Benthic macroinvertebrates based new biotic score “ETHbios” for assessing ecological conditions of highland streams and rivers in Ethiopia

Limnologica 52 (2015) 11–19

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Benthic macroinvertebrates based new biotic score “ETHbios” for assessing ecological conditions of highland streams and rivers in Ethiopia Aschalew L. a,∗ , Otto Moog b a

Ethiopian Institute of Agricultural Research (EIAR), National Fishery and Aquatic Life Research Centre, P.O. Box 64, Sebeta, Ethiopia University of Natural Resources and Life Sciences, Department of Water, Atmosphere and Environment, Institute of Hydrobiology and Aquatic Ecosystem Management, Max-Emanuel-Strasse 17, A-1180 Vienna, Austria b

a r t i c l e

i n f o

Article history: Received 17 October 2014 Received in revised form 21 February 2015 Accepted 27 February 2015 Available online 11 March 2015 Keywords: Benthic macroinvertebrates Biotic score River quality assessment Organic pollution Highland rivers

a b s t r a c t The study describes the development of a macroinvertebrate based biotic score system (ETHbios) for assessing the ecological status of rivers in the Ethiopian highlands. The ETHbios is basically developed on the principle of the BMWP approach (version of the South African Scoring System) but excludes taxa that don’t occur in Ethiopia and includes some of Ethiopian fauna. Macroinvertebrates were collected from 104 sites distributed in a total area about 98,000 square kilometers in the upper Awash, Rift-Valley, Wabi-Shebele and Genale basins. A sensitivity score was assigned to 59 taxa based on guide score, taxon distribution across river quality classes, reference score and autecological knowledge. To define the ranges of the five river quality classes (high, good, moderate, poor and bad), the ETHbios values of sites were correlated with the corresponding ecological status of the sites derived by the Ethiopian Multimetric Index. The validation procedure was done by comparing the ETHbios with selected environmental parameters (conductivity, dissolved oxygen, biological oxygen demand and total phosphorus); the analysis showed significantly high correlations (r > 0.5; p < 0.05). ETHbios can be considered as rapid, inexpensive but scientifically sound monitoring method that can be used to evaluate the ecological conditions of running waters in the highlands of Ethiopia. © 2015 Elsevier GmbH. All rights reserved.

Introduction Ethiopia, like many other developing countries, is experiencing increasing problems due to deterioration in river quality resulting adverse effects on human health, increased water treatment costs and reduction in yields from river fisheries (Zinabu and Elias, 1989; Mereta et al., 2013; Aschalew, 2014). Until present conventional physical–chemical methods are used in some streams for monitoring the river water quality. However, the effects of a variety of stressors (e.g. water and sand abstraction, catchment and river bank degradation, reservoir flushing, diversion, etc.) cannot be detected and water management decisions may suffer under too little knowledge of environmental consequences with this method. In addition, the lack of robust surface water quality monitoring method results a greater level of uncertainty when

∗ Corresponding author. Tel.: +251 913033838; fax: +251 113380657. E-mail addresses: [email protected] (A. L.), [email protected] (O. Moog). http://dx.doi.org/10.1016/j.limno.2015.02.002 0075-9511/© 2015 Elsevier GmbH. All rights reserved.

developing management strategies for water resources and infrastructure development. This can lead to wasted investment and a failure to implement effective pollution control measures. In the past years, however, interest has been shown by environmental and water quality monitoring institutions in the application of biological water quality monitoring methods using bio-indicators, which tend to be lower cost and more effective than physical–chemical methods (Mereta et al., 2013; Aschalew, 2014). To implement this plea a sound scientifically proven method to evaluate the ecological status of rivers has been developed recently, the Ethiopian Multimetric Index (Aschalew, 2014; Aschalew and Moog, 2015). Although the development of the multimetric index followed general rules (see Hering et al., 2006), the application of this method requires time and well trained personnel. Consequently this study presents a new biotic score as less sophisticated method than multimetric index for assessing Ethiopian highland streams and rivers. Biological organisms in a water body are natural monitors of environmental quality and can reveal the the effects of sporadic as well as cummulative pollution and habitat degradation (Barbour

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et al., 1999). Among resident aquatic biota benthic macroinvertebrates are often the taxa group of choice for biomonitoring in streams and rivers (Rosenberg and Resh, 1993). Macroinvertebrates present several advantages compared to other groups of organisms. They are ubiquitous and abundant even in small streams that make biomonitoring to be carried out in almost any type of streams and rivers. They are good indicators of several anthropogenic pressures such as water pollution (Armitage et al., 1983) and hydro-morphological alterations (Negishi et al., 2002). They consist thousands of species that originate from different systematic categories with different environmental needs – thus they are differentially sensitive to pollutants of various types. The sampling and processing methods are well-developed and taxonomic keys are available to identify most benthic macroinvertebrates. The quality assessment of surface water based on biological methods started about 165 years ago when Kolenati (1848), Hassal (1850) and Cohn (1853) observed that organisms that occur in polluted water are different from organisms that occur in clean water. Since that time hundreds of methods for biological river quality assessment have been developed (Birk et al., 2012). The first indices were nearly simultaneously developed in the US and Europe around 1950 (Beck, 1954; Pantle and Buck, 1955). The Trent Biotic Index (Woodiwiss, 1964) is seen as the origin of most modern biotic indices and scores. The biotic scores have a long history of development in Europe (Armitage et al., 1983; Hering et al., 2003, 2004; Birk et al., 2012) and North America (Hilsenhoff, 1988). Further it is adapted to Asian countries (Sharma and Moog, 1996; Ofenboeck et al., 2010) and Africa (Chutter, 1972, 1994; Ollis et al., 2006; Elias et al., 2014). In Africa the British BMWP/ASPT system was modified and the South African Scoring System (SASS) was developed (Chutter, 1994, 1995, 1998), tested and adapted over several years as a macroinvertebrate based biotic index for river assessments. The SASS was intended to be a rapid, inexpensive but scientifically sound method for the detection of water quality degradation or for revealing trends in water quality change over time. Today the SASS method is the standard for the rapid bioassessment of rivers in South Africa (Dallas, 1997; Dickens and Graham, 2002) and it forms an integral component of the River Health Program (Dickens and Graham, 2002). More recently, the SASS approach was adapted on local freshwater macroinvertebrate taxa to be used in other regions of southern Africa, including Zimbabwe (Phiri, 2000), Namibia (Namibian Scoring System NASS; Palmer and Taylor, 2004) and Botswana (Okavango Assessment System OKAS; Dallas, 2009). While the value of the score system in determining the ecological river status has been well studied in the southern part of African countries (see above), only few studies have been undergone in the eastern and northern Africa. Based on two master theses, Obubu (2010) for Uganda and Koblinger and Trauner (2013) for Burkina Faso, the fact that the SASS approach basically work in other parts of the African continent was confirmed. Scores are allocated to specific indicator organisms at a particular taxonomic level based on specific requirements in terms of physical and chemical conditions (Armitage et al., 1983; Hilsenhoff, 1988). However these scores may require adaptation for application to other regions, not only because some macroinvertebrates may be absent from the respective area and replaced by other taxa, but also because some taxa may exhibit different pollution tolerances from region to region (Buss and Salles, 2006). The advantage of the biotic score is that only qualitative sampling is required without the need to count abundances per taxon (Armitage et al., 1983) and the results can directly be translated to water quality classes that avail the information more accessible to managers and decision makers (Ofenboeck et al., 2010). Moreover the scoring system is widely used as they allow a large number of sites to be examined

in relatively low cost and short time (Rosenberg and Resh, 1993) and require moderately trained experts. The research question in this study is whether benthic macroinvertebrate diversity-expressed in a low taxonomic resolution (mostly family level), can be related to river integrity assessment in highlands of Ethiopia. We also wanted to test that benthic macroinvertebrates and environmental data collected from wide geographic area could be used to develop simple biotic index for river monitoring in Ethiopia. Materials and methods Study area The present study was conducted in the upper section of Awash, Wabe-shebele, Genale and Rift Valley basins, lying between 6◦ 57 N and 9◦ 05 N latitude and 38◦ 07 E and 40◦ 06 E longitudes (Fig. 1). Most sampling sites are located in Ethiopian montane grasslandwoodlands ecoregion with altitude ranging from 1900 to 2500 m above sea level. Rainfall distribution in the study area is bimodal, short rainy season from February to April while the main rains occur from June to September (NMA, 2012). The study area is characterized by scarce coverage of natural vegetation. State protected forests in the upper Awash (e.g. Chilimo, Suba forest) and central rift valley (e.g. Wondogenet forest) are covered by indigenous natural forest such as Juniperus procera, Podocarpus falcatus, Prunus africanum, Olea europaea, and Hagenia abyssinica. The head waters of Genale and Wabe-shebele basins are dominated by Erica aroborea, Erica trimera, Alchemilla haumannii and Alchemilla ellenbeckii at higher altitudes, and J. procera, Hagenia abyssinica and different grass species in the protected escarpments (e.g. Bale National Park and Adaba forest). In unprotected areas of all basins, the natural vegetation was cleared and replaced by farmland, grazing land and/or Eucalyptus plantation. Sampling sites were distributed strategically in the national park, protected forest areas, rural–agricultural areas and urbanindustrial sites to represent different stress gradients. The major threats in rural areas are removal of riparian vegetation, nutrient loading from farmlands, flushing of reservoirs with a consecutive siltation of the river bed, sand excavation, intensive livestock grazing and watering, water extraction and diverse in stream activities. In urban areas the major pollutants comprise diffuse and punctual loads of untreated domestic and industrial wastes. Sample collection and laboratory analysis Water quality parameters including temperature, pH, dissolved oxygen and conductivity were measured in situ using a portable WTW multi-parameter probe (Model HQ40D, HACH Instruments). Two liters of water were collected from each investigation site and stored in ice box until return to JIJE labo P.L.C. and National Fishery Research center for laboratory analysis. In the laboratory, total phosphorus (TP) and biochemical oxygen demand (BOD5 ) were measured following the standard methodology described in APHA (1997). Macroinvertebrates were collected using standard square frame (side 25 cm) hand net with mesh size of 500 ␮m following multi-habitat approach (Barbour et al., 1999; Moog, 2007). In the laboratory, each sample was passed through a set of sieves (5000, 3000, 2000, 1000 and 500 ␮m mesh size) to separate size class of macroinvertebrate groups and dilutes the formalin under tap water. Identification was performed to the lowest possible taxonomic level based on the available keys. Coleoptera, Hemiptera, Molluscs, Trichoptera and Ephemeroptera were identified by taxonomic specialists residing in Vienna, Austria (see acknowledgments).

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Fig. 1. Spatial distribution of study sites in four drainage basins of Ethiopia.

Scoring of indicator taxa The sensitivity of taxa to organic pollution, siltation and some hydro-morphological degradation were scored based on a 10-point system similar to Biological Monitoring Working Party (BMWP). Taxa with high scores indicate high sensitivity to stressors and low scores indicate high tolerance to stressors. Since the sensitivity of taxon varies from region to region based on natural attributes and intensity of anthropogenic degradation, scores are allocated to the regional condition. The procedure for assigning scores was accomplished in two major steps:

Step 1. Calculation of a ‘guide score’ based on Ofenboeck et al. (2010) for five class system.

Step 2. Validation and plausibility check of results by experts’ judgment. The ‘guide score’ is calculated on the basis of the distribution and frequency of taxa among the river quality classes based on applications of the Ethiopian multimetric index (Aschalew, 2014; Aschalew and Moog, 2015) calculated from the same data set as follows: Guide score =

SI SII SIII SIV SV ∗ 10 + ∗ 7.75 + ∗ 5.5 + ∗ 2.25 + Stot Stot Stot Stot Stot

where SI , SII , SIII , SIV , SV are the total numbers of sites in each of the respective river quality classes (multimetric river quality class) where the taxon was recorded and Stot is the total number of sites where the taxon was found.

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The final values were assigned on the basis of experts’ consensus (step 2) based on: • Numerical proportioning applied to taxon occurrences and abundances along river quality classes. • Reference scores of taxon obtained from related scoring systems mainly SASS and HKHbios score (Chutter, 1994; Ofenboeck et al., 2010). • Autecological knowledge of benthic invertebrate taxon. • Associating taxon occurrences or abundances with water quality data. Calculation of biotic score (ETHbios) ETHbios was calculated as the sum of sensitivity score of each taxon present in a sample as follows: ETHbios =

n 

Scorei

i=1

The Average Score Per Taxon (ASPT) was calculated as ETHbios divided by total number of taxa considered in the calculation.

n

ASPT =

i=1

Scorei

n

where Scorei is the score of taxon i and n is the number of taxa considered in the calculation. Threshold values for ETHbios score The definition of class boundaries was based on the distribution of ETHbios values across multimetric river quality classes derived from the same dataset. If there is no overlap between the 25th percentile of the upper class and the 75th percentile of the lower class, the mean value of the 25th percentile of the upper class and the 75th percentile of the lower class was defined as class boundary. However, if there is an overlap between the 25th percentile of the upper class and the 75th percentile of the lower class, the mean value of the upper class was considered as the class boundary (see Fig. 3). Frequency scatter plots in combination with Spearman rank correlation were applied to correlate the ETHbios score systems against selected environmental parameters which were known to indicate stream quality deterioration. Results

not represented in poor and bad quality class (Fig. 2(a and b)). The expert opinion on autecological demands of Psephenidae is in agreement with this finding (Shepard and Lee, 2007). In addition earlier studies awarded high sensitive score (e.g. 8 and 10 in HKHbios and SASS respectively). Hence the final score was set to be 8. Example 3 (Family Baetidae with 2 species). To use Baetidae as indicator it was necessary to split them into three categories based on their genus-specific species richness: Baetidae with 1 species; Baetidae with 2 species and Baetidae with more than 2 species. The guide score for Baetidae with 2 species was 6.3. The distribution showed the highest percentage of representation in good river quality class (40%) and moderate quality class (44%) and low representations in poor river quality class (16%). These findings agreed well with the knowledge about the ecological requirements of this family (Chutter, 1994; Moog et al., 1997; Schmidt-Kloiber and Hering, 2012). The score obtained from earlier scoring system also strongly support the result obtained in the present study. Therefore, the final score for this taxon was assigned 6. Example 4 (Red Chironomidae). Basically the various species among the family Chironomidae have a wider ecological preference and tolerance level. The taxon Red Chironomidae is well known in the public and because of their red color they are called ‘bloodworms’ among naturalists, fish farmers and fishermen. From the taxonomic point of view it is rather impossible to give the Red Chironomidae a scientific name because there are many species within the sub-families and tribes Orthocladiinae, Tanypodinae, Tanytarsini and Chironominae that have a hemoglobin-like substance in their body makes them appear from reddish to deep blood-red. The hemoglobin-like substance stores oxygen, which allows them to live under nearly anaerobic conditions. But, as this ‘functional group’ contains many species, they basically have a wide spectrum of habitats and do not only live in areas with high pollution. The bar diagram in Fig. 2(4a) and a guide score of 5.6 clearly confirms this fact in Ethiopian rivers. However, the abundances in different quality classes showed remarkably high abundances in polluted sites (Fig. 2(4b)) as it was observed in previous studies (Chutter, 1994; Ofenboeck et al., 2010). Therefore, the final score assigned was 1. This corresponds with the score of the genus Chironomus (also called bloodworm) that can be easily differentiated due to its tubular gills near the end of the abdomen. Finally we included Red Chironomidae (easily identified by color) and Chironomus (identified with basic taxonomic knowledge) in Table 1 following experts comment.

Taxa scoring Four examples are given below to explain the procedure we followed to assign the sensitivity score. Example 1 (Family Perlidae). The guide score calculated for family Perlidae was 9.5. The distribution among river quality classes showed that the highest percentage of records was under high river quality (88%), followed by good river quality (12%). The average abundances for different quality classes also showed remarkably high abundance in the high quality class (Fig. 2(1a and b)). These findings are in agreement with the knowledge about the ecological requirements of this family (Graf et al., 1995; Schmidt-Kloiber and Hering, 2012). Moreover from other similar scoring systems, the score for Perlidae is high. Therefore, the guide score was rounded to whole number and the final score was assigned 10. Example 2 (Family Psephenidae). The calculated guide score for Psephenidae was 8.12. The distribution among river quality classes showed the highest percentage of representation under high river quality (42%) and good river quality class (48%). Only 10% of Psephenidae was represented in moderate river quality and it was

With this procedure a total of 59 taxa were assigned a sensitivity score. Most scores were assigned to family level identification as in BMWP, SASS and HKHbios. However some taxa were scored either at genus/species level to increase the discrimination efficiency among water quality classes or at order level due to taxonomic difficulties (Table 1). Class boundaries for river quality classes The threshold values were established using a five-class scheme based on the distribution of ETHbios under multimetric river quality classes. The result from box and whisker plot revealed that interquartile ranges of total score showed no overlap between high/good, good/moderate and poor/bad river quality classes. However due to high overlaps in the whisker among quality classes, the average of the 25th percentile of the upper class and the 75th percentile of the lower class was used to set threshold values (Fig. 3a). Accordingly, threshold values between high/good, good/moderate and poor/bad river quality classes were calculated as 111, 67.1 and 12 respectively. On the other hand, high interquartile overlap was

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Fig. 2. Percentage of occurrence (a) and average abundance per square meter area (b) amongst river quality class (multimetric based river quality class). Perlidae (1), Psephnidae (2), Baetidae with 2 sp. (3) and Red Chironmidae (4).

Fig. 3. Box and whisker plots of ETHbios values (a) and the corresponding ASPT values (b) versus river quality classes (multimetric index).

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Table 1 Newly adapted ETHbios scoring list. Common name

Taxon

Score

Stone flies Caddis flies Beetles Mayflies

Perlidae (Neoperla sp.) Lepidostomatidae, Philopotamidae Scirtidae Baetidae > 2 spp., Acanthiop sp., Heptageniidae (Afronurus sp.), Leptophlebiidae Hydropsychidae > 2 spp. Tricorythidae Leptoceridae, Ecnomidae Psephenidae, Stenelmis sp., Microdinodes sp. Hydrocarina Potamidae Aeshnidae, Lestidae Elmidae Tipulidae

10 10 10 9

Caddis flies Mayflies Caddis flies Beetles Water mites Crabs Dragonflies Beetles Flies

9 8 8 8 8 7 7 7 7

Pisidium sp. Limpets

7 6

Baetidae with 2 sp., Caenidae Hydropsychidae with 2 sp. Gomphidae Naucoridae Tabanidae Hydropsychidae with 1sp. Coenagrionidae, Libellulidae Mesoveliidae, Veliidae, Gerridae Hydrophilidae, Dytiscidae, Gyrinidae, Haliplidae Ceratopogonidae excl. Bezzia-Gr., Baetidae with 1 sp.

6 6 6 6 6 5 5 5 5

Bugs

Corixidae, Pleidae Belostomatidae, Notonectidae, Nepidae

4 3

Discussion

Leeches Snails

Hirudinea Physidae, Bulimus sp.

3 3

Bezzia-group, Musidae, Chironomidae with predominantly Tanytarsini and Tanypodinae Psychodidae, Ephydridae, Culicidae, Red Chironomidae, Chironomus sp., Syrphidae

3 2

Oligochaeta

1

ETHbios was developed to provide a simple biomonitoring tool for assessing ecological status of Ethiopian highland streams, specifically for woodland and grassland ecoregions located above 1800 m a.s.l. The score for different taxa was developed from a large dataset (104 sites) exposed to a wide range of anthropogenic influences. The scores were assigned not only in response to organic pollution but also to effects of land use changes including eutrophication, siltation and sand excavation. Indicator taxa were scored according to their occurrence across a river quality class (as obtained from the multimetric index). Scores were assigned to family and genus/species-level, where less tolerant taxa to human degradation acquire high score while the most resistant ones received low score. A 10-point scoring system following BMWP (Armitage et al., 1983) and NEPBIOS (Sharma and Moog, 1996) was applied but a flexible scoring system was used for Baetidae and Hydropsychidae diversity as described in SASS (Chutter, 1994). This is mainly because Baetidae and Hydropsychidae were the most diverse and abundant taxa in the region (Harrison and Hynes, 1988; Malicky and Graf, 2012) and cover wide pollution gradients (Hur et al., 2000; Beketov, 2004). This approach increased the discrimination efficiency of Baetidae and Hydropsychidae among river quality classes and supported expert ecological judgment as well. Taxa that were rarely observed in the samples were not scored but if one of these taxa showed clear preference for a given river quality class, the score was adopted from Chutter (1994) assuming that the eco-geographic location and the diversity in South Africa is comparable to regions in Ethiopia (Harrison and Hynes, 1988). Out of the total 104 taxa recorded from all sites, scores were assigned only to 59 taxa which showed clear water quality preferences. ETHbios was meaningful when assessed together with various factors that may influence the scores. The results from correlation analysis showed that a significant correlation (p > 0.05; Spearman rank correlation) was observed between ETHbios and most environmental parameters responsible for structuring benthic macroinvertebrate communities. ASPT-values showed strong correlation with dissolved oxygen (r2 = 0.6), conductivity (r2 = 0.56),

Snails Mayflies Caddis flies Dragonflies Bugs Flies Caddis flies Dragonflies Bugs Beetles Flies Mayflies

Flies

Worms

5 4

1

observed between moderate and good river quality classes and we considered the mean value of the moderate class (value = 44) as a threshold. The corresponding ASPT value on the other hand showed no interquartile overlap among river quality classes. Therefore a uniform rule, the average value of the 25th percentile of the upper class and 75th percentile of the lower class, was applied (Fig. 3b). Previous studies showed that total biotic scores and average scores per taxon are associated with different benefits and drawbacks, and therefore their use in a complementary fashion was suggested (Armitage et al., 1983; Chutter, 1994). With this sense, previously established threshold values were evaluated by combining the scores in scatter plot following the procedure described in Dallas (2007). The result showed that five clearly distinguished biological bands were identified (Fig. 4). Therefore, combined threshold values were suggested instead of separately used total score or ASPT score values. As indicated in Fig. 4, ‘high’ river quality class was clearly separated from the closest ‘good’ river quality class. Thus ETHbios threshold values between ‘high’ and ‘good’ obtained from box and whisker plot were revised based on expert consensus. Accordingly total score and ASPT values were set at 115 and 6.5 respectively, and a slight overlap between these two classes was observed. ETHbios and environmental parameters Those parameters identified as ‘best’ in CCA analysis for structuring benthic macroinvertebrate community also gave the highest

Fig. 4. Scatter plot showing ASPT as a function of ETHbios score, multimetric based river quality class (high, good, moderate, poor and bad) was indicated by symbols.

correlation with the ETHbios scores. A strong correlation was shown between ASPT values and total phosphorus (r2 = 0.778); BOD5 (r2 = 0.624); DO (r2 = 0.60); Conductivity (r2 = 0.556); % of urban (r2 = 0.68) and % forest cover (r2 = 0.41). The correlation of total score with selected environmental parameters was also strong (Fig. 5), and % of forest showed comparatively higher correlation with total score (r2 = 0.596) than ASPT (r2 = 0.46).

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Fig. 5. Correlation between pertinent physicochemical parameters with ETHbios (total score and the corresponding ASPT). Actual values of conductivity, dissolved oxygen (DO), biological oxygen demand (BOD5 ) and total phosphorus (TP) were log transformed.

BOD5 (r2 = 0.624), total phosphorus (r2 = 0.778) and % urban (r2 = 0.68) which may indicate better performance of the index to organic pollution (Fig. 5). This result agreed well with Ofenboeck (2010) who indicated strong correlation between HKHbios (Scoring system for Hindu Kush Himalayan regions) and ortho-phosphate

(r2 = 0.66). Total score also showed strong correlation with % forest (r2 = 0.596) which may indicate the importance of this index in relation with catchment degradation leading to reduced biodiversity (Chutter, 1998; Dickens and Graham, 2002; Dallas, 2007) (Fig. 6).

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Fig. 6. Correlation analysis between ETHbios scores and forest cover. Actual value of forest cover was Arcsin square root transformed.

Table 2 Suggested ETHbios threshold values. River quality class

Color

ETHbios score

ASPT-ETHbios

Interpretation

1 2 3 4 5

Blue Green Yellow Orange Red

>115 65–114 45–64 12–44 <12

>6.5 5.01–6.4 4–5 2.4–3.99 <2.4

High water quality; low level of degradation Good water quality; slight ecological degradation Moderate water quality; significant ecological disturbance Poor water quality; major degradation Bad water quality; heavily degraded

Compared to ASPT score, the total score showed high variability between river quality classes and this could be explained in terms of benthic macroinvertebrate site specific diversity. For instance, in some high river quality class where the streams are dominated by boulders and high flow velocity, less biotic diversity were obtained and consequently a lower total score was recorded. Such natural factors affecting benthic invertebrates diversity is reported by previous authors including longitudinal zonation of river abiotic conditions (Vannote et al., 1980), discharge (Jungwirth et al., 2003), substrate suitability and heterogeneity (Resh, 1995; Moog, 2007). On the other hand, ASPT values were less influenced by such factors because the few taxa present showed appropriate sensitivity and hence represents the river quality. This finding agreed well with Armitage et al. (1983) who demonstrated less sensitivity of ASPT than the BMWP index. Chutter (1994) also pointed out that ASPT is a more reliable measure of the health of good quality rivers than the SASS score. Summarizing the evaluation procedures, there are authors who favor the total scores or the ASPT or use a combined technique. On the basis of the observed strong level of agreement (Cohen’s kappa,  = 0.84) with multimetric based river quality classes developed from the same data set (Aschalew, 2014; Aschalew and Moog, 2015), we recommend a combined ETHbios system. Moreover, threshold values suggested for Ethiopian highland streams (Table 2) are comparable with reference biotic score systems; BMWP (Armitage et al., 1983), SASS (Chutter, 1994), NEPBIOS (Sharma and Moog, 1996), and HKHbios (Ofenboeck et al., 2010). It is clear that a number of biotic scores based on benthic macroinvertebrates have been developed and are successfully used for the bioassessment of rivers in different parts of the world (e.g. Birk et al., 2012; Elias et al., 2014). Biotic scores like ETHbios are not sophisticated in terms of sampling procedure, sample processing and data analysis/interpretation. Despite their wider applicability and simplicity in biomonitoring, biotic score systems must be carefully interpreted using supplementary data (Resh, 1995). Some of

the limitations of the system include the restricted applicability of the index to a particular stressor type usually to organic degradable components of running waters (Chutter, 1972; Armitage et al., 1983). Therefore, when using ETHbios, the dominant stressor type in the area should be properly defined to provide more precise information on the status of the water quality. Nevertheless, it is the aim of this study to provide easily applicable biomonitoring method that could be accepted and applied in Ethiopia. ETHbios offers cost effective and reliable information for monitoring highland streams of Ethiopia, provided that trained people should be able to identify invertebrates at the lowest family level. Lower than family level identification was reported to be costly and time consuming (Furse et al., 1984; Schmidt-Kloiber and Nijboer, 2004) and require more specialized knowledge and techniques. ETHbios uses a large number of family level indicators that simplify taxonomic complications. However, for specific taxa groups such as Baetidae and Hydropsychidae, family level identification was not sufficient for introducing effective ETHbios and thus a higher taxonomic resolution (genus–species level) is required. Anyhow, the application of this request in praxis is rather simple, as it is sufficient to note if there are different taxa among a family, but not to give them a scientific name.

Concluding remarks The use of biotic score system for the assessment Ethiopian stream requires adaptation of sensitivity score to suit local condition. Since this index considers taxa richness and taxon’s specific sensitivity to pollution, the method may be more effective in dry months of the year (November to February) than rainy months. ETHbios could be used to detect sites suffering from point and diffused source of pollution and it provides a degree of quantification of the impact which can be applied by different authorities for monitoring ecological conditions of running waters.

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Acknowledgments We would like to thank Austrian Partnership Program in Higher Education and Research for Development (APPEAR) for financing this study. We are thankful to EIAR-National Fishery and Aquatic Life Research Center especially Dr. Adamneh Dagne and Ato Yared Tigabu for logistic and financial support during field work. We also thank Mr. Siltanu and Mr. Alemayehu for field support and Dr. Zenebe Tadesse for editorial comments. Special thanks to taxonomic specialists from Vienna, Austria: Dr. Wolfram Graf (Plecoptera, Trichoptera), Dr. Manfred Jäch (Coleoptera), Dr. Herbert Zettel (Hetroptera) and Mag. Alexander Reischütz (Mollusca) for their help. References APHA, 1997. Standard Methods for the Examination of Water and Wastewater, 19th ed. American Public Health Association, Washington DC, USA. Armitage, P.D., Moss, D., Wright, J.F., Furse, M.T., 1983. 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