Influence of a dam and tributaries on macrobenthos communities and ecological water quality in the Kebir–Rhumel wadi (Northeast Algeria)

The study was conducted in the Kebir–Rhumel watershed (catchment area c. 8800 km²), upstream and downstream of the Beni Haroun Dam, Northeast Algeria (lat 36°32′36.9″N, long 6°16′07.5″E) (Fig. 1). Beni Haroun, the largest dam in the country (40 km²), originates at the confluence of the Rhumel and Enndja wadi. Below the dam, the Kebir wadi flows northwards for 56 km until it mouths into the Mediterranean Sea. The climatic conditions of the basin are typical of a Mediterranean climate type and are characterized by moderate annual average temperature (18°C) and high rates of precipitation from December to March/April (Mébarki, 1982, 1984). The Rhumel wadi catchment exhibits areas highly disturbed by human activities, while the Kebir wadi drains a basin with relatively few anthropogenic impacts and has a largely natural, undammed watershed.

Figure 1

Locations of the sampling sites upstream and downstream of the Beni Haroun dam in the Kebir–Rhumel catchment (-UC, -DC: upstream and downstream of the confluence; -TR: tributary).

Eight sites were sampled in April, July, and November 2017. Four sites were upstream of the Beni Haroun dam, in the Rhumel wadi: (i) in the Smendou tributary (S-TR), (ii) in the confluence of the Rhumel and the Smendou wadi (R-C), and (iii) upstream and downstream of the tributary confluence (R-UC and R-DC, respectively). This river section was surrounded by a large agricultural area. The river substrate in this section consists mainly of stones and a few boulders, while the water is murky with a foul odor along the mainstem. The other four stations were located 4 km below the dam, on the Kebir wadi: (i) in the Tara tributary (T-TR), (ii) in the Kebir and Tara wadi confluence (K-C), and (iii) upstream and downstream of the confluence (K-UC and K-DC, respectively) (Fig. 1). These stations were surrounded by mountainous areas, and the effect of the anthropogenic disturbances was assumed to be low. The riverbanks in this section were less vegetated, and the substrate was stony except in the confluence (K-C) where the substrate consisted mainly of sand, gravel, stone and few large boulders.

Benthic macroinvertebrates were collected using a Surber sampler (30 cm × 30 cm; 500 µm mesh). We established eight haphazardly sampling locations along a ~35 m reach of the stream, including all possible microhabitats within each site for benthic macroinvertebrate sampling. At each site, the organisms were pooled. In total, the benthic fauna samples covered a 0.72 m² area at each site. Samples were fixed in 70% ethanol in the field and returned to the laboratory, where macroinvertebrates were sorted, counted, and identified to the lowest practical taxonomic level (usually family, genus, or species) using a binocular loupe (40× magnification; OPTIKA), a stereomicroscope (400× magnification; Motic), and taxonomic keys (Dommanget, 1994; Himmi et al., 1995; Poisson, 1957; Tachet et al., 2010).

On each sampling occasion, punctual measures of water temperature (°C), dissolved oxygen (DO, mg l⁻¹), electrical conductivity (EC, µS cm⁻¹), salinity (Sal, Practical Salinity Unit [PSU]), and pH were collected with a WTW^™ 305i Multi-Parameter and a Mettler Toledo pH meter. We also estimated current velocity (cm s⁻¹) via the time taken for a flutter to travel a measured distance downstream. In parallel with macroinvertebrate sampling, water samples were collected in plastic bottles and taken to the laboratory for the determination of the following parameters: ammonium (NH₄⁺, mg l⁻¹), nitrites (NO₂⁻, mg l⁻¹), nitrates (NO₃⁻, mg l⁻¹), and orthophosphates (PO₄³⁻, mg l⁻¹). These parameters were analyzed according to standardized methods presented by AFNOR (Association Française de NORmalisation).

The normality of data and homogeneity of variances were tested using the Shapiro–Wilk and Levene’s tests, respectively. One-way analysis of variance (ANOVA) and Kruskal–Wallis tests were conducted to test the significance of among-site differences in environmental variables and biological metrics. Principal component analysis (PCA) was applied to ordinate sites according to physicochemical parameters using XLSTAT software.

To explore taxonomical changes in the macroinvertebrate community, taxonomic richness (S = number of taxa), Shannon–Wiener diversity index (H’), Ephemeroptera, Plecoptera, and Trichoptera (EPT), and the ratio EPT/(EPT + Chironomidae) were calculated. In addition, we estimated the ecological water quality at all sampling sites using the Hilsenhoff Biotic Index (BI), the Biological Monitoring Working Party (BMWP) index, and the Average Score Per Taxon (ASPT).

The Shannon–Wiener diversity index is commonly used in the type calculation and the percentage of each species within the benthic community. This index was calculated as: H′=−∑i=1sPi ln Pi H' = - \sum\limits_{i = 1}^s {Pi\;ln\;Pi} Where ‘Pi’ is the relative abundance of i^th taxon of the community and ‘S’ is the total number of species in the sample (Jørgensen et al., 2005).

The EPT family richness is equal to the total number of families belonging to three orders of aquatic insects considered as very sensitive to pollution: EPT; thus, their richness increases when water quality increases and vice versa (Table 1).

The metric EPT/(EPT + Chironomidae) is calculated by dividing the total abundance of EPT by EPT plus the total abundance of Chironomidae. This ratio expresses the relationship between the generally pollution-intolerant EPT organisms and the generally pollution-tolerant Chironomidae organisms. A value close to 0 indicates poor biotic condition, whereas a higher ratio (values 0.75 or greater) indicates a low number of Chironomids, denoting a fairly even distribution among these four groups (James, 2002).

The Hilsenhoff Biotic Index (BI) was developed to detect organic pollution and is based on the genus and species-level tolerance values (Bode et al., 2002). The formula for calculating the BI is: BI=∑Xitin BI = {{\sum \left( {{X_i}{t_i}} \right)} \over {\rm{n}}} Where ‘X_i’ is the number of individuals in the ‘i^th’taxon, ‘t_i’ is the pollution tolerance score of the ‘i^th’ taxon, and ‘n’ is the total number of organisms in the sample.

BI values range from 0 to 10 and are classified into seven quality categories (Table 2).

The BMWP’ (Alba-Tercedor & Sanchez-Ortega, 1988) is a Spanish adaptation of the British Biological Monitoring Working Party (BMWP) score system (Armitage et al., 1983). The BMWP’ index considers the sensitivity of invertebrates to pollution. A score between 1 and 10 is assigned to each family along an increasing gradient of pollution sensitivity. It is calculated by adding the tolerance scores of all the macroinvertebrate families (and Oligochaeta) in the sample (Alba-Tercedor & Pujante, 2000). The five classes of quality and their boundaries used in the quality mapping are shown in Table 3.

Furthermore, the has been calculated as the ratio between the BMWP’ score and the number of taxa in the sample: ASPT=BMWP′ scoreTotal number of families {\rm{ASPT}} = {{{\rm{BMWP}}'\;{\rm{score}}} \over {{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{families}}}}

ASPT is suitable for assessing the impact of organic pollution based on five quality classes (Table 3) (Mandaville, 2002).

The correlations between macroinvertebrate metrics and physicochemical values were determined using a Pearson’s correlation analysis. All statistical analyses were carried out using the statistical software package SPSS 26 (IBM Corp, 2019). We used a one-way analysis of similarity (ANOSIM) (Chapman & Underwood, 1999) to identify significant differences in community structure among sites. In addition, the similarity percentage procedure (SIMPER) analysis (Clarke & Warwick, 2001) was also applied to determine the species that best contributed to dissimilarity among sections. ANOSIM and SIMPER were conducted using PAST 4.08 software (Hammer et al., 2001). The relationship between macroinvertebrate assemblage structure and physicochemical parameters was analyzed by canonical correspondence analysis (CCA) in CANOCO 4.5 software (ter Braak & Šmilauer, 2002). Only taxa representing at least 0.15% of the total abundance were included in the CCA to minimize the effects of rare taxa. To stabilize the variances and improve normality, the abundance (x) of each taxon and the environmental variables data were log10(x + 1)-transformed (Sokal & Rohlf, 1995).

In this study, a total of 11 720 individuals in 91 taxa of benthic macroinvertebrates were recorded and identified during the three sampling seasons, above and below the Beni Haroun dam in Kebir–Rhumel wadi (Table 4).

Above the dam, the benthic macroinvertebrate communities were dominated by Diptera, Ephemeroptera, Oligochaeta, and Heteroptera with 36.92%, 25.03%, 15.01%, and 12.52%, respectively. Below the dam, Diptera, Ephemeroptera, and Trichoptera were abundant with 42.97%, 32.52%, and 13.45%, respectively (Fig. 2). Chironomidae, Simulium (S.) pseudequinum, Baetis pavidus, Caenis luctuosa, and Hydropsyche lobata were the most abundant taxa above and below the dam. Also, Tubifex sp. and Micronecta sp. were only dominant in stations above the dam. In terms of diversity, the Kebir wadi (Kr) segment and the tributary T-TR were the richest sites with 54 and 46 taxa, respectively. Diptera and Ephemeroptera were highly diversified in the station T-TR (11 taxa each), followed by Trichoptera with 7 taxa. In the S-TR station, Diptera, Coleoptera, and Odonata showed the highest richness with 10, 8, and 5 taxa, respectively (Table 4).

Macroinvertebrate densities and taxon richness were relatively higher below the dam than above and also higher in tributaries compared to the stations in the mainstem. Plecoptera have been observed only below the dam. Generally, this taxon is known to be sensitive to external influence (Cuffney et al., 2010; Evans-White et al., 2009; Villalobos-Jimenez et al., 2016). On the other hand, Oligochaeta and Hirudinea have been observed only above the Beni Haroun dam. These taxa are usually considered moderately tolerant or tolerant to organic enrichment and pollution (Cortelezzi et al., 2018; Lenat, 1993; Wang et al., 2012).

One-way ANOSIM yielded a significant difference between benthic community structure in the sampling stations (global test R = 0.7, p = 0.0001). Pairwise ANOSIM test indicated that the following sites were significantly different except between R-DC and two stations, R-UC and R-C, and between K-DC and two stations, K-UC and K-C (Table 5). The SIMPER analysis (Table 6) recorded the highest average dissimilarity (70.58%) between the tributaries S-TR and T-TR. The dissimilarity between the Rhumel segment (Rh) and S-TR (59.84%) was mainly caused by Caenis luctuosa, Micronecta sp., Naididae, Simulium. (W.) pseudequinum, and Baetis pavidus.

The assemblage composition differed most across the dam (between above and below the dam) and between the tributaries (S-TR and T-TR) and mainstem segments (Rh and Kr). The low dissimilarities between R-UC and R-C and between K-UC and T-TR show slight effects of the tributaries on the assemblage structure in confluence stations. Faster-flowing tributaries have effects on the physical channel, such as increased channel depth and width, which would have influenced habitats (Wallis et al., 2008). In our study, the similarity of assemblages at the mainstem stations confirms the negligible effects of the tributaries (generally of low flow) in modifying the physicochemical characteristics of the channel to observe detectable effects on macroinvertebrate assemblages. The main differences in macrobenthos assemblages between the mainstem and the tributaries might be due to differences in habitat, including water depth, velocity, and substrate. On the other hand, the appearance of sensitive taxa and change in assemblage structure below the dam compared to above the dam sites likely reflected the change in environmental variables caused by the dam.

The results of the analyzed physical and chemical parameters of water in eight sampling stations are shown in Table 7.

The ANOVA demonstrated that almost all physicochemical parameters of water in this study, except temperature and pH, showed a significant difference between above and below the dam. The average water temperature ranged between 12.33 ± 4.93 and 18 ± 7°C. Water pH has shown very small variation, with average values ranging from 8.10 ± 0.53 to 9.33 ± 0.35, meaning that in all sites the water is alkaline. The highest average of DO was recorded in the T-TR with 7.93 ± 0.40 mg l⁻¹, and the lowest amount was 4.83 ± 0.99 mg l⁻¹ at the station R-DC. The R-DC station presented the highest average values in EC and salinity, with 1728.33 ± 358.34 µS cm⁻¹ and 0.92 ± 0.34 PSU, respectively; the lowest values were recorded at the station T-TR, with 718.33 ± 163.58 µS cm⁻¹ and 0.29 ± 0.08 PSU, respectively. As for the nutrient concentrations (ammonium, nitrite, nitrate, and phosphate), they have shown significant differences observed between the tributaries and mainstem, and they were higher immediately above the dam than below the dam.

The Rhumel wadi is greatly affected by anthropogenic activities such as land use activities, discharge of household sewage, and industrial wastes. The Beni Haroun dam has constituted a barrier, which can disrupt the continuity of the fluvial environment by the accumulation of sediment, nutrients, and organic matter supplied by the Rhumel wadi. This was confirmed by Kondolf (1997) and Takao et al. (2008). Wiatkowski (2011) emphasized the contribution of the sedimentation process in reservoirs to decrease the pollution in rivers flowing through them.

Results of PCA indicated that PC1 and PC2 accounted for 75.57% and 15.66% of the total variance of environmental variables, respectively (Fig. 3). The distribution of the stations in the first factorial plane shows a clear differentiation of the sampling sectors based on the physical and chemical variables. The PCA ordination plot has suggested that stations above the dam were positively influenced by temperature, salinity, conductivity, and nutrient loads. The dam here clearly had a remarkable effect on the physicochemical characteristics of the river.

A summary description of the calculated biotic indices and the community structure indices of the sampling stations is provided in Table 8. According to Mason (2002), Shannon–Wiener index values in this study (ranging from 1.46 and 2.57) were in the range indicating moderate-polluted environment, with relatively high index values below the dam. It is obvious from the observation during the sampling process that the mainstem above the dam was more polluted than the section below the dam.

Figure 2

Relative composition in macroinvertebrate orders in stations above (A) and below (B) the Beni Haroun dam.

The EPT, EPT/(EPT + Chironomidae), and BI index values classified the majority of the stations in the same quality class, with the highest values recorded in the stations below the dam.

The high values of EPT (8.33), BMWP’ (108.66), and ASPT (6.05) collectively indicate that the water quality at T-TR is good. The biological indices, except ASPT, allocated to the majority of the stations passable and clear water quality. Based on ASPT, the mean water quality in all sampling stations, except T-TR, was poor to very poor.

In our research, there are differences in water quality classification with different indices, whereas the application of EPT and BMWP’ index seems to be more reliable and to better reflect the environmental condition since they are both based on the presence of sensitive species to disturbances. Logically, the section below the dam had a relatively higher water quality than the ASPT category, as indicated by the classification of other biotic indices and physicochemical evaluation of water quality. The differences in quality class allocation among indices may be due to different values limiting the quality class levels and the systems of categorization.

Pearson correlation analyses between biological indices and physicochemical parameters show that temperature and DO had a significant correlation with all the biological indices (Table 9). pH and salinity had significant correlation with the majority of biological indices, except EPT/(EPT + Chironomidae). NO₃⁻ exhibited significant negative correlation with only ASPT. Nutrients had no significant correlation with taxonomic richness (S), EPT, and BMWP’. The Shannon–Wiener index (H’) has a strong correlation (p < 0.01) with all physicochemical parameters, except NO₃⁻. Also, ASPT was significantly correlated to all the physicochemical parameters (Table 9).

The results of Pearson’s correlation approach have shown the major influence of water physicochemical variables on the taxonomic richness and the diversity of macroinvertebrates in the study area. Similar studies have documented the negative effects of chemical variables on the richness and diversity of benthic macrofauna (Lewin et al., 2013; Zhushi Etemi et al., 2020). Chen et al. (2015) found that the diversity of macrobenthic assemblages was negatively correlated with the nutrient enrichment in the water body. The BI index is inversely related to the quality of water and increases with river pollution. Although biotic indices are intensely influenced by anthropogenic activities, it should be clear that they can also respond relatively to a range of potential physicochemical parameters (Sharifinia et al., 2016).

A multivariate CCA was applied to summarize the relationships between macroinvertebrate assemblages and water physicochemical parameters above and below the dam (Fig. 4). The parameters that have negligible variance were omitted from the ordination. Above the dam, the results of CCA analysis showed that Simulium ornatum, Ceratopogenidae, Limnius intermedius, and Caenis luctuosa abundances were closely and positively related to DO concentration and pH. Helobdella stagnalis, Cloeon dipterum, and Naididae showed preferences for high nitrate concentrations. Temperature, conductivity, and nitrite were positively related to the abundance of Tubifex sp. and Esolus filum (Fig. 4). Below the dam, Choroterpes lindrothi, Choroterpes atlas, Caenis pusilla, Ecdyonurus rothschildi and Cloeon dipterum (Ephemeroptera), Simulium hispaniola, Limnophora riparia (Diptera), Isoperla sp. (Plecoptera), Stenelmis consobrina, Limnius intermedius (Coleoptera), Chimarra marginata (Trichoptera), and Ancylus fluviatilis (Gastropoda) showed preferences for well-oxygenated waters and a great sensitivity to temperature, conductivity, salinity, and nitrate. These last parameters were positively related to the abundance of Gomphus lucasi (Odonata), Simulium ornatum (Diptera), Dugesia gonocephala (Turbellaria), and Aulonogyrus striatus (Coleoptera) (Fig. 4).

Figure 3

PCA ordination plot explaining the variation of physicochemical parameters among sampling sites. DC: downstream of the confluence; PCA, principal component analysis; T-TR, Tara tributary; UC, upstream of the confluence.

According to the results of CCAs applied to the data above and below the Beni Haroun dam, water temperature, conductivity, and DO concentration were the most important factors that affected the macrobenthic assemblage. The CCA ordination plots obviously divided tolerant macroinvertebrate species affected by temperature, conductivity, and nutrients from sensitive species to decrease in DO concentration. Similarly, De Jonge et al. (2009), Sharifinia et al. (2012), and Raphahlelo et al. (2022) reported that nutrients, water temperature, conductivity, and DO directly affect the composition, life cycle, and distribution of macrobenthic communities.

As illustrated by the CCA analysis, the stations above the dam had the most moderately pollution-tolerant species, such as the Chironomidae, Tubifex sp., Micronecta sp., and Physa acuta. The total absence of Plecoptera and the presence of Tubifex sp. only at Rhumel wadi confirm its very degraded state. On the other hand, the CCA ordination plot showed a strong relationship between sensitive taxa and good oxygenation below the dam.

Figure 4

CCA ordination diagram illustrating the relationships between macroinvertebrate taxa and physicochemical parameters in sites above (A) and below (B) the Beni Haroun dam. The codes of taxa are provided in Table 4. CCA, canonical correspondence analysis.