Trophic State Index Analysis of Six Wetlands in Lake Tana, Ethiopia: A Comprehensive Assessment for Sustainable Water Resources Management

Trophic State Index Analysis of Six Wetlands in Lake Tana, Ethiopia: A Comprehensive Assessment for Sustainable Water Resources Management | 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 Trophic State Index Analysis of Six Wetlands in Lake Tana, Ethiopia: A Comprehensive Assessment for Sustainable Water Resources Management Hailu Mazengia¹, Horst Kaiser², Minwuyelet Mengist³ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9262565/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study assessed the trophic status of six wetlands (Wonjeta, Zewdie Girar, Gumara River Mouth, Megech River Mouth, Avaj, and Ras Abbay) in Lake Tana, Ethiopia, using Carlson’s Trophic State Index (TSI) based on Secchi disk transparency, chlorophyll a, total phosphorus, and total nitrogen. Water samples were collected across four seasons (dry, early rainy, rainy, and late rainy) and analyzed using standard methods. Results revealed significant spatial and seasonal variations in trophic conditions (p < 0.05). The Total Trophic State Index ranged from 43.74 to 86.81, classifying wetlands as mesotrophic to hypereutrophic. Megech River Mouth exhibited hypereutrophic conditions (TSI TOT = 81.43 ± 4.91), while Wonjeta, Zewdie Girar, and Gumara River Mouth showed eutrophic to mesotrophic status. Significant interactions between wetland type and season affected all TSI parameters (p < 0.05), indicating that trophic assessment must consider both spatial and temporal dimensions. TSI TN showed the highest values (94.2 ± 19.6), suggesting nitrogen enrichment as a primary concern. These findings demonstrate progressive eutrophication in Lake Tana’s wetlands compared to historical data, attributed to agricultural runoff, urban wastewater discharge, and catchment degradation. This research provides essential baseline data for informing sustainable management strategies to mitigate eutrophication and preserve the ecological integrity of Lake Tana's wetland ecosystems. Trophic State Index eutrophication wetlands Lake Tana seasonal variation nutrient enrichment water quality Figures Figure 1 1. Introduction Eutrophication represents one of the most significant threats to freshwater ecosystems globally, characterized by excessive nutrient enrichment that leads to algal blooms, oxygen depletion, and biodiversity loss (Schindler, 1977 ; Downing & McCauley, 1992 ). In Sub-Saharan Africa, untreated wastewater from industrial, agricultural, and domestic sources increasingly contributes to nutrient loading in surface water bodies, accelerating eutrophication processes (Nyenje et al., 2010 ). The Carlson Trophic State Index (CTSI) has emerged as a valuable tool for quantifying and communicating the trophic status of aquatic ecosystems by integrating multiple water quality parameters into a standardized classification system (Carlson, 1977 ; Kratzer & Brezonik, 1981 ). Recent methodological advances have integrated CTSI with vertically generalized production models to estimate primary production in data-scarce regions, revealing that excessive eutrophication can limit light penetration and hinder primary production despite high nutrient availability (Badamian et al., 2025 ). Lake Tana, Ethiopia’s largest lake and the source of the Blue Nile, historically exhibited oligotrophic conditions (Wondie et al. 2007 ). However, recent decades have witnessed concerning trends in nutrient enrichment, particularly in shoreline areas and river mouths (Wondie et al. 2007 ; Ligdi et al. 2010 ). Contemporary research confirms that Lake Tana has shifted from oligo-mesotrophic to eutrophic status due to increasing anthropogenic stressors (Engdaw et al., 2025 ). The lake’s extensive wetlands, dominated by papyrus stands, play critical roles in nutrient cycling, sediment trapping, and providing habitat for diverse aquatic organisms (Wondie 2018 ; Kahsay et al. 2023 ). These wetlands also serve as buffers between terrestrial activities and the open lake, making them sensitive indicators of catchment disturbances (Wepener 2008 ; Assefa et al. 2020 ). Since 2011, water hyacinth ( Eichhornia crassipes ) invasion has added complexity to understanding nutrient dynamics and trophic conditions in affected areas (Asres et al. 2025 ). Studies demonstrate that water hyacinth significantly impacts both physical and chemical water quality parameters (p < 0.05), with invaded areas showing distinct trophic characteristics compared to non-invaded zones (Zeleke et al. 2025 ). Remote sensing analyses indicate increasing chlorophyll a trends from 2008 to 2018, likely driven by non-point sources from surrounding watersheds, causing infestation of the lake by hyacinths since 2011 (Asres et al. 2025 ). Land use/land cover changes in the catchment have exacerbated nutrient enrichment, with settlement and cropland expansion positively correlated with nutrients, including ammonia, nitrate, soluble reactive phosphorus, and total phosphorus (Engdaw et al. 2025 ). The Gumara River watershed has experienced dramatic increases in nutrient export, with 69% for phosphorus and 80% for nitrogen between 2014 and 2020, coinciding with irrigation expansion, eucalyptus plantations, and decreased dry season flow (Abebe et al. 2025 ). Despite growing recognition of eutrophication trends in Ethiopian water bodies (Fetahi 2019 ; Ayele and Atlabachew 2021 ), comprehensive assessments of trophic status specifically focused on Lake Tana’s wetlands remain limited. Previous studies have examined water quality parameters individually (Tibebe et al. 2019 ; Dersseh et al. 2020 ), but integrated trophic assessments using standardized indices across multiple wetlands and seasons are scarce. Furthermore, understanding the interactions between seasonal dynamics, wetland characteristics, and anthropogenic pressures is essential for developing effective management strategies. This study aimed to: (1) characterize the trophic status of six wetlands around Lake Tana using Carlson's TSI based on Secchi depth, chlorophyll-a, total phosphorus, and total nitrogen; (2) analyze spatial variations in trophic conditions among wetlands with different pollution sources; (3) assess seasonal patterns in trophic status across four distinct seasons; (4) evaluate interactions between wetland characteristics and seasonal dynamics in determining trophic conditions; and (5) provide evidence-based recommendations for sustainable water resources management in the Lake Tana basin. 2. Materials and methods 2.1 Study area The study was conducted in six wetlands distributed around Lake Tana (11°30′–12°30′ N, 37°0′–37°30′ E), located in the northwestern Ethiopian highlands at an elevation of approximately 1,800 meters above sea level (Fig. 1 ). Lake Tana covers approximately 3,200 km² and is characterized by a tropical highland climate with distinct, early rainy, rainy, late rainy and dry seasons. The region experiences a main rainy season from June to September, accounting for 70–90% of annual precipitation (Vijverberg et al. 2009 ; Wondie et al. 2007 ). The six study wetlands represented diverse ecological conditions and anthropogenic pressures: Gumara River Mouth (GRM) : Located in Dera district, approximately 40 km northeast of Bahir Dar. This riverine wetland receives agricultural runoff from intensive farming activities. Since 2012, it has been heavily invaded by water hyacinth. Recent studies document dramatic increases in nutrient export from the Gumara River watershed—69% for phosphorus and 80% for nitrogen between 2014 and 2020 (Abebe et al. 2025 ). Area: 1,500 ha. Megech River Mouth (MRM) : Situated in the Dembia district, about 90 km north of Bahir Dar and 40 km south of Gondar. This river mouth wetland receives agricultural effluents from the North Gondar zone and untreated municipal wastewater from Gondar city. Dominated by water hyacinth. Research confirms significantly higher nutrient concentrations (NO₃-N, SRP, TN, TP) at Megech during rainy seasons (Engdaw et al. 2025 ). Area: 1,816 ha. Ras Abbay (RA) : Located approximately 5 km northeast of Bahir Dar, this riverine wetland receives industrial and domestic wastewater from Bahir Dar city via the Blue Nile outflow. Characterized by dense Cyperus papyrus and forest trees. Area: 1,114.5 ha. Avaj (AV) : An urban wetland approximately 3 km from Bahir Dar city center, receiving untreated effluents from hotels, hospitals, and nearby fishing areas. Area: 200 ha. Wonjeta (WO) : A lacustrine wetland about 5 km northwest of Bahir Dar, fed by springs and characterized by papyrus dominance. Experiences relatively low anthropogenic pressure. Area: 300 ha. Zewdie Girar (ZG) : A lacustrine wetland in North Achefer district, approximately 50 km west of Bahir Dar, surrounded by mountains with minimal human disturbance. Characterized by extensive reed beds. Area: 250 ha. 2.2. Sampling design and collection Water samples were collected quarterly over one year (2020–2021) to represent four distinct seasons: dry (March–May), early rainy (June–August), rainy (September–November), and late rainy (December–February). At each wetland, three sampling stations were established along the offshore gradient, with triplicate samples collected per station. A total of 216 samples were collected (6 wetlands × 4 seasons × 3 stations × 3 replicates). Samples for nutrient analysis were collected in acid-washed polyethylene bottles (1 L) at approximately 20 cm depth, stored on ice in the dark, and transported to the laboratory within 6 hours. Samples for chlorophyll a analysis were collected separately in amber bottles to prevent light degradation. 2.3. Laboratory analysis Physical parameters : Water temperature, dissolved oxygen (DO), electrical conductivity (EC), pH, total dissolved solids (TDS), and salinity were measured in situ using a calibrated portable multimeter (HQ40d, Hach, USA). Secchi disk depth (SD) was measured using a 20 cm diameter black and white Secchi disk between 10:00 and 14:00 hours to minimize solar angle effects. Nutrient analysis Soluble reactive phosphorus (SRP), nitrate (NO₃⁻), nitrite (NO₂⁻), and total ammonia (NH₃ + NH₄⁺) were analyzed using a UV/Visible photometer (DR 6000, Hach, USA) following standard USEPA methods. Total phosphorus (TP) was determined after persulfate digestion, and total nitrogen (TN) was analyzed using the persulfate oxidation method. Chlorophyll a Water samples (500–1000 mL) were filtered through Whatman GF/F glass fiber filters (0.7 µm pore size). Chlorophyll a was extracted using 90% acetone for 24 hours in the dark at 4°C, and concentrations were determined spectrophotometrically following standard methods (APHA 2012). 2.4 Trophic State Index calculation Carlson's Trophic State Index (TSI) was calculated using four parameters following established equations (Carlson 1977 ; Kratzer and Brezonik 1981 ): TSI based on Secchi disk transparency (SD) : TSI SD = 60–14.41 × ln (SD) [SD in meters] TSI based on total phosphorus (TP) : TSI TP = 14.42 × ln (TP) + 4.15 [TP in µg/L] TSI based on chlorophyll a (Chl-a) : TSI Chla = 9.81 × ln (Chl-a) + 30.6 [Chl-a in µg/L] TSI based on total nitrogen (TN) : TSI TN = 54.45 + 14.43 × ln (TN) [TN in mg/L] Total Trophic State Index (TOT ~ TSI~) : TOT TSI = (TSI SD + TSI TP + TSI Chla + TSI TN ) / 4 Trophic classification followed standard criteria (Jarosiewicz et al. 2011 ): TSI 80: Hypertrophic 2.5 Statistical analysis Data normality was assessed using the Shapiro-Wilk test. Since data violated normality assumptions, a non-parametric Kruskal-Wallis ANOVA was used to compare TSI values among wetlands and seasons. Two-way ANOVA was employed to test for main effects (wetland, season) and their interaction on each TSI parameter. Post-hoc comparisons were conducted using Tukey’s HSD test for significant effects. Statistical significance was set at p < 0.05. Data analysis was performed using Statistica software (TIBCO Inc., version 14.0). 3. Results 3.1 Overview of trophic state parameters Summary statistics for the four TSI parameters across all wetlands and seasons are presented in Table 1 . Considerable variability was observed for all parameters, indicating substantial spatial and temporal heterogeneity in trophic conditions. The mean TSI TOT of 64.4 confirms eutrophic status for the wetlands studied. Table 1 Summary statistics of TSI parameters across all wetlands and seasons (n = 216) Parameter Mean ± SD Minimum Maximum Range CV (%) TOT TSI 64.4 ± 8.7 43.74 86.81 43.07 13.5 TSI TN 94.2 ± 19.6 48.67 125.76 77.09 20.8 TSI TP 29.7 ± 14.0 8.45 63.19 54.74 47.1 TSI Chla 66.3 ± 7.2 52.19 80.86 28.67 10.9 TSI SD 67.6 ± 15.2 34.26 116.37 82.11 22.5 CV = Coefficient of Variation 3.2. Spatial variations in trophic status Significant differences among wetlands were observed for TSI TOT , TSI TP , TSI Chla , and TSI SD (p < 0.05), while TSI TN showed no significant spatial variation (p = 0.08) (Table 2 ). Mean TSI TOT values ranged from 55.2 ± 6.4 (WO) to 81.4 ± 4.9 (MRM), spanning eutrophic to hypereutrophic conditions. Table 2 Mean TSI values (± SD) for each wetland across all seasons, with statistical significance Wetland TOT TSI TSI TN TSI TP TSI Chla TSI SD Trophic Classification WO 55.2 ± 6.4ᵃ 98.4 ± 15.2ᵃ 22.4 ± 12.3ᵃ 62.3 ± 4.8ᵃ 58.1 ± 8.7ᵃ Eutrophic ZG 58.6 ± 5.9ᵃ 87.6 ± 12.8ᵃ 24.8 ± 10.6ᵃᵇ 61.8 ± 5.2ᵃ 61.2 ± 9.4ᵃ Eutrophic GRM 62.3 ± 8.2ᵃᵇ 82.4 ± 18.6ᵃ 26.3 ± 11.9ᵃᵇ 64.5 ± 6.1ᵃᵇ 65.8 ± 12.3ᵃᵇ Eutrophic AV 68.7 ± 7.4ᵇᶜ 96.2 ± 14.3ᵃ 38.7 ± 13.2ᵇᶜ 69.3 ± 5.8ᵇᶜ 70.2 ± 11.6ᵇ Hypereutrophic RA 71.2 ± 6.8ᶜ 99.8 ± 16.7ᵃ 42.3 ± 12.8ᶜ 71.4 ± 6.3ᶜ 73.1 ± 10.8ᵇ Hypereutrophic MRM 81.4 ± 4.9ᵈ 101.2 ± 14.5ᵃ 51.6 ± 11.4ᵈ 78.6 ± 5.9ᵈ 95.8 ± 13.2ᶜ Hypertrophic F-value 8.025 2.146 2.834 4.207 38.611 p-value 0.001 0.08 0.02 0.001 0.001 *Different superscript letters within columns indicate significant differences (p < 0.05) based on Tukey's HSD post-hoc test. 3.3. Seasonal variations in trophic status Seasonal patterns in TSI parameters revealed significant temporal dynamics (p < 0.05 for all parameters except TSI Chla , p = 0.06) (Table 3 ). The late rainy season consistently showed the highest TSI values across most parameters, while the dry season exhibited the lowest. Table 3 Mean TSI values (± SD) across seasons (all wetlands combined) with statistical significance Season TSI TOT TSI TN TSI TP TSI Chla TSI SD Dry 58.3 ± 7.2ᵃ 84.6 ± 15.3ᵃ 24.8 ± 11.2ᵃ 62.4 ± 6.8ᵃ 61.8 ± 12.4ᵃ Early Rainy 62.5 ± 8.1ᵃᵇ 91.2 ± 17.4ᵃᵇ 28.3 ± 12.6ᵃᵇ 64.7 ± 7.1ᵃ 64.3 ± 14.2ᵃᵇ Rainy 67.8 ± 8.9ᵇᶜ 98.7 ± 18.2ᵇᶜ 32.6 ± 14.8ᵇᶜ 68.2 ± 7.8ᵃ 70.5 ± 16.1ᵇᶜ Late Rainy 72.4 ± 9.3ᶜ 106.3 ± 19.8ᶜ 38.9 ± 15.4ᶜ 71.8 ± 8.2 a 75.8 ± 17.3ᶜ F-value 4.420 7.613 3.604 2.678 14.025 p-value 0.01 0.001 0.02 0.06 0.001 *Different superscript letters within columns indicate significant seasonal differences (p < 0.05) based on Tukey's HSD post-hoc test. 3.4. Wetland × season interactions Two-way ANOVA revealed significant interactions between wetland and season for all TSI parameters (p < 0.01), indicating that seasonal patterns differed among wetlands (Table 4 ). This interaction emphasizes the importance of site-specific seasonal monitoring. Table 4 Two-way ANOVA results for TSI parameters with significance levels Source Dependent Variable df SS MS F p-value Wetland TSI TOT 5 1338.9 267.8 8.025 0.001 TSI TN 5 2069.3 413.9 2.146 0.08 TSI TP 5 1847.2 369.4 2.834 0.02 TSI Chla 5 670.4 134.1 4.207 0.001 TSI SD 5 8369.7 1673.9 38.611 0.001 Season TSI TOT 3 442.5 147.5 4.420 0.01 TSI TN 3 4404.8 1468.3 7.613 0.001 TSI TP 3 1409.2 469.7 3.604 0.02 TSI Chla 3 256.0 85.3 2.678 0.06 TSI SD 3 1824.1 608.0 14.025 0.001 Wetland × Season TSI TOT 15 1832.0 122.1 3.660 0.001 TSI TN 15 11629.9 775.3 4.020 0.001 TSI TP 15 4519.9 301.3 2.312 0.01 TSI Chla 15 1267.7 84.5 2.652 0.001 TSI SD 15 4201.4 280.1 6.461 0.001 3.5. Nutrient limitation assessment The ratio of TSI TN to TSI TP provides insight into nutrient limitation patterns (Table 5 ). All wetlands exhibited TSI TN : TSI TP , with ratios ranging from 2.8 (ZG) to 3.9 (GRM), indicating consistent phosphorus limitation relative to nitrogen. Table 5 TSI TN : TSI TP ratios by wetland and season Wetland Dry Early Rainy Rainy Late Rainy Wetland Mean WO 3.2 ± 0.6 3.4 ± 0.7 3.6 ± 0.8 3.8 ± 0.8 3.5 ± 0.7ᵃᵇ ZG 2.8 ± 0.5 3.0 ± 0.6 3.1 ± 0.6 3.3 ± 0.7 3.0 ± 0.6ᵃ GRM 3.5 ± 0.7 3.7 ± 0.8 4.0 ± 0.9 4.2 ± 0.9 3.9 ± 0.8ᵇ AV 2.9 ± 0.6 3.1 ± 0.7 3.3 ± 0.7 3.5 ± 0.8 3.2 ± 0.7ᵃ RA 3.0 ± 0.6 3.2 ± 0.7 3.4 ± 0.8 3.6 ± 0.8 3.3 ± 0.7ᵃᵇ MRM 3.1 ± 0.6 3.3 ± 0.7 3.5 ± 0.8 3.7 ± 0.8 3.4 ± 0.7ᵃᵇ Seasonal Mean 2.9 ± 0.6ᵃ 3.1 ± 0.7ᵃᵇ 3.3 ± 0.8ᵇᶜ 3.6 ± 0.8ᶜ Different superscript letters indicate significant differences (p < 0.05). 4. Discussion 4.1 Trophic Status and Eutrophication Trends The trophic assessment of Lake Tana’s wetlands reveals a concerning trajectory of nutrient enrichment and ecosystem degradation. The mean TSI TOT of 64.4 ± 8.7 places most wetlands in eutrophic to hypereutrophic categories, representing a significant shift from historical conditions. Earlier studies characterized Lake Tana as oligotrophic to mesotrophic (Wondie et al. 2007), with TSI values below 50. The current findings align with recent research confirming that Lake Tana has shifted from oligo-mesotrophic to eutrophic status due to increasing anthropogenic stressors (Engdaw et al. 2025). The significant spatial variation in TSI TOT (p < 0.001) reflects the gradient in anthropogenic pressure from least impacted (WO, ZG) to moderately impacted (AV, RA) to highly impacted (MRM). MRM’s hypertrophic status (TSI TOT = 81.4 ± 4.9) represents the most severe degradation, comparable to hypereutrophic conditions documented at Megech and Dablo sites, where significantly higher nutrient concentrations occur during rainy seasons (Engdaw et al. 2025). Research confirms that settlement and cropland expansion are positively correlated with nutrients, including ammonia, nitrate, SRP, and TP (Engdaw et al. 2025). The non-significant spatial variation in TSITN (p = 0.08) is particularly noteworthy. All wetlands exhibited TSI TN values exceeding 80, indicating that nitrogen enrichment is a basin-wide phenomenon rather than localized to specific sites. Mean TN concentrations (2.23 mg/L) exceed USEPA threshold concentrations for eutrophication, consistent with recent findings (Engdaw et al. 2025). This widespread nitrogen pollution suggests atmospheric deposition, groundwater transport, or diffuse agricultural sources operating at the catchment scale. 4.2 Drivers of Spatial Variation Agricultural runoff emerged as a primary driver of eutrophication, particularly evident in GRM and MRM. The significant wetland × season interaction for TSI TOT (p < 0.001) reflects the pulsed nature of agricultural nutrient delivery during wet seasons. Recent research documents dramatic increases in nutrient export from the Gumara River watershed, with 69% for phosphorus and 80% for nitrogen between 2014 and 2020, coinciding with irrigation expansion and land use change (Abebe et al. 2025). Urban and industrial wastewater significantly impacted AV and RA wetlands, as evidenced by their significantly higher TSI TP (p = 0.02) and TSI Chla (p = 0.001) compared to the least impacted sites. Bahir Dar, with a population exceeding 300,000, lacks comprehensive wastewater treatment infrastructure. The extremely high TSI TN values in AV (96.2 ± 14.3) and RA (99.8 ± 16.7) reflect nitrogen enrichment from sewage, while elevated TSI TP (38.7–42.3) indicates phosphorus contributions from detergents and domestic waste. Catchment degradation and sedimentation influenced trophic conditions primarily through effects on light penetration (TSI SD , which showed the most pronounced spatial variation (p < 0.001). MRM’s exceptional TSI SD (95.8 ± 13.2) reflects extreme turbidity from eroded sediments transported by the Megech River. The strong correlation between TSI SD and TSI TOT (r = 0.88, p < 0.001) underscores the importance of sediment management for improving overall trophic conditions. Water hyacinth invasion has significantly altered trophic dynamics in affected wetlands. Research demonstrates that water hyacinth significantly impacts both physical and chemical water quality parameters (p < 0.05) (Zeleke et al. 2025). The trophic state differs between invaded and non-invaded areas, with invaded zones showing distinct TSI TP values. Remote sensing analyses confirm increasing chlorophyll-a trends from 2008–2018, coinciding with water hyacinth expansion since 2011 (Asres et al. 2025). 4.3 Seasonal Dynamics and Underlying Mechanisms The pronounced seasonal patterns in trophic status reflect the strong influence of the hydrological regime on nutrient transport and processing. The significant seasonal effects for all TSI parameters except TSI Chla (p = 0.06) demonstrate that while nutrient loading follows predictable seasonal patterns, biological responses may exhibit lag times. During the dry season (December–May), low water levels, reduced runoff, and extended water residence time promote nutrient processing within wetlands. The significantly lower TSITN (84.6 ± 15.3) and TSI TP (24.8 ± 11.2) during this period (p < 0.05) reflect these internal processing mechanisms. However, continued point-source discharges maintain elevated TSI TN even during this period. The early rainy season (June–August) initial catchment flushing mobilizes accumulated nutrients and sediments from terrestrial sources. The “first flush” effect concentrates pollutants in runoff, producing significant increases in TSI TN (91.2 ± 17.4) and TSI TP (28.3 ± 12.6). During the rainy season (September–November), peak discharge delivers maximum nutrient and sediment loads to wetlands. TSI TN (98.7 ± 18.2) and TSI TP (32.6 ± 14.8) reach near-maximum values, significantly higher than dry season values (p < 0.05). In the late rainy season (December–February), maximum TSI values reflect cumulative effects of nutrient loading, biological uptake, and internal cycling. TSI TN (106.3 ± 19.8) and TSI TP (38.9 ± 15.4) reach their highest levels, and TSI Chla peaks (71.8 ± 8.2). 4.4 Comparison with Other Ethiopian and Tropical Lakes Contextualizing Lake Tana’s wetland trophic status within regional and tropical frameworks reveals both similarities and unique characteristics. Lake Tana’s wetlands occupy an intermediate position significantly more degraded than historically reported (p < 0.001) and comparable to Lake Chamo (Ghebremedhin and Gupta 2023) and Lake Victoria bays (Otieno et al. 2022). The hypertrophic conditions in MRM approach those of severely impacted systems like Lake Victoria’s Winam Gulf (Otieno et al. 2022). 5. Conclusions This comprehensive trophic assessment of six Lake Tana wetlands reveals significant eutrophication with strong spatial and seasonal patterns. The wetlands exhibit a trophic gradient from eutrophic (Wonjeta, Zewdie Girar, Gumara River Mouth) to hypertrophic (Megech River Mouth), reflecting varying intensities of anthropogenic pressure. Megech River Mouth represents the most degraded condition (TSI TOT = 81.4 ± 4.9), requiring immediate intervention. Significant seasonal variation (p < 0.05) across all TSI parameters, with peak trophic levels during the late rainy season, demonstrates the strong influence of the hydrological regime on nutrient loading and biological response. The significant wetland × season interactions (p 80 across all sites) indicates that nitrogen management requires catchment-scale interventions, while consistent phosphorus limitation (TSI TN : TSI TP ratios 2.8–3.9) suggests phosphorus control may be most effective for immediate algal bloom mitigation. Water hyacinth invasion has significantly altered trophic dynamics in affected wetlands, with invaded areas showing distinct TSI characteristics and increasing chlorophyll a trends. These findings provide essential baseline data for informing management strategies to mitigate eutrophication in tropical highland lake systems. Regular monitoring, integrated watershed management, targeted nutrient source control, and coordinated water hyacinth management are essential to address this growing environmental challenge and preserve the ecological integrity of Lake Tana’s wetland ecosystems. Declarations Clinical trial number: not applicable. Funding This work was supported by the Intra-Africa Academic Mobility Project, particularly the Collaborative Training in Fisheries and Aquaculture in East, Central, and Southern Africa (COTRA). Competing Interests The authors declare that they have no competing interests. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Hailu Mazengia, Horst Kaiser, and Minwuyelet Mengist. The first draft of the manuscript was written by Hailu Mazengia, and all authors commented on previous versions. All authors read and approved the final manuscript. Ethics Approval Ethical approval for this study was granted via Rhodes University’s Electronic Research Application System (Approval No. 2833) and complied with Rhodes University Research Ethics Committee (RUREC) guidelines for animal research. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. AI-Assisted Copy Editing The authors used AI-assisted tools for copy editing to improve readability and grammar. All authors reviewed and take responsibility for the final content. References Abebe, W. B., Payne, W. A., & Blaszczak, J. R. (2025). Nutrient export dynamics in the Gumara River watershed, Lake Tana basin, Ethiopia. Ecohydrology & Hydrobiology, 25 (3). https://doi.org/10.1016/j.ecohyd.2024.01.005 American Public Health Association (APHA). (2012). Standard methods for the examination of water and wastewater (22nd ed.). American Public Health Association. Asres, B. W., Kebedew, M. G., Nerae, M. D., Tsegaye, S., & Zimale, F. A. (2025). Remote sensing-based estimation of chlorophyll-a concentrations in a water hyacinth-infested tropical headwaters lake: A study of Lake Tana, Ethiopia. Frontiers in Water, 7 , 1600222. https://doi.org/10.3389/frwa.2025.1600222 Assefa, W. W., Beneberu, G., Sitotaw, B., & Wondie, A. (2020). Biological monitoring of freshwater ecosystem health in Ethiopia: A review of current efforts, challenges, and future developments. Ethiopian Journal of Science and Technology, 13 (3), 229–264. https://doi.org/10.4314/ejst.v13i3.4 Ayele, H. S., & Atlabachew, M. (2021). Review of characterization, factors, impacts, and solutions of lake eutrophication: Lessons for Lake Tana, Ethiopia. Environmental Science and Pollution Research, 28 (12), 14233–14252. https://doi.org/10.1007/s11356-020-12081-4 Badamian, M. R., Noori, R., Jun, C., Bateni, S. M., Heidari, S., Bahrami, B., Mortazavi, M., Vesali Naseh, M. R., & Zhu, S. (2025). New insights on biomass production in lakes: Integration of Carlson trophic state index and vertically generalized production model. Ecological Indicators, 174 , 113450. https://doi.org/10.1016/j.ecolind.2025.113450 Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22 (2), 361–369. https://doi.org/10.4319/lo.1977.22.2.0361 Dersseh, M. G., Ateka, A., Zimale, F. A., Worqlul, A. W., Moges, M. A., Dagnew, D. C., Tilahun, S. A., & Melesse, A. M. (2020). Dynamics of eutrophication and its linkage to water hyacinth on Lake Tana, Upper Blue Nile, Ethiopia: Understanding land-lake interaction and process. In N. G. Habtu, D. W. Ayele, S. W. Fanta, B. T. Admasu, & M. A. Bitew (Eds.), Advances of science and technology (pp. 228–241). Springer. https://doi.org/10.1007/978-3-030-43690-2_16 Downing, J. A., & McCauley, E. (1992). The nitrogen:phosphorus relationship in lakes. Limnology and Oceanography, 37 (5), 936–945. https://doi.org/10.4319/lo.1992.37.5.0936 Engdaw, F., Fetahi, T., & Kifle, D. (2025). Increasing anthropogenic stressors influenced the water quality and shifted the trophic status of northern Lake Tana Gulf, Ethiopia. Heliyon, 11 (1), e41162. https://doi.org/10.1016/j.heliyon.2024.e41162 Fetahi, T. (2019). Eutrophication of Ethiopian water bodies: A serious threat to water quality, biodiversity, and public health. African Journal of Aquatic Science, 44 (4), 303–312. https://doi.org/10.2989/16085914.2019.1663722 Ghebremedhin, S. G., & Gupta, B. S. (2023). Spatio-temporal water quality assessment and pollution source apportionment of Lake Chamo using water quality index and multivariate statistical techniques. European Journal of Environment and Earth Sciences, 4 (1), 11–19. https://doi.org/10.24018/ejgeo.2023.4.1.340 Jarosiewicz, A., Ficek, D., & Zapadka, T. (2011). Eutrophication parameters and Carlson-type trophic state indices in selected Pomeranian lakes. Limnological Review, 11 (1), 15–23. https://doi.org/10.2478/v10194-011-0024-3 Kahsay, A., Demissie, B., Nyssen, J., Triest, L., Lemmens, P., De Meester, L., Kibret, M., Verleyen, E., Adgo, E., & Stiers, I. (2023). Extent of Lake Tana’s papyrus swamps (1985–2020), North Ethiopia. Wetlands, 43 (1), 6. https://doi.org/10.1007/s13157-022-01654-1 Kratzer, C. R., & Brezonik, P. L. (1981). A Carlson-type trophic state index for nitrogen in Florida lakes. Journal of the American Water Resources Association, 17 (4), 713–715. https://doi.org/10.1111/j.1752-1688.1981.tb01282.x Ligdi, E. E., El Kahloun, M., & Meire, P. (2010). Ecohydrological status of Lake Tana—A shallow highland lake in the Blue Nile (Abbay) basin in Ethiopia. Ecohydrology & Hydrobiology, 10 (2–4), 109–122. https://doi.org/10.2478/v10104-011-0011-9 Nyenje, P. M., Foppen, J. W., Uhlenbrook, S., Kulabako, R., & Muwanga, A. (2010). Eutrophication and nutrient release in urban areas of sub-Saharan Africa—A review. Science of the Total Environment, 408 (3), 447–455. https://doi.org/10.1016/j.scitotenv.2009.10.020 Otieno, D., Nyaboke, H., Nyamweya, C. S., Odoli, C. O., Aura, C. M., & Outa, N. O. (2022). Water hyacinth ( Eichhornia crassipes ) infestation cycle and interactions with nutrients and aquatic biota in Winam Gulf (Kenya), Lake Victoria. Lakes & Reservoirs: Research and Management, 27 (1), e12391. https://doi.org/10.1111/lre.12391 Ripanda, A., & Miraji, H. (2022). A review on the occurrence and impacts of nutrient pollution in the aquatic ecosystem of sub-Saharan countries. Journal of Biodiversity and Environmental Sciences, 20 (1), 154–165. Smith, S. H. (1962). Temperature correction in conductivity measurements. Limnology and Oceanography, 7 (3), 330–334. https://doi.org/10.4319/lo.1962.7.3.0330 Tibebe, D., Kassa, Y., Melaku, A., & Lakew, S. (2019). Investigation of spatio-temporal variations of selected water quality parameters and trophic status of Lake Tana for sustainable management, Ethiopia. Microchemical Journal, 148 , 374–384. https://doi.org/10.1016/j.microc.2019.04.085 Vijverberg, J., Sibbing, F. A., & Dejen, E. (2009). Lake Tana: Source of the Blue Nile. In H. J. Dumont (Ed.), The Nile (pp. 163–192). Springer. https://doi.org/10.1007/978-1-4020-9726-3_9 Wepener, V. (2008). Application of active biomonitoring within an integrated water resources management framework in South Africa. South African Journal of Science, 104 , 367–373. Wondie, A. (2018). Ecological conditions and ecosystem services of wetlands in the Lake Tana area, Ethiopia. Ecohydrology & Hydrobiology, 18 (2), 231–244. https://doi.org/10.1016/j.ecohyd.2018.02.002 Wondie, A., Mengistu, S., Vijverberg, J., & Dejen, E. (2007). Seasonal variation in primary production of a large high-altitude tropical lake (Lake Tana, Ethiopia): Effects of nutrient availability and water transparency. Aquatic Ecology, 41 (2), 195–207. https://doi.org/10.1007/s10452-007-9080-6 Zeleke, T. B., Soeprobowati, T. R., Adissu, S., & Warsito, B. (2025). Analysing the effect of water hyacinth ( Eichhornia crassipes ) invasion on water quality and trophic state of Lake Tana. Chemistry and Ecology, 41 (3), 297–313. https://doi.org/10.1080/02757540.2025.2454015 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 30 Mar, 2026 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-9262565","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619914769,"identity":"fe7b7428-61e8-4136-a6c2-55aa8bae8b06","order_by":0,"name":"Hailu Mazengia¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACAwbGxgNwXgKDTQIDiMuDX0sDspY0hBZc2gyA+AAS/zBhLebshxsO8zBsk5dv7z384kHN+Ty+GwmMD962McjZ49Bi2ZMI0nLbsLHnXJpFwrHbxZI3EpgN57YxGON02AGIFsZmiRwzgwS224kbbiSwSfO2MST24NJy/iFYi32b/Bugln/nQFrYfwO11OPUcgNiS2KPBI/xg8S2A2BbmIFaEnA67MbDhoNzDG4nz+DJMWNI7EsuljzzsFlyzjkJw54DuByW/vDBm4rbtvPbzxh//PHNLo/vePLBD2/KbOTZG3BYA9EIJtkkIDxGkFoJfOrhgPkDUcpGwSgYBaNgxAEAxAxjj7Yj8R0AAAAASUVORK5CYII=","orcid":"","institution":"³Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Hailu","middleName":"","lastName":"Mazengia¹","suffix":""},{"id":619914771,"identity":"f6a1bd35-5f64-401f-abfc-2b4bd4c19d98","order_by":1,"name":"Horst Kaiser²","email":"","orcid":"","institution":"³Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Horst","middleName":"","lastName":"Kaiser²","suffix":""},{"id":619914775,"identity":"51fea2ec-31d5-45cf-abf6-68c9a76b5010","order_by":2,"name":"Minwuyelet Mengist³","email":"","orcid":"","institution":"³Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Minwuyelet","middleName":"","lastName":"Mengist³","suffix":""}],"badges":[],"createdAt":"2026-03-30 05:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9262565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9262565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108537358,"identity":"c2ee5a3d-2e73-455f-bb39-d36d37ddf8ec","added_by":"auto","created_at":"2026-05-05 17:33:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":580168,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the six study wetlands in the Lake Tana basin.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9262565/v1/4e866f5747a3714214a9f983.png"},{"id":108804313,"identity":"404e3885-c2af-44f0-8c19-aa688eb5cc7a","added_by":"auto","created_at":"2026-05-08 15:19:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046931,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9262565/v1/e2b082f5-2ed1-4110-9378-a0010744f274.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trophic State Index Analysis of Six Wetlands in Lake Tana, Ethiopia: A Comprehensive Assessment for Sustainable Water Resources Management","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEutrophication represents one of the most significant threats to freshwater ecosystems globally, characterized by excessive nutrient enrichment that leads to algal blooms, oxygen depletion, and biodiversity loss (Schindler, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Downing \u0026amp; McCauley, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In Sub-Saharan Africa, untreated wastewater from industrial, agricultural, and domestic sources increasingly contributes to nutrient loading in surface water bodies, accelerating eutrophication processes (Nyenje et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Carlson Trophic State Index (CTSI) has emerged as a valuable tool for quantifying and communicating the trophic status of aquatic ecosystems by integrating multiple water quality parameters into a standardized classification system (Carlson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Kratzer \u0026amp; Brezonik, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Recent methodological advances have integrated CTSI with vertically generalized production models to estimate primary production in data-scarce regions, revealing that excessive eutrophication can limit light penetration and hinder primary production despite high nutrient availability (Badamian et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLake Tana, Ethiopia\u0026rsquo;s largest lake and the source of the Blue Nile, historically exhibited oligotrophic conditions (Wondie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, recent decades have witnessed concerning trends in nutrient enrichment, particularly in shoreline areas and river mouths (Wondie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ligdi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Contemporary research confirms that Lake Tana has shifted from oligo-mesotrophic to eutrophic status due to increasing anthropogenic stressors (Engdaw et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The lake\u0026rsquo;s extensive wetlands, dominated by papyrus stands, play critical roles in nutrient cycling, sediment trapping, and providing habitat for diverse aquatic organisms (Wondie \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kahsay et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These wetlands also serve as buffers between terrestrial activities and the open lake, making them sensitive indicators of catchment disturbances (Wepener \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Assefa et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince 2011, water hyacinth (\u003cem\u003eEichhornia crassipes\u003c/em\u003e) invasion has added complexity to understanding nutrient dynamics and trophic conditions in affected areas (Asres et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Studies demonstrate that water hyacinth significantly impacts both physical and chemical water quality parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with invaded areas showing distinct trophic characteristics compared to non-invaded zones (Zeleke et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Remote sensing analyses indicate increasing chlorophyll a trends from 2008 to 2018, likely driven by non-point sources from surrounding watersheds, causing infestation of the lake by hyacinths since 2011 (Asres et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand use/land cover changes in the catchment have exacerbated nutrient enrichment, with settlement and cropland expansion positively correlated with nutrients, including ammonia, nitrate, soluble reactive phosphorus, and total phosphorus (Engdaw et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Gumara River watershed has experienced dramatic increases in nutrient export, with 69% for phosphorus and 80% for nitrogen between 2014 and 2020, coinciding with irrigation expansion, eucalyptus plantations, and decreased dry season flow (Abebe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing recognition of eutrophication trends in Ethiopian water bodies (Fetahi \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ayele and Atlabachew \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), comprehensive assessments of trophic status specifically focused on Lake Tana\u0026rsquo;s wetlands remain limited. Previous studies have examined water quality parameters individually (Tibebe et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dersseh et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but integrated trophic assessments using standardized indices across multiple wetlands and seasons are scarce. Furthermore, understanding the interactions between seasonal dynamics, wetland characteristics, and anthropogenic pressures is essential for developing effective management strategies.\u003c/p\u003e \u003cp\u003eThis study aimed to: (1) characterize the trophic status of six wetlands around Lake Tana using Carlson's TSI based on Secchi depth, chlorophyll-a, total phosphorus, and total nitrogen; (2) analyze spatial variations in trophic conditions among wetlands with different pollution sources; (3) assess seasonal patterns in trophic status across four distinct seasons; (4) evaluate interactions between wetland characteristics and seasonal dynamics in determining trophic conditions; and (5) provide evidence-based recommendations for sustainable water resources management in the Lake Tana basin.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study was conducted in six wetlands distributed around Lake Tana (11\u0026deg;30\u0026prime;\u0026ndash;12\u0026deg;30\u0026prime; N, 37\u0026deg;0\u0026prime;\u0026ndash;37\u0026deg;30\u0026prime; E), located in the northwestern Ethiopian highlands at an elevation of approximately 1,800 meters above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Lake Tana covers approximately 3,200 km\u0026sup2; and is characterized by a tropical highland climate with distinct, early rainy, rainy, late rainy and dry seasons. The region experiences a main rainy season from June to September, accounting for 70\u0026ndash;90% of annual precipitation (Vijverberg et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wondie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe six study wetlands represented diverse ecological conditions and anthropogenic pressures:\u003c/p\u003e \u003cp\u003e \u003cb\u003eGumara River Mouth (GRM)\u003c/b\u003e: Located in Dera district, approximately 40 km northeast of Bahir Dar. This riverine wetland receives agricultural runoff from intensive farming activities. Since 2012, it has been heavily invaded by water hyacinth. Recent studies document dramatic increases in nutrient export from the Gumara River watershed\u0026mdash;69% for phosphorus and 80% for nitrogen between 2014 and 2020 (Abebe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Area: 1,500 ha.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMegech River Mouth (MRM)\u003c/b\u003e: Situated in the Dembia district, about 90 km north of Bahir Dar and 40 km south of Gondar. This river mouth wetland receives agricultural effluents from the North Gondar zone and untreated municipal wastewater from Gondar city. Dominated by water hyacinth. Research confirms significantly higher nutrient concentrations (NO₃-N, SRP, TN, TP) at Megech during rainy seasons (Engdaw et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Area: 1,816 ha.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRas Abbay (RA)\u003c/b\u003e: Located approximately 5 km northeast of Bahir Dar, this riverine wetland receives industrial and domestic wastewater from Bahir Dar city via the Blue Nile outflow. Characterized by dense \u003cem\u003eCyperus papyrus\u003c/em\u003e and forest trees. Area: 1,114.5 ha.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAvaj (AV)\u003c/b\u003e: An urban wetland approximately 3 km from Bahir Dar city center, receiving untreated effluents from hotels, hospitals, and nearby fishing areas. Area: 200 ha.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWonjeta (WO)\u003c/b\u003e: A lacustrine wetland about 5 km northwest of Bahir Dar, fed by springs and characterized by papyrus dominance. Experiences relatively low anthropogenic pressure. Area: 300 ha.\u003c/p\u003e \u003cp\u003e \u003cb\u003eZewdie Girar (ZG)\u003c/b\u003e: A lacustrine wetland in North Achefer district, approximately 50 km west of Bahir Dar, surrounded by mountains with minimal human disturbance. Characterized by extensive reed beds. Area: 250 ha.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling design and collection\u003c/h2\u003e \u003cp\u003eWater samples were collected quarterly over one year (2020\u0026ndash;2021) to represent four distinct seasons: dry (March\u0026ndash;May), early rainy (June\u0026ndash;August), rainy (September\u0026ndash;November), and late rainy (December\u0026ndash;February). At each wetland, three sampling stations were established along the offshore gradient, with triplicate samples collected per station. A total of 216 samples were collected (6 wetlands \u0026times; 4 seasons \u0026times; 3 stations \u0026times; 3 replicates).\u003c/p\u003e \u003cp\u003eSamples for nutrient analysis were collected in acid-washed polyethylene bottles (1 L) at approximately 20 cm depth, stored on ice in the dark, and transported to the laboratory within 6 hours. Samples for chlorophyll a analysis were collected separately in amber bottles to prevent light degradation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Laboratory analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePhysical parameters\u003c/b\u003e: Water temperature, dissolved oxygen (DO), electrical conductivity (EC), pH, total dissolved solids (TDS), and salinity were measured in situ using a calibrated portable multimeter (HQ40d, Hach, USA). Secchi disk depth (SD) was measured using a 20 cm diameter black and white Secchi disk between 10:00 and 14:00 hours to minimize solar angle effects.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNutrient analysis\u003c/strong\u003e \u003cp\u003eSoluble reactive phosphorus (SRP), nitrate (NO₃⁻), nitrite (NO₂⁻), and total ammonia (NH₃ + NH₄⁺) were analyzed using a UV/Visible photometer (DR 6000, Hach, USA) following standard USEPA methods. Total phosphorus (TP) was determined after persulfate digestion, and total nitrogen (TN) was analyzed using the persulfate oxidation method.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eChlorophyll a\u003c/strong\u003e \u003cp\u003eWater samples (500\u0026ndash;1000 mL) were filtered through Whatman GF/F glass fiber filters (0.7 \u0026micro;m pore size). Chlorophyll a was extracted using 90% acetone for 24 hours in the dark at 4\u0026deg;C, and concentrations were determined spectrophotometrically following standard methods (APHA 2012).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Trophic State Index calculation\u003c/h2\u003e \u003cp\u003eCarlson's Trophic State Index (TSI) was calculated using four parameters following established equations (Carlson \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Kratzer and Brezonik \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1981\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cb\u003eTSI based on Secchi disk transparency (SD)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e = 60\u0026ndash;14.41 \u0026times; ln (SD) [SD in meters]\u003c/p\u003e \u003cp\u003e \u003cb\u003eTSI based on total phosphorus (TP)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e = 14.42 \u0026times; ln (TP)\u0026thinsp;+\u0026thinsp;4.15 [TP in \u0026micro;g/L]\u003c/p\u003e \u003cp\u003e \u003cb\u003eTSI based on chlorophyll a (Chl-a)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e = 9.81 \u0026times; ln (Chl-a)\u0026thinsp;+\u0026thinsp;30.6 [Chl-a in \u0026micro;g/L]\u003c/p\u003e \u003cp\u003e \u003cb\u003eTSI based on total nitrogen (TN)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e = 54.45\u0026thinsp;+\u0026thinsp;14.43 \u0026times; ln (TN) [TN in mg/L]\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal Trophic State Index (TOT\u0026thinsp;~\u0026thinsp;TSI~)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTOT \u003csub\u003eTSI\u003c/sub\u003e = (TSI \u003csub\u003eSD\u003c/sub\u003e + TSI \u003csub\u003eTP\u003c/sub\u003e + TSI \u003csub\u003eChla\u003c/sub\u003e + TSI \u003csub\u003eTN\u003c/sub\u003e) / 4\u003c/p\u003e \u003cp\u003eTrophic classification followed standard criteria (Jarosiewicz et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTSI\u0026thinsp;\u0026lt;\u0026thinsp;40: Oligotrophic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI 40\u0026ndash;50: Mesotrophic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI 50\u0026ndash;60: Eutrophic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI 60\u0026ndash;70: Eutrophic (transitional)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI 70\u0026ndash;80: Hypereutrophic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTSI\u0026thinsp;\u0026gt;\u0026thinsp;80: Hypertrophic\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eData normality was assessed using the Shapiro-Wilk test. Since data violated normality assumptions, a non-parametric Kruskal-Wallis ANOVA was used to compare TSI values among wetlands and seasons. Two-way ANOVA was employed to test for main effects (wetland, season) and their interaction on each TSI parameter. Post-hoc comparisons were conducted using Tukey\u0026rsquo;s HSD test for significant effects. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analysis was performed using Statistica software (TIBCO Inc., version 14.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview of trophic state parameters\u003c/h2\u003e \u003cp\u003eSummary statistics for the four TSI parameters across all wetlands and seasons are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Considerable variability was observed for all parameters, indicating substantial spatial and temporal heterogeneity in trophic conditions. The mean TSI \u003csub\u003eTOT\u003c/sub\u003e of 64.4 confirms eutrophic status for the wetlands studied.\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\u003eSummary statistics of TSI parameters across all wetlands and seasons (n\u0026thinsp;=\u0026thinsp;216)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOT\u003csub\u003eTSI\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eCV\u0026thinsp;=\u0026thinsp;Coefficient of Variation\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spatial variations in trophic status\u003c/h2\u003e \u003cp\u003eSignificant differences among wetlands were observed for TSI \u003csub\u003eTOT\u003c/sub\u003e, TSI \u003csub\u003eTP\u003c/sub\u003e, TSI \u003csub\u003eChla\u003c/sub\u003e, and TSI \u003csub\u003eSD\u003c/sub\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while TSI \u003csub\u003eTN\u003c/sub\u003e showed no significant spatial variation (p\u0026thinsp;=\u0026thinsp;0.08) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mean TSI \u003csub\u003eTOT\u003c/sub\u003e values ranged from 55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 (WO) to 81.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 (MRM), spanning eutrophic to hypereutrophic conditions.\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\u003eMean TSI values (\u0026plusmn;\u0026thinsp;SD) for each wetland across all seasons, with statistical significance\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOT \u003csub\u003eTSI\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrophic Classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEutrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEutrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEutrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypereutrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypereutrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9ᵈ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4ᵈ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9ᵈ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypertrophic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.146\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.834\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e38.611\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Different superscript letters within columns indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) based on Tukey's HSD post-hoc test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Seasonal variations in trophic status\u003c/h2\u003e \u003cp\u003eSeasonal patterns in TSI parameters revealed significant temporal dynamics (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all parameters except TSI \u003csub\u003eChla\u003c/sub\u003e, p\u0026thinsp;=\u0026thinsp;0.06) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The late rainy season consistently showed the highest TSI values across most parameters, while the dry season exhibited the lowest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean TSI values (\u0026plusmn;\u0026thinsp;SD) across seasons (all wetlands combined) with statistical significance\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 \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTOT\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate Rainy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3ᶜ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.420\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.613\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.604\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.678\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e14.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Different superscript letters within columns indicate significant seasonal differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) based on Tukey's HSD post-hoc test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Wetland \u0026times; season interactions\u003c/h2\u003e \u003cp\u003eTwo-way ANOVA revealed significant interactions between wetland and season for all TSI parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that seasonal patterns differed among wetlands (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This interaction emphasizes the importance of site-specific seasonal monitoring.\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\u003eTwo-way ANOVA results for TSI parameters with significance levels\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \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\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\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\u003eWetland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTOT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1338.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2069.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e413.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1847.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e369.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e670.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8369.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1673.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeason\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTOT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e442.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4404.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1468.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1409.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e469.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e256.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1824.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e608.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWetland \u0026times; Season\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTOT\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1832.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11629.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e775.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4519.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e301.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1267.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSI \u003csub\u003eSD\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4201.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e280.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Nutrient limitation assessment\u003c/h2\u003e \u003cp\u003eThe ratio of TSI \u003csub\u003eTN\u003c/sub\u003e to TSI \u003csub\u003eTP\u003c/sub\u003e provides insight into nutrient limitation patterns (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All wetlands exhibited TSI \u003csub\u003eTN\u003c/sub\u003e: TSI \u003csub\u003eTP\u003c/sub\u003e, with ratios ranging from 2.8 (ZG) to 3.9 (GRM), indicating consistent phosphorus limitation relative to nitrogen.\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\u003eTSI\u003csub\u003eTN\u003c/sub\u003e: TSI\u003csub\u003eTP\u003c/sub\u003e ratios by wetland and season\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly Rainy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLate Rainy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWetland Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8ᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7ᵃ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeasonal Mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDifferent superscript letters indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Trophic Status and Eutrophication Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trophic assessment of Lake Tana\u0026rsquo;s wetlands reveals a concerning trajectory of nutrient enrichment and ecosystem degradation. The mean TSI \u003csub\u003eTOT\u003c/sub\u003e of 64.4 \u0026plusmn; 8.7 places most wetlands in eutrophic to hypereutrophic categories, representing a significant shift from historical conditions. Earlier studies characterized Lake Tana as oligotrophic to mesotrophic (Wondie et al. 2007), with TSI values below 50. The current findings align with recent research confirming that Lake Tana has shifted from oligo-mesotrophic to eutrophic status due to increasing anthropogenic stressors (Engdaw et al. 2025).\u003c/p\u003e\n\u003cp\u003eThe significant spatial variation in TSI \u003csub\u003eTOT\u003c/sub\u003e (p \u0026lt; 0.001) reflects the gradient in anthropogenic pressure from least impacted (WO, ZG) to moderately impacted (AV, RA) to highly impacted (MRM). MRM\u0026rsquo;s hypertrophic status (TSI \u003csub\u003eTOT\u003c/sub\u003e = 81.4 \u0026plusmn; 4.9) represents the most severe degradation, comparable to hypereutrophic conditions documented at Megech and Dablo sites, where significantly higher nutrient concentrations occur during rainy seasons (Engdaw et al. 2025). Research confirms that settlement and cropland expansion are positively correlated with nutrients, including ammonia, nitrate, SRP, and TP (Engdaw et al. 2025).\u003c/p\u003e\n\u003cp\u003eThe non-significant spatial variation in TSI\u0026lt;sub\u0026gt;TN\u0026lt;/sub\u0026gt;\u0026nbsp;(p = 0.08) is particularly noteworthy. All wetlands exhibited TSI\u003csub\u003eTN\u003c/sub\u003e values exceeding 80, indicating that nitrogen enrichment is a basin-wide phenomenon rather than localized to specific sites. Mean TN concentrations (2.23 mg/L) exceed USEPA threshold concentrations for eutrophication, consistent with recent findings (Engdaw et al. 2025). This widespread nitrogen pollution suggests atmospheric deposition, groundwater transport, or diffuse agricultural sources operating at the catchment scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Drivers of Spatial Variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAgricultural runoff emerged as a primary driver of eutrophication, particularly evident in GRM and MRM. The significant wetland \u0026times; season interaction for TSI \u003csub\u003eTOT\u003c/sub\u003e (p \u0026lt; 0.001) reflects the pulsed nature of agricultural nutrient delivery during wet seasons. Recent research documents dramatic increases in nutrient export from the Gumara River watershed, with 69% for phosphorus and 80% for nitrogen between 2014 and 2020, coinciding with irrigation expansion and land use change (Abebe et al. 2025).\u003c/p\u003e\n\u003cp\u003eUrban and industrial wastewater significantly impacted AV and RA wetlands, as evidenced by their significantly higher TSI\u003csub\u003eTP\u003c/sub\u003e (p = 0.02) and TSI \u003csub\u003eChla\u0026nbsp;\u003c/sub\u003e(p = 0.001) compared to the least impacted sites. Bahir Dar, with a population exceeding 300,000, lacks comprehensive wastewater treatment infrastructure. The extremely high TSI \u003csub\u003eTN\u0026nbsp;\u003c/sub\u003evalues in AV (96.2 \u0026plusmn; 14.3) and RA (99.8 \u0026plusmn; 16.7) reflect nitrogen enrichment from sewage, while elevated TSI\u003csub\u003eTP\u0026nbsp;\u003c/sub\u003e(38.7\u0026ndash;42.3) indicates phosphorus contributions from detergents and domestic waste.\u003c/p\u003e\n\u003cp\u003eCatchment degradation and sedimentation influenced trophic conditions primarily through effects on light penetration (TSI\u003csub\u003eSD\u003c/sub\u003e, which showed the most pronounced spatial variation (p \u0026lt; 0.001). MRM\u0026rsquo;s exceptional TSI\u003csub\u003eSD\u003c/sub\u003e (95.8 \u0026plusmn; 13.2) reflects extreme turbidity from eroded sediments transported by the Megech River. The strong correlation between TSI\u003csub\u003eSD\u0026nbsp;\u003c/sub\u003eand TSI\u003csub\u003eTOT\u003c/sub\u003e (r = 0.88, p \u0026lt; 0.001) underscores the importance of sediment management for improving overall trophic conditions.\u003c/p\u003e\n\u003cp\u003eWater hyacinth invasion has significantly altered trophic dynamics in affected wetlands. Research demonstrates that water hyacinth significantly impacts both physical and chemical water quality parameters (p \u0026lt; 0.05) (Zeleke et al. 2025). The trophic state differs between invaded and non-invaded areas, with invaded zones showing distinct TSI\u003csub\u003eTP\u003c/sub\u003e values. \u0026nbsp;Remote sensing analyses confirm increasing chlorophyll-a trends from 2008\u0026ndash;2018, coinciding with water hyacinth expansion since 2011 (Asres et al. 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Seasonal Dynamics and Underlying Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pronounced seasonal patterns in trophic status reflect the strong influence of the hydrological regime on nutrient transport and processing. The significant seasonal effects for all TSI parameters except TSI\u003csub\u003eChla\u003c/sub\u003e (p = 0.06) demonstrate that while nutrient loading follows predictable seasonal patterns, biological responses may exhibit lag times.\u003c/p\u003e\n\u003cp\u003eDuring the dry season (December\u0026ndash;May), low water levels, reduced runoff, and extended water residence time promote nutrient processing within wetlands. The significantly lower TSI\u0026lt;sub\u0026gt;TN\u0026lt;/sub\u0026gt;\u0026nbsp;(84.6 \u0026plusmn; 15.3) and TSI\u003csub\u003eTP\u0026nbsp;\u003c/sub\u003e(24.8 \u0026plusmn; 11.2) during this period (p \u0026lt; 0.05) reflect these internal processing mechanisms. However, continued point-source discharges maintain elevated TSI\u003csub\u003eTN\u0026nbsp;\u003c/sub\u003eeven during this period.\u003c/p\u003e\n\u003cp\u003eThe early rainy season (June\u0026ndash;August) initial catchment flushing mobilizes accumulated nutrients and sediments from terrestrial sources. The \u0026ldquo;first flush\u0026rdquo; effect concentrates pollutants in runoff, producing significant increases in TSI\u003csub\u003eTN\u003c/sub\u003e (91.2 \u0026plusmn; 17.4) and TSI\u003csub\u003eTP\u003c/sub\u003e (28.3 \u0026plusmn; 12.6). During the rainy season (September\u0026ndash;November), peak discharge delivers maximum nutrient and sediment loads to wetlands. TSI\u003csub\u003eTN\u0026nbsp;\u003c/sub\u003e(98.7 \u0026plusmn; 18.2) and TSI\u003csub\u003eTP\u003c/sub\u003e (32.6 \u0026plusmn; 14.8) reach near-maximum values, significantly higher than dry season values (p \u0026lt; 0.05). In the late rainy season (December\u0026ndash;February), maximum TSI values reflect cumulative effects of nutrient loading, biological uptake, and internal cycling. TSI\u003csub\u003eTN\u003c/sub\u003e (106.3 \u0026plusmn; 19.8) and TSI\u003csub\u003eTP\u003c/sub\u003e (38.9 \u0026plusmn; 15.4) reach their highest levels, and TSI \u003csub\u003eChla\u003c/sub\u003e peaks (71.8 \u0026plusmn; 8.2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Comparison with Other Ethiopian and Tropical Lakes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContextualizing Lake Tana\u0026rsquo;s wetland trophic status within regional and tropical frameworks reveals both similarities and unique characteristics. Lake Tana\u0026rsquo;s wetlands occupy an intermediate position significantly more degraded than historically reported (p \u0026lt; 0.001) and comparable to Lake Chamo (Ghebremedhin and Gupta 2023) and Lake Victoria bays (Otieno et al. 2022). The hypertrophic conditions in MRM approach those of severely impacted systems like Lake Victoria\u0026rsquo;s Winam Gulf (Otieno et al. 2022).\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis comprehensive trophic assessment of six Lake Tana wetlands reveals significant eutrophication with strong spatial and seasonal patterns. The wetlands exhibit a trophic gradient from eutrophic (Wonjeta, Zewdie Girar, Gumara River Mouth) to hypertrophic (Megech River Mouth), reflecting varying intensities of anthropogenic pressure. Megech River Mouth represents the most degraded condition (TSI\u003csub\u003eTOT\u003c/sub\u003e = 81.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9), requiring immediate intervention.\u003c/p\u003e \u003cp\u003eSignificant seasonal variation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across all TSI parameters, with peak trophic levels during the late rainy season, demonstrates the strong influence of the hydrological regime on nutrient loading and biological response. The significant wetland \u0026times; season interactions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) emphasize the necessity of site-specific, seasonally-targeted monitoring and management approaches.\u003c/p\u003e \u003cp\u003eThe basin-wide nitrogen enrichment (TSI\u003csub\u003eTN\u003c/sub\u003e \u0026gt; 80 across all sites) indicates that nitrogen management requires catchment-scale interventions, while consistent phosphorus limitation (TSI\u003csub\u003eTN\u003c/sub\u003e: TSI\u003csub\u003eTP\u003c/sub\u003e ratios 2.8\u0026ndash;3.9) suggests phosphorus control may be most effective for immediate algal bloom mitigation. Water hyacinth invasion has significantly altered trophic dynamics in affected wetlands, with invaded areas showing distinct TSI characteristics and increasing chlorophyll a trends.\u003c/p\u003e \u003cp\u003eThese findings provide essential baseline data for informing management strategies to mitigate eutrophication in tropical highland lake systems. Regular monitoring, integrated watershed management, targeted nutrient source control, and coordinated water hyacinth management are essential to address this growing environmental challenge and preserve the ecological integrity of Lake Tana\u0026rsquo;s wetland ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Intra-Africa Academic Mobility Project, particularly the Collaborative Training in Fisheries and Aquaculture in East, Central, and Southern Africa (COTRA).\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Hailu Mazengia, Horst Kaiser, and Minwuyelet Mengist. The first draft of the manuscript was written by Hailu Mazengia, and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthics Approval\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted via Rhodes University\u0026rsquo;s Electronic Research Application System (Approval No. 2833) and complied with Rhodes University Research Ethics Committee (RUREC) guidelines for animal research.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAI-Assisted Copy Editing\u003c/p\u003e\n\u003cp\u003eThe authors used AI-assisted tools for copy editing to improve readability and grammar. All authors reviewed and take responsibility for the final content.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbebe, W. B., Payne, W. A., \u0026amp; Blaszczak, J. R. (2025). Nutrient export dynamics in the Gumara River watershed, Lake Tana basin, Ethiopia. \u003cem\u003eEcohydrology \u0026amp; Hydrobiology, 25\u003c/em\u003e(3). https://doi.org/10.1016/j.ecohyd.2024.01.005\u003c/li\u003e\n \u003cli\u003eAmerican Public Health Association (APHA). (2012). \u003cem\u003eStandard methods for the examination of water and wastewater\u003c/em\u003e (22nd ed.). American Public Health Association.\u003c/li\u003e\n \u003cli\u003eAsres, B. W., Kebedew, M. G., Nerae, M. D., Tsegaye, S., \u0026amp; Zimale, F. A. (2025). Remote sensing-based estimation of chlorophyll-a concentrations in a water hyacinth-infested tropical headwaters lake: A study of Lake Tana, Ethiopia. \u003cem\u003eFrontiers in Water, 7\u003c/em\u003e, 1600222. https://doi.org/10.3389/frwa.2025.1600222\u003c/li\u003e\n \u003cli\u003eAssefa, W. W., Beneberu, G., Sitotaw, B., \u0026amp; Wondie, A. (2020). Biological monitoring of freshwater ecosystem health in Ethiopia: A review of current efforts, challenges, and future developments. \u003cem\u003eEthiopian Journal of Science and Technology, 13\u003c/em\u003e(3), 229\u0026ndash;264. https://doi.org/10.4314/ejst.v13i3.4\u003c/li\u003e\n \u003cli\u003eAyele, H. S., \u0026amp; Atlabachew, M. (2021). Review of characterization, factors, impacts, and solutions of lake eutrophication: Lessons for Lake Tana, Ethiopia. \u003cem\u003eEnvironmental Science and Pollution Research, 28\u003c/em\u003e(12), 14233\u0026ndash;14252. https://doi.org/10.1007/s11356-020-12081-4\u003c/li\u003e\n \u003cli\u003eBadamian, M. R., Noori, R., Jun, C., Bateni, S. M., Heidari, S., Bahrami, B., Mortazavi, M., Vesali Naseh, M. R., \u0026amp; Zhu, S. (2025). New insights on biomass production in lakes: Integration of Carlson trophic state index and vertically generalized production model. \u003cem\u003eEcological Indicators, 174\u003c/em\u003e, 113450. https://doi.org/10.1016/j.ecolind.2025.113450\u003c/li\u003e\n \u003cli\u003eCarlson, R. E. (1977). A trophic state index for lakes. \u003cem\u003eLimnology and Oceanography, 22\u003c/em\u003e(2), 361\u0026ndash;369. https://doi.org/10.4319/lo.1977.22.2.0361\u003c/li\u003e\n \u003cli\u003eDersseh, M. G., Ateka, A., Zimale, F. A., Worqlul, A. W., Moges, M. A., Dagnew, D. C., Tilahun, S. A., \u0026amp; Melesse, A. M. (2020). Dynamics of eutrophication and its linkage to water hyacinth on Lake Tana, Upper Blue Nile, Ethiopia: Understanding land-lake interaction and process. In N. G. Habtu, D. W. Ayele, S. W. Fanta, B. T. Admasu, \u0026amp; M. A. Bitew (Eds.), \u003cem\u003eAdvances of science and technology\u003c/em\u003e (pp. 228\u0026ndash;241). Springer. https://doi.org/10.1007/978-3-030-43690-2_16\u003c/li\u003e\n \u003cli\u003eDowning, J. A., \u0026amp; McCauley, E. (1992). The nitrogen:phosphorus relationship in lakes. \u003cem\u003eLimnology and Oceanography, 37\u003c/em\u003e(5), 936\u0026ndash;945. https://doi.org/10.4319/lo.1992.37.5.0936\u003c/li\u003e\n \u003cli\u003eEngdaw, F., Fetahi, T., \u0026amp; Kifle, D. (2025). Increasing anthropogenic stressors influenced the water quality and shifted the trophic status of northern Lake Tana Gulf, Ethiopia. \u003cem\u003eHeliyon, 11\u003c/em\u003e(1), e41162. https://doi.org/10.1016/j.heliyon.2024.e41162\u003c/li\u003e\n \u003cli\u003eFetahi, T. (2019). Eutrophication of Ethiopian water bodies: A serious threat to water quality, biodiversity, and public health. \u003cem\u003eAfrican Journal of Aquatic Science, 44\u003c/em\u003e(4), 303\u0026ndash;312. https://doi.org/10.2989/16085914.2019.1663722\u003c/li\u003e\n \u003cli\u003eGhebremedhin, S. G., \u0026amp; Gupta, B. S. (2023). Spatio-temporal water quality assessment and pollution source apportionment of Lake Chamo using water quality index and multivariate statistical techniques. \u003cem\u003eEuropean Journal of Environment and Earth Sciences, 4\u003c/em\u003e(1), 11\u0026ndash;19. https://doi.org/10.24018/ejgeo.2023.4.1.340\u003c/li\u003e\n \u003cli\u003eJarosiewicz, A., Ficek, D., \u0026amp; Zapadka, T. (2011). Eutrophication parameters and Carlson-type trophic state indices in selected Pomeranian lakes. \u003cem\u003eLimnological Review, 11\u003c/em\u003e(1), 15\u0026ndash;23. https://doi.org/10.2478/v10194-011-0024-3\u003c/li\u003e\n \u003cli\u003eKahsay, A., Demissie, B., Nyssen, J., Triest, L., Lemmens, P., De Meester, L., Kibret, M., Verleyen, E., Adgo, E., \u0026amp; Stiers, I. (2023). Extent of Lake Tana\u0026rsquo;s papyrus swamps (1985\u0026ndash;2020), North Ethiopia. \u003cem\u003eWetlands, 43\u003c/em\u003e(1), 6. https://doi.org/10.1007/s13157-022-01654-1\u003c/li\u003e\n \u003cli\u003eKratzer, C. R., \u0026amp; Brezonik, P. L. (1981). A Carlson-type trophic state index for nitrogen in Florida lakes. \u003cem\u003eJournal of the American Water Resources Association, 17\u003c/em\u003e(4), 713\u0026ndash;715. https://doi.org/10.1111/j.1752-1688.1981.tb01282.x\u003c/li\u003e\n \u003cli\u003eLigdi, E. E., El Kahloun, M., \u0026amp; Meire, P. (2010). Ecohydrological status of Lake Tana\u0026mdash;A shallow highland lake in the Blue Nile (Abbay) basin in Ethiopia. \u003cem\u003eEcohydrology \u0026amp; Hydrobiology, 10\u003c/em\u003e(2\u0026ndash;4), 109\u0026ndash;122. https://doi.org/10.2478/v10104-011-0011-9\u003c/li\u003e\n \u003cli\u003eNyenje, P. M., Foppen, J. W., Uhlenbrook, S., Kulabako, R., \u0026amp; Muwanga, A. (2010). Eutrophication and nutrient release in urban areas of sub-Saharan Africa\u0026mdash;A review. \u003cem\u003eScience of the Total Environment, 408\u003c/em\u003e(3), 447\u0026ndash;455. https://doi.org/10.1016/j.scitotenv.2009.10.020\u003c/li\u003e\n \u003cli\u003eOtieno, D., Nyaboke, H., Nyamweya, C. S., Odoli, C. O., Aura, C. M., \u0026amp; Outa, N. O. (2022). Water hyacinth (\u003cem\u003eEichhornia crassipes\u003c/em\u003e) infestation cycle and interactions with nutrients and aquatic biota in Winam Gulf (Kenya), Lake Victoria. \u003cem\u003eLakes \u0026amp; Reservoirs: Research and Management, 27\u003c/em\u003e(1), e12391. https://doi.org/10.1111/lre.12391\u003c/li\u003e\n \u003cli\u003eRipanda, A., \u0026amp; Miraji, H. (2022). A review on the occurrence and impacts of nutrient pollution in the aquatic ecosystem of sub-Saharan countries. \u003cem\u003eJournal of Biodiversity and Environmental Sciences, 20\u003c/em\u003e(1), 154\u0026ndash;165.\u003c/li\u003e\n \u003cli\u003eSmith, S. H. (1962). Temperature correction in conductivity measurements. \u003cem\u003eLimnology and Oceanography, 7\u003c/em\u003e(3), 330\u0026ndash;334. https://doi.org/10.4319/lo.1962.7.3.0330\u003c/li\u003e\n \u003cli\u003eTibebe, D., Kassa, Y., Melaku, A., \u0026amp; Lakew, S. (2019). Investigation of spatio-temporal variations of selected water quality parameters and trophic status of Lake Tana for sustainable management, Ethiopia. \u003cem\u003eMicrochemical Journal, 148\u003c/em\u003e, 374\u0026ndash;384. https://doi.org/10.1016/j.microc.2019.04.085\u003c/li\u003e\n \u003cli\u003eVijverberg, J., Sibbing, F. A., \u0026amp; Dejen, E. (2009). Lake Tana: Source of the Blue Nile. In H. J. Dumont (Ed.), \u003cem\u003eThe Nile\u003c/em\u003e (pp. 163\u0026ndash;192). Springer. https://doi.org/10.1007/978-1-4020-9726-3_9\u003c/li\u003e\n \u003cli\u003eWepener, V. (2008). Application of active biomonitoring within an integrated water resources management framework in South Africa. \u003cem\u003eSouth African Journal of Science, 104\u003c/em\u003e, 367\u0026ndash;373.\u003c/li\u003e\n \u003cli\u003eWondie, A. (2018). Ecological conditions and ecosystem services of wetlands in the Lake Tana area, Ethiopia. \u003cem\u003eEcohydrology \u0026amp; Hydrobiology, 18\u003c/em\u003e(2), 231\u0026ndash;244. https://doi.org/10.1016/j.ecohyd.2018.02.002\u003c/li\u003e\n \u003cli\u003eWondie, A., Mengistu, S., Vijverberg, J., \u0026amp; Dejen, E. (2007). Seasonal variation in primary production of a large high-altitude tropical lake (Lake Tana, Ethiopia): Effects of nutrient availability and water transparency. \u003cem\u003eAquatic Ecology, 41\u003c/em\u003e(2), 195\u0026ndash;207. https://doi.org/10.1007/s10452-007-9080-6\u003c/li\u003e\n \u003cli\u003eZeleke, T. B., Soeprobowati, T. R., Adissu, S., \u0026amp; Warsito, B. (2025). Analysing the effect of water hyacinth (\u003cem\u003eEichhornia crassipes\u003c/em\u003e) invasion on water quality and trophic state of Lake Tana. \u003cem\u003eChemistry and Ecology, 41\u003c/em\u003e(3), 297\u0026ndash;313. https://doi.org/10.1080/02757540.2025.2454015\u003c/li\u003e\n\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":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Trophic State Index, eutrophication, wetlands, Lake Tana, seasonal variation, nutrient enrichment, water quality","lastPublishedDoi":"10.21203/rs.3.rs-9262565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9262565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed the trophic status of six wetlands (Wonjeta, Zewdie Girar, Gumara River Mouth, Megech River Mouth, Avaj, and Ras Abbay) in Lake Tana, Ethiopia, using Carlson\u0026rsquo;s Trophic State Index (TSI) based on Secchi disk transparency, chlorophyll a, total phosphorus, and total nitrogen. Water samples were collected across four seasons (dry, early rainy, rainy, and late rainy) and analyzed using standard methods. Results revealed significant spatial and seasonal variations in trophic conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Total Trophic State Index ranged from 43.74 to 86.81, classifying wetlands as mesotrophic to hypereutrophic. Megech River Mouth exhibited hypereutrophic conditions (TSI\u003csub\u003eTOT\u003c/sub\u003e = 81.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91), while Wonjeta, Zewdie Girar, and Gumara River Mouth showed eutrophic to mesotrophic status. Significant interactions between wetland type and season affected all TSI parameters (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that trophic assessment must consider both spatial and temporal dimensions. TSI\u003csub\u003eTN\u003c/sub\u003e showed the highest values (94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6), suggesting nitrogen enrichment as a primary concern. These findings demonstrate progressive eutrophication in Lake Tana\u0026rsquo;s wetlands compared to historical data, attributed to agricultural runoff, urban wastewater discharge, and catchment degradation. This research provides essential baseline data for informing sustainable management strategies to mitigate eutrophication and preserve the ecological integrity of Lake Tana's wetland ecosystems.\u003c/p\u003e","manuscriptTitle":"Trophic State Index Analysis of Six Wetlands in Lake Tana, Ethiopia: A Comprehensive Assessment for Sustainable Water Resources Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 17:33:20","doi":"10.21203/rs.3.rs-9262565/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T22:27:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T22:05:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T22:04:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2026-03-30T05:12:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3f6a92f3-bafb-40bd-9ff7-4eb581bd601f","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T17:33:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 17:33:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9262565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9262565","identity":"rs-9262565","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}