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Diatom community structure and relationship with water quality in Lake Sibaya, KwaZulu-Natal, South Africa
South African Journal of Botany 123 (2019) 161–169
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South African Journal of Botany journal homepage: www.elsevier.com/locate/sajb
Diatom community structure and relationship with water quality in Lake Sibaya, KwaZulu-Natal, South Africa A. Kock a,⁎, J.C. Taylor b,c, W. Malherbe a a b c
Water Research Group, Unit for Environmental Sciences and Management, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa Unit for Environmental Sciences and Management, North-West University, Private Bag X6001, Potchefstroom, North West Province 2520, South Africa South African Institute for Aquatic Biodiversity (SAIAB), Private Bag 1015, Grahamstown, 6140, South Africa
a r t i c l e
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Article history: Received 28 September 2018 Received in revised form 8 February 2019 Accepted 6 March 2019 Available online xxxx Edited by WA Stirk Keywords: Biodiversity Bio-monitoring Diatom indices Eutrophication Wetlands
a b s t r a c t Monitoring of wetland ecosystems is crucial as these ecosystems provide numerous services and resources to both humans and the biota found therein. Wetlands are characterised by variations in inundation time and depth and therefore it is often difficult to make use of invertebrates and fish as biological indicators. Diatoms may be used for monitoring of wetland ecosystems as they are widely distributed, are microscopic with few habitat restrictions, communities are species rich and they respond rapidly to environmental changes. Very little information is available on wetland diatom communities in South Africa. Wetland ecosystems include Ramsar sites such as Lake Sibaya which faces increased forestry and rural development within the lake's catchment area. Therefore, this study aimed to determine the diatom community structure of Lake Sibaya and its response to changes in water quality. Water and diatom samples were collected from four sites in Lake Sibaya during August 2015, December 2015 and February 2016. All samples were collected and analysed according to standard methodologies. From the results it was clear that the lake had a lower diatom diversity than that described in a previous study in 1966, with Cocconeis placentula, Gomphonema sp., Epithemia adnata and Nitzschia sp. being the dominant taxa. Both the diatom community structure and measured water quality parameters showed the lake to be nutrient enriched. However, as nutrients accumulate in wetlands it is unknown whether the increased nutrients arise from anthropogenic disturbances or natural sources. © 2019 SAAB. Published by Elsevier B.V. All rights reserved.
1. Introduction Wetlands are important ecosystems that provide numerous important ecological services such as water storage, biochemical cycling and maintenance of biodiversity (Matlala et al., 2011). Humans also benefit from wetland ecosystems as these ecosystems provide them with essential resources like water, wood (plant products) and food (Kotze, 2010). Wetlands are found across the world and make up approximately 6% of the world's surface area (DWAF, 2004). The importance of wetlands can thus not be overlooked and adequate monitoring of these ecosystems is vital. However, as wetlands act as ‘sinks’ where water and sediment accumulate, they are more susceptible to pollution (Dallas and Day, 2004; Malan and Day, 2012; Humphries and BenitezNelson, 2013; Dalu and Froneman, 2016). This makes these ecosystems extremely sensitive to anthropogenic activities including mining, forestry and agricultural drainage that potentially affect the water quality (Malan and Day, 2012; de Necker et al., 2016). ⁎ Corresponding author at: 11 Hoffman Street, Potchefstroom, North-West University, Potchefstroom Campus, Building F20, Room 63, 2531 Potchefstroom, South Africa. E-mail address: [email protected] (A. Kock).
https://doi.org/10.1016/j.sajb.2019.03.013 0254-6299/© 2019 SAAB. Published by Elsevier B.V. All rights reserved.
To monitor these ecosystems, the complete spectrum of information (chemical, physical and bacteriological measurements) is needed for the aquatic ecosystem (Li et al., 2010). However, Li et al. (2010) stated that it has been shown that biological indicators can replace traditional monitoring techniques as these organisms are constantly exposed to the environmental changes in their natural environment. Bio-monitoring can effectively be used to aid in the management of wetlands as it provides a time integrated indication of the environmental conditions (Dalu and Froneman, 2016). Numerous bio-monitoring techniques have been implemented in South Africa which include the National Aquatic Ecosystem Health Monitoring Programme (NAEHMP) (DWAF, 2016a) and the River Ecostatus Monitoring Programme (REMP) (DWAF, 2016b), previously the River Health Programme (RHP). The NAEHMP consists of numerous indices that incorporate riparian vegetation, macro-invertebrates and fish as biological responders (DWAF, 2016b). Due to wetland ecosystems' varied inundation time and depths as well as the availability of habitat and representative biota, it is problematic to use fish and invertebrates as biological indicators in shallow lotic waters. However, diatoms are useful to monitor wetland ecosystems as they are present in ecosystems that lack refuge and requisite habitats for higher organisms. Diatoms are sensitive to organic matter
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and nutrient contamination making them suitable organisms in monitoring the chemical status of ecosystems (Pandey et al., 2017). Diatoms form part of the phylum Bacillariophyta and are unique unicellular micro-organisms that form a vital link within the food web structure of aquatic ecosystems as they are amongst the main primary producers in these ecosystems (Round et al., 1990; Dixit et al., 1992; Schmitt-Jansen and Altenburger, 2005; Taylor et al., 2005, 2007; Smucker and Vis, 2011; Pandey et al., 2017). Diatoms plays a vital role in the food web as a source of food for benthic consumers, they are also important in ecosystems were sediment lacks oxygen as they play a crucial role in the carbon cycle as oxygen producers (Schmitt-Jansen and Altenburger, 2005). A good understanding of the relationship between environmental changes (physico-chemical parameters) and the diatom community makes these organisms ideal biological indicators (Brazner et al., 2007; Julius and Theriot, 2010; Smol and Stoermer, 2010). Diatom frustules can be preserved for long periods after cell death, even when the system has dried out, due to their siliceous cell wall (Julius and Theriot, 2010; Smol and Stoermer, 2010; Matlala et al., 2011; Pandey et al., 2017), making them unique from other algae. They are useful biological indicators as they are found in all aquatic biotypes, each species has its own specific water quality requirements, diatoms respond rapidly to environmental changes and anthropogenic activities and are present in most aquatic and sub-aerial environments (Dixit et al., 1992; Reid et al., 1995; Morales et al., 2001; Julius and Theriot, 2010; Smol and Stoermer, 2010; Stevenson et al., 2010; Dalu and Froneman, 2016; Pandey et al., 2017). Diatoms are useful in long-term monitoring programmes as they are simple to sample and generating data are cost effective, time efficient and comparable between ecosystems (Gell et al., 2002; Dalu and Froneman, 2016). Diatoms have successfully been implemented to monitor past and present environmental conditions and aquatic pollution worldwide (Stevenson et al., 2010; La Hée and Gaiser, 2012; Dalu and Froneman, 2016). Numerous studies have reported on diatoms' sensitivity to inorganic contamination, organic matter and organic toxicants (Pandey et al., 2017). Diatoms have recently been included in the NAEHMP in South Africa even though they are not routinely used for environmental monitoring in southern Africa (Dalu and Froneman, 2016). Previous studies on diatom communities within wetland ecosystems in South Africa showed that there is a strong correlation between the diatom composition and physico-chemical parameters (Matlala et al., 2011). As diatoms have a siliceous cell wall they can aid in determination of the long-term water quality conditions within wetlands including those that have recently dried out (Pandey et al., 2017). According to Gaiser et al. (2005), coastal wetlands have been monitored using diatoms, especially to determine a salinity gradient in South Florida, USA. However, they also stated that implementing these techniques elsewhere in the world may be problematic. There is a general paucity of information on aquatic ecosystems in South Africa's Ramsar wetlands, this includes a lack of information on the diatom communities. Limited aquatic sampling and monitoring has been carried out in Lake Sibaya, this is of concern as the lake is threatened by rural development, herbicide and pesticide spraying as well as forestry (Ward and Kyle, 1990; Humphries and BenitezNelson, 2013; DWS, 2015a). The only known study on the diatom community from Lake Sibaya was by Archibald (1966). During their study two sites were sampled on the eastern shore of the lake and one sites' location is uncertain, however, it might be on the eastern shore due to the species present. Their study identified a total of 107 diatom species from the three sites. Due to the lake's endorheic nature and the anthropogenic activities impacting the lake it is necessary to monitor this ecosystem. The present study aimed to determine the current diatom community structure within Lake Sibaya as well as its relationship with selected water quality parameters. It was hypothesised that Lake Sibaya diatom community would be negatively impacted based on diatom index scores, due to the development of rural areas and forestry in the catchment.
2. Materials and methods 2.1. Study area Lake Sibaya (Fig. 1) is situated on the east coast of South Africa roughly 430 km north-east of Durban (Ward and Kyle, 1990; Combrink et al., 2011; Humphries and Benitez-Nelson, 2013; Stager et al., 2013). The lake is South Africa's largest natural coastal freshwater lake (Ward and Kyle, 1990; Combrink et al., 2011; Stager et al., 2013) and is cut-off from the ocean by large vegetated sand dunes on the eastern side with the western side of the lake shore being extremely flat (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013; Stager et al., 2013). The lake is 20 m above sea level (Bowen, 1978; Combrink et al., 2011; Stager et al., 2013) and has a surface area of approximately 60–70 km2 (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013). The lake has a maximum depth of 43 m with a mean depth of 13 m (Bowen, 1978) and supports large numbers of fauna and flora including the second largest Nile crocodile and hippopotamus populations in the KwaZulu-Natal Province (Ward and Kyle, 1990). The lake has the ability to support hundreds of crocodiles and 250 hippopotami (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013). It is divided into five main regions namely the main basin, southern basin, south-western basin, western arm and the northern arm (Allanson, 1979; Bruton, 1979; Combrink et al., 2011; DWS, 2015b). Four sites were selected during this study namely Site 1 (LS1), Site 2 (LS2), Site 3 (LS3) and Site 4 (LS4) (Fig. 1). Sites 1 and 2 were situated within the main basin and appeared undisturbed with little external impacts. Site 2 however, could only be sampled once during August 2015 due to a lack of substrata during the last two surveys. Site 3 was situated within the southern basin close to forestry and rural development with livestock present at the study site during all surveys. Site 4 was situated within the western arm of the lake close to Mseleni town. The Department of Water and Sanitation (DWS) indicated that both sites 3 and 4 are influenced by forestry and rural development (DWS, 2015a). There is also a sewage treatment plant close to Site 4. The sites were sampled during three surveys in August 2015 (winter), December 2015 (start of summer) and February 2016 (end of summer). 2.2. Physico-chemical factors Before the sampling of diatoms, in situ parameters were measured at each site using an Extech DO610 meter (Extech Instruments, A FLIR Company, USA) to measure the dissolved oxygen and temperature and a Extech EC610 (Extech Instruments, A FLIR Company, USA) to measure the electrical conductivity. Water samples were collected in 500 ml pre-cleaned sample bottles from each site for nutrient analysis. Collected samples were frozen and transported back to the laboratory for further analysis. At the laboratory all samples were defrosted and nutrient analyses were performed using a Merck Spectroquant Pharo 300 UV–VIS Spectrophotometer (Merck KGaA, Germany) and the relevant test kits. The following variables, with kit method number were analysed: alkalinity (1.11109.0001), ammonium (1.14752.0001), nitrate (1.09713.0001), nitrite (1.14776.0001), orthophosphate (1.14848.0001) and sulfates (1.14791.0001). For the present study only nitrogen and phosphate data are reported on as these nutrients are the main contributors to eutrophication. 2.3. Diatom sampling, preparation and analysis Diatoms were sampled, prepared and analysed according to Taylor et al. (2005). Epiphytic diatoms were sampled from aquatic vegetation from all sites. Ten submerged stems, approximately 10–15 cm in length, were retrieved and inserted into zip lock bags with 50 ml lake water. The stems were vigorously rubbed in order to detach the diatoms from the stems. The water containing the diatoms was transferred to
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Fig. 1. Map of Lake Sibaya indicating the lake's position as well as the location of the four sampling sites (LS = Site).
aid of Taylor et al. (2007) and Archibald (1966). After identification, diatom valves were counted to a total number of ~400 valves or until the entire microscope slide was counted.
sterile honey jars with 70% ethanol (final concentration not exceeding 20%) to preserve the collected sample. Diatom microscope slides were prepared according to the hot hydrochloric acid (HCl) and potassium permanganate (KMnO4) method as this is the preferred method to use in South Africa as the samples typically have high levels of organic matter (Taylor et al., 2005). Samples were left to settle for 24 h. Marked test tubes were filled with 2–3 ml (depending on material concentration) of the shaken sample and treated with KMnO4 and left for 24 h after which 1–2 ml of 32% HCl was added to the sample. Samples were boiled on a hotplate and treated with one drop hydrogen peroxide (to ensure complete digestion of organic matter). The samples were then washed through centrifuging, decanting the supernatant and re-suspending the sample in distilled water. This process was repeated four times. The dilute diatom samples were pipetted onto cleaned coverslips and mounted to microscope slides with Pleurax (Refraction index, 1.73). Prepared microscope slides were viewed under a Nikon 80i compound light microscope equipped with differential interference contrast (DIC) and a 100 × 1.4 N. A. oil immersion objective. Diatoms were identified to species level where possible otherwise to genus level through
To determine the ecological status of each study site diatom indices were calculated with the Omnidia software package (Lecointe et al., 1993). Four indices were selected for the present study as 80% or more identified species were used to calculate these indices final scores. These included: (1) Generic Diatom Index (GDI) (Coste and Ayphassorho, 1991) as the final score is based on the tolerance of diatom genera to pollution (Matlala, 2010). (2) Specific Pollution sensitivity Index (SPI) (Cemagref, 1982) as this index includes the most species in its final calculation (more than 1400 species) (Matlala, 2010). (3) Trophic Diatom Index (TDI) (Kelly and Whitton, 1995) as it classifies diatoms into five different sensitivity categories with regard to nutrient status. (4) Percentage Pollution Tolerant Valves (%PTV) (Kelly and Whitton, 1995) as it indicates the percentage of diatoms that are tolerant to organic pollution. According to Kelly and Whitton (1995), the %
Table 1 Table used to interpret GDI and SPI score to determine the ecological status of the ecosystem.
Table 2 Table used to interpret TDI score to determine the ecological status of the ecosystem.
2.4. Diatom indices
Index score (up to 20)
Ecological status
Index score (up to 20)
Ecological status
N17 15–17 12–15 9–12 b9
Oligotrophic Oligo-mesotrophic Mesotrophic Meso-eutrophic Eutrophic
0–20 21–40 41–60 61–80 N80
Oligotrophic Oligo-mesotrophic Mesotrophic Meso-eutrophic Eutrophic
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Table 3 Table used to interpret %PTV score to determine the ecological status of the ecosystem. Index score (up to 20)
Ecological status
b20 21–40 41–60 N60
Site free from organic pollution Some evidence of organic pollution Organic pollution likely to contribute to eutrophication Heavily contaminated with organic pollution
PTV should be taken into account when the TDI is used as the %PTV indicates whether the TDI where influenced by organic pollution or nutrients. These first two indices are interpreted based on values between 0
and 20 for the GDI and SPI (Table 1) and values between 0 and 100 for the TDI and %PTV (Tables 2 and 3 respectively). 2.5. Statistical analysis To determine the average and standard error of the mean for the diatom indices scores as well as water quality variables GraphPad Prism Version 5 was used. To determine the response of the diatom indices to environmental variables a nonparametric Spearman correlation (p b .05) was used, Tukey's multiple comparison test and a one-way Anova test were performed. Dominant species over the surveys were determined through SIMPER analysis by using Primer (Version 7).
Fig. 2. Column graphs illustrating the mean and standard error for nitrogen (A), phosphate (B), temperature (C), percentage dissolved oxygen (D) and electrical conductivity (E) for all four sites over three surveys (August 2015, December 2015 and February 2016) at Lake Sibaya. For percentage dissolved oxygen letter a, b and c indicates significant differences between sites.
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To determine the seasonal influence on the diatom community structure a non-metric multidimensional scaling (nMDS) plot based on the Bray Curtis similarity matrix was drawn. To determine the influence of the environmental variables on the diatom community a constrained Canonical Correspondence Analysis (CCA) was carried out with Canoco version 5 (Riato et al., 2017). Diatom community data were log transformed before analysis. 3. Results 3.1. Water quality Mean nitrogen concentrations (Fig. 2A) indicated all sites as nutrient enriched and in a mesotrophic ecological state when compared to South Africa's water quality guidelines volume 7 (DWAF, 1996). These guidelines also indicated that measured phosphate values (Fig. 2B) for all sites would allow their classification as eutrophic. There were no significant differences (p N .05) between the means of measured nitrogen and phosphate values for all sites sampled. In Fig. 2C the mean temperature is seen as consistent between the study sites. All sites had temperatures ranging between 20 and 25 °C, with no significant difference (p N .05) between the means of sampled sites. Percentage dissolved oxygen (Fig. 2D) measured between some sites differed significantly (p b .05) from one another as illustrated on the graph. Percentage dissolved oxygen differed the most between sites 1 and 4 with a difference of approximately 20%. Electrical conductivity (Fig. 2E) was consistent between all sites with measured values ranging between 366 and 688 μS/cm with no significant (p N .05) difference between the site's means. 3.2. Diatom community A complete species list of species sampled during the three surveys in 2015 and 2016 is presented in Table 4 with the dominant species contributing the most to the CCA marked in bold. The dominant species (based on a 70% contribution) were identified for each survey with the use of the software package Primer (version 7). The winter survey's (August 2015) dominant species included Cocconeis placentula, Epithemia adnata, Gomphonema sp., Gomphonema insigne and Mastoglia sp. Dominant species during the summer survey (December 2015) were Gomphonema sp., Navicula interruptestriata, Tabularia fasciculata, Ulnaria ulna, Gomphonema pseudoaugur and Anorthoneis sp. During the second summer survey in February 2016 the dominant diatom species identified included Gomphonema pseudoaugur, Cocconeis placentula, Epithemia adnata, Seminavis sp., Gomphonema sp. and Epithemia sorex. The calculated GDI scores (Fig. 3A) indicated sites 1, 3 and 4 were in a mesotrophic state with site 2 in a meso-eutrophic ecological state. From Fig. 3B the SPI values indicated sites 1, 3 and 4 were in a mesoeutrophic state with site 2 as a mesotrophic ecological state. The average TDI values for all sites were higher than 60 indicating the sites were either mesotrophic or mesoeutrophic. The TDI values are in agreement with the SPI and GDI values which also indicated these sites as nutrient enriched. The overall indices scores for the lake over the four sites indicated the lake as mesotrophic according to the GDI score and in a mesoeutrophic ecological state according to the SPI and TDI indices. A %PTV score b 20 was calculated for all sites indicating that inorganic nutrients contributed the highest to the final TDI scores for all sites. Table 5 shows the Spearman correlation between the diatom indices and water quality variables. Only the SPI index correlated significantly (p b .05) with the measured phosphate. A non-metric multidimensional scaling (nMDS) (Fig. 4) plot indicated temporal variation between the sites. From the plot it was clear that there was site variation between warmer and cooler months with the sites sampled during the summer (December and February) grouping together. A hierarchical cluster indicated 40% similarity between
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Table 4 Diatom species present in Lake Sibaya system during three surveys in 2015 and 2016. The dominant species are marked in bold. Species
Abbreviation Date described
Amphora sp. Amphora veneta Kützing Amphora lacustris Archibald Anomoeoneis sphaerophora (Ehrenberg) Pfitzer Anorthoneis sp. Aulacoseira granulata (Ehrenberg) Simonsen Aulacoseira muzzanensis (Meister) Krammer Cocconeis pediculus Ehrenberg Cocconeis placentula (Ehrenberg) Grunow Cocconeis sp. Craticula sp. Cymbella cymbiformis Agardh Diploneis sp. Diploneis ovalis (Hilse) Cleve Diploneis zanzibarica (Grunow) Hustedt Discostella pseudostelligera (Hustedt) Houk & Klee Encyonema minutum (Hilse.) D.G. Mann Encyonema sp. Encyonopsis sp. Encyonopsis minuta Krammer & Reichardt Encyonopsis subminuta Krammer & Reichardt Epithemia adnata (Kützing) Brébisson Epithemia sorex Kützing Fragilaria sp. Gomphonema sp. Gomphonema insigne Gregory Gomphonema parvulum Kützing Gomphonema pseudoaugur Lange-Bertalot Gomphonema sp. 2 Gomphonema sp. 3 Gyrosigma acuminatum (Kützing) Rabenhorst Hantzschia distinctepunctata Hustedt Karayevia ploenensis (Hustedt) Bukhtiyarova Mastogloia smithii Thwaites Mastogloia sp. 1 Mastogloia sp. 2 Mastogloia sp. 3 Navicula sp. Navicula cryptotenelloides Lange-Bertalot Navicula interruptestriata Schwabe & Simonsen Navicula radiosa Kützing Navicula zanoni Hustedt Navicymbula pusilla Krammer Nitzschia sp. Hassall Pinnularia sp Pinnularia subcapitata Gregory Placoneis sp. Placoneis placentula (Ehrenberg) Heinzerling Rhopalodia sp. Rhopalodia gibberula (Ehrenberg) O.Müller Rhopalodia musculus (Kützing) O.Müller Sellaphora sp. 1 Sellaphora sp. 2 Sellaphora pupula (Kützing) Mereschkowksy Seminavis sp. Seminavis strigosa (Hustedt) Danieledis & Economou-Amilli Tabularia fasciculata (Agardh) D.M. Williams & Round Tryblionella apiculata Gregory Ulnaria ulna (Nitzsch) Compère
AMPH AVEN – ASPH ANOT AUGR AMUZ CPED CPTG COCO CRAT CCYM DIPL DOVA – DPST ENMI ENCY ENCP ECPM ESUM EADN ESOR FRAG GOMP GINS GPAR GPSA GOMS GOPS GYAC HDIS – MSMI MAST MASP MASS NAVI NCTO NITS NRAD NZAN NCPU NITZ PINU PSEL PLAC PPLC RHOP RGBL RMUS SELL SELS SPUP SMNA SMST
1844 1844 2006 1871 1868 1979 1991 1838 1884 1837 1868 1830 1894 1891 1937 2004 1990 1834 1997 1997 1997 1838 1844 1819 1832 1856 1849 1979 1832 1832 1853 1921 1999 1848 1848 1848 1848 1822 1993 1961 1844 1949 2003 1845 1843 1992 1903 1908 1895 1895 1899 1902 1902 1902 1990 2003
TFAS
1986
TAPI FUBI
1857 1980
sites sampled in the summer. Winter samples did not group together showing variation between these sites. Fig. 5 is a CCA tri-plot illustrating the influence of the measured environmental variables on the diatom community structure. The CCA analysis determined that 60.1% of the species variation can be explained by the measured environmental variables. Species contributing b 10% to the variation were excluded from the graph. The majority of the species grouped together on the left half of the graph, with these species having a higher affinity to nitrogen and temperature. These species include
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Fig. 3. Column graph indicating the mean and standard error for the GDI (A), SPI (B), TDI (C) and %PTV (D) indices for the four sites in Lake Sibaya over three surveys.
Aulacoseira granulata, Cocconeis placentula, Diploneis ovalis, Encyonopsis minuta and Navicula sp. These are all cosmopolitan species with A. granulata, C. placentula and Navicula sp. occurring in nutrient enriched ecosystems. Species on the right had a higher affinity for phosphate, electrical conductivity, pH and dissolved oxygen. 4. Discussion 4.1. Water quality The quality of aquatic ecosystems can be determined based on the concentration of water quality variables present in the ecosystem. Nutrients are an important driving force in aquatic ecosystems as they form the basis for plant growth (Dallas and Day, 2004), or cause eutrophication in these ecosystems that poses a threat to their sustainability (Ryther and Dunstan, 1971; Clark and Tilman, 2017; Tromboni and Dodds, 2017). Wetlands are particularly susceptible to nutrient enrichment as these ecosystems act as sinks (Humphries and Benitez-Nelson, 2013). However, the quantity of aquatic macrophytes may have an influence on the nutrient concentrations within the ecosystem. Thus an aquatic ecosystem may be eutrophic even if the measured nutrients concentrations are low. Measured nitrogen concentrations for all sites indicated mesotrophic conditions when these values were compared to South Africa's water quality guidelines for aquatic ecosystems (DWAF, 1996); however it should be noted that these guidelines mostly pertain to riverine ecosystems. When comparing the measured orthophosphate values to the water quality guidelines, they showed the ecosystem to be in a
eutrophic state. The Department of Water and Sanitation (DWS) compiled a report on Lake Sibaya based on water quality data they collected from 1980 to 2015 and when the present studies' data were compared to the report the measured nitrogen values corresponded to the DWS report, however, the phosphate concentrations did not correspond, with the DWS reporting much lower dissolved inorganic phosphate values (DWS, 2015a). However, Allanson (1979) indicated nitrogen concentrations of the system to be between 17 and 32 μg/L while the orthophosphate was between 14 and 25 μg/L. Allanson (1979) also reported that there was no significant variation in the nutrient concentrations through the water column in the Lake Sibaya system. Increased nutrient concentrations have however been recorded in the lake over recent years, with Humphries and Benitez-Nelson (2013) reporting increased nutrient concentrations especially in the western arm (Site 4) of the lake and the DWS (2015a) also reported increased nutrients in the sediment of the western arm. The high concentrations of nutrients can be ascribed to various causes. These causes Table 5 Spearman correlation between diatom indices and environmental variables. Values in bold are significantly correlated (p b .05).
PO4 NO3 % Dissolved oxygen Temperature Electrical conductivity
SPI
GDI
TDI
−0.64 −0.27 −0.15 0.28 0.33
−0.23 −0.49 −0.09 −0.19 0.55
−0.33 −0.55 −0.33 0.01 0.58
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Non-metric MDS Transform: Log(X+1) Resemblance: S17 Bray-Curtis similarity
LS 3.1
2D Stree: 0.08
LS 3.3
LS 2.1
LS 4.2 LS 1.3
Season Aug Dec Feb
LS 4.3 LS 3.2
LS 1.1
LS 1.2
LS 4.1
Fig. 4. Non-metric multidimensional scaling (nMDS) based on the Bray Curtis similarity coefficient between seasonal samples from 2015 to 2016 at Lake Sibaya.
include increased human settlement, the development of Mseleni Town and forestry development around the lake. These factors are all contributing causes to eutrophication in Lake Sibaya (Humphries and BenitezNelson, 2013; DWS, 2015a). As temperature influences the metabolic rate and chemical reactions of organisms it is an important environmental variable to measure (DWAF, 1996). Recorded temperatures for all sites did not fluctuate more than 3 °C between surveys. As these are not unnatural fluctuations for temperature between seasons this will not have an adverse effect on the aquatic biota (DWAF, 1996). During 1968–1976, Allanson (1979) recorded the average monthly temperatures of the lake to be 19 °C for August, 22 °C for December and 24 °C for February. The present study's
measured temperature values were within the limits measured by Allanson (1979). Oxygen is crucial to an organism's survival (DWAF, 1996; Dallas and Day, 2004), with aquatic organisms making use of dissolved oxygen in the water column. According to Dallas and Day (2004) as well as Patil et al. (2012), oxygen and temperature have a negative correlation with one another. This was however not noted during the present study and might be due to photosynthesis and turbulent water (wind action). The high recorded nutrient concentrations and temperatures can result in an increase in photosynthesis in aquatic vegetation and primary producers, which would increase the dissolved oxygen within the aquatic ecosystem (Elwood et al., 1981; Hill et al., 1995). Site 4 had the lowest measured dissolved oxygen between the sites and this could be attributed to increased anthropogenic activities (rural development and waste discharges) and nutrient enrichment (DWAF, 1996; Humphries and Benitez-Nelson, 2013). The DWS (2015a) reported a decrease in water quality to the lakes southern basin (Site 3) and western arm (Site 4) due to increased forestry and rural development. As the pressure on the lake intensifies, due to development within the catchment, the lake will continue to decline in terms of water quality (Humphries and Benitez-Nelson, 2013). The water quality analyses in the present study were just a once-off snap-shot of the ecosystem's water quality at the time of sampling and do not represent the water quality over a period of time. As water quality samples are influenced over time by numerous factors (Matlala et al., 2011) it is recommended to use aquatic communities to assess the health of the aquatic environment as they are exposed to the environment for a prolonged period (during their entire life time in the case of diatoms), thus reflecting the physical and chemical disturbances in the ecosystem (de la Rey et al., 2004). 4.2. Diatom community
Fig. 5. Canonical correspondence analysis (CCA) biplot illustrating the influence of these four environmental variables on the diatom community of Lake Sibaya. Only species contributing 10–100% of the variation are illustrated. (Abbreviations in Table 1).
As diatoms are constantly exposed to the aquatic environment they are good indicators of the lake water quality, thus water quality and the dominant diatom species could be used to represent the environmental conditions over time. The present study identified 59 species while a previous study by Archibald (1966) identified 107 different species. During the present study, nine species that were reported by Archibald (1966) were found, namely Amphora lacustris, Aulacoseira granulata (previously known as Melosira granulate), Cocconeis
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placentula, Diploneis ovalis, Epithemia sorex, Gomphonema parvulum, Navicula zanonii, Navicymbella pusilla (previously known as Cymbella Pusilla) and Rhopalodia gibberula. Archibald (1966) described nine new species with only one of these species observed during the present study namely Amphora lacustris. The difference in diatom species between the studies may be due to intensity in sampling (monitoring vs biodiversity studies) or due to changes in the aquatic environment over the past 51 years (DWS, 2015b). From the CCA bi-plot (Fig. 5) it is clear that the majority of the commonly occurring species have a high affinity to nitrogen and temperature. This suggests that these variables influence the diatom community structure of the lake. The species in the left half of the CCA are those usually present in ecosystems that are either mesotrophic or eutrophic and rich in electrolytes (Bellinger et al., 2006; Taylor et al., 2007). It is also well documented that members of the Gomphonema parvulum species complex are indicators of eutrophication (Riato et al., 2014). These data correlate with both the measured water quality variables showing that the ecosystem is nutrient enriched, as well as the DWS (2015a) report indicating the lake water quality is declining due to nutrient enrichment. A study by Riato et al. (2017) focused on how physical and chemical water variables influence the structuring of the diatom community in temporary depressional wetlands within the Mpumalanga Province (South Africa). Their study showed the important structuring effect of environmental variables (especially alkalinity and ionic composition) on the diatom community. The diatom community composition varied between summer and winter months (Fig. 4) in response to temperature. Sites sampled during the summer (December and February) grouped together, while the winter (August) survey indicated spatial differences between each site. It is clear from the CCA bi-plot as well that temperature influences the community structure as the majority of the species occurred in sites with an above average water column temperature. 4.3. Diatom indices Calculated diatom indices (GDI, SPI, TDI and %PTV) indicated the lake as nutrient enriched in agreement with what is known of the ecology of the dominant diatom species and the significant correlation between PO4 and SPI. The SPI as well as the GDI indices indicated the lake is in a mesotrophic state and both indices had a negative correlation with the measured water quality variables. This negative correlation was expected as a decline in the quality of the ecosystem is associated with nutrient enrichment (Dalu and Froneman, 2016). Studies done by Blanco et al. (2004) on six Spanish lakes concluded that the SPI index can be applied to those lentic systems as a significant correlation was found between the SPI and total nitrogen concentration. The low GDI score is also a useful indication of pollution or impact within the lake (Matlala et al., 2011) originating from flows that enter the lake as runoff from rural development, forestry and agriculture within the catchment of the lake. The calculated TDI score showed the lake to be either eutrophic, mesoeutrophic or mesotrophic. This score is in agreement with the SPI, GDI, the diatom community structure as well as the measured water quality variables. However, Kelly and Whitton (1995) cautioned that as the %PTV is an indication whether organic pollution or inorganic nutrients contributed the highest to the TDI score that the %PTV index has to be used together with the TDI. As the calculated %PTV score was b 20% for all sites this indicates that the TDI score reflected inorganic rather than organic nutrient pollution (Kelly and Whitton, 1995). Physical and chemical constituents of aquatic ecosystems, such as nutrients, can however, be as a result of natural processes and may not necessarily be influenced by anthropogenic activities alone (Riato et al., 2017). This is further complicated by the fact that Lake Sibaya is a natural sink for nutrients (as are all wetlands). However, clear changes in water chemistry as shown by several studies coupled with a clear
change in the diatom community composition would indicate that over the intervening years between the present study and that of Archibald (1966) there have been water quality impacts that have changed both the water quality variables as well as the structure of biotic aquatic communities. 5. Conclusion This study documented Lake Sibaya's diatom diversity and selected water quality variables over three surveys in 2015 and 2016. Measured water quality variables showed the lake to be nutrient enriched with high nitrogen and phosphate concentrations measured during the study. The diatom community as well as the calculated diatom indices indicated the lake to be nutrient enriched. The dominant diatom species documented are those adapted to and generally present in nutrient enriched ecosystems. This observation correlates with the measured nitrogen and phosphate data showing the ecosystem to be nutrient enriched. This increase in nutrients may be due to forestry, rural development and the development of Mseleni town within the catchment of the lake (Humphries and Benitez-Nelson, 2013; DWS, 2015a). Further studies are necessary on the nutrient concentrations of wetland ecosystems in South Africa as the nutrient concentrations of wetlands are poorly understood (Malan and Day, 2012). The increased nutrient concentrations may arise from natural causes but this is considered unlikely when taking into account changes in the community structure of the diatoms. However, an article published by Bate et al. (2018) focussed on the quality of groundwater and the effects of population growth on groundwater. The study indicated that rural development could have an effect on groundwater quality as poor management of septic tanks and disposal of chemicals, grease, disinfectants, bleach and detergents into toilets could have an effect on groundwater quality (Bate et al., 2018). They found that the nitrite concentration was much higher than expected indicating that the Lake Sibaya ecosystem is impacted by pollution (Bate et al., 2018). Conflict of interest The authors declare there is no conflict of interest. Acknowledgements This work was funded by the Water Research Commission (Project K5/2352). Opinions expressed, and conclusions arrived at, are those of the authors and are not necessarily those of the WRC. J. C. Taylor is the recipient of South African National Research Foundation (NRF) incentive funding. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and therefore the NRF does not accept any liability in regard thereto. This is contribution number 262 of the North-West University (NWU) Water Research Group. A special thanks to Dr. M. Ferreira, Mr. J. Beukes, Ms. M. Truter, Ms. E. Bester and Ms. S van der Wal for all the assistance in the field. References Allanson, B.R., 1979. Lake Sibaya. Vol. 36. Kluwer Academic Publishers Group. Archibald, R.E.M., 1966. Some new and rare diatoms from South Africa 2. Diatoms from Lake Sibayi and Lake Nhlange in Tongaland (Natal). Nova Hedwigia 12, 476–498. Bate, G., Mkhwanazi, M., Simonis, J., 2018. The effects of population growth on the groundwater quality of a sandy rural catchment. Transactions of the Royal Society of South Africa 1–11. Bellinger, B.J., Cocquyt, C., Reilly, C.M.O., 2006. Benthic diatoms as indicators of eutrophication in tropical streams. Hydrobiologia 573, 75–87. Blanco, S., Ector, L., Bécares, E., 2004. Epiphytic diatoms as water quality indicators in Spanish shallow lakes. Vie et Milieu 54, 71–79. Bowen, S.H., 1978. Benthic diatom distribution and grazing by Sarotherodon mossambicus in Lake Sibaya, South Africa. Freshwater Biology 8, 449–453.
A. Kock et al. / South African Journal of Botany 123 (2019) 161–169 Brazner, J.C., Danz, N.P., Niemi, G.J., Regal, R.R., Trebitz, A.S., Howe, R.W., Hanowski, J.M., Johnson, L.B., Ciborowski, J.J.H., Johnston, C.A., Reavie, E.D., Brady, V.J., Sgro, G.V., 2007. Evaluation of geographic, geomorphic and human influences on Great Lakes wetland indicators: a multi-assemblage approach. Ecological Indicators 7, 610–635. Bruton, M.N., 1979. The breeding biology and early development of Clarias gariepinus (Pisces: Clariidae) in Lake Sibaya, South Africa, with a review of breeding in species of the subgenus Clarias (Clarias). Transactions of the Zoological Society of London 35, 1–45. Cemagref, B., 1982. Etude des méthodes biologiques d'appréciation quantitative de la qualité des eaux. [A study on the biological methods of qualitative assessment of water quality. A report of the Water Quality Division Lyon-Outflow Rhône River section catchment]. Rapport division Qualite des Eaux Lyon-A.F. Bassin RhôneMéditerranée-Corse, Pierre-Bénite, p. 218 (in French). Clark, M., Tilman, D., 2017. Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters 12, 064016. Combrink, X., Korrûbel, J.L., Kyle, R., Taylor, R., Ross, P., 2011. Evidence of a declining Nile crocodile (Crocodylus niloticus) population at Lake Sibaya, South Africa. South African Journal of Wildlife Research 41, 145–157. Coste, M., Ayphassorho, H., 1991. Etude de la qualité des eaux du bassin Artois Picardie à l'aide des communautés de diatomées benthiques (Application des indices diatomiques). Rapport Cemagref Bordeaux – Agence de l'Eau Artois Picardie, p. 227 (in French). Dallas, H.F., Day, J.A., 2004. The effect of water quality variables on aquatic ecosystems. WRC Report No. TT 224/04. Water Research Commission, Pretoria. Dalu, T., Froneman, P.W., 2016. Diatom-based water quality monitoring in southern Africa: challenges and future prospects. Water SA 42, 551–559. de la Rey, P.A., Taylor, J.C., Laas, A., van Rensburg, L., Vosloo, A., 2004. Determining the possible application value of diatoms as indicators of general water quality: a comparison with SASS 5. Water SA 30, 325–332. de Necker, L., Ferreira, M., van Vuren, J.H.J., Malherbe, W., 2016. Aquatic invertebrate community structure of selected endorheic wetlands (pans) in South Africa. Inland Waters 6, 303–313. Department of Water Affairs and Forestry (DWAF), 1996. South African water quality guidelines. Aquatic Ecosystems. Vol. 7. Department of Water Affairs and Forestry, Pretoria. Department of Water Affairs and Forestry (DWAF), 2004. Development of a framework for the assessment of wetland ecological integrity in South Africa. Phase 1: Situation Analysis. by MC Uys. Contributors G Marneweck and P Maseti. Report No. 0000/00/ REQ/0904 ISBN No.:0-621-354740. Resource Quality Services. Department of Water Affairs and Forestry, Pretoria, South Africa. Department of Water Affairs and Forestry (DWAF), 2016a. National Aquatic Ecosystem Health Monitoring Programme. http://www.dwaf.gov.za/iwqs/rhp/naehmp.aspx access 26 April 2018. Department of Water Affairs and Forestry (DWAF), 2016b. River Eco-status Monitoring Programme. http://www.dwaf.gov.za/iwqs/rhp/rhp_background.aspx access 26 April 2018. Department of Water and Sanitation (DWS), 2015a. Resource directed measures: Reserve determination study of selected surface water and groundwater resources in the Usuthu/Mhlanthuze water management area. Lake Sibaya – EWR specialist reports. Report produced by Tlou Consulting (Pty) Ltd. Report no: RDM/WMA6/CON/COMP/ 1813. Department of Water and Sanitation (DWS), 2015b. Resource directed measures: Reserve determination study of selected surface water and groundwater resources in the Usuthu/Mhlanthuze water management area. Lake Sibaya – Intermediate EWR assessment report. Report produced by Tlou Consulting (Pty) Ltd. Report no: RDM/ WMA6/CON/COMP/1713. Dixit, S.S., Smol, J.P., Kingston, J.C., Charles, D.F., 1992. Diatoms: powerful indicators of environmental change. Environmental Science and Technology 26, 23–33. Elwood, J.W., Newbold, J.D., Trimble, A.F., Stark, R.W., 1981. The limiting role of phosphorous in a woodland stream ecosystem: effects of P enrichment on leaf decomposition and primary producers. Ecology 62, 146–158. Gaiser, E.E., Wachnicka, A., Ruiz, P., Tobias, F., Ross, M., 2005. Diatom indicators of ecosystem change in subtropical Coastal Wetlands. Estuarine indicators. CRC Press, Boca Raton, FL, pp. 127–144. Gell, P.A., Sluiter, I.A., Fluin, J., 2002. Marine Freshwater River connected wetlands in north-west Victoria, Australia. Marine and Freshwater Research 53, 981–992. Hill, W.R., Ryon, M.G., Schilling, E.M., 1995. Light limitation in a stream ecosystem: responses by primary producers and consumers. Ecology 76, 1297–1309.
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South African Journal of Botany journal homepage: www.elsevier.com/locate/sajb
Diatom community structure and relationship with water quality in Lake Sibaya, KwaZulu-Natal, South Africa A. Kock a,⁎, J.C. Taylor b,c, W. Malherbe a a b c
Water Research Group, Unit for Environmental Sciences and Management, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa Unit for Environmental Sciences and Management, North-West University, Private Bag X6001, Potchefstroom, North West Province 2520, South Africa South African Institute for Aquatic Biodiversity (SAIAB), Private Bag 1015, Grahamstown, 6140, South Africa
a r t i c l e
i n f o
Article history: Received 28 September 2018 Received in revised form 8 February 2019 Accepted 6 March 2019 Available online xxxx Edited by WA Stirk Keywords: Biodiversity Bio-monitoring Diatom indices Eutrophication Wetlands
a b s t r a c t Monitoring of wetland ecosystems is crucial as these ecosystems provide numerous services and resources to both humans and the biota found therein. Wetlands are characterised by variations in inundation time and depth and therefore it is often difficult to make use of invertebrates and fish as biological indicators. Diatoms may be used for monitoring of wetland ecosystems as they are widely distributed, are microscopic with few habitat restrictions, communities are species rich and they respond rapidly to environmental changes. Very little information is available on wetland diatom communities in South Africa. Wetland ecosystems include Ramsar sites such as Lake Sibaya which faces increased forestry and rural development within the lake's catchment area. Therefore, this study aimed to determine the diatom community structure of Lake Sibaya and its response to changes in water quality. Water and diatom samples were collected from four sites in Lake Sibaya during August 2015, December 2015 and February 2016. All samples were collected and analysed according to standard methodologies. From the results it was clear that the lake had a lower diatom diversity than that described in a previous study in 1966, with Cocconeis placentula, Gomphonema sp., Epithemia adnata and Nitzschia sp. being the dominant taxa. Both the diatom community structure and measured water quality parameters showed the lake to be nutrient enriched. However, as nutrients accumulate in wetlands it is unknown whether the increased nutrients arise from anthropogenic disturbances or natural sources. © 2019 SAAB. Published by Elsevier B.V. All rights reserved.
1. Introduction Wetlands are important ecosystems that provide numerous important ecological services such as water storage, biochemical cycling and maintenance of biodiversity (Matlala et al., 2011). Humans also benefit from wetland ecosystems as these ecosystems provide them with essential resources like water, wood (plant products) and food (Kotze, 2010). Wetlands are found across the world and make up approximately 6% of the world's surface area (DWAF, 2004). The importance of wetlands can thus not be overlooked and adequate monitoring of these ecosystems is vital. However, as wetlands act as ‘sinks’ where water and sediment accumulate, they are more susceptible to pollution (Dallas and Day, 2004; Malan and Day, 2012; Humphries and BenitezNelson, 2013; Dalu and Froneman, 2016). This makes these ecosystems extremely sensitive to anthropogenic activities including mining, forestry and agricultural drainage that potentially affect the water quality (Malan and Day, 2012; de Necker et al., 2016). ⁎ Corresponding author at: 11 Hoffman Street, Potchefstroom, North-West University, Potchefstroom Campus, Building F20, Room 63, 2531 Potchefstroom, South Africa. E-mail address: [email protected] (A. Kock).
https://doi.org/10.1016/j.sajb.2019.03.013 0254-6299/© 2019 SAAB. Published by Elsevier B.V. All rights reserved.
To monitor these ecosystems, the complete spectrum of information (chemical, physical and bacteriological measurements) is needed for the aquatic ecosystem (Li et al., 2010). However, Li et al. (2010) stated that it has been shown that biological indicators can replace traditional monitoring techniques as these organisms are constantly exposed to the environmental changes in their natural environment. Bio-monitoring can effectively be used to aid in the management of wetlands as it provides a time integrated indication of the environmental conditions (Dalu and Froneman, 2016). Numerous bio-monitoring techniques have been implemented in South Africa which include the National Aquatic Ecosystem Health Monitoring Programme (NAEHMP) (DWAF, 2016a) and the River Ecostatus Monitoring Programme (REMP) (DWAF, 2016b), previously the River Health Programme (RHP). The NAEHMP consists of numerous indices that incorporate riparian vegetation, macro-invertebrates and fish as biological responders (DWAF, 2016b). Due to wetland ecosystems' varied inundation time and depths as well as the availability of habitat and representative biota, it is problematic to use fish and invertebrates as biological indicators in shallow lotic waters. However, diatoms are useful to monitor wetland ecosystems as they are present in ecosystems that lack refuge and requisite habitats for higher organisms. Diatoms are sensitive to organic matter
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and nutrient contamination making them suitable organisms in monitoring the chemical status of ecosystems (Pandey et al., 2017). Diatoms form part of the phylum Bacillariophyta and are unique unicellular micro-organisms that form a vital link within the food web structure of aquatic ecosystems as they are amongst the main primary producers in these ecosystems (Round et al., 1990; Dixit et al., 1992; Schmitt-Jansen and Altenburger, 2005; Taylor et al., 2005, 2007; Smucker and Vis, 2011; Pandey et al., 2017). Diatoms plays a vital role in the food web as a source of food for benthic consumers, they are also important in ecosystems were sediment lacks oxygen as they play a crucial role in the carbon cycle as oxygen producers (Schmitt-Jansen and Altenburger, 2005). A good understanding of the relationship between environmental changes (physico-chemical parameters) and the diatom community makes these organisms ideal biological indicators (Brazner et al., 2007; Julius and Theriot, 2010; Smol and Stoermer, 2010). Diatom frustules can be preserved for long periods after cell death, even when the system has dried out, due to their siliceous cell wall (Julius and Theriot, 2010; Smol and Stoermer, 2010; Matlala et al., 2011; Pandey et al., 2017), making them unique from other algae. They are useful biological indicators as they are found in all aquatic biotypes, each species has its own specific water quality requirements, diatoms respond rapidly to environmental changes and anthropogenic activities and are present in most aquatic and sub-aerial environments (Dixit et al., 1992; Reid et al., 1995; Morales et al., 2001; Julius and Theriot, 2010; Smol and Stoermer, 2010; Stevenson et al., 2010; Dalu and Froneman, 2016; Pandey et al., 2017). Diatoms are useful in long-term monitoring programmes as they are simple to sample and generating data are cost effective, time efficient and comparable between ecosystems (Gell et al., 2002; Dalu and Froneman, 2016). Diatoms have successfully been implemented to monitor past and present environmental conditions and aquatic pollution worldwide (Stevenson et al., 2010; La Hée and Gaiser, 2012; Dalu and Froneman, 2016). Numerous studies have reported on diatoms' sensitivity to inorganic contamination, organic matter and organic toxicants (Pandey et al., 2017). Diatoms have recently been included in the NAEHMP in South Africa even though they are not routinely used for environmental monitoring in southern Africa (Dalu and Froneman, 2016). Previous studies on diatom communities within wetland ecosystems in South Africa showed that there is a strong correlation between the diatom composition and physico-chemical parameters (Matlala et al., 2011). As diatoms have a siliceous cell wall they can aid in determination of the long-term water quality conditions within wetlands including those that have recently dried out (Pandey et al., 2017). According to Gaiser et al. (2005), coastal wetlands have been monitored using diatoms, especially to determine a salinity gradient in South Florida, USA. However, they also stated that implementing these techniques elsewhere in the world may be problematic. There is a general paucity of information on aquatic ecosystems in South Africa's Ramsar wetlands, this includes a lack of information on the diatom communities. Limited aquatic sampling and monitoring has been carried out in Lake Sibaya, this is of concern as the lake is threatened by rural development, herbicide and pesticide spraying as well as forestry (Ward and Kyle, 1990; Humphries and BenitezNelson, 2013; DWS, 2015a). The only known study on the diatom community from Lake Sibaya was by Archibald (1966). During their study two sites were sampled on the eastern shore of the lake and one sites' location is uncertain, however, it might be on the eastern shore due to the species present. Their study identified a total of 107 diatom species from the three sites. Due to the lake's endorheic nature and the anthropogenic activities impacting the lake it is necessary to monitor this ecosystem. The present study aimed to determine the current diatom community structure within Lake Sibaya as well as its relationship with selected water quality parameters. It was hypothesised that Lake Sibaya diatom community would be negatively impacted based on diatom index scores, due to the development of rural areas and forestry in the catchment.
2. Materials and methods 2.1. Study area Lake Sibaya (Fig. 1) is situated on the east coast of South Africa roughly 430 km north-east of Durban (Ward and Kyle, 1990; Combrink et al., 2011; Humphries and Benitez-Nelson, 2013; Stager et al., 2013). The lake is South Africa's largest natural coastal freshwater lake (Ward and Kyle, 1990; Combrink et al., 2011; Stager et al., 2013) and is cut-off from the ocean by large vegetated sand dunes on the eastern side with the western side of the lake shore being extremely flat (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013; Stager et al., 2013). The lake is 20 m above sea level (Bowen, 1978; Combrink et al., 2011; Stager et al., 2013) and has a surface area of approximately 60–70 km2 (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013). The lake has a maximum depth of 43 m with a mean depth of 13 m (Bowen, 1978) and supports large numbers of fauna and flora including the second largest Nile crocodile and hippopotamus populations in the KwaZulu-Natal Province (Ward and Kyle, 1990). The lake has the ability to support hundreds of crocodiles and 250 hippopotami (Ward and Kyle, 1990; Humphries and Benitez-Nelson, 2013). It is divided into five main regions namely the main basin, southern basin, south-western basin, western arm and the northern arm (Allanson, 1979; Bruton, 1979; Combrink et al., 2011; DWS, 2015b). Four sites were selected during this study namely Site 1 (LS1), Site 2 (LS2), Site 3 (LS3) and Site 4 (LS4) (Fig. 1). Sites 1 and 2 were situated within the main basin and appeared undisturbed with little external impacts. Site 2 however, could only be sampled once during August 2015 due to a lack of substrata during the last two surveys. Site 3 was situated within the southern basin close to forestry and rural development with livestock present at the study site during all surveys. Site 4 was situated within the western arm of the lake close to Mseleni town. The Department of Water and Sanitation (DWS) indicated that both sites 3 and 4 are influenced by forestry and rural development (DWS, 2015a). There is also a sewage treatment plant close to Site 4. The sites were sampled during three surveys in August 2015 (winter), December 2015 (start of summer) and February 2016 (end of summer). 2.2. Physico-chemical factors Before the sampling of diatoms, in situ parameters were measured at each site using an Extech DO610 meter (Extech Instruments, A FLIR Company, USA) to measure the dissolved oxygen and temperature and a Extech EC610 (Extech Instruments, A FLIR Company, USA) to measure the electrical conductivity. Water samples were collected in 500 ml pre-cleaned sample bottles from each site for nutrient analysis. Collected samples were frozen and transported back to the laboratory for further analysis. At the laboratory all samples were defrosted and nutrient analyses were performed using a Merck Spectroquant Pharo 300 UV–VIS Spectrophotometer (Merck KGaA, Germany) and the relevant test kits. The following variables, with kit method number were analysed: alkalinity (1.11109.0001), ammonium (1.14752.0001), nitrate (1.09713.0001), nitrite (1.14776.0001), orthophosphate (1.14848.0001) and sulfates (1.14791.0001). For the present study only nitrogen and phosphate data are reported on as these nutrients are the main contributors to eutrophication. 2.3. Diatom sampling, preparation and analysis Diatoms were sampled, prepared and analysed according to Taylor et al. (2005). Epiphytic diatoms were sampled from aquatic vegetation from all sites. Ten submerged stems, approximately 10–15 cm in length, were retrieved and inserted into zip lock bags with 50 ml lake water. The stems were vigorously rubbed in order to detach the diatoms from the stems. The water containing the diatoms was transferred to
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Fig. 1. Map of Lake Sibaya indicating the lake's position as well as the location of the four sampling sites (LS = Site).
aid of Taylor et al. (2007) and Archibald (1966). After identification, diatom valves were counted to a total number of ~400 valves or until the entire microscope slide was counted.
sterile honey jars with 70% ethanol (final concentration not exceeding 20%) to preserve the collected sample. Diatom microscope slides were prepared according to the hot hydrochloric acid (HCl) and potassium permanganate (KMnO4) method as this is the preferred method to use in South Africa as the samples typically have high levels of organic matter (Taylor et al., 2005). Samples were left to settle for 24 h. Marked test tubes were filled with 2–3 ml (depending on material concentration) of the shaken sample and treated with KMnO4 and left for 24 h after which 1–2 ml of 32% HCl was added to the sample. Samples were boiled on a hotplate and treated with one drop hydrogen peroxide (to ensure complete digestion of organic matter). The samples were then washed through centrifuging, decanting the supernatant and re-suspending the sample in distilled water. This process was repeated four times. The dilute diatom samples were pipetted onto cleaned coverslips and mounted to microscope slides with Pleurax (Refraction index, 1.73). Prepared microscope slides were viewed under a Nikon 80i compound light microscope equipped with differential interference contrast (DIC) and a 100 × 1.4 N. A. oil immersion objective. Diatoms were identified to species level where possible otherwise to genus level through
To determine the ecological status of each study site diatom indices were calculated with the Omnidia software package (Lecointe et al., 1993). Four indices were selected for the present study as 80% or more identified species were used to calculate these indices final scores. These included: (1) Generic Diatom Index (GDI) (Coste and Ayphassorho, 1991) as the final score is based on the tolerance of diatom genera to pollution (Matlala, 2010). (2) Specific Pollution sensitivity Index (SPI) (Cemagref, 1982) as this index includes the most species in its final calculation (more than 1400 species) (Matlala, 2010). (3) Trophic Diatom Index (TDI) (Kelly and Whitton, 1995) as it classifies diatoms into five different sensitivity categories with regard to nutrient status. (4) Percentage Pollution Tolerant Valves (%PTV) (Kelly and Whitton, 1995) as it indicates the percentage of diatoms that are tolerant to organic pollution. According to Kelly and Whitton (1995), the %
Table 1 Table used to interpret GDI and SPI score to determine the ecological status of the ecosystem.
Table 2 Table used to interpret TDI score to determine the ecological status of the ecosystem.
2.4. Diatom indices
Index score (up to 20)
Ecological status
Index score (up to 20)
Ecological status
N17 15–17 12–15 9–12 b9
Oligotrophic Oligo-mesotrophic Mesotrophic Meso-eutrophic Eutrophic
0–20 21–40 41–60 61–80 N80
Oligotrophic Oligo-mesotrophic Mesotrophic Meso-eutrophic Eutrophic
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Table 3 Table used to interpret %PTV score to determine the ecological status of the ecosystem. Index score (up to 20)
Ecological status
b20 21–40 41–60 N60
Site free from organic pollution Some evidence of organic pollution Organic pollution likely to contribute to eutrophication Heavily contaminated with organic pollution
PTV should be taken into account when the TDI is used as the %PTV indicates whether the TDI where influenced by organic pollution or nutrients. These first two indices are interpreted based on values between 0
and 20 for the GDI and SPI (Table 1) and values between 0 and 100 for the TDI and %PTV (Tables 2 and 3 respectively). 2.5. Statistical analysis To determine the average and standard error of the mean for the diatom indices scores as well as water quality variables GraphPad Prism Version 5 was used. To determine the response of the diatom indices to environmental variables a nonparametric Spearman correlation (p b .05) was used, Tukey's multiple comparison test and a one-way Anova test were performed. Dominant species over the surveys were determined through SIMPER analysis by using Primer (Version 7).
Fig. 2. Column graphs illustrating the mean and standard error for nitrogen (A), phosphate (B), temperature (C), percentage dissolved oxygen (D) and electrical conductivity (E) for all four sites over three surveys (August 2015, December 2015 and February 2016) at Lake Sibaya. For percentage dissolved oxygen letter a, b and c indicates significant differences between sites.
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To determine the seasonal influence on the diatom community structure a non-metric multidimensional scaling (nMDS) plot based on the Bray Curtis similarity matrix was drawn. To determine the influence of the environmental variables on the diatom community a constrained Canonical Correspondence Analysis (CCA) was carried out with Canoco version 5 (Riato et al., 2017). Diatom community data were log transformed before analysis. 3. Results 3.1. Water quality Mean nitrogen concentrations (Fig. 2A) indicated all sites as nutrient enriched and in a mesotrophic ecological state when compared to South Africa's water quality guidelines volume 7 (DWAF, 1996). These guidelines also indicated that measured phosphate values (Fig. 2B) for all sites would allow their classification as eutrophic. There were no significant differences (p N .05) between the means of measured nitrogen and phosphate values for all sites sampled. In Fig. 2C the mean temperature is seen as consistent between the study sites. All sites had temperatures ranging between 20 and 25 °C, with no significant difference (p N .05) between the means of sampled sites. Percentage dissolved oxygen (Fig. 2D) measured between some sites differed significantly (p b .05) from one another as illustrated on the graph. Percentage dissolved oxygen differed the most between sites 1 and 4 with a difference of approximately 20%. Electrical conductivity (Fig. 2E) was consistent between all sites with measured values ranging between 366 and 688 μS/cm with no significant (p N .05) difference between the site's means. 3.2. Diatom community A complete species list of species sampled during the three surveys in 2015 and 2016 is presented in Table 4 with the dominant species contributing the most to the CCA marked in bold. The dominant species (based on a 70% contribution) were identified for each survey with the use of the software package Primer (version 7). The winter survey's (August 2015) dominant species included Cocconeis placentula, Epithemia adnata, Gomphonema sp., Gomphonema insigne and Mastoglia sp. Dominant species during the summer survey (December 2015) were Gomphonema sp., Navicula interruptestriata, Tabularia fasciculata, Ulnaria ulna, Gomphonema pseudoaugur and Anorthoneis sp. During the second summer survey in February 2016 the dominant diatom species identified included Gomphonema pseudoaugur, Cocconeis placentula, Epithemia adnata, Seminavis sp., Gomphonema sp. and Epithemia sorex. The calculated GDI scores (Fig. 3A) indicated sites 1, 3 and 4 were in a mesotrophic state with site 2 in a meso-eutrophic ecological state. From Fig. 3B the SPI values indicated sites 1, 3 and 4 were in a mesoeutrophic state with site 2 as a mesotrophic ecological state. The average TDI values for all sites were higher than 60 indicating the sites were either mesotrophic or mesoeutrophic. The TDI values are in agreement with the SPI and GDI values which also indicated these sites as nutrient enriched. The overall indices scores for the lake over the four sites indicated the lake as mesotrophic according to the GDI score and in a mesoeutrophic ecological state according to the SPI and TDI indices. A %PTV score b 20 was calculated for all sites indicating that inorganic nutrients contributed the highest to the final TDI scores for all sites. Table 5 shows the Spearman correlation between the diatom indices and water quality variables. Only the SPI index correlated significantly (p b .05) with the measured phosphate. A non-metric multidimensional scaling (nMDS) (Fig. 4) plot indicated temporal variation between the sites. From the plot it was clear that there was site variation between warmer and cooler months with the sites sampled during the summer (December and February) grouping together. A hierarchical cluster indicated 40% similarity between
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Table 4 Diatom species present in Lake Sibaya system during three surveys in 2015 and 2016. The dominant species are marked in bold. Species
Abbreviation Date described
Amphora sp. Amphora veneta Kützing Amphora lacustris Archibald Anomoeoneis sphaerophora (Ehrenberg) Pfitzer Anorthoneis sp. Aulacoseira granulata (Ehrenberg) Simonsen Aulacoseira muzzanensis (Meister) Krammer Cocconeis pediculus Ehrenberg Cocconeis placentula (Ehrenberg) Grunow Cocconeis sp. Craticula sp. Cymbella cymbiformis Agardh Diploneis sp. Diploneis ovalis (Hilse) Cleve Diploneis zanzibarica (Grunow) Hustedt Discostella pseudostelligera (Hustedt) Houk & Klee Encyonema minutum (Hilse.) D.G. Mann Encyonema sp. Encyonopsis sp. Encyonopsis minuta Krammer & Reichardt Encyonopsis subminuta Krammer & Reichardt Epithemia adnata (Kützing) Brébisson Epithemia sorex Kützing Fragilaria sp. Gomphonema sp. Gomphonema insigne Gregory Gomphonema parvulum Kützing Gomphonema pseudoaugur Lange-Bertalot Gomphonema sp. 2 Gomphonema sp. 3 Gyrosigma acuminatum (Kützing) Rabenhorst Hantzschia distinctepunctata Hustedt Karayevia ploenensis (Hustedt) Bukhtiyarova Mastogloia smithii Thwaites Mastogloia sp. 1 Mastogloia sp. 2 Mastogloia sp. 3 Navicula sp. Navicula cryptotenelloides Lange-Bertalot Navicula interruptestriata Schwabe & Simonsen Navicula radiosa Kützing Navicula zanoni Hustedt Navicymbula pusilla Krammer Nitzschia sp. Hassall Pinnularia sp Pinnularia subcapitata Gregory Placoneis sp. Placoneis placentula (Ehrenberg) Heinzerling Rhopalodia sp. Rhopalodia gibberula (Ehrenberg) O.Müller Rhopalodia musculus (Kützing) O.Müller Sellaphora sp. 1 Sellaphora sp. 2 Sellaphora pupula (Kützing) Mereschkowksy Seminavis sp. Seminavis strigosa (Hustedt) Danieledis & Economou-Amilli Tabularia fasciculata (Agardh) D.M. Williams & Round Tryblionella apiculata Gregory Ulnaria ulna (Nitzsch) Compère
AMPH AVEN – ASPH ANOT AUGR AMUZ CPED CPTG COCO CRAT CCYM DIPL DOVA – DPST ENMI ENCY ENCP ECPM ESUM EADN ESOR FRAG GOMP GINS GPAR GPSA GOMS GOPS GYAC HDIS – MSMI MAST MASP MASS NAVI NCTO NITS NRAD NZAN NCPU NITZ PINU PSEL PLAC PPLC RHOP RGBL RMUS SELL SELS SPUP SMNA SMST
1844 1844 2006 1871 1868 1979 1991 1838 1884 1837 1868 1830 1894 1891 1937 2004 1990 1834 1997 1997 1997 1838 1844 1819 1832 1856 1849 1979 1832 1832 1853 1921 1999 1848 1848 1848 1848 1822 1993 1961 1844 1949 2003 1845 1843 1992 1903 1908 1895 1895 1899 1902 1902 1902 1990 2003
TFAS
1986
TAPI FUBI
1857 1980
sites sampled in the summer. Winter samples did not group together showing variation between these sites. Fig. 5 is a CCA tri-plot illustrating the influence of the measured environmental variables on the diatom community structure. The CCA analysis determined that 60.1% of the species variation can be explained by the measured environmental variables. Species contributing b 10% to the variation were excluded from the graph. The majority of the species grouped together on the left half of the graph, with these species having a higher affinity to nitrogen and temperature. These species include
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Fig. 3. Column graph indicating the mean and standard error for the GDI (A), SPI (B), TDI (C) and %PTV (D) indices for the four sites in Lake Sibaya over three surveys.
Aulacoseira granulata, Cocconeis placentula, Diploneis ovalis, Encyonopsis minuta and Navicula sp. These are all cosmopolitan species with A. granulata, C. placentula and Navicula sp. occurring in nutrient enriched ecosystems. Species on the right had a higher affinity for phosphate, electrical conductivity, pH and dissolved oxygen. 4. Discussion 4.1. Water quality The quality of aquatic ecosystems can be determined based on the concentration of water quality variables present in the ecosystem. Nutrients are an important driving force in aquatic ecosystems as they form the basis for plant growth (Dallas and Day, 2004), or cause eutrophication in these ecosystems that poses a threat to their sustainability (Ryther and Dunstan, 1971; Clark and Tilman, 2017; Tromboni and Dodds, 2017). Wetlands are particularly susceptible to nutrient enrichment as these ecosystems act as sinks (Humphries and Benitez-Nelson, 2013). However, the quantity of aquatic macrophytes may have an influence on the nutrient concentrations within the ecosystem. Thus an aquatic ecosystem may be eutrophic even if the measured nutrients concentrations are low. Measured nitrogen concentrations for all sites indicated mesotrophic conditions when these values were compared to South Africa's water quality guidelines for aquatic ecosystems (DWAF, 1996); however it should be noted that these guidelines mostly pertain to riverine ecosystems. When comparing the measured orthophosphate values to the water quality guidelines, they showed the ecosystem to be in a
eutrophic state. The Department of Water and Sanitation (DWS) compiled a report on Lake Sibaya based on water quality data they collected from 1980 to 2015 and when the present studies' data were compared to the report the measured nitrogen values corresponded to the DWS report, however, the phosphate concentrations did not correspond, with the DWS reporting much lower dissolved inorganic phosphate values (DWS, 2015a). However, Allanson (1979) indicated nitrogen concentrations of the system to be between 17 and 32 μg/L while the orthophosphate was between 14 and 25 μg/L. Allanson (1979) also reported that there was no significant variation in the nutrient concentrations through the water column in the Lake Sibaya system. Increased nutrient concentrations have however been recorded in the lake over recent years, with Humphries and Benitez-Nelson (2013) reporting increased nutrient concentrations especially in the western arm (Site 4) of the lake and the DWS (2015a) also reported increased nutrients in the sediment of the western arm. The high concentrations of nutrients can be ascribed to various causes. These causes Table 5 Spearman correlation between diatom indices and environmental variables. Values in bold are significantly correlated (p b .05).
PO4 NO3 % Dissolved oxygen Temperature Electrical conductivity
SPI
GDI
TDI
−0.64 −0.27 −0.15 0.28 0.33
−0.23 −0.49 −0.09 −0.19 0.55
−0.33 −0.55 −0.33 0.01 0.58
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Non-metric MDS Transform: Log(X+1) Resemblance: S17 Bray-Curtis similarity
LS 3.1
2D Stree: 0.08
LS 3.3
LS 2.1
LS 4.2 LS 1.3
Season Aug Dec Feb
LS 4.3 LS 3.2
LS 1.1
LS 1.2
LS 4.1
Fig. 4. Non-metric multidimensional scaling (nMDS) based on the Bray Curtis similarity coefficient between seasonal samples from 2015 to 2016 at Lake Sibaya.
include increased human settlement, the development of Mseleni Town and forestry development around the lake. These factors are all contributing causes to eutrophication in Lake Sibaya (Humphries and BenitezNelson, 2013; DWS, 2015a). As temperature influences the metabolic rate and chemical reactions of organisms it is an important environmental variable to measure (DWAF, 1996). Recorded temperatures for all sites did not fluctuate more than 3 °C between surveys. As these are not unnatural fluctuations for temperature between seasons this will not have an adverse effect on the aquatic biota (DWAF, 1996). During 1968–1976, Allanson (1979) recorded the average monthly temperatures of the lake to be 19 °C for August, 22 °C for December and 24 °C for February. The present study's
measured temperature values were within the limits measured by Allanson (1979). Oxygen is crucial to an organism's survival (DWAF, 1996; Dallas and Day, 2004), with aquatic organisms making use of dissolved oxygen in the water column. According to Dallas and Day (2004) as well as Patil et al. (2012), oxygen and temperature have a negative correlation with one another. This was however not noted during the present study and might be due to photosynthesis and turbulent water (wind action). The high recorded nutrient concentrations and temperatures can result in an increase in photosynthesis in aquatic vegetation and primary producers, which would increase the dissolved oxygen within the aquatic ecosystem (Elwood et al., 1981; Hill et al., 1995). Site 4 had the lowest measured dissolved oxygen between the sites and this could be attributed to increased anthropogenic activities (rural development and waste discharges) and nutrient enrichment (DWAF, 1996; Humphries and Benitez-Nelson, 2013). The DWS (2015a) reported a decrease in water quality to the lakes southern basin (Site 3) and western arm (Site 4) due to increased forestry and rural development. As the pressure on the lake intensifies, due to development within the catchment, the lake will continue to decline in terms of water quality (Humphries and Benitez-Nelson, 2013). The water quality analyses in the present study were just a once-off snap-shot of the ecosystem's water quality at the time of sampling and do not represent the water quality over a period of time. As water quality samples are influenced over time by numerous factors (Matlala et al., 2011) it is recommended to use aquatic communities to assess the health of the aquatic environment as they are exposed to the environment for a prolonged period (during their entire life time in the case of diatoms), thus reflecting the physical and chemical disturbances in the ecosystem (de la Rey et al., 2004). 4.2. Diatom community
Fig. 5. Canonical correspondence analysis (CCA) biplot illustrating the influence of these four environmental variables on the diatom community of Lake Sibaya. Only species contributing 10–100% of the variation are illustrated. (Abbreviations in Table 1).
As diatoms are constantly exposed to the aquatic environment they are good indicators of the lake water quality, thus water quality and the dominant diatom species could be used to represent the environmental conditions over time. The present study identified 59 species while a previous study by Archibald (1966) identified 107 different species. During the present study, nine species that were reported by Archibald (1966) were found, namely Amphora lacustris, Aulacoseira granulata (previously known as Melosira granulate), Cocconeis
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placentula, Diploneis ovalis, Epithemia sorex, Gomphonema parvulum, Navicula zanonii, Navicymbella pusilla (previously known as Cymbella Pusilla) and Rhopalodia gibberula. Archibald (1966) described nine new species with only one of these species observed during the present study namely Amphora lacustris. The difference in diatom species between the studies may be due to intensity in sampling (monitoring vs biodiversity studies) or due to changes in the aquatic environment over the past 51 years (DWS, 2015b). From the CCA bi-plot (Fig. 5) it is clear that the majority of the commonly occurring species have a high affinity to nitrogen and temperature. This suggests that these variables influence the diatom community structure of the lake. The species in the left half of the CCA are those usually present in ecosystems that are either mesotrophic or eutrophic and rich in electrolytes (Bellinger et al., 2006; Taylor et al., 2007). It is also well documented that members of the Gomphonema parvulum species complex are indicators of eutrophication (Riato et al., 2014). These data correlate with both the measured water quality variables showing that the ecosystem is nutrient enriched, as well as the DWS (2015a) report indicating the lake water quality is declining due to nutrient enrichment. A study by Riato et al. (2017) focused on how physical and chemical water variables influence the structuring of the diatom community in temporary depressional wetlands within the Mpumalanga Province (South Africa). Their study showed the important structuring effect of environmental variables (especially alkalinity and ionic composition) on the diatom community. The diatom community composition varied between summer and winter months (Fig. 4) in response to temperature. Sites sampled during the summer (December and February) grouped together, while the winter (August) survey indicated spatial differences between each site. It is clear from the CCA bi-plot as well that temperature influences the community structure as the majority of the species occurred in sites with an above average water column temperature. 4.3. Diatom indices Calculated diatom indices (GDI, SPI, TDI and %PTV) indicated the lake as nutrient enriched in agreement with what is known of the ecology of the dominant diatom species and the significant correlation between PO4 and SPI. The SPI as well as the GDI indices indicated the lake is in a mesotrophic state and both indices had a negative correlation with the measured water quality variables. This negative correlation was expected as a decline in the quality of the ecosystem is associated with nutrient enrichment (Dalu and Froneman, 2016). Studies done by Blanco et al. (2004) on six Spanish lakes concluded that the SPI index can be applied to those lentic systems as a significant correlation was found between the SPI and total nitrogen concentration. The low GDI score is also a useful indication of pollution or impact within the lake (Matlala et al., 2011) originating from flows that enter the lake as runoff from rural development, forestry and agriculture within the catchment of the lake. The calculated TDI score showed the lake to be either eutrophic, mesoeutrophic or mesotrophic. This score is in agreement with the SPI, GDI, the diatom community structure as well as the measured water quality variables. However, Kelly and Whitton (1995) cautioned that as the %PTV is an indication whether organic pollution or inorganic nutrients contributed the highest to the TDI score that the %PTV index has to be used together with the TDI. As the calculated %PTV score was b 20% for all sites this indicates that the TDI score reflected inorganic rather than organic nutrient pollution (Kelly and Whitton, 1995). Physical and chemical constituents of aquatic ecosystems, such as nutrients, can however, be as a result of natural processes and may not necessarily be influenced by anthropogenic activities alone (Riato et al., 2017). This is further complicated by the fact that Lake Sibaya is a natural sink for nutrients (as are all wetlands). However, clear changes in water chemistry as shown by several studies coupled with a clear
change in the diatom community composition would indicate that over the intervening years between the present study and that of Archibald (1966) there have been water quality impacts that have changed both the water quality variables as well as the structure of biotic aquatic communities. 5. Conclusion This study documented Lake Sibaya's diatom diversity and selected water quality variables over three surveys in 2015 and 2016. Measured water quality variables showed the lake to be nutrient enriched with high nitrogen and phosphate concentrations measured during the study. The diatom community as well as the calculated diatom indices indicated the lake to be nutrient enriched. The dominant diatom species documented are those adapted to and generally present in nutrient enriched ecosystems. This observation correlates with the measured nitrogen and phosphate data showing the ecosystem to be nutrient enriched. This increase in nutrients may be due to forestry, rural development and the development of Mseleni town within the catchment of the lake (Humphries and Benitez-Nelson, 2013; DWS, 2015a). Further studies are necessary on the nutrient concentrations of wetland ecosystems in South Africa as the nutrient concentrations of wetlands are poorly understood (Malan and Day, 2012). The increased nutrient concentrations may arise from natural causes but this is considered unlikely when taking into account changes in the community structure of the diatoms. However, an article published by Bate et al. (2018) focussed on the quality of groundwater and the effects of population growth on groundwater. The study indicated that rural development could have an effect on groundwater quality as poor management of septic tanks and disposal of chemicals, grease, disinfectants, bleach and detergents into toilets could have an effect on groundwater quality (Bate et al., 2018). They found that the nitrite concentration was much higher than expected indicating that the Lake Sibaya ecosystem is impacted by pollution (Bate et al., 2018). Conflict of interest The authors declare there is no conflict of interest. Acknowledgements This work was funded by the Water Research Commission (Project K5/2352). Opinions expressed, and conclusions arrived at, are those of the authors and are not necessarily those of the WRC. J. C. Taylor is the recipient of South African National Research Foundation (NRF) incentive funding. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and therefore the NRF does not accept any liability in regard thereto. This is contribution number 262 of the North-West University (NWU) Water Research Group. A special thanks to Dr. M. Ferreira, Mr. J. Beukes, Ms. M. Truter, Ms. E. Bester and Ms. S van der Wal for all the assistance in the field. References Allanson, B.R., 1979. Lake Sibaya. Vol. 36. Kluwer Academic Publishers Group. Archibald, R.E.M., 1966. Some new and rare diatoms from South Africa 2. Diatoms from Lake Sibayi and Lake Nhlange in Tongaland (Natal). Nova Hedwigia 12, 476–498. Bate, G., Mkhwanazi, M., Simonis, J., 2018. The effects of population growth on the groundwater quality of a sandy rural catchment. Transactions of the Royal Society of South Africa 1–11. Bellinger, B.J., Cocquyt, C., Reilly, C.M.O., 2006. Benthic diatoms as indicators of eutrophication in tropical streams. Hydrobiologia 573, 75–87. Blanco, S., Ector, L., Bécares, E., 2004. Epiphytic diatoms as water quality indicators in Spanish shallow lakes. Vie et Milieu 54, 71–79. Bowen, S.H., 1978. Benthic diatom distribution and grazing by Sarotherodon mossambicus in Lake Sibaya, South Africa. Freshwater Biology 8, 449–453.
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