Assessment of Water Quality Using Benthic Macroinvertebrates and Biotic Indices Based on Year-Long Monitoring in the Kızılırmak River (Nevşehir, Türkiye)

Assessment of Water Quality Using Benthic Macroinvertebrates and Biotic Indices Based on Year-Long Monitoring in the Kızılırmak River (Nevşehir, Türkiye) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Water Quality Using Benthic Macroinvertebrates and Biotic Indices Based on Year-Long Monitoring in the Kızılırmak River (Nevşehir, Türkiye) Fatih TÜRE, Ayşe Nur SÖNMEZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9488372/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study evaluated the water quality, benthic macroinvertebrate community structure, and biotic indices of the Kızılırmak River at the Sarıhıdır Village (Nevşehir, Türkiye) using a year-long high-frequency monitoring approach. A total of 48 sampling events were conducted between March 2025 and February 2026, consisting of monthly sampling with weekly replicates, during which physicochemical parameters and benthic macroinvertebrate fauna were analyzed simultaneously. The physicochemical results revealed consistently high levels of electrical conductivity (EC), total dissolved solids (TDS), and total phosphorus (TP) throughout the year, indicating persistent ionic load and nutrient pressure in the study area. According to the Surface Water Quality Regulation, the station corresponds to Class III water quality based on EC values. The benthic macroinvertebrate community comprised 15 taxa and was characterized by low diversity but high individual abundance. Gammarus balcanicus was the dominant taxon throughout the year, followed by Bithynia tentaculata. Saprobic index values indicated β–α mesosaprobic conditions, while BMWP scores suggested moderately polluted water quality. Pearson and Spearman correlation analyses demonstrated that the community structure was primarily controlled by temperature, dissolved oxygen, and ionic parameters. In terms of functional feeding groups, the benthic macroinvertebrate community was clearly dominated by shredders. Overall, the study area does not represent a completely degraded system but rather an ecosystem subjected to chronic environmental pressure driven by elevated ionic load and nutrient inputs. Physicochemical parameters high-frequency monitoring ionic load biotic indices river ecosystem salinization Figures Figure 1 Figure 3 1. Introduction Freshwater ecosystems, although representing a relatively small proportion of the total water resources on Earth, are of great importance due to their high biological diversity (Dudgeon et al., 2006 ). In recent years, anthropogenic pressures such as population growth, urbanization, industrialization, and intensive agricultural activities have negatively affected the structure and functioning of freshwater ecosystems (Carpenter et al., 1998 ; Poonam et al., 2013 ). Physicochemical parameters commonly used in water quality assessment primarily reflect instantaneous conditions and are often insufficient for capturing long-term environmental changes. In contrast, biological monitoring provides more reliable and holistic results in the evaluation of aquatic ecosystems by integrating the responses of organisms to environmental conditions over time (Vitecek et al., 2021 ). In this context, benthic macroinvertebrates are considered one of the most widely used biological quality elements in water quality studies due to their varying tolerance to different levels of pollution, relatively long life cycles, and limited mobility (Hering et al., 2006 ; Akay & Dalkıran, 2020 ; Akyıldız & Duran, 2021 ). The European Union Water Framework Directive (WFD) (2000/60/EC) mandates the use of biological quality elements for the protection and sustainable management of water resources (European Commission, 2000 ). Accordingly, biotic indices developed based on benthic macroinvertebrates are widely used in the assessment of water quality (Birk et al., 2012 ). The structure of benthic macroinvertebrate communities in freshwater ecosystems is sensitive to changes in physicochemical parameters, and environmental variables such as temperature, dissolved oxygen, conductivity, and nutrients directly affect the distribution of organisms. Therefore, the composition and seasonal dynamics of benthic macroinvertebrate communities play a critical role in assessing the ecological status of river ecosystems (Bonada et al., 2006 ; Birk et al., 2012 ; Arslan et al., 2016 ). The Kızılırmak River, the longest river in Türkiye, is of great ecological and economic importance and is utilized for various purposes, including agricultural irrigation, drinking water supply, industry, and recreation. However, the river is subject to significant pressures on water quality, particularly due to its passage through settlements and its exposure to intensive agricultural activities. Nutrient inputs from agricultural sources, along with domestic and industrial discharges and other pollutants, have led to increased pollution levels in the river ecosystem and have adversely affected biological diversity (Aras & Fındık, 2018 ; Aras & Fındık, 2023 ; Tarım ve Orman Bakanlığı, 2022 ). Previous studies have shown that water quality in certain sections of the Kızılırmak River ranges from moderate to low (Tülek, 2006 ). Although biomonitoring approaches are widely used, most studies rely on low-frequency sampling designs, which may fail to adequately capture short-term ecological variability (Birk et al., 2012 ). In addition, the performance of commonly used biotic indices under conditions of high ionic load has not yet been sufficiently investigated (Cañedo-Argüelles et al., 2013 ). This issue becomes particularly important in river systems where geological background and anthropogenic pressures jointly shape water chemistry. Studies conducted on the Kızılırmak River have mostly been limited to basin-scale assessments and have focused on revealing the general distribution of water quality and chemical characteristics in different sections of the river (Şimşek et al., 2022 ). However, studies that comprehensively address the intra-annual dynamics of benthic macroinvertebrate composition and their reflection on water quality indices in the Sarıhıdır Village section of Nevşehir, where local pollutant pressures (particularly agricultural drainage and domestic discharges) are concentrated, are quite limited. In this respect, Sarıhıdır Village represents a critical monitoring point that characterizes anthropogenic pressures due to its representation of post-dam river dynamics (Bayramhacılı Dam), its exposure to intensive agricultural activities, and its proximity to settlement areas. Furthermore, the physical accessibility of the station and habitat diversity enable high-frequency and continuous monitoring of benthic macroinvertebrate communities, allowing for a detailed assessment of the ecological status and biological diversity of this section of the river. Accordingly, this study aims to reveal the year-long variation of benthic macroinvertebrate fauna in the study area and to evaluate water quality using biotic indices within the framework of the WFD. This study provides an alternative perspective to conventional water quality assessments by revealing short-term variations in benthic macroinvertebrate communities through a high-frequency sampling approach. 2. Materials and Methods 2.1. Description of the Study Area A total of 48 benthic macroinvertebrate samples, along with physicochemical measurements, were obtained from a sampling station located on the Kızılırmak River at Sarıhıdır Village (Ürgüp, Nevşehir), near the Avanos district, between March 2025 and February 2026. Sampling was carried out monthly with weekly replicates (Table 1 , Fig. 1 .). Physicochemical parameters including water temperature, pH, dissolved oxygen (DO), salinity, electrical conductivity (EC), and total dissolved solids (TDS) were measured in situ using a multiparameter probe (AZ 86031 Combo Water Quality Meter) and recorded in field logs. In addition, water samples were collected monthly over a 12-month period in 1 L polyethylene bottles for subsequent chemical analyses. Table 1 Information on sampling station. Station Latitude Longitude Kızılırmak (Sarıhıdır Village / Nevşehir) 38.733354° 34.929680° 2.2. Sampling Benthic macroinvertebrate sampling was conducted using a hand net with a mesh size of 500 µm, applying the kick-sampling method for 2–3 minutes over a 100 m stretch of the stream, taking into account different ecological zones of the river. During sampling, particular attention was given to fast-flowing sections of the stream, as well as to collecting individuals from various habitat types representing the full range of stream characteristics, including different substrate structures (rocky, stony, gravelly, and sandy bottoms), areas with low current velocity, sections with and without riparian vegetation, and both shaded and sunlit locations (Hering et al., 2004 ). The collected benthic samples were preserved in plastic bottles containing 4% formaldehyde solution and transported to the Hydrobiology Research Laboratory of Nevşehir Hacı Bektaş Veli University. In the laboratory, the samples were first sorted under a LEICA EZ-4D stereo microscope, and then identified to family, genus, and species levels using a LEICA DM-500 light microscope. Identification was carried out using taxonomic keys and diagnostic characteristics provided by Sládeček and Košel ( 1984 ), İpek and Özbek ( 2022 ), Özbek and Balık ( 2009 ), Karaman and Pinkster ( 1987 ), Nilsson ( 1996 ), Epler ( 2001 ), and Brinkhurst and Wetzel ( 1984 ). 2.3. Biotic Indices and Statistical Analyses Species diversity was assessed using the Shannon–Wiener index, and the relationship between species and population density was evaluated using the Shannon Evenness index (Shannon & Weaver, 1949 ). To address multicollinearity among physicochemical parameters, identify the most appropriate variables, and determine the relationships among them, multicollinearity testing and Pearson correlation analysis were performed using IBM SPSS 22 software (Marcoulides & Raykov, 2019 ). The relationships between the selected environmental parameters and the taxa identified in this study were analyzed using Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) in PAST 4.03 software. The statistical significance of the ordination axes was tested using the Monte Carlo permutation test (Ter Braak & Verdonschot, 1995 ; Legendre & Anderson, 1999 ). In addition, non-parametric Spearman correlation analysis was applied to examine the relationships between physicochemical parameters and the identified taxa (Spearman, 1987 ). Biotic indices based on benthic macroinvertebrates were used to assess water quality. The saprobic index (SI) was applied to determine the level of organic pollution (Zahrádková & Soldán 2013 ). The Biological Monitoring Working Party (BMWP) index was used to evaluate water quality based on taxa, while the Average Score Per Taxon (ASPT) index was calculated to estimate the average tolerance level of the community. In the calculation of the saprobic index, taxa-specific tolerance values to organic pollution were considered, and saprobic status was determined for each sampling period (Junqueira et al., 2010 ; Hawkes, 1998 ). The BMWP index was calculated by summing predefined scores assigned to benthic macroinvertebrate taxa according to their sensitivity to pollution. The ASPT index was obtained by dividing the BMWP score by the total number of taxa (Armitage et al., 1983 ; Metcalfe, 1989 ). Functional feeding group (FFG) analysis was conducted to determine the feeding strategies of the identified taxa (Cummins et al., 2005 ; Merritt et al., 2008 ). All indices were calculated using ASTERICS 4.04 software (AQEM/STAR Ecological River Classification System). Based on these indices, water quality was evaluated in accordance with international biomonitoring approaches. In addition, water quality classes of the sampling station were interpreted based on selected physicochemical parameters according to the Surface Water Quality Regulation (SWQR, 2021 ) (Table 2 ). Table 2 Quality class ranges according to the SWQR (DO: dissolved oxygen; EC: electrical conductivity; TP: total phosphorus). Quality classes pH EC (µS/cm) DO (mg/L) Nitrate (mg/L) TP (mg/L) Fluoride (µg/L) I (High-quality water) 6.0–9.0 8 < 3 1000 10 > 0.2 > 1500 3. Results and Discussion The monthly mean values of physicochemical parameters are presented in Table 3 , while their temporal trends are illustrated in Fig. 2 . Water temperature exhibited clear seasonal variation, with the lowest values recorded in winter (January: 6.18 ± 0.59°C) and the highest at the end of summer (August: 22.30 ± 1.29°C). pH values remained slightly alkaline throughout the year (6.80–7.94) and showed no pronounced fluctuations. The lowest pH values were observed during winter (February: 6.80 ± 0.08), while the highest values occurred in summer (July: 7.94 ± 0.08). Dissolved oxygen (DO) concentrations displayed a seasonal pattern inversely related to temperature. The lowest values were measured during the warmest summer months (July: 4.73 ± 1.18 mg/L), whereas the highest concentrations were recorded in colder months (December: 12.95 ± 0.68 mg/L; January: 12.42 ± 2.45 mg/L; February: 12.68 ± 0.13 mg/L). This pattern can be attributed to the increased solubility of oxygen in water at lower temperatures. Salinity values remained within a narrow range throughout the year (1.00–1.18 ppt) and did not exhibit a clear seasonal trend. EC and total TDS were consistently high over the year (EC: 1868.50–2215.00 µS/cm; TDS: 944.00–1110.00 mg/L) and showed parallel variation. Both parameters reached their annual maxima during the autumn period (October). As shown in Fig. 2 , there is a clear inverse relationship between temperature and DO, while EC and TDS exhibit a largely parallel pattern. Overall, temperature and DO demonstrated pronounced seasonal variation, whereas pH, salinity, EC, and TDS remained relatively stable throughout the year. Furthermore, it is well known that an increase in water temperature directly raises conductivity by increasing the kinetic energy and mobility of ions. As noted by Dewangan et al. ( 2023 ), temperature also influences the solubility of certain minerals, further complicating TDS determinations. Table 3 Mean ± standard deviation values of physicochemical parameters according to sampling period (Temp.: Temperature; DO: dissolved oxygen; EC: electrical conductivity; TDS: total dissolved solids). Months Temp. (°C) pH DO (mg/L) Salinity (ppt) EC (µS/cm) TDS (mg/L) March 9.93 ± 0.44 7.23 ± 0.07 9.75 ± 0.13 1.13 ± 0.01 2105.00 ± 5.77 944.00 ± 83.54 April 13.23 ± 1.87 7.62 ± 0.15 12.23 ± 1.35 1.13 ± 0.01 2122.50 ± 20.62 1060.00 ± 8.17 May 16.38 ± 2.96 7.70 ± 0.11 9.35 ± 1.49 1.11 ± 0.03 1955.50 ± 184.73 1006.25 ± 78.25 June 17.95 ± 0.93 7.73 ± 0.13 7.00 ± 1.56 1.06 ± 0.02 1868.50 ± 115.34 992.25 ± 62.35 July 19.53 ± 1.02 7.94 ± 0.08 4.73 ± 1.18 1.08 ± 0.02 2047.50 ± 29.86 1037.50 ± 34.03 August 22.30 ± 1.29 7.85 ± 0.18 5.93 ± 2.12 1.07 ± 0.01 2042.50 ± 22.17 1022.50 ± 9.57 September 19.13 ± 1.28 7.78 ± 0.02 5.68 ± 3.07 1.10 ± 0.05 2062.25 ± 136.46 1026.25 ± 69.21 October 16.05 ± 0.87 7.68 ± 0.39 5.60 ± 3.85 1.18 ± 0.09 2215.00 ± 158.85 1110.00 ± 80.42 November 13.35 ± 1.82 7.02 ± 0.01 8.45 ± 2.40 1.11 ± 0.03 2080.00 ± 60.55 1042.50 ± 32.02 December 9.33 ± 1.58 6.98 ± 0.06 12.95 ± 0.68 1.06 ± 0.01 2020.00 ± 14.14 1012.50 ± 5.00 January 6.18 ± 0.59 7.01 ± 0.02 12.42 ± 2.45 1.00 ± 0.01 1927.75 ± 76.70 956.50 ± 38.28 February 8.75 ± 2.24 6.80 ± 0.08 12.68 ± 0.13 1.04 ± 0.01 2012.50 ± 5.00 1012.50 ± 5.00 Average 14.34 ± 4.96 7.45 ± 0.34 8.90 ± 3.03 1.09 ± 0.05 2038.25 ± 88.73 1018.56 ± 46.61 When the annual mean values of physicochemical parameters were evaluated according to the water quality classes of the SWQR, pH values were found to correspond to Class I water quality. In terms of DO, water quality ranged between Class I, II, and III throughout the year; however, based on the annual mean, it was classified as Class I. In contrast, EC values consistently indicated Class III water quality throughout the year. Multicollinearity among physicochemical variables was assessed using tolerance (T) and variance inflation factor (VIF) values (Table 4 ). All variables yielded tolerance values above 0.1 and VIF values below 10, indicating the absence of significant multicollinearity (Elsayed, 2021 ). Accordingly, all variables were retained and included in subsequent analyses. Table 4 Multicollinearity analysis results of physicochemical parameters (T: tolerance; VIF: variance inflation factor). Variable T VIF Temp. (°C) 0.252 3.970 pH 0.288 3.469 DO (mg/L) 0.485 2.062 Salinity 0.357 2.801 EC (µS/cm) 0.306 3.264 TDS (mg/L) 0.484 2.067 The results of some chemically analyzed parameters on a monthly basis are presented in Table 5 (Fig. 3 ). Nitrate concentrations varied throughout the year, with the lowest values recorded at the beginning of summer (June: 0.92 mg/L) and the highest during the autumn period (September: 5.37 mg/L). According to the SWQR classification, these values indicate that water quality ranged between Class I - III. TP values showed a marked increase, particularly during autumn and winter, with the highest value recorded in January (3.45 mg/L). According to SWQR classification, these values correspond to Class III water quality. In contrast, the low TP values observed during the summer months (July: 0.03 mg/L) indicate relatively better water quality conditions during this period. Sulfate concentrations were found to be relatively high and stable throughout the year (340–431 mg/L). Fluoride levels generally remained low and mostly fell within Class I–II water quality according to SWQR criteria. Table 5 Monthly analysis results of chemical parameters. (TP: total phosphorus) Months Nitrate (mg/L) Nitrite (mg/L) TP (mg /L) Sulfate (mg/L) Fluoride (µg/L) March 2.51 < 0.001 0.15 345 1010 April 2.42 < 0.001 0.11 344 1000 May 1.39 < 0.001 0.17 347 < 10 June 0.92 < 0.001 0.14 340 < 10 July 2.03 < 0.001 0.03 402 220 August 1.67 < 0.001 0.72 431 610 September 5.37 < 0.001 1.53 377 < 10 October 1.32 < 0.001 2.97 372 1000 November 3.14 < 0.001 2.95 374 1000 December 3.45 < 0.001 2.94 379 1070 January 3.44 < 0.001 3.45 383 1000 February 3.43 < 0.001 3.15 380 1000 A total of 15 benthic macroinvertebrate taxa were identified within the scope of the study. Examination of the species composition revealed that the community consisted of organisms belonging to the groups Gastropoda, Oligochaeta, Hirudinea, Crustacea, and Insecta (Table 6 ). Phylum: Arthropoda Class: Malacostraca Order: Amphipoda Family: Gammaridae Leach, 1814 Gammarus balcanicus Schäferna, 1923 Order: Isopoda Family: Asellidae Asellus aquaticus (Linnaeus, 1758) Class: Insecta Order: Diptera Family: Chironomidae Chironomidae sp . Order: Hemiptera Family: Micronectidae Micronecta scholtzi (Fieber, 1860) Phylum: Annelida Class: Clitellata Order: Tubificida Family: Naididae Limnodrilus sp. Claparède, 1862 Tubifex Lamarck, 1816 Psammoryctides Hrabe, 1964 Nais sp. Order: Rhynchobdellida Family: Glossiphoniidae Helobdella stagnalis (Linnaeus, 1758) Order: Arhynchobdellida Family: Erpobdellidae Erpobdella octoculata (Linnaeus, 1758) Phylum: Mollusca Class: Gastropoda Order: Littorinimorpha Family: Bithyniidae Bithynia tentaculata (Linnaeus, 1758) Order: Basommatophora Family: Lymnaeidae Galba truncatula (O.F.Müller, 1774) Family: Physidae Physella acuta (Draparnaud, 1805) Class: Bivalvia Order: Sphaeriida Family: Sphaeriidae Pisidium sp. Phylum: Plathyhelminthes Class: Acentrosomata Order: Tricladida Family: Planariidae Planaria sp. The total number of individuals of benthic macroinvertebrates varied throughout the year, with the highest value recorded in December (4337) and the lowest in February (549). During the entire sampling period, G. balcanicus was identified as the dominant taxon, accounting for the majority of the total individuals (21860). This species showed a marked increase, particularly during late summer and autumn (June–December), reaching its maximum abundance in December. The second most dominant species was Bithynia tentaculata (2270), which exhibited a relatively regular distribution throughout the year, with higher abundances especially during spring and summer. Oligochaeta taxa, including Limnodrilus sp., Tubifex sp., Nais sp. , and Psammoryctides sp. , were primarily observed during spring and summer and were represented at low to moderate densities. Erpobdella octoculata was present throughout the year, with increased abundance during spring and autumn. Other taxa, such as Chironomidae sp., Micronecta scholtzi, Physella acuta , and Galba truncatula , were recorded at low frequencies and in limited months, suggesting either low population densities or occurrence under specific environmental conditions. The benthic macroinvertebrate community exhibited low taxonomic diversity but high abundance, indicating a structure strongly shaped by environmental stress and dominated by tolerant taxa, particularly G. balcanicus. Table 6 Monthly distribution and abundance of benthic macroinvertebrate taxa. Species March April May June July August September October November December January February Asellus aquaticus 6 9 20 43 52 13 10 12 13 8 4 5 Bithynia tentaculata 125 260 278 61 505 454 106 89 92 105 120 75 Chironomidae sp. - 14 - - - - - - - - - - Erpobdella octoculata 16 90 7 10 26 26 58 25 16 8 4 9 Galba truncatula 2 - - - - - - - - - - - Gammarus balcanicus 510 570 550 2240 2790 3560 2530 1900 2130 4200 450 430 Helobdella stagnalis 1 - - - - 5 10 3 1 - - 2 Limnodrilus sp. 23 68 35 14 6 44 5 3 8 12 4 12 Micronecta scholtzi - - - - - - - - 1 - - - Nais sp. - - - 2 4 - 2 3 - - - - Physella acuta - - - - 1 - - - - - - - Pisidium sp. 10 12 7 - 20 25 4 2 5 - 3 4 Planaria sp. 28 15 11 8 44 34 62 5 3 4 - 12 Psammoryctides sp. - 8 16 5 - - - - - - - - Tubifex sp. - 28 - - - 17 7 - 2 - - - The abundance, taxa richness, and biotic indices of the benthic macroinvertebrate community were calculated on a monthly basis (Table 7 ). Saprobic index values ranged between 2.252 and 2.545, indicating that the environment was generally characterized as β–α mesosaprobic (moderately polluted) (Barinova & Dyadichko, 2022 ). BMWP scores varied between 19 and 32, corresponding to moderately polluted water quality (Metcalfe, 1989 ). ASPT values ranged from 3.167 to 3.556, suggesting that the water quality was biologically indicative of heavily polluted conditions (Metcalfe, 1989 ; Armitage et al., 1983 ). The generally low values of the Shannon–Wiener diversity index (0.167–1.4) and the limited range of the Shannon Evenness index (0.093–0.648) indicate that the benthic community was not well balanced and was dominated by a few tolerant taxa. Notably, despite a marked increase in abundance during the summer and autumn periods, diversity remained low, further supporting the dominance of tolerant species. The relatively low taxa richness (6–10 taxa) also reflects the limited diversity of the community. When all biotic indices are considered together, the water quality of the study area can be classified predominantly within Class II–III throughout the year, indicating that the system is subject to chronic environmental stress and that the biological community is structured by tolerant taxa adapted to these conditions. Annual evaluation of the biotic indices, based on all sampling periods, indicated an overall Class III water quality status, with other index results consistently falling within similar water quality categories. Table 7 Monthly variation of biotic indices, diversity metrics, and abundance of benthic macroinvertebrates Months Abundance (ind/m²) Taxa_N Saprobic Index BMWP ASPT Shannon Wiener Shannon Evenness Water Quality Class March 721 9 2.323 30 3.333 0.994 0.452 II-III April 1074 10 2.420 26 3.429 1.400 0.648 III May 924 8 2.275 24 3.429 1.074 0.517 II-III June 2383 8 2.545 21 3.500 0.316 0.152 II-III July 3448 9 2.312 27 3.375 0.660 0.300 II-III August 4178 9 2.273 27 3.375 0.575 0.262 II-III September 2794 10 2.532 27 3.375 0.460 0.200 II-III October 2042 9 2.445 27 3.375 0.338 0.154 II-III November 2271 10 2.397 32 3.556 0.310 0.134 II-III December 4337 6 2.309 21 3.500 0.167 0.093 II-III January 585 6 2.252 19 3.167 0.656 0.366 II-III February 549 8 2.343 27 3.375 0.797 0.383 II-III Annual 25306 15 2.349 40 3.333 0.574 0.212 III The Pearson correlation analysis conducted to determine the relationships between physicochemical parameters and biological metrics revealed several important and statistically significant associations (Table 8 ). A strong positive correlation between temperature and pH, along with a strong negative correlation between temperature and DO, confirms that increasing temperature reduces oxygen solubility and reflects the expected thermal effect in aquatic systems (Caissie, 2006 ; Verberk et al., 2011 ). Similarly, a strong negative relationship was also observed between pH and DO. In terms of ionic parameters, significant positive correlations between salinity and both EC and TDS indicate that these variables collectively reflect the ionic composition of the water (Rusydi, 2018 ). The positive relationship between EC and TDS further supports this interpretation. When examining the relationships between biological metrics and environmental variables, the negative correlation between dissolved oxygen and both abundance and taxa richness suggests that more tolerant species become dominant under low-oxygen conditions. This is consistent with the observed pattern of low diversity and high abundance during the summer period. The strong positive relationship between taxa richness and BMWP indicates that improvements in species diversity are associated with better water quality conditions (Yazdian et al., 2014 ). Taken together, the results of the Pearson correlation analysis demonstrate that the structure of the benthic macroinvertebrate community is primarily controlled by temperature, dissolved oxygen, and ionic parameters (EC and TDS). Table 8 Results of Pearson correlation analysis between physicochemical parameters and biological metrics. Temp.(°C) Temp.(°C) pH DO Salinity EC TDS Abundance (ind/m²) Taxa N Saprobic Index BMWP ASPT 1 0.893** -0.869** 0.349 0.083 0.423 0.556 0.552 0.118 0.241 0.089 pH 1 -0.779** 0.416 0.104 0.365 0.354 0.468 -0.097 0.048 0.112 DO 1 -0.416 -0.211 -0.381 -0.462 -0.537 -0.396 -0.350 -0.176 Salinity 1 0.764** 0.600* -0.025 0.668* -0.203 0.585* 0.307 EC 1 0.639* 0.052 0.579* -0.274 0.669* 0.494 TDS (mg/L) 1 0.292 0.480 -0.248 0.316 0.205 Abundance (ind/m²) 1 0.008 0.249 -0.028 -0.410 Taxa N 1 -0.278 0.814** 0.547 Saprobic Index 1 -0.012 -0.185 BMWP 1 0.613* ASPT 1 CCA applied to evaluate the relationships between the benthic macroinvertebrate community and environmental variables was not statistically significant (p > 0.05). Similarly, the results of RDA did not reveal a statistically significant relationship between environmental variables and community structure at the model level (p > 0.05). The Spearman correlation analysis revealed several significant relationships between environmental variables and benthic macroinvertebrate taxa (Table 9 ). A strong negative correlation was found between temperature and dissolved oxygen (rₛ = −0.853, p < 0.01), confirming the inverse relationship between these parameters. Additionally, a strong positive correlation was detected between temperature and pH (rₛ = 0.951, p < 0.01). Among the biological variables, a significant negative relationship was observed between Asellus aquaticus and dissolved oxygen (rₛ = −0.729, p < 0.01), indicating that this species can tolerate low-oxygen conditions (O'Callaghan et al., 2019 ). In terms of ionic parameters, a strong positive correlation was found between electrical conductivity (EC) and salinity (rₛ = 0.818, p < 0.01), and a moderate positive correlation between EC and total dissolved solids (TDS) (rₛ = 0.669, p < 0.05). A significant positive relationship was also identified between G. balcanicus and temperature (rₛ = 0.629, p < 0.05), indicating that the abundance of this species increases under higher temperature conditions. Furthermore, a negative correlation was observed between Erpobdella octoculata and dissolved oxygen (rₛ = −0.579, p < 0.05), suggesting that this species is also tolerant to low-oxygen environments. Table 9 Significant Spearman correlations between environmental variables and benthic macroinvertebrates Variables rₛ p-value Interpretation Temperature – DO -0.853 < 0.01 Strong negative correlation Temperature – pH 0.951 < 0.01 Strong positive correlation DO – Asellus aquaticus -0.729 < 0.01 Significant negative correlation EC – Salinity 0.818 < 0.01 Strong positive correlation EC – TDS 0.669 < 0.05 Moderate positive correlation Temperature – Gammarus balcanicus 0.629 < 0.05 Significant positive correlation DO – Erpobdella octoculata -0.579 < 0.05 Significant negative correlation Overall, the lack of statistically significant results in multivariate analyses does not necessarily imply the absence of ecological relationships. Rather, this outcome can be attributed to the limitations of the single-station sampling design and the complex nature of species-specific tolerance strategies. The strong agreement between the patterns observed in the ordination diagrams and the results of the Spearman correlation analysis clearly and consistently indicates that the benthic macroinvertebrate community is primarily structured along gradients of temperature, dissolved oxygen, and ionic parameters. In terms of functional feeding groups (FFGs), the benthic macroinvertebrate community was clearly dominated by shredders, which accounted for 52.07% of the total individuals (Table 10 ). Gathering collectors represented the second most abundant group (20.68%), followed by other feeding types (17.28%), filtering collectors (4.86%), scrapers (2.95%), and predators (2.16%). The dominance of shredders suggests that energy flow in the study area is primarily driven by the processing of coarse particulate organic matter (CPOM), highlighting the importance of leaf litter and terrestrial plant inputs in the ecosystem energy cycle (Cummins et al., 2005 ; Merritt et al., 2008 ). The relatively high proportion of gathering collectors indicates that fine particulate organic matter (FPOM) also constitutes an important energy source (Tomanova et al., 2006 ; Kelly et al., 2002 ). In contrast, the low contributions of scrapers (2.95%) and filtering collectors (4.86%) suggest a limited availability of particulate organic matter within the water column at the sampling station. The notable proportion of individuals classified under “other feeding types” (17.28%) reflects the presence of taxa exhibiting omnivorous or mixed feeding strategies (Merritt et al., 2008 ). The low proportion of predators (2.16%) is consistent with limited prey diversity under the prevailing environmental stress conditions. Table 10 Relative abundance of functional feeding groups (FFGs) of benthic macroinvertebrates. FFG group Percentage (%) Shredders 52.07 Gathering collectors 20.68 Filtering collectors 4.86 Predators 2.16 Scrapers 2.95 Other feeding types 17.28 The Kızılırmak Basin is predominantly characterized by a semi-arid climate, with limited precipitation, high temperatures, and a hydrological regime marked by significant potential evapotranspiration. Long-term assessments conducted in the basin indicate that arid and semi-arid climatic conditions prevail over extensive areas, exerting considerable influence on both the quantity and quality dynamics of water resources (Tarım ve Orman Bakanlığı, 2022 ). The region is dominated by volcanic lithology, and gypsum-rich formations are present in the Upper Kızılırmak Basin (Koç et al., 2018 ). Due to the relatively high solubility of gypsum compared to many other minerals, surface and groundwater interacting with these formations acquire substantial amounts of total dissolved solids (TDS), calcium, and sulfate, which directly deteriorate water quality. Streams and springs originating from gypsum-rich areas exhibit markedly elevated electrical conductivity (EC) values (1100–5200 µS/cm), and gypsum-derived karstic waters are particularly enriched in TDS and sulfate (Kaçaroğlu et al., 2001 ). These findings clearly indicate that the elevated EC, TDS, and sulfate concentrations observed in the study area are primarily controlled by gypsum-bearing lithological units and climatic conditions. This results in a continuous input of ions into the river system, leading to chronically elevated ionic loads. In addition, intensive agricultural activities in the study area further enhance this ionic load by increasing the transport of nutrients and dissolved substances via surface runoff (Carpenter et al., 1998 ). The elevated total phosphorus (TP) and nitrate concentrations observed particularly during autumn and winter clearly reflect the influence of agricultural drainage on water chemistry. This dual influence (geological + agricultural) imposes chronic chemical stress on the aquatic environment, resulting in the elimination of sensitive species and providing a competitive advantage to tolerant taxa. The high abundance and dominance of G. balcanicus in the study area indicate that this species possesses a broad ecological tolerance and is capable of adapting to a wide range of environmental conditions (Karaman & Pinkster, 1987 ). In benthic macroinvertebrate communities, the dominance of tolerant taxa is generally considered an indicator of environmental stress and high tolerance capacity (Bonada et al., 2006 ). However, Gammarus species are not fully resistant to extreme environmental conditions and are known to perform better in habitats with sufficient dissolved oxygen levels. Indeed, oxygen uptake and metabolic activity in these organisms are directly dependent on the oxygen content of the water, and low dissolved oxygen levels can induce physiological stress (Sutcliffe, 1984 ). Furthermore, reduced oxygen availability has been shown to significantly alter benthic macroinvertebrate community structure, allowing only tolerant species to persist under such conditions (MacNeil et al., 2000 ). In this context, the generally favorable dissolved oxygen levels observed in the study area may represent a key factor supporting the persistence of G. balcanicus populations. Therefore, the dominance of this species reflects not only its biological characteristics but also the combined influence of geological background and anthropogenic pressures shaping the environmental conditions of the study area. This study demonstrates that evaluating benthic macroinvertebrate communities through a high-frequency sampling approach enables a more precise characterization of water quality dynamics. In particular, the use of weekly replicates allows short-term environmental fluctuations and their effects on biological communities to be captured more effectively, providing a significant methodological advantage over conventional low-frequency monitoring approaches (Hering et al., 2006 ; Birk et al., 2012 ). In the Sarıhıdır section of the Kızılırmak River, the annual dynamics of the benthic macroinvertebrate community were evaluated together with water quality using biotic indices and physicochemical parameters. The results indicate that the study area is clearly characterized by low diversity, high abundance, and dominance of tolerant taxa. In conclusion, this study reveals that water quality in the investigated section of the Kızılırmak River is at a moderate level and that the benthic macroinvertebrate community is dominated by tolerant species. Moreover, it highlights the importance of high-frequency data collection and integrated, multidisciplinary approaches in biomonitoring studies. 4. Conclusion The results obtained indicate that physicochemical parameters, particularly electrical conductivity (EC) and total dissolved solids (TDS), play a key role in shaping the structure of benthic macroinvertebrate communities. Nutrient dynamics also emerged as an important indicator of anthropogenic pressure on the system. In particular, the marked increase in total phosphorus (TP) concentrations during autumn and winter clearly reflects the influence of nutrient inputs associated with surface runoff and agricultural drainage. Saprobic index values indicate that the sampling station is located within the β–α mesosaprobic transition zone, suggesting the presence of moderate to high levels of organic pollution (Sládeček, 1973 ). When considered together with the elevated ionic load, this finding highlights the simultaneous influence of multiple stressors acting on the system. The high-frequency sampling approach applied in this study provides a significant methodological advantage. Conventional biomonitoring studies typically rely on low sampling frequencies, which may overlook short-term environmental variability. In contrast, the combination of monthly sampling and weekly replicates enabled a more precise detection of temporal changes in both physicochemical parameters and biological community structure. In conclusion, the study area is subject to chronic environmental pressure driven by elevated ionic load and nutrient inputs, and these conditions play a decisive role in structuring the benthic macroinvertebrate community. Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Clinical trial number: not applicable. Funding This work was supported by 2209-A University Students Research Projects Support Program of TUBITAK (Project Numbers: 1919B012415915) Acknowledgements The authors would like to thank the Nevşehir Public Health Laboratory for their support in conducting the chemical analyses. References Akay, E., & Dalkıran, N. (2020). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9488372","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639638064,"identity":"9d47a914-2050-4dfd-a6b1-432677d091ad","order_by":0,"name":"Fatih TÜRE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBAC9gYGhsNwFhAksIFIHjxaeA4wA7UkgFmoWiTwaWEGa5FIgGhhIKiF/fzBw4U/DufzS749+Jjnj00en/QBxgdv2xjqzBtwaOFJZjg8I+Gw5czZecnGvG1pxWx8CcyGc9sYJGQOYNdizwDUwpNw2MDgdo6Z5MyGw4ltPAxs0rxALbhcxsP/GKLF/uYZ858z/vwHaWH/jVeLBMwWCR4zhg9sB8C2MOPX8tjgME9auoHEmRxjiY9tyUAtjM2Sc85JSM7A6bDEx595bKwN+NvPGH5I+GOXOL+H+eCHN2U2/DhDGQtgbGDAEy2jYBSMglEwCogAAEA4UIx6bdt2AAAAAElFTkSuQmCC","orcid":"","institution":"Nevşehir Hacı Bektaş Veli University","correspondingAuthor":true,"prefix":"","firstName":"Fatih","middleName":"","lastName":"TÜRE","suffix":""},{"id":639638065,"identity":"eccef61d-7acb-40c4-b395-d8c241e16c22","order_by":1,"name":"Ayşe Nur SÖNMEZ","email":"","orcid":"","institution":"Nevşehir Hacı Bektaş Veli University","correspondingAuthor":false,"prefix":"","firstName":"Ayşe","middleName":"Nur","lastName":"SÖNMEZ","suffix":""}],"badges":[],"createdAt":"2026-04-21 20:09:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9488372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9488372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109330486,"identity":"503238ef-3eeb-4c45-a486-ca156ebde6a4","added_by":"auto","created_at":"2026-05-15 16:00:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":665715,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the sampling station on the Kızılırmak River (Sarıhıdır Village, Nevşehir, Türkiye)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9488372/v1/29adb5f17fc346244cc8817c.png"},{"id":109330488,"identity":"52bbc08f-bffd-47fd-90d9-7147734fb4d9","added_by":"auto","created_at":"2026-05-15 16:00:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79816,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variation of chemical parameters\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9488372/v1/6a55b528e0894d9ad000cfcc.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Water Quality Using Benthic Macroinvertebrates and Biotic Indices Based on Year-Long Monitoring in the Kızılırmak River (Nevşehir, Türkiye)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFreshwater ecosystems, although representing a relatively small proportion of the total water resources on Earth, are of great importance due to their high biological diversity (Dudgeon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In recent years, anthropogenic pressures such as population growth, urbanization, industrialization, and intensive agricultural activities have negatively affected the structure and functioning of freshwater ecosystems (Carpenter et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Poonam et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Physicochemical parameters commonly used in water quality assessment primarily reflect instantaneous conditions and are often insufficient for capturing long-term environmental changes. In contrast, biological monitoring provides more reliable and holistic results in the evaluation of aquatic ecosystems by integrating the responses of organisms to environmental conditions over time (Vitecek et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this context, benthic macroinvertebrates are considered one of the most widely used biological quality elements in water quality studies due to their varying tolerance to different levels of pollution, relatively long life cycles, and limited mobility (Hering et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Akay \u0026amp; Dalkıran, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Akyıldız \u0026amp; Duran, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The European Union Water Framework Directive (WFD) (2000/60/EC) mandates the use of biological quality elements for the protection and sustainable management of water resources (European Commission, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Accordingly, biotic indices developed based on benthic macroinvertebrates are widely used in the assessment of water quality (Birk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe structure of benthic macroinvertebrate communities in freshwater ecosystems is sensitive to changes in physicochemical parameters, and environmental variables such as temperature, dissolved oxygen, conductivity, and nutrients directly affect the distribution of organisms. Therefore, the composition and seasonal dynamics of benthic macroinvertebrate communities play a critical role in assessing the ecological status of river ecosystems (Bonada et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Birk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arslan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Kızılırmak River, the longest river in T\u0026uuml;rkiye, is of great ecological and economic importance and is utilized for various purposes, including agricultural irrigation, drinking water supply, industry, and recreation. However, the river is subject to significant pressures on water quality, particularly due to its passage through settlements and its exposure to intensive agricultural activities. Nutrient inputs from agricultural sources, along with domestic and industrial discharges and other pollutants, have led to increased pollution levels in the river ecosystem and have adversely affected biological diversity (Aras \u0026amp; Fındık, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aras \u0026amp; Fındık, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tarım ve Orman Bakanlığı, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have shown that water quality in certain sections of the Kızılırmak River ranges from moderate to low (T\u0026uuml;lek, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough biomonitoring approaches are widely used, most studies rely on low-frequency sampling designs, which may fail to adequately capture short-term ecological variability (Birk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, the performance of commonly used biotic indices under conditions of high ionic load has not yet been sufficiently investigated (Ca\u0026ntilde;edo-Arg\u0026uuml;elles et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This issue becomes particularly important in river systems where geological background and anthropogenic pressures jointly shape water chemistry.\u003c/p\u003e \u003cp\u003eStudies conducted on the Kızılırmak River have mostly been limited to basin-scale assessments and have focused on revealing the general distribution of water quality and chemical characteristics in different sections of the river (Şimşek et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, studies that comprehensively address the intra-annual dynamics of benthic macroinvertebrate composition and their reflection on water quality indices in the Sarıhıdır Village section of Nevşehir, where local pollutant pressures (particularly agricultural drainage and domestic discharges) are concentrated, are quite limited. In this respect, Sarıhıdır Village represents a critical monitoring point that characterizes anthropogenic pressures due to its representation of post-dam river dynamics (Bayramhacılı Dam), its exposure to intensive agricultural activities, and its proximity to settlement areas. Furthermore, the physical accessibility of the station and habitat diversity enable high-frequency and continuous monitoring of benthic macroinvertebrate communities, allowing for a detailed assessment of the ecological status and biological diversity of this section of the river. Accordingly, this study aims to reveal the year-long variation of benthic macroinvertebrate fauna in the study area and to evaluate water quality using biotic indices within the framework of the WFD. This study provides an alternative perspective to conventional water quality assessments by revealing short-term variations in benthic macroinvertebrate communities through a high-frequency sampling approach.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e \u003cp\u003eA total of 48 benthic macroinvertebrate samples, along with physicochemical measurements, were obtained from a sampling station located on the Kızılırmak River at Sarıhıdır Village (\u0026Uuml;rg\u0026uuml;p, Nevşehir), near the Avanos district, between March 2025 and February 2026. Sampling was carried out monthly with weekly replicates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.). Physicochemical parameters including water temperature, pH, dissolved oxygen (DO), salinity, electrical conductivity (EC), and total dissolved solids (TDS) were measured in situ using a multiparameter probe (AZ 86031 Combo Water Quality Meter) and recorded in field logs. In addition, water samples were collected monthly over a 12-month period in 1 L polyethylene bottles for subsequent chemical analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation on sampling station.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKızılırmak (Sarıhıdır Village / Nevşehir)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.733354\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.929680\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling\u003c/h2\u003e \u003cp\u003eBenthic macroinvertebrate sampling was conducted using a hand net with a mesh size of 500 \u0026micro;m, applying the kick-sampling method for 2\u0026ndash;3 minutes over a 100 m stretch of the stream, taking into account different ecological zones of the river. During sampling, particular attention was given to fast-flowing sections of the stream, as well as to collecting individuals from various habitat types representing the full range of stream characteristics, including different substrate structures (rocky, stony, gravelly, and sandy bottoms), areas with low current velocity, sections with and without riparian vegetation, and both shaded and sunlit locations (Hering et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The collected benthic samples were preserved in plastic bottles containing 4% formaldehyde solution and transported to the Hydrobiology Research Laboratory of Nevşehir Hacı Bektaş Veli University. In the laboratory, the samples were first sorted under a LEICA EZ-4D stereo microscope, and then identified to family, genus, and species levels using a LEICA DM-500 light microscope. Identification was carried out using taxonomic keys and diagnostic characteristics provided by Sl\u0026aacute;deček and Košel (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), İpek and \u0026Ouml;zbek (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u0026Ouml;zbek and Balık (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Karaman and Pinkster (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), Nilsson (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), Epler (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and Brinkhurst and Wetzel (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Biotic Indices and Statistical Analyses\u003c/h2\u003e \u003cp\u003eSpecies diversity was assessed using the Shannon\u0026ndash;Wiener index, and the relationship between species and population density was evaluated using the Shannon Evenness index (Shannon \u0026amp; Weaver, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1949\u003c/span\u003e). To address multicollinearity among physicochemical parameters, identify the most appropriate variables, and determine the relationships among them, multicollinearity testing and Pearson correlation analysis were performed using IBM SPSS 22 software (Marcoulides \u0026amp; Raykov, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationships between the selected environmental parameters and the taxa identified in this study were analyzed using Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) in PAST 4.03 software. The statistical significance of the ordination axes was tested using the Monte Carlo permutation test (Ter Braak \u0026amp; Verdonschot, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Legendre \u0026amp; Anderson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In addition, non-parametric Spearman correlation analysis was applied to examine the relationships between physicochemical parameters and the identified taxa (Spearman, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiotic indices based on benthic macroinvertebrates were used to assess water quality. The saprobic index (SI) was applied to determine the level of organic pollution (Zahr\u0026aacute;dkov\u0026aacute; \u0026amp; Sold\u0026aacute;n \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Biological Monitoring Working Party (BMWP) index was used to evaluate water quality based on taxa, while the Average Score Per Taxon (ASPT) index was calculated to estimate the average tolerance level of the community. In the calculation of the saprobic index, taxa-specific tolerance values to organic pollution were considered, and saprobic status was determined for each sampling period (Junqueira et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hawkes, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The BMWP index was calculated by summing predefined scores assigned to benthic macroinvertebrate taxa according to their sensitivity to pollution. The ASPT index was obtained by dividing the BMWP score by the total number of taxa (Armitage et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Metcalfe, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Functional feeding group (FFG) analysis was conducted to determine the feeding strategies of the identified taxa (Cummins et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Merritt et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). All indices were calculated using ASTERICS 4.04 software (AQEM/STAR Ecological River Classification System).\u003c/p\u003e \u003cp\u003eBased on these indices, water quality was evaluated in accordance with international biomonitoring approaches. In addition, water quality classes of the sampling station were interpreted based on selected physicochemical parameters according to the Surface Water Quality Regulation (SWQR, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality class ranges according to the SWQR (DO: dissolved oxygen; EC: electrical conductivity; TP: total phosphorus).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDO (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNitrate\u003c/p\u003e \u003cp\u003e(mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFluoride (\u0026micro;g/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI (High-quality water)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026ndash;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eII (Moderate polluted water)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026ndash;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u0026ndash;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1001\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIII (Polluted water)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026ndash;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe monthly mean values of physicochemical parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while their temporal trends are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Water temperature exhibited clear seasonal variation, with the lowest values recorded in winter (January: 6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u0026deg;C) and the highest at the end of summer (August: 22.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u0026deg;C). pH values remained slightly alkaline throughout the year (6.80\u0026ndash;7.94) and showed no pronounced fluctuations. The lowest pH values were observed during winter (February: 6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08), while the highest values occurred in summer (July: 7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08).\u003c/p\u003e \u003cp\u003eDissolved oxygen (DO) concentrations displayed a seasonal pattern inversely related to temperature. The lowest values were measured during the warmest summer months (July: 4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 mg/L), whereas the highest concentrations were recorded in colder months (December: 12.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 mg/L; January: 12.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45 mg/L; February: 12.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 mg/L). This pattern can be attributed to the increased solubility of oxygen in water at lower temperatures.\u003c/p\u003e \u003cp\u003eSalinity values remained within a narrow range throughout the year (1.00\u0026ndash;1.18 ppt) and did not exhibit a clear seasonal trend. EC and total TDS were consistently high over the year (EC: 1868.50\u0026ndash;2215.00 \u0026micro;S/cm; TDS: 944.00\u0026ndash;1110.00 mg/L) and showed parallel variation. Both parameters reached their annual maxima during the autumn period (October).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there is a clear inverse relationship between temperature and DO, while EC and TDS exhibit a largely parallel pattern. Overall, temperature and DO demonstrated pronounced seasonal variation, whereas pH, salinity, EC, and TDS remained relatively stable throughout the year.\u003c/p\u003e \u003cp\u003eFurthermore, it is well known that an increase in water temperature directly raises conductivity by increasing the kinetic energy and mobility of ions. As noted by Dewangan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), temperature also influences the solubility of certain minerals, further complicating TDS determinations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation values of physicochemical parameters according to sampling period (Temp.: Temperature; DO: dissolved oxygen; EC: electrical conductivity; TDS: total dissolved solids).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp. (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDO (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalinity (ppt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTDS (mg/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2105.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e944.00\u0026thinsp;\u0026plusmn;\u0026thinsp;83.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2122.50\u0026thinsp;\u0026plusmn;\u0026thinsp;20.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1060.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1955.50\u0026thinsp;\u0026plusmn;\u0026thinsp;184.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1006.25\u0026thinsp;\u0026plusmn;\u0026thinsp;78.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1868.50\u0026thinsp;\u0026plusmn;\u0026thinsp;115.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e992.25\u0026thinsp;\u0026plusmn;\u0026thinsp;62.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2047.50\u0026thinsp;\u0026plusmn;\u0026thinsp;29.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1037.50\u0026thinsp;\u0026plusmn;\u0026thinsp;34.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2042.50\u0026thinsp;\u0026plusmn;\u0026thinsp;22.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1022.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e19.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2062.25\u0026thinsp;\u0026plusmn;\u0026thinsp;136.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1026.25\u0026thinsp;\u0026plusmn;\u0026thinsp;69.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2215.00\u0026thinsp;\u0026plusmn;\u0026thinsp;158.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1110.00\u0026thinsp;\u0026plusmn;\u0026thinsp;80.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2080.00\u0026thinsp;\u0026plusmn;\u0026thinsp;60.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1042.50\u0026thinsp;\u0026plusmn;\u0026thinsp;32.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2020.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1012.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1927.75\u0026thinsp;\u0026plusmn;\u0026thinsp;76.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e956.50\u0026thinsp;\u0026plusmn;\u0026thinsp;38.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e12.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2012.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1012.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.90\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2038.25\u0026thinsp;\u0026plusmn;\u0026thinsp;88.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1018.56\u0026thinsp;\u0026plusmn;\u0026thinsp;46.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen the annual mean values of physicochemical parameters were evaluated according to the water quality classes of the SWQR, pH values were found to correspond to Class I water quality. In terms of DO, water quality ranged between Class I, II, and III throughout the year; however, based on the annual mean, it was classified as Class I. In contrast, EC values consistently indicated Class III water quality throughout the year.\u003c/p\u003e \u003cp\u003eMulticollinearity among physicochemical variables was assessed using tolerance (T) and variance inflation factor (VIF) values (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All variables yielded tolerance values above 0.1 and VIF values below 10, indicating the absence of significant multicollinearity (Elsayed, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, all variables were retained and included in subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulticollinearity analysis results of physicochemical parameters (T: tolerance; VIF: variance inflation factor).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp. (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of some chemically analyzed parameters on a monthly basis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nitrate concentrations varied throughout the year, with the lowest values recorded at the beginning of summer (June: 0.92 mg/L) and the highest during the autumn period (September: 5.37 mg/L). According to the SWQR classification, these values indicate that water quality ranged between Class I - III.\u003c/p\u003e \u003cp\u003eTP values showed a marked increase, particularly during autumn and winter, with the highest value recorded in January (3.45 mg/L). According to SWQR classification, these values correspond to Class III water quality. In contrast, the low TP values observed during the summer months (July: 0.03 mg/L) indicate relatively better water quality conditions during this period. Sulfate concentrations were found to be relatively high and stable throughout the year (340\u0026ndash;431 mg/L). Fluoride levels generally remained low and mostly fell within Class I\u0026ndash;II water quality according to SWQR criteria.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly analysis results of chemical parameters. (TP: total phosphorus)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrite (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP (mg /L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSulfate (mg/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFluoride (\u0026micro;g/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 15 benthic macroinvertebrate taxa were identified within the scope of the study. Examination of the species composition revealed that the community consisted of organisms belonging to the groups Gastropoda, Oligochaeta, Hirudinea, Crustacea, and Insecta (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylum: Arthropoda\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Malacostraca\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Amphipoda\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Gammaridae Leach, 1814\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eGammarus balcanicus\u003c/em\u003e Sch\u0026auml;ferna, 1923\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Isopoda\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Asellidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAsellus aquaticus\u003c/em\u003e (Linnaeus, 1758)\u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Insecta\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Diptera\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Chironomidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eChironomidae sp\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Hemiptera\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Micronectidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMicronecta scholtzi\u003c/em\u003e (Fieber, 1860)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylum: Annelida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Clitellata\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Tubificida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Naididae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLimnodrilus sp.\u003c/em\u003e Clapar\u0026egrave;de, 1862\u003c/p\u003e \u003cp\u003e \u003cem\u003eTubifex\u003c/em\u003e Lamarck, 1816\u003c/p\u003e \u003cp\u003e \u003cem\u003ePsammoryctides\u003c/em\u003e Hrabe, 1964\u003c/p\u003e \u003cp\u003e \u003cem\u003eNais sp.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Rhynchobdellida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Glossiphoniidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eHelobdella stagnalis\u003c/em\u003e (Linnaeus, 1758)\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Arhynchobdellida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Erpobdellidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eErpobdella octoculata\u003c/em\u003e (Linnaeus, 1758)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylum: Mollusca\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Gastropoda\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Littorinimorpha\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Bithyniidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBithynia tentaculata\u003c/em\u003e (Linnaeus, 1758)\u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Basommatophora\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Lymnaeidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eGalba truncatula\u003c/em\u003e (O.F.M\u0026uuml;ller, 1774)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Physidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePhysella acuta (Draparnaud, 1805)\u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Bivalvia\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Sphaeriida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Sphaeriidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePisidium sp.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePhylum: Plathyhelminthes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClass: Acentrosomata\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eOrder: Tricladida\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFamily: Planariidae\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePlanaria sp.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe total number of individuals of benthic macroinvertebrates varied throughout the year, with the highest value recorded in December (4337) and the lowest in February (549). During the entire sampling period, \u003cem\u003eG. balcanicus\u003c/em\u003e was identified as the dominant taxon, accounting for the majority of the total individuals (21860). This species showed a marked increase, particularly during late summer and autumn (June\u0026ndash;December), reaching its maximum abundance in December. The second most dominant species was \u003cem\u003eBithynia tentaculata\u003c/em\u003e (2270), which exhibited a relatively regular distribution throughout the year, with higher abundances especially during spring and summer.\u003c/p\u003e \u003cp\u003eOligochaeta taxa, including \u003cem\u003eLimnodrilus sp., Tubifex sp., Nais sp.\u003c/em\u003e, and \u003cem\u003ePsammoryctides sp.\u003c/em\u003e, were primarily observed during spring and summer and were represented at low to moderate densities. \u003cem\u003eErpobdella octoculata\u003c/em\u003e was present throughout the year, with increased abundance during spring and autumn. Other taxa, such as \u003cem\u003eChironomidae sp., Micronecta scholtzi, Physella acuta\u003c/em\u003e, and \u003cem\u003eGalba truncatula\u003c/em\u003e, were recorded at low frequencies and in limited months, suggesting either low population densities or occurrence under specific environmental conditions. The benthic macroinvertebrate community exhibited low taxonomic diversity but high abundance, indicating a structure strongly shaped by environmental stress and dominated by tolerant taxa, particularly \u003cem\u003eG. balcanicus.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly distribution and abundance of benthic macroinvertebrate taxa.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAsellus aquaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBithynia tentaculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChironomidae sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eErpobdella octoculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGalba truncatula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGammarus balcanicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHelobdella stagnalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLimnodrilus sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMicronecta scholtzi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNais\u0026nbsp;sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhysella acuta\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePisidium sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlanaria\u0026nbsp;sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePsammoryctides\u0026nbsp;sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTubifex sp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe abundance, taxa richness, and biotic indices of the benthic macroinvertebrate community were calculated on a monthly basis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Saprobic index values ranged between 2.252 and 2.545, indicating that the environment was generally characterized as β\u0026ndash;α mesosaprobic (moderately polluted) (Barinova \u0026amp; Dyadichko, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). BMWP scores varied between 19 and 32, corresponding to moderately polluted water quality (Metcalfe, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eASPT values ranged from 3.167 to 3.556, suggesting that the water quality was biologically indicative of heavily polluted conditions (Metcalfe, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Armitage et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). The generally low values of the Shannon\u0026ndash;Wiener diversity index (0.167\u0026ndash;1.4) and the limited range of the Shannon Evenness index (0.093\u0026ndash;0.648) indicate that the benthic community was not well balanced and was dominated by a few tolerant taxa. Notably, despite a marked increase in abundance during the summer and autumn periods, diversity remained low, further supporting the dominance of tolerant species. The relatively low taxa richness (6\u0026ndash;10 taxa) also reflects the limited diversity of the community.\u003c/p\u003e \u003cp\u003eWhen all biotic indices are considered together, the water quality of the study area can be classified predominantly within Class II\u0026ndash;III throughout the year, indicating that the system is subject to chronic environmental stress and that the biological community is structured by tolerant taxa adapted to these conditions.\u003c/p\u003e \u003cp\u003eAnnual evaluation of the biotic indices, based on all sampling periods, indicated an overall Class III water quality status, with other index results consistently falling within similar water quality categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonthly variation of biotic indices, diversity metrics, and abundance of benthic macroinvertebrates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbundance (ind/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaxa_N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprobic Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBMWP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eASPT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eShannon Wiener\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eShannon Evenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWater Quality Class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eII-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Pearson correlation analysis conducted to determine the relationships between physicochemical parameters and biological metrics revealed several important and statistically significant associations (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). A strong positive correlation between temperature and pH, along with a strong negative correlation between temperature and DO, confirms that increasing temperature reduces oxygen solubility and reflects the expected thermal effect in aquatic systems (Caissie, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Verberk et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, a strong negative relationship was also observed between pH and DO.\u003c/p\u003e \u003cp\u003eIn terms of ionic parameters, significant positive correlations between salinity and both EC and TDS indicate that these variables collectively reflect the ionic composition of the water (Rusydi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The positive relationship between EC and TDS further supports this interpretation.\u003c/p\u003e \u003cp\u003eWhen examining the relationships between biological metrics and environmental variables, the negative correlation between dissolved oxygen and both abundance and taxa richness suggests that more tolerant species become dominant under low-oxygen conditions. This is consistent with the observed pattern of low diversity and high abundance during the summer period. The strong positive relationship between taxa richness and BMWP indicates that improvements in species diversity are associated with better water quality conditions (Yazdian et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, the results of the Pearson correlation analysis demonstrate that the structure of the benthic macroinvertebrate community is primarily controlled by temperature, dissolved oxygen, and ionic parameters (EC and TDS).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Pearson correlation analysis between physicochemical parameters and biological metrics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemp.(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp.(\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbundance (ind/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTaxa N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSaprobic Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBMWP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eASPT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.869**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.779**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.764**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.600*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.668*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.585*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.639*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.579*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.669*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance (ind/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxa N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.814**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaprobic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.613*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCCA applied to evaluate the relationships between the benthic macroinvertebrate community and environmental variables was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, the results of RDA did not reveal a statistically significant relationship between environmental variables and community structure at the model level (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe Spearman correlation analysis revealed several significant relationships between environmental variables and benthic macroinvertebrate taxa (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). A strong negative correlation was found between temperature and dissolved oxygen (rₛ = \u0026minus;0.853, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the inverse relationship between these parameters. Additionally, a strong positive correlation was detected between temperature and pH (rₛ = 0.951, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Among the biological variables, a significant negative relationship was observed between Asellus aquaticus and dissolved oxygen (rₛ = \u0026minus;0.729, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that this species can tolerate low-oxygen conditions (O'Callaghan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of ionic parameters, a strong positive correlation was found between electrical conductivity (EC) and salinity (rₛ = 0.818, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a moderate positive correlation between EC and total dissolved solids (TDS) (rₛ = 0.669, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A significant positive relationship was also identified between \u003cem\u003eG. balcanicus\u003c/em\u003e and temperature (rₛ = 0.629, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the abundance of this species increases under higher temperature conditions. Furthermore, a negative correlation was observed between Erpobdella octoculata and dissolved oxygen (rₛ = \u0026minus;0.579, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that this species is also tolerant to low-oxygen environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant Spearman correlations between environmental variables and benthic macroinvertebrates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003erₛ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature \u0026ndash; DO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong negative correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature \u0026ndash; pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong positive correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO \u0026ndash; \u003cem\u003eAsellus aquaticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant negative correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC \u0026ndash; Salinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong positive correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC \u0026ndash; TDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate positive correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature \u0026ndash; \u003cem\u003eGammarus balcanicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant positive correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO \u0026ndash; \u003cem\u003eErpobdella octoculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificant negative correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, the lack of statistically significant results in multivariate analyses does not necessarily imply the absence of ecological relationships. Rather, this outcome can be attributed to the limitations of the single-station sampling design and the complex nature of species-specific tolerance strategies. The strong agreement between the patterns observed in the ordination diagrams and the results of the Spearman correlation analysis clearly and consistently indicates that the benthic macroinvertebrate community is primarily structured along gradients of temperature, dissolved oxygen, and ionic parameters.\u003c/p\u003e \u003cp\u003eIn terms of functional feeding groups (FFGs), the benthic macroinvertebrate community was clearly dominated by shredders, which accounted for 52.07% of the total individuals (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Gathering collectors represented the second most abundant group (20.68%), followed by other feeding types (17.28%), filtering collectors (4.86%), scrapers (2.95%), and predators (2.16%).\u003c/p\u003e \u003cp\u003eThe dominance of shredders suggests that energy flow in the study area is primarily driven by the processing of coarse particulate organic matter (CPOM), highlighting the importance of leaf litter and terrestrial plant inputs in the ecosystem energy cycle (Cummins et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Merritt et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The relatively high proportion of gathering collectors indicates that fine particulate organic matter (FPOM) also constitutes an important energy source (Tomanova et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kelly et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the low contributions of scrapers (2.95%) and filtering collectors (4.86%) suggest a limited availability of particulate organic matter within the water column at the sampling station. The notable proportion of individuals classified under \u0026ldquo;other feeding types\u0026rdquo; (17.28%) reflects the presence of taxa exhibiting omnivorous or mixed feeding strategies (Merritt et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The low proportion of predators (2.16%) is consistent with limited prey diversity under the prevailing environmental stress conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative abundance of functional feeding groups (FFGs) of benthic macroinvertebrates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFG group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShredders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGathering collectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiltering collectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScrapers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther feeding types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Kızılırmak Basin is predominantly characterized by a semi-arid climate, with limited precipitation, high temperatures, and a hydrological regime marked by significant potential evapotranspiration. Long-term assessments conducted in the basin indicate that arid and semi-arid climatic conditions prevail over extensive areas, exerting considerable influence on both the quantity and quality dynamics of water resources (Tarım ve Orman Bakanlığı, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The region is dominated by volcanic lithology, and gypsum-rich formations are present in the Upper Kızılırmak Basin (Ko\u0026ccedil; et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the relatively high solubility of gypsum compared to many other minerals, surface and groundwater interacting with these formations acquire substantial amounts of total dissolved solids (TDS), calcium, and sulfate, which directly deteriorate water quality. Streams and springs originating from gypsum-rich areas exhibit markedly elevated electrical conductivity (EC) values (1100\u0026ndash;5200 \u0026micro;S/cm), and gypsum-derived karstic waters are particularly enriched in TDS and sulfate (Ka\u0026ccedil;aroğlu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These findings clearly indicate that the elevated EC, TDS, and sulfate concentrations observed in the study area are primarily controlled by gypsum-bearing lithological units and climatic conditions.\u003c/p\u003e \u003cp\u003eThis results in a continuous input of ions into the river system, leading to chronically elevated ionic loads. In addition, intensive agricultural activities in the study area further enhance this ionic load by increasing the transport of nutrients and dissolved substances via surface runoff (Carpenter et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The elevated total phosphorus (TP) and nitrate concentrations observed particularly during autumn and winter clearly reflect the influence of agricultural drainage on water chemistry. This dual influence (geological\u0026thinsp;+\u0026thinsp;agricultural) imposes chronic chemical stress on the aquatic environment, resulting in the elimination of sensitive species and providing a competitive advantage to tolerant taxa.\u003c/p\u003e \u003cp\u003eThe high abundance and dominance of \u003cem\u003eG. balcanicus\u003c/em\u003e in the study area indicate that this species possesses a broad ecological tolerance and is capable of adapting to a wide range of environmental conditions (Karaman \u0026amp; Pinkster, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). In benthic macroinvertebrate communities, the dominance of tolerant taxa is generally considered an indicator of environmental stress and high tolerance capacity (Bonada et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, Gammarus species are not fully resistant to extreme environmental conditions and are known to perform better in habitats with sufficient dissolved oxygen levels. Indeed, oxygen uptake and metabolic activity in these organisms are directly dependent on the oxygen content of the water, and low dissolved oxygen levels can induce physiological stress (Sutcliffe, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, reduced oxygen availability has been shown to significantly alter benthic macroinvertebrate community structure, allowing only tolerant species to persist under such conditions (MacNeil et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this context, the generally favorable dissolved oxygen levels observed in the study area may represent a key factor supporting the persistence of \u003cem\u003eG. balcanicus\u003c/em\u003e populations. Therefore, the dominance of this species reflects not only its biological characteristics but also the combined influence of geological background and anthropogenic pressures shaping the environmental conditions of the study area.\u003c/p\u003e \u003cp\u003eThis study demonstrates that evaluating benthic macroinvertebrate communities through a high-frequency sampling approach enables a more precise characterization of water quality dynamics. In particular, the use of weekly replicates allows short-term environmental fluctuations and their effects on biological communities to be captured more effectively, providing a significant methodological advantage over conventional low-frequency monitoring approaches (Hering et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Birk et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Sarıhıdır section of the Kızılırmak River, the annual dynamics of the benthic macroinvertebrate community were evaluated together with water quality using biotic indices and physicochemical parameters. The results indicate that the study area is clearly characterized by low diversity, high abundance, and dominance of tolerant taxa.\u003c/p\u003e \u003cp\u003eIn conclusion, this study reveals that water quality in the investigated section of the Kızılırmak River is at a moderate level and that the benthic macroinvertebrate community is dominated by tolerant species. Moreover, it highlights the importance of high-frequency data collection and integrated, multidisciplinary approaches in biomonitoring studies.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe results obtained indicate that physicochemical parameters, particularly electrical conductivity (EC) and total dissolved solids (TDS), play a key role in shaping the structure of benthic macroinvertebrate communities. Nutrient dynamics also emerged as an important indicator of anthropogenic pressure on the system. In particular, the marked increase in total phosphorus (TP) concentrations during autumn and winter clearly reflects the influence of nutrient inputs associated with surface runoff and agricultural drainage.\u003c/p\u003e \u003cp\u003eSaprobic index values indicate that the sampling station is located within the β\u0026ndash;α mesosaprobic transition zone, suggesting the presence of moderate to high levels of organic pollution (Sl\u0026aacute;deček, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). When considered together with the elevated ionic load, this finding highlights the simultaneous influence of multiple stressors acting on the system.\u003c/p\u003e \u003cp\u003eThe high-frequency sampling approach applied in this study provides a significant methodological advantage. Conventional biomonitoring studies typically rely on low sampling frequencies, which may overlook short-term environmental variability. In contrast, the combination of monthly sampling and weekly replicates enabled a more precise detection of temporal changes in both physicochemical parameters and biological community structure.\u003c/p\u003e \u003cp\u003eIn conclusion, the study area is subject to chronic environmental pressure driven by elevated ionic load and nutrient inputs, and these conditions play a decisive role in structuring the benthic macroinvertebrate community.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by 2209-A University Students Research Projects Support Program of TUBITAK (Project Numbers: 1919B012415915)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Nevşehir Public Health Laboratory for their support in conducting the chemical analyses.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkay, E., \u0026amp; Dalkıran, N. 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Elsevier.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Physicochemical parameters, high-frequency monitoring, ionic load, biotic indices, river ecosystem, salinization","lastPublishedDoi":"10.21203/rs.3.rs-9488372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9488372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the water quality, benthic macroinvertebrate community structure, and biotic indices of the Kızılırmak River at the Sarıhıdır Village (Nevşehir, T\u0026uuml;rkiye) using a year-long high-frequency monitoring approach. A total of 48 sampling events were conducted between March 2025 and February 2026, consisting of monthly sampling with weekly replicates, during which physicochemical parameters and benthic macroinvertebrate fauna were analyzed simultaneously. The physicochemical results revealed consistently high levels of electrical conductivity (EC), total dissolved solids (TDS), and total phosphorus (TP) throughout the year, indicating persistent ionic load and nutrient pressure in the study area. According to the Surface Water Quality Regulation, the station corresponds to Class III water quality based on EC values. The benthic macroinvertebrate community comprised 15 taxa and was characterized by low diversity but high individual abundance. \u003cem\u003eGammarus balcanicus\u003c/em\u003e was the dominant taxon throughout the year, followed by Bithynia tentaculata. Saprobic index values indicated β\u0026ndash;α mesosaprobic conditions, while BMWP scores suggested moderately polluted water quality. Pearson and Spearman correlation analyses demonstrated that the community structure was primarily controlled by temperature, dissolved oxygen, and ionic parameters. In terms of functional feeding groups, the benthic macroinvertebrate community was clearly dominated by shredders.\u003c/p\u003e \u003cp\u003eOverall, the study area does not represent a completely degraded system but rather an ecosystem subjected to chronic environmental pressure driven by elevated ionic load and nutrient inputs.\u003c/p\u003e","manuscriptTitle":"Assessment of Water Quality Using Benthic Macroinvertebrates and Biotic Indices Based on Year-Long Monitoring in the Kızılırmak River (Nevşehir, Türkiye)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 16:00:14","doi":"10.21203/rs.3.rs-9488372/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"196171401083773746624212274849631177660","date":"2026-05-13T10:18:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T12:30:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226063022934929893172553006270410993743","date":"2026-05-08T06:24:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189344294665079875760499705161853378311","date":"2026-05-07T11:54:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69298172515576339196651573344172028519","date":"2026-05-06T13:53:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260139022804947653653960588455226233202","date":"2026-05-06T08:50:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315004897493529729074481299927156060164","date":"2026-05-06T08:37:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T08:22:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:21:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T13:20:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-04-21T19:57:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83a96c88-c5ed-463d-8f2c-13b77dc20358","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"196171401083773746624212274849631177660","date":"2026-05-13T10:18:05+00:00","index":46,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T12:30:50+00:00","index":45,"fulltext":""},{"type":"reviewerAgreed","content":"226063022934929893172553006270410993743","date":"2026-05-08T06:24:59+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"189344294665079875760499705161853378311","date":"2026-05-07T11:54:28+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"69298172515576339196651573344172028519","date":"2026-05-06T13:53:45+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"260139022804947653653960588455226233202","date":"2026-05-06T08:50:50+00:00","index":34,"fulltext":""},{"type":"reviewerAgreed","content":"315004897493529729074481299927156060164","date":"2026-05-06T08:37:10+00:00","index":32,"fulltext":""},{"type":"reviewersInvited","content":"24","date":"2026-05-06T08:22:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T13:21:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T13:20:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T16:00:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 16:00:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9488372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9488372","identity":"rs-9488372","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}