The Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics for Ecological Biomonitoring in The Little Akaki River, Addis Ababa, Ethiopia

The Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics for Ecological Biomonitoring in The Little Akaki River, Addis Ababa, Ethiopia | 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 The Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics for Ecological Biomonitoring in The Little Akaki River, Addis Ababa, Ethiopia Alachew Adino, Seyoum Mengistou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8475505/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the relationship between macroinvertebrate metrics and water quality along this river is crucial for assessing the river ecological health. This study aims to evaluate the relationship between macroinvertebrate metrics and water quality parameters along the Little Akaki River. The research was conducted across seven sampling sites in April 2024, employing a multi-habitat sampling methodology. The measurement of physico-chemical parameters (temperature, DO, pH and conductivity) was done in triplicate using HQ40-d multimeter probe. Macroinvertebrates were collected from gravel, sand, mud, vegetation, riffles and pools with a 500 µm D-frame net. Most of the physicochemical parameters showed significant differences between the sampling sites (p < 0.05). DO ranged from 0.57 ± 0.05 to 6.39 ± 0.04 mg L − 1 . A total of 5575 macroinvertebrates were collected. The percentage of Ephemeroptera metrics positively correlated with dissolved oxygen and percent saturation (r = 0.677 and 0.598). A plot of RDA analysis separated the macroinvertebrate taxa which showed significant correlation with DO, HQI, SRP, TDS, pH, and TP. The study reveals a significant correlation between macroinvertebrate metrics and water quality parameters providing clear evidence that the health and diversity of macroinvertebrate communities are intricately connected to the ecological conditions present in their aquatic habitats. Dissolved oxygen Little Akaki River Macroinvertebrate Metrics Water Quality Figures Figure 1 Figure 2 1. Introduction Freshwater ecosystems are vital to human existence, providing essential ecosystem services that sustain billions of people globally (Aylward et al., 2005 ; Covich et al., 1999 ; Smith et al., 2007 ). Fresh water is only 2.5% of the aquatic environments on Earth (Giordano, 2009 ; Gleick, 1993 ; Zedler & Kercher, 2005 ). The majority of freshwater, approximately 68.6%, is locked in polar ice caps, while 30.1% is stored as groundwater (Carpenter et al., 2011 ; Giordano, 2009 ; Gleick, 1993 ). The proportion of the river is about 0.46% is accessible for ecosystem and human use (Carpenter et al., 2011 ; Gleick, 1993 ). The finite nature of freshwater resources necessitates sustainable management strategies through scientific understanding (Baron et al., 2002 ; Mishra, 2023 ; Richter et al., 2003 ). Among the most delicate yet biologically varied and ecologically significant systems are river ecosystems, which support a variety of commercial endeavors while providing environmental services including flood control, nutrient cycling, carbon sequestration, and biodiversity support (Sabater & Elosegi, 2014 ). However, their ecological integrity is increasingly compromising by population growth, sedimentation, urbanization, intensive agriculture, deforestation, riparian habitat degradation, flow diversion, waste disposal and climate (Benoy et al., 2012 ; Sathe et al., 2020 ; Van Looy et al., 2017 ). Water quality is the chemical, physical, and biological properties of water (Bartram & Ballance, 1996 ). These physical, chemical, and biological characteristics of water change with the season, the watershed natural environment, the pattern of land use, and, to a considerable extent, human activity (Bartram & Ballance, 1996 ; Karmakar et al., 2019 ). It is most often used about a set of guidelines that may be used to measure compliance, which is typically attained through water treatment. Biomonitoring is the most common tool that has been used to monitor the integrity of freshwater ecosystems (Covich et al., 1999 ; Rosenberg, 1998 ). It could provide a movie picture of an ecosystem condition, while physicochemical parameters show snapshot information (Barbour, 1999 ). Macroinvertebrates are crucial for evaluating the ecological state of freshwater habitats because they are sensitive water quality indicators and the general health of ecosystems (Abebe Beyene et al., 2009 ; Aschalew Lakew & Moog, 2015; Getachew Beneberu & Seyoum Mengistou, 2010). Most of the aquatic environments in developing countries are subjected to degradation, mainly due to domestic as well as industrial wastes (Getachew Beneberu, 2013 ; Getachew Beneberu & Seyoum Mengistou, 2015; Zinabu Gebre-Mariam & Elias Dadebo, 1989). One of the major environmental issues facing the Little Akaki River is sedimentation, which could have a negative impact on aquatic ecosystems. Assessing the ecological health of this river requires an understanding of the connection between water quality factors and macroinvertebrate metrics. We investigated the notion that, in tropical water bodies, there is no correlation between macroinvertebrate measures and water quality indicators. The aim of this study was assessing the relation between macroinvertebrate metrics and water quality parameters in the Little Akaki River. 2. Material and Method 2.1. Study Area Description The Little Akaki River, which provides drinking water, agriculture, livestock management, and recreational activities for the local people downstream and acts as a drainage system for the Addis Ababa government, was the subject of the study (Deshu Mamo et al., 2021). This river is located at the geographical coordinates of 8° 47' 19.64" N and 38° 45' 25.3" E, with an elevation ranging from 2050 and 2619 meters above sea level (Fig. 2 ). From the Gefersa Reservoir in the northwest, the river flows past the city center and industrial areas until arriving at Aba-Samuel. The Akaki catchment region, which spans 540 km² and is known for its afro-alpine environment, includes the Little Akaki River as one of its principal tributaries (Adino & Mengistou, 2025 ; Deshu Mamo et al., 2021). This area experiences mean annual precipitation of 1254 mm and average daily temperatures between 9.9 and 24.6°C. Throughout its catchment region, the river flows through areas of Addis Ababa that are polluted by both point and nonpoint sources. These sources include open defecation, unpermitted solid waste disposal along riverbanks, sewage waste and septic tank discharge into the river, residential, commercial, and industrial operations, and the use of agrochemicals (Deshu Mamo et al., 2021). 2.2. Sampling site description Seven sampling sites were thought to be adequate for testing the hypothesis, they were selected. It is dependent on the availability of the biotopes we require for sampling, as well as accessibility and recognition ease. Sites 1 and 2, which are situated outside of Addis Ababa, have superior riparian vegetation cover and are comparatively less affected. Because of the high human activity in the area, there is minimal riparian vegetation at the remaining locations (3, 4, 5, 6, and 7) (Adino & Mengistou, 2025 ). The remaining sites (3, 4, 5, 6, and 7) little riparian vegetation due to the intense human activities in the area. Along the stream and river banks studied only a few remnant plants like Acacia sp ., Eucalyptus , Syzygium guineense , shrubs, and some grasses were observed. The description of the sites is based on (Aschalew Lakew & Moog, 2015). While the central and downstream locations were darkly stained due to the reduction of ferrous-sulfide and the presence of foul sewage, the top portions of the rivers had rough bottoms and were noticeably translucent (Table 1 ). Chemical and sewage-like smells emanated from the stretch of the river that received industrial waste (Adino & Mengistou, 2025 ). Table 1 Study site with geographic location and major human activities (Adino & Mengistou, 2025 ) Sites Sampling site code Northing Easting Elevation (m) Major features and human activities Upstream Gefersa Site 1 9° 5' 26.66" 38° 37' 53.29" 2619 The location featured pools of somewhat silted conditions and was mostly composed of boulder and stone riffles. The riparian vegetation was good, and the banks were stable. There were livestock watering and just minor urban developments. Down Gefersa Site 2 9° 3' 42.37" 38° 38' 36.82" 2555 Boulder-based riffles, pools with sizable silt and muck contents, and healthy riparian flora were the site's defining features. Additionally, it was impacted by mining operations, urbanization, cattle grazing, and locations with mild environmental impacts. Kolfa Bridge Site 3 9° 1' 11.75" 38° 42' 31.93" 2349 In addition to pools that were heavily silted and muddy, the site was characterized by riffles with pebbles and occasional cobbles and little riparian vegetation. Significant environmental deterioration resulted from urban development's strong influence on the location, which included unlawful solid waste disposal and car washing. Alert Hospital Site 4 8° 59' 17.81" 38° 42' 24.91" 2266 The site had no riparian vegetation present and was characterized by riffles made up of many cobbles and pools filled with silt, mud, and sludge. major urban expansion occurred there, resulting in major environmental deterioration from industrial operations, car washes, slaughterhouses, and the disposal of solid and liquid waste. Kadisko Site 5 8° 55' 48.97" 38° 45' 24.3" 2161 The site has little riparian vegetation and a little stream with pools that contain sizable amounts of mud, sludge, and silt. Urban growth, including industrial activities, open defecation, inappropriate solid waste disposal, and locations that have been negatively impacted by these causes, have a significant influence on it. Gelan Site 6 8° 54' 13.86" 38° 44' 43.8" 2085 There was little riparian vegetation and high silt levels at the location. Sites that have seen significant environmental degradation, high levels of urbanization, industrial activity, waste disposal methods, animal grazing, and little agricultural involvement. Aba Samuel Site 7 8° 47' 19.64" 38° 42' 15.55" 2050 The site was distinguished by the absence of riparian vegetation and lakes with a high silt content. Cattle watering, a significantly damaged landscape, and a lack of urban development. 2.3. Field data collection The timing of benthic invertebrate sampling was determined by the study objectives and prevailing climatic conditions. Seven sampling sites were established (Table 1 ), and each station was georeferenced using GPS to ensure consistency across sampling periods. Physicochemical parameters were measured across various biotopes. Macroinvertebrates were gathered from gravel, sediment, mud, vegetation, pool and riffles at every station. Water quality and macroinvertebrate samples data were collected from the seven sites within the confines of the Little Akaki River from April to May 2024. During the sample period, environmental variables (physical, chemical, and hydro morphological parameters) related to the diversity and abundance of indicator organisms were measured. Latitude, longitude, and altitude were determined using a GPS unit brand Garmin model GPS 72H, manufactured in Taiwan by the Garmin Corporation. Water quality parameters recorded from the surface water of the river including dissolved oxygen (DO), temperature, pH, and electrical conductivity, were measured in situ using multimeter probe (Model HQ4300, manufactured by HACH company in the USA) and TDS was recorded using the Pocket Pro TDS meter (MSIP-REM-HAH-140321539, HACH company in China), and turbidity was measured using turbidity meter (T-100, manufactured by OAKTON company in Singapore). The measurements were taken in triplicate, with the average taken as one measurement per site. Water samples were collected in 500 mL polyethylene bottles from the river surface. These samples were transported in an icebox to Addis Ababa University Seyoum Mengistou Limnology Laboratory for nutrient analysis. Velocity was measured by using the floating method, which measures the time it takes a floating object to travel a certain distance (the travel time should be at least 20 seconds). V= \(\:\:\frac{L}{t}\) , where L is the length of the distance and t is the time. To correct for the mean velocity, an adjustment was made because the floating method measures only the surface velocity, which is higher than the mean velocity. $$\:Vmean=\text{V}\text{s}\text{u}\text{r}\text{f}\text{a}\text{c}\text{e}\:\text{x}\:0.85$$ . A standardized D-frame wire mesh with an area of 25 × 25 cm and a mesh size of 500 µm was used to collect macroinvertebrates from gravel, sand, mud, vegetation and stone (Barbour, 1999 ). Key biotopes such gravel, sand, mud, vegetation, pools, and riffles were all included in the sample process, and all sampling sites were kept consistent by using a multi-habitat sampling system (MHS). A total of 20 minutes of jabbing was the normal sampling effort (Rogers et al., 2002 ), It was allocated to vegetation, riffle, pool, sand mud, and gravel. For every site, the sample time was the same. To prevent upstream sample units from being disturbed, benthic macroinvertebrate sampling started downstream of the reach against the water's flow. Using sieves with mesh sizes of 500 µm and 250 µm, the macroinvertebrates were extracted and separated from debris and algal mats in the field. The samples were then composited in a white plastic sorting tray after being collected from gravel, sediment, mud, vegetation, pools, and riffles. The specimens were then put in jars with 4% formalin (Barbour, 1999 ; Griffin et al., 2015 ), and labeled accordingly with site name, sampling site code, and collection dates. 2.4. Laboratory analysis The nutrient levels in the water sample were analyzed using a UV/visible spectrophotometer (6405, manufactured by JENAY Ltd. in the United Kingdom), following the standard methods APHA ( 2005 ). Measurements for ammonia (NH 3 ), nitrate (NO 3 - ), and soluble reactive phosphorus (SRP) were performed on filtered water samples, while total phosphorus (TP) was performed on unfiltered water samples. Filtration was carried out using glass fiber filter paper (GF/F). Triplicate samples were taken for each parameter. Ammonia was determined by the phenate method; nitrate was determined by using the sodium salicylate method; and for TP and SRP, the ascorbic acid method was used, and for TSS was determined using Gravimetric method APHA ( 2005 ). Macroinvertebrate samples collected in the field were processed in a laboratory for further examination. The data sheet was copied with the information from the sample container before processing. Samples were sorted in a white plastic tray before being poured into vials in the lab after being washed through a series of sieves (2000, 1000, 500 and 250 µm mesh size) with tap water (Aschalew Lakew, 2014 ). Forceps were used to remove visible creatures from the substrate, which were then put into specimen vials. A light microscope was used to sort the organisms that were kept in the smaller section of the sieve. On-site composites of the samples were stored in 70% ethanol for later sorting and identification. Each sample's benthic macroinvertebrate population was counted. to ascertain the percentage of functioning feeder groupings along the river, as well as their relative quantity, diversity, and composition. Using a dissecting microscope, common identification keys, and a field guide “Aquatic Invertebrates of South African Rivers and Guide to aquatic macroinvertebrates of the Upper Midwest Waters”(Bouchard, 2004 ; Gerber & Gabriel, 2002 ). Identification was done all the way down to the family level Counting was done on the entire composited sample without sub-sampling. 2.5. Determination of Macroinvertebrate Metrics ETHbios, ASPT-ETHbios, Trophic/habit, Shannon Diversity Index, number of EOT, Taxa richness, Taxa abundance, and the Family Biotic Index are some of the frequently utilized macroinvertebrate metrics in this study. The scoring system assigns a number between 1 and 10, with the most sensitive taxa having the highest score and the tolerant benthic macroinvertebrates having the lowest (Aschalew Lakew & Moog, 2015). 2.6. Data Analysis First, Microsoft Office Excel (2016) was used to organize the row data, generate table and figures. The Shapiro-Wilk test was then used to test for normality in the water quality data. The data was then subjected to the non-parametric Kruskal- Wallis tests to determine the size of the spatial variations. IBM SPSS version 20 was used to calculate the mean and standard deviation and perform the Kruskal-Wallis analysis. Macroinvertebrate taxa were correlated with habitat quality and physicochemical characteristics using Pearson correlation analysis. Using the CANOCO 5 program, the relationship between the environment and the composition of communities of macroinvertebrates was assessed (Smilauer & Leps, 2014 ). The physicochemical data was log (Log 10 (X + 1)) except pH transformed before analysis, and the macroinvertebrate count data was Hellinger transformed (Legendre & Gallagher, 2001 ). The species response with a gradient length of 2.1 standard deviations in DCA indicated that a linear model was appropriate (Leps & Smilauer, 2003 ). A linear model was also utilized for redundancy analysis (RDA). The relationship between the data concerning benthic macroinvertebrates and the seven parameters of water quality was examined. To determine whether the relationships between macroinvertebrate taxa and environmental variables were significant at p < 0.05, the Monte Carlo test with 999 permutations was employed (Leps & Smilauer, 2003 ). PAST (Paleontological Statistics software) package was used for diversity index (Hammer & Harper, 2001 ). 3. Results 3.1. Environmental Parameters The mean value of physicochemical parameters is presented in (Table 2 ). Most of the physicochemical parameters measured in the field and laboratory showed a significant difference between the sampling sites ( p < 0.05). The mean DO concentrations spanned from 0.57 ± 0.05 to 7.50 ± 0.04 mg L − 1 , with site 1 showing the highest DO level and site 5 the lowest. The lowest pH reading was recorded at site 2 while highest at site 6 (8.85). In the sampling sites, the temperature ranged between 18.17 ± 0.06°C and 24.17 ± 0.29°C. The highest was recorded at site 4 and at site 1, the lowest was recorded. Electrical conductivity (EC) across the sites ranged from 169.93 ± 1.96 to 1259.67 ± 0.58 (µS cm − 1 ); site 5 had the maximum value, the lowest at site 1. The highest turbidity was recorded at site 7 (750.67 ± 8.14 NTU), while the lowest at site 1 (16.20 ± 0.52 NTU). The total dissolved solids (TDS) exhibited a range from 108.33 ± 2.52 to 761.67 ± 16.04 mg L − 1 . Site 5 exhibited the highest TDS value, while site 1 had the lowest. Total suspended solids (TSS) ranged from 16 ± 0.00 to 140 ± 0.00 mg L − 1 . The highest TSS was recorded at site 5 and lowest at site 1. The mean of all nutrient parameters showed significant differences among the sites ( p < 0.05). The highest TP was recorded at site 5 (1488.80 ± 0.01 µg L − 1 ), while low at site 1 (3.47 ± 0.00 µg L − 1 ). Similarly, the highest SRP was high at site 5 (1372.80 ± 0.00 µg L − 1 ) and lowest again at site 1 (0.1 ± 0.00 µg L − 1 ). NH 3 was highest at site 3 (353.49 ± 0.00 µg L − 1 ) and lowest at site 1 (23.00 ± 0.00 µg L − 1 ). Table 2 Environmental Parameters Results (Mean ± Sd) Parameters Sites p-value (spatial) 1 2 3 4 5 6 7 DO (mg L − 1 ) 7.50 ± 0.01 6.39 ± 0.04 3.02 ± 0.18 3.94 ± 0.03 0.57 ± 0.05 5.92 ± 0.02 2.85 ± 0.04 0.004 % of saturation 99.50 ± 0.17 96.33 ± 0.06 45.97 ± 2.70 61.40 ± 0.62 8.77 ± 0.81 86.03 ± 0.21 41.10 ± 0.52 0.003 Flow (m S − 1 ) 0.07 ± 0.0 0.21 ± 0.0 0.42 ± 0.0 0.59 ± 0.0 0.49 ± 0.0 0.65 ± 0.0 0.00 ± 0.0 0.002 pH 6.59 ± 0.04 4.78 ± 0.27 6.93 ± 0.18 7.65 ± 0.00 6.52 ± 0.07 8.85 ± 0.21 6.51 ± 0.04 0.004 Temp ( 0 C) 18.17 ± 0.06 20.67 ± 0.12 22.50 ± 0.10 24.17 ± 0.29 24.07 ± 0.12 21.67 ± 0.12 21.27 ± 0.06 0.003 Cond (µS cm − 1 ) 169.93 ± 1.96 188.77 ± 1.99 717.33 ± 2.52 792.00 ± 0.00 1259.67 ± 0.58 1043.00 ± 12.12 1054.67 ± 1.15 0.004 Turb (NTU) 16.20 ± 0.52 244.67 ± 14.74 30.47 ± 1.62 25.70 ± 4.95 169.33 ± 6.66 100.67 ± 1.15 750.67 ± 8.14 0.003 TDS (m gL − 1 ) 108.33 ± 2.52 138.33 ± 1.53 496.33 ± 2.52 556.67 ± 6.11 761.67 ± 16.04 639.33 ± 16.92 680.67 ± 34.53 0.003 TSS (mg L − 1 ) 16 ± 0.00 84 ± 0.00 30 ± 0.00 26 ± 0.00 140 ± 0.00 63 ± 0.00 98 ± 0.00 0.003 TP (µg L − 1 ) 3.47 ± 0.00 362.13 ± 0.01 1007.47 ± 0.01 750.80 ± 0.00 1488.80 ± 0.01 1217.47 ± 0.01 903.47 ± 0.00 0.003 SRP (µg L − 1 ) 0.10 ± 0.00 104.80 ± 0.00 822.13 ± 0.00 627.46 ± 0.00 1372.80 ± 0.00 1002.81 ± 0.01 776.13 ± 0.00 0.003 NH 3 (µg L − 1 ) 23.00 ± 0.00 320.62 ± 0.00 353.49 ± 0.00 33.01 ± 0.00 345.38 ± 0.00 83.47 ± 0.00 109.67 ± 0.00 0.003 NO 3 − (µg L − 1 ) 0.16 ± 0.00 0.21 ± 0.00 0.18 ± 0.00 0.19 ± 0.00 0.18 ± 0.00 0.26 ± 0.00 0.28 ± 0.00 0.003 3.2. Distribution of Macroinvertebrates Taxa and Macroinvertebrates Metrics in Sites A total of 5,575 macroinvertebrates were collected from vegetation, riffles, pools, sand, mud, and GSM (gravel, sand, and mud). These organisms represented four classes—Hexapoda, Oligochaeta, Gastropoda, and Bivalvia—and three phyla: Arthropoda, Annelida, and Mollusca. Site 1 recorded the highest diversity, with 21 taxa spanning 11 orders and 32 families, whereas Site 7 had the lowest taxa richness. In terms of abundance, Site 6 yielded the greatest number of individuals (1,102), followed closely by Site 1 (1,080), while Site 7 had the fewest (131). Because most taxa at Site 6 were tolerant species, Site 2 exhibited lower abundance compared to Site 6 (Table 3 ). Across the seven sampling sites, macroinvertebrate metrics were compiled, revealing a total of 11 orders and 32 families. Taxa richness ranged from 6 to 21, with Site 1 hosting the highest number (21), followed by Site 2 (approximately 15), and Site 6 with the lowest. Overall, the sites contained about six taxa on average (Table 3 ). Table 3 macroinvertebrate metrics for seven sampling sites (Adino & Mengistou, 2025 ) Category Metrics Sites Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Taxa Richness Number of taxa 21 15 12 11 10 13 6 Total abundance 1080 341 984 920 1017 1102 131 N o Ephemeroptera 492 36 29 22 0 6 0 N o Ephemeroptera Odonata, Trichoptera 646 70 43 22 0 6 0 N o of Hemiptera 300 38 15 6 0 6 5 N o of Diptera 37 85 533 407 642 448 53 N o of Chironomidae 9 73 382 230 327 154 46 N o of Oligochaeta 6 82 380 467 350 631 73 Composition Percentage of Ephemeroptera 45.56 10.56 2.95 2.39 0.00 0.54 0.00 Percentage of Ephemeroptera, Odonata, Trichoptera 59.81 20.53 4.37 2.39 0.00 0.54 0.00 Percentage of Diptera 3.43 24.93 54.17 44.24 63.13 40.65 40.46 Percentage of Chironomidae 0.83 21.41 38.82 25.00 32.15 13.97 35.11 Percentage of EOT/chiron 6.65 0.28 0.01 0.01 0.00 0.00 0.00 Percentage of Oligochaeta 0.56 24.05 38.62 50.76 34.41 57.26 55.73 %Non-insects 5.46 36.07 38.62 52.72 36.87 57.53 55.73 Diversity SDI 2.44 2.37 1.46 1.49 1.70 1.51 1.05 Tolerance Percentage of Dominant taxa 28.70 24.05 38.82 48.04 44.35 52.63 55.73 Percentage of Sensitive taxa 60.37 25.51 4.27 3.37 0.59 8.08 0.00 Percentage Tolerant taxa 39.63 74.49 95.73 96.63 99.41 91.92 100.00 Family Biotic Index 5.69 6.65 7.08 7.34 6.60 7.59 7.15 ETHbios 96 72 50 41 30 48 12 ASPT-ETHbios 4.6 4.8 4.2 3.7 3.0 3.7 2.0 Functional feeding group Percentage of Collector gatherer 45.65 67.16 81.91 85.22 81.32 76.77 94.66 Percentage of scrapers 8.98 7.92 0.00 1.96 2.46 0.27 0.00 Trophic/habit Percentage of Burrowers 16.67 56.89 77.44 84.02 78.37 76.50 90.84 Percentage of Swimmer 49.26 27.86 18.70 13.70 10.03 12.25 4.58 3.3. Relationship Between Physicochemical Parameters and Macroinvertebrate Taxa The result of the multivariate RDA showing the relationship between environmental parameters and macroinvertebrates taxa) and the composition of the macroinvertebrate assemblages at the level of the family is summarized in Fig. 2 . Axis 1 of the RDA plot separated that sensitive and moderately sensitive macroinvertebrate groups, including Caenidae, Baetidae, Notonectidae, Gomphidae, Corixidae, Dystiscidae, and Coenagrionidae, which are frequently found at site one, were associated with high levels of DO and habitat quality index, and we can say the ecological integrity gradient. It had a negative correlation with Syphididae, Physidae, Chironomidae, and Oligochaete. Axis 2 of the RDA plot separated in the middle section of the sampling sites, Chironomidae, and Oligocheata, had a positive correlation with conductivity and TP. Circles indicate sampling sites. Abbreviated as sites. The black arrow shows the major physicochemical factors that shape the benthic community: DO = dissolved oxygen, pH = power of hydrogen, Cond = conductivity, Turb = turbidity, NO 3 = Nitrate, TP = total phosphorus, NH 3 = ammonia, TSS = total suspended solids, and HQI = habitat quality index. Macroinvertebrates are abbreviated (Caen = Caenidae, Baet = Baetidae, Noton = Notonectidae, Gomph = Gomphidae, Corix = Corixidae, Dysti = Dytiscidae, Coenagri = Coenagrionidae, Tipuli = Tipulidae, Dixi = Dixidae, Culici = Culicidae, Syrphi = Syrphidae, Oligo = Oligochaeta, Psychodi = Psychodidae, Chiro = Chironomidae, Physi = Physidae, and Ephydrid = Ephydridae. 3.4. Relationship Between Physicochemical Parameters Macroinvertebrate Metrics The results of Pearson correlation analysis showed that the percentage of Ephemeroptera metrics positively correlated with dissolved oxygen (r = 0.677). Concurrently, there is a negative correlation with temperature, conductivity, and total phosphorus (r = -0.813, -0.752, and − 0.823, respectively). The percentage of Ephemeroptera and percentage of EOT showed an inverse relationship with temperature (r = -0.813 and − 0.835, respectively). Percentage of dipterans had a positive relationship was observed in both temperature (r = 0.904) and TP (r = 0.924) and a strong negative correlation with DO (r = -0.892). The percentage of Chironomids had an inverse relationship with DO (r = -0.840). The percentage of Oligochaeta had a positive correlation with temperature and conductivity (r = 0.641 and 0.758). SDI had a positive correlation with DO (r = 0.643). The percentage of sensitive taxa had a positive relation nship with DO (r = 0.781) and a strong negative correlation with temperature and conductivity (r = 0.856 and 0.823). ASPT- ETHbios had a positive DO (r = 0.683). The percentage of collectors had an inverse relationship with DO (r = -0.750) and a positive correlation with temperature and conductivity (r = 0.750 and 0.776). The percentage of burrowers had a strong negative correlation with DO (r = -0.720) as well as a positive relationship with temperature (r = 0.806), conductivity (r = 0.793), and TP (r = 0.785). 4. Discussion 4.1. Environmental Parameters The result of this study on the physicochemical parameters of the Little Akaki River provide important new insight in to the dynamics of water quality across various sampling locations. The mean of DO concentration in this study ranges from 0.57 ± 0.05 to 6.39 ± 0.04 mg L − 1 . The lowest DO level observed at sampling site (site 5) is mainly attributed to point and nonpoint sources of pollution that have caused a reduction in the amount of DO in water. Various factors influence it, including temperature, turbulence, the partial pressure of solutes, photosynthesis, the presence of organic matter, and the decomposer organisms inhabiting the area (Aschalew Lakew, 2012 ). The DO levels recorded in this study were lower than those reported for other river experiencing significant human impacts, such as the Modjo River, which has a minimum concentration of 6.1 ± 4.01 mg L − 1 , and the Great Akaki River, with a minimum of 3.1 ± 2.4 mg L − 1 (Baye Sitotaw, 2006 ). In contrast, research conducted on The River Little Akaki indicated DO values as low as 0.3 mgL − 1 in the section that runs through Addis Ababa (Deshu Mamo et al., 2021). According to FDRE-EPA ( 2003 ) DO concentration greater than 5m g L − 1 is essential to sustain aquatic life. In this study, the DO concentration at sampling site 1, site 2 and site 6 met this standard. However, the DO level at site 3, site 4, site 5 and site 7 fell below 5m g L − 1 . In the present study, the average pH values ranged from 4.78 ± 0.27 to 8.85 ± 0.21. The maximum pH was observed at the sampling site (site 6), the observed pH values in those specific sites could be influenced by various anthropogenic activities such as the discharge of industrial waste as well as natural processes including the decomposition of organic material. The sampling site (site 2) exhibited the lowest pH value, which can be ascribed to a comparatively minimal impact from human activities. Changes in pH levels significantly impact aquatic organisms, including fish and benthic fauna (Aschalew Lakew, 2012 ). The findings align with existing literature that highlight the strong correlation between water pH and the concentrations of carbon dioxide (CO 2 ) as well as alkaline substances (Tanjung & Hamuna, 2019 ). The reported pH were comparable those reported by previous studies, such as the Little Akaki River (7.31 to 8.13) (Deshu Mamo et al., 2021). The pH values recorded for the Little Akaki River fell within the acceptable range of 6.5 to 8.5 as established by the World Health Organization (WHO, 2011 ). There were significant differences in the water temperature at the sampling locations ( p < 0.05). The highest temperature observed at site 4 could be because of the flood of industrial effluent from textiles, clothes, tanneries, paints, plastics, rubber, and boiler activities, which were likely to discharge warm water into the river ecology. Downstream minor fluctuation in temperature was recorded, potentially resulting from changes in altitude that enhance solar radiation exposure, as well as differing degrees of water pollution (Matta et al., 2017 ). An increase in temperature results in a reduction of gas solubility in water, elevates the metabolic rates of organisms, and accelerates the decomposition of organic matter. This chain of events ultimately contributes to oxygen depletion and the deterioration of aquatic life (Aschalew Lakew, 2012 ). This study align with the previous studies conducted in the Little Akaki River (19.6 to 23.89°C) (Deshu Mamo et al., 2021), Kebena River (17–21°C) (Salami, 2016 ) in Addis Ababa city and Modjo River (21.50–24.93°C) (Abrha Mulu et al., 2013). According to WHO ( 2008 ) and (USEPA, 2000 ), temperatures of river water generally range from 5 to 30°C and should be < 32°C for the protection of aquatic ecosystems, respectively. Therefore, the recorded temperature in the Little Akaki River were within acceptable range for supporting aquatic life. In this study, electrical conductivity (EC) ranged from 169.93 ± 1.96 to 1259.67 ± 0.58 µS cm − 1 . The maximum EC was recorded at site 5, likely due to the presence of nearby fuel stations, agrochemicals that have been leaching from irrigated vegetable, waste from alcohol factories, automotive repair shops, electrical wiring production, damp open areas, soldiering activities as well as, domestic and commercial waste. The elevated organic load from rivers in the town, along with disturbances in the watershed, leads to an increase in the ionic concentration of the receiving water body, which in turn raises its conductivity (Amare Mezgebu et al., 2019). The conductivity observed in this study is comparable to that of Modjo Upstream 180 ± 154 to 1,197 ± 137 µS cm − 1 reported by Kassahun Tessema et al. ( 2020 ). The EC at site 1, site 2, site 3 and site 4 did not exceed the FDRE-EPA ( 2003 ) (1000 µS cm − 1 ), while EC at site 5, site 6, and site 7 exceeded the standards by FDRE-EPA ( 2003 ). In this study, TDS and TSS range from 108.33 ± 2.52 to 761.67 ± 16.04 mg L − 1 and 16 ± 0 to 140 ± 0 mg L − 1 , respectively. The maximum levels of TDS and TSS, were documented at sampling sites 5 while their lowest values were obtained at the sampling site 1. The highest concentration of TDS, or TSS at site 5 may be caused by human activities that introduce dissolved and suspended solids into the system of rivers, such as the Addis Ababa City abattoir wastes, home and sewer wastewaters that contain salts and detergents, canals, surface drainage for irrigated plants, metal industry, and garages that transport soil and agrochemicals. In contrast to previous research findings, the TDS concentration in Little Akaki River water samples was greater than that of the result of the study reported in Little Akaki River (0.83 to 915.57 mg L − 1 ) (Deshu Mamo et al., 2021), Kebena (40.43–640 mg L − 1 ) (Salami, 2016 ) in Addis Ababa City, Ethiopia. According to the South African Department of Water Affairs and Forestry (DWAF, 1996 ), TSS levels in irrigation water samples that are too high could have negative effects such as surface crust formation, which hinders water percolation into the soil and affects soil aeration, suspended particles covering plant leaves, and reduced photosynthetic activity of plants. The TDS values site 1 and site 2 did not exceed in the standard limit of domestic uses 0-450 mg L − 1 (DWAF, 1996 ) while site 3 to site 7 exceeded the standard limit. The sample location (site 3) had the highest NH 3 -N concentrations and possibly due to point-source nitrogen inputs like agricultural Addis Ababa Abattoir and nonpoint sources like chemical fertilizers, sewage discharge, biological degradation of manure. In contrast to previous research, the concentration of NO 3 –N of this study result was lower than the study made by Deshu Mamo et al. (2021) 0.49–72.86 mg L − 1 . The highest TP and SRP might be due to discharges from industrial facilities, decomposition of organic matter, runoff from agricultural fields. The concentration of nitrogen and phosphorus in industrial effluent is primarily influenced by the management of raw materials, the quantity of spent yeast present in the effluent, and the volume of phosphorus-containing chemicals utilized in the clean-in-place systems (Tesfalem Fikresilasie, 2011 ). In this research, there was an observed increase in the trends of various physicochemical parameters, including EC, TDS, TSS, turbidity, TP, and SRP. The study observed a decline in dissolved oxygen and percent of saturation from the upstream to the downstream locations. However, no uniform pattern was identified for NH 3 -N, NO 3 –N, or pH levels in either the upstream or downstream sections. 4.2. Relationship Between Physicochemical Parameters and Macroinvertebrate Taxa Environmental variables strongly shaped the macroinvertebrate community. The DO was among the environmental variables that affect the macroinvertebrate composition of the streams in Ethiopia (Solomon Tesfay et al., 2019). RDA demonstrated a variation in pollutant levels, indicating that upstream regions exhibited significantly superior water quality compared to downstream locations, which displayed diminished water quality. Furthermore, there was a positive correlation observed with macroinvertebrate taxa that are less able to tolerate pollution (e.g. Caenidae, Baetidae, Coenagrionidae, and Notonectidae) and DO was noted at the upstream site (site 1). Their autecological preference for high oxygen levels and low levels of organic pollution is the cause of this. This is in line with the result of the study made in the upper and middle Awash River by Tesfaye Muluye et al. (2024). In this study, macroinvertebrate taxa from the families Baetidae, Caenidae, Coenagrionidae, and Notonectidae exhibited a favorable association with dissolved oxygen levels and were negatively related to nutrients, conductivity, and TSS. A similar, study was reported by Tesfaye Muluye et al. (2024), made in upper and middle Awash River, Ethiopia In sites four and six, the highly tolerant taxa Chironomidae and Oligochaeta were prevalent, exhibiting significant correlations with total phosphorus (TP) and conductivity. This indicates that Dipteran and Oligochaeta taxa are capable of withstanding polluted aquatic environments (Bere & Nyamupingidza, 2014 ). These findings align with previous research on water pollution. This study is similar to the result of the study made in upper and middle Awash River by (Geda Kebede et al., 2020); Tesfaye Muluye et al. (2024). 4.3. Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics Analysis of the relationship between environmental variables and macroinvertebrate metrics indicated a significant correlation ( p < 0.05). The percentage of Ephemeroptera and EOT exhibited a significant positive correlation with dissolved oxygen levels, which can be ascribed to their ecological preference for elevated oxygen concentrations and reduced pollution from organic sources. This study is in line with the result of the study made in the upper and middle Awash River, Ethiopia (Tesfaye Muluye et al., 2024). The study indicates a preference for tolerant taxa within the Chironomidae and Oligochaeta groups, which consequently shapes the observed distribution pattern of sensitivity and tolerance to the depletion of dissolved oxygen. The percentage Ephemeroptera and EOT showed a stronger negative correlation to electrical conductivity; this might result from the difference in their tolerance values. The importance of temperature in monitoring aquatic ecosystems has been validated by its strong positive correlation coefficient (r ≥ 0.6) with metrics that increase with disturbances, such as % Diptera, % Chironomidae, % Oligochaeta, % collector gatherer, and % burrower. A similar result was also indicated in the study by Solomon Akalu et al. (2011) on the Greater Akaki River, Ethiopia. Electrical conductivity had a strong positive relationship with the percentages of Oligochaeta and Chironomidae. According to Odume et al. ( 2016 ), certain Diptera exhibit a range of specialized adaptations that allow them to thrive in severely disturbed habitats. For instance, members of the Chironomidae family have a unique molecule called hemoglobin that helps their bodies absorb oxygen more readily when they are in hypoxic environments. The Syrphidae family, however, can absorb ambient oxygen in contaminated environments because of their extending respiratory tubes. This observation aligns with previous research that has associated the reduction of mayfly taxa, which are susceptible to current changes, with rising levels of conductivity (Aschalew Lakew & Moog, 2015; Extence et al., 2013 ). Sites 3 to 7 are identified as areas of degradation in the downstream locations, where the highly tolerant taxa Chironomidae are prevalent and exhibit significant correlations with total dissolved solids (TDS), ammonia (NH 3 ), total phosphorus (TP), and soluble reactive phosphorus (SRP) concentrations. 5. Conclusion This study assessed the relationship between water quality parameters and macroinvertebrate metrics for ecological biomonitoring in the Little Akaki River. The findings indicate that the water quality and ecological integrity of the river have been significantly degraded due to anthropogenic activities. Significant levels of water contamination and associated ecological degradation were found at impacted locations by assessment of different physicochemical parameters and community of benthic macroinvertebrates metrics. The percentage of Ephemeroptera and the percentage of EOT showed an inverse relationship with temperature. The percentage of dipterans that had a positive relationship was observed in both temperatures. The ET and EOT taxa have high abundance in the upstream of the river and lower downstream. Electrical conductivity had a strong positive relationship with the percentages of Oligochaeta and Chironomidae. The percentage of Ephemeroptera and EOT showed a stronger negative correlation to electrical conductivity. A significant relationship was observed between physicochemical parameters and macroinvertebrate metrics. Develop a systematic monitoring program aimed at evaluating essential physicochemical parameters, including temperature, pH, dissolved oxygen, turbidity, and nutrient concentrations. The collected data will facilitate the identification of trends and potential stressors that may impact macroinvertebrate populations and the overall health of the ecosystem. Further studies are necessary to assess the relationship between water quality parameters and various biotic and abiotic factors and Future research should build on this work as a reference point and prioritize autecological studies of macroinvertebrate taxa at the level of species. Declarations CRediT authorship contribution statement Alachew Adino: conceptualization (equal), formal analysis (equal), methodology (equal), software (equal), writing – original draft (equal), writing – review and editing (equal). Seyoum Mengistou: method ology (equal), supervision (equal), validation (equal), visualization (equal). Declaration of competing interest The authors have no relevant financial or non-financial interests to disclose. Funding This research is funded by the Austrian Development Cooperation (ADC) with grant number (ADC project 0612-00/2022 (AQUAHUB2)). Author Contribution Alachew Adino: conceptualization (equal), formal analysis (equal), methodology (equal), software (equal), writing – original draft (equal), writing – review and editing (equal). Seyoum Mengistou: method ology (equal), supervision (equal), validation (equal), visualization (equal). Acknowledgements The authors thank Aquatic Ecosystem and Environmental Management (AEEM) joint MSc Program of Addis Ababa University, Bahir Dar University, Egerton University and National Fishery and Aquatic Life Research Center (NFALRC), Sebeta in collaboration with Austrian Development Cooperation for funding the research through University of Natural Resources and Life Sciences, (BOKU), Vienna, Austria. 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Zinabu Gebre-Mariam, & Elias Dadebo. (1989). Water resources and fisheries management in the Ethiopian rift valley lakes. Sinet , 12 (2), 95–109. Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":368672,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area and sampling sites along with the Little Akaki River (LAR) (Adino \u0026amp; Mengistou, 2025)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8475505/v1/f230c69ed0077cdcc5943c7e.png"},{"id":99679720,"identity":"5c9ba8e1-2be1-4bbe-ae60-39a6f6c99135","added_by":"auto","created_at":"2026-01-07 08:43:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127883,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy Analysis (RDA) triplot between sampling sites, species, and environment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8475505/v1/07c2736b5547b933391d8e8f.png"},{"id":100373326,"identity":"b320cb7d-b5da-499f-a299-d8da90bb745d","added_by":"auto","created_at":"2026-01-16 08:14:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1642784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8475505/v1/57787fff-b683-41b3-940d-f10356fd1713.pdf"},{"id":99796259,"identity":"6b7488f0-961e-49f5-811f-5d9ce0b8527e","added_by":"auto","created_at":"2026-01-08 13:40:52","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":38864,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8475505/v1/08b3b9aa96b9cc9cf4d4ae13.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics for Ecological Biomonitoring in The Little Akaki River, Addis Ababa, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFreshwater ecosystems are vital to human existence, providing essential ecosystem services that sustain billions of people globally (Aylward et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Covich et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Fresh water is only 2.5% of the aquatic environments on Earth (Giordano, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gleick, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Zedler \u0026amp; Kercher, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The majority of freshwater, approximately 68.6%, is locked in polar ice caps, while 30.1% is stored as groundwater (Carpenter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Giordano, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gleick, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The proportion of the river is about 0.46% is accessible for ecosystem and human use (Carpenter et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gleick, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The finite nature of freshwater resources necessitates sustainable management strategies through scientific understanding (Baron et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mishra, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Richter et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Among the most delicate yet biologically varied and ecologically significant systems are river ecosystems, which support a variety of commercial endeavors while providing environmental services including flood control, nutrient cycling, carbon sequestration, and biodiversity support (Sabater \u0026amp; Elosegi, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, their ecological integrity is increasingly compromising by population growth, sedimentation, urbanization, intensive agriculture, deforestation, riparian habitat degradation, flow diversion, waste disposal and climate (Benoy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sathe et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Looy et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWater quality is the chemical, physical, and biological properties of water (Bartram \u0026amp; Ballance, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). These physical, chemical, and biological characteristics of water change with the season, the watershed natural environment, the pattern of land use, and, to a considerable extent, human activity (Bartram \u0026amp; Ballance, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Karmakar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is most often used about a set of guidelines that may be used to measure compliance, which is typically attained through water treatment. Biomonitoring is the most common tool that has been used to monitor the integrity of freshwater ecosystems (Covich et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rosenberg, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It could provide a movie picture of an ecosystem condition, while physicochemical parameters show snapshot information (Barbour, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Macroinvertebrates are crucial for evaluating the ecological state of freshwater habitats because they are sensitive water quality indicators and the general health of ecosystems (Abebe Beyene et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Aschalew Lakew \u0026amp; Moog, 2015; Getachew Beneberu \u0026amp; Seyoum Mengistou, 2010). Most of the aquatic environments in developing countries are subjected to degradation, mainly due to domestic as well as industrial wastes (Getachew Beneberu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Getachew Beneberu \u0026amp; Seyoum Mengistou, 2015; Zinabu Gebre-Mariam \u0026amp; Elias Dadebo, 1989). One of the major environmental issues facing the Little Akaki River is sedimentation, which could have a negative impact on aquatic ecosystems. Assessing the ecological health of this river requires an understanding of the connection between water quality factors and macroinvertebrate metrics. We investigated the notion that, in tropical water bodies, there is no correlation between macroinvertebrate measures and water quality indicators. The aim of this study was assessing the relation between macroinvertebrate metrics and water quality parameters in the Little Akaki River.\u003c/p\u003e"},{"header":"2. Material and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area Description\u003c/h2\u003e \u003cp\u003eThe Little Akaki River, which provides drinking water, agriculture, livestock management, and recreational activities for the local people downstream and acts as a drainage system for the Addis Ababa government, was the subject of the study (Deshu Mamo et al., 2021). This river is located at the geographical coordinates of 8\u0026deg; 47' 19.64\" N and 38\u0026deg; 45' 25.3\" E, with an elevation ranging from 2050 and 2619 meters above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From the Gefersa Reservoir in the northwest, the river flows past the city center and industrial areas until arriving at Aba-Samuel. The Akaki catchment region, which spans 540 km\u0026sup2; and is known for its afro-alpine environment, includes the Little Akaki River as one of its principal tributaries (Adino \u0026amp; Mengistou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Deshu Mamo et al., 2021).\u003c/p\u003e \u003cp\u003eThis area experiences mean annual precipitation of 1254 mm and average daily temperatures between 9.9 and 24.6\u0026deg;C. Throughout its catchment region, the river flows through areas of Addis Ababa that are polluted by both point and nonpoint sources. These sources include open defecation, unpermitted solid waste disposal along riverbanks, sewage waste and septic tank discharge into the river, residential, commercial, and industrial operations, and the use of agrochemicals (Deshu Mamo et al., 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling site description\u003c/h2\u003e \u003cp\u003eSeven sampling sites were thought to be adequate for testing the hypothesis, they were selected. It is dependent on the availability of the biotopes we require for sampling, as well as accessibility and recognition ease. Sites 1 and 2, which are situated outside of Addis Ababa, have superior riparian vegetation cover and are comparatively less affected. Because of the high human activity in the area, there is minimal riparian vegetation at the remaining locations (3, 4, 5, 6, and 7) (Adino \u0026amp; Mengistou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The remaining sites (3, 4, 5, 6, and 7) little riparian vegetation due to the intense human activities in the area. Along the stream and river banks studied only a few remnant plants like \u003cem\u003eAcacia sp\u003c/em\u003e., \u003cem\u003eEucalyptus\u003c/em\u003e, \u003cem\u003eSyzygium guineense\u003c/em\u003e, shrubs, and some grasses were observed. The description of the sites is based on (Aschalew Lakew \u0026amp; Moog, 2015). While the central and downstream locations were darkly stained due to the reduction of ferrous-sulfide and the presence of foul sewage, the top portions of the rivers had rough bottoms and were noticeably translucent (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chemical and sewage-like smells emanated from the stretch of the river that received industrial waste (Adino \u0026amp; Mengistou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy site with geographic location and major human activities (Adino \u0026amp; Mengistou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSampling site code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorthing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEasting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor features and human activities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpstream Gefersa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026deg; 5' 26.66\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 37' 53.29\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe location featured pools of somewhat silted conditions and was mostly composed of boulder and stone riffles. The riparian vegetation was good, and the banks were stable. There were livestock watering and just minor urban developments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown Gefersa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026deg; 3' 42.37\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 38' 36.82\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBoulder-based riffles, pools with sizable silt and muck contents, and healthy riparian flora were the site's defining features. Additionally, it was impacted by mining operations, urbanization, cattle grazing, and locations with mild environmental impacts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKolfa Bridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026deg; 1' 11.75\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 42' 31.93\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn addition to pools that were heavily silted and muddy, the site was characterized by riffles with pebbles and occasional cobbles and little riparian vegetation. Significant environmental deterioration resulted from urban development's strong influence on the location, which included unlawful solid waste disposal and car washing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlert Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026deg; 59' 17.81\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 42' 24.91\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe site had no riparian vegetation present and was characterized by riffles made up of many cobbles and pools filled with silt, mud, and sludge. major urban expansion occurred there, resulting in major environmental deterioration from industrial operations, car washes, slaughterhouses, and the disposal of solid and liquid waste.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKadisko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026deg; 55' 48.97\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 45' 24.3\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe site has little riparian vegetation and a little stream with pools that contain sizable amounts of mud, sludge, and silt. Urban growth, including industrial activities, open defecation, inappropriate solid waste disposal, and locations that have been negatively impacted by these causes, have a significant influence on it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGelan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026deg; 54' 13.86\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 44' 43.8\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThere was little riparian vegetation and high silt levels at the location. Sites that have seen significant environmental degradation, high levels of urbanization, industrial activity, waste disposal methods, animal grazing, and little agricultural involvement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAba Samuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026deg; 47' 19.64\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u0026deg; 42' 15.55\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe site was distinguished by the absence of riparian vegetation and lakes with a high silt content. Cattle watering, a significantly damaged landscape, and a lack of urban development.\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Field data collection\u003c/h2\u003e \u003cp\u003eThe timing of benthic invertebrate sampling was determined by the study objectives and prevailing climatic conditions. Seven sampling sites were established (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and each station was georeferenced using GPS to ensure consistency across sampling periods. Physicochemical parameters were measured across various biotopes. Macroinvertebrates were gathered from gravel, sediment, mud, vegetation, pool and riffles at every station. Water quality and macroinvertebrate samples data were collected from the seven sites within the confines of the Little Akaki River from April to May 2024. During the sample period, environmental variables (physical, chemical, and hydro morphological parameters) related to the diversity and abundance of indicator organisms were measured. Latitude, longitude, and altitude were determined using a GPS unit brand Garmin model GPS 72H, manufactured in Taiwan by the Garmin Corporation. Water quality parameters recorded from the surface water of the river including dissolved oxygen (DO), temperature, pH, and electrical conductivity, were measured \u003cem\u003ein situ\u003c/em\u003e using multimeter probe (Model HQ4300, manufactured by HACH company in the USA) and TDS was recorded using the Pocket Pro TDS meter (MSIP-REM-HAH-140321539, HACH company in China), and turbidity was measured using turbidity meter (T-100, manufactured by OAKTON company in Singapore). The measurements were taken in triplicate, with the average taken as one measurement per site. Water samples were collected in 500 mL polyethylene bottles from the river surface. These samples were transported in an icebox to Addis Ababa University Seyoum Mengistou Limnology Laboratory for nutrient analysis. Velocity was measured by using the floating method, which measures the time it takes a floating object to travel a certain distance (the travel time should be at least 20 seconds).\u003c/p\u003e \u003cp\u003eV=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{L}{t}\\)\u003c/span\u003e\u003c/span\u003e, where L is the length of the distance and t is the time.\u003c/p\u003e \u003cp\u003eTo correct for the mean velocity, an adjustment was made because the floating method measures only the surface velocity, which is higher than the mean velocity.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Vmean=\\text{V}\\text{s}\\text{u}\\text{r}\\text{f}\\text{a}\\text{c}\\text{e}\\:\\text{x}\\:0.85$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eA standardized D-frame wire mesh with an area of 25 \u003cb\u003e\u0026times;\u003c/b\u003e 25 cm and a mesh size of 500 \u0026micro;m was used to collect macroinvertebrates from gravel, sand, mud, vegetation and stone (Barbour, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Key biotopes such gravel, sand, mud, vegetation, pools, and riffles were all included in the sample process, and all sampling sites were kept consistent by using a multi-habitat sampling system (MHS). A total of 20 minutes of jabbing was the normal sampling effort (Rogers et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), It was allocated to vegetation, riffle, pool, sand mud, and gravel. For every site, the sample time was the same. To prevent upstream sample units from being disturbed, benthic macroinvertebrate sampling started downstream of the reach against the water's flow. Using sieves with mesh sizes of 500 \u0026micro;m and 250 \u0026micro;m, the macroinvertebrates were extracted and separated from debris and algal mats in the field. The samples were then composited in a white plastic sorting tray after being collected from gravel, sediment, mud, vegetation, pools, and riffles. The specimens were then put in jars with 4% formalin (Barbour, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Griffin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and labeled accordingly with site name, sampling site code, and collection dates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Laboratory analysis\u003c/h2\u003e \u003cp\u003eThe nutrient levels in the water sample were analyzed using a UV/visible spectrophotometer (6405, manufactured by JENAY Ltd. in the United Kingdom), following the standard methods APHA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Measurements for ammonia (NH\u003csub\u003e3\u003c/sub\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), and soluble reactive phosphorus (SRP) were performed on filtered water samples, while total phosphorus (TP) was performed on unfiltered water samples. Filtration was carried out using glass fiber filter paper (GF/F). Triplicate samples were taken for each parameter. Ammonia was determined by the phenate method; nitrate was determined by using the sodium salicylate method; and for TP and SRP, the ascorbic acid method was used, and for TSS was determined using Gravimetric method APHA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Macroinvertebrate samples collected in the field were processed in a laboratory for further examination. The data sheet was copied with the information from the sample container before processing. Samples were sorted in a white plastic tray before being poured into vials in the lab after being washed through a series of sieves (2000, 1000, 500 and 250 \u0026micro;m mesh size) with tap water (Aschalew Lakew, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Forceps were used to remove visible creatures from the substrate, which were then put into specimen vials. A light microscope was used to sort the organisms that were kept in the smaller section of the sieve. On-site composites of the samples were stored in 70% ethanol for later sorting and identification. Each sample's benthic macroinvertebrate population was counted. to ascertain the percentage of functioning feeder groupings along the river, as well as their relative quantity, diversity, and composition. Using a dissecting microscope, common identification keys, and a field guide \u0026ldquo;Aquatic Invertebrates of South African Rivers and Guide to aquatic macroinvertebrates of the Upper Midwest Waters\u0026rdquo;(Bouchard, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gerber \u0026amp; Gabriel, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Identification was done all the way down to the family level Counting was done on the entire composited sample without sub-sampling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Determination of Macroinvertebrate Metrics\u003c/h2\u003e \u003cp\u003eETHbios, ASPT-ETHbios, Trophic/habit, Shannon Diversity Index, number of EOT, Taxa richness, Taxa abundance, and the Family Biotic Index are some of the frequently utilized macroinvertebrate metrics in this study. The scoring system assigns a number between 1 and 10, with the most sensitive taxa having the highest score and the tolerant benthic macroinvertebrates having the lowest (Aschalew Lakew \u0026amp; Moog, 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data Analysis\u003c/h2\u003e \u003cp\u003eFirst, Microsoft Office Excel (2016) was used to organize the row data, generate table and figures. The Shapiro-Wilk test was then used to test for normality in the water quality data. The data was then subjected to the non-parametric Kruskal- Wallis tests to determine the size of the spatial variations. IBM SPSS version 20 was used to calculate the mean and standard deviation and perform the Kruskal-Wallis analysis. Macroinvertebrate taxa were correlated with habitat quality and physicochemical characteristics using Pearson correlation analysis. Using the CANOCO 5 program, the relationship between the environment and the composition of communities of macroinvertebrates was assessed (Smilauer \u0026amp; Leps, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The physicochemical data was log (Log\u003csub\u003e10\u003c/sub\u003e (X\u0026thinsp;+\u0026thinsp;1)) except pH transformed before analysis, and the macroinvertebrate count data was Hellinger transformed (Legendre \u0026amp; Gallagher, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The species response with a gradient length of 2.1 standard deviations in DCA indicated that a linear model was appropriate (Leps \u0026amp; Smilauer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). A linear model was also utilized for redundancy analysis (RDA). The relationship between the data concerning benthic macroinvertebrates and the seven parameters of water quality was examined. To determine whether the relationships between macroinvertebrate taxa and environmental variables were significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the Monte Carlo test with 999 permutations was employed (Leps \u0026amp; Smilauer, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). PAST (Paleontological Statistics software) package was used for diversity index (Hammer \u0026amp; Harper, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Environmental Parameters\u003c/h2\u003e \u003cp\u003eThe mean value of physicochemical parameters is presented in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most of the physicochemical parameters measured in the field and laboratory showed a significant difference between the sampling sites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mean DO concentrations spanned from 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 to 7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with site 1 showing the highest DO level and site 5 the lowest. The lowest pH reading was recorded at site 2 while highest at site 6 (8.85). In the sampling sites, the temperature ranged between 18.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u0026deg;C and 24.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u0026deg;C. The highest was recorded at site 4 and at site 1, the lowest was recorded. Electrical conductivity (EC) across the sites ranged from 169.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 to 1259.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 (\u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); site 5 had the maximum value, the lowest at site 1. The highest turbidity was recorded at site 7 (750.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14 NTU), while the lowest at site 1 (16.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 NTU). The total dissolved solids (TDS) exhibited a range from 108.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 to 761.67\u0026thinsp;\u0026plusmn;\u0026thinsp;16.04 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Site 5 exhibited the highest TDS value, while site 1 had the lowest. Total suspended solids (TSS) ranged from 16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 to 140\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The highest TSS was recorded at site 5 and lowest at site 1. The mean of all nutrient parameters showed significant differences among the sites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest TP was recorded at site 5 (1488.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while low at site 1 (3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Similarly, the highest SRP was high at site 5 (1372.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and lowest again at site 1 (0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). NH\u003csub\u003e3\u003c/sub\u003e was highest at site 3 (353.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and lowest at site 1 (23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental Parameters Results (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Sd)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e (spatial)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of saturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e99.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e96.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e45.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e61.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e86.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e41.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow (m S\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e8.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e24.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e21.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCond (\u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e169.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e188.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e717.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e792.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1259.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1043.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1054.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurb (NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e244.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e30.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e169.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e100.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e750.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS (m gL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e108.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e138.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e496.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e556.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e761.67\u0026thinsp;\u0026plusmn;\u0026thinsp;16.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e639.33\u0026thinsp;\u0026plusmn;\u0026thinsp;16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e680.67\u0026thinsp;\u0026plusmn;\u0026thinsp;34.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e362.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1007.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e750.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1488.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1217.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e903.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e104.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e822.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e627.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1372.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e1002.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e776.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e320.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e353.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e33.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e345.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e83.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e109.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Distribution of Macroinvertebrates Taxa and Macroinvertebrates Metrics in Sites\u003c/h2\u003e \u003cp\u003eA total of 5,575 macroinvertebrates were collected from vegetation, riffles, pools, sand, mud, and GSM (gravel, sand, and mud). These organisms represented four classes\u0026mdash;Hexapoda, Oligochaeta, Gastropoda, and Bivalvia\u0026mdash;and three phyla: Arthropoda, Annelida, and Mollusca. Site 1 recorded the highest diversity, with 21 taxa spanning 11 orders and 32 families, whereas Site 7 had the lowest taxa richness. In terms of abundance, Site 6 yielded the greatest number of individuals (1,102), followed closely by Site 1 (1,080), while Site 7 had the fewest (131). Because most taxa at Site 6 were tolerant species, Site 2 exhibited lower abundance compared to Site 6 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Across the seven sampling sites, macroinvertebrate metrics were compiled, revealing a total of 11 orders and 32 families. Taxa richness ranged from 6 to 21, with Site 1 hosting the highest number (21), followed by Site 2 (approximately 15), and Site 6 with the lowest. Overall, the sites contained about six taxa on average (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emacroinvertebrate metrics for seven sampling sites (Adino \u0026amp; Mengistou, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSite 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSite 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSite 6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSite 7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eTaxa Richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal abundance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e Ephemeroptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e Ephemeroptera Odonata, Trichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e of Hemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e of Diptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003e of Chironomidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo of\u003c/span\u003e Oligochaeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eComposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Ephemeroptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Ephemeroptera,\u003c/p\u003e \u003cp\u003eOdonata, Trichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Diptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Chironomidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of EOT/chiron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Oligochaeta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Non-insects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Dominant taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Sensitive taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage Tolerant taxa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily Biotic Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eETHbios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASPT-ETHbios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFunctional feeding group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Collector gatherer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of scrapers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrophic/habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Burrowers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Swimmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.58\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Relationship Between Physicochemical Parameters and Macroinvertebrate Taxa\u003c/h2\u003e \u003cp\u003eThe result of the multivariate RDA showing the relationship between environmental parameters and macroinvertebrates taxa) and the composition of the macroinvertebrate assemblages at the level of the family is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Axis 1 of the RDA plot separated that sensitive and moderately sensitive macroinvertebrate groups, including Caenidae, Baetidae, Notonectidae, Gomphidae, Corixidae, Dystiscidae, and Coenagrionidae, which are frequently found at site one, were associated with high levels of DO and habitat quality index, and we can say the ecological integrity gradient. It had a negative correlation with Syphididae, Physidae, Chironomidae, and Oligochaete. Axis 2 of the RDA plot separated in the middle section of the sampling sites, Chironomidae, and Oligocheata, had a positive correlation with conductivity and TP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCircles indicate sampling sites. Abbreviated as sites. The black arrow shows the major physicochemical factors that shape the benthic community: DO\u0026thinsp;=\u0026thinsp;dissolved oxygen, pH\u0026thinsp;=\u0026thinsp;power of hydrogen, Cond\u0026thinsp;=\u0026thinsp;conductivity, Turb\u0026thinsp;=\u0026thinsp;turbidity, NO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Nitrate, TP\u0026thinsp;=\u0026thinsp;total phosphorus, NH\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ammonia, TSS\u0026thinsp;=\u0026thinsp;total suspended solids, and HQI\u0026thinsp;=\u0026thinsp;habitat quality index. Macroinvertebrates are abbreviated (Caen\u0026thinsp;=\u0026thinsp;Caenidae, Baet\u0026thinsp;=\u0026thinsp;Baetidae, Noton\u0026thinsp;=\u0026thinsp;Notonectidae, Gomph\u0026thinsp;=\u0026thinsp;Gomphidae, Corix\u0026thinsp;=\u0026thinsp;Corixidae, Dysti\u0026thinsp;=\u0026thinsp;Dytiscidae, Coenagri\u0026thinsp;=\u0026thinsp;Coenagrionidae, Tipuli\u0026thinsp;=\u0026thinsp;Tipulidae, Dixi\u0026thinsp;=\u0026thinsp;Dixidae, Culici\u0026thinsp;=\u0026thinsp;Culicidae, Syrphi\u0026thinsp;=\u0026thinsp;Syrphidae, Oligo\u0026thinsp;=\u0026thinsp;Oligochaeta, Psychodi\u0026thinsp;=\u0026thinsp;Psychodidae, Chiro\u0026thinsp;=\u0026thinsp;Chironomidae, Physi\u0026thinsp;=\u0026thinsp;Physidae, and Ephydrid\u0026thinsp;=\u0026thinsp;Ephydridae.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Relationship Between Physicochemical Parameters Macroinvertebrate Metrics\u003c/h2\u003e \u003cp\u003eThe results of Pearson correlation analysis showed that the percentage of Ephemeroptera metrics positively correlated with dissolved oxygen (r\u0026thinsp;=\u0026thinsp;0.677). Concurrently, there is a negative correlation with temperature, conductivity, and total phosphorus (r = -0.813, -0.752, and \u0026minus;\u0026thinsp;0.823, respectively). The percentage of Ephemeroptera and percentage of EOT showed an inverse relationship with temperature (r = -0.813 and \u0026minus;\u0026thinsp;0.835, respectively). Percentage of dipterans had a positive relationship was observed in both temperature (r\u0026thinsp;=\u0026thinsp;0.904) and TP (r\u0026thinsp;=\u0026thinsp;0.924) and a strong negative correlation with DO (r = -0.892). The percentage of Chironomids had an inverse relationship with DO (r = -0.840). The percentage of Oligochaeta had a positive correlation with temperature and conductivity (r\u0026thinsp;=\u0026thinsp;0.641 and 0.758). SDI had a positive correlation with DO (r\u0026thinsp;=\u0026thinsp;0.643). The percentage of sensitive taxa had a positive relation nship with DO (r\u0026thinsp;=\u0026thinsp;0.781) and a strong negative correlation with temperature and conductivity (r\u0026thinsp;=\u0026thinsp;0.856 and 0.823). ASPT- ETHbios had a positive DO (r\u0026thinsp;=\u0026thinsp;0.683). The percentage of collectors had an inverse relationship with DO (r = -0.750) and a positive correlation with temperature and conductivity (r\u0026thinsp;=\u0026thinsp;0.750 and 0.776). The percentage of burrowers had a strong negative correlation with DO (r = -0.720) as well as a positive relationship with temperature (r\u0026thinsp;=\u0026thinsp;0.806), conductivity (r\u0026thinsp;=\u0026thinsp;0.793), and TP (r\u0026thinsp;=\u0026thinsp;0.785).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Environmental Parameters\u003c/h2\u003e \u003cp\u003eThe result of this study on the physicochemical parameters of the Little Akaki River provide important new insight in to the dynamics of water quality across various sampling locations. The mean of DO concentration in this study ranges from 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 to 6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The lowest DO level observed at sampling site (site 5) is mainly attributed to point and nonpoint sources of pollution that have caused a reduction in the amount of DO in water. Various factors influence it, including temperature, turbulence, the partial pressure of solutes, photosynthesis, the presence of organic matter, and the decomposer organisms inhabiting the area (Aschalew Lakew, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The DO levels recorded in this study were lower than those reported for other river experiencing significant human impacts, such as the Modjo River, which has a minimum concentration of 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the Great Akaki River, with a minimum of 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Baye Sitotaw, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In contrast, research conducted on The River Little Akaki indicated DO values as low as 0.3 mgL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the section that runs through Addis Ababa (Deshu Mamo et al., 2021). According to FDRE-EPA (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) DO concentration greater than 5m g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is essential to sustain aquatic life. In this study, the DO concentration at sampling site 1, site 2 and site 6 met this standard. However, the DO level at site 3, site 4, site 5 and site 7 fell below 5m g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In the present study, the average pH values ranged from 4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 to 8.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21. The maximum pH was observed at the sampling site (site 6), the observed pH values in those specific sites could be influenced by various anthropogenic activities such as the discharge of industrial waste as well as natural processes including the decomposition of organic material. The sampling site (site 2) exhibited the lowest pH value, which can be ascribed to a comparatively minimal impact from human activities. Changes in pH levels significantly impact aquatic organisms, including fish and benthic fauna (Aschalew Lakew, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The findings align with existing literature that highlight the strong correlation between water pH and the concentrations of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) as well as alkaline substances (Tanjung \u0026amp; Hamuna, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reported pH were comparable those reported by previous studies, such as the Little Akaki River (7.31 to 8.13) (Deshu Mamo et al., 2021). The pH values recorded for the Little Akaki River fell within the acceptable range of 6.5 to 8.5 as established by the World Health Organization (WHO, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere were significant differences in the water temperature at the sampling locations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest temperature observed at site 4 could be because of the flood of industrial effluent from textiles, clothes, tanneries, paints, plastics, rubber, and boiler activities, which were likely to discharge warm water into the river ecology. Downstream minor fluctuation in temperature was recorded, potentially resulting from changes in altitude that enhance solar radiation exposure, as well as differing degrees of water pollution (Matta et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). An increase in temperature results in a reduction of gas solubility in water, elevates the metabolic rates of organisms, and accelerates the decomposition of organic matter. This chain of events ultimately contributes to oxygen depletion and the deterioration of aquatic life (Aschalew Lakew, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This study align with the previous studies conducted in the Little Akaki River (19.6 to 23.89\u0026deg;C) (Deshu Mamo et al., 2021), Kebena River (17\u0026ndash;21\u0026deg;C) (Salami, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Addis Ababa city and Modjo River (21.50\u0026ndash;24.93\u0026deg;C) (Abrha Mulu et al., 2013). According to WHO (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and (USEPA, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), temperatures of river water generally range from 5 to 30\u0026deg;C and should be \u0026lt;\u0026thinsp;32\u0026deg;C for the protection of aquatic ecosystems, respectively. Therefore, the recorded temperature in the Little Akaki River were within acceptable range for supporting aquatic life. In this study, electrical conductivity (EC) ranged from 169.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 to 1259.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The maximum EC was recorded at site 5, likely due to the presence of nearby fuel stations, agrochemicals that have been leaching from irrigated vegetable, waste from alcohol factories, automotive repair shops, electrical wiring production, damp open areas, soldiering activities as well as, domestic and commercial waste. The elevated organic load from rivers in the town, along with disturbances in the watershed, leads to an increase in the ionic concentration of the receiving water body, which in turn raises its conductivity (Amare Mezgebu et al., 2019). The conductivity observed in this study is comparable to that of Modjo Upstream 180\u0026thinsp;\u0026plusmn;\u0026thinsp;154 to 1,197\u0026thinsp;\u0026plusmn;\u0026thinsp;137 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e reported by Kassahun Tessema et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The EC at site 1, site 2, site 3 and site 4 did not exceed the FDRE-EPA (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) (1000 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), while EC at site 5, site 6, and site 7 exceeded the standards by FDRE-EPA (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, TDS and TSS range from 108.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 to 761.67\u0026thinsp;\u0026plusmn;\u0026thinsp;16.04 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 16\u0026thinsp;\u0026plusmn;\u0026thinsp;0 to 140\u0026thinsp;\u0026plusmn;\u0026thinsp;0 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The maximum levels of TDS and TSS, were documented at sampling sites 5 while their lowest values were obtained at the sampling site 1. The highest concentration of TDS, or TSS at site 5 may be caused by human activities that introduce dissolved and suspended solids into the system of rivers, such as the Addis Ababa City abattoir wastes, home and sewer wastewaters that contain salts and detergents, canals, surface drainage for irrigated plants, metal industry, and garages that transport soil and agrochemicals. In contrast to previous research findings, the TDS concentration in Little Akaki River water samples was greater than that of the result of the study reported in Little Akaki River (0.83 to 915.57 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Deshu Mamo et al., 2021), Kebena (40.43\u0026ndash;640 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Salami, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Addis Ababa City, Ethiopia. According to the South African Department of Water Affairs and Forestry (DWAF, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), TSS levels in irrigation water samples that are too high could have negative effects such as surface crust formation, which hinders water percolation into the soil and affects soil aeration, suspended particles covering plant leaves, and reduced photosynthetic activity of plants. The TDS values site 1 and site 2 did not exceed in the standard limit of domestic uses 0-450 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (DWAF, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) while site 3 to site 7 exceeded the standard limit.\u003c/p\u003e \u003cp\u003eThe sample location (site 3) had the highest NH\u003csub\u003e3\u003c/sub\u003e-N concentrations and possibly due to point-source nitrogen inputs like agricultural Addis Ababa Abattoir and nonpoint sources like chemical fertilizers, sewage discharge, biological degradation of manure. In contrast to previous research, the concentration of NO\u003csub\u003e3\u003c/sub\u003e\u0026ndash;N of this study result was lower than the study made by Deshu Mamo et al. (2021) 0.49\u0026ndash;72.86 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The highest TP and SRP might be due to discharges from industrial facilities, decomposition of organic matter, runoff from agricultural fields. The concentration of nitrogen and phosphorus in industrial effluent is primarily influenced by the management of raw materials, the quantity of spent yeast present in the effluent, and the volume of phosphorus-containing chemicals utilized in the clean-in-place systems (Tesfalem Fikresilasie, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this research, there was an observed increase in the trends of various physicochemical parameters, including EC, TDS, TSS, turbidity, TP, and SRP. The study observed a decline in dissolved oxygen and percent of saturation from the upstream to the downstream locations. However, no uniform pattern was identified for NH\u003csub\u003e3\u003c/sub\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u0026ndash;N, or pH levels in either the upstream or downstream sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Relationship Between Physicochemical Parameters and Macroinvertebrate Taxa\u003c/h2\u003e \u003cp\u003eEnvironmental variables strongly shaped the macroinvertebrate community. The DO was among the environmental variables that affect the macroinvertebrate composition of the streams in Ethiopia (Solomon Tesfay et al., 2019). RDA demonstrated a variation in pollutant levels, indicating that upstream regions exhibited significantly superior water quality compared to downstream locations, which displayed diminished water quality. Furthermore, there was a positive correlation observed with macroinvertebrate taxa that are less able to tolerate pollution (e.g. Caenidae, Baetidae, Coenagrionidae, and Notonectidae) and DO was noted at the upstream site (site 1). Their autecological preference for high oxygen levels and low levels of organic pollution is the cause of this. This is in line with the result of the study made in the upper and middle Awash River by Tesfaye Muluye et al. (2024). In this study, macroinvertebrate taxa from the families Baetidae, Caenidae, Coenagrionidae, and Notonectidae exhibited a favorable association with dissolved oxygen levels and were negatively related to nutrients, conductivity, and TSS. A similar, study was reported by Tesfaye Muluye et al. (2024), made in upper and middle Awash River, Ethiopia In sites four and six, the highly tolerant taxa Chironomidae and Oligochaeta were prevalent, exhibiting significant correlations with total phosphorus (TP) and conductivity. This indicates that Dipteran and Oligochaeta taxa are capable of withstanding polluted aquatic environments (Bere \u0026amp; Nyamupingidza, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings align with previous research on water pollution. This study is similar to the result of the study made in upper and middle Awash River by (Geda Kebede et al., 2020); Tesfaye Muluye et al. (2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics\u003c/h2\u003e \u003cp\u003eAnalysis of the relationship between environmental variables and macroinvertebrate metrics indicated a significant correlation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The percentage of Ephemeroptera and EOT exhibited a significant positive correlation with dissolved oxygen levels, which can be ascribed to their ecological preference for elevated oxygen concentrations and reduced pollution from organic sources. This study is in line with the result of the study made in the upper and middle Awash River, Ethiopia (Tesfaye Muluye et al., 2024). The study indicates a preference for tolerant taxa within the Chironomidae and Oligochaeta groups, which consequently shapes the observed distribution pattern of sensitivity and tolerance to the depletion of dissolved oxygen. The percentage Ephemeroptera and EOT showed a stronger negative correlation to electrical conductivity; this might result from the difference in their tolerance values. The importance of temperature in monitoring aquatic ecosystems has been validated by its strong positive correlation coefficient (r\u0026thinsp;\u0026ge;\u0026thinsp;0.6) with metrics that increase with disturbances, such as % Diptera, % Chironomidae, % Oligochaeta, % collector gatherer, and % burrower. A similar result was also indicated in the study by Solomon Akalu et al. (2011) on the Greater Akaki River, Ethiopia.\u003c/p\u003e \u003cp\u003eElectrical conductivity had a strong positive relationship with the percentages of Oligochaeta and Chironomidae. According to Odume et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), certain Diptera exhibit a range of specialized adaptations that allow them to thrive in severely disturbed habitats. For instance, members of the Chironomidae family have a unique molecule called hemoglobin that helps their bodies absorb oxygen more readily when they are in hypoxic environments. The Syrphidae family, however, can absorb ambient oxygen in contaminated environments because of their extending respiratory tubes. This observation aligns with previous research that has associated the reduction of mayfly taxa, which are susceptible to current changes, with rising levels of conductivity (Aschalew Lakew \u0026amp; Moog, 2015; Extence et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Sites 3 to 7 are identified as areas of degradation in the downstream locations, where the highly tolerant taxa Chironomidae are prevalent and exhibit significant correlations with total dissolved solids (TDS), ammonia (NH\u003csub\u003e3\u003c/sub\u003e), total phosphorus (TP), and soluble reactive phosphorus (SRP) concentrations.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study assessed the relationship between water quality parameters and macroinvertebrate metrics for ecological biomonitoring in the Little Akaki River. The findings indicate that the water quality and ecological integrity of the river have been significantly degraded due to anthropogenic activities. Significant levels of water contamination and associated ecological degradation were found at impacted locations by assessment of different physicochemical parameters and community of benthic macroinvertebrates metrics. The percentage of Ephemeroptera and the percentage of EOT showed an inverse relationship with temperature. The percentage of dipterans that had a positive relationship was observed in both temperatures. The ET and EOT taxa have high abundance in the upstream of the river and lower downstream. Electrical conductivity had a strong positive relationship with the percentages of Oligochaeta and Chironomidae. The percentage of Ephemeroptera and EOT showed a stronger negative correlation to electrical conductivity. A significant relationship was observed between physicochemical parameters and macroinvertebrate metrics. Develop a systematic monitoring program aimed at evaluating essential physicochemical parameters, including temperature, pH, dissolved oxygen, turbidity, and nutrient concentrations. The collected data will facilitate the identification of trends and potential stressors that may impact macroinvertebrate populations and the overall health of the ecosystem. Further studies are necessary to assess the relationship between water quality parameters and various biotic and abiotic factors and Future research should build on this work as a reference point and prioritize autecological studies of macroinvertebrate taxa at the level of species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e \u003cp\u003eAlachew Adino: conceptualization (equal), formal analysis (equal), methodology (equal), software (equal), writing \u0026ndash; original draft (equal), writing \u0026ndash; review and editing (equal). Seyoum Mengistou: method ology (equal), supervision (equal), validation (equal), visualization (equal).\u003c/p\u003e\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research is funded by the Austrian Development Cooperation (ADC) with grant number (ADC project 0612-00/2022 (AQUAHUB2)).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAlachew Adino: conceptualization (equal), formal analysis (equal), methodology (equal), software (equal), writing \u0026ndash; original draft (equal), writing \u0026ndash; review and editing (equal). Seyoum Mengistou: method ology (equal), supervision (equal), validation (equal), visualization (equal).\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank Aquatic Ecosystem and Environmental Management (AEEM) joint MSc Program of Addis Ababa University, Bahir Dar University, Egerton University and National Fishery and Aquatic Life Research Center (NFALRC), Sebeta in collaboration with Austrian Development Cooperation for funding the research through University of Natural Resources and Life Sciences, (BOKU), Vienna, Austria. The authors thank the Department of Zoological Sciences, Addis Ababa University for field and laboratory support.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed in this study can be found upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbebe Beyene, Taffere Addis, Demeke Kifle, Worku Legesse, Kloos, H., \u0026amp; Ludwig Triest. (2009). Comparative study of diatoms and macroinvertebrates as indicators of severe water pollution: Case study of the Kebena and Akaki rivers in Addis Ababa, Ethiopia. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 381\u0026ndash;392.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbrha Mulu, Tenalem Ayenew, \u0026amp; Shifare Berhe. (2013). Impact of slaughterhouses effluent on water quality of Modjo and Akaki River in Central Ethiopia. \u003cem\u003eInt. J. Sci. 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Water resources and fisheries management in the Ethiopian rift valley lakes. \u003cem\u003eSinet\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), 95\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dissolved oxygen, Little Akaki River, Macroinvertebrate Metrics, Water Quality","lastPublishedDoi":"10.21203/rs.3.rs-8475505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8475505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the relationship between macroinvertebrate metrics and water quality along this river is crucial for assessing the river ecological health. This study aims to evaluate the relationship between macroinvertebrate metrics and water quality parameters along the Little Akaki River. The research was conducted across seven sampling sites in April 2024, employing a multi-habitat sampling methodology. The measurement of physico-chemical parameters (temperature, DO, pH and conductivity) was done in triplicate using HQ40-d multimeter probe. Macroinvertebrates were collected from gravel, sand, mud, vegetation, riffles and pools with a 500 \u0026micro;m D-frame net. Most of the physicochemical parameters showed significant differences between the sampling sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). DO ranged from 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 to 6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A total of 5575 macroinvertebrates were collected. The percentage of Ephemeroptera metrics positively correlated with dissolved oxygen and percent saturation (r\u0026thinsp;=\u0026thinsp;0.677 and 0.598). A plot of RDA analysis separated the macroinvertebrate taxa which showed significant correlation with DO, HQI, SRP, TDS, pH, and TP. The study reveals a significant correlation between macroinvertebrate metrics and water quality parameters providing clear evidence that the health and diversity of macroinvertebrate communities are intricately connected to the ecological conditions present in their aquatic habitats.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Physicochemical Parameters and Macroinvertebrate Metrics for Ecological Biomonitoring in The Little Akaki River, Addis Ababa, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 08:43:05","doi":"10.21203/rs.3.rs-8475505/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a510395f-0f55-4050-98c4-e338ad9618fe","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T22:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 08:43:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8475505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8475505","identity":"rs-8475505","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}