E Lubbe Orcid.Org 0000-0002-6693-5760

Aquatic invertebrate assessment of the Seekoeivlei Nature Reserve

E Lubbe orcid.org 0000-0002-6693-5760

Dissertation submitted in fulfilment of the requirements for the degree Master of Science in Environmental Sciences at the North-West University

Supervisor: Dr CW Malherbe Co-supervisor: Prof V Wepener

Graduation May 2018 23441852

TABLE OF CONTENTS

ACKNOWLEDGEMENTS iv

SUMMARY v

LIST OF FIGURES vii

LIST OF TABLES xii

ABBREVIATIONS xiv

CHAPTER 1: GENERAL INTRODUCTION 1

1.1. INTRODUCTION 1

1.1.1. Wetlands 1

1.1.2. Wetland Importance, Functions and Values 2

1.1.3. The Ramsar Convention 3

1.1.4. Wetlands in South Africa 5

1.1.5. Seekoeivlei Nature Reserve 6

1.1.6. Water quality 7

1.1.7. Sediment quality 8

1.1.8. Aquatic invertebrates 9

1.2. PROBLEM STATEMENT 9

1.3. HYPOTHESIS 10

1.4. AIMS AND OBJECTIVES 10

1.5. CHAPTER BREAKDOWN 10

CHAPTER 2: SEEKOEIVLEI NATURE RESERVE AND SITE SELECTION 12

2.1. BACKGROUND 12

2.1.1. Rainfall and climate 12

2.1.2. Geology and soils 13

2.1.3. Hydrology 13

i 2.1.4. Terrestrial vegetation 14

2.1.5. Wetland classification 14

2.1.6. Fauna 15

2.1.7. Anthropogenic activities 16

2.2. SITE SELECTION 17

CHAPTER 3: WATER AND SEDIMENT ANALYSIS 40

3.1. INTRODUCTION 40

3.1.1. Water quality 40

3.1.2. Sediment quality 41

3.1.3. Aim and objective for this chapter 42

3.2. MATERIALS AND METHODS 42

3.2.1. Water quality methods 42

3.2.1.1. Water sampling protocol 42

3.2.1.2. Laboratory analyses 42

3.2.2. Sediment quality methods 43

3.2.2.1. Sediment sampling protocol 43

3.2.2.2. Laboratory analyses 43

3.3. STATISTICAL ANALYSES 44

3.4. RESULTS 45

3.4.1. Water quality 45

3.4.2. Sediment quality 55

3.5. DISCUSSION 62

3.5.1. Water quality 62

3.5.2. Sediment quality 64

3.6. CONCLUSION 65

ii CHAPTER 4: ZOOPLANKTON DIVERSITY 66

4.1. INTRODUCTION 66

4.1.1. Aim and objective for this chapter 67

4.2. MATERIALS AND METHODS 67

4.2.1. Sampling protocol 67

4.2.2. Statistical analyses 68

4.3. RESULTS 69

4.4. DISCUSSION 79

4.5. CONCLUSION 81

CHAPTER 5: MACROINVERTEBRATE DIVERSITY 83

5.1. INTRODUCTION 83

5.1.1. Aim and objective for this chapter 84

5.2. MATERIALS AND METHODS 84

5.2.1. Sampling protocol 84

5.2.2. Statistical analyses 85

5.3. RESULTS 85

5.4. DISCUSSION 101

5.5. CONCLUSION 104

CHAPTER 6: GENERAL CONCLUSION 105

CHAPTER 7: REFERENCES 109

APPENDICES 121

iii

ACKNOWLEDGEMENTS

I would like to acknowledge and extend my sincere gratitude to the following people and organisations who assisted me in various ways:

Firstly, I would like to thank my Heavenly Father for His love, grace and mercy throughout this study.

 My supervisor, Dr. Wynand Malherbe for granting me with this opportunity and for helping me in the field and laboratory and always having an open door, valuable insight and guidance.  My co-supervisor, Prof Victor Wepener for always making time to respond to any questions and to read through my dissertation when there was no time.  Ms. Anja Greyling, my friend who helped so much with fieldwork, assisted with creating the study area maps and assisted in formatting and support.  Mr. Hannes Erasmus, my friend for his assistance with sampling and identification of aquatic macroinvertebrates.  Ms. Jana Klem, for her assistance with sampling and unconditional support.  Ms. Lizaan de Necker, for her help with the identification of zooplankton specimens and assistance in proofreading and formatting.  Seekoeivlei Nature Reserve for allowing us to sample within the reserve.  The North-West University for use of laboratory equipment.  The financial assistance of the Water Research Commission.  Mr. Jonathan Joubert for his assistance in formatting and proofreading and for his unconditional support, understanding and patience.  My parents, Pieter and Ria Lubbe, I thank both of them for their unconditional love and support.

SUMMARY

The maintenance of wetlands is greatly encouraged because of their importance in the hydrological cycle and the habitat they provide for a variety of organisms. Wetlands are known for their connection between the terrestrial and aquatic environments that leads to a habitat of which certain organisms depend on for survival. The Ramsar Convention was originally adopted for the preservation of birds, their migratory routes and breeding areas that depends on wetland environments. Later the spectrum was broadened to preserve all aspects of wetlands as well as to encourage the wise use of wetlands.

The Seekoeivlei Nature Reserve is one of the 23 wetlands that is designated as a Ramsar Wetland of International Importance in South Africa. The Seekoeivlei Wetland as a whole cover approximately 12 000 hectares and consists of approximately 220 oxbows formed by the meandering of the Klip River in the Frees State Province. The Seekoeivlei Wetland is considered important because of the Klip River, being an important tributary of the Vaal River. The Vaal River supplies the majority of water to the main industrial areas in Gauteng Province. However, very little is known about the aquatic biodiversity of the Seekoeivlei Wetland. Therefore, the aim of this research project was to establish the diversity, community structure and the distribution of the zooplankton and aquatic macroinvertebrates of the Seekoeivlei Nature Reserve.

Water and sediment samples were collected from 21 selected sites located throughout the Seekoeivlei Nature Reserve and just outside of the reserve. Zooplankton and aquatic macroinvertebrate samples were collected from 17 of the 21 selected sites whereas the remaining sites only water and sediment samples were collected. All samples were collected during three seasonal surveys in July 2016 (winter), December 2016 (summer) and March 2017 (autumn). Water and sediment samples were collected in situ and transported back to the laboratory for further analyses. Water samples were analysed to determine nutrient and metal concentrations. Sediment analyses were conducted to determine particle size, percentage organic and metal concentrations. Water and sediment samples showed natural levels of nutrients and sediment present in the Seekoeivlei Wetland.

Zooplankton and aquatic macroinvertebrates were sampled using accepted techniques followed by the identification to the lowest taxonomic level possible. Zooplankton biodiversity showed a total of 17 taxa from eight families and four orders that were identified during this study. Seasonality originally was hypothesised to have an impact

v SUMMARY on the distribution of the zooplankton, but statistical analyses showed no significant differences between the various seasons. Wetland type were also hypothesised to have an impact on the zooplankton distribution and communities, and it was found that the majority zooplankton taxa were rather present in the oxbow and pan sites than in the river. A total of 87 macroinvertebrate taxa from 51 families and 14 orders were identified during this study. The zooplankton and macroinvertebrate diversity are potentially greater as many of the invertebrates could not be identified to species level due to inadequate keys. Functional feeding groups within the macroinvertebrate communities showed that the most abundant groups were the predators and grazers.

This study was successful in identifying and describing the diversity of zooplankton and aquatic macroinvertebrates present in the Seekoeivlei Nature Reserve. This project provided updated information regarding the aquatic invertebrate diversity that could potentially feed into the management plan as well as increasing the understanding of this dynamic ecosystem.

Keywords: Ramsar, Seekoeivlei Nature Reserve, floodplain wetland, water quality, sediment quality, zooplankton, aquatic macroinvertebrates.

LIST OF FIGURES

Figure 1.1: Map of South Africa with the 23 designated Ramsar sites.

Figure 1.2: Regional setting of the town Memel in the Free State.

Figure 2.1: Rainfall data of the Free State from 2012 - 2016 (www.dwa.gov.za/Hydrology/Provincial rain/Default.aspx)

Figure 2.2: Conceptual overview of the classification system for wetland ecosystems (Ollis et al., 2015).

Figure 2.3: Different land uses present in the study area of the Seekoeivlei Nature Reserve and Klip River during sampling surveys.

Figure 2.4: Map of the Seekoeivlei Nature Reserve with the selected sitesduring this study.

Figure 3.1: Physico-chemical water quality variables measured at the Seekoeivlei Nature Reserve for sampling surveys from 2016 – 2017 using spatial samples as replicates. Bars and error bars represent mean and standard error from each site (n=3).

Figure 3.2: Physico-chemical water quality variables measured at the Seekoeivlei Nature Reserve for sampling surveys from 2016 – 2017 using temporal samples as replicates. Bars and error bars represent mean and standard error from each site (n=21).

Figure 3.3: Water nutrient variables from selected sites in the Seekoeivlei Nature Reserve during winter (July, 2016), summer (December, 2016) and autumn (March, 2017). Bars indicate mean concentrations using temporal samples as replicates, whereas the error bars indicate the standard error (n=3).

Figure 3.4: Dissolved metal concentrations (µg/l) in water samples of the Seekoeivlei Nature Reserve. Bars and error bars represent the mean and standard error of the concentrations from 2016 to 2017.

Figure 3.5: PCA bi-plot of the combined water quality variables for the 2016-2017 surveys. This bi-plot explains 34.48% of water quality variables

vii LIST OF FIGURES variance on the first axis and a further 19.21% of variance on the second axis. (DO – dissolved oxygen; EC – electrical conductivity).

Figure 3.6: Sediment grain size distributions (percentages) of the Seekoeivlei Nature Reserve for winter (July 2016), summer (December 2016) and autumn (March 2017) sampling surveys. Sites with no bars present is the sites where no samples were collected. Bars and error bars represent the mean and standard error of the of the mean percentages. (> 4000 µm = gravel, 2000-4000 µm = very coarse sand, 500-2000 µm = coarse sand, 212-500 µm = medium sand, 53-212 µm = very fine sand, <53 µm = mud)

Figure 3.8: Spatial variation of Al, As, Cr, Fe, Ni and Pb concentrations in the sediment (µg/g) from the Seekoeivlei Nature Reserve sampled sites. Bars and error bars represent mean and standard error of the concentrations from July 2016-March 2017 (n=3).

Figure 3.9: Spatial variation of Zn, Mn, Co, Cu and Cd concentrations in the sediment (µg/g) from the Seekoeivlei Nature Reserve sampled sites. Bars and error bars represent a mean and standard error of the concentrations from July 2016-March 2017 (n=3).

Figure 3.10: PCA bi-plot of the combined sediment quality variables of the Seekoeivlei Nature Reserve. This bi-plot explains a total of 76.46% variance, with 65.49% explained on the first axis 65.49% and 10.97% on the second axis (> 4000µm - gravel; 2000-4000 µm - Very coarse sand; 500-2000 µm – Coarse sand; 212-500 µm – Medium sand; 53-212 µm – Very fine sand; <53 µm – Mud).

Figure 4.1: Diversity indices for the zooplankton taxa sampled in the Seekoeivlei Nature Reserve from 2016 to 2017. Bars and error bars represent mean

viii LIST OF FIGURES and standard error of the mean with the error bars indicating temporal variation. (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

Figure 4.2: Diversity indices for the zooplankton taxa sampled in the Seekoeivlei Nature Reserve during July 2016 (winter), December 2016 (summer) and March 2017 (autumn). Bars and error bars represent mean and standard error (n=17). (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

Figure 4.3: Non-multidimensional scaling (NMDS) plot of the zooplankton data sampled in the Seekoeivlei Nature Reserve during winter (July 2016), summer (December 2016) and autumn (March 2017).

7.21% is displayed on the second axis (p value: NO3 = 0.008, NO2 =

0.048, NH4 = 0.006, temperature = 0.002).

Figure 4.6: Venn diagrams representing unique and shared contribution of water quality variables (a) and different wetland structures (b) on the zooplankton community structure and diversity. Only 37.3% of the total data are explained.

Figure 4.7: CCA plot showing species present at the different wetland types for all sampled sites during the three surveys. With 10.81% total variation explained, of which 5.69% are explained in the first axis and the remaining 5.12% on the second axis.

ix LIST OF FIGURES Figure 5.1: Diversity indices for the aquatic macroinvertebrate taxa sampled in the Seekoeivlei Nature Reserve for 2016 to 2017. Bars and error bars represent mean and standard error. The replicates at each site are represented by the seasonal survey data and therefore provide indication temporal variation (n=3). (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

Figure 5.2: Diversity indices for the aquatic macroinvertebrate taxa sampled in the Seekoeivlei Nature Reserve during July 2016 (winter), December 2016 (summer) and March 2017 (autumn). Bars and error bars represent mean and standard deviation (n=17). (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

Figure 5.3: Non-multidimensional scaling (NMDS) plot of the macroinvertebrate taxa sampled during winter (July 2016), summer (December 2016) and autumn (March 2017) in the Seekoeivlei Nature Reserve.

Figure 5.5: RDA tri-plot (interactive forward selection) using species and water quality variables for the sampled sites during winter (July 2016), summer (December 2016) and autumn (March 2017) in the Seekoeivlei Nature Reserve. This tri-plot explains 25.97% of total variation, 15.19% on the first axis and 10.78% on the second axis (p value: temperature

= 0.002, Ni = 0.004, NO2 (nitrites) = 0.004, NO3 (nitrates) = 0.01, Mg = 0.016, K = 0.01, % DO (dissolved oxygen) = 0.062).

x LIST OF FIGURES Figure 5.7: CCA plot for all sampled sites during the three surveys. With 9.20% total variation explained, of which 6.44% explained on the first axis and the remaining 2.76% on the second axis.

Figure 5.9: PCA bi-plot containing the Functional Feeding Groups in the Seekoeivlei Nature Reserve. This bi-plot explains 60.39% of the explained variation. First axis explains 36.59% and the second axis the remaining 23.80%.

LIST OF TABLES

Table 1.1: Attributes are given as functions and services of a wetland ecosystem (Richardson, 1994)

Table 2.1: Site description of Pampoen Spruit.

Table 2.2: Site description of Site 1.

Table 2.3: Site description of Site 2a

Table 2.4: Site description of Site 2b.

Table 2.5: Site description of Wildemans Spruit.

Table 2.6: Site description of Site 3a.

Table 2.7: Site description of Site 3b.

Table 2.8: Site description of Site 3c.

Table 2.9: Site description of Site 3d.

Table 2.10: Site description of Site 3e.

Table 2.11: Site description of Site 4a.

Table 2.12: Site description of Site 4b.

Table 2.13: Site description of Site 4c.

Table 2.14: Site description of Site 4d.

Table 2.15: Site description of Site 5.

Table 2.16: Site description of Site 6.

Table 2.17: Site description of Site 7.

Table 2.18: Site description of Site 8.

Table 2.19: Site description of Site 9.

Table 2.20: Site description of Site 10a.

Table 2.21: Site description of Site 10b.

Table 3.1: The classification for sediment grain size analysis (Malherbe et al., 2010; Wentworth, 1922)

xii LIST OF TABLES Table 3.2: Classification of organic content in sediment (USEPA, 2001).

Table 3.3: Target water quality ranges for freshwater systems in South Africa (TWQR) (DWAF, 1996).

Table 3.4: Ecological categories for the classification of Wetlands in South Africa (adapted from Malan & Day, 2012).

Table 4.1: List of the zooplankton diversity sampled in the Seekoeivlei Nature Reserve for three surveys from 2016 to 2017.

Table 4.2: The SIMPER Analysis results showing the most abundant species present during each sampling survey (contribution cut off = 70%). Showing an average similarity of 32.29% during the winter survey (July 2016), 35.84% during the summer survey (December 2016) and 44.46% during the autumn survey (March 2017).

Table 5.1: List of aquatic macroinvertebrate diversity recorded in the Seekoeivlei Nature Reserve for three surveys from 2016 to 2017

Table 5.2: SIMPER Analysis results showing the most abundant species present during each sampling survey. Season 1 showed an average similarity of 33.11% during the winter survey (July, 2016), Season 2 showed 24.90% average similarity which was during the summer survey (December 2016) and the Season 3 showed 31.73% average similarity which was during the autumn survey (March 2017). LIST OF APPENDICES

Appendix A: Water and Sediment Quality

Appendix B: Zooplankton Diversity

Appendix C: Macroinvertebrate Diversity

xiii

LIST OF ABBREVIATIONS

ANOSIM Analysis of Similarity

ANOVA Analysis of Variance

ASTM American Society for Testing and Materials

CCA Canonical Correspondence Analysis

DO Dissolved Oxygen

DWAF Department of Water Affairs and Forestry, South Africa

EC Electrical Conductivity

FROC Fish Reference Frequency of Occurrence

FS DTEEA Free State Department of Tourism, Environmental and Economic Affairs

GSM Gravel, Sand, Mud

ICP-MS Inductively Coupled Plasma Mass Spectrophotometry

LHC Lateral Hydrological Connectivity

NMDS Non-metric Multidimensional Scaling

PCA Principle Component Analysis

Ps Pampoen Spruit

RDA Redundancy Analysis

SIMPER Similarity Percentage

TDS Total Dissolved Solids

TWQR Target Water Quality Range

USEPA United States Environmental Protection Agency

WHO World Health Organisation

Ws Wildemans Spruit

xiv

GENERAL INTRODUCTION

1.1. INTRODUCTION

1.1.1. Wetlands

Wetlands are among the most important ecosystems on earth (Mitsch & Gosselink, 2000), and provide many functions that are valuable to the environment and society. According to USEPA (2002), these functions include the transfer- and storage of water, decomposition of organic material, production of plants and animals, communities and habitats of living creatures within the ecosystem (Mitsch & Gosselink, 2015). In order to study a wetland, one must first understand what a wetland is and distinguish between different types of wetlands.

According to Ramsar Convention Secretariat (2013), wetlands are defined as areas where water is the primary factor that controls the environment and the associated plant and animal life. Ramsar Article 2.2 (2013) states that “Wetlands should be selected for the List on account of their international significance in terms of ecology, botany, zoology, limnology, or hydrology” and indicates that “in the first instance, wetlands of international importance to a waterfowl at any season should be included.”

Batzer & Boix (2016) define wetlands as “…those areas that are inundated or saturated by surface or ground water at a frequency and duration sufficient to support, and that under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated soil conditions. Wetlands generally include swamps, marshes, bogs, and similar areas.”

The definition provided in the South African National Water Act (No 36 of 1998) is that a wetland is a: “land that is transitional between terrestrial and aquatic systems where the water table is usually at or near the surface or land that is periodically covered with shallow water and usually inhabited by hydrophytic vegetation.”

pg. 1 CHAPTER: 1 GENERAL INTRODUCTION 1.1.2. Wetland Importance, Functions and Values

Wetlands have a great importance in the environment, which includes fish nurseries, grazing, nutrient retention and many more services and products (Davies & Day, 1998). Previously mentioned functions lead to services such as the control of flooding, the cleaning and filtering of water and even recreational values (fishing and bird watching) (USEPA, 2002). Some functions of wetlands affect the water quality of downstream systems. Wetlands tend to slow water down during flood periods which reduces the volume of water reaching downstream systems. The flow rate of water will slow down after the flooding period, which will lead to the sediments present in the water being deposited (Reddy & Gale, 1994). During sediment deposition, the chemical constituents that are associated with these sediments also become deposited and trapped. These chemical constituents (nutrients and toxicants) get degraded to simpler molecules due to the action of bacteria, fungi and protozoa which are present in the wetland systems amongst the plant roots and sediments. Biota is considered one of the factors that remove dissolved constituents from the water column (Davies & Day, 1998). Thus, wetlands have been found to be efficient at removing chemical constituents from the water column i.e. nutrients.

Richardson (1994) states that wetland values are directly derived from wetland function. According to Maltby & Acreman (2011), recognized values of the wetland must be sustainable in such a way that the resources and services it provides must support sustainable development and not degrade the wetland. Table 1.1 shows the attributes (functions) and values (services) of wetland ecosystems. These functions and values are directly involved with the water quality of a wetland.

Table 1.1: Functions and services of a wetland ecosystem (Richardson, 1994)

Functions Services Hydrological flux and storage Flood control and flood storage Biological productivity Sediment control Biogeochemical cycling and Waste water treatment system storage Decomposition Nutrient removal from agricultural runoff and wastewater systems Community/wildlife habitat Recreation Hunting Preservation of flora and fauna Timber production.

There is a danger in listing the functions and values of a wetland ecosystem because of

pg. 2 CHAPTER: 1 GENERAL INTRODUCTION the differences between every wetland ecosystem. Richardson (1994) mentions four principles to list appropriate functions and values for a specific wetland. These four principles state that every wetland must be assessed before any disturbances and include the assessments of natural and constructed wetlands with their ecological, hydrological and biogeochemical functions (Richardson, 1994). Principle one states that all wetlands are not equal with regards to functions and values. The second principle states that a constructed wetland may or may not be equal to a natural wetland in the terms of functions and values (Richardson, 1994). The third principle states that the functions and values of wetland systems and other systems on the landscape are coupled together. And the last principle states that the functions and values that wetland ecosystems provide exceed their boundaries. Only after each wetland has been thoroughly assessed, can a decision be made on the way to protect the specific wetland ecosystem.

1.1.3. The Ramsar Convention

The Convention on Wetlands was adopted in February 1971 in the Iranian city of Ramsar and is officially called The Convention on Wetlands of International Importance especially as Waterfowl Habitat. This Convention is defined as: “…an intergovernmental treaty which provides the framework for national action and international cooperation for the conservation and wise use of wetlands.” This reflects on the original emphasis on the conservation of wetlands as a habitat for waterfowl (Ramsar Convention Secretariat, 2013). Over the years the Ramsar Convention broadened its scope to the implementation of wetland conservation, which now includes all aspects of wetlands and the wise use thereof. The reason for this is that the treaty recognized wetlands as ecosystems that are vital for the conservation of biodiversity as well as the well-being of human communities. In 2013 the Ramsar Convention listed 2 060 wetlands globally, (covering 197 millions of hectares) as wetlands of international importance, which are called Ramsar sites. Today there are 2 271 wetlands listed over 169 countries with a coverage of 219 175 951 hectares (The Ramsar Secretariat, 2017).

For a wetland to be classified as a Ramsar site there are nine criteria to which it must adhere (Ramsar Convention on Wetlands, 1971). Criterion 1 is based on a wetland that represent a rare or unique sample of a natural or near-natural wetland type that is found in the appropriate biogeographic region. The following three criteria (criteria 2, 3 and 4) are based on species and ecological communities. For instance, a wetland should

pg. 3 CHAPTER: 1 GENERAL INTRODUCTION support vulnerable or endangered species or threatened ecological communities, plant or animal species important for maintaining the biological diversity of a region and plant or animal species that are at a critical stage in their life cycle. Criteria 5 and 6 are based on water birds, meaning a wetland should regularly support 20 000 or more water birds and it should support 1% of the individuals in a population of one species or subspecies. Criteria 7 and 8 focus on the support of fish with regards to indigenous fish and if the wetland is an important habitat for the fish to spawn, feed and migrate. The ninth criterion is based on other taxa, meaning a wetland should support 1% of individuals in a population of one species or subspecies of “wetland-dependent non-avian animal species” (Ramsar Convention, 2013).

The Ramsar Convention in South Africa was established in December 1975. The first two sites that were designated was Barberspan in the North-West Province and De Hoop Vlei in the Western Cape Province. Currently, there are 23 sites (covering 557 028 hectares) that are designated as Ramsar Wetlands of International Importance in South Africa (Figure 1.1). The Seekoeivlei Nature Reserve is one of these Ramsar sites (declared in 1997) which covers 4 754 hectares (Ramsar Convention, 2013). The latest site, Bot – Kleinmond Estuarine System (Western Cape Province) was designated in 2017.

pg. 4 CHAPTER: 1 GENERAL INTRODUCTION

Figure 1.1: Map of South Africa with the 23 designated Ramsar sites.

1.1.4. Wetlands in South Africa

In South Africa wetlands have been lost over the years due to different impacts. In certain areas such as on coastal plains, more than 50% of wetland habitats have already been lost (Shearer, 1997) and a large quantity of the remaining wetlands are under constant threat (Kotze, Breen & Quinn, 1995; Wamsley, 1988). Physical and direct impacts include draining for agriculture or forestry, urban development (e.g. the building of roads and dams), grazing, discharge of pollutants and the mining of wetland soils cause loss and degradation to/of wetlands.

The majority of research on wetlands includes bird diversity, ecosystem services such as flood attenuation and the provision of livestock grazing, the functions that wetlands provide such as groundwater recharge and discharge (Day & Malan, 2010) and a number of cultural and recreational benefits that wetlands provide to communities.

pg. 5 CHAPTER: 1 GENERAL INTRODUCTION According to Day & Malan (2010), there is a lack of research regarding aquatic biota in wetlands.

Of the 23 designated Ramsar sites, only 14 are freshwater wetlands which includes pans, freshwater lakes and floodplain wetlands. The Seekoeivlei Wetland can be classified as a floodplain depression (Ollis et al., 2015).

The Seekoeivlei Nature Reserve, near the town of Memel in the Free State (Figure 1.2), is considered important in providing ecosystem services such as water purification and flood attenuation. Furthermore, the wetland is important because it forms part of the upper reaches of the Klip River catchment, which is an important tributary of the Vaal River (Youthed, 2014). The Klip River contributes approximately 46% of surface water flow in the upper Vaal River catchment (DWAF, 2004). According to Wepener et al. (2011), the Vaal River has been described as one of the most important rivers in South Africa. It is an important source of water not only for Gauteng but also for parts of neighbouring provinces such as the Free State, North-West and Mpumalanga (Tempelhoff, 2009).

1.1.5. Seekoeivlei Nature Reserve

The Seekoeivlei Wetland is considered a Ramsar site for four reasons. Firstly, it is the only protected area in the Free State that covers Amersfoort Highveld Clay Grassland and the Eastern Temperate Freshwater Wetland veld type (McCarthy et al., 2010) which is poorly protected according to the Free State Department of Tourism, Environmental and Economic Affairs (FS DTEEA, 2008). Secondly, when looking at the interior of South Africa it is the largest protected area of wetlands (Mucina & Rutherford, 2006). Thirdly, it provides habitat for numerous plant and animal species that are threatened in the Free State Province; and lastly, this is considered an important breeding site for numerous bird species, including those that are endangered (McCarthy et al., 2010). This wetland also provides food for livestock and game during winter times.

pg. 6 CHAPTER: 1 GENERAL INTRODUCTION

Figure 1.2: Regional setting of the town Memel in the Free State. Before Seekoeivlei was declared as a nature reserve in 1978 (McCarthy et al., 2010), the wetland was impacted by artificial drainage channels because of commercial farming. These human interventions had an impact on wetland geo-hydrological processes that led to the decrease in function and integrity of the Seekoeivlei Wetland (Youthed, 2014). Dirt roads that run through the wetland were also identified to cause erosion or sedimentation of the wetland (Zabala, 2008).

The Seekoeivlei Wetland is approximately 16 km long and varies in width from a few hundred meters up to 2 km in places (McCarthy et al., 2010). In the Wetland Rehabilitation Plan completed by Youthed (2014) the study looked at hydrology, geomorphology, and vegetation, with no research on the aquatic micro- and macroinvertebrate assemblages.

1.1.6. Water quality

South Africa’s water quality monitoring of rivers are more than satisfactory with a long history of both water quality and biological monitoring (Malan & Day, 2012). Unfortunately, not much is known of the water quality of South Africa’s wetlands. When

pg. 7 CHAPTER: 1 GENERAL INTRODUCTION comparing wetlands and rivers, the water chemistry differs, spatially and temporally (Malan & Day, 2012). Rivers are sources of sediment while wetlands are sinks. Furthermore, wetlands are accumulating systems meaning it will have an impact on water quality through the accumulation of sediments. According to Malan & Day (2012), nutrient levels that can be the cause of eutrophication is important in accumulation systems and is the reason why there is a poor understanding of the water quality of wetlands in South Africa, whether under natural or impacted conditions. A few main factors that indeed have an influence on the water quality of wetlands is the water source, drainage pattern, residence time and inundation depth of the wetland (Malan & Day, 2005).

Water sources of a wetland can vary from an underground spring to overland run-off. The shorter the retention of water, the less evaporation will occur; and salts and other constituents cannot concentrate. The length of residence time of the water has an influence on the period of time that the water is in contact with the sediment (Malan & Day, 2005) i.e. the time that it takes for exchange processes between the water column and sediments. According to Malan & Day (2012), nitrogen in organic matter can be changed when there are lower water levels, which causes the surrounding substrate to become more aerated, which leads to an increase in decomposition.

1.1.7. Sediment quality

De Klerk et al. (2012) mention stressors such as metal contamination and nutrient enrichment have a direct impact on water and sediment quality in wetlands. Not only water quality is affected by pollutants but the sediment quality as well (Malan & Day, 2012). Sediments can be regarded as a sink for contaminants such as metals and sometimes sediments contain higher concentrations of pollutants than the surrounding water body (de Klerk et al., 2012).

According to Karbassi et al. (2007), the deposition of contaminants such as metals have the potential to be toxic to aquatic biota. Sediments play an important role in the adsorption of metals and can be a potential reservoir for metals that can influence water quality. Metals and pollutants not only bind to sediments but to organic matter too, which can change the chemical and physical conditions of sediments (de Klerk et al., 2012).

Grain size is considered as a fundamental property of sediments of its effect on the transport and depositing properties of sediment particles (Blott & Pye, 2001). Organic

pg. 8 CHAPTER: 1 GENERAL INTRODUCTION matter is another factor to be included in the analyses since organic content of sediment is an important factor that determines the sorption potential of the system (Schorer, 1997).

1.1.8. Aquatic invertebrates

Aquatic invertebrates are found in freshwater rivers and wetlands and live either in or on the bottom substrate, swim in the water column or live on the surface of the water (Suren & Sorrell, 2010). According to Batzer & Boix (2016), ecologists tend to focus on groups of invertebrates, i.e. microinvertebrates (or zooplankton) and macroinvertebrates. Although zooplankton of wetlands are not as well studied as macroinvertebrates, both groups are important food sources and an important component for a healthy ecosystem.

According to Ferreira et al. (2012), the most prominent feature of wetland ecosystems, are the aquatic invertebrate communities (zooplankton and macroinvertebrates). The invertebrate communities are considered as very important because they are regarded as possible indicators of the ecological integrity of wetlands (Bird & Day, 2009). They can provide insight into seasonal variation in biological assemblages and the ecological services that the wetland can provide (Chipps et al., 2006). Distribution of invertebrates in wetlands can also be affected by the vegetation present. Biotopes with more vegetation tend to contain higher invertebrate diversity than more open-water biotopes (Bird et al., 2014).

1.2. PROBLEM STATEMENT

It is said that floodplain wetlands, with associated biota, are important for the biota in rivers that specifically depend on the link between the two wetland structures, i.e. floodplain wetland and river (Malherbe et al., 2015). However, little information is available in South Africa to support this statement. Recent studies of floodplain wetlands in South Africa include the Phongolo floodplain (Dube et al., 2017) and the floodplain wetlands associated with the Harts River (Malherbe et al., 2015). In both these studies the importance of the link between the river and the wetland system was demonstrated.

The Seekoeivlei can be classified as a floodplain wetland system and although it is a Ramsar site and in a nature reserve, there are no published records on the aquatic invertebrate diversity. Studies that have been done on the Seekoeivlei Nature Reserve

pg. 9 CHAPTER: 1 GENERAL INTRODUCTION include physical studies on the hydrology and geomorphology (Tooth et al., 2002). Biotic studies are limited to bird diversity and nematode diversity.

There is thus a need to obtain baseline information on the Seekoeivlei Wetland and the associated Klip River. This information will be important for future management and monitoring actions in the reserve.

1.3. HYPOTHESIS

The following hypotheses will be tested during this study:

Water and sediment quality has an influence on the aquatic invertebrate diversity of the Seekoeivlei Nature Reserve.

Aquatic invertebrate diversity will show seasonal variation in the Seekoeivlei Nature Reserve.

Different wetland types have an influence on the community structure of the aquatic invertebrate diversity.

1.4. AIMS AND OBJECTIVES

The aims of this study were to determine the changes in water and sediment quality together with the aquatic invertebrate diversity (zooplankton and macroinvertebrates) in relation to hydrological regime and inundation of the floodplain wetlands. To achieve these aims the following objectives were set:

1. The assessment of the water and sediment quality of the Seekoeivlei Wetland during three surveys representing three different hydrological periods. 2. Determine the zooplankton and aquatic macroinvertebrate diversity of the Seekoeivlei Wetland during three surveys representing three different hydrological periods. 3. Relating water and sediment quality together with hydroperiod to changes in different wetland types in the Seekoeivlei Wetland.

pg. 10 CHAPTER: 1 GENERAL INTRODUCTION 1.5. CHAPTER BREAKDOWN

Chapter 1: General introduction: contains the introduction to wetlands, importance of the wetlands, overview of the Ramsar Convention and introductory comments on important aspects of the study. The problem statement, aims, objectives and hypotheses are presented for the project.

Chapter 2: Study area and site selection: the available information on the Seekoeivlei Nature Reserve will be reviewed as well as the classification of the wetland. The 21 selected sites will be described in terms of habitat availability and wetland type.

Chapter 3: Water and sediment analysis: in this chapter the methodology of sample collection, results and analyses of the water and sediment in the Seekoeivlei Nature Reserve is provided. The spatial and temporal data results are discussed.

Chapter 4: Zooplankton diversity: this chapter includes the methodology of the zooplankton sample collection, results and analyses of the Seekoeivlei Nature Reserve. Results include graphic representations of the statistical analyses. The spatial and temporal trends in community structure are discussed.

Chapter 5: Macroinvertebrate diversity: methods used to collect and identify the macroinvertebrates in the Seekoeivlei Nature Reserve are presented. Macroinvertebrates were divided in functional feeding groups, and the results presented in this chapter. Statistical analyses were performed and used to create the appropriate graphic representations. The spatial and temporal trends in community structure are discussed.

Chapter 6: General conclusion and recommendations: provides some concluding remarks and recommendations that emanated from the study.

Chapter 7: References: A list of all the references used throughout this dissertation is presented.

Appendices: Raw data of the water and sediment, zooplankton diversity and aquatic macroinvertebrate diversity results of the Seekoeivlei Nature Reserve.

pg. 11

SEEKOEIVLEI NATURE RESERVE AND SITE SELECTION

2.1. BACKGROUND

The Seekoeivlei Nature Reserve is situated in the Free State Province, just outside the small town of Memel. This reserve was declared a nature reserve in 1979 and a Ramsar site in 1997 (Zabala & Policy, 2008).

The Free State Department of Tourism, Environmental and Economic Affairs (FS DTEEA, 2008) states that there are 18 Nature reserves in the Free State with a cover of approximately 107 996 km2 and 18 735 mapped wetlands with an estimated cover of 2 2 129 km . The Seekoeivlei Nature Reserve covers an estimated area of 4 754 ha (McCarthy et al., 2010) and the wetland covers 3 000 ha of the reserve. The Seekoeivlei Wetland as a whole is approximately 12 000 ha and consists of 220 oxbows which formed over centuries by the meandering course of the Klip River (Tooth & McCarthy, 2007). This wetland consists of distinctive aquatic habitats that contain numerous oxbow lakes, active and abandoned channels and back swamps (McCarthy et al., 2010). This floodplain complex is 28 km long and up to 1.5 km wide.

2.1.1. Rainfall and climate

In the upper Klip River catchment, the mean annual rainfall ranges from 700 mm to 1200 mm, falling mainly from November to March (McCarthy et al., 2010) (rainfall data of the Free State is presented in Figure 2.1), with annual potential evaporation of 1600 mm to 1800 mm (Tooth et al., 2002). According to Marren et al. (2006), the maximum mean monthly temperature in the summer is between 13 ˚C and 25 ˚C and the minimum mean monthly temperature in the winter is -1 ˚C and 15 ˚C.

pg. 12 CHAPTER: 2 STUDY AREA

Figure 2.1: Rainfall data of the Free State from 2012 - 2016 (www.dwa.gov.za/Hydrology/Provincial rain/Default.aspx) 2.1.2. Geology and soils

The Seekoeivlei Wetland is underlain by sediments consisting of the lower Beaufort and Upper Ecca Groups of the Karoo Sequence (du Preez & Marneweck, 1996). The Beaufort group consists of various mudstones and sandstones which form part of the Normandien formation (McCarthy et al., 2010). Highly resilient dolerite dykes and sills cut through the sediments and occur throughout the reserve (Tooth & McCarthy, 2007). According to Tooth & McCarthy (2007), erosion of the sandstone/shale valleys is restricted to lateral erosion which, over time, creates space for meanders. This process is only possible to the level of the dolerite. The moment the river enters a dense thick valley of dolerite the meandering will stop abruptly. The Seekoeivlei Nature Reserve is generally flat to slightly undulating, which becomes more uneven in the mountainous catchment that is south-east of the floodplain (du Preez & Marneweck, 1996).

2.1.3. Hydrology

Changes in the channel of the Klip River Valley are linked to the lithology variations, specifically the modification from sandstone to dolerite (Tooth et al., 2002). Wide valleys

pg. 13 CHAPTER: 2 STUDY AREA occur where the sandstone are present on one or both sides of the valley, whereas the narrow valleys occur where the dolerite are present on both sides of the valley. According to Tooth et al. (2002), when looking at the longitudinal profile of the Klip River, the narrowing of the river because of the dolerite valleys is associated with a steepening channel-bed gradient.

Wetlands that are vulnerable to erosion will result in sediment that is removed rather than deposited. This will lead to the formation of deep gullies that drains the water, and thereby destroys the function and values of the wetland (Collins, 2005).

2.1.4. Terrestrial vegetation

The vegetation of the reserve can be characterised as grassland, woodland and thicket as well as hygrophilous communities (du Preez & Marneweck, 1996). With the altitude as high as 1 680 m to 1 700 m above sea level the catchment is characterised by Afromontane grassland, which is dominated by Themeda triandra and Tristachya leucothrix. Other dominating grass species in the floodplain is Aristida junciformis and Eragrostis curvula which is part of the drier floodplain. The grassland terrain is vulnerable to overgrazing and trampling effect of livestock that are present (McCarthy et al., 2010). Most of the tree species present were introduced by commercial farmers such as Pinus spp. (pines); Eucalyptus spp. (gums); Salix spp. (willows) and Populus spp. (poplars) which is also considered invasive species (McCarthy et al., 2010).

2.1.5. Wetland classification

The Classification System for Wetlands and other Aquatic Ecosystems in South Africa (Ollis et al., 2013) should be referred to, in order to identify what type of wetland Seekoeivlei is. An example of the classification table given by Ollis et al. (2015) is presented in Figure 2.2. According to Ollis et al. (2015), Seekoeivlei falls within the North Eastern Uplands ecoregion and landscape setting is defined as a plain. Seekoeivlei is distinguished as a floodplain wetland (Ollis et al., 2015). To further classify the specific wetland, it is necessary to look at the source of water and how it moves into, through and out of the wetland system, in other words, the hydrological regime (Ollis et al., 2015). The Klip River, which is the primary source of water for the Seekoeivlei, is classified as a perennial river. Based on the water quality parameters, Seekoeivlei is classified as a natural freshwater wetland (Ollis et al., 2015). With a mean pH of 7.36, the Seekoeivlei Wetland is categorised as a circum-neutral wetland.

pg. 14 CHAPTER: 2 STUDY AREA Substratum and soil present in the Seekoeivlei Wetland consist of the full scale ranging from boulders, gravel, very coarse sand, coarse sand, medium sand, very fine sand and mud (Ollis et al., 2015; Wentworth 1922). Aquatic vegetation consists of floating attached aquatic vegetation as well as submerged aquatic vegetation. In the Seekoeivlei Wetland three types of wetland structures were classified, i.e. river, pans and oxbow lakes. The pans represent the waterbodies that were further from the river than the oxbow lakes. Oxbow lakes are old river channels that flow near the river.

Figure 2.2: Conceptual overview of the classification system for wetland ecosystems (Ollis et al., 2015). 2.1.6. Fauna

There is an assemblage of rare, vulnerable and endangered animal species in the Seekoeivlei Nature Reserve. In the reserve itself, 31 mammal species have been reported including the Hippopotamus amphibious (hippopotamus), which was reintroduced in 1999. A number of these species are important in terms of their conservation status including the Hippotragus equinus (roan antelope), Ourebia ourebi (oribi) and Leptailurus serval (serval) which are rare and endangered species (du Preez & Marneweck, 1996). The Connochaetes gnou (black wildebeest) and the Pelea capreolus (grey rhebok) are two species present in the reserve that are endemic to South Africa (FS DTEEA, 2008). Other mammals in the reserve include Syncerus caffer

pg. 15 CHAPTER: 2 STUDY AREA (buffalo), Redunca arundinum (reedbuck), Alcelaphus buselaphus caama (red hartebeest), Equus zebra (zebra), small antelope and smaller mammals (mongoose and rodents) (du Preez & Marneweck, 1996 & FS DTEEA, 2008). Mammals present in the Seekoeivlei Nature Reserve that have a near-threatened status on the Red Data List includes the Mellivora capensis (honey badger), Rhinolophus capensis (Cape horseshoe bat), Rhinolophus clivosus (Geoffroy’s horseshoe bat), Myotis myotis (mouse-eared bat) as well as the Hydrictis maculicollis (spotted-necked otter) (FS DTEEA, 2008 & MammalMAP, 2017).

According to the Fish Reference of Occurrence (FROC; Kleynhans et al., 2007), Austroglanis sclateri (rock catfish), Labeobarbus aeneus (smallmouth yellowfish), Enteromius anoplus (chubbyhead barb), Enteromius neefi (sidespot barb), Cyprinus carpio (common carp), Labeo capensis (mudfish) and Labeo umbratus (moggel) can be expected in this Vaal River catchment area. Of these species, the largemouth yellowfish, smallmouth yellowfish, moggel, mudfish and sharptooth catfish are commercially and recreationally important species in the Free State (FS DTEEA, 2008).

2.1.7. Anthropogenic activities

In the Seekoeivlei Nature Reserve, there are two dominant human activities i.e. agricultural activities and tourism. McCarthy et al. (2010) mentioned that commercial farming began in the late nineteenth century which led to the existence of the town Memel in the early twentieth century. This commercial farming brought some changes to the wetland such as trees, mainly poplars and willows that were introduced. This introduction can be a good explanation for the great bird diversity present today (McCarthy et al., 2010).

Cattle and sheep were also introduced to the area which led to an increase in grazing. According to Collins (2005), grazing can have both positive and negative impacts on the wetland. The diversity of habitats is increased because of some short grazed areas and other areas left with tall grass. Unfortunately, there are some areas that are completely grazed and leads to the decrease of habitats. This heavy grazing can cause important grazing species to be replaced with less productive species in a specific area (Collins, 2005). Grazing and trampling can lead to erosion in some wetlands or even some areas of a wetland. The different land use zones in relation to the samplings sites (described in the following section) are shown in Figure 2.3.

pg. 16 CHAPTER: 2 STUDY AREA

Figure 2.3: Different land uses present in the study area of the Seekoeivlei Nature Reserve and Klip River during sampling surveys. 2.2. SITE SELECTION

Sites were selected according to the different wetland types and habitats present in the Seekoeivlei Nature Reserve. This floodplain wetland consists of the Klip River, floodplain depressions and flats. It is important to sample in different habitats to make a conclusion about the biodiversity present in the wetland. The study was conducted at 21 preselected sites (Figure 2.4) in the Klip River (Sites 1, 4c, 6, 7 10a), Pampoen Spruit (Site Ps), Wildemans Spruit (Site Ws) and wetland sites (Sites 2a, 2b, 3a, 3b, 3c, 3d, 3e, 4a, 4b, 4d, 5, 8, 9, 10b). Sampling surveys were completed in the dry winter season (July 2016), wet summer season (December 2016) and austral autumn (late summer rain), (March 2017). A detailed site description for each site is provided below (Table 2.1 – Table 2.21).

pg. 17 CHAPTER: 2 STUDY AREA

Figure 2.4: Map of the Seekoeivlei Nature Reserve with the selected sites during this study.

pg. 18 CHAPTER: 2 STUDY AREA Table 2.1: Site description of Pampoen Spruit.

Pampoen Spruit (Ps)

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27°40'9.50" S; 29°33'48.15" E Altitude 1 708 m Classification Inland stream naturally, permanently inundated. Description Small tributary from the rural development, Zamani (Zabala, 2008) just outside Memel. Shallow stream (on the western side of the Klip River), with no vegetation present. Pollution is present in the stream in the form of litter and potential effluent from the township Zamani. The water level was the highest during the third survey (average depth of approximately 60 cm). Grazing in the riparian zone evident by horses, cattle and goats.

pg. 19 CHAPTER: 2 STUDY AREA

Table 2.2: Site description of Site 1.

Site 1 (Klip River)

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 41' 12.8" S; 29° 34' 43.4" E Altitude 1 709 m Classification River. Description Klip River flowing towards the town Memel – sampled downstream of the R34 road bridge. Shallow water where sediment and water samples were collected. Water levels were two to three meters higher in the third survey. No riparian vegetation except for the one willow tree. Run in the river present in the deeper middle section of the river. Grazing evident by cattle.

pg. 20 CHAPTER: 2 STUDY AREA Table 2.3: Site description of Site 2a

Site 2a

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 39' 00.3" S; 29° 35' 10.1" E Altitude 1 700 m Classification Natural floodplain depression, permanently saturated and inundated. Distance from Klip 163 m River Average depth of approximately 3 m. Description Biggest floodplain depression (on the eastern side of the river), large bird activity present. Vegetation around the pan present such as Cyperus fastigiatus (tall slender sedge) and Juncus effusus (soft rush). The fringing vegetation covered approximately 2-3 m. Grazing present by the sheep and cattle on the farm.

pg. 21 CHAPTER: 2 STUDY AREA Table 2.4: Site description of Site 2b.

Site 2b

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27° 38' 34.4" S; 29° 35' 02.1" E Altitude 1 701 m Classification Natural floodplain oxbow lake, permanently inundated. Distance from Klip 18.3 m River Average depth of 5 m Description Floodplain oxbow lake on the eastern side of the river). Some vegetation present along the edges of the lake. Deeper water (approximately 5 m) to the middle with Isolepis fluitans (watergrass) present. Could not sample during survey 3 because of the rain which caused flooding. Grazing evident by the cattle.

pg. 22 CHAPTER: 2 STUDY AREA Table 2.5: Site description of Wildemans Spruit.

Wildemans Spruit (Ws)

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 37' 52.0" S; 29° 35' 32.4" E Altitude 1 710 m Classification Inland stream naturally, permanently inundated. Description Small tributary flowing into reserve, from the upper north- eastern reaches through agricultural land, towards the southern side of the reserve.

pg. 23 CHAPTER: 2 STUDY AREA Table 2.6: Site description of Site 3a.

Site 3a

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 38' 37.87" S; 29° 34' 44.66" E Altitude 1 702 m Classification Natural floodplain oxbow lake, permanently saturated and inundated. Description Oxbow lake one of five, west side of the river. There is a clear difference between the seasons when looking at the abundance of vegetation and taking the depth in consideration. Little to no vegetation was present during the first survey and during the third survey the sampling area was a complete marshland, with complete vegetation cover. Open water was much deeper during the third survey (deeper than 1 m), whereas during the second survey it had an average depth of not more than 1 m and the first survey not more than approximately 50 cm. Little to no grazing present in this part of the nature reserve.

pg. 24 CHAPTER: 2 STUDY AREA Table 2.7: Site description of Site 3b.

Site 3b

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 38' 43.7" S; 29° 34' 45.5" E Altitude 1 702 m Classification Natural floodplain oxbow lake, permanently saturated and inundated. Description Oxbow lake number two on the west side of the river. Deep water is over 1 m deep. Vegetation such as Cyperus fastigiatus (tall slender sedge), Persicaria lapathifolia (pale persicaria), Lagarosiphon major (curly water thyme) is present on the outer edges of the lake. Little to no grazing present in this part of the nature reserve.

pg. 25 CHAPTER: 2 STUDY AREA Table 2.8: Site description of Site 3c.

Site 3c

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 38' 48.2" S; 29° 34' 41.1" E Altitude 1 702 m Classification Natural floodplain oxbow lake, permanently saturated and inundated. Description Oxbow lake number three, on the west side of the river. More shallow (average depth of 60 cm up to 1 m) than the first two oxbow lakes. Again much more vegetation was present the third survey, which includes Cyperus fastigiatus (tall slender sedge), Persicaria lapathifolia (pale persicaria), Typha capensis (short bulrush). Little to no grazing present in this part of the nature reserve.

pg. 26 CHAPTER: 2 STUDY AREA Table 2.9: Site description of Site 3d.

Site 3d

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 38' 50.0" S; 29° 34' 39.0" E Altitude 1 702 m Classification Natural floodplain oxbow lake, permanently saturated and inundated. Description Oxbow lake number four, on the west side of the river. During survey one it was not deeper than 30 cm, second survey it had an average depth of no more than 1 m and during third survey the water depth was approximately 1 m. Vegetation present on outer edges as well as deeper sections in the lake. Vegetation includes Cyperus fastigiatus (tall slender sedge), Persicaria lapathifolia (pale persicaria), Typha capensis (short bulrush). Little to no cattle grazing present in this part of the nature reserve. There are only antelope present in this part of the reserve.

pg. 27 CHAPTER: 2 STUDY AREA Table 2.10: Site description of Site 3e.

Site 3e

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27° 38' 56.7" S; 29° 34' 41.6" E Altitude 1 702 m Classification Natural floodplain oxbow lake, permanently saturated and inundated. Description Oxbow lake number five, on the west side of the river, but closer to the river (see map). A shallow lake, not deeper than 1 meter. Because of the flooding during the third survey, site 3e could not be reached. Cyperus fastigiatus (tall slender sedge), Persicaria lapathifolia (pale persicaria), Typha capensis (short bulrush) were noted as the vegetation present during the second survey. During the third survey, the whole area of the oxbow lakes (Sites 3a, 3b, 3c, 3d & 3e) was a complete flooded marshland. Little to no grazing present in this part of the nature reserve.

pg. 28 CHAPTER: 2 STUDY AREA Table 2.11: Site description of Site 4a.

Site 4a

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27° 37' 32.5" S; 29° 34' 55.7" E Altitude 1 701 m Classification Inland floodplain depression (pan), permanently saturated. Description Floodplain site inside the reserve on the western side of the river. Very shallow pan (average depth of approximately 30 cm) with a lot of Isolepis fluitans (watergrass) present. This site was inaccessible during the third survey. Little to no grazing present in this part of the nature reserve.

pg. 29 CHAPTER: 2 STUDY AREA Table 2.12: Site description of Site 4b.

Site 4b

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27°37'35.59" S; 29°35'1.29" E Altitude 1 702 m Classification Permanently saturated floodplain depression (pan). Description Floodplain site, closer to the river (western side of the river) than site 4a (approximately 100 m from the Klip River). Still, shallow open water present with not much vegetation than Isolepis fluitans (watergrass). As with site 4a, site 4b could not be reached during the third survey. Both sites 4a and 4b are shallow pans, not deeper than 1 m. Little grazing evident from Syncerus caffer (buffaloes) and Hippopotamus amphibious (hippopotami) in this part of the nature reserve.

pg. 30 CHAPTER: 2 STUDY AREA Table 2.13: Site description of Site 4c.

Site 4c (Klip River)

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 37' 47.0" S; 29° 35' 09.8" E Altitude 1 702 m Classification Inland river, permanently inundated. Description Klip River site next to the fence in the reserve at the south side of the reserve. Stones and boulders were present with little vegetation. As can be seen from the pictures above, specifically the third survey the whole system from the upper reaches were flooded due to large amounts of water that came into the reserve. Due to large amounts of rain the river was much deeper during the third survey, this site can be classified as a run.

pg. 31 CHAPTER: 2 STUDY AREA Table 2.14: Site description of Site 4d.

Site 4d

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27° 37' 31.5" S; 29° 35' 11.3" E Altitude 1 701 m Classification Permanently saturated and inundated floodplain depression. Description Floodplain depression on the east side of the river. Hippopotamus amphibious (hippopotamus) present on the site. The open water had an average depth of 2 m, which is deep enough for the hippopotamus to move about. Vegetation such as Isolepis fluitans (watergrass) made it difficult to sample sediment. This site was inaccessible during the third survey. Little grazing evident from the hippopotami.

pg. 32 CHAPTER: 2 STUDY AREA Table 2.15: Site description of Site 5.

Site 5

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27° 37' 02.0" S; 29° 34' 38.7" E Altitude 1 701 m Classification Inland, natural depression, permanently inundated and saturated. Description Big pan on the west side of the river inside the reserve. Flamingos were present during the first survey Not much vegetation present except for the Isolepis fluitans (watergrass) on the floor of the pan. This site was inaccessible during the third survey. Little to none grazing present at this site.

pg. 33 CHAPTER: 2 STUDY AREA Table 2.16: Site description of Site 6.

Site 6

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 35' 30.6" S; 29° 35' 31.7" E Altitude 1 700 m Classification Inland natural river permanently inundated. Description This open water section of the river in the reserve, which is at a bird hide, is the sampling area. Vegetation present at the edges of the site is Juncus effuses (soft rush), Utricularia stellaris (bladderworts), Spirodela spp. (duckweed), Typha capensis (short bulrush), Cyperus marginatus (sedge), Phragmites australis (common reed), Persicaria lagarosiphon (pale persicaria). Only edges of the site are shallow enough to sample. Approximate depth of 1 m up to 6 m. Grazing present mostly by Redunca arundinum (reedbuck) and Connochaetes gnou (black wildebeest).

pg. 34 CHAPTER: 2 STUDY AREA Table 2.17: Site description of Site 7.

Site 7

Survey 1 (July 2016) Survey 2 (December 2016)

Survey 3 (March 2017)

Coordinates 27° 34' 57.8" S; 29° 35' 09.1" E Altitude 1 696 m Classification Inland natural river, permanently inundated. Description Further downstream is the second bird hide next to a river site that expands into open water and forms a deep depression of over 1 m deep. Otters and birds werepresent at the site. During the first and second surveys this site were accessible from the eastern side but during the third survey, the eastern side was inaccessible and the site could only be reached from the western side of the reserve. Typha capensis (short bulrush), Phragmites australis (common reed) are the vegetation present at this site. Grazing present mostly by Redunca arundinum (reedbuck) and Connochaetes gnou (black wildebeest).

pg. 35 CHAPTER: 2 STUDY AREA Table 2.18: Site description of Site 8.

Site 8

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27°33'32.79" S; 29°35'8.76" E Altitude 1 694 m Classification Natural oxbow lake, permanently saturated and inundated. Description Small depression (mostly backwater) directly next to the river on the western side and lower reaches of the river in the northern part of the reserve. Deep water body, over 1 m deep. Vegetation is dominated by Phragmites australis (common reed). This site was inaccessible during the third survey. Grazing present mostly by Redunca arundinum (reedbuck), Equus zebra (zebra) and Connochaetes gnou (black wildebeest).

pg. 36 CHAPTER: 2 STUDY AREA Table 2.19: Site description of Site 9.

Site 9

Survey 2 (December 2016) Survey 2 (December 2016)

Coordinates 27°33'22.2" S; 29°35'28.1" E Altitude 1 694 m Classification Natural oxbow lake, permanently saturated. Description Oxbow site on the eastern side of the river, close to the boundary of the reserve. Water present only during rainy season. During the third survey, this site was flooded and could not be reached. Isolepis fluitans (watergrass) present in the water. Pan is shallow (average depth of 1 m) enough to sample effectively. Grazing present mostly by Redunca arundinum (reedbuck) and Connochaetes gnou (black wildebeest).

pg. 37 CHAPTER: 2 STUDY AREA Table 2.20: Site description of Site 10a.

Site 10a (Klip River)

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27°32'24.35" S; 29°35'12.13" E Altitude 1 692 m Classification Inland river, permanently inundated. Description Lower reaches of the river at the northern side of the reserve. Can be classified as a run. Present vegetation at the site is Cyperus marginatus (sedge). This site was inaccessible during the third survey. Little to no grazing present at this site.

pg. 38 CHAPTER: 2 STUDY AREA Table 2.21: Site description of Site 10b.

Site 10b

Survey 1 (July 2016) Survey 2 (December 2016)

Coordinates 27°32'26.13" S; 29°35'7.59" E Altitude 1 693 m Classification Small natural depression (mostly backwater), permanently saturated and inundated. Description Small pan on the western side of the river in the northern part of the reserve. Riparian vegetation had a greater abundance during the second survey. The dominant vegetation is Cyperus marginatus (sedge) and Isolepis fluitans (watergrass). Deeper than 1 meter to the middle reaches of the pan with a steep decline (average depth of 5 m). This site was inaccessible during the third survey. Grazing evident from Redunca arundinum (reedbuck) and Connochaetes gnou (black wildebeest).

pg. 39

WATER AND SEDIMENT ANALYSIS

3.1. INTRODUCTION

3.1.1. Water quality

Water resources in South Africa are generally scarce in many parts and as such these systems need to be conserved and monitored. The basis of aquatic ecosystems is water and if it is not monitored the ecosystem with its biodiversity could be severely impacted. Man depends on services provided by aquatic systems which include: providing a pleasing environment; recreational uses; providing water for food for dependent communities and maintaining biodiversity in the aquatic ecosystem (DWAF, 1996).

Water quality data, specifically long-term data sets for wetlands, are limited for South African wetlands (Malan & Day, 2012). In South Africa wetlands and rivers are managed and monitored separately (Malan & Day, 2012). Even though wetlands are considered an important component of the hydrological cycle, there is still a gap in the research and monitoring of South African wetlands (Ferreira et al., 2012). Hydrology of wetlands creates the unique physico-chemical conditions that makes it a different ecosystem compared to terrestrial ecosystems (Mitsch & Gosselink, 2000).

Standard water quality variables that are monitored routinely in South African monitoring programmes include: system variables (salts, dissolved oxygen, turbidity, temperature), nutrients (phosphate, nitrite, nitrate) and toxic substances (metals and organic substances that are listed in the South African Water Quality Guidelines for Aquatic Ecosystems) (DWAF, 1996; Palmer et al., 2005).

Stressors that have an influence on water quality of rivers and wetlands include toxic or trace metal contamination and nutrient enrichment (de Klerk et al., 2012). These stressors are a result of cultivation, industrial developments, mining and overgrazing (de

pg. 40 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS Klerk et al., 2012). Although there are no mining activities near the Seekoeivlei Nature Reserve, which will suggest theoretically there will be no metal pollution, it is still important to provide a baseline on the metal concentrations. According to Jeffrey (2005), the Free State, in particular, the Klip River is considered as one of the coal resources in South Africa, where future mining could occur in the future.

Water quality is one of the most important factors that can influence the wetland ecosystem (de Klerk et al., 2012). According to Malan & Day (2012), water quality in rivers differ from the water quality in wetland systems. This difference can be explained by the fact that rivers have more constant movement of water, including the present nutrients and sediments (Malan & Day, 2012). In contrast, there is no continuous movement of water in wetlands and these systems receive water from different sources that can influence the type and concentration of chemical constituents present (Malan & Day, 2012). There are no flow areas in wetlands, causing a residence time for constituents to concentrate in one space. According to Malan & Day (2012), the shorter the residence time is in the water, the less time there is for salts to evaporate or nutrients and metals to concentrate.

Since water quality can differ between river and wetland systems it is important to consider the lateral exchange of nutrients and metals between rivers and wetland systems, specifically floodplains (Thoms et al., 2000). According to Amoros & Bornette (2002), the increase in dissolved nutrient in floodplains can be ascribed to the connectivity to the river, which provides the floodplain with nutrient-rich water.

3.1.2. Sediment quality

Sediment quality is essential to the functioning of aquatic ecosystems. According to Burton (2002), sediments are important to the food web and serve as a reservoir of contaminants for bioaccumulation and trophic transfer. Development of sediment quality guidelines has grown since the 1980s for future uses in analysis of contaminated sediments. Contaminants include pathogens, nutrients; metals and organic materials. If the loading of the contaminants is large enough, the sediments will accumulate extreme quantities which will disturb the ecosystem, whether it is directly or indirectly (Burton, 2002).

According to Mitsch & Gosselink (2007), the complex exchange processes between the water column and present sediments take place in flowing water systems, i.e. rivers. Malan & Day (2012) states that nutrients and metals that are bound to sediments can

pg. 41 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS be released under certain pH and dissolved oxygen as well as when sediment are disturbed under storm conditions.

Three characteristics that are usually included in aquatic health assessments of sediments are moisture content; grain size distribution as well as organic content. Physical characteristics of sediment can be determined through grain size, organic content as well as the moisture content, which then leads to the composition of the biological assemblages (Venter & van Vuren, 1997).

3.1.3. Aim and objective for this chapter:

Limited water and sediment data are available for Seekoeivlei Nature Reserve. The aim of this chapter is to determine the variation of the water quality and sediment quality during the three surveys, winter (July 2016), summer (December 2016) and autumn (March 2017). The first objective of this chapter was to determine the spatial and temporal water quality of the Seekoeivlei Nature Reserve, through the assessment of various physico-chemical and metal analyses. The second objective of this chapter is to determine the spatial and temporal sediment quality of the Seekoeivlei Nature Reserve in terms of grain size, organic content and metal analyses.

3.2. MATERIALS AND METHODS

3.2.1. Water quality methods

3.2.1.1. Water sampling protocol

Water samples were collected in pre-washed polyethylene bottles during the three surveys (July 2016, December 2016 and March 2017) at each site (described in Chapter 2). Samples were frozen until analyses were completed in the laboratories of the North-West University. In-situ physico-chemical parameters were measured using the ExStik EC500 & DO600 (Extech Instruments, A FLIR Company, USA) multi meters at each site. These parameters included electrical conductivity (µS/cm), total dissolved solids (mg/l), oxygen saturation (%), oxygen concentration (mg/l), pH and temperature (°C).

pg. 42 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS 3.2.1.2. Laboratory analyses

A Merck Pharo 300 Spectroquant (Merck KGaA, Germany) with the associated kits were used to determine the following variables: ammonium (NH4-N); nitrate (NO3-N); -2 nitrite (NO2-N); ortho-phosphate (PO4-P); sulphate (SO4 ) and turbidity (NTU). The methods for these variables are set in the Merck Pharo 300 Spectroquant manual (methods adapted from Vlok et al., 2013).

All water samples were defrosted and 50 ml of each sample was filtered through pre- weighed 0.45µm cellulose filter using a vacuum pump (methods adapted from Malherbe et al., 2015). Following microwave digestion, the samples were analysed with an Agilent 7500CE Inductively Coupled Plasma – Mass Spectrophotometer (ICP-MS) for selected metals and major cations.

3.2.2. Sediment quality methods

3.2.2.1. Sediment sampling protocol

Surface (top 5 cm) sediment samples were collected in pre-washed polyethene jars at each site during the three surveys during July 2016, December 2016 and March 2017 Samples were frozen until analyses were done in the laboratories at the North-West University.

3.2.2.2. Laboratory analyses

Sediment analysis was carried out using the methods described in ASTM (2000) and USEPA (2001). Sediment from each site was dried at 60 ˚C, in a Labcon 5016U oven for three days. Approximately 0.2 g sediment (weighed to the nearest 4 decimals) was transferred to Teflon vessels and 10 ml 65% nitric acid was added. The Teflon vessels were then placed in the Milestone Ethos Easy Maxi-44 microwave Digestion System and digested for 35 min at a temperature of 200˚C followed by a 15 min. cooling period (methods adapted from Greenfield et al., 2011).

A second aliquot of dried sediment was used to determine grain size and organic content. Approximately 100 g (weighed to the nearest 4 decimals) from each site was sieved with a KingTest VB 200 300 Sieve Shaker. Mesh sizes from 53 µm to 4000 µm were used. The grain sizes present in the Seekoeivlei Nature Reserve were classified according to Table 3.1 (Wentworth, 1922; Malherbe et al., 2010).

pg. 43 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS Table 3.1: The classification for sediment grain size analysis (Malherbe et al., 2010; Wentworth, 1922)

Grain size (µm) Classification >4000 Gravel 2000 – 4000 Very coarse sand 500 – 2000 Coarse sand 212 – 500 Medium sand 53 – 212 Very fine sand <53 Mud

The organic content was determined for each site, where an exact mass of sediment in a crucible was weighed and placed in furnace for a minimum of six hours at 600 ˚C until all the organic matter was incinerated. The crucibles were allowed to cool before being re-weighed to calculate the percentage organic matter lost (methods adapted from ASTM, 2000; USEPA, 2001). The classification of the percentage organic matter was carried out according to Table 3.2.

Table 3.2: Classification of organic content in sediment (USEPA, 2001).

Class % Organic Matter Very low < 0.5 Low 0.5 – 1.0 Moderately 1.0 – 2.0 Medium 2.0 – 4.0 High > 4.0

3.3. STATISTICAL ANALYSES

One-way analysis of variance (ANOVA) was conducted to determine significant differences in spatial and temporal variation of the water and sediment quality variables. Data were tested for normality through the use of the Kolmogorov-Smirnov test (p < 0.05). Significant difference was tested if p < 0.05, with the use of the Tukey’s post-hoc statistical analysis test. Kruskal-Wallis post-hoc test was performed if data were not normally distributed (methods adapted from de Klerk et al., 2012). Appropriate graphs were constructed in order to visualise the differences between the sites as well as the

pg. 44 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS difference between the seasons. Univariate statistical analysis was carried out with GraphPad Prism 5.

In order to determine whether there were spatial and temporal patterns evident in the water and sediment quality data, multivariate statistical analyses were carried out. The Principle Component Analysis (PCA), which is an ordination method, was used to determine whether there are any differences in the water quality composition between the sampled sites (Van den Brink et al., 2003). The PCA is a linear response model which show distribution of the environmental variables. The program which was used for these analyses was Canoco Version 5.

3.4. RESULTS 3.4.1. Water quality

The results of the in situ physicochemical water quality variables for the three surveys from 2016 to 2017 are summarised in Figure 3.1. Several of the sites were inaccessible during the autumn survey (March 2017, rainy season) as described in Chapter 2, therefore no results were available for Sites 2b, 3e, 4a, 4b, 4c, 5, 8, 9, 10a and 10b.

Water temperature remained fairly constant across the sites with slight changes throughout the seasons. There was however a difference between winter and the two remaining seasons summarised in Figure 3.2. Temperatures were found to be lowest during winter (July; between 3.7 ºC to 13.2 ºC) and increased towards summer (December; between 19.8 ºC to 31.1 ºC). Even with these differences in temperature, no significant differences were found between the seasons. Sites 4a, 4b, 4d and 8 showed the highest variation in temperatures (between 3.7 ºC (Site 4a) to 31.1 ºC (Site 4b)), but no significant differences were found. With regard to the pH values measured at the different sites (Figure 3.1) all the sites showed the same seasonal trend with decreasing levels observed during summer and autumn (Figure 3.2). All of the pH levels measured at the sites ranged between 6 and 9 with only a few sites showing values less than 6.

Throughout the sites the dissolved oxygen (DO) showed slight fluctuations (Figure 3.1), with no significant differences. From winter to summer and autumn there were a general decrease in DO, with values ranging between 1.15 mg/l to 13.9 mg/l (11.3% to 146% oxygen saturation). With regard to the seasonal DO, no significant differences were found. Sites 4c and 10b had relatively high electrical conductivity (EC) values when

pg. 45 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS compared to the rest of the sites (between 237 µS/cm to 251 µS/cm), but no significant differences because of the variability (Figure 3.1). Figure 3.2 showed a general decrease in EC from winter to summer and autumn (between 251 µS/cm to 60 µS/cm), with no significant differences.

With regard to total dissolved solids (TDS) sites varied from 7.81 mg/l present at Site 3c to 171 mg/l present at Site 4c, but no significant differences between the sites (Figure 3.1). Once again, a general decrease can be observed from the winter survey to the summer and autumn surveys, with no significant differences. Turbidity showed clear variation between the sites (Figure 3.1), with values ranging from 1 NTU (Site Ws) to 93 NTU (Site 2a), with sites 3e showing the highest temporal variation. Increasing turbidity occurred from winter to summer, followed by a decrease towards autumn, with the highest spatial variation present during summer. Variation between the sites as well as the season showed no significant differences.

pg. 46 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 47 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 48 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS In Figure 3.3, the nutrient analysis is shown for the three surveys and these results were compared to DWS Target Water Quality Ranges (TWQR) (Table 3.3) for aquatic ecosystems (DWAF, 1996). Unfortunately, this classification is for river systems, however, Malan & Day (2012) focused on the water quality ranges of wetland systems. These ranges will determine in what ecological category the system will be classified as (Table 3.4).

Table 3.3: Target water quality ranges for freshwater systems in South Africa (TWQR) (DWAF, 1996).

Physico-chemical Chronic effect Acute effect Unit TWQR variables value (CEV) value (AEV)

Aluminum (Al) µg/l 5-10 10-20 100-150 Arsenic (As) µg/l 10 20 130 Cadmium (Cd) µg/l 0.15-0.4 0.3-0.8 3-13 Chromium (Cr) µg/l 7-12 14-24 200-340 Copper (Cu) µg/l 0.3-1.4 0.53-2.8 1.6-12 Dissolved oxygen % 80-120 - - (DO) Iron (Fe) µg/l > 10 - - Lead (Pb) µg/l 0.2-1.2 0.5-2.4 4-16 Manganese (Mn) µg/l 180 370 1300 Nitrogen µg/l > 15 - - pH µg/l > 5 - - Phosphorous µg/l > 15 - - Temperature µg/l > 10 - - Zinc (Zn) µg/l 2 3.6 36

Table 3.4: Ecological categories for the classification of Wetlands in South Africa (adapted from Malan & Day, 2012).

Total Ecological Phosphates Phosphorous Ammonium Nitrate & Nitrite Category (mg/l) (mg/l) (mg/l) (mg/l) A ≤ 0.01 ≤ 0.02 ≤ 0.03 ≤ 0.015 B/C ≤ 0.04 ≤ 0.06 ≤ 0.05 ≤ 0.07 D/E > 0.04 > 0.06 > 0.05 > 0.07 Ecological category- where A category indicates natural/least-impacted water quality and an E category indicates extreme contamination.

pg. 49 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS With regards to nitrates, sites showed a similar trend except for Site 9 that had the highest concentration (4.4 mg/l), during the summer survey. Nitrate concentrations was found to be the highest at the sites during summer (between 0.6 mg/l to 4.4 mg/l) while values decreased towards autumn (between 1.6 mg/l to 2.5 mg/l) and winter (between 0.3 mg/l to 1.4 mg/l). Within these variations no significant differences were found. Nitrite concentrations showed an increase in concentrations at Sites 3c and 3e, which also showed the highest temporal variation (between 0.4 mg/l to 3.1 mg/l). Sites 4a, 4b and 9 showed no temporal variation. Nitrite concentrations followed the same trend between seasons as the nitrate concentrations, with increased values from winter (between 0.006 mg/l to 0.06 mg/l) towards summer (between 0.02 mg/l to 0.09 mg/l) and decreased toward autumn (between 0.01 mg/l to 0.03 mg/l).

The phosphate concentrations were found to be highest at Site 2b (between 0.09 mg/l to 3.5 mg/l) with the highest concentration during the winter survey. Although Site 2b showed a remarkable increase in phosphate concentration, there were no significant differences between all of the sites. Ammonium concentrations showed fluctuations between the sites, with the highest concentration at Site 4a (between 0.27 mg/l to 0.51 mg/l). Ammonium concentrations were found to be lowest during winter (between 0.03 mg/l to 0.51 mg/l) and increased towards summer (between 0.05 mg/l to 0.32 mg/l). Sulfate concentrations remained relatively similar across the sites as well as the seasons, with no significant differences.

Overall nutrient concentrations were higher during the summer and autumn surveys, except for phosphates. These higher concentrations can be due to higher rainfall that results in high runoff conditions containing nutrient concentrations (de Klerk et al., 2012). The ecological category of the Seekoeivlei Wetland is a D/E category indicating a severely impacted system (Table 3.4).

pg. 50 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 51 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS Metal analysis results were presented in Figure 3.4, which also indicates the temporal variation between the three sampling surveys through the error bars. These results show that, spatially, Sites 4a and 4b had higher concentrations than the other sites with regards to most of the metal concentrations but this was not significant. The exception was for Co, Mn and Cu. The Co and Mn concentrations showed that Site 4b had lower concentrations than Site 4a, whereas with Cu concentration, Site 4b was higher than 4a. With regards to the Co concentrations of Site 4a, it was significantly different (p < 0.05) from the rest of the sites, and Site 4b was significantly different (p < 0.05) from the rest of the sites regarding the Cu concentrations.

With regards to the temporal variation, the majority of metal concentrations showed that Sites 4a and 4b had the highest temporal variation. The metal concentrations that does not display this trend is As and Fe. The As and Fe concentrations showed higher temporal variation at Sites 8 and 5 respectively. The Cr concentrations remained fairly constant through the sampling surveys, however, analyses showed that there were significant differences between winter (July 2016) and summer (December 2016) and also between winter and autumn (March 2017). Between the remaining sites, no significant differences were found.

pg. 52 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 53 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS In the PCA (Figure 3.5) it is clear on the second axis that there is temporal variation since the warmer seasons (summer and autumn) have separated from the cold season (winter) The first axis showed a difference between winter (July 2016) and summer (December 2016) with the water quality variables even though the ANOVA statistic results showed no significant difference between winter and summer. These water quality variables include DO, phosphates, conductivity, Na, Sr, Ca that were more evident in the winter survey. Whereas in the summer survey the following water variables were higher: ammonium, iron, turbidity, nitrite, As, V and P. With regard to the spatial differences, specifically between the river and wetland sites, the PCA showed little variation. On the first axis the majority of the river sites were grouped on the right- hand side (Sites 7, 10a, Ws), whereas the wetland sites were grouped more on the left- hand side. Even with these visual differences, statistically there were no significant differences between the sites.

Environmental variables

Jul (winter) Dec (summer) Mar (autumn)

Figure 3.5: PCA bi-plot of the combined water quality variables for the 2016-2017 surveys. This bi-plot explains 34.48% of water quality variables variance on the first axis and a further 19.21% of variance on the second axis (DO – dissolved oxygen; EC – electrical conductivity).

pg. 54 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS 3.4.2. Sediment quality

The Seekoeivlei Wetland mainly consisted of 53-212µm (very fine sand) and 500-2000 µm (coarse sand) (Figure 3.6). The majority of sites were dominated by coarse sand, throughout the surveys and sites, except for Sites Ps and 1 which consisted more of very fine sand, especially during the winter survey. During the summer survey more mixed samples of sediment were present, except for Site 10a, which consisted largely of very fine sand. It was evident from Figure 3.7 that medium sand (212 - 500 µm) increased towards autumn.

Organic content for the Seekoeivlei Nature Reserve is shown in Figures 3.7 (A and B). According to Figure 3.7A, it is clear that the organic content was predominantly high in March, but these results are inconclusive because in the autumn survey (March) not all sites were accessible. Organic content (Figure 3.7B) for all sites ranged from 3.4 - 31.1% during all three sampling surveys from 2016 - 2017. It is clear that the river sites (i.e. sites Ps, 1, 4c) have predominantly less organic content compared to the wetland sites (i.e. sites 3a-4b). It is clear from the graph that there are differences between Sites Ps, 1, 4c and 10a compared to the rest of the sites. Statistically significant differences were found between Sites Ps and 3b, 3d, 4b, 4d as well as between Sites 1 and 2a, 3b, 4d and 5. Significant differences were also found between Sites 4c and 3b, 4b and Sites 10a and 3b and4b. Remaining sites showed no significant differences.

pg. 55 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

Figure 3.6: Sediment grain size distributions (percentages) of the Seekoeivlei Nature Reserve for winter (July 2016), summer (December 2016) and autumn (March 2017) sampling surveys. Sites with no bars present are the sites where no samples were collected. Bars and error bars represent the mean and standard error of the percentages. (> 4000 µm = gravel, 2000-4000 µm = very coarse sand, 500-2000 µm = coarse sand, 212- 500 µm = medium sand, 53-212 µm = very fine sand, <53 µm = mud)

pg. 56 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

Figure 3.7: Organic content (USEPA, 2001) of sediment for the selected sites of the Seekoeivlei Nature reserve taken in winter (July 2016), summer (December 2016) and autumn (March 2017). Figure 3.7A showing temporal variation and Figure 3.7B showing spatial variation with regards to organic content. Bars and error bars represent mean and standard deviation. The metal concentrations analysed in the sediment of the Seekoeivlei Nature Reserve included Al, As, Cr, Fe, Ni, Pb, Zn, Mn, Co, Cu and Cd (Figures 3.8 and 3.9). Three additional metals that were not detected during the ICP-MS analyses for the metal concentrations in the water samples were included here i.e Al, Pb and Cd. With regard to Al, spatial variation can be observed in Figure 3.8, where it is clear that Sites Ps and 1 had significantly lower concentrations compared to the rest of the sites. Sites 4c, 6, 10a and 10b also showed lower concentrations of Al, but not significantly different.

When comparing Sites Ps and 1 to the rest of the sites, it showed that the concentrations of all the tested metals were lower at these two sites (Figures 3.8 and

pg. 57 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS 3.9). Statistical differences were found for As and Cr between Sites 1 and the majority of the remaining sites. This was also true for Site Ps, which showed significant differences from the rest of the sites. Metals such as Fe, Ni, Pb and Zn were significantly different between Sites 1 and Ps and the rest of the sites except for Sites 4c, 4d, 5, 6 and 7. No significant differences were found with Mn, Co, Cu and Cd between Sites Ps and 1 compared to the rest of the sites, except for Site 1 and Ws, which showed a significant difference.

The majority of the metals had similar trends except for Mn, which indicated that Site Ws had a higher concentration than the other sites. Site Ws had a higher concentration of between 2000 and 3000 µg/g, which also proved to be significantly different from the rest of the sites.

When comparing Cd (Figure 3.8), more spatial variation was present but this variation was not significantly different. Although Site 10a showed more temporal variation than the rest of the sites, there were no significant differences. Spatial variation present in Cd, showed more variation than the other metals, but the only significant differences were present between Sites Ps and 3b, Sites 1 and 3b and3d as well as between Sites 3b and 6. With regards to Co, significant temporal variation (presented through the error bars) was present between the winter survey (July 2016) and the summer survey (December 2016) as well as the winter survey and the autumn survey (March 2017).

pg. 58 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 59 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS

pg. 60 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS One-way ANOVA were performed which indicated that no significant difference was present between the sites and the seasons with regards to grain size and organic content. According to the PCA bi-plot (Figure 3.10), there was no spatial or temporal variation in the sediment variables analysed.

The majority of the sites are clustered together except for Sites 6 and 10b (circled in red in Figure 3.10). In this bi-plot, the grain size of > 4000 µm were the highest at sites 10b (winter, July 2016) and 6 (autumn, March 2017). Grain size of 53-212 µm were more prominent at Site 1 from the winter and summer survey. Few sites were not clustered or separate (Sites 6 and 10b) like the majority of sites. These include Sites Ws, 3d, 5 and 8 (circled in blue in Figure 3.10). In the case of Site Ws, the Mn concentration was the highest during the winter survey. With regards to Sites 3d, 5 and 8 from the summer survey, the main factor was >4000 µm and 2000-4000 µm (gravel and very coarse sand) that influenced the distribution of these sites.

Environmental variables Jul (winter) Dec (summer) Mar (autumn)

Figure 3.10: PCA bi-plot of the combined sediment quality variables of the Seekoeivlei Nature Reserve. This bi-plot explains a total of 76.46 % variance, with 65.49% explained on the first axis 65.49 % and 10.97% on the second axis (> 4000µm - gravel; 2000-4000 µm - Very coarse sand; 500-2000 µm – Coarse sand; 212-500 µm – Medium sand; 53-212 µm – Very fine sand; <53 µm – Mud).

pg. 61 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS 3.4. DISCUSSION

3.4.1. Water quality

Nitrates, nitrites and ammonium fall under the category of inorganic nitrogen described in the guideline for aquatic ecosystems (DWAF, 1996). The DWAF (1996) guideline for inorganic nitrogen indicates that the value should not change more than 15% from the reference condition because it will have an impact on the trophic status. A system with a concentration lower than 0.5 mg/l can be classified as an oligotrophic system, concentration between 0.5 mg/l – 2.5 mg/l would be classified as mesotrophic, concentrations between 2.5 mg/l – 10 mg/l would be classified as an eutrophic system and hypertrophic system when the concentrations are greater than 10 mg/l (DWAF, 1996).

When comparing this classification to the results from the Seekoeivlei results it was evident that sites from the winter survey (July 2016) such as Sites Ps, 3c, 3d, and 5 can be classified as oligotrophic systems and the rest of the sites can be classified as mesotrophic systems. Results from the summer survey (December 2016) showed that the majority of sites can be classified as mesotrophic systems except for Sites 3e, 4c, 9 and 10a which can be classified as eutrophic systems. According to the results from the autumn survey (March 2017), it is evident sites Ps, 2a and 3a were eutrophic and sites 1, Ws, 3b, 3c, 3d, 4c, 6 and 7 were mesotrophic.

Higher concentrations in phosphates were observed at Site 2b in the Seekoeivlei Wetland, indicating that Site 2b is at risk of eutrophication. As phosphates is considered as one of the most important factors in the process of eutrophication. Similar results were observed by a study on the seasonal variation of water and sediment quality parameters in reed pans done by de Klerk et al. (2012). Eutrophication can cause the depletion of oxygen, accumulation of metabolic products, as well as the development of cyanobacterial blooms, which can have an effect on the water quality and health of the ecosystem (Hoagland & Franti, 2008; Oberholster & Ashton, 2008). There was an increase in DO during winter which can be explained by the decrease in temperature as well as the mixture and movement of water which leads to an increase in DO (de Klerk et al., 2012). With these results of Site 2b that had higher phosphate and DO concentrations during winter, it can be said that the present concentration phosphates will not have a long term negative effect on the ecosystem health. The EC that showed

pg. 62 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS a decreasing seasonal trend towards summer and autumn can be explained by the increased rainfall which resulted in a dilution effect (Marneweck, 2004).

Using the inorganic nitrogen and orthophosphate ranges set out by Malan & Day (2012), the Ecological category of the Seekoeivlei is D/E, meaning it is a severely impacted wetland which can have detrimental effects on the aquatic invertebrates.

According to Mellis et al. (2004), organic content adsorbs Ni, which can explain why Sites 4a and 4b had higher concentrations of Ni, since these sites also had higher organic content. Zn and Cu were also particularly high at Sites 4a and 4b. Zn occurs naturally in water through weathering and erosion, is also present in fungicides, insecticides and fertilizers and can also be adsorbed by organic content. Cu is found in in fungicides, algaecides and insecticides and can also be added to fertilizers and animal feeds as a nutrient to support the growth of the plants and animals (WHO, 2004). In the case of Seekoeivlei Wetland, and specifically with Sites 4a and 4b inside the reserve no fertilizers are used in the reserve. According to DWAF (1996), Cu can also increase the toxicity of Zn. The high concentration of Mn at Site 4a can be explained by the fact that this site contained large amounts of organic content. Mn is a naturally occurring element in water and soil. According to the World Health Organisation (2003), Mn can occur from the wash-off from plant and surfaces and can also occur because of dead plant and animal material as well as animal excrement. High levels of Mn were found at Site 4a and this can be due to both natural and anthropogenic inputs. Manganese is a naturally occurring element in water and soils and also accumulate due to degradation of dead plant and animal material as well as animal excrement (such as occurs in the nature reserve (WHO 2003).

The As concentration results of the Seekoeivlei indicated concentrations of less than 1 µg/l. Arsenic occurs naturally in water through the dissolution of rocks and minerals (Smedley & Kinniburgh, 2002; WHO, 2011) and according to the World Health Organization (2011), the level of As in natural waters ranges between 1 and 2 µg/l. Iron (Fe) is a natural component in water, coming from rocks through weathering and is also present in pesticides and fertilizers (Diagomanolin et al., 2004). For this reason, the elevated Fe concentrations at Site 2a can possibly be ascribed to the agricultural activities in the vicinity of this site, which includes the use of pesticides and fertilizers. Chromium can enter a system naturally through weathering of rocks and run-off from the terrestrial systems (Kotaś & Stasicka, 2000), but can also enter through wastewater from chemical industries. Levels of Cr were however of natural levels (0.5-2 µg/l) as set

pg. 63 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS out by the WHO (2003) guidelines and so inputs from fungicides and chemical wastewater may not be occurring, as there are no chemical industries in the area.

Increases in As, Fe and Ni in the summer and decreases in Cr and Zn in autumn may have been due to a variety of factors, such as changes in organic content, water temperature, DO, pH and higher rainfall (de Klerk et al., 2012). Increase in rainfall in summer and autumn, as was found during this study, results in the dissolving of metals and the transportation thereof, leading to the higher concentrations measured during the summer and autumn surveys (de Klerk et al., 2012).

3.4.2. Sediment quality

Sediment properties such as grain size are considered important for transportation and sedimentation processes. According to de Klerk et al. (2012), smaller grain sizes have higher surface area leading to an increased ability to adsorb metals. There is not much gravel present in the Seekoeivlei Wetland. Because this is river-floodplain system a more concentrated flow of water occurs (Ollis et al., 2013), which leads to different grain size fractions of sediment at the different sites (Malherbe et al., 2015).

Organic material is added to the soils in the form of plant or animal detritus by means of natural processes including physical, chemical and biological (Sahrawat, 2004). According to Nguyen (2000), organic matter accumulation provides a storage place of carbons and nutrients as well as an environment for microbial denitrification. High organic content at the wetland sites (floodplain and back swamps) created conditions that are favourable for the accumulation of contaminants in the sediment (Sahrawat, 2004).

Corresponding spatial trends were found in the metal and organic content analyses. According to de Klerk et al. (2012), the organic content of sediment plays an important role in the decomposition of organic material which in turn releases metals from the sediment. Thus, if there is more decomposition of organic material, more metals are released into the aquatic system, if present.

Low metal concentrations present at Site Ps and Site 1 can be ascribed to the fact these two sites are stream and river sites. The metal, Al occurs naturally throughout the Earth’s crust, but it is introduced into the water through the dissolution of rocks and minerals (WHO, 2010; WHO, 2011), and adsorbs to humic and fluvic acid (DWAF, 1996). Arsenic may also be found in industrial effluent such as mining (WHO 2011) but

pg. 64 CHAPTER: 3 WATER AND SEDIMENT ANALYSIS since no severe mining activities occur around the Seekoeivlei Nature Reserve, the As levels found in this study may be attributed to natural weathering of rocks and minerals as described by Tooth et al. (2002).

The increase of Mn at Site Ws can be ascribed to natural occurring concentration. The analysis of sediment in the selected sites of the Seekoeivlei Wetland showed similar results as the study on seasonal variation of water and sediment quality parameters by de Klerk et al. (2012). These results being that the metal concentrations within the sediment were higher than the metals in the overlying water. This can be explained by the fact that metals can be accumulated by sediments through a variety of processes (de Klerk et al., 2012).

3.5. CONCLUSION

Nutrients were largely within the TWQR with the exception of phosphates during the winter season, this was however temporary and most probably will not have long term negative effects on the wetlands. However, continuous monitoring should be implemented to assess fluctuations over a long time series. Remaining nutrients increased towards summer and autumn which had higher rainfall and therefor higher runoff conditions.

The results of the metal analysis indicated no significant metal contamination and most of the levels that were found were due to natural inputs such as weathering, runoff from the terrestrial systems and wet precipitation. In spite of this, elevated Fe concentrations were observed, possibly arising from anthropogenic inputs, such as pesticides and fertilizers used on nearby crops. These concentrations did however, not exceed the relevant guidelines.

Overall, the sediment quality in the Seekoeivlei Nature Reserve is in a fair state. The elevation in metals at specific sites can be ascribed to the amount of organic matter present at these sites and no significant metal contamination was found. Higher levels of metals were found in the sediment than in the water due to accumulation and the fact that more fine sediment were present, which have higher ability to adsorb metals.

Thus, with the water and sediment quality, it is evident that the Seekoeivlei Nature Reserve is in fair condition and it can be concluded that the water quality and sediment quality pose little threat to the wetland’s organisms.

pg. 65

ZOOPLANKTON DIVERSITY

4.1. INTRODUCTION

Wetland biological communities are neglected in South African ecosystem studies (Kotze et al., 2005, Malherbe, 2015). This means that, for lesser known systems such as the Seekoeivlei Wetlands no information is available. Floodplain depressions are developed through a riverine floodplain which gets replenished through flooding (Chessman & Hardwick, 2014). According to Chessman & Hardwick (2014), the water regime within a floodplain depression has an influence on the invertebrate assemblages. Therefore, knowledge on these invertebrate assemblages is important since wetlands are known for the variety of species, including zooplankton, it supports (Toruan, 2012). Three wetland types, based on the lateral connectivity to the river, were present within the floodplain depression of the study area, i.e. river, pan and oxbow lakes. In terms of lateral connectivity, pans are further from the river than the oxbow lakes.

Zooplankton are rapidly reproducing organisms and play an important role in energy flow and nutrient cycling in aquatic ecosystems (Wickramasinghe et al., 2012). Sharma & Sharma (2011) state that zooplankton are the integral components of the food webs in freshwater systems. They are small bodied animals, with low power of movement and are passively carried by water currents (Biswas & Panigrahi, 2015). These organisms are regarded as good bioindicators (Biswas & Panigrahi, 2015), and today there is an increase in demand by environmental monitoring programs for the use of zooplankton as indicators for water quality and environmental changes (Wickramasinghe et al., 2012).

There are three common zooplankton groups described from South African lotic systems. The Cladocera suborder varies in length from less than 1 mm to approximately 5 mm, and are filter-feeders. With the exception of two families, Chydoridae and Macrothrix sp., which are found with vegetation and sediments (Seaman et al., 1999),

pg. 66 CHAPTER: 4 ZOOPLANKTON DIVERSITY all other taxa occur in open water bodies. Copepods are micro-crustaceans that vary in length from 1 mm up to 5 mm and are generally found in temporary water bodies, river backwaters, marshy areas, vleis and coastal lakes (Rayner, 2001). Although copepods do not occur in the main flow of rivers they are associated with river systems (Rayner, 2001). The Class Ostracoda is an important part of zooplankton in African inland water systems (Martens, 2001). Even when they are considered as important they are often neglected during ecological surveys. Ostracods can be found in aquatic and even humid environments and are tolerant to harsh environmental conditions (Rayner, 2001). Most of the families are scavengers while some prey on other ostracods and copepods.

In South Africa a few studies have been conducted on the zooplankton communities present in wetland habitats (Bird et al. 2014; Dube et al., 2017; Ferreira et al., 2012; Hart 2012; Riato et al., 2014;). The study completed by Hart et al. (2012) was on the zooplankton abundance and composition in the Rietvlei Dam and Ferreira et al. (2012) studied the aquatic invertebrate communities of perennial pans in Mpumalanga. A study on the influence of biotope on invertebrate assemblages in lentic environments in the Western Cape was done by Bird et al. (2014). According to the study done by Riato et al. (2014), zooplankton taxa that were found in the freshwater pans in Mpumalanga Province consisted of Cladocera, Calanoida, Cyclopoida and Ostrocoda. Dube et al. (2017) reported that the zooplankton community (Cladocera, Calanoida and Cyclopoida) structure in the Phongolo floodplain wetlands showed no significant temporal variation.

4.1.1. Aims for this chapter

Zooplankton biodiversity within the Seekoeivlei Nature Reserve has no recorded data available. Therefore, the first aim of this chapter was to determine the zooplankton diversity present in the three wetland types in Seekoeivlei Nature Reserve. The second aim was to determine the spatial and temporal variation within the zooplankton diversity and the physico-chemical factors that drive the zooplankton distribution.

4.2. MATERIALS AND METHODS

4.2.1. Sampling protocol

Zooplankton samples were collected by filtering 200 litres of water through a plankton net, 1m x 1m with a mesh size of 100 microns. Samples were placed in 500 ml washed

pg. 67 CHAPTER: 4 ZOOPLANKTON DIVERSITY polyethylene jars, fixed in 70% ethanol, stored in a mobile fridge and transported back to the laboratory for further analyses. In the North-West University laboratory, the samples were rinsed under running water using a sieve with a mesh size of 100 micron. The zooplankton were separated from organic matter with the aid of a Nikon, C-LEDS dissection microscope. The separated zooplankton were identified with the aid of a Nikon SMZ 1500 compound microscope to the lowest possible taxonomic group using appropriate guides (Day et al., 1999; Day et al., 2001). Zooplankton taxa were placed in 70% ethanol for future reference purposes.

4.2.2. Statistical analyses

Univariate indices were applied to assess the diversity of the study area. The Margalef’s index (d) is a method that uses a simple count of the number of different individuals and species at the specific site (Odum & Barrat, 2005). The Pielou’s evenness index (J’) calculates how evenly the individuals in a community are distributed among the different species (De Necker et al., 2016). The Shannon diversity index (H’) is the most widely used diversity index and it uses the species richness and equitability of components for the diversity calculation (Clarke and Warwick, 2001). One-way analysis of variance (ANOVA) was conducted to determine differences in spatial and temporal variation within the zooplankton diversity. More detail on the statistical methodology can be found in Chapter 3.

Multivariate analysis involves the observation and analysis of more than one statistical outcome variable at a time. This analysis makes it easier to identify spatial and temporal patterns present in the community structures because each species is considered as a variable (Clarke and Warwick, 2001).

To determine similarities between sites a NMDS (Non-metric Multi-Dimensional Scaling) bi-plot based on a Bray-Curtis similarity coefficient was plotted (Clarke and Warwick, 2001). To identify the community structure through the identification of spatial and temporal differences within the community structures (Malherbe et al., 2010), the NMDS was used. This estimates the distances between samples using the sample matrix that was calculated by the similarity coefficient. This NMDS still calculates the measured variables even if there is data missing. The NMDS technique was determined together with an Analysis of Similarity (ANOSIM), which is a summary statistic with a non- parametric permutation which provides two variables to interpret significance (Clarke

pg. 68 CHAPTER: 4 ZOOPLANKTON DIVERSITY and Gorley, 2015). A SIMPER analysis was also performed which indicates the contribution of each species between the samples.

Canoco Version 5 was used for the Redunancy Analysis (RDA) as well as an interactive forward selection RDA, which is a constrained ordination that uses the environmental variables that had a significant outcome on the zooplankton community structure using a Monte Carlo permutation procedure (Shurin, 2001). A RDA is a constrained ordination which uses the zooplankton community structure with environmental variables for each sampling survey (van den Brink et al., 2003). A canonical correspondence analysis (CCA) is another multivariate constrained ordination that focus on the relative differences (van den Brink et al., 2003). Venn diagrams (using the variation partitioning results) shows visual results of the influence of water quality and wetland type on the zooplankton diversity as well as the shared effect on the zooplankton diversity.

4.3. RESULTS

A total of eight families and four orders were collected at the sampling sites of the Seekoeivlei Nature Reserve (Table 4.1). The dominant group throughout the study was the Cladocera, of which there were five families. Detailed site and species abundance data are presented in Appendix B.

Spatial variation of diversity indices data are presented in Figure 4.1 where the replicates were represented by the three surveys conducted for each site. There were no significant spatial differences (p<0.05). The total number of species present at each site ranged from 4 to 22. The highest number of species were found at Sites 2a (pan) and 3c (oxbow), whereas the lowest number of species were found in Sites 4b (pan) and 9 (oxbow). Both the highest and lowest number of species were present in the oxbow and pan sites, whereas the river sites had a moderate number of species. Although there was an increase in abundance at Sites 3d (oxbow), 4a (pan) and 5 (pan) there was no significant differences. These were also the sites that showed the greatest seasonal variation as shown by the error bars. The species richness (Margalef’s index) remained fairly constant except for Site 3e which was the highest. The Pielou’s evenness showed a similar evenness for all sites except for Site 4a which was lower but had greater temporal variation. The Shannon-Wiener diversity index represents the diversity at each site with no difference between the sites during the sampling surveys.

pg. 69 CHAPTER: 4 ZOOPLANKTON DIVERSITY Temporal variation of the diversity indices data were presented in Figure 4.2 where each site is used as a replicate (n=17). As with the spatial variation there were no significant temporal differences (p>0.05). The total number of species varied between 15 (July, winter), 12 (December, summer) and 13 (March, autumn). Total individuals varied between 2 288 and 12 191 individuals present during each season. The highest number of individuals were found during the winter survey, which also had the highest number of species. The lowest number of individuals were found during the autumn survey. The Margalef’s index, Pielou’s evenness index and Shannon-Wiener diversity index all remained fairly constant throughout all three seasons.

pg. 70 CHAPTER: 4 ZOOPLANKTON DIVERSITY

Table 4.1: List of the zooplankton diversity sampled in the Seekoeivlei Nature Reserve for three surveys from 2016 to 2017.

Phylum Subphylum Class Subclass Order Suborder Superfamily Family Subfamily Genus Species Arthropoda Crustacea Branchiopoda Phyllopoda Diplostraca Cladocera Bosminidae Bosmina B. longirostris

Chydoridae Pleuroxus

Monospilus

Daphnidae Daphnia D. laevis

Daphnia sp A

Simocephalus

Moinidae Moina

Macrothricidae Macrothrix M. propinqua

Maxillopoda Copepoda Calanoida Diaptomidae

Cyclopoida Cyclopidae

Ostrocoda Podocopa Podocopida Darwinulocopina Darwinuloidea Darwinulidae

Cytheroidea Limnocytheridae Limnocytherinae Gomphocythere

Cypridocopina Cyprioidea Cyprididae Cypridopsinae Zonocypris

Cyprididae Herpetocyprininae Parastenocypris P. junodi

Cyprioidea Cyprididae Hexapoda Entognatha Collembola Entomobryomorpha Isotomidae Symphypleona Dicyrtomidae Dicyrtominae

pg. 71 CHAPTER: 4 ZOOPLANKTON DIVERSITY

Figure 4.1: Diversity indices for the zooplankton taxa sampled in the Seekoeivlei Nature Reserve from 2016 to 2017. Bars and error bars represent mean and standard error of the temporal data from each site (n=3). (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

pg. 72 CHAPTER: 4 ZOOPLANKTON DIVERSITY

pg. 73 CHAPTER: 4 ZOOPLANKTON DIVERSITY The NMDS bi-plot is based on the Bray-Curtis similarity matrix (Figure 4.3). The stress value of 0.17 gives an indication that the groupings are a good representation of the data in a two dimensional field. Zooplankton diversity and abundances were distinctly different at Sites 3b (winter), 3d (autumn and winter), 4a (summer) and 5 (summer) from the other sites. The ANOSIM significance analyses indicated significant differences between sites, i.e. 0.1% between season summer and winter; 2.7% between winter and autumn and 1.3% between summer and autumn. But the global R value indicated that these groupings were similar to one another based on season (Global R 0.246).

Figure 4.3: Non-multidimensional scaling (NMDS) plot of the zooplankton data sampled in the Seekoeivlei Nature Reserve during winter (July 2016), summer (December 2016) and autumn (March 2017). Based on the Bray-Curtis similarity matrix the SIMPER analyses showed that the average site similarity of the winter survey was 32.29%, the summer survey was 35.84% and the autumn survey was 44.46% (Table 4.2). According to the SIMPER analyses, it is clear that the most abundant species were Calanoida and Cyclopoida throughout all three surveys. During the winter survey, Daphnia sp., calanoids and cyclopoid naupli were the most dominant, calanoids and cyclopoids during the summer survey, and cyclopoids with their naupli during the autumn survey.

pg. 74 CHAPTER: 4 ZOOPLANKTON DIVERSITY Table 4.2: SIMPER Analysis results showing the most abundant species present during each sampling survey (contribution cut off = 70%). Showing an average similarity of 32.29% during the winter survey (July 2016), 35.84% during the summer survey (December 2016) and 44.46% during the autumn survey (March 2017).

Winter (July) Average similarity: 32.29

Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % %

Daphnia sp. 214.40 8.00 0.81 24.77 24.77

Calanoida sp 68.40 7.95 1.17 24.63 49.40

Cyclopoida sp. nauplia 282.73 6.77 0.63 20.96 70.36

Summer (December) Average similarity: 35.84

Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % %

Calanoida sp. 111.65 15.00 1.42 41.85 41.85

Cyclopoida sp. 71.60 10.20 1.12 28.45 70.29

Autumn (March) Average similarity: 44.46

Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % %

Cyclopoida sp. 61.14 23.69 2.38 53.29 53.29

Cyclopoida sp. nauplia 36.57 13.86 1.73 31.18 84.47

pg. 75 CHAPTER: 4 ZOOPLANKTON DIVERSITY The RDA tri-plot explains 38.68% of the total variation in the community structure data with p value of 0.006 (Figure 4.3). Again the sites from the same surveys were grouped together, indicating temporal variation. Limited spatial variation was present, but with the exceptions of Sites 3d (July; winter), 4b (December; summer) and 5 (December; summer). Species that were separated from the others were Cyclopoid sp. nauplia, Daphnia sp., Daphnia laevis and Moina sp.

Species

Jul Dec Mar

Figure 4.4: Redundancy Analysis (RDA) plot for all sampled sites during July 2016 (winter), December 2016 (summer) and March 2017 (autumn), in the Seekoeivlei Nature Reserve. The tri-plot explains 38.68% of the total variation in the data of which 22.37% is displayed on the first axis and 16.31% is displayed on the second axis. The RDA tri-plot with interactive-forward-selection explains 22.34% of the total data variation with p < 0.05 (significant), for the differences across all sampling surveys, with

pg. 76 CHAPTER: 4 ZOOPLANKTON DIVERSITY regard to water quality and zooplankton diversity. In this tri-plot, four variables (nitrates, nitrites, ammonium and temperature) had significant influence on the distribution of taxa at specific sites. Site 9 was the most associated with nitrates during the summer survey and had no significant difference between the seasons (p<0.05).

Environmental variables Species

Jul Dec Mar

Figure 4.5: RDA tri-plot (interactive forward selection) for all sampled sites during winter (July 2016), summer (December 2016) and autumn (March 2017) in the Seekoeivlei Nature Reserve. This tri-plot explains 22.34% of the total data variation of which 15.13% is displayed on the first axis and 7.21% is displayed on the second axis (p value: NO3 = 0.008, NO2 = 0.048, NH4 = 0.006, temperature = 0.002). Variation partitioning results (calculated through the CCA analyses) of water quality variables (a) and wetland types (b) effect on the zooplankton diversity, were presented by a Venn diagram in Figure 4.6. Only 37.3% of the total data are explained, with the

pg. 77 CHAPTER: 4 ZOOPLANKTON DIVERSITY shared effect between zooplankton, water quality and wetland type being 4.2% (p = 0.002). The highest proportion of the variation in zooplankton diversity and community structure was explained by the effect of water quality variables (a = 32.1%, p = 0.002). The rest of the zooplankton variation were explained by the wetland types (b = 0.9%, p = 0.27). Neither the water quality, wetland types nor the shared effect were significantly different.

Figure 4.6: Venn diagrams representing unique and shared contribution of water quality variables (a) and different wetland types (b) on the zooplankton community structure and diversity. Only 37.3 % of the total data are explained. The CCA plot (Figure 4.7) showed the zooplankton distribution influenced by the wetland type based on the variation partitioning results. This plot explains 10.81% of the total variation with a p value of 0.27. Zonocypris sp. had the highest abundance at Site 7, which was identified as a river site. Site 2b, identified as a pan within the floodplain depression, had the highest abundance of Pleuroxus sp. Parastenocypris junodi, Isotomidae and Dicyrtominae were all present in the bottom right quadrant, whereas Pleuroxus sp. and Bosmina longirostris were grouped in the bottom left quadrant.

pg. 78 CHAPTER: 4 ZOOPLANKTON DIVERSITY

Wetland type Species

4.4. DISCUSSION

Zooplankton taxa respond differently to their environments based on their feeding methods, food preferences, water clarity and vulnerability to predators (Hart, 2012). When the univariate and multivariate analyses were performed it showed that zooplankton had no temporal variation and little to no spatial variation across the wetland types. According to Dallas & Day (2004), many organisms are adapted to seasonal changes with regards to temperature. The zooplankton communities showed no significant change from the winter (July 2016) to summer (December 2016) to autumn (March 2017) surveys. Dube et al. (2017) also found that season did not influence the zooplankton community structure in depression wetlands in the Phongolo floodplain because they are passive dispersers and cannot actively move between sites. They are thus dependent on other means of dispersal such as wind or animal vectors.

pg. 79 CHAPTER: 4 ZOOPLANKTON DIVERSITY Within the results of this study it was clear that the most abundant species was Daphnia laevis, Daphnia sp., Moina sp., Cyclopoida. nauplia, Cyclopoida adults and Calanoida during the July (winter) and December (summer) sampling surveys. According to Rayner (2001), Cyclopoida are the most abundant and successful copepods in South African freshwater habitats. Firstly, the majority of the Cyclopoida species are carnivores and prey on other zooplankton and rotifers. Secondly, they can be found in all types of water habitats such as lakes, ponds, streams, rivers and temporary pools (Rayner, 2001) and are not confined to a specific water habitat.

Site 3d (during the July survey) showed a dominance in species such as Daphnia sp., Moina sp. and D. laevis as well as Cyclopoida nauplia. A total of 1216 D. laevis individuals were present at Site 3d during the winter survey. During this survey at this specific site the water was clear and no submerged vegetation or algae were present, and the majority of cladocera could be seen with the naked eye. According to DeMott & Pape (2005), Daphnia species in shallow ponds where no fish are present have a high growth rate in comparison with deep lakes where fish are present. Site 3d was a shallow oxbow lake and could therefore be a reason why such high abundances were found in this site.

Moina sp. was most abundant at sites 4a and 5 during the December (summer) survey. According to Seaman et al. (1999), most of the Moina species occur in small temporary ponds. Sites 4a and 5, which are classified as pans, were drier during the winter survey and only four individuals of the Moina sp. were present at Site 4a and no individuals at Site 5. This is an indication that Sites 4a and 5 are, to a degree, temporary ponds which is the preferred habitat of the Moina sp. Furthermore, according to Rajagopal et al. (2010), the abundance of Moina sp. will be influenced by organic enrichment, and lead to the increase in abundance of Moina sp. at the present site. The nutrient levels at Sites 4a and 5 were elevated during the summer survey (see Chapter 3) and this coincided with raised water levels and more organic material such as watergrass (Isolepis fluitans) at Site 4a and duckweed (Spirodela spp.) at Site 5, which is another explanation of the abundance of the Moina sp. at these two sites.

Monospilus sp. and Pleuroxus sp. showed higher numbers during the winter (July) survey (Sites 7 and 8). These two species are part of the Chydoridae family and according to Griffiths et al. (2015), this family arebottom dwelling organisms. Site 7 and Site 8 are depressions and are situated in the backwater areas of the Seekoeivlei

pg. 80 CHAPTER: 4 ZOOPLANKTON DIVERSITY Wetland. During the July sampling survey there were little to no vegetation present at these sampling sites, increasing the available benthic habitat which is the preferred habitat of the Monospilus sp. and Pleuroxus sp.

The zooplankton community composition was influenced more by the water quality variables than the wetland type (Figure 4.6). This can be due to the zooplankton taxa responding to water quality variables (Hart, 2012). Zooplankton communities in the Seekoeivlei Wetland showed no significant temporal variation which can be ascribed to the fact that zooplankton have low dispersal abilities (Padial et al., 2014). These organisms lay their eggs which can then be dispersed through wind, animal vectors or surface water, in other words passive dispersal (Havel & Shurin, 2004). According to Havel & Shurin (2004), zooplankton are restricted to single continents and are known to be narrowly endemic. Zooplankton communities preferred the oxbow and pan sites as their habitat. The zooplankton communities found in the Seekoeivlei Wetland are known to occur in ponds, temporary waters, backwaters of rivers and marshy areas (Seaman et al., 1999; Rayner, 2001).

In order to establish the overall zooplankton community, the Seekoeivlei Nature Reserve was compared to a study done by Ferreira et al. (2012) on the aquatic invertebrate communities of perennial pans in Mpumalanga Province. During this study the selected pans (perennial and ephemeral) were surveyed during winter (July) and autumn (May) where the most abundant taxa were Copepoda and Cladocera. It was stated in this study that the perennial pans had more stable zooplankton communities, in contrast to the ephemeral pans that represented other zooplankton taxa. It is evident from the study done by Ferreira et al. (2012) that the zooplankton taxa stayed consistent throughout the different seasons, which was also observed in the zooplankton taxa in the Seekoeivlei Wetland.

4.5. CONCLUSION

The statistical analysis of the zooplankton data from the Seekoeivlei Wetland showed no significant spatial or temporal variation, meaning seasonality does not have an influence on the zooplankton diversity A possible reason for this is that zooplankton have low dispersal abilities and cannot actively disperse seasonally from one site to the next. The zooplankton diversity was the highest during the winter survey that could possibly be ascribed to the fact that they prefer more lentic systems. Zooplankton

pg. 81 CHAPTER: 4 ZOOPLANKTON DIVERSITY communities were more abundant in the oxbow and pan sites than in the river sites. This is possibly due to the fact that many of the zooplankton communities found are known to commonly occur in ponds, temporary waters, backwaters of rivers and marshy areas. Interesting finding was the high abundance of Cladocera that were only present during the winter survey which could be ascribed to the fact that no fish were present and the water level were the lowest during, which is the preferred habitat. Whereas in the summer there were fish present and the abundance of the Cladocera showed a decrease. Water quality variables such as nitrates, nitrites, ammonium and temperature had a significant effect on the zooplankton diversity.

pg. 82

MACROINVERTEBRATE DIVERSITY

5.1. INTRODUCTION

Monitoring of aquatic ecosystems through the study of the water quality and biomonitoring is essential for the management of water resources (Malherbe et al., 2010). Inland wetlands are distributed worldwide and have been impacted over recent years by human activities such as food sources and recreation (Muñoz, 2010). Inland wetlands can be described as water bodies that are isolated from each other. In South Africa wetlands have been neglected in terms of biological research as well as monitoring (Ferreira et al., 2012). With the lack of research into wetlands, the invertebrate communities of wetlands are especially not considered in South African studies (Malherbe et al., 2015). According to Malherbe et al. (2015), the availability of information of the biological communities of wetlands in South Africa is limited in certain areas, i.e. Seekoeivlei Nature Reserve.

Macroinvertebrates in freshwater ecosystems are the most common organisms found (Thirion, 2007; Griffiths et al., 2015). These macroinvertebrates spend most of their lifecycles as larvae and are distributed over a variety of habitats (Batzer & Boix, 2016). Habitat, competition for food, predation as well as prey distribution is important in determining the distribution and abundance of the macroinvertebrates (Brooks et al., 2005). Aquatic macroinvertebrate communities and diversities offer good reflection of the water quality within an aquatic ecosystem (Thirion, 2007). Macroinvertebrates are known to retain and breakdown organic material, recycle nutrients and minerals as well as contribute to the energy processes at different trophic levels (Malherbe et al., 2010).

Macroinvertebrates can be used as indicators of wetland integrity, following two approaches (Ferreira et al., 2012). According to Ferreira et al. (2012), one of the approaches is to determine the diversity and species richness. The first approach is

pg. 83 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY mostly successful when the study is to assess the biodiversity or the communities’ response to a contaminant (Ferreira et al., 2012). The second approach is to consider the functional traits of the taxa present. Functional traits include the feeding groups, their habit and the mechanism of breathing. Feeding groups include scrapers, filter- feeders, shredders, collectors and predators. Habits include burrowers, skaters, gliders, clingers, swimmers and flyers. Breathing mechanisms include gills, tracheal gills, air breathers and other mechanisms such as through the skin or respiratory siphon, respiratory atrium, elytra and spiracular gills (Ferreira et al., 2012). According to Cummins et al. (2005), the functional trait approach is the more appropriate and rapid approach to characterize the condition of the ecosystem.

5.1.1. Aim and objective for this chapter

There is no available information with regards to the aquatic macroinvertebrates within the Seekoeivlei Nature Reserve. Therefore, the first objective of this chapter was to determine the zooplankton and aquatic macroinvertebrate diversity in the Seekoeivlei Nature Reserve. The second objective was to determine whether there were any spatial and temporal variation within the zooplankton and macroinvertebrate diversity and determine what physico-chemical factors drive the macroinvertebrate distribution.

5.2. MATERIALS AND METHODS

5.2.1. Sampling protocol

At each of the 17 selected sites, macroinvertebrates were sampled during the three surveys (July 2016 (winter), December 2016 (summer) and March 2017 (autumn)). Macroinvertebrates were sampled using a sweep net with a frame of 30 cm x 30 cm with a mesh size of 0.5 mm. All the available biotopes, such as marginal and riparian vegetation, and GSM (Gravel, Sand, Mud) were sampled for approximately 10 - 15 minutes per site, depending on the amount of habitat available. The samples were placed in clean 500 ml polyethylene jars and fixed in 70% ethanol before being transported back to the laboratory for further analyses.

In the laboratory, each sample was rinsed with tap water using a sieve with a mesh size of 250 µm. The macroinvertebrates were separated from all inorganic and organic debris under a Nikon Model C-LEDS microscope and identified to the lowest possible taxonomic level using suitable guides (Day et al., 1999; Day et al., 2001; Day et al.,

pg. 84 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY 2001; Day & De Moor, 2002; Day & De Moor, 2002; De Moor et al., 2003; De Moor et al., 2003; Day et al., 2003; Stals & de Moor, 2007). The different taxa were enumerated and preserved in 70%.

5.2.2. Statistical analyses

Statistical analyses included univariate indices and multivariate analyses. Univariate indices applied were Maraglef’s species richness (d), Shannon diversity index (H’), Pielou’s evenness index (J’). One- way analysis of variance (ANOVA) was used to determine significant differences in spatial and temporal variation of the univariate indices. Data were tested for normality through the use of the Kolmogorov-Smirnov test (p < 0.05). Significant difference was tested if p < 0.05, with the use of the Tukey’s post- hoc statistical analysis test. Kruskal-Wallis post-hoc test was performed if data were not normally distributed (methods adapted from de Klerk et al., 2012). The multivariate analyses consisted of constructing NMDS bi-plots using the Bray-Curtis similarity coefficient and analysing statistical differences between groupings using ANOSIM (Primer Version 7). With the use of Canoco Version 5 RDA tri-plots were constructed with interactive forward selection using the Monte Carlo permutation procedure to determine the pyhysico-chemical factors responsible for the ordination. If appropriate a CCA (Canonical Correspondence Analysis) were constructed which is a unimodal response model and the analysis of compositional data (Van den Brink et al., 2003). All the analyses were conducted using the methodology explained in Chapter 3 and Chapter 4.

5.3. RESULTS

A total of 87 taxa belonging to 14 orders and 51 families were collected from the various sampling sites during the three sampling surveys in the Seekoeivlei Nature Reserve (Table 5.1). A total of 56 taxa were found during the winter survey (July 2016), 64 taxa during the summer survey (December 2016) and 53 taxa during the autumn survey (March 2017). The highest total number of individuals counted was during the summer survey, which was 3366 individuals present at all sampled sites. Detailed site and species abundance data are presented in Appendix C. These data were used to conduct the spatial and temporal analyses.

pg. 85 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Table 5.1: List of aquatic macroinvertebrate diversity recorded in the Seekoeivlei Nature Reserve for three surveys from 2016 to 2017

Phylum Subphylum Class Subclass Order Suborder Family Subfamily Species Arthropoda Hexapoda Insecta Pterygota Ephemeroptera Pisciforma Baetidae Cloeon & Procloeon sp.

Furcatergalia Caenidae

Caenospella sp.

Odonata Zygoptera Coenagrionidae Agriocnemis sp.

Ceriagrion sp.

Enallagma sp.

Pseudagrion sp.

Teinobasis sp.

Lestidae Lestus sp.

Protoneuridae Ellattoneura glauca

Anisoptera Aeshnidae Anax sp.

Corduliidae Hemicordulia sp.

Libellulidae Bradinopyga sp.

Notiothemis sp.

Orthetrum sp.

Tetrathemis sp.

Lepidoptera Crambidae

Hemiptera Heteroptera Aphelocheiridae Aphelocheirus sp.

Belostomatidae Belostomatinae Appasus sp.

Corixidae Micronectinae Micronecta sp.

Sigara sp.

Hebridae Hebrus sp.

Gerridae

Aquarius sp.

Leptopodidae

Mesoveliidae Mesovelia sp.

Naucoridae Naucoris sp.

Nepidae Ranatra sp.

Notonectidae Anisops sp.

pg. 86 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Phylum Subphylum Class Subclass Order Suborder Family Subfamily Species Enitharus sp.

Pleidae Plea sp.

Saldidae

Coleoptera Adephaga Aspidytidae Aspidytes sp.

Curculionidae

Neochetina sp.

Dytiscidae Africophilus sp.

Derovatellus sp.

Hydroporinae Hydrovatus sp.

Laccophilinae Laccophilus sp.

Neptosternus sp.

Philodytes sp.

Elmidae

Gyrinidae Gyrinus sp.

Haliplidae Haliplus sp.

Hydraenidae

Hydrophilidae Berosus sp.

Enochrus sp.

Hydrochinae Hydrochus sp.

Scirtidae Cyphon sp.

Sphaeriusidae Microsporus sp.

Spercheidae Spercheus sp.

Diptera Brachycera Ceratopogonidae Culicoidinae Bezzia sp.

Culicoides sp.

Chaoboridae Chaoborinae Chaoborus sp.

Corethrella sp

Chironomidae Chironominae

Orthocladiinae

Tanypodinae

Culicidae Malaya sp.

Dolichopodidae

pg. 87 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Phylum Subphylum Class Subclass Order Suborder Family Subfamily Species Ephydridae

Muscidae

Sciomyzidae

Simuliidae Simulium (Byssodon) griseicolle

Tabanidae

Trichoptera Annulipalpia Ecnomidae

Psychomyiidae

Arachnida Pontarachnidae

Potamonautes depressus Malacostrace Decapoda Potamonautidae depressus Potamonautes unispinus

Annelida Oligochatea Hirudinea Alboglossiphonia sp.

Helobdella stagnalis

Oosthuizobdella sp.

Placobdelloides sp.

Polychaeta Rhabdocoela Typhloplanidae Mesostoma sp.

Nematoda

Platyhelminthes Rhabditophora

Mollusca Gastropoda Pulmonata Bassommatophora Bulinidae Bulininae Bulinus tropicus

Planorbidae Planorbinae Ceratophallus natalensis

Gyraulus connollyi

Segmentorbis planodiscus

Hygrophila Burnupia sp.

Lymnaeidae Lymnaea columella

Lymnaea natalensis

Lymnaea truncatula

Ancylidae Ferrissia cawstoni

Bivalvia Venerioda Sphaeriidae Pisidium costulosum

pg. 88 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY The univariate diversity data presented in Figure 5.1 represent spatial (site) data with survey data as replicates (three surveys in most cases). Site 4b (pan) had the lowest total number of species (Figure 5.1a) with only eight species in the winter survey and five species during the summer survey. Whereas Sites 10a (river) and 10b (pan) had the highest total number of species, ranging from 21 to 24 species. Site 10 had 23 species during the winter survey and 21 species during the summer survey, whereas Site 10b had 23 species during winter survey and 24 species present during the summer survey. Even with the clear visual differences between Sites 4b and 10a, 10b, statistical analyses (ANOVA) showed no significant differences. During the summer survey Site 9 (oxbow) showed a low total number of species (11) as well as total individuals (60). The most individuals (702) were recorded at Site 2a during the summer survey (Figure 5.1b). Sites 3a and 3b, both oxbow sites, showed a decrease in total individuals (59 and 54). The mean total individuals per site was found to be the lowest at Site 3a and 3b. However, Site 4b had the lowest actual abundances during the summer (19) and winter (331) surveys. With regard to the total number of individuals there were no significant differences between the sites.

Species richness (Margalef’s, Figure 5.1c) showed small variations between the sites showing the highest species richness present (4.72) at Site 6 during the autumn survey, and the lowest (1.21) at Site 4b during the winter survey. The Pielou’s evenness index (J’) calculates how evenly the individuals in a community are distributed among the different species (de Necker et al., 2016) with values between 0 and 1, with the value 0 that represents and uneven distribution and the value of 1 that represents even distribution of abundance. The results (Figure 5.1d) showed the highest value (0.91) at Site 6 during the autumn survey and the lowest value (0.47) at Site 4b during the winter survey. When assessing significant differences there were no significant differences with regards to spatial variation. The Shannon diversity index (H’) uses the species richness and equitability of components for the diversity calculation (Clarke and Warwick, 2001). The Shannon diversity index (Figure 5.1e) indicated that, during the autumn survey, the highest value (2.78) was at Site 2a and the lowest value (0.81) at Site 4b was during the summer survey at Site 4b. However, spatial variation indicated significant difference between Site 4b and Sites 5, 6, 10a and 10b.

Temporal diversity data are presented in Figure 5.2 where each site is used as a replicate (n=17). According to Figure 5.2a, the autumn survey had the highest total number of species present (26), but no significant differences between the seasons.

pg. 89 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY Total individuals (Figure 5.2b) was the highest during the summer survey (702) with a decrease during the autumn survey (426). Figures 5.2c, d, and e shows a general increase towards the autumn survey. Species richness (Figure 5.2c) was the lowest (4.37) with an increase during autumn (5.1). Pielou’s evenness index (Figure 5.2d) showed the lowest concentration during winter (0.85) but according to the column statistics, summer was the lowest with a value of 0.83, increasing in autumn (0.91). Shannon-Wiener diversity index (Figure 5.2e) was between 0.98 to 2.62 during the winter survey, decreasing in summer (from 0.81 to 2.33) and increasing in autumn (from 1.78 to 2.78). Although there are visual differences between the seasons, none of the temporal univariate indices had significant differences.

pg. 90 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Figure 5.1: Diversity indices for the aquatic macroinvertebrate taxa sampled in the Seekoeivlei Nature Reserve for 2016 to 2017. Bars and error bars represent mean and standard error. The replicates at each site are represented by the seasonal survey data and therefore provide indication temporal variation (n=3). (a) Total species, (b) Total individuals, (c) Margalef’s index, (d) Pielou’s evenness index, and (e) Shannon Wiener index.

pg. 91 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

pg. 92 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY The NMDS plot which is based on the Bray-Curtis similarity matrix had a stress value of 0.21 indicating a bad 2D representation of the data (Figure 5.3). According to Figure 5.3, there is temporal variation present, where winter samples grouped together and summer samples grouped together. Spatial variation is present; it is only Site 4b that was slightly further from the rest of the groupings. An ANOSIM analysis was performed and it showed a significant difference of 0.1% between winter and summer, as well as between winter and autumn and 10.4% difference between summer and autumn. With regards to the global R value, these groupings were similar to one another based on season (Global R value 0.397).

Figure 5.3: Non-multidimensional scaling (NMDS) plot of the macroinvertebrate taxa sampled during winter (July 2016), summer (December 2016) and autumn (March 2017) in the Seekoeivlei Nature Reserve.

The SIMPER analysis of the macroinvertebrate diversity were presented in Table 5.2. Average similarity of 33.11% was present during winter, 24.90% during summer and 31.73% during autumn. According to these results, it is clear that Micronecta sp. were the most abundant species during all three surveys. During the winter survey (July), Tanypodinae sp., Chironominae sp. and Micronecta sp. were the most dominant. During the summer survey (December) more species were dominant, which included Micronecta sp., Bulinus tropicus, Enitharus sp., Laccophilus sp. and Ceratophallus natalensis. The species that were most dominant during the autumn survey (March) was Baetidae sp., Cloeon sp, Procloeon sp., Micronecta sp. and Appasus sp.

pg. 93 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY Table 5.2: SIMPER Analysis results showing the most abundant species present during each sampling survey. Season 1 showed an average similarity of 33.11% during the winter survey (July, 2016), summer (December 2016) showed 24.90% average similarity and the autumn survey (March 2017) showed 31.73% average similarity.

Winter (July) Average similarity: 33.11% Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % % Tanypodinae sp. 54.31 16.62 1.78 50.21 50.21 Chironomidae sp. 27.56 4.97 0.70 15.00 65.21 Micronecta sp. 12.81 3.15 0.94 9.53 74.74 Summer (December) Average similarity: 24.90 % Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % % Micronecta sp. 32.47 6.35 0.72 25.50 25.50 Bulinus tropicus 27.24 4.39 0.67 17.65 43.15 Enitharus sp. 14.71 3.42 0.91 13.72 56.87 Laccophilus sp. 9.29 2.50 0.92 10.04 66.91 Ceratophallus natalensis 35.18 1.31 0.35 5.28 72.19 Autumn (March) Average similarity: 31.73% Average Average Similarity/Standard Contribution Cumulative Species Abundance Similarity deviation % % Baetidae sp. 32.29 10.47 1.33 33.01 33.01 Cloeon & Procloeon sp. 12.43 6.09 1.98 19.19 52.20 Micronecta sp. 22.43 4.02 1.04 12.68 64.88 Appasus sp. 5.29 2.91 1.18 9.19 74.06

The RDA tri-plot (Figure 5.4) represent 32.81% of the total data variation, with a p value of 0.028. The first axis explained 17.66% and the second axis explained 15.15%, which means the variables on the first axis carry more values than the variables on the second axis. Majority of the species were abundant during the summer survey (present on the first axis). It is clear in this tri-plot that the winter survey was mostly grouped together in the lower right quadrant, and the autumn survey was mostly grouped together in the lower left quadrant. Most of the sites during the December survey were on the first axis and most of the sites during the July and March surveys were situated on the second axis. The species associated with the December survey was Micronecta sp., Baetidae sp., Cloeon & Procloeon sp., Pseudagrion sp., Enitharus sp. and Ceratophallus

pg. 94 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY natalensis. The species associated with theJuly survey was Chironomidae sp., Mesostoma sp. and Tanypodinae sp.

Species

Jul Dec Mar

Figure 5.4: Redundancy Analysis (RDA) plot showing species diversity for all the sampled sites during the three surveys (July 2016 (winter); December 2016 (summer) and March 2017 (autumn)), in the Seekoeivlei Nature Reserve. This tri-plot explains 32.81% of the total data variation of which 17.66% is displayed on the first axis and remaining 15.15% is displayed on the second axis. The RDA tri-plot with interactive-forward selection explained 25.97% of the total data variation with a p value of < 0.05 (significant) for the differences across all sampling surveys, with regard to water quality and the macroinvertebrate diversity (Figure 5.5). This RDA plot showed that seven variables had a significant influence on the distribution of some taxa at specific sites. These variables included percentage dissolved oxygen (DO), K, Mg, Ni, nitrites, nitrates and temperature.

pg. 95 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Environmental variables Species

Jul Dec Mar

Variation partitioning results of water quality variables (a) and wetland types (b) effect on the aquatic macroinvertebrate diversity, are presented in the Venn diagram in Figure 5.6. Only 25.1% of the total data are explained, with the shared effect between the aquatic macroinvertebrates, water quality and wetland type being 1.9% (p = 0.002). The highest proportion of the variation in macroinvertebrate diversity and community structure was explained by the effect of water quality (20.8%, p = 0.002). The remaining 0.9%, explained the macroinvertebrate diversity and the effect of wetland types on the diversity and community structure (p = 0.006). This indicates that the water quality, wetland types and the shared effect were significantly different.

pg. 96 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Figure 5.6: Venn diagrams representing unique and shared contribution of water quality variables (a) and different wetland types (b) on the aquatic macroinvertebrate diversity and community structure. Only 25.1% of the total data explained. The CCA plot showed the macroinvertebrate distribution (calculated through the variation partitioning results) influenced by the wetland type, presented in Figure 5.7. This plot explains 9.20% of the total variation with a p value of 0.006. During this study it was determined that the Seekoeivlei Wetland is a floodplain depression which consisted of river, pan and oxbow sites. In this CCA plot only 11% of the species are illustrated, these were the species that were influenced the most by the wetland type, resulting in 16 species present in the graph. Species such as Helobdella stagnalis, Sigara sp. and Chaoborus sp. were more abundant in the pan sites. Pisidium costulosum, Plea sp. and Lestus sp., for example had the highest abundance present in river sites. Whereas Philodytes sp. and Orthocladiinae sp. had the highest abundance present in oxbow sites. Remaining species in the graph had the highest abundance in river sites.

pg. 97 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Wetland type Species

Figure 5.7: CCA plot for all sampled sites during the three surveys. With 9.20% total variation explained, of which 6.44% explained on the first axis and the remaining 2.76% on the second axis. Abundance of each functional feeding group is presented in Figure 5.8 which showed that the most dominant group was the predator feeding group. The highest abundance within the predator group were at Sites 4a and 4b. The group of grazers showed second highest, specifically at Site 2a. It is clear that the abundance of predators were less present in the river sites i.e. Sites 6, 7 and 10a. More detailed tables can be found in Appendix C.

pg. 98 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Figure 5.8: Graph of Functional Feeding Groups present in the Seekoevlei Nature Reserve from 2016 to 2017. Percentage abundance within the Functional Feeding Groups (FFG) at each site. FFG = Shredder, scraper, scavenger, predator, parasitic/predator, omnivorous, grazer, filter-feeder, collector-gatherer and carnivorous. The PCA bi-plot presented in Figure 5.9, represents a total variation of 60.39%. The lowest abundance occurred within the parasitic, filter-feeder and omnivorous groups. The predator feeding group had the highest abundance at Sites 4a and 4b (both classified as depression pans) with total of 366 and 342 individuals respectively. Whereas the scavenger had the highest abundance at Sites 3d (oxbow) and 6 (river site) with a total of 132 and 73 individuals, respectively. The feeding group, scraper, had the highest abundance present in Sites 2a and 2b, both classified as depression pans, with total of 80 (Site 2a) and 87 (Site 2b) individuals present.

pg. 99 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY

Species

Sites

pg. 100 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY 5.4. DISCUSSION

Aquatic macroinvertebrate taxa are commonly used for biomonitoring, as they are adapted to specific habitats, temperatures, DO and are therefore good indicators of disturbances present in the ecosystem (de Klerk & Wepener, 2013). During the winter (July 2016) survey 56 taxa were found, during the summer survey (December 2016) 64 taxa and during the autumn survey (March 2017) 53 taxa were found. When comparing this data to a study on the macroinvertebrate assemblage changes of reed pans on the Mpumalanga Highveld done by de Klerk & Wepener (2013), they also found that the macroinvertebrate species increased during the warmer season (summer). This increase of macroinvertebrates can be ascribed to the increase in productivity during warmer seasons (de Klerk & Wepener, 2013). Although there was a difference between the seasons, according to the statistical analyses, it was not significant. Even with the autumn (March 2017) survey where most of the sites were inaccessible, it had little to no effect on the number of individuals sampled. There was a small difference in species collected when comparing the autumn survey with the winter (July 2016) and summer (December 2016) survey, where the following species were only collected during the autumn survey: Gerridae sp., Ranatra sp., Saldidae sp., Culicoides sp., Muscidae sp. and Tabanidae species.

When the univariate and multivariate analyses were performed it showed that the macroinvertebrates had significant spatial differences based on the Shannon-Wiener diversity index between Sites 4b and 5, 6, 10a and 10b. This could be because the species richness and evenness were lower at Site 4b than the other four sites, where there were more species present and evenness indicated no dominant species were present.

According to the study done by Malherbe et al. (2015), water quality has an influence on macroinvertebrate community structure. During this study the community composition of fish and macroinvertebrates were determined in relation to water quality parameters. The RDA analysis (Figure 5.4) indicated that some water quality variables were significant (p < 0.05) in explaining the variance in the communities. These water quality variables include DO, K, Mg, Ni, nitrites, nitrates and temperature (Figure 5.4) had a significant effect on the macroinvertebrate distribution with regards to their abundance.

Community structure can be influenced through the habitat connectivity of the aquatic ecosystem (Leibold & Norberg, 2004). This means that different species can occur in

pg. 101 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY the different habitats, i.e. river, pan and oxbow. The CCA analysis (Figure 5.5) showed the species mostly influenced by the habitat included Sigara sp.(Hemiptera), H. stagnallis (Hirudinea) and Chaoborus sp. (Diptera) which were most abundant in the pan sites. According to Oosthuizen & Siddall (2002), leeches are commonly found in ponds, swamps and slow-moving streams. The Chaoborus sp. is a small family of flies, commonly known as the phantom midges that are the most common in lakes and other standing waterbodies (Harrison et al., 2003). However, aside from their habitat preference not much is known about their taxonomy. Most common species present in the river sites consisted mostly of Odonata and Mollusca. Pulmonates are commonly found among aquatic vegetation (marginal or submerged) and can survive fluctuating water levels (Appleton, 2002). According to Leibold & Norberg (2004), this is true when considering pans (in the case of Seekoeivlei Wetland, floodplain depression) of a wetland and not larger lakes.

Culicoides sp. from the family Ceratopogonidae are adapted to suck blood and are considered as pests for all vertebrates. They are found in nearly every aquatic or even semi-aquatic habitat in large numbers (de Meillon & Wirth, 2003). According to de Meillon & Wirth (2003), Culicoides sp. are found even in wet and damp places such as under bark or in decomposing plant material. The Muscidae family is a large family that represents the most common organism, the housefly (Harrison et al., 2003). The Muscidae and Tabanidae families are part of the suborder, Brachycera. The Tabanidae family are closely associated to water and the larvae will survive in damp soil near the water or in the water itself and come up to the water surface to breathe air (Harrison et al., 2003).

Enitharus sp., that forms part of the Notonectidae family, is fast swimmers and predaceous (de Moor et al., 2003; Scholtz & Holm, 2008), which was one of the dominant species at Site 10b during the summer survey. Micronecta is part of the largest feeding group present in the Seekoeivlei Nature Reserve, the predators. Pseudagrion sp. is common near running waters and widespread (Scholtz & Holm, 2008), and are classified as a predator.

The abundance of H. stagnalis (parasitic/predator) where the highest during the winter survey (July) at Site 4a (floodplain depression). According to Learner & Potter (1974), H. stagnalis are more common and prefer water bodies that are neither hard nor soft. For the water to be classified as hard or soft, the calcium levels should be between 14 - 24 mg/l. when looking at the calcium levels of Site 4a it is clear that it falls in this range,

pg. 102 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY and be classified as neither hard nor soft water which is the preferable water quality that the species H. stagnalis prefers.

Sites 4a and 4b had the highest abundance of predators that included taxa from Hemiptera, Coleoptera and Odonata. One of the species that had the most influence on the abundance in this group, was the species Philodytes sp. from the Dytiscidae family. According to Miller et al. (2005), Philodytes sp. occur relatively widespread and prefer open water habitats. The other two species that influenced the abundance was Aquarius sp. and Mesovelia sp, from the families Gerridae and Mesovelidae respectively. These are both water gliders that prefer open water (Reavell, 2003), such as Sites 4a and 4b.

Important to notice that there is one invasive snail species, Lymnaea (Pseudosuccina) columella (Table 5.1), present in the Seekoeivlei Nature Reserve (de Kock et al., 1989). According to de Kock & Wolmarans (2008), Lymnaea columella were introduced in South Africa in the early 1940’s and is the third most widespread freshwater snail species in South Africa. It was suspected by Brown (1980) that the species had been imported in South Africa with aquatic plants and fish. According to de Kock et al. (1989), L. columella can be found widespread in Natal and the Free-State, and a recent study by de Kock & Wolmarans (2014) indicated that this species can also be found in the southern areas of the Kruger National Park. In 2003 Appleton reported that there were high numbers of these species present in KwaZulu-Natal. The presence of this species can cause a reduction in food availability to other indigenous grazing invertebrates (Appleton, 2003).

There were two dominant snail species during the summer survey in the Seekoeivlei Nature Reserve. These two species are Ceratophallus natalensis (Krauss, 1848) and Bulinus tropicus (Krauss, 1848), both part of the family Planorbidae. Bulinus tropicus serves as the intermediate host of a conical fluke (Calicophoron microbothrium) of cattle (Appleton, 2002). Both of these had the highest abundance in Site 2a, where more birds are present, and cattle and sheep were present. This same site had the highest abundance within the grazer feeding group.

The Seekoeivlei Nature Reserve was compared to national wetland in order to ascertain the aquatic macroinvertebrate biodiversity within different habitat types. In South Africa, the Seekoeivlei Wetland was compared to the floodplain wetland associated to the Harts River, a study done by Malherbe et al. (2015). The aim of this study was to

pg. 103 CHAPTER: 5 MACROINVERTEBRATE DIVERSITY determine the fish and macroinvertebrate communities of the floodplain wetlands that are associated with the Harts River. The macroinvertebrates were collected from the major biotypes i.e. vegetation and GSM, which was the same biotypes present in the Seekoeivlei Wetland sites. The Harts River study concluded that there were different macroinvertebrate communities present between the river and floodplain sites, and that the macroinvertebrate traits for their habitat preference could be the reason why the communities differed at the sites (Malherbe et al., 2015). In the Seekoeivlei it showed (Figure 5.5) that some taxa preferred the river sites while others preferred the floodplain depression sites. The Seekoeivlei Wetland also represented the same taxa communities than in the study done by Malherbe et al. (2015) in the Harts River associated floodplain wetlands. In the results of the Harts River floodplain wetlands it was found that the taxa found in the floodplain was more than in the river, i.e. 21 taxa in the floodplain wetland and 19 in the Harts River (Malherbe et al., 2015). With regards to the Seekoeivlei Wetland, a total of 121 taxa were found in the floodplain wetland (floodplain depression pan and oxbow lakes) and a total of 60 taxa in the river sites. Only 39 taxa were found in all three the wetland types, which shows the same trend than the Harts River study (Malherbe et al., 2015).

5.5. CONCLUSION

From this study, it can be concluded that the Seekoevlei Nature Reserve provides habitat for numerous macroinvertebrate taxa. There was temporal variation seen within the macroinvertebrate diversity, but the variation was not significant. The macroinvertebrate community structure did not show little to no spatial variation. The aquatic macroinvertebrate diversity was the highest during summer that could be ascribed to an increase in productivity. The majority macroinvertebrate species were more abundant in the pan and river sites, which can be due to the preference of more vegetation or flowing water. When the Seekoeivlei Wetland macroinvertebrate biodiversity was compared to other national wetlands, it showed that the Seekoeivlei Wetland had a rich biodiversity of macroinvertebrate taxa. It is evident from these results what species can be found in the Seekoeivlei Nature Reserve, and further studies can be conducted for more information on the Seekoeivlei Nature Reserve. It can be concluded that water quality variables had more influence on the macroinvertebrate diversity and community structure than the wetland types.

pg. 104

GENERAL CONCLUSION AND RECOMMENDATIONS

6.1. CONCLUSION

The Seekoeivlei Wetland is an important wetland because it forms part of the upper reaches of the Klip River Catchment, which is one of the main and important tributaries of the Vaal River (McCarthy et al., 2010). According to McCarthy et al. (2010), the Vaal River supplies the majority of water for South Africa’s main industrial areas in the Gauteng Province. With the Klip River as the primary water source of the Seekoeivlei Wetland it could have an impact on the Vaal River, and must therefore be assessed.

This current study completed at the Seekoeivlei Nature Reserve was the first aquatic assessment with regards to zooplankton and macroinvertebrates. Other studies that were conducted at the Seekoeivlei Nature Reserve included the study done by McCarthy et al. (2010) about the role of the geomorphology for floodplain wetlands. In South Africa there are a lack of information of the biological communities of wetlands (Malherbe et al., 2015), and especially of the Ramsar sites in South Africa. The current study of the Seekoeivlei Nature Reserve was conducted in order to update and provide a general insight into the invertebrate communities present in the wetland.

Water quality parameters, which consisted of in situ parameters and nutrient analysis, of all the sites revealed no spatial variation. The only parameter that had a significant difference between the seasons, was temperature, which showed a significant difference between July (winter, 2016) and December (summer, 2016); July and March (autumn, 2017). When comparing the water quality results of the Seekoeivlei Nature Reserve to the South African water quality guidelines (DWAF, 1996) it indicated that the Seekoeivlei Wetland is in a fair condition due to the natural level of concentration metals and nutrients. However, during this study not all the nutrients were tested, and according to Malan & Day (2012) the Seekoeivlei Wetland is severely impacted,

pg. 105 CHAPTER: 6 GENERAL CONCLUSION AND RECOMMENDATIONS indicating that the metals and nutrient concentrations present were not caused by anthropogenic activities. No significant spatial or temporal differences of metal concentrations in the water and sediment were found in the Seekoeivlei Nature Reserve.

The community structure of zooplankton in the Seekoeivlei Nature Reserve appeared not to be significantly different with regards to spatial variation. Although some of the statistical analyses showed no significant differences, the ANOSIM results showed temporal differences in the zooplankton community. According to De Necker et al. (2016), other environmental factors, such as geography, size of the habitat, food availability and water chemistry can also have an influence on the zooplankton community structure. Nutrients (nitrates, nitrites, ammonium) and temperature had a significant (p<0.05) effect on the zooplankton community structure. Another factor that influenced the zooplankton community structure was the wetland type, i.e. river, oxbow and pan. The zooplankton diversity was high within the Seekoeivlei Nature Reserve when compared to other national and international wetlands (Dube et al., 2017; Biswas & Panigrahi, 2015).

The macroinvertebrate community structure in the Seekoeivlei Nature Reserve varied throughout the sampling surveys across the wetland. Based on the univariate and multivariate analyses the macroinvertebrate community showed no significant spatial or temporal variation across the wetland. The RDA interactive-forward-selection showed that some of the water quality variables (%DO, K, Mg, Ni, nitrates, nitrites and ammonium) had a significant effect on the community structure. The macroinvertebrates that were affected by the water quality variables, differed from the macroinvertebrates affected by the wetland type. Present in the pan sites included Sigara sp., H. stagnalis and Chaoborus sp. and in the river sites consisted more of Odonata and Mollusca, which showed that different taxa prefer different habitats. When considering the macroinvertebrates at the functional feeding group level two main groups, i.e. predators and grazers were identified. The predators were the most abundant at Sites 4a and 4b, which are classified as pans and the grazers were the most abundant at Site 2a which is also classified as a depression pan. When comparing the macroinvertebrate diversity within the Seekoeivlei Nature Reserve to another national wetland (Malherbe et al., 2015), it showed that the Seekoeivlei Nature Reserve had a relatively high diversity of macroinvertebrate taxa.

pg. 106 CHAPTER: 6 GENERAL CONCLUSION AND RECOMMENDATIONS The aims of this study were to assess the water and sediment quality, determine the zooplankton and macroinvertebrate diversity of the Seekoeivlei Wetland as well as the relationship between the water quality, wetland types and aquatic zooplankton and macroinvertebrate diversity. The study investigated three major hypotheses to achieve the aim. The first hypothesis, that water and sediment quality has an influence on the aquatic macroinvertebrate community structure of the Seekoeivlei Nature Reserve, is accepted in terms of the water quality. With regard to the sediment quality, the hypothesis is rejected because of the natural levels of metals present in the wetland.

The second hypothesis, that there is seasonal variation in the zooplankton and aquatic macroinvertebrate diversity of the Seekoeivlei Nature Reserve, is also accepted.

The third hypothesis, which stated that wetland type has an influence on the zooplankton and aquatic macroinvertebrate community structure of the Seekoeivlei Nature Reserve, is also accepted.

In conclusion, the water quality of the Seekoeivlei Nature Reserve is in a fair condition according to the TWQR. It also contains high diversity zooplankton and aquatic macroinvertebrates. Thus, indicating that the Seekoeivlei Nature Reserve supports a biologically diverse wetland. These results will provide vital baseline information on the water and sediment quality as well as on the diversity of the zooplankton and macroinvertebrates of the Seekoeivlei Nature Reserve. It can also add to the general aquatic invertebrate biodiversity knowledge of one of the South African Ramsar sites.

pg. 107 CHAPTER: 6 GENERAL CONCLUSION AND RECOMMENDATIONS 6.2. Recommendations

For future studies at the Seekoeivlei Nature Reserve the following additional work should be conducted:

 This study found that functional feeding groups can provide insight on the diversity and community structure. Further research into this component is necessary and will be valuable to determine the food webs present in the Seekoeivlei.  Fish should also be added to the future studies, which can provide useful information on the Seekoeivlei Wetland ecosystem.  Currently, the pollution and waste disposal of the informal settlement, Zamani, does not show an effect on the Seekoeivlei Wetland, but strategies will have to be put in place to prevent future problems regarding pollution.  Where possible, South African Invertebrate identification guides need to be updated as this study relied on books published between 1999 and 2008, where many of the identification keys only go up to family level.

pg. 108

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pg. 120

APPENDIX A: WATER AND SEDIMENT QUALITY

Table A1.1: Water quality data showing nutrient concentrations (mg/l) and metal concentrations dissolved in water samples (µg/l) collected from Sites Ps – 3e at the Seekoeivlei Nature Reserve during the July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys (Ps = Pampoen Spruit, Ws = Wildemans Spruit).

Season Ps 1 2a 2b Ws 3a 3b 3c 3d 3e

July, winter 0.30 0.50 0.60 0.50 0.50 0.50 0.50 0.40 0.30 0.60 December, 2.30 0.70 1.10 1.00 0.90 1.40 0.60 2.00 1.00 3.10 Nitrates summer March, 2.50 2.10 2.50 2.30 2.50 1.6 1.7 1.9 autumn July, winter 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 December, 0.08 0.03 0.07 0.04 0.04 0.05 0.03 0.09 0.04 0.02 Nitrites summer March, 0.03 0.02 0.03 0.02 0.01 0.01 0.01 0.01 autumn July, winter 0.8 0.97 0.79 3.5 0.57 0.93 0.38 1.2 0.57 0.41 December, 0.21 0.13 0.53 0.09 0.14 0.42 0.14 0.28 0.19 0.21 Phosphates summer March, 0.11 0.07 0.37 0.09 0.11 0.09 0.09 0.06 autumn July, winter 0.17 0.18 0.14 0.1 0.11 0.16 0.08 0.05 0.03 0.05 December, 0.15 0.17 0.15 0.05 0.05 0.05 0.05 0.21 0.09 0.21 Ammonium summer March, 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 autumn July, winter 56 59 89 59 74 46 52 38 54 34 December, 98 48 98 56 77 74 77 69 25 74 Sulfates summer March, 66 67 56 66 83 66 25 25 autumn July, winter 0.00038 1.3E-05 0.000741 0.0000404 0.0000376 0.0003621 0.0003231 0.0005557 0.0007944 0.0001776 December, 0.00059 0.00043 0.001119 0.0007936 0.0006673 0.0004441 0.0006543 0.0003186 0.00066 0.0002909 As summer March, 0.00054 0.00025 0.0006697 0.0003031 0.0001719 0.0003121 0.0002852 0.0002656 autumn July, winter 0.00146 0.00334 0.002874 0.003584 0.003563 0.003624 0.002221 0.004784 0.00298 0.003111 Cr December, 0.00417 0.00518 0.004508 0.004775 0.004942 0.004407 0.00362 0.003565 0.004396 0.004858

pg. 121 APPENDIX A WATER AND SEDIMENT QUALITY

Season Ps 1 2a 2b Ws 3a 3b 3c 3d 3e

summer March, 0.0052 0.00563 0.005009 0.005626 0.005032 0.005813 0.005811 0.005488 autumn July, winter 0.06668 0.01944 0.1218 0.003723 0.0000547 0.0743 0.006603 0.004744 0.01634 0.01215 December, 0.02837 0.00622 0.1532 0.006292 0.01296 0.005926 0.01223 0.005434 0.005771 0.007953 Fe summer March, 0.01903 0.00918 0.06384 0.007117 0.008558 0.004664 0.0035 0.004382 autumn July, winter 0.00057 1.6E-05 0.001758 0.0004046 0.0003657 0.00118 0.001261 0.001192 0.002398 0.0000769 December, 0.00173 0.00064 0.001182 0.0009582 0.001506 0.001494 0.000764 0.001027 0.001182 0.001192 Ni summer March, 0.00111 0.00065 0.000985 0.0007062 0.0005481 0.0006975 0.0007386 0.0006948 autumn July, winter 0.00033 7.9E-05 0.00007945 0.0003157 0.0002368 0.00007945 0.00007945 0.000087 0.002201 0.0003103 December, 0.00041 0.0008 0.001918 0.001941 0.0006333 0.006555 0.0002388 0.00007945 0.0003845 0.0005036 Zn summer March, 0.00061 0.00216 0.006734 0.002249 0.001766 0.001757 0.001624 0.002068 autumn July, winter 0.00081 1.3E-05 0.0004928 0.06326 0.0003973 0.001744 0.0015 0.002502 0.3799 0.0005945 December, 0.00644 0.00416 0.005205 0.4873 0.003568 0.02735 0.009408 0.009268 0.009256 0.008345 Mn summer March, 0.0618 0.06705 0.7598 0.003866 0.002995 0.005851 0.002577 0.00418 autumn July, winter 0.0002 1.9E-06 0.0001849 0.0001472 0.0000483 0.0003974 0.0004005 0.0006349 0.0008887 0.0001193 December, 0.0004 0.00021 0.0001964 0.0002873 0.0002592 0.0002372 0.0001777 0.000198 0.0002295 0.0002817 Co summer March, 0.00024 0.00021 0.001433 0.0000555 0.000019 0.0000205 0.0000333 0.0000201 autumn July, winter 0.00327 0.00124 0.003329 0.0008373 0.001475 0.003733 0.001184 0.002332 0.001023 0.002074 December, 0.00396 0.00206 0.003443 0.003326 0.003182 0.00364 0.002185 0.004302 0.003647 0.004027 Cu summer March, 0.00225 0.00204 0.001205 0.0013 0.002117 0.003543 0.002612 0.001612 autumn

pg. 122 APPENDIX A WATER AND SEDIMENT QUALITY

Table A1.2: Water quality data showing nutrient concentrations (mg/l) and metal concentrations dissolved in water samples (µg/l) collected from Sites 4a – 10b at the Seekoeivlei Nature Reserve during the July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys.

Season 4a 4b 4c 4d 5 6 7 8 9 10a 10b

July, 1.00 1.40 1.00 0.90 0.30 0.50 0.50 0.50 0.50 0.50 winter December, Nitrates 1.00 1.00 2.90 1.30 1.40 1.90 2.00 1.90 4.40 3.00 2.10 summer March, 2.00 2.40 2.10 autumn July, 0.02 0.02 0.06 0.05 0.06 0.05 0.03 0.02 0.02 0.02 winter December, Nitrites 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.03 0.03 summer March, 0.01 0.01 0.01 autumn July, 0.05 0.20 0.05 0.05 0.43 0.41 0.76 0.52 0.61 0.34 winter December, Phosphates 0.27 0.30 0.10 0.13 0.32 0.22 0.12 0.10 0.25 0.12 0.14 summer March, 0.07 0.27 0.10 autumn July, 0.51 0.05 0.03 0.03 0.14 0.05 0.03 0.04 0.13 0.17 winter December, Ammonium 0.27 0.30 0.10 0.13 0.32 0.22 0.12 0.10 0.25 0.12 0.14 summer March, 0.05 0.05 0.05 autumn July, 46.00 31.00 30.00 25.00 73.00 51.00 41.00 49.00 41.00 37.00 winter December, Sulfates 93.00 78.00 41.00 105.00 81.00 84.00 89.00 73.00 80.00 105.00 69.00 summer March, 25.00 25.00 25.00 autumn July, 0.00001322 0.00194 0.0000269 0.0004261 0.001505 0.0003145 0.0000147 0.0000171 0.000035 0.000585 winter December, As 0.001238 0.00135 0.0005508 0.0006999 0.00137 0.0004938 0.0008498 0.001513 0.00131 0.0009036 0.0006086 summer March, 0.0003153 0.000396 0.0004775 autumn July, Cr 0.002275 0.00378 0.003735 0.002459 0.003963 0.004075 0.004457 0.004114 0.004292 0.00251 winter

pg. 123 APPENDIX A WATER AND SEDIMENT QUALITY

Season 4a 4b 4c 4d 5 6 7 8 9 10a 10b

December, 0.005311 0.00522 0.005847 0.00468 0.005525 0.00616 0.005375 0.004144 0.00488 0.005863 0.005488 summer March, 0.005482 0.00543 0.004681 autumn July, 0.0732 0.07553 0.0002978 0.01362 0.01488 0.01551 0.0002978 0.000047 0.0002978 0.007943 winter December, Fe 0.0724 0.06473 0.006695 0.01317 0.1254 0.04776 0.04289 0.04066 0.1143 0.04964 0.0367 summer March, 0.008018 0.02023 0.006812 autumn July, 0.00532 0.00404 0.0001099 0.001005 0.002917 0.000885 0.0000976 0.0002119 0.0001517 0.0007998 winter December, Ni 0.001283 0.00114 0.000995 0.001134 0.001306 0.001973 0.00168 0.002554 0.00196 0.001697 0.0006808 summer March, 0.0005227 0.001332 0.0007914 autumn July, 0.01543 0.0124 0.0002187 0.0000643 0.00007945 0.00007945 0.00007945 0.00007945 0.00007945 0.0009543 winter December, Zn 0.002081 0.0007 0.006229 0.001502 0.0008727 0.00198 0.0007737 0.0005116 0.00221 0.0005771 0.001458 summer March, 0.001456 0.006338 0.002902 autumn July, 3.152 0.00386 0.04648 0.0002841 0.001113 0.000417 0.0001422 0.0005819 0.002256 0.0000495 winter December, Mn 0.0105 0.00428 0.08325 0.0008974 0.006371 0.001509 0.1075 0.01818 0.00306 0.1454 0.001948 summer March, 0.001329 0.0007128 0.1272 autumn July, 0.01476 0.00153 0.000055 0.0000802 0.001126 0.0001326 0.0000638 0.0000595 0.00006 0.0001595 winter December, Co 0.0007832 0.00062 0.0001793 0.0001292 0.00063 0.0003886 0.0008391 0.0005264 0.00015 0.0005897 0.0001128 summer March, 0.0000231 0.0001041 0.0002101 autumn July, 0.006636 0.02744 0.00127 0.001905 0.00284 0.002344 0.0009475 0.001714 0.001208 0.005218 winter December, Cu 0.003045 0.00368 0.00361 0.002625 0.001774 0.002214 0.002251 0.002836 0.00266 0.001974 0.001528 summer March, 0.003394 0.00274 0.001444 autumn

pg. 124 APPENDIX A WATER AND SEDIMENT QUALITY

Table A2: Physico-chemical water quality variables measured at the Seekoeivlei Nature Reserve for sampling surveys from 2016 – 2017

Total Dissolved Dissolved Electrical Temperature dissolved Turbidity Sites Season pH Oxygen oxygen conductivity (˚C) solids (NTU) (mg/l) (%) (µS/cm) (mg/l) July, winter 11.20 8.36 8.87 80.00 185.20 127.30 52.00 December, 25.50 7.76 5.80 76.00 158.50 112.60 75.00 Ps summer March, 24.30 7.64 7.68 104.50 142.40 98.50 14.00 autumn July, winter 8.60 8.19 10.97 93.80 63.50 44.50 13.00 December, 24.00 7.69 7.55 88.00 82.00 56.00 23.00 1 summer March, 23.50 7.17 8.05 84.70 60.00 40.30 25.00 autumn July, winter 12.70 7.81 9.40 81.30 191.00 129.70 93.00 December, 21.60 6.87 3.22 39.70 122.30 81.80 41.00 2a summer March, 21.80 5.98 1.15 11.30 114.20 79.90 4.00 autumn July, winter 11.00 7.69 12.20 112.00 108.70 76.30 9.00 December, 25.90 7.11 5.06 62.40 104.90 73.20 17.00 2b summer March, ------autumn July, winter 7.90 7.96 10.20 85.00 91.50 66.50 5.00 December, 20.30 7.44 5.20 59.60 115.80 79.80 14.00 Ws summer March, 23.30 6.85 7.60 77.70 70.90 48.40 1.00 autumn July, winter 5.60 8.60 12.10 99.00 10.00 71.20 43.00 December, 24.00 7.34 3.92 46.00 99.80 65.80 64.00 3a summer March, 22.50 6.25 6.50 69.50 67.80 45.30 7.00 autumn 3b July, winter 7.10 7.87 9.50 81.60 149.00 104.00 10.00

pg. 125 APPENDIX A WATER AND SEDIMENT QUALITY

Total Dissolved Dissolved Electrical Temperature dissolved Turbidity Sites Season pH Oxygen oxygen conductivity (˚C) solids (NTU) (mg/l) (%) (µS/cm) (mg/l) December, 27.10 7.61 7.80 93.10 88.20 60.60 19.00 summer March, 21.50 6.40 7.30 80.40 64.50 47.30 7.00 autumn July, winter 8.00 8.35 10.05 79.70 111.50 7.81 25.00 December, 23.50 7.23 3.90 50.60 89.80 64.20 88.00 3c summer March, 21.20 6.44 6.27 69.80 68.50 47.80 8.00 autumn July, winter 7.40 7.86 11.50 96.90 135.80 94.20 20.00 December, 25.90 7.03 5.04 59.00 106.30 75.60 40.00 3d summer March, 20.90 6.65 6.45 68.00 68.00 47.60 8.00 autumn July, winter 8.20 8.23 10.41 84.00 109.10 77.70 15.00 December, 24.00 7.58 4.05 48.70 97.70 68.80 84.00 3e summer March, ------autumn July, winter 3.70 6.90 5.20 36.00 109.80 78.00 20.00 December, 30.50 7.43 7.60 101.00 101.50 71.80 33.00 4a summer March, ------autumn July, winter 8.50 6.40 8.90 77.00 78.00 54.80 55.00 December, 31.10 7.63 8.44 108.90 100.40 70.00 39.00 4b summer March, ------autumn July, winter 9.60 3.70 8.13 76.60 237.00 171.00 5.00 December, 4c 22.50 7.06 3.63 41.60 106.20 72.30 15.00 summer March, 23.40 6.58 6.14 63.10 66.00 45.60 9.00

pg. 126 APPENDIX A WATER AND SEDIMENT QUALITY

Total Dissolved Dissolved Electrical Temperature dissolved Turbidity Sites Season pH Oxygen oxygen conductivity (˚C) solids (NTU) (mg/l) (%) (µS/cm) (mg/l) autumn July, winter 13.20 8.20 10.20 84.60 185.00 125.00 20.00 December, 22.70 6.96 2.30 26.00 113.00 78.90 16.00 4d summer March, ------autumn July, winter 11.90 7.50 9.12 84.00 156.30 111.00 57.00 December, 30.80 7.28 8.80 122.00 110.50 77.00 26.00 5 summer March, ------autumn July, winter 10.10 7.90 7.80 64.50 163.70 110.80 9.00 December, 23.40 6.70 2.72 33.60 102.50 48.00 23.00 6 summer March, 24.80 6.40 5.70 73.00 92.40 61.30 16.00 autumn July, winter 9.30 8.33 8.03 77.80 87.10 64.10 7.00 December, 25.60 7.53 10.70 136.00 69.10 65.80 9.00 7 summer March, 24.10 6.10 3.83 43.50 89.00 61.30 9.00 autumn July, winter 8.70 8.33 9.11 70.90 103.80 72.30 8.00 December, 28.70 8.28 8.50 108.70 186.60 130.60 9.00 8 summer March, ------autumn July, winter DRY December, 25.10 6.80 6.10 72.10 119.60 88.10 9.00 9 summer March, ------autumn 10a July, winter 7.70 8.12 9.10 75.80 96.00 65.50 5.00

pg. 127 APPENDIX A WATER AND SEDIMENT QUALITY

Total Dissolved Dissolved Electrical Temperature dissolved Turbidity Sites Season pH Oxygen oxygen conductivity (˚C) solids (NTU) (mg/l) (%) (µS/cm) (mg/l) December, 19.80 7.20 7.40 85.80 98.20 70.40 3.00 summer March, ------autumn July, winter 7.80 8.01 7.60 62.70 251.00 17.40 21.00 December, 23.20 9.60 13.90 146.00 90.40 63.90 2.00 10b summer March, ------autumn

Table A3: Sediment quality data showing sediment grain size, organic content and metal concentrations within the sediment samples (µg/g) collected from the Site Ps – 3e at the Seekoeivlei Nature Reserve during the July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys (Ps = Pampoen Spruit, Ws = Wildemans Spruit).

Season Ps 1 2a 2b Ws 3a 3b 3c 3d 3e

July, winter 0.54 0.09 1.09 0.74 0.00 0.59 2.07 2.00 0.00 0.24 % > 4000 December, summer 0.70 0.00 0.11 0.64 0.68 0.68 1.06 0.00 5.79 1.07 March, autumn 1.06 0.00 0.00 5.01 0.00 2.44 0.61 0.17

July, winter 1.77 2.40 12.35 5.36 6.61 5.65 13.32 14.71 10.45 12.02 %2000- December, summer 8.53 1.50 7.87 7.01 10.71 9.99 14.17 9.35 23.45 9.25 4000 March, autumn 10.71 1.53 2.12 3.08 3.98 13.18 6.96 6.59

July, winter 18.28 9.23 43.84 24.73 46.75 36.05 47.09 34.74 54.63 45.54 % 500-2000 December, summer 27.82 7.83 42.95 29.67 55.02 47.86 51.42 48.66 46.39 43.58 March, autumn 21.67 6.75 23.40 22.26 43.28 34.61 55.19 49.94

July, winter 12.69 17.64 16.88 12.54 21.85 16.74 18.70 17.31 14.85 13.29 % 212-500 December, summer 11.58 26.01 18.75 14.62 18.30 17.56 16.30 16.80 12.51 18.89 March, autumn 18.31 23.07 29.76 24.94 23.14 19.46 13.49 19.68

July, winter 47.37 55.40 18.08 37.53 18.70 30.36 14.43 21.73 14.28 17.75 % 53-212 December, summer 39.21 53.62 24.11 37.80 12.07 19.98 12.69 18.50 8.92 17.20 March, autumn 41.45 54.38 32.86 37.26 24.96 25.21 16.64 19.91

pg. 128 APPENDIX A WATER AND SEDIMENT QUALITY

Season Ps 1 2a 2b Ws 3a 3b 3c 3d 3e

July, winter 16.98 13.55 6.17 17.66 4.65 8.34 2.70 6.35 3.39 8.19 % < 53 December, summer 10.68 9.45 4.07 7.27 2.64 2.76 3.49 1.36 1.19 3.89 March, autumn 5.13 6.04 9.47 4.30 2.23 2.34 1.69 1.54

July, winter 4.26 3.22 13.68 5.88 10.21 9.74 23.85 11.70 21.34 12.85 % December, summer 6.06 2.72 15.92 11.07 12.03 12.20 17.89 12.45 16.36 13.15 Organics March, autumn 4.04 4.38 21.55 19.23 16.91 16.07 10.64 14.45

July, winter 7680.20 4759.90 33232.32 16341.84 36521.74 29876.24 40646.77 33374.38 39898.99 34566.33 Al December, summer 9793.97 4296.57 41045.92 25073.23 48954.08 31782.18 41069.65 30169.08 60970.87 43383.08 March, autumn 13139.71 11332.50 34116.16 35227.27 37995.17 38507.46 28731.71 42500.00

July, winter 1.36 0.86 2.96 2.76 2.57 3.09 4.10 3.01 4.03 3.63 As December, summer 2.35 0.82 3.74 2.10 2.48 3.18 4.07 2.41 3.63 3.48 March, autumn 2.10 1.52 2.36 2.89 2.56 2.64 3.03 2.59

July, winter 15.11 12.26 56.77 43.67 55.70 61.78 72.44 69.56 70.73 80.94 Cr December, summer 28.77 20.49 70.92 56.59 73.29 59.16 68.61 62.42 70.10 70.97 March, autumn 26.37 26.10 43.69 48.79 57.25 57.34 49.12 57.70

July, winter 8626.90 5524.75 21775.25 20923.47 26545.89 27896.04 28009.95 29950.74 30580.81 34591.84 Fe December, summer 14115.58 6799.02 23017.86 20237.37 23923.47 25024.75 20611.94 19855.07 27888.35 33880.60 March, autumn 12708.33 11547.50 21787.88 27626.26 24782.61 22649.25 24853.66 23533.16

July, winter 6.88 4.14 21.39 15.03 20.29 22.31 29.65 26.01 27.30 25.89 Ni December, summer 11.57 5.16 24.55 17.32 27.40 18.61 22.64 17.70 28.93 29.13 March, autumn 12.35 8.96 17.97 24.72 22.30 21.95 18.26 21.54

July, winter 6.55 3.49 21.89 19.40 19.32 24.17 26.97 26.58 24.95 27.55 Pb December, summer 18.70 6.55 28.55 21.19 19.38 23.22 34.50 22.60 27.62 23.57 March, autumn 11.47 9.48 18.64 14.73 23.47 23.44 21.63 24.09

July, winter 18.56 11.91 49.32 33.49 49.35 59.28 73.93 63.18 88.41 60.31 Zn December, summer 33.92 21.27 54.92 45.56 46.45 46.73 52.31 40.94 75.92 69.38 March, autumn 24.38 18.79 43.54 49.02 58.86 50.10 39.12 54.85

July, winter 411.68 239.75 438.13 920.41 4748.79 814.60 1055.47 746.31 650.76 1218.88 Mn December, summer 901.51 230.59 514.54 534.60 1463.27 831.93 550.00 704.83 545.63 1237.06 March, autumn 622.79 338.25 335.10 1424.49 735.75 837.06 1301.95 707.91

pg. 129 APPENDIX A WATER AND SEDIMENT QUALITY

Season Ps 1 2a 2b Ws 3a 3b 3c 3d 3e

July, winter 5.04 3.25 11.06 15.69 22.23 18.50 20.86 21.88 15.95 21.51 Co December, summer 11.81 5.48 15.98 13.04 14.34 13.55 12.39 12.93 10.02 17.69 March, autumn 8.39 6.61 10.07 18.17 11.06 11.44 12.99 10.52

July, winter 12.69 7.61 35.45 23.72 47.66 33.51 54.85 41.95 42.40 41.02 Cu December, summer 23.91 10.84 45.26 27.02 32.58 26.06 48.83 28.04 39.47 34.20 March, autumn 15.17 11.42 21.77 29.82 30.89 29.35 23.19 28.21

July, winter 0.01 0.00 0.08 0.05 0.04 0.09 0.18 0.23 0.15 0.11 Cd December, summer 0.05 0.01 0.10 0.07 0.05 0.07 0.16 0.08 0.15 0.12 March, autumn 0.030392 0.02 0.07 0.06 0.09 0.11 0.05 0.11

Table A4: Sediment quality data showing sediment grain size, organic content and metal concentrations within the sediment samples (µg/g) collected from the Site 4a – 10b at the Seekoeivlei Nature Reserve during the July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys.

Season 4a 4b 4c 4d 5 6 7 8 9 10a 10b 0.31 0.57 0.00 0.00 1.23 0.00 0.72 0.00 20.80 July, winter % > 4000 December, summer 0.77 4.90 3.64 1.41 7.63 0.55 0.14 9.56 0.00 0.00 0.65 0.68 13.28 0.26 March, autumn July, winter 28.77 5.13 4.54 4.53 7.28 1.66 10.17 12.38 4.53 %2000- 15.94 10.91 3.70 24.52 26.91 6.01 3.49 23.05 6.91 0.28 6.25 4000 December, summer 5.46 6.92 3.43 March, autumn 49.31 44.37 22.30 50.26 23.49 45.84 42.61 16.87 31.47 July, winter % 500-2000 December, summer 49.87 38.81 15.48 52.10 37.33 24.69 20.04 47.11 38.00 3.67 44.47 37.73 16.38 23.49 March, autumn 10.29 20.83 12.26 19.84 25.57 22.36 19.77 17.28 20.15 July, winter % 212-500 December, summer 17.43 20.81 23.39 11.72 12.30 27.09 27.54 10.81 20.76 13.13 19.55 23.94 20.72 24.86 March, autumn 8.22 22.26 49.21 19.64 32.71 22.88 20.61 28.24 33.22 July, winter % 53-212 December, summer 13.27 19.32 46.68 7.22 12.29 32.75 41.97 7.19 25.72 67.21 22.98 24.55 30.54 38.84 March, autumn

pg. 130 APPENDIX A WATER AND SEDIMENT QUALITY

Season 4a 4b 4c 4d 5 6 7 8 9 10a 10b 1.89 3.52 9.50 3.62 7.90 4.92 4.29 3.10 7.95 July, winter % < 53 December, summer 1.09 3.26 5.37 1.69 1.89 6.84 5.13 1.33 5.32 4.10 3.91 3.44 9.62 1.80 March, autumn July, winter 18.22 18.66 4.54 16.78 4.91 16.53 13.49 6.22 7.99 % 17.88 26.01 5.62 21.71 17.97 4.99 5.54 12.36 31.05 4.19 9.60 Organics December, summer 16.57 20.80 10.75 March, autumn 41889.95 45000.00 8835.75 32709.36 13185.71 31519.61 39879.23 15107.84 13196.97 July, winter Al December, summer 62015.31 65646.77 14235.15 55348.26 50724.64 26990.05 18058.54 64719.39 57010.05 8696.60 21082.52 31576.35 12892.34 29904.31 March, autumn 3.63 3.24 1.68 2.85 2.55 2.50 2.36 2.27 1.62 July, winter As December, summer 3.29 4.79 1.85 2.84 3.18 4.15 1.50 2.71 2.41 4.32 3.01 1.89 1.10 3.00 March, autumn 74.31 74.15 26.38 65.22 44.33 62.50 70.39 37.28 27.30 July, winter Cr December, summer 73.11 74.28 25.54 68.73 65.89 65.50 26.34 77.76 63.54 13.81 31.41 39.61 14.37 39.62 March, autumn 27799.04 26183.57 12144.93 24408.87 19957.14 22313.73 27681.16 22681.37 12313.13 July, winter Fe December, summer 26326.53 26940.30 14905.94 23398.01 23734.30 27611.94 9673.17 33724.49 19994.97 7004.85 16609.22 16298.03 7236.84 24593.30 March, autumn 24.93 26.50 8.46 23.91 12.59 23.22 24.37 15.08 9.53 July, winter Ni December, summer 28.65 29.70 9.80 25.67 25.05 18.18 8.30 27.37 24.69 5.79 11.29 18.18 8.29 15.75 March, autumn 27.42 26.01 12.45 27.17 15.87 26.32 22.57 9.70 21.99 July, winter Pb December, summer 24.53 25.80 10.35 22.89 24.71 17.53 9.01 20.24 19.28 18.32 14.57 15.01 7.41 17.59 March, autumn 62.44 67.87 29.93 62.41 21.02 65.37 82.54 27.38 62.27 July, winter Zn December, summer 72.45 70.65 30.25 64.98 63.26 29.18 21.98 58.52 76.06 29.71 29.08 41.75 23.28 37.94 March, autumn 610.29 696.14 672.95 521.43 651.19 477.21 406.28 1033.33 302.02 July, winter Mn December, summer 559.69 481.09 620.05 460.45 435.02 836.32 459.51 447.45 555.03 236.84 258.98 595.81 110.38 616.27 March, autumn

pg. 131 APPENDIX A WATER AND SEDIMENT QUALITY

Season 4a 4b 4c 4d 5 6 7 8 9 10a 10b 14.27 16.37 9.68 15.25 13.90 14.63 13.11 18.97 7.58 July, winter Co December, summer 10.65 11.82 8.58 10.11 9.87 13.99 4.81 11.82 8.83 5.26 7.30 8.66 3.88 10.45 March, autumn 41.17 38.50 16.68 41.77 17.67 37.65 31.86 29.17 89.17 July, winter Cu December, summer 36.02 36.97 10.86 37.16 35.24 19.02 11.71 26.51 31.03 47.04 15.91 21.46 11.58 18.22 March, autumn 0.11 0.13 0.05 0.14 0.01 0.13 0.04 0.02 0.04 July, winter Cd December, summer 0.10 0.12 0.02 0.10 0.09 0.02 0.01 0.04 0.18 0.15 0.02 0.05 0.02 0.04 March, autumn

pg. 132

APPENDIX B: ZOOPLANKTON DIVERSITY

Table B1: Zooplankton taxa sampled in the Seekoeivlei Nature Reserve (2a – 10b) during the July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys.

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total Cladocera 13633 July, 2 4 2 24 32 winter Bosmina December, 8 8 longirostris summer March, autumn July, 2 5 45 168 8 56 24 180 224 712 winter December, Pleuroxus sp. 20 108 12 40 48 20 248 summer March, 84 15 12 40 4 155 autumn July, 3 51 54 winter December, Monospilus sp. 4 4 summer March, 28 9 4 41 autumn July, 10 16 54 894 188 1216 128 55 40 296 120 8 8 183 3216 winter December, Daphnia sp. 4 44 18 48 183 448 24 26 24 28 32 879 summer March, 4 4 autumn July, 4 4 232 92 17 12 39 400 winter December, Daphnia laevis 10 14 4 422 8 96 554 summer March, autumn Simocephalus July, 18 211 92 34 355

pg. 133 APPENDIX B ZOOPLANKTON DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total sp. winter December, 24 10 32 12 8 73 400 12 8 8 40 627 summer March, 8 8 20 36 autumn July, 1240 60 4 40 64 8 28 1444 winter December, Moina sp. 20 52 76 56 80 24 8 2992 184 96 976 32 44 20 4 4664 summer March, 12 16 28 autumn July, winter Macrothrix December, propinqua summer March, 44 12 8 108 172 autumn Copepoda 10163

July, 38 32 10 8 30 36 40 28 92 128 144 40 136 168 96 1026 winter December, Calanoida sp. 64 84 10 36 168 104 56 96 208 184 32 160 60 448 48 140 1898 summer March, 44 12 8 108 172 autumn July, 32 18 22 16 26 228 52 24 56 56 52 16 100 72 144 914 winter December, Cyclopoida sp. 84 92 52 160 28 48 56 264 20 108 44 172 28 52 1208 summer March, 128 36 56 52 12 40 104 428 autumn July, 127 174 6 2874 60 420 44 36 208 84 208 4241 winter Cyclypoid December, 8 12 20 nauplia sp. summer March, 64 20 52 44 4 20 52 256 autumn Ostrocoda 380

pg. 134 APPENDIX B ZOOPLANKTON DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total July, 4 4 8 winter Darwinulidae December, 6 1 8 8 84 32 16 88 243 sp. summer March, autumn July, winter Gomphocythere December, 34 12 46 sp. summer March, 10 4 4 18 autumn July, 8 8 winter Cypriodoidea December, 3 8 11 sp. summer March, autumn July, winter Parastenocypris December, 12 12 24 junodi summer March, autumn July, winter December, Zonocypris sp. 4 12 16 summer March, 6 6 autumn Collembola 2

July, winter December, Isotomidae sp. 1 1 summer March, autumn

pg. 135 APPENDIX B ZOOPLANKTON DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total July, winter Dicyrtominae December, 1 1 sp. summer March, autumn

pg. 136 APPENDIX B ZOOPLANKTON DIVERSITY

Table B2: Detailed list of the abundance and diversity indices of the zooplankton taxa collected during the sampling surveys of July (winter, 2016), December (summer, 2016) and March (autumn, 2017).

Total individuals Margalef's index Pielou's evenness Shannon-Wiener Sites Season Total species (S) (N) (d) index (J') index (H') July, winter 6 213 0.93 0.65 1.16 2a December, summer 8 244 1.27 0.82 1.71 March, autumn 8 376 1.18 0.83 1.72 July, winter 8 254 1.26 0.54 1.12 2b December, summer 6 320 0.87 0.88 1.57 March, autumn

July, winter 5 108 0.85 0.82 1.31 3a December, summer 7 210 1.12 0.68 1.32 March, autumn 5 88 0.89 0.90 1.46 July, winter 4 1129 0.43 0.43 0.59 3b December, summer 7 224 1.11 0.89 1.74 March, autumn 4 124 0.62 0.77 1.06 July, winter 8 440 1.15 0.77 1.61 3c December, summer 7 468 0.98 0.75 1.45 March, autumn 6 160 0.99 0.84 1.50 July, winter 6 5826 0.58 0.72 1.29 3d December, summer 7 268 1.07 0.78 1.52 March, autumn 4 28 0.90 0.92 1.28 July, winter 8 624 1.09 0.92 1.92 3e December, summer 8 168 1.37 0.80 1.65 March, autumn

July, winter 6 136 1.02 0.85 1.53 4a December, summer 3 3248 0.25 0.29 0.32 March, autumn

July, winter

4b December, summer 4 784 0.45 0.80 1.11 March, autumn

pg. 137 APPENDIX B ZOOPLANKTON DIVERSITY

Total individuals Margalef's index Pielou's evenness Shannon-Wiener Sites Season Total species (S) (N) (d) index (J') index (H') July, winter 7 716 0.91 0.70 1.37 4d December, summer 5 608 0.62 0.77 1.24 March, autumn

July, winter 4 504 0.48 0.76 1.05 5 December, summer 6 2096 0.65 0.78 1.40 March, autumn

July, winter 6 428 0.83 0.86 1.54 6 December, summer 5 104 0.86 0.93 1.49 March, autumn 4 80 0.69 0.84 1.17 July, winter 4 272 0.54 0.71 0.99 7 December, summer 8 408 1.164 0.79 1.64 March, autumn 5 288 0.71 0.80 1.29 July, winter 6 684 0.77 0.80 1.43 8 December, summer 5 184 0.77 0.92 1.48 March, autumn

July, winter

9 December, summer 4 744 0.45 0.74 1.03 March, autumn

July, winter 7 372 1.01 0.73 1.42 10a December, summer 4 104 0.65 0.88 1.22 March, autumn

July, winter 6 704 0.76 0.90 1.61 10b December, summer 5 268 0.72 0.78 1.26 March, autumn

pg. 138

APPENDIX C: MACROINVERTEBRATE DIVERSITY

Table C1: Macroinvertebrate taxa sampled in the Seekoeivlei Nature Reserve (Sites 2a – 10b) during July (winter, 2016), December (summer, 2016) and March (autumn, 2017) surveys.

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total Ephemeroptera 1377

July, winter 12 28 4 2 1 13 16 1 8 47 200 8 145 485

December, 1 2 2 3 1 4 15 9 7 3 37 84 Baetidae sp. summer March, 6 22 36 68 16 4 74 226 autumn July, winter 2 2 5 1 1 16 11 4 15 14 6 7 84

December, Cloeon & 3 1 8 1 28 5 18 6 70 Procloeon sp. summer March, 9 12 14 8 8 4 32 87 autumn July, winter

December, 5 1 6 Caenidae sp. summer March, 7 7 autumn July, winter 5 4 1 26 126 38 13 213

December, 5 30 47 9 91 Caenospella sp. summer March, 24 24 autumn Odonata 306

July, winter

December, 2 2 Agriocnemis sp. summer March, autumn July, winter

Ceriagrion sp. December, 6 1 7 summer

pg. 139 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total March, autumn July, winter 4 11 15

December, 1 1 Enallagma sp. summer March, autumn July, winter 1 2 3 2 6 14

December, 3 13 23 4 1 76 120 Pseudagrion sp. summer March, 2 1 15 18 autumn July, winter 15 15

December, 4 4 Teinobasis sp. summer March, 1 2 3 autumn July, winter 1 1 2 4

December, 2 2 Lestus sp. summer March, 5 5 autumn July, winter

December, 5 5 Ellattoneura glauca summer March, autumn July, winter 1 1

December, 4 1 6 1 1 13 Anax sp. summer March, 21 2 23 46 autumn July, winter

December, Hemicordulia sp. 2 2 summer March,

pg. 140 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total autumn July, winter

December, 13 4 17 Bradinopyga sp. summer March, 2 1 3 autumn July, winter 1 1

December, Notiothemis sp. summer March, autumn July, winter

December, Orthetrum sp. summer March, 4 4 autumn July, winter 1 1

December, 2 1 3 Tetrathemis sp. summer March, autumn Lepidoptera 14

July, winter 9 1 10

December, 1 1 Crambidae sp. summer March, 1 1 1 3 autumn Hemiptera 1959

July, winter

December, Aphelocheirus sp. summer March, 1 1 autumn July, winter 3 3 Appasus sp. December, 23 3 1 3 1 3 4 2 2 7 49

pg. 141 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total summer March, 8 1 6 11 4 5 2 37 autumn July, winter 4 37 17 4 15 2 9 3 15 9 5 16 28 41 205

December, 5 40 2 26 35 6 84 49 9 102 13 12 74 95 552 Micronecta sp. summer March, 5 27 3 84 3 2 33 157 autumn July, winter 1 4 4 1 1 2 1 7 3 21 11 4 1 61

December, 25 1 2 7 8 2 11 1 5 3 4 8 77 Sigara sp. summer March, 3 2 5 autumn July, winter 1 1

December, Hebrus sp. summer March, autumn July, winter

December, Gerridae sp. summer March, 2 2 1 5 autumn July, winter

December, 2 2 Aquarius sp. summer March, autumn July, winter

December, 2 1 3 Leptopodidae sp. summer March, 2 1 4 1 8 autumn July, winter 1 1

Mesovelia sp. December, 2 1 3 summer

pg. 142 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total March, 1 1 autumn July, winter 1 1

December, Naucoris sp. summer March, autumn July, winter

December, Ranatra sp. summer March, 1 1 autumn July, winter 1 1 2 1 1 1 1 15 23

December, Anisops sp. summer March, 1 86 87 autumn July, winter 2 2

December, 2 27 27 15 7 9 24 2 20 11 1 26 3 3 8 65 250 Enitharus sp. summer March, 4 16 2 9 100 131 autumn July, winter 7 1 2 86 1 3 2 56 158

December, 17 5 1 2 1 7 16 2 19 4 37 111 Plea sp. summer March, 2 11 4 17 autumn July, winter

December, Saldidae sp. summer March, 2 5 7 autumn Coleoptera 481

July, winter

Aspidytes sp. December, 1 1 2 summer

pg. 143 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total March, autumn July, winter 1 1

December, Curculionidae sp. summer March, autumn July, winter 16 7 23

December, 1 2 3 Neochetina sp. summer March, autumn July, winter

December, 1 3 4 Africophilus sp. summer March, autumn July, winter

December, 3 1 10 14 Derovatellus sp. summer March, autumn July, winter 1 2 1 1 1 3 2 9 20

December, 1 3 5 1 1 11 Hydrovatus sp. summer March, 2 1 3 autumn July, winter 5 7 43 10 17 5 6 93

December, 2 12 11 44 4 4 10 1 2 7 13 13 10 10 15 158 Laccophilus sp. summer March, 1 2 2 2 3 10 autumn July, winter

December, Neptosternus sp. 3 3 summer March, 2 1 3

pg. 144 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total autumn July, winter 2 1 2 5

December, 11 3 3 6 2 1 1 1 28 Philodytes sp. summer March, 1 2 3 autumn July, winter

December, 1 2 3 Elmidae sp. summer March, autumn July, winter

December, 1 11 12 Gyrinus sp. summer March, autumn July, winter 1 1 1 1 4

December, 1 1 1 3 Haliplus sp. summer March, autumn July, winter 2 3 5

December, 1 1 2 1 1 6 Hydraenidae sp. summer March, autumn July, winter 1 1

December, Berosus sp. summer March, autumn July, winter 2 2

December, 1 4 4 8 17 Enochrus sp. summer March, 1 2 3 autumn

pg. 145 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total July, winter 3 3

December, Hydrochus sp. summer March, autumn July, winter 2 2 1 5

December, 1 1 Cyphon sp. summer March, autumn July, winter 1 1 1 3

December, Microsporus sp. summer March, autumn July, winter

December, 7 3 9 1 5 1 1 27 Spercheus sp. summer March, 2 2 autumn Diptera 1628

July, winter 2 1 3

December, 1 2 1 1 5 Bezzia sp. summer March, autumn July, winter

December, Culicoides sp. summer March, 3 3 autumn July, winter 1 1 7 3 3 15

December, Chaoborus sp. summer March, 4 4 autumn

pg. 146 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total July, winter

December, Corethrella sp. summer March, 2 2 autumn July, winter 3 9 5 31 48 65 23 160 26 46 3 14 8 441

December, 19 1 2 4 2 3 46 1 78 Chironominae sp. summer March, 9 2 2 13 autumn July, winter

December, 1 1 7 9 Orthocladiinae sp. summer March, 1 1 2 autumn July, winter 31 83 16 27 44 18 138 73 149 44 64 34 18 20 84 26 869

December, 11 2 1 3 1 2 1 16 1 2 42 2 1 2 31 2 120 Tanypodinae sp. summer March, 7 14 5 10 1 5 42 autumn July, winter 1 1

December, Malaya sp. summer March, autumn July, winter

December, 2 2 Dolichopodidae sp. summer March, autumn July, winter 4 4

December, Ephydridae sp. summer March, autumn Sciomyzidae sp. July, winter 1 1 2

pg. 147 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total December, 1 1 summer March, 2 1 3 autumn July, winter

Simulium December, 8 8 (Byssodon) summer griseicolle March, autumn July, winter

December, Tabanidae sp. summer March, 1 1 autumn Trichoptera 12

July, winter 1 3 4

December, Ecnomidae sp. summer March, 1 1 autumn July, winter 6 6

December, 1 1 Psychomyiidae sp. summer March, autumn Arachnida 109

July, winter 1 1 1 6 3 9 19 40

December, 3 1 16 7 18 1 5 1 1 10 63 Pontarachnidae sp. summer March, 1 2 3 6 autumn Decapoda 10

Potamonautes July, winter 1 1

depressus December, 1 1 2 depressus summer

pg. 148 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total March, 2 1 1 4 autumn July, winter

December, Potamonautes 1 1 2 unispinus summer March, 1 1 autumn Hirudinea 364

July, winter 2 1 3 6

December, Alboglossiphonia 2 1 4 1 1 5 14 sp. summer March, 2 2 autumn July, winter 1 4 4 5 5 200 4 3 226

December, Helobdella 2 1 1 7 5 15 63 1 1 5 101 stagnalis summer March, 1 1 2 autumn July, winter

December, Oosthuizobdella 3 3 sp. summer March, autumn July, winter

December, Placobdelloides 9 1 10 sp. summer March, autumn Polycheata 105

July, winter 6 4 9 3 35 12 19 16 104

December, Mesostoma sp. summer March, autumn Polychaeta sp. July, winter

pg. 149 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total December, 1 1 summer March, autumn Nematoda 4

July, winter 1 3 4

December, Nematoda sp. summer March, autumn Mollusca 1425

July, winter 3 3 1 2 1 2 3 4 1 2 13 35

December, 143 31 49 33 44 48 30 3 4 1 6 1 2 14 54 463 Bulinus tropicus summer March, 18 10 1 6 2 2 39 autumn July, winter 1 2 1 1 9 14

December, Ceratophallus 390 6 30 1 98 1 3 15 47 7 598 natalensis summer March, 2 4 6 autumn July, winter 2 19 2 8 31

December, 27 9 34 3 1 18 92 Gyraulus connollyi summer March, 19 3 22 autumn July, winter

Segmentorbis December, planodiscus summer March, 1 1 autumn July, winter 3 3

December, 1 1 Burnupia sp. summer March, autumn

pg. 150 APPENDIX C MACROINVERTEBRATE DIVERSITY

Species Season 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total July, winter 6 12 5 1 24

December, 3 1 4 Lymnaea columella summer March, 1 1 autumn July, winter 1 2 2 5

December, Lymnaea 1 4 1 6 natalensis summer March, 1 1 autumn July, winter 9 2 1 12

December, Lymnaea 1 1 truncatula summer March, 1 1 autumn July, winter 3 2 5 10

December, 1 1 2 Ferrissia cawstoni summer March, autumn July, winter 9 34 43

December, Pisidium 1 6 2 9 costulosum summer March, 1 1 autumn

pg. 151 APPENDIX C MACROINVERTEBRATE DIVERSITY

Table C2: Detailed list of the abundance and diversity indices of the macroinvertebrate taxa collected during the sampling surveys of July (winter, 2016), December (summer, 2016) and March (autumn, 2017).

Total individuals Margalef's index Pielou's evenness Shannon-Wiener Sites Season Total species (S) (N) (d) index (J') index (H') July, winter 10 59 2.21 0.67 1.54 2a December, summer 24 702 3.51 0.50 1.60 March, autumn 26 134 5.10 0.85 2.78 July, winter 17 207 3.00 0.69 1.95 2b December, summer 16 138 3.04 0.73 2.03 March, autumn

July, winter 9 59 1.96 0.83 1.83 3a December, summer 23 154 4.37 0.74 2.33 March, autumn 10 107 1.93 0.84 1.93 July, winter 11 54 2.51 0.72 1.74 3b December, summer 15 180 2.70 0.77 2.08 March, autumn 20 84 4.29 0.70 2.11 July, winter 9 100 1.74 0.64 1.40 3c December, summer 19 129 3.70 0.70 2.06 March, autumn 18 213 3.17 0.62 1.80 July, winter 8 86 1.57 0.641 1.33 3d December, summer 23 267 3.94 0.70 2.16 March, autumn 9 45 2.10 0.81 1.78 July, winter 16 270 2.68 0.57 1.59 3e December, summer 16 188 2.87 0.66 1.83 March, autumn

July, winter 12 350 1.88 0.53 1.31 4a December, summer 12 51 2.80 0.84 2.07 March, autumn

July, winter 8 331 1.21 0.47 0.98 4b December, summer 5 19 1.36 0.50 0.81

pg. 152 APPENDIX C MACROINVERTEBRATE DIVERSITY

Total individuals Margalef's index Pielou's evenness Shannon-Wiener Sites Season Total species (S) (N) (d) index (J') index (H') March, autumn

July, winter 21 223 3.70 0.86 2.62 4d December, summer 14 157 2.57 0.62 1.64 March, autumn

July, winter 12 161 2.17 0.68 1.69 5 December, summer 15 160 2.76 0.78 2.10 March, autumn

July, winter 25 244 4.37 0.68 2.20 6 December, summer 16 122 3.12 0.73 2.03 March, autumn 20 56 4.72 0.91 2.74 July, winter 15 151 2.79 0.78 2.11 7 December, summer 21 244 3.64 0.68 2.06 March, autumn 23 426 3.63 0.72 2.25 July, winter 21 427 3.30 0.53 1.62 8 December, summer 16 111 3.19 0.81 2.25 March, autumn

July, winter

9 December, summer 11 60 2.44 0.81 1.95 March, autumn

July, winter 23 251 3.98 0.71 2.24 10a December, summer 21 310 3.49 0.73 2.21 March, autumn

July, winter 23 393 3.68 0.72 2.25 10b December, summer 24 371 3.89 0.72 2.30 March, autumn

pg. 153 APPENDIX C MACROINVERTEBRATE DIVERSITY

Table C3: Detailed list of families and corresponding feeding groups of the macroinvertebrate taxa collected from the Seekoeivlei Nature Reserve during July (winter, 2016), December (summer, 2016) and March (autumn, 2017).

Family 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total Carnivorous 546

Ceratopogonidae 0 3 17 15 9 9 24 0 0 11 5 4 67 16 18 0 14 212 Chaoboridae 10 2 2 0 0 0 4 0 0 1 0 0 29 161 0 91 34 334 Collector-gatherer 249

Baetidae 30 33 0 1 1 0 16 0 0 0 0 0 44 21 1 82 0 229 Caenidae 0 5 0 0 0 0 0 0 0 0 0 0 12 1 0 2 0 20 Filter-feeder 100

Simuliidae 0 4 0 3 0 0 0 0 0 0 0 0 29 0 0 2 1 39 Haliplidae 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 Elmidae 0 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0 0 5 Sphaeriusidae 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Culicidae 0 0 0 0 0 0 0 0 1 0 0 6 1 0 0 0 0 8 Scirtidae 0 0 0 0 0 0 1 0 0 0 0 7 0 0 0 36 0 44 Grazer 1854

Crambidae 0 9 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 14 Psychomyiidae 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Bulinidae 164 6 1 7 11 4 3 3 1 0 0 8 6 2 0 2 7 225 Planorbidae 438 44 52 43 148 15 94 21 4 90 32 3 137 29 16 106 145 1417 Lymnaeidae 3 6 3 2 4 0 0 0 0 0 0 0 1 0 0 2 1 22 Ancylidae 0 0 0 0 0 0 0 0 0 0 0 0 4 2 0 6 1 13 Sphaeriidae 0 0 0 0 0 0 1 0 0 0 0 16 0 0 0 36 0 53 Hydraenidae 0 0 1 0 0 0 1 2 0 0 1 1 86 1 0 1 15 109 Omnivorous 11

Tabanidae 1 0 0 2 5 0 0 0 0 0 0 0 0 0 0 0 0 8 Potamonautidae 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 3 Parasitic/predatory 63

Hirudinea 11 4 1 1 7 3 2 1 0 1 3 16 0 2 1 9 1 63

pg. 154 APPENDIX C MACROINVERTEBRATE DIVERSITY

Family 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total Predator 3513

Coenagrionidae 3 14 25 46 10 8 23 1 0 11 7 5 18 31 10 29 22 263 Lestidae 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 1 5 Protoneuridae 0 0 1 0 1 2 0 0 0 2 0 1 0 0 0 0 1 8 Aeshnidae 21 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 22 Corduliidae 0 0 4 0 5 8 0 0 0 0 0 2 0 0 0 0 0 19 Libellulidae 15 0 3 0 10 1 0 0 0 3 6 0 1 0 1 1 0 41 Aphelocheiridae 0 0 2 0 0 0 0 0 0 0 0 0 1 2 0 1 1 7 Belostomatidae 31 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 34 Hebridae 0 0 0 0 0 0 0 1 0 7 3 0 0 0 0 0 3 14 Gerridae 0 0 10 5 33 52 67 26 160 26 92 2 2 0 0 0 9 484 Leptopodidae 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 7 0 11 Mesoveliidae 0 2 31 35 55 20 139 89 150 46 106 1 7 21 2 115 28 847 Naucoridae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Nepidae 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Notonectidae 6 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 5 13 Pleidae 17 7 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 32 Saldidae 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 0 0 7 Aspidytidae 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 3 0 5 Dytiscidae 8 2 21 13 7 34 5 206 17 74 8 6 4 9 0 10 42 466 Gyrinidae 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 11 0 12 Dolichopodidae 2 0 0 0 0 9 0 1 0 0 0 0 0 0 0 0 0 12 Typhloplanidae 0 0 0 6 4 9 3 35 12 19 16 0 0 0 0 0 0 104 Spercheidae 9 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 10 Corixidae 26 35 60 36 50 51 32 6 3 8 1 8 1 2 0 16 67 402 Ecnomidae 0 2 28 38 69 16 16 0 0 17 5 4 89 209 7 11 182 693 Scavenger 339

Ephydridae 0 0 6 30 1 98 0 1 0 5 15 51 0 1 0 0 16 224 Hydrophilidae 1 0 0 10 0 34 0 3 1 19 0 21 0 0 0 4 8 101 Sciomyzidae 2 0 0 1 0 0 0 0 0 9 0 1 0 0 0 0 1 14

pg. 155 APPENDIX C MACROINVERTEBRATE DIVERSITY

Family 2a 2b 3a 3b 3c 3d 3e 4a 4b 4d 5 6 7 8 9 10a 10b Total Scraper 189

Chironomidae 80 87 0 2 0 0 1 0 0 9 0 0 1 2 0 6 1 189 Shredder 569

Curculionidae 1 43 48 18 9 18 25 4 0 21 20 28 132 25 3 16 158 569

pg. 156