Rapid industrialisation, urbanisation and population growth have resulted in an increased deterioration of water quality in river systems (Wong and Wong 2003; Dalu and Froneman 2016; Tudesque et al. 2014; Teittinen et al. 2015). River pollution is a result of several pollutant sources, which are linked to man-made discharges such as wastewater discharge and non-point sources, from diffuse sources such as land drainage and agricultural surface runoff (Tudesque et al. 2014; Bere et al. 2016a; Dalu et al. 2017a, b; Nhiwatiwa et al. 2017). Nutrient enrichment leading to eutrophication in streams greatly reduces habitat heterogeneity, therefore directly and indirectly impacting aquatic biota (Kelly and Whitton 1995; Bere 2007; Dalu and Froneman 2016). The water quality of these aquatic ecosystems has become a subject of ongoing concern worldwide and has resulted in the need for river water quality monitoring. Regular monitoring of aquatic water quality is therefore not only to predict the further deterioration in ecosystem health, but also provides a scope to assess current investments for pollution control (Soininen and Könönen 2004; Bellinger and Sigee 2010; Nhiwatiwa et al. 2017).

Studies have shown that diatoms are better related to water quality, while macroinvertebrates were better indicators of catchment disturbances (Sonneman et al. 2001). However, a study by Belore et al. (2002) found that diatoms and macroinvertebrates were equally important in predicting water quality. As a result, some scholars (e.g. Resende et al. 2010; Mangadze et al. 2016) have advocated the use of both diatoms and macroinvertebrates for effective water quality monitoring. Therefore, the current study used a combined approach (i.e. diatoms and macroinvertebrates) to assess the impact on pollution on river water quality. Most studies in the region on water quality assessments tend to concentrate on a single parameter, either macroinvertebrates (Dallas 1997; Bere et al. 2016a, b) or diatoms (De la Rey et al. 2004; Bere and Mangadze 2014; Dalu et al. 2015, 2016). The study’s main aim was to investigate the responses of benthic diatoms or macroinvertebrates to water quality changes along the Ngamo River which drains part of the urban and agricultural area in the Midlands Province of Zimbabwe. The study further assessed how the benthic diatom and macroinvertebrate fauna respond to changes in water quality.

Materials and methods

Study area

The study was carried out on Ngamo River, in the Midlands Province of Zimbabwe. The river is approximately 8 km in length and drains sections of a local town Gweru, flowing past farming areas and finally draining into the Anchor Reservoir (Fig. 1). The mean annual rainfall is 684 mm, with the least rainfall occurring during the cool-dry season (i.e. July and August—mean 1 mm) and high amount of rainfall occurring during the hot-wet season (i.e. December—159 mm). The mean temperature is 18.1 °C, with the highest average being observed in the hot-dry season (i.e. October ~ 21.5 °C) and the lowest mean temperatures in the year occurring in July (i.e. 12.8 °C). The continuous discharge of sewage effluent keeps the river flowing even during the dry season. Four sites sampled over three seasons in 2016, hot-wet (March), cool-dry (June) and hot-dry (September), were selected along the Ngamo River, with site 1 located about 100 m from below the sewage treatment works (WTW) discharge point, site 2 in the farmlands, site 3 approximately 1 km downstream of site 2 and site 4 above the reservoir mouth.

Fig. 1
figure 1

Map showing study locations near Gweru urban area, Midlands Province, Zimbabwe

Physico-chemical variables

Water conductivity, dissolved oxygen (DO), pH, salinity, total dissolved solids (TDS) and temperature were measured on-site using WTW 340i multiparameter meter (Xylem, Germany). Depth-integrated 1 L water samples were collected for the determination of turbidity, total nitrogen (TN), nitrates, ammonia, total phosphorus (TP) and reactive phosphates in the laboratory using the standard methods for the examination of water and wastewater (Clesceri et al. 1998). All samples were stored on ice in the field.



Prior to sampling, all rock substrata were gently shaken in stream water to remove any loosely attached sediment. At least ten rocks and/or stones (~80–120 mm diameter) were randomly collected at each sampling site and the top surface brushed into a 500 mL polyethylene container with distilled water and preserved in 70% ethanol [see Taylor et al. (2005) for detailed sampling methodology]. In the laboratory, the diatom samples were set aside for 2 days to sediment before being processed using potassium permanganate method according to Taylor et al. (2005). Diatoms were identified to species level using a phase-contrast light microscope (Olympus CX) at 1000× according to Taylor et al. (2007). Species richness as the number of different species at a site was used as a measure of diatoms diversity. Diatom keys developed by Taylor et al. (2007) were used to classify diatoms into four classes: A (clean water, i.e. no pollution), B (moderately polluted), C (strongly polluted) and D (electrolyte rich).


Macroinvertebrates were collected by kicking benthic sediment and rocks with feet and sweeping through vegetation in a zigzag manner using a kick net (1.5 m handle, mesh size 500 µm, dimension 30 × 30 cm) to form an integrated sample and then preserved in 500 mL polyethylene container with 70% ethanol according to Dickens and Grahams (2002), Dalu et al. (2012) and Bere et al. (2016a) methods. At each site, ~6 min was spent sampling all available aquatic habitats (i.e. kicking substratum—rocks, sediment (5 m transect length) was 2 min; sweeping littoral macrophytes (2 m2) and marginal vegetation (5 m transect length) was 2 min each, respectively). Macroinvertebrates were identified to family level using identification guides by Thirion et al. (1995) and Gerber and Gabriel (2002). Several macroinvertebrate community measures were computed to assess the ability of macroinvertebrates to respond to water quality changes: South African Scoring System version 5 (SASS5) scores (i.e. the sum of pre-determined taxa tolerance values of all macroinvertebrates within a particular sample), average score per taxa (ASPT; calculated by dividing the SASS5 score by the sample number of taxa; see Dickens and Grahams (2002) for detailed methodology), diversity indices (Shannon–Wiener, Simpsons, Margalef), %Ephemeroptera, Plecoptera and Trichoptera (EPT) abundance, %Ephemeroptera abundance, %Diptera abundance, %Trichoptera abundance and EPT/Chironomidae ratio. The SASS5 and ASPT scores used to determine the condition of a site were based on Thirion et al. (1995): excellent: SASS >100 and ASPT >7; good: 80–100 and 5–7; fair: 60–80 and 3–5; poor: 40–60 and 2–3 and very poor: <40 and <2, respectively.

Data analysis

The data collected did not meet the assumptions of parametric tests such as normality and homogeneity of variance. Hence, the data were not normal as confirmed by the Shapiro–Wilk normality test. Therefore, a non-parametric test, Kruskal–Wallis, was used to test for differences in physico-chemical variables and community metrics among sites and seasons. Pairwise comparisons using the Kruskal–Wallis multiple comparisons of p values for physico-chemical variables among sites and seasons was carried out to assess the significant differences indicated by the Kruskal–Wallis test in STATISTICA version 12.0 (StataCorp 2011). Spearman rank correlations were carried out to determine the relationships that existed between the physico-chemical variables vs community metrics and diversity indices.

A detrended canonical correspondence analysis (DCCA) was used to examine whether linear or unimodal analysis ordination methods should be employed. The DCCA gradient lengths were examined, and, since the longest gradient was greater than 3, a unimodal canonical correspondence analysis (CCA) model was used (ter Braak and Verdonschot 1995; Lepš and Šmilauer 2003) to study diatom and macroinvertebrate communities and their relation with physico-chemical variables. CCA was carried out to assess the relationships between water quality parameters (i.e. physico-chemical variables) and diatom and macroinvertebrate community data. All data was log (x + 1) transformed prior to analysis. The down-weighting option and the automatic stepwise forward selection procedure, with Monte Carlo significance test (permutation: n = 9999, p < 0.05), were selected to examine the direct effects of physico-chemical variables on the variation of diatoms and macroinvertebrates community composition. All statistical analysis was carried out in CANOCO version 4.5 (ter Braak and Šmilauer 2002).


Physico-chemical variables

Conductivity, salinity, turbidity and total dissolved solids (TDS) showed a decreasing trend from site 1 to 4 (Table 1). High total nitrogen (TN; 2.42 mg L−1) and phosphorus (TP; 1.85 mg L−1) were observed at site 1. The water quality at site 4 was relatively good compared to other sites based on the physico-chemical variables (Table 1). Very low dissolved oxygen concentrations were recorded throughout the duration of the study (mean range 0.95–3.5 mg L−1) due to sewage discharge (Table 1). For all measured physico-chemical variables, no significant site variation was observed (p > 0.05). Conductivity, salinity, turbidity and total dissolved solids (TDS) showed an increasing trend across seasons, similar to all nutrient variables measured. Water quality was low during the hot-dry season in comparison to the rest of the seasons. Ammonium, pH, salinity, total phosphorus and temperature were significantly different (p < 0.05) within seasons (Table 1). Using multiple comparisons, significant differences for ammonia (z′ = 2.550, p = 0.032), pH (z′ = 3.138, p = 0.005), total phosphorus (z′ = 2.451, p = 0.043) and temperature (z′ = 2.402, p = 0.049) were observed between the hot-wet and hot-dry seasons.

Table 1 Mean (±SD) summary of physical and chemical variables in the Ngamo River

Community structure and metric responsiveness


A total of 30 macroinvertebrates families belonging to eight orders were recorded during the study period (Table S1). Chironomidae, Culicidae and Hydroptilidae were the most dominant macroinvertebrate families forming >60% abundance. Macroinvertebrate taxa richness increased in a downstream trend. The %EPT, %Trichoptera, %Ephemeroptera and EPT/Chironomidae ratio were zero at site 1 and high at sites 3 and 4 (Table 2). The diversity indices (Margalef, Shannon–Wiener, Simpsons), SASS5 and ASPT scores all increased at downstream sites, whereas, %Diptera decreased (Table 2). Based on the SASS5 and ASPT scores, the water quality ranged from very poor to fair. Significant differences were observed for %Trichoptera (H = 8.303, p = 0.040) across the study sites, whereas significant seasonal differences were observed for taxa richness (H = 6.598, p = 0.037) and the Margalef diversity index (H = 6.038, p = 0.049) (Table 2).

Table 2 Macroinvertebrates and diatom community metrics (mean ± standard deviation) for the four study sites recorded on the Ngamo River


Eighty-four diatom taxa belonging to 35 genera were recorded across four study sites and seasons, with Nitzschia, Navicula, Gomphonema and Craticula being the most dominant groups (Table S2). Diatom species richness per sites ranged from 11 to 27 taxa, with the species richness increasing at downstream sites. The most frequently occurring species were Tryblionella fasciculata Agardh (100%), Gomphonema pumilum Reichardt (83%), Sellaphora pupula Kützing (75%), Gomphonema parvulum Kützing (75%) and Craticula accomoda Hustedt (67%) (Table S2).

Spearman rank correlation showed that most physico-chemical variables were strongly related (p < 0.05) to macroinvertebrate and diatom community metrics and diversity indices (Table 3). Strongly pollutant-tolerant species dominated sites 1 and 2. Most class A species (e.g. Achnanthes standeri Cholnoky, Eunotia spp. and Pinnularia divergens Smith) which prefer clean waters were dominant at the downstream sites 3 and 4 during the hot-wet and cool-dry seasons. Species belonging to three diatoms classes, A, B and D, increased in frequency of occurrence from sites 1 to 4, while species belonging to class C species declined downstream. All sites and seasons were found to be very similar (p > 0.05) in terms of diatom classes (Table 2) and were very poor in terms of water quality.

Table 3 Spearman correlation coefficient between physico-chemical variables vs community metrics and diversity indices along the Ngamo River

Relationship between water quality and biological indicator


The CCA analysis identified two variables, reactive phosphorus (RP) and TN concentrations, as significantly important (p < 0.01) in explaining macroinvertebrate community structure accounting for 43.9% of species variance. CCA axes 1 and 2 accounted for 24.7 and 19.2% variance, respectively. Reactive P and TN were positively associated with the second axis. The CCA axes 1 and 2 separated the sites into roughly four groups based on season, location and pollution levels. Hot-wet (sites 1, 2) and cool-dry (sites 2, 3) seasons were associated with Elmidae, Pyralidae and Hydroptilidae, and were positively associated with axis 1 and 2 being characterised by high TP and RP (Fig. 2a). Cool-dry (sites 1, 2, 3) and hot-dry (sites 1, 4) were associated with pollution-tolerant Oligochaetae, Syrphidae and Culicidae. On the other hand, hot-wet (site 4) and cool-dry (site 3) seasons were associated with pollution-sensitive taxa such as Notonectidae, Caenidae, Pleidae, Coenagrionidae and Veliidae (Fig. 2a).

Fig. 2
figure 2

CCA triplot showing the effects of environmental variables on: a macroinvertebrates and b diatom community structure in the Ngamo River. CD cool-dry, HW hot-wet, HD hot-dry. The numbers next to the seasons are site numbers. Abbreviations are further highlighted in Tables 1, S1 and S2


CCA analysis of the first two axes explained 31.3%, with axes 1 and 2 explaining 16.7 and 14.6%, respectively, of the total diatom community. The RP concentration was highly negatively associated with the first axis. Similar to macroinvertebrate community analysis, the CCA axes 1 and 2 separated the sites into roughly four groups based on season, location and pollution levels. Site 4 during the hot-dry season was generally characterised by low RP (Fig. 2b) and was associated with species such as Encyonopsis subminuta, Navicula arvensis var. maios, Nitzschia communis and Placoneis dicephala. Cool-dry season, sites 2, 3 and 4, were associated with diatom species such as Nitzschia pura, Pseudostaurosira brevistriata and Rhoicosphenia abbreviata. Most of the study sites were associated with pollution-tolerant taxa Gomphonema, Navicula and Nitzschia (Fig. 2b).


The current study managed to highlight that the Ngamo River was in a very poor state using a combined approach of nutrients, diatom and macroinvertebrate community assessments and metrics across all study sites and seasons. The use of a combined approach provides important and better information of the river state (Mangadze et al. 2016). Overall, the macroinvertebrate and diatom community metrics were almost similar in their abilities to predict river quality, with macroinvertebrate communities being driven by TP and RP, while diatom communities were driven by RP concentrations. In areas with high RP or TP due to organic pollution, i.e. sewage spillages, only pollution-tolerant taxa, e.g. Diptera, Gomphonema, Navicula, Nitzschia, were found and sensitive disappeared, e.g. Trichoptera, Ephemeroptera, Diplonies, Sellaphora. The current results are in agreement with other studies (e.g. Ometo et al. 2000; Tudesque et al. 2014; Teittinen et al. 2015; Bere et al. 2016a, b; Dalu et al. 2016, 2017a, b) that predict catchment activities greatly impacts on river state.

Nutrients were significantly high across the study sites, which could be attributed to raw sewage discharge and agriculture. These findings are similar to those observed by Bere and Nyamupingidza (2014) and Teittinen et al. (2015) who found that urban streams generally had high nutrients due to sewage pollution. There were differences with regard to levels of nutrient enrichment depending on seasonal variation that causes dilution of the raw sewage from burst pipes or partially treated sewage discharged from treatment works by the rain water. The continuous sewage input into the river resulted in a marked increase in ammonia and nitrates in hot-dry season as there was also no dilution (Nhiwatiwa et al. 2017; Dalu et al. 2017a).

The fact that one or more sites had high catchment impact suggests that local river conditions may exert an important influence on biotic richness (Ometo et al. 2000). High Diptera larvae abundance within the study sites was probably due to organic pollution and this was coupled by high relative abundance of Oligochaetes (Ndebele-Murisa 2012). These taxa are highly adapted to organic pollution, and hence they can survive in highly polluted waters (Brinkhurst and Kennedy 1965; Kazanci and Girgin 1998). The CCA analysis highlighted that many of these physico-chemical variables (e.g. TN, RP) were found to impact significantly on the benthic macroinvertebrate community. However, the unexplained variation might be a result of other unmeasured physico-chemical variables, e.g. substratum embeddedness, water velocity and heavy metals.

The diatom assemblages within the Ngamo River were dominated by a few genera such as Navicula, Nitzschia and Gomphonema, which agrees with observations similar to other studies (Kelly and Whitton 1995; Teittinen et al. 2015). The fact that the river system was dominated by strong pollution-tolerant species reflects that the river quality was in very poor state. Diatom species richness was low at the first two sites as there were closer to catchment impacts i.e. agriculture and urban area. These sites were strongly polluted and only few diatom genera such as Gomphonema, Navicula and Nitzschia could tolerate these extremes of pollution.

In conclusion, using a multivariate combined approach of nutrients and diatom and macroinvertebrate community metrics and biological indices allowed going further in the river ecological diagnostic of the Ngamo River and facilitated the consideration of the biological communities compartment into decision-making processes related to river conservation and restoration. The different catchment activities, e.g. agriculture and sewage spillages, had a significant implication to the river state and had a huge impact on the river water quality, benthic diatom and macroinvertebrate communities. Therefore, it is of paramount importance to understand the catchment processes that cause changes in river ecosystems as a result of land use. This relatively pilot study in an understudied region of the world highlighted that the river state of the Ngamo River was very poor. We advocate for further studies to quantify the impact of the urban area and agriculture on the entire river system catchment through increased sampling sites and assessing nutrient loading over time and space.