{"paper_id":"3af8ab2d-c8cc-4c3f-8a72-ada45eaf8422","body_text":"Effects of Multiple Variables on Epilithic Algal Communities in the Pülümür Stream System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Multiple Variables on Epilithic Algal Communities in the Pülümür Stream System Banu KUTLU, Serdar ÇETİNDAĞ, Fatma ÇEVİK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8147545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study evaluates the influence of multiple environmental stressors—including nutrient enrichment, physicochemical variability, and trace metal contamination—on the spatial and seasonal dynamics of epilithic algal communities in the semi-arid Pülümür Stream (Eastern Türkiye). Ten stations were sampled seasonally between February 2022 and January 2023 to characterize gradients in temperature, dissolved oxygen, pH, nutrients, and heavy metals in both water and sediments. Dissolved oxygen ranged from 9.72 to 12.27 mg/L, temperature from 2.76 to 18.03°C, and pH from 8.82 to 9.93. Nitrate (0.43–4.50 mg/L), phosphate (0.38–2.48 mg/L), and metal concentrations (notably Pb, Cd, Zn, Mn, and Fe) exhibited pronounced spatial variability associated with downstream anthropogenic pressures. Multivariate analyses (PCA, CA, CCA) revealed that nutrients (NO₃⁻, PO₄³⁻) and metals—particularly Pb, Cd, Mn, and Zn—were the primary drivers shaping algal composition. Pollution-tolerant taxa such as Nitzschia palea , Gomphonema parvulum , and Achnanthidium minutissimum predominated at nutrient- and metal-rich sites, whereas sensitive species persisted in upstream reaches. These findings demonstrate strong species–environment relationships and highlight epilithic algae as effective bioindicators for detecting early ecological degradation in semi-arid river systems. The study provides essential baseline data to support ecological monitoring and sustainable watershed management strategies in the region.. Epilithic algae PCA CCA heavy metals bioindicators Pülümür Stream Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Epilithic algal communities were assessed across ten stations in a stream. • Nutrient and heavy metal gradients strongly shaped algal distribution patterns. • Pollution-tolerant diatoms dominated nutrient- and metal-rich downstream sites. • CCA identified phosphate, nitrate, Pb, and Cd as key drivers of community change. • Findings support bioindicator-based monitoring for sustainable river management. Introduction Lotic ecosystems provide essential ecological services—such as water supply, nutrient cycling, fisheries, recreation, and biodiversity conservation—and are therefore regarded as critical components of global freshwater resources. However, nearly two-thirds of freshwater habitats worldwide are currently classified as moderately to severely threatened due to escalating anthropogenic pressures ( Dudgeon et al., 2006 ; Schowe & Harding, 2014 ) . Rapid population growth, expansion of agricultural and industrial activities, and increasing water demand intensify pollution loads and accelerate the degradation of riverine environments ( Nhiwatiwa et al., 2017 ; Petersen et al., 2017 ). These pressures alter water quality, reduce ecological resilience, and compromise the ecological integrity of freshwater systems ( Wu et al., 2012 ; Venkatachalapathy & Karthikeyan, 2015 ) . Anthropogenic disturbances frequently propagate downstream and can restructure ecological processes such as nutrient retention, primary production, and biotic interactions ( Teittinen et al., 2015 ; Bere et al., 2016 ; Dalu & Froneman, 2016 ) . In response to these widespread pressures, biological monitoring has become indispensable. The European Water Framework Directive (WFD) emphasizes the integration of biological quality elements—including phytobenthos, epilithic algae, macroinvertebrates, macrophytes, and fish—with physicochemical and hydromorphological parameters to assess the ecological status of freshwaters (EU, 2014; EU, 2023) . Among these indicators, epilithic algae are particularly valuable due to their rapid response to changes in nutrient concentrations, heavy metal contamination, and flow variability, making them sensitive and reliable ecological indicators. Excessive anthropogenic activities—such as agricultural runoff, livestock waste discharge, domestic sewage, and industrial effluents—contribute significantly to nutrient and metal accumulation in lotic systems, often resulting in shifts in algal community composition and reductions in diversity ( Dalu et al., 2017 ; Alloway, 2013 ; Kutlu & Sarıgül, 2022 ) . These changes serve as early warning signals of ecological degradation, particularly in semi-arid regions where hydrological instability amplifies pollutant impacts. The Pülümür Stream, located in a semi-arid region of Eastern Türkiye, is exposed to multiple environmental stressors, including agricultural practices, livestock activities, and urban wastewater inputs. Despite evidence of nutrient and metal enrichment in certain segments of the stream, the ecological responses of epilithic algal communities to these combined pressures remain insufficiently investigated. Therefore, the present study aims to (i) examine the spatial and seasonal distribution of epilithic algal communities in the Pülümür Stream, (ii) identify key environmental drivers influencing algal composition using multivariate statistical analyses, and (iii) evaluate the potential of epilithic algae as bioindicators for ecological monitoring in semi-arid river systems. The findings offer critical insight into the ecological condition of the Pülümür Stream and provide a scientific foundation for sustainable water quality management and restoration efforts. Materials and Methods Study Area and Sampling Seasonal sampling was carried out between February 2022 and January 2023 at ten stations (St1–St10) along the Pülümür Stream (Fig. 1 ). The stations were selected to represent upstream, midstream, and downstream segments and to capture gradients influenced by land use, tributary inflows, and anthropogenic pressures. Substrate types ranged from fine sand and gravel to cobbles and boulders. Stations close to agricultural, livestock, urban, and landfill areas were included to characterize the effects of environmental stressors. Geographic coordinates of all stations are provided in Fig. 1 . Measurement of physicochemical parameters and water sampling Water samples were collected 0.5 m below the surface using pre-cleaned 2.5 L polyethylene bottles following the “Sampling and Analysis Methods” of the Turkish Water Pollution Control Regulation. Samples were transported at 4°C in light-protected glass bottles. In situ measurements of temperature (°C), pH, and dissolved oxygen (DO) were made using a YSI Professional Plus Portable Multi-Parameter instrument. Nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), and phosphate (PO₄³⁻) concentrations were determined volumetrically according to TS 4956 standards. Water heavy metals (coded as W) were analyzed after acid digestion. A 50 mL subsample was treated with 1 mL HCl and 4 mL HNO₃ and heated to 250°C for 3 h until a clear solution formed. Digested samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES; SPECTRO Arcos, Canada). Selected metals (Cu, Pb, Zn, Ni, Co, Mn, Fe, As, Cd, Mg, Al, Hg) were confirmed with ICP-MS (ACME Laboratory, Canada). Sediment sampling and analysis Surface sediments (0–10 cm) were collected with an Ekman dredge (20 × 20 × 20 cm) and stored in polyethylene bags at − 20°C until analysis. Samples were digested using a modified aqua regia solution (HCl:HNO₃:H₂O = 1:1:1) for 1 h, and metal concentrations were quantified by ICP-MS (ACME Laboratory, Canada) ( Alloway, 2013 ; Mü ller, 1969) . Sediment metals were denoted as “C.” Epilithic algal sampling and identification Epilithic algae were seasonally collected by brushing submerged stones and cobbles and preserved in 4% formaldehyde. Samples were cleaned using 30% H₂O₂ and mounted in Naphrax for permanent slide preparation. Algal identification followed standard taxonomic keys ( Krammer, 2002 ; Lange-Bertalot et al., 2017 ; Bey & Ector, 2013 ) . In vivo chlorophyll-a fluorescence was measured using a Turner Designs 10-AU fluorometer following the manufacturer’s instructions. Results were expressed as µg/L. Data processing and statistical analysis A total of 39 environmental variables were analyzed, including physicochemical parameters, nutrients, and heavy metals in water and sediment. Skewed data were log(x + 1) transformed prior to analysis. Principal Component Analysis (PCA) was applied to determine major environmental gradients. Analysis of Variance (ANOVA) tested differences among stations. Canonical Correspondence Analysis (CCA) was used to relate epilithic algal composition to environmental variables. Cluster analysis using Bray–Curtis similarity identified spatial groupings of stations. Similarity Percentage (SIMPER) analysis determined species contributing ≥ 1% to between-group dissimilarities. ANOSIM and SIMPER were performed in PAST 3.0 ( Hammer et al., 2009 ; Seaby & Henderson, 2007 ) . Results Dissolved oxygen (DO) ranged from 9.72 to 12.27 mg/L during the study period, with the highest values recorded in spring. Water temperature varied between 2.76°C and 18.03°C, while pH fluctuated from 8.82 to 9.93. In vivo chlorophyll-a concentrations ranged from 0.28 to 2.04 µg/L. Nitrate (NO₃⁻) levels varied between 0.43 and 4.50 mg/L, with minimum values observed at Station 9 in summer and maximum values at Station 6 in autumn. Ammonium (NH₄⁺) ranged from 0.01 to 0.49 mg/L, and nitrite (NO₂⁻) varied between 0.008 and 0.18 mg/L, with the highest NO₂⁻ concentration detected at Station 10 in summer. Phosphate (PO₄³⁻) ranged from 0.38 to 2.48 mg/L, showing strong spatial and seasonal variation (Fig. 2 ). Heavy metal concentrations in water displayed distinct patterns. Minimum and maximum values were as follows: Cu (0.9–2.4 µg/L), Pb (7.1–28.2 µg/L), Zn (1.0–3.37 µg/L), Ni (0.2–1.4 µg/L), Co (0.03–0.16 µg/L), Mn (0.17–1.0 µg/L), Fe (8.0–9.76 µg/L), As (1.1–3.4 µg/L), Cr (5.8–7.8 µg/L), and Cd (0.03–0.16 µg/L). The metal concentration order in water was Pb > Fe > Cr > As > Zn > Cu > Ni > Mn > Cd > Co. Sediment metal concentrations were considerably higher than water values. Ranges included: Al (1.03–2.04 mg/kg), Cd (0.09–0.39 mg/kg), Cr (54–121 mg/kg), Cu (18–19 mg/kg), Fe (231–401 mg/kg), Co (14.4–29.7 mg/kg), K (0.12–0.23 mg/kg), Mg (1.09–3.1 mg/kg), Na (5–125 mg/kg), Pb (6.4–17.5 mg/kg), Zn (46–89 mg/kg), As (3.6–26.8 mg/kg), Hg (0.02–0.07 mg/kg), and Mn (320–691 mg/kg). Sediment metals followed the order Mn > Fe > Na > Cr > Zn > Co > As > Cu > Pb > Mg > Al > Cd > K > Hg. The Spearman correlation heatmap revealed strong positive associations among most heavy metals (Pb, Zn, Fe, Cu, Cd, Ni), indicating shared anthropogenic or geogenic sources. Conversely, DO, pH, and temperature negatively correlated with metals, suggesting that increased metal pollution deteriorates key water quality parameters (Fig. 2 ). PCA results showed that PC1 and PC2 explained 32.89% and 25.42% of the total variance (58.31% overall). Metal variables (Pb, Cd, Zn, Fe) grouped strongly on PC1, while physicochemical factors such as DO, pH, and temperature were more associated with PC2 (Fig. 3 ). Key contributors included NO₂⁻, NH₄⁺, Pb (water), Cd (sediment), As (sediment), Hg (sediment), and Pb (sediment). Taxonomic composition and diversity A total of 452 epilithic algal taxa were identified across 10 stations. Bacillariophyceae were dominant (41.3%), followed by Cyanophyceae (19.9%), Euglenophyceae (10.6%), Chlorophyceae (5.1%), and Dinophyceae (3.9%). SIMPER analysis identified major contributors including Peridinium bipes, Cocconeis placentula var. euglypta , Aulacoseira italica , Nitzschia spp., Encyonema spp., Gomphonema spp., Rhoicosphenia spp., Fragilaria capucina, Pseudostaurosira brevistriata, Synedra ulna , and Ulnaria ulna . Shannon Diversity Index (H’) values ranged from 1.017 to 1.034 across stations, while evenness (J’) varied between 0.56 and 0.60, indicating generally low diversity. The highest diversity was observed at Station 10, with the lowest at Station 2. Simpson Dominance Index values ranged from 0.39 to 0.40, reflecting dominance of a few tolerant taxa. Correspondence Analysis (CA) revealed station-level groupings: St3, St6, St9, and St10 clustered closely, indicating similar algal composition, while St2 was isolated, suggesting distinct community characteristics (Fig. 4 ). CCA and species–environment relationships CCA showed that axes 1 and 2 explained 41.2% and 30.8% of total variance, respectively (Fig. 5 ). pH and nitrate (NO₃⁻) showed strong opposing gradients, while DO was positively associated with temperature and Fe. Cyanobacteria and Chlorophyta aligned with nutrient-rich, metal-influenced conditions, whereas Bacillariophyta showed affinity for oxygenated sections with moderate nutrient levels. Dominant taxa included Achnanthidium minutissimum , Gomphonema parvulum , and Nitzschia palea , which occurred frequently at pollution-impacted stations. Cosmopolitan species such as Cocconeis placentula and Ulnaria ulna were also widespread. Discussion The spatial heterogeneity observed in epilithic algal communities along the Pülümür Stream clearly reflected the influence of multiple environmental gradients, consistent with classic theories of lotic ecosystem functioning (Allan & Castillo, 2007). Upstream stations characterized by relatively undisturbed hydromorphology and low nutrient availability supported oligotrophic and sensitive taxa, a pattern frequently documented in high-altitude or minimally impacted rivers (Soininen, 2004; Stevenson et al., 2010). In contrast, downstream sites subjected to agricultural runoff, livestock waste, and urban discharges were dominated by eutrophic and pollution-tolerant species, mirroring findings from heavily impacted rivers worldwide (Bere et al., 2016 ; Dalu et al., 2017 ). These shifts demonstrate that community structure responds predictably to nutrient enrichment, hydrological variability, and contaminant loading, reinforcing the importance of species-level bioassessment in ecological monitoring programs (Kelly et al., 2008 ; Rimet & Bouchez, 2012). Canonical Correspondence Analysis (CCA) further highlighted that manganese (Mn), zinc (Zn), cobalt (Co), and dissolved oxygen (DO) were the most influential variables shaping algal composition. Elevated concentrations of Mn, Zn, and Co reduced taxonomic richness and contributed to the dominance of tolerant taxa, a trend also reported in metal-enriched streams influenced by mining, agriculture, or industrial discharges (Ivorra et al., 2002 ; Morin et al., 2008; Singh et al., 2020). Conversely, higher nitrate (NO3-) and DO levels supported a more diverse algal community, which aligns with research showing that moderate nutrient availability and oxygenation enhance metabolic activity and facilitate species coexistence (Guo et al., 2017; Sabater et al., 2019). Together, these results confirm that metal–nutrient interactions can exert complex and sometimes opposing effects on benthic algal assemblages, depending on local environmental conditions. The pronounced sensitivity of epilithic algae to both nutrient enrichment and heavy metal contamination reinforces their value as early-warning indicators in riverine monitoring frameworks (Biggs & Kilroy, 2000; Pan et al., 2021). The strong association of Mn and Zn with stations exhibiting low diversity mirrors observations in other semi-arid basins where agricultural and urban pressures intensify metal inputs (Zhang et al., 2019 ; Lopez-Doval et al., 2020). Therefore, routine monitoring of these metals should be integrated into watershed management programs. Strategies such as improving agricultural fertilizer efficiency, controlling livestock waste discharge, and upgrading urban wastewater infrastructure are essential to reduce ecological stress in downstream regions (Carpenter et al., 2011; EU WFD, 2023). Overall, the results emphasize the importance of integrating biological indicators with chemical assessments to detect early signs of ecological degradation. The strong species–environment relationships identified in this study highlight the sensitivity of epilithic algae to nutrient–metal interactions, making them reliable tools for long-term monitoring. For the Pülümür Stream, improving fertilizer management, reducing livestock waste inputs, and enhancing wastewater treatment facilities are essential measures to mitigate pollutant loads. These recommendations are aligned with international river basin management strategies and contribute directly to broader sustainability objectives, including SDG 15 on biodiversity conservation (UNEP, 2021; Reid et al., 2019). Our findings demonstrate that epilithic algal communities in the Pülümür Stream are strongly influenced by nutrient enrichment and heavy metal contamination. PCA revealed two major stress gradients: PC1 representing metal load (Pb, Cd, Zn, Fe) and PC2 reflecting physicochemical conditions (DO, pH, temperature). Similarly, CCA showed that CCA1 captures eutrophication and metal contamination effects, while CCA2 contrasts oxygen regime with metal toxicity. Species-level responses confirmed these patterns: tolerant taxa (Nitzschia palea, Gomphonema parvulum) dominated nutrient- and metal-rich downstream sites, whereas sensitive species persisted upstream. These results emphasize the need for integrated watershed management, including nutrient control, improved wastewater treatment, and continuous monitoring of trace metals. Such measures are essential to maintain ecological integrity and align with global conservation goals (SDG 15). Conclusion This study demonstrated that epilithic algal communities are highly sensitive indicators of environmental stress in the Pülümür Stream. Spatial shifts from oligotrophic, diversity-rich assemblages in upstream sections to pollution-tolerant and metal-resistant taxa in downstream areas reflected the cumulative impacts of agricultural runoff, livestock activities, and urban wastewater. Multivariate analyses (PCA and CCA) revealed that nutrient levels (NO₃⁻, PO₄³⁻) and trace metals (Mn, Zn, Co, Pb) were the primary drivers shaping algal composition, with elevated metal concentrations associated with reduced diversity and ecological degradation. These findings highlight the importance of integrating biological indicators with physicochemical monitoring to obtain a comprehensive assessment of freshwater ecosystem health. Reducing nutrient and heavy metal inputs through improved agricultural practices, enhanced wastewater management, and strengthened watershed protection measures is essential for restoring ecological integrity. Overall, the study provides critical baseline data for the sustainable management of the Pülümür Stream and supports broader global conservation efforts, including the objectives of the United Nations Sustainable Development Goal (SDG) 15. Maintaining the ecological resilience of river systems like Pülümür is vital for preserving freshwater biodiversity and ensuring long-term environmental sustainability. Declarations Funding This study was supported by the Munzur University Scientific Research Projects Coordination Unit under Project Number YLMUB021-18 . Author Contribution Author Information & ContributionsAuthor InformationAuthor: Banu KUTLUGiven names: BanuFamily name: KUTLUEmail: [email protected] affiliation: Basic Science, Fisheries Faculty, Munzur University, Tunceli, TürkiyeAuthor: Serdar ÇETİNDAĞGiven names: SerdarFamily name: ÇETİNDAĞEmail: [email protected] affiliation: Tunceli Provincial Health Directorate, Tunceli, TürkiyeAuthor: Fatma ÇEVİKGiven names: FatmaFamily name: ÇEVİKEmail: [email protected] affiliation: Basic Science, Fisheries Faculty, Çukurova University, Adana, TürkiyeAuthor Contributions StatementB.K. designed the study, conducted the field sampling, and wrote the main parts of the manuscript.S.Ç. performed the data analyses, including statistical evaluations, and contributed to the interpretation of the results.F.Ç. carried out the laboratory analyses, including nutrient and heavy metal measurements, and contributed to data validation.All authors reviewed, edited, and approved the final version of the manuscript and agreed to its submission. Acknowledgement This study was supported by the Scientific Research Projects Coordination Unit of Munzur University under Project No. YLMUB021-18. I would like to thank the BAP Unit for their support. References Alloway, B. J. (2013). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability (Vol. 22). Springer. Bere, T., Dalu, T., & Mwedzi, T. (2016). Detecting the impact of heavy metal contaminated sediment on benthic macroinvertebrate communities in tropical streams. Science of the Total Environment, 572, 147–156. https://doi.org/10.1016/j.scitotenv.2016.07.204 Bere, T., Mangadze, T., & Mwedzi, T. (2016). Variation partitioning of diatom species data matrices: Understanding the influence of multiple factors on benthic diatom communities in tropical streams. Science of the Total Environment, 566, 1604–1611. https://doi.org/10.1016/j.scitotenv.2016.06.058 Bey, M. Y., & Ector, L. (2013). Atlas des diatomées des cours d’eau de la région Rhône-Alpes. DREAL Rhône-Alpes, 1–5, 172–181. Cantonati, M., & Lowe, R. L. (2014). Lake benthic algae: Toward an understanding of their ecology. Freshwater Science, 33, 475–486. https://doi.org/10.1086/676140 Chessman, B., Growns, I., Currey, J., & Plunkett Cole, N. (1999). Predicting diatom communities at the genus level for the rapid biological assessment of rivers. Freshwater Biology, 41, 317–331. https://doi.org/10.1046/j.1365-2427.1999.00433.x Commission Directive 2014/101/EU of 30 October 2014 amending Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for Community action in the field of water policy (Text with EEA relevance). Official Journal of the European Union, L311, 32–35. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32014L0101 Dalu, T., & Froneman, P. W. (2016). Diatom-based water quality monitoring in southern Africa: Challenges and future prospects. Water SA, 42(4), 551–559. https://doi.org/10.4314/wsa.v42i4.05 Dalu, T., Wasserman, R. J., Magoro, M. L., Mwedzi, T., Froneman, W., & Weyl, O. L. F. (2017). Variation partitioning of benthic diatom community matrices: Effects of multiple variables on benthic diatom communities in an Austral temperate river system. Science of the Total Environment, 601–602, 1498–1508. Dudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z. I., Knowler, D. J., Lévêque, C., Naiman, R. J., Prieur-Richard, A. H., Soto, D., Stiassny, M. L., & Sullivan, C. A. (2006). Freshwater biodiversity: Importance, threats, status and conservation challenges. Biological Reviews, 81(2), 163–182. https://doi.org/10.1017/S1464793105006950 Ector, L., Compère, P., Vidal, H., & Gentili, R. (2000). Compte rendu du 18e colloque de l’Association des diatomistes de langue française. Cryptogamie Algologie, 21, 213–258. https://doi.org/10.1016/S0181-1568(01)80003-9 European Commission. (2023). WFD Reporting Guidance 2022, Final Draft V6.6, 26 October 2023. Brussels: European Commission. Gupta, D. K., Chatterjee, S., & Datta, S. (2014). Role of phosphate fertilizers in heavy metal uptake and detoxification of toxic metals. Chemosphere, 108, 134–144. https://doi.org/10.1016/j.chemosphere.2014.01.030 Hammer, Q., Harper, D. A. T., & Ryan, P. D. (2009). PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 4, 9. Hill, B. H., Herlihy, A. T., Kaufmann, P. R., Stevenson, R. J., McCormick, F. H., & Burch-Johnson, C. (2000). Use of periphyton assemblage data as an index of biotic integrity. Journal of the North American Benthological Society, 19, 50–67. https://doi.org/10.2307/1468281 Ivorra, N., Hettelaar, J., Kraak, H. S., Sabater, M., & Admiraal, W. (2002). Responses of biofilms to combined nutrient and metal exposure. Environmental Toxicology and Chemistry, 21(3), 626–632. https://doi.org/10.1002/etc.5620210323 John, J. (2003). Bioassessment on health of aquatic systems by the use of diatoms. In R. S. Ambasht & N. K. Ambasht (Eds.), Modern Trends in Applied Aquatic Ecology (pp. 1–20). New York: Kluwer Academic/Plenum Publishers. Kelly, M., Juggins, S., & Guthrie, R. (2008). Assessment of ecological status in U.K. rivers using diatoms. Freshwater Biology, 53, 403–422. Kinally, C., Fuller, R., Larsen, B., Hu, H., & Lanphear, B. (2025). A review of lead exposure attributional studies. Science of the Total Environment, 90, 179838. https://doi.org/10.1016/j.scitotenv.2025.179838 Krammer, K. (2002). Diatoms of the European Inland Waters and Comparable Habitats, Vol. 3: Cymbella. Gantner Verlag. Kutlu, B., & Sarigül, A. (2022). The assessment of health risk from heavy metals with water indices for irrigation and the portability of Munzur Stream: A case study of the Ovacik area (Ramsar site), Türkiye. Oceanological and Hydrobiological Studies, 52(1), 111–123. https://doi.org/10.26881/oahs-2023.1.09 Kutlu, B., Özcan, T., & Özcan, G. (n.d.). Tunceli Province Water Resources and Properties. Munzur University Press. ISBN: 978-605-67909-0-4 Lange-Bertalot, H., Hofmann, G., Werum, M., & Cantonati, M. (2017). Freshwater Benthic Diatoms of Central Europe: Over 800 Common Species Used in Ecological Assessment. Koeltz Botanical Books. Luo, H. B., Luo, L., Huang, G., Liu, P., Li, J. X., Hu, S., Wang, F. X., Xu, R., & Huang, X. (2009). Total pollution effect of urban surface runoff. Journal of Environmental Sciences, 21(9), 1186–1193. https://doi.org/10.1016/S1001-0742(08)62402-X Mar, S. S., & Okazaki, M. (2012). Investigation of Cd contents in several phosphate rocks used to produce fertilizer. Microchemical Journal, 104, 17–21. https://doi.org/10.1016/j.microc.2012.03.020 Milovanovic, M. (2007). Water quality assessment and determination of pollution sources along the Axios/Vardar River, Southeastern Europe. Desalination, 213(1–3), 159–173. Mirzahasanlou, J. P., Ramezanpour, Z., Nejadsattari, T., Namin, J., & Asri, Y. (2020). Temporal and spatial distribution of diatom assemblages and their relationship with environmental factors in Balikhli River (NW Iran). Ecohydrology & Hydrobiology, 20, 102–111. https://doi.org/10.1016/j.ecohyd.2019.04.002 Muller, G. (1969). Index of geoaccumulation in sediments of the Rhine River. GeoJournal, 2, 108–118. Nhiwatiwa, T., Dalu, T., & Brendonck, L. (2017). Impact of irrigation-based sugarcane cultivation on the Chiredzi and Runde Rivers quality, Zimbabwe. Science of the Total Environment, 587–588, 316–325. https://doi.org/10.1016/j.scitotenv.2017.02.155 ’guessan, Y. M., Probst, J. L., Bur, T., & Probst, A. (2009). Trace elements in stream bed sediments from agricultural catchments (Gascogne region, SW France): Where do they come from? Science of the Total Environment, 407(8), 2939–2952. https://doi.org/10.1016/j.scitotenv.2008.12.047 Palmer, C. M. (1980). Algae and Water Pollution. New York: Castle House Publications Ltd. Petersen, C. R., Jovanovic, N. Z., Le Maitre, D. C., & Grenfell, M. C. (2017). Effects of land use change on streamflow and stream water quality of a coastal catchment. Water SA, 43(1), 139–152. https://doi.org/10.4314/wsa.v43i1.16 Ponader, K. C., & Potapova, M. G. (2007). Diatoms from the genus Achnanthidium in flowing waters of the Appalachian Mountains (North America): Ecology, distribution and taxonomic notes. Limnologica, 37, 227–241. https://doi.org/10.1016/j.limno.2007.01.004 Potapova, M. G., & Charles, D. F. (2002). Benthic diatoms in USA rivers: Distributions along spatial and environmental gradients. Journal of Biogeography, 29(2), 167–187. https://doi.org/10.1046/j.1365-2699.2002.00668.x Reimer, C. W., Henderson, M. V., & Patrick, R. (2001). Bibliography, addenda, and corrigenda for the diatoms of the United States. Proceedings of the Academy of Natural Sciences of Philadelphia, 151, 129–155. Sabater, S., & Roca, J. R. (1992). Ecological and biogeographical aspects of diatom distribution in Pyrenean springs. British Phycological Journal, 27, 203–213. Schowe, K. A., & Harding, J. S. (2014). Development of two diatom-based indices: A biotic and a multimetric index for assessing mine impacts in New Zealand streams. New Zealand Journal of Marine and Freshwater Research, 48(2), 163–176. https://doi.org/10.1080/00288330.2013.852113 Seaby, R. M., & Henderson, P. A. (2007). Community Analysis Package (Version 4.1.3). Pisces Conservation Ltd. Soininen, J., Paavola, R., & Muotka, T. (2004). Benthic diatom communities in boreal streams: Community structure in relation to environmental and spatial gradients. Ecography, 27, 330–342. https://doi.org/10.1111/j.0906-7590.2004.03749.x Song, Z., Song, G., Tang, W., Zhao, Y., Yan, D., & Zhang, W. (2021). Spatial and temporal distribution of Mo in the overlying water of a reservoir downstream from mining area. Journal of Environmental Sciences, 102, 256–262. https://doi.org/10.1016/j.jes.2020.09.033 Szczepocka, E., & Szulc, B. (2009). The use of benthic diatoms in estimating water quality of variously polluted rivers. Oceanological and Hydrobiological Studies, 38, 17–26. https://doi.org/10.2478/v10009-009-0012-x TSWQR. (2016, August 10). Turkish Surface Water Quality Regulation: Quality criteria of surface water resources according to their classes in terms of general chemical and physicochemical parameters. Official Gazette, No. 29797. Retrieved from https://www.resmigazete.gov.tr/eskiler/2016/08/20160810-9.htm Teittinen, A., Taka, M., Ruth, O., & Soininen, J. (2015). Variation in stream diatom communities in relation to water quality and catchment variables in a boreal, urbanized region. Science of the Total Environment, 530, 279–289. https://doi.org/10.1016/j.scitotenv.2015.05.101 Telesh, I. V. (2004). Plankton of the Baltic estuarine ecosystems with emphasis on Neva Estuary: A review of present knowledge and research perspectives. Marine Pollution Bulletin, 49, 206–219. https://doi.org/10.1016/j.marpolbul.2004.02.009 Van Dam, H., Mertens, A., & Sinkeldam, J. (1994). A coded checklist and ecological indicator values of freshwater diatoms from The Netherlands. Netherlands Journal of Aquatic Ecology, 28(1), 117–133. https://doi.org/10.1007/BF02334251 Venkatachalapathy, R., & Karthikeyan, P. (2015). Diatom indices and water quality index of the Cauvery River, India: Implications on the suitability of bio-indicators for environmental impact assessment. In Environmental Management of River Basin Ecosystems (pp. 707–727). Cham: Springer International Publishing. Winter, G., & Duthie, C. (2000). Epilithic diatoms as indicators of stream total N and total P concentration. Journal of the North American Benthological Society, 19, 32–49. https://doi.org/10.2307/1468280 Wu, N., Cai, Q., & Fohrer, N. (2012). Development and evaluation of a diatom-based index of biotic integrity (D-IBI) for rivers impacted by run-of-river dams. Ecological Indicators, 18, 108–117. https://doi.org/10.1016/j.ecolind.2011.10.013 Zhang, G. Q., Luo, W., Chen, W. F., & Zheng, G. X. (2019). A robust but variable lake expansion on the Tibetan Plateau. Science Bulletin, 64(18), 1306–1309. https://doi.org/10.1016/j.scib.2019.07.018 Zhou, M., He, L., Huang, J. M., Zhang, M., Wang, Q. P., Wan, B. H., Xiong, M. R., & Liu, Z. G. (2023). Spatiotemporal variation of epilithic algal flora communities and their relationship with environmental factors in Zhuhu Lake of Poyang Lake (in Chinese). Environmental Science and Pollution Research, 40(4), 36–46. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8147545\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":571412276,\"identity\":\"0794014c-1aec-4126-a9d6-bcac34922c8e\",\"order_by\":0,\"name\":\"Banu KUTLU\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACCQYGxgOMDUAWO4gwsCBGCzMDRAvPAZAWCVK0SCRAbSUEJGfkHzjMu8Muz+Dm86sbfhRIMPC3dyfg1SItkcxwmPdMcrHB7Zyymz1Ah0mcObsBrxY5sJY25sQNt3PSbvAAtRhI5BKlpT5xw80zaTf/EKMF4rC2w4kbbrAfu02ULZI9jw0Ozj1zPHHmmRy22zIGEjwE/SJxPPHhg7c7qhP7jh9/dvPNHxs5/vZe/FqQAI8BmCRWOQiwPyBF9SgYBaNgFIwgAADEtkuniQAzEQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Munzur University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Banu\",\"middleName\":\"\",\"lastName\":\"KUTLU\",\"suffix\":\"\"},{\"id\":571412278,\"identity\":\"7e5fde46-7e0d-4c2a-9660-0ec6557b6688\",\"order_by\":1,\"name\":\"Serdar ÇETİNDAĞ\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tunceli ProvincialHealth Directorate Tunceli\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Serdar\",\"middleName\":\"\",\"lastName\":\"ÇETİNDAĞ\",\"suffix\":\"\"},{\"id\":571412279,\"identity\":\"df810adb-a31f-4fa9-947a-ebe6cdfd9d09\",\"order_by\":2,\"name\":\"Fatma ÇEVİK\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Cukurova University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fatma\",\"middleName\":\"\",\"lastName\":\"ÇEVİK\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-11-18 16:23:05\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8147545/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8147545/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":100363801,\"identity\":\"8e7783ca-09c5-422d-b277-9471acc53ccf\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:51:48\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":820940,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"sonn.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/ae2e200be69e9150d93694d7.docx\"},{\"id\":100363652,\"identity\":\"8b7c213e-a0f1-45b3-9d7a-6bad08bc3eb2\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:50:59\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":6016,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"1079e8f15f364f7096a37cba0e33db08.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/41aa2fc09cd23b513497c8ba.json\"},{\"id\":100363282,\"identity\":\"832ca60f-586b-43dc-8082-25f82bc3a849\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:49:16\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":101418,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"1079e8f15f364f7096a37cba0e33db081enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/ec9637293fe6e1932b417006.xml\"},{\"id\":100051256,\"identity\":\"cf8e4ff4-de87-4b9d-ad72-b11e8e47cbed\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":21182,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/a8bc8b7891dffdd19cc134b2.png\"},{\"id\":100051260,\"identity\":\"7962e048-dcb3-4283-b003-7abd77e5ba68\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":191127,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/0f69c06e4c9997ea94d95768.png\"},{\"id\":100364252,\"identity\":\"b2d11cd1-a310-4d88-9a37-1d06a4c8a4f7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:53:06\",\"extension\":\"png\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":30500,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/47c5164362a62d91c23b7df7.png\"},{\"id\":100363646,\"identity\":\"3e082369-588f-4dce-9433-ff40bf991d34\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:50:57\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":67218,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/51d3eeeebea5317075bb1474.png\"},{\"id\":100364215,\"identity\":\"d78b9322-9994-4186-b1af-76caad5e172f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:52:57\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":20481,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/a77053932d24dc5a37f4181b.png\"},{\"id\":100051264,\"identity\":\"31c1ea3b-a4f9-469d-8a2e-592b8a5d11a4\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"xml\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":98141,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"1079e8f15f364f7096a37cba0e33db081structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/637ea7477901f4503a7c46d0.xml\"},{\"id\":100364183,\"identity\":\"ad283d56-8465-4d06-ae67-1f367ace30f2\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:52:50\",\"extension\":\"html\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":111239,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/d219380be4dd029286ce69e5.html\"},{\"id\":100051250,\"identity\":\"33a6c7a9-c67c-458b-84f3-eeedf39809d1\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":93280,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSampling locations.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/a267953f254645f2d22800a9.png\"},{\"id\":100051259,\"identity\":\"717cf11e-2185-4359-ac36-8149c106fec5\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":471901,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpearman’s rank correlations of environmental factors\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/fd0fc5afc879da66b71b4165.png\"},{\"id\":100051251,\"identity\":\"e763d20f-ca0c-4743-b4f1-4ad70b277a82\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":120781,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePCA biplot: PC1 (metal load) and PC2 (physicochemical conditions) illustrate ecological gradients between pollution stress and habitat quality\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/26451c80f63a8f73fd940a96.png\"},{\"id\":100051254,\"identity\":\"366742ae-a6df-44cc-8501-25d97cd3d850\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71147,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrespondence analysis of taxa\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/0acb02be0446c3ffa5179c66.png\"},{\"id\":100051253,\"identity\":\"4d626bd2-38b6-4d6e-a9bb-96f8881186fb\",\"added_by\":\"auto\",\"created_at\":\"2026-01-12 13:04:41\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":114693,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCCA ordination: CCA1 (nutrient-metal interaction) and CCA2 (oxygen regime vs. metal toxicity) explain species distribution patterns\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/cdd702a12250f5b4a6017dd8.png\"},{\"id\":103056374,\"identity\":\"90afdbfa-11ea-4a0b-9390-1061ad01beb9\",\"added_by\":\"auto\",\"created_at\":\"2026-02-20 09:08:27\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1446054,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8147545/v1/9d26b30a-83a4-4a8f-97ac-b23c244a8fb8.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Effects of Multiple Variables on Epilithic Algal Communities in the Pülümür Stream System\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cp\\u003e\\u0026bull; Epilithic algal communities were assessed across ten stations in a stream.\\u003c/p\\u003e\\u003cp\\u003e\\u0026bull; Nutrient and heavy metal gradients strongly shaped algal distribution patterns.\\u003c/p\\u003e\\u003cp\\u003e\\u0026bull; Pollution-tolerant diatoms dominated nutrient- and metal-rich downstream sites.\\u003c/p\\u003e\\u003cp\\u003e\\u0026bull; CCA identified phosphate, nitrate, Pb, and Cd as key drivers of community change.\\u003c/p\\u003e\\u003cp\\u003e\\u0026bull; Findings support bioindicator-based monitoring for sustainable river management.\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eLotic ecosystems provide essential ecological services\\u0026mdash;such as water supply, nutrient cycling, fisheries, recreation, and biodiversity conservation\\u0026mdash;and are therefore regarded as critical components of global freshwater resources. However, nearly two-thirds of freshwater habitats worldwide are currently classified as moderately to severely threatened due to escalating anthropogenic pressures \\u003cem\\u003e(\\u003c/em\\u003eDudgeon et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Schowe \\u0026amp; Harding, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e. Rapid population growth, expansion of agricultural and industrial activities, and increasing water demand intensify pollution loads and accelerate the degradation of riverine environments \\u003cem\\u003e(\\u003c/em\\u003eNhiwatiwa et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Petersen et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). These pressures alter water quality, reduce ecological resilience, and compromise the ecological integrity of freshwater systems \\u003cem\\u003e(\\u003c/em\\u003eWu et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Venkatachalapathy \\u0026amp; Karthikeyan, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAnthropogenic disturbances frequently propagate downstream and can restructure ecological processes such as nutrient retention, primary production, and biotic interactions \\u003cem\\u003e(\\u003c/em\\u003eTeittinen et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Bere et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Dalu \\u0026amp; Froneman, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e. In response to these widespread pressures, biological monitoring has become indispensable. The European Water Framework Directive (WFD) emphasizes the integration of biological quality elements\\u0026mdash;including phytobenthos, epilithic algae, macroinvertebrates, macrophytes, and fish\\u0026mdash;with physicochemical and hydromorphological parameters to assess the ecological status of freshwaters \\u003cem\\u003e(EU, 2014; EU, 2023)\\u003c/em\\u003e. Among these indicators, epilithic algae are particularly valuable due to their rapid response to changes in nutrient concentrations, heavy metal contamination, and flow variability, making them sensitive and reliable ecological indicators.\\u003c/p\\u003e \\u003cp\\u003eExcessive anthropogenic activities\\u0026mdash;such as agricultural runoff, livestock waste discharge, domestic sewage, and industrial effluents\\u0026mdash;contribute significantly to nutrient and metal accumulation in lotic systems, often resulting in shifts in algal community composition and reductions in diversity \\u003cem\\u003e(\\u003c/em\\u003eDalu et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Alloway, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Kutlu \\u0026amp; Sarıg\\u0026uuml;l, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e. These changes serve as early warning signals of ecological degradation, particularly in semi-arid regions where hydrological instability amplifies pollutant impacts.\\u003c/p\\u003e \\u003cp\\u003eThe P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream, located in a semi-arid region of Eastern T\\u0026uuml;rkiye, is exposed to multiple environmental stressors, including agricultural practices, livestock activities, and urban wastewater inputs. Despite evidence of nutrient and metal enrichment in certain segments of the stream, the ecological responses of epilithic algal communities to these combined pressures remain insufficiently investigated.\\u003c/p\\u003e \\u003cp\\u003eTherefore, the present study aims to (i) examine the spatial and seasonal distribution of epilithic algal communities in the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream, (ii) identify key environmental drivers influencing algal composition using multivariate statistical analyses, and (iii) evaluate the potential of epilithic algae as bioindicators for ecological monitoring in semi-arid river systems. The findings offer critical insight into the ecological condition of the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream and provide a scientific foundation for sustainable water quality management and restoration efforts.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Area and Sampling\\u003c/h2\\u003e \\u003cp\\u003eSeasonal sampling was carried out between February 2022 and January 2023 at ten stations (St1\\u0026ndash;St10) along the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The stations were selected to represent upstream, midstream, and downstream segments and to capture gradients influenced by land use, tributary inflows, and anthropogenic pressures. Substrate types ranged from fine sand and gravel to cobbles and boulders. Stations close to agricultural, livestock, urban, and landfill areas were included to characterize the effects of environmental stressors. Geographic coordinates of all stations are provided in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eMeasurement of physicochemical parameters and water sampling\\u003c/h3\\u003e\\n\\u003cp\\u003eWater samples were collected 0.5 m below the surface using pre-cleaned 2.5 L polyethylene bottles following the \\u0026ldquo;Sampling and Analysis Methods\\u0026rdquo; of the Turkish Water Pollution Control Regulation. Samples were transported at 4\\u0026deg;C in light-protected glass bottles.\\u003c/p\\u003e \\u003cp\\u003eIn situ measurements of temperature (\\u0026deg;C), pH, and dissolved oxygen (DO) were made using a YSI Professional Plus Portable Multi-Parameter instrument. Nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), and phosphate (PO₄\\u0026sup3;⁻) concentrations were determined volumetrically according to TS 4956 standards.\\u003c/p\\u003e \\u003cp\\u003eWater heavy metals (coded as W) were analyzed after acid digestion. A 50 mL subsample was treated with 1 mL HCl and 4 mL HNO₃ and heated to 250\\u0026deg;C for 3 h until a clear solution formed. Digested samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES; SPECTRO Arcos, Canada). Selected metals (Cu, Pb, Zn, Ni, Co, Mn, Fe, As, Cd, Mg, Al, Hg) were confirmed with ICP-MS (ACME Laboratory, Canada).\\u003c/p\\u003e\\n\\u003ch3\\u003eSediment sampling and analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eSurface sediments (0\\u0026ndash;10 cm) were collected with an Ekman dredge (20 \\u0026times; 20 \\u0026times; 20 cm) and stored in polyethylene bags at \\u0026minus;\\u0026thinsp;20\\u0026deg;C until analysis. Samples were digested using a modified aqua regia solution (HCl:HNO₃:H₂O\\u0026thinsp;=\\u0026thinsp;1:1:1) for 1 h, and metal concentrations were quantified by ICP-MS (ACME Laboratory, Canada) \\u003cem\\u003e(\\u003c/em\\u003eAlloway, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; M\\u0026uuml;\\u003cem\\u003eller, 1969)\\u003c/em\\u003e. Sediment metals were denoted as \\u0026ldquo;C.\\u0026rdquo;\\u003c/p\\u003e\\n\\u003ch3\\u003eEpilithic algal sampling and identification\\u003c/h3\\u003e\\n\\u003cp\\u003eEpilithic algae were seasonally collected by brushing submerged stones and cobbles and preserved in 4% formaldehyde. Samples were cleaned using 30% H₂O₂ and mounted in Naphrax for permanent slide preparation. Algal identification followed standard taxonomic keys \\u003cem\\u003e(\\u003c/em\\u003eKrammer, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Lange-Bertalot et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Bey \\u0026amp; Ector, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn vivo chlorophyll-a fluorescence was measured using a Turner Designs 10-AU fluorometer following the manufacturer\\u0026rsquo;s instructions. Results were expressed as \\u0026micro;g/L.\\u003c/p\\u003e\\n\\u003ch3\\u003eData processing and statistical analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 39 environmental variables were analyzed, including physicochemical parameters, nutrients, and heavy metals in water and sediment. Skewed data were log(x\\u0026thinsp;+\\u0026thinsp;1) transformed prior to analysis.\\u003c/p\\u003e \\u003cp\\u003ePrincipal Component Analysis (PCA) was applied to determine major environmental gradients. Analysis of Variance (ANOVA) tested differences among stations. Canonical Correspondence Analysis (CCA) was used to relate epilithic algal composition to environmental variables.\\u003c/p\\u003e \\u003cp\\u003eCluster analysis using Bray\\u0026ndash;Curtis similarity identified spatial groupings of stations. Similarity Percentage (SIMPER) analysis determined species contributing\\u0026thinsp;\\u0026ge;\\u0026thinsp;1% to between-group dissimilarities. ANOSIM and SIMPER were performed in PAST 3.0 \\u003cem\\u003e(\\u003c/em\\u003eHammer et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Seaby \\u0026amp; Henderson, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e\\u003cem\\u003e)\\u003c/em\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eDissolved oxygen (DO) ranged from 9.72 to 12.27 mg/L during the study period, with the highest values recorded in spring. Water temperature varied between 2.76\\u0026deg;C and 18.03\\u0026deg;C, while pH fluctuated from 8.82 to 9.93. In vivo chlorophyll-a concentrations ranged from 0.28 to 2.04 \\u0026micro;g/L. Nitrate (NO₃⁻) levels varied between 0.43 and 4.50 mg/L, with minimum values observed at Station 9 in summer and maximum values at Station 6 in autumn. Ammonium (NH₄⁺) ranged from 0.01 to 0.49 mg/L, and nitrite (NO₂⁻) varied between 0.008 and 0.18 mg/L, with the highest NO₂⁻ concentration detected at Station 10 in summer. Phosphate (PO₄\\u0026sup3;⁻) ranged from 0.38 to 2.48 mg/L, showing strong spatial and seasonal variation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eHeavy metal concentrations in water displayed distinct patterns. Minimum and maximum values were as follows: Cu (0.9\\u0026ndash;2.4 \\u0026micro;g/L), Pb (7.1\\u0026ndash;28.2 \\u0026micro;g/L), Zn (1.0\\u0026ndash;3.37 \\u0026micro;g/L), Ni (0.2\\u0026ndash;1.4 \\u0026micro;g/L), Co (0.03\\u0026ndash;0.16 \\u0026micro;g/L), Mn (0.17\\u0026ndash;1.0 \\u0026micro;g/L), Fe (8.0\\u0026ndash;9.76 \\u0026micro;g/L), As (1.1\\u0026ndash;3.4 \\u0026micro;g/L), Cr (5.8\\u0026ndash;7.8 \\u0026micro;g/L), and Cd (0.03\\u0026ndash;0.16 \\u0026micro;g/L). The metal concentration order in water was Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Fe\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;As \\u0026gt;\\u0026thinsp;Zn\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cu\\u0026thinsp;\\u0026gt;\\u0026thinsp;Ni\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Co.\\u003c/p\\u003e \\u003cp\\u003eSediment metal concentrations were considerably higher than water values. Ranges included: Al (1.03\\u0026ndash;2.04 mg/kg), Cd (0.09\\u0026ndash;0.39 mg/kg), Cr (54\\u0026ndash;121 mg/kg), Cu (18\\u0026ndash;19 mg/kg), Fe (231\\u0026ndash;401 mg/kg), Co (14.4\\u0026ndash;29.7 mg/kg), K (0.12\\u0026ndash;0.23 mg/kg), Mg (1.09\\u0026ndash;3.1 mg/kg), Na (5\\u0026ndash;125 mg/kg), Pb (6.4\\u0026ndash;17.5 mg/kg), Zn (46\\u0026ndash;89 mg/kg), As (3.6\\u0026ndash;26.8 mg/kg), Hg (0.02\\u0026ndash;0.07 mg/kg), and Mn (320\\u0026ndash;691 mg/kg). Sediment metals followed the order Mn\\u0026thinsp;\\u0026gt;\\u0026thinsp;Fe\\u0026thinsp;\\u0026gt;\\u0026thinsp;Na\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Zn\\u0026thinsp;\\u0026gt;\\u0026thinsp;Co\\u0026thinsp;\\u0026gt;\\u0026thinsp;As \\u0026gt;\\u0026thinsp;Cu\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mg\\u0026thinsp;\\u0026gt;\\u0026thinsp;Al\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;K\\u0026thinsp;\\u0026gt;\\u0026thinsp;Hg.\\u003c/p\\u003e \\u003cp\\u003eThe Spearman correlation heatmap revealed strong positive associations among most heavy metals (Pb, Zn, Fe, Cu, Cd, Ni), indicating shared anthropogenic or geogenic sources. Conversely, DO, pH, and temperature negatively correlated with metals, suggesting that increased metal pollution deteriorates key water quality parameters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePCA results showed that PC1 and PC2 explained 32.89% and 25.42% of the total variance (58.31% overall). Metal variables (Pb, Cd, Zn, Fe) grouped strongly on PC1, while physicochemical factors such as DO, pH, and temperature were more associated with PC2 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Key contributors included NO₂⁻, NH₄⁺, Pb (water), Cd (sediment), As (sediment), Hg (sediment), and Pb (sediment).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eTaxonomic composition and diversity\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 452 epilithic algal taxa were identified across 10 stations. Bacillariophyceae were dominant (41.3%), followed by Cyanophyceae (19.9%), Euglenophyceae (10.6%), Chlorophyceae (5.1%), and Dinophyceae (3.9%). SIMPER analysis identified major contributors including \\u003cem\\u003ePeridinium bipes, Cocconeis placentula\\u003c/em\\u003e var. \\u003cem\\u003eeuglypta\\u003c/em\\u003e, \\u003cem\\u003eAulacoseira italica\\u003c/em\\u003e, \\u003cem\\u003eNitzschia\\u003c/em\\u003e spp., \\u003cem\\u003eEncyonema\\u003c/em\\u003e spp., \\u003cem\\u003eGomphonema\\u003c/em\\u003e spp., \\u003cem\\u003eRhoicosphenia\\u003c/em\\u003e spp., \\u003cem\\u003eFragilaria capucina, Pseudostaurosira brevistriata, Synedra ulna\\u003c/em\\u003e, and \\u003cem\\u003eUlnaria ulna\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eShannon Diversity Index (H\\u0026rsquo;) values ranged from 1.017 to 1.034 across stations, while evenness (J\\u0026rsquo;) varied between 0.56 and 0.60, indicating generally low diversity. The highest diversity was observed at Station 10, with the lowest at Station 2. Simpson Dominance Index values ranged from 0.39 to 0.40, reflecting dominance of a few tolerant taxa.\\u003c/p\\u003e \\u003cp\\u003eCorrespondence Analysis (CA) revealed station-level groupings: St3, St6, St9, and St10 clustered closely, indicating similar algal composition, while St2 was isolated, suggesting distinct community characteristics (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eCCA and species–environment relationships\\u003c/h3\\u003e\\n\\u003cp\\u003eCCA showed that axes 1 and 2 explained 41.2% and 30.8% of total variance, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). pH and nitrate (NO₃⁻) showed strong opposing gradients, while DO was positively associated with temperature and Fe. Cyanobacteria and Chlorophyta aligned with nutrient-rich, metal-influenced conditions, whereas Bacillariophyta showed affinity for oxygenated sections with moderate nutrient levels.\\u003c/p\\u003e \\u003cp\\u003eDominant taxa included \\u003cem\\u003eAchnanthidium minutissimum\\u003c/em\\u003e, \\u003cem\\u003eGomphonema parvulum\\u003c/em\\u003e, and \\u003cem\\u003eNitzschia palea\\u003c/em\\u003e, which occurred frequently at pollution-impacted stations. Cosmopolitan species such as \\u003cem\\u003eCocconeis placentula\\u003c/em\\u003e and \\u003cem\\u003eUlnaria ulna\\u003c/em\\u003e were also widespread.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe spatial heterogeneity observed in epilithic algal communities along the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream clearly reflected the influence of multiple environmental gradients, consistent with classic theories of lotic ecosystem functioning (Allan \\u0026amp; Castillo, 2007). Upstream stations characterized by relatively undisturbed hydromorphology and low nutrient availability supported oligotrophic and sensitive taxa, a pattern frequently documented in high-altitude or minimally impacted rivers (Soininen, 2004; Stevenson et al., 2010). In contrast, downstream sites subjected to agricultural runoff, livestock waste, and urban discharges were dominated by eutrophic and pollution-tolerant species, mirroring findings from heavily impacted rivers worldwide (Bere et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Dalu et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). These shifts demonstrate that community structure responds predictably to nutrient enrichment, hydrological variability, and contaminant loading, reinforcing the importance of species-level bioassessment in ecological monitoring programs (Kelly et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Rimet \\u0026amp; Bouchez, 2012).\\u003c/p\\u003e \\u003cp\\u003eCanonical Correspondence Analysis (CCA) further highlighted that manganese (Mn), zinc (Zn), cobalt (Co), and dissolved oxygen (DO) were the most influential variables shaping algal composition. Elevated concentrations of Mn, Zn, and Co reduced taxonomic richness and contributed to the dominance of tolerant taxa, a trend also reported in metal-enriched streams influenced by mining, agriculture, or industrial discharges (Ivorra et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Morin et al., 2008; Singh et al., 2020). Conversely, higher nitrate (NO3-) and DO levels supported a more diverse algal community, which aligns with research showing that moderate nutrient availability and oxygenation enhance metabolic activity and facilitate species coexistence (Guo et al., 2017; Sabater et al., 2019). Together, these results confirm that metal\\u0026ndash;nutrient interactions can exert complex and sometimes opposing effects on benthic algal assemblages, depending on local environmental conditions.\\u003c/p\\u003e \\u003cp\\u003eThe pronounced sensitivity of epilithic algae to both nutrient enrichment and heavy metal contamination reinforces their value as early-warning indicators in riverine monitoring frameworks (Biggs \\u0026amp; Kilroy, 2000; Pan et al., 2021). The strong association of Mn and Zn with stations exhibiting low diversity mirrors observations in other semi-arid basins where agricultural and urban pressures intensify metal inputs (Zhang et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Lopez-Doval et al., 2020). Therefore, routine monitoring of these metals should be integrated into watershed management programs. Strategies such as improving agricultural fertilizer efficiency, controlling livestock waste discharge, and upgrading urban wastewater infrastructure are essential to reduce ecological stress in downstream regions (Carpenter et al., 2011; EU WFD, 2023).\\u003c/p\\u003e \\u003cp\\u003eOverall, the results emphasize the importance of integrating biological indicators with chemical assessments to detect early signs of ecological degradation. The strong species\\u0026ndash;environment relationships identified in this study highlight the sensitivity of epilithic algae to nutrient\\u0026ndash;metal interactions, making them reliable tools for long-term monitoring. For the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream, improving fertilizer management, reducing livestock waste inputs, and enhancing wastewater treatment facilities are essential measures to mitigate pollutant loads. These recommendations are aligned with international river basin management strategies and contribute directly to broader sustainability objectives, including SDG 15 on biodiversity conservation (UNEP, 2021; Reid et al., 2019).\\u003c/p\\u003e \\u003cp\\u003eOur findings demonstrate that epilithic algal communities in the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream are strongly influenced by nutrient enrichment and heavy metal contamination. PCA revealed two major stress gradients: PC1 representing metal load (Pb, Cd, Zn, Fe) and PC2 reflecting physicochemical conditions (DO, pH, temperature). Similarly, CCA showed that CCA1 captures eutrophication and metal contamination effects, while CCA2 contrasts oxygen regime with metal toxicity. Species-level responses confirmed these patterns: tolerant taxa (Nitzschia palea, Gomphonema parvulum) dominated nutrient- and metal-rich downstream sites, whereas sensitive species persisted upstream. These results emphasize the need for integrated watershed management, including nutrient control, improved wastewater treatment, and continuous monitoring of trace metals. Such measures are essential to maintain ecological integrity and align with global conservation goals (SDG 15).\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study demonstrated that epilithic algal communities are highly sensitive indicators of environmental stress in the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream. Spatial shifts from oligotrophic, diversity-rich assemblages in upstream sections to pollution-tolerant and metal-resistant taxa in downstream areas reflected the cumulative impacts of agricultural runoff, livestock activities, and urban wastewater. Multivariate analyses (PCA and CCA) revealed that nutrient levels (NO₃⁻, PO₄\\u0026sup3;⁻) and trace metals (Mn, Zn, Co, Pb) were the primary drivers shaping algal composition, with elevated metal concentrations associated with reduced diversity and ecological degradation.\\u003c/p\\u003e \\u003cp\\u003eThese findings highlight the importance of integrating biological indicators with physicochemical monitoring to obtain a comprehensive assessment of freshwater ecosystem health. Reducing nutrient and heavy metal inputs through improved agricultural practices, enhanced wastewater management, and strengthened watershed protection measures is essential for restoring ecological integrity.\\u003c/p\\u003e \\u003cp\\u003eOverall, the study provides critical baseline data for the sustainable management of the P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream and supports broader global conservation efforts, including the objectives of the United Nations Sustainable Development Goal (SDG) 15. Maintaining the ecological resilience of river systems like P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r is vital for preserving freshwater biodiversity and ensuring long-term environmental sustainability.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis study was supported by the Munzur University Scientific Research Projects Coordination Unit under Project Number \\u003cb\\u003eYLMUB021-18\\u003c/b\\u003e.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthor Information \\u0026amp; ContributionsAuthor InformationAuthor: Banu KUTLUGiven names: BanuFamily name: KUTLUEmail: kutlubanu@gmail.comPrimary affiliation: Basic Science, Fisheries Faculty, Munzur University, Tunceli, T\\u0026uuml;rkiyeAuthor: Serdar \\u0026Ccedil;ETİNDAĞGiven names: SerdarFamily name: \\u0026Ccedil;ETİNDAĞEmail: serdarcetindag@hotmail.comPrimary affiliation: Tunceli Provincial Health Directorate, Tunceli, T\\u0026uuml;rkiyeAuthor: Fatma \\u0026Ccedil;EVİKGiven names: FatmaFamily name: \\u0026Ccedil;EVİKEmail: fcevik@cu.edu.trPrimary affiliation: Basic Science, Fisheries Faculty, \\u0026Ccedil;ukurova University, Adana, T\\u0026uuml;rkiyeAuthor Contributions StatementB.K. designed the study, conducted the field sampling, and wrote the main parts of the manuscript.S.\\u0026Ccedil;. performed the data analyses, including statistical evaluations, and contributed to the interpretation of the results.F.\\u0026Ccedil;. carried out the laboratory analyses, including nutrient and heavy metal measurements, and contributed to data validation.All authors reviewed, edited, and approved the final version of the manuscript and agreed to its submission.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThis study was supported by the Scientific Research Projects Coordination Unit of Munzur University under Project No. YLMUB021-18. I would like to thank the BAP Unit for their support.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAlloway, B. J. (2013). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability (Vol. 22). Springer.\\u003c/li\\u003e\\n\\u003cli\\u003eBere, T., Dalu, T., \\u0026amp; Mwedzi, T. (2016). Detecting the impact of heavy metal contaminated sediment on benthic macroinvertebrate communities in tropical streams. Science of the Total Environment, 572, 147\\u0026ndash;156. https://doi.org/10.1016/j.scitotenv.2016.07.204\\u003c/li\\u003e\\n\\u003cli\\u003eBere, T., Mangadze, T., \\u0026amp; Mwedzi, T. (2016). Variation partitioning of diatom species data matrices: Understanding the influence of multiple factors on benthic diatom communities in tropical streams. Science of the Total Environment, 566, 1604\\u0026ndash;1611. https://doi.org/10.1016/j.scitotenv.2016.06.058\\u003c/li\\u003e\\n\\u003cli\\u003eBey, M. Y., \\u0026amp; Ector, L. (2013). Atlas des diatom\\u0026eacute;es des cours d\\u0026rsquo;eau de la r\\u0026eacute;gion Rh\\u0026ocirc;ne-Alpes. DREAL Rh\\u0026ocirc;ne-Alpes, 1\\u0026ndash;5, 172\\u0026ndash;181.\\u003c/li\\u003e\\n\\u003cli\\u003eCantonati, M., \\u0026amp; Lowe, R. L. (2014). Lake benthic algae: Toward an understanding of their ecology. Freshwater Science, 33, 475\\u0026ndash;486. https://doi.org/10.1086/676140\\u003c/li\\u003e\\n\\u003cli\\u003eChessman, B., Growns, I., Currey, J., \\u0026amp; Plunkett Cole, N. (1999). Predicting diatom communities at the genus level for the rapid biological assessment of rivers. Freshwater Biology, 41, 317\\u0026ndash;331. https://doi.org/10.1046/j.1365-2427.1999.00433.x\\u003c/li\\u003e\\n\\u003cli\\u003eCommission Directive 2014/101/EU of 30 October 2014 amending Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for Community action in the field of water policy (Text with EEA relevance). Official Journal of the European Union, L311, 32\\u0026ndash;35. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32014L0101\\u003c/li\\u003e\\n\\u003cli\\u003eDalu, T., \\u0026amp; Froneman, P. W. (2016). Diatom-based water quality monitoring in southern Africa: Challenges and future prospects. Water SA, 42(4), 551\\u0026ndash;559. https://doi.org/10.4314/wsa.v42i4.05\\u003c/li\\u003e\\n\\u003cli\\u003eDalu, T., Wasserman, R. J., Magoro, M. L., Mwedzi, T., Froneman, W., \\u0026amp; Weyl, O. L. F. (2017). Variation partitioning of benthic diatom community matrices: Effects of multiple variables on benthic diatom communities in an Austral temperate river system. Science of the Total Environment, 601\\u0026ndash;602, 1498\\u0026ndash;1508.\\u003c/li\\u003e\\n\\u003cli\\u003eDudgeon, D., Arthington, A. H., Gessner, M. O., Kawabata, Z. I., Knowler, D. J., L\\u0026eacute;v\\u0026ecirc;que, C., Naiman, R. J., Prieur-Richard, A. H., Soto, D., Stiassny, M. L., \\u0026amp; Sullivan, C. A. (2006). Freshwater biodiversity: Importance, threats, status and conservation challenges. Biological Reviews, 81(2), 163\\u0026ndash;182. https://doi.org/10.1017/S1464793105006950\\u003c/li\\u003e\\n\\u003cli\\u003eEctor, L., Comp\\u0026egrave;re, P., Vidal, H., \\u0026amp; Gentili, R. (2000). Compte rendu du 18e colloque de l\\u0026rsquo;Association des diatomistes de langue fran\\u0026ccedil;aise. Cryptogamie Algologie, 21, 213\\u0026ndash;258. https://doi.org/10.1016/S0181-1568(01)80003-9\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Commission. (2023). WFD Reporting Guidance 2022, Final Draft V6.6, 26 October 2023. Brussels: European Commission.\\u003c/li\\u003e\\n\\u003cli\\u003eGupta, D. K., Chatterjee, S., \\u0026amp; Datta, S. (2014). Role of phosphate fertilizers in heavy metal uptake and detoxification of toxic metals. Chemosphere, 108, 134\\u0026ndash;144. https://doi.org/10.1016/j.chemosphere.2014.01.030\\u003c/li\\u003e\\n\\u003cli\\u003eHammer, Q., Harper, D. A. T., \\u0026amp; Ryan, P. D. (2009). PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 4, 9.\\u003c/li\\u003e\\n\\u003cli\\u003eHill, B. H., Herlihy, A. T., Kaufmann, P. R., Stevenson, R. J., McCormick, F. H., \\u0026amp; Burch-Johnson, C. (2000). Use of periphyton assemblage data as an index of biotic integrity. Journal of the North American Benthological Society, 19, 50\\u0026ndash;67. https://doi.org/10.2307/1468281\\u003c/li\\u003e\\n\\u003cli\\u003eIvorra, N., Hettelaar, J., Kraak, H. S., Sabater, M., \\u0026amp; Admiraal, W. (2002). Responses of biofilms to combined nutrient and metal exposure. Environmental Toxicology and Chemistry, 21(3), 626\\u0026ndash;632. https://doi.org/10.1002/etc.5620210323\\u003c/li\\u003e\\n\\u003cli\\u003eJohn, J. (2003). Bioassessment on health of aquatic systems by the use of diatoms. In R. S. Ambasht \\u0026amp; N. K. Ambasht (Eds.), Modern Trends in Applied Aquatic Ecology (pp. 1\\u0026ndash;20). New York: Kluwer Academic/Plenum Publishers.\\u003c/li\\u003e\\n\\u003cli\\u003eKelly, M., Juggins, S., \\u0026amp; Guthrie, R. (2008). Assessment of ecological status in U.K. rivers using diatoms. Freshwater Biology, 53, 403\\u0026ndash;422.\\u003c/li\\u003e\\n\\u003cli\\u003eKinally, C., Fuller, R., Larsen, B., Hu, H., \\u0026amp; Lanphear, B. (2025). A review of lead exposure attributional studies. Science of the Total Environment, 90, 179838. https://doi.org/10.1016/j.scitotenv.2025.179838\\u003c/li\\u003e\\n\\u003cli\\u003eKrammer, K. (2002). Diatoms of the European Inland Waters and Comparable Habitats, Vol. 3: Cymbella. Gantner Verlag.\\u003c/li\\u003e\\n\\u003cli\\u003eKutlu, B., \\u0026amp; Sarig\\u0026uuml;l, A. (2022). The assessment of health risk from heavy metals with water indices for irrigation and the portability of Munzur Stream: A case study of the Ovacik area (Ramsar site), T\\u0026uuml;rkiye. Oceanological and Hydrobiological Studies, 52(1), 111\\u0026ndash;123. https://doi.org/10.26881/oahs-2023.1.09\\u003c/li\\u003e\\n\\u003cli\\u003eKutlu, B., \\u0026Ouml;zcan, T., \\u0026amp; \\u0026Ouml;zcan, G. (n.d.). Tunceli Province Water Resources and Properties. Munzur University Press. ISBN: 978-605-67909-0-4\\u003c/li\\u003e\\n\\u003cli\\u003eLange-Bertalot, H., Hofmann, G., Werum, M., \\u0026amp; Cantonati, M. (2017). Freshwater Benthic Diatoms of Central Europe: Over 800 Common Species Used in Ecological Assessment. Koeltz Botanical Books.\\u003c/li\\u003e\\n\\u003cli\\u003eLuo, H. B., Luo, L., Huang, G., Liu, P., Li, J. X., Hu, S., Wang, F. X., Xu, R., \\u0026amp; Huang, X. (2009). Total pollution effect of urban surface runoff. Journal of Environmental Sciences, 21(9), 1186\\u0026ndash;1193. \\u003c/li\\u003e\\n\\u003cli\\u003ehttps://doi.org/10.1016/S1001-0742(08)62402-X\\u003c/li\\u003e\\n\\u003cli\\u003eMar, S. S., \\u0026amp; Okazaki, M. (2012). Investigation of Cd contents in several phosphate rocks used to produce fertilizer. Microchemical Journal, 104, 17\\u0026ndash;21. https://doi.org/10.1016/j.microc.2012.03.020\\u003c/li\\u003e\\n\\u003cli\\u003eMilovanovic, M. (2007). Water quality assessment and determination of pollution sources along the Axios/Vardar River, Southeastern Europe. Desalination, 213(1\\u0026ndash;3), 159\\u0026ndash;173.\\u003c/li\\u003e\\n\\u003cli\\u003eMirzahasanlou, J. P., Ramezanpour, Z., Nejadsattari, T., Namin, J., \\u0026amp; Asri, Y. (2020). Temporal and spatial distribution of diatom assemblages and their relationship with environmental factors in Balikhli River (NW Iran). Ecohydrology \\u0026amp; Hydrobiology, 20, 102\\u0026ndash;111. https://doi.org/10.1016/j.ecohyd.2019.04.002\\u003c/li\\u003e\\n\\u003cli\\u003eMuller, G. (1969). Index of geoaccumulation in sediments of the Rhine River. GeoJournal, 2, 108\\u0026ndash;118.\\u003c/li\\u003e\\n\\u003cli\\u003eNhiwatiwa, T., Dalu, T., \\u0026amp; Brendonck, L. (2017). Impact of irrigation-based sugarcane cultivation on the Chiredzi and Runde Rivers quality, Zimbabwe. Science of the Total Environment, 587\\u0026ndash;588, 316\\u0026ndash;325. https://doi.org/10.1016/j.scitotenv.2017.02.155\\u003c/li\\u003e\\n\\u003cli\\u003e\\u0026rsquo;guessan, Y. M., Probst, J. L., Bur, T., \\u0026amp; Probst, A. (2009). Trace elements in stream bed sediments from agricultural catchments (Gascogne region, SW France): Where do they come from? Science of the Total Environment, 407(8), 2939\\u0026ndash;2952. https://doi.org/10.1016/j.scitotenv.2008.12.047\\u003c/li\\u003e\\n\\u003cli\\u003ePalmer, C. M. (1980). Algae and Water Pollution. New York: Castle House Publications Ltd.\\u003c/li\\u003e\\n\\u003cli\\u003ePetersen, C. R., Jovanovic, N. Z., Le Maitre, D. C., \\u0026amp; Grenfell, M. C. (2017). Effects of land use change on streamflow and stream water quality of a coastal catchment. Water SA, 43(1), 139\\u0026ndash;152. https://doi.org/10.4314/wsa.v43i1.16\\u003c/li\\u003e\\n\\u003cli\\u003ePonader, K. C., \\u0026amp; Potapova, M. G. (2007). Diatoms from the genus Achnanthidium in flowing waters of the Appalachian Mountains (North America): Ecology, distribution and taxonomic notes. Limnologica, 37, 227\\u0026ndash;241. https://doi.org/10.1016/j.limno.2007.01.004\\u003c/li\\u003e\\n\\u003cli\\u003ePotapova, M. G., \\u0026amp; Charles, D. F. (2002). Benthic diatoms in USA rivers: Distributions along spatial and environmental gradients. Journal of Biogeography, 29(2), 167\\u0026ndash;187. https://doi.org/10.1046/j.1365-2699.2002.00668.x\\u003c/li\\u003e\\n\\u003cli\\u003eReimer, C. W., Henderson, M. V., \\u0026amp; Patrick, R. (2001). Bibliography, addenda, and corrigenda for the diatoms of the United States. Proceedings of the Academy of Natural Sciences of Philadelphia, 151, 129\\u0026ndash;155.\\u003c/li\\u003e\\n\\u003cli\\u003eSabater, S., \\u0026amp; Roca, J. R. (1992). Ecological and biogeographical aspects of diatom distribution in Pyrenean springs. British Phycological Journal, 27, 203\\u0026ndash;213.\\u003c/li\\u003e\\n\\u003cli\\u003eSchowe, K. A., \\u0026amp; Harding, J. S. (2014). Development of two diatom-based indices: A biotic and a multimetric index for assessing mine impacts in New Zealand streams. New Zealand Journal of Marine and Freshwater Research, 48(2), 163\\u0026ndash;176. https://doi.org/10.1080/00288330.2013.852113\\u003c/li\\u003e\\n\\u003cli\\u003eSeaby, R. M., \\u0026amp; Henderson, P. A. (2007). Community Analysis Package (Version 4.1.3). Pisces Conservation Ltd.\\u003c/li\\u003e\\n\\u003cli\\u003eSoininen, J., Paavola, R., \\u0026amp; Muotka, T. (2004). Benthic diatom communities in boreal streams: Community structure in relation to environmental and spatial gradients. Ecography, 27, 330\\u0026ndash;342. https://doi.org/10.1111/j.0906-7590.2004.03749.x\\u003c/li\\u003e\\n\\u003cli\\u003eSong, Z., Song, G., Tang, W., Zhao, Y., Yan, D., \\u0026amp; Zhang, W. (2021). Spatial and temporal distribution of Mo in the overlying water of a reservoir downstream from mining area. Journal of Environmental Sciences, 102, 256\\u0026ndash;262. https://doi.org/10.1016/j.jes.2020.09.033\\u003c/li\\u003e\\n\\u003cli\\u003eSzczepocka, E., \\u0026amp; Szulc, B. (2009). The use of benthic diatoms in estimating water quality of variously polluted rivers. Oceanological and Hydrobiological Studies, 38, 17\\u0026ndash;26. https://doi.org/10.2478/v10009-009-0012-x\\u003c/li\\u003e\\n\\u003cli\\u003eTSWQR. (2016, August 10). Turkish Surface Water Quality Regulation: Quality criteria of surface water resources according to their classes in terms of general chemical and physicochemical parameters. Official Gazette, No. 29797. Retrieved from https://www.resmigazete.gov.tr/eskiler/2016/08/20160810-9.htm\\u003c/li\\u003e\\n\\u003cli\\u003eTeittinen, A., Taka, M., Ruth, O., \\u0026amp; Soininen, J. (2015). Variation in stream diatom communities in relation to water quality and catchment variables in a boreal, urbanized region. Science of the Total Environment, 530, 279\\u0026ndash;289. https://doi.org/10.1016/j.scitotenv.2015.05.101\\u003c/li\\u003e\\n\\u003cli\\u003eTelesh, I. V. (2004). Plankton of the Baltic estuarine ecosystems with emphasis on Neva Estuary: A review of present knowledge and research perspectives. Marine Pollution Bulletin, 49, 206\\u0026ndash;219. https://doi.org/10.1016/j.marpolbul.2004.02.009\\u003c/li\\u003e\\n\\u003cli\\u003eVan Dam, H., Mertens, A., \\u0026amp; Sinkeldam, J. (1994). A coded checklist and ecological indicator values of freshwater diatoms from The Netherlands. Netherlands Journal of Aquatic Ecology, 28(1), 117\\u0026ndash;133. https://doi.org/10.1007/BF02334251\\u003c/li\\u003e\\n\\u003cli\\u003eVenkatachalapathy, R., \\u0026amp; Karthikeyan, P. (2015). Diatom indices and water quality index of the Cauvery River, India: Implications on the suitability of bio-indicators for environmental impact assessment. In Environmental Management of River Basin Ecosystems (pp. 707\\u0026ndash;727). Cham: Springer International Publishing.\\u003c/li\\u003e\\n\\u003cli\\u003eWinter, G., \\u0026amp; Duthie, C. (2000). Epilithic diatoms as indicators of stream total N and total P concentration. Journal of the North American Benthological Society, 19, 32\\u0026ndash;49. https://doi.org/10.2307/1468280\\u003c/li\\u003e\\n\\u003cli\\u003eWu, N., Cai, Q., \\u0026amp; Fohrer, N. (2012). Development and evaluation of a diatom-based index of biotic integrity (D-IBI) for rivers impacted by run-of-river dams. Ecological Indicators, 18, 108\\u0026ndash;117. https://doi.org/10.1016/j.ecolind.2011.10.013\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, G. Q., Luo, W., Chen, W. F., \\u0026amp; Zheng, G. X. (2019). A robust but variable lake expansion on the Tibetan Plateau. Science Bulletin, 64(18), 1306\\u0026ndash;1309. https://doi.org/10.1016/j.scib.2019.07.018\\u003c/li\\u003e\\n\\u003cli\\u003eZhou, M., He, L., Huang, J. M., Zhang, M., Wang, Q. P., Wan, B. H., Xiong, M. R., \\u0026amp; Liu, Z. G. (2023). Spatiotemporal variation of epilithic algal flora communities and their relationship with environmental factors in Zhuhu Lake of Poyang Lake (in Chinese). Environmental Science and Pollution Research, 40(4), 36\\u0026ndash;46. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Epilithic algae, PCA, CCA, heavy metals, bioindicators, Pülümür Stream\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8147545/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8147545/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study evaluates the influence of multiple environmental stressors\\u0026mdash;including nutrient enrichment, physicochemical variability, and trace metal contamination\\u0026mdash;on the spatial and seasonal dynamics of epilithic algal communities in the semi-arid P\\u0026uuml;l\\u0026uuml;m\\u0026uuml;r Stream (Eastern T\\u0026uuml;rkiye). Ten stations were sampled seasonally between February 2022 and January 2023 to characterize gradients in temperature, dissolved oxygen, pH, nutrients, and heavy metals in both water and sediments. Dissolved oxygen ranged from 9.72 to 12.27 mg/L, temperature from 2.76 to 18.03\\u0026deg;C, and pH from 8.82 to 9.93. Nitrate (0.43\\u0026ndash;4.50 mg/L), phosphate (0.38\\u0026ndash;2.48 mg/L), and metal concentrations (notably Pb, Cd, Zn, Mn, and Fe) exhibited pronounced spatial variability associated with downstream anthropogenic pressures. Multivariate analyses (PCA, CA, CCA) revealed that nutrients (NO₃⁻, PO₄\\u0026sup3;⁻) and metals\\u0026mdash;particularly Pb, Cd, Mn, and Zn\\u0026mdash;were the primary drivers shaping algal composition. Pollution-tolerant taxa such as \\u003cem\\u003eNitzschia palea\\u003c/em\\u003e, \\u003cem\\u003eGomphonema parvulum\\u003c/em\\u003e, and \\u003cem\\u003eAchnanthidium minutissimum\\u003c/em\\u003e predominated at nutrient- and metal-rich sites, whereas sensitive species persisted in upstream reaches. These findings demonstrate strong species\\u0026ndash;environment relationships and highlight epilithic algae as effective bioindicators for detecting early ecological degradation in semi-arid river systems. The study provides essential baseline data to support ecological monitoring and sustainable watershed management strategies in the region..\\u003c/p\\u003e\",\"manuscriptTitle\":\"Effects of Multiple Variables on Epilithic Algal Communities in the Pülümür Stream System\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-12 13:04:36\",\"doi\":\"10.21203/rs.3.rs-8147545/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"df74ef4e-07e9-475f-805d-d275f29fc4fe\",\"owner\":[],\"postedDate\":\"January 12th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-16T20:24:24+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-12 13:04:36\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8147545\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8147545\",\"identity\":\"rs-8147545\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}