INTRODUCTION

Surface freshwater resources are increasingly being subjected to pollution and a wide range of pollutants such as nutrients, biocides, pathogenic microorganisms, industrial and household chemicals, metals and pharmaceutical products can be found fresh waters (Bedoya et al., 2009; Ertaş et al., 2020; Qadri and Faiq, 2020; Sukatar et al., 2020). A comprehensive range of legislation has been established in Europe for conservation of freshwaters. Water Framework Directive (WFD), which represents the single most important piece of EU legislation relating to the quality of fresh and coastal waters, aims to attain good ecological status (WFD, 2000).

The water quality of lotic ecosystems should be assessed by the use of physical, chemical and biological parameters (Atique et al., 2019). The use of traditional methods in running waters, by evaluating the physical and chemical parameters, reflect instantaneous situation and insufficient because the obtained data provides information restricted to the sampling time (Keck et al., 2017). So, biomonitoring techniques using bioindicators were developed. The use of benthic macroinvertebrates in assessing the water quality of lotic ecosystems has gained much recognition among biologists and there have been many different biotic indices for such assessments (Birk et al., 2012; Chang et al., 2014). In Turkey the assessment of water quality is based mainly on physicochemical variables. An ecosystem analysis using benthic macroinvertebrates together with physical and chemical variables has recently been conducted in Turkey (Girgin and Kazancı, 1994; Kazancı and Girgin, 2001; Kalyoncu et al., 2008; Zeybek et al., 2014; Yorulmaz et al., 2015; Duran, 2011; Zeybek, 2017; Özbek et al., 2018; Akay and Dalkıran, 2019; Ertaş et al., 2020; Yorulmaz and Ertaş, 2021; Ertaş and Yorulmaz, 2021). However, biotic indices were developed for countries or regions and their effective application to Turkish ecosystems requires significant research. Most of the biological approaches are particular to specific geographic regions. Various countries use various indices with different levels of identification of organisms, and different assumptions of final interpretation of results (Korycińska and Królak, 2006). The Saprobe indices in Germany, Biological Monitoring Working Party- BMWP and Average Score Per Taxon-ASPT in England, Belgian Biotic Index-BBI in Belgium, seem to give the most reliable results specific to geographic regions. The Turkish-BMWP biotic index (TR-BMWP), the first assessment method using benthic invertebrates, has recently been developed (Kazanci et al., 2016).

Benthic macroinvertebrates response to the change of water quality and have been widely used (Hering et al., 2006; Yorulmaz et al., 2015; Gültekin et al., 2017). Biological composition of the ecosystem is an important component of determination of water quality (Graf et al., 1995; Custodio and Penaloza, 2019; Tirsch and Male, 1984; Sanders et al., 1983; Ward and Loftis, 1986).

The industrial development, intensive agricultural activities and rapidly increasing population in the Gediz River Basin cause pollution on the surface waters and deterioration of the water quality (Şenol et al., 2016). Besides, the pollution situation and the ecological fauna of the upper basin of Gediz River have not been researched yet. With this respect, it is aimed to determine the water quality of Ilke Stream, located in upper basin of Gediz River, by using benthic macroinvertebrates and physicochemical parameters. The other objective of this study is to compare the results of water quality indices and to determine the most adequate estimate of water quality in accordance with physicochemical characteristics of the examined watercourse.

MATERIALS AND METHODS

Gediz River Basin is located between 38°04′39°13′ N with 26°42′29°45′ E. Ilke Stream is one of the most important streams of Gediz River Basin which has approximately 17 500 km2 drainage area and 1.95 billion m3 surface water potential. Ilke Stream is fed by the Gümele and Alaağaç creeks and reaches the Gediz River (Fig. 1). This study was carried out between January 2019 and February 2020. Six sampling points were chosen and samplings were carried out monthly. Sampling points were chosen according to criteria for selecting operational monitoring sites given in WFD Annex V 1.3.2. (WFD, 2000).

Fig. 1.
figure 1

Sampling area and sampling points.

Full size image

Benthic macroinvertebrates were sampled monthly, using a classic bottom kick net with 91 × 30 size and 250 µm mesh according to the literature (AQEM Consortium, 2002). Collected organisms were immediately fixed in formaldehyde (4%) in the field and then transferred to 70% ethyl alcohol. The benthic macroinvertebrates were first sorted out then identified to the lowest possible taxon (species, genus or families) and counted under a stereomicroscope. Simultaneous with macroinvertebrate samplings, water samples were taken and analysed for PO4–P, NH4–N, NO2–N, NO3–N, Cl– and BOD5 parameters, using spectrophotometer. All analyses were done by following the standard methods (APHA, 1998). Water temperature (°C), pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), oxygen saturation (Sat. O2) were measured in the field by portable equipment.

Water quality assessment by physicochemical parameters was done according to Klee (1991). Saprobe Index (SI), several modifications of Biological Monitoring Working Party (BMWP), BMWP Original (BMWP-O), BMWP Spanish (BMWP-S), BMWP Hungarian (BMWP-H), BMWP Polish (BMWP-P), BMWP Czech (BMWP-C), BMWP Greek (BMWP-G) and Average Score Per Taxon (ASPT), ASPT Original (ASPT-O), ASPT Hungarian (ASPT-H), ASPT Czech (ASPT-C), Belgian Biotic Index (BBI), Family Biotic Index (FBI), Percentage of Ephemeroptera, Plecoptera, Trichoptera taxa (EPT-Taxa [%]), Ratio of EPT taxa and Oligochaete (EPT/OL [%]), Simpson’s (SDI), Shannon-Wiener (SWDI) and Margalef (MDI) diversity indices were applied on benthic macroinvertebrate data set by using ASTERICS Software Programme. Similarity analyses were assessed by using Bray- Curtis similarity index (Somerfield, 2008; Yoshioka, 2008). Pearson’s based correlation analysis was undertaken by using SPSS version. Principal component analysis (PCA) was applied to determine the effectiveness of physicochemical parameters.

RESULTS

The mean, standard deviation and range values of physicochemical parameters were given in Table 1.

Table 1. Range, Mean and Standard deviations of physicochemical parameters
Full size table

The results of correlation matrix of physicochemical based on PCA and the biplots, which are the graphical representation of factor loadings in different components (Component 1 and Component 2) are given in Fig. 2. The sampling points function to determine the location of the ordinations. In the current study, the eigenvalues are greater than 1 at the Component 1 and Component 2. According to PCA, 92% variance between components was determined. The PCA 1 has positive loading on temperature, T°C, EC, TDS, BOI5, NH4–N, NO2–N, NO3–N, PO4–P and Cl– and negative loading on pH, DO and Sat. O2. Positive loading on NH4–N, NO2–N, NO3–N and PO4–P have been considered to be related to organic pollution. Thus, components are representing pollution from agricultural and domestic wastewaters.

Fig. 2.
figure 2

Biplots for PCA analysis of physicochemical variables.

Full size image

A total of 33 taxa and 4861 individuals were revealed. The maximum numbers of individuals were collected at sampling point 1, while the minimum numbers of individuals were collected at sampling point 5. Lowest numbers of individuals were collected in autumn while highest numbers of individuals were collected in spring (Fig. 3).

Fig. 3.
figure 3

The total numbers and percent of benthic macroinvertebrates.

Full size image

Ephemeroptera was dominant order at the all sampling points along in four season. Trichoptera was the second dominant order at the sampling points 1, 2, 3 and 4, respectively. Oligochaeta and Diptera are the most dominant benthic macroinvertebrate orders at the sampling point 6 (Fig. 4).

Fig. 4.
figure 4

Distribution of taxa on order base.

Full size image

As a result of the UPGMA analysis, the sampling points 1 and 2 were the most similar to each other according to physicochemical parameters (97%) and benthic macroinvertebrates (93%). The second most similar sampling points to each other was determined between the 2-3 (96%) and 1-3 (95%) according to physicochemical parameters (Fig. 5). Sampling points 2-3 were similar according to benthic macroinvertebrates (88%).

Fig. 5.
figure 5

The similarities of the sampling points (a) Physicochemical parameters, (b) Benthic macroinvertebrates).

Full size image

The average biological quality scores of biotic indices and water quality classes are shown in Table 2.

Table 2 Average score values of indices and water quality classes
Full size table

In this study, the random sample cases (10% select case) were applied on the biotic and diversity indices to verify datasets (Table 3). In this correlation analysis, the significant negative correlation was determined between SI and BMWP-C (r = –0.925, p < 0.01); SI and BMWP-G (r = –0.938, p < 0.01); SI and ASPT-O (r = –0.979, p < 0.01); SI and ASPT-C; SI and BBI (r = –0.964, p < 0.01). On the other hand, the significant positive correlation was determined between SI and FBI (r = 0.949, p < 0.01). Also, significant positive correlations were determined between BMWP-S and BMWP-H (r = 0.980, p < 0.01); BMWP-S and BMWP-C (r = 0.937, p < 0.01). BMWP-P showed significant positive correlation with BMWP-G (r = 0.945, p < 0.01) while BMWP-P showed significant negative correlation with FBI (r = –0.918, p < 0.01). ASPT-O showed significant positive correlation with ASPT-C (r = 0.971, p < 0.01) while ASPT-O showed significant negative correlation with FBI (r = –0.977, p < 0.01). ASPT-H showed significant positive correlation with ASPT-C (r = 0.957, p < 0.01) and BBI (r = 0.978, p < 0.01). ASPT-C showed significant positive correlation with BBI (r = 0.985, p < 0.01). SWDI showed significant positive correlation with SDI (r = 0.985, p < 0.01). According to Pearson’s based correlations, The SI, BMWP, ASPT and BBI indices were the more adequate estimate of water quality in Ilke Stream.

Table 3 Pearson’s correlation assessment between indices
Full size table

DISCUSSION

For aquatic life in streams, dissolved oxygen concentrations are important variables that influence the composition of freshwater communities (Bremond and Vuichard, 1973; EPA, 1997; Nikolsky, 1963; Wetzel, 2001). In this study, highest DO and Sat. O2 values were observed at the upstream stations of the Ilke Stream. The BOD5 is of great importance as it is a measure of organic contamination in water. The BOD5 value of a region gives the amount of organic matter in that region, in other words, the amount of organic pollutants (Kocataş, 2008; Kalyoncu and Zeybek, 2011). In this study, highest BOD5 values were observed at the downstream stations of the Ilke Stream. Yorulmaz et al. (2015) reported that the highest BOD5 values were measured at the sampling points effected by the discharge of trout farms in their studies. Similarly, the fifth sampling point on Ilke Stream was chosen after the discharge point of trout farms, however BOD5 values were relatively low when compared with other studies given above. The highest Cl–, EC and TDS values in this study were measured in sampling point 6 in the downstream area. Elements that limit efficiency in aquatic environments are mostly PO4–P, NH4–N, NO2–N, NO3–N (Moss et al., 1987; Zeybek and Kalyoncu, 2016; Özbek et al., 2018). Ammonium content increases by deterioration of organic materials, organic fertilizer and chemical fertilization with inorganic ammonium and discharge of domestic and industrial wastewater. Nitrite, which is formed as an intermediate product as a result of oxidation of ammonium nitrogen, is either absent or as trace in unpolluted waters. Nitrate is found in very small amounts in unpolluted waters (Tanyolaç, 2004). In this study, highest PO4–P, NH4–N, NO2–N, NO3–N values were observed at the downstream stations of the Ilke Stream.

Principal component analysis (PCA) was applied to reduce the dimensionality of a data set consisting of a large number of interrelated variables (Kazi et al., 2009; Najar and Khan, 2012; Shrestha and Kazama, 2007). In the present study, the PCA analysis showed a clear spatial and temporal division, and the sampled stations subdivide into two groups. PCA separates slightly downstream from unpolluted upstream and T °C, EC, TDS, BOI5, NH4–N, NO2–N, NO3–N, PO4–P and Cl– accounted for 92% of total variance. The last sampling points (SP 6) were separated from others with relatively lower pH and DO levels and higher nutrient levels which could be explained by organic pollution, originating from agricultural activities, aquaculture and urban waste waters.

In several studies on different streams and rivers, parallel to our findings, Insecta was found the most dominant class (Kalyoncu et al., 2008; Kalyoncu and Zeybek, 2011; Zeybek et al., 2014; Gültekin et al., 2017; Özbek et al., 2018; Koşalşahin and Zeybek, 2019). According to Moisan and Pelletier (2008), Diptera and Oligochaeta are tolerant to pollution. Existent abundant of the organic matter are favourable for benthic macroinvertebrates such as Oligochaete’s (Rashid and Pandit, 2014). Limnodrilus sp. and Tubifex tubifex were found abundant at sampling points 5 and 6. These organisms are indicators for polysaprobic (heavy polluted) aquatic systems (Kalyoncu and Zeybek, 2011; Arslan et al., 2016; Zeybek, 2017). The members of Plecoptera and Trichoptera orders are sensitive to pollution; their number and species diversity decrease when pollution increases (Plafkin et al., 1989). On the other hand, Ephemeroptera species (eg Baetis sp.) have a wider range of tolerances (Tanyolaç, 2004). Ephemeroptera, Plecoptera and Trichoptera are the most dominant orders in the upstream sampling points (1, 2 and 3).

[%] EPT was one of the metrics that gave the best response to the physical and chemical variables of water in biomonitoring studies. According to Ode et al. (2005), the Ephemeroptera, Plecoptera and Trichoptera orders are sensitive to anthropogenic effects in aquatic ecosystems. For this reason, [%] EPT is one of the preferred metrics for water quality research (Hering et al., 2006; Oliveira et al., 2011; Helson and Williams, 2013; Huang et al., 2015). The highest [%] EPT-Taxa scores were obtained at the sampling points 1, 2 and 3 while the lowest [%] EPT-Taxa scores were obtained at the sampling point 6. The lowest [%] EPT- OL scores are obtained at the sampling points 5 and 6 while no score could be obtained at the sampling point 1 and 2 because of absence of Oligochaeta species. According to observations of Akay and Dalkıran (2019) on Yalakdere stream, the average [%] EPT was found over 53% at upstream sampling points while it was found less than 1% in sampling point 4 which was damaged by high way construction.

Kantzaris et al. (2002) recorded that the BMWP, ASPT and Land Quality Indicators (LQI) were insufficient in evaluating water quality while BBI and IBE were proper. Kalyoncu et al. (2008) stated that the BBI and physicochemical data were more proper to evaluate the water quality. Kazancı et al. (2010) mentioned that the BMWP and ASPT were sufficient to evaluate water quality in Aksu Stream. Ogleni and Topal (2011) mentioned that the BMWP and ASPT were sufficient to evaluate water quality. Kalyoncu and Zeybek (2011) found that SWDI and BBI seem to be the most reliable to determine the water quality. Yorulmaz et al. (2015) applied five biotic indices and noted that the FBI was insufficient in evaluating water quality while ASPT, BMWP, SI, and BBI were appropriate. Zeybek (2017) found that the most appropriate indices for the physical and chemical indices were BMWP-O and ASPT-O and ASPT-C. Yorulmaz and Ertaş (2021) found that the the BMWP Polish, BMWP Spanish, BMWP Hungarian, BMWP Greek versions and all versions of ASPT indices are more proper than FBI and BBI indices to determine the water quality of Selendi Stream. Ertaş and Yorulmaz (2021) found that the BMWP (Original), BMWP (Spanish), BMWP (Greek) and ASPT indices are suitable for assessing the water qualiy in Kelebek Stream. Ertaş et al. (2021) found that the ASPT, BBI, BMWP-O, BMWP-S, BMWP-G and EPT-Taxa [%] are more proper than FBI and SI indices to determine the water quality of Kozluoluk Stream. Ertaş et al. (2022) found that the BMWP-O, BMWP-S, BMWP-H, ASPT-O, ASPT-H and ASPT-C indices were more proper than SI, FBI and BBI indices to determine the water quality of Balaban Stream.

CONCLUSION

This is the first study that determines the benthic fauna of Ilke Stream, for this reason, all taxa are new records for the stream. In this study, SI, BBI, BMWP and ASPT indices were in correlation. The FBI index was not in correlation with other indices and found inappropriate to determine the water quality of Ilke Stream. Detailed studies should be carried out to determine more reliable and useful results about the water quality of streams in Turkey and a specific biotic index according to the ecological characteristics of Turkey should be developed.