CHAPTER 3 Ecological integrity using SASS 5 for the uMngeni, Tugela, Umvoti, Umdloti and Umfolozi Rivers in KwaZulu-Natal
Rivers can be assessed by various indicators such as the vegetation types, the fish populations, the types of macro-invertebrates for their ecological integrity and the state of health (Barbour et al., 1996; Thirion, 2007). Any change in the structures of the aquatic macro-invertebrate community will provide information on the effects or direct stress of the water body. These stressors are the water quality, pollution, hydrological and geomorphological processes and habitat alterations (Dallas, 2000; Álvarez-Cabria et al., 2010; Holt and Miller, 2011). Due to their wide distribution, macro-invertebrates have been known to be ideal ecological indicator. They are easily sampled, sensitive to even the slightest changes in ecosystem states, have a large-scale applicability and can be used across regions (Álvarez-Cabria et al., 2010). In South Africa, several methodologies incorporate aquatic macro-invertebrates as biological indicators. The South African Scoring System, Version 5 (SASS 5) (Dickens and Graham, 2002), the Macro-Invertebrate Response Assessment Index (MIRIA) (Thirion, 2007) and the use of multivariate statistical analysis are currently used throughout South Africa. The ecosystem variables that are used in these assessments include water quality and habitat variables which are referred to as ecological driver components which are the main components of the South African Scoring System (SASS 5) used as a biological index of water quality (Dickens and Graham, 2002).
This South African Scoring System is now the benchmarked guidelines where all rivers can be assessed on its ecological integrity and community structures. The technique also provide valuable information regarding the current state of ecological integrity of the aquatic macro-invertebrate communities (Dickens and Graham, 2002; Thirion, 2007). The credibility of the South African Scoring System is not questionable as it has been revised and improved upon since it was developed in 1994 and is now in its 5th revision, hence the acronym SASS 5 (Dickens and Graham, 2002). Different families show different tolerance to pollutions and range from highly tolerant families (e.g. Muscidae and Psychodidae) to less tolerant families (e.g. Oligoneuridae).
The Macro-Invertebrate Response Assessment Index (MIRAI) method used the information generated by SASS to evaluate the water-quality and -quantity impacts and at the same time assess the habitat suitability for aquatic macro-invertebrates (Thirion, 2007). This method delivers to the end user the habitat-based cause-and-effect which then can be used to interpret the deviation of the aquatic macro-invertebrate assemblage attributes from a pre-established reference condition (Thirion, 2007). The most often used approach nationally is the SASS 5 method (Thirion, 2007). Van den Brink et al. (2003) indicated that several multivariate statistical techniques have also been used to evaluate the structure of aquatic macro-invertebrate assemblages and their response to different altered ecosystem driver components. To determine community structure, Multivariate statistical analyses techniques is the most often used. This method also derives the patterns in various ecosystems (Ter Braak, 1994; Van den Brink et al., 2003; O‟Brien et al., 2009). Statistical analysis for this study was undertaken by a qualified statistician
Materials and methods
South African Scoring System (SASS) (refer to Annexure for example of spreadsheet for SASS sampling)
Samples of different micro-invertebrates were taken from the five sites of the uMngeni River, Tugela River, Umvoti River, Umdloti River and Umfolozi River during summer and winter respectively to ascertain differences due to seasonal variations. The surveys were undertaken using the SASS 5 method which involves the collection of macro-invertebrates according to the standardised SASS protocol at three different habitat types or biotopes according to Dickens and Graham (2002). The three different biotopes include stones (in current, out of current and bedrock) sampled for 2 min, marginal vegetation (total length of 2 m), and gravel, sand and mud (GSM) sampled from 30 to 60 s. Sampling was done with a standard SASS net (1 mm mesh and dimensions of 30 x 30 x 30 cm) and analysed separately according to the standardised protocol in order to be able to consider habitat availability. Specimen samples were preserved in 10% neutral buffered formaldehyde and stained with phloxine dye and transported to the laboratory for identification with the aid of a dissection microscope and guided by the macro-invertebrate guide (Kleynhans, 1999; Dickens and Graham, 2002).
SASS results are expressed both as index score (SASS score) and the average score per recorded taxon (ASPT) and the results (SASS scores and ASPT values) were then analysed using the SASS data interpretation guidelines (Dallas, 2005; Dallas, 2007). SASS assessment were done the same way as investigations carried out in previous studies on other rivers (CRUZ, 2000; O’Brien et al.,2005; Malherbe, 2006; Cloete et al., 2008; Ferreira et al., 2008; Malherbe et al., 2008; Stryftombolas, 2008; O’Brien et al., 2009; O’Brien, 2010).
Results and Discussion
South African Scoring System (SASS 5)
Each river was sampled in both seasons making a total of ten assessments. From the assessments, the number of taxa as well as the diversity was noted. The ASPT value was generated by dividing the SASS score by the number of taxa for each sampled site. Tables 3.2 indicated the SASS scores, Number of taxa and the ASPT for each of the rivers under this investigation.
The SASS 5 assessment for all the five rivers under this investigation seems to have some sort of consistency in the number of taxa which ranged between 16 and 33. The Tugela River had the most number of taxa in Site 1W and differed slightly from Site 1S. The lowest number of taxa was noted at the Umdhloti River ranging between 16 and 23 with the lowest ASPT value of 4.56 and 4.94. Across all rivers, the ASPT values for the winter assessments seem to be much better than the summer values.
Previous investigation on the Umvoti River showed that SASS scores were better during high flow periods as compared to low flow periods (Carminati, 2008). This could be due to the low flow periods having little effect on the organisms associated with rock and stones that form homes for these organisms. However, the organisms are easily washed down the river due to the high pressure of the flow during the high flow periods. The summer months are predominated by random rainfall. Hence, the summer months had a much lower SASS score as compared to the winter months. Furthermore, sedimentation as well as abstractions contributed to the water flows of the rivers. The rivers which are more affected by sedimentation and abstractions due to the industrial influence are the Umvoti River, Umgeni River and Tugela River. These sedimentation and abstraction resulted in flow modification had a rippled effect on the lower reaches of the river. The Umvoti River is associated with effluents from the paper mill (SAPPI) and the sewage plants near the Stanger region. These effluents affected the biodiversity of the river itself due to the increased chemical and waste pollution that these plants would contribute and ultimately affect the SASS score of the river (Stryftombolas, 2008; O’Brien, 2010a). The Umdhloti River is mainly affected by the extensive sand mining operation currently taking place just after the Verulam area. The sand mining is impacting on the biodiversity of the riparian vegetation and that of the river itself.
The most taxa collected for uMngeni River was 28 which were at the site nearest the lower reaches of the river. For Tugela River, Umvoti River, Umdhloti River and Umfolozi River, the number of taxa collected were 33, 25, 23 and 29 respectively. The least taxa collected for uMngeni River, Tugela River, Umvoti River, Umdhloti River and Umfolozi River were 19, 17, 19, 16 and 22 respectively (Tables 3.2 to 3.6).
The SASS scores for all the rivers are relatively low but constant with the lowest being that of Umdloti River. Deterioration of water quality due to industrial and domestic influence could be the driving factor for the low scores. The poor water quality due to the increased pollution creates a poor habitat for organisms and the macro-invertebrate community structures that occupied these rivers. As conservationist’s, there is a need to implement stricter measures to reduce the effects of pollution resulting from effluents in industry and effluents from sewage plants as well as prevent illegal sand mining to prevent further destruction of the rivers under this investigation.
Assessment of fish populations and bacterial levels of the uMngeni, uThukela, Umvoti, Umdloti and Umfolozi Rivers, KwaZulu-Natal
Fresh water bodies tend to end in estuaries at the lower reaches of the system. The fresh water bodies such as rivers and estuaries form homes and nesting areas for most fish species. Any pollution in the river or fresh water system will affect fish populations of a river system. In many investigations, fish assemblages are commonly used as key indicators to describe the ecological state of aquatic ecosystems (Maceda-Veiga and De Sostoa, 2011). The determination of fish assemblages is a good ecological indicator due to their longevity and the ability to move through various habitats (DWAF, 1999; Todd and Roux, 2000; Whitfield and Elliott, 2002; Van der Oost et al., 2003; Harrison and Whitfield, 2004; Maceda-Veiga and De Sostoa, 2011; Cabral et al., 2012; Gamito et al., 2012). There are limitations to this as the fish population can be affected in different ways from each river due to variation and diversity in environmental conditions (Whitfield and Elliott, 2002; Harrison and Whitfield, 2004; Cabral et al., 2012; Gamito et al., 2012).
Estuaries are highly known to provide nursery areas for marine fish (Harrison et al., 2000; Turpie., 2002). The diversity of fish species is linked directly to the characteristic of an estuary (Harrison et al., 2000). Estuaries experience a fluctuation in salt concentrations due to seawater and fresh water constantly influencing the salinity, temperature, dissolved oxygen sedimentation and turbidity. This places a considerable physiological demand on the fishes that occupy these systems (Harrison and Whitfield, 2006; Elliott et al., 2007).
This chapter centres around the fish populations during winter and summer seasons in uMngeni River, Tugela River, Umvoti River, Umdloti River and Umfolozi River.
The uMngeni River is blessed with abundance of fish species. It has been reported that the uMngeni River boasts about 48 species of freshwater fish. Thirty six of the fishes are indigenous while 12 fishes are alien. Furthermore, 57 fish species are found in the uMngeni Estuary in Durban (DWAF, 2017).
The major problem facing water bodies is the issue of pathogen transport. The process of identifying microorganisms that can potentially spread through the water supply is quite a daunting task (Salgot et al., 2001). In most river systems, the bacterial indicators such as coliforms are used to assess water quality. However, the presence of other microorganisms such as protozoa and viruses is often disregarded during these monitoring activities (Straub and Chandler, 2003).
The selection of quality microbial indicator is essential. There are specific characteristics that could be used to select an appropriate indicator, and they include An indicator that is universally present in the faeces of humans and warm-blooded animals in large numbers It must readily be detected by simple methods Can grow in natural waters, the general environment or water distribution systems Be persistent in water and the degree to which it is removed by water treatment is comparable to those of waterborne pathogens (WHO;1990; NHMRC-ARMCANZ, 2003).
The presence of different bacterial species was done in most rivers that are associated with industries, agricultural process, sewage treatment plants as well as domestic wastes. The summer/winter test for bacteriophages in the uMngeni River had ealier revealed a vast amount of contamination in the river system (Lin et al., 2012).
This chapter does not involve an extensive investigation into the fish populations, but addresses the type of fish currently existing in each of the rivers under investigation. The determination of the Fish Response Assessment index (FRAI) has been extensively investigated and it would be a futile exercise to undertake such an investigation again. However, the FRAI in South Africa is commonly used to determine the state of ecological integrity of fish assemblages in aquatic ecosystems and is implemented by the National River Health Programme (RHP) (Kleynhans, 2007). The Chapter also addresses the microbial content of each river under investigation.
Materials and Methods
Field sampling Fish
Sampling was done at the sites as it was done in previous chapters. Both summer and winter samples were taken to ascertain changes in the population type during seasonal fluctuations. The survey was undertaken as per previous investigations, with modifications on fresh water ecosystems (Meador et al., 1993; Barbour et al., 1999). Samplings were done on three different occasions with the best sample size as noted on the table of results. In summary, the netting techniques included the use of a seine net (12 mm mesh, 5 m long). This net was hauled through all shallow (less than 1 m depth) habitats onto sand banks at all sites dominated by sandy bottoms. Additionally, a medium sized seine net (22 mm mesh, 30 m long, fitted with a bag) was used through deep (greater than 1 m) open water habitats at all of the sandy bottomed sites. The habitats that were sampled include slow (<0.3 m/s) deep (> 1m), slow shallow (< 1m), fast (>0.3 m/s) deep and shallow as well as areas with marginal and overhanging vegetation. The physical condition of the area was also noted. Changes in the environmental conditions are related to fish stress and formed the basis of ecological response interpretation
Soil samples from each river were collected from the sampling areas in clean 100 ml bottles. The bottles were washed first with the water from the sample area before collections were done. Three samples were taken from each area. The samples were then transported to the laboratory for further analysis.
Nutrient Media Preparation
Fifty eight grams of MacConkey Agar Purple was weighed and dispense in an Erlenmeyer flask containing 1L of distilled water. The agar was mixed well and allowed to stand for 10 min. The agar was autoclaved for 20 min at 121oC and at 2 atmospheric pressures before being poured into sterile petri dishes and allowed to set before use.
Methodology – Soil analysis
Ninety nine millilitres of distilled water was poured into an Erlenmeyer Flask. Soil samples weighing 1g was diluted in each flask for each river to make a final solution of 100 g/ml. The flasks were left to agitate on an orbital shaker for 15 min at 100 rpm. A 10-fold serial dilution was prepared by pipetting 1ml of the original sample and diluting it serially on culture tubes containing 9 ml of distilled water – 1×101,1×102,1×103,1×104,1×105 and 1×106. The 1×106 dilution was taken and passed through a sterile filter paper embedded on a funnel assembly of a vacuum pump. The samples were allowed to run completely through the filter. The filter paper was removed from the vacuum pump with sterile forceps and aseptically placed on the surface of a Salmonella Shigella Agar. Plates were sealed with parafilm and incubated upside down for 48 h at 37oC. Colonies forming unit/100 ml after incubation were then counted.
1.1 Study motivation
1.2 Study Hypothesis
2. CHAPTER 2
2.2. Materials and methods
2.3 Results and discussion
3. CHAPTER 3
3.2 Materials and methods
3.3 Results and Discussion
4 CHAPTER 4
4.2 Materials and Methods
5. CHAPTER 5
6. CHAPTER 6
6.3: Results and Discussion
7. CHAPTER 7
7.3 Results and Discussion
7.4 Key policies and water management systems
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Integrating indigenous knowledge systems into indigenous agricultural and industrial water management that impacts changes in riverine biodiversity: A conservation perspective