba

Belene Relative Abundance

Figure 16. Belene relative abundance. Identified fish species in open water.

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3.2.8. Vardim Station.

A total of 5 species were identified on shore water (Figure 17) and 19 species in open water (Figure 18), with 2 species of community interest: Aspius aspius and Sabanejewia aurata romanica. The dominant fish species at Vardim Station were: Alburnus alburnus followed by Sabanejewia aurata romanica and Ruti- lus rutilus. Less frequent species Ponticola kessleri, Pseudorasbora parva, Silurus glanis and Vimba vimba, with only one observed individual per species, were also identified.

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Vardim Relative Abundance

Figure 17. Vardim relative abundance. Identified fish species in shore water.

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70.0%

80.0%

Alburnus alburnus Aspius aspius Blicca bjoerkna Carassius gibelio Chondrostoma nasus Esox lucius Leuciscus idus Lepomis gibbosus Neogobius fluviatilis Neogobius… Neogobius melanostomus Perca fluviatilis Pseudorasbora parva Rutilus rutilus Sabanejewia aurata… Sander lucioperca Silurus glanis Squalius cephalus Vimba vimba

Vardim Relative…

Figure 18. Vardim relative abundance. Identified fish species in open water.

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3.2.9. Iantra station.

The total number of fish species observed at this station was 6 species on shore water (Figure 19) and 14 species in open water (Figure 20), with 3 species of community interest: Aspius aspius, Rhodeus amarus and Sabanejewia aurata romanica. The dominant fish species at Iantra Station were: Alburnus alburnus, Neogobius melanostomus and Rhodeus amarus. Less frequent species found were: Esox lucius and Rutilus rutilus with only one observed specimen per species.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Iantra Relative Abundance

Figure 19. Iantra relative abundance. Identified fish species in shore water.

Iantra Relative AbundanceColumn1 Column2

Alburnus alburnus 46.3%

Aspius aspius 2.5%

Carassius gibelio 2.5%

Chondrostoma nasus 4.1%

Esox lucius 0.8%

Leuciscus idus 3.3%

Neogobius fluviatilis 0.8%

Neogobius gymnotrachelus 3.3%

Neogobius melanostomus 13.2%

Pseudorasbora parva 5.8%

Rhodeus amarus 11.6%

Rutilus rutilus 0.8%

Sabanejewia aurata romanica 3.3%

Squalius cephalus 1.7%

Figure 20. Iantra relative abundance. Identified fish species in open water.

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3.2.10. Batin Station.

Batin station capture on shore water was of only 4 specimens belonging to 4 different species (Figure 21) and 17 species in open water (Figure 22). Species of community interest found were Aspius aspius, Rhodeus amarus, Eudontomyzon mariae and Barbus barbus. The dominant fish species were: Alburnus al- burnus, Neogobius melanostomus, Blicca bjoerkna and Rutilus rutilus. Less frequent species for this station with only one observed specimen per species was identified as Alburnoides bipunctatus, Cyprinus carpio, Leuciscus idus, and Rhodeus amarus.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Alburnus alburnus

Alburnoides bipunctatus

Cyprinus carpio

Neogobius

fluviatilis Batin Relative Abundance

Figure 21. Batin relative abundance. Identified fish species in shore water (only 1 specimen per species was captured).

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Alburnus alburnus Aspius aspius Barbus barbus Blicca bjoerkna Carassius gibelio Chondrostoma nasus Esox lucius Eudontomyzon mariae Leuciscus idus Neogobius fluviatilis Neogobius gymnotrachelus Neogobius melanostomus Perca fluviatilis Proterorhinus semilunaris Rhodeus amarus Rutilus rutilus Sander lucioperca

Batin Relative Abundance

Figure 22. Batin relative abundance. Identified fish species in open water.

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3.2.11. Kosui station.

Highly contaminated in heterotrophs and SRB, Kosui station presented a total number of 16 fish species on shore water (Figure 23) and 18 species in open water (Figure 24) study. Species of community interest found were Aspius aspius, Rhodeus amarus, Eudontomyzon mariae and Barbus barbus. The dominant fish species at Kosui Station were Neogobius melanostomus, Rhodeus amarus and Alburnus alburnus. Less frequent species for this location with only one observed specimen per species were Sander lucioperca, Hy- pophthalmichthys nobilis, Proterorhinus marmoratus, and Pseudorasbora parva.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Kosui Relative Abundance

Figure 23. Kosui relative abundance. Identified fish species in shore water.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

Kosui Relative Abundance Figure 24. Kosui relative abundance. Identified fish species in open water.

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3.3. GIS water distribution.

The water flow oscillates depending on the factors generated by the Danube River tributaries (Figure 25), on the precipitations in the hydrographic basin area, and also depending on the relief (Figure 26). The relief can determine the amount of water distributed to the tributaries or the amount of water retained in the soil. This implicitly determines the amount of nutrients and toxic elements that are in the water, as previous studies with GIS analysis took place regarding spatial distribution of contaminants such as heavy metals in the Danube River revealed (Ilie et al 2017). The flow in the Garla Mare section is decreasing up to the value of 3400 m³ s^-1, being below the multiannual average of December (5200 m³ s^-1).

Figure 25. Hydrographic basin of the Danube Sector including tributaries.

Figure 26. Hydrographic area and elevation.

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Downstream of this sector, flows will be declining, except for the first part of the period when they will in- crease in the Kosui sector.

For most sediments, the particles are easily eroded by aquatic currents (Inman 1949). Shields' diagram (Shields 1936) allows the delimitation of laminar transport from turbulent transport, Shields number being used to calculate the initiation of the motion of a sedimentary particle in a fluid. The lowest values of the Shields parameter are found in the area of sands (between 0.06 and 2.00 mm), the sand being small enough in size and mass but at the same time too large to adhere to the bed of the riverbed. Knowing that sediments also retain biotic and abiotic particles, the volume of water, the flow rate and the mass of suspended particles determine the amount of toxic elements moving in the total volume of water.

4. Discussions

Determining the sources responsible for both organic and inorganic pollution as well as knowing their short- and long-term effects are vital to water management and habitat safety, as well as for human health (Cotner

& Biddanda 2002). Effective measures to counteract pollution can be taken after periodic surveys and cau- salities between different trophic levels as well as anthropogenic activities can be made with the use of suf- ficient data. Microbiology assays can provide the critical information required to identify whether the contamination is human or animal in origin as the hotspots of faecal pollution in the Danube River, despite the major known contributors, can be expected to be from diffuse rather than point sources. The sites with the highest faecal contamination indicators were Belene (S7), Kosui (S11) and Bogdan Secian (S3), with vast ichthyofauna diversity found in Kosui counting 16 species in shore water and 18 in open water and Belene with 4 species in shore water and 18 species in open water, but relatively pour in Bogdan Secian were 4 species were found in shore and 6 in open water. High heterotrophic bacteria densities were found in sites with vast ichthyofauna diversity as in: Batin (S10) that shown 4 species in shore water and 17 species in open water, Vardim (S8) with 5 species in shore water and 19 species in open water, and Garla Mare with 20 species in shore water and 12 in open water. Dobrina (S4) site with 4 identified species in shore water and 11 in open water and Bogdan Secian (S3) counted the fewest fish species and the lowest heterotrophic bacteria densities. The most prominent density of SRB was found in Kosui (S11) where the highest number of species was also identified, however no correlation could be made with other sites as SRB were found in both high and low ichthyofauna diversity sites (S1, S4, S9, S10).

5. Conclusions

Although it remains certain that microbiota is an integral part of the ecosystem and that some strains affect fish health at some degree, periodic surveys and further research is required to confirm the supposition that there could be a strong link between different microbial densities and ichthyofauna habitat preference in River water. During our survey we could not establish a strong correlation between the known microbiological pollution indicators and the ichthyofauna diversity

Acknowledgments. The authors would like to thank Hanganu Dorin for sampling and for in situ analysis of the specimens.

Conflicts of interest. The authors declare no conflict of interest.

Funding. This research received no external funding.

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