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EFFECTS OF DDT ON AQUATIC ORGANISMS IN THE LUVUVHU RIVER

by

KERRY ANNE BRINK

Submitted in fulfillment of the requirements for the degree

PHILOSOPHIAE DOCTOR

in

Aquatic Health

Department of Zoology

at

University of Johannesburg

November 2009

Supervisor: Professor J.H.J. van Vuren (UJ) Co-Supervisor: Professor R. Bornman (UP)

AFFIDAVIT: MASTER'S AND DOCTORAL STUDENTS TO WHOM IT MAY CONCERN

This serves to confirm that I e.if\A Prinr &ink Full Name(s) and Surname

ID Number SOS %O

Student number CiaC00100 enrolled for the

Qualification PHD (\%/)..0;tic, Faculty Science 0Cf)-ArkliIrri* d\- ZrOCAC104, A Herewith declare that m y acadetnic work is in line with the Pla g iaris P4lic y of the Universit y of Johannesburg which I am familiar.

I further declare that the work •resented in the ekorc,\ (minor dissertation/dissertatio is authentic and ori g inal unless clearly indicated otherwise and in such instances full reference to the source is acknowled ged and I do not pretend to receive an y credit for such acknowled ged quotations, and that there is no cop yrig ht infrin gement in m y work. I declare that no unethical research practices were used or material gained throug h dishonesty. I understand that pla g iarism is a serious offence and that should I contravene the Pla g iarism Policy notwithstandin g sig nin g this affidavit, I ma y be found g uilty of a serious criminal offence (perj ury) that would amon gst other conseq uences compel the UJ to inform all other tertiar y institutions of the offence and to issue a correspondin g certificate of reprehensible academic conduct to whomever request such a certificate from the institution.

Signed at Johannesburg on this 1 6 of tfnGi i1 2010 . Signature Print name xtftq Er;trk

SHANAZ KHAN BRANCH MANAGER ■ aledban& Untied , Reg No 1951/000009/06 45 UPPER MALL, CNR HYDE PARK SHOPPING CENTRE, HYDE PARK MAGISTERIAL DISTRICT OF JOHANNESBURG COMMISSIONER OF OATHS EX OFFICIO

STAMP COMMISSIONER OF OATHS Affidavit certified by a Commissioner of Oaths This affidavit conforms with the requirements of the JUSTICES OF THE PEACE AND COMMISSIONERS OF OATHS ACT 16 OF 1963 and the applicable Regulations published in the GG GNR 1258 of 21 July 1972; GN 903 of 10 July 1998; GN 109 of 2 February 2001 as amended. Abstract

The toxicant dichlorodiphenyl-trichloroethane, commonly known as DDT, is a broad spectrum insecticide and is currently banned in most countries due to its toxic effects. However, in some countries restricted use of DDT has been authorized as an effective vector control within malarial control programmes. South Africa is one such country, where spraying of DDT occurs in three provinces including the Limpopo Province, KwaZulu Natal and Mpumalanga. Specifically in the Limpopo Province, spraying of DDT has been ongoing for almost 56 years within the eastern malaria belt of the province. Despite this long term spraying there is still a scarcity of data regarding DDT and its effects on indigenous aquatic organisms in South Africa. Any research regarding DDT will therefore be of the utmost value.

It was in this context that the present study was initiated, which primarily aimed to assess the extent of contamination within DDT sprayed areas in South Africa and the associated effects on indigenous species, whilst identifying techniques that could be used in future monitoring of these areas. This assessment was done in the Luvuvhu River catchment at three reference sites and four exposure sites situated within the areas where indoor residual spraying of DDT is done annually.

At these sites the extent of DDT contamination within the water, sediment and biota (using the bioindicator species C. gariepinus from only the lentic sites) in the Luvuvhu river was evaluated. The results showed that DDT concentrations were well above recommended levels in all three of the measured phases, with the highest concentrations predominantly observed at the Xikundu weir. This site was particularly impacted by DDT due to a combination of its close proximity to the DDT sprayed areas, concentration accumulation from upstream sources and environmental conditions that accentuated contamination. These elevated levels of DDT did, however, not induce significant quantifiable effects in the bioindicator C. gariepinus or in the fish and macro-invertebrate community structures.

Specifically, the effects in the catfish, C. gariepinus, were assessed using a range of biomarkers specific to the endocrine disrupting effects of DDT, including indirect measures of vitellogenin (calcium, zinc, magnesium and alkali-labile phosphate (ALP) that are all present on the VTG molecule in high abundances), gonad-somatic index (GSI), condition factor (CF), analysis of covariance (ANCOVA) manipulated gonads, protein carbonyls (PC) and intersex. Although none of these biomarkers could be significantly correlated with the DDT contaminations, DDT was shown to induce a slight sub-organismal effect by slightly inducing the synthesis of ALP and Ca as well as reducing the gonad mass (shown by GSI and adjusted gonad mass biomarkers) and body condition. In contrast, the fish and macro- invertebrate communities showed no conclusive relationship with DDT contamination, using a variety of methodologies, including informal assessments, univariate diversity indices,

ii multivariate statistics, abundance models, fish response assessment index (FRAI) as well as average score per taxon (ASPT) and Ephemeroptera, Plecoptera and Trichoptera (EPT) richness. In conclusion, it was shown that DDT concentrations within the Luvuvhu River only induced effects at the lower levels of complexity, which highlights the importance of the utilisation of biomarkers to measure more subtle long-term effects as compared to the usage of community level effects.

In order to validate the biomarkers measured in the field, the fish species C. gariepinus was chronically exposed to environmentally relevant concentrations of p,p'-DDT, including 0.659 pg/I, 1.36 pg/I, 2 pg/I and 2.724 pg/I. The results proved to be of significant value in evaluating the dose-response relationship and identifying the baseline levels of a suite of biomarkers for the indigenous C. gariepinus exposed to DDT concentrations. These results in turn contributed toward identifying biomarkers that could be successfully utilised for future monitoring of DDT within South Africa, which included calcium, ALP, intersex, condition factor, GSI and ANCOVA treated gonads. Furthermore, within the laboratory exposures of C. gariepinus, the reproductive success of the adults and associated juveniles were also evaluated using hatching success and survival rates as measurable endpoints. Although a response was evident, too many variables influenced the responses of the exposed juveniles for a definite conclusion to be given as to the effects of DDT on juveniles.

In addition to the assessment of the effects of DDT on the aquatic ecosystems, other activities were also identified to have inducted an effect. These included effects related to agriculture and afforestation in the upper reaches as well as effects associated with the rural communities in the middle reaches. However, with appropriate monitoring and management of the catchment, it was shown that these activities can have a reduced impact.

In conclusion, although DDT concentrations were present in the water, sediment and adipose tissue of the fish species C. gariepinus, the effects related to these concentrations were generally small. Only subtle changes related to DDT were evident sub-organismally with no changes present within the community structures of both fish and macro- invertebrates. Despite these low level impacts, it is recommended that DDT contamination is monitored and managed effectively during the continuation of DDT spraying within South Africa's vector control programme. This is particularly important when considering that C. gariepinus was shown to exhibit a higher tolerance to DDT in contrast with other studies that have shown larger impacts related to DDT in more susceptible species 0. mossambicus as well as human populations within the Luvuvhu River catchment.

Table of contents

CHAPTER 1. INTRODUCTION 1.1 INTRODUCTION 2 1.2 BACKGROUND AND LITERATURE REVIEW OF DDT 2 1.2.1 DDT 2 1.2.2 History of DDT in South Africa 2 1.2.3 Fate of DDT in aquatic ecosystems 3 1.2.4 Effects of DDT in aquatic ecosystems 5 Background 5 Reproductive effects of DDT 6 1.2.5 Biomonitoring contamination and effects 9 Biomonitoring 9 Bioindicators 10 1.3 RATIONALE OF THE STUDY 11 1.4 AIMS AND OBJECTIVES 12 1.5 STRUCTURE OF THESIS 13 1.6 REFERENCES 14

CHAPTER 2. DESCRIPTION OF STUDY AREA 2.1 GENERAL DESCRIPTION OF THE LUVUVHU RIVER CATCHMENT 20 2.2 POSSIBLE IMPACTING FACTION IN THE LUVUVHU RIVER 20 2.1.1 DDT Spraying 20 2.1.2 Rural Communities 21 2.1.3 Sewage Treatment Plants 21 2.1.4 Forestry and Agriculture 21 2.1.5 Mining 22 2.1.6 Dams and Weirs 22 2.3 SAMPLING SITES 23 2.4 REFERENCES 28

CHAPTER 3. DESCRIPTION OF HABITAT IN LUVUVHU RIVER 3.1 INTRODUCTION 31 3.2 METHODS 31 3.3 RESULTS 32 3.3.1 Habitat description of each site 33 3.3.2 Major impacts on the habitat 37 3.4 DISCUSSION 40 3.5 REFERENCES 41

CHAPTER 4. PESTICIDE AND METALCONTAMINATION IN THE LUVUVHU RIVER 4.1 INTRODUCTION 44 4.2 MATERIALS AND METHODS 44 4.2.1 Water analysis 44 Physico-chemical and nutrient analyses 44 Pesticide and metal analysis 45 4.2.2 Sediment analysis 46 Sediment composition 46 Pesticide and metal analysis 46 4.2.3 Biota analysis 47 Selection criteria of male C. gariepinus 47 Sampling 48 Fish Biology: mass, length and age 49

iv

d. Pesticides and metal analysis 49 4.2.4 Statistics 50 4.3 RESULTS 50 4.3.1 Water analysis 50 Physico-chemical parameters and nutrients 50 DDT and pesticide concentrations 53 Metal analysis 55 4.3.2 Sediment analysis 57 Sediment properties 57 DDT and pesticide analysis 59 Metal analysis 62 4.3.3 Bioaccumulation 64 Fish Biology 64 DDT Analysis 65 Metal Analysis 67 4.4 DISCUSSION 69 4.4.1 Water analysis 69 a. Physico-chemical and nutrient concentration 69 b. DDT and pesticide concentrations 70 c. Metal concentrations 72 4.4.2 Sediment analyses 73 a. Sediment properties 73 b. DDT and pesticide concentrations 74 c. Metal concentrations 75 4.4.3 Bioaccumulation 76 Fish biology 76 DDT and pesticide concentrations 76 d. Metal concentrations 78 4.5 REFERENCES 80

CHAPTER 5. SUB-ORGANISMAL EFFECTS OF DDT IN C. GARIEPINUS 5.1 INTRODUCTION 90 5.2 METHODS 94 5.2.1 Field Procedures 94 5.2.2 Laboratory Procedures 94 Alkali-labile phosphate 94 Plasma metal analysis 95 Protein carbonyls 95 Gonad condition 96 Histology 96 Condition factor 96 5.2.3 Statistics 96 5.3 RESULTS 96 5.3.1 Vitellogenin 96 5.3.2 Protein carbonyls 98 5.3.3 Gonad condition 99 5.3.4 Intersex 101 5.3.5 Condition factor 101 5.4 DISCUSSION 102 5.4.1 Vitellogenin 102 5.4.2 Protein carbonyls 103 5.4.3 Gonad condition 105 5.4.4 Intersex 107 5.4.5 Condition factor 108

v

5.5 REFERENCES 108

CHAPTER 6. LABORATORY EXPOSURES OF C. GARIEPINUS OF p,p-DDT 6.1 INTRODUCTION 117 6.2 METHODS 117 6.2.1 Experimental design 117 Advantages and disadvantages of C. gariepinus in toxicity testing 117 Culture and handling of test species 118 Approach 118 6.2.2 Test Conditions 118 6.2.3 Adult Exposure 119 Sample collection 120 Sample analyses 120 6.2.4 Juvenile Exposure 120 6.2.5 Statistics 121 6.3 RESULTS 121 6.3.1 Adult male biomarkers 121 Fish Biology 121 Vitellogenin 122 Protein carbonyls 123 Gonad condition 124 Intersex 124 Condition factor 125 6.3.2 Juveniles 125 6.4 DISCUSSION 127 6.4.1 Biomarkers 127 Vitellogenin 127 Protein carbonyls 128 Gonad condition 128 Intersex 129 Condition factor 129 6.4.2 Juveniles 130 Hatching success 130 Juvenile survival 131 6.5 REFERENCES 131

CHAPTER 7. DETERMINATION OF A SUITABLE SUITE OF BIOMARKERS FOR BIOMONITORING 7.1 INTRODUCTION 137 7.2 METHODS 137 7.3 RESULTS AND DISCUSSION 137 7.4 REFERENCES 140

CHAPTER 8. THE EFFECTS OF DDT ON FISH COMMUNITIES 8.1 INTRODUCTION 142 8.2 METHODS 144 8.2.1 Sampling 144 8.2.2 Data analysis 145 Occurrence of fish 145 Relative abundance 145 Non-parametric diversity indices 146 Abundance models 146 FRAI 148

vi 8.2.3 Statistics 150 8.3 RESULTS 151 8.3.1 Occurrence of fish 151 8.3.2 Informal assessment of number of species and relative abundances 152 8.3.3 Species diversity indices 153 8.3.4 Abundance models 157 8.3.5 FRAI 158 8.3.6 Multivariate statistics 161 8.3.7 Factors influencing fish abundance and diversity 165 8.4 DISCUSSION 167 8.4.1 Fish community changes 167 a. Latonyanda River 167 b. Hasana 168 c. Tshikonelo 169 d. Xikundu 170 e. Mhinga 171 8.4.2 Fish community assemblage methodologies 172 8.5 REFERENCES 174

CHAPTER 9. MACRO-INVERTEBRATE COMMUNITIES 9.1 INTRODUCTION .179 9.2 METHODS 181 9.2.1 Sampling 181 9.2.2 Data treatment 181 a. Occurrence of macro-invertebrates 181 b. Relative abundance 181 c. ASPT and EPT (Ephemeroptera, plecoptera, trichoptera) richness 181 d. Non-parametric diversity indices 182 9.2.3 Statistics 182 9.3 RESULTS 182 9.3.1 Occurrence of macro-invertebrate families 182 9.3.2 Informal assessments of number of taxon and relative abundances 183 9.3.3 Sensitivity scores 184 9.3.4 Ephemeroptera, plecoptera and trichoptera (EPT) richness 187 9.3.5 Species diversity 187 9.3.6 Multivariate statistics 188 9.3.7 Factors influencing macro-invertebrate abundances and diversity 194 9.4 DISCUSSION 195 9.4.1 Macro-invertebrate community changes 195 a. Latonyanda River 195 b. Hasana 196 c. Tshikonelo 196 d. Xikundu 197 e. Mhinga 198 9.4.2 Macro-invertebrate community assemblage methodologies 198 9.5 REFERENCES 199

CHAPTER 10. GENERAL DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 10.1 INTRODUCTION 204 10.2 CURRENT STATUS OF THE LUVUVHU RIVER 204 10.2.1 Summary of the possible current impacts evaluated 204 Physical habitat 204 Physico-chemical and nutrient analysis of water 204 DDT contamination in water, sediment and C. gariepinus 205

VII

d. Pesticide and metal screen 205 10.2.2 Summary of the current effects in fish 206 a. Sub-organismal effects 206 b. Community effects 206 c. Level of biological complexity 207 10.2.3 Current impacts in macro-invertebrates 207 a. Community effects 207 10.3 LABORATORY ANALYSIS 207 10.4 A SUITABLE SUITE OF BIOMARKERS FOR MONITORING 208 10.5 IDENTIFY EFFECTIVENESS OF COMMUNITY LEVEL MEASUREMENTS 209 10.6 GENERAL CONCLUSIONS AND RECOMMENDATIONS 209 10.6.1 Major concerns in the Luvuvhu River catchment 209 Risk of DDT bioaccumulation and biomagnification 209 Effects of DDT on aquatic biota 209 Agriculture and afforestation 210 Rural village communities 210 10.6.2 Incorporating biomarkers in South Africa 211 10.6.3 Considerations for biomonitoring in the Luvuvhu River 212 10.6.4 Recommendations for future biomonitoring in IRS areas 213 10.6.5 Significance of laboratory work 213 10.6.6 Additional limitations of this study and recommendations for future research 214 10.5 REFERENCES 215

APPENDICES APPENDIX 1 216 APPENDIX 2 229 APPENDIX 3 224 APPENDIX 4 226 APPENDIX 5 228 APPENDIX 6 229 APPENDIX 7 232

viii Abbreviation Table

AChE Acetylcholinesterase IHI Index of Habitat Integrity ALP Alkali-labile phosphate IRS Indoor residual spraying ANCOVA Analysis of Covariance LF Low flow ANOSIM One-way analysis of similarity LS Log series models ANOVA Analysis of variance MCDA Multi Criteria Decision Analysis Approach ASPT Average score per taxon MDA Malondialdehyde ATSDR Agency for toxic substances and disease registry Mg Magnesium BOD Biological oxygen demand MIRAI Macro-invertebrate response assessment index BS Broken stick models N Abundance of species BSA Bovine Serum Albumin NMDS Non-metric multi-dimensional scaling Ca Calcium NOAA National Oceanic and Atmospheric Association CCME Canadian Council of Ministers of the Environment NTMP National Toxicants Monitoring Programme CF Condition factor Q p'-D DT 1, 1 , 1 -trichloro-2-(o -chlorophenyI)-2-(p chlorophenyl)-ethane COD Chemical oxygen demand OC Organochlorine CPUE Catch per unit effort OP Organophosphate DDD Tetrachlorodiphenylethane P Probability DDE Dichlorodiphenyl-dichloroethane p,p'-DDD 1,1-dichloro-2,2-bis(p chlorophenyl) ethane DDT Dichlorodiphenyl-trichloroethane, p,p'-DDT 1,1 ,1-trichloro-2,2-bis(p -chlorophenyl df Degrees of freedom PC Protein carbonyls DWAF Departement of Water Affairs and Forestry, now DWA PCA Principle component analysis (Department of Water Afffairs) EDC Endocrine disrupting chemicals PCB Polychlorinated biphenyls ELISA Enzyme linked immunosorbent assay POP Persistent organic pollutants EPT Ephemeroptera, Plecoptera Trichoptera RHP River Health Programme EXTOXNET Extension Toxicology Network S Number of species FAIT Fish assemblage integrity index SASS South African scoring system FO Frequency of occurrence SE Standard error FRAI Fish response assessment index SIMPER Similarity percentages GC-MS Gas chromatography mass spectrophotometer TDS Total dissolved salts GnRh Gonadotrophin releasing hormone TLN Truncated log normal models GS Geometric series models TOC Total organic carbon GSI Gonad somatic index TWQR South African target water quality range GSM Gravel sand and mud UJ University of Johannesburg GST Glutathione-s-transferase USEPA US environmental protection agency HCI Hydrochloric Acid UV Ulta-Violet Rays HCR fish habitat availability index VTG Vitellogenin HD Historical data WHO World Health Organisation HF High flow Zn Zinc ICP-MS Inductive coupled plasma mass spectrophotometer

ix Acknowledgments

I would like to first and foremost acknowledge and thank the NRF and University of Johannesburg for the three year funding I received during this project and to the people that made this project possible, including Prof van Vuren, Prof Bornman and Dr Barnhoorn, for which I am very grateful.

Then a special thank you to my loving husband, Emile Brink! Thank you so very much for your undying support during this project. Your patience, incredible encouragement, help during those slimy catfish exposures in the early hours of the morning, editing of this PhD, and scientifically minded discussions are just a few of the many, things you've done for me during this project. Words can't express my gratitude to you!

Then to the many people that have helped and supported me during this project, including my mom Karen, Kieren, Richard, Giela, Liesel, Liam, Taneshka, Bridget, Solly, Lizl, Jessica, Gillian, and my parents in-law Anne-Marie and Pieter. Chapter 1 Introduction

1.1 INTRODUCTION 2 1.2 BACKGROUND AND LITERATURE REVIEW OF DDT 2 1.2.1 DDT 2 1.2.2 History of DDT in South Africa 2 1.2.3 Fate of DDT in aquatic ecosystems 3 1.2.4 Effects of DDT in aquatic ecosystems 5 a. Background 5 b. Reproductive effects of DDT 6 1.2.5 Biomonitoring contamination and effects 9 a. Biomonitoring 9 b. Bioindicators 10 1.3 RATIONALE OF THE STUDY 11 1.4 AIMS AND OBJECTIVES 12 1.5 STRUCTURE OF THESIS 13 1.6 REFERENCES 14 Chapter 1

1.1 INTRODUCTION

In consideration of the following background and literature review, the rationale of this study was determined along with the aims, objectives and structure of this thesis.

1.2 BACKGROUND AND LITERATURE REVIEW OF DDT

1.2.1 DDT

The toxicant dichlorodiphenyl-trichloroethane, commonly known as DDT, is classified as an organochlorine (as it is an organic compound that contains chlorine atoms) as well as a persistent organic pollutant (POP) (as it is a toxic substance that resists degradation and can severely affect human and environmental health globally) (Bouwman, 2004). DDT is characterised as a white powder, with little to no odour as well as other physical and chemical properties, which can be observed in Appendix 1 (ATSDR, 2002). It metabolises to DDE and DDD and exists in two forms p,p'- and o,p'-DDT, which are degraded to the respective DDE and DDD isomers (these will all commonly be referred to as DDT in the present study, unless otherwise specified) (Kime, 1998). p,p'-DDT is a synthetic compound that was first discovered in 1939 to have insecticidal properties. This compound is commonly referred to as DDT because the technical grade DDT (sprayed as a pesticide) is primarily composed of the active ingredient p,p'-DDT (1,1,1- trichloro-2,2-bis(p-chlorophenyl), making up 65-80% of the pesticide. The remaining percentage is made up of its isomer o,p'-DDT (1,1,1-trichloro-2-(o-chlorophenyI)-2- (pchloropheny1)-ethane) (15-21%) and its metabolite p,p'-DDD (1,1-dichloro-2,2- bis(pchlorophenyl) ethane) (approximately 4%). It is a broad spectrum insecticide that was once very popular due to its effectiveness, long residual persistence and low cost in agricultural practices, but is now only used in the control of disease vectors due to its toxicity (ATSDR, 2002).

1.2.2 History of DDT in South Africa

In the early 20th century the organochlorine pesticide DDT was used as an effective pest control within the agricultural sector of most countries worldwide. In South Africa, along with its agricultural use, DDT was also incorporated into a malarial control programme as it was relatively inexpensive; easy to produce, distribute and utilise; and was highly effective (Wells and Leonard, 2006). In this programme, technical grade DDT was applied to the interior of village huts via spraying called indoor residual spraying (IRS) (Tren and Baste, 2004; Metcalf, 1995). This programme proved to be a major success, with rapid declines in malarial incidences observed throughout the three provinces (now known as KwaZulu Natal, Limpopo and Mpumalanga), which were mostly influenced by this disease (Department of Health, South Africa, 2002).

2 Chapter 1

In the early 1970's the use of DDT for agricultural practices was banned worldwide, as it was shown by scientists to be highly persistent and toxic to humans and wildlife (Falandysz, 1994). South Africa was one of the countries that incorporated this ban within the agricultural sector, but continued to spray DDT in all of the provinces due to its effective eradication of malaria. However, in 1996 the South African government changed their policy and utilised deltamethrin as an alternative to DDT in some of the provinces. The literature regarding the time frames and the provinces that underwent this transformation was unfortunately inconclusive. Mabaso et al. (2004) made no mention of DDT spraying within Mpumalanga and Limpopo during the transition to pyrethroids in 1996, but did state that KwaZulu Natal specifically used the pyrethroid deltamethrin in 1997-1999 and that utilisation of DDT in IRS was reinstated in 2000, due to vector resistance. Gerritsen at al. (2008) generalised and said that in South Africa IRS has used DDT and pyrethroids in the past, and that DDT spraying was "scaled up since 2000". Tren and Bate (2004) mentioned that DDT spraying was stopped in 1996 in KwaZulu Natal and Mpumalanga and in 1999 in the Limpopo Province. In contrast, monthly reports done by the Department of Health (2002) suggested that DDT spraying was stopped in all three provinces, but never elaborated as to the time frame in which it had occurred. In contrast, Bornman et al. (2009) stated that in the Limpopo province, DDT spraying was introduced in 1945 and since 1966 has been sprayed annually. Despite these inconsistencies in the literature, it is still evident that DDT spraying was relatively continuous over the last 60 years in all of the provinces.

In 2004, a global, multilateral agreement known as the Stockholm convention on POPs came into force with the aim of protecting human and environmental health from the effects of POPs (such as DDT) worldwide by enforcing restrictive use and production or banning of these substances (Bouwman, 2004). Although South Africa is a party to this convention, an exemption for the use of DDT as a malaria control was secured on the premise that alternatives be incorporated where and when possible in order to eventually lead to the elimination of DDT as a vector control (Tren and Bate, 2004).

1.2.3 Fate of DDT in aquatic ecosystems

Upon entering the environment DDT distributes itself between the various phases within the environment including atmosphere, water, sediment, biota and vegetation similar to other organochlorines (OCs). Many factors can influence the distribution between these phases and are all reviewed extensively by Nowel et al. (1999). However, the most notable factor that influences the distribution of the contaminants within and between the environmental phases is the partition coefficient of contaminants. For instance, the organic-bases of DDT generally have a higher partitioning coefficient than the organic-bases of other contaminants such as heavy metals. This characteristic allows such compounds to absorb strongly in lipid/organic-rich environments such as biota, vegetation and sediment. This together with DDTs extremely long half life (reviewed in ATSDR (2002)), which results from it is resistant to volatilisation, leaching or degradation, makes DDT highly persistent and present in

3 Chapter 1

normally much greater concentrations in the sediment and biota than in the water and atmosphere (ATSDR, 2002; Kime, 1998).

In the aquatic environments DDT initially enter the aqueous phase and then redistributes into the sediment, biota or vegetation. When contaminants in the water enter the sediment phase desorption occurs into the pore water, the overlying water or the organic fraction of the sediment. Contaminants then remain in this sediment phase until certain conditions allow them to transform and/or to redistribute back into the aqueous phase or into the biological phase. Consequently, uptake in the biological phase can occur from both the water and sediment (otherwise known as bioconcentration) from biologically available (bioavailable) OCs, which are influenced by physical, chemical or biological differences (van der Oost et al., 2003). Thereafter, Uptake occurs by passive diffusion via semi-permeable membranes including gills, mouth, and gastro-intestinal tract (USEPA, 2000). Another significant route of uptake into organisms is via the food chain. Otherwise known as biomagnification, DDT can be absorbed from the prey of carnivorous species via passive diffusion from the gastrointestinal tract.

Despite the route of uptake, once within the biota, DDT can be eliminated, transformed and/or transported to various tissues for sequestration. Although DDT elimination is often negligible in biota due to its lipophilic nature, according to Nowell et al. (1999) OC with such characteristics could be eliminated by the kidney through the urine or transferred to the juveniles (Nowel et al., 1999). However, a major influencing factor in the elimination of a contaminant is the presence of other contaminants. Johnson (1973) concluded this from the findings of a study by Macek et al. (1970) that found that dieldrin residues inhibited the elimination of DDT. Nevertheless, if contaminants are not directly eliminated the aquatic organisms attempts to transform and/or sequester the compound. In the biotransformation process contaminants are either altered by inserting a functional group, which would increase their reactivity known as phase I transformation, or by reacting them with highly polar species to form more soluble compounds that are more readily excreted (phase II transformation) (Nowell et al., 1999). Of the various DDT metabolites, DDE is the most resistant to such transformation and is therefore often more persistent than the other metabolites. This is primarily due to the formation of halogen atoms that are induced during metabolism from DDT to DDE, which tends to prevent oxidation and other biotransformation processes (Walker et al., 2001; Paasivirta, 1991). Depending on the success of the transformation and excretion of contaminants, the organism then can store the DDT in an inert tissue such as in the adipose tissue, bone, or scales, which reduces its general circulation and becomes relatively inert in the biological phase. However, the storage in adipose tissue, particularly apparent in DDT, does not always guarantee permanent sequestration within an organism, as adverse conditions may lead to the utilisation of these lipid reserves and hence the release of contaminants back into the blood stream (Connell et al., 1999).

4 Chapter 1

1.2.4 Effects of DDT in aquatic ecosystems

a. Background

When exposed to sub-lethal concentrations of most contaminants, aquatic organisms do not bioaccumulate without inducing an effect, which vary in biological complexity depending on degree of exposure (duration and concentration) and to a lesser extent on the exposure mode (e.g. skin contact and ingestion), susceptibility of organism to toxicants (life cycle stage, specificity of species, excretion rates, genetic selection, stress, metabolic rates, accessibility to toxicant) and toxicant properties (Murray et al., 2003). Generally, when a foreign chemical is introduced into an organism it initially exerts effects on sub-cellular molecules such as genes, proteins and enzymes. Then if exposure continues, further alterations occur that start to influence cellular structures and functioning such as energy expenditure or secretion of a hormone. When these changes are severe enough the structure of the organs deteriorate, which lead to effects on the organ's functioning. Once the organs are affected the organism's general health, growth, ability to reproduce and function normally reduce, which in turn leads to changes observed within the populations and communities (Vasseur and Cossu-Leguille, 2006; Arcand-Hoy and Benson, 1998).

Populations and communities can either be directly or indirectly influenced. Direct influences are related to the lethal effect on individuals within a population and community. According to Connell et al. (1999), when populations are exposed to high lethal concentrations they may respond by reducing or rising in abundance relative to their tolerance. A species which is more sensitive may be eliminated (or show reduced abundance) before a more tolerant species is, which may increase in abundance in the absence of competitors. These alterations consequently influence the entire community structure within an ecosystem. In contrast to lethal concentrations, when organisms are exposed to low sub-lethal concentrations the contaminants may not necessarily eliminate a species directly, but may indirectly alter population structures and survival of species. That is, when an individual is exposed to sub-lethal concentrations, contaminants may influence the reproductive output, the recruitment and/or growth that can lead to a reduction in the abundance of species within population and changes in the community. Nevertheless, whether the influence on the populations is direct with lethal concentrations or indirect with sub-lethal concentrations, the resulting effects are generally irreversible and long-term and therefore according to many reviews much effort must be taken to avoid such changes. In order to prevent these high level impacts it is recommended that the effects of lower levels of biological complexity be measured as early warning signs of stress. This is commonly done using biological indicators called biomarkers (Connell et al., 1999).

Biomarkers, as defined by Hyne and Maher (2003), are indicators of sub-lethal changes in organisms resulting from exposure to contaminants xenobiotics. Apart from their ability to measure the extent of sub-organismal effects and act as short term indicators of future, more

5 Chapter 1 ecologically relevant effects (as shown above), have numerous other advantages. These include their ability to provide information on the relative toxicity of chemicals in both the field and laboratory, to provide temporally and spatially integrated measures of bioavailable pollutants that can detect intermittent pollution effects. Further to this they can also be used to identify specific contamination, if the responses are specific to a particular contaminant (called biomarkers of exposure i.e. acetylcholine esterase is a specific biomarker of organophosphates and carbamates, metallothioneins of metals and delta-aminolevulinic acid of lead (Slabbed et al., 2004)). Despite these advantages there are also some disadvantages that need to be considered. Firstly, natural abiotic and biotic factors interfere with the biomarker responses. Secondly, contaminants interact with each other causing interference with the organisms' responses. Thirdly, some biomarkers are not sensitive enough to detect pollutant exposure or effects at environmentally realistic concentrations and lastly biomarkers often require extensive technical expertise (Moolman, 2004; Amiard et al., 2000; Lagadic et al., 2000; Connell et al., 1999). However, these limitations can be overcome by utilising a suite of biomarkers that considers different aspects of the organisms' responses.

b. Reproductive effects of DDT

When organisms are exposed to DDT (as well as most other OC) a cascade of biological effects start (as explained above) predominantly with endocrine disrupting mechanisms, such as altering hormone secretion, interfering with hormone-receptor interaction or modifying the metabolism of circulating hormones and are therefore known as endocrine disrupting chemicals (EDCs) (Rodrigues et al., 2007). These changes, depending on the DDT isomer and metabolite present, in turn induce alterations in the functioning of the reproductive system, nervous system, behaviour and immune systems; resulting in many different biological systems experiencing effects. Many of these effects, and the mechanisms in which they formed within aquatic organisms, have been reviewed by large organisations such as USEPA (1997, 2006) and NOAA (2002) as well as Huang et al. (2003), Kime (1998) and Vouk and Sheehan (1983).

Regardless of these varying effects, the majority of the effects due to DDT and its isomers are usually found to occur on the reproductive functioning. This is because the endocrine system is largely related to reproduction, which may manifest at various life-history stages (USEPA, 1997). In some cases the lethal or sub-lethal effects are observed directly in the life history stage which is exposed, while in other situations the sub-lethal effect of lower life stages is observed in later stages or in the progeny of exposed parents (Vouk and Sheehan, 1983). These can ultimately lead to changes in the organisms' population and in turn the aquatic communities. However, according to a review by Mills and Chichester (2005) such deductions should be done with caution, because even though there is strong evidence that EDCs such as DDT can affect the reproductive health of aquatic organisms (including fish and invertebrates), mainly through endocrine disrupting effects, it is not to say that these

6 Chapter 1 organismal impacts will influence the population levels. For example, a study by Price and Depledge (1998) showed in polychaete worms exposed to another EDC (4-n-nonylphenol) that although there were endocrine induced changes in egg production and egg viability there was no subsequent effects present on populations. Furthermore, very few studies linking the endocrine disruptive effects of individuals with the consequences at population and community level have been done. According to Ford et al. (2007) the reason for this is four-fold. Firstly, there is not enough data available to create viable models for the population level effects of endocrine disruption. Secondly, reproductive disorders are mainly investigated in species that are difficult to model the populations. Thirdly, there is a general lack of population ecologists within this field and lastly, there is often a lack of funding for research. According to many, the best documented case within the aquatic ecosystem of endocrine disruption causing alterations in populations was in a study by Bryan and Gibbs (1991). The authors showed a causal decline in the populations of gastropods exposed to tributyltin, which is associated with the imposex phenomenon. For further review of the existing literature available on the potential of endocrine disruption to induce population level effects on aquatic organisms refer to Taylor et al. (1999) and Taylor and Harrison (1999).

Even though endocrine changes (i.e. sub-organismal changes) can not always predict higher level changes, measurements of changes using biomarkers are often more responsive to subtle chronic effects of EDCs (such as DDT) and therefore can be used to successfully show sub-organismal effects. In fact, because EDCs are such high profile contaminants they have attracted a vast amount of attention, with many biomarkers been shown to identify sub-organismal effects caused by EDCs and have been reviewed by many including large organisation such as USEPA (1997) and NOAA (2002). Briefly, these sub-organismal effects from EDCs may include anything from detoxifying enzymes, to those related to oestrogen mimicking in males, oxidative stress, and other hormones, proteins, and enzymes from numerous areas within the body, as listed in Table 1.1.

7 Chapter 1

Table 1.1. Literature review of some biomarkers used to identify exposure and toxicity of DDT contamination in aquatic biota.

Biomarker Mechanism of action comments Literature

Oestrogen mimicking in males

Zona radiata Synthesis of zona radiate protein mediated by oestrogen mimicking in Arukwe et a/. males (1998)

Vitellogenin Synthesis of VTG protein mediated by oestrogen mimicking in males Cheek et a/. (2001)

Total calcium High calcium present in VTG protein and therefore an indirect Verslycke et al. measure of VTG (2002)

Serum alkali-labile phosphate High ALP present in VTG protein and therefore an indirect measure of Versonnen et al. VTG (2004)

Total protein High protein present in VTG protein and therefore an indirect measure Lv et a/. (2006) of VTG

Total magnesium High magnesium present in VTG protein and therefore an indirect Lv et a/. (2006) measure of VTG

Testosterone Presence of oestrogen mimickers reduces testosterone plasma levels Bjerselius et al., in males (2001)

11-ketotestosterone Presence of oestrogen mimickers reduces ketotestosterone plasma Bjerselius et a/. levels in males (2001)

Oxidative stress

Lipid peroxidase activity — Activated in response to oxidative stress (DDT causes oxidative Ferreira et a/. malondialdehyde (MDA) stress-apoptosis/protein inactivation) (2005)

Glutathione peroxidase activity Activated in response to oxidative stress (DDT causes oxidative Barrosa et al. stress) (1994)

Protein carbonyls Indirect measure of oxidative stress on plasma proteins. By product of Pa rvez & protein oxidation Ra isudd in (2005)

Liver catalase activity Oxidative stress causes induction of catalase Pandey et a/. (2001)

Glutathione-s-transferase Involved in metabolism of OC pesticides Porte et al. (2000) (GST)

Other

Contaminants interfere with the activity of this enzyme that catalyses Lavado et al. P450 aromatase the final rate-limiting step in the conversion of androgens to oestrogen (2004) Key enzymatic activities involved in steroid hormone synthesis and Lavado et a/. UDP-glucuronyl transferases clearance (2004) Benguira & Hontela Plasma cortisol Probably not viable as baseline concentration are high (2000) Chapter 1

1.2.5 Biomonitoring contamination and effects

a. Biomonitoring

Biomonitoring is an essential element when assessing contaminants such as DDT within aquatic ecosystems. It is the most common form of monitoring, which is defined as the regular systematic use of living organisms to evaluate changes in environment or water quality in laboratory or field by assessing either bioaccumulation, biological effects, health (occurrence of disease) and/or ecosystem integrity (van der Oost et al., 2003). The successful utilisation of biomonitoring over the traditionally used chemical monitoring occurred when it became apparent that biomonitoring procedures are able to reflect short- term events of contamination, which chemical monitoring can not do. This is particularly important in DDT monitoring as these generally lipophilic contaminants are unstable within the water phase and do not remain in this phase for long. Other advantages of biomonitoring organisms as opposed to water is that they are able to integrate overall environmental conditions, they are more readily understood by the public compared to chemical pollutant loading and they are the only practical means of evaluating management criteria for non-point source impacts such as DDT spraying (Heath, 1999).

In general, biomonitoring is classified into two different types, the biomonitoring of accumulation of contaminants and effects-based biomonitoring that incorporates biomarkers and/or ecological effects, all of which can be utilised depending on the objectives (Connell, et al., 1999). Biomonitoring the bioaccumulation of contaminants within aquatic organisms is done to determine the current health status of an ecosystem. This is not only important in terms of managing and protecting aquatic ecosystems required by the National Water Act (Act No. 24 of 1998), but also to monitor the possible health risks that contaminants may impose on humans through food intake (Heath, 1999). Nevertheless, a number of difficulties are apparent when monitoring the bioaccumulation of contaminants, particularly with the objective of managing and protecting aquatic ecosystems in mind. Firstly, bioaccumulation of a contaminant does not necessarily indicate if aquatic organisms are influenced or impacted by the contaminant. Secondly, bioaccumulation is influenced by a large range of natural fluctuations such as the size and sex, which make it difficult to determine changes in the contamination status and thirdly, bioaccumulation techniques are generally very expensive, particularly those involving OCs and other pesticides. However, all of these disadvantages can be overcome, particularly if used in second or third phase monitoring (i.e. after a potential threat has been identified, via effects monitoring), and if the bioindicator (organism) that is selected is (Phillips and Rainbow, 1994):

Minimally influenced by natural fluctuations, Representative of the study area and does not have excessive migratory patterns, Has behavioural patterns that allows the bioindicator in contact with both the water and sediment and

9 Chapter 1

• Easily obtainable within the aquatic ecosystem being monitored.

In contrast to bioaccumulation monitoring, effects-based monitoring is able to detect toxic effects of contamination within organisms and is therefore often utilised in conjunction with the monitoring of contaminant concentrations. As discussed previously the effects brought about by contaminants can be manifested at a variety of biological levels from effects within an organism to those relating to population or communities, which are measured using biomarkers and bioassessments respectively. For successful biomonitoring, these measurements must adhere to a number of criteria before they can be selected as indicators of impacts on organisms. These include a quantitative relationship should exist between the response and the contaminant dose and the response should:

Be related to an adverse effect in an organism or in populations and communities, Have sufficient sensitivity to contaminant, Not reflect short term fluctuations in contaminant availability, Be easily detectable above natural variability, and Be easy to measure in laboratory and/or field, without necessitating the use of very expensive equipment, complicated procedures or high running costs (Phillips and Rainbow, 1994).

b. Bioindicators

Regardless of the monitoring type and measurements used, a key element in biomonitoring is the indicator organism(s) used. In aquatic systems biomonitoring traditionally makes use of either fish or invertebrates and sometimes periphyton (Kotze, 2002; Barbour et al., 1999; Heath, 1999; Kime, 1998).

Fish are advantageous in that they are present in all but the most polluted waters, they are relatively long-lived thereby indicating long-term effects, they are mobile and as such are able to integrate diverse aspects of large-scale habitats. Further to this fish assemblages generally include a range of species that represent a variety of trophic levels and are generally at the top of the aquatic food chain allowing them to be reflective of integrated environmental health from lower trophic levels. They are important for assessing contamination which may affect human consumers, they are the most well known and are more likely understood by communities during management practices, they are relatively easy to identify to species level in the field so fish can be safely released unharmed, and there is generally a vast amount of information regarding fish biology, ecology, bioaccumulation mechanisms, biomarkers and ecological effects. There are however some difficulties, including the fact that sampling can be intensive with selective gear often required and the fish mobility makes point source monitoring difficult (Kotze, 2002; Barbour et al., 1999; Heath, 1999; Kime, 1998).

10 Chapter 1

Like fish, aquatic invertebrates are present in most ecosystems no matter how polluted, constitute a broad range of trophic levels and pollutant tolerances, thus providing strong information for interpreting cumulative effects, and also have vast amounts of information regarding biology, ecology, bioaccumulation, biomarkers and ecological effects, although to a lesser extent. They are however advantageous over fish in that they are generally sedentary so they are better indicators of localised conditions, they have a short life span, which allows for more rapid responses at the community level, they are easily sampled and have an acknowledged sensitivity to general impacts. They unfortunately are also disadvantaged over fish in that they are highly influenced by seasonal changes, they are small in size therefore making analyses difficult and taxonomic identification is more complex than for fish (Kotze, 2002; Barbour et al., 1999; Heath, 1999; Kime, 1998).

1.3 RATIONALE OF THE STUDY

In South Africa, DDT spraying has been on going for almost 56 years in certain parts of Limpopo, Mphumalanga and KwaZulu Natal (Section 1.2.2). Despite this long term spraying there is still a scarcity of data regarding DDT (and its metabolites DDE and DDD) and their effects on indigenous aquatic organisms in South Africa, with no standardised monitoring programmes in place, which are essential to ensure appropriate management of these contaminants. Therefore, any research regarding DDT will be of the utmost value not only for South Africa, but for most other developing countries that spray DDT with no effective monitoring in aquatic ecosystems in place. Considering this, a major research project was carried out that assessed the adverse health effects of DDT on aquatic organisms and attempted to link these effects to adverse health in humans within the DDT sprayed area of the Luvuvhu River catchment in Limpopo province (Bornman et al., 2009). The study represented in this thesis formed an integral part of this Water Research Commission (WRC) project, with particular focus on the contamination and the reproductive effects of DDT on aquatic organisms in the Luvuvhu River.

Although the current extent of DDT contamination was assessed for temporal and spatial baseline analyses in the water, sediment and biota, elevated concentrations doesn't always imply that a toxic response is created (Phillips and Rainbow, 1994). Therefore, measuring contaminant specific effects of DDT should serve as an adjunct, or perhaps even as an alternative, to measuring DDT (due to the high costs required for these analyses) in order to evaluate the current extent of DDT toxicity. The extent of effects can occur at various levels of biological complexity (i.e. from sub-cellular to organ to community level changes) depending on the concentration and duration of DDT (Amiard et al., 2000) and therefore the effects at both sub-organismal levels (measured using various reproduction-based indicators/biomarkers) in the indigenous fish species Clarias gariepinus and community levels in both fish and invertebrates were evaluated in this thesis. Furthermore, in measuring the extent of the effects related to DDT the methodologies that best define the effects could also be evaluated, so as to refine current and recommend alternative methods,

11 Chapter 1 which can ultimately be used for biomonitoring DDT in South Africa. However, although measuring methods in field conditions are essential for defining appropriate methodologies, laboratory studies are particularly advantageous in that they offer more controlled conditions (Slabbert et al., 2004). Consequently, in this thesis laboratory studies were carried out in conjunction with those in the field, not only to standardise the methodologies that evaluate possible DDT effects in C. gariepinus, but also to identify baseline effect levels and sensitivity to pollutants. Furthermore, since community effects are difficult to measure in the laboratory (Connell et al., 1999), only the sub-organismal biomarkers were assessed for comparisons. There was however an additional evaluation of juvenile responses to DDT exposure. This could be used to extrapolate to population changes, which were not assessed in the field or in the laboratory of this study.

1.4 AIMS AND OBJECTIVES

The overall aim of this study was to identify the reproductive effects of the contaminant DDT that can be used as indicators in monitoring the aquatic ecosystem in a DDT sprayed catchment.

The specific objectives were to:

To identify the current status of DDT contamination in the aquatic ecosystem of the Luvuvhu River, as well as to screen for a selected number of possible pesticides, metals and other impacts that may influence results.

To identify at what level of biological complexity effects were occurring in fish in the Luvuvhu River, by measuring sub-organismal, organismal and community responses.

To identify macro-invertebrate community responses related to DDT contamination.

To assess the sub-organismal effects of C. gariepinus exposed to DDT concentrations within the laboratory.

To evaluate the hatching success and survival of juveniles exposed to DDT reproduced from exposed C. gariepinus parents.

To identify adequate sub-organismal biomarkers of DDT contamination in the reproductive system of male C. gariepinus, by measuring their effectiveness in the field and standardising them in the laboratory.

To assess the effectiveness of fish and aquatic macro-invertebrate communities, using well known techniques, as successful bioindicators of DDT contamination.

12 Chapter 1

1.5 STRUCTURE OF THESIS

This thesis is composed of 10 chapters that each cover a specific objective as presented in section 1.4:

Chapter 1 includes a review of literature regarding DDT history, fate and effects as well as the rationale, aims and objectives.

Chapter 2 includes a brief introduction to the Luvuvhu River catchment and the proposed sites utilised in the current study.

Chapter 3 identifies the habitat characteristics and the impacts observed at each of the lotic sites.

Chapter 4 assesses the current status of DDT contamination in the Luvuvhu River as well as other possible pesticides and metals present.

Chapter 5 identifies the sub-organismal effects of DDT on the reproductive system of C. gariepinus and the most suitable methods for monitoring within a DDT sprayed area.

Chapter 6 standardises the biomarkers utilised to determine the sub-organismal reproductive effects of C. gariepinus exposed to environmentally relevant p,p'-DDT contamination as well as assessing the juvenile responses.

Chapter 7 determines a suitable suite of biomarkers for use in future monitoring from results obtained from Chapter 5 and 6.

Chapter 8 assesses fish community changes related to DDT contamination using well established techniques.

Chapter 9 assesses whether the macro-invertebrate community changes are related to DDT contamination using well established techniques.

Chapter 10 provides general discussions, conclusions of the study and recommendations for future research.

13 Chapter 1

1.6 REFERENCES

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Arcand-Hoy LD and Benson WH. 1998. Fish reproduction: an ecologically relevant indicator of endocrine disruption. Environ. Tox. Chem. 17: 49-57.

Arukwe A, Celius T, Walther T and Goksoy A. 1998. Plasma levels of vitellogenin and eggshell zona radiate proteins in 4-nonylphenol and o,p'-DDT treated juvenile atlantic salmon (Salmo salary. Mar. Enviro. Res. 46: 133-136.

Agency for toxic substances and disease registry (ATSDR). 2002. Toxicological profile for zinc. CAS# 7440-66-6. Obtained from official ATSDR website: http://www.atsdr.cdc.gov/toxpro2.html . Retrieved 1/2/2008.

Barbour MT, Gerritsen J, Snyder BD and Stribling JB. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, and fish. USEPA. Second Edition. Report no. EPA 841-B-99-002.

Barrosa SBM, Pimente R, Simizu K, Azzalis LA, Costa IS and Junqueira VBC. 1994. Dose- dependent study of liver lipid peroxidation related parameters in rats treated with pp'-DDT. Tox. Letters. 70(1): 33-38.

Benguira S and Hontela A. 2000. Adrenocorticotrophin- and cyclic adenosine 39,59- monophosphatestimulated cortisol secretion in interrenal tissue of rainbow trout exposed in vitro to DDT compounds. Environ. Tox. Chem. 19: 842-847.

Bjerselius R, Lundstedt-Enkel K, Mayer I and Dimberg K. 2001. Male goldfish reproductive behaviour and physiology are severely affected by exogenous exposure to 17b-estradiol. Aqua. Tox. 53: 139-152.

Bornman MS, Van Vuren JHJ, Barnhoorn IEJ, Aneck-Hahn N, De Jager CJ, Genthe B, Pieterse GM and Van Dyk JC. 2009. Environmental exposure and health risk assessment in an area where ongoing DDT spraying occurs. Water Research Commission (WRC) Report No. K5/1674.

Bouwman H. 2004. South Africa and the Stockholm convention on persistent organic pollutants. S.A. J. Sci. 100: 323-328.

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Bryan and Gibbs. 1991. Imposex at low concentrations of tributyltin (TBT) on marine organisms: a reviews. In: Newman MC and Macintosh (Eds.). Metal Ecotoxicology: consepts and applications. Lewis, Ann Arbor. pp. 323-261.

Cheek A.O. Brouwer TH, Carroll S, Manning S, McLachlan JA, and Brouwer M. 2001. Experimental evaluation of vitellogenin as a predictive biomarker for reproductive disruption. Environ. Health Perspect. 109: 681-690.

Connell D, Lam P, Richardson B and Wu R. 1999. Introduction to ecotoxicology. Blackwell Science. UK.

Department of Health, South Africa. 2002 Obtained from. www.doh.gov.za on1 April 2009.

Falandysz, J. 1994. Polychlorinated biphenyl concentrations in cod-liver: evidence of a steady state condition of these compounds in the Baltic area oils and levels noted in Atlantic oils. Arch. Environ. Contam. Toxicol. 27: 266-271.

Ferreira M, Moradas-Ferreira P and Reis-Henriques MA. 2005. Oxidative stress biomarkers in two resident species, mullet (Mugil cephalus) and flounder (Platichthys flesus), from a polluted site in River Douro Estuary, Portugal. Aqua. Tox. 71: 39-48.

Ford AT, Martins I, and Fernandes TF. 2007. Population level effects of intersexuality in the marine environment. Sci. Tot. Environ. 274: 102-111.

Gerritsen AAM, Kruger P, Schim MF and van der Loeff MFS and Grobusch MP.2008. Malarial incidence in Limpopo Province, South Africa, 1988-2007. Malar. J. 7: 162.

Heath RGM. 1999. A catchment-based assessment of the metal and pesticide levels of fish from the Crocodile River, Mpumalanga. Unpublished Phd Thesis. Rand Afrikaans University, South Africa.

Huang Y, Twidwell DL, Elrod JC. 2003. Occurrence and effects of endocrine disrupting chemicals in the environment. Prac. Period Haz. Tox. Radioactive waste Man. 7: 241-252.

Hyne RV and Maher WA. 2003. Invertebrate biomarkers: links to toxicosis that predict population decline. Ecotox. Environ. Saf. 54: 366-374.

Johnson DW. 1973. Pesticide residues in fish. In: Edwards CA. Environmental pollution by pesticides. Plenum press. New York. pp. 200.

Kime, DE. 1998. Endocrine disruption in fish. Dordrecht: Kluwer academic publishers. New York.

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Kotze, P. 2002. The ecological integrity of the Klip River and the development of a sensitivity weighted fish index of biotic integrity (SIBI). PhD thesis, unpublished. Rand Afrikaans University, South Africa.

Lagadic L, Caquet T, Amiard JC and Ramade F (Eds.). 2000. Use of biomarkers for environmental quality assessment. A.A. Balkema, Rotterdam.

Lavado R, Thibaut R, Raldua D, Martin R and Porte C. 2004. First evidence of endocrine disruption in feral carp from the Ebro River. Toxicol. Appl. Pharm. 196: 247— 257.

Lv X, Shao J, Song M, Zhou Q and Jiang G. 2006. Vitellogenic effects of 17 6-estradiol in male Chinese loach (Misgurnus anguillicaudatus). Comp. Biochem. Physiol. 143 (C): 127- 133.

Mabaso MLH, Sharp B and Lengeler C. 2004. Historical review of malarial control in southern Africa with emphasis on the use of indoor residual house-spraying. Trop. Med. Int. Health. 9(8): 846-856.

Macek KJ Rodgers CR, Stalling DL and Korm S. 1970. Trans. Am. Fish Soc. 99: 689. Sited by: Johnson DW. Pesticide residues in fish. In: Edwards CA. 1993. Environmental pollution by pesticides. Plenum press. New York. pp. 200.

Metcalf RL. 1995. Insect control technology. In: Kroschwitz J and Howe-Grant M (Eds.). Kirk- Othemer encyclopedia of chemical technology. Volume 14. New York, NY: John Wiley and Sons, Inc. p. 524-602.

Mills LJ and Chichester C. 2005. Review of evidence: are endocrine-disrupting chemicals in aquatic environment impacting fish populations? Sci. Tot. Environ. 343: 1 — 34.

Moolman L. 2004. The use of selected freshwater gastropods as biomonitors to assess water quality. MSc dissertation, PhD thesis, unpublished. Rand Afrikaans University, South Africa.

Murray K, Slabbert L and Moloi B. 2003. Needs assessment and development framework for a tested implementation plan for the initialisation and execution of a National Toxicants Monitoring Programme (NTMP). Final Report for Department of Water Affairs and Forestry.

National Oceanic and Atmospheric Association. 2002. Pait AS and Nelson JO. Endocrine disruption in fish: an assessment of recent research and results. NOAA Tech. Memo. NOS NCCOS CCMA 149. Silver Spring, MD: NOAA, NOS, Centre for Coastal Monitoring and Assessment 55pp.

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Nowel LH, Capel PD and Dileanis PD. 1999. Pesticides in stream sediment and aquatic biota. Lewis publishers. New York. pp.306.

Paasivirta J.1991. Chemical ecotoxicology. CRC Press. New York. pp. 36-37.

Pandey S, Ahmad I, Parvez S, Bin-Hafeez B, Hague R and Raisuddinarch S. 2001. Effect of endosulfan on antioxidants of freshwater fish Channa punctatus bloch: 1. protection against lipid peroxidation in liver by copper preexposure. Environ. Contam. Toxicol. 41: 345-352.

Parvez S and Raisuddin S. 2005. Protein carbonyls: novel biomarkers of exposure to oxidative stress-inducting pesticides in freshwater fish Channa punctata (Bloch). 2005; 20: 112-117.

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Price U and Depledge MH. 1998. Effects of the xenooestrogen nonylphenol on the polychaete Dinophilus gyrociliatus In: Depledge and Billinghurst (1999) Ecological significance of endocrine disruption in marine invertebrates. Mar. Poll. Bull. 39: 32 -28.

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USEPA. 1997. Special report on environmental endocrine disruption: an effects assessment and analysis. EPA/630/R-96/012.

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Van der Oost R, Beyer J and Vermeulen NPE. 2003. Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environ. Tox. Pharm. 13: 57-149.

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Verslycke T, Vandenbergh GF, Versonnen B, Arijs K and Janssen CR. 2002. Induction of vitellogenesis in 17a-ethinylestradiol-exposed rainbow trout (Oncorhynchus mykiss): a method comparison. Comp. Biochem. Physiol. 132 (C): 483-492.

Versonnen BJ, Goemans G, Belpaire C and Janssen CR. 2004. Vitellogenin content in European eel (Anguilla anguilla) in Flanders, Belgium. Environ. Poll. 128: 363-371.

Vouk VB and Sheehan PJ, editors. 1983. Methods for assessing the effects of chemicals on reproductive functions. John Wiley and Sons. New York.

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Wells M. and Leonard L. 2006. DDT contamination in South Africa. Unpublished report, Groundwork.

18 Chapter 2 Description of the Study Area

2.1 GENERAL DESCRIPTION OF THE LUVUVHU RIVER CATCHMENT 20 2.2 POSSIBLE IMPACTING FACTORS ON THE LUVUVHU RIVER 20 2.2.1 DDT Spraying 20 2.2.2 Rural Communities 21 2.2.3 Sewage Treatment Plants 21 2.2.4 Forestry and Agriculture 21 2.2.5 Mining 22 2.2.6 Dams and Weirs 22 2.3 SAMPLING SITES 23 2.4 REFERENCES 28 Chapter 2

2.1 GENERAL DESCRIPTION OF THE LUVUVHU RIVER CATCHMENT

The Luvuvhu River (Figure 2.1) is situated within the Limpopo Province in the northern part of South Africa (Heath, 1999). It originates in the Soutpansberg Mountains and traverses the Albasini Dam in the upper reaches. From there it meanders eastwards into the middle reaches, where it joins up with a number of tributaries such as the Latonyanda, Dzindi, Mutshindudi, Mbwedi and Mutate Rivers, whereupon it traverses another major dam, the Nandoni Dam. After approximately 200 km from its origin, the Luvuvhu River then converges with the Limpopo River in the Kruger National Park at Crook's Corner.

The terrain surrounding the Luvuvhu River is predominately made up of undulating landscapes, hills and low mountains with a moderate relief and vegetation that consists mostly of sour lowveld bushveld, with patches of afromontane forest and Soutpansberg arid mountain bushveld. The geology of this terrain consists of granite and gneisses (sandstone, quartzite and shale) rock types, while the soils vary from sandy loam in the uplands to clayey soils in the bottomlands. As for the general climate in the catchment, it could be described as hot and humid, with a predominantly summer rainfall varying from 2025 mm in the Soutpansberg Mountains to 300 mm in the mature river reaches (Heath & Claassen, 1999).

Since the geography of the area surrounding the Luvuvhu River differed considerably between the river reaches, there was also considerable variation in the land use within the catchment (Sate of rivers report, 2001). In the upper reaches the land use was dominated by commercial farming including agriculture and forestry with a small percentage of ecotourism surrounding the Albasini Dam. While, in contrast, the middle reaches of the catchment is heavily populated with rural settlements, predominantly using land for subsistence farming of cattle and agriculture. Areas surrounding Thohoyandou were particularly populated with various urban, semi-urban and rural settlements. Whilst further eastward, in the lower parts of the Luvuvhu catchment the river meandered into the north of the Kruger National Park (KNP) and as such the land is only utilised as a wilderness area.

2.2 POSSIBLE IMPACTING FACTORS ON THE LUVUVHU RIVER

2.2.1 DDT Spraying

Since DDT spraying is the main focus of this thesis, details regarding the possible impacts and effects were discussed in detail in Sections 1.2 and 1.3.

20 Chapter 2

2.2.2 Rural Communities

In 2001 the total basin population of the Luvuvhu River catchment was approximately 770 000 (DWAF, 2004). Large portions of this population live in underdeveloped rural villages, with poor reticulation infrastructure such as running water and refuse removal systems. As a consequence of this, the communities are highly dependent on the natural resources of the Luvuvhu River, such as the riparian vegetation and aquatic resources. Such utilisation however often results in the deterioration of the river health status (State of rivers report, 2001; Davies and Day, 1998). For instance, people washing and bathing in the rivers (Plate 2.1) can lead to increased nutrient concentrations that ultimately stimulate algal growth and eutrophication. Other examples of impacts by communities related to river destruction include overexploitation of freshwater fish (as fish are a very important protein source for the local villages), excessive vegetation clearing for firewood and overgrazing by livestock, increased bank erosion by the removal of clay from river banks for brick making, and increased litter and waste polluting the aquatic integrity of the river.

2.2.3 Sewage Treatment Plants

The major potential threat of contamination from a sewage treatment plant within the Luvuvhu River is close to Thohoyandou. The sewage treatment plant is located on the Mvudi River upstream of Thohoyandou (-23.002672°S; 30.475500°E). According to State of rivers report (2001), the effluent from this sewage treatment plant is directed to a nearby fish farm. Unfortunately no major data was available on the possible discharges into the Luvuvhu River, although recently a report monitoring the faecal pollution downstream of this sewage treatment plant was obtained from DWAF showing high concentrations of E. coli and faecal coliforms (DWAF, 2009). Unfortunately only the bacterial contamination was assessed, with no data available on the levels of oxygen and biological oxygen demand (BOD), which severely influence the aquatic biological integrity of receiving water (Allanson, 1995).

2.2.4 Forestry and Agriculture

The most dominant commercial activities within the Luvuvhu River catchment are forestry and agriculture, both of which are situated within the upper catchments. The afforestation covers an area of approximately 14 600 ha and is primarily made up of eucaluptus and pine tree forests (State of rivers report, 2001). The effects of these forests on the surrounding river systems have been well documented (Allanson, 1995; Dallas and Day, 2004), with the most predominant problem being the reduction the runoff of water from the upper reaches. According to DWAF (2004), most of the available resource in the Luvuvhu catchment is significantly impacted by this reduced runoff, while other less significant, impacts caused by the reduction/removal of the riparian zone in afforestation activities, have also been shown to exist. These include changing light availability and water temperature, changed energy

21 Chapter 2 inputs, and acidification in certain areas (Dallas and Day, 2004). The remaining areas within the upper reaches of the catchment are predominantly utilised for commercial crop farming (Plate 2.2). The main produce include subtropical fruits such as citrus, mangos, bananas, litchis, pawpaws, macadamias as well as potatoes, cabbages, tomatoes and ginger (Claassen et al., 2005). The influence of such crops on the surrounding aquatic ecosystems has been well reviewed, with many identifying common farming practices such as the land preparation, irrigation, fertiliser and pesticide application as causes for increasing turbidity, sedimentation, nutrient concentration and pesticide concentration (Dallas and Day, 2004).

In contrast, in the lower parts of the Luvuvhu River catchment (downstream of Albasini Dam) the primary form of agriculture is subsistence farming of both crops and livestock. The impacts of such crop farming on the aquatic resources are similar to those of commercial crop farming, with the additional impact of bank and donga erosion due to poor agricultural practices that are often associated with the subsistence farmers within the rural areas (Claassen et al., 2005). Impacts of subsistence farmers with livestock include organic enrichment, bacterial contamination, destruction to riparian zone (as seen in Plate 2.3) and sedimentation, all of which are mostly incurred by the livestock within the river channel and the riparian zone.

2.2.5 Mining

According to DWAF in 2004, two major mines existed in the Luvuvhu River catchment including the Tshikondeni coal mine and the Geocapro (Venmag) magnesite mine (DWAF, 2004). However, according to Claassen et al. (2005) the magnesite mine has since ceased operation and has no potential threat to aquatic ecosystem, with only the Tshikondeni coal mine still operational. Despite the rich coal reserves available in the Soutpansberg basin, this mine is the only coal mine within the catchment. Driven by ISCOR, the Tshikondeni coal mine is about 40 km from Pafuri Gate of the Kruger National Park (KNP) and 100 km from Tshipise (-22.512439°S; 30.948228°E). Although the documented impacts of this mine on the aquatic resources are minimal, according to Claassen et al. (2005) the mine does have a potential impact of sulphuric acid contamination within the catchment.

2.2.6 Dams and Weirs

The Luvuvhu River is sprawled with dams and weirs alike. The major dams include the Albasini Dam and the Nandoni Dam, while the major weirs include the Xikundu weir (see below); the Mhinga/Lambani weir (-22.768314°S; 30.889022°E); and the Malamulele weir (DWAF, 2004). The general impact of these dams and weirs on the Luvuvhu River can be significant, with numerous ecological assessments highlighting the negative effects dams and impoundments have on river systems. Davies and Day (1998) reviewed these effects in detail, which briefly included alterations in the riparian and aquatic vegetation, ecological

22 Chapter 2 diversity, migratory movements of fish, trophic structure, species composition, physical structure, flow regimes and water quality.

2.3SAMPLING SITES

Seven sites were selected and analysed in this study and are summarised in Figure 2.1. They included two major dam sites, one site at a weir and four riverine (lotic) sites.

Figure 2.1 Locality of study sites in the Luvuvhu River catchment. 1: Latonyanda River site, 2: Albasini Dam, 3: Hasana, 4: Nandoni Dam, 5: Tshikonelo, 6: Xikundu, and 7: Mhinga. Illustration not to scale.

Site 1 was situated on the Latonyanda River under the bridge of the R524 between Makhado/Louis Trichardt and Thohoyandou (-23.061779° S; 30.249457° E) and is shown in the photograph represented in Plate 2.4. This river is one of the Luvuvhu River's major tributaries and was selected as a lotic reference site based on its location outside of the DDT sprayed area.

Site 2 was the Albasini Dam (-23.107542° S; 30.113794° E), selected as a DDT lentic (dam) control site because it was situated approximately 45 km outside the DDT sprayed area (Plate 2.5). This dam was built in 1952 in the upper reaches of the Luvuvhu River catchment for the primary purpose of supplying water to the Levubu irrigation scheme, but today also supplies domestic water to Makhado/Louis Trichardt (DWAF, 2004). Apart from this important functional purpose, the dam also holds aesthetic and recreational value,

23 Chapter 2

particularly within the private conservancy around the Albasini Dam, thus bringing ecotourism to the area (State of rivers report, 2001).

Site 3 was situated in the rural area of Hanani (between R524 and R578) and was named Hasana (-23.083839°S; 30.470957°E). This lotic site (Plate 2.6) was located east of the Albasini Dam in the upper middle reaches of the Luvuvhu River catchment, outside the DDT sprayed areas. The surrounding land use is predominantly rural, with visible subsistence farming and utilisation of aquatic resources in the vicinity.

Site 4 was selected at the Nandoni Dam (-22.976833°S; 30.583278°E) and is situated north east of the previously mentioned sites close to Thohoyandou. The dam, represented in Plate 2.7, is currently the largest dam within the Luvuvhu River catchment. It was recently constructed by DWAF in order to supply water to the sprawling regional centre of Thohoyandou as well as a number of smaller communities in the area (DWAF, 2004). The reason for its selection as a site was primarily based on the close proximity (within nearby villages) of this lentic site to the DDT sprayed area and was thus identified as an exposure site.

Site 5, named Tshikonelo (-22.843889°S; 30.741389°E), was a lotic site surrounded by subsistence farming of rural communities from Tshikonelo and other surrounding villages. This site was situated downstream from the confluence of the Mutshindudi tributary, within the middle reaches of the Luvuvhu River and was classified as an exposed site since DDT spraying occurred within close proximity (in nearby villages) (Plate 2.8).

Site 6 was selected just down stream of the Xikundu weir (-22.808056°S; 30.798611°E) (Plate 2.9), where there was sufficient biotope diversity for fish and macro-invertebrate sampling (as prescribed by respective sampling methodologies, which will be discussed in detail in Chapter 8 and Chapter 9). This site was approximately 8 km downstream of Tshikonelo and was also surrounded by rural communities, while being within the proposed area of DDT spraying and contamination. The weir at this site (managed by DWAF) was built to supply domestic water to many of the surrounding rural villages, with the impacts of on the aquatic ecosystem being considerably reduced due to the presence of a fish ladder that allows fish to migrate upstream.

Site 7 was the last selected site and was named Mhinga (-22.761389°S; 30.894722°E) due to its close proximity to the Mhinga village. As such there was visible utilisation of aquatic resources in the Luvuvhu River, by the surrounding rural communities (Plate 2.10). This site was approximately 15 km east of the Xikundu weir and close to the KNP boarder, but was still possibly contaminated with DDT from indoor residual spraying (IRS).

24 Chapter 2

Photo 2.1. Rural people bathing and washing in the Luvuvhu River at Xikundu fish weir.

Photo 2.2. Commercial farming in the upper parts of the Luvuvhu River catchment

Photo 2.3. Livestock of local subsistence farmers within the riparian zone of the Luvuvhu River at Xikundu.

25 Chapter 2

'44

Photo 2.4. Photographs of the site on the Latonyanda River.

Photo 2.5. Albasini Dam at dawn.

Photo 2.6. A photograph of the Luvuvhu River in the low flow at Hasana.

26 Chapter 2

Photo 2.7. This is a photograph of the Nandoni Dam from the dam wall.

Photo 2.8. Tshikonelo site with extensive erosion and truck prints.

_ Photo 2.9. Photographs of the weir at Xikundu with extensive bank erosion (left) and the fish ladder (right).

27 Chapter 2

Photo 2.10. This photograph at Mhinga shows the extensive erosion and rural activities such as bathing and cattle farming.

2.4 REFERENCES

Allanson BR. 1995. An introduction to the management of inland water ecosystems in south Africa. Water Research Commission (WRC). Report No. TT 72/95.

Claassen M, Damon M, King NA, Letsaolo A, Moilwa N, Moloi B, Ramoelo A and Visser A. 2005. The feasibility of developing payments for catchment protection services and improved livelihoods in South Africa. CSIR, Pretoria. Report No. ENV-P-C 2005-014.

Davies B and Day JA. 1998. Vanishing waters. UCT Press.

Dallas HF and Day JA. 2004. The effect of water quality variables on aquatic ecosystems. Water Research Commission (WRC). Report No: U224/04.

Department of Water Affairs and Forestry (DWAF). 2004. Luvuvhu/Letaba WMA: internal strategic perspective report. Report No. WMA 02/000/00/0304.

Department of Water Affairs and Forestry (DWAF). 2009. Communications obtained from http://www.dwaf.gov.za/communications/Q&A/2009/Q118June09 . 1November2009.

Heath RGM. 1999. A catchment-based assessment of the metal and pesticide levels of fish from the Crocodile River, Mpumalanga. PhD thesis, unpublished. Rand Afrikaans University, South Africa.

Heath RGM and ClaasSen M. 1999. An overview of the pesticide and metal levels present in populations of the larger indigenous fish species selected in South African rivers. Water Research Commission (WRC). Report No: 428/1/99.

28 Chapter 2

State of the Rivers Report. 2001. Letaba and Luvuvhu river systems. Water Research Commission (WRC). Report no: TT 165/01.

29

Chapter 3 Description of Habitat in Luvuvhu River

3.1 INTRODUCTION 31 3.2 METHODS 31 3.3 RESULTS 32 3.3.1 Habitat description of each site 33 3.3.2 Major impacts on the habitat 37 3.4 DISCUSSION 40 3.5 REFERENCES 41 Chapter 3

3.1 INTRODUCTION

Both the physical instream and riparian habitats of an aquatic system are essential components for the normal functioning of aquatic ecosystems and should thus remain healthy and relatively unchanged (Rabeni, 2000). Although these habitats often change due to natural fluctuations, habitats may also be influenced by anthropogenic activities within a river catchment (Kotze, 2002). Despite the cause, alterations in habitat can interfere with the interpretation of DDT by influencing the bioavailabilities of the contaminant and the responses of the aquatic ecosystem (Edwards, 1973).

When considering the bioavailability of contaminants, numerous alterations in the habitat can influence the uptake of DDT. For instance, DDT concentrations can be influenced by an increase in suspended particles in the water via erosion, an increase in organic matter via sewage contamination and reduced quantities of water via abstraction, just to name a few (Phillips and Rainbow, 1994; Edwards, 1973).

Changing habitats also influence the responses of aquatic organisms, which according to Barbour (1991) follows a sigmoid curve. The author explained that as the habitat quality decreases only subtle differences will initially be seen followed by a proportional decrease in the biological condition. If habitat condition continues to deteriorate, the biological community will eventually plateau, resulting in an abundance of tolerant, opportunistic species that thrive in areas of reduced competition (Dallas and Day, 2004; Karr and Dudley, 1981). Thus evaluating the habitat condition and availability could be used to explain possible changes in the biotic responses and therefore rendering this assessment essential in the present study. Therefore the aim was to assess the habitat characteristics at each site and to identify any observed impacts on the instream and riparian habitats that may be present. In order to determine this, both the site-specific physical characteristics and the South African developed Index of Habitat Integrity (IHI) (Kleynhans, 1996) were assessed, respectively.

3.2 METHODS

The physical characteristics of the habitats as well as the impacts were assessed in all of the sites in two high flows and two low flows from 2006 to 2008 (except for Albasini Dam and Nandoni Dam, in all four seasons and Latonyanda in low flow 2006) (Table 3.1). As mentioned in Chapter 2 the two dams in the Luvuvhu River are not natural occurrences within the catchment. As such the physical habitats at these sites were drastically altered, with a common reduction in habitat diversity despite their maturity levels and associated differences in the physical and chemical conditions (Davies and Day, 1998). Since the fish community structures that are the most influenced by these differences were not assessed in these lentic environments in the present study (due to lack of the appropriate resources required to assess communities in these ecosystems), it was not necessary to measure the

31 Chapter 3

fluctuating physical habitat characteristics that may have differed between the sites (Davies and Day, 1998; Whitton, 1975). With regards to the Latonyanda site, the first sampling season, low flow 2006, was not measured as another site was initially selected in the first sampling season. This site was however found to be an inadequate representation of the Luvuvhu River catchment as a reference site for no DDT contamination, as the river habitat and biological characteristics were not comparable to the other sites.

Table 3.1. Dates of sampling regimes and associated abbreviations used in figures and tables. Date Flow regime Abbreviation Sites Sampled October 2006 Low flow LF 2006 Hasana, Tshikonelo, Xikundu, Mhinga March 2007 High flow HF 2007 Latonyanda, Hasana, Tshikonelo, Xikundu, Mhinga October 2007 Low flow LF 2007 Latonyanda, Hasana, Tshikonelo, Xikundu, Mhinga February 2008 High flow HF 2008 Latonyanda, Hasana, Tshikonelo, Xikundu, Mhinga

For the remaining sites, the physical characteristics of the instream channel and riparian habitat were described by using a number of criteria from DWAF's site characterisation field- manual (Dallas, 2005). The criteria included channel morphology, stream dimensions, canopy cover, riparian vegetation, aquatic vegetation, as well as substratum composition, biotopes (substrate and flow type combinations) and velocity—depth classes (velocity and depth combinations), which are defined in Table 3.2.

The impacts on the physical habitat were then assessed using the IHI. Kleynhans (1996) developed this IHI to assess the number and severity of anthropogenic perturbations on the river such as water abstraction, pollution, erosion and exotic species, and the damage they potentially inflict on the habitat integrity of the system. In order to calculate this index, each disturbance such as pollution was assigned an impact rating and a confidence score (Kleynhans, 1996). The two values were then used to calculate an impact score for each disturbance using the formula: (impact rating/25) x (the weight of that impact). This was then used to calculate the final IHI score by adding up all the impact scores from each disturbance, expressing it as a percentage and then subtracting the resulting score from 100.

3.3 RESULTS

In order to get a clearer understanding of the habitat alterations, a site-specific description of the actual habitat and the impacts of anthropogenic activities were assessed. Since there was very little variation between the habitats of each season, all the physical habitat descriptions and impacts were based on summarised observations from all four seasons except for the velocity-depth classes and biotope, which differed seasonally.

32 Chapter 3

3.3.1 Habitat description of each site

The habitat descriptions for each of the sites were based on the physical characteristics observed and summarised in Table 3.3 as well as the depth flow classes and biotope observed and noted in Table 3.4 and Table 3.5, respectively.

Table 3.2. Definition for substrate composition, biotopes and velocity-depth classes (Dallas, 2005).

Description Substratum composition Bedrock Large sheets of rock Boulder >256 mm diameter Cobble 100 — 256 mm diameter Pebble 16 — 100 mm diameter Gravel 2 -16 mm diameter Sand 0.06 — 2 mm diameter Silt/mud/clay <0.06 mm diameter

Biotopes Backwater An area alongside, but physically separated from the channel, connected to it downstream. Slackwater An area of no perceptible flow which is hydraulically detached from the main flow but is within the main channel; barely perceptible or no flow. Pool An area with direct hydraulic contact with upstream and downstream water; barely perceptible flow. Glide A glide exhibits smooth boundary turbulent flow, with clearly perceptible flow, without any surface disturbance. A glide can occur over any substrate. Run A run is characterised by a rippled flow type and can occur over any substrate apart from silt. Riffle Riffles may have undular standing waves or breaking standing waves and occur over alluvial substrates from gravel to cobble. Rapid Rapids have undular standing waves or breaking waves, and occur over a fixed substrate such as boulder or bedrock.

Velocity-depth categories Slow — Shallow Shallow pools and backwaters (less than 0.3 m/s velocity and less than 0.5 m depth) Slow — Deep Deep pools, backwaters and slow glides (less than 0.3 m/s velocity and more than 0.5 m depth) Fast — Shallow Riffles, rapids and runs (more than 0.3 m/s velocity and less than 0.5 m depth) Fast — Deep Usually rapids and runs and glides. (mores than 0.3 m/s velocity and more than 0.5 m depth)

The site along the Latonyanda River is situated slightly upstream from an elevated bridge with side channel supports along the R524 road. For the most part the bridge caused limited damage to the channel condition but there was slight inundation surrounding the supports (increasing the limited slow-deep and pool biotopes available in this reach) as well as evidence of erosion along steep sides. Both however, recovered downstream of the bridge. The dominating riparian zone included grasses, shrubs and trees that formed a partially closed canopy over the river that has a width of between 2 - 5 meters, with a moderate encroachment of exotic vegetation. The main substratums were cobbles and pebbles (both covered with algae) that yielded runs and riffles and a mixture of gravel, sand and mud (GSM) that yielded glides. With regards to the aquatic habitat, the water was generally clear with lower temperatures than the other sites, whilst the average depth of the water was less than 0.5 meters deep, probably lower than natural conditions due to the large utilisation of

33 Chapter 3

water by the surrounding forestry and agriculture (Claassen et al., 2005). These lower depths resulted in the depth-flow classes of slow-shallow and fast-shallow dominating at this site.

Table 3.3. Generalised physical characteristics of each site within the Luvuvhu River.

Latonyanda Hasana Tshikonelo Xikundu Mhinga Riverine zone head-middle zone middle zone middle zone middle zone middle zone

Slope steep-gradual gradual gradual gradual gradual

Velocity slower slower slower slower slower

Active width of stream 2-5 20-50 20-50 20-50 20-50

Dominant depth (m) <0.5 m >0.5 m >0.5 m >0.5 m >0.5 m

Dominant substratum type cobbles/ bedrock cobbles/ cobbles/pebbles/ boulders/cobbles/ pebbles/GSM pebbles/GSM gravel pebbles Temperature (°C) 21 -23 26 - 30 23 - 33 24 - 28 25 - 28

Water clarity low (clear) intermediate intermediate Intermediate-high Intermediate-high

Light reaching stream (canopy) partially open open open open open

Riparian vegetation type grasses/shrubs/ grasses/reeds reeds reeds reeds trees

Hasana was substantially wider than the Latonyanda site with an active width of between 20 to 50 meters. This site had no canopy cover with the majority of the riparian vegetation type being reeds and grass and little to no exotic vegetation encroachment. As can be seen from Table 3.6 the banks had a large amount of erosion which impacted the river bed through a moderate amount of sedimentation. The channel was dominated with algae covered bedrock and as such there were many glides with few cobble forming riffles, while the section devoid of bedrock was dominated by the GSM substratum type. With regards to the water at this site, the water temperatures were between 26 and 30°C and the water had an intermediate clarity. Furthermore, although water flows were not measured in this study, according to Kleynhans (1996) there was a large reduction in the volume of water in this area compared to historical flows.

Tshikonelo was in the same riverine zone as Hasana and as such had a similar gradual slope, wide channel, higher water temperatures, intermediate water clarity and an open canopy. The riparian zone consisted mostly of reeds, which was largely removed causing erosion and a fair amount of sedimentation. The most dominant substratum present in the river bed was GSM with the major available habitats consisting of slow-deep, slow-shallow, pools and a few riffles. The water flow was most probably lower than what it would have been under natural conditions since the Nandoni Dam abstracted a major amount of water upstream. However, it is suspected that the Mushindudi tributary, entering the Luvuvhu River approximately 4 km upstream, alleviated a significant portion of this impact.

At Xikundu, the presence of the weir resulted in a number of instream impacts. These include water inundation approximately 4 km upstream, causing a reduction in instream

34 Chapter 3 habitat, with the majority of the habitat being slow-deep along a channel of 20-50 meters wide and a substratum consisting mostly of cobbles, pebbles and gravel. Downstream from the weir there was a reduction of flow due to water abstraction, from a nearby located water purification plant, which led to a few sand banks. Even with this abstraction a more diverse amount of biotopes were found, including some riffles, runs, pools and glides, as compared to the inundated region upstream of the weir. Apart from the weir's impacts, intense erosion was evident within the riparian zone, resulting in infrequent strips of vegetation dominated by reeds and some grasses. The water was generally quite turbid, with high temperatures ranging from 24 to 28 °C.

At Mhinga the impacts of the Xikundu weir were substantially reduced, although there was probably a reduced amount of flow due to the abstraction at Xikundu. The active width of the stream was still between 20 to 50 meters, with a gradual slope, slow velocities, high water temperatures and high clarity, which were possibly accentuated by the high erosion on the banks of the river. The riparian zone that was present consisted mainly of reeds and therefore no canopy was present. As for the substratum, the dominant combination of substratum included boulders, cobbles, pebbles and GSM and the dominant biotopes were runs and riffles, while the depth flow classes were mainly slow deep with some slow and fast shallow.

Table 3.4. The dominant velocity-depth classes observed in this study for each site and season on the Luvuvhu River (as determined using method of Dallas (2005)).

Slow-Deep Fast-Deep Slow-Shallow Fast-Shallow Latonyanda LF 2006 - HF 2007 2 0 4 4 LF 2007 1 0 3 4 HF 2008 2 0 4 4

Hasana LF 2006 4 0 4 2 HF 2007 3 0 4 3 LF 2007 4 0 4 2 HF 2008 4 0 3 3

Tshikonelo LF 2006 4 1 3 3 HF 2007 4 2 2 3 LF 2007 4 2 3 2 HF 2008 4 2 2 3

Xikundu LF 2006 - - HF 2007 4 3 2 2 LF 2007 4 1 4 2 HF 2008 4 3 2 2

Mhinga LF 2006 4 2 3 2 HF 2007 4 2 2 3 LF 2007 4 2 0 2 HF 2008 4 3 2 4 Hyphen-no data; LF-low flow, HF-high flow, Availability scores: 0-absent, 1-rare, 2-sparce, 3-common, 4-abundant, 5-entire.

35 • • •

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Chapter 3

3.3.2 Major impacts on the habitat

The IHI was utilised in order to quantify the individual impacts that may influence the physical instream (Table 3.6) and riparian (Table 3.7) habitats as well as their accumulative impacts on the habitat integrity at each site (Figure 3.1.) (Kleynhans, 1996). With regards to the instream habitat integrity, the site along the Latonyanda River had a score of 80% which can be classified as largely natural with a few modifications mainly as a result of water abstraction. At the other sites within the Luvuvhu River, the instream habitat integrity was also influenced by water abstraction from two major dams (Albasini and Nandoni dams) as well as from a number of weirs, with Hasana and Tshikonelo having habitat scores of 65% and 67%, respectively. Xikundu had a largely modified habitat of 51% due to its proximity to the weir, but it did recover down stream at Mhinga where the habitat improved to a 70% (moderately modified). As for the riparian habitat, the integrity was largely natural at Latonyanda, while the other sites along the Luvuvhu River suffered from severe erosion and indigenous vegetation removal that resulted in scores of 78%, 75%, 52%, and 72% at Hasana, Tshikonelo, Xikundu and Mhinga, respectively.

—f— Riparian —s— I nstream

90

0 largely natural >, 80 Ic ti) C4-.c 70 moderately modified Cti :.. 60 a: I largely modified 50 Lotanyanda Hasana Tshikonelo Xikundu Mhinga

Figure 3.1. The instream and riparian zone habitat integrity of the Luvuvhu River observed at each of the lotic sites (as determined using IHI of Kleynhans (1996)).

37 Chapter 3

Table 3.6. Impact on the instream habitat integrity of the Luvuvhu River observed at each site (as determined using method by Kleynhans (1996)). Instream Impacts Impact General remarks Water abstraction Latonyanda Moderate No weirs evident but irrigation and forestry impact Hasana Serious Albasini dam upstream and water tanks filled regularly Tshikonelo Serious Nandoni dam upstream and water tanks filled regularly Xikundu Serious Weir upstream Mhinga Large Weir upstream

Flow modification Latonyanda Moderate Moderate Hasana Large Impact from abstraction but alleviated by Latonyanda River Tshikonelo Large Impact from abstraction is large but is alleviated by the Mutshindudi tributary that enters Luvuvhu 4 km upstream Xikundu Large Weir in close vicinity Mhinga Large Impacted by abstraction but alleviated by small tributaries entering Luvuvhu

Bed modification Latonyanda Small Little sedimentation evident Hasana Moderate River banks destroyed by livestock and locals Tshikonelo Large River banks destroyed by livestock, distribution of river sand for easy access of vehicles to river bed Xikundu Large River banks destroyed by livestock Mhinga Large River banks destroyed by livestock, there are in-channel supports of bridge that modifies bed slightly

Channel modification Latonyanda Small Small Hasana Moderate Moderate Tshikonelo Moderate Distribution of river sand for easy access of vehicles to river bed Xikundu Large Upstream weir disrupts channel flow Mhinga Moderate Bridge with in-channel supports

Water quality Latonyanda Moderate Surrounded by agricultural lands so there is a high probability of agricultural chemicals contaminated at this site Hasana Large Slight odour present, continuous washing of clothes with detergents, and down stream from agricultural lands with a high probability of agricultural chemicals having a negative impact Tshikonelo Small Little activity Xikundu Moderate Washing of clothes and foam at night in HF'08 (source unknown) Mhinga Moderate Continuous washing of clothes with detergents

Inundation Latonyanda Small Slight inundation under bridge caused by side channel supports Hasana Small Upstream from site due to instream bridge Tshikonelo None None Xikundu Serious Large proportion of this reach is inundated upstream of weir Mhinga Small Very little inundation observed

Exotic macrophytes All sites None None

Exotic fauna All sites None None

Solid waste disposal Latonyanda Small Small amounts of litter observed Hasana Small Small amounts of litter observed Tshikonelo None None Xikundu Small Small amounts of litter observed Mhinga Small Small amounts of litter observed

38 Chapter 3

Table 3.7. Impact of various modifications on the riparian habitat integrity of the different lotic sites within the Luvuvhu River (as determined using method by Kleynhans (1996)). Riparian Impacts Impact General remarks Indigenous vegetation removal Latonyanda Moderate Erosion of steep slopes Hasana Moderate Over grazing of livestock and trampling by locals Tshikonelo Large Over grazing of livestock and trampling by locals Xikundu Serious Over grazing of livestock, trampling by locals and isolated subsistence cultivation within riparian zone Mhinga Serious Over grazing of livestock and trampling by locals

Exotic vegetation encroachment Latonyanda Moderate Peanut butter cassia the most obvious exotic species Hasana None None Tshikonelo None None Xikundu None None Mhinga None None

Bank erosion Latonyanda Moderate Surrounding the bridge but recovered downstream Hasana Large Vegetation destroyed by local communities and their livestock Tshikonelo Large Vegetation destroyed by local communities and their livestock Xikundu Serious Vegetation destroyed by local communities and their livestock Mhinga Serious Vegetation destroyed by local communities and their livestock

Channel modification Latonyanda None None Hasana Small Higher flows may influence vegetation Tshikonelo Small Higher flows may influence vegetation Xikundu Moderate Destruction of vegetation by weir Mhinga Small Higher flows may influence vegetation

Water abstraction Latonyanda None None Hasana Small A reduction in flow would cause a slight impact Tshikonelo None None Xikundu Large Downstream of weir some vegetation does not receive water Mhinga Small A reduction in flow would cause a slight impact

Inundation Latonyanda None None Hasana None None Tshikonelo None None Xikundu Large Upstream from weir most of the riparian vegetation is inundated by water Mhinga None None

Flow modification Latonyanda None None Hasana Small Lower flows may influence vegetation Tshikonelo Small Lower flows may influence vegetation Xikundu Moderate Lower flows may influence vegetation downstream of weir Mhinga Small Lower flows may influence vegetation

Water quality All sites None None

39 Chapter 3

3.4 DISCUSSION

The habitat condition and availability varied throughout the various sites of the Luvuvhu River catchment. Although most of the variation was as a result of surrounding anthropogenic activities, some of the variations were due to the natural succession of the river. The largest of which was between the Latonyanda River site that was in the upper reaches of the catchment and those within the Luvuvhu River that were within the middle reaches of the catchment.

As for the influence of anthropogenic activities, it was evident that the catchment was predominantly altered by abstraction and rural activities. Abstraction was an influencing factor at all of the sites in the Luvuvhu River. Kleynhans (1996) showed a considerable decrease in the flow from 1961 to 1996 and in 2004 DWAF reported that approximately 72% of the total available river water was allocated to irrigation, afforestation, and rural and urban community utilisation. At most of the sites in the present study however, this abstraction was alleviated by the contributions of the perennial tributaries. The site that was most visibly influenced by the abstraction was at Xikundu due to the close proximity of the weir. Both the instream and riparian habitats were characterised by the IHI by Kleynhans (1996) as being largely modified however there was a rapid recovery of the habitat integrity down stream at Mhinga.

Another influencing factor was the rural activities surrounding and within the instream and riparian habitat. The activities were predominantly in the middle reaches of the Luvuvhu River, at sites characterised by higher temperatures and pH, wide river beds, no canopies, and intermediate turbidity's, and according to the IHI scoring system were moderately modified. The impacts primarily resulted from the high dependence of the surrounding underdeveloped rural villages on the natural resources. For instance, washing (clothes and cars) and bathing were a daily occurrence using chemicals that could influence the water quality (see Chapter 4). Further impacts were observed on the riparian habitat integrity. Many of the indigenous trees and shrubs within and beyond the riparian zone were removed by the local communities for firewood and the remaining grasses and sedges were often destroyed by the overgrazing of the local communities' livestock. In addition, at Tshikonelo the riparian zone as well as the river bed was impacted by the removal of river sand for building. These disturbances to the riparian zone in turn caused severe erosion at most of the lotic sites along the Luvuvhu River. This erosion mostly resulted in sedimentation within the instream habitat which can ultimately result in an imbalance in the trophic structure due to reduced phytoplankton and macrophytes from lack of light or reduced food resources for foraging fish (Allanson, 1995). The removal of the overhanging vegetation probably had less of an impact due to the naturally broad width of the river which naturally has a lot of light reaching the river.

In contrast to the sites in the Luvuvhu River, the site in the Latonyanda River was not influenced by rural activities, but was rather surrounded by extensive forestry and

40 Chapter 3

agriculture. However, as was shown by the IHI scoring system the physical habitat at this site was hardly impacted by these activities, with the majority of the habitat alterations occurring as a result of the nearby bridge.

3.5 REFERENCES

Allanson BR. 1995. An introduction to the management of inland water ecosystems in South Africa. Water Research Commission. Report no: TT72/95.

Barbour MT. 1991. Stream surveys — the importance of the relation between habitat quality and biological condition. In: Peters N.E. and Walling D.E. (Eds.). Sediment and stream water quality in a changing environment: trends and explanation. Proceedings of a symposium held at Vienna. pp. 161-168.

Claassen M, Damon M, King NA, Letsaolo A, Moilwa N, Moloi B, Ramoelo A and Visser A. 2005. The feasibility of developing payments for catchment protection services and improved livelihoods in South Africa. CSIR, Pretoria. Report No. ENV-P-C 2005-014.

Dallas HF. 2005. River health programme: site characterisation field-manual and field-data sheets. Resource quality services, Department of Water Affairs and Forestry.

Dallas HF. and Day JA. 2004. The effect of water quality variables on aquatic ecosystems. Water Research Commission. Report no: TT -224/04.

Davies B and Day JA. 1998. Vanishing waters. UCT Press. pp. 268-284.

Edwards CA. 1973. Environmental pollution by pesticides. Plenum press, New York.

Karr JR. and Dudley DR 1981. Ecological perspective on water quality goals. Environ. Man. 5(1): 55-68.

Kleynhans CJ. 1996. A qualitative procedure for the assessment of the habitat integrity status of the Luvuvhu River (Limpopo system, South Africa). J Aq. Ecosys. Health. 5: 41-54.

Kotze P. 2002. The ecological integrity of the Klip River and the development of a sensitivity weighted fish index of biotic integrity (SIBI). PhD Thesis, unpublished. Rand Afrikaans University, South Africa.

Phillips DJH and Rainbow PS.1994. Biomonitoring of trace aquatic contaminants. Chapman and Hall.

Rabeni CF. 2000. Evaluating physical habitat integrity in relation to the biological potential of streams. Hydrobiologia. 422/423: 245-256.

41 Chapter 3

Whitton BA. 1975. River ecology. Blackwell Scientific Publications, Great Britain.

42

CHAPTER 4 Pesticides and metal contamination in Luvuvhu River

4.1 INTRODUCTION 44 4.2 MATERIALS AND METHODS 44 4.2.1 Water analysis 44 Physico-chemical and nutrient analyses 44 Pesticide and metal analysis 45 4.2.2 Sediment analysis 46 Sediment composition 46 Pesticide and metal analysis 46 4.2.3 Biota analysis 47 African feral catfish — C. gariepinus 47 Sampling 48 Fish Biology: mass, length and age 49 Pesticides and metal analysis 49 4.2.4 Statistics 50 4.3 RESULTS 50 4.3.1 Water analysis 50 Physico-chemical parameters and nutrients 50 DDT and pesticide concentrations 53 Metal analysis 55 4.3.2 Sediment analysis 57 Sediment properties 57 DDT and pesticide analysis 59 Metal analysis 62 4.3.3 Bioaccumulation 64 Fish Biology 64 DDT Analysis 65 Metal Analysis 67 4.4 DISCUSSION 69 4.4.1 Water analysis 69 Physico-chemical and nutrient concentration 69 DDT and pesticide concentrations 70 Metal concentrations 72 4.4.2 Sediment analyses 73 Sediment properties 73 DDT and pesticide concentrations 74 Metal concentrations 75 4.4.3 Bioaccumulation 76 Fish biology 76 DDT and pesticide concentrations 76 Metal concentrations 78 4.5 REFERENCES 80 Chapter 4

4.1 INTRODUCTION

Increasing concentrations of contaminants present within aquatic ecosystems worldwide are predominantly due to anthropogenic activities such as agricultural applications, urban development and industrial releases (Phillips and Rainbow, 1993). In the case of DDT however, current contamination is primarily due to IRS (indoor residual spraying) that controls the mosquito vector of the malarial epidemic (Mabaso et al., 2004; Tren and Baste, 2004; Falandysz, 1994). Once in the environment, DDT and its resulting metabolites DDE and DDD may continue to persist in various environmental phases, long after sources are discontinued. This is due to DDT's characteristically high lipophilicity, chemical stability and very slow biodegradation (Borga et al., 2001; Connell et al., 1999). When biota are exposed to these features of DDT in the water, sediment and/or food chain, uptake or bioaccumulation is almost spontaneous. According to Vallack et al. (1998), the lipophilic nature of DDT reduces its solubility in water which promotes its accumulation into biota and sediment from the water (bioconcentration), whilst the resistance of DDT to metabolic degradation in biota allows residues to persist and accumulate within the food chains (biomagnification). The combination of uptake from these two environments (bioconcentration and biomagnification) then ultimately leads to greater bioaccumulation of DDT or its metabolites in biota.

In South Africa, despite the fact that DDT has been sprayed for over 56 years, there is still a paucity of data on these concentrations in both the terrestrial and aquatic ecosystems (Awofolu and Fatoki, 2003). The aim of this study was therefore to assess the current status of DDT in the water, sediment and biota (represented by the fish species C. gariepinus) in the aquatic ecosystem of the Luvuvhu River catchment where DDT is currently being sprayed. However, DDT is not the only possible contaminant in the Luvuvhu River. As shown in Chapter 2, agricultural, rural, urban, forestry, sewage treatment plant and coal mine activities could also contribute toward contamination of other organochlorine (OC) pesticides and metals in the catchment and interfere with DDT contamination and effects, in turn influencing the interpretation thereof. Consequently, these contaminants were also screened in the present study.

4.2 MATERIALS AND METHODS

4.2.1 Water analysis

a. Physico-chemical and nutrient analyses

The aquatic habitat is a combination of the physical and the chemical (physico-chemical) constituents making up the water characteristics. Measuring these constituents is essential due to two main reasons. Firstly, anthropogenic activities can render these naturally occurring constituents toxic under certain conditions (Dallas and Day, 2004) and secondly,

44 Chapter 4 naturally different physico-chemical constituents can influence contaminant toxicology (Phillips and Rainbow, 1993). Therefore at each site of the seven sites, in situ measurements were taken using standard physical water quality measurements including the temperature (°C), dissolved oxygen (mg/I and %) (Cyberscan - D0100 dissolved oxygen meter), pH (waterproof pHScan meter) and conductivity/TDS (Cyberscan — CON400 conductivity meter). In order to measure the chemical constituents (including nutrients and trace metals) three water samples were collected in polyethylene bottles and transported back to the laboratory on ice and frozen at -20°C until further analysis. The nutrients that were measured included ammonia, chemical oxygen demand (COD), calcium, nitrate, nitrite, ortho-phosphate and sulphate, while the trace metals were measured as explained in Section 4.2.1b.

In addition to this, historical water quality data was required for comparisons with the current data. This historical data was obtained from DWAF (2005), and incorporated the median values obtained between 1970 and 2005 at various monitoring stations in close proximity to the sites in the current study. These monitoring stations included Station A9H007Q01 located upstream from Latonyanda site, Station A9R001Q01 at Albasini Dam, Station A9H001Q01 upstream from Hasana and Station A9012Q01 upstream from Mhinga.

b. Pesticide and metal analysis

Four water samples were collected seasonally from randomly selected locations at all seven sites, in ethanol (pure grade) washed glass bottles covered in tin foil to prevent UV degradation. Then the samples were transported on ice to the laboratory and stored at 4°C for later pesticide analysis. In the laboratory, the pesticides were extracted and quantified on a gas chromatography mass spectrophotometer (GC-MS) according to Cacho et al. (1995) as explained in Bornman et al. (2009). The pesticide analysis included the OC pesticides: o,p'- and p,p'-DDT, -DDE and —DDD, alpha-BHC, gamma-BHC (lindane), heptachlor, aldrin, dieldrin, beta-BHC, delta-BHC, heptachlor epoxide, endosulfan I, endosulfan II, endosulfan sulfate, alpha-chlordane, gamma-chlordane, endrin, endrin aldehyde, endrin ketone, and methoxychlor as well as the alkylphenols: nonylphenol and octylphenol and PCB 153. Detection levels varied for each pesticide and sample set and are represented in the results. Due to the cost constrains only the OC, alkylphenols and PCBs were measured, an indirect screen for organophosphates (OPs) and carbamates was done using the indirect AChE enzyme analysis (Appendix 2).

For the analysis of the metals in the water, duplicate samples were collected at each site in plastic bottles rinsed with 2% HCI (Bornman et al., 2009). The samples were then frozen and stored at -20°C until further analysis. In preparation for the analysis the samples were diluted in 1% nitric acid and determined by Water lab (Pty) Ltd using a Varian UltaMass 700 inductive coupled plasma mass spectrophotometer (ICP-MS). All samples were analysed for aluminium (Al), arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron

45 Chapter 4

(Fe), manganese (Mn), nickel (Ni), lead (Pb), mercury (Hg) and zinc (Zn). Detection limits were 0.1 pg/I for all the metals measured.

4.2.2 Sediment analysis

As was done for water sample collection, the sediment samples were collected from all sites in all seasons in ethanol (pure grade) washed glass bottles and transported on ice to the laboratory, where the samples were stored at 4°C for later sediment composition, pesticide and metal analysis.

Sediment composition

The physical characteristics of the sediment were evaluated in conjunction with contaminants. Specifically the particle size and organic percentage was assessed as they have been shown to strongly influence interpretation of results (Phillips and Rainbow, 1993). For instance higher concentrations of pollutants will be observed in smaller grain sized sediment samples since they have greater surface area, whilst samples with higher organic content will have higher percentage of hydrophobic contaminants such as pesticides.

In order to measure these two components, three sediment samples were collected from each of the 7 sites, kept on ice and refrigerated in the laboratory until assessment was done as described by Barker (2006). The sediment particle sizes were measured by sieving 80 g of wet sediment through an array of sieves with grid sizes ranging from <125, >125, >300, >500 and >700 microns. Then the mass of sediment in each sieve was determined after the drying process and expressed as a percentage of the 80 g sediment. As for the percentage organics, 1 g of wet soil was dried at 110°C for 24 hours. The dried sample was then weighed (includes both organic and inorganic constituents) and incinerated at 600°C for 6 hours to destroy all the organic constituents. The remaining inorganic constituents in the form of ash were thereafter weighed. The mass of the organic content (otherwise known as total organic carbon (TOC)) was then obtained by subtracting the inorganic (incinerated) mass from the dried mass (Barker 2006; Heath, 1999).

Pesticide and metal analysis

In the laboratory the same pesticides measured in the water were measured in the sediment. The pesticides were first extracted from the sediment using the method by Naude et al. (1998) and then quantified using the GC-MS according to Villaverde et al., (2008) and Bordet et al., (2002), as explained in Bornman et al. (2009) (Detection levels varied for each pesticide and sample set and are represented in the results). The easily reducible metal concentrations in the sediment were digested using the methodology described by Bervoets and Blust (2003). Sediment was dried to a constant mass in a 60°C oven and 1 ml was added to 15 ml of 0.1 M NH 2OH.HCI in 0.01 M HNO 3 . After 30 hours of incubation, the

46 Chapter 4 sediment was then filtered to get excess debris from metal containing substrate and measured by Water lab (Pty) Ltd on ICP—MS. The detection limits were 0.01 pg/g for all of the metals.

4.2.3 Biota analysis

a. African feral catfish -C. gariepinus

Selection criteria of male catfish The biota assessments were represented by the fish species, C. gariepinus. This species was selected as it complied with the majority of criteria required for a bioindicator of contamination (Connell et al., 1999), including the following advantages (Heath and Claassen, 1999):

Can accumulate a relatively large amount of pollutants without being killed, Relatively sedentary and do not have large migratory patterns as compared to more migratory fish such as eel, Readily found throughout the Luvuvhu River and in fact in South Africa in almost all levels of pollution, Readily exposed to DDT pollution due to its omnivorous feeding habits Relatively long lived, Large enough to ensure adequate quantities of material for analyses, Rather hardy, Relatively easy to handle, Been shown to represent metal and pesticide contamination present within the aquatic ecosystem.

The male specimens were particularly focused on in this study as many of the biomarkers selected were specific to male changes due to the oestogenic mimicking-effects of DDT (Kime, 1998) and because the sex of organisms is a large influencing factor in bioaccumulation and effects monitoring (Phillips and Rainbow, 1993).

Biology, distribution and feeding behaviour catfish C. gariepinus have a strongly compressed, elongated cylinderical body (Skelton, 2001). They are characterised by an extremely long dorsal and anal fin that both extend toward the base of the rounded caudal fin. They have no scales and a skin that is often marbed in shades of black or gray and a white abdomen. Their heads are large and depressed, with small eyes, a sub-terminal mouth and 4 pairs of long filamentous barbels (maxillaries longest).

47 Chapter 4

This catfish species is one of the most widely distributed fish species in Africa, found throughout Southern Africa to Algeria and from West Africa to the Nile (de Graaf and Janssen, 1996). It occurs in almost any habitat, but favours slow, turbid pools in large sluggish rivers and is commonly found in dams and lakes. This species is able to withstand harsh non-perennial environments as they are above to move overland under damp conditions if necessary. They are primarily able to do this due to the presence of an accessory air-breathing organ that allows the catfish to absorb oxygen from atmospheric air.

Catfish are primarily omnivorous and preys, scavenges or grubs on virtually any available organic food source including fish, birds, frogs, crabs, insects and even plant matter such as seeds and plankton (Skelton, 2001). They normally breed in summer after rains to flooded shallow grassy verges of rivers and lakes. After fertilisation growth is rapid, but greatly dependent on local conditions. Individuals are mature after a year and live for approximately 8 years.

b. Sampling

In order to assess the bioaccumulation of metals and pesticides in C. gariepinus 10 adult males were proposed as a significant number for collection at Albasini Dam, Nandoni Dam and Xikundu in all four seasons i.e. 10 per a site and season as depicted in Table 4.1. Despite the initial aim to collect 10 C. gariepinus using three 100 mm mesh size gill nets, only two data sets were fully completed. There were two possible reasons for the lack of fish caught. Firstly, there may not have been fish present, perhaps a reduction in fish populations due to overfishing or other unfavourable conditions. Secondly, due to inefficient sampling techniques, despite that all efforts were taken to reduce this influence by sampling both day and night in all habitat diversities. For instance, the mesh size may have been incorrect for the population sizes that were present. Furthermore, no catfish were collected at the lotic sites (due to the lack of appropriate sampling gear such as nets) except at Xikundu, which was extensively sampled with nets upstream of the weir. Therefore, no analyses at the lotic sites could be done. Nevertheless in the fish collected from the lentic sites the muscle was removed, placed in plastic Falcon tubes and stored at -20°C until metal analysis could be done. As for pesticide analysis, the adipose tissue was removed from each catfish, placed into foil and also stored at -20°C until further analysis could be done.

Table 4.1. Number of C. gariepinus collected in all 12 data sets from low flow (LF) and high flow (HF) of 2006, 2007 and 2008.

FLOW REGIME Albasini Nandoni Xikundu LF 2006 9 4 3 HF 2007 9 1 3 LF 2007 6 3 10 HF 2008 7 4 10

48 Chapter 4

Fish biology: mass, length and age

According to Philips and Rainbow (1993) size, mass and age may affect accumulation of contaminants and therefore need to be taken into consideration when analysing contaminant results. To determine the age the pectoral spines were collected and prepared according to Staples (1970). Sections of approximately 1 mm were cut perpendicular to the basal recess of the pectoral spine, using a hacksaw blade. The number of rings on each of the thin sections, placed in ethanol, was counted using a dissecting microscope (x10). As for the gonad maturity it was macroscopically estimated using the gonad development index described in Heath (1999).

Pesticides and metal analysis

The DDT residues were measured in the fat of the male C. gariepinus collected from all the seasons except in the high flow 2007, where the adipose tissue samples were inadequate for analysis. These samples were taken to the Food and Drug Assurance (FDA) laboratories (Pretoria, South Africa) for further analyses. Samples were first thawed, extracted and reconstituted into 250 pl hexane before GC-MS was used to quantify the DDT concentrations as described by Bornman et al. (2009) (detection levels varied for each pesticide and sample set and are represented in the results). The other pesticides screened for in the water and sediment were, however, not analysed in the fish tissue due to cost constraints.

In order to measure the metals, frozen muscle tissue from C. gariepinus was thawed and dried to a constant weight in a 60°C oven for 96 hours. A sample of 0.5 g dry tissue was then added to an acid solution made of 7 ml 65% nitric acid and 1 ml 30% hydrogen peroxide, according to the application notes for digestion in a Milestone ETHOS microwave (cookbook digestion, REV. 03_04). The samples were placed in the Milestone ETHOS microwave and digested for 30 minutes at 200°C. The digested samples were then diluted (200 x) in 1% nitric acid and metal concentrations were determined using a Varian UltaMass 700 ICP-MS at the University of Johannesburg. These facilities were utilised instead of those from Water lab, as an alternative was required because the WRC project only allowed for the screening of metals in the water and sediment. Nevertheless, the samples were analysed for the same metals as those in the water and sediment. Indium was used as an internal standard to correct for interferences from high-dissolved solids. All concentrations were validated with reference materials. Certified values and recoveries for the reference materials are presented in Table 4.2. Detection levels varied for each metal and sample set and are represented in the results.

49 Chapter 4

Table 4.2. Mean recoveries for metals (pg/g) from standard reference material (DOLT-3).

Element DOLT-3 Recovery As 10.2 9.07 Cd 19.4 18.81 Cu 31.2 28.54 Fe 1484 1610 Pb 0.32 0.51 Zn 86.6 70.73

4.2.4 Statistics

The Graphpad v.4 software was utilised to draw all the graphs represented in this chapter. The significant variations between the sites and seasons for each contaminant were tested by one-way analysis of variance (ANOVA) using SPSS v.15 (Zar, 1996). Data was tested for homogeneity of variance using Levene's test, prior to applying post-hoc comparisons. Post-hoc comparisons were made using the Scheffe test for homogeneous or Dunnett'sT3 test for non-homogenous data. Also using SPSS v.15, Pearson's correlation was calculated to determine if there were any relationships between contaminants (metals and pesticides) in the sediment and fluctuating sediment components (organic content and particle sizes), as well as in the biota between contamination and biological influencing factors. The use of either one of the two tests resulted in the determination of significant differences (p<0.05) between variables. Furthermore, principle component analysis (PCA) was used to discriminate between the different sites and seasons, defined by square root transformed pesticides in the water and sediment and metals in the sediment (metals in the water could not be analyses since there were too few analyses). The PCA analyses were all performed with the PRIMER v.4 software.

4.3 RESULTS

4.3.1 Water analysis

a. Physico-chemical parameters and nutrients

The physico-chemical and nutrients at all sites, except at Latonyanda in low flow 2006 (Section 3.2), along with median historical data obtained between 1970 and 2005 from DWAF stations are represented in Table 4.3. As can be seen at all sites within the low flow 2006, the water oxygen was not successfully measured due to instrumental error. Considering the water quality at the different sites, it evident that at Latonyanda the water was generally clear, with high dissolved oxygen and lower temperatures. The pH was lower (no significance, p>0.05) than historical data, while there was an increase in nitrate in 2007 and an increase in ammonia and sulphate in high flow 2008. The water at Albasini Dam ranged from 21°C to 28°C in the high flow 2008, whilst the oxygen was not successfully measured in the high flow 2007. In the latter two seasons (low flow 2007 and high flow 2008), the pH, conductivity, TDS, ammonia, nitrate, nitrite, phosphate and sulphate

50 Chapter 4

concentrations were generally higher (not significantly, p>0.05). At Hasana the water temperatures were generally higher (between 26 and 30°C), while the oxygen of the water was low in the 2007 sampling regimes, but recovered in the following season. Although most of the nutrients were above the historical values found at this site, the increase was only very slight with the maximum increase found in ortho-phosphate levels in the high flow 2008 and sulphate levels in all seasons except in the first season. Nandoni Dam water then showed high pH levels, increased conductivity, TDS and phosphate levels in the low flow 2007, higher nitrate levels in high flow 2007 and higher sulphate levels in high flow 2008. Followed by Tshikonelo which had water quality similar to that found previously with a reduction in sulphate and slight increase in pH, conductivity in the low flows, ammonia in the high flow 2007, nitrate in low flow 2006 and ortho-phosphate in high flow 2008. At Xikundu the water was generally quite turbid especially in the high flow regimes with few chemical components above historical levels. In the low flow 2006 the pH and ammonia were higher than normal, but recovered in the following sampling regime, while an increase in nitrates and nitrites in the low flow 2007 and a higher orthophosphate in the high flow of 2008 were found. Lastly, Mhinga had generally stable water quality, with only a higher ortho-phosphate concentration being observed in the high flow 2007 and higher sulphate in the high flow 2008.

51

Chapter 4

Table 4.3. Physico-chemical water quality variables in the Luvuvhu River with historical data obtained between 1970 and 2005 from DWAF stations (italised).

/I)

m) (mg /c

te

S /I)

0 /I) ha (p /I)

112 ity (mg mg (mg iv ia hosp

t E

(1) ( te

s- n n C 0. w e 43) ha

0. 0) duc ho-p lp n E U) t CI ) X Or Su Co Ammo Oxyg 0 0.

HF 2007 21.7 8.17 95 7.12 85 43 bd 0.4 bd 0.01 bd LF 2007 23.0 8.46 107 7.81 49 25 0.01 2.0 0.07 0.03 5 HF 2008 23.8 9.30 110 7.31 73 37 0.80 bd bd 0.05 8 Historical na na na 7.50 82 59 0.02 0.2 na 0.01 2

LF 2006 20.9 7.53 260 126 0.03 2.5 0.01 0.01 bd HF 2007 24.0 - 7.62 299 149 bd bd 0.02 0.01 bd i Dam LF 2007 24.7 8.74 115 8.48 825 413 0.01 0.0 0.12 0.09 5 in s HF 2008 28.0 10.00 127 8.61 305 153 0.60 0.3 0.01 0.21 10

Alba Historical na na na 7.9 229 164 0.02 0.08 na 0.013 6

LF 2006 26.3 - - 8.63 148 75 0.02 0.8 0.02 0.09 bd HF 2007 30.8 7.10 95 7.70 129 65 bd bd 0.01 0.00 10 LF 2007 26.3 7.74 111 7.28 117 0.01 bd 0.04 0.07 5 HF 2008 27.8 10.64 130 7.90 105 bd bd 0.01 0.16 8 Historical na na na 7.85 125 89 0.02 0.2 na 0.02 4

LF 2006 25.8 - 8.93 141 71 0.03 bd 0.01 0.02 7 m HF 2007 27.0 6.09 80 7.41 164 82 bd 3.1 bd 0.03 bd i Da

n LF 2007 22.4 9.81 114 8.93 424 214 0.02 1.0 0.06 0.13 1

do 9 n HF 2008 29.5 10.38 136 8.28 151 75 bd bd bd 0.03

Na Historical na na na 7.85 125 89 0.02 0.2 na 0.02 4

LF 2006 22.8 - - 9.00 173 87 0.02 1.7 0.01 0.02 6

lo HF 2007 33.1 7.32 102 8.86 127 80 0.13 bd 0.01 0.04 4 LF 2007 25.4 10.70 114 8.03 174 0.06 bd 0.01 0.09 bd

hikone HF 2008 27.6 8.39 108 7.48 132 66 bd 0.6 0.01 0.11 bd Ts Historical na na na 7.97 144 105 0.02 0.2 na 0.02 5

LF 2006 24.8 - - 8.56 178 89 0.10 0.3 0.02 0.02 bd c HF 2007 28.2 6.10 85 7.65 142 72 0.02 0.7 bd 0.01 bd V c c LF 2007 18.0 12.00 130 7.30 182 0.06 1.0 0.12 0.06 5 -IC R HF 2008 28.9 9.27 123 6.96 139 70 bd 0.3 0.01 0.14 7 Historical na na na 7.97 144 105 0.02 0.2 na 0.02 5

LF 2006 28.0 - 8.14 175 88 0.04 bd 0.01 0.02 bd HF 2007 25.0 7.96 106 7.39 138 bd bd bd 0.10 5 LF 2007 27.5 9.33 120 8.10 182 91 0.07 bd 0.04 0.04 bd HF 2008 26.5 6.82 141 70 bd 0.7 bd 0.08 8 Historical na na na 7.97 144 105 0.02 0.2 na 0.02 5 LF-low flow; HF-high flow; Bd-concentrations below detection; na-variables not analysed; hyphen-technical errors.

52 Chapter 4

b. DDT and pesticide concentrations

The concentrations of DDT in the water were summarised in Table 4.4 for all sites and seasons except Latonyanda low flow 2006 (Section 3.2). The high flow 2008 was the only season that showed any contamination in the water. DDE was the only metabolite detected, with concentrations ranging from below detection (<0.5 pg/I) to 1.2 pg/I. Of the detected DDT contaminants, summarised in Figure 4.1, all sites were above acceptable levels (0.1 pg/I) defined in South Africa (Kempster et al., 1980) and Australia (Australian water quality guidelines, 2000) except for Nandoni Dam, with Xikundu and Hasana showing the highest concentrations present.

Table 4.4. DDT concentrations in the water (Ng/1) from the Luvuvhu River.

o,p'-DDT p,p'-DDT o,p'-DDE p,p'-DDE o,p'-DDD p,p'-DDD LF 2006 All sites <0.10 <0.10 <0.10 <0.10 <0.10 <0.10

HF 2007 All sites <0.10 <0.10 <0.10 <0.10 <0.10 <0.10

LF 2007 All sites <0.10 <0.10 <0.10 <0.10 <0.10 <0.10

HF 2008 Latonyanda <0.5 <0.5 <0.5 0.8 <0.5 <0.5 Albasini Dam <0.5 <0.5 <0.5 0.6 <0.5 <0.5 Hasana <0.5 <0.5 <0.5 1.0 <0.5 <0.5 Nandoni Dam <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 Tshikonelo <0.5 <0.5 <0.5 0.5 <0.5 <0.5 Xikundu <0.5 <0.5 1.2 0.6 <0.5 <0.5 Mhinga <0.5 <0.5 <0.5 0.6 <0.5 <0.5 LF-Iow flow, HF-high flow, Bold-highlights the values obtained; <0.5 and <0.1 pg/I represent concentrations below detection levels.

In conjunction with the DDT analysis other possible contaminants were screened for in order to identify any additional contamination which may be present at the time of sampling. The results, given in Table 4.5, showed that only three of the 19 screened pesticides were present in the Luvuvhu River water. These included PCB 153, dieldrin and lindane, all of which were only present in the high flow 2008 and at concentrations that were above South African (DWAF, 1996) and Australian water quality guidelines (2000) (Table 4.5 and Figure 4.2). Spatial representation of these contaminants showed that Xikundu had the greatest concentrations of dieldrin and lindane, while Hasana was the only site that had PCB 153 contamination.

The combined influence of these pesticide concentrations and DDT concentrations on the spatial and temporal trends was illustrated on a PCA plot in Figure 4.3. No clear spatial pattern was observed for the contaminants on either of the PC plots in Figure 4.3a, however

53 Chapter 4 there was a separation of data from the high flow 2008 on the PC1 axis (Figure 4.3b). As shown in Table 4.6, most of this temporal variation was due to the presence of elevated lindane and dieldrin levels.

Water LF 2006 2. ER HF 2007 OLF 2007 1.5 MHF 2008

1. 0 O (.4 0. 6

QG 0.0 LAT ALB HAS NAN TSHI XIK MHI

Sediment

15.61 10.6 1. 5.6 1 QG 0.6 0 0.61 O. 0.2 n 0.0 I I I I LAT ALB HAS NAN TSHI XIK MHI

Catfish 68 68 48 p) 38 28 18 8

6 4 2 QG 0 rALB NAN XIK

Figure 4.1. Spatial and temporal representation of the E DDT (sum of DDT, DDE and DDD in Table 4.4, Table 4.8, Table 4.14) with guidelines (QG) derived for Canada, Australia and USA aquatic ecosystems. The sites included Latonyanda (LAT), Albasini dam (ALB), Hasana (HAS), Nandoni Dam (NAN), Tshikonelo (TSHI), Xikundu (XIK) and Mhinga (MHI) in the low flow (LF) of 2006 and 2007 and high flow (HF) of 2007 and 2008.

54 Chapter 4

Table 4.5. Minimum, median and maximum pesticide and metal concentrations (Ng/1) recorded in water in 2007-2008 (present study), 2005-2006 (data recorded by Barker (2006)) and 1992- 1993 (data recorded by Heath and Claassen (1999)), with water quality guideline for SA (DWAF, 1996).

Present study 2005 - 2006 1992-1993 SA water quality guidelines Min Median Max Min Max Min Max Pesticides

PCB153 <0.1 <0.1 2.8 na na na na 80 Dieldrin <0.1 <0.1 3.5 na na na na 0.005 Lindane <0.1 <0.1 9.4 na na na na 0.015

Metals Al <0.1 0 77.95 50 61 bd bd 10 As <0.1 <0.1 <0.1 na na bd bd 10 <0.1 1 Cd <0.1 <0.1 2 3 3 .0.01 Co <0.1 0 0.58 na na 13 13 na Cr <0.1 <0.1 <0.1 56 57 3 15 7 Cu <0.1 0 1.58 39 58 12 90 0.8

Fe <0.1 232.07 756.88 38 68 210 1310 8300 Hg <0.1 <0.1 <0.1 na na bd bd 0.04 Mn <0.1 10.69 130.02 33 79 40 500 180 Ni <0.1 0 1.59 13 40 13 40 na Pb <0.1 <0.1 <0.1 17 41 25 25 0.5 Zn <0.1 0 3.61 68 73 70 190 2 na-not analysed; bd-below detection in historical studies; asterisks-concentrations were derived for medium water hardness (Heath and Claassen, 1999); aobtained from Canadian guidelines and bold-represents values that were above South African water quality guidelines; <0.1 pg/I-below detection in the present study for both pesticides and metals.

c. Metal analysis

Metal concentrations were also assessed in the water, in order to identify any other sources of pollution that might have been present within the Luvuvhu River. The concentration ranges, for all sites and seasons of the present study (except Latonyanda in low flow 2006), were represented as many of the samples were below detection (refer to the appendix 3 for the unabridged concentrations). These ranges are listed in Table 4.5 along with historical data obtained from previous studies (1992-1993 and 2005-2006). Upon comparison it was evident that the current metal concentrations differed considerably from the historically measured metals. Most of the water metal concentrations decreased from the previous years' analyses, with only Al showing higher concentrations. When the Al concentrations were compared against the acceptable water quality levels defined by DWAF (1996) in Table 4.5, the maximum concentration found was about 16 times higher than the target level. In fact, further inspection of Al concentrations at each site and season in Figure 4.2 showed that most of the concentrations obtained in the last two sampling seasons were well above the South African target water quality range (TWQR). Zn was another metal that was above the TWQR level, but only slightly at Hasana, Nandoni and Tshikonelo in the low flow of 2007 as shown in Figure 4.2. Further spatial interpretation of these metals on PCA plots however could not be done since there was too few trace metals measured in the water. As for the Fe concentrations, although they were above the guidelines set out for Canadian environments, Fe is largely influenced by the natural geological chemistry of an area and as

55 Chapter 4 such it is suspected that the concentrations were within the normal limits of the Luvuvhu River catchment (Heath and Claassen, 1999).

EEEMLF 2006 HF 2007 4 O LF 2007 HF 2008 3 cr) 2

r. 2

O 2 1

WOG -WOG LAT ALB HAS NAN TSHI XIK MHI LAT ALB HAS NAN TSHI XIK MHI g il p m iu in Alum

TWQ R WOG LAT ALB HAS NAN TSHI XIK MHI LAT ALB HAS NAN TSHI XIK MHI

TWQ R

LAT ALB HAS NAN TSHI XIK MHI

Figure 4.2. Spatial and temporal representation of the pesticide and metal concentrations measured in water above SA target water quality range (TWQR) and international guidelines for when no SA guidelines available (WQG) (see Table 4.5 for ranges). The sites include Latonyanda (LAT), Albasini dam (ALB), Hasana (HAS), Nandoni Dam (NAN), Tshikonelo (TSHI), Xikundu (XIK) and Mhinga (MHI) in the low flow (LF) of 2006 and 2007 and high flow (HF) of 2007 and 2008.

Table 4.6. Eigenvector coefficients for the first two PC's for pesticides in the water.

PC1 PC2 81.6% 9% o,p'-DDE 0.16 0.13 p,p'-DDE 0.26 -0.64 o,p'-DDD 0.00 0.00 o,p'-DDT 0.00 0.00 p,p'-DDD 0.00 0.00 p,p'-DDT 0.00 0.00 PCB 153 0.25 0.20 Dieldrin 0.50 0.66 Lindane 0.77 -0.31 Absolute values of the coefficients greater than 60% of the maximum coefficient is shown in bold (PC1: 60% of 0.77 = 0.462 ; PC2: 60% of 0.66 = 0.396)

56 Chapter 4

(a) A Latonyanda Albasini Dam i P Hasana Nandoni Dam Tshikonelo

0 .0 Xikundu . cv X Mhinga 0 0 a a_

,, • £} -1 I I 1 I I -1 0 1 2 3 4 PC1

(b)

i A LF 2006 0 V HF 2007 LF 2007 0 0 HF 2008 0

o

0

0 -1 0 -1 0 1 2 3 4 PC1

Figure 4.3. Principal component analysis of pesticides in the water (square root transformed) in order to compare the sites (a) and the flow regimes (low flows (LF) and high flows (HF)) (b) using the same data set.

4.3.2 Sediment analysis

a. Sediment properties

The percentage of particle grain size at each site is represented in Figure 4.4, where the gravel and sand represent particles ranging from 4 mm to 212 pm and the mud represents the finer grain sizes of clay (<3.9 pm) and silt (3.9 — 62.5 pm). Upon evaluation of the graph, Xikundu showed the highest percentage of fine grain sediment, the general lotic sites showed a dominance of sand particles and the two dams (Albasini and Nandoni) showed an even distribution of grain sizes.

The percentage of organic matter for each flow regime and site is listed in Table 4.7. The organic matter was generally low throughout, the study compared to other studies, with levels ranging from 0.47% to only 10.12%. The sites with the most abundant organic matter were observed at Albasini Dam, Nandoni Dam and Xikundu in the low flow 2006 and the two high flow regimes, while the abundance in the low flow 2007 was greatest at Hasana, Tshikonelo and Xikundu.

57

Chapter 4

Latonyanda Albasini Dam 10 100-' 9 90- 8 80- 7 70- 6 60- 5 50- 4 40- 3 30- 2 20- 10- 0 LF '06 HF'07 LF'07 HF '08 AVG LF '06 HF'07 LF'07 HF '08 AVG Hasana Nandoni Dam 100 9 8 7 6 50 4 3 2

LF '06 HF'07 LF'07 HF '08 AVG LF '06 HF'07 LF'07 HF '08 AVG Tshikonelo Xikundu 100-' 100-, 90- 90- 80- 80- 70- 70- 60- 60- 50- 50- 40- 40- 30- 30- 20- 20- 10- 10- 0 "PM UMW 1.191111" LF '06 HF '07 HF.'08 AVG LF '06 HF'07 LF .'07 HF '08 AVG

Mhinga 10 9 8 7 6 5 Mud 4 Sand 3 2 Gravel

LF '06 HF'07 LF'07 HF '08 AVG

Figure 4.4. Percentage particle sizes ranging from 2 — 4 mm (gravel), 212 — 2000 pm (sand) and <212 pm (mud) for all sites and seasons (except Latonyanda, low flow 2006) and average (AVG) of all seasons. LF and HF represented the low flow and high flow regimes, respectively.

58 Chapter 4

Table 4.7. The mean (± SE) of the percentage organic matter present in the sediment at all sites and seasons. LF 2006 HF 2007 LF 2007 HF 2008 Latonyanda 0.94 (0.14) 1.16(0.56) 0.74 (0.03) Albasini Dam 5.03 (0.49) 5.10 (0.49) 1.37 (0.07) 4.84 (0.23) Hasana 0.47 (0.02) 0.62 (0.06) 10.12 (0.05) 0.47 (0.01) Nandoni Dam 2.54 (0.27) 3.40 (0.05) 1.62 (0.20) 1.49 (0.31) Tshikonelo 0.76 (0.03) 0.80 (0.08) 6.42 (5.82) 1.49 (0.12) Xikundu 5.13 (0.05) 1.68 (0.16) 6.95 (2.81) 1.62 (0.16) Mhinga 0.75 (0.09) 0.43 (0.02) 2.81 (5.42) 1.18 (0.09) LF-low flow; HF-high flow; Hyphen-no data available.

b. DDT and pesticide analysis

Table 4.8 shows the results of the DDT contamination in the sediment of the Luvuvhu River. As can be seen, no DDT was evident in the first two sampling seasons, however in the following seasons DDT contamination was measured. In the low flow 2007 the Latonyanda site showed small concentrations of DDT and DDD, whilst Hasana showed increased concentrations of DDE. Despite the recovery of both these sites in the following season, there was an increase in DDT and DDE concentration in the sediment at Nandoni, Tsikonello and Xikundu. The site with the highest concentrations was Xikundu with a total of 11.17 pg/kg of DDT (1% TOC normalised) in the high flow 2008, approximately three times higher than the highest guideline proposed (Table 4.8 and Figure 4.1). Furthermore, Xikundu was also contaminated in this season with heptochlor epoxide and endrin aldehyde and in the low flow 2007 with endosulfane II (Figure 4.5). The combined spatial and temporal representation of the contaminants is then illustrated in Figure 4.6. The PC1 axis represented 91% of the variance, with the majority of the patterns related to higher concentrations of DDT and DDE in the high flow of 2008 (Table 4.9). The second axis resulted in very little of the variance with a coefficient of 4%, it was nevertheless evident that Xikundu showed the highest levels of pesticide contamination in the sediment.

In order to ensure that these spatial trends were not influenced by the natural properties of the sediment, the sediment contaminants were correlated with various sediment components discussed previously, including the percentage of organic matter and the various grain sizes. The results for the pesticide contamination observed in Table 4.10 however showed no significant correlations present.

59 Chapter 4

Table 4.8. DDT concentrations (Ng/kg) (concentrations normalised to 1% TOC) in the sediment from the Luvuvhu River.

o,p'-DDT p,p'-DDT o,p'-DDE p,p'-DDE o,p'-DDD p,p'-DDD LF 2006 All sites <0.020 <0.020 <0.020 <0.020 <0.020 <0.020

HF 2007 All sites <0.020 <0.020 <0.020 <0.020 <0.020 <0.020

LF 2007 Latonyanda <0.10 0.10 (0.08) <0.10 <0.10 <0.10 0.10 (0.08) Albasini Dam <0.10 <0.10 <0.10 <0.10 <0.10 <0.10 Hasana <0.10 <0.10 0.20 (0.02) <0.10 <0.10 <0.10 Nandoni Dam <0.10 <0.10 <0.10 <0.10 <0.10 <0.10 Tshikonelo <0.10 <0.10 <0.10 <0.10 <0.10 <0.10 Xikundu <0.10 <0.10 <0.10 <0.10 <0.10 <0.10 Mhinga <0.10 <0.10 <0.10 <0.10 <0.10 <0.10

HF 2008 Latonyanda, Albasini, Hasana <0.5 <0.5 <0.5 <0.5 <0.5 <0.5 Nandoni Dam <0.5 1.40 (0.934) <0.5 3.70 (2.48) <0.5 <0.5 Tshikonelo <0.5 1.80 (1.21) <0.5 3.80 (2.55) <0.5 <0.5 Xikundu <0.5 4.10 (2.53) 1.20 (0.74) 12.80 (7.9) <0.5 <0.5 Mhinga <0.5 <0.5 <0.5 <0.5 <0.5 <0.5

Guidelines USAa 4.16 4.16 3.16 3.16 4.88 4.88 Canada° 1.19 1.19 1.42 1.42 3.54 3.54 Australia` 2.2 2 2

a MacDonad et al. (2000); b Canadian environmental quality guidelines (1999b); `Australian sediment quality guidelines (2000); bold-highlights the values obtained; hyphen-no data available. LF-Iow flow; HF-high flow; detection limits include 0.02 pg/kg for low flow 2006 and high flow 2007, 0.1 pg/kg for low flow 2007 and 0.5 pg/kg for high flow 2008.

Table 4.9. Eigenvector coefficients for the first two PC's for pesticides in sediment.

PC1 PC2 91% 4% o,p'-DDT 0.00 0.00 p,p'-DDT 0.49 0.27 o,p'-DDE 0.17 -0.66 p,p'-DDE 0.83 0.14 o,p'-DDD 0.00 0.00 p,p'-DDD 0.00 0.02 Heptachlor epoxide 0.15 -0.49 Endosulfan II -0.02 -0.13 Endrin aldehyde 0.15 -0.47 Absolute Values of coefficients greater than 60% of the maximum coefficient is shown in bold (PC1: 60% of 0.83 = 0.498 ; PC2: 60% of 0.66 = 0.396)

60

Chapter 4

EZE3 LF 2006 E. 2.61 EBE HF 2007 a = LF 2007 :121 ME HF 2008 1 1.1 0 1. 7c. o b

0.0 LAT ALB HAS NAN TSHI XIK MHI LAT ALB HAS NAN TSHI XIK MHI

1.

1'7 0.7

.c 0.5 "43 0 0.2

0 LAT ALB HAS NAN TSHI XIK MHI

Figure 4.5. Spatial and temporal variation of sediment pesticide concentrations above sediment quality guidelines. (a) Canadian guidelines (1999) and (b) Australian guidelines (2000) (see Table 4.11). The sites included Latonyanda (LAT), Albasini dam (ALB), Hasana (HAS), Nandoni Dam (NAN), Tshikonelo (TSHI), Xikundu (XIK) and Mhinga (MHI) in the low flow (LF) of 2006 and 2007 and high flow (HF) of 2007 and 2008.

A Latonyanda *. Albasini Dam Q Hasana ■ • Nandoni Dam 410 Tshikonelo • co o O Xikundu a- • X Mhinga 0

-1 0 1 2 3 4 5 PC1

A LF 2006 HF 2007

00 LF 2007 ■(:). HF 2008

0 •

O

0 1 2 3 4 5 PC1

Figure 4.6. Two identical PCA plots with the same data set of pesticides in the sediment represented with (a) a spatial and (b) a temporal key in order to identify the relationships between the sites and sampling flow regimes (two low flows (LF) and high flows (HF)), respectively.

61 Chapter 4

Table 4.10. Pearson's correlations between contaminants (metals and DDT) and fluctuating sediment components.

Gravel Sand Mud Organic matter Al Fe Mn Pesticides None*

Metals As -0.04 0.03 0.00 0.20 -0.39 0.75 -0.18 Cu -0.16 0.32 -0.20 -0.38 0.69 -0.47 0.48 Fe -0.25 -0.12 0.31 0.59 Mn -0.36 -0.08 0.35 0.45 - Bold-significantly (p<0.05) correlated components; Hyphen-data not represented; *No significant correlations observed.

c. Metal analysis

In Table 4.11 the minimum, maximum and median concentrations of sediment metals from all the sites over all the seasons sampled in the Luvuvhu River were listed together, as few concentrations of significance were represented on unabridged format that resulted in unnecessary lists of data (see appendix 4 for complete list of sediment concentrations), along with the international sediment quality guidelines for all the important metals (MacDonald et al., 2000; Canadian environmental quality guidelines, 1999b; Ingersoll et al., 1996). These international sediment quality guidelines were used as an alternative as there were no available sediment guidelines for South African ecosystems. Upon comparison with these guidelines it was found that the current trace and heavy metals were very low and well within the sediment guideline values.

Although the concentrations were of no major concern, PCA was performed to identify if any sites showed a tendency toward higher metal concentrations, which is represented in Figure 4.7. On the PC1 axis of Figure 4.7a, Mhinga was clearly separated from the other sites. This was primarily due to higher Co concentrations, which can be seen in Table 4.12. While on the second (PC2) axis in Figure 4.7b the low flow 2007 was separated according to higher Zn concentrations.

As previously mentioned, the sediment properties may influence the concentration of a contaminant measured in sediments. Therefore the relationship between metals and organic content and particle sizes was evaluated. The result represented in Table 4.10 were however inconsistent. It was found that Fe and Mn were significantly positively correlated with organic matter, while Cu showed a significantly negative relationship. As for the influencing metals, there were only two relationships observed, one correlation was between Fe and As and the second correlation was between Al and Cu.

62 Chapter 4

Table 4.11. Range of pesticide and metal concentrations in sediment (pg/g) for all sites and seasons (except Latonyanda low flow 2006) in the Luvuvhu River and international sediment guidelines.

Present study Sediment Quality Guidelines (SQG)

min median max USA e ' b Canadian Australian ° Pesticides Endosulfan II <0.0001 <0.0001 0.0006 na na na Heptachlor epoxide <0.0005 <0.0005 0.001 0.002 0.0006 na Endrin aldehyde <0.0005 <0.0005 0.0009 na na na

Metals Al 2.94 7.34 21.08 58 000 na na As <0.01 <0.01 0.04 10 6 20 Cd <0.01 <0.01 <0.01 1 0.6 2

Co 0.08 0.41 1.11 20 e na na Cr 0.00 0.01 0.09 43 37 80 Cu 0.02 0.11 0.33 32 36 65 Fe 0.19 16.37 117.35 200 000 na na Hg <0.01 <0.01 <0.01 0.18 0.17 0.15 Mn 2.70 11.84 50.02 730 na na Ni <0.01 0.01 0.95 23 na 21 Pb <0.01 <0.01 0.06 36 35 50 Zn 0.07 0.20 1.08 121 123 200 a MacDonald et a/. (2000), b Ingersoll et al. (1996), Canadian environmental quality guidelines (1999), ° Australian sediment quality guidelines (2000) and e natural concentrations of cobalt in sediment is less than 20 pg/g (Nagpal, 2004). na-not available; bold-above sediment quality guidelines and detection limits include 0.0001 pg/g for endosulfan II, 0.0002 pg/g for remaining pesticides and 0.01 pg/g for all metals.

Table 4.12. Eigenvector coefficients for the first two PC axis's for trace metals in sediment

PC1 PC2 47% 32% As 0.01 -0.03 Cr 0.00 0.04 Co 0.87 0.37 Cu 0.06 0.05 Ni 0.27 0.20 Pb 0.03 -0.06 Zn 0.41 -0.90 Absolute values of coefficients greater than 60% of the maximum coefficient is shown in bold (PC1: 60% of 0.76 = 0.456 ; PC2: 60% of 0.91 = 0.546)

63 Chapter 4

(a)

0.5 latonyanda * Albasini Dam 0 Hasana Nandoni Darn ze 0 411 Tshikonelo 0 4. O Xikundu a, X Mhinga

-0.5

0 0.5 0 0.5 1.0 PC1

(b) 0.5 * LF 2006 HF 2007 LF 2007 )1( HF 2008 * O

-0.5 *

-1.0 0.5 0 0.5 1.0 PC1

Figure 4.7. Two identical principal component plots of sediment metal burdens representing different groupings, with (a) representing the relationship between the sites and (b) representing the relationship between the four flow regimes (two low flows (LF) and two high flows (HF))

4.3.3 Bioaccumulation

a. Fish biology

The range of age, size and maturity of the C. gariepinus sampled were summarised in Table 4.13. As was expected due to the selective sampling for size, most of the fish sampled showed similar attributes with moderate fluctuations around the median. Although there was no seasonal changes there were some spatial differences. In the low flow 2006, Nandoni Dam specimens were generally older and larger than the other two sites, whilst Xikundu fish were generally more mature in this season. Then in the high flow 2007, the ages of the fish

64 Chapter 4 did not correspond to the size of the fish, with Xikundu fish being the youngest with the largest body mass, length and gonad maturity. Similar trends were also observed for the following two seasons. At Nandoni Dam fish were generally the largest and most mature compared to the other sites but according to the pectoral spines the fish were of a younger age.

Table 4.13. The mean (min - max) factors influencing accumulation and effects including age, length, weight, gonad maturity for each site and season. Age (years) Mass (kg) Length (cm) Maturity (%) Dev. Mat. LF 2006 Albasini Dam 2.38 (2 - 3) 0.87 (0.6 - 1.2) 46.03 (39.5 - 51.5) 89 11 Nandoni Dam 4.00 (3 - 5) 1.87 (0.5 - 3.5) 54.75 (40.5 - 71.5) 100 0 Xikundu 3.33 (2 - 5) 1.23 (0.6 - 2.5) 51.17 (42.5 - 59.0) 33 67

HF 2007 Albasini Dam 4.50 (3 - 6) 1.39 (0.8 - 2.1) 51.1 (44.5 - 59.5) 67 33 Nandoni Dam- 3.00 1.11 48 100 0 Xikundu 3.50 (3 - 4) 1.49 (0.7 - 2.6) 54.5 (43.5 - 68.5) 33 67

LF 2007 Albasini Dam 4.00 (2 - 8) 1.62 (0.7 - 3.6) 55.35 (42.5 - 70.0) 80 20 Nandoni Dam 3.33 (2 - 5) 2.53 (2.0 - 3.1) 63.00 (55.5 - 68.0) 67 33 Xikundu 5.50 (4 - 8) 1.66 (1.4 - 2.2) 57.67 (54.5 - 65.5) 100 0

HF 2008 Albasini Dam 3.50 (2 - 6) 1.39 (0.6 - 3.4) 51.25 (41.5 - 68.0) 50 50 Nandoni Dam 6.00 (4 - 8) 2.85 (1.7 -4.0) 65.25 (59.0 - 72.0) 25 75 Xikundu 6.00 (4 - 10) 1.40 (0.9 - 2.0) 55.75 (52.0 - 61.0) 50 50 LF-low flow; HF-high flow; Dev-developing; Mat-mature; fish were collected in this season, despite no data available for pesticide bioaccumulation and only one fish sampled at this site.

b. DDT Analysis

A summary of the bioaccumulation of DDT in C. gariepinus males collected from the three lentic sites is given in Table 4.14 (also refer to Appendix 5 for the figure representing spatial and temporal differences of DDT bioaccumulated). In contrast to the results in the sediment and water, DDT was present in the adipose tissue of C. gariepinus from all the sites and seasons that were measured (i.e. in all lentic sites and all seasons, except in all sites in the high flow 2007, which had inadequate adipose tissue). A concentration gradient along the Luvuvhu River course was evident (Figure 4.1) with increasing DDT metabolites from Albasini Dam through to Nandoni Dam and Xikundu sites. The concentrations ranged from below detection levels of certain DDT metabolites at Albasini Dam to concentrations as high as 37 mg/kg at Xikundu. In general, the concentrations were above the Canadian guidelines of 0.014 mg/kg diet wet weight (CCME, 1999a) stipulated for the total concentration of DDT (i.e. the sum of all metabolites) allowed, with Xikundu having the highest unacceptable concentrations present. These were utilized instead of South African guidelines as there were no official guidelines available for comparison, probably due to the very large paucity of

65 Chapter 4 information regarding the bioaccumulation of DDT in South African fish up until now. Of the DDT compounds that were present the p,p'-DDE metabolite was present in the highest concentrations, however as was shown in Table 4.15 the concentrations were significantly positively correlated with age and maturity. Upon assessment of the spatial and temporal changes observed in the PCA plots, predominantly defined by PC1 in Figure 4.8, Xikundu was the most dissimilar primarily due to higher p,p'-DDE and p,p'-DDT concentrations (Table 4.16).

Table 4.14. The mean (± SE) DDT concentrations (mg/kg) in the adipose tissue of C. gariepinus in all lentic sites and seasons except high flow 2007 in the Luvuvhu River.

o,p'-DDT p,p'-DDT o,p'-DDE p,p'-DDE o,p'-DDD p,p'-DDD LF 2006 Albasini Dam <0.01 <0.01 <0.01 0.21 (0.24) <0.01 0.06 (0.05) Nandoni Dam <0.01 <0.01 <0.01 0.87 (0.68) <0.01 0.34 (0.13) Xikundu <0.01 <0.01 <0.01 3.56 (2.92) <0.01 2.42 (2.20)

LF 2007 Albasini Dam 0.09 (0.04) 0.22 (0.23) <0.05 2.85 (4.69) <0.05 0.26 (0.23) Nandoni Dam 0.18 (0.07) 1.37 (1.05) <0.05 12.02 (8.79) 0.16 (0.07) 2.45 (1.43) Xikundu 2.25 (0.31) 18.79 (2.35) 0.09 (0.05) 37.53 (6.38) 0.53 (0.19) 8.05 (1.66)

HF 2008 Albasini Dam 0.07 (0.02) 0.13 (0.15) 0.06 (0.10) 2.53 (2.17) 0.04 (0.14) 0.40 (0.49) Nandoni Dam 0.09 (0.06) 0.66 (1.86) 0.33 (0.89) 16.57 (0.47) 0.47 (1.38) 3.50 (6.70) Xikundu 0.42 (0.65) 4.54 (10.48) 0.28 (0.36) 34.47 (65.01) 0.56 (0.78) 8.20 (9.47) LF-low flow; HF-high flow; bold-values above detection limits; detection limits were 0.01 mg/kg for low flow 2006 and 0.05 mg/kg for low flow 2007 and high flow 2008.

Table 4.15. Pearson's correlation between bioaccumulated contaminants and biological factors that are significantly correlated.

age length mass maturity DDT metabolites p,p'-DDE 0.54 0.10 -0.05 0.42

Metals Al -0.31 -0.25 -0.19 -0.07 Cd -0.08 -0.28 -0.24 0.08 Mn -0.32 0.02 0.08 -0.07 Pb -0.36 -0.36 -0.31 -0.17 Zn 0.27 0.19 0.26 -0.27 Bold-significant (p<0.05) correlations

66 Chapter 4

(a) * Albasini Dam 2 Nandoni Dam O Xikundu O

O 0 •

-2 -6 -4 -2 0 2 4 PC1 (b) 2— A LF 2006 V HF 2007 • LF 2007 0 HF 2008 O A •

O O

-2 -6 -4 -2 0 2 4 PC1

Figure 4.8. Two identical PCA plots representing the spatial and temporal variation of DDT metabolites in C. gariepinus in order to compare the sites (a) and sampling flow regimes (low fow (LF) and high flow (HF)) (b). No samples were available for the high flow 2007.

Table 4.16. Eigenvector coefficients for the first two PC coordinations for DDT metabolites in C. gariepinus. PC1 PC2 93.3% 5.6% o,p'-DDT -0.16 0.31 p,p'-DDT -0.49 0.78 o,p'-DDE -0.06 -0.16 p,p'-DOE -0.77 -0.38 o,p'-DDD -0.12 -0.11 p,p'-DDD -0.35 -0.33 Absolute values of the coefficients greater than 60% of the maximum coefficient is shown in bold (PC1: 60% of 0.77 = 0.461 ; PC2: 60% of 0.78 = 0.467)

c. Metal analysis

The results of the metal analysis in the muscle of the catfish were summarised in Table 4.17. The average metal concentrations measured were generally low and very similar to the values obtained in catfish caught within the Luvuvhu River in 1992/1993, with a few slight fluctuations in Al, As, Cd, Co and Mn.

Spatial and temporal patterns caused by metals in the tissue were graphically represented on the PCA plots in Figure 4.9. The first two PCs accounted for 93% of the variance

67 Chapter 4

(summation of the PC1 and PC2 axis as listed in Table 4.18) with the remaining three PCs having negligible eigenvalues. Upon inspection of the trends it was evident that the bioaccumulation of metals in catfish showed no clear spatial patterns (Figure 4.9a), with the majority of the variance related to temporal differences (Figure 4.9b). The high flow 2008 flow regime was graphically separated from the other seasons along the PC1 axis (57% variance) primarily due to increased bioaccumulation of Zn (Table 4.18). The remaining flow regimes were then separated along the PC2 axis, with the low flow 2006 caused by higher Al concentrations.

In Table 4.15, only the metals that were significantly correlated with the natural biological fluctuations were listed. The metals, including Al, Cd, Mn and Pb showed significant negative correlations, suggesting that higher concentrations were predominantly present in younger/smaller fish, whilst Zn showed a positive correlation suggesting higher concentrations in larger fish. Therefore an adjustment was required that would reduce the effects of size on the Zn bioaccumulation in order to accurately assess the spatial and temporal variations. This was done using ANCOVA (described in detail in Chapter 5, Section 5.2.2d) as recommended by Phillips and Rainbow (1993). The results showed (Figure 4.10) that the Zn concentrations had a similar trend at all sites, with the highest concentrations being in the high flow 2008 and the lowest in the low flow 2007.

68 - •• •▪•

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- Albas X Nando Alba

Albas J Chapter 4

(a) (b) Albasini Dam 0 LF 2006 Nandoni Dam HF 2007 ‹), Xikundu 17 LF 2007 A HF 2006 0 4 0

* 0 g 2 2

0

0 * 0 • 0 • • V V 0 • -2 *0 -2 ° -4 -2 0 2 1 -4 -2 1 4 PC1 PC1

Figure 4.9. Two identical PCA plots superimposed with (a) spatial and (b) temporal keys of muscle burdens in C. gariepinus across lentic sites and low (LF) and high (HF) flow regimes.

Table 4.18. Eigenvector coefficients for the first two PC's for metal burdens in muscle.

PC1 PC2 57% 36% Al -0.18 0.76 Cd -0.02 0.03 Cr 0.03 -0.001 Cu 0.03 0.38 Mn -0.05 0.50 Ni 0.00 0.00 Pb -0.05 0.10 Zn 0.98 0.16 Absolute values of coefficients greater than 60% of the maximum coefficient is shown in bold

Figure 4.10. ANCOVA adjusted variation of zinc concentrations measured in C. gariepinus. The sites included Albasini dam (ALB), Nandoni Dam (NAN) and Xikundu (XIK) in the low flow (LF) of 2006 and 2007 and high flow (HF) of 2007 and 2008.

68 Chapter 4

4.4 DISCUSSION

4.4.1 Water analysis

a. Physico-chemical and nutrient concentration

Measuring the physico-chemical and nutrient concentrations is essential as these variables fluctuate and are often shown to influence both the bioaccumulation and organismal responses within the aquatic ecosystems (Phillips and Rainbow, 1993). In the Latonyanda River, high nutrient levels of ammonia, nitrate and sulphate concentrations were measured in various sampling regimes. The ammonia, measured as the toxic un-ionised form in the high flow 2008, was found at a concentration of 40 times more than historical data and when compared against the TWQR of less than 0.007 mg/I, the Latonyanda site could be described as being highly enriched with ammonia. However, according to Heath and Claassen (1999) the TWQR values were unrealistic for South African rivers and upon inspection of the Canadian and Rand Water guidelines (cited by Kotze, 2002) the concentrations were more acceptable. Thus, suggesting that the ammonia concentrations were perhaps not abnormally high and were just slightly elevated in the high flow 2008. It is suspected that this increase may have been due to agricultural practices within the surrounding areas of this site (Dallas and Day, 2004). In contrast to the ammonia, the nitrate concentration of 2 mg/I in the low flow 2007 at the Latonyanda site was generally higher than the guidelines. Despite there being no South African based guidelines (TWQR), Dallas and Day (2004) noted that nitrate concentrations are seldom greater than 0.1 mg/I under natural conditions, while Rand Water suggested that concentrations should be less than 1 mg/I in natural conditions. Nevertheless, the increased concentrations of the nitrates were probably also a result of agricultural activities in the surrounding area as was the case for the ammonia. As for the higher than historical values of sulphate concentrations observed in the high flow 2008 at the Latonyanda site, comparisons with guidelines showed that these concentrations were only very slightly out of normal range. In fact, according to the Canadian water quality guidelines, concentrations below 100 mg/I are acceptable. Be that as it may, the cause of this slight increase in sulphate concentrations was probably also due to runoff from agricultural activities (WHO, 2004), as mining (the main source of sulphate contamination) was not an influencing factor in this section of the catchment (Heath and Claassen, 1999; Chapter 2).

Another site that was suspected of being influenced by the agricultural and forestry activities in the Luvuvhu River was the Albasini Dam. In this dam the conductivity was as high as 824 pS/cm in the low flow 2007 and there was a contamination of both nitrogen (ammonia, nitrite, and nitrate) and phosphorous (ortho-phosphate) based nutrients predominantly in the latter two sampling seasons. These were possibly a result of the agricultural activities surrounding the area which was accentuated in comparison to the Latonyanda site conceivably due to the greater retention ability of lentic systems (Dallas and Day, 2004). Further contamination was also observed with sulphates in the high flow 2008, which could

69 Chapter 4 also have been a result of runoff from fertilisers as explained above. Apart from the agricultural activities being a source of nutrient enrichment, nutrients may have also been added into the system through sewage effluent, industrial discharge or detergents (Dallas and Day, 2004). However, no data was found that identified these as sources in the vicinity.

The Nandoni dam also showed similar nutrient enrichment, which could have accumulated from upstream agricultural practices. Another possible reason for the higher conductivity and phosphate levels at this site may be due to contamination from the Thohoyandou sewage treatment works, which was upstream from the dam. According to Dallas and Day (2004), sewage effluent can be a major source of nutrients and organic enrichment within a river. Although the nutrients were assessed, the organic enrichment in the water was not assessed to validate this potential source. However, organic content within the sediment was analysed and concentrations were generally quite low (Table 4.7).

With regards to the lotic sites of the Luvuvhu River, all were generally characterised by higher temperatures and pH values (Table 4.3). The higher temperatures were probably due to the natural physical habitat characteristics of the middle riverine zone, whilst the increased pH was possibly due to increased washing, bathing and utilisation of alkaline-based detergents in the river (Pillay and Buckley, 2000). Furthermore, all of the lotic sites had fluctuations in the physico-chemistry and nutrient concentrations. However, most of the measurements were only slightly higher than historical values, with concentrations generally having an ideal water quality range (as illustrated above with sulphates, nitrates and phosphates). Therefore, although a number of factors may be the cause of these fluctuations, it is suspected that these fluctuations are mostly due to the dynamic characteristics of the water phase (DWAF, 1996).

b. DDT and pesticide concentrations

Unfortunately very few studies have published data regarding the fate of DDT after IRS. However, it is known from pesticide usage in agriculture that there is generally an increase of contamination into nearby aquatic ecosystems during the application seasons, whist for the remainder of the year concentrations remain below detection levels (Nowel et al., 1999; Kreuger, 1998; Kilikidis et al., 1992). Similar trends could however not be observed in the present study, with no DDT concentrations measured in the water coinciding with the time of DDT application on huts, except perhaps in the high flow 2008, which was the only IRS season where DDT was measured. Upon evaluation of rainfall influencing the general lack of correlation with the IRS season, no relationship could be found. As seen in Table 4.19 (Appendix 7), the DDT concentrations did not coincide with the rainfall levels. Although, there was perhaps a correlation between the higher concentrations in February 2008 with reduced rainfall evident, which resulted from a lack of dilution within this season.

70 Chapter 4

Table 4.19. Rainfall data during the study period as obtained from weather SA. Total rainfall over Total Rainfall Month of each Period for each Total monthly a period of 14 Season during field field trip field trip Rainfall (mm) days prior to trip(mm) field trip (mm) LF Oct 06 2 - 8 Oct 06 1.0 0 0.8 HF March 07 5 - 11 March 07 94.2 44.7 0 LF Oct 07 22 - 28 Oct 07 73.8 12.1 13.3 HF Feb 08 11 - 17 Feb 08 15.5 1.3 1.9

The possible cause for the temporal changes of DDT in the water measured in the other sampling regimes might have been due to the accumulative effects of differing conditions during and after spraying and the instability of DDT in water. A variety of environmental and spraying conditions may influence the transport of DDT from its source to surface waters for example, the effectiveness of application on huts (extent of loss into atmosphere); alterations in climate (duration, amount and intensity of rainfall, direction and extent of air currents, UV exposure and timing of rainfall after DDT application); atmospheric conditions (percentage particulate matter), soil characteristics (erosion) and microbial activity (US Department of Health and Human Services, 2002; Phillips and Rainbow 1993; Edwards, 1973) all can influence the amount of contamination within an ecosystem. Then, if the contaminant reaches the aquatic ecosystem successfully, it usually does not remain in the aquatic phase for long and accumulates quickly either in the sediment or biota due to its lipophilic nature.

In the high flow regime of 2008, all the sites except Nandoni Dam were contaminated with toxic levels of the DDE metabolite. Of the contaminated sites, Xikundu showed the greatest contamination of DDE in the water, with a concentration of 1.8 pg/I. This exceeded the guideline concentrations set out by South African and other countries to protect the wildlife from adverse effects by a factor of approximately 80 to 1800 and was also much higher than most other South African river systems analysed, such as in the East London Harbour with concentrations ranging from 0.006 to 0.0189 pg/I (Fatoki and Awofolu, 2003), Olifants River with levels below detection (Grobler, 1994) and in the Isipingo River (Grobler et al., 1996). This difference was however not surprising since Xikundu was in close proximity to the DDT sprayed villages and was probably contaminated from combined sources, including surface runoff and atmospheric transportation. The close proximity to sprayed areas, however, does not explain the presence of both lindane and the banned substance dieldrin at Xikundu. If these pesticides were not recently introduced into the system through usage or possible the local environmental conditions at Xikundu might have contributed toward the net increase in contamination residence time in the water. According to Barker et al. (1991) during certain conditions, such as temperature and flow fluctuations, in a lake environment, water contaminant burdens can increase in the water from the re-suspension of sediments that have accumulated pesticides over the years.

In contrast to Xikundu, Nandoni showed no DDT contamination in the water. However, since Nandoni Dam was situated in the DDT sprayed areas it was probably contaminated with DDT. The possible reason for not measuring DDT could have been the large percentage of

71 Chapter 4 water present that may have diluted the DDT to below detection levels. Although the volume of water in the Nandoni Dam could not be found in literature, according to DWAF (2004) Nandoni Dam is approximately 6 times larger than the Albasini Dam, which was found to be contaminated.

The presence of DDT in the non-sprayed areas suggests that contamination was transported and deposited to these sites either from the legal IRS spraying of DDT or simply from the illegal spaying of DDT in the upper catchment. With regards to the legal spraying of DDT, concentrations may have been transported to the non-sprayed areas via the atmosphere, runoff or groundwater. According to Wheatley (1973) the atmosphere is a key transport medium and a vast reservoir for pesticides, particularly if DDT is already in the vapour phase as in the case of the indoor residual spraying applications (resulting in spray drift) and therefore the more probable form of transport as compared to the latter two. This is because runoff would only be possible if the source was in close proximity to the upper non-sprayed sites and groundwater is not a possibility as it generally only flows 1 to 2 meters per day toward topographically low areas (opposite direction to non-sprayed sites) (Levin, 1990). Therefore to conclude, if the source of DDT contamination was from the legal IRS spraying of DDT in the lower catchment, it was probably transported to the non-sprayed areas via the atmosphere. However, since the atmospheric transport of DDT and the possibility of illegal spraying were not analysed in this study, the exact cause of DDT contamination within the non-sprayed areas could not be conclusively identified.

c. Metal concentrations

Both essential and non essential metals in the water can become toxic to aquatic organisms if concentrations are high enough. In the present study however, all of the metals measured were within the required water quality range for healthy aquatic ecosystems except for Al and Zn. Al concentrations in the Luvuvhu River water were much higher than target levels defined by DWAF (1996). Many studies have shown that acid rain is associated with increased Al concentrations (Dallas and Day, 2004; DWAF, 1996; CCME, 2003). However, in the current study the pH values of the water were predominantly alkaline. Thus, acid rain could not have been the cause of the increased concentrations. Another possible reason for these higher than target level concentrations could be due to background geology and that the guidelines proposed by DWAF are rather unrealistic for the Luvuvhu River. Similar observations were found by Barker (2006) and Heath and Claassen (1999) for rivers flowing into the Kruger National Park.

Another metal that was higher than the target levels defined by DWAF (1996) in the water was Zn. Upon spatial and temporal evaluation of the contamination, there were only high concentrations of Zn in the low flow 2007 at Hasana, Nandoni and Tshikonelo. These three sites were in close proximity to Thohoyandou where numerous sources of Zn have been previously documented. A study by Odiyo et al. (2005) showed that Zn in the aquatic ecosystem could be attributed to the runoff from galvanised household materials and car

72 Chapter 4 washes. Whilst an extensive review by ATSDR (2006) showed that water can also be contaminated with Zn from runoff from roofs and cultivated lands, automobiles, mine drainage (this is not really a factor in the Luvuvhu River catchment) and from sewer overflows. This suggests that the sites in the present study were perhaps contaminated from these sources via tributaries surrounding Thohoyandou or from terrestrial runoff after rainfall. This however was only evident in the low flow of 2007. The possible reason for such temporal specificity could be due to the increased rainfall during this period. According to Weather SA, rainfall in October 2007 (low flow 2007 sampling period) was 100-200 mm, whilst in the other sampling months the rainfall was below 100 mm.

4.4.2 Sediment analyses

a. Sediment properties

Sediment particle sizes can have a large influence on the contaminant concentrations measured (Phillips and Rainbow, 1993). Generally, the finer grain sizes such as clay and silt (together classified as mud) absorb higher concentrations of pollutants than sand and gravel. This is mainly due to the smaller grain sizes having a greater surface area. It is therefore essential to quantify the percentage of each grain size of a sediment sample measuring contaminant concentrations. In the present study, the percentage of the particle sizes differed at all seven sites, with very little seasonal variations observed. Upon inspection of the site averages for all the seasons, the highest percentage of fine grain sediment was present at Xikundu. It is suspected that it was as a result of increased settling of smaller particles due to slower flows as was also shown in weirs within the Crocodile River by Heath (1999).

Another sediment property that has been shown to influence pollutant concentrations is the level of organic matter present in the sediment (Phillips and Rainbow, 1993). Organic matter has a high affinity for hydrophobic pollutants and thus tends to form complexes with these pollutants, rendering them unavailable for uptake into the aquatic ecosystem. Therefore, higher percentages of organic matter would probably result in increased contaminant concentrations within the sediment, which would yield false positive results and incorrect interpretation. In the present study, the organic matter was generally low throughout the study, as compared to Heath (1999) within the same area, with levels ranging from 0.47% to only 10.12%. The sites with the most abundant organic matter were observed at Albasini Dam, Nandoni Dam and Xikundu, which was most probably as a result of particles settling as shown by Heath (1999). Despite the organic content being particularly high in the lentic sites, Hasana showed the highest percentage of organic matter in the low flow 2007. A possible source of this organic matter at Hasana was perhaps due to a combination of an increase in runoff of debris from the surrounding agricultural activities caused by the high percentage of rainfall during the season (Weather SA, 2009) and possibly from crude sewage originating from humans and their livestock faeces in the river channel or perhaps

73 Chapter 4 the nearby municipal waste water treatment plant (although this source was not likely as it was situated downstream from Hasana) (Dallas and Day, 2004).

b. DDT and pesticide concentrations

As mentioned previously, the hydrophobic nature of DDT does not allow it to remain in the water column for long and so quickly sorbs to particulate matter, sediment and biota (Vallack et al., 1998; EXTOXNET, 1996). DDT tightly binds to the sediment, and can remain in this phase for very long periods and therefore accumulate and show any short term temporal variations that may occur in the water (Phillips and Rainbow, 1993). In the present study the contamination in the sediment had the same temporal tendencies as was found in the water. That is, the sediment also had the predominant contaminations of DDT metabolites and other pesticides in the high flow 2008, but were present at concentrations about 20 times higher than that of water. This higher percentage of pesticides in the sediment compared to the water thus further reflects the preferential partitioning of DDT to sediment found throughout literature, which is able to reflect historical events.

As for the spatial analysis of DDT and the other pesticides measured in the sediment, Xikundu and to a much lesser extent Nandoni Dam, had the majority of the contamination in the Luvuvhu River. Although this was expected for DDT contamination, it was surprising for endosulfan, heptachlor and endrin since the proximity to the probable sources of these pesticides were closer to the Albasini Dam, which had no contamination. However, Phillips and Rainbow (1993) reviewed a number of factors that could influence the spatial variability of contaminants in the sediment. Firstly, the concentrations of contaminants are largely influenced by the rate of sedimentation of particulates. Although the degree of sedimentation at Xikundu was not measured, it was extremely possible that sedimentation was high at this site due to a combination of reduced water flow (Dallas and Day, 2004) and the severe erosion present (Chapter 3). This factor, together with the smaller percentage of grain size may have further contributed toward greater OC concentrations present, including DDT. Although the grain size was not significantly correlated with any contaminant in the present study, there was a higher percentage of mud (<212 pm) present at Xikundu (Figure 4.4).

Furthermore, as was shown in Table 4.10, no other sediment characteristics showed significant correlations with OC contamination, despite numerous publications emphasizing their influence on sediment accumulation. A possible reason for this may be due to the lack of consistent contamination of OCs (i.e. many below detection) in the present study or as in the case of the organic matter, the percentages were all relatively low. However, there was a possible relationship in the low flow 2007 at Hasana, between increased organic matter and a slight contamination of 0.2 pg/kg DDE in the sediment. According to Edwards (1973), the organic matter attracts the lipophilic DDT and allows for a fast and persistent binding in the sediment phase, which has been shown by many studies including Nakata et al. (2005) and Xue et al. (2006).

74 Chapter 4

c. Metal concentrations

As with pesticides, metals also bind to sediment in aquatic ecosystems under certain conditions, and as such sediments are a sink for pollutants (McIntosh, 1991). Generally however metals within the sediment can still influence the aquatic organisms in an ecosystem via two routes (Phillips and Rainbow, 1993). Firstly, if organisms are in direct contact with the bioavailable (i.e. loosely bound) metals they can bioaccumulate metal concentrations, especially those that feed, breed, or live in or close to the sediment. Secondly, although concentrations in the sediment are generally more stable than in water, metals can still desorb under certain conditions, such as changes in temperature or in the presence of certain chemicals, and redistribute into the aquatic phase, making the metals more bioavailable to aquatic organisms.

In South Africa, many studies have identified the concentration of pollutants such as metals in the sediment, including a number that have identified metal contamination in the Luvuvhu River sediment (Barker, 2006) and other nearby river catchments (Heath, 1999). However, comparisons with these studies could not be accurately done primarily because of the different extraction procedures. For instance, in the study by Barker (2006) the total concentrations of metals in the sediment were measured, whilst in the present study only the bioavailable portion of the total metal concentrations were assessed. Furthermore, there were additional comparative difficulties as there were no South African guidelines available for metal concentrations in the sediment. Nevertheless, Canada, Australia and the USA, amongst others, have compiled guidelines for the non-toxic metal concentrations of sediment in aquatic ecosystems (Table 4.11). Upon comparisons with these guidelines it was evident that none of the metals were at any concentration that can be toxic to aquatic organisms and that the spatial and temporal differences were rather a result of natural fluctuations. This was also shown in the PCA plots, where a temporally higher concentration of Zn was observed in the low flow 2007 and although similar temporal changes were evident in the water, there was no direct spatial correlation observed, with concentrations being highest at Latonyanda, Albasini Dam and Mhinga in contrast to Hasana, Nandoni Dam and Tshikonelo in the water.

Similar to pesticides, the contamination of metals in sediment is also largely influenced by the sediment size and organic content (Panda et al., 2006; Luoma, 1990). Analyses of the sediment grain size showed that there were no significant correlations with metal concentrations. This was probably due to the generally low concentrations of metals in the sediment. However, analyses of the organic content showed that there was a significant positive relationship with Mn and Fe concentrations. The primary reason for this was due to the specificity of the extraction reagent, hydroxylamine hydrochlorine in nitric acid medium used in this study. This reagent, which is utilised to obtain the easily reducible fraction, does not only release metals that are bound to Mn and Fe oxides, but also appears to release substantial amounts of trace elements bound to organic matter (Filguerias et al., 2002). As for As and Cu, they were correlated with Fe and Al, respectively, suggesting that they

75 Chapter 4

coexist and co-precipitate with these trace metals on the sediment. Thus agreeing with many studies that have shown that Fe controls the mobility of As from sediment (Varsanyi and Kovacs, 2006), whilst the opposite was true for Cu. The Cu trace metal is generally said to be associated with Fe oxides (Filguerias et al., 2002). However in this study Cu could not be related to Fe, but rather to Al. Furthermore, a negative correlation with organic matter was also observed, which contradicts most studies. These results thus suggest that other factors influenced the Cu concentration, although this was probably not contamination as concentrations were very low, ranging from 0.02 to 0.33 pg/g.

4.4.3 Bioaccumulation

Fish biology

Due to size selective sampling of the catfish C. gariepinus in the Luvuvhu River, interpretation of the age, mass, length and maturity results were generally biased and will therefore not be critically discussed. However, it should be noted that although the average age of the fish did not correspond to the mass and length averages, there was a significant (p<0.05) Pearson's correlation between age and both mass and length, namely 0.52 and 0.44, respectively. This suggests that in future studies the mass and/or length can be used as indirect measures of age in C. gariepinus, which requires considerably more effort to determine.

DDT and pesticide concentrations

Total DDT in the present study had an average concentration of 18.62 mg/kg in the adipose tissue of the catfish, C. gariepinus. Apart from this concentration being well above the Canadian guideline value of 0.014 mg/kg, it was also much higher than most other reports available in literature. Needless to say, comparisons with the few South African studies published also highlighted the higher than normal concentrations in the present study. Of the 11 published studies found up until now (summarised in Table 4.20), the highest concentration was 4.023 mg/kg, which was measured in fish from the Letaba River in 1990- 1992 (Heath and Claassen, 1999). In the same study, fish bioaccumulation data was also presented for the Luvuvhu River, which showed an average of 1.8 mg/kg DDT bioaccumulation, about 18 times lower than the current data in the Luvuvhu River. This large difference however was not because of the absence of DDT in the catchment in 1992, since DDT was still being sprayed as a malaria control during this time (Mabaso et al., 2004), but rather due to differing species, sampling sites, or sampling times (Heath and Claassen, 1999). Firstly, the species C. gariepinus from the present study had a higher probability of accumulating DDT than the species from 1992 study (B. unitaeniatus, L. congoro and L. rosae, refer to Table 8.3 in Chapter 8 for full genus and species name) due to its higher trophic position, generally higher body size and lipid content (McIntyre and Beauchamp, 2007; Cheung et al., 2007; Dietz et al., 2000). Another possible reason could have been that the sampling site in the Luvuvhu River in 1992 may not have been

76 Chapter 4 contaminated with large quantities of DDT. As was found in the present study, DDT concentrations can indeed differ drastically between sites, for example the DDT concentrations at Xikundu were very high in water and sediment and then 20 km downstream at Mhinga there were no concentrations observed (unfortunately more complementary comparisons could not be made as no bioaccumulation data in C. gariepinus at Mhinga was not measured). Then lastly the time of sampling may have contributed toward the lack of DDT in the 1992. In the study the fish were only sampled in August of 1992, which was about two months before the annual residual DDT spraying occurred. However, such fluctuations are rather unlikely as DDT is a largely lipophilic and persistent contaminant within biota. Although as shown in the present study (below), there could possibly be a chance of concentrations coinciding with the application of DDT.

With regards to the temporal sampling in the present study, the bioaccumulated DDT concentrations generally coincided with the application of DDT within the local communities of the Luvuvhu River. In the low flow 2006 C. gariepinus were predominantly contaminated with lower concentrations of p,p'-DDE and p,p'-DDD metabolites, which were a result of no recent input of DDT into the system as sampling in this season was done prior to the annual residue DDT spraying (Bornman et al., 2009). In contrast, the low flow 2007 sampling was done during the annual application of DDT and thus showed higher concentrations of all metabolites including the relatively unstable o,p'- and p,p'-DDT compounds in C. gariepinus. As was expected, the majority of the DDT metabolites were much lower in the following sampling season of 2008 (four months later), with the majority of the contamination being the p,p'-DDE metabolite. In fact this metabolite was measured at concentrations frequently higher than any of the other metabolites throughout the study. This was primarily due to DDE being far more stable and persistent in fish tissue than any of the other metabolites, which is induced by the formation of halogen atoms during the metabolism from DDT to DDE (Paasivirta, 1991; US department of health and human services, 2002). Such halogenated chemicals tend to be resistant to oxidation and other chemical mechanisms that may cause degradation (Walker et al., 2001) and also tend toward avoiding depuration (Wang and Simpson, 1996).

As for the spatial trends, the C. gariepinus also showed a positive relationship between the DDT contamination and its application on the huts in the catchment. As was evident from Figure 4.1, there was a general downstream increase of DDT concentration in the Luvuvhu River, which was consistent with the areas that were sprayed with DDT. Xikundu generally had the highest concentrations of DDT bioaccumulation, which was not only due to its close proximity to the source and accumulation from upstream sources but also because certain conditions may have favoured the loading of DDT at this site as explained in both the water and sediment.

77 Chapter 4

c. Metal concentrations

In contrast to pesticide bioaccumulation studies in South Africa, there are numerous studies published on the bioaccumulation of metals in South Africa. Since the aim of this Chapter was just to screen metals in the Luvuvhu River for interfering effects with DDT metabolites, an extensive review was not incorporated into this thesis. Thus for further reviews refer to Heath and Claassen (1999) and Barker (2006).

Upon assessment of the metal concentrations in C. gariepinus in the present study, it was evident that most of the metals were below the concentrations observed for unexposed C. gariepinus in other studies. For example, the As concentrations in the present study ranged from 0.01 to 0.12 mg/kg, which was well below the concentration of 4.72 mg/kg and 0.3 mg/kg observed by Soeroes et al. (2005) and Chen and Folt (2000), respectively. Similar observations were shown for the remaining metals (except Zn) with comparison with studies measuring unexposed C. gariepinus including Vinodhini and Narayanan (2008), Barker (2006), Coetzee et al. (2002), Avenant-Oldewage and Marx (2000), Chen and Folt (2000), Heath (1999), Heath and Claassen (1999), Adeyeye et al. (1996), and Van den Heever and Frey, (1994).

With regards to Zn, concentrations varied between 11.38 and 110.68 mg/kg in the present study. Comparisons with other Zn bioaccumulation studies however showed a number of inconsistencies with regards to the concentration expected under natural conditions. A study by Murugan et al. (2008) showed in the catfish Channa punctatus muscle, Zn concentrations in unexposed conditions ranged from 4.14 to 4.62 mg/kg. Another study by Adeyeye (1996) showed much lower concentrations of 0.49 mg/kg Zn in the C. gariepinus muscles, whilst Kotze et al. (1999) observed "normal" concentrations at 28 mg/kg Zn in C. gariepinus from a control site in the Olifants River. However, a study by Murphy et al. (1978) reported Zn concentrations of between 48 and 173 mg/kg in fish tissue from relatively uncontaminated aquatic ecosystems. Despite these inconsistencies it was evident that the Zn concentrations measured in the present study were mostly above normal concentrations as compared to literature. Similarly, high Zn concentrations were observed in the water (Section 4.4.1); although no correlations between the sites or seasons were observed. Another possible explanation could be that the Zn concentrations were naturally higher in C. gariepinus in the Luvuvhu River or that the fish are continuously being exposed to excess Zn contamination, since the study by Heath and Claassen (1999) measured similar concentrations in 1992/1993. Nevertheless, excess Zn concentrations within aquatic organisms have been shown to induce numerous lethal and sub-lethal effects on fish (Moolman, 2004; Van Dyk, 2003; Kruger, 2002), including changes in reproductive responses, which may interfere with

78 Chapter 4

Table 4.20. A comparison of the total DDT bioaccumulated in various fish from South African aquatic ecosystems. Reference Date River Tissue Species DDT (mg/kg wet wt) Greichus et al., 1977 1974 Hartbeespoort Dam whole fish C. flaviventris 0.78 0. mossambicus 0.78

Greichus et al., 1977 1974 Voelvlei Dam whole fish L. macrochirus 0.26 M. salmoides 1.94

Butler et al., 1983 1974 Kosi Bay unknown A. berda 0.51 M. cephalus 0.51

Piek et al., 1981 <1981 Olifants River/ Letaba River / muscle/liver/skin Barbus species 0.92 Crocodile River C. gariepinus 3.03 Labeo species 0.42 0. mossambicus 2.43

Butler et al., 1983 1981 Kosi Bay unknown A. berda bd M. cephalus bd

De kock et a/., 1987 1983 Wilderness Lake system unknown L. amia 0.10 0. mossambicus 0.03

Bouwman et a/., 1990 <1990 Pongola River muscle E. depressirostus 0.009 H. vittatus 0.09 0. mossambicus 0.04 S. intermedius 0.02

Grobler, 1994 1990 Olifants River whole fish C. gariepinus 0.02 (head and guts O. mossambicus 0.01 removed) S. intermedius 0.15

Grobler et al., 1996 1991 Isipingo Estuary whole fish A. berda 0.002 (head and guts O. mossambicus 0.008 removed) M. cephalus 0.05 L. amia 0.18

Roux et a/., 1994 1992 Crocodile River muscle C. carpio 0.004 0. mossambicus 0.02 C. gariepinus 0.06 marequensis 0.12

Heath et al., 1999 1992 Luvuvhu River various tissue various species 1.76 Crocodile River various tissue various species 0.52 Letaba River various tissue various species 4.02 Olifants River various tissue various species 0.95 Sabie River various tissue various species 1.06

Claassen, 1996 <1996 Voelvlei Dam various tissue 0. mossambicus 0.13 carpio 0.08 M. dolomieu 0.18

Bornman et al., 2007 2004 -2006 Rietvlei Nature Reserve adipose tissue C. gariepinus 0.60

79 Chapter 4

DDT effects in the current study. Although in the present study severe reproductive effects were probably not likely caused by Zn, since the Zn concentrations present in this study were generally much lower than those observed in studies related to reproductive effects in C. gariepinus. In addition, the males are generally less susceptible to reproductive effects related to Zn than the females (Kime, 1998; Olsson et al., 1987).

Upon evaluation of the spatial and temporal variations, it was evident that Zn concentrations showed the same temporal tendencies at all the sites, with the high flow 2008 sampling regime showing the highest concentrations. Unfortunately, the exact source of this Zn concentration was unknown and therefore it is recommended that these concentrations be monitored within the Luvuvhu River. As for the spatial variations, there were no apparent trends observed at any of the sites.

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Chapter 5 Sub-organismal effects of DDT in C. gariepinus

5.1 INTRODUCTION 90 5.2 METHODS 94 5.2.1 Field procedures 94 5.2.2 Laboratory procedures 94 Alkali-labile phosphate 94 Plasma metal analysis 95 Protein carbonyls 95 Gonad condition 96 Histology 96 Condition factor 96 5.2.3 Statistics 96 5.3 RESULTS 96 5.3.1 Vitellogenin 96 5.3.2 Protein carbonyls 98 5.3.3 Gonad condition 99 5.3.4 I ntersex 101 5.3.5 Condition factor 101 5.4 DISCUSSION 102 5.4.1 Vitellogenin 102 5.4.2 Protein carbonyls 103 5.4.3 Gonad condition 105 5.4.4 Intersex 107 5.4.5 Condition factor 108 5.5 REFERENCES 108 Chapter 5

5.1 INTRODUCTION

As has been previously mentioned, DDT has numerous sub-lethal effects in an organism including changes in DNA, proteins, cells, processes or organs of the reproductive system, nervous system, behavioural system and immune systems (Vasseur and Cossu-Leguille, 2006; Bayley et al., 2002; US toxicological profile, 2002; Kime, 1998; EXTOXNET, 1996; Holden, 1973). The most predominant of these effects are those induced by endocrine disruption (lwaniuk et al., 2006), which allows for many opportunities to measure effects using biomarkers. Indeed, countless biomarkers have been shown to measure endocrine disrupting effects of DDT or similar contaminants. Developing countries, such as South Africa, require methodologies that are cost effective and that don't utilise specialised equipment or require extensive technical expertise, with the result that many of the international methodologies are not applicable.

Therefore, the objectives of this chapter were to assess if DDT contamination caused endocrine disruptive effects within C. gariepinus in the Luvuvhu River, to identify the benchmarks in South Africa and to identify the most suitable and effective biomarkers that can detect these effects in South African ecosystems. The endocrine disruptive effects of DDT were measured using a number of biomarkers selected according:

Their ability to identify DDT contamination, Their ability to indicate endocrine disruption (most predominant effects of DDT), Level of biological complexity (subcellular to organismal), Tissue measured in blood (particularly advantageous in that sacrificing fish can be avoided) and The number of resources they require (financial and labour insensitive as well as requiring technical expertise).

As can be seen in Table 5.1, the biomarkers satisfying most of the criteria include the indirect measured vitellogenin (VTG) including alkali-labile phosphate (ALP), calcium (Ca), zinc (Zn), and magnesium (Mg); protein carbonyls (PC); gonad somatic index (GSI); ANCOVA adjusted gonads; intersex and condition factor (CF) and are discussed in detail in the following paragraphs.

Vitellogenin One of the most extensively reviewed mechanisms of action of endocrine disruptors (such as DDT) in fish is the mimicking of the female hormone, oestrogen (Cheek et al., 2001). Under normal conditions oestrogen stimulates the production of VTG in the liver, which is transported to the ovary where it enters the mature female's oocyte to function as an essential protein in egg development (Marin and Matozzo, 2004). However, in the presence of DDT, such processes in the mature female may be inhibited. According to Overdorster and Cheek (2001), continuous oestrogenic stimuli may trigger negative feedback on endogenous estradiol (oestrogen) production which may lead to inhibited vitellogenin

90 Chapter 5

synthesis and ovarian growth. In contrast, the male and juvenile fish exposed to oestrogenic mimicking compounds may produce excess VTG. Hence, the abnormal presence of excessive VTG in male and juvenile plasma can be used as a biological indicator of exposure to oestrogenic compounds (Lomax et al., 1998).

Table 5.1. Selection criteria of the biomarkers that were selected as indicators of endocrine disruption.

Biomarker Indicative of Indicative of Level of biological Resources Tissue required DDT exposure Endocrine organisation required disruption VTG ELISA Yes Yes Sub-cellular Financial, Plasma Not available ALP Yes Yes Sub-cellular Labour, Plasma Spectrophotometer Ca, Mg, Zn Yes Yes Sub-cellular ICP-MS Plasma

PC Yes Indirectly Sub-cellular Labour, Plasma/liver Spectrophotometer Gonad condition Yes Yes Organ None Gonads

Intersex Yes Yes Organ Labour Gonads

Condition factor Yes Yes Organismal None None

Donohoe and Curtis (1996) showed in juvenile rainbow trout (Oncorhynchus mykiss) exposed to DDT and DDE, that plasma VTG could be successfully used as a very sensitive marker of oestrogen exposure. Similar conclusions were obtained using the Japanese medaka (Oryzias latipes), sunshine bass (Morone spp.) channel catfish (lctalurus punctatus), indian catfish (Heteropneustes fossils), common carp (Cyprinus carpio), and largescale sucker (Catostomus macrocheilus) (Chatterjee et al., 2001; Hinck et al., 2005; Lavado et al., 2004; Nimrod and Benson, 1996; Thomson et al., 2000). Despite the wide usage of VTG as an indicator of exposure, the degree to which it causes adverse effects in organisms and communities remains unclear. However, Huang et al. (2003) suggested that VTG should be used in combination with other reproductive parameters in order to better quantify the effects on an organism.

Many assays for measuring VTG concentrations directly in fish plasma have been developed. Examples include, the enzyme-linked immunosorbent assay (ELISA), radio- immunoassays (RIA), western blot analyses, and Vg mRNA determination by DNA hybridization strategies (Marin and Matozzo, 2004). Undoubtedly, the most favoured of these is the ELISA due to it being a relatively rapid and simple tool for monitoring EDC contamination. However, this method is disadvantaged in that it is limited to only a few species and not one of which is commercially available for any South African indigenous fish species (Verslycke et al., 2002). A rapid, cost effective alternative to the ELISA, is the alkali- labile phosphate (ALP) assay. This assay has been shown to successfully estimate the amount of VTG present in plasma by colorimetrically measuring the amount of phosphate

91 Chapter 5 groups bound to VTG (Bjornsson and Haux, 1985). Similarly, concentrations of Ca, Zn, and Mg can be used as an indirect measure of VTG as they have been shown as major components bound to the VTG molecule and increase in concentration in the presence of VTG (Lv at al., 2006).

Protein carbonyls Disruption to the endocrine system can be indirectly caused by DDT via oxidative stress (Dowling et al., 2006). Oxidative stress is an imbalance between the higher production of free radicals and a lesser antioxidant defense system (Abdollahi et al., 2004). Increased productions of these free radicals can be formed by both natural metabolic functioning and exposure to contaminants like DDT (Almroth et al., 2005). The contaminant-produced free radicals are usually induced via the malfunction of enzymes in cytochrome P450 system or through the redox cycling of the xenobiotics (van der Oost et al., 2003). Once produced, these highly reactive and unstable free radicals tend to seek stability by reacting to any cellular macromolecules and in doing so result in structural and/or functional alterations. This consequently causes an endless possibility of sites for disruption to various biological systems including the endocrine system. In the endocrine system, a possible cause for disruption would be if the steroid hormones were to be oxidized. Since most hormones are transported through the blood and bound to water soluble proteins, analysis of protein oxidation in the plasma would be a relevant protocol to predict a possible source of endocrine disruption by DDT (Voet et al., 1999). Apart from the plasma, the liver could also be useful for such assessment as a large portion of the endocrine system is based in the liver which can be influenced by oxidative stress and in turn result in endocrine disruption (Kime, 1999).

One of the most convenient techniques used to measure the direct oxidative damage in the blood is the Protein Carbonyl (PC) assay (Parvez, et al., 2005). This assay accurately measures carbonylated proteins, irreversibly formed by either the free radical induced conversion of the amino group to a carbonyl group or by secondary mechanisms resulting from the reactions of free radicals with other cellular constituents (Almroth et al., 2005).

Gonad condition Gonad maturation and spawning readiness are frequently measured using fish condition indices such as gonadosomatic index (GSI) (Huang et al., 2003). This index, calculated as the percentage of gonad mass to total body mass, is based on the broad assumption that proportionally larger gonads indicate greater development and because reduction in relative gonad mass can occur in response to increased contaminant exposure, GSI can be used to estimate the degree of effects on reproduction (Schweer, 2002). Indeed, numerous studies have successfully used this index to monitor the reproductive effects of EDCs such as DDT exposed fish. Fiest et al. (2005) showed, using white sturgeon (Acipenser transmontanus), significant negative correlations between DDT and its metabolites and GSI. Similar observations were found in the Paralichthys dentatus (summer flounder) (Mills et al., 2001), 0. latipes (Papoulias et al., 2003), Percopsis omiscomaycus (trout-perch) (Gibbons at al.,

92 Chapter 5

1998), 0. mykiss (Sheahan et al., 2002), and Micropterus salmoides (large mouth bass) (Sepulveda et al., 2003) among others, exposed to various EDC's.

Although measurements of the GSI have been shown to successfully monitor reproductive effects, there is much controversy surrounding their use primary due to the influence of fluctuating environmental conditions on the index (VCI, 2005). Some of which may include, age, temperature, health, fish density, time of year or inter-individual variation in gonad weight during spawning season. In order to compensate for this large variability, the authors of VCI (2005) suggested that all examined samples should be of a sufficient size to rule out misinterpretation in the field. Schweer (2002) concurred with this suggestion, but further criticised the use of this ratio-based index when comparing different stages of development. Then Packard and Boardman (1999) went so far as to discourage the entire usage of GSI as an indicator of gonad condition. These authors showed the inability of GSI to actually remove the strong influence of body size on gonad condition (measured as mass) (p<0.05), which was the intension of the ratio derived index (USEPA, 2003). Instead an alternative using the more statistically sound methodology involving Analysis of Covariance (ANCOVA) was suggested to remove the obscuring effects of fish mass on gonad mass. Consequently, both these methodologies were performed and compared in this thesis.

Histology In comparison with gonad mass as measured by GSI and ANCOVA, histology is a more comprehensive and informative method for analyzing alterations in gonadal morphology brought about by 00 including DDT (Pieterse, 2004). OCs can induce a number of alterations in both ovarian and testicular morphology, which has been extensively reviewed by Kime (1998). One of the most ecologically damaging alterations observed in fish is the gonadal intersexuality induced by the oestrogen mimicking components in males. This feminization has been shown to have deleterious effects on fertilization success, which may ultimately have severe population-level consequences, although the extent of these conditions on fish populations remains to be thoroughly evaluated (Jobling and Tyler, 2003). Most studies have rather focused on identification and cause of intersex gonads in fish. For example, Cheek at al. (2001) observed intersex in most of the life stages of medaka exposed to DDT whilst Lye et al. (1997) found intersexuality in the flounder (Pleuronectes flesus). A field study by Hinck et al. (2005) in the pesticide ridden Columbia River showed intersex in the smallmouth bass (Micropterus dolomieu). While in South Africa, the sharptooth catfish (C. gariepinus) present in the Rietvlei Nature Reserve, also showed signs of intersex due to exposure to nonylphenol (Bornman et al., 2007).

Although this phenomenon has been universally attributed to endocrine disruption by EDC contaminants such as DDT, an important consideration is that many teleosts, including catfish, may exhibit sex reversal due to natural changes, such as changes in temperature, behaviour, salinity, light, water quality, pH or nutrition (Haniffa et al., 2004; Baroiller et al., 1999).

93 Chapter 5

Condition factor The CF is a general indicator of fish health that correlates the fish's body mass to its length and is in fact often used in aquaculture, to monitor feeding intensity, age and growth rates (Anene, 2005). Within an ecotoxicological context however the CF is based on the fact that there is usually depletion in the energy resources available, as these resources are utilised to cope with the increased stresses posed by a toxicant. An astounding number of ecotoxiciological based studies, including those measuring DDT, have incorporated this index as it is particularly popular due to its simplistic and cost effective nature. There are many different approaches available to measure CF and are reviewed by Stevenson and Woods (2006). In the current study a more meaningful approach was used where a standardised mass was predicted on the basis of a statistical formula for a specified test species and then compared against the actual mass for each of the respective individuals (Hagenaars et al., 2008).

5.2 METHODS

5.2.1 Field procedures

The sub-organismal effects of DDT were assessed in male C. gariepinus collected from Albasini Dam, Nandoni Dam and Xikundu (as described in Chapter 4) in the low flow 2007 and high flow 2008. The main reason that only the last two seasons were analysed was because resources were only available in the latter part of the project. Nevertheless from the male fish collected the total length (cm) and total wet mass (g) were recorded before the condition factor was determined. Plasma samples were then collected from the caudal aorta into vacutainers coated with EDTA and aprotinin (heparinisation), kept on ice and centrifuged at 3 600 rpm for 10 minutes at 4°C and the resulting plasma stored at -20°C. The gonads were removed, weighed and prepared for histological analysis, while the liver (PC) and muscle (AChE, see Appendix 2) were removed and stored in Hendrikson's stabilising buffer solution (50m1 glycerol, 20 mM Tris-HCI, 5mM B-mercaptoethanol, 0.5 mM EDTA, 0.02% bovine serum albumin (BSA), pH 7) at -80°C.

5.2.2 Laboratory procedures

a. Alkali-labile phosphate

Protein-bound phosphate was extracted from 10 pl of plasma according to the procedure described by Brasfield et al. (2002), with minor modifications. To the plasma, 1.5 ml of 10% trichloroacetic acid (TCA) (w/v) was added before it was precipitated for 15 hours at 4°C. Thereafter, the sample was centrifuged at 7 000 rpm on the Sorval RMC14 from Du Pont for 10 minutes to obtain a pellet. The resulting pellet was re-suspended in 1 ml ice cold 5% TCA (w/v) and incubated at 50°C for 30 minutes and again centrifuged at 7 000 rpm for 10 minutes. The pellets were further washed with 1 ml of 100 % ethanol (incubated for 1 minute at 80°C); then chloroform:ether:ethanol (1:2:2) solution, 100% acetone and 100%

94 Chapter 5 ether, respectively. The ether formed pellets were then allowed to dry for 10 minutes and 250 pl 2 M sodium hydroxide was added. The samples were incubated at 100°C for 15 minutes and allowed to cool before the sample was neutralised with 250 pl 2 M hydrogen chloride. Extracts were then assayed spectrophotometrically on the universal microplate reader from Biotek Instruments, Inc, using a modified method derived by Stanton (1968). Extracts were initially diluted 4 x using a 1:1 2 M HCI: 2 M NaOH. Then 100 pl 2.5% molybdate solution (2.5% ammonium molybdate tetrahydrated in 2.5 M sulphuric acid) and 25 pl Fiske-Subbarow solution (1 g in 6.3 ml distilled water) were added to the diluted extracts, incubated for 10 minutes and measured at 660 nm. A standard solution of 10 mg phosphate/ml was prepared with 4.39 g KH 2PO4 in 100 ml distilled water and used to obtain a standard curve between 0 and 10 pg phosphate/ml. The results were expressed as pg phosphate/ mg protein, using a BSA standard.

Plasma metal analysis

Plasma was thawed and 500 pl was added to an acid solution made of 8 ml 65% nitric acid and 2 ml 30% hydrogen peroxide according to the application notes for digestion in a Milestone ETHOS microwave (cookbook digestion, REV. 03_04). The samples were then placed in a Milestone ETHOS microwave and digested for 26 minutes at temperatures varying from 85 - 230°C. The digested samples were diluted (10 x) in 1% nitric acid and metal concentrations were determined using a Varian UltaMass 700 ICP-MS. All samples were analysed for Ca, Zn, and Mg. Indium was used as an internal standard to correct for interferences from high-dissolved solids. All concentrations were validated with reference materials, with the certified values and recoveries for the reference materials sited in Table 4.2 (Chapter 4).

Protein carbonyls

Liver and blood plasma were tested for protein carbonyl content using the assay modified by Parvez and Raisuddin (2005). The liver was homogenised in 1 ml of 0.1 M phosphate buffer (pH 7.4), containing 1.17% KCI, and centrifuged at 13 300 rpm for 30 minutes a 4°C, whilst the blood plasma, previously prepared, was thawed. The supernatants of both liver and blood were added to 10 mM 2,4-dinitrophenylhydrazine in 2 M HCI (v:v) and incubated for 1 hour at room temperature. Thereafter proteins from the supernatant were precipitated with 500 pl of cold (4°C) 6% TCA and separated in centrifuge at 13 000 rpm for 3 minutes at 4°C. To the resultant pellet, 1 ml of ethanol:ethylacetate (1:1) was added and let to incubate at room temperature for 10 minutes and centrifuged again as given above. This procedure was then repeated or a third time. The washed out protein containing pellet was thereafter solubilised in 400 pl of 6 M guanidine hydrochloride in 50% formic acid for 10 minutes at room temperature and centrifuged at 14 000 rpm for 7 minutes. The samples were measured in duplicate on a spectrophotometer at 405 nm, against a guanidine hydrochloride blank. The results were expressed as nanomoles of DNPH incorporated/mg protein, using a BSA standard.

95 Chapter 5

Gonad condition

The gonad condition was measured using the GSI index and ANCOVA. The GSI was simply calculated as the percentage of gonad mass (g) to total body mass, whilst ANCOVA was slightly more complex. For the examination of data by ANCOVA, gonad mass (g) was plotted against fish body mass (kg) for each site of each season. Linear regression lines were then fit (best fit) to each data set. The sample mean deviated from best fit mean was measured (sum of squares), which was used to determine a common slope between all the groups. This was in turn utilised to calculate an adjusted mean without confounding effects of body size for each site of each season and tested for significant differences using analysis of variance (ANOVA) (Lowry, 2009).

Histology

The dissected gonads were fixed in Bouin's solution made up of 225 ml picric acid, 75 ml 40% formaldehyde and 15 ml glacial acetic acid. After 48 hours, the gonads were dehydrated in graded 50% ethanol twice and 70% ethanol. The gonads were taken to the Pathology department at Onderstepoort in South Africa for further dehydration, embedding in paraffin wax, section cutting and staining with hematoxylin and eosin. The resulting slides were examined for intersex using light microscopy with a range of magnifications between 20 x and 100 x (Barnhoorn et al., 2004).

Condition factor

The condition factor was calculated using the formula K = W/(aL b) according to Hagenaars et al. (2008). Where W is the total body mass (g) and L is the total length (cm). The parameters a and b were determined using the best fit values from the length-mass relationship (W = aL b) of total number of fish within the study.

5.2.3 Statistics

As described in Section 4.2.4 in Chapter 4.

5.3 RESULTS

5.3.1 Vitellogenin

VTG was measured using four major components present on its molecule including ALP, Ca, Mg and Zn (Lv et al., 2006; Bjornsson and Haux, 1985). The concentrations of Ca were shown to be significantly (p<0.05) influenced by the maturity of each male catfish, which resulted in significant correlations with gonad weight, (Table 5.2) and GSI (Table 5.3). The reason for this is unknown since most of the studies showed that males were not influenced

96

Chapter 5

by seasonal fluctuations and maturity (Srivastav and Srivastav, 1998). Nevertheless, only mature specimens were utilised for Ca analysis and is illustrated in Figure 5.1. In the figure there were no significant (p<0.05) spatial or seasonal differences observed, however the concentrations were higher in the high flow regime than the low flow regime and fish at Albasini Dam always showed the highest Ca levels, while Nandoni Dam showed the lowest. Upon comparison with DDT bioaccumulation (Table 5.2) no significant correlation was observed with Ca levels from mature fish.

Similarly, no DDT correlations were found with ALP concentrations in the plasma, however there were significant spatial and temporal differences observed. Albasini Dam and Nandoni Dam fish had significantly high ALP concentrations in the high flow 2008 compared to Xikundu fish from the same season and all sites from the low flow 2007. In the low flow 2007, the spatial tendencies were similar to those of Ca, with the highest levels at Albasini Dam and lowest levels at Nandoni Dam. Upon comparison with Ca and ALP concentrations, a significant positive correlation was observed when all fish maturities were analysed (Table 5.3).

The Mg and Zn concentrations both showed insignificant (p<0.05) spatial and temporal differences (Figure 5.1). However, both showed contrasting temporal differences to the Ca and ALP biomarkers with higher levels in fish from the low flow compared to the high flow. The Mg levels in the low flow were both higher at Albasini Dam and Nandoni Dam than Xikundu, whilst Zn levels were highest at Albasini Dam and Xikundu. In the high flow regime very slight spatial variances were evident with Xikundu having the highest Mg concentrations and Nandoni Dam having the highest Zn concentrations. As for a relationship with DDT bioaccumulation, both Mg and Zn showed significantly negative correlations (Table 5.2).

Table 5.2. Pearson's correlation between DDT metabolites and endocrine disruptive effects as well as the correlation between factors that may influence the biomarker results. Significant correlations (p<0.05) represented in bold.

CF GSI-t GSI-m PC-p PC-I Mg Ca-t Ca-m Zn ALP DDT metabolites o,p'-DDT -0.05 0.08 -0.29 0.15 -0.40 -0.11 -0.46 -0.37 -0.21 -0.30 p,p'-DDT -0.12 0.07 -0.31 0.10 -0.46 -0.18 -0.43 -0.32 -0.30 -0.31 p,p '-DDE -0.13 -0.01 -0.21 -0.38 -0.40 -0.41 0.26 0.31 -0.41 0.13 p,p'-DDE -0.23 0.06 -0.42 -0.21 -0.48 -0.27 -0.09 0.09 -0.44 -0.20 o,p '-DDD -0.16 0.02 -0.30 -0.29 -0.46 -0.41 0.04 0.12 -0.43 -0.02 p,p '-DDD -0.20 0.02 -0.40 -0.26 -0.51 -0.43 -0.06 0.06 -0.54 -0.11

Biotic fluctuations Age 0.05 -0.02 -0.16 -0.18 -0.18 0.12 0.11 0.23 -0.09 0.06 Body length 0.28 0.11 -0.09 -0.08 -0.23 -0.03 -0.08 -0.14 -0.02 0.1 Body weight 0.34 0.03 -0.08 0.03 -0.16 -0.04 -0.02 -0.12 0.03 0.12 Gonad weight 0.02 0.87 0.81 0.08 0.12 0.3 -0.43 -0.30 0.28 -0.22 Maturity -0.27 0.66 0.00 0.26 -0.03 0.16 -0.52 0.00 0.18 -0.34

Bold represents significant correlations (p<0.05), t - total, m - mature, p - plasma, I - liver.

97 Chapter 5

(a) (b) 1-1Albasini Dam c a) 250-- Nandoni Dam 0 • Xikundu C. 3- 200- E EC) 2- ea

cs) o 1-

a) 3.0 0 LF 2007 HF 2008 LF 2007 HF 2008

(c )

LF 2007 HF 2008 LF 2007 HF 2008

Figure 5.1. The mean (± SE) plasma VTG content expressed as alkali-labile phosphate (a), calcium (b), magnesium (c) and zinc (d) of male catfish from last two seasons (low flow (LF) 2007 and high flow (HF) 2008) at Albasini Dam, Nandoni Dam and Xikundu. Asterisks indicates significant differences from other sites (p<0.05).

5.3.2 Protein carbonyls

The protein carbonyl concentrations in both the blood and liver are shown in Figure 5.2. In the blood the PC varied between 0.2 and 1.2 nmol/mg. In the low flow 2007, the amount of PC in the male catfish were significantly higher (p<0.05) at Albasini Dam, with Nandoni Dam and Xikundu having similar concentrations of 0.7 and 0.6 nmol/mg, respectively. In the high flow 2008 however, no significant spatial variations were observed. The concentrations were all generally low with Albasini Dam catfish having the lowest score of 0.21 nmol/mg and Xikundu having the highest score of 0.35 nmol/mg. Upon comparison with other biomarkers (Table 5.3), a significant negative correlation was observed with the Ca concentrations in the plasma. In the liver, the concentration ranges were similar to those in the blood. In the low flow 2007 no significant spatial differences were observed, although there was a similar trend to blood with the highest concentrations found at Albasini Dam and the lowest at Xikundu. The high flow 2008 also showed no significant spatial differences, but did show a major reduction in PC from Albasini Dam catfish to both Nandoni Dam and Xikundu. Correlations with DDT metabolites show significant negative relationship between liver PC and p,p'-DDT, p,p'-DDE, o,p'-DDD and p,p'-DDD (Table 5.2). Furthermore, PC in

98

Chapter 5 the liver was also significantly positively correlated with Mg and Zn concentrations within the plasma (Table 5.3).

(a) (b) 0Albasini Dam 1.4- 1.0- Nandoni Dam Xikundu c o.s- 1.0- 2 a. o. E 0.8- 0.6- E 0 0.6- O 0.4- E c 0.4- 3 0 0 0.2- 0. 0.2- a. 0.0 0.0 LF 2007 HF 2008 LF 2007 HF 2008

Figure 5.2. The mean (± SE) protein carbonyls (PC) measured in the blood (a) and in the liver (b) from male C. gariepinus from three sites in the low flow (LF) 2007 and high flow (HF) 2008. Asterisks indicates significant differences from other sites (p<0.05). Asterisks indicates significant differences from other sites (p<0.05).

5.3.3 Gonad condition

The gonad conditions were measured with the GSI index and represented in Figure 5.3. Since the GSI is largely influenced by the reproductive stage of fish (further supported by the significantly positive correlations between the GSI and maturity, Table 5.2), spatial and temporal variations should only be assessed with fish that have the same developmental stage (Mills and Chichester, 2005). Therefore, in the present study the spatial and temporal variations were illustrated using GSI from developing males, mature males and all males. In the developing fish the gonad conditions were very low in all sites ranging from 0.036% in fish from Albasini Dam in the low flow 2007 to 0.083% in fish from the Albasini Dam in the high flow 2008 and as such were not used to analyse spatial and temporal variations in this study. However, in the mature fish, the GSI values were much higher, with concentrations ranging from 0.14% at Xikundu in the high flow 2008 to 0.29% at Albasini Dam in the low flow 2007. None of the values showed significant differences (p<0.05) between either the sites or the seasons, although a number of tendencies were found in the mature fish. Spatially, the GSI values from fish in the low flow 2007 declined from Albasini Dam to Xikundu by 0.02%, whilst fish in the high flow 2008 had the highest GSI values at Nandoni Dam and lowest at Xikundu. As for the temporal variations, most of the fish seemed to tend toward lower GSI values in the high flow 2008. When these spatial and temporal variations of GSI from the mature fish were compared against the variations of the GSI average for all fish combined (Figure 5.3c), very few similarities were evident in the low flow 2007. However, in the high flow 2008 the spatial distribution of gonad condition for the average combined value was similar to that of the mature fish grouping with highest values at Albasini Dam and lowest values at Xikundu. Furthermore, in Table 5.3 the total GSI was correlated with Mg in the plasma.

99

Chapter 5

(a) F(3,2)=0.43 (p=0.43) (b) F(3,3)=1.59 (p=0.27)

0.125- 1.75-

0.100-

0.075- a) 1.00- r Cl) a) Q. 0.75- 0 0.050- o o 0 0.50- 0.025- ' ow 0.25- 0.000 0.00 LF 2007 HF 2008 17 I i 1 LF 2007 HF 2008 (c) F(4,49)=1.58 (p=0.22) (d) F(4,27)=0.82 (p=0.53)

0.4- g ds

e 0.3- na o

Di VA g 0 0.2- ture 0.1- ma

0.0 LF 2007I HF 2008 LF 2007 HF 2008 (e) F(5,65)=1.39 (p=0.25) (f) F(5,51)=1.29 (p=0.29)

0.45- 4.5- 0.40- 4.0- 0.35- 3.5- 0.30- o) 3.0- u) a* !" 0.25- o 2.5- as 67) 0.20- c 2.0 - o ' 0.15- 0 1.5- 0.10- 1.0- 0.05- 0.5- 0.00 I 0.0 LF 2007 HF 2008 LF 2007 HF 2008I Figure 5.3. The mean (± SE) gonad somatic index (GSI) and ANCOVA adjusted gonad means representing the gonad condition of developing gonads (a and b), mature gonads (c and d) and a average/mean of the two (e and f) for the last two seasons (low flow (LF) 2007 and high flow (HF) 2008).

The gonad means adjusted for body size using ANCOVA is represented in Figure 5.3b,d,f. In comparison to GSI results, there were similar spatial and temporal tendencies, but were generally accentuated. For the ANCOVA adjusted developing gonads there was a large reduction at Nandoni Dam in the high flow 2008, whilst for the mature gonads the levels were particularly high (although mature gonads were only represented by one individual at Nandoni Dam). Nevertheless, in both the developing and mature gonads the gonads tended

100

Chapter 5 to be lower at Xikundu compared to Albasini and Nandoni Dams, whilst when all maturities were combined the Nandoni Dam and Albasini Dams were lowest in the low flow and high flow, respectively.

5.3.4 Intersex

Upon the evaluation of male C. gariepinus gonads, there was no intersex observed either macroscopically or histologically. No visible structural changes were observed on the gonads and upon the histological evaluation there was no indication of female oocytes present within any of the testicular tissue sampled. These histological sections were therefore not graphically represented. For the transverse sections of C. gariepinus showing both normal and intersex gonads refer to Barnhoorn et al. (2004) and Bornman et al. (2007).

5.3.5 Condition factor

In Figure 5.4, the mean CF values showed very little variations and only ranged from 0.91 to 1.07. Although no significant spatial and temporal differences (p<0.05) were found, the condition factors in C. gariepinus from the two dams were higher as compared to those CF values in C. gariepinus at Xikundu, for all seasons. Furthermore, no real seasonal trends were observed at any of the sites. When the values were correlated with the DDT loads (Chapter 4) and other influencing factors, no relationships were found (Table 5.2 and Table 5.3).

Albasini Dam 1.2- Nandoni Dam 1.1- e 1.0- Xikundu I- 0 0.9- 1-3 0.8- .4 0.7- c 0.6- 0.5- :5 0.4- c 0 3- 0 0.2- 0.1- 0.0 LF 2007 HF 2008

Figure 5.4. The spatial and temporal variations of the general body condition as measured by the condition factor (Mean (± SE)). LF represents low flow and HF represents high flow.

101 Chapter 5

Table 5.3. Correlations between the different biomarkers measured in C. gariepinus. Significant correlations (p<0.05) represented in bold.

CF GSI-t GSI-m PC-p PC-I Mg Ca-t Ca -m Zn ALP Effects CF 1.00 -0.10 0.04 -0.23 -0.06 -0.04 0.25 0.23 -0.02 -0.02 GSM 1.00 1.00 0.04 0.14 0.39 -0.47 na 0.32 -0.23 GSI-m 1.00 -0.16 0.24 0.40 -0.33 -0.33 0.31 -0.06 PC-p 1.00 0.16 -0.05 -0.68 -0.68 0.19 -0.16 PC-I 1.00 0.44 0.06 0.06 0.41 0.04 Mg 1.00 -0.15 -0.11 0.63 -0.21 Ca-t 1.00 na -0.19 0.33 Ca-m 1.00 -0.08 0.26 Zn 1.00 -0.36 ALP 1.00 Bold represents significant correlations (p<0.05), t - total, m - mature, p - plasma, I - liver.

5.4 DISCUSSION

5.4.1 Vitellogenin

The oestrogen mimicking effects of DDT on VTG levels were evaluated under field conditions in male C. gariepinus. In the presence of some OCs such as DDT, VTG is synthesised in the liver where it is loaded with phosphorylated lipids as well as Ca, Mg and Zn, which can be utilised as indirect measures of VTG circulating in the blood (Lv et al. 2006). In the current study no significant positive correlations between these VTG components and bioaccumulated DDT metabolites could be found, although a number of relationships were observed with regards to the environmental contaminations. For example, Ca concentrations for all the sites in the high flow 2008 were generally higher than in the previous season, which corresponds with higher OC concentrations in the water and sediment during this period (Chapter 4). This was in agreement with many studies that have reported increased plasma Ca (hypercalcaemia) in the presence of oestrogen mimicking chemicals within the water and sediment (Gillespie and de Peyster, 2004; Guerreiro et al., 2002; Tsai and Wang, 2000). Although the hypercalcaemia could not be directly associated with circulating VTG levels in the present study (as VTG was not assessed due to the lack of standardised methodologies for measuring VTG concentrations in C. gariepinus), a number of studies have shown the correlation between Ca and VTG including a study on the Chinese loach, European eel and Rainbow trout (Lv et al., 2006; Versonnen et al., 2004; Verslycke et al., 2002). Nevertheless, further investigation of this in the fish species C. gariepinus is recommended. As for the spatial tendencies, the catfish from the Albasini Dam showed the highest Ca concentrations. This trend was however contrasting with the spatial trend of DDT contamination, with the lowest DDT concentrations generally observed at Albasini Dam. These results therefore suggest that Ca is only sensitive to large environmental changes and not small changes as was observed between the sites. This is in agreement with Lv et al. (2006) who only observed increased Ca concentrations at higher exposure concentrations of estradiol.

102 Chapter 5

ALP levels showed similar temporal and spatial trends to Ca. Higher concentrations in the high flow 2008 were evident and related to higher OC concentrations in the water, whilst no real relationship between the sites and OC contamination could be established. This similarity with Ca was not only illustrated in Chapter 6 during the exposure studies, but also shown in a study by Verslycke et al. (2002) and Versonnen et al. (2004). Using fish exposed to 17a — ethinylestradiol both authors showed that plasma ALP and Ca assays have the same sensitivity to oestrogen effects. According to many authors this is largely because each Ca ion is bound to the phosphate groups on the VTG molecule (Fuentes et al., 2007; Gosh and Thomas, 1995). However, the concentrations in the present study contradicted this, with Ca measuring more than a 100 times greater than the ALP. It is strongly suspected that this is due to the insensitivity of the ALP protocol utilised in the present study. Although many have successfully used this protocol, it has been shown to be disadvantaged in that it is quite elaborate and requires extensive manipulations compared to the relatively simple measurements of Ca on the ICP-MS (Verslycke et al., 2002).

As for Mg and Zn, relatively few reports were found in literature that assessed their adequacy for measuring VTG in the presence of oestrogen mimicking chemicals. Most of which had contradicting results, with some showing them as reliable indicators (Lv et al., 2006; Bjornsson and Haux, 1958), whilst others demonstrating their insensitivities (Blaise et al., 1999). The results of this study tend to agree with the latter publications as there were no positive correlations observed with DDT contamination or with Ca and ALP concentrations. The proposed reason for the insensitivity of these metals is that their respective amounts that bind to the VTG molecules are generally smaller. Zn was reported as binding to the VTG molecule at half the concentration as Ca, whilst Mg was reported as being bound to the VTG molecules at concentrations ten times lower than that of Ca (Anderson, 1998; Montorzi et al., 1995; Ghosh and Thomas, 1995). Therefore it can be concluded that, unlike Ca, these essential metals in the plasma could not be used as indicators of contamination and that the spatial and temporal variations observed in Figure 5.1 were only indicative of natural fluctuations, due to changes in uptake from water, sediment or food (Phillips and Rainbow, 1993).

5.4.2 Protein carbonyls

Within severely polluted environments an increase in PC would indicate that normal protein metabolism is disrupted through oxidative stress. PCs can therefore be used as indicators of oxidative stress, which can in turn indirectly identify endocrine disruption from the liver and/or plasma. In the present study, there was a generally higher PC concentration in the field compared to laboratory control fish (Chapter 6). This suggests that conditions in the field may have induced higher than normal oxidative stress that could be due to anything from changes in age, feeding behaviours, nutritional factors, habitat changes, behavioural changes, xenobiotics and even parasite infections (Falfushynska and Stolyar, 2009; Padmimi et al., 2008; Martinez-Alvarez et al., 2005). The majority of these effects (all except nutritional and behavioural changes) were taken into account with no significant correlations

103 Chapter 5

observed except for some DDT concentrations. In the liver samples, a significantly negative correlation between some of the DDT metabolites and PC were observed. This suggests that as DDT metabolite concentrations increase, the concentrations of PC (oxidative stress) decreases, which contradicts many studies showing that DDT contamination increase oxidative stress in fish species (Abdollahi et al., 2004). However, a study by Almroth et al. (2005) illustrated that PC levels can appear to be reduced in the presence of higher levels of oxidative stress that can stabilize proteins and increase their half-life through aggregation, cross-linking and/or decreasing solubility compared to proteins exposed to milder oxidation, which are readily degraded. However, these mechanisms are not applicable in the presence of severe oxidative stress, which will evidently supersede the stabilizing mechanisms that occur in less oxidative environments. In consideration of this, it can be said that despite the increase in oxidative stress compared to laboratory studies, the levels were not severe and degrading and that the reduced PC levels, particularly observed at Xikundu in the high flow 2008, could be due to an increase in oxidants that can decrease the fish susceptibility to proteolytic degradation.

Upon comparison with other biomarkers (Table 5.3), the PC in the liver showed significant positive correlations with both Mg and Zn in plasma. Since decreasing PC represents increasing oxidative stress the positive correlations suggests that higher oxidative stress is related to lower Mg and Zn ions in the plasma, which were probably a result of reduced dietary uptake since they could not be used as measures of VTG. According to Martinez- Alvarez et al., (2005), Zn along with other dietary minerals such as Mn, Cu, Se are utilized by antioxidant enzymes that prevent oxidative stress, so when there is a deprivation of these metals, oxidative stress increases (Hidalgo et al., 2002). However, no studies were found that showed Mg to have a role in antioxidant processes in aquatic organism. Be that as it may, a number of studies using rats and human cells did show that Mg deficiency can increase oxidative stress, as Mg ions function to inhibit this stress (Stafford et al., 1993; Regan and Guo, 2001). Therefore, it can be concluded that PC measuring oxidative stress in the liver of C. gariepinus may have been influenced by changes in Mg and Zn concentrations in the plasma. The exact reason for this is unknown and thus it is recommended that further investigations are done to assess why metals in the plasma influence liver oxidative stress and does not induce plasma stress and what influence does metals in the liver have on oxidative stress in C. gariepinus.

As for the PC measured in the plasma, similar spatial trends to liver PC were observed in the low flow regime. Significantly higher PC levels were observed at Albasini Dam compared to Xikundu, suggesting that plasma proteins may have also been stabilized in the presence of higher contaminant concentrations as shown in the liver. In contrast, in the fish from the high flow regime these processes were not apparent, with PC levels being greatest at Xikundu (i.e. positive linear relationship between PC and contamination). These concentrations were however very low in relation to the rest of the samples, ranging from 0.21 to 0.35 nmol/mg protein and thus the spatial trend evident may just have been due to natural fluctuations. The reason for these low concentrations could be due to increased

104 Chapter 5 metabolic activity, increased presence of vitamins and other nutrients or perhaps due to changes in fish behaviour that may have enhanced their ability to deal with oxidative stress (Martinez-Alvarez et al., 2005). Unfortunately, none of these factors were assessed in the present study and therefore definite conclusions could not be made as to the reason for these low concentrations.

Another possible contributing factor toward the lower oxidative stress in the high flow could have been the presence of Ca in the plasma, as a significant negative correlation between the Ca and PC in the plasma was evident (i.e. increased Ca concentrations are related to lower PC concentrations that represent increased oxidative stress, as explained previously) . However, no other studies were found that indicated that Ca induced oxidative stress in plasma. Although this could be a new finding, it is probable that the relationship could be due to a component that is related to Ca concentration. Indeed in the previous section, Ca was hypothesized to be indicative of the VTG component. It could therefore be concluded that VTG, rather than Ca, may have contributed toward the reduced oxidative stress measured in the plasma. Upon comparison with literature only a couple of studies were found (in only honey bees and dragon lizards and no fish) but, they all showed a negative correlation between VTG and oxidative stress and attributed it to the fact that VTG can act as an antioxidant and scavenge ROS thereby reducing oxidative stress (Olsson et al., 2009; Seehuus et al., 2006). Therefore, in the present study the increase in VTG levels may influence resulting oxidative stress in males. However it should be kept in mind that the relationship between Ca and VTG was not determined (as VTG was not directly measured) and was only based on current contaminant concentrations and similar results observed in literature. Nevertheless it is recommended that further analysis on the effect of VTG on oxidative stress be assessed in fish.

In conclusion, the liver was shown to be a better indicator of oxidative stress than plasma, as liver appeared to be a more sensitive biomarker of oxidative effects than plasma and was perhaps not so influenced by fluctuating Ca/VTG concentrations in the plasma. The PC concentrations in the liver suggested that contamination in the Luvuvhu River did not induce severe oxidative stress and that Xikundu showed the greatest amount of oxidative stress if the hypothesis by Almroth and her colleagues (2005) is accepted. Since this was the only known study to have found such results, it is recommended that further investigations on this hypothesis be done before adequate conclusions can be made.

5.4.3 Gonad condition

A reduction in the gonad mass can occur in response to certain types of OCs, with many field and laboratory studies endorsing this as an effective indicator of contamination (Angus et al., 2005, Mills and Chichester, 2005; Schweer, 2002). What is often overlooked in these studies is the influence of natural variations of gonad development on spatial and temporal variations, which can lead to incorrect conclusions about contamination effects i.e. lower gonad condition values can be due to lower gonad maturities rather than from contamination

105 Chapter 5 effects. An example of this occurrence was shown in a study assessing the gonad condition in cunner fish caught at different sites within a Canadian River (Billard and Khan, 2003). The results showed that the gonad conditions were lower at an un-impacted site than at contaminated sites. Although the results initially suggested that the fish at the un-impacted site were stressed, it was rather attributed toward differences in the gonad maturation, with the fish from the un-impacted sites having gonads that were already spent compared to the ripe stage of the gonads from contaminated site fish.

In the present study, in order to identify whether reproductive variations influence the ability of gonad mass to identify contaminant effects, the gonad conditions in the fish were separated into three respective groups containing the developing fish only, mature fish only and all fish with all maturation stages and then compared. The graphs depicting the combination of maturation stages (Figure 5.3 e and f) showed a lower gonad condition in fish from Nandoni Dam in the low flow 2007 compared to the other sites. This was however deceptive as comparison with gonad conditions of the developing and mature fish groupings showed opposite spatial tendencies (i.e. higher gonad conditions at Nandoni Dam). The reason for these spatial differences between the groupings was primarily due to the percentage of developing fish versus mature fish within the composite sample (Table 4.13 in Chapter 4). That is, the percentage of developing fish with naturally lower gonad mass (Cavaco et al., 1997) was greater at Nandoni Dam as compared to the other sites, which was the reason for the reduced mean gonad condition at Nandoni Dam rather than from contaminant stress as the initial analysis suggested. Similar tendencies were also apparent at Nandoni Dam in the high flow 2008, with 75% of the composite group consisting of developing fish. The influence of maturity on spatial comparisons was further supported by the lack of differing spatial trends between the composite and separate reproductive stage groups in Albasini Dam and Xikundu. Since the samples were made up of 50% mature and 50% developing, a consistent spatial trend was evident throughout the groupings. From these results it is strongly advised that fish with different reproductive stages are separately analysed within monitoring studies, as was recommended by a review by Mills and Chichester (2005). In the present study, only the mature fish were utilised for spatial and temporal comparisons as a higher percentage of mature fish were sampled, allowing for comparisons to be more statistically sound.

To assess the gonad condition, the GSI ratio approach is the most commonly utilised method within ecotoxicological studies. The GSI is a ratio of gonad mass to body mass, which attempts to normalise the effects of body mass on the gonad mass. Upon analysing the GSI in the mature fish it was evident that all of the concentrations were particularly low. In the low flow 2007 (measured in October) the low GSI concentrations corresponded with the naturally low spawning capacity between July and November at all sites (Yalcin et al., 2001). However in the high flow 2008, the GSI values should have been much higher as February to April is the peak period for C. gariepinus spawning in the southern hemisphere. According to DeGraaf and Janssen (1996) the GSI during these periods are generally measured between 6 and 10%, about 10 to 30 times greater than the GSI values during the

106 Chapter 5 high flow period. Similar low GS! values were observed in C. gariepinus sampled from two dams in the Rietvlei Nature Reserve, which was shown to be extensively contaminated with effluent from upstream anthropogenic activities (Barnhoorn et al., 2004). These results suggest that gonad conditions are extremely reduced in the Luvuvhu River during the spawning season. However, the extent of reduced gonad condition could be exaggerated as comparisons with laboratory C. gariepinus showed an average GSI value of 0.67% in mature control fish (Chapter 6). Nevertheless spatial comparisons in the high flow 2008 did highlight a reduced condition (although not significant) in fish from Xikundu, which corresponds to the higher DDT concentrations at this site (Chapter 4).

In contrast to GSI, the ANCOVA adjusted gonad mass was proposed as a more efficient alternative to measure gonad condition. Comparisons between the two methods showed very similar temporal and spatial tendencies in the mature catfish (Figure 5.3c and d). Both methodologies showed that fish in the low flow 2007 at Albasini Dam and Xikundu had greater gonad conditions than those in the high flow 2008. The Nandoni Dam however showed no temporal differences in the GSI approach, whilst in the ANCOVA approach Nandoni Dam fish had higher gonad condition in the high flow compared to the low flow. However it must be noted that Nandoni Dam in the high flow 2008 was only represented by one specimen so accurate conclusions can not be obtained from this result. Nevertheless, ANCOVA was also shown to increase the distinction between the spatial variations observed in the low flow as compared to GSI. That is, GSI values decreased by 0.02% from Albasini Dam to Xikundu, whilst the ANCOVA adjusted values showed greater spatial differences. These results suggest that although the two methodologies showed the same trends, ANCOVA adjusted gonads were more effective in distinguishing smaller differences. Furthermore in comparison to the strong emphasis on the utilisation of ANCOVA adjusted gonads instead of GSI by Packard and Boardman (1999), the results of the current study show very little differences. A possible reason for this could be that the gonad mass was generally small and there was no drastic differences between the various sites and seasons.

5.4.4 Intersex

The results of the present study showed no instances of intersex within any of the male testis sampled, even in the presence of endocrine disrupting chemicals. However, in the same study (Water Research Commission project K5-1674), Marchand et al. (2008) found oocytes within testicular tissue of 0. mossambicus. This suggests that C. gariepinus was more resistant to endocrine disruption than 0. mossambicus, perhaps because the contaminant concentrations within the Luvuvhu River were low enough for C. gariepinus to metabolise, transform, sequester and/or eliminate contaminants more effectively than 0. mossambicus (Connell et al., 1999). This lack of effect in C. gariepinus was also shown in the Chapter 6, where catfish had no intersex after 21 days of exposure to low concentrations of p,p'-DDT. However, in the presence of many high concentration contaminants, including nonylphenol, C. gariepinus have shown to exhibit extensive intersex (Barnhorn et al., 2004).

107 Chapter 5

5.4.5 Condition factor

As is shown in Chapter 6, CF in C. gariepinus did not seem to be a major indicator of DDT contamination. However, the slightly lower health conditions evident at Xikundu during this study may have been caused by the increased DDT contamination observed at this site (Figure 4.9, Chapter 4). In spite of this there was still no significant correlation between the general CF and OC concentrations (water, sediment or adipose tissue). This is predominantly due to the large influence of environmental variations on fish body conditions. Since CF is related to changes in the body mass and length, fluctuations according to food availability, habitat quality, breeding activity or even age (Filbert and Hawkins, 1995; Lobon- Cervia et al., 1991) are as expected, although in the present study neither age nor breeding activity could be related to the CF through Pearson's correlation. The food availability and habitat quality may however have resulted in some of the spatial fluctuations in the CF. At the two predominantly lentic sites, Nandoni Dam and to a lesser extent Albasini Dam catfish had higher CF than those from Xikundu, suggesting that perhaps the lake habitats were more conducive to better catfish condition. In fact, according to Davies and Day (1998) rapid growth in dams is a common occurrence in fish species such as catfish, as the physico- chemical features provide conditions for greater food quantities.

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Chapter 6 Laboratory exposures of C. gariepinus to p,p-DDT

6.1 INTRODUCTION 117 6.2 METHODS 117 6.2.1 Experimental design 117 Advantages and disadvantages of C. gariepinus in toxicity testing 117 Culture and handling of test species 118 Approach 118 6.2.2 Test Conditions 118 6.2.3 Adult exposure 119 Sample collection 120 Sample analyses 120 6.2.4 Juvenile exposure 120 6.2.5 Statistics 121 6.3 RESULTS 121 6.3.1 Adult male biomarkers 121 Fish biology 121 Vitellogenin 122 Protein carbonyls 123 Gonad condition 124 Intersex 124 Condition factor 125 6.3.2 Juveniles 125 6.4 DISCUSSION 127 6.4.1 Biomarkers 127 Vitellogenin 127 Protein carbonyls 128 Gonad condition 128 Intersex 129 Condition factor 129 6.4.2 Juveniles 130 Hatching success 130 Juvenile survival 131 6.5 REFERENCES 131 Chapter 6

6.1 INTRODUCTION

The primary objective of this chapter is to validate the biomarkers selected and measured in the field (including ALP, Ca, Mg, Zn, PC, GSI, gonad mass (ANCOVA), intersex and CF) for future use in the biomonitoring of DDT sprayed areas, utilising the indigenous fish species C. gariepinus. In order to do this a chronic exposure to environmentally relevant DDT concentrations under laboratory conditions is necessary, which will (1) identify the basal levels of biomarkers in C. gariepinus so as to establish a clear distinction between natural variation and stress, (2) identify the sensitivity of the respective biomarkers to DDT, and (3) eliminate external factors that may influence the biomarkers' responses (Slabbed et al, 2004; Cosson and Amiard, 2000; Phillips and Rainbow, 1993).

In addition to assessing the responses of the adults with the proposed biomarkers, the responses of exposed juveniles were also assessed. This was primarily done to identify whether DDT influences juveniles at environmentally relevant concentrations as firstly, there is a large paucity of data regarding juvenile responses to DDT contamination in the species C. gariepinus and secondly, from literature it seems that not all species react similarly to the same DDT concentrations, with some showing effects at much higher concentrations than others (Kime, 1998). The endpoint that were specifically selected included hatching success and survival of juveniles, which were primarily selected as they both can theoretically influence recruitment in natural populations and thus have marked effects on adult populations in future generations (Ankley and Johnson, 2001).

6.2 METHODS

6.2.1 Experimental design

a. Advantages and disadvantages of C. gariepinus in toxicity testing

As was shown in Section 4.2.3 and 5.2.1, the fish species C. gariepinus is advantageous for monitoring of both bioaccumulation and biomarker effects in the field. Although it was primarily selected for laboratory exposures because it was utilised as a bioindicator in the Luvuvhu River, it also has a number of advantages that allowed it to be a suitable species for toxicity testing including:

They are large fish and therefore allow for ample tissue sampling for analyses, There is a large amount on research data available on the culturing techniques in this species as they are a relatively important economic species, They have relatively high tolerances to pollution (i.e. they are strong enough not to die after low concentrations of pollutants) and , They are known to have high fertilization rates and They can easily adapt to laboratory conditions. Nevertheless, the utilization of C. gariepinus in toxicity testing does pose some problems in that they are relatively

117 Chapter 6

large and thus require more space for culturing and testing and their life cycles are relatively long (months-years).

Culture and handling of test species

For an extensive review on the culture, handling and general biology of C. gariepinus refer to Tucker (1985), Viljoen (1999) and De Graaf and Janssen (1996).

Approach

For each exposure treatment mature males and females were exposed chronically (21 days, as recommended by Ankley et al. (2001) and Jensen et al. (2004)) to sub-lethal concentrations of p,p'-DDT (this isomer was selected as it is most predominant within the technical grade DDT sprayed in South Africa). The males were not only used to standardise the biomarkers that were utilised in the field study, but were also utilised to fertilise eggs from the exposed female in order to spawn Fl generations, which were further exposed for 96 hours.

6.2.2 Test Conditions

For the purposes of the present study a flow-through system was selected as an appropriate bioassay, as it is particularly useful in chronic toxicity testing in that it minimizes toxicant properties such as volatilization, precipitation, absorption and oxygen demand and that it eliminates the build up of waste products and excess food (Schmitz, 1996). Sixteen 100 L glass experimental tanks along with 4 stock tanks were set up for each exposure group in an environmental room in the aquarium of the University of Johannesburg (UJ), as represented in Figure 6.1. In two of the 900 L stock tanks a volume of p,p'-DDT stock solution was added. This sock solution was prepared to a concentration of 50 mg/I by dissolving p,p'- DDT powder in the solvent, grade ethanol, ensuring that the concentration of solvent did not exceed 20 p1/1 (according to Hutchinson et al. (2006), concentrations above this concentration would influence the organisms responses). The resulting stock was then added to the stock tanks containing 900 L aged tap water in a volume that was dependent on the nominal concentrations of p,p'-DDT to be tested including 0.659 pg/I, 1.36 pg/I, 2 pg/I and 2.724 pg/I. These concentrations were determined using the medians, maximum and 20% of the maximum total DDT concentrations in water from the Luvuvhu River. This was pumped into the respective exposure tanks and exchanged at a rate of once every 48 hours. Contaminated water was left in activated carbon for 1 week before being disposed of. As for the control groups, the aquarium set up was prepared in the same manner as described for the exposure groups, with the solvent added in the same volumes as added in the respective DDT exposure groups.

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LEGEND I. 900 L stock tank

4,4'-. DDT pumped out into exposure tanks After 48hrs 4,4'-DDT pumped out into activated carbon Stock tank Water left in activated carbon for 1 week

male male male male fish fish fish fish

Figure 6.1. Layout of one of the four flow-through systems setup for each exposure treatment of p,p'-DDT (referred to as 4,4'-DDT in the figure) using a male C. gariepinus exposure group as an example.

6.2.3 Adult exposure

The mature C. gariepinus (selection based primarily on size, but also on the size of abdomen and genital papillae of females) were obtained from broodstock fish bred at Bushveld catfish aquaculture farm. The broodstock were safely transported to the aquarium facilities at UJ, where they were left to acclimate between one and three months in holding tanks. After this acclimation period, a set of 16 fish (4 males and 4 females, for the control and p,p'-DDT exposure, respectively) were each transferred into 100 L experimental tanks. The limited number of replicates was attributed to the combination of the large size of the fish that reduces the number of fish per tank and the limited facilities available for the project.

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Nevertheless, according to Ankley et al. (2001), a minimum of four replicates can be used per treatment in order to obtain a statistically sound grouping. Once within the 100 L tanks the fish were left to acclimate, with an attached biological filter, for a further six weeks under summer photoperiod (day:night = 12:12) and a constant water temperature of 27°C. This was done to ensure that the adult fish induce mature gonads for breeding (De Graaf and Janssen, 1996). The catfish were chronically exposed for 21 days in these tanks, during which time the water quality variables were assessed. The variables, including temperature, conductivity, dissolved oxygen, oxygen saturation and pH (measured using meters described in 5.2.1a), were analysed as they have been shown to influence bioavailability of pollutants such as p,p'-DDT (Phillips and Rainbow, 1994). 12 hours prior to the completion of the 21 days a single injection of 0.5 ml/kg Aquaspawn (a decapeptide, GnRH, which stimulates the release of pituitary hormones) was injected into the dorsal muscle of the male and female in order to induce spermiation and ovulation. The female catfish were stripped after 12 hour incubation and the eggs collected whilst the male catfish were sacrificed before sperm could be collected from testes.

Sample collection

The male catfish were measured, weighed and sampled for blood in the same manner as described in Section 5.2.1, before ethically severing the vertebrae. Thereafter the pectoral spine was sampled as explained in Section 4.2.3c, while the gonads were sampled as for histology (described in Section 5.2.2) and fertilisation. The latter was done by extracting mature sperm cells from the testes that were properly dried (as water activates fish sperm cells), by gently applying pressure to the testes and removing the sperm from the epididymis of a testes.

Sample analyses

The fish biology parameters (mass, length, age) and biomarkers (ALP, metals, PC, gonad condition, histology and CF) were analysed as discussed in Section 4.2.3 and Section 5.2.2, respectively.

6.2.4 Juvenile exposure

Within 10 minutes of collection, eggs from 2 females were each mixed with an equal ratio of semen from 4 males and then fertilized using an equal volume of water (Viljoen, 1999). Fertilized eggs were then gently stirred and dispensed evenly on incubation sieves with a 1mm mesh size. The incubation sieves were suspended vertically in the 8 flow-through exposure tanks (2 females eggs each fertilised by 4 males, resulting in 8 data sets) and 8 controls, for each of the DDT concentrations. After approximately 48 - 72 hours, the number of eggs hatched and surviving free embryos (with yolk sacs) were counted. Thereafter the survival of free embryos (about 120 hours after fertilization) and resulting juveniles were monitored every 24 hours for 4 days. The amount of free and dead embryos was counted.

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The hatching success was calculated using the number of inseminated ova divided by the number of free embryos and multiplied by 100 (Viljoen, 1999). The percentage of juveniles (free embryos) that survived after 120 hours were calculated by, dividing the number of surviving embryos with the total number of hatched embryos and multiplying that by 100 to get a percentage (Viljoen, 1999). As few of the free embryo's survived after 120 hours, the larval phase (stage where the yolk sac is completely absorbed and is dependent on exogenous feeding) was not considered in this study.

6.2.5 Statistics

As described in Section 5.2.3.

6.3 RESULTS

6.3.1 Adult male biomarkers

a. Fish biology

The age, size and maturity are known to significantly influence bioaccumulation of contaminants in fish (Phillips and Rainbow, 1994). Therefore it was essential to consider these influencing factors in this study. Since all of the fish were found to be mature, only the age and size of the C. gariepinus utilised were summarised in Table 6.1. As was expected, most of the fish had very similar ages and sizes, the largest of which were the fish utilised in the 0.66 pg/I p,p'-DDT exposure set. On average the remaining exposure sets had fish that ranged between 2 and 4 years old, weighed between 0.6 and 0.9 kg and were between 52 and 60 cm in length.

Table 6.1 The mean (min - max) factors influencing catfish accumulation and effects including age, length, and mass, for each set of DDT exposure concentrations.

DDT Exposures Age (years) Body mass (kg) Body length (cm) 0.66 pg/I Exposed 3.0 (2-4) 1.14 (1.1-1.2) 62.17 (61-63) Control 3.3 (3-4) 1.27 (1.2-1.4) 63.00 (62-64)

1.36 pg/I Exposed 2.5 (2-3) 0.92 (0.7-1.1) 56.00 (53-60) Control 2.0 (2) 0.84 (0.7-0.9) 56.75 (55-59)

2.00 pg/I Exposed 3.0 (2-4) 0.86 (0.8-1.0) 56.18 (56-57) Control 2.3 (2-3) 0.77 (0.6-0.9) 55.16 (52-59)

2.72 pg/I Exposed 2.8 (2-3) 0.84 (0.7-0.9) 56.00 (53-58) Control 3.7 (3-4) 0.90 (0.7-1.0) 57.63 (56-60)

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b. Vitellogenin

The plasma VTG content was expressed as ALP, Ca, Mg and Zn and represented in Figure 6.2. The ALP concentrations were only measured for in the males exposed to 1.36, 2.0 and 2.72 pg/I p,p'-DDT, since there was not enough plasma sampled from the males exposed to 0.66 pg/I p,p'-DDT. Nevertheless, it was shown that a general increase in ALP concentrations with increase in p,p'-DDT exposure with a significantly (p<0.05) lower concentration in the control males of 2.72 pg/I exposure grouping. The metals, showed no significant differences between control and exposed fish, although there was a general tendency of a higher metal concentration in the plasma of exposed males. With regards to Ca concentrations in the blood, only 0.66, 1.36 and 2.0 pg/I DDT exposed fish showed higher concentrations than their respective controls. In contrast, 2.72 pg/I DDT exposed fish showed that no large changes were observed from the control fish. As for Mg and Zn, these metals showed also higher concentrations in the exposed groups versus the control groups, particularly with Mg in the first three DDT concentrations and Zn in only the first two concentrations.

(a) (b) =CONTROL =EXPOSED 6 60 a 5 50 40 -15) 3 E 30 2 co 20 a. tm 1 10 a. 0.66 1 36 200 2.72 0 66 1 36 2 00 2 72 p,p'-DDT pg/I p,p'-DDT pg/I (c) (d) 60- 8 1 7 50- eo =. 6 1 40- i i E lb z 4 a) co c 3 cm izi 20- (4 2 10-

0 066 136 200 272 0 66 1 36 11200 2 72I p,p'-DDT pg/I p,p'-DDT pg/I

Figure 6.2. The mean (±SE) plasma VTG content expressed as calcium, magnesium and alkali- labile phosphate in male catfish exposed to 0.66, 1.36, 2.0 and 2.72 pg/I p,p'-DDT in the ambient water and their respective solvent controls. The asterisks represents significant differences (p<0.05).

In order to identify if any of the VTG indicators were influenced by the respective p,p'-DDT concentration exposures as a whole, a Pearson's correlation was performed. As can be seen in Table 6.2, none of the parameters were significantly correlated (p<0.05) with the different p,p'-DDT exposure concentrations. Other possible contributing factors to the

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effects of the fish, included the biotic parameters such as age, size and gonad mass were. Upon evaluation of these factors with effects, however, the only correlations were found with Zn concentrations in the plasma. Upon comparisons with the various biomarkers, there was a significant relationship between the plasma ALP, Ca and Mg concentrations.

Table 6.2. Pearson's correlation between biomarkers and p,p'-DDT, effects and biotic fluctuations.

ALP Ca Mg Zn PC-p PC-I GS! CF p,p'-DDT 0.28 0.26 0.19 -0.08 -0.14 -0.14 0.00 0.07

Effects Ca 0.41 Mg 0.59 0.56 Zn -0.01 0.32 -0.24 PC-p -0.14 -0.07 -0.22 -0.08 PC-I -0.20 -0.03 0.05 -0.11 -0.08 GS! 0.09 0.27 0.09 -0.04 0.18 -0.06 CF -0.14 0.07 0.09 0.22 0.17 -0.24 0.21

Biotic fluctuations Age -0.20 0.07 -0.24 0.36 0.16 -0.22 -0.07 0.11 Body length 0.01 -0.04 -0.08 0.44 -0.02 0.21 0.07 0.30 Body mass -0.01 -0.02 -0.20 0.42 0.11 -0.07 0.06 0.74 Gonad mass 0.12 0.02 -0.04 0.74 0.14 -0.09 0.92 0.38 Bold-represents significant correlations (p<0.05), p-plasma, and I-liver. PC-protein carbonyls, GSI-gonadosomatic index and CF-condition factor

c. Protein carbonyls

Figure 6.3 shows the PC concentrations measured in the blood and liver. In the blood, the PC concentrations were all very low with no significant differences observed. In comparison to the controls, the concentrations of exposed fish were generally lower, with fish exposed to 2.0 pg/I p,p'-DDT having the highest concentration of PCs. Similar results were observed in the liver, with higher PC in fish exposed to 2.0 pg/I p,p'-DDT and was also higher than their associated controls. While the exposed fish of 1.36 and 2.72 pg/I p,p'-DDT, had lower PC concentrations compared to their respective control fish. Furthermore upon comparison with influencing factors no significant relationships could be found (Table 6.2).

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(a) (b) =CONTROL =EXPOSED 0.20- 0.7 C I c 0. 6 '5 "a 15' 0.15- T2' 0. 5 a 0. C) 0.4 --I? 0. 10- o 1-) 0.3 E c 0.05- g 0.2 C.) R a, 0.1

0.00 o. 0.66 1.36 2.00I 2.72I 0.66 1.36 2.00 2.72 p,p'-DDT pg/I p,p'-DDT pg/I

Figure 6.3. The mean (±SE) protein carbonyls (PC) measured in the blood (a) and liver (b) in male catfish exposed to 0.66, 1.36, 2.0 and 2.72 pg/I p,p'-DDT in the ambient water and their respective solvent controls.

Gonad condition

The gonad condition was measured with GSI and ANCOVA adjusted gonad mass and represented in Figure 6.4. Both analyses, showed no significant differences between any of the control and p,p'-DDT exposure groups. All control groups had slightly better gonad condition, except for those exposed to 0.66 pg/I p,p'-DDT, which had a better gonad condition than the control catfish. Upon evaluation of the relationship GSI had with p,p'- DDT, other biomarkers and biotic fluctuating factors in Table 6.2, no significant (p<0.05) correlations were observed except for the positive correlation with gonad mass, which was expected as GSI is a direct measure of gonad mass.

(a) (b) F(7, 15)=0.46 (p=0.86) =CONTROL =EXPOSED

0.66 1 36 2 00 2 72 0 66 136 200 272 p,p' - DDT pg/I p,p'-DDT1 pg/I 1

Figure 6.4. The mean (±SE) gonad condition (GSI) and ANCOVA adjusted gonad means in male catfish exposed to 0.66, 1.36, 2.0 and 2.72 pg/I p,p'-DDT in the ambient water and their respective solvent controls.

Intersex

The macroscopic and histological evaluation of the testes showed no indication of female oocytes present within either the control or exposed male fish. Therefore there was no

124 Chapter 6 graphical representation and for the appropriate references illustrating normal and intersex gonads refer to Section 5.3.4.

f. Condition factor

In Figure 6.5, there was very little deviation of the mean CF values between any of the exposure groups. No significant differences were observed, with the only exposure groups showing decreased body condition, compared to control groups, were those at 0.66 and 2.72 pg/I p,p'-DDT. Upon evaluation of correlations with influencing factors such as age, length, mass, and gonad mass in Table 6.2, there were significant (p<0.05) positive relationships observed with all variables except age.

Figure 6.5. The mean (±SE) condition factor (CF) in male catfish exposed to 0.66, 1.36, 2.0 and 2.72 pg/I p,p'-DDT in the ambient water and their respective solvent controls.

6.3.2 Juveniles

As shown in Table 6.3, most of the control fish had successfully produced an Fl generation in this study, except for male 3 and 4 from the 1.36 pg/I p,p'-DDT exposures. The 2.0 pg/I p,p'-DDT groupings had none of the control adults successfully spawned juveniles suggesting an inherent problem in fish, perhaps a result of handling stress. As such it was considered that the juvenile exposures of the catfish exposed to 2.0 pg/I p,p'-DDT could not be accurately interpreted and was consequently not further discussed. In all the exposures, the only parents that yielded a successful Fl generation were those exposed to 0.66 pg/I p,p'-DDT. However, as shown in Table 6.4, the hatching success was very low and after 72 hours there were no survivors.

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Table 6.3. The success of the artificial reproduction of 2 exposed and control females (FM 1 and 2) with 4 respectively exposed and control males (M1 — M4) to different DDT concentrations. DDT Exposures Parents that successfully propagated an Parents that unsuccessfully propagated F1 generation an F1 generation 0.66 pg/I FM1xM2 FM2xM1 FM2xM3 FM1xM1 Exposed FM FM2xM2 FM FM2xM4 FM2xM1M2xMl

FM1xM1 FM2xM2 Control FM1xM3 FM2xM3M2xM3

FM2xM4 1.36 pg/I FM1xM1 FM2xM1 FM FM2xM2 Exposed FM FM2xM3 FM1xM4 FM2xM4 FM1xM1 FM2xM1 FM1xM3 FM2xM3 Control FM FM2xM2 FM FM2xM4 2.00 pg/I FM1xM1 FM2xM1 FM1xM2 FM2xM2 Exposed FM1xM3 FM2xM3 FM1xM4 FM2xM4 FM FM2xM1 FM1xM2 FM2xM2 Control FM1xM3 FM2xM3 FM FM2xM4 2.72 pg/I FM1xM1 FM2xM1 FM1xM2 FM2xM2 Exposed FM1xM3 FM2xM3 FM1xM4 FM2xM4 FM1xM1 FM2xM1 FM1xM2 FM2xM2 Control FM1xM3 FM2xM3 FM1xM4 FM2xM4 First column represents parents that successfully produced an F1 generation (i.e. during exposure to 0.66 pg/I the parents FM1+M2 successfully produced F1 generation, whilst during 1.36 pg/I FM1+M1 did not successfully produce an F1 generation).

Table 6.4. The median percentage hatching success and survival of juveniles exposed to different concentrations from parents exposed to the same concentrations

Hatching success % Survival of hatchlings % Survival of embryos % (72 hrs) (120 hrs) Control 28 69 9 0.66 pg/I 4.25 0 0 1.36 pg/I 0 0 0 2.72 pg/I 0 0 0

126 Chapter 6

6.4DISCUSSION

6.4.1 Biomarkers

a. Vitellogenin

Plasma VTG is often used as an indicator of the presence of oestrogen mimicking chemicals in males (Kime, 1998). In order to determine the influence of p,p'-DDT on the activation of VTG levels in the present study, Ca, Zn, Mg and ALP concentrations were measured. The results showed no significant dose-dependent increase in any of the indirect biomarkers in the plasma of the male C. gariepinus. Perhaps the reason for this lack of significance was due the DDT concentrations or exposure time being too low. As shown in Chinese loaches by Lv et al. (2006), the vitellogenic responses in male fish are significantly time- and dose- dependent. Nevertheless there was a positive response of Ca and ALP, albeit not significant, to the DDT concentrations with reasonable correlation values (Table 6.2), and is illustrated in Figure 6.2a and Figure 6.2b. The Ca concentrations in the male catfish were all generally higher in the exposed fish groups compared to the control groups and increased with increasing p,p'-DDT concentrations, with the exception of 2.0 pg/I p,p'-DDT exposed group. This suggests that there was an increase in VTG levels due to DDT contamination, provided that Ca is reflective of VTG concentrations. Although this has been shown by numerous studies (Gillespie and de Peyster, 2004; Verslycke et al., 2002), it is recommended that there be further investigation into this relationship using C. gariepinus. With regards to the ALP biomarker, the concentrations were significantly related to the Ca concentrations, observed by a high positive correlation and as such ALP showed the same trends when exposed to p,p'-DDT. This trend was also observed in Chapter 5 and is therefore further discussed in Section 5.4.1. Regardless of this similarity, the ALP concentrations have successfully demonstrated significant correlations with VTG levels in fish plasma in past studies (Bjornsson and Haux, 1985) and thus can be concluded to be a good indicator of increased DDT levels (as was observed for Ca), again provided that ALP is reflective of VTG concentrations, which was not identified in the present study.

The Mg concentrations were found to be significantly correlated with both ALP and Ca concentrations in the plasma. These relationships therefore suggest that Mg measured in the plasma was perhaps related to VTG concentrations as shown by a few studies including that of Lv et al. (2006) and Bjornsson and Haux (1985). However, as was shown in Figure 6.2 the Mg means showed different trends to the ALP and Ca concentration means, with higher Mg concentrations in fish exposed to 1.36 pg/I p,p'-DDT. This along with the lack of a relationship observed in the field between ALP or Ca and Mg suggests that perhaps Mg was not an indicator of VTG concentration and was rather induced by other mechanisms related to DDT contamination. For instance a study by Logaswamy et al. (2007) and Heath (1995) showed that there was an increase of Mg in the blood of fish as a result of renal damage and dysfunction caused by certain pesticides and metals, however, no studies were found that made mention of these effects from DDT exposure.

127 Chapter 6

With regards to the Zn concentrations, it was reported that Zn bind to VTG at half the concentration as Ca (see Section 5.4.1), however, as can be seen in Figure 6.2d no such relationships were found. In fact according to Pearsons correlation no significant relationship was found with any of the other indirect measures of VTG suggesting that Zn was perhaps not related to VTG or if Zn was, there was another cause of the increased concentrations in the plasma. No related studies were found that showed similar results, with an increase in Zn probably a result of natural fluctuations.

Protein carbonyls

As is known PCs are an indication of oxidative stress, which may be induced by exposure to pollutants such as DDT. In the present study, the PC in the plasma and liver of exposed fish were generally below the concentrations observed in the control fish. The lower PC concentrations in the exposed fish could be explained by the fact that higher oxidation can stabilise proteins that would consequently yield a reduction in PC as discussed in Section 5.4.2 (Almroth et al., 2005). However, it was perhaps more likely that the concentrations of p,p'-DDT were not sufficient to induce significant oxidative stress as the PC levels were rather low (ranging from 0.1 to 0.4 nmol/mg protein) when compared with other studies including the study by Parvez et al. (2005) that had concentrations ranging from 0.5 nmol/mg to 2.5 nmol/mg in catfish species Channa punctata that was obtained using the same methodology utilised in this thesis. Therefore the resulting trends were perhaps due to natural fluctuations within the plasma and liver of the fish, which then further explained the lack of significant correlations between any influencing biotic factors, DDT or the other biomarkers, unlike the PC measured in fish from the Luvuvhu River.

Gonad condition

Despite most studies successfully utilising GSI as a measure of gonad condition to monitor the reproductive effects of DDT (Fiest et al., 2005; Mills et al., 2001; Schweer, 2002), gonad mass treated with ANCOVA had been shown to better reflect gonad condition in this study (Chapter 5, Section 5.4.3) and others. Similarly, the results observed in the exposed fish show the same tendencies and contrasted with the results of Packard and Boardman (1999) that showed the GSI should not be utilised and that ANCOVA treated gonads were a far better indication of gonad condition. Nevertheless, both indicators showed no significant (p<0.05) reflection of DDT impact for any of the exposed groupings and the least changes in gonad condition was observed in fish exposed to the lowest p,p'-DDT concentration, 0.66 pg/I. This suggests that the p,p'-DDT concentration of 0.66 pg/I in the water had no influence on gonad condition (as measured by mass) after 21 days of exposure, with a very slight reduction in testicular condition upon exposure to 1.36, 2.0 and 2.72 pg/I p,p'-DDT. These weak associations could have perhaps been due to the DDT concentrations being too low to show any significant changes in the gonad condition for the time exposed. A study done by Tricklebank et al. (2002) also showed weak associations between gonad condition

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and tissue DDT concentrations in damselfish. Although no data was available on the concentrations in the water of this study, the residues present in the damselfish were relatively lower. Other fish species have, however, been more responsive to lower concentrations of DDT for example 0. mossambicus showed structural changes in the gonads exposed to DDT concentrations in the water as low as 1 pg/I (Bhattacharya and Pandey, 1989), unfortunately the GSI or ANCOVA treated gonads were not measured so exact comparisons could not be made.

Intersex

Intersex was not observed at any dose of p,p'-DDT in C. gariepinus in the present study, which was perhaps due to the low DDT exposure doses. As was established by Papoulias et al. (2003) in medaka exposed to o,p'-DDE concentrations between 5 and 500 pg/I, intersex is usually (with a few exceptions) only induced at higher DDT metabolite concentrations. For instance according to Metcalfe et al. (2000) in the medaka fish the nominal concentration of 5 pg/I o,p'-DDT is the lowest level required for the induction of testis-ova. Another study using medaka exposed for 2 weeks showed that intersex was only induced at concentrations above 4.32 pg/I DDT (Cheek et al., 2001), whilst Edmunds et al. (2000) identified an entire sex reversal of medaka when exposed to 227 000 pg/I. Calson et al. (2000), however, showed in trout and salmon no intersex when exposed to concentrations between 1 000 and 80 000 ppb (equivalent to pg/I).

Condition factor

The CF is commonly utilised as an indicator of the general 'well being' of individual fish, which relates the total body mass to the body length. The condition of the fish during the present study exposures remained relatively constant for the various exposure groupings. Again suggesting that exposure to DDT in this study did not induce an effect in the body mass of the individual fish. This is consistent with other studies that have also shown the insensitive nature of the CF to lower pollutant concentrations (Bervoets and Blust, 2003; Pait and Nelson, 2002). This was probably due to two factors. Firstly, the CF is an indicator of the organismal/individual level of biological complexity (compared to the sub-organismal levels of complexity) and as was shown in Chapter 1, effects at this higher level of complexity generally occurs when organisms are exposed to pollutants for longer and/or at higher concentrations that was utilised in this exposure study. Secondly, it is well known that CF is largely influenced by naturally fluctuating factors such as age and breeding activity (Phillips and Rainbow, 1994) and as such these factors could have interfered with the already low changes in body mass that may occur due to the DDT exposures. However, upon evaluation of the correlations with the possible influencing factors, it was shown that age was not significantly associated with CF and that although the gonad condition was significantly related to the CF, this result should be interpreted with caution as the gonad capacity is directly proportional to the length and mass of the fish, which are both essential components of the CF.

129 Chapter 6

6.4.2 Juveniles

a. Hatching success

As can be seen in Table 6.4, the hatching success of the control specimens were all particularly low when compared to a study by Viljoen (1999) that utilised a similar approach where the hatching success was up to 99%. As the protocol, primarily based on the procedures by Viljoen (1999) and de Graaf and Janssen (2001), for the artificial spawning procedure was closely followed throughout the exposure study, these results could not be attributed to varying procedures. Furthermore, the water conditions such as fluctuating temperatures, oxygen, conductivity and pH were all regularly monitored and kept constant within controlled environmental rooms, which discounted these components as influencing factors on the hatching success of the fish during this study as was cautioned by de Graaf and Janssen (2001).

Apart from these, there are a number of other possible reasons that may have influenced the reproductive success of the control specimens. Firstly, in contrast to the control specimens in the Viljoen study, the specimens in this study were exposed to ethanol (solvent control). However, the presence of ethanol was probably not the cause of the reduced hatching success in the present study as a critical review on the acute and chronic effects of solvents by Hutchinson et al. (2006) showed that ethanol concentrations below 20 p1/1 would not induce any effect, which was far above the concentrations utilised in the exposures of this study, which ranged between 4 and 10 p1/1 pure graded ethanol.

Secondly, the specimens were probably more stressed than those in the study by Viljoen (1999) as the tanks were much smaller in the environmental rooms (compared to a three meter diameter Aquatan dam used by Viljoen) and provided no cover as they were glass. Furthermore, the specimens may have been stressed due to excessive handling, despite all the endeavours taken to reduce this stress. According to Tucker (1989) stress can directly cause reproductive inhibitions in adults by reducing or blocking gametogenesis, atresia of gametes and/or can indirectly cause reproductive inhibitions through mechanisms such as pathogens and reduced food intake caused by stress. Although no pathogens were observed in the catfish, it was evident during the exposures that some of the fish did not consume sufficient quantities of food, which according to Tucker (1989) will influence the quantity and quality of gametes that will be produced. However, since the food intake was not monitored within the scope of this study, accurate conclusions pertaining to this could not be made in the present study.

Another possible cause for the reduced hatching success was that the final oocyte maturation in the females was not always a 100% successful. It was noted that in some of the females of the unhatched juveniles, there were oocytes that were macroscopically more round than those female oocytes producing progeny. According to de Graaf and Janssen (2001), oocytes are only round before hypophysation (artificially induced spawning) where

130 Chapter 6 upon the pituitary hormone adenohypophysis is activated and utilised to complete the final oocyte maturation that yields a more translucent and flat oocyte. However, in the present study, the final maturation of some of the oocytes did not occur due to the inactivation of the adenohypophysis, but probably as a result of the inefficiency of the gonadotrophin releasing hormone (GnRH) hormone (Aquaspawn) injected into the females to activate the pituitary gland (Kime, 1998). The exact cause for this is unknown as there were no consistent deviations in the materials and methods involved in this procedure.

Although the hatching success was comparatively low in the control specimens, there was still an indication that DDT may have influenced the hatching success of juveniles. As shown in Table 6.3, the only exposed adult fish that successfully produced an Fl generation were those exposed to 0.66 pg/I p,p'-DDT. In these exposed juveniles, the hatching success was lower than in the control juveniles. These results along with the lack of hatching success from adults exposed to 1.36 and 2.72 pg/I p,p'-DDT show that the early stages of development are sensitive to very low concentrations of p,p'-DDT, which may have been accentuated by the accumulative effects of DDT on both the male and female parents as well as the juveniles (Kime, 1998). A similar sensitivity was found in medaka in a study by Cheek et al. (2001). In their study, there was a significant reduction in the reproductive success of medaka exposed to concentrations of 0.23, 1.36 and 4.32 pg/I DDT.

b. Juvenile survival

Apart from the natural reduction in free embryo survival observed with the control specimens, there was a notable decline in survival of the hatchlings exposed to 0.66 pg/I p,p'-DDT (as mentioned above this was the only exposure concentration that hatched successfully). According to the results by Viljoen (1999), free embryos are very sensitive to pollution and as was shown by Halter and Johnson (1974) in coho salmon the mean survival times of the early life-stages can be considerably reduced when exposed to DDT concentrations in the water as low as 0.5 pg/I. The possible reasons for these sensitivities are that early life-stages are particularly susceptible to DDT in the water primarily due to the high lipid content of juveniles that attract DDT and their relative size, which leads to high DDT uptake (Kruse and Scarneccia, 2002; Petterson and Kristenson. 1998; Crawford and Guarino, 1976). It, however, seems from literature that not all species react similarly to the same DDT concentrations. For instance Crawford and Guarino (1976) only found marked effects on early life stages when exposed to concentrations between 50 and 100 pg/I DDT.

6.5 REFERENCES

Almroth BC, Sturve J, Berglund A and Forlin L. 2005. Oxidative damage in eelpout (Zoarces viviparous), measured as protein carbonyls and TBARS, as biomarkers. Aqua. Tox. 73: 171- 180.

131 Chapter 6

Ankley GT, Jensen, KM, Kahl MD, Korte JJ, Makynen EA. 2001. Description and evaluation of a short-term reproduction test with the fathead minnow (Pimephales promelas). Environ Tox. Chem. 20(6): 1276-1290.

Ankley GT and Johnson RD. 2005. Small fish models for identifying and assessing the effects of endocrine-disrupting chemicals. ILAR. 45: 469-483.

Bervoets L and Blust R. 2003. Metal concentrations in water, sediment and gudgeon (Gobio gobio) from a pollution gradient: relationship with fish condition factor. Environ. Poll. 126: 9- 19.

Bhattacharya L. and Pandey KA. 1989. Inhibition of steroidogenesis and pattern of recovery in the testes of DDT exposed cichlid — Oreochromis mossambicus. Bangladesh. J. Zool. 17:1-14. In Kime DE. 1998. Endocrine disruption in fish. Kluwer Academic Publishers, Boston, USA.

Bjornsson BT and Haux C. 1985. Distribution of calcium, magnesium and inorganic phosphate in plasma of estradio1-1713 treated rainbow trout. J. Comp. Physiol. 155(B): 347- 352.

Calson DB, Curtis LR and Williams DE. 2000. Salmonid sexual development is not consistently altered by embryonic exposure to endocrine-active chemicals. Environ. Health. Pers. 108(3): 249-255.

Cheek AN, Brouwer TH, Carroll S, Manning S, McLachlan JA, and Brouwer M. 2001. Experimental evaluation of vitellogenin as a predictive biomarker for reproductive disruption. Enviro. Heal. Pers. 109(7): 168-178.

Cosson RP and Amiard JC. 2000. Use of metallothioneins as biomarkers of exposure to metalsl. In: Lagadic L, Caquet T, Amiard JC and Ramade F (Eds). Use of biomarkers for environmental quality assessment. A.A. Balkema.

Crawford RB and Guarino AM. 1976. Effects of DDT in Fundulus: studies on toxicity, fate and reproduction. Arch. Environ. Contam. 4: 334-348.

De Graaf G and Janssen J. 1996. Handbook on the artificial reproduction and pond rearing of the African catfish Clarias gariepinus in sub-Saharan Africa. FAO, Fisheries technical paper 362; Rome.

Edmunds JSG, McCarthy RA and Ramsdell RS. 2000. Permanent and functional male-to- female sex reversal in d-rr strain Medaka (Olyzias latipes) following egg microinjection of o,p'-DDT. Envion. Health. Pers. 108(3): 219-224.

132 Chapter 6

Exotoxnet, Extension Toxicology Network. 1996. Pesticide Information Profiles, DDT. Oregon State University.

Fiest GW, Webb MAH, Gundersen DT, Foster EP, Schreck CB, Maule AG and Fitzpatrick MS. 2005. Evidence of detrimental effects of environmental contaminants on growth and reproductive physiology of white sturgeon in impounded areas of the Columbia River. Environ. Health Perspect. 113(12): 1675-1682.

Gillespie DK and de Peyster A. 2004. Plasma calcium as a surrogate measure for vitellogenin in fathead minnows (Pimephales promelas). Ecotox. Environ. Saf. 58: 90-95.

Halter MT and Johnson HE. 1974. Acute toxicities of a polychlorinated biphenyl (PCB) and DDT alone and in combination to early life stages of coho salmon (Onchorhynchus kisutch). J. Fish. Res. Board of Can. 31: 1543-1547. In: World health organization. 1989. Environmental health criteria for DDT and its derivatives - environmental aspects. ISBN 92 4 154283 7.

Heath AG. 1995. Water pollution and fish physiology. Second edition, published CRC Press. pp 359.

Hutchinson TH, Shillabeer N, Winter MJ and Pickford DB. 2006. Acute and chronic effects of carrier solvents in aquatic organisms: a critical review. Aqu. Tox. 76: 69-92.

Jensen KM, Kahl MD, Makynen EA, Korte JJ, Leino RL, Butterworth BC and Ankley GT. 2004. Characterization of responses to the antiandrogen flutamide in a short-term reproduction assay with the fathead minnow. Aqua. Tox. 70: 99-110.

Kime DE. 1998. Endocrine disruption in fish. Kluwer Academic Publishers, Boston, USA.

Kruse GO and Scarneccia DL. 2002. Contaminant uptake and survival of white sturgeon embryos. Amer. Fish. Soc. Sym. 28: 151-160.

Logaswamy S, Radha G, Subhashini S and Logankumar K. 2007. Alterations in the levels of ions in blood and liver of freshwater fish, Cyprinus carpio var. communis exposed to dimetholate. Environ. Monit. Assess. 131: 439-444.

Lv X, Shao J, Song M, Zhou Q and Jiang G. 2006. Vitellogenic effects of 1713-estradiol in male Chinese loach (Misgurnus anguillicaudatus). Comp. Biochem. Physiol. 143(C): 127- 133.

Metcalfe TL, Metcalfe CD, Kiparissis Y, Niimi AJ, Foran CM and Benson WH. 2000. Gonadal development and endocrine responses in Japanes Medaka (Oryzias latipes) exposed to o,p'-DDT in water or through maternal transfer. Environ. Tox. Chem. 19(7): 1893-1900.

133 Chapter 6

Mills LJ, Gutjahr-Gobell RE, Haebler RA, Horowitz DJB, Jayaraman S, Pruell RJ, McKinney RA, Gardner GR and Zaroogian GE. 2001. Effects of estrogenic (o,p-DDT; octylphenol) and anti-androgenic (p,p-DDE) chemicals on indicators of endocrine status in juvenile male summer flounder (Paralichthys dentatus). Aqua. Tox. 52: 157-176.

Packard GC and Boardman TJ. 1999. The use of percentages and size-specific indices to normalize physiological data for variation in body size: wasted time, wasted effort? Comp. Biochem. Physiol. 122: 37-44.

Pait AS and Nelson JO. 2002. Endocrine disruption in fish: an assessment of recent research and results. NOAA Tech. Memo. NOS NCCOS CCMA 149. Silver Spring, MD: NOAA, NOS, Centre for Coastal Monitoring and Assessment 55pp. Silver Spring, Maryland.

Papoulias DM, Villalobos SA, Meadows J, Noltie DB, Giesy JP and Tillitt DE. 2003. In Ovo exposure to o,p'-DDE affects sexual development but not sexual differentiation in Japanese Medaka (Oryzias latipes). Environ. Health Pers. 2003. 11(1): 29-32.

Parvez S and Raisuddin S. 2005. Protein carbonyls: novel biomarkers of exposure to oxidative stress-inducting pesticides in freshwater fish Channa punctata (Bloch). Environ. Toxicol. Pharmacol. 20: 112— 117.

Petterson GI and Kristenson P. 1998 Bioaccumulation of lipophilic substances in fish early life stages. Environ. Tox. Chem. 17(7): 1385-1895.

Phillips DJH and Rainbow PS. 1993. Biomonitoring of trace aquatic contaminants. Chapman and Hall. pp. 244.

Schmitz RJ. 1996. Introduction to water pollution biology. Texas: Golf Publishing Company.

Schweer G. 2002. Draft detailed review paper on a fish two-generation toxicity test. EPA, Battelle Report No. 68-W-01-02.

Slabbert JL, Venter EA, Joubert A, Voorster A, de Wet LPD, van Vuren JHJ, Barnhoorn I and Damelin LH. 2004. Biomarker assays for the detection of sub-lethal toxicity in the aquatic environment — a preliminary investigation. Water Research Commission (WRC) Report No. 952/1/04.

Tricklebank KA, Kingsford MJ and Rose HA. 2002. Organochlorine pesticides and hexachlorobenzene along the central coast of New South Wales: multi-scale distributions using the territorial damselfish Parma microlepis as an indicator. Enviro. Poll. 116: 319-335.

Tucker CS. 1985. Channel catfish culture. Amsterdam: Elsevier Science Publishers.

134 Chapter 6

US EPA. 1999. Environmental Protection Agency technical fact sheet: DDT. Oregon State University.

US toxicological profile for DDT, DDE, and DDD. 2002. US department of health and human services. Agency for Toxic Substances and Disease Registry.

Vallack HW, Bakker DJ, Brandt I, Brostrom-Lunden E, Brouwer A, and Bull KR. 1998. Controlling persistent organic pollutants — what next? Environ. Tox. Pharm. 6: 143-175.

Verslycke T, Vandenbergh GF, Versonnen B, Arijs K, and Janssen CR. 2002. Induction of vitellogenesis in 17aethinylestradiol-esposed rainbow trout (Oncorhynchus mykiss): a method comparison. Comp. Biochem. Physiol. 132(C): 483-492.

Viljoen A. 1999. Effects of zinc and copper on the post ovulatory reproductive potential of the sharptooth catfish, Clarias gariepinus. MSc dissertation, unpublished. Rand Afrikaans University, Johannesburg, South Africa.

Vouk VB and Sheehan PJ (editors). 1983. Methods for assessing the effects of chemicals on reproductive functions. John Wiley and Sons.

135 Chapter 7 Determination of a suitable suite of biomarker for biomonitoring

7.1 INTRODUCTION 137 7.2 METHODS 137 7.3 RESULTS AND DISCUSSION 137 7.4 REFERENCES 140 Chapter 7

7.1 INTRODUCTION

Much controversy surrounds the use of DDT that is currently sprayed in numerous developing countries as a vector control, as it is a highly persistent pesticide that has a vast array of toxic effects. It is therefore of utmost importance to monitor the status and trends of DDT in these areas. Unfortunately, because most of the DDT spraying occurs in developing countries, there are few monitoring programmes put in place due to a general lack of resources (Phillips and Rainbow, 1993). In South Africa, DWA (Department Water Affairs) only recently considered the usefulness of sub-organismal monitoring in assessing the effects of contaminants (Wepener, 2008). Unfortunately, a large lack of data exists with regards to the monitoring of these effects in South African ecosystems, including those related to DDT contamination. Therefore, in identifying the effects of DDT in the Luvuvhu River, it was also possible to establish suitable biomarkers specific to DDT for use within a monitoring programme, specifically in the indigenous C. gariepinus. Hence, the objective was to establish a suitable battery of biomarkers that can be used to detect sub-lethal effects of DDT for use within a monitoring programme.

7.2 METHODS

In order to establish the suitability of a biomarker to DDT contamination, a number of criteria need to be met (Slabbert et al. 2004; Lagadic et al., 2000; Phillips and Rainbow, 1993). These include the following:

The biomarker must be able to identify DDT effects, The biomarker must be sensitive, reliable/repeatable (give same results to similar concentrations of exposure) and scientifically sound, The biomarker must be cost effective, The biomarker must be usable under field and laboratory conditions, The biomarker must be able to detect effects above natural fluctuations, and The biomarker must be able to detect effects in indigenous bioindicator, C. gariepinus.

These criteria were then evaluated for each biomarker, which were summarised with a set of advantages and disadvantages. The biomarkers included ALP, Ca, Zn, Mg, GSI, ANCOVA adjusted gonad mass, intersex, and CF, which were already analysed and discussed in the field (Chapter 5) and laboratory (Chapter 6).

7.3 RESULTS AND DISCUSSION

The results and discussion of chapter 5 and 6 are combined in this chapter as it is a concluding chapter.

137 Chapter 7

In Table 7.1 it was evident that the biomarker that showed the most potential was that of Ca measured in the plasma. Although no significant correlations with DDT were found, there were in both the field and the laboratory strong indications that Ca concentrations increase in the presence of DDT in C. gariepinus. Furthermore, it was evident that Ca was generally detectable above natural fluctuations, easy to analyse and interpret with no advanced skills required, and most importantly it is cost effective. Therefore, this biomarker can be recommended for future use in the monitoring of DDT, but the mechanisms responsible for the positive reactions should still be investigated i.e. if VTG is responsible for the hypercalcemia discussed in Chapter 6.

Similar conclusions and recommendations have been found for ALP, another biomarker that is an indirect measure of VTG. However, this biomarker was not found to be as cost effective and required more expert skills than Ca. Furthermore, the techniques used to measure ALP were more insensitive than Ca, although it would probably still be able to highlight areas of concern.

As for Mg and Zn measured in the plasma, these biomarkers were probably not related to VTG contamination as no consistent relationships with Ca or ALP were observed in either the field or laboratory exposures. In Mg, however, there was a slight response to p,p'-DDT in the laboratory, but no such response was found in the field which was contaminated with p,p-DDE, suggesting that this Mg may be specifically induced by the p,p'-DDT metabolite. This conclusion is however rather bold, as the p,p'-DDT metabolite was measured in the tissue of the organisms, and many other influencing factors at play in the field could have adjusted the final measurements. From this, a recommendation can be made that further research be done on the mechanisms responsible for changes in Mg concentration in the blood. In the case of Zn, the results were far less contradictory with both laboratory and field fish showing no relationship and was shown to be highly influenced by external concentrations of Zn.

In contrast to Ca and ALP, the PC that measure the oxidative stress caused by DDT showed no promise as an indicator for monitoring DDT contamination. This was because despite the fact that these carbonylated proteins measure a very low level of biological complexity, they resulted in contradicting data regarding DDT contamination. In addition, they require relatively complex techniques, which are inappropriate for unskilled workers and they are somewhat expensive to monitor. Therefore, the use of PC as direct indicators of chronic DDT effects is not recommended.

At the organ level of biological complexity the gonads were assessed using the well known GSI and intersex biomarkers, as well as the lesser known indicator utilising gonad mass adjusted by ANCOVA. The results showed that the gonad mass measured with GSI and ANCOVA were very similar. They both showed slight changes due to DDT exposure, they were both easily measurable, extremely cost effective, reliable and scientifically sound, and effective in C. gariepinus. The only disadvantage that was observed was that they were

138 Chapter 7

influenced by natural fluctuations, which should always be considered in further investigations. Although they both showed the same responses it is recommended that both of these biomarkers be utilised, as ANCOVA is slightly more sensitive and at higher concentrations could be a more accurate indication of spatial trends. As for the intersex, no testes-ova were observed in this study. It is suspected that C. gariepinus is less sensitive to such changes and require much higher levels of contaminants to induce such effects. As such effects have been shown in C. gariepinus exposed to DDT contamination this biomarker was recommended for monitoring DDT. As a positive response was noted between this biomarker and DDT it is recommended for DDT monitoring.

Lastly, the condition factor had no response to DDT expose in this study, primarily because it measures high levels of complexity. However, it was recommended for use in monitoring programmes as it was shown to be cost effective, reliable, scientifically sound and usable in both field and laboratory studies. Care must however be taken when using this biomarker as it can be influenced by natural external fluctuations.

Table 7.1. Criteria to establish suitability of each biomarker for future use.

ANCOVA ALP Ca Mg Zn PC GS! lntersex CF gonads

Indicative of DDT Yes Yes Not No Not Yes Yes No No contamination always always

Measurable in Yes Yes Yes Yes Yes Yes Yes No Yes C. gariepinus

Sensitive (level of Yes Yes Yes No No Slightly Slightly No No biological (sub- (sub- (sub- (sub- (sub- (organ) (organ) (organ) (organismal) complexity) cellular) cellular) cellular) cellular) cellular) Reliable Yes Yes Not No Not Yes Yes Yes Yes always always

Scientifically sound Unknown Unknown Unknown Unknown Yes Yes Yes Yes Yes (VTG=ALP?) (VTG=ALP?) (VTG=ALP?) (VTG=ALP?)

Cost effective Spect. Yes Yes Yes Spect. Yes Yes Histology Yes

Usable in field Yes Yes No Yes No Yes Yes Yes Yes and lab

Detectable above Yes Yes Yes No Yes Not Not Yes Not natural fluctuations always always always

RECOMMENDED YES YES NO NO NO YES YES YES YES

139 Chapter 7

7.4 REFERENCES

Lagadic L, Caquet T, Amiard JC and Ramade F (Eds.). 2000. Use of biomarkers for environmental quality assessment. A.A. Balkema, Rotterdam.

Phillips DJH and Rainbow PS. 1993. Biomonitoring of trace aquatic contaminants. Chapman and Hall. Pp. 244.

Slabbert JL, Venter EA, Joubert A, Vorster A, de Wet LPD, van Vuren JHJ, Barnhoorn IEJ and Damelin LH. 2004. Biomarker assays for the detection of sub-lethal toxicity in the aquatic environment — a preliminary investigation. Water Research Commission (WRC) Report No. 952/1/04.

Wepener V. 2008. Application of active biomonitoring within an integrated water resources management framework in South Africa. SA. J. Sci. 104: 367-373.

140 CHAPTER 8 The effects of DDT on fish communities

8.1 INTRODUCTION 142 8.2 METHODS 144 8.2.1 Sampling 144 8.2.2 Data analysis 145 Occurrence of fish 145 Relative abundance 145 Non-parametric diversity indices 146 Abundance models 146 FRAI 148 8.2.3 Statistics 150 8.3 RESULTS 151 8.3.1 Occurrence of fish 151 8.3.2 Informal assessment of number of species and relative abundances 152 8.3.3 Species diversity indices 153 8.3.4 Abundance models 157 8.3.5 FRAI 158 8.3.6 Multivariate statistics 161 8.3.7 Factors influencing fish abundance and diversity 165 8.4 DISCUSSION 167 8.4.1 Fish community changes 167 Latonyanda River 167 Hasana 168 Tshikonelo 169 Xikundu 170 Mhinga 171 8.4.2 Fish community assemblage methodologies 172 8.5 REFERENCES 174 Chapter 8

8.1 INTRODUCTION

Fish community structure has been widely used to assess the effect of human impacts on aquatic ecosystems including water quality deterioration and habitat. This is primarily due to a number of advantages related to measuring fish communities. These include the ability of fish communities to identify large scale effects, the ease to identify taxonomically, their high public profile so effects can be related to human welfare and their ability to allow for a multitrophic approach (Attrill and Depledge, 1997). Consequently, a vast amount of literature is available on the responses fish communities have to anthropogenic changes within aquatic ecosystems, including a vast array of contaminants such as pesticides (Eaton and Lydy, 2000; Kerr and Vass, 1973), metals (Phillips and Rainbow, 1993), pulp and paper mill effluents (Karels and Niemi, 2002), sewage outfall contamination (Mills and Chichester, 2001), PCBs (Mayon et al., 2006) and industrial wastewater treatment plants (Yoem et al., 2007).

The changes in the fish communities from these contaminants are usually a result of either lethal effects that eliminate sensitive species or by sub-lethal effects that cause changes in reproductive output, recruitment and/or growth (Connell, 1999). However, very rarely have studies successfully shown the exact cause of the community changes within field studies. This is primarily because (1) complex chemical contamination makes it difficult to attribute harmful community effects to a particular pollutant, (2) additional compounding factors such as other chemicals, habitats, nutrients, physical stress are responsible for false interpretation of community changes, and (3) generally a pollutant has multiple biological targets that may be altered and the same targets may be altered by different pollutants making it difficult to identify exact causes (Vasseur and Cossu-Lequille, 2006). These disadvantages however can be overcome if contamination and effects are large enough in a study area with the majority of contamination only related to one pollutant. Therefore, as the majority of contamination was due to DDT, with some exceptions of dieldrin and lindane contamination, the possibility of a relationship with fish community structure integrity is higher.

Consequently, the objective of this chapter was to assess if there were effects of DDT at the community level of biological complexity using a battery of bioassessments, as recommended by Russel (1997). These included the assessment of the fish composition, the informal assessment of the number of species and abundances, species richness and evenness indices, diversity indices, abundance models, multi-metric assessments and multivariate statistics. The evaluation of the extent of contamination using these bioassessments can also be used to refine the effectiveness of well known techniques to assess community level effects.

The informal assessments considered the species composition, number of species and the abundances' of each species. According to Philippi et al. (1998) changes in the species composition can provide a sensitive measure of ecologically relevant changes in the environment as it reflects a combination of environmental and historical events. Whilst the

142 Chapter 8 informal assessment of the number of species and the abundances thereof was shown by Russel (1997) as an important technique used in the assessment of aquatic communities since it provides a rough indication of the nature and extent of change in community assemblages.

Species diversity is a function of both the number of species as well as distribution and abundance within individual species. Evaluation thereof is based on the principle that impacts usually reduce the number of species in a community by eliminating certain sensitive species and by increasing the abundance of a few tolerant, opportunistic species (Connell et al., 1999). Numerous measures of species diversity have been developed, internationally, to provide a mathematical means of describing and comparing differences in species diversity and can be divided into three main categories including species richness indices, diversity indices (based on the proportional abundances of species) and abundance models (Magurran, 1988). The species richness indices essentially only measure the number of species per a specified number of individuals and are advantageous in that it provides an instantly comprehensible expression of diversity with no influence of abundances.

The second diversity category is the diversity indices that are a measure of the relationship between the species richness and evenness combined, where the evenness is a single expression of the distribution of the abundances for all the species, with a high evenness representing a community with all species with equal abundances (Robinson and Sandgren, 1984). The most commonly used diversity index is the Shannon index that despite its popularity has numerous flaws (Magurran, 2004; Russel, 1997; Cao et al., 1996). An alternative to this diversity index was the lesser known alpha-log series index. According to Magurran (1988), this index is a much better indicator of diversity than the Shannon index since it is not unduly influenced by sample size; it has good discriminatory abilities and is relatively simple to calculate.

The third diversity category that carries the most information about ecological and environmental conditions is the theoretically-based species abundance models (Peters and Bork, 1999). These models generally describe the distribution of individuals among species observed in fish communities and are usually examined in relation to four main models (Magurran, 1988). The models are seen to represent a progression ranging from the geometric series where a few species are dominant with the remainder fairly uncommon, through to the log series and log normal distributions where species of intermediate abundance become more common and ending with broken stick model in which species are as equally abundant as is observed within the natural environment. These changes in the community distributions can reflect changes in the resource availability and niche pre- emption (or occupation), which have been found to change in the presence of stress. Shifts in the models can therefore be used to identify negative influences on fish communities. For example a shift from a log-normal distribution indicating large species-rich communities to a geometric-series pattern that indicates a species poor community occurring under a harsh

143 Chapter 8

environmental regime can be an indication of degradation in the fish communities due to reduced resources and niche space.

Another method that was used in addition to the univariate methods mentioned above was the multivariate analyses. The classification, ordination and discriminatory tests of the multivariate analyses provide a quantitative comparison of species composition and abundances between communities (Connell et al., 1999). These multivariate analyses are particularly suitable in biomonitoring, as they are able to detect subtle changes not evident in univariate analyses and are thus more sensitive in discriminating between sites (Phillips and Rainbow, 1998). Furthermore, the multivariate analyses are advantageous as species that cause the dissimilarities/similarities are highlighted and that environmental variables responsible for community changes can be identified. Despite these advantages the multivariate analyses do have a few disadvantages (Cao et al., 1996). Firstly, since the approaches were similarity based, they could not be directly used to show whether conditions were improving or deteriorating at a specific site. And secondly, the approaches measure the difference between samples on a relative scale, which is dependent on the other samples in the grouping.

The fish response assessment index (FRAI) was predominantly established for use within the framework of the Ecostatus and Ecoclassification methodologies. These methodologies were primarily derived to gain insight into the causes and sources of the deviation of the present ecological status of biophysical attributes from the reference condition for the application in ecological reserve determination in South Africa (Kleynhans and Louw, 2008). The specific purpose of the FRAI is to provide a habitat-based cause-and-effect underpinning, to interpret the deviation of the fish assemblage from the reference condition and was defined as "an assessment index based on the environmental intolerances and preferences of the reference fish assemblage and the response of the constituent species of the assemblage to particular groups of environmental determinants" (Kleynhans, 2008; Kleynhans et al. (2008)). This assessment was utilised as an alternative to the multi-metric fish assemblage integrity index (FAIT) (Kleynhans, 1999). Although they both essentially use the same information, the procedures and results are entirely different. According to the authors that developed the FRAI, this index is advantageous in that it has a much stronger cause-effect basis than FAIT, it could be used for site-specific investigations and naturally low fish species diversities could be assessed.

8.2 METHODS

8.2.1 Sampling

Sampling for the purpose of assessing the status of fish communities occurred at only five riverine sites in the Luvuvhu River, namely Latonyanda, Hasana, Tshikonelo, Xikundu and Mhinga in all four flow regimes mentioned in Chapter 4. The Latonyanda and Xikundu sites however were not sampled in the low flow 2006. The Latonyanda site was not sampled as

144 Chapter 8 another site was initially selected as a reference site, but was found as an inadequate lotic reference site as discussed in Section 3.2. Whereas Xikundu was not sampled in the first sampling season, due to insufficient time and resources available. The Albasini and Nandoni Dams were not sampled in any of the seasons due to two main reasons. Firstly, as is well known, reservoirs have substantially different physical, chemical and biological properties from that of flowing rivers, thereby making comparisons between these types of environments difficult (Davies and Day, 1998) and secondly, even if one was to sample these reservoirs, accurate assessment of community assemblages was not practical as sampling was very intensive; requiring specialised sampling techniques, funding, time, and labour.

At each of the riverine sites fish were collected using electronarcosis and a seine net. Electronarcosis was primarily used to sample fish from riffles, runs, rapids, and vegetated habitats in a known amount of time. Whilst the seine net was used to sample fish primarily from sandy pools (the amount of drags were also noted at each site). Following collection, fish were identified to species level using the key from Skelton (2001) and safely returned to the water.

8.2.2 Data analysis

Occurrence of fish

In order to informally identify changes in the distribution of fish species in the current study, comparisons were made with species distributions obtain from previous studies and from the reference frequency of occurrence (FO) data. The majority of the historical data was obtained from reports specifically aimed at identifying species distributions in the lower segments of the Luvuvhu River found within the Kruger National Park (Muller and Villet, 2004; Russel, 1997; Pienaar, 1978) however, there was some published data found for the upper reaches of the Luvuvhu River in Fouche et al. (2005). Apart from the historical data, the reference frequency of occurrence data is a reference list of species that should occur in a segment of a river. Each species was rated, by specialists, according to its expected presence in a specific reach, where 1 = <10%, 2 = 10-25%, 3 = 25-50%, 4 = 50-75% and 5 = >75% (Kleynhans et al., 2008).

Relative abundance

Collection of biota for absolute abundance of populations is very intensive and according to Barbour et al. (1999) is not a required endpoint to determine the effects of pollution in lotic ecosystems. Therefore, only the relative abundance was used as an indicator of contaminant stress on fish and invertebrate communities. Furthermore, manipulation of fish data into catch per unit effort (CPUE) values was required to overcome the differences in the sampling efforts between the sites (Schneider, 2000; Kleynhans, 1999). To determine the CPUE, the total catch for the relevant sampling technique was divided by their total sampling

145 Chapter 8

effort, which would be time (minutes) for electronarcosis and number of drags for seine net. This CPUE was then finally standardised to 30 minutes and 2 drags for each respective technique, and rounded off to the nearest 1.

Non-parametric diversity indices

Margalef's species richness, Pilou's evenness index, Shannon diversity index and alpha-log series diversity index of fish can be used to describe the response of a community to the quality of the environment. The Margalef's species richness was selected to measure the number of taxa (species/families) present for a given number of individuals, incorporating total number of individuals and total number of taxa, while Pielou's evenness index was selected as it represents how individuals are distributed over the taxa, and Shannon diversity index because it incorporates both the species richness and evenness components. All of the other indices were then calculated on the statistical program PRIMER v5. In addition, the alpha-log series diversity index was also included as an alternative to the controversial Shannon diversity index and was calculated as shown in the log series model analysis in Magurran (1988).

Abundance models

For fish communities, assemblages can be described according to one of four statistical models: the geometric series, log series, truncated log normal series or broken stick model (Magurran, 1988). In order to identify which of these models fits best to the current community abundance data, both goodness of fit tests and visual inspection of rank abundance plots were utilised as suggested by Magurran (1988). Once a model has been ascribed to the fish communities it can be used to highlight sites with environmental stress. That is, the regression of a community from a broken stick model down to a geometric series model indicates that there is a decrease in the community equitability (evenness). The broken stick model predicts a very equally abundant community, as compared to the log series and log normal distributions where species of intermediate abundance are less common and ending in the geometric series model in which there is an extremely uneven distribution with a few dominant species. This change in community distribution has been attributed to negative influences such as anthropogenic stressors and can therefore be used in the present study to highlight sites with deterioration in community distributions (Magurran, 1988).

Goodness of fit tests The present/observed number of species made use of CPUE data (see relative abundance) and were put into eight log2 abundance classes, with upper abundance boundaries of 2, 4, 8, 16, 32, 64, 128, 256 fish species for log series, truncated log normal and broken stick models. In order to test the goodness of fit of these observed abundances to a specific model, it was necessary to first calculate the expected abundance for each model. The calculations for this expected abundance differed between the models and are given in detail

146 Chapter 8 in Magurran (1988). Briefly, in the geometric series the expected abundances of each species ranked from most abundant to least abundant were determined on the assumption that the dominant species will use proportion K of some limiting resource, while the second most dominant species will take proportion K of the remaining resource and so on until all species have been accounted for. In the Log series model the expected abundance was calculated by determining the number of species expected to have one individual, two individuals and so on using the form; ax, axe/2, ax3/3 ... ax"/n. The expected abundance (also measured by the number of individuals per species) of the truncated log normal model was determined by estimating the total area under the log normal curve that was not sampled. Lastly, the expected broken stick abundances were calculated by ranking the species from most abundant to least abundant and expressing it in terms of a standard species distribution. In the latter three models the respective expected numbers of species were summed into log2 abundance classes as was done for the observed abundances.

Once the expected abundances were calculated according to Magurran (1988) for each of the models, the Chi square test was used to statistically compare this with the observed data using the following equation:

X2 = E[(observed — expected)2/expected]

The resulting X2 value was then used to determine the probability (P) that the observed abundances are different to the expected abundances. This exact probability was calculated on an online calculator (see references) using the X2 value and the degrees of freedom (df). The df was calculated differently for each model; geometric series df = number of observed species — 1, log series df = number of classes — 1, truncated log normal df = number of classes — 3, and broken stick df = number of classes — 1. The percentage similarity between the expected model curve and the observed curve was then derived in order to identify which model curve the observed data follows (Magurran, 1988).

Rank abundance plots The rank abundance plot is the most frequently utilised method for plotting species abundance data in order to identify which of the four models best describes the abundance distribution data. In these plots the abundance of each species is logged and plotted against the species' sequence (or rank) that is on the x-axis (Figure 8.1) (Magurran, 1988). For the abundance plots in the present study the standardised CPUE values (see relative abundance) for each site and season were logged and plotted against the species' sequence, in the order from the most abundant to the least abundant species. Each of these observed abundance distribution graphs were then used to identify which species abundance distribution model fit best, by visually comparing their curves with the typical shape of the four models observed on a rank abundance plot.

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e. FRAI

The fish response assessment index (FRAI) is an alternative to the fish assemblage integrity index (FAIT) which assesses the response of fish assemblages to particular groups of environmental determinants. (Kleynhans, 2008). For an in depth explanations on the procedures refer to Kleynhans (2008), whilst the verified spreadsheets that calculates the FRAI was obtained from the South African governmental department of Resource Quality Services (RQS) in Department of Water Affairs (DWA).

Briefly, at each site the observed fish species were transformed to a FO score of between 1 and 5 (as shown in Section 8.2.2a). Each species was scored according their presence at different sampling points at each site (considering the abundances and habitat sampled) and the resulting score would then represent the percentage of occurrence of each species at a site (Kleynhans, 2008). The resulting sets of observed FO scores were compared with those reference FO scores, however not all of the reference species were included. This follows the suggestion by Kleynhans (2008) that species should be excluded from the assessment if the sampling procedures did not allow for species capture. For instance, the eel species A. benalensis (refer to Table 8.1 for genus) was historically present within the Luvuvhu River, however they were excluded from the FRAI assessment as there is an inherent difficulty in the adequately sampling of eels and therefore the chances of catching an eel without specialised techniques is low (Naismith and Knights, 1990). Other species that were excluded on this premise were the A. marmorata, Anguilla mossambica, Hydrocynus vittatus, Labeo congoro, Labeo rosae, Labeo ruddi, Macusenius. macrolepidotus, Micropterus salmoides, Petrocephalus spp, Synodontis zambezensis and Schilbe intermedius (Table 8.1).

Table 8.1. List of the species names and abbreviations used in the text and tables, respectively.

ABEN Anguilla bengalensis BUNI Barbus eutaeniatus MACU Micralestes acutidens AMOS Anguilla mossambica BVIV Barbus viviparous MBRE Mesobola brevianalis AURA Amphilus uranoscopus CGAR Clarias gariepinus MMAC Marcusenius macrolepidotus BANN Barbus annectens CPAR Chiloglanis paratus MSAL Micropterus salmoides BEUT Barbus eutaeniatus CPRE Chiloglanis pretoriae OMOS Oreochromis mossambicus BFRI Barbus bifrenatus CSWI Chiloglanis swierstrai OPER Opsaridium peringueyi BIMB Brycinus imberi GCAL Glossogobius callidus PCAT Petrocephalus wesselsi BUN Barbus lineomaculatus GGIU Glossogobius giuris PPHI Pseudocrenilabrus philander BMAR Labeobarbus marequensis HVIT Hydrocynus vittatus SINT Schilbe intermedius BNEE Barbus neefi LCON Labeo congoro SZAM Synodontis zambezensis BPAU Barbus paludinosus LCYL Labeo cylindricus TREN Tilapia rendalli BRAD Barbus radiatus LMOL Labeo molybdinus TSPA Tilapia sparrmanii BTOP Barbus toppini LROS Labeo rosae BTRI Barbus trimaculatus LRUD Labeo ruddi

Six metric groups including velocity-depth, flow modification, migration, cover, physico- chemical and introduction of alien species were assessed in order to identify change in fish assemblages. The assessments of these were based on a Multi Criteria Decision Analysis

148 Chapter 8

Approach (MCDA) that follows a rating, ranking and weighting procedure. For each metric a set of habitat indicators (Table 8.2) were ranked according to the number of species with a high and very high preference for that indicator, with the habitat indicator containing the highest number of high preference species having the highest rank of 1. These were then weighted in terms of how each indicator differed from one another. Thereafter, the components were rated in terms of the degree to which fish assemblages changed compared to reference conditions from 0 (no change) to 5 (extreme modification from reference). Once a value was obtained for each metric group, the groups were compared and weighted against each other; with the groups that had the highest influence on fish populations weighted the highest.

From these metric calculations an adjusted FRAI value and category was obtained through a series of formulations based on the percentage of reference. The categories were defined for the EcoStatus (see Section 8.1) and are represented in Table 8.3. In order to determine the cause of the resulting ecological category score, the habitat preferences of each fish species not sampled (particularly those with a high reference FO) (Appendix 6) were analysed with the change in habitat from reference conditions (Chapter 3), since fish populations may have been reduced due to changes in one of the river habitats described in Table 8.2.

Table 8.2. Metric groups with their accompanying habitat indicators that provides a good indication of changes in fish assemblages (Kleynhans, 2008).

Metric groups Velocity- Cover Flow Migration Introduced Physico-chemical depth class modification modification changes species Overhanging Intolerant of Intolerant of modified Catchment scale Impact of competing/predaceous Fast deep vegetation flow changes physico-chemistry movements species Undercut Movement Moderately Frequency of occurrence (widespread) Fast shallow banks and Moderately intolerant between reaches tors intolerant of competing sp rootwads

ica of segments d Movement within t in t Moderately Slow deep Substrate Moderately tolerant reach or Impact of habitat modifying species bita tolerant segment Ha Instream Frequency of occurrence (widespread) Slow shallow Tolerant Tolerant vegetation of habitat modifying species Water column

149 Chapter 8

Table 8.3. The ecological categories defined by Kleynhans (2008).

Ecological Score % Description categories A 100 Unmodified, natural. B 80-99 Largely natural with few modifications. A small change in natural habitats and biota may have taken place but the ecosystem functions are essentially unchanged C 60-79 Moderately modified. A loss and change of natural habitat and biota have occurred but the basic ecosystem functions are still predominantly unchanged. D 40-59 Largely modified. A large loss of natural habitat, biota and basic ecosystem functions have occurred. E 20-39 Seriously modified. The loss of natural habitat, biota and basic ecosystem functions are extensive. F 0-19 CriticaVExtremely modified. Modifications have reached a critical level and the system has been modified completely with an almost complete loss of natural habitat and biota. In the worst instances the basic ecosystem functions have been destroyed and the changes are irreversible.

8.2.3 Statistics

In order to identify a cause-effect relationships a two-tailed Pearson's correlation analyses was computed using SPSS release 12.0.1. The community endpoints, described earlier in this chapter, were pooled regardless of sampling season and sites, since no significant differences between the flow regimes were found for fish species. The pooled data for fish communities included the number of species (S), the abundance of species (N), Margalef's species richness, Pilou's evenness index, Shannon diversity index, alpha-log series diversity index and models, which were assigned a score between 1 (geometric series), 2 (geometric series/log series), 3 (log series), 4 (log series/log normal), 5 (log normal), 6 (log normal/broken stick) and 7 (broken stick). A number of possible environmental influences were then correlated with this data, which include the following as discussed below.

Correlations were done with standard physico-chemical water quality measurements (methodology and results discussed in Chapter 4), including temperature (°C), dissolved oxygen (mg/I), pH, conductivity, TDS and nutrients measured in mg/I (ammonia, calcium, nitrate, nitrite, ortho-phosphate, sulphate). All the metals in water and sediment, with concentration units of mg/I and mg/g respectively, were then correlated (refer to Chapter 4 for exact metals), but only those that showed a significant correlation with fish were noted (too many metals). Similarly, DDT and its metabolites, measured in pg/I in the water and pg/kg in the sediments, were analysed (Chapter 4). Physical anthropogenic habitat disturbances (measured using IHI) were also correlated along with fish habitat availability index (HCR) (Kleynhans, 1996).

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100 1 t I 1 t 1 0. 1

I . .... Inoken stick I ••• ..... 1 I I I m I u c I m 1 so log normal 0.1 I .0 I a 1 I 1 \ I \% series I 0.01 • • 1 • I • 1 geometric I series

0.001 species sequence

Figure 8.1. Typical rank abundance plots of four species abundance models: geometric series, log series, log normal and broken stick (redrawn from Magurran, 1988).

Multivariate statistics were used to compare community patterns because subtle changes in the community composition across sites may be masked when the characteristics of a site is combined, as is in the case of univariate analyses, such as ANOVA. In order to assess any spatial and temporal changes in the community structures, the Bray-Curtis cluster determination, non-metric multi-dimensional scaling (NMDS) and analysis of similarities were conducted using PRIMERv5 (Clarke and Warwick, 1994). For these analyses, the abundance data for each species were initially square root transformed to yield a similarity matrix that was used to plot both a Bray-Curtis dendrogram and an NMDS plot. The Bray- Curtis dendrogram was used to visually identify the extent of spatial differences, while the MDS ordinations were used to visually identify seasonal differences. Once the dissimilarities and similarities have been identified for each site and season, the similarity percentages (SIMPER) were then calculated in order to identify which species were principally responsible for the differences or similarities and the ANOSIM was calculated to identify any significant differences between the sites or seasons. For further details on the interpretation of these statistics, refer to Clarke and Warwick, 1994.

8.3 RESULTS

8.3.1 Occurrence of fish

The fish species sampled from all riverine sites are represented in Table 8.4. Upon comparison with the reference data (FO and historical) there was a substantial decline in the number of species at all sites. At Latonyanda, only 13 indigenous species were observed

151 Chapter 8 through out the season as compared to 23 species that should have been sampled (FO according to Kleynhans et al. (2008)). This shows that only 57% of the fish species that should have been sampled were actually found. Of the species that were not found, four were previously wide spread (>50% chance that species should have occurred within the reach), namely A. benalensis, A. uranoscopus, B. eutaenia, and B. neefi (refer to Table 8.1 for genus), and 8 were previously scarce (<50% chance that species should have occurred within the reach) including A. mossambica, 0. mossambicus, B. viviparus, C. gariepinus, L. molybdinus, M. macrolepidotus, Petrocephalus, O.mossambicus and T. rendalli. At Hasana a total of 15 species were caught during this survey, which was 62% of the total species that should have been collected. The unsampled species included A. uranoscopus, B. paludinosus, B. viviparous, L. molybdinus, B. eutaenia, B. lineomaculatus, B. neefi, B. radiatus, B. toppini, C. gariepinus, A. benalensis, A. mossambica, and 0. peringueyi, of which none were previously wide spread in this reach of the river. At Tshikonelo 60% of the total species that should be present was not collected. However, only 8% of this, including B. viviparous, L. molybdinus, and P. philander, was of real concern as these species should have been widely spread along this reach of the river. The remaining 52% of species not found A. benalensis, A. uranoscopus, B. eutaenia, B. lineomaculatus, B. neefi, B. paludinosus, B. radiatus, B. toppini, C. paratus, G. giuris, C. swierstrai, H. vittatus, M. macrolepidotus, 0. peringueyi, Petrocephalus spp, S. intermedius, and S. zambezensis were those that had been previously scarce according to FO and historical data. Downstream from Xikundu 15 different species were sampled out of the 36 species that should have been collected under reference conditions. However, out of the remaining 60% of unsampled species only 2 were wide spread (B. viviparus and P. philander) and 18 were scarce species. While at the last site, Mhinga, 13 different fish species were observed, which was only 36% of what should have been caught. Of the unsampled fish species most were previously scarce except for M. brevianalis.

8.3.2 Informal assessment of number of species and relative abundances

The total number of fish, measured in CPUE, from each site and survey is shown in Table 8.5. A total of 353 specimens, representing 41 different species, were caught between 2006 and 2008. As was expected from literature, there was a large spatial heterogeneity in the Luvuvhu River with differences between the sites, with regards to both fish species type and abundance. For each of the 5 sampling sites only 6 species, including L.marequensis, B. trimaculatus, C. pretoriae, L. cylindricus, M. acutidens, and T. sparrrnanii, were common throughout and most of which had the highest abundances. Despite this heterogeneity, comparisons between sites could still be made. That is, it was evident in all seasonal samples that there was a definite smaller number of fish species found at Mhinga (5 — 7) and to an even lesser extent, at Tshikonelo (3 — 9), as compared to Xikundu and Hasana, with species numbers between 8 and 13, and 7 and 11, respectively. In fact, Xikundu had the most thriving fish community with the highest number of fish species and abundances of all surveys, with the exception of the low flow 2007 where Hasana showed greater fish species diversity. With regards to seasonality, a few differences in species and abundances between

152 Chapter 8 the seasons were observed. Latonyanda showed a seasonal fluctuation in the presence of four fish species, B lineomaculatus, T. sparrmanii, B. radiatus and B. trimaculatus. The former two were only present during the high flow regimes, while the latter two were only found during the low flow regimes. Seasonal changes in the remaining sites were not really visible, but there was a tendency toward an increase in number of species from 2006 to 2008 at Tshikonelo and towards a higher diversity and abundance in the high flows at Xikundu.

8.3.3 Species diversity indices

Non-parametric diversity indices represented in Figure 8.2 were used to describe different attributes of the fish communities obtained in the present study. No real seasonal trends were observed, but a number of interesting spatial fluctuations were identified. The graphs showed that there was an increase in the fish species richness and diversity between Latonyanda and Hasana in all the seasons, except for the slight deterioration in species richness and diversity observed at Hasana in the high flow 2008. From Hasana to Tshikonelo the species diversity was greatly reduced in the first three sampling seasons and in fact showed the lowest scores, but was followed by a dramatic recovery in the last high flow sampled, which is especially evident in the alpha-log series diversity index. At Xikundu the species richness and diversity was higher than at Tshikonelo in all the measured flow regimes except the high flow 2008. Nevertheless, there was a generally good recovery from the low index scores at Tshikonelo, before the species richness and diversity once again dropped at Mhinga for all of the seasons.

153 Chapter 8

Table 8.4. The reference frequency of occurrence (FO), historical data (HD) and current data (CD) from all seasons.

Latonyanda Hasana Tshikonelo Xikundu Mhinga FO HD CD FO HD CD FO HD CD FO HD CD FO HD CD ABEN 4 1 1 1 1 AMAR P P AMOS 1 P 1 P 1 P P 1 1 AURA 4 P P 3 P 2 P 1 P 1 P BANN 3 2 P 2 P BEUT 4 P 2 P 3 P 1 1 BFRI P 1 P P 1 P P BIMB 1 2 P 2 P BLIN 4 P P 2 P 2 P P P BMAR 4 P P 5 P P 5 P P 5 P P 5 P P BNEE 5 P 2 P 2 P

BPAU 3 P 3 P 3 P P BRAD P 2 2 1 P 1 P BTOP 2 2 2 P 2 P BTRI 4 P P 3 P P 3 P P 4 P P 4 P P BUNI 4 P P 3 P P 3 P P 3 P P 3 P BVIV 3 P 3 P 4 P 4 P 4 P P CCAR 2 P P CGAR 1 P P P 2 P P 3 P P 3 P P CPAR 2 3 P 3 P CPRE 3 P P 5 P P 5 P P 5 P P 5 P P CSWI 2 2 2 GCAL 1 P 1 P P 1 P GGIU 2 2 P 2 P HVIT 1 2 P 2 P LCON 2 P 2 P LCYL 3 P P 3 P P 4 P P 4 P P 4 P P LMOL 3 P 3 P 4 P 4 P P 4 P P LROS 2 P 3 P 3 P LRUD - 2 P 2 P MACU 3 P P 4 P P 5 P P 5 P P 5 P P MBRE 2 P P 4 P P 5 P P 5 P P 5 P MMAC 2 2 2 P 2 P MSAL - - P P OMOS 2 P 3 P P 4 P P 4 P P 4 P P OPER - 1 2 2 P 2 P PCAT 2 2 2 P 2 P PPHI 4 P P 4 P P 4 P 4 P 4 P P SINT 2 P 2 2 P P 2 P SZAM P 1 2 P 2 P TREN 1 P 3 P P 3 P P 3 P P 3 P P TSPA 3 P 3 P 3 P 3 P P 3 P P P-species present; hyphen-species absent; FO scoring system: 1 (<10%), 2 (10-25%), 3 (25-50%), 4 (50-75%) and 5 (>75%) (Kleynhans et al., 2008).

154

Table 8.5. Num ber of ind ividuals (norma lised to CPU E un its) for each species caug ht in the Luvuvhu River. X p I— LL C LL CO .)e co r- CO C C4 LL N 0 C u- C C

LL CL O —I O CO lL O CO LL O LL. O CO U- O CO P r-- 0 LL a O u- O O LL O CO U- O CO U- O U- CD LL 0 CO <40:10303c000COC0000:10000—i—i 2DGC u.=12tKn.i—D5Oca.0002 ce<1:0220n.zNice 0 Z< a— e 00000 00000 t .. < Z I I I i I I I Cal i r I r ... Cal I CalNi i N CD I I .4. i i CsI r ..--

CV Ce CD (4) e— a— cn v CT) O CO 0) I . ) Ns

• I 1 I i I i are<=:>-000tYKLO

CV el Csl 1 Ma—li aI to uj IT cv C.) tO 0) .."" N— cr, I I I 1 I I S ,— I I ee—o I I, CO Csi Z.,„ 0 N. — I 0 I ) )

a■ 00000 I . I I I .. I r-- Cal TrIO) A 0 0) i ..

'r C•1 1.0 l -

I i I rs i 1— p C.1 LC) r Cs/ CV C•1 i 1••••• at I r I a— i i I— Cal - r") 1— h i I r I SCOcr i I aui N I I TI• i Cal I a- i CO I CV I I Cal I

u) < CO I co Z CO 0) CO CO C.1 a— is a— (a) CV CO Cal CO C^) u) CO 10 0) CO cr) N O ch .o To C .c ..o LL LL C C E N 0. a) a) 0. N N 0 Co Co C C CC N C 0. a) a) co N w ca rn 0 0 0 0

Chapter 8

Latonyanda Hasana O Tshikonelo EMI Xi kundu eza Mhinga

3.5 3.0- 2.5- 2.0- 1.5- 1.0- 0.5. 0.0 LF 2006 m miHF 2007 LF 1007 HF 1008

1.00

0.75-

0.50-

0.25-

0.00 LF 1006 HF 1007 LF 1007 HF 1008I n

2.5

2.0

1.5

1.0

0.5

0.0 LF 200 6 HF 2007 n LF 2007 HF 1008 (d) 201

15

5.01

2.5

0.0 in iq min§ H. In LF 2006 HF 2007 LF 2007 HF 1008

Figure 8.2. Non-parametric diversity indices including Margalef's species richness (a), Pilou's evenness index (b), Shannon diversity index (c) and alpha-log series diversity index (d) in the 5 lotic sites in the Luvuvhu River in two low flow (LF) and two high flow (HF) seasons.

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8.3.4 Abundance models

It has been said that although the broken stick model represents the most stable and equitable (even) community distributions; it is not always the most appropriate goal for natural communities (Russel, 1997). Instead, the log normal distribution has been suggested as a more appropriate model. However, in the present study the majority of the sites and seasons (accurately depicted), had a log series model (as shown in Table 8.6 and Figure 8.3). This was not a result of degradation due to increased toxicant contamination, but rather a result of the smaller sample sizes; where rare species are often not sampled and hence resulting in a truncated log normal distribution that is virtually indistinguishable from a log series abundance distribution (Magurran, 1998). Therefore, the regression of the models from a log series to geometric series distribution can then be used to highlight negative environmental changes in most sites and seasons. In the present study however, accurate changes in model distribution between sites were difficult due to the inherent spatial heterogeneity between sites. Nevertheless, it was evident that there was an increase in fish equitability at Xikundu in the high flow 2007, while a reduction in fish species equitability was observed in the low flow 2007 at Hasana and Mhinga and in the high flow 2008 at Xikundu.

Inspection of temporal changes in the fish distribution showed different results at all of the sites. At the Latonyanda River, the fish community showed no temporal changes with a log series distribution found in all seasons. The Hasana fish assemblages however showed that there was deterioration from the log series distribution in the first year of sampling to a geometric series distribution in the low flow 2007, however there was a recovery in the following sampling season. The fish assemblages at Tshikonelo had generally low species numbers, which have been shown to describe more than one model (Magurran, 1988) and therefore, could not be correctly identified during any sampling period at this site. At Xikundu, the fish communities followed both a broken stick and log series distribution in the high flow 2007, but then deteriorated to a log series-log normal distribution in low flow 2007 and then to a geometric series-log series distribution in the high flow 2008. Similar events occurred at Mhinga, with a log series distribution in the first two sampling seasons and then a geometric series in the low flow 2007. No comments could be made for the high flow 2008 fish assemblages, as there were again too few number of species sampled at this site.

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Table 8.6. The percentage probability (using Chi squared test) that each respective site and season is similar to geometric series (GS), log series (LS), truncated log normal(TLN) and broken stick (BS) models, respectively, with models predicted from RA (rank abundance) plots in Figure 8.3.

GS LS TLN BS RA plots Latonyanda LF 2006 - - HF 2007 0.00% 31.10% 0.00% 0.30% LS LF 2007 0.00% 80.10% 0.00% 0.00% LS HF 2008 0.00% 49.60% 0.00% 0.00% LS

Hasana LF 2006 0.00% 92.30% 44.90% 0.00% LS HF 2007 0.00% 80.00% 0.00% 0.00% LS/LN LF 2007 91.00% 63.00% 0.00% 0.00% GS HF 2008 16.80% 80.80% 28.70% 0.00% LS

Tshikonelo LF 2006 99.90% 97.00% 0.00% 0.00% HF 2007 99.80% 99.90% 0.00% 0.00% LF 2007 0.00% 72.40% 0.00% 0.00% GS/LS/LN HF 2008 90.60% 99.80% 55.90% 0.00% GS/LS

Xikundu LF 2006 - - HF 2007 0.00% 52.20% 0.00% 76.65% LS/BS LF 2007 0.00% 58.20% 0.00% 0.00% LS/LN HF 2008 10.00% 79.50% 34.70% 0.00% GS/LS

Mhinga LF 2006 0.00% 80.50% 0.00% 0.00% LS HF 2007 0.00% 23.50% 0.00% 0.00% LS/LN LF 2007 93.40% 39.40% 0.00% 10.31% GS HF 2008 93.80% 97.10% 0.00% 0.00% LF-low flow, HF-high flow, hyphen-not sampled and Asterisk-rank abundance plots that could not be adequately assigned to a model via visual inspection

8.3.5 FRAI

The FRAI scores for each of the sites and seasons are represented in Figure 8.4. Most of the sites showed values that were characterised by a C class ecological category which represented a moderately modified fish assemblage. The reasons for these results were determined by the preferences of fish that were absent and are explained in detail for each site.

In the Latonyanda River site the FRAI values ranged between 65 and 74. In the high flow 2007 most of the high reference FO species that were not sampled had a high preference for overhanging vegetation and bank undercuts including B. trimaculatus, P. philander, L. molybdinus and B. viviparus. Then in the following season (low flow 2007) the FRAI value was slightly higher due to a reduced number of species that changed from reference. The

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majority of the species not caught were sensitive to flow and water quality changes (including M. acutidens, C. pretoriae and B. lineomaculatus) rather than habitat suggesting different drivers influencing the fish assemblages in this season. Similar changes were apparent in the high flow 2008, although to a greater extent. The FRAI values were much lower in this season since there were more sprecies sensitive to flow and water quality that were not sampled.

At Hasana all the seasons showed an increase in FRAI values, with the low flow 2007 and high flow 2007 fish assemblages characterised by B/C category. This was mainly due to an increased number of high FO being caught, with the only species not present were those that prefer vegetative cover (B. Paludinousus and B viviparus) and substrate (A. uranoscopus). In contrast, the low flow 2006 and high flow 2008 showed a C category, with 10 and 7 species not found that should have been present. No predominant preferences could be identified with fish species sensitive to lack of cover, flow, water quality and various velocity depth classes.

The FRAI values obtained at Tshikonello were generally the lowest compared to all the other sites. This was primarily due to the low number of specimens caught in all of the seasons (Table 8.4). The species with the reduced FO compared to reference (T. sparmani, B. trimaculatus, T. rendali, B. unitaenia, B. eutaeniatus, P. philander, 0. mossambicus, B. viviparus, L. molybdinus, M. brevianalis and M. acutidens) showed various preferences for flow and water quality, with the majority showing preferences to overhanging vegetation, which was lacking in most parts of the site.

At Xikundu there was a considerable recovery measured in the high flow regimes despite the large changes in habitat due to the weir, with a B/C FRAI ecological category score in the high flow 2007. In both these seasons only C. paratus, P. philander, B. viviparus, B. trimaculatus and T. sparmani were not sampled. The most common preference of all these species was overhanging vegetation and slow flow, with varying sensitivities to changes in flow and water quality. In contrast in the low flow 2007 the FRAI value was lower. The majority of the high reference FO species not sampled had higher sensitivities to water quality and flow.

The fish assemblage at Mhinga declined in all of the seasons, with FRAI values varying between 63 in high flow 2008 and 67 in remaining seasonal surveys. The number of species that were sampled was considerably low compared to the reference ranging from 5 to 7 species. The species with a high FO that were not sampled varied between the various seasons and had different habitat preferences.

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Hasana LF'06 Latonyanda HF'07 Latonyanda LF'07 Latonyanda HF'08

8 c co v c 0 .0 <

1- 0.1 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 Rank Rank Rank Rank Tshikonelo LF'06 Hasana HF'07 Hasana LF'07 Hasana HF'08

8 8 8 c c c co co co -0 -0 v c c c 0 0 0 .0 .0 .0 a < a

2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 Rank Rank Rank Rank MhInga LF'06 Tshikonelo HF'07 Xikundu LF'07 Tshikonelo HF'08 1

8 8 a c Co 0 1 v dance v c c 0 0 .0 .0 a Abun a 0.1 0.1 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 Rank Rank Rank Rank Xikundu HF'07 Tshikonelo LF'07 Xikundu HF'08

8

nce c co

da -0

n c 0 .0

Abu a

2 4 6 8 10 12 14 16 16 Rank Rank Rank MhInga HF'07 MhInga LF'07 Mhlnga HF'08

8 8 c c Co CO v v c c 0 = .0 .0 < .c 0.1 2 4 6 8 10 12 14 16 2 4 6 8 10 12 14 16 Rank Rank Rank

Figure 8.3. Rank abundance plots for fish communities observed in the Luvuvhu River in two low flow (LF) and two high flow (HF) seasons.

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iii4 LF 2006 HF 2007 B/C CILF 2007 HF 2008

C

Figure 8.4. The adjusted FRAI index value for each of the sites and seasons (two low flow (LF) and high flow (HF) seasons) along with their ecological categories B/C (natural and modified) and C (moderately modified).

8.3.6 Multivariate statistics

According to the MDS plot and ANOSIM in Figure 8.5, no significant temporal differences were observed for any of the sites. There were however some significant spatial differences shown by ANOSIM between both Latonyanda and Mhinga, and Hasana and Tshikonelo (Table 8.7). In fact, as can be seen in Figure 8.6 the Bray-Curtis dendrograms showed that all of the sites were largely different with no similar spatial clusters/groups and similarities of 60% or more, except for low flow 2006 (Figure 8.6a) where Hasana and Mhinga had an average similarity of 58, primarily due to similarly high C. pretoriae abundances (Table 8.8). Nevertheless, the multivariate analysis of communities in high flow 2007 (Figure 8.6b) revealed that Tshikonelo had a predominantly different fish community structure, with an average dissimilarity of 94 primarily due to the generally low species present at this site (Table 8.9). The remaining sites in this season were also distributed on the Bray-Curtis dendrogram by the previously mention species, with Latonyanda and Xikundu having a higher abundances of M. acutidens and M. brevianalis than at Hasana and Mhinga. During low flow 2007 (Figure 8.6c) Latonyanda, Hasana and Xikundu were separated from the other two sites due to their low M. acutidens abundances (Table 8.10), while Xikundu was separated from Latonyanda and Hasana due to its higher abundances of C. pretoriae. During the high flow 2008 (Figure 8.6d) Hasana had the least common fish assemblage according to dendograms, primarily due to high abundances of P. philander species (Table 8.11). Latonyanda was also different from the other sites and this was attributed to the L. marequensis and M. acutidens found in high abundances, whilst Xikundu had high G. callidus and C. pretoriae.

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Sooss:0.10 Global R = -0.06 (p = 0.74) A • • A A •

A A • A • A • A •

Figure 8.5. MDS ordination showing the seasonal differences between combined fish taxa of low (r) and high (A) flow regimes with one-way analysis of similarity (ANOSIM).

Table 8.7. One-way analysis of similarity (ANOSIM) testing for spatial differences in fish assemblage structures. Significant differences (P<0.05) represented in bold.

Global R

Latonyanda vs Hasana 0.43 0.09 Latonyanda vs Tshikonelo 0.69 0.06 Latonyanda vs Xikundu 0.78 0.10 Latonyanda vs Mhinga 0.76 0.03 Hasana vs Tshikonelo 0.64 0.03 Hasana vs Xikundu 0.37 0.11 Hasana vs Mhinga 0.26 0.17 Tshikonelo vs Xikundu 0.41 0.09 Tshikonelo vs Mhinga 0.54 0.06 Xikundu vs Mhinga 0.07 0.46

Table 8.8. The importance ratings of fish species in the low flow 2006, presented as a percentage of the total contribution for the similarity (a) and dissimilarity (b) between the sites. Only those taxa responsible for 50% of the cumulative contribution are presented. See Table 8.1 for species (taxon) abbreviations. Similarity

Taxon Abundance Avg. similarity % contribution Hasana, Mhinga Total 58 CPRE 35 35 61

Dissimilarity Taxon Abundance Avg. dissimilarity % contribution Hasana, Mhinga Tshikonelo Total 85 CPRE 35 3 50 58

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U-

20.-

40-

80-

80-

100-

U-

20-

40-

60-

80-

100

0--

20-

40-

60-

80-

0-

20-

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80-

100-

Figure 8.6. Bray-Curtis dendrograms of fish assemblages showing spatial differences in (a) low flow 2006, (b) high flow 2007, (c) low flow 2007 and (d) high flow 2008 at Latonyanda (L), Hasana (H), Tshikonelo (T), Xikundu (XW) and Mhinga (M).

163 Chapter 8

Table 8.9. The importance ratings of fish species in the high flow 2007, presented as a percentage of the total contribution between the sites 1 to 7 (no similarities greater than 20%, therefore no groupings). Only those species responsible for 50% of the cumulative contribution are presented. See Table 8.1 for species abbreviations.

Dissimilarity

Taxon Abundance Avg. dissimilarity % contribution Latonyanda Tshikonelo Total 94 MACU 90 0 45 48 BMAR 66 4 31 33 Tshikonelo Xikundu Total 93 MBRE 0 50 22 23 MACU 0 40 18 19 BMAR 4 39 15 17 Tshikonelo Mhinga Total 84 CPRE 2 31 28 33 OMOS 2 27 24 28 Hasana Tshikonelo Total 81 BMAR 35 4 38 47 OMOS 11 2 11 14 Latonyanda Mhinga Total 75 MACU 90 0 32 42 BMAR 66 28 13 18 Latonyanda Hasana Total 69 MACU 90 0 34 49 BMAR 66 35 12 17 Hasana Xikundu Total 66 MBRE 0 50 17 26 MACU 0 40 14 21 Xikundu Mhinga Total 57 MBRE 50 0 16 28 MACU 40 0 13 22 Latonyanda Xikundu Total 50 MACU 90 40 12 24 MBRE 0 50 12 24 Hasana Mhinga Total 48 CPRE 2 31 17 36 OMOS 11 27 10 20

164 Chapter 8

Table 8.10. The importance ratings of fish species in the low flow 2007, presented as a percentage of the total contribution between site 1 to 7 (no similarities greater than 20%, therefore no groupings). Only those species responsible for 50% of the cumulative contribution are presented. See Table 8.1 for species abbreviations. Dissimilarity Taxon Abundance Avg. Dissimilarity % Contribution Latonyanda Xikundu Total 97 CPRE 0 51 55 57 Latonyanda Tshikonelo Total 95 MACU 0 77 66 70 Hasana Mhinga Total 92 MACU 2 194 49 54 Latonyanda Mhinga Total 89 MACU 0 194 54 61 Hasana Tshikonelo Total 87 MACU 2 77 51 58 Tshikonelo Xikundu Total 85 MACU 77 4 48 56 Mhinga Xikundu Total 82 MACU 194 4 48 59 Latonyanda Hasana Total 78 BMAR 16 1 16 21 BUNI 2 11 10 13 OMOS 0 9 10 13 PPHI 2 11 9 12 Hasana Xikundu Total 78 CPRE 3 51 39 48 BUNI 12 0 9 11 Tshikonelo Mhinga Total 59 MACU 77 194 30 47 BMAR 3 69 16 27

8.3.7 Factors influencing fish abundance and diversity

In Table 8.12 only the variables that showed significant correlations with the indicators were noted. As can be seen there were very few correlations between the indices and the possible causes and no meaningfully significant (p<0.05) correlations were observed for the abundance and evenness, while the number of species, Margalef's species richness, the Shannon diversity index and alpha-log series diversity index showed some correlations. pH was negatively correlated with the number of species present and the Shannon and alpha- log series diversity indices, while TDS also had a negative correlation, it was only with the alpha-log series diversity index. Zinc in the sediment also showed a significant negative correlation with fish species richness and alpha-log series, suggesting a possible influence on fish communities. In contrast, the DDE and DDD concentrations were significantly positively correlated with the number of species and species richness, however this relationship was more likely due to other influences. As can be expected there were positive correlations observed between species richness and phosphate.

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Table 8.11. The importance ratings of fish species in the high flow 2008, presented as a percentage of the total contribution between site 1 to 7 (no similarities greater than 20%, therefore no groupings). Only those species responsible for 50% of the cumulative contribution are presented. See Table 8.1 for species abbreviations.

Dissimilarity Taxon Abundance Avg. Dissimilarity % Contribution Latonyanda Tshikonelo Total 92 BMAR 23 1 33 36 MACU 17 1 24 26 Hasana Mhinga Total 91 PPHI 24 0 38 42 CPRE 1 13 19 21 Latonyanda Hasana Total 90 PPHI 1 24 24 27 BMAR 23 1 23 26 Hasana Tshikonelo Total 90 PPHI 24 0 46 51 TSPA 7 0 13 15 Latonyanda Xikundu Total 88 BMAR 23 2 22 25 MACU 17 3 15 17 Xikundu Mhinga Total 88 GCAL 11 0 17 35 BMAR 2 7 8 16 Hasana Xikundu Total 87 PPHI 24 0 29 33 CPRE 1 13 14 16 Latonyanda Mhinga Total 83 MACU 17 0 22 26 BMAR 23 7 21 25 Tshikonelo Mhinga Total 72 CPRE 3 13 30 42 BMAR 1 7 17 23 Tshikonelo Xikundu Total 64 GCAL 1 11 19 30 CPRE 3 13 19 29

Table 8.12. Pearson's correlation coefficients between variables and fish endpoints that were significantly correlated (p<0.05).

S N D J H a MODELS FRAI pH -0.59 -0.16 -0.38 -0.10 -0.47 -0.59 -0.24 -0.31 TDS -0.24 0.14 -0.17 -0.16 -0.34 -0.78 -0.35 -0.24 Phosphate 0.29 -0.35 0.50 0.31 0.43 -0.04 -0.20 -0.17 2,4 DDE (s) 0.53 -0.12 0.57 0.21 0.41 -0.09 -0.09 0.19 4,4 DDD (s) 0.50 -0.17 0.68 0.27 0.44 -0.03 -0.07 0.36 2,2 DDE (w) 0.48 -0.11 0.53 0.16 0.34 -0.05 0.26 Zinc (s) -0.61 0.47 -0.68 -0.40 -0.55 -0.13 -0.45 Bold-significant correlations (p<0.05), S-number of species, N-abundance, D-species richness, J-evenness, H-Shannon diversity, a-alpha-log series diversity, s-sediment and w-water.

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8.4 DISCUSSION

The fish community structures were primarily assessed in order to determine the extent of DDT impacts within the Luvuvhu River. Since the different methodologies for the bioassessments were interrelated, each site is discussed separately in Section 8.4.1, in order to identify if there were changes within the community assemblages in the Luvuvhu River. The methods that best describe community changes will follow in Section 8.4.2.

8.4.1 Fish community changes

a. Latonyanda River

In the Latonyanda River there was only a slight reduction in the fish community diversity compared to the other sites, with a definite reduction in B. eutenia and B. neefi distribution. The largely reduced abundances of these species was perhaps due to an altered environmental condition, similar to the findings by Fouche et al. (2005). The authors also showed a lack of these two species in the upper reaches of the Luvuvhu River and attributed this to the fact that these species were more specialist and sensitive species. A lower fish diversity was found at this site with regards to seasonal changes, as was shown by the Shannon diversity index and informal assessments, in the low flow 2007, with a sudden absence of C. pretoriae, T. sparrmanii, M. acutidens, and B. lineomaculatus. C. pretoriae was not found again at this site (also not present in the high flow 2008) possibly due to a deterioration in either the water flow requirements or water quality changes (Kleynhans, 2008). The flow was however not a likely influencing factor as these species would have then recovered in the following high flow season, while the water quality may have influenced the populations as there was 0.1 pg/kg of bioavailable DDE and DDT measured in the sediment and higher than normal nitrate levels. However, it should be kept in mind that these contaminants may not have been the only factors that were influencing the fish communities, with many other possibilities causing changes such as other contaminants that were not measured and variability in sampling times and effort. With regards to M. acutidens, and B. lineomaculatus these species did recover in the following high flow, suggesting that these species showed natural seasonal fluctuations. Indeed, similar fluctuations of these species were shown by Fouche et al. (2001), where the species were only found in the summer months. In fact, according to the informal assessments, there was a generally lower fish species abundance in the low flow 2007. This could perhaps be due to a reduction in an already lower habitat availability at this site, with an average water surface width of 2 meters and depth of less than 0.5 meters (Bicknell, 2005; Kaeming et al., 2007; Fouche et al., 2005) that yielded this site as strongly dissimilar to other sites within the Luvuvhu River (as shown by the multivariate analyses).

The FRAI and alpha-log series indices showed slightly different temporal trends. Although the FRAI values for the various sampling regimes were all categorised as a 'C', indicating moderate modification of fish assemblage, there were some seasonal fluctuations. The

167 Chapter 8 results of the FRAI showed that the fish assemblage in the high flow 2008 had the highest change from reference, with the absence of 8 species that should have been sampled using the sampling techniques of the current study. Most of these species had high preferences to water quality changes and flow changes (Appendix 6), which suggested that a negative influence may have occurred during this season. Upon inspection of the water and sediment quality the presence of lindane was found in the water in the high flow 2008 (Figure 4.3, Chapter 4), which may have influenced the presence of some species.

The alpha-log series index showed the high flow regimes having a reduced diversity compared to the low flow regimes. This was surprising since the informal assessment showed that there were more species and number of individuals present in the high flow regimes than in the low flow regime. Similar results were found by Russel (1997), who explained the reason for this inconsistency was because, the indices utilise species richness that depicts the ratio between the number of individuals and number of species and as such a sample set with a lower number of species and abundance can produce the same or higher index value as a sample set with higher number of species and abundances. Although these attributes can and have been shown to be advantageous, it can lead to a misinterpretation of data. Therefore, in the present study the alpha-log series diversity index should be utilised with caution. Upon comparison with other studies it is evident that the latter two sampling seasons (high flow 2008 and low flow 2007) were perhaps negatively influenced and that the good diversities depicted in these seasons by alpha-log series index were indeed misleading. Especially in the low flow 2007 that showed a reduction in important species such as C. pretoriae.

b. Hasana

The fish assemblages of Hasana were generally unchanged, with the majority of the species not sampled due to sampling inefficiencies. A large percentage of fish species that were absent at this site had high preferences to slow deep habitats. This suggests that sampling of slow deep habitats were not as efficient at this site (as well as the remaining sites). The habitats were sampled with a seine net that could have been too small for the average depth. According to Hayes (1983), seining is not very effective in deep water over rough bottoms as the fish often escape underneath the lead line. Once these sampling inefficiencies were corrected for, the fish assemblages at Hasana seemed to be in generally good condition with A. uranoscopus, B. viviparous, and L. molybdinus the only species that were not sampled that were predicted as being widespread within this reach. In the high and low flows of 2007 the diversity indices and FRAI scores (B/C category) showed that fish assemblages were moderately natural with only slight changes. However, in the high flow regime of 2008 the fish diversity was slightly reduced, as is shown by all the indicators except the abundance models. The possible cause for this reduced fish community integrity was perhaps changes in water quality, since there was a lower abundance of sensitive species at this site including C. pretoriae and L. marequensis. Indeed, comparisons with contaminants, there was a presence of DDE and PCB 153 within the water that may have

168 Chapter 8 resulted in changes of the fish community during the high flow 2008. A similar study by Eaton and Lydy (2000) showed similar reduction in fish communities in the presence of a combination of OC contaminants, although as was shown in this study there was no significant relationship (negative correlation) between the indicators and the individual OC concentrations. The possibility of other influencing factors such as migration and physico- chemical assessments were excluded, as C. pretoriae is not a natural migratory fish and the water quality variables, as shown in Table 4.3, were all within limits.

The abundance models at Hasana showed deterioration in the community distribution from a log series model to a geometric model in the low flow 2007. This signifies that the communities in the 2007 low flow had a generally lower number of species that dominated the available resources, with the rare and more sensitive species extinct. This was however not the case in the present study as there was an abundance of sensitive species, suggesting that these abundance curves should be analysed with caution and are perhaps not as effective in identifying environmental deterioration as was shown by Magurran (1988). This deterioration of the abundance models was probably a result of the generally lower number of species present within the Luvuvhu River. Similar conclusions were found by Russel (1997), who also showed a reduced species diversity in the Luvuvhu River.

c. Tshikonelo

Tshikonelo had the most dissimilar fish community structure compared to all the other sites in the first three seasons. This was generally attributed to lower diversities and abundances, which was observed with the informal descriptions and the diversity indices including Shannon diversity index and the alpha-log series diversity index. Upon initial evaluation of these indicators it was evident that no consistently high contaminants were correlated with the low fish diversity at this site and that the possible cause for the large absence of fish could be due to sampling inefficiency, fish movement to better habitats or more favourable conditions such as natural fish migration. As for the recovery in the species diversity in the last season (high flow 2008) it was primarily a result of increased number of juveniles from adult species that were not generally caught using the techniques in this study, including C. gariepinus, L. rosae and T. rendalli. This increased FO of juvenile specimens suggests that there was an increase spawning success from the previous seasons. This could either have been because conditions were more favourable during this season for adequate spawning such as, increased habitat availability as shown by higher HCR scores (Chapter 3), or adults had naturally migrated to other sections of the river in previous seasons.

The large dissimilarities observed at Tshikonelo by the diversity indices and informal descriptions were however not seen by the FRAI index. In fact, all the seasons showed very similar scores at Tshikonelo; all identified as an ecological category of 'C' which indicates that community integrity was only moderately modified. The primary cause of the change was attributed to a reduced amount of habitat available for fish, with a large lack of species that have a high preference for vegetation including B. annectens, B. eutaenia, B.

169 Chapter 8 paludinosus, B. viviparous, L. molybdinus and P. philander. Indeed many habitat perturbations were evident including water extraction trucks, actual removal of river sand, and cattle grazing within the riparian zone which resulted in erosion and general loss of habitat. According to Russel (1997), there has been a marked reduction of marginal vegetation in Luvuvhu River over the years and in fact also showed a reduction in both B. viviparus and P. philander.

The multivariate analysis seemed to identify more subtle changes between the fish communities than the other indicators (Cao et al., 1996). For instance, the reduced abundance of C. pretoriae and L. marequensis were a major cause of dissimilarity compared to other sites in all of seasons. These species were shown as a very sensitive species to changes in flow and/or water quality, suggesting an increased perturbation at this site. As was shown in Chapter 4 the water quality at this site was not significantly reduced, suggesting that reduced flow (combined with reduced habitat) may have influenced the presence of these species, perhaps due to reduced discharges from Nandoni Dam and Mutshindudi River. In the low flow 2007 the large dissimilarity was primarily a result of the appearance of a shoal of M. acutidens that was probably migrating upstream after the first summer rains (Skelton, 2001).

d. Xikundu

In comparison to all the sites, Xikundu had the most thriving fish community according to the diversity indices. It had high numbers of sensitive species such as C. pretoriae, L. molybdinus and M. acutidens, despite this site having high pesticide contamination. In chapter 4 it was shown that the majority of contamination occurred at Xikundu in the high flow 2008, however the fish communities did not reflect this in any of the indicators utilised. This suggests that the concentrations within the Luvuvhu River did not induce any observable changes within the fish communities, which may be because the concentrations of the contaminants were not high enough or the exposure period was not long enough to induce a population/community level effect (Connell, 1999). However, it should be kept in mind that the influence of a wide range of environmental factors, including habitat could, have masked the effects of pollution, thereby reducing the sensitivity of fish community indices to this pollution (Mayon et al., 2007). Indeed, there were large changes in the natural habitat at Xikundu, with a weir and a substantial amount of bank erosion. Under normal circumstances this habitat would have negatively influenced fish communities. However the presence of a fish ladder may have provided a fair amount of protection and habitat for fish, as demonstrated by Jensen et al. (1999).

Upon evaluation of seasonal changes that occurred at Xikundu it was evident that the low flow 2007 had the most changed fish community assemblage, which was apparent in the FRAI index and in both the diversity indices (alpha-log series index and Shannon diversity index) as well as the abundance models. The FRAI results showed that this season was a lot more modified (although not so modified that it was in an entirely different category) than

170 Chapter 8 the other seasons. The reason for this was due to the absence of a large amount of specimens that had a high probability of being sampled including C. paratus, T. sparmanii, B. unitaeniatus, L. cylindricus, I. molybdinus, P. Philander, B. trimaculatus and B. viviparous. Most of these species had a strong sensitivity to reduced vegetation, which suggested that these specimens may have migrated upstream in search of better habitats as a consequence of the weir or in the case of M. acutidens and L. cylindricus for an annual breeding migration upstream (Skelton, 2001). As for the alpha-log series index, it seemed to accurately identify change in species abundance in contrast to the previous sites. This may have been due to the higher number of species present at this site allowing for greater discriminatory abilities between the seasons, although this was in contrast to Magurran (1988) who emphasised that this index is not unduly influenced by sample size. In the abundance models Xikundu showed deterioration in the community distribution models from high flow 2007 through to the low flow 2007 and even further deterioration in the high flow 2008. This signifies that there was a reduced equitability (evenness) throughout the seasons, with reduced species with high dominance. Although this temporal trend concurred with the contamination present at this site, the presence of intolerant species suggests that the abundance models miss-represent the effects present at this site.

e. Mhinga

Mhinga had a similar fish community structure to Tshikonelo, with slightly higher fish diversities throughout the sampling periods. However, there were many fish species that should have been sampled compared to reference data, although most were scarce. As described in previous site discussions, the lack of sampling of these species was not entirely due to their extinction but rather that they were not sampled due to the difficulty with the sampling methodologies. Once these inefficiencies were accounted for there was still a low number of species with varying abundances throughout the sampling regimes. The lowest species and abundances (high evenness) were observed in the high flow 2008 by the informal assessments and the FRAI index. The informal assessments showed that the majority of species were either absent or largely reduced compared to the other seasons. With regards to the FRAI index results, although there was no considerable differences between the seasons (i.e. all within moderately modified category), the high flow 2008 showed the lowest FRAI score compared to all sites and seasons. This was attributed to the absence of a large number of species that should have been sampled using the current sampling techniques. They included many various types of species with differing habitat preferences. These results suggest that there was a large impact on the fish community structures in the high flow 2008. Although no species with the same preferences were consistently absent, there was a combination of contaminants above water quality guidelines in this flow regime. As shown in Chapter 4 there was a combination of DDE, dieldrin and lindane in the water, which may have resulted in the change in fish community observed, similar to that of Hasana.

171 Chapter 8

These results were however not reflected in the species richness index, alpha-log series diversity index or the abundance models. All these indicators showed that the fish diversity in the low flow 2007 was the most reduced. These reduced values in the diversity indices suggest that there was a decline in the desired state of the fish community. However, upon inspection of the informal abundances and species numbers in the low flow 2007, higher abundances and number of species were found. The main reason for this is because the indices essentially depict the ratio between the number of individuals and the number of species and as such result in conflicting results as explained for the Latonyanda fish community above. This suggests that the results depicted by the alpha-log series diversity and species richness indices are not an adequate representation of fish community structures and that the low flow 2007 fish communities were largely misrepresented. Nevertheless, the main cause for the high abundances in the low flow 2007 at Mhinga was the collection of a M. acutidens shoal, perhaps also migrating upstream as shown in Tshikonello. As for the abundance models these could also not be utilised as indicators of fish community changes as explained earlier.

8.4.2 Fish community assemblage methodologies

The informal assessments considered the species composition, number of species and the abundances of each species. According to Philippi et al. (1998) changes in the species composition can provide a sensitive measure of ecologically relevant changes in the environment as it reflects a combination of environmental and historical events. In order to determine such changes, comparisons with reference conditions (condition of aquatic communities in the absence of human disturbance and pollution) need to be made. In the current study this comparison with reference communities were largely influenced by the sampling techniques, however once these influences on the results were excluded the species composition was able to in some cases used as an indicator of anthropogenic disturbances as suggested by Fausch et al., (1990). As for the number and abundances of each species Russel (1997) concluded that investigations of these raw data are essential procedures within community studies that provide a rough indication of the nature and extent of change in community diversity. In deed, in the present study a quick estimation of community changes could be done. Furthermore, the informal assessments were advantageous as they provided explanations for changes observed in more complex methodologies used in this study including the abundance models, diversity indices, FRAI and multivariate analyses. However Russel (1997) showed that problems do exist with this approach. The author noted that the results are based entirely on the perceptions of the observer, with many tendencies of less obvious changes often overlooked, which may have been the case in the current study. Therefore utilisation of this should be done in combination with other methodologies

Diversity indices are simple and easy to calculate but are for the most part controversial with many practical problems observed including there high sensitivity to sample size and that they are often unable to demonstrate anthropogenic effects of biotic communities (Lande,

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1996, Magurran, 1988; Cao et al., 1996). The Shannon diversity index has attracted most of the criticism, however in the present study this index, along with the species richness and evenness was able to identify sites that were anthropogenically impacted. The alpha-log series index showed similar results, but demonstrated better discrimination between the impacted and unimpacted sites. There were however some contradictory results observed, with lower number of species and abundances producing the same or higher index value as a sample set with higher number of species and abundances due to ratio aspect of the indices. Similar results were shown by Russel (1997) who recommended that this index should not be utilised in monitoring fish communities.

The purpose of the FRAI is to provide a habitat-based cause-and-effect underpinning to interpret the deviation of the fish assemblage from the reference condition (Kleynhans, 2008). Unlike the assessment of the species composition, the FRAI considers all factors that may influence the results (such as sampling error and natural habitat changes) before analysis of the extent the current fish community deviates from reference. In the current study this lead to much smaller deviations observed between the sites compared to informal assessments, with all the sites showing similar ecological categories. Furthermore this index was advantageous as it was able to distinguish the causes of changes in the fish communities by examining the preferences of species most likely to be sampled at each site. Therefore it can be concluded that the FRAI can be effectively used to analyse changes in fish communities as was shown by Kleynhans (2008).

Few studies have used abundance models as indicators of fish community structures. The most prominent study was done by Russel (1997) in South Africa and concluded that models could be used to assess the effects of pollution on fish communities. However, in the current study there were a few disadvantages for the utilisation of the abundance models. Firstly, accurate spatial changes using the abundance models could not be done due to the inherent spatial heterogeneity between sites. Secondly the sensitivity of the abundance models was largely reduced due to the reduced sample size. These sample sizes resulted in a lower number of rare species collected, which made the distinction between log normal and log series models virtually indistinguishable. Thirdly the geometric model did not accurately depict the community distributions. According to Magurran (1988) the geometric model signifies that there are a lower number of species that dominated the available resources with the rare and more sensitive species extinct. However in the current study this was not the case as there was an abundance of sensitive species at the sites with geometric model and was probably rather a result of the generally lower number of species present within the Luvuvhu River. These results suggest that these abundance models should be analysed with caution and are perhaps not as effective in identifying environmental deterioration as was shown by Magurran (1988).

Multivariate analyses are increasingly used to assess spatial and temporal changes in fish community structures and can identify where environmental deteriorations occur. As was mentioned by Cao et al., (1996) this technique was able to detect subtle changes in the fish

173 Chapter 8 communities in the present study. This was primarily because the distribution of individuals and the identity of each of the species were accounted for. Furthermore, the utilisation of the multivariate analyses was advantageous as clear changes in the fish communities were evident and the causes of the changes could be identified using SIMPER analysis.

8.5 REFERENCES

Attrill MJ and Depledge MH. 1997. Community and population indicators of ecosystem health: targeting links between levels of biological organisation. Aqua. Tox. 38: 183-197.

Barbour MT, Gerritsen J, Snyder BD and Stribling JB. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macro-invertebrates and fish. Second Edition. EPA 841-B-99-002. U.S. Environmental Protection Agency; Office of Water; USA.

Bicknell J. 2005. Patterns of fish diversity in the Rupununi savannahs, guiana shield. lwokrama International Centre.: http://www. iwokrama.orq/library/pdfdown load/BicknellRupu nun i Fish Diversity2005. pdf. Date accessed 1 October 2008.

Cao Y, Bark AW and Wiliams WP. 1996. Measuring the responses of macro-invertebrate communities to water pollution: a comparison of multivariate approaches, biotic and diversity indices. Hydrobiologia. 341: 1-19.

Chi squared probability can be calculated on numerous online sources, the present study used a calculator from http://www.fortunecitv. co . uk/meltinqpot/back/360/product/iava/cdfdemonmain. htm I. Date accessed 1 October 2008.

Clarke KR and Warwick RM. 1994. Change in marine communities: an approach to statistical analysis and interpretation. Manual'for the PRIMER statistical programme. Natural Environmental Research Council.

Connell D, Lam P, Richardson B and Wu R. 1999. Introduction to ecotoxicology. UK: Blackwell Science.

Davies B and Day J. 1998. Vanishing Waters. University of Cape Town Press. pp 487.

Eaton HJ and Lydy MJ. 2000. Assessment of water quality in Wichita, Kansas, using an index of biotic integrity and analysis of bed sediment and fish tissue for organochlorine insecticides. Arch. Enviro. Contam. and Toxic. 39(4) 531 — 540.

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Fausch KD, Lyons J, Karr JR and Angermeier Pl. 1990. Fish communities as indicators of environmental degradation. Am. Fish. Soc. Sym. 8: 123-144.

Fouche PSO, Foord SH, Potgieter N, van der Waal BCW and van Ree T. 2005. Towards an understanding of factors affecting the biotic integrity of rivers in the Limpopo province: niche partitioning, habitat preference and microbiological status in rheophilic biotopes of the Luvuvhu and Mutale Rivers. Water Research Commission (WRC) Report no. 1197/1/05. Pretoria, South Africa.

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Jensen W, Berthold K, Bohmer J and Beiter T. 1999. Fish communities and migrations in the vicinity of fishways in a regulated river (Germany). Limnologica. 29: 425-235.

Kaeming MA, Graeb BDS, Hoagstrom CW and Willis DW. 2007. Patterns of fish diversity in a mainstem Missouri River reservoir and associated delta in South Dakota and Nebraska, USA. River. Res. Applic. 23: 786-791.

Karels AE, and Niemi A. 2002. Fish community responses to pulp and paper mill effluents at the southern Lake Saimaa, Finland. Environ. Poll. 116: 309-317.

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Kleynhans CJ. 1996. A qualitative procedure for the assessment of the habitat integrity status of the Luvuvhu River (Limpopo system, South Africa). J Aq. Ecosys. Health. 5: 41-54.

Kleynhans CJ. 1999. The development of a fish index to assess the biological integrity of South African rivers. Water SA. 25(3): 265-278.

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Kleynhans CJ, Louw MD and Moolman J. 2008. Reference frequency of occurrence of fish species in South Africa. Joint Water Research Commission and Department of Water Affairs and Forestry report.

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Magurran AE. 2004. Measuring biological diversity. Wiley-Blackwell. pp. 106-115.

Mayon N, Bertrand A, Leroy D, Malbrouck C, Mandiki SNM, Silvestre F and Goffart A. 2006. Multiscale approach of fish responses to different types of environmental contaminations: A case study. Sci. Tot. Environ. 367: 715-731.

Mills LJ and Chichester C. 2005. Review of evidence: Are endocrine-disrupting chemicals in the aquatic environment impacting fish populations? Sci. Tot. Environ. 343: 1-34.

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Naismith IA and Knights B. 1990. Modelling of unexploited and exploited populations of eels, Anguilla Anguilla (L.), in theThames Estuary. J. Fish Biol. 37(6): 975-986.

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177 CHAPTER 9 Effects of DDT on macro-invertebrate communities

9.1 INTRODUCTION 179 9.2 METHODS 181 9.2.1 Sampling 181 9.2.2 Data treatment 181 Occurrence of macro-invertebrates 181 Relative abundance 181 ASPT and EPT (Ephemeroptera, Plecoptera, Trichoptera) richness 181 Non-parametric diversity indices 182 9.2.3 Statistics 182 9.3 RESULTS 182 9.3.1 Occurrence of macro-invertebrate families 182 9.3.2 Informal assessments of number of taxon and relative abundances 183 9.3.3 Sensitivity scores 184 9.3.4 Ephemeroptera, Plecoptera and Trichoptera (EPT) richness 187 9.3.5 Species diversity 187 9.3.6 Multivariate statistics 188 9.3.7 Factors influencing macro-invertebrate abundances and diversity 194 9.4 DISCUSSION 195 9.4.1 Macro-invertebrate community changes 195 Latonyanda River 195 Hasana 196 Tshikonelo 196 Xikundu 197 Mhinga 198 9.4.2 Macro-invertebrate community assemblage methodologies 198 9.5 REFERENCES 199 Chapter 9

9.1 INTRODUCTION

Invertebrates are frequently used in both international and local (River Health Programme (RHP)) monitoring programmes as indicators of water quality changes. As was illustrated by the Watershed Science Institute report, invertebrates are "much like the 'canary in the coal mine', the response of aquatic insects gives an early warning of possible harm to a water body" such as organic pollution, wastewater discharges, trout farm effluent, effects of agriculture, afforestation, metal pollution and experimental insecticide treatments (Dallas, 2002). As such macro-invertebrates communities are not suitable to identify the specific effects of DDT contamination, but rather the combination of all contaminants within an aquatic ecosystem. However as was shown in Chapter 3 and 4, the impacts in the Luvuvhu River are limited to DDT and a few other factors including some OC and Zn contamination, as well as abstraction and sedimentation. Therefore, in the present study an attempt to identify the effect of DDT on macro-invertebrates is done with consideration of the other impacts. The purposes of identifying macro-invertebrate community changes related to DDT were two fold. Firstly, as macro-invertebrates have short life-cycles their response to DDT at a community level of complexity is faster than those of fish, which would perhaps be a better indication of community level changes within this study. Secondly, to refine methodologies that would best indicate community level effects, which include the commonly utilised sensitivities of families (average score per taxon), EPT (Ephemeroptera, Plecoptera Trichoptera) richness, diversity indices, informal assessments and multivariate assessments.

The average score per taxon (ASPT) was developed as a consequence of the South African scoring system (SASS) index, which are both commonly utilised for the bioassessment of the river health in South Africa (Chutter, 1995). These two metrics are usually used in conjunction with each other and are both based on the varying degrees of sensitivity macro- invertebrates have to water quality changes (ranging from extremely tolerant to extremely sensitive). They were primarily designed to act as a signal to changes in general water quality in a reduced time frame, and as such are part of the RHP in South Africa, with many publications and technical reports available showing their abilities in first line monitoring processes (Dickens and Graham, 2002; Henning, 2001; Roux, 2001). However since the SASS5 methodology was not followed, with the 15 minute analysis restriction not followed and the samples taken back to the laboratory for more extensive analysis, the SASS score could not be incorporated into this study. In contrast, the ASPT score was included in this study as, despite the methodology not being followed, this index would still contribute toward indentifying changes in the number of sensitive macro-invertebrate families present at each site.

The EPT richness index is widely used and accepted as one of the best candidate benthic metrics used in USA (Barbour et al., 1999) and Australia (Marshall et al., 2001). It is based on the generally high sensitivity of Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) (EPT richness) to disturbances and as such a decrease in the

179 Chapter 9 number of these orders would indicate an increase in perturbations. Apart from identifying disturbances the Watershed Science Institute report showed that the EPT index is also able to establish reference conditions, used to set protection and restoration goals, choose control measures, evaluate the effectiveness of biomonitoring programme improvement measures and monitor watershed condition changes in the early stages of a project. Unfortunately, no studies were found that utilised this EPT approach within South Africa however, since it was found that the majority of these orders in South Africa had an average sensitivity of between 9 and 12, the index could be utilised in this study. One of the disadvantages of using this index is the fact that EPT is limited to regional comparisons, as the composition of the fauna varies between regions, with some systems naturally having more EPT than others (Marshall et al., 2001).

The diversity indices, informal assessments and multivariate assessments were explained in detail in Chapter 8. All were utilised in the macro-invertebrate assessments, except for the abundance models. These models were not considered for the macro-invertebrate community analysis due to two reasons. Firstly, no literature was found that formed rank- abundance models using the family level resolution, presumably because the loss of information that is inherent to the family identification level would have a marked influence on these models. Secondly, the fish abundance models utilising species level resolution were shown to be ineffective in identifying environmental deterioration.

Another index that was not utilised within the macro-invertebrate analysis is the MARAI. The MARAI is the macro-invertebrate index utilised in the locally derived EcoStatus evaluation. It was not included in this study as it compares the current macro-invertebrate community with a reference community (similar to the FRAI as explained in Section 8.1) and as such requires a macro-invertebrate reference list. According to Thirion (2008) a reference list must either be derived from a suitable minimally impacted site or via desk top reviews in combination with expert knowledge of the area, along with suitable historic data and data from similar sites from different rivers. Unfortunately, since the main objective of the present study was to determine the effects related to DDT the reference site was selected based on its impacts related to DDT contamination and as such a proper reference list based on suitably minimally impacted sites could not be defined. Furthermore, a reference list could not be determined via the latter means as it requires expert knowledge of macro- invertebrates in various rivers within the Limpopo province, which was not obtainable within the time-frame of this study. Consequently, the MIRAI could not be incorporated into this study.

180 Chapter 9

9.2 METHODS

9.2.1 Sampling

As explained in Section 8.2.1 only the five riverine sites in the Luvuvhu River were sample. At each site the macro-invertebrates were collected as per the standard SASS 5 sampling protocol developed by Chutter (1998). A SASS net with a 1 mm mesh size (30 cm x 30 cm) was used to collect specimens that were dislodged from their respective habitats of stones, mud or vegetation. This sampling was standardised to disturbing cobbles and bedrock for 6 minutes, gravel sand and mud for 1 minute and marginal vegetation for 2 square meters. The organisms were then preserved in 10% neutral buffered formalin (100 ml 40% formaldehyde, 4 g NaH 2PO4 and 6.5 g Na2HPO4 made up to 1 litre) and then transferred and stored in 70% ethanol in the laboratory. This was followed by the identification of each specimen to family level. This taxonomic resolution was selected as it was the most cost and time effective and is able to distinguish between polluted sites (Marshal et al., 2006; Waite et al., 2004: Baley et al., 2001; Krassulya, 2001).

9.2.2 Data treatment

Occurrence of macro-invertebrates

Although no suitable reference lists were available for the Luvuvhu River (see section 8.1 for further discussion), there was historical data available that could be used to informally identify deterioration in the distribution of macro-invertebrate assemblages compared to the previous years. The majority of this historical data was obtained from the Rivers database version 3, Fouche et al. (2005), Muller and Villet (2004), and Henning (2001).

Relative abundance

Collection of biota for absolute abundance of populations is very intensive and according to Barbour et al. (1999) is not a required endpoint to determine the effects of pollution in lotic ecosystems. Therefore, only the relative abundance was used as an indicator of contaminant stress on macro-invertebrate communities.

ASPT and EPT (Ephemeroptera, Plecoptera, Trichoptera) richness

Once each macro-invertebrate family was identified, the taxa were assigned a pre-defined sensitivity score varying from 1 to 15 depending on the sensitivity to impaired water quality, with higher scores associated with sensitive taxa (Chutter, 1995). The ASPT score was then calculated by dividing the sum of all sensitivity scores with the number of taxon present. This was followed by the EPT richness that evaluated the total number of families occurring

181 Chapter 9

in the order Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) was calculated and by adding the number of families into one score (Marshall et al., 2001).

d. Non-parametric diversity indices

Margalef's taxa richness, Pilou's evenness index, Shannon diversity index and alpha-log series were assessed as described in Section 8.2.2c.

9.2.3 Statistics

Pearson's correlation and multivariate statistics were assessed in the same manner as described in section 8.2.3.

9.3 RESULTS

9.3.1 Occurrence of macro-invertebrate families

Upon evaluation of Table 9.1 the macro-invertebrate families sampled in this thesis differed slightly from historical data obtained from 1978 to 2001. The families that were not present within the current study, included Amphipoda, Chlorocyphidae, Corbiculidae, Hydropsychidae, Libellulidae and Muscidae.

Table 9.1. The historical data and combined current data (all sites and seasons) of macro- invertebrate assemblages.

Historical data Present study 1978-1997' 19992 2000-2001 3 2006-2008 Aeshnidae P P P Amphipoda• P Ancylidae P P P Athericidae P P P Atyidae P Baetidae P P P P Belostomatidae P Blephariceridae - P Caenidae P P P P Ceratopogonidae - P P P Chironomidae P P P P Chlorocyphidae P P Coenagrionidae P P Corduliidae P P Corixidae P P Culicidae P P P Dixidae P Dytiscidae P P Ecnomidae P Elmidae P P P P

182 Chapter 9

Historical data Present study 1978-1997' 19992 2000-2001 2 2006-2008 Gerridae P P P Gomphidae P P P Gyrinidae P P P Helodidae P P Heptageniidae P P P P Hrundinea P Hydracarina P P P Hydraenidae P Hydrophilidae P Hydropsychidae P P P Hydroptilidae P P P Leptoceridae P P P Leptophlebiidae P P P Lestidae P Libellulidae P P Lymnaeidae P P Muscidae P Naucoridae P P P P Nepidae P P Notonectidae P P Oligochaeta** P P P Oligoneuridae P P Palaemonidae - P Perlidae P P P Philopotamidae P P Physidae P P Planorbinae P P P Playcnemidae P Pleidae P - P Polycentropodidae P Potamonautidae P P P Pyralidae P Simuliidae P P P P Sphaeriidae P P Synlestidae P Syrphidae P Tabanidae P P P P Thiaridae P Tipulidae P P P P Tricorythidae P P P P Unionidae P Veliidae - P P P 'Muller and Villet (2004), 'Henning (2001) and 3Fouche et al. (2005). Bold represents families that are not present in this study. Scientific classification of those groups not a family include *Order and **Sub-class.

9.3.2 Informal assessments of number of taxon and relative abundances

In Table 9.2 the number of taxa and relative abundances are summarised. Although the variations were very slight, some seasonal and spatial differences were present. When considering the seasonal differences it was found that the low flow regimes had higher

183 Chapter 9 numbers of families and abundances. As for the spatial differences, the Latonyanda generally had the lowest number of taxa and abundances of all of the sites, while Hasana showed a vast improvement in the number of taxa and abundances present. At Tshikonelo it was found that the total abundances of Baetidae, Hydropsychidae and Chironomidae were very high, particularly in the low flow 2006, whilst in the other seasons at this site the abundances and number of taxon varied slightly. Whilst at Xikundu in the low flow 2007 a high number of families' with high abundances of Chironomidae, Elmidae, Atyidae and Coenagrionidae were found. Lastly, at Mhinga the macro-invertebrate assemblage were very similar to those found at Xikundu; however there was Hydrophillidae, Hydroptilidae, Philopotamidae, Belostamatidae, and Synlestidae that were not present at Xikundu.

9.3.3 Sensitivity scores

As shown in Figure 9.1, the sensitivity scores were generally classified as good, with the exception of Tshikonelo in the high flow 2008 being excellent and Hasana in the low flow 2007 being fair. At Latonyanda the good ASPT was primarily characterised by the presence of Heptageniidae (13), Helodidae (12), Athericidae (10), Blephariceridae (15) and Dixidae (10). An improvement in the ASPT scores was observed in the assemblages present at Hasana and Tshikonelo, with high sensitivity taxon including Athericidae (10), Philopotamidae (10), Polycentropodidae (12), Pyralidae (12), Oligoneuridae (15), Heptageniidae (13) and Perlidae (12). However, a slight deterioration in the ASPT scores was observed at Xikundu, while a general recovery of the number of sensitive taxa was noted at Mhinga.

Figure 9.1. The average sensitivity score per a macro-invertebrate family taxon along with their condition categories of fair (3-5), good (5-7) and excellent (>7) defined by Thirion et al. (1995). At Latonyanda (LAT), Hasana (HAS), Tshikonelo (TSHI), Xikundu (XIK) and Mhinga (Mhinga) in the two low flow (LF) and two high flow (HF) regimes.

184

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9.3.4 Ephemeroptera, Plecoptera and Trichoptera (EPT) richness

The number of Plecoptera, Ephemeroptera and Trichoptera (EPT) is a measure of degradation of habitat and water quality and is illustrated in Figure 9.2. Upon seasonal comparisons it was evident that the EPT richness was highest in the high flow regimes as compared to the low flow regimes at all the sites, except at Latonyanda in the high flow 2008 and at Xikundu in high flow 2007. Spatially, the Latonyanda showed the lowest number of these specialised macro-invertebrate families. At Hasana the richness improved in all of the seasons (except low flow 2007), but deteriorated at Tshikonelo (except in the low flow 2007) and to a greater extent at Xikundu. Mhinga community assemblages, however, showed a good recovery in all the sampling regimes.

Figure 9.2. The number of Plecoptera (stoneflies), Ephemeroptera (mayflies) and Trichoptera (caddisflies) (EPT taxa) present in the Luvuvhu River at Latonyanda (LAT), Hasana (HAS), Tshikonelo (TSHI), Xikundu (XIK) and Mhinga (Mhinga) in the two low flow (LF) and two high flow (HF) regimes.

9.3.5 Species diversity

The diversity indices, represented in Figure 9.3, are used to describe the response of the macro-invertebrate community to the water quality at each of the sites. Although no large difference was evident, there was a seasonally low taxa richness and diversity in the high flow regimes with two exceptions. Firstly, at Latonyanda and Tshikonelo a slightly lower diversity was found in the low flow 2007, which was due to an increase in unevenness (illustrated by low evenness index in Figure 9.3b). Secondly, there was a lower than normal low flow macro-invertebrate community diversity at Hasana in 2007, with a lower family richness.

Upon spatial comparison, there were no sites that particularly stood out throughout the four seasonal sampling periods, with Tshikonelo, Xikundu, Hasana and Mhinga having the lowest community diversities in the four respective seasons from 2006 to 2008. In the low flow

187 Chapter 9

2006, Tshikonelo showed the lowest family richness and an exceptionally high abundance of Baetidae (Table 9.2). In the following season, Xikundu had the lowest diversity as a result of low evenness, particularly caused by generally low family abundances and a high abundance of Hydropsychidae. The log series diversity index was however not as influenced by the low abundances and showed that Mhinga had the lowest macro- invertebrate diversity. Whilst in the low flow 2007, Hasana had the lowest richness and diversity indices, particularly observed with the log-series diversity index. Finally, considering the last sampling season, high flow 2008, a lower macro-invertebrate diversity was found at Mhinga as given by the Shannon index. This was primarily due to a higher than normal abundance of Atyidae and Chironomidae, with a much lower overall abundance. In contrast, the alpha-log series index was lowest at Tshikonelo.

9.3.6 Multivariate statistics

The MDS plot shown in Figure 9.4 illustrates the significant differences between the low flow and high flow regimes obtained by ANOSIM calculations. These differences were generally a result of higher abundances in the low flow regime (Table 9.2), although the high flow did have higher abundances of Atyidae, Tricorythridae, Leptophlebiidae, Heptageniidae, Oligochaeta and Oligoneuridae that contributed towards the dissimilarity of the two flow regimes. Due to this high difference between flow regimes, the spatial variations were separated according to each sampling regime.

As shown in Figure 9.5, there were no consistent spatial trends between any of the flow regimes, although Latonyanda was found to be significantly different from Hasana and Tshikonelo (Table 9.3). In the low flow of 2006 (Figure 9.5a), comparisons between all the sites showed that Tshikonelo had the most dissimilar macro-invertebrate community assemblage. This was primarily based on the dominance of the mayfly family Baetidae, which then differed in the following seasons (Table 9.4). In the high flow of 2007 (Figure 9.5b), the Latonyanda macro-invertebrate community was predominantly dissimilar (average dissimilarity of 70 -74) based on higher abundances of Heptagenidae and Ceratopogonidae, with few Baetidae and Leptophlebidae (Table 9.5). Similarly large dissimilarities in Latonyanda communities were found in the low flow 2007 (Figure 9.5c), although in this season, the Chironomidae and Veliidae where the characterising taxa contributing toward the average dissimilarity (Table 9.6). It should also be noted that Hasana was also fairly dissimilar in this season, which was a direct result of an above normal number of Caenidae present. Then in the last sampling season (high flow 2008), the major difference in the ordination distance between Tshikonelo, Xikundu and Mhinga (Figure 9.5d) was due to a high abundance of Atyidae and Tricorythridae (Table 9.7).

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r-1 Latonyanda OM Hasana CI Tshikonelo Xikundu EZZZIMhinga

HF '08 (b) 1.0— 0.8- 0.6- I 111 0.4- 0.2-

0.0 . LF1'06 IILF '07 HF '08 (C ] 3— 1 li IF I

ILF ''06 LF ''0707 HF '08 (d)7— 6- 5- 4- 3- 2- I - 1 - LFI'06 H.LF ''070711 11HF '08 li

Figure 9.3. Non-parametric diversity indices for macro-invertebrates including Margalef's taxa richness (a), Pilou's evenness index (b), Shannon diversity index (c) and log series (d) for macro-invertebrates from 5 sites collected in two low flows (LF) and two high flows (HF).

189 Chapter 9

Stress: 0.19 Global R = 0.30* (p=0.001) C, • • •

A 6. v • • A A • • A A A

A A

Figure 9.4. MDS ordination showing the significant (ANOSIM) seasonal differences between low flow and high flow regimes based on macro-invertebrate taxa of low ( ■ ) and high (A) flow regimes.

Table 9.3. One-way analysis of similarity (ANOSIM) testing for seasonal and spatial differences in macro-invertebrate assemblage structures.

Global R P

Latonyanda vs Hasana 0.41 0.03

Latonyanda vs Tshikonelo 0.41 0.03 Latonyanda vs Xikundu 0.30 0.10 Latonyanda vs Mhinga 0.57 0.03 Hasana vs Tshikonelo 0.33 0.06 Hasana vs Xikundu -0.06 0.57 Hasana vs Mhinga -0.06 0.57 Tshikonelo vs Xikundu 0.00 0.51 Tshikonelo vs Mhinga 0.26 0.11 Xikundu vs Mhinga -0.13 0.74 Bold-significant differences (p<0.05)

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Figure 9.5. Bray-Curtis dendrograms showing spatial differences in (a) low flow 2006, (b) high flow 2007, (c) low flow 2007 and (d) high flow 2008 for macro-invertebrate assemblages in the Luvuvhu River at Latonyanda (L), Hasana (H), Tshikonelo (T), Xikundu (XW) and Mhinga (M).

191 Chapter 9

Table 9.4. The importance ratings of macro-invertebrate families in the low flow of 2006, presented as a percentage of the total contribution of the similarity and dissimilarity between the sites. Only those taxa responsible for 50% or more of the cumulative contribution are presented. Similarity

Taxon Abundance Avg. similarity % contribution Hasana, Mhinga Total 41 Corbiculidae 55 9 22 Atyidae 66 7 16 Chironomidae 17 4 9

Dissimilarity Taxon Abundance Avg. dissimilarity % contribution Hasana, Mhinga Tshikonelo Total 85 Baetidae 9 897 47 55

Table 9.5. The importance ratings of macro-invertebrate families in the high flow of 2007, presented as a percentage of the total contribution of the similarity and dissimilarity between the sites. Only those taxa responsible for 50% or ore of the cumulative contribution are presented. Similarity Taxon Abundance Avg. similarity % contribution Hasana, Xikundu, Mhinga Total 48 Hydropsychidae 53 10 22 Leptophlebiidae 49 9 18 Aytidae 23 8 16

Dissimilarity Taxon Abundance Avg. dissimilarity % contribution Latonyanda Tshikonelo Total 70 Baetidae 3 74 15 22 Hydropsychidae 0 48 10 37 Heptageniidae 24 0 5 45 Ceratopogonidae 19 1 4 50 Tshikonelo Hasana, Xikundu, Mhinga Total 58 Baetidae 10 74 13 22 Leptophlebiidae 49 24 8 14 Hydropsychidae 53 48 6 11 Tricorythridae 3 30 5 9 Latonyanda Hasana, Xikundu, Mhinga Total 74 Hydropsychidae 0 53 14 19 Leptophlebiidae 34 49 10 13 Heptageniidae 24 1 6 8 Aytidae 2 23 5 7

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Table 9.6. The importance ratings of macro-invertebrate families in the low flow of 2007, presented as a percentage of the total contribution of the similarity and dissimilarity between the sites. Only those taxa responsible for 50% or more of the cumulative contribution are presented. Similarity Taxon Abundance Avg. similarity % contribution Tshikonelo, Xikundu, Mhinga Total 40 Corbiculidae 67 8 20 Elmidae 46 6 34 Baetidae 74 6 48

Dissimilarity Taxon Abundance Avg. dissimilarity % contribution Latonyanda Hasana Total 63 Caenidae 10 96 11 18 Veliidae 84 1 11 17 Chironomidae 117 55 8 13 Latonyanda Tshikonelo, Xikundu, Mhinga Total 74 Chironomidae 117 38 9 13 Veliidae 84 3 9 13 Corbiculidae 0 67 8 11 Baetidae 52 74 6 9 Hasana Tshikonelo, Xikundu, Mhinga Total 61 Caenidae 96 16 9 15 Baetidae 101 74 8 13 Planorbidae 5 44 5 8 Chironomidae 55 38 5 8 Hydropsychidae 1 43 5 8

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Table 9.7. Importance ratings of macro-invertebrate families in the high flow 2008, presented as a percentage of the total contribution of the similarity and dissimilarity between the sites. Only those taxa responsible for 50% or more of the cumulative contribution are presented. Similarity Taxon Abundance Avg. similarity % contribution Tshikonelo, Xikundu, Mhinga Total 46 Atyidae 95 13 29 Chironomidae 41 7 44 Baetidae 22 5 55

Dissimilarity Taxon Abundance Avg. dissimilarity % contribution Latonyanda Hasana Total 66 Baetidae 17 71 18 27 Chironomidae 19 42 7 11 Coenagrionidae 24 1 7 11 Latonyanda Tshikonelo, Xikundu, Mhinga Total 74 Atyidae 0 95 21 28 Tricorythridae 0 75 15 20 Hasana Tshikonelo, Xikundu, Mhinga Total 62 Atyidae 6 95 17 27 Tricorythridae 2 75 12 20

9.3.7 Factors influencing macro-invertebrate abundances and diversity

As shown in Table 9.8, the high flow and low flow regimes were assessed separately (in contrast to fish assemblages) as there were significant seasonal variations observed (Figure 9.4) and that only those variables with significant correlations were noted. In the low flow regimes there was a significantly negative correlation between the ASPT scores, phosphate and 2,4'-DDE in the sediment. The latter was primarily a result of the 0.2 pg/kg DDE in the sediment of Hasana during low flow 2007. Another variable that may have influenced the community structures was oxygen in the water, as indicated as being significantly positively correlated with the taxa richness, number of taxa and abundance.

Table 9.8. Pearson's correlation coefficients between variables and macro-invertebrate endpoints that were significantly correlated (p<0.05).

No. of taxa Abundance Taxa richness Evenness Shannon D ASPT Low flow regimes Oxygen 0.95 0.96 0.92 0.73 0.82 0.19 Phosphate 0.69 -0.10 0.47 0.41 0.49 -0.98 2,4'-DDE (s) -0.63 -0.22 -0.41 -0.06 -0.19 -0.83

High flow regimes Ammonia -0.85 -0.50 -0.23 0.29 0.13 -0.19 Nitrite 0.26 0.42 -0.14 -0.64 -0.59 0.18 Zinc (s) 0.14 0.13 -0.11 -0.84 -0.89 -0.19 Bold: significant correlations (p<0.05), ASPT: average taxon sensitivity, s: sediment.

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As for the correlations observed in the high flow regimes, it was evident that ammonia was significantly correlated with the number of taxa, nitrate was negatively correlated with evenness and zinc in the sediment was correlated with the Shannon diversity index.

9.4 DISCUSSION

As macro-invertebrate communities are far more responsive to contamination and anthropogenic impacts than fish community structures, they were utilised as an additional approach to assess community level effects related to DDT contamination and to highlight the most effective methodologies available that would best indicate DDT contamination for future use within biomonitoring programmes.

Since the different methodologies for the bioassessments were interrelated, each site was discussed separately in Section 9.4.1, in order to identify if there were changes within the community assemblages in the Luvuvhu River. The methods that best describe community changes will follow in Section 9.4.2.

9.4.1 Macro-invertebrate community changes

a. Latonyanda River

The Latonyanda macro-invertebrate assemblage had the lowest number of species and abundances including Plecoptera, Ephemeroptera and Trichoptera as shown by the EPT index. This was not a result of water quality, as the ASPT score was good and the cause for dissimilarity by SIMPER showed higher numbers of Heptageniidae (12), but probably due the lower habitat availability compared to other sites. However this did not influence the structure of the macro-invertebrate assemblages, with the functional feeding groups largely similar to assemblages at other sites, with a low number of shredders (macro-invertebrates that feed on decomposing plant matter) and high number of grazers (macro-invertebrates that feed on periphyton and algae).

Upon temporal evaluation of the Latonyanda, strong seasonal differences were observed. The diversity was generally high in the low flow regimes, perhaps due to the natural reproductive cycling, as no environmental variables could be correlated to the changes. The exception to this was the low flow 2007, which had slightly reduced diversity due to the unevenness, particularly a result of Chironomidae and Veliidae. The presence of these families suggest that there was perhaps an imbalance in the community structure due to an external source, such as increase in decomposed organic matter (Thirion et al., 1995), which was discussed in Chapter 4.

195 Chapter 9

Hasana

Upon inspection of the indicators, the Hasana macro-invertebrate assemblages generally had higher sensitivity scores, EPT richness and abundances than the Latonyanda in all the seasons except in the low flow 2007. In fact in this season, the assemblages were one of the most degraded communities according to the ASPT score and EPT richness. An average sensitivity score of 4.5 was observed, which is an indicator of a "fair" water quality (most of the sites had good scores ranging from 5 to 7.6). Whilst, the reduced number of Plecoptera, Ephemeroptera and Trichoptera families suggested that there was a degradation of habitat or water quality at this site (Barbour et al., 1999). A possible explanation for this deterioration observed in the low flow 2007, could be due to the o,p'-DDE concentrations in the sediment, which were significantly correlated with the ASPT score, or due to the higher nitrite concentrations (Chapter 4). These results were however surprising, as the concentration of the DDE and nitrites were much lower than what was found in other sites, which had seemingly unperturbed assemblages.

Another possible cause of this lower number of sensitive taxa, may be due to increased sewage pollution in the low flow 2007. This was not only observed in the high concentrations of organic matter in the sediment, but also highlighted by the high abundance of Caenidae and Baetidae (also contributing toward the high dissimilarities observed at Hasana) (Allanson, 1995; Davies and Day, 1998; Rudek et al. 1991). The Caenidae family are predominantly shredders that feed on decomposing plant material and coarse organic matter and the Baetidae family are also predominantly made up of grazers/scrapers of diatoms, algae and phytoplankton (Barbour et al., 1999).

Tshikonelo

As was shown in the macro-invertebrate assemblage at Hasana, DDT was not an influencing factor at Tshikonelo. At this site higher concentrations of DDD and DDE were observed in the high flow 2008 in the sediment (3.8 pg/g) and water (0.5 pg/I), respectively (Chapter 4). However the ASPT score of 7.6 showed that the water quality was "excellent" according to Thirion et al. (1995) and the EPT richness showed the highest number of Plecoptera, Ephemeroptera and Trichoptera families, which are generally the most sensitive to degradation. This suggests that these DDT concentrations in both the water and sediment were not an influencing factor on macro-invertebrate community structures. According to a review done by EXTOXNET (1996), aquatic macro-invertebrates can experience mortality at ranges between 0.18 pg/I to 7 pg/I after 96 hours of continuous exposure, while mortalities after 48 hours of exposure were found between concentrations of 4.7 pg/I to 15 pg/I. Thus, the lack of DDE influence on communities could be due to the macro-invertebrate families being exposed in shorter durations or perhaps the effects were only present at sub-cellular levels, which were not assessed in the present study.

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Upon evaluation of temporal variations, strong seasonal differences were observed (as in Latonyanda) with higher numbers in the low flow regimes, which may be due to the natural reproductive cycling of the macro-invertebrates. However in contrast, the sensitivities and EPT richness generally yielded lower scores, with no shredders present in the low flow regimes. The lack of shredders suggests that there was a reduction in the amount of plant material present in the low flows, which corresponds to the reduction of instream vegetation. While the generally reduced sensitivities and the distinctly lower number of Oligoneuridae and stoneflies in the low flow regimes suggest that perhaps there was a reduced water quality in these sampling regimes. This corresponds with many studies that have shown an increase in community structural perturbations in the low flow. What was evident from the studies was that the lower water flows usually resulted in either a reduction of suitable habitat for sensitive taxa (Henning, 2001) or an increase in nutrient and toxicant concentrations, which were not found at such high concentrations in the more diluted high flow regimes (Garnier et al., 1995; Gasith and Resh, 1999; Moreau et al., 1998). However, upon inspection of influencing factors in the water in the current study (Chapter 4) there were few seasonal changes observed, although there was a seasonal increase in the conductivity in the low flow regimes.

Of the two low flow regimes the macro-invertebrate communities were mostly affected in the low flow 2006. This was largely due to the particularly high presence of Baetidae, Hydropsycidae and Chironomidae. This could be a result of higher nutrient levels present (albeit slightly above ideal scoring range and not significant) or organic pollution such as sewage outfall, although no such point source was observed (Bredenhand 2005; Barbour et al., 1999).

d. Xikundu

As was shown in Chapter 4, the concentration of contaminates (DDT, dieldrin and lindane) increased at Xikundu, particularly in the high flow 2008 in the water and sediment. This was however not reflected in the macro-invertebrate assemblages in the high flow 2008, with lower scores observed in the high flow 2007 and low flow 2007. The EPT and ASPT specifically showed a reduced number of sensitive families including those belonging to Plecoptera, Ephemeroptera and Trichoptera at Xikundu compared to Tshikonelo and Mhinga specifically in the high flow and low flow 2007. In the low flow 2007, assemblages were changed largely due to a reduced number of Oligoneuridae and a general increase in low sensitive families, suggesting that there was a negative change in the water quality in this season. As was explained at Tshikonelo, this could have been due to the dilution effect that may have occurred. Whilst in the high flow 2007, macro-invertebrate communities were largely dissimilar to the other sites due to the low diversity observed as a result of generally lower abundances and a high abundance of Hydrophychidae. This suggests that an imbalance in the community structure may have occurred, perhaps due to increased food availabilities. Hydrophychidae are predominantly made up of species that are filter feeders

197 Chapter 9 of algae, debris and other macro-invertebrate larvae, which may have increased in availability in this high flow regime (Scott et al., 2009).

e. Mhinga

At Mhinga, the macro-invertebrate communities showed a slight improvement from Xikundu with higher abundances of Perlidae and Philopatomidae, and slightly higher sensitivity scores and EPT richness in all regimes, except in the high flow 2008. In this season, the diversity was largely reduced compared to the other sites due to the unevenness of the community. The SIMPER analyses showed that there was a very high abundance of Atyidae and Chironomidae present in a community that had on average low family abundances. The presence of both these families have been shown to increase due to decomposing fine particulate organic matter, perhaps a result of the increase in Atyidae and Chironomidae (Davies and Day, 1998; Thirion et al., 1995), although evaluation of organic matter in sediment (Chapter 4, Table 4.7) there was no relationship observed. Another possible cause for the change in community assemblages in the high flow 2008 could have been due to the generally low abundances found in the stones and GSM habitats, which may have resulted from reduced sampling success due to increased water velocities in the high flow.

9.4.2 Macro-invertebrate community assemblage methodologies

All of the bioassessments showed strong seasonal differences. In general the sensitivity assessments including the ASPT scores and the EPT richness were reduced within the low flow regimes. This was attributed to either the reduced habitat related to the low flows and/or the reduced water quality that is inherent in low flows due to the lack of dilution evident. In contrast, the informal assessments and diversity analysis had strong seasonal differences related to normal reproductive cycling, with normally higher taxa richness and diversity in the high flows.

Despite these strong seasonal changes, all of these bioassessments, together with multivariate statistics, were able to highlight areas where impacts were occurring when interpreted in combination. Unfortunately, none of the community changes were related to DDT impacts. It was strongly suspected that the reason for this may be because the concentrations and durations of DDT exposure were too low to induce an effect at such high levels of biological organisation (Connell et al., 1999). As lower levels of biological organisation where not measured, the exact extent of impact by DDT on the macro- invertebrates could not be quantified in this study.

Nevertheless, these bioassessments can still be utilised to monitor macro-invertebrate community level effects within biomonitoring programmes. Some inherent factors influencing the use of these bioassessments in biomonitoring were noticed. Specifically it was evident the EPT richness and ASPT scores showed very similar results. Therefore only

198 Chapter 9 one of these two indices is necessary to include in a monitoring programme assessing the changes in the macro-invertebrate sensitivities. Since ASPT is commonly utilised in the RHP, it would be the most obvious index of choice for use in South Africa. With regards to the diversity indices, all of the indices including richness, evenness, Shannon-diversity and log-series diversity indices were related to taxa richness and abundances. However, since the latter two theoretically should identify diversity, only one would be required for use in biomonitoring. Comparisons between the two show that the log—series diversity index tended to accentuate some of the community changes measured in the Shannon diversity index (as shown in the fish), although the Shannon diversity index was more responsive to the changes in the evenness. As the evenness index is measured, the log-series diversity index would therefore be a better indicator of diversity than Shannon as was found in Chapter 8. In addition, the informal assessments and multivariate statistics should be included into any biomonitoring programme, as the informal assessments form an integral part of the interpretation of all of the diversity indices and the multivariate statistics were particularly valuable in showing spatial and temporal trends and identifying the taxa causing variations. Therefore it is recommended that during monitoring of macro-invertebrate communities that ASPT, informal assessments, diversity indices (including richness, evenness and log-series diversity) and multivariate statistics are utilised to highlight community structural changes and the causes thereof.

9.5 REFERENCES

Allanson BR. 1995. An introduction to the management of inland water ecosystems in South Africa. Water Research Commission (WRC) Report No. TT72/95. Pretoria, South Africa.

Baley RC, Norris RH and Reynaldson TB. 2001. Taxonomic resolution of benthic macroinvertebrate communities in bioassessments. J. N. Am. Benthol. Soc. 20(2): 280-286

Barbour MT, Gerritsen J, Snyder BD, Stribling JB. 1999. Rapid Bioassessment protocols for use in streams and wadeable Rivers: periphyton, Benthic macroinvertebrates and Fish, Second Edition. EPA 841-B-99-002. U.S. Environmental Protection Agency; Office of Water; USA.

Bredenhand E. 2005. Evaluation of macro-invertebrates as bio-indicators of water quality and the assessment of the impact of the Klein Plaas dam on the Eerste River. MSc dissertation, unpublished. University of Stellenbosh, South Africa.

Chutter FM. 1995. The role of aquatic organisms in the management of river basins for sustainable utilisation. Water Sci. Tech. 325: 283-291.

199 Chapter 9

Chutter FM. 1998. Research on the rapid biological assessment of water quality impacts in streams and rivers. Water Research Commission (WRC) Report No. TT422/1/98. Pretoria, South Africa.

Connell D, Lam P, Richardson B and Wu R. 1999 Introduction to ecotoxicology. UK: Blackwell Science.

Extension Toxicology Network (EXTOXNET). 1996. Pesticide Information Profiles: DDT. files maintained and archived at Oregon State University.

Dallas HF. 2002. Spatial and temporal heterogeneity in lotic systems implications for defining reference conditions for macro-invertebrates. Water Research Commission (WRC) Report No. KV 138/02. Pretoria, South Africa.

Dallas HF. 2005. River health programme: site characterisation field-manual and field-data sheets. Resource quality services, Department of Water Affairs and Forestry.

Davies B and Day J. 1998. Vanishing waters. University of Cape Town Press, South Africa.

Dickens CWS and Graham PM. 2002. PM. The South African Scoring System (SASS), Version 5, Rapid Bioassessment method for rivers. Afr. J. Aqu. S. 27: 1-10.

Fouche PSO, Foord SH, Potgieter N, van der Waal BCW and van Ree T. 2005. Towards an understanding of factors affecting the biotic integrity of rivers in the Limpopo province: niche partitioning, habitat preference and microbiological status in rheophilic biotopes of the Luvuvhu and Mutale Rivers. Water Research Commission (WRC) Report No. 1197/1/05. Pretoria, South Africa.

Garnier J, Billen G, and Coste M. 1995. Seasonal succession of diatoms and Chlorophyceae in the drainage network of the Seine River: Observations and modelling. Limnol. Oceanogr. 40(4): 750-765.

Gasith A and Resh VH. 1999. Streams in Mediterranean climate regions: abiotic influences and biotic responses to predictable seasonal events. Ann. Rev. of Ecol. And Syst. 30: 51- 81.

Henning D. 2001. Evaluation of the role of SASS4 as an aquatic biomonitoring method in the ecological risk assessment process and in the determination of resource directed measures for the Luvuvhu River. MSc dissertation, unpublished. Rand Afrikaans University, Johannesburg.

200 Chapter 9

Krassulya N. 2001. Choice of methodology for marine pollution monitoring in intertidal soft- sediment communities. Upsala, CBMs Skriftserie 3: 131-148.

Marshall C, Harch BD, Choy SC and Smith MJ. 2001. Aquatic macro-invertebrates as indicators of ecosystem health. Design and implementation of baseline monitoring (DIBM3). Chapter 8.

Marshal EJP, Wert TM and Kleijn D. 2006. Impacts of an agri-environment field margin prescription on the flora and fauna of arable farmland in different landscapes. Agric. Ecos. Envir. 113: 36 — 44.

Moreau S, Bertru G, and Buson C. 1998. Seasonal and spatial trends of nitrogen and phosphorus loads to the upper catchment of the river Vilaine (Brittany): relationships with land use. Hydrobiologia. 373/374: 247-258.

Muller WJ and Villet MH. 2004. Similarities and differences between rivers of the Kruger National Park. Water Research Commission (WRC) Report No. 881/1/04. Pretoria, South Africa.

Rivers database version 3. 2007. Rivers Database: Data Owners: Mick Angliss and Gerhard Diedricks. Department of Water Affairs and Forestry.

Roux DJ. 2001. Development of procedures for the implementation of the National River Health Programme in the province of Mpumalanga. Water Research Commission (WRC) Report No. 850/1/01.11. Pretoria, South Africa.

Rudek J, Paerl HW, Mallin MA and Bates PW. 1991. Seasonal and hydrological control of phytoplankton nutrient limitation in the lower Neuse River estuary, North Carolina. Mar. Ecol. Prog. Ser. 75: 133-142.

Sanchez W, Katsiadaki I, Piccini B, Ditche J-M, and Porcher J-M. 2008. Biomarker responses in wild three-spined stickleback (Gasterosteus aculeatus L.) as a useful tool for freshwater biomonitoring: a multiparametric approach. Enviro. Int. 34: 490-498.

Scott KM, de Moor FC and Kohly N. 2009. Life history alternatives in the genus Cheumatopsyche (Trichoptera: Hydropsychidae) in Southern Africa. Downloaded on 5 April 2009: http://www. biolog iezentrum. at.

Thirion C, Mocke A and Woest R. 1995. Biological monitoring of streams and rivers using SASS4: a user manual. DWAF, IWQS report No. N0000/00/REQ/1195.

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Thirion C. 2008. Module E: Macroinvertebrate response assessment index in river ecoclassification: manual for ecostatus determination (version 2). Joint Water Research Commission (WRC) and Department of Water Affairs and Forestry report.

Waite IR, Herlihy AT, Larsen DP, Urquhart NS and Klemm DJ. 2004. The effect of macro- invertebrate taxonomic resolution in large landscape bioassessments: an example for the mid-Atlantic Highlands, USA. Freshwater Biol. 49(4): 474-489.

Watershed Science Institute. The EPT Index. Watershed Condition Series. Technical Note 3. www.wsi.nrcs.usda.gov . Obtained on 1 April 2009.

202 Chapter 10 General discussion, conclusions and recommendations

10.1 INTRODUCTION 204 10.2 CURRENT STATUS OF THE LUVUVHU RIVER 204 10.2.1 Summary of the possible current impacts evaluated 204 Physical habitat 204 Physico-chemical and nutrient analysis of water 204 DDT contamination in water, sediment and C. gariepinus 205 Pesticide and metal screen 205 10.2.2 Summary of the current effects in fish 206 Sub-organismal effects 206 Community effects 206 Level of biological complexity 207 10.2.3 Current impacts in macro-invertebrates 207 a. Community effects 207 10.3 LABORATORY ANALYSIS 207 10.4 A SUITABLE SUITE OF BIOMARKERS FOR MONITORING 208 10.5 IDENTIFY EFFECTIVENESS OF COMMUNITY LEVEL MEASUREMENTS 209 10.6 GENERAL CONCLUSIONS AND RECOMMENDATIONS 209 10.6.1 Major concerns in the Luvuvhu River catchment 209 Risk of DDT bioaccumulation and biomagnification 209 Effects of DDT on aquatic biota 209 Agriculture and afforestation 210 Rural village communities 210 10.6.2 Incorporating biomarkers in South Africa 211 10.6.3 Considerations for biomonitoring in the Luvuvhu River 212 10.6.4 Recommendations for future biomonitoring in IRS areas 213 10.6.5 Significance of laboratory work 213 10.6.6 Additional limitations of this study and recommendations for future research 214 10.7 REFERENCES 215 Chapter 10

10.1 INTRODUCTION

This study was initiated due to the general scarcity of data regarding DDT contamination and its effects in South Africa. The overall aim was to determine the extent of contamination within a currently DDT sprayed area in South Africa and to assess the extent of the associated effects, whilst identifying a methodology that could be used in future monitoring of DDT sprayed areas. In order to evaluate this, firstly, the current status of the Luvuvhu River was assessed by quantifying the impacts observed (including those related to habitat, water quality and contamination) (objective 1) and by assessing the current extent of DDT effects by measuring the various levels of biological complexity in fish (objective 2) and the community level in macro-invertebrates (objective 3). Secondly, the responses of aquatic fish to DDT contamination were measured under controlled laboratory conditions, in order to assess the baseline biomarker responses of C. gariepinus to DDT contamination (objective 4) and to identify the effects of DDT on the juveniles of exposed adults (objective 5). Thirdly, the effects observed in the field were compared with those in the controlled laboratory conditions in order to standardise suitable biomarkers for future use in field biomonitoring studies (objective 6). And lastly, the effectiveness of the techniques used to analyse fish and aquatic macro-invertebrate communities for further use within future monitoring of DDT contaminated sites were identified (objective 7).

10.2 CURRENT STATUS OF THE LUVUVHU RIVER

10.2.1 Summary of the possible current impacts evaluated

Physical habitat

As was shown in Chapter 3, the physical habitat of the Luvuvhu River was influenced by numerous anthropogenic activities in the catchment. There was extensive abstraction of water for irrigation, afforestation, rural and urban community utilisation, as well as extensive rural activities in the instream and riparian habitat of all riverine sites in the Luvuvhu River, except for the Latonyanda River. These activities yielded impacts such as bank erosion, flow modification, water quality changes, bed modification and some solid waste disposal. Of all the lotic sites selected, Xikundu showed the greatest impacts particularly due to the accumulative effects of the close proximity to the weir and the larger rural activities within the instream and riparian habitats.

Physico-chemical and nutrient analysis of water

The physico-chemical parameters and nutrients in the Luvuvhu River water (discussed in Chapter 4) were generally close to normal, apart from the various fluctuations that were observed, which were probably due to the dynamic characteristics of the water phase. This was despite the extensive washing of clothes and cars as well as bathing in most of the

204 Chapter 10 segments assessed. However, there was an indication of increased nutrients and conductivity possibly due to runoff of fertilisers and sewage contamination.

DDT contamination in water, sediment and C. gariepinus

In chapter 4 it was found that the concentrations in the water analyses were found to vary from below detection to 1.2 pg/I. The temporal and spatial variations showed that concentrations were highest in the high flow regime in February 2008 at all sites, specifically at Xikundu. The possible reason for this temporal trend was attributed to the spraying of DDT and the reduced rainfall (Appendix 7) which caused a reduction in the dilution effect that may have occurred in the other seasons. Xikundu was shown to be primarily contaminated with values above South African guidelines which are hypothesised to have been attributed to the close proximity of the site to the IRS sprayed area, the combination of various sources upstream and/or the environmental conditions that may have accentuated the contamination at this site.

The spatial and temporal variations of the DDT concentrations in the sediment were found to be very similar to that of the water, with the highest concentrations found in the high flow 2008 and at Xikundu and attributed to the same changes. The concentrations were, however, measured in higher concentrations than in the water, which was due to the lipophilic nature of DDT and therefore its high adsorption to sediment. These sediment concentrations were furthermore shown to be influenced by external environmental variables such as sedimentation and erosion, which were particularly high at Xikundu.

In contrast to the water and sediment concentrations, the concentrations measured in the tissue of the male catfish species C. gariepinus were found to be more stable and better indicators of the extent of DDT contamination in the Luvuvhu River catchment. This was because the concentrations could be related to the application of DDT in IRS areas, both spatially and temporally. Upon evaluation of the extent of DDT contamination within the fish species, it was evident that the concentrations were extremely high in comparison to historical data, obtained from various tissues including adipose tissue. The highest of which were measured in fish from Xikundu due to a combination of the proximity to the source, accumulation from upstream sources (it's the confluence) and increased concentrations observed in the water and sediment.

Pesticide and metal screen

In an attempt to determine other possible causes of effects, the pesticides and metals were evaluated in the water and sediment of the Luvuvhu River catchment. It was found that the majority of pesticides were present at Xikundu within the Luvuvhu River, including concentrations of lindane, dieldrin, endosulfan, heptachlor and endrin. This was surprising as Xikundu was not surrounded by major commercial agricultural practices, which was the

205 Chapter 10

case for Albasini Dam where few pesticides were measured and no concentrations were found upstream or downstream from this site. A possible reason for this is attributed to the fact that Xikundu may have certain conditions that make this site particularly susceptible to contamination such as increased sedimentation, unless contaminants were a result of undocumented close proximity utilisation of pesticides.

The metal concentrations in all the phases were shown (in Chapter 4) to generally be within normal limits for South African ecosystems. However, a higher than normal concentration of Zn was observed in the water and fish. Although this was cause for concern, the Zn concentrations were not expected to induce significant effects on the aquatic ecosystems. This was apparent upon comparisons with South African water quality guidelines and with other studies evaluating Zn reproductive effects.

10.2.2 Summary of the current effects in fish

Sub-organismal effects

In Chapter 5, the results showed that few significant correlations were observed between the biomarkers and DDT contamination. Nevertheless, DDT was shown to induce an effect by slightly inducing the synthesis of ALP, Ca and reducing the gonad mass (shown by GSI and adjusted gonad mass biomarkers). These biomarkers were indicative of effects ranging from sub-cellular to organ level of complexity, with no effects observed at organismal level.

Community effects

In Chapter 7, fish communities were shown to be largely impacted within the Luvuvhu River. One of the major influencing causes of this interpretation was the large decline in the species distributions compared to reference species distributions. However, this was not due to the absence of fish species, but mainly because the species were not sampled due to the lack of specialised sampling techniques such as angling and gill nets. Nevertheless, once these sampling inefficiencies were accounted for, the fish communities did highlight some important results. None of the fish communities were influenced by DDT nor any other contaminant measured in this study. It was strongly believed that this was due to the fact that the exposure duration and/or concentrations were too low to induce an effect at the community level of biological organisation. The fish communities were affected by changes in the physical habitat. This consisted of both natural and anthropogenically induced, such as reduced vegetation and increased erosion in the riparian zone from cattle and human activity. Further to this, there were also changes in the habitat availability due to the flow alterations as a result of the presence of impoundments (weir at Xikundu and the Nandoni Dam upstream from Tshikonelo). As for the spatial changes, Tshikonelo and Mhinga had the most altered fish community structures, whilst Xikundu, and to a lesser extent Hasana, had less modified community structures. Upon evaluation of temporal trends, there were a

206 Chapter 10 number of fluctuations apparent, with no real seasonal changes contributing toward the changes. What was not evident from the results of this study was the effect of fishing by the local communities on the health of the fish communities within the Luvuvhu River catchment. At all of the sites, extensive fishing by local people using gill nets, seine nets, cast nets and traps was observed and therefore further investigations into the resulting effects are recommended.

c. Level of biological complexity

As effects can occur at different levels of complexity, it was necessary to assess the extent of the current effects of DDT by measuring sub-cellular, organ, organismal and community level changes. Upon evaluation of the biomarkers and community bioassessments, effects were only evident at the sub-cellular and organ level of biological complexity, with no effects occurring at organismal level as measured by intersex and condition factor nor community level as measured by various indicators of community assemblage changes. This is despite the fact that, in this study area, DDT had been sprayed for approximately 56 years with very high concentrations measured in the adipose tissue. This leads to the conclusion that the concentrations of DDT within the Luvuvhu River catchment did not induce effects at higher levels of complexity, which are generally reversible with no long term consequences.

10.2.3 Current impacts in macro-invertebrates

a. Community effects

The macro-invertebrate community structures were evaluated to assess whether they were better indicators of community level effects than fish (Chapter 8). The communities were strongly influenced by seasonal changes, but were still able to identify negative effects. As was shown by the fish community results in Chapter 7, there were no significant effects on macro-invertebrate communities due to DDT contamination. The changes that were apparent at most of the sites were hypothesised as being due to organic (sewage) pollution and increased nutrient levels as a result of the locals washing and bathing, on a daily basis. The Latonyanda macro-invertebrate community was the most dissimilar compared to all the other sites in the Luvuvhu River, however the site that showed the most change from natural conditions was Hasana. The lack of effects related to DDT in this study, further illustrates the low sensitivity of high order effects, even in macro-invertebrates that would probably respond quicker than fish communities due to their fast life cycle.

10.3 LABORATORY ANALYSIS

When considering the sub-organismal effects (Chapter 6), specifically with regards to GSI and CF, no changes in the growth of the catfish were noted after exposure to the different environmentally relevant DDT concentrations. Fortunately, Ca and ALP concentrations did

207 Chapter 10

show subtle dose dependent increases in exposed male catfish, while the overall reproductive success of the exposed juveniles indicated that an escalating effect, due to the increasing DDT concentrations (albeit the extent unknown), on the spawning success of the adults may have occurred. To summarise, the results showed that no severe effects were found in adult C. gariepinus exposed to 0.66, 1.36, 2 and 2.72 pg/I DDT concentrations; a possibility of a dose-response relationship was evident suggesting that prolonged duration and increased concentration of DDT exposure may lead to irreversible long term negative effects in C. gariepinus. The juvenile exposures highlighted the accumulative effects of DDT on the reproductive success of C. gariepinus, showing again, like so many other studies, that early life stages of these fish are more sensitive to toxicants than adult fish.

10.4 A SUITABLE SUITE OF BIOMARKERS FOR MONITORING

Upon comparing the biomarker results from the field with the laboratory (Chapter 7), it was evident that the biomarkers that are most able to identify DDT effects included ALP, Ca, GSI, ANCOVA gonads, intersex and CF. These effects were all related to endocrine disrupting effects within fish and the results make a significant contribution toward biomarker data. Specifically, the indirect measures of VTG, including ALP and Ca and the ANCOVA treated gonads, have very little data available worldwide.

Table 10.1. Ranking of biomarkers applicable for future monitoring

ANCOVA ALP Ca Mg Zn PC GSI Intersex CF gonads

Indicative of DDT 3 3 1 0 1 2 2 0 0 contamination Measurable in C. gariepinus 4 4 4 4 4 4 4 0 4

Sensitive (level of biological 4 4 4 0 0 2 2 0 0 complexity) Reliable 3.5 3.5 2 0 2 4 4 4 4

Scientifically sound 2 2 2 2 2 4 4 4 4

Cost effective 2 3.5 3.5 3.5 2 3.5 3.5 2 4

Usable in field 3 3 0 3 0 2 2 3 4 and lab Detectable above natural 2 3 3 0 3 2 2 3 2 fluctuations TOTAL 23.5 26 19.5 12.5 14 23.5 23.5 16 18

RECOMMENDED YES YES NO NO NO YES YES YES YES Ranks: (1) least applicable, (2) moderately acceptable, (3) recommended, (4) highly recommended.

The biomarkers specifically selected for the monitoring and management of DDT have been shown to have a number of advantages over direct monitoring of DDT levels. These being reduced resources, which include time and cost, non-lethal, capable of showing endocrine disruptive effects, ability to indirectly identify DDT contamination and reducing the level of

208 Chapter 10 needed technical expertise (Table 10.1). As was evident the biomarker with the highest ranking throughout all criteria was the Ca, ALP and both gonad condition biomarkers.

10.5 IDENTIFY EFFECTIVENESS OF COMMUNITY LEVEL MEASUREMENTS

A variety of community level methods were assessed to determine if they could be correlated with DDT contamination. In doing so, their effectiveness in measuring changes in the respective communities could also be analysed for recommendation for use in future studies and are each explained in detail in Chapter 7 and 8. In summary it was shown that although no significant relationship between DDT and the community changes were identified, changes and impacts could still be identified using a number of bioassessments in fish and macro-invertebrates, which are recommended for use in monitoring community level disturbances. In the fish communities the methods of choice included informal assessments, diversity indices including the species richness, evenness and alpha-log series diversity index, the FRAI and multivariate analysis. While in the macro-invertebrate communities, the bioassessments that were the most suitable for the measurement of community structural changes included informal assessments, ASPT, diversity indices including species richness, evenness and log series diversity index, as well as the multivariate statistics.

10.6 GENERAL CONCLUSIONS AND RECOMMENDATIONS

10.6.1 Major concerns in the Luvuvhu River catchment

Risk of DDT bioaccumulation and biomagnification

The extremely high concentrations of DDT measured in the male C. gariepinus found in the Luvuvhu River catchment are significant, as it highlights the extent of contamination that is occurring from IRS within the vector control programme. This contamination does not only pose a risk to the health of the fish through biomagnification, but also threatens other aquatic life and humans that readily consume these fish (as seen above). Apart from finding an alternative vector control to DDT, it is essential that areas within the vector control programme are properly managed in order to reduce contamination and exposure to humans and the ecosystem. Furthermore, it is of the utmost importance that humans are educated in ways that would reduce their risk of contamination through fish, although for a more comprehensive discussion on the risks of DDT to humans in the Luvuvhu River catchment refer to Bornman et al. (2009).

Effects of DDT on aquatic biota

In the current study it was shown that despite the high concentrations measured in the study area and the long-term spraying, DDT only induced low level, sub-organismal effects. However, it should be noted that the full extent of the sub-organismal impact may have been

209 Chapter 10

masked with the use of a generally tolerant and hardy species, C. gariepinus. Nevertheless, with continued exposure and lack of appropriate management these effects may increase to levels that will result in irreversible changes in the aquatic integrity, including changes in the more tolerant species. As such, this highlights the importance of initiating a monitoring programme that would support the management of DDT, and its associated effects, within the Luvuvhu River.

Although it is highly recommended to monitor DDT, an alternative malaria control needs to be considered. Recently much emphasis has gone into integrated vector control (IVC) management as an alternative to the more harmful chemical controls that are currently in use (WHO, 2004).

Agriculture and afforestation

The upper reaches of the Luvuvhu River catchment were dominated by extensive agriculture and afforestation practices. It was shown in the present study that these activities may have caused reduced water quantities, increased nutrients from runoff of fertilisers and increased pesticide concentrations, including DDT. Although the initial two impacts have been monitored regularly by Department of Water Affairs (DWA) stations, little monitoring of pesticides and other contaminants have been done. This is of particular concern as this study found that there was contamination of both legal and illegal pesticides, including DDT contamination in the areas upstream from the IRS areas, suggesting the possibility of illegal spraying of DDT, as well as the presence of the banned substance dieldrin, both of which are toxic and characterised as persistent organic pollutants (POPs). It is therefore recommended, particularly in the light that South Africa is a party to the Stockholm convention on POPs (Bouwman, 2004), which restricts/bans the use of a number of pesticides, that the concentrations of pesticides are monitored on a frequent basis, not only for the Luvuvhu River catchment, but also in other aquatic resources within South Africa. Furthermore, farming activities should be more closely monitored for illegal use of pesticides and that fines are enforce where necessary.

Rural village communities

The rural communities in the middle reaches of the Luvuvhu River catchment are largely dependent on the natural resources of the river, such as riparian vegetation and aquatic resources primarily due to the poor reticulation infrastructure such as running water, electricity and refuse removal systems (DWAF, 2004). Their utilisation of the natural resources have been shown (in the present study) to impact the river habitat. That is, at many of the sites it was often observed that the local villages used the aquatic resources for washing, drinking and bathing, whilst the riparian zone was used for cattle grazing, firewood, planting of crops and the river sand for building material. These activities caused many alterations to the natural habitat of the Luvuvhu River including increased bank erosion, destruction to riparian and instream habitats, and possibly increased nutrient enrichment due

210 Chapter 10

to washing and bathing. Many of these can, however, be avoided and it is therefore recommended that several steps be taken to ensure the integrity of the Luvuvhu River catchment, including educating the local communities in the correct subsistence farming methods that would avoid disturbing the riparian habitats and in the sustainable utilisation of their aquatic resources. In addition, the provision of basic services to the rural villages including water, electricity and refuse removal systems as well as assisting in job creation will help to reduce their dependence on the aquatic ecosystems.

These villages are also known to be highly dependent on the fish as a major source of protein in their diet (DWAF, 2004). This was evident at most of the sites in the present study, where the locals were observed fishing using many methods including gill nets, fyke nets, cast nets, rods and seine nets in almost all possible habitats, including the fish ladder at the Xikundu weir. Although the impacts of this on the fish populations could be great, it was unfortunately not accurately identified in this study. It is therefore recommended that more intensive research be done as to the impact of the village communities on the fish population, particularly the larger species such as 0. mossambicus, C. gariepinus, Tilapia spp, and L. marequensis, before an accurate conclusion can be made. Apart from this research, it is also recommended that villages are educated as to practices that ensure more sustainable utilisation of the fish populations with the least amount of impact.

The nutrient enrichment at some of the sites in this study suggested that there may have been sewage contamination within the Luvuvhu River catchment. However, unfortunately there is a gap in the data with regards to this, as organic enrichment was not thoroughly assessed in this study.

10.6.2 Incorporating biomarkers in South Africa

In South Africa, biomarkers as indicators of aquatic impacts have largely been overlooked. The results in this study have highlighted the importance of measuring the sub-organismal effects of pollutants within aquatic ecosystems, using these fairly new assessment procedures as opposed to the community level monitoring that have dominated biomonitoring programme procedures in South Africa since inception (Roux, 2001). Unfortunately although much has been done the last 10 years, there is still a large paucity of data surrounding sub-organismal effects and as such initiating monitoring programmes incorporating biomarkers is difficult. There is increasing interest from water quality managers and therefore it is highly recommended that more research is done to identify biomarkers that can be utilised within biomonitoring programmes. Lastly, and very importantly, the general lack of data regarding biomarkers in South Africa highlights the significance of this project/research in the initiation of incorporating biomarkers within South Africa.

211 Chapter 10

10.6.3 Considerations for biomonitoring in the Luvuvhu River

As highlighted above, in order to adequately manage the spraying of DDT for vector control, it is essential to incorporate a monitoring programme. From the conclusions drawn in the present study a number of recommendations have been compiled that will advise for the creation of a biomonitoring program, specifically within the Luvuvhu River.

Measuring DDT contamination: In the present study it was found that the bioaccumulation of DDT in catfish was a more reliable indication of past exposure and contamination of DDT than sediment and water. Therefore, if contaminants are to be monitored by measuring their concentrations within the catchment, it is recommended that biota be utilised rather than sediment and water.

Xikundu weir: As was shown previously, apart from DDT concentrations, Xikundu generally had the highest concentrations of pesticides. The reason for this was hypothesised as being due to the local conditions being favourable to contamination or nearby spraying of pesticides. Regardless of this, it is recommended that Xikundu be monitored and managed as a high priority site within the water resource management of the Luvuvhu River catchment, in order to prevent future negative consequences that may occur at this site.

Sampling periods: Strong seasonal changes were evident within many of the results in the present study. Therefore, it is suggested that monitoring of DDT spraying in the Luvuvhu River is done bi-annually (one sampling before and one after DDT spraying).

Measuring metals: In the Luvuvhu River the concentrations of metals were generally very low. However, the high Zn concentrations which were measured in the water, sediment and catfish are cause for some concern. Further analysis into the cause of these concentrations is therefore recommended and the continuous monitoring of Zn within all phases of the ecosystem.

Biomonitoring DDT effects: As was shown in this study, the bioaccumulation of contaminants could not be utilised as an indicator of organismal effects and responses. This highlights the need for the biomonitoring of effects in organisms in conjunction with bioaccumulation monitoring.

Of all the effect orientated methodologies measured in this study including biomarkers (sub- organismal effects), fish community assemblages and macro-invertebrate community assemblages, it is recommended that future biomonitoring of DDT specific effects only incorporate biomarkers as the first line of assessment. This is primarily because the concentrations of DDT present within the catchment only induced measurable effects at a sub-organismal level of biological organisation, which was in contrast to the community level effects that were seldom attributed to DDT contamination. The biomarkers of choice would

212 Chapter 10 be calcium and ALP as an indirect measure of VM in the plasma (preferably after further validation of these alternative methods), as well as GSI, gonad mass (ANCOVA treated data), intersex and CF.

If community structures are to be assessed in either the fish or macro-invertebrate assemblages a number of methods have been shown to use in unison. For the fish, this includes, informal assessments, diversity indices, FRAI and multivariate analyses. Whilst for the macro-invertebrate assessments, the methods included, the informal assessments, diversity indices, ASPT and multivariate analyses.

Species selection for biomarker monitoring: As was shown, the tolerant species C. gariepinus showed very subtle responses to DDT, despite the high bioaccumulated concentrations. Further research is therefore recommended into assessing sub-organismal responses using biomarkers in more sensitive and susceptible fish species, such as L. marequensis or perhaps even C. pretoriae.

10.6.4 Recommendations for future biomonitoring in IRS areas

It is recommended that before a monitoring programme is initiated within an area, a preliminary assessment of the study area is done on the water, sediment and biota contamination and effects. This is essential to gain insight into the current extent (baseline values) of contamination and effects, which will allow for the selection of appropriate techniques at appropriate levels of complexity, the most suitable species, and the identification of sites of concern, additional contaminants and sources of impacts (Phillips and Rainbow, 1993).

10.6.5 Significance of laboratory work

Although the field work formed the majority of this thesis, the laboratory exposures were equally important. The laboratory exposure results significantly contributed toward identifying the responses of fish to DDT concentrations as they were far more conclusive than the results obtained in the field. This is because there was a reduced number of influencing factors present in the more controlled laboratory environment. Consequently, these tests provided valuable contributions in determining baseline levels of biomarkers for species C. gariepinus. Furthermore, these exposure studies were significant in that the results contributed toward identifying the dose-response of DDT in fish, as shown in Section 10.3

Even though the juvenile studies did show susceptibility to DDT, the results were still relatively inconclusive and it is therefore recommended that in depth future studies be done which concentrate on the effects of DDT on juveniles. It is furthermore suggested that these studies use smaller indigenous species that are less susceptible to the exposure setup and have shorter live cycles in order to obtain more conclusive results.

213 Chapter 10

10.6.6 Additional limitations of this study and recommendations for future research

Although biomarkers were only assessed in the last two sampling seasons, it was sufficient to determine the sub-organismal effects of DDT. This was because the first two sampling seasons had generally lower DDT contamination and since the sub- organismal effects were generally low and more subtle, it is suspected that the effects would have been negligible in these first two seasons. Nevertheless, this was a limitation to this study.

The inefficiency of sampling at some of the sites influenced some of the fish community results. Although after these sampling inefficiencies were taken into account during the interpretation of fish data, impacts could still be highlighted.

As only the male C. gariepinus were assessed in this study, it is recommended that future studies evaluate the effects of DDT in female C. gariepinus in South Africa.

The sewage contamination (organic enrichment within water) and the rate of sedimentation, which were hypothesised to induce impacts within the Luvuvhu River, were not assessed in the present study. It is therefore recommended that future studies in the Luvuvhu River assess these influencing variables.

Excessive levels of oestrogen, estradiol and testosterone are common measures of endocrine disruption, but were not measured in the present study as these methods are rather costly and one of the aims of this study was to identify cost effective methodologies. It is recommended that these methodologies are quantified in C. gariepinus (or other suitable indicator species) exposed to DDT contamination in South Africa.

The transport of DDT from the huts to the river was unfortunately not assessed in this study. Knowledge of the means of transportation and the associated routes are essential in the effective management of DDT within the catchment, and it is therefore recommended that future studies assess the distribution and transportation of DDT in South Africa.

As was previously mentioned, once the VTG-ELISA methodology is functional it is recommended that the indirect measures of VTG, including Ca and ALP, are validated.

Since it is hypothesised that the population structures are affected by the fishing habits of the local communities, it is suggested that future studies attempt to identify the extent of the impact of this fishing on the natural fish populations within the Luvuvhu River.

214 Chapter 10

The sub-organismal effects of DDT on macro-invertebrates are largely unknown, with only the community level of effects commonly measured. As was seen macro- invertebrate communities are not sensitive to lower chronic concentrations of DDT, thereby highlighting the need to research macro-invertebrate responses at lower level of biological complexity.

Although biomagnification forms a significant contribution in the transport of DDT in the aquatic ecosystem, these mechanisms were not assessed in the present study. It is recommended that future research goes into the distribution of DDT in various levels of the food chain in the Luvuvhu River. Not only is this research important as no current data is available in South Africa, but also because the conclusions would provide both health and water quality managers with better insight into the risks DDT poses on various levels of the food chain, including the risks on humans.

10.7 REFERENCES

Bornman MS, Van Vuren JHJ, Barnhoorn IEJ, Aneck-Hahn N, De Jager CJ, Genthe B, Pieterse GM and Van Dyk JC. 2009. Environmental exposure and health risk assessment in an area where ongoing DDT spraying occurs. Water Research Commission (WRC) Report No. K5/1674.

Bouwman H. 2004. South Africa and the Stockholm convention on persistent organic pollutants. S.A. J. Sci. 100: 323-328.

Department of Water Affairs and Forestry. 2004. Luvuvhu/Letaba Water Management Area (WMA): internal strategic perspective. Prepared by Goba Moahloli Keeve Steyn (Pty) Ltd in association with Tlou and Matji, Golder Associates Africa and BKS on behalf of the Directorate: National Water Resource Planning. DWAF Report No. P WMA 02/000/00/0304.

Phillips DJH and Rainbow PS. 1993. Biomonitoring of trace aquatic contaminants. Chapman and Hall.

Roux, DJ (Ed). 2001. Development of procedures for the implementation of the National River Health Programme in the province of Mpumalanga. WRC Report No. 850/1/01. Water Research Commission, Pretoria.

World Health Organisation. 2004. Global strategic framework for intergrated vector control. Obtained from http://whqlibdoc.who.int/ho/2004/WHO CDS CPE PVC 2004 10.pdf. 1November2009.

215 Appendix 1

Appendix 1 Chemical and physical identity of DDT obtained from ATSDR 2002*

Characteristic p,p'-D DT pp.-ODE p,p'-DDD

syrionymts) 4,4"-DDT; 1,1,1-trichtoro-2,2-bis 4,4 '-DDE; dicihlorodiphenyl- 4 ,4'-0 Da, ODD; 1 ,1 -dichtoro-2,2- (p-chlorophenyl)ethane; dichloro- dichloroethane; 1,1-dichloro- bis(p-chlorophenyl)ethane; 1,1-his diphenA trichloroethane; DDT; 1,1'- 2,2-bis(p-chtorophenyl) ethylene; (4-chlorophenyl)-2,2-dichtoroethane; (2,2,2-trichloroethylidene)bis(4-chloro- 1,1 '-(Z 2-dichloroethylidene)bis(4- TDE; tetrachlorodiphenylethane benzene); -o-bis(p-chlorophenyI)- chlorobenzene); ODE 0, B, p -trichloroethane

Registered trade names) Genitor, Anofex, Detoxan, Neocid, No data ODD; Rothane; Dilene, TDE Gesarol, Pentachtorin, Dicophane, Chlorophenothane' Chemical formula Chemical structure

Identification numbers: CAS registry 50-29-3 72-55-9 72-54-8 NIOSH RTECS KJ3325000 KV9450000 K10700000 EPA hazardous waste U061 No data U060 OHM/TADS 7216510 No data 7215098 DOT/UN/NAtIMCO shipping IMCO 6.1; UN2761 No data NA2761; TOE HSDB 200 1625 285 NCI C00465 C00555 C00475

Characteristic o,p'-D DT nji-DDF 0,p'-n1n

Synonyn (s) 4A -DDT 1.1.1-trichloro-2- 2.4'-DDE; 1,1-dichlom-2- 2.4-1)DD MtotEtne: c,p'-DI3D: io-chloropherly1)-2-(p-ehbro) ny1)- (o-ctforopteny1)-2-(p-chlorophenyl) 1,1-di chloro2-{o-chicropheyb- ethane; al-diehicancipterriltnchbro- ethylene; 1 -choro-2-12,2-tichloro-1- 2-(p-ctilorophenyl)ethane; c,p'-TCE; ethane (4-chorophenygetheny benzene Choditane; 2-(c-chloropheny1)- 2-0-chiotopnenyft-1,1-0 cribroetnane Rte3in*reti trade names) No data No data I yaraciten

Che.rrical formula cw-i9cIa CyH CIS CullaCt4 Chamiew etucture

F- ') C .110,I

tesenmIcatIon monams: CAS tegialty '89-02-8 2=4:14-82-6 53-19-0 NICGI I rrccs ito data Na data KH 73E0000 EPA lucardoua wcatc Ito data Na data No data OHM/TADS No data No data No data Pfirtt IN/14 AnArm nhinring tin data No data Nn data HSDB No data No data 3240 NCI No data No data C04933

•ka mrornaton =mired son HSDB 1998a, .1399b. 1929c, 1992z, or idcwan and Neal 1922 e)xept wlere noted. olossaen etai. lest

CAS= Chemical Abstacrs Santee: CIOT/UN,NNIMCO= Cebarblent of Trolsportabcntl2nited Nations'Noth Atrericalntematonal Maritime Danaercus Gocds PA. Environmental Protection Acency; HSDB = Hazardcus Substances Data Sank; NCI = Najaral Career Insets*: NICSH = Natiarul Institute or Dccupalcoal Safety and Health: CHS21F135 = tk1 and Hazardous Bratenasr 1 era-nrcal Asestarce pea :_,.,sterrot IiKZ = Registry at toxic Ideas C eienvca albs:an:es

*Agency for toxic substances and disease registry (ATSDR). 2002. Toxicological profile for zinc. CAS# 7440-66-6. Obtained from official ATSDR website: http://www.atsdr.cdc.gov/toxpro2.html . Retrieved 1/2/2008.

216 Appendix 1

Property p,p'-DDT pp'-DDE p,p'-DDD

Molecular weight 354.49' 31603" 320.05' Color Colorless crystals, White Colorless crystals, white wh ite powdef powder

Physical state Solid* Crystalline solid Solid Melting palnl 109 89 • C" 109-110*e" Boiling point Decomposes 336•V . 350 • Ce Density 0.943-0.99 g/cral No data 1.385 gicrri3 Odor Odorless or weak No data Odorless aromatic adore

Odor threshold: Water 0.35 mg/kg" No data No data Air ND data No data No data Solubility Water 0.025 mg/L at 25 ..C° 0.12 mg/L at 25 • C' 0.090 mgtt at 25 • C' Organic solvents Slightly soluble in Lipids and most No data" ethanol, very soluble organic solvents in ethyl ether and acetone'

Partition coefficients: Log K„ 6.91° 6.51' 6.02° Log Km 5.1e 4.TC 5.181 Vapor pressure 1.60x1e at 20 • C, 6.Dx105 at 25 • C, torr° 1.35x104 at 25 • C, tort tore

Henry's law constant 8.3x105 at -m'irriol" 2.1 x105 atm- m3frnol° 4.0x105 atrn -m3/mol" Autoignition temperature No data No data No data

Flashpoint 72.2-77.2 •C No data No data Flammability limits No data No data No data

Conversion ?factors ppm(vIv) to mg/m' in air at 20 •C Nat applicable' Not applicable" Not applicable' mg/m3 to ppm(v,v) rn air at 20 •C Not applicable Not applicable Not applicable Explosive 'Emits Na data No data No data

217 Appendix 1

Property o,poi-DDT o,p'-DDE o,p'-17DDD

Moecu:ar weight 354.4P 318.033 320,05 6 Color White crysteine No data fkto data powder' Physical state Solid` No data Solid Matting point 74.2 • C.' No data 76-78 • C Boiling point No data No data No data Density 0.98-0.99 git cm.= No data No data Odor Odorless or weak No data No data aromatic adore Odor threshold: Water No data Na data No data Air No data No data No data Solubility: Water 0.085 rngil_ at 25 • et 0.14 mg/ at 25 • Cb 0_1 mg.IL at 25 • C° Organic solvents No data No data Soluble in ethanol, isooctane, carbon tetrach foridet Partition coefficients: Log ue 6.791. 6.0013 5_87D Log IC, 5.351 5.191 5.191 Vapor pressure 1.1x10''at 20 •C, 6.110-6 at 25 • C, tore 1.94x10-6 at 30 • C, tare' tom' Henry's law constant 5.9x10'' atm-rOmolb 1.8x10` atm-m3frnalb 8 .17x11134 atm-m llmor Autoignition temperature No data No data No data Flashpoint No data No data No data Flammability limits No data No data No data Conversion /factors ppin(v/v) to rngfrre in air at 20 •C Not applicable Not applicable Not applicable mgfrn3 to ppm(vIv) M. air at 20 •C Not applicable Not applicable Not applicable Explosive limits No data No data No data

°All information obtained from HSDB 1999a, 199gb. 19r09o, 1999O unless otherwise noted 'Howard and Meyfan 1997 Werschueren 1985 aNtOSH 1985 'Sax 1979 tide 1998 gChemical is expected to be soluble in most crean5c compounds. 'Swann et at 1981 'Sal lejic 1984 IMeylan vial 1992 (values estimated from a fragment constant method) Exists partially in paxtioalate farm in air. Convers.ion factors are only applicable for compounds that are entirely in the vapor phase.

218 Appendix 2

Appendix 2 Assessment of AChE as indicators of organophosphate and carbamate contamination

A2.1 INTRODUCTION

Organophosphates and carbamate contamination, primarily from agricultural spraying, have both been shown to induce endocrine disruption (Kime, 1998). In order to discount the influence of these insecticides on the fish endocrine system, it is necessary to identify the extent of there contamination. However, in the present study both organophosphates and carbamates were not quantified. This was mainly due to the historical data showing low to no concentrations in the Luvuvhu River and the quantification being very expensive. Nevertheless, the inhibition of acetylcholinesterase (AChE) can be utilised as an alternative, with many studies recommending the utilisation of this neural enzyme as a biomarker of exposure in fish including C. gariepinus (Somnuek et al., 2007; Parveen et al., 2004; Slabbert et al., 2004; Hyne et al., 2003). Therefore, the aim of this study was to assess organophosphate and/or carbamate using the AChE biomarker in muscle of C. gariepinus. As no studies have shown positive dose-response relationships in C. gariepinus fish species, the extent of contamination could not be assessed. Consequently, only spatial and temporal differences could be evaluated.

A2.2 METHODS

Muscle tissue stored in Hendrikson stabilising buffer solution at -80°C was thawed before it was tested for AChE activity using the method of Ellman et al. (1961) as described by Venter et al. (2003). While keeping the thawed muscle at 4°C it was homogenised thoroughly in three volumes of buffer (0.25M sucrose, 50 mM Tris-HCI, pH 7.4) and centrifuged at 11 000 rpm for 20 minutes in a Sorvall RMG 14 rotor from Du Pont. The resulting supernatant containing the AChE was then reacted with a reaction mixture of 10 pl 10 mM Ellman's reagent, 10 pl 30 mM s-Acetylthiochloine iodide and 210 pl 0.09 M potassium phosphate (pH 7.4), which was allowed to incubate for 5 minutes at room temperature before 5 pl of sample was added. For the controls, 5 pl of distilled water was added instead of sample. The absorbance was recorded at 405 nm in two minute intervals over a period of 12 minutes on the spectrophotometer ECX800 Universal from Biotek Instruments. The results were calculated as nmol.min -l .mg-l protein content. The total protein content was quantified at 630 nm using Bradford's reagent (Bradford, 1976) and bovin serum albumin (BSA) standard. The statistical analysis of AChE data was the same as that mentioned in Chapter 6.

219 Appendix 2

A2.3 RESULTS AND DISCUSSION

Although most studies have shown that the inhibition of AChE in fish is a suitable biomarker for diagnosing organophosphate and/or carbamate exposure indirectly, some studies have shown that other contaminants can also affect AChE levels within fish. A recent study by Ballesteros et al. (2009) showed in Jenynsia multidentata fish that endosulfan inhibits activity of AChE in muscle tissue, while a couple of other studies have shown in both fish and mussel species the influence of chromium (VI), detergents (sodium dodecyl sulphate) and other heavy metals (Frasco et al., 2005; Flammarion et al., 2002; Guilhermino et al., 2000; Guilhermino et al., 1998). In the present study, all of these contaminants excluding the detergents were measured within the water and sediment and none of them could be directly correlated with the AChE activity measured in the fish. Then, since no known studies have analysed the effects of detergents on AChE inhibition in vertebrates, the organophosphates and carbamates were assumed to be the only contaminants present in the Luvuvhu River that could significantly influence AChE in muscle tissue.

PAlbasini Nandoni M Xikundu

LF '07 HF '08

Figure A2.1. AChE indicating the exposure of organophosphorous and carbamate contamination in C. gariepinus.

Although comparisons with other studies were difficult due to the differing conditions (tissue, technique and species) present, evaluation of temporal and spatial variations could be assessed. Upon assessment of the sites and season differences, no significant differences were observed (Figure A2.1). Nevertheless it was evident that Xikundu in the low flow of 2007 had the lowest AChE values, with the remaining sites all similar except for Nandoni Dam. This suggested that Xikundu catfish were more stressed than those at the other sites and exposed to the highest concentrations of organophosphates and carbamates. This was surprising as this site was further away from the agricultural hub than the other two sites, but showed similar spatial tendencies to the other pesticides present in the river. As can be seen in Chapter 5, the possible reason for increased pesticide contamination at Xikundu could be due to the physical habitat conditions that may have allowed for increased pesticide absorption into the various abiotic/biotic phases (i.e. sediment or biota) of this site.

220

Appendix 2

Table A2.1. Pearson's correlation with natural factors that may influence AChE. Age Body length Body mass Gonad mass Maturity

AChE 0.05 -0.05 0.13 -0.14 -0.31

As for seasonal differences there were no trends observed in the figure. This lack of seasonal difference was further supported by the lack of significant correlations (P<0.05) with seasonal fluctuating factors such as gonad mass and maturity (Table A2.1). In addition to the seasonal factors other biotic factors were also assessed including age, body length and body mass and were also not shown to influence AChE activity. Other studies however contradicted these results by showing a significant link between AChE activity and fish body length (Mulkievicz et al., 2007; Flammarion et al., 2002; Chuiko et al., 1997). The probable reason for this was because the length range was very small in the present study in comparison to the other studies.

From the spatial and temporal analysis the AChE inhibition was only higher at Xikundu in the low flow. This suggests that if there is an increased endocrine disruption measured in C. gariepinus at this site, it may be also influenced by organophosphate and/or carbamate. However, when correlated with the endocrine disruptive effects no significant correlations were observed (Table A2.2)

Table A2.2. Pearson's correlation between AChE and endocrine disruptive biomarkers CF GS! (mature) Mg Ca Alp Zn

AChE 0.04 -0.01 -0.07 0.193 0.08 -0.08

A2.4 REFERENCES

Ballesteros ML, Durando PE, Nores ML, Diaz MP, Bistoni MA and Wunderlin DA. 2009. Endosulfan induces changes in spontaneous swimming activity and acetylcholinesterase activity of Jenynsia multidentata (Anablepidae, Cyprinodontiformes). Environ. Poll. Article in press on 16 March 2009. DOI: 10.1016/j.envpol.2009.01.001.

Bradford MM. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilising the principle of protein-dye binding. Analyt. Biochem. 72: 249- 254.

Chuiko GM, Zhelnin Y and Pod'gornaya VA. 1997. Seasonal fluctuations in brain acetylcholinesterase activity and soluble protein content in Roach (Rutilus rutilus L.): a freshwater fish from northwest Russia. Comp. Biochem. Physiol. 117(C): 251-257.

221 Appendix 2

Ellman GL, Courtney KD, Andres V and Featherstone RM. 961. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharm. 7: 88-95.

Flammarion P, Noury P and Garric J. 2002. The measurement of cholinesterase activities as a biomarker in chub (Leuciscus cephalus): the fish length should not be ignored. Environ. Poll. 120: 325 — 330.

Frasco MF, Fournier D, Carvalho F and Guilhermino L. 2005. Do metals inhibit acetylchloinesterase (ACHE)? Implementation of assay conditions for the use of AChE activity as a biomarker of metal toxicity. Biomarkers. 10: 360-375.

Guilhermino L, Barros P, Silva MC and Soares AM. 1998. Should the use of inhibition of cholinesterases as a specific biomarker of organophosphate and carbamate pesticides be questioned. Biomarkers. 3: 157-163.

Guilhermino L, Lacerda MN, Nogueira AJA and Soares AMVM. 2000. In vitro and in vivo inhibition of Daphnia magna acetylcholinesterase by surfactant agents: possible implications for contamination biomonitoring. Sci. Tot. Environ. 247: 137-141.

Hyne RV and Maher WA. 2003. Invertebrate biomarkers: links to toxicosis that predict population decline. Ecotox. Eniron. Saf. 54: 366 — 374.

Kime DE. 1998. Endocrine disruption in fish. Kluwer Academic publishers. pp. 25-27.

Mulkiewicz E, Napierska D and Podolska M. 2007. Integrated use of biomarkers in flounder Platichthys flesus from the Polish coastal area of the Baltic Sea. ICES CM conference poster.

Parveen M, Kumar S and Singh P. 2004. Kinetic analysis of the in vitro inhibition of liver AChE in air breathing fish Clarias batrachus (Linnaeus, 1758). EU J Fish. Aqua. Sci. 21: 143-144.

Slabbert JL, Venter EA, Joubert A, Borster A, de Wet LPD, van Vuren JHJ, Barnhoorn I, Damelin LH. 2004. Biomarker assays for the detection of sub-lethal toxicity in the aquatic environment — a preliminary investigation. Water Research Commission (WCR) Report no. 952/1/04.

Somnuek C, Cheevaporn V, Saengkul C and Beamish FWH. 2007. Variability in acetylcholinesterase upon exposure to chlorpyrifos and carbaryl in hybrid catfish. Sci. Asia. 33: 301-305.

222 Appendix 2

Venter EA, Slabbert JL, Jouber A, Vorster A and Barnhoorn I. 2003. biomarker assays for the detection of sub-lethal toxicity in fish — operation manual. Water Research Commission (WRC) Report no. 952/2/03.

223 ▪

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2 (I NF- X ~ Appendix 5

Appendix 5 The spatial and temporal representation of DDT concentrations in the adipose tissue of C. Gariepinus

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228 Appendix 6

Appendix 6 Expected species habitat preferences in the Luvuvhu River (DWAF, 2007)

SPECIES VELOCITY-DEPTH PREFERENCE COVER PREFERENCE HIGH - VERY HIGH (>3)

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AMAR FALSE FALSE 4.40 FALSE FALSE 3.90 4.20 FALSE FALSE AMOS 3.40 3.30 3.40 FALSE FALSE 4.10 4.90 FALSE FALSE AURA 4.60 4.60 FALSE FALSE FALSE FALSE 5.00 FALSE FALSE BANN FALSE FALSE 5.00 FALSE FALSE FALSE FALSE FALSE 4.70 BEUT 4.30 4.70 FALSE FALSE 4.10 4.40 4.10 FALSE FALSE BFRI FALSE FALSE 4.70 4.30 FALSE FALSE FALSE FALSE 4.00 BIMB FALSE FALSE 4.70 FALSE FALSE FALSE FALSE FALSE 4.70 BLIN FALSE FALSE 3.70 4.70 FALSE FALSE 3.90 FALSE FALSE BMAR 4.10 4.40 4.40 3.40 FALSE FALSE 4.50 FALSE 4.10 BNEE FALSE FALSE 3.30 4.70 3.90 3.30 4.40 FALSE FALSE BPAU FALSE FALSE 3.90 3.90 4.20 FALSE FALSE 3.60 3.50 BRAD FALSE FALSE 4.70 5.00 4.70 FALSE FALSE FALSE FALSE BTOP FALSE FALSE 3.30 4.30 4.70 FALSE FALSE FALSE FALSE BTRI FALSE FALSE 3.90 3.20 3.90 FALSE FALSE FALSE FALSE BUNI FALSE FALSE 5.00 4.30 4.60 FALSE FALSE FALSE FALSE BVIV FALSE FALSE FALSE 4.80 4.90 FALSE FALSE 3.20 FALSE CCAR FALSE FALSE 4.70 3.20 FALSE FALSE FALSE FALSE FALSE CGAR FALSE FALSE 4.30 3.40 FALSE FALSE FALSE FALSE FALSE CPAR 4.20 4.90 FALSE FALSE FALSE FALSE 4.90 FALSE FALSE CPRE 4.30 4.90 FALSE FALSE FALSE FALSE 4.90 FALSE FALSE CSWI FALSE 4.70 FALSE FALSE FALSE FALSE 4.90 FALSE FALSE GCAL FALSE FALSE FALSE 4.70 FALSE FALSE 4.90 FALSE FALSE GGIU FALSE FALSE FALSE 4.60 FALSE FALSE 4.90 FALSE FALSE HVIT 3.60 FALSE 4.70 FALSE 3.40 FALSE FALSE FALSE 4.90 LCON 5.00 FALSE 5.00 FALSE FALSE FALSE 5.00 FALSE 3.40 LCYL 3.40 4.80 FALSE FALSE FALSE FALSE 4.90 FALSE FALSE LMOL 3.30 4.30 3.70 FALSE FALSE FALSE 4.70 FALSE FALSE LROS FALSE FALSE 4.70 FALSE FALSE FALSE 5.00 FALSE FALSE LRUD FALSE FALSE 4.70 FALSE FALSE FALSE 4.70 FALSE FALSE MACU FALSE FALSE 4.30 4.30 3.10 FALSE FALSE FALSE 4.00 MBRE FALSE FALSE 4.30 4.20 FALSE FALSE FALSE FALSE 5.00 MMAC FALSE FALSE 4.20 3.70 3.80 5.00 FALSE FALSE FALSE MSAL FALSE FALSE 4.50 FALSE 3.10 FALSE 3.10 3.20 FALSE OMOS FALSE FALSE 4.60 3.80 FALSE FALSE FALSE FALSE 3.90 OPER 3.20 FALSE 3.30 FALSE FALSE FALSE FALSE FALSE 4.40 PCAT FALSE FALSE 4.70 4.30 3.30 5.00 FALSE FALSE FALSE PPHI FALSE FALSE FALSE 4.30 4.50 3.20 FALSE FALSE FALSE SINT FALSE FALSE 5.00 FALSE FALSE FALSE FALSE FALSE 4.70 SZAM FALSE FALSE 5.00 FALSE FALSE 5.00 FALSE FALSE FALSE TREN FALSE FALSE 4.90 3.90 4.30 FALSE FALSE 4.10 FALSE TSPA FALSE FALSE FALSE 4.30 4.50 FALSE FALSE 3.60 FALSE

229 Appendix 6

SPECIES FLOW INTOLERANCE TOLERANCE: MODIFIED PHYSICO-CHEM a a M W A N A T. - ' w 8 30 3 0 3c v T. .-. 0 CI' 5

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230 Appendix 6

SPECIES MIGRATION

n twee be

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it 8 reac w ies ec Sp AMAR FALSE FALSE 5.00 3.00 >100km (Up to 100 miles) AMOS FALSE FALSE FALSE 3.00 Up to watershed, >100km AURA 1.00 FALSE FALSE 3.00 Very local BANN FALSE 3.00 FALSE 3.00 Far (50km?) BEUT FALSE 3.00 FALSE 3.00 Local BFRI FALSE 3.00 FALSE 3.00 Far (50km?) BIMB FALSE 3.00 FALSE 3.00 Local - restricted to Lowveld BLIN FALSE 3.00 FALSE 3.00 Local BMAR FALSE 3.00 FALSE 3.00 Far (100km?) BNEE 1.00 FALSE FALSE 3.00 Very local movement BPAU FALSE 3.00 FALSE 3.00 8km reported / specialist thinks much further (50km) BRAD FALSE 3.00 FALSE 3.00 Local BTOP FALSE 3.00 FALSE 3.00 Far (50km?) BTRI FALSE 3.00 FALSE 3.00 Far (50+km?) BUNI FALSE 3.00 FALSE 3.00 Far (50km?) BVIV FALSE 3.00 FALSE 3.00 0-10km CGAR FALSE 3.00 FALSE 3.00 Long distances CPAR FALSE 3.00 FALSE 3.00 Local CPRE FALSE 3.00 FALSE 3.00 Local CSWI FALSE 3.00 FALSE 3.00 Local GCAL 1.00 FALSE FALSE 3.00 Local GGIU 1.00 FALSE FALSE 3.00 From the coast - might breed in estuaries and move up into Lowveld HVIT FALSE 3.00 FALSE 3.00 Local - restricted to Lowveld LCON FALSE 3.00 FALSE 3.00 up to 100km LCYL FALSE 3.00 FALSE 3.00 Far (50km?) LMOL FALSE 3.00 FALSE 3.00 Far (50km?) LROS FALSE 3.00 FALSE 3.00 Far (50km?) LRUD FALSE 3.00 FALSE 3.00 Far (50km?) MACU FALSE 3.00 FALSE 3.00 50 km MBRE FALSE 3.00 FALSE 3.00 Local MMAC FALSE 3.00 FALSE 3.00 Far (50km?) OMOS FALSE 3.00 FALSE 3.00 0-20km OPER FALSE 3.00 FALSE 3.00 Local (10-20km) PCAT FALSE 3.00 FALSE 3.00 Far (50km?) PPHI 1.00 FALSE FALSE 3.00 8km reported SINT FALSE 3.00 FALSE 3.00 Far (50km?) SZAM FALSE 3.00 FALSE 3.00 Further than local (10 km) TREN FALSE 3.00 FALSE 3.00 8km reported TSPA FALSE 3.00 FALSE 3.00 8km reported

231 Appendix 7

Appendix 7 Rainfall before and during study as obtained from Weather SA

Oct-06 Rainfall Mar-07 Rainfall Oct-07 Rainfall Feb-08 Rainfall (mm) (mm) (mm) (mm) 14 days prior to sampling trip 2006/09/18 0.0 2007/02/19 0.4 2007/10/08 0.0 2008/01/28 0.0 2006/09/19 0.0 2007/02/20 0.0 2007/10/09 0.0 2008/01/29 0.0 2006/09/20 0.0 2007/02/21 0.0 2007/10/10 1.0 2008/01/30 0.0 2006/09/21 0.0 2007/02/22 0.0 2007/10/11 0.8 2008/01/31 0.0 2006/09/22 0.0 2007/02/23 0.0 2007/10/12 0.0 2008/02/01 0.0 2006/09/23 0.0 2007/02/24 0.0 2007/10/13 0.0 2008/02/02 0.0 2006/09/24 0.0 2007/02/25 42.2 2007/10/14 0.0 2008/02/03 0.0 2006/09/25 0.0 2007/02/26 0.0 2007/10/15 10.2 2008/02/04 0.0 2006/09/26 0.0 2007/02/27 2.1 2007/10/16 0.1 2008/02/05 0.0 2006/09/27 0.0 2007/02/28 0.0 2007/10/17 0.0 2008/02/06 0.0 2006/09/28 0.0 2007/03/01 0.0 2007/10/18 0.0 2008/02/07 0.0 2006/09/29 0.0 2007/03/02 0.0 2007/10/19 0.0 2008/02/08 1.3 2006/09/30 0.0 2007/03/03 0.0 2007/10/20 0.0 2008/02/09 0.0 2006/10/01 0.0 2007/03/04 0.0 2007/10/21 0.0 2008/02/10 0.0 During sampling trip 2006/10/02 0.0 2007/03/05 0.0 2007/10/22 0.0 2008/02/11 0.0 2006/10/03 0.0 2007/03/06 0.0 2007/10/23 0.0 2008/02/12 0.0 2006/10/04 0.0 2007/03/07 0.0 2007/10/24 5.4 2008/02/13 0.1 2006/10/05 0.8 2007/03/08 0.0 2007/10/25 5.9 2008/02/14 1.5 2006/10/06 0.0 2007/03/09 0.0 2007/10/26 2.0 2008/02/15 0.3 2006/10/07 0.0 2007/03/10 0.0 2007/10/27 0.0 2008/02/16 0.0 2006/10/08 0.0 2007/03/11 0.0 2007/10/28 0.0 2008/02/17 0.0

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