Macroinvertebrate assemblages in agricultural ...

Macroinvertebrate assemblages in agricultural, mining, and urban tropical streams: implications for conservation and management Tongayi Mwedzi, Taurai Bere & Tinotenda Mangadze

Environmental Science and Pollution Research ISSN 0944-1344 Environ Sci Pollut Res DOI 10.1007/s11356-016-6340-y

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Author's personal copy Environ Sci Pollut Res DOI 10.1007/s11356-016-6340-y

RESEARCH ARTICLE

Macroinvertebrate assemblages in agricultural, mining, and urban tropical streams: implications for conservation and management Tongayi Mwedzi 1 & Taurai Bere 1 & Tinotenda Mangadze 1

Received: 5 August 2015 / Accepted: 21 February 2016 # Springer-Verlag Berlin Heidelberg 2016

Abstract The study evaluated the response of macroinvertebrate assemblages to changes in water quality in different land-use settings in Manyame catchment, Zimbabwe. Four land-use categories were identified: forested commercial farming, communal farming, Great Dyke mining (GDM) and urban areas. Macroinvertebrate community structure and physicochemical variables data were collected in two seasons from 41 sites following standard methods. Although not environmentally threatening, urban and GDM areas were characterised by higher conductivity, total dissolved solids, salinity, magnesium and hardness. Chlorides, total phosphates, total nitrogen, calcium, potassium and sodium were significantly highest in urban sites whilst dissolved oxygen (DO) was significantly higher in the forested commercial faming and GDM sites. Macroinvertebrate communities followed the observed changes in water quality. Macroinvertebrates in urban sites indicated severe pollution (e.g. Chironomidae) whilst those in forested commercial farming sites and GDM sites indicated relatively clean water (e.g. Notonemouridae). Forested watersheds together with good farm management practices are important in mitigating impacts of urbanisation and agriculture. Strategies that reduce oxygen-depleting substances must be devised to protect the health of Zimbabwean streams. The study affirms the wider applicability of the South African Scoring System in different land uses. Responsible editor: Thomas Hein * Tongayi Mwedzi [email protected]; [email protected]

1

School of Wildlife, Ecology and Conservation, Chinhoyi University of Technology, Off Harare-Chirundu Rd, P. Bag 7724, Chinhoyi, Zimbabwe

Keywords Land-use . Water quality . Forested watershed . Dissolved oxygen . SASS

Introduction Water pollution has become a common problem that requires urgent attention worldwide. This is clearly the result of the ever increasing human population which has brought dramatic changes to landscapes (Walsh 2000). While worldwide progress has been made in controlling the acute effects of pointsources of water pollution, it has become increasingly clear that non-point-source pollution from agriculture, mining and urban land uses has caused long-term, cumulative harm to stream ecosystems (Waite et al. 2000). Management of water resources in Zimbabwe is based on two acts: the Zimbabwe National Water Authority Act (chapter 20:25) of 1996 and the Water Act (chapter 20:24) of 1998. Using these acts, the Zimbabwe National Water Authority (ZINWA) manages water resources on a catchment basis with involvement of stakeholders (through catchment councils) in each catchment area. Following the reforms in the water act in the late 90s, Zimbabwe was widely seen, as a leader in innovation, policy reform and service provision in the water sector in Southern Africa. However, the gains of this sector were quickly reversed following the economic downturn. There has been very limited new investment in infrastructure by the institutions responsible for service provision. This has led to a decline in operations and maintenance of key water resource infrastructure. Streams draining urban areas of Zimbabwe are therefore characterised by industrial and sewage effluent (mostly untreated) and sewage spillages from burst pipes (Nhapi 2009; Bere and Nyamupingidza 2014). There is also irregular collection of garbage (Nhapi 2009). While the legal framework has spelt the importance of

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catchment plans, they are not being implemented. Thus, there is lot of theory surrounding governance of water resources in Zimbabwe which is hardly put to practice (Manzungu and Kujinga 2002). Monitoring and enforcement of the existing regulations in Zimbabwe is poor because of economic hardships and lack of political will to deal with offenders (Nhapi 2009). While the country is faced with breakdown in service provision and inadequate maintenance of wastewater treatment plants, the population coupled with urbanisation has continued to grow exerting unbearable pressure on water resources (Nhapi 2009). Zimbabwe’s urbanization rates are among the highest in the world (Garland and Herzer 2009; Mangizvo and Kapungu 2010). Gumindoga et al. (2014) showed that urban areas increased rapidly in the upper Manyame catchment, Zimbabwe, e.g. by more than 600 % in the Mukuvisi subcatchment between 1986 and 2008. There have also been numerous land-use changes in the farming areas following the fast track land reform program of 2000 (Matsa and Muringaniza 2010). This has had serious implications on water as it is impossible to separate land-use activities from the water resources (Dube and Swatuk 2002). For instance, woodland decreases of more than 40 % in the upper Manyame catchment resulted in 84.8 % increase in runoff in the Mukuvisi sub-catchment (Gumindoga et al. 2014). It is therefore apparent that sustainable water management is impossible where land is not being sustainably managed (Dube and Swatuk 2002). Land-use change, from natural forest to urban developed, mining or agriculture, is now a major concern in developing countries because of the associated disturbances that lead to soil erosion, sedimentation, nutrient enrichment and input of toxic substances to aquatic habitats and biological communities (Stewart et al. 2001; Jun et al. 2011). Land-use change has been identified as the major stressor on stream ecological conditions (Zhang et al. 2013). For example, agriculture is known to increase landscape vulnerability to surface runoff leading to loss of riparian complexity and in-stream habitats, changes in hydrology and increased inputs of herbicides/pesticides and fine sediments into the river (Zhang et al. 2012). Likewise, urban areas have a disproportionately large negative influence on the stream system (Zhang et al. 2013). This influence has been shown to increase with increase in percentage area under urban development (Roy et al. 2001). Urban areas create impervious areas (e.g. through parking lots, roof tops etc.) thereby increasing surface runoff, modifying channel morphology and increasing loads of sediments, nutrients and contaminants (Zhang et al. 2012, 2013). The effects of these land-use changes are manifested in changes in biotic communities, as the patterns of these biota are responsive to the nature of the prevailing physical and chemical conditions (Sponseller et al. 2001). Alterations in biotic composition can thus be used to reflect changes in water

quality in a more integrated manner than traditional monitoring of water chemistry because it integrates responses to a range of pollutants occurring over different times (Calow and Petts 1994). Water quality in this study refers to the condition or state of water relative to the requirements of one or more biotic species and/or to any human need or purpose (Johnson et al. 1997). Macroinvertebrates are often preferred water quality indicators for a number of reasons. The reasons include the fact that they are ubiquitous to all streams, occur in high numbers, have relatively long life cycles, are sedentary in nature, have a wide range of feeding habits, are dependent on the land environment around the stream and respond to multiple aquatic perturbations with a known range of environmental stress (Rosenberg and Resh 1993; Ndebele-Murisa 2012; Bere and Nyamupingidza 2014). Most of the studies relating macroinvertebrate community composition to changes in land-use patterns have mainly been carried out in the temperate regions. However land-use impacts are regionally specific due to the strong influence of cultural, historical, climatic and landscape settings on indicator-disturbance relationships (Zhang et al. 2012). Additionally, the socio-economic drivers of anthropogenic activities vary from place to place. Thus, understanding the relationship between land-use patterns, water quality and macroinvertebrate composition provides a starting point for establishing stream water quality control regulations, conservation goals, ecological restoration efforts and necessary research hypotheses for management of tropical lotic systems in Zimbabwe (Mangadze et al. 2015). To date, most studies that sought to show the applicability of SASS in Zimbabwe have mainly concentrated on a single geographic setting, e.g. urban areas (Phiri 2000; Bere and Nyamupingidza 2014), and authors recommended an explicit evaluation of SASS in other regions or on a bigger scale. The current study focused on the relationship between land-use-induced changes in water quality and macroinvertebrate assemblages in the Manyame catchment, a remarkably disturbed and the most urbanized catchment in Zimbabwe having undergone substantial land-use changes in the past 60 years. The Manyame catchment passes through four administrative provinces, i.e. Harare, Mashonaland East, Mashonaland West and Mashonaland Central covering areas characterised by urbanization, industrialization and varied agricultural activities. Some of the streams also pass through part of the Great Dyke—a seam of ore-bearing rock that goes from the north to the south of Zimbabwe. The Great Dyke represents an important resource for the national economy and the local communities’ livelihoods as it contains vast mineral resources including the platinum group of metals, gold, nickel, copper and chrome among others. Some of the biggest mining companies such as Zimbabwe Mining and Steel Company and thousands of small-scale miners and illegal artisanal miners operate within this section of the Great Dyke (Makore and

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Zano 2012). The predominance of different anthropogenic activities in the Manyame catchment therefore provides a unique opportunity to assess the response of macroinvertebrate assemblages to changes in water quality in contrasting land-use settings. The objectives of this study were: (1) to assess land-useinduced changes in water quality across different land-use settings and (2) to evaluate the response of macroinvertebrate assemblages to changes in water quality in different land-use settings, i.e. forested commercial farming, communal farming, Great Dyke mining (GDM) and urban areas. We hypothesised that macroinvertebrate community composition in the study streams is a function of landuse-induced changes in water quality. Thus, understanding the influences of land-use activities on aquatic biota is a stepping stone towards conservation and management of these resources.

Materials and methods Study area and study design The study was carried out on wadeable streams/rivers that are tributaries of the Manyame river in the Manyame catchment, Zimbabwe (Fig. 1). The area receives average rainfall of about 700 mm annually, most of which falls in summer (November to April) and experiences warm to high summer temperatures of 22 to 27° C (Meteorological Services Department of Zimbabwe, data from 1965 to 2014). Field reconnaissance was used in the selection of 41 sites (Fig. 1) that were used in this study. A spatially balanced probabilistic design (Stevens and Olsen 2004) was used to select sampling sites in the different land-use categories. Of the 41 sites selected, four were in forested commercial farming areas, 21 were in communal farming areas, seven were in urban areas and nine were in GDM areas. The forested commercial farming areas have lower human populations as individuals own single large pieces of land. They thus suffer less of the impacts associated with increasing human populations such as increased waste generation, deforestation river bank cultivation and overgrazing. They thus served as the best available “least disturbed” sites. Communal agricultural sites were picked in areas that suffered deforestation, had poor agricultural practices (e.g. overgrazing, stream bank cultivation, soil erosion) and had relatively higher human population compared to the forested commercial farming areas. These factors (deforestation, poor agricultural practices and higher population densities) in communal areas were expected to have negative effects on water quality of streams in these areas. Negative effects were also expected from urban areas where streams are polluted by the direct discharge of raw municipal sewerage into public streams, frequent sewer bursts

and untreated effluent from industries (Ministry of Environment and Natural Resources Management 2010). Furthermore, most urban areas in Zimbabwe are located on the watershed. Consequently streams draining urban areas in the study area were headwaters. The study area also encompasses streams draining part of the Great Dyke, a linear geological feature that extends nearly north-south through the centre of Zimbabwe passing just to the west of the capital, Harare. It consists of a band of short, narrow ridges and hills spanning for approximately 550 km. It has large commercial deposits of nickel, copper, cobalt, gold and platinum group metals. The stream beds in some sections of these areas are comprised of ultramafic rocks which are strongly enriched in magnesium-bearing minerals (Makore and Zano 2012). Being so rich in minerals, mining is the major socio-economic activity along the streams that pass through the Great dyke. The mining methods used by many of the illegal gold panners in the area lead to environmental degradation and high rates of siltation in the rivers. The area was therefore expected to show signs of ecosystem degradation and poor river health. Sampling was done twice in April (at the end of the rainy season when all the streams were flowing) and September (during the dry season) 2013 to capture the two flow extremes typical of the study area. Physicochemical variables A YSI Pro-plus Multi-Parameter Water Quality Meter (Xylem Inc, USA) was used to measure conductivity, dissolved oxygen (DO), total dissolved solids (TDS), chloride, salinity and temperature at each sampling site. Water samples were collected at each site following standard methods (APHA 1988) and taken to the lab for analysis. In the lab, total concentrations of lead (Pb), magnesium (Mg), calcium (Ca), potassium (K), sodium (Na), zinc (Zn), iron (Fe), cadmium (Cd), chromium (Cr), copper (Cu), cobalt (Co), nickel (Ni) and total hardness were determined using a flame atomic absorption spectrophotometer (Varian Australia Pty Ltd, Victoria Australia). Total phosphates (TP) was determined following the nesslerization method (APHA 1988), and Total nitrogen (TN) were determined by oxidising nitrogenous compounds to nitrate by heating with alkaline per sulphate solution (Korroleff 1972). Chemical oxygen demand (COD) was determined by oxidation of potassium dichromate in an acid medium following Jirka and Carter (1975). Percentage riparian vegetation cover was visually estimated at each sampling station over a 20 to 30 m riparian width. Macroinvertebrates sampling Macroinvertebrate sampling followed the South African Scoring System version 5 protocol (SASS5; Dickens and

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Fig. 1 Manyame sub-catchment showing sampling sites for macroinvertebrate assemblages in contrasting land-use settings

Graham 2002). At each sampling site, a handheld net (mesh size 500 μm) was used to collect macroinvertebrates within a 20–30 m reach comprising a relatively homogenous riffle section. Riffle sections were selected because they are relatively shallow, and they are vulnerable to physicochemical impacts (Ziglio et al. 2006). Where available, the three major habitats identified by Dickens and Graham (2002) were sampled. These habitats include: stones (including bedrock or any solid object), aquatic vegetation (marginal, floating and submerged) and gravel (including sand mud, silt and clay). Stones were sampled by kicking, dislodging and collecting the invertebrates into the net for approximately 2 min. A total length of approximately 2 m of aquatic vegetation spread over more locations was sampled by pushing the net vigorously into

the vegetation, moving backwards and forward. Gravel was stirred by scraping with the feet for at least half a minute, whilst sweeping the net over the disturbed area to catch removed biota. Samples that may have been missed by the sampling procedure were hand-picked for approximately 1 min. Snails and fast-moving pond skaters were also noted. The three sub samples from the three habitats were pooled, and macroinvertebrates were identified to family level except in the case of Oligochaeta and Hirudinea which were identified to class level and Amphipoda which was identified to order level. Identification was done using taxonomic keys by Geber and Gabriel (2002). Macroinvertebrate families that could be identified in the field were returned to the stream, while those that could not be identified immediately were preserved in

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10 % formalin in polythene bottles and transported to the laboratory for identification.

Results Water quality

Data analysis Taxa richness, dominance, and Shannon Wiener Diversity (H 1 ) index calculated using Paleontological Statistics (PAST) software Version 2.16 (Hammer et al. 2012) were used as measures of community structure. Two biotic metrics, i.e. South African Scoring System (SASS) score and Average Score Per Taxon (ASPT) were also calculated. SASS scores were calculated by summing the quality scores of all the families present at a given site, irrespective of abundance (Dickens and Graham 2002). ASPT was calculated for each site by dividing the SASS score by number of taxa following Dickens and Graham (2002). A higher ASPT and SASS score was considered to reflect better water quality. Having checked the data for normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test), one-way ANOVA was used to compare means of physicochemical variables across the four land-use categories. Where significant differences were observed, Tukey’s Honesty Significant Difference (HSD) test was used to indicate the sources of variation. ANOVA and Tukey’s HSD analysis were done using SPSS (version 16). Kruskal-Wallis was used to test for significant differences of taxa richness, Shannon wiener diversity index, evenness, SASS score and ASPT score among different land-use categories as the data were not normally distributed and could not be transformed in any way. The Mann-Whitney test was used as a post hoc test to indicate the sources of variation in this instance. Macroinvertebrate abundances were then analysed using Detrended Correspondence Analysis (DCA) to assess variation in assemblage composition in the different land-use categories. DCA is a non-linear multivariate approach that describes the relationship among individual invertebrate taxa with a gaussian response curve (i.e. a bell-shaped response curve with an optimum represented as a point on an ordination diagram; Jongman et al. 1995). This approach was chosen because it is considered a more ecologically appropriate method than linear models (e.g. Principal Components Analysis; ter Braak 1986; Jongman et al. 1995) and necessary for the simultaneous examination of multiple taxa particularly since each taxon may respond differentially to environmental gradients (Lenat 1984). DCA is an improvement over both PCA and correspondence analysis (CA) methods (Wartenberg et al. 1987; Peet et al. 1988). Prior to analyses, abundances were log transformed to reduce the effects of extreme values (Gauch 1982). Corresponding environmental factors and external information on site characteristics were then used to interpret the emanating ordinations. All DCAs generated for this study were done using Canoco for Windows version 5.

Of the 25 physicochemical variables that were assessed, seven (7) trace metals Pb, Cu, Zn, Fe, Cr, Cd and Co were below the detection limit of the atomic absorption spectrophotometer ( 0.05). Mg, hardness, TDS, salinity and conductivity were significantly higher in urban sites and GDM sites (ANOVA, p < 0.05) while resistivity was significantly lower in the same compared to the other two land-use categories (ANOVA, p < 0.05, Table 1). Chloride, TP, TN, Ca, K and Na were significantly highest in urban sites (ANOVA, p < 0.05) compared to the other three land-use categories. Dissolved oxygen was significantly highest in the GDM and in the commercial farming sites compared to the other two land-use categories (ANOVA, p < 0.05, Table 1). Communal and forested commercial farming areas were mainly distinguished by DO, which was significantly (Tukey, p < 0.05) higher in the later compared to the former. There were no significant differences in all the other variables (ANOVA, p > 0.05, Table 1). Community composition and metrics A total of 62 macroinvertebrate families, representing 12 orders were recorded (Table 3). Furthermore, two classes Oligochaeta and Hirudinea (not classified beyond class level) and one order, Amphipoda, (classified up to order level) were recorded (Table 3). Of all the metrics used to compare the macroinvertebrate families in this study, only SASS, ASPT and taxa richness significantly differed among land-use categories (Table 2, Kruskal-Wallis, p < 0.05). SASS and ASPT scores were significantly highest in the forested commercial farming sites and the GDM sites compared to the rest of the land-use categories while taxa richness was significantly lowest in urban sites compared to all the other land-use categories (Table 2). Relationship between land use and macroinvertebrates The distribution of macroinvertebrates in the two different sampling periods (April and September) among the different land uses is shown in Fig. 2a, b, respectively. Figure 2a, b shows similar trends of macroinvertebrate distribution in the different sampling seasons. In Fig. 2a (April), DCA axis 1 and 2 accounted for 20.40 % variation in macroinvertebrate composition with axis 1 explaining 12.30 % variation. In Fig. 2b (September), DCA axis 1 and 2 accounted for 22.77 % variation in macroinvertebrate composition with axis 1 explaining

Author's personal copy Environ Sci Pollut Res Table 1

Mean (± standard deviation) of physicochemical variables recorded in different land-use categories in Manyame Catchment, Zimbabwe

Variable

Forested commercial farming

Communal farming

Urban areas

Great Dyke mining

Vegetation cover (%) Temperature (°C)

46.3 ± 28.98 21.2 ± 0.52 5.04 ± 1.35a 140.0 ± 88.11 13064 ± 13431a 98.1 ± 60.83 0.07 ± 0.05 64.2 ± 81.55 0.02 ± 0.01 4.28 ± 1.78

51.9 ± 21.94 20.0 ± 1.83 3.93 ± 1.621.62 214.2 ± 143.46 6597 ± 3209b 156.4 ± 104.01 0.11 ± 0.08 57.9 ± 100.91 0.02 ± 0.02 2.34 ± 1.15

42.1 ± 28.85 20.6 ± 2.33 3.30 ± 2.33 639.3 ± 335.24a 1902 ± 804 459.1 ± 236.25a 0.34 ± 0.19a 237.0 ± 337.26a 0.33 ± 0.63a 7.75 ± 7.86a

38.9 ± 26.43 22.5 ± 1.63 6.58 ± 1.18a 535.5 ± 118.14a 1775 ± 833 368.2 ± 78.44a 0.30 ± 0.16a 12.68 ± 5.59 0.00 ± 0.01 1.99 ± 0.53

112.5 ± 44.02 7.39 ± 7.11 7.86 ± 3.09 1.36 ± 1.35 8.7 ± 5.45 0.02 ± 0.02 50 ± 31.26

118.3 ± 56.92 8.95 ± 5.91 12.86 ± 9.01 1.67 ± 0.97 13.07 ± 5.01 0.02 ± 0.02 68.4 ± 44.74

175.7 ± 161.85 17.02 ± 3.40a 30.92 ± 6.51a 9.34 ± 13.87a 27.2 ± 9.82a 0.02 ± 0.02 147.26 ± 27.21a

97.0 ± 69.86 23.6 ± 2.16a 14.01 ± 4.36 0.13 ± 0.15 1.47 ± 1.53b 0.04 ± 0.02 132.13 ± 16.74a

Dissolved Oxygen (mg/l)1 Conductivity(μS/cm)1 Resistivity (ohms)1 Total Dissolved Solids (mg/l)1 Salinity(PPT)1 Chloride (mg/l)1 Total Phosphates (mg/l)1 Total Nitrogen (mg/l)1 Chemical Oxygen Demand (mg/l) Magnesium (mg/l)1 Calcium (mg/l)1 Potassium (mg/l)1 Sodium (mg/l)1 Nickel (mg/l) Hardness (mg/l)1 1

Variable significantly differed between sites (p < 0.05). Superscript letters indicate values that significantly differ with others in the same row (Tukey’s HSD, p < 0.05)

15.78 % variation. Eigen values and gradient lengths for each of the first two axes in the DCAs (Fig. 2a, b) were relatively high, e.g. for April; Axis 1 Eigen value = 0.63, gradient length = 4.68; Axis 2 Eigen value = 0.42, gradient length = 4.06 and for September; Axis 1 Eigen value = 0.59, gradient length = 4.99; Axis 2 Eigen value = 0.25, gradient length = 2.47. This indicates good separation of the different land uses in macroinvertebrate ordination space, particularly along DCA axis 1. DCA axis 1 generally separated the different pollution gradients in the study area with similar sites grouping closely together. The forested commercial farming sites and GDM sites grouped together to the left of the ordination plot while urban areas were grouped to the right. However, communal farming sites were the most variable in their macroinvertebrate communities and occurred across the entire ordination space plotting at each end of both ordination

Table 2 Means ± standard deviation of multi metric indices (taxa richness, SASS score, ASPT score, dominance, Shannon Wiener Diversity (H1) index) for macroinvertebrates sampled in different land-use categories in Manyame Catchment, Zimbabwe

Taxa richness1 1

Total SASS Score ASPT1 Dominance (H1)

axes. Macroinvertebrates occurring in the communal sites were therefore predominantly a subset of those occurring in all the other different land uses. Forested commercial farming sites and GDM sites were associated with pollution-sensitive taxa such as Amphipoda, Notonemouridae, Heptageniidae and Perlidae. Macroinvertebrates characterising urban sites on the other hand included pollution tolerant taxa such as Syrphidae, Oligochaeta and Chironomidae.

Discussion Water quality The results indicate that land-use pattern has an effect on water quality in surrounding streams as has been reported in other

Forested commercial farming

Communal farming

Urban areas

Great Dyke mining

6.5 ± 2.6 37.8 ± 15.9a 5.8 ± 0.7a 0.3 ± 0.3 1.5 ± 0.6

5.4 ± 2.8 22.2 ± 14.0 4.0 ± 0.8 0.4 ± 0.2 1.1 ± 0.5

4.0 ± 2.9 a 15.1 ± 13.2 3.6 ± 1.7 0.5 ± 0.3 1.0 ± 0.7

6.8 ± 3.0 36.1 ± 15.5a 5.5 ± 1.5a 0.4 ± 0.3 1.4 ± 0.6

Variable significantly differed between sites (Kruskal-wallis, p < 0.05). Superscript letters indicate values that significantly differ with others in the same row (Mann-Whitney, p < 0.05)

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Author's personal copy Environ Sci Pollut Res Table 3 Macroinvertebrates sampled in different land-use categories in Manyame Catchment, Zimbabwe and acronyms used in DCAs on Fig. 2 Order

Family

Amphipoda

Table 3 (continued) Order

Family

Acronym

Amp

Odonata Odonata

Gomphidae Lestidae

Gom Lest

Acronym

Basommatophora

Ancylidae

Ancy

Odonata

Libellulidae

Lib

Coleoptera Coleoptera

Dytiscidae Elmidae

Dyt Elm

Odonata Pelecypoda

Platycnemidae Corbiculidae

Plat Corb

Coleoptera

Gyrinidae

Gyr

Pelecypoda

Sphaerilidae

Sph

Coleoptera Coleoptera

Helodidae Hydraenidae

Hel Hyda

Pelecypoda Trichoptera

Unionidae Barbarochthonidae

Uni Bar

Coleoptera Coleoptera

Hydrophilidae Psephenidae

Hydp Pse

Trichoptera Trichoptera

Ecnomidae Hydropsychidae

Ecn Hyds

Decapoda

Potamonautidae

Pot

Trichoptera

Leptoceridae

Lptc

Diptera

Athericidae

Ath

Diptera Diptera Diptera

Ceratopogonidae Chironomidae Culicidae

Cer Chi Cul

Trichoptera Trichoptera

Parecnomina Philopotamidae

Par Phi

Pisuliidae Polycentropodidae

Diptera

Dixidae

Dix

Trichoptera Trichoptera Hirudinae (class)

Pis Pol Hir

Diptera Diptera Diptera

Muscidae Notonemouridae Perlidae

Mus Ntnm Per

Oligochaeta (class)

Diptera Diptera Diptera Diptera

Psychodidae Simuliidae Syrphidae Tabanidae

Pscd Sim Syr Tab

Diptera Ephemeroptera Ephemeroptera

Tipulidae Baetidae Caenidae

Tip Bae Cae

Ephemeroptera Ephemeroptera Gastropoda Gastropoda Gastropoda

Heptageniidae Leptophlebiidae Lymnaeidae Physidae Planorbidae

Hep Lptp Lym Phy Plan

Gastropoda Hemiptera Hemiptera Hemiptera Hemiptera Hemiptera Hemiptera Hemiptera Hemiptera Hemiptera Lepidoptera

Thiaridae Belostomatidae Corixidae Gerridae Hydrometridae Naucoridae Nepidae Notonectidae Pleidae Veliidae Pyralidae

Thi Bel Corx Ger Hydm Nau Nep Ntnc Plei Vel Pyr

Megaloptera Odonata Odonata Odonata Odonata Odonata Odonata

Corydalidae Aeshnidae Chlorocyphidae Chlorolestidae Calopterygidae Coenagrionidae Corduliidae

Cory Aes Chlc Chll Cal Coe Cord

studies (Gratwicke 1998; Broussard and Turner 2009; Nielsen et al. 2012). High concentration of pollutants, especially TP and TN in urban areas can be attributed to sewage (and industrial effluents) and storm water runoff. Several studies have shown that urban streams worldwide are often polluted by urban runoff and combined sewer outflows (Roy et al. 2001; Nhapi 2009; Broussard and Turner 2009; Zhang et al. 2012, 2013; Bere and Nyamupingidza 2014). The Manyame catchment is no exception, sewage being a prominent problem in the area. Raw sewage is being discharged into streams draining urban areas as reported by Bere and Nyamupingidza (2014) and Mangadze et al. (2015). Gratwicke (1998) showed that season has an effect on the SASS index in Yellow Jacket and Mazoe rivers, Zimbabwe. The seasonal differences can be attributed to the dilution effect of increased flows during the rainy season. However, Gratwicke (1998) was not looking at urban headwater streams. Our study investigated urban headwater streams with minimum fluctuations. Fluctuations in stream flow in these headwater streams are usually associated with particular storm events. The rest of the flow that is recorded throughout the year is industrial waste and sewage. The reduced pollution observed in forested commercial farming areas confirms the hypothesis that the forested areas maintain relatively higher water quality compared to nonforested areas. Other studies also found forested areas to be least impacted by pollution (Sponseller et al. 2001; Zhang et al. 2013). Forest land cover tends to reduce runoff of water, sediments, nutrients and toxicants, maintain more stable flows, water temperatures and channel morphologies, and supply coarse organic material and debris to provide food

Oli

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8.10%

a)

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Environ Sci Pollut Res

Lptc Hydp

Par Pse Pol

Sph Hir

Plan

Corb

Lym

Aes

Amp Ger

Hyda

Dyt

Gom

Mus

Coe Hep

Chll Hyds

Cory Chi Phy Nep

Oli

Cul Ntnm Phi Cal Nau Chlc TabCer Bae Bel Pscd Pot Pyr GyrLptp

12.30%

Ntnc

Thi

-1

Elm Lib

Plei

Vel

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6.99%

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-1

b)

Syr

Coe

Bar

Plan Bel Cory Lest Vel Nau Phy Corb Lib Cer Dyt Aes Chll Bae Pse Oli Cae Ger Per Tab Syr Gyr Cal Hep Hydp Tip Lym Sim Pot Corx Chi 15.78% Ath Ntnc Cul Chlc Ecn Gom

-4

Cord

-1

X

6

Forested Commercial farming sampling sites

○

Communal Areas

Great Dyke mining sites

♦

urban sites

Fig. 2 a and b Detrended correspondence analysis (DCA) score plot for macroinvertebrates sampled in the Manyame catchment in a April and b September (Macroinvertebrate taxa names given in full in Table 3)

and habitat for aquatic life (Sponseller et al. 2001; Jun et al. 2011; Zhang et al. 2013). Consequently, streams with preserved watersheds have greater biodiversity and better stream integrity (Jun et al. 2011). A watershed with poor forest cover is more vulnerable to soil erosion, and this intensifies the deterioration of the water quality. All the metals that could lead to metal contamination were not detected in our study. The total metal concentrations soluble in water are governed by an array of factors including pH, salinity and redox conditions, and the absence of these metals in this study can be attributed to the alkaline pH (>7) the area is known to have (Bere and Mangadze 2014). Metals have

been shown to be sparingly soluble under alkaline conditions (Chuan et al. 1996; Spencer 2002). However, future studies should consider analysing the heavy metals in sediments instead of in the water column. This is imperative as heavy metals usually decant to the sediments from which they can possibly impair macroinvertebrates. GDM sites had the highest amount of Mg (23.6 ± 2.16 mg/ l) since the stream beds are composed of ultramafic rocks which are well-endowed with magnesium-bearing minerals (Makore and Zano 2012). Magnesium was also significantly high in urban areas (17.02 ± 3.40 mg/l) where pollution is rampant. While magnesium is relatively higher in the GDM and urban areas, it is neither a health nor environmental problem. Hamzaoui and Arab (2012) observed high Mg levels of up to 351 mg/l that also did not have an adverse effect on wildlife. Magnesium is common in natural waters with natural concentrations that range from 1 to 100 mg/l, depending on the rock types (Chapman 1996). Hardness is a measure of the amount of divalent cations present in water and the capacity of their salts to precipitate soap (Lutz 2004). The most common of such cations are calcium and magnesium (AWWA 1990) which are very high in the GDM area (as a result of the geological situation) and urban areas (as a result of pollution). Hence, the samples in these two areas had hard water (total hardness above 100 mg/l; WHO 2006), 132.13 ± 16.74 mg/l for urban areas and 147.26 ± 27.21 mg/l for the great dyke. However, water softening is not necessary unless total hardness exceeds 200 mg/l (AWWA 1990). Furthermore, hardness is not considered a critical water parameter in determining suitability for aquatic organisms (Chapman 1996). TDS, which was also significantly higher in urban areas and GDM sites, is a measure of inorganic salts (principally calcium, magnesium, potassium, sodium, bicarbonates, chlorides and sulphates) organic matter and other dissolved materials in water (Weber-Scannell and Duffy 2007). TDS concentration and composition in natural waters is determined by the geology, atmospheric precipitation and the water balance, i.e. evaporation and precipitation (Weber-Scannell and Duffy 2007). Elevated TDS levels are known to occur in streams draining mining sites after large volumes of rock are exposed to weathering elements such as air and water, causing rapid dissolution of rocks and minerals into waters draining the site (Bernhardt et al. 2012). In urban areas, TDS is increased by pollution from sewage, urban runoff and industrial wastewater. Concentrations of calcium, potassium, magnesium and sodium were all significantly higher in urban areas compared to other land-use categories. Water with TDS less than 600 mg/l as in all sites in our study is generally considered to be good drinking water (WHO 2006). However, the increase in TDS (mean recorded value of 459.1 ± 236.25) in urban areas should be a cause of concern as it has potential for disturbing ecosystem integrity if it continues to increase.

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Elevated levels of TDS are known to affect benthic macroinvertebrates negatively (Timpano et al. 2011; Bernhardt et al. 2012; Cormier et al. 2013a, b). TDS causes toxicity through increases in salinity (leading to osmotic stress), changes in the ionic composition of the water and toxicity of individual ions (Weber-Scannell and Duffy 2007). A linear relationship between TDS and conductivity has been demonstrated before hence the increase in conductivity in GDM sites and urban areas (Timpano et al. 2011). Timpano et al. (2011) calculated effect probabilities of 0, 50 and 100 % to be associated with conductivity values of 332; 625 and 1 366 μS/cm, respectively, in Central Appalachian Streams, Virginia. The conductivity values of 639.3 ± 335.24 observed in urban areas should thus be an issue of concern as it is now at the level where it may start to have an effect on biota. Dissolved oxygen which was significantly highest in the GDM and commercial farming sites is known to vary spatially and temporally because of respiration by organisms, photosynthesis by plants, atmospheric losses and gains, changes in pressure and temperature and groundwater inflow (Connollym et al. 2004). The high DO in the GDM sites can be attributed to the higher stream velocities to do with the dyke terrain. However, anthropogenic impacts have been solely responsible for the reduction in DO in many aquatic systems (Connollym et al. 2004). Water resources that receive poorly treated urban wastewater and pollutants from agriculture are thus characterised by low dissolved oxygen (Nhapi et al. 2004) as was observed in the urban and communal faming sites. Low dissolved oxygen levels in riverine systems may suffocate aquatic life (Nhapi et al. 2004) resulting in changes in community composition and often a loss of diversity (Connollym et al. 2004). Relationship between land use and macroinvertebrates Macroinvertebrate assemblages closely followed the observed changes in pollution levels as a result of land-use-induced changes in water quality with the least disturbed sampling sites being associated with macroinvertebrate communities that were different from highly polluted sampling sites. Taxa richness, SASS and ASPT scores also followed this trend being highest in the forested commercial farming sites and the GDM sites compared to the rest of the land-uses. Based on the DCA, the polluted urban sites were characterized by species such as Syrphidae, Oligochaeta and Chironomidae. These species are known to tolerate polluted environments because they have a high concentration of haemoglobin (e.g. Oligochaeta and Chironomidae) or specialised body structures (e.g. a breathing siphon in Syrphidae larva) that enables them to increase oxygen uptake in highly polluted environments (Gooderham and Tsyrlin 2002). Only a few specialised families can adapt in polluted environments; hence, such environments are bound to have fewer families (Xu et al. 2013).

Communal farming sites which were characterised by fairly cleaner water compared to urban sites showed the highest amount of variation in ordination space. These communal farming sites were characterised by macroinvertebrates occurring in all the other different land uses. Palmer et al. (2004) allude that biota are indicative of the upper limits of the range of changes in water quality that they can tolerate. Thus, the species which develop well in polluted zones may also occur in fairly clean water as has been observed in this study and as also observed by Bere and Nyamupingidza (2014). The forested commercial farming sites were grouped together with the GDM sites being characterised by high DO. Although the GDM sites recorded higher values of Mg, TDS, salinity, conductivity and hardness, these variables were not limiting as discussed above. GDM sites therefore also showed notably high SASS and ASPT scores being characterised by families such as Notonemouridae, Heptageniidae, Perlidae and order Amphipoda. These families are known to occur in relatively clean waters (Geber and Gabriel 2002). The DCA therefore clearly presented a gradient of pollution and its adverse effects on macroinvertebrates and singled out urban development as the single most degrading land-use. The fact that macroinvertebrate biodiversity was higher and indicated good water quality in the forested agricultural sites and progressively declined in less forested communal areas until the least biodiversity was recorded in urban areas shows the importance of forested catchments in restoring ecosystem integrity. Ndebele-Murisa (2012) showed how Mukuvisi woodlands vegetation wetland patches greatly improved water quality in Mukuvisi river, Zimbabwe, from pH values of between 2.22 and 4.75 (highly uninhabitable for most biota) to pH values between 6.36 and 7.25 (with high macroinvertebrate biodiversity). This shows the increasing importance of maintaining forested watershed which apparently had received less attention in macroinvertebrate studies as indicated by Moore and Palmer (2005). Forested watersheds are therefore important in reversing the usually undesirable effects of agriculture and urbanisation including sediment and chemical pollution. This however does not exclude good farm management practices (like contour ploughing and avoiding stream bank cultivation) that characterise the forested agricultural sites in our study area. Good farm management practices combined with riparian buffers will help protect streams and maintain high levels of diversity. Palmer et al. (2005) further asserts that streams in highly urbanized areas should not be viewed as lost causes as riparian buffer restoration may mitigate some of the impacts on stream biodiversity. Urban development, which was identified as the single most ecosystem-degrading land use in our study, is expected to increase irreversibly globally (Irwin and Bockstael 2006). Thus, the maintenance of riparian buffers in urban areas is going to be even more important. The fact that GDM sites had the highest biodiversity together with the forested commercial sites shows the

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importance of dissolved oxygen in maintaining ecosystems intact. Diversity, SASS and ASPT scores increased with increases in DO. Dissolved oxygen is an important parameter in ecosystem integrity being a key indicator of environmental quality (Connollym et al. 2004). DO concentrations below 4.0 mg/L, have potentially adverse impacts on aquatic life. It is therefore imperative that more stringent measures are taken to address the increasing amounts of pollution in headwater streams of Zimbabwean urban areas which are currently characterised by raw sewage and industrial wastes. Strategies that reduce the pollutants contributing to oxygen depletion must be devised. Total phosphates in particular are known to have a very significant impact on algal production (which depletes DO). The accuracy of the South African Scoring System (SASS) in evaluating land-use-induced changes in water quality in this study shows the robustness of this inexpensive method in continuous monitoring. This further confirms the wider applicability of SASS in monitoring anthropogenic activities in the study region corroborating findings by Gratwicke (1998); Phiri (2000); Mtetwe et al. (2002) and Bere and Nyamupingidza (2014). However, most of these studies were done in a small setting using few sites, e.g. Bere and Nyamupingidza (2014) only studied one geographic setting (urban areas) using eight sites. Results of this study indicate the wider applicability of SASS despite differences in geographic set up demonstrating the effects of land-use-induced changes in ecosystem health which have implications for conservation and management. Used predictively, SASS is useful in measuring continued land-use-induced changes in ecosystem health over large geographic areas without the necessity for costly field surveys. Conclusion Variations in macroinvertebrate communities among the four land-use settings identified in this study can be broadly attributed to land-use-induced changes in water quality as we hypothesised. This study emphasizes the importance of forested watersheds together with good farm management practices in mitigating the impacts of agriculture, fostering macroinvertebrate conservation and consequently ecosystem integrity. Dissolved oxygen was identified as an important parameter in ecosystem integrity being a key indicator of environmental quality. Urbanisation was identified as the most ecosystemdegrading land-use in the Manyame catchment. It is therefore imperative that more stringent measures are taken to address the increasing amounts of pollution in headwater streams of Zimbabwean urban areas which are currently characterised by raw sewage and industrial wastes. Strategies that reduce the pollutants contributing to oxygen depletion must be devised. More research is needed to separate possible environmental impairments arising from mining and those arising from the

geological situation of the rivers in the Great Dyke. The information gained in this study augments previous work on the use of macroinvertebrates in other regions and affirms the wider applicability of SASS in evaluating land-use-induced changes in water quality in the study region. Acknowledgments This study was made possible by the provision of funds from International Foundation for Science and British Ecological Society

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