Seasonal Evaluation of Microbial Quality of Water Resources Feeding İğneada Floodplain Forests

Seasonal Evaluation of Microbial Quality of Water Resources Feeding İğneada Floodplain Forests | 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 Article Seasonal Evaluation of Microbial Quality of Water Resources Feeding İğneada Floodplain Forests Utku Güner, Ahmet Burak Dumlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8830939/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background/aim: The İğneada Floodplain Forests represent one of the largest forest-marsh ecosystems in Türkiye, hosting significant biodiversity. However, increasing tourism investments, camping activities, and seasonal population growth in the region pose potential pressure on the water resources feeding this ecosystem. This study aimed to determine the fecal pollution levels of the basins and sub-basins feeding the İğneada Floodplain Forests using microbiological culture methods and to identify the pollution sources (human or animal) using molecular microbial source tracking (MST) via detection of host-specific Bacteroidales 16S rRNA genes. Materials and methods: Water samples were collected seasonally in 2021 and 2022 from 18 stations across the Bulanık, Çavuşköprü, and Efendi stream basins. Microbiological quality was assessed using membrane filtration and pour plate methods. For molecular analysis, qPCR was performed using universal (BacUni), human-specific (HF183, Bachum), and ruminant-specific (BacCow, BacR) markers. The relationships between microbial data, physicochemical parameters, and climatic factors were evaluated statistically using PCA, K-means clustering, and Spearman correlation analyses. Results: Microbiological analyses revealed high levels of fecal pollution, particularly in the Bulanık Creek (Station 9B) and Çavuşköprü Creek (Station 12Ç), which were identified as outliers with extreme pollution loads. A strong positive correlation was found between human-specific molecular markers and fecal indicator bacteria (Spearman r>0.8; p<0.001), indicating a predominance of human-induced pollution. While seasonal temperature increases correlated with Clostridia and total colony counts, anthropogenic factors were found to be more dominant than climatic factors in driving fecal pollution. Conclusion: The water resources feeding the İğneada Floodplain Forests are under significant anthropogenic pressure, primarily from human-origin fecal contamination. The findings underscore the urgent need for management strategies to mitigate pollution and ensure the sustainability of this critical wetland ecosystem. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology Floodplain forests water quality microbial source tracking qPCR iğneada fecal pollution Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The İğneada floodplain forests (longoz) constitute one of the largest forest-marsh ecosystems in Turkey. This area has become a vital wetland hosting various distinct ecosystems (Camur-Elipek et al., 2015 ). Therefore, preserving the quality of the water basins feeding the floodplain is of vital importance for the sustainability of its ecosystems and biodiversity. However, in recent years, tourism investments in the region, developing camping activities in Demirköy and İğneada, and the consequent seasonal population increases have created potential pressure on regional water resources. Additionally, cattle breeding activities continue in the region, albeit not at a commercial scale. In this study, the fecal pollution of the basins and sub-basins feeding the İğneada Floodplain Forests was determined using the microbiological culture method. To ascertain whether the pollution was of human or animal origin, molecular tracking was performed by detecting host-specific Bacteroidales 16S rRNA genes, and these results were compared with microbiological culture results. Furthermore, the relationship between the data obtained from the study and the climatic elements of the region was statistically evaluated. 2. Materials and methods 2.1. Overview of the study area The İğneada Floodplain Forests were formed by the accumulation of alluvium carried by streams flowing from the Yıldız Mountains to the Black Sea coast and the seasonal flooding of the region. The İğneada settlement physically divides the coastal dunes within the National Park into two. The northern coastal dunes extend from the eastern part of Erikli Lake to İğneada (Fig. 1 ). The southern coastal dunes extend from the canal region connecting Mert Lake to the sea down to the south of Saka Lake, reaching a width of 50–60 meters in places. These approximately 10 km long dunes are of great importance with plant species specific to the southwest of the Black Sea. A study conducted in 2015 identified 227 bird species, demonstrating the importance of the İğneada Floodplain Forests and its surroundings for birds (Kaya, 2015 ). The hydrological focus of the study consists of surface flows feeding the Mert, Erikli, Hamam, Pedina, Deniz, and Saka Lakes within the National Park. Three main basins feeding the region have been identified: Bulanık Creek basin, Çavuşköprü Creek basin, and Efendi Creek basin. Within the scope of this study, a total of 18 sampling stations were determined by examining the continuous and seasonal streams feeding the floodplain forests. Eleven of these stations are located in the Bulanık Creek, the largest basin, and its sub-basin, Yavuz Creek. Of the remaining stations, 4 were selected from the Çavuşköprü Creek basin and 3 from the Efendi Creek basin (Fig. 2 ). Station coding was done primarily by number order, followed by the initial letter of the stream/lake name (Table 1 ). Table 1 Sample codes and coordinates of locations of sampling stations. Code Sampling Point UTM 50-DATUM 6 o – 35T Basin X Y 1S Lake Saka 582470.00 d D 4628279.00 m K Yavuz Creek Basin 2Y Yavuz Stream – Inside the National Park 579734.00 d D 4628940.00 m K 3Y Yavuz Stream – National Park Entrance 578124.00 d D 4629118.00 m K 4Y Yavuz Stream – Lower Branch 577798.00 d D 4628597.00 m K 5Y Yavuz Stream – Upper Branch 577483.00 d D 4628697.00 m K 6D Sea Lake 582054.00 d D 4629469.00 m K Bulanık Stream Basin 7B Bulanık Stream – Next to the Nursery 579968.00 d D 4630009.00 m K 8B Bulanık Stream – National Park Entrance 577463.00 d D 4630446.00 m K 9B Bulanık Stream – Demirköy Exit 568825.00 d D 4630534.00 m K 10B Bulanık Stream – Demirköy Entrance 565412.00 d D 4632443.00 m K 18B Bulanık Stream – Lower Branch 570944.00 d D 4625297.00 m K 11M Mert Lake 581098.00 d D 4635684.00 m K Cavuskopru Stream Basin 12Q Cavuskopru Stream 577806.00 d D 4636273.00 m K 13I Igneada Stream 578070.00 d D 4636567.00 m K 14A Golden Creek 577561.00 d D 4635727.00 m K 15E Erikli Lake 582873.00 d D 4638016.00 m K Efendi Creek Basin 16E Efendi Creek – National Park Entrance 578217.00 d D 4640045.00 m K 17E Efendi Creek – The Confluence Point of Springs 575305.00 d D 4642065.00 m K 2.2. Sampling and transfer to the laboratory To determine the microbial pollution indicators of the basins and sub-basins feeding the İğneada Floodplain Forests, samples were collected from the stations in the research area seasonally during 2021 and 2022. Two sterile 500 ml polypropylene (PP) containers were used for surface water samples for microbiological analyses, and 1500 ml sterile PP containers were used for physicochemical analyses. Samples were collected in February, May, August, and November for the winter, spring, summer, and autumn periods, respectively. 2.3. Physicochemical analysis Physicochemical parameters such as water temperature ( o C), pH, oxidation-reduction potential (ORP, mV), electrical conductivity (EC, µS/cm), total dissolved solids (TDS, ppm), and luminescent dissolved oxygen (LDO, % and mg/L) were measured at the stations during sampling using a calibrated Hach Lange® Hq40d Multi device. Color (Pt-Co and m -1 ), turbidity (ntu), and total alkalinity (mg/L CaCO 3 ) parameters of the samples brought to the laboratory in 1500 ml PP containers were analyzed. Spectrophotometric methods were used for color analysis, turbidimetric methods for turbidity analysis, and titrimetric methods for total alkalinity determination. Hach Lange ® DR2800 spectrophotometer and 2100Q turbidimeter devices were utilized for these analyses. 2.4. Microbiological culture analysis Membrane filtration and pour plate methods were used in the analysis of samples brought to the laboratory in sterile 500 ml light-impermeable PP containers, in accordance with standards and literature (Table 2 ). All membrane filtration analyses were performed on a 100 ml sample volume. However, analyses of samples with dense growth were repeated by decimally diluting up to 10 − 2 ml volume with sterile saline peptone water. Confirmation or counting was performed on plates with countable growth. The final analysis result was calculated according to the 100 ml volume, considering the dilution factor of these selected plates. Table 2 Parameters analyzed in microbiological culture study. Microbiological Analysis Parameter Unit of Measurement Qualification Reference Method Method Total Coliform CFU/100ml Fecal ISO 9308-1:2014 Membrane Filtration Fecal Coliform CFU/100ml Fecal SM 9222-D Membrane Filtration Enterococcus (Fecal Streptococcus) CFU/100ml Fecal ISO 7899-2 Membrane Filtration Clostridia CFU/100ml Non-fecal TS 8020 EN 26461–2 Membrane Filtration, Plate Casting Clostridium perfringens (vegetative + spore) CFU/100ml Fecal 98/83/EC Membrane Filtration Pseudomonas aeruginosa CFU/100ml Non-fecal pathogenic ISO 16266 Membrane Filtration Total number of colonies that reproduced at 37°C CFU/ml Total live ISO 6222 Plaque Casting Total number of colonies that reproduced at 22°C CFU/ml Total live ISO 6222 Plaque Casting In the pour plate method, the amount of sample to be analyzed was transferred to a sterile empty petri dish. Subsequently, the appropriate agar medium, heated to 100 o C and cooled to 45 o C, was added to the petri dish, homogenized with the sample, and left for incubation at the desired temperature for the appropriate duration. 2.5. Molecular analysis via qPCR Quantitative PCR (qPCR) was performed to determine the source of the microbial load at the designated stations. After filtering 100 ml samples, the filters were stored at -20 o C in accordance with the literature (Gilpin et al., 2013 ) and subsequently subjected to pre-analysis steps for extraction. The stored filters were divided into small pieces (Vadde, 2018 ) and transferred to DNeasy PowerWater (Qiagen ® ) kits under aseptic conditions, and extraction was performed according to the manufacturer's instructions.Seven-point 10-fold serially diluted (ranging from 5 to 10 7 genome copies/reaction) ultrapure synthetic DNA with target sequences was used as a positive control to generate a standard curve for each qPCR analysis. All qPCR studies were performed in triplicate with a final reaction volume of 20 µL. The primer and probe sequences used in the study are shown in Table 3 . Table 3 Presentation of the methods used in qPCR analyses and general information about these methods. Source Method Primer/Probe Sequence(5'-3') Bonding Temperature ( ° C) Reference Universal BacUni (TaqMan) BacUni- 520F BacUni-690R BacUni-656P CGTTATCCGGATTTATTGGGTTTA CAATCGGAGTTCTTCGTGATATCTA FAM-TGGTGTAGCGGTGAAA-MGB 60 (Kildare et al., 2007 ) Human HF183 (TaqMan) HF183F BacR287R BacP234P ATCATGAGTTCACATGTCCG CTTCCTCTCAGAACCCCTATCC FAM-CTAATGGAACGCATCCC-MGB 60 (Green et al., 2014 ) Cattle BacR (TaqMan) BacR_f BacR_r BacR_P GCGTATCCAACCTTCCCG CATCCCCATCCGTTACCG FAM-CTTCCGAAAGGGAGATT-NFQ-MGB 60 (Reischer, Kasper, Steinborn, Mach and Farnleitner, 2006 ) 2.6. Acquisition of climate and geographic parameter data The average temperature and total precipitation data for the years 2021 and 2022 of the study area were obtained from the Kırklareli/Demirköy meteorological measurement station and internet-based online channels (Meteoblue); instantaneous air temperature was obtained with the Hach Lange ® Hq40d Multi device, and instantaneous wind measurements were obtained with an anemometer (BeneTech ® ). 3. Results 3.1. Reduction of data with Principal Component Analysis (PCA) To visualize and interpret the microbiological culture data of the sampling points more clearly, eight parameters were subjected to Principal Component Analysis (PCA). As a result of this analysis, 8 microbiological parameters were gathered under 2 factors. The first factor explained 49.17% of the variance, while the second factor explained 15.90%; cumulatively, 65.08% of the variance was explained (KMO: 0.615; p < 0.001). According to the rotation analysis result, "Fecal Coliform, Clostridium perfringens , and Enterococci " parameters were gathered under Factor 1, while " Clostridia and Pseudomonas aeruginosa " parameters were gathered under Factor 2. "Total Coliform" and "Total colony counts at 37 o C and 22 o C parameters showed distribution in both factors. Based on this grouping, Factor 1 was named the "Fecal Bacteria Group" and Factor 2 was named the "Non-Fecal Bacteria Group" in the visualization of the data. 3.2. Evaluation of physicochemical and microbiological culture analysis results K-Means clustering analysis performed on the microbiological analysis results of 144 water samples showed that the majority of the samples (137 samples) had average pollution levels and gathered in a single cluster (Cluster 3). However, it was determined that 7 samples deviated significantly from this average and exhibited outlier pollution values. The distribution of these outlier samples is summarized in Table 4 . Table 4 General information about the clusters according to the K-means clustering analysis results. 1st Group 2nd Group 3rd Group 4th Group 5th Group Number of Samples 1 1 137 4 1 Station and Period 12Q (Summer 2022) 9B (2021-Spring) Stations and periods where average reproduction levels were observed. • 9B (Summer 2021) • 9B (2022-Winter) • 9B (2022-Spring) • 9B (2022-Fall) 9B (Summer 2022) Parameter(s) in which the anomaly was observed Total Coliform, Fecal Coliform, Total Colony Counts Growing at 22°C and 37°C Clostridium perfringens Enterococcus Pseudomonas aeruginosa Total number of samples = 144 Spearman rank correlation analysis was performed to determine the relationship between the microbiological culture and physicochemical analysis results of the water samples (p < 0.05). Spearman correlation analysis revealed various significant relationships between parameters. It was observed that Total Coliform (r = 0.502), Clostridia (r = 0.580), and total colony count growing at 37 o C (r = 0.515) increased with increasing water temperature (p < 0.001). Conversely, a negative relationship was detected between the dissolved oxygen (LDO) level in water and fecal indicators (p < 0.05). Furthermore, it was determined that the increase in turbidity and color of the water was associated with the increase in the fecal coliform count (p < 0.003). When climate data were examined, a moderate-strong positive relationship was found between seasonal average temperature and "Non-Fecal Group" bacteria (r = 0.612), while the relationship between "Fecal Group" bacteria and temperature was weaker (r = 0.38). A weak negative relationship was observed between seasonal total precipitation amount and the "Fecal Group" (r = -0.311). These findings suggest that anthropogenic factors may be more dominant than climatic factors in the dynamics of fecal pollution. 3.3. Evaluation of molecular analysis results qPCR efficiencies showed similarities with molecular MST analysis efficiencies performed in different geographies. The measurement ranges of the primers varied between 5 and 10 7 depending on the samples. The detection frequencies of the primers are shown in Table 5 . Table 5 Seasonal detection frequencies of MST methods in water samples of İğneada Longoz Forest basin and sub-basins Season Number of Samples Number of Positive Samples (%) BacUni (Universal) HF183 (Human) Bachum (Human) BacCow (Cattle) BacR (Cattle) Winter 36 36 (100) 34 (94.4) 33 (91.6) 31 (86,1) 29 (80.5) Spring 36 36 (100) 35 (97.2) 35 (97.2) 34 (94.4) 31 (86,1) Summer 36 36 (100) 36 (100) 36 (100) 36 (100) 36 (100) Autumn 36 36 (100) 34 (94.4) 32 (88.8) 32 (88.8) 31 (86,1) Total 144 144 (100) 139 (96.5) 136 (94.4) 133 (92.3) 127 (88,1) 3.4. Statistical evaluation of the relationship between qPCR and microbiological culture results Spearman rank correlation analysis was performed to determine the relationship between microbiological culture analysis results and qPCR analysis results. Since the data were not normally distributed, Spearman correlation was used. As a result of the analysis, a statistically significant positive relationship was found between all examined parameters (p < 0.001). BacUni, the universal Bacteroidales marker, showed the highest correlation with Total Coliform (r = 0.862) and total colony count at 37 o C (r = 0.816). Among human-specific markers, HF183 exhibited strong correlations with Fecal Coliform (r = 0.861), Enterococci (r = 0.731), and the Fecal Group (r = 0.864). Similarly, the Bachum marker showed high correlation with Fecal Coliform (r = 0.843) and the Fecal Group (r = 0.851). Among cattle-specific markers, BacCow showed a positive relationship with Fecal Coliform (r = 0.705) and the Fecal Group (r = 0.724), while the BacR marker exhibited correlation with Fecal Coliform (r = 0.712) and the Fecal Group (r = 0.732) (Fig. 3 ). 3.5. Effects of climatic and seasonal factors on microbiological and physicochemical water quality Measurements made throughout 2021–2022 in the İğneada Floodplain Forests research area showed that climatic and seasonal changes have a determining role in water quality (Fig. 4 ). While precipitation generally intensified in the winter and spring months, average temperatures showed distinct differences according to seasons. When the relationship of these climatic dynamics with microbiological parameters was examined statistically, a moderate to strong positive relationship was detected between seasonal average temperature and parameters such as Clostridia and total colony count at 37 o C (Spearman r > 0.6). In contrast, a weak negative relationship was observed between fecal group bacteria data and seasonal total precipitation. 4. Discussion This study has revealed with numerical and molecular data that the microbiological water quality of the basins feeding the İğneada Floodplain Forests is under serious pressure, especially in regions where human activities are intense. The Bulanık Creek basin, the largest basin in the research area, constitutes a critical example for understanding pollution dynamics. In particular, it was determined that microbiological quality levels were quite high in all seasonal measurements performed in 2021–2022 at the Demirköy Exit station (9B) of Bulanık Creek, which passes through the Demirköy district center of Kırklareli and enters the National Park borders. The quality level at this station was measured as an average of 34,675 cfu/100ml for total coliform bacteria and 11,688 cfu/100ml for fecal coliforms. These values indicate a highly polluted water profile according to water quality standards. According to the analysis results carried out at stations before Demirköy and at other stations after the Demirköy exit until it empties into Deniz Lake, it is observed that this fecal pollution seen at station 9B tends to be diluted until discharge. Conversely, the microbiological load of station 18B from the sub-branches of Bulanık Creek, flowing north of Sivriler village and far from settlements, is quite low compared to other stations (average total coliform 661 cfu/100ml, fecal coliform 191 cfu/100ml). This situation numerically proves the determining role of anthropogenic pressure on pollution. One of the most striking findings of the study is the extreme fecal pollution event observed in the Çavuşköprü Creek basin, especially in the summer of 2022. During this period, the fecal coliform count reached an extreme level of 97000 cfu/100 ml. This situation was confirmed by K-Means clustering analysis, and it was determined that the sample taken from this station (12Ç) showed a high amount of outliers compared to the other 137 samples (p < 0.05). This result suggests a point and sudden fecal waste discharge into the stream. Similarly, in a study conducted on Acarlar Lake feeding Acarlar Floodplain, it was stated that the high level of total coliform bacteria presence seen in the winter period at a sampling point could be the result of a point source of fecal pollution (Ertürk, 2005 ). On the other hand, analysis results in Mert Lake where the stream discharges (average fecal coliform 518 cfu/100ml) show that the floodplain ecosystem absorbs this fecal pressure to a certain extent. When the microbiological culture analysis results of all basins in the research area are examined, it is determined that the microbial load increases during the summer periods. This situation is consistent with bacteriological studies conducted in national parks in the American and European continents, observing a general increase in microbial load levels in lakes and other water sources in hot weather (Crabill et al., 1999 ; McCarthy, 2020 ; Schoonover and Lockaby, 2006 ; Xue et al., 2018 ). Correlations established between physicochemical parameters and microbiological data also presented important findings. While a positive relationship was detected between water temperature and many microbiological parameters (total coliform, Clostridia , Enterococci ) (p < 0.05), an inverse relationship was found between the amount of dissolved oxygen in water and microbiological indicators. This finding is supported by a study conducted on the Kshipra River in India revealing a similar negative correlation (Diwan et al., 2018 ). When the performed qPCR analysis results were compared with culture-based fecal indicator bacteria (FIB) results, it was clearly demonstrated that the fecal pollution in the basins is largely of human origin. A very strong positive relationship was found between human-origin MST markers HF183 and Bachum and "fecal group bacteria" parameters (Spearman r > 0.8; p < 0.001). This correlation is statistically significantly stronger than the relationship between cattle-origin markers (BacCow and BacR) and FIB data (Spearman r < 0.8). Although the relationship between MST methods and FIB data is controversial in the literature (Green et al., 2019 ; Yasar et al., 2021 ), many researchers have detected a moderate to high positive relationship similar to our findings (Ahmed et al., 2010 ; Gourmelon et al., 2010 ; Harwood et al., 2014 ; Malla et al., 2018 ). The fact that analysis results obtained with human-specific MST methods are also at very high levels, especially at stations where high amounts of fecal bacteria growth are seen (9B, 12Ç), is the most important evidence strengthening this study. When the effect of climatic factors on pollution is examined, while positive relationships are found between seasonal average temperature and microbiological parameters, it is observed that these relationships are generally at weak to moderate levels. It is understood that climatic factors affecting the distribution of fecal group bacteria have a complex interaction and anthropogenic pressures shape this dynamic more. This situation is consistent with the study conducted by Lenart-Boron et al. (2016) on the Bialka River, stating that although fecal pollution in the river varies depending on the climate, it reaches the highest levels especially during periods when the number of people for recreational purposes in the region increases. In conclusion, the quality of water resources feeding the İğneada Floodplain Forests is under the threat of predominantly human-origin fecal pollution, especially in basins under the influence of settlements and increasing tourism activities. This study emphasizes the importance of developing urgent management strategies and protection measures for the conservation of this rare ecosystem by revealing the extent and source of this threat with numerical and molecular data. Declarations Funding information This study was supported by the Trakya University Scientific Research Projects Coordination Unit. Project number: 2021/48. Author Contribution A.B.D. and U.G. wrote the main manuscript text, figures . Acknowledgement We would like to thank "TRAKYA UNIVERSITY SCIENTIFIC RESEARCH PROJECTS UNIT (TÜBAP)" for supporting your work. 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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-8830939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597045059,"identity":"ef5d7420-16b5-4f0a-8099-8dba008b8a2b","order_by":0,"name":"Utku Güner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYPACGyBmA7MYG4jUkiaBpIWZKC2HSdBicH6N2YOPe87XmbcfS5P4wWAju+EA/7EPeLXceGNuOOPZbQmZM2nHJHsY0ow3HGBmnoFfyxkzaZ4DtyUkGNLbJHgYDieCtOB3GEjLnwPnJCT4n7dJ/mH4T4SW8z1m0gwHDkhISKQdk+ZhOEBYi+QNtjLJngPJkjMkniVbyxgkG888zGyMVwvf+cPbJH4csOOX4E8zvPmmwk6273jjY7xaGCQS4EwWCQYDIEUwJvkPwJnMeKNjFIyCUTAKRi4AAKf+SB0WOyk1AAAAAElFTkSuQmCC","orcid":"","institution":"Trakya University","correspondingAuthor":true,"prefix":"","firstName":"Utku","middleName":"","lastName":"Güner","suffix":""},{"id":597045062,"identity":"9e5108fb-9fd4-46e7-8413-fa1fdc436889","order_by":1,"name":"Ahmet Burak Dumlu","email":"","orcid":"","institution":"Trakya University","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"Burak","lastName":"Dumlu","suffix":""}],"badges":[],"createdAt":"2026-02-09 13:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8830939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8830939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103629073,"identity":"fcbfcb5a-d7e3-40e8-aa66-df0fa1035437","added_by":"auto","created_at":"2026-02-27 22:09:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186220,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite image showing the boundaries of İğneada Longoz Forests National Park.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/69fe6b442cf1cb08714b43a6.jpeg"},{"id":103629075,"identity":"2fbd0d43-3b9a-43cb-adc9-4dfa4c45193c","added_by":"auto","created_at":"2026-02-27 22:09:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":627899,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of sampling stations within the region\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/cb2e686ce0294860044e1116.png"},{"id":104399457,"identity":"8951842d-22f9-41f9-a6d2-41c24bc0a501","added_by":"auto","created_at":"2026-03-11 12:06:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184973,"visible":true,"origin":"","legend":"\u003cp\u003eLine graph of Spearman rank correlation analysis data performed between qPCR and microbiological culture analysis results.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/7268f73e995b62e93bbd11c8.jpeg"},{"id":103629077,"identity":"8f2d07af-b98d-42c3-b4af-387545a09e0c","added_by":"auto","created_at":"2026-02-27 22:09:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330186,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral overview of the microbiological culture analysis results of the study (*12Ç station 2022-summer period total coliform, fecal group, total viable numbers growing at 22 \u003csup\u003e°\u003c/sup\u003eC and 37 \u003csup\u003e°\u003c/sup\u003eC were 168000, 98070 cfu/100ml; 278000, 116500 cfu/ml, respectively)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/f49927edc061bef5288d7d04.jpeg"},{"id":104407502,"identity":"61f5045b-f6fe-4269-9954-3fe9fdeef89c","added_by":"auto","created_at":"2026-03-11 12:38:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2542919,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/dcf57598-63b6-4349-8565-de9c9fe6537f.pdf"},{"id":103629076,"identity":"8ea81308-19b2-476c-94d6-e9095cfc4992","added_by":"auto","created_at":"2026-02-27 22:09:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":745561,"visible":true,"origin":"","legend":"","description":"","filename":"AhmetBurakDumluSupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8830939/v1/47cc2460e7498dff600b4584.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Evaluation of Microbial Quality of Water Resources Feeding İğneada Floodplain Forests","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe İğneada floodplain forests (longoz) constitute one of the largest forest-marsh ecosystems in Turkey. This area has become a vital wetland hosting various distinct ecosystems (Camur-Elipek et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, preserving the quality of the water basins feeding the floodplain is of vital importance for the sustainability of its ecosystems and biodiversity.\u003c/p\u003e \u003cp\u003eHowever, in recent years, tourism investments in the region, developing camping activities in Demirk\u0026ouml;y and İğneada, and the consequent seasonal population increases have created potential pressure on regional water resources. Additionally, cattle breeding activities continue in the region, albeit not at a commercial scale.\u003c/p\u003e \u003cp\u003eIn this study, the fecal pollution of the basins and sub-basins feeding the İğneada Floodplain Forests was determined using the microbiological culture method. To ascertain whether the pollution was of human or animal origin, molecular tracking was performed by detecting host-specific \u003cem\u003eBacteroidales\u003c/em\u003e 16S rRNA genes, and these results were compared with microbiological culture results. Furthermore, the relationship between the data obtained from the study and the climatic elements of the region was statistically evaluated.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Overview of the study area\u003c/h2\u003e \u003cp\u003eThe İğneada Floodplain Forests were formed by the accumulation of alluvium carried by streams flowing from the Yıldız Mountains to the Black Sea coast and the seasonal flooding of the region. The İğneada settlement physically divides the coastal dunes within the National Park into two. The northern coastal dunes extend from the eastern part of Erikli Lake to İğneada (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The southern coastal dunes extend from the canal region connecting Mert Lake to the sea down to the south of Saka Lake, reaching a width of 50\u0026ndash;60 meters in places. These approximately 10 km long dunes are of great importance with plant species specific to the southwest of the Black Sea. A study conducted in 2015 identified 227 bird species, demonstrating the importance of the İğneada Floodplain Forests and its surroundings for birds (Kaya, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe hydrological focus of the study consists of surface flows feeding the Mert, Erikli, Hamam, Pedina, Deniz, and Saka Lakes within the National Park. Three main basins feeding the region have been identified: Bulanık Creek basin, \u0026Ccedil;avuşk\u0026ouml;pr\u0026uuml; Creek basin, and Efendi Creek basin. Within the scope of this study, a total of 18 sampling stations were determined by examining the continuous and seasonal streams feeding the floodplain forests. Eleven of these stations are located in the Bulanık Creek, the largest basin, and its sub-basin, Yavuz Creek. Of the remaining stations, 4 were selected from the \u0026Ccedil;avuşk\u0026ouml;pr\u0026uuml; Creek basin and 3 from the Efendi Creek basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Station coding was done primarily by number order, followed by the initial letter of the stream/lake name (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eSample codes and coordinates of locations of sampling stations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSampling Point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUTM 50-DATUM 6 \u003csup\u003eo\u003c/sup\u003e \u0026ndash; 35T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLake Saka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582470.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4628279.00 m K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eYavuz Creek Basin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYavuz Stream \u0026ndash; Inside the National Park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579734.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4628940.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYavuz Stream \u0026ndash; National Park Entrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578124.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4629118.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYavuz Stream \u0026ndash; Lower Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577798.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4628597.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYavuz Stream \u0026ndash; Upper Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577483.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4628697.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSea Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582054.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4629469.00 m K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eBulanık Stream Basin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulanık Stream \u0026ndash; Next to the Nursery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579968.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4630009.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulanık Stream \u0026ndash; National Park Entrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577463.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4630446.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulanık Stream \u0026ndash; Demirk\u0026ouml;y Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e568825.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4630534.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulanık Stream \u0026ndash; Demirk\u0026ouml;y Entrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565412.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4632443.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBulanık Stream \u0026ndash; Lower Branch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e570944.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4625297.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMert Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e581098.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4635684.00 m K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCavuskopru Stream Basin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12Q\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCavuskopru Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577806.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4636273.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgneada Stream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578070.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4636567.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGolden Creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577561.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4635727.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErikli Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582873.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4638016.00 m K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEfendi Creek Basin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfendi Creek \u0026ndash; National Park Entrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578217.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4640045.00 m K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfendi Creek \u0026ndash; The Confluence Point of Springs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e575305.00 d D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4642065.00 m K\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 \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling and transfer to the laboratory\u003c/h2\u003e \u003cp\u003eTo determine the microbial pollution indicators of the basins and sub-basins feeding the İğneada Floodplain Forests, samples were collected from the stations in the research area seasonally during 2021 and 2022. Two sterile 500 ml polypropylene (PP) containers were used for surface water samples for microbiological analyses, and 1500 ml sterile PP containers were used for physicochemical analyses. Samples were collected in February, May, August, and November for the winter, spring, summer, and autumn periods, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Physicochemical analysis\u003c/h2\u003e \u003cp\u003ePhysicochemical parameters such as water temperature (\u003csup\u003eo\u003c/sup\u003eC), pH, oxidation-reduction potential (ORP, mV), electrical conductivity (EC, \u0026micro;S/cm), total dissolved solids (TDS, ppm), and luminescent dissolved oxygen (LDO, % and mg/L) were measured at the stations during sampling using a calibrated Hach Lange\u0026reg; Hq40d Multi device. Color (Pt-Co and m\u003csup\u003e-1\u003c/sup\u003e), turbidity (ntu), and total alkalinity (mg/L CaCO\u003csub\u003e3\u003c/sub\u003e) parameters of the samples brought to the laboratory in 1500 ml PP containers were analyzed. Spectrophotometric methods were used for color analysis, turbidimetric methods for turbidity analysis, and titrimetric methods for total alkalinity determination. Hach Lange\u003csup\u003e\u0026reg;\u003c/sup\u003e DR2800 spectrophotometer and 2100Q turbidimeter devices were utilized for these analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Microbiological culture analysis\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMembrane filtration and pour plate methods were used in the analysis of samples brought to the laboratory in sterile 500 ml light-impermeable PP containers, in accordance with standards and literature (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All membrane filtration analyses were performed on a 100 ml sample volume. However, analyses of samples with dense growth were repeated by decimally diluting up to 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e ml volume with sterile saline peptone water. Confirmation or counting was performed on plates with countable growth. The final analysis result was calculated according to the 100 ml volume, considering the dilution factor of these selected plates.\u003c/p\u003e \u003c/li\u003e \u003c/ul\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\u003eParameters analyzed in microbiological culture study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMicrobiological Analysis Parameter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUnit of Measurement\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eQualification\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eReference Method\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMethod\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eISO 9308-1:2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFecal Coliform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSM 9222-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterococcus (Fecal Streptococcus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eISO 7899-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClostridia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-fecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTS 8020 EN 26461\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration, Plate Casting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClostridium perfringens\u003c/em\u003e (vegetative\u0026thinsp;+\u0026thinsp;spore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFecal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98/83/EC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-fecal pathogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eISO 16266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMembrane Filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of colonies that reproduced at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal live\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eISO 6222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlaque Casting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of colonies that reproduced at 22\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal live\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eISO 6222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlaque Casting\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\u003eIn the pour plate method, the amount of sample to be analyzed was transferred to a sterile empty petri dish. Subsequently, the appropriate agar medium, heated to 100 \u003csup\u003eo\u003c/sup\u003eC and cooled to 45 \u003csup\u003eo\u003c/sup\u003eC, was added to the petri dish, homogenized with the sample, and left for incubation at the desired temperature for the appropriate duration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Molecular analysis via qPCR\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eQuantitative PCR (qPCR) was performed to determine the source of the microbial load at the designated stations. After filtering 100 ml samples, the filters were stored at -20 \u003csup\u003eo\u003c/sup\u003eC in accordance with the literature (Gilpin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and subsequently subjected to pre-analysis steps for extraction. The stored filters were divided into small pieces (Vadde, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and transferred to DNeasy PowerWater (Qiagen\u003csup\u003e\u0026reg;\u003c/sup\u003e) kits under aseptic conditions, and extraction was performed according to the manufacturer's instructions.Seven-point 10-fold serially diluted (ranging from 5 to 10\u003csup\u003e7\u003c/sup\u003e genome copies/reaction) ultrapure synthetic DNA with target sequences was used as a positive control to generate a standard curve for each qPCR analysis. All qPCR studies were performed in triplicate with a final reaction volume of 20 \u0026micro;L. The primer and probe sequences used in the study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \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\u003ePresentation of the methods used in qPCR analyses and general information about these methods.\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=\"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=\"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\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer/Probe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSequence(5'-3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBonding Temperature ( \u003csup\u003e\u0026deg;\u003c/sup\u003e C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eUniversal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacUni\u003c/p\u003e \u003cp\u003e(TaqMan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacUni- 520F\u003c/p\u003e \u003cp\u003eBacUni-690R\u003c/p\u003e \u003cp\u003eBacUni-656P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCGTTATCCGGATTTATTGGGTTTA\u003c/p\u003e \u003cp\u003eCAATCGGAGTTCTTCGTGATATCTA\u003c/p\u003e \u003cp\u003eFAM-TGGTGTAGCGGTGAAA-MGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Kildare et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHuman\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHF183\u003c/p\u003e \u003cp\u003e(TaqMan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHF183F\u003c/p\u003e \u003cp\u003eBacR287R\u003c/p\u003e \u003cp\u003eBacP234P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATCATGAGTTCACATGTCCG\u003c/p\u003e \u003cp\u003eCTTCCTCTCAGAACCCCTATCC\u003c/p\u003e \u003cp\u003eFAM-CTAATGGAACGCATCCC-MGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Green et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCattle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacR\u003c/p\u003e \u003cp\u003e(TaqMan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacR_f\u003c/p\u003e \u003cp\u003eBacR_r\u003c/p\u003e \u003cp\u003eBacR_P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCGTATCCAACCTTCCCG\u003c/p\u003e \u003cp\u003eCATCCCCATCCGTTACCG\u003c/p\u003e \u003cp\u003eFAM-CTTCCGAAAGGGAGATT-NFQ-MGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Reischer, Kasper, Steinborn, Mach and Farnleitner, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\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 \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Acquisition of climate and geographic parameter data\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe average temperature and total precipitation data for the years 2021 and 2022 of the study area were obtained from the Kırklareli/Demirk\u0026ouml;y meteorological measurement station and internet-based online channels (Meteoblue); instantaneous air temperature was obtained with the Hach Lange\u003csup\u003e\u0026reg;\u003c/sup\u003e Hq40d Multi device, and instantaneous wind measurements were obtained with an anemometer (BeneTech\u003csup\u003e\u0026reg;\u003c/sup\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Reduction of data with Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eTo visualize and interpret the microbiological culture data of the sampling points more clearly, eight parameters were subjected to Principal Component Analysis (PCA). As a result of this analysis, 8 microbiological parameters were gathered under 2 factors. The first factor explained 49.17% of the variance, while the second factor explained 15.90%; cumulatively, 65.08% of the variance was explained (KMO: 0.615; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to the rotation analysis result, \"Fecal Coliform, \u003cem\u003eClostridium perfringens\u003c/em\u003e, and \u003cem\u003eEnterococci\u003c/em\u003e\" parameters were gathered under Factor 1, while \"\u003cem\u003eClostridia\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\" parameters were gathered under Factor 2. \"Total Coliform\" and \"Total colony counts at 37 \u003csup\u003eo\u003c/sup\u003eC and 22 \u003csup\u003eo\u003c/sup\u003eC parameters showed distribution in both factors. Based on this grouping, Factor 1 was named the \"Fecal Bacteria Group\" and Factor 2 was named the \"Non-Fecal Bacteria Group\" in the visualization of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Evaluation of physicochemical and microbiological culture analysis results\u003c/h2\u003e \u003cp\u003eK-Means clustering analysis performed on the microbiological analysis results of 144 water samples showed that the majority of the samples (137 samples) had average pollution levels and gathered in a single cluster (Cluster 3). However, it was determined that 7 samples deviated significantly from this average and exhibited outlier pollution values. The distribution of these outlier samples is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eGeneral information about the clusters according to the K-means clustering analysis results.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1st Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2nd Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5th Group\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\u003eNumber of Samples\u003c/b\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStation and Period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12Q (Summer 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9B (2021-Spring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStations and periods where average reproduction levels were observed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; 9B (Summer 2021)\u003c/p\u003e \u003cp\u003e\u0026bull; 9B (2022-Winter)\u003c/p\u003e \u003cp\u003e\u0026bull; 9B (2022-Spring)\u003c/p\u003e \u003cp\u003e\u0026bull; 9B (2022-Fall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9B (Summer 2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParameter(s) in which the anomaly was observed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Coliform, Fecal Coliform, Total Colony Counts Growing at 22\u0026deg;C and 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eClostridium perfringens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnterococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eTotal number of samples\u0026thinsp;=\u0026thinsp;144\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpearman rank correlation analysis was performed to determine the relationship between the microbiological culture and physicochemical analysis results of the water samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Spearman correlation analysis revealed various significant relationships between parameters. It was observed that Total Coliform (r\u0026thinsp;=\u0026thinsp;0.502), Clostridia (r\u0026thinsp;=\u0026thinsp;0.580), and total colony count growing at 37 \u003csup\u003eo\u003c/sup\u003eC (r\u0026thinsp;=\u0026thinsp;0.515) increased with increasing water temperature (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, a negative relationship was detected between the dissolved oxygen (LDO) level in water and fecal indicators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, it was determined that the increase in turbidity and color of the water was associated with the increase in the fecal coliform count (p\u0026thinsp;\u0026lt;\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eWhen climate data were examined, a moderate-strong positive relationship was found between seasonal average temperature and \"Non-Fecal Group\" bacteria (r\u0026thinsp;=\u0026thinsp;0.612), while the relationship between \"Fecal Group\" bacteria and temperature was weaker (r\u0026thinsp;=\u0026thinsp;0.38). A weak negative relationship was observed between seasonal total precipitation amount and the \"Fecal Group\" (r = -0.311). These findings suggest that anthropogenic factors may be more dominant than climatic factors in the dynamics of fecal pollution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Evaluation of molecular analysis results\u003c/h2\u003e \u003cp\u003eqPCR efficiencies showed similarities with molecular MST analysis efficiencies performed in different geographies. The measurement ranges of the primers varied between 5 and 10\u003csup\u003e7\u003c/sup\u003e depending on the samples. The detection frequencies of the primers are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\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\u003eSeasonal detection frequencies of MST methods in water samples of İğneada Longoz Forest basin and sub-basins\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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of Samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eNumber of Positive Samples (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBacUni (Universal)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHF183 (Human)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBachum (Human)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eBacCow (Cattle)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eBacR (Cattle)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (86,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (80.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (86,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (86,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127 (88,1)\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 \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Statistical evaluation of the relationship between qPCR and microbiological culture results\u003c/h2\u003e \u003cp\u003eSpearman rank correlation analysis was performed to determine the relationship between microbiological culture analysis results and qPCR analysis results. Since the data were not normally distributed, Spearman correlation was used. As a result of the analysis, a statistically significant positive relationship was found between all examined parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eBacUni, the universal \u003cem\u003eBacteroidales\u003c/em\u003e marker, showed the highest correlation with Total Coliform (r\u0026thinsp;=\u0026thinsp;0.862) and total colony count at 37 \u003csup\u003eo\u003c/sup\u003eC (r\u0026thinsp;=\u0026thinsp;0.816). Among human-specific markers, HF183 exhibited strong correlations with Fecal Coliform (r\u0026thinsp;=\u0026thinsp;0.861), \u003cem\u003eEnterococci\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.731), and the Fecal Group (r\u0026thinsp;=\u0026thinsp;0.864). Similarly, the Bachum marker showed high correlation with Fecal Coliform (r\u0026thinsp;=\u0026thinsp;0.843) and the Fecal Group (r\u0026thinsp;=\u0026thinsp;0.851). Among cattle-specific markers, BacCow showed a positive relationship with Fecal Coliform (r\u0026thinsp;=\u0026thinsp;0.705) and the Fecal Group (r\u0026thinsp;=\u0026thinsp;0.724), while the BacR marker exhibited correlation with Fecal Coliform (r\u0026thinsp;=\u0026thinsp;0.712) and the Fecal Group (r\u0026thinsp;=\u0026thinsp;0.732) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Effects of climatic and seasonal factors on microbiological and physicochemical water quality\u003c/h2\u003e \u003cp\u003eMeasurements made throughout 2021\u0026ndash;2022 in the İğneada Floodplain Forests research area showed that climatic and seasonal changes have a determining role in water quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While precipitation generally intensified in the winter and spring months, average temperatures showed distinct differences according to seasons. When the relationship of these climatic dynamics with microbiological parameters was examined statistically, a moderate to strong positive relationship was detected between seasonal average temperature and parameters such as \u003cem\u003eClostridia\u003c/em\u003e and total colony count at 37 \u003csup\u003eo\u003c/sup\u003eC (Spearman r\u0026thinsp;\u0026gt;\u0026thinsp;0.6). In contrast, a weak negative relationship was observed between fecal group bacteria data and seasonal total precipitation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study has revealed with numerical and molecular data that the microbiological water quality of the basins feeding the İğneada Floodplain Forests is under serious pressure, especially in regions where human activities are intense.\u003c/p\u003e \u003cp\u003eThe Bulanık Creek basin, the largest basin in the research area, constitutes a critical example for understanding pollution dynamics. In particular, it was determined that microbiological quality levels were quite high in all seasonal measurements performed in 2021\u0026ndash;2022 at the Demirk\u0026ouml;y Exit station (9B) of Bulanık Creek, which passes through the Demirk\u0026ouml;y district center of Kırklareli and enters the National Park borders. The quality level at this station was measured as an average of 34,675 cfu/100ml for total coliform bacteria and 11,688 cfu/100ml for fecal coliforms. These values indicate a highly polluted water profile according to water quality standards. According to the analysis results carried out at stations before Demirk\u0026ouml;y and at other stations after the Demirk\u0026ouml;y exit until it empties into Deniz Lake, it is observed that this fecal pollution seen at station 9B tends to be diluted until discharge. Conversely, the microbiological load of station 18B from the sub-branches of Bulanık Creek, flowing north of Sivriler village and far from settlements, is quite low compared to other stations (average total coliform 661 cfu/100ml, fecal coliform 191 cfu/100ml). This situation numerically proves the determining role of anthropogenic pressure on pollution.\u003c/p\u003e \u003cp\u003eOne of the most striking findings of the study is the extreme fecal pollution event observed in the \u0026Ccedil;avuşk\u0026ouml;pr\u0026uuml; Creek basin, especially in the summer of 2022. During this period, the fecal coliform count reached an extreme level of 97000 cfu/100 ml. This situation was confirmed by K-Means clustering analysis, and it was determined that the sample taken from this station (12\u0026Ccedil;) showed a high amount of outliers compared to the other 137 samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This result suggests a point and sudden fecal waste discharge into the stream. Similarly, in a study conducted on Acarlar Lake feeding Acarlar Floodplain, it was stated that the high level of total coliform bacteria presence seen in the winter period at a sampling point could be the result of a point source of fecal pollution (Ert\u0026uuml;rk, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). On the other hand, analysis results in Mert Lake where the stream discharges (average fecal coliform 518 cfu/100ml) show that the floodplain ecosystem absorbs this fecal pressure to a certain extent.\u003c/p\u003e \u003cp\u003eWhen the microbiological culture analysis results of all basins in the research area are examined, it is determined that the microbial load increases during the summer periods. This situation is consistent with bacteriological studies conducted in national parks in the American and European continents, observing a general increase in microbial load levels in lakes and other water sources in hot weather (Crabill et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McCarthy, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schoonover and Lockaby, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelations established between physicochemical parameters and microbiological data also presented important findings. While a positive relationship was detected between water temperature and many microbiological parameters (total coliform, \u003cem\u003eClostridia\u003c/em\u003e, \u003cem\u003eEnterococci\u003c/em\u003e) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), an inverse relationship was found between the amount of dissolved oxygen in water and microbiological indicators. This finding is supported by a study conducted on the Kshipra River in India revealing a similar negative correlation (Diwan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the performed qPCR analysis results were compared with culture-based fecal indicator bacteria (FIB) results, it was clearly demonstrated that the fecal pollution in the basins is largely of human origin. A very strong positive relationship was found between human-origin MST markers HF183 and Bachum and \"fecal group bacteria\" parameters (Spearman r\u0026thinsp;\u0026gt;\u0026thinsp;0.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This correlation is statistically significantly stronger than the relationship between cattle-origin markers (BacCow and BacR) and FIB data (Spearman r\u0026thinsp;\u0026lt;\u0026thinsp;0.8). Although the relationship between MST methods and FIB data is controversial in the literature (Green et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yasar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), many researchers have detected a moderate to high positive relationship similar to our findings (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gourmelon et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Harwood et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Malla et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The fact that analysis results obtained with human-specific MST methods are also at very high levels, especially at stations where high amounts of fecal bacteria growth are seen (9B, 12\u0026Ccedil;), is the most important evidence strengthening this study.\u003c/p\u003e \u003cp\u003eWhen the effect of climatic factors on pollution is examined, while positive relationships are found between seasonal average temperature and microbiological parameters, it is observed that these relationships are generally at weak to moderate levels. It is understood that climatic factors affecting the distribution of fecal group bacteria have a complex interaction and anthropogenic pressures shape this dynamic more. This situation is consistent with the study conducted by Lenart-Boron et al. (2016) on the Bialka River, stating that although fecal pollution in the river varies depending on the climate, it reaches the highest levels especially during periods when the number of people for recreational purposes in the region increases.\u003c/p\u003e \u003cp\u003eIn conclusion, the quality of water resources feeding the İğneada Floodplain Forests is under the threat of predominantly human-origin fecal pollution, especially in basins under the influence of settlements and increasing tourism activities. This study emphasizes the importance of developing urgent management strategies and protection measures for the conservation of this rare ecosystem by revealing the extent and source of this threat with numerical and molecular data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding information\u003c/h2\u003e \u003cp\u003eThis study was supported by the Trakya University Scientific Research Projects Coordination Unit. Project number: 2021/48.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.B.D. and U.G. wrote the main manuscript text, figures .\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank \"TRAKYA UNIVERSITY SCIENTIFIC RESEARCH PROJECTS UNIT (T\u0026Uuml;BAP)\" for supporting your work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed, W., Yusuf, R., Hasan, I., Goonetilleke, A. \u0026amp; Gardner, T. Quantitative PCR assay of sewage-associated Bacteroides markers to assess sewage pollution in an urban lake in Dhaka, Bangladesh. \u003cem\u003eCan. J. 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Hydrol.\u003c/em\u003e \u003cb\u003e331\u003c/b\u003e (3\u0026ndash;4), 371\u0026ndash;382 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVadde, K. K. \u003cem\u003eApplication of Microbial Source Tracking Techniques to Characterize Fecal Pollution entering Taihu Lake (China)\u003c/em\u003e (The University of Liverpool (United Kingdom), 2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue, J., Lin, S., Lamar, F. G., Lamori, J. G. \u0026amp; Sherchan, S. Assessment of fecal pollution in Lake Pontchartrain, Louisiana. \u003cem\u003eMar. Pollut. Bull.\u003c/em\u003e \u003cb\u003e129\u003c/b\u003e (2), 655\u0026ndash;663 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasar, S. A. et al. Quantitative detection of human-and canine-associated Bacteroides genetic markers from an urban coastal lagoon. \u003cem\u003eWater Sci. Technol.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e (7), 1732\u0026ndash;1744 (2021).\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":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Floodplain forests, water quality, microbial source tracking, qPCR, iğneada, fecal pollution","lastPublishedDoi":"10.21203/rs.3.rs-8830939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8830939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/aim:\u003c/strong\u003e The İğneada Floodplain Forests represent one of the largest forest-marsh ecosystems in Türkiye, hosting significant biodiversity. However, increasing tourism investments, camping activities, and seasonal population growth in the region pose potential pressure on the water resources feeding this ecosystem. This study aimed to determine the fecal pollution levels of the basins and sub-basins feeding the İğneada Floodplain Forests using microbiological culture methods and to identify the pollution sources (human or animal) using molecular microbial source tracking (MST) via detection of host-specific \u003cem\u003eBacteroidales\u003c/em\u003e 16S rRNA genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods:\u003c/strong\u003e Water samples were collected seasonally in 2021 and 2022 from 18 stations across the Bulanık, Çavuşköprü, and Efendi stream basins. Microbiological quality was assessed using membrane filtration and pour plate methods. For molecular analysis, qPCR was performed using universal (BacUni), human-specific (HF183, Bachum), and ruminant-specific (BacCow, BacR) markers. The relationships between microbial data, physicochemical parameters, and climatic factors were evaluated statistically using PCA, K-means clustering, and Spearman correlation analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Microbiological analyses revealed high levels of fecal pollution, particularly in the Bulanık Creek (Station 9B) and Çavuşköprü Creek (Station 12Ç), which were identified as outliers with extreme pollution loads. A strong positive correlation was found between human-specific molecular markers and fecal indicator bacteria (Spearman r\u0026gt;0.8; p\u0026lt;0.001), indicating a predominance of human-induced pollution. While seasonal temperature increases correlated with \u003cem\u003eClostridia\u003c/em\u003e and total colony counts, anthropogenic factors were found to be more dominant than climatic factors in driving fecal pollution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The water resources feeding the İğneada Floodplain Forests are under significant anthropogenic pressure, primarily from human-origin fecal contamination. The findings underscore the urgent need for management strategies to mitigate pollution and ensure the sustainability of this critical wetland ecosystem.\u003c/p\u003e","manuscriptTitle":"Seasonal Evaluation of Microbial Quality of Water Resources Feeding İğneada Floodplain Forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 22:09:42","doi":"10.21203/rs.3.rs-8830939/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T07:13:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T02:08:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149117979590982319573521055665396467800","date":"2026-03-11T08:35:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-28T22:38:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23836654005506236048934275491553965209","date":"2026-02-25T15:56:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T02:30:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T02:21:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T14:47:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T07:00:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-23T06:55:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d55735c4-4549-41bf-a635-18929ceea857","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63532946,"name":"Biological sciences/Ecology"},{"id":63532947,"name":"Earth and environmental sciences/Ecology"},{"id":63532948,"name":"Earth and environmental sciences/Environmental sciences"},{"id":63532949,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-04-23T09:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 22:09:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8830939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8830939","identity":"rs-8830939","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}