an assessment of the effects of sugar mill activities …

AN ASSESSMENT OF THE EFFECTS OF SUGAR MILL ACTIVITIES ON THE ECOLOGICAL INTEGRITY OF THE MVOTI AND AMATIKULU RIVERS,

KWA-ZULU NATAL

by

ALEXANDRA VIRGINIA CARMINATI

DISSERTATION

SUBMITTED IN FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE

MAGISTER SCIENTAE

IN

AQUATIC HEALTH

in the

FACULTY OF SCIENCE

at the

UNIVERSITY OF JOHANNESBURG

SUPERVISOR: PROFESSOR V. WEPENER

March 2008

SUMMARY

The Mvoti River is referred to as a ‘working river’ in that it is highly utilized and

developed. It is also, however, in a severely degraded state according to recent

ecological assessments that were carried out on the Lower Mvoti River in 2000 and 2005.

Previously, this was mostly attributed to the specific activities related to a pulp and paper

mill. However, findings of the abovementioned studies indicated that there are multiple

stressors present in the system and to derive the combined effect of stressors in an

environment affected by multiple activities, a characterization of the different

contributing activities is required. Thus, this assessment was undertaken to determine the

impact of one of the contributing stressors, i.e. sugar milling activities on the ecological

integrity of the Mvoti River. The survey assessments, incorporating local and

international accredited methods and techniques, were carried out over a high and low

flow period during 2006. A BACI (before-after controlled impact)-research design was

applied in this study. Four sites were selected on the Mvoti River, up and downstream of

the Glendale Distillery (GDR and GDS) and the Ushukela Milling Company (USR and

USS). The former sites were selected to assess the impact of a sugar mill alone and the

latter to determine the combined effects of sugar milling and pulp and paper activities.

Additionally, 2 sites were selected on the Amatikulu River, up and downstream of the

Amatikulu Mill (AR and AS), to assess the effect of the sugar milling activities alone.

This study consists of two components, namely the abiotic driver component and the

biotic response component. The abiotic driver component involved the analysis of water

quality, sediment and habitat quality in which physico-chemical variables of water and

sediment were carried out during high and low flow periods and the habitat indices,

Habitat Quality Index (HQI) and Integrated Habitat Assessment System (IHAS) were

implemented. The biotic response component involved the assessment of the

macroinvertebrate and fish community structures, as well as the determination of acute

and chronic toxicity and mutagenicity resulting from exposure to mill effluent. The

macroinvertebrate communities were assessed using the South African Scoring System

Version 5 (SASS5) index and the fish communities were assessed using the Fish

i

Assemblage Integrity Index (FAII). The toxicological assessments were carried out via

the use of the Direct Estimation of Ecological Effect Potential, or DEEEP, methodology,

and biomarkers.

The water quality, sediment and biomarker data were analysed using multivariate

statistical analyses and the macroinvertebrate and fish data were analysed using both

univariate and multivariate statistical analyses. Univariate diversity indices,

distributional k-dominance plots, hierarchical clustering and non-metric dimensional

scaling (NMDS) was done using Primer Version 6 and the Principle Component Analysis

(PCA) and Redundancy Analysis (RDA) were performed using Canoco Version 4.5.

Multivariate analysis was used to determine any spatial and temporal trends occurring

among the sites in the study and the environmental variables responsible for these trends.

Univariate diversity indices were used to indicate species diversity and component

species distribution.

The sugar milling activities have numerous effects on the water, sediment and habitat

quality of the systems. Effluent discharge and irrigation, as well as use of fertilizer have

resulted in organic pollution and the increase of nitrate, sulfate, chloride and phosphate

ions. This in turn depletes the oxygen content and increases the conductivity of the water

body. These effects are more prominent below the dual impact of the Sappi Stanger and

Ushukela Sugar mill. A very low content of organic material was found at all sites

except the one below the pulp and sugar mill, which has a very high organic content.

Neither of these two extremes have a positive effect on the biota as a low organic content

means less nutrients and a high organic content, less oxygen. Habitat quality is impacted

by water abstraction by the mills which leads to the deposition of sediment in the system

and loss of valuable habitat for the biota. The removal of riparian vegetation due to

sugarcane farming has lead to bank erosion, thus encouraging siltation. The IHAS and

HQI indices showed that habitat conditions are better during low flow due to more habitat

availability and the greatest impact occurs downstream of the pulp and sugar mill.

ii

The statistical analysis of the fish data indicated that the species abundance at Sites USR

and USS were lower than Sites GDR and GDS during both high and low flows and this is

probably due to the poor water quality occurring at the Ushukela sites. As for spatial and

temporal analysis, it was found that that the fish community structure at the sites at each

mill was different, but flow periods did not have an influence. The spatial trend indicates

that the Glendale sites are in one group, the Ushukela sites in another and the Amatikulu

sites in yet another. This is mainly due to the different quality of water occurring at the

Glendale and Ushukela sites and the change in community structure at the Amatikulu

sites as a result of a massive flood that occurred in 2006. The dual impact of the pulp and

sugar mill is once again responsible for the poor water quality at the Ushukela sites. FAII

scores indicate that the Glendale and Amatikulu sites are in a much better condition

compared to the Ushukela sites as more hardy species were found at the Ushukela sites.

The site below the dual impact, Site USS, is in an F category whereas the site above it,

Site USR, is in a D category indicating an impact. This is probably due to the effluent

discharge by the pulp and sugar mills.

In terms of macroinvertebrates, the univariate analyses indicate that the sampling sites on

the Mvoti River have a higher number of species compared to the reference sites during

both flows. This is due to impacts other than the mills at Site GDR, and flow and channel

modification at Site USR. Also, Site USS showed a high number of hardy species due to

organic pollution from the mills, giving it a higher abundance. On the Amatikulu River,

however, Site AR, has a higher species number than Site AS, and is especially significant

during low flow. This is due to the loss of the stones biotope at Site AS resulting from

sedimentation brought on by the activities of the Amatikulu Sugar Mill upstream. The

RDA bi-plot separates the Mvoti River sites from the Amatikulu River sites because of

the difference in oxygen content. More oxygen occurs at the Amatikulu sites than at the

Mvoti sites indicating better conditions on the Amatikulu River. The lower oxygen

content at Site USS, may be attributed to the organic effluent discharged by the Sappi

Stanger and Ushukela Sugar Mills as the high organic content leads to a lowered oxygen

concentration, and a thus a lower species diversity. This also occurs at Site GDS but to a

lesser extent, indicating a smaller impact by the Glendale Distillery. The SASS5 scores

iii

also reflected the conditions determined by the univariate and multivariate statistical

analyses at every site.

As for the biomarker component, a decreasing trend in CEA indicated that pollution of

the Mvoti River increases as one moves downstream from the Glendale sites to the

Ushukela sites. The brunt of the pollution thus occurs at the lowest site, Site USS,

because it is impacted by both the Sappi Stanger Mill and the Ushukela Sugar Mill, as

well as by runoff from the surrounding sugarcane fields. The AChE and EROD results

indicated that pesticide and PAH exposure at Site GDS is less than that found at Site

USS. This indicates that the effluent discharged by the Glendale Distillery does not have

a detrimental affect when compared to the other sites in the study. This is supported by

the results of the DEEEP methodology that indicated that the Glendale Distillery effluent

was not toxic or mutagenic via the fish and water flea test, and the Ames Salmonella

Mutagenicity test. The greatest exposure occurs at Site USS and this is because it is

impacted by both the Sappi Stanger Mill as well as the Ushukela Sugar Mill. The fish

lethality test indicated an LC10 value of 75% due to the low oxygen concentration

resulting from the high organic nature of the effluent. This in turn is probably due to the

presence of PAHs as the effluent was also found to be mutagenic. Site AR was found to

have a high EROD induction, which is probably due to effluent runoff from surrounding

sugarcane fields or due to exposure to other industrial effluent, as the Amatikulu Sugar

Mill is situated further downstream. The Ames mutagenicity test, however, did not

indicate mutagenicity in this effluent. The water flea test indicated an LC10 of 50% -

75%, of which the cause is unknown. Site AS could not be analyzed due to the absence

of resident fish at this site resulting from the flood.

On the whole, the sugar milling activities are having an adverse effect on the abiotic and

biotic components in the Mvoti and Amatikulu Rivers. The combined effect of the pulp

and sugar mills, however, is more severe. Nevertheless, management by all parties needs

to be undertaken in order to prevent further degradation at the Glendale and Amatikulu

sites and to rectify the situation at the Ushukela sites.

iv

OPSOMMING

Die Mvotirivier word beskou as ‘n “werkende rivier” aangesien dit water voorsien aan

verskeie gebruikers tot op die punt dat geen bykomende ontwikkeling moontlik is nie.

Onlangse studies in 2000 en 2005 het getoon dat die ekologiese toestand van die

Mvotirivier in die Stanger omgewing erg verswak is. Die resultate van hierdie studies,

wat meestal op die aktiwiteite van die Stanger papiermeule gefokus het, het getoon dat

daar veelvoudige impakte in die sisteem teenwoordig is. Die studies het getoon dat die

afsonderlike impakte van die veelvoudige aktiwiteite gekarakteriseer moet word voordat

die gekombineerde effek uitgewerk kan word.

Vervolgens is besluit om ondersoek in te stel na die moontlike impakte van die

suikermeules op die ekologiese integriteit van die Mvoti en Amatikuluriviere. Die

veldopnames is gedoen deur beide nasionaal en internasionaal geakkrediteerde metodes

te gebruik gedurende die hoog en laagvloei periodes in 2006. Die navorsingsontwerp het

dus ‘n “voor-na impak” ontwerp behels waar lokaliteite geselekteer is om toestande

stroomop en stroomaf van die suikermeule te moniteer.Vier lokaliteite is gekies op die

Mvotirivier deur lokaliteite stroomop en stroomaf van die Glendale Distillery (GDR en

GDS) en die Ushukela Milling Company (USR en USS) te plaas. Die eersgenoemde

lokaliteite was gebruik om die impak van slegs die suikermeule te bepaal terwyl die

laasgenoemde gebruik is om die gekombineerde impakte van die suiker en papiermeules

te bepaal. Lokaliteite is ook op die Amatikulurivier geplaas stroomop en stroomaf van

die Amatikulu suikermeule om die impak van die meule op die rivier te bepaal.

Die studie is saamgestel uit twee komponente naamlik die abiotiese en biotiese

komponente. Die abiotiese komponent het bestaan uit die analise van die waterkwaliteit,

sediment en habitat kwaliteit. Die fisiese en chemiese veranderlikes van die

waterkwaliteit en sediment veranderlikes is bepaal gedurende die hoog en laagvloei

periodes,tesame met twee habitat kwaliteits indekse, die Habitat Quality Index (HQI) en

Integrated Habitat Assessment System (IHAS) wat toegepas is om die habitat kwaliteit te

bepaal.

v

Die biotiese komponente het bestaan uit die makro-invertebraat en vis

gemeenskapstrukture tesame met akute en chroniese toksisiteit toetse en mutageniese

toetse, as gevolg van blootstelling aan die suikermeule uitvloeisel. Die makro-

invertebraat gemeenskap se kwaliteit is bepaal deur van die South African Scoring

System Version 5 (SASS5) gebruik te maak terwyl die Fish Assemblage Integrity Index

(FAII) op die vigemeenskap toegepas is. Die toksikologiese assessering is gedoen deur

die Direct Estimation of Ecological Effect Potential (DEEEP) metodes sowel as die

gebruik van biomerkers.

Enkel en multiveranderlike statistiese analises is gebruik om die makro-invertebraat en

vis gemeenskappe te analiseer terwyl die waterkwaliteit, sediment en biomerker data

alleenlik met multiveranderlike statistiese analises geanaliseer is. Enkelveranderlike

diversiteits indekse, dominansie uitstippings, hierargiesegroep uitstippings en NMDS

uitstippings was gedoen deur van Primer Version 6 gebruik te maak terwyl

komponentanalises (PCA en RDA) met Canoco Version 4.5 uitgevoer is. Die

multiveranderlike analises was gebruik sodat enige tyd-ruimtelike verskille tussen

lokaliteite bepaal kon word sowel as die omgewings veranderlikes wat vir hierdie

verskille verantwoordelik is. Die enkelveranderlike analises was gebruik as aanduiding

van die spesies diversiteit en verspreiding by die verskillende lokaliteite.

Suikermeul aktiwiteite is bekend daarvoor dat dit ‘n verskeidenheid effekte op die water,

sediment en habitat kwaliteit van akwatiese ekosisteme veroorsaak. Landbou

besproeiing, uitvloeisel en bemesting stowwe veroorsaak organiese besoedeling wat dan

die toename van nitrate, sulfate, chloriedes en fosfaat ione in akwatiese sisteme

veroorsaak. Hierdie besoedeling veroorsaak dan ‘n verlaging in die opgeloste suurstof

vlakke en ‘n verhoging in die elektriese konduktiwiteit van die water. Hierdie effekte

was meer prominent onderkant die gekombineerde invloede van die Sappi Stanger

papiermeule en die Ushukela suikermeule.

Die organiese inhoud van die sediment was laag by alle lokaliteite behalwe die enkele

Ushukela meule lokaliteit onderkant die papier en suikermeules. Beide ‘n lae en ‘n hoë

vi

organiese inhoud het geen positiewe effekte op die biota nie aangesien ‘n lae organiese

inhoud ‘n aanduiding is van lae nutrient vlakke terwyl ‘n hoë organiese inhoud beteken

dat daar minder opgeloste suurstof teenwoordig sal wees. Die habitat kwaliteit in die

sisteem het effekte van waterontrekking deur die meules getoon deurdat die sediment

ladings in die sisteem verhoog is en dit lei dan tot ‘n verlaging in geskikte habitat vir

biota. Die verwydering van die oewerplantegroei as gevolg van die suikerriet plantasies

gee aanleiding tot oewererosie wat dan siltasie veroorsaak. Die IHAS en HQI indekse

het getoon dat die habitat kondisies beter was gedurende die laagvloei periode as gevolg

van verhoogde habitat beskikbaarheid terwyl die grootste habitat degradasie stroomaf van

die papier en suikermeules waargeneem was.

Die makroinvertebraat enkelveranderlike analises het getoon dat die stroomaf lokaliteite

op die Mvotirivier ‘n hoër telling individue per spesies het, gedurende die hoog sowel as

laagvloei, as vergeleke met die verwysings lokaliteite. Dit is as gevolg van impakte

stroomop van lokaliteit GDR sowel as die vloei en rivierkanaal veranderings teenwoordig

by lokaliteit USR. Verder het lokaliteit USS ook ‘n groter hoveelheid geharde spesies

bevat as gevolg van die effek van die invloei van uitvloeisel wat ryk is aan organiese

stowwe. Lokaliteit AR in die Amatikulurivier het meer spesies diversiteit bevat as

lokaliteit AS stroomaf en dit was veral opsigtelik gedurende die laagvloei periode. Die

verlaagde spesies telling by lokaliteit AS was as gevolg van die afwesigheid van die

klippe biotoop na aanleiding van sedimentasie veroorsaak deur die Amatikulu

suikermeule se aktiwiteite. Die RDA uitstipping het onderskei tussen die Mvotirivier

lokaliteite en die Amatikulurivier as gevolg van die verhoogde suurstof konsentrasies by

die eersgenoemde. Die verlaagde suurstof vlakke by lokalitiet USS kan aan die organiese

afloopwater wat deur die papiermeule en die suikermeule uitgelaat word, toegeskryf

word. Die verhoogde organiese vlakke lei tot ‘n verlaging in die suurstof konsentrasie en

dus ‘n laer spesie diversiteit. Hierdie verskynsel was ook tot ‘n mate waargeneem by

lokaliteit GDS en dus gee dit ‘n aanduiding van die kleiner impak van die Glendale

Distillery. Die resultate van die SASS5 tellings het die resultate van die enkel en

multiveranderlike statistiese analises beaam.

vii

Die vis resultate het getoon dat die spesies diversiteit by lokaliteite USR en USS laer was

as wat by lokaliteite GDR en GDS gedurend sowel die hoog en laagvloei periodes

waargeneem was. Dit kan waarskynlik toegeskryf word aan die swak waterkwaliteit wat

by die Ushukela lokaliteite waargeneem was. Die visgemeenskappe by die verskillende

lokaliteite het verskille tussen die verskillende vloei periodes getoon. Daar was ook

ruimtelike verskille geidentifiseer deurdat die Glendale, Ushukela en die Amatikulu

lokaliteite afsonderlik gegroepeer het. Dit is meestal as gevolg van die waterkwaliteit

wat tussen die Glendale en Ushukela lokaliteite verskil. Die Amatikulurivier groepeer

apart aangesien die gemeenskapstruktuur by die lokaliteite die resultaat is van ‘n groot

vloed gebeurtenis in 2006. Die gekombineerde invloed van die papier en suikermeules is

as die oorsaak van die swak waterkwaliteit by die Ushukela lokaliteite geidentifiseer. Die

FAII resultate van die Glendale en Amatikulu lokaliteite was in ‘n beter kondisie as die

Ushukela lokaliteite as gevolg van die teenwoordigheid van tolerante spesies by die

Ushukela lokaliteite. Die lokaliteit USS, onderkant die papier en suikermeule, is in ‘n

kritiese kategorie (F) terwyl die lokaliteit stroomop (Lokaliteit USR) in ‘n kategorie D

was. Dit gee ‘n aanduiding van die grootte van die impak veroorsaak deur die uitvloeisel

van afvoer water vanaf die papier en suikermeules.

Die biomerker resultate het getoon dat die CEA resultate verlaag vanaf die Glendale

lokaliteite na die Ushukela lokaliteite as gevolg van besoedeling wat die Mvotirivier

invloei. Die grootste deel van die besoedeling kom dus voor by die laaste lokaliteit

(USS) as gevolg van die gekombineerde impakte van die Sappi Stanger papiermeule, die

Stanger rioolwerke en die Ushukela suikermeule. Afloop van die omringende suikerriet

plantasies kan ook ‘n bydra lewer as ‘n bykomende impak op die sisteem. Die AchE en

EROD resultate het getoon dat daar moontlik pestisiedes en PAH blootstellings voorkom

by lokaliteit USS vergeleke met lokaliteit GDS. Dit is ‘n aanduiding dat die Glendale

Distillery se uitvloeisel geen negatiewe effekte het as dit vergelyk word met die ander

lokaliteite in die studie area. Hierdie observasie is beaam deur die resultate van die

DEEEP toetse wat getoon het dat die Glendale Distillery se uitvloeisel nie toksies of

mutagenies is nie. Die hoogste toksisiteit is waargeneem by lokaliteit USS as gevolg van

die veelvoudige impakte waargeneem by hierdie lokaliteit. Die vis mortaliteit toets het ‘n

viii

LC10 waarde van 75% getoon as gevolg van die lae suurstof inhoud veroorsaak deur die

hoë organiese inhoud van die uitvloeisel. Die uitvloeisel bevat moontlik ook PAH’s

aangesien dit mutageniese aksie getoon het. Die biomerkers van lokaliteit AR het hoë

EROD induksie getoon wat moontlik die gevolg is van die afloop vanaf die suikerriet

plantasies of moontlik ook die blootstelling van die akwatiese ekosisteem aan ander

industriele aflope in die omgewing. Die Ames toets van hierdie afvoer water het geen

mutageniese aksie getoon nie. Die enigste toksisiteit is bespeur deur die Daphnia toets

wat ‘n LC10 van 50 – 75% getoon het. Geen biomerkers was toegepas by lokaliteit AS

nie aangesien geen vis gevang kon word as gevolg van die vloed in 2006.

Die finale bevinding was dat die suikermeul aktiwiteite wel effekte op die abiotiese en

biotiese komponente van die Mvoti en Amatikulurivier getoon het. Die gekombineerde

effekte van die papiermeule, rioowerk afvoer en suikermeule het ernstige effekte op die

rivier sisteme. Die verdere degradasie van die akwatiese ekosisteme by die Glendale,

Amatikulu en Ushukela lokaliteite kan slegs deur goeie bestuur van alle betrokke partye

en impakte verhoed word.

ix

TABLE OF CONTENTS Summary i

Opsomming v

Table of Contents x

List of Figures xiv

List of Tables xvii

Acknowledgements xxi

Chapter 1 : Introduction 1.1 Study rationale 1

1.2 Existing literature on the impacts of sugar mill activities

on aquatic ecosystems 2

1.3 Description of the study area 6

1.3.1 Detailed description of the study sites 7

1.4 Aim and Objectives 12

1.5 Study Layout 12

Chapter 2: Water Quality, Sediment and Habitat Integrity 2.1 Introduction 17

2.1.1 Water Quality 17

2.1.2 Sediment 18

2.1.3 Habitat 20

2.2 Materials and Methods 21


2.2.1.1 Sampling Protocol 21

2.2.1.2 Laboratory Analysis 22

2.2.2 Sediment 22


x

2.2.2.2 Laboratory Analysis 22

2.2.2.3 Statistical Analysis 24

2.2.3 Habitat 25


2.3 Results 26


2.3.1.1 Site GDR 26

2.3.1.2 Site GDS 28

2.3.1.3 Site USR 29

2.3.1.4 Site USS 31

2.3.1.5 Site AR 31

2.3.1.6 Site AS 32

2.3.1.7 Spatial and Temporal Analysis 32

2.3.2 Sediment 34

2.3.2.1 Moisture content, Organic content and Grain size determination 34

2.3.2.2 Spatial and Temporal Analysis 35

2.3.3 Habitat 36

2.4 Discussion 39


2.4.2 Sediment 42

2.4.3 Habitat 43

2.5 Conclusion 46

xi

Chapter 3: Macro-Invertebrates and Fish 3.1 Introduction 48


3.2.1 Macro-Invertebrates 51

3.2.2 Fish 52

3.2.3 Statistical Analysis 54

3.3 Results 56

3.3.1 Spatial and Temporal Analysis 56

3.3.1.1 Macro-Invertebrates 56

3.3.1.2 Fish 66

3.3.2 Biotic Indices 74


3.3.2.2 Fish 76

3.4 Discussion 77

3.4.1 Spatial and Temporal Analysis 77


3.4.1.2 Fish 79

3.4.2 Biotic Indices 81


3.4.2.2 Fish 82

3.5 Conclusion 83

Chapter 4: DEEEP and Biomarkers 4.1 Introduction 85


4.2.1 Sampling Protocol 89

4.2.2 DEEEP Analysis 90

4.2.2.1 The Fish and Invertebrate Lethality Tests 90

4.2.2.2 The Ames Salmonella Mutagenicity Test 91

4.2.3 Biomarkers 93

xii

4.2.3.1 Cellular Energy Allocation 93

4.2.3.2 Acetylcholinesterase 94

4.2.3.3 Ethoxyresorufin-O-deethylase 95

4.2.4 Statistical Analysis 96

4.3 Results 96

4.3.1 DEEEP 96

4.3.2 Biomarkers 98

4.4 Discussion 103

4.5 Conclusion 105

Chapter 5: Conclusion and Recommendations 5.1 Conclusion 107

5.2 Recommendations 110

Chapter 6: References 112

xiii

LIST OF FIGURES

Figure 1.1 A map of the Kwazulu Natal province indicating the location of the Mvoti

and Amatikulu Rivers and the sites assessed in this study. The position of

the mills is also shown.

Figure 1.2 The Glendale Reference Site at high flow (A) and at low flow (B).

Figure 1.3 The Glendale Sampling Site at high (A) and low flow (B).

Figure 1.4 The Ushukela reference site at high flow (A) and low flow (B).

Figure 1.5 Ushukela Sampling Site at high (A) and low flow (B).

Figure 1.6 The Amatikulu reference site at high (A) and low (B) flow.

Figure 1.7 The Amatikulu Sampling Site at high (A) and low (B) flow.

Figure 1.8 The layout of the study in which drivers and responder components are

addressed.

Figure 2.1 A map of the Kwazulu Natal province indicating the location of the Mvoti

and Amatikulu Rivers and the sites assessed in this study.

Figure 2.2 PCA bi-plot of water quality variables and sampling sites for both high and

low flow data. The L suffix indicates the low flow period.

Figure 2.3 PCA bi-plot of grain size distribution and sampling sites for both high and

low flow data. The L suffix indicates the low flow period.

xiv

Figure 2.4 Graphical representation of IHAS and HQI scores during the (A) high flow

period and (B) the low flow period. Polynomial trend-lines are included

in order to illustrate trends.

Figure 3.1 Graphical representations of the total number of species (A) and

individuals (B) during the high and low flow periods. Polynomial trend-

lines are included in order to illustrate trends.

Figure 3.2 Univariate diversity indices indicating Margalef Species Richness (A) and

Pielou’s Eveness Index (B) during the high and low flow periods.

Polynomial trend-lines included in order to illustrate trends.

Figure 3.3 Graphical representation of Shannon-Wiener (A) and Simpson (B)

Diversity Indices during the high and low flow periods. Polynomial trend-


Figure 3.4 Bray-Curtis similarity matrix-based cluster analysis (A) and NMDS plot

indicating the different groups according to presence/absence data during

high and low flow.

Figure 3.5 k-Dominance plot of the high and low flow macro-invertebrate data

ranking species based on their order of importance.

Figure 3.6 RDA bi-plot showing the position of the sampling sites and the water

quality and sediment variables responsible for the macro-invertebrate

community structure groupings present at each site. The L suffix indicates

the low flow period.

xv


abundances (B) during the high and low flow periods. Polynomial trend-


Figure 3.8 Univariate diversity indices indicating Margalef Species Richness (A) and

Pielou’s Eveness Index (B) during the high and low flow periods.

Polynomial trend-lines are included in order to illustrate trends.


Diversity Indices during the high and low flow periods. Polynomial trend-


Figure 3.10 Bray-Curtis similarity matrix-based cluster analysis (A) and NMDS plot

indicating the different groups according to presence/absence data during

high and low flow.

Figure 3.11 RDA tri-plot performed with forward selection to indentify the significant

water quality variables responsible for the community structure groupings

and the dominant species present at the particular sites. The L suffix

indicates the low flow period.

Figure 3.12 Graphical representation of the FAII scores calculated for the combined

high and low flow periods. Polynomial trend-lines are included in order to

illustrate trends.

Figure 4.1 The carbohydrate (A), lipid (B) and protein (C) reserves of the resident

fish, O. mossambicus at the study sites. Error bars represent standard error

of the mean and bars with common letters differ significantly from each

other (p<0.05).

xvi

Figure 4.2 The total energy available (A), the ETS activity (B) and the total energy

budget (C) of O. mossambicus. Error bars represent standard error of the

mean and bars with common letters differ significantly from each other

(p<0.05).

Figure 4.3 The mean AChE concentrations in the brain tissue of (A) and EROD levels

in liver (B) of resident O. mossambicus from the different study sites.

Error bars represent standard error of the mean. Post-hoc tests were not

performed for EROD as at least one group had fewer than 2 cases.

Figure 4.4 PCA bi-plot showing CEA, AChE and EROD results for the sites. The

numbers represent biomarker responses in individual fish from the

following sites 1=GDR, 2=GDS, 3=USR, 4=USS and 5=AR.

LIST OF TABLES

Table 1.1 Global positioning system (GPS) coordinates for the sites assessed in the

study.

Table 2.1 Organic content classification system in sediment (USEPA, 1991a)

Table 2.2 Grain size categories according to Cyrus et al. (2000).

Table 2.3 Rating system for the IHAS and HQI habitat quality indices.

xvii

Table 2.4 Results of the descriptive statistics of the historical water quality

parameters provided by DWAF from the Glendale Weir Monitoring

Station U4H800 from 1983 to 1996.

Table 2.5 Physical, chemical and biological water variables for all sites during the

high flow period. The Target Water Quality Ranges (TWQR) for

domestic and aquatic ecosystems are also given.

Table 2.6 Physical, chemical and biological water variables for all sites during the

low flow period. The Target Water Quality Ranges (TWQR) for

domestic and aquatic ecosystems are also given.

Table 2.7 The water quality data acquired by Mackay et al. 2000 for two sites on the

Mvoti River that was collected over a period of 6 weeks. The median

values are shown here. The TWQR for both domestic use and aquatic

ecosystems are also indicated.

Table 2.8 Percentage moisture and organic content of all sites during high and low

flow periods.

Table 2.9 Percentage grain size distribution of sediment from the Mvoti and

Amatikulu sites during the high flow period (March 2006), with sieve

sizes ranging from >4000 μm to 53 μm.

Table 2.10 Percentage grain size distribution of sediment from the Mvoti and

Amatikulu sites during the low flow period (July 2006).

Table 2.11 Total IHAS and HQI percentage scores and ecological classes for habitat

integrity of the Mvoti and Amatikulu sites during the high flow period.

xviii


integrity of the Mvoti and Amatikulu sites during the low flow period.

Table 3.1 The Ecological classes and relevant conditions determined from the

SASS5 and ASPT scores which are also shown (adapted from O’ Brien et

al., 2005).

Table 3.2 Sampling habitat, technique and effort for fish at all sites during the high

flow period.

Table 3.3 Sampling habitat, technique and effort for fish at all sites during the low

flow period.

Table 3.4 The different FAII integrity classes listed from A to F accompanied by a

description and a score for each class (Kleynhans, 1999).

Table 3.5 The percentage contribution and cumulitive percentage contribution for

groups 1 to 5 during high and low flow determined by SIMPER analysis.

Table 3.6 The fish species diversity and abundances sampled in the Mvoti and

Amatikulu Rivers during the high flow period. The common name, FAII

abbreviation and Estuarine Dependant Categories (Whitfield, 1998) of the

fish are presented.


Amatikulu Rivers during the low flow period. The common name, FAII


fish are presented.

xix

Table 3.8 SASS5 results indicating the SASS5 score, number of taxa and ASPT

which were used to determine the EC for each site during the high and

low flow periods. The IHAS score was also included

Table 4.1 The mutation ratios (MRs) obtained with the two tester strains, the number

of colonies on the negative control plates and the results of the positive

controls and sterility checks for the mutagenicity test performed on the

effluent from the three mills.

xx

xxi

ACKNOWLEDGEMENTS

“I lift up my eyes to the hills – where does my help come from?

My help comes from the Lord, the Maker of heaven and earth.”

Psalms 121:1

To my Rock, my Fortress, my Life: my Lord and Savior, Jesus Christ.

To God be the glory forever and ever.

I would like to acknowledge with heartfelt thanks, the following people and institutions:

My mom, Anastasia Carminati, for always loving me, believing in me, supporting me and helping

me through every step of the way.

The rest of my family and friends for encouraging me and keeping me sane! Special thanks to

Mary Carminati for helping me get up when I was down. To Catherine Carminati, Michael

Müller and Dieter Harms for keeping my passion for rivers alive, and to Anthony D’aubrey for

your tolerance, comfort, love and laughs during the tough times. Thank-you.

Prof. V. Wepener and Gordon O’Brien for their continued support, guidance and supervision, and

for allowing me this opportunity to further my studies.

Wynand Malherbe, Martin Ferreira and the rest of the Paper Mill Study (PMS) team, thank you

so much for your incessant help, knowledge, and patience, and for getting me out of some very

sticky situations in the field!

Dr Amina Nel, Irene Stryftombolas, Liezel Whitlow, Kerry Mills and Bridget Shaddock for their

help in the laboratory. The long hours paid off….

Finally to The University of Johannesburg and Sappi for their financial assistance and support.

Dedicated in loving memory of Virginia Michialidis.

Σ’αγαπω παρα πολυ Гίάγίάκα μου, ευχαρηστω γία ολα.

Chapter 1

Introduction

Chapter 1 Introduction 1.1 Study rationale

Recent Ecological Risk Assessments (ERA) of the Lower Mvoti and Estuary carried out

by the Coastal Research Unit of Zululand Environmental in 2000 (Mackay et al., 2000)

and by the Paper Mill Study Team at the Zoology Department, University of

Johannesburg (UJ) in 2005 (O ‘Brien et al., 2005), indicated that the lower Mvoti River is

ecologically in a severely degraded, highly modified state. In addition, these studies

reveal that the highly modified state of this aquatic environment’s drivers (instream

habitat, water quality, flow) have resulted in an ecosystem response that relates to a

critically modified state of its biological communities. This is particularly relevant below

the confluence of the Nchaweni and Mvoti Rivers. These findings also indicated that to

differentiate between the combined effects of stressors in the environment that is exposed

to multiple activities, it is necessary to characterize the ecological effect of the individual

activities.

The O’Brien et al. (2005) study focused on the characterisation of the effects and risk of

the pulp and paper industry activities to the local aquatic ecosystems of the lower Mvoti

River. A number of other potential stressor sources were also identified amongst which

the sugar industry plays an important role. Thus this study was conducted to determine

whether there were any differences between the ecosystem responses influenced by

effluent from sugar mills and effluents from paper mills. The study hypothesis that was

formulated stated that effluents from sugar mills and therefore the impacts caused by

them are distinctly different from other activities and the effect on the receiving aquatic

environment can be quantified using biological responses as indicators.

1

1.2 Existing literature on the impacts of sugar mill activities on aquatic

ecosystems 1.2.1 Water Quality

The water quality of rivers is impacted by both the cultivation and processing of sugar

crops. In terms of cultivation, runoff and leaching can lead to pollution of the surface

water of natural watercourses. The major pollutants here are nutrients (namely nitrates

and phosphates) and agrochemicals, particularly pesticides, and sedimentation. Nitrates

and phosphates are mainly derived from fertilizers which can cause eutrophication of

waterways (Cheesman, 2005). Sediment loads arise from soil erosion that occurs because

of the lack of adequate soil conservation practices. In South Africa, sugarcane is mainly

situated in the catchment areas of gently to steeply curving land which encourages runoff

into the major rivers (Tudor-Owen and Wyatt, 1991). What makes the situation worse is

that sugarcane is often grown right up to the banks of rivers resulting in the removal of

natural riparian vegetation, which is a problem because the filtering of sediment and

agrochemicals by vegetation does not occur, increasing their concentrations in surface

waters (Arthington et al., 1997). Bunn et al. (1997) noted that indigenous riparian leaf

litter, which drives the dynamics of the aquatic community, is replaced by cane leaf litter

that does not provide the necessary resources that would normally be obtained from

indigenous vegetation. In fact, it is likely that this may result in a pollution threat as great

as that posed by runoff of nutrients from cane fields (Arthington et al., 1997). Lastly,

irrigation can increase runoff and deep drainage (Cheesman, 2005).

The main consideration in terms of sugar processing is the pollution arising from the

discharge of mill wastewater into waterways. Relatively large volumes of water are

abstracted from rivers for sugar processing and the wastewater resulting from this is often

used to irrigate the sugarcane (Cheesman, 2005), consequently running back into the

river. Since sugarcane consume large amounts of water, estimated at 7.5 Ml/ha water for

100t/ ha cane (Bakker, 1999), rainfall to supply such needs is just not sufficient.

Therefore, irrigation using wastewater is practiced. These effluents are often rich in

2

organic matter having high biological (BOD) and chemical oxygen demands (COD),

which leads to lowered levels of dissolved oxygen (DO) in surface waters. In addition,

heavy metals, lime, oil or grease, from poor maintenance of equipment, and cleaning

agents are likely to be found in the effluent. Wastewater generated from cane washing is

muddy and has a high BOD. Water from cooling systems of barometric condensers is

often contaminated with sugar and regarded as one of the major sources of potentially

environmentally damaging waste in a cane mill. Mills can also produce acidic and basic

wastes from cleaning of equipment (Cheesman, 2005).

1.2.2 Water Consumption

Large volumes of water are used in sugarcane production, both in terms of cultivation

and processing. The main consideration in terms of sugarcane cultivation is the irrigation

of sugar crops (Cheesman, 2005). The South African sugar belt is divided into two areas

in terms of irrigation. Firstly, the areas where irrigation is used to increase yields, called

‘supplementary irrigation’ and secondly, areas where irrigation is necessary to guarantee

continuous production of sugarcane, called ‘total irrigation’. These areas are classified

according to local rainfall conditions (Thompson, 1977). The rainfall that occurs at the

sites of this particular study, i.e. the coastal regions of KwaZulu-Natal, is sufficient for

continuous cane production, but the practice of supplementary irrigation is carried out in

order to increase yields (Cheesman, 2005). The practical and economic advisability can

be determined for any grower by The South African Sugar Association (S.A.S.A). For

advice concerning total irrigation, the rain-fed yield is assumed to be zero. For

supplementary irrigation schemes, between 25 mm and 50 mm of water is usually

administered per application (Thompson, 1977).

Schmidt (1998, 2000) reported that in South Africa, 87,000 ha of the 412,000 ha under

production were irrigated in 1996/97. This 87,000 ha of land includes 47,000 ha in the

northern parts where total irrigation is practiced, and the remaining 40,000 ha in the

coastal and midland regions of KwaZulu-Natal where supplementary irrigation is

required (Cheesman, 2005). Consequently, this has resulted in large volumes of water

3

being abstracted for sugarcane irrigation in South Africa. In other parts of the world,

similar situations have been observed. For example, in the Bundaberg and Burdekin

regions as well as in the Ord Irrigation Area in Australia, water consumption for

sugarcane irrigation is estimated at 3.8 Ml / ha / year (Kingston, 1994), 10 Ml / ha / year

(Raine, 1995) and 15 – 53. 8 Ml / ha / year (Wood et al., 1998), respectively. In

Mauritius, irrigation occurs at 22% of the total cane cultivation area (Jhoty et al., 2001).

Sugarcane factories can also consume large amounts of water. The processes that require

the use of water include the cleaning of cane, the extraction of juice and the production of

steam for processing and generating electricity. The United Nations Environment

Programme (UNEP) reports that the washing of cane alone requires 3-10 m³ water per

tonne (Cheesman, 2005). With regards to water consumed during the extraction of juice,

Albert-Thenet in Payne (1991) reports that water is the most extensive material used in

sugar manufacture and that because it is freely available to the operator it is often used

indiscriminately (Cheesman,2005).

1.2.3 Sediment

Siltation is one of the main impacts of sugarcane farming on aquatic ecosystems. It is the

deposition of finely divided soil and rock particles upon the bottom of stream and river

beds (Vennie, 2007) and is the result of the removal of vegetation in catchment areas and

subsequent soil erosion during early establishment of cane and following harvest. Soil

erosion is influenced by high-intensity rainfall (Umrit and Ng Kee Kwong, 1999),

irrigation (Inamdar et al., 1995; Chapman, 1997), wind, temperature, soil type (Ahmad,

1996; Hartemink, 2003), soil disturbance during cultivation and increasing slope

gradients (Landrey, 1978; SASA, 2002). Surface drainage water running from cane

fields into rivers can take with it soil sediments arising from erosion that contain

insoluble chemical residues such as phosphates and ammonium.

The absence of riparian vegetation due to sugarcane farming increases the risk of erosion

because vegetation stabilizes the soil and protects bare ground against exposure to water

4

inputs. Erosion risk is greatest when the soil infiltration rate is low, increasing run-off

into surface waters (Cheesman, 2005). Eroded silt can smother river-bed gravels,

harming aquatic plants, invertebrates and the eggs of fish. Trout spawning beds in 29 out

of 51 river reaches across southern England contained more than 15% of fine sediments,

which may result in the death of 50% of the eggs and larvae. In the Rivers Test and

Itchen, for example, over 95% of fine sediments came from the surrounding land, where

cultivated crops are a major land use. Erosion and deposition account for over half of the

river maintenance activities in some Environment Agency Regions (Bingham, 2006).

1.2.4 Macro-Invertebrates

It has been found that sugar mill effluent discharge reduces the aquatic macro-

invertebrate diversity as a result of decreased DO levels. In tropical north Queensland,

Australia, increased pollution in a sugarcane stream lead to decreased diversity and the

fauna was found to be dominated by Oligochaeta and one species of Chironomidae in

heavily polluted situations (Pearson and Penridge, 1987). This study also showed that the

discharge of organic effluent from a sugar mill into this stream had similar effects on the

macro-invertebrate fauna to those found in temperate streams. The DO concentration here

fell below 6 multiplied by 5 mg l-1, and the effects were most severe below 3 multiplied

by 5 mg l-1.

1.2.5 Fish

High levels of pollution from sugar mill effluents impose considerable stress on native

fish populations. Studies on the Ameca River by Lopez-Lopez et al. (2003) in Mexico

showed that sugar mill effluent containing pressed pulp, organochlorines,

organophosphates, herbicides, fungicides and sulphur produce multiple damage in fish.

As a result of this, the Ameca River has undergone a severe loss of fish biodiversity over

the last 10 years. It has also been stated that the changes in fish fauna is also related to

5

water extraction from the basin. Due to the negative effects that pollutants often have on

enzyme activity, enzymes were used as biomarkers of sublethal stress in this study. These

included alkaline phosphatase, acetylcholinesterase and gamma-glutamyl transpeptidase.

In addition to this, the levels of lipoperodixation were used as a biomarker. It was found

that the organophosphate insecticides inhibited acetylcholinesterase, ultimately blocking

synaptic transmission in the nervous system and affecting vital functions such as

respiration. Lipoperoxidation levels were increased, leading to the disruption of cellular

function in fish. In addition to the effects of wastewater on fish, the removal of

indigenous riparian vegetation also has a negative effect resulting from the loss of

shading and consequent disruption of the food chain (Arthington et al., 1997).

1.3 Description of Study Area The Mvoti River catchment covers approximately 2728 km2 and falls within the

KwaZulu-Natal province. The highest point of the catchment is approximately 1500

meters above sea level and can be found at Mount Alida (Harris and Kelly, 1991). From

there it flows towards Greytown and then to Stanger, which is approximately 7 km away

from the Mvoti Estuary that opens up into the Indian Ocean (Tharme, 1996).

Both subsistence and commercial agriculture occur in the Mvoti River catchment

(DWAF, 2004). The upper catchment area is covered by exotic forests, irrigated and dry

land farming whereas in the central part, this cover is limited. The central part comprises

a dense rural population but heavy industry is absent in this area. The lower part of the

catchment has a sparse amount of forestry, and is dominated by sugarcane and grazing.

Towns and villages have developed in the upper and lower areas of the catchment

(Tharme, 1996) and heavy industry consists of the Glendale Distillery and Ushukela

Sugar Mill as well as the Sappi Stanger Mill (Harris and Kelly, 1991).

Overgrazing and the abstraction of water from the Mvoti River for both commercial and

subsistence agricultural irrigation as well as industrial use occurs in the lower reaches of

6

the catchment. A considerable amount of eroded soil is deposited in the channel as a

result, transforming it into a sinuous narrow river that is braided and experiences

unnatural low flows throughout the year (Tharme, 1996).

The Amatikulu River is north of the Mvoti River and has a meandering pattern at the

lowland zone. It joins up with the Inyoni River at this zone, running parallel to the Indian

Ocean. The lower part of the catchment is impacted by a sewage treatment plant and the

Amatikulu Sugar Mill. Agricultural and farming practices also occur in this area, and

informal settlements are prevalent.

1.3.1 Detailed description of the study sites Sites were selected so as to determine whether sugar mill effluent elicited a different

ecosystem response than paper mill effluent. Four sites were selected on the Mvoti

River, up and downstream of the Glendale Distillery and the Ushukela Sugar Mill. The

former sites were selected to assess the impact of a sugar mill alone and the latter to

determine the combined effects of sugar milling and pulp and paper activities.

Additionally, two sites were selected on the Amatikulu River, up and downstream of the

Amatikulu Mill, to assess the effect of the sugar milling activities alone. Assessments of

these sites were carried out over two flow periods, high flow and low flow, and were

selected based on historical flow regimes. Table 1.1 indicates the GPS coordinates of

these sites and Figure 1.1 indicates their position.

7Site Latitude Longitude

Table 1.1 Global positioning system (GPS) coordinates for the sites assessed in the study.

GDR 29º 18’ 21.6” S 31º 05’ 44.8” EGDS 29º 17’ 45.1” S 31º 11’ 21.3” EUSR 29º 22’ 33.2” S 31º 17’ 33.3” EUSS 29º 22’ 13.5” S 31º 18’ 38.2” EAR 29º 02’ 14” S 31º 31’ 15.7” EAS 29º 03’ 26.4” S 31º 31’ 46.9” E

Figure 1.1 A map of the Kwazulu Natal province indicating the location of the

Mvoti and Amatikulu Rivers and the sites assessed in this study. The position of

the mills is also shown.

8

Glendale Reference Site (GDR) This site is situated above the impacts of the mills and is thus a reference site for all the

sites below it. The sugar-cultivated land in this area is sparse, but there is evidence of

rural settlement runoff and agricultural activity. Continuous reeds and grasses, as well as

patchy shrubs and trees occur on the banks of the active channel.

A B

Figure 1.2 The Glendale Reference Site at high flow (A) and at low flow (B).

Glendale Sampling Site (GDS) This site is below GDR and is impacted by the Glendale Distillery upstream. It is

impacted more by residential areas than by sugar plantations. Water abstraction here is

minimal. A dense amount of reeds, shrubs and trees, as well as patchy grass occurs on

the banks.

9

A B Figure 1.3 The Glendale Sampling Site at high (A) and low flow (B).

Ushukela Reference Site (USR)

This site is upstream of the Sappi Stanger Mill and the Ushukela Sugar Mill. There are

extensive sugarcane cultivated lands, residential areas and farming activities occurring

here. A lagoon has been artificially constructed to accommodate water abstraction for

sugar mill practices and there is evidence of vegetation removal.

A B Figure 1.4 The Ushukela reference site at high flow (A) and low flow (B).

Ushukela Sampling Site (USS) USS is a stream containing stagnant water and is downstream of sugar and paper mill

impacts. It is impacted by sugarcane farming and discharge of sugar mill effluent. The

river is impounded by a tunnel over which a road has been constructed. This disrupts its

natural flow regime.

10

A B

Figure 1.5 Ushukela Sampling Site at high (A) and low flow (B). Amatikulu Reference Site (AR) This site is found on the Amatikulu River above the impacts of the Amatikulu Mill.

Roads, sugar-cultivated land and grazing are extensive. There is also evidence of

farming and agricultural activities occurring close to this site.

A B Figure 1.6 The Amatikulu reference site at high (A) and low (B) flow.

Amatikulu Sampling Site (AS)

Site AS is a slow-flowing stream with cloudy water and is situated below the Amatikulu

Sugar Mill. Dense reeds, continuous grasses, shrubs and trees occur. The site is

impacted by water abstraction, sediment deposition and runoff from informal settlements.

Sugar-cultivated lands and residential areas are extensive at this site. A big flood that

occurred during the high flow period also impacted the river.

11

A B he Amatikulu Sampling Site at high (A) and low (B) flow.

The aim of this project was to determine the effect of sugar milling activities on the

In order to meet this aim, the following objectives were set:

Use invertebrate and fish community responses to determine the effect of sugar

Use the Direct Estimation of Ecological Effect Potential (DEEEP) protocols to

Use biomarker responses to determine the effect of sugar mills alone and combined

Compare the biological community responses, DEEEP assessment and biomarker

Figure 1.7 T

1.4 Aim and Objectives

ecological integrity state of the Mvoti River in KwaZulu-Natal.

•

mills alone and combined effect of sugar and pulp mill activities on the ecological

integrity of the Mvoti River.

•

assess the toxicity effect of sugar mills alone and combined effect of sugar and pulp

mill activities on the ecological integrity of the Mvoti River.

•

effect of sugar and pulp mill activities on the ecological integrity of the Mvoti

River.

•

responses from the Mvoti River to responses in the adjacent Amatikulu River that is

only subjected to sugar mill activities.

12

1.5 Study Layout

Biomonitoring of the abiotic and biotic components of both the Mvoti and Amatikulu

Rivers entailed the implementation of the South African River Health Programme (RHP).

The RHP was developed by the Department of Water Affairs and Forestry (DWAF), The

Water Research Commission (WRC), the Department of Environmental Affairs and

Tourism (DEAT) and various consultants to monitor and assess South Africa’s

freshwater resources (Mackay et al., 2000). The programme entailed the selection and

use of reference and sampling sites and the application of ecological driver indicators and

biological responder indicators (indices), to assess the condition or “health” of rivers in

South Africa. Application of additional biological response indicators was also included

to assess the risk posed by the toxicity of the effluent associated with the mills to aquatic

organisms. This entailed the implementation of Direct Estimation of Ecological Effect

Potential (DEEEP) methodology and assessment of biomarker responses in resident fish

populations in the vicinity of the sugar mills (Figure 1.8).

M i b

Fish attribute state

Biomarker assessments EROD, CEA, AChE

Toxicity DEEEP methods

Habitat inc. IHAS and HQI indices

Sediment inc. Grain size, moisture & organic content.

Study Layout

Water Quality inc. Physico/chemical and microbe content

Mvoti River

Site selection

Low flow 2005 / High flow 206Survey period

-

System

Four Sites Site selection

Two surveys: high flow vs. low flow 2006 Survey period

Ass

essm

ent t

echn

ique

s an

d

pro

cedu

res

Driv

ers

System

Two Sites

Amatikulu River (control)

Ames test, invertebrate and fish mortality tests

FAII

SASS5

FRAI

MARAI

Res

pond

ers

13

Figure 1.8 The layout of the study in which drivers and responder components are

addressed.

1.5.1 Driver components: water quality, sediment, habitat

The abiotic driver assessment procedures include the assessment of physico-chemical

variables of the water and sediment, according to the Resource Directed Measures

(RDM) methodology and the use of two habitat driver indices, the IHAS (Invertebrate

Habitat Assessment System) and HQI (Habitat Quality Index) for habitat integrity. The

term water quality is used to describe the physical, chemical, biological and aesthetic

properties of water that determine its fitness for a variety of uses, and for the protection

of the health and integrity of aquatic ecosystems (DWAF, 1996). The physico-chemical

variables assessed will provide indications of any probable contamination of the Mvoti

and Amatikulu Rivers in the form of system variable change (oxygen, temperature and

pH), microbial contamination, and nutrient enrichment. All sediment sampling

techniques are standard methods set out by the United States Environmental Protection

Agency (USEPA). Moisture and organic content, as well as grain size are measured to

determine the sediment’s influence on toxicity (USEPA, 2001), the productivity of the

system (Mackay et al., 2000) and sediment transport and deposition (Freeman and

Rowntree, 2005), respectively. Lastly, habitat integrity is the composition of balanced

physico-chemical and habitat characteristics that are integrated on a temporal and spatial

scale and are comparable to the characteristic of natural habitats within a region. An

assessment of the habitat structure is essential since aquatic community structure is

determined by habitat availability and diversity and the information obtained frequently

supports the interpretation of biological results (Roux, 1999).

14

1.5.2 Biotic response components: macro-invertebrates and fish

The biotic response indicator methodologies are in the form of various indices relating to

the health of the aquatic ecosystem. The indices include the South African Scoring

System (SASS5) for invertebrates, the Fish Assemblage Integrity Index (FAII) and the

Fish Response Assemblages Index (FRAI) for fish (Roux, 2001). The SASS5 index is a

standardized macroinvertebrate sampling technique that has been accredited and widely

used in the RHP. It was designed for systems containing low to moderate flow hydrology

and is most effective in lotic systems with diverse biotopes specifically including riffles

and rapids (Dickens and Graham, 2002). The information gained from this protocol is

then incorporated into the Macro-Invertebrate Response Asessment Index, or MIRAI, to

determine the ecological category (EC) of the system in terms of macro-invertebrate

responses. The fish data is evaluated by implementing the FAII and the FRAI which

were developed by Kleynhans (1999) and Kleynhans et al. (2005) respectively. The FAII

index assesses the attributes of fish assemblages in terms of the occurrence of a species

expected to be present in segments of the river (Roux, 2001) and the FRAI is based the

fact that the quantity and quality of the habitat that is available can be used indirectly as

an indication of the effect of stressors on individual fish and fish populations (Kleynhans

et al., 2005). Thus the FAII provides a general assessment that does not have a strong

cause-and-effect basis and the FRAI provides a habitat-based cause-and-effect foundation

to deduce how the fish assemblage deviates from the reference condition (Kleynhans,

2007).

1.5.3 Additional biotic response components: DEEEP and biomarker responses

The DEEEP methodology involves the implementation of three basic toxicity tests,

namely the fish and water flea lethality tests and the Ames Salmonella mutagenicity test.

The water flea and fish used in these tests were Daphnia pulex and Danio rerio,

15

16

respectively. These tests aim to measure the acute toxicity of wastewater discharges and

receiving waters. The Ames Salmonella mutagenicity test detects mutagens in

wastewater discharges and receiving waters (Slabbert, 2004). The biomarker assays,

Cellular Energy Allocation (CEA), 7–ethoxy resorufin O-deethylase (EROD) and

acetylcholinesterase (AChE), were carried out on the muscle, liver and brain,

respectively, of the resident fish Oreochromis mossambicus from each site. CEA serves

as a biomarker of cellular energetics and is based on the fact that exposure of organisms

to pollutants has adverse effects on their energy budgets at a cellular level (De Coen and

Janssen, 1997). EROD activity in fish is a well-established in vivo biomarker of exposure

to certain planar halogenated and polycyclic aromatic hydrocarbons (PHHs and PAHs)

and other structurally similar compounds (Whyte and Tillitt, 1999). AChE is an enzyme

that is important for cellular neurotransmitter functioning in that it cleaves the

neurotransmitter achetylcholine. Exposure to organophosphates, carbamates or heavy

metals inhibit acetylcholinesterase, causing an accumulation of acetylcholine at the nerve

synapse, ultimately blocking synaptic transmission in the nervous system and affecting

vital functions such as respiration. For this reason, acetylcholinesterase activity has been

proposed as a useful molecular biomarker of direct neurotoxic effects (Bocquene et al.,

1990).

Chapter 2

Water quality, Sediment and Habitat

Integrity

Chapter 2 Water Quality, Sediment and Habitat Integrity

2.1 Introduction 2.1.1 Water Quality Life cannot exist without water because it is the major component of all living things. It

is important both physiologically and ecologically in that it plays an essential role in

temperature control in organisms and it is the medium in which many organisms live.

The term water quality refers to the suitability of water for a particular purpose (Boyd,

2000) and is used to describe the physical, chemical, biological and aesthetic properties

of water that determine its fitness for a variety of uses, and for the protection of the health

and integrity of aquatic ecosystems (DWAF, 1996a).

In South Africa, the water situation has been described as one of ‘water stress’ (DWAF,

1999) due to the fact that it is a semi-arid country, with annual rainfall below the world

average and has high evaporation rates (DWAF, 1996a). Furthermore, there is an

increase in the demand on water by a growing population of about 40 million (DWAF,

1999a). Our water resources therefore have to be carefully conserved and protected. The

Department of Water Affairs and Forestry (DWAF) aims to ensure that the quality of the

water resources remains fit for recognized water users and that the viability of aquatic

ecosystems is maintained and protected (DWAF, 1996b). For this reason, DWAF

developed National Water Quality Guidelines for domestic, industrial, agricultural,

recreational and aquatic ecosystems. The criteria with respect to aquatic ecosystems

were developed by Roux et al. (1996) and were calculated from acute and chronic

toxicity tests on a number of representative species.

The activities in the adjacent catchment of the study area that may alter the water quality

in the lower Mvoti River are sugar-cultivated lands, rural settlements, the Glendale

Distillery, Ushukela Sugar Mill and the Sappi Stanger Mill. Negative impacts on the

water quality in the lower Amatikulu River include the sewage treatment plant, the

Amatikulu Sugar Mill and sugar-cultivated lands in the vicinity.

17

The quality of the water in systems affected by sugar cane farming is influenced by two

main factors, namely, eutrophication and effluent discharge (Cheesman, 2005).

Eutrophication is the enrichment of waters by inorganic plant nutrients, usually nitrogen

and phosphorus. This may lead to a change in the dominant biota and a decrease in

species diversity; an increase in turbidity, sedimentation rate, and plant and animal

biomass; the development of anoxic conditions and lastly, the shortening of the life-span

of the system (Mason, 1991). In terms of sugar cane farming, nitrates and phosphates are

mainly derived from fertilizers and enter the receiving ecosystem by means of runoff and

leaching.

The irrigation of sugar cane with effluent is a common practice on the cane fields in

Kwa-Zulu Natal. This is due to the fact that rainfall cannot supply the large amounts of

water (approx. 7.5 Ml/ha water for 100 t/ ha cane) that are consumed by the cane. The

high BOD and CODs that these effluents have often lead to lowered levels of DO in

surface waters (Cheesman, 2005). Water used for irrigation must be free of phytotoxic

substances and excessive concentrations of minerals which can have adverse osmotic

effects on plants (Boyd, 2000). The concentration of salts is a variable of concern in

water used for irrigation because high salt concentrations could have an effect on both

fauna and flora. Excessive salt concentrations have resulted in growth reduction in plants

(Jensen, 1983) and water loss through osmosis and death (Wurts, 1998) in aquatic

invertebrates and fish. Salt concentration can be used to describe the water quality for

irrigation by measuring the electrical conductivity of the water (Harris and Kelly, 1991).

2.1.2 Sediment

Sediment is sand, gravel, mud and/or clay, and organic material in varying combinations

(Hay et al., 2005). The sediment in an aquatic ecosystem has various roles, namely,

providing a feeding, spawning, and nurturing habitat for many aquatic organisms.

Sediment also retains many types of pollutants that may arise from many sources

including urban and agricultural runoff, municipal and industrial discharges and

atmospheric deposition. Benthic and other sediment-associated organisms may be

negatively affected by these foreign compounds in both a lethal and sublethal manner.

18

This in turn directly or indirectly affects other species, such as fish, wildlife and humans,

by direct consumption or bioaccumulation through the food chain (USEPA, 2001).

Water content is a measurement of sediment moisture usually expressed as a percentage

of the whole sediment weight. It is known to influence toxicity and is used to aid in the

interpretation of sediment quality investigations (USEPA, 2001). It is the primary route

of exposure for many infaunal benthic invertebrates in contaminated sediments (Hoffman

et al., 1995).

Organic particles arise from the deposition of leaf litter and make the sediment more

cohesive. Crabs and other invertebrates feed on this organic material (Hay et al., 2005).

However, organic material in large amounts is considered as organic pollution. This is

because they have high biological (BOD) and chemical oxygen demands (COD), which

leads to lowered levels of DO in surface waters. This in turn affects living organisms as

the oxygen content is decreased (Cheesman, 2005). Sugar mill activities result in large

amounts of organic material being deposited in aquatic systems through the discharge of

effluent from the mills (Cheesman, 2005). Grain size distribution is determined in order

to evaluate sediment transport and deposition. The grain size of sediment influences its

ability to be transported by the flow regime, and when this regime is unable to take it

over different gradients or past obstacles, a build-up of sediment occurs, called deposition

or siltation. (Freeman and Rowntree, 2005). Siltation is one of the main impacts of sugar

cane farming on aquatic ecosystems (Cheesman, 2005) and can be detected by the

presence of a high percentage of finer sediments (Venter and van Vuren, 1997,).

Sediment transportation increases as its coarseness increases, resulting in a lower

diversity of benthic invertebrates since their biotope is removed. Fine grain-sediments

tend to have higher organic carbon content and are more likely to be a depository for

pollutants in comparison to larger sediment grain size fractions such as sand and gravel

(USEPA, 2001).

19

2.1.3 Habitat

Habitat integrity refers to the physico-chemical and habitat characteristics that are

balanced and integrated on a temporal and spatial scale that is comparable to the

characteristic of natural habitats within a region. Habitat structure influences the

integrity of aquatic ecosystems and includes factors such as stable substrate, instream

cover, bank stability, canopy, channel width/depth, riparian vegetation, gradient and

channel morphology (Roux, 1999).

Since natural ecosystems consist of organisms, populations and communities that interact

with one another and the abiotic environment, they respond specifically to multiple

stressors presented to them and have different ranges of tolerance to these stressors.

Chemical measures alone are not sufficient for assessing the integrity of aquatic

ecosystems. Amongst others, an assessment of the habitat structure is essential since

habitat availability and diversity are major determinants of aquatic community structure.

Furthermore, the information obtained from such non-biological indicators frequently

supports the interpretation of biological results (Roux, 1999). For this reason, a habitat

index called the Invertebrate Habitat Assessment Index (IHAS) was designed. The index

is designed in such a way that it allows different operators to obtain very similar scores.

It is seen as an improvement on the Habitat Quality Index (HQI) since it allows for less

subjectivity (McMillan, 2002).

The main effects that sugar cane farming have on a river system with regards to habitat

quality is firstly, the removal of natural riparian vegetation since sugarcane is often

grown right up to the banks of rivers (Arthington et al., 1997). Bunn et al. (1997) noted

that indigenous riparian leaf litter, which drives the dynamics of the aquatic community,

is replaced by cane leaf litter that does not provide the necessary resources that would

normally be obtained from indigenous vegetation. Secondly, the abstraction of large

amounts of water results in the alteration of channel morphology. This, in turn, results in

the disappearance of key biotopes needed for the survival of many invertebrate species.

20

The objective of this chapter is to assess the abiotic and habitat structure of the lower

Mvoti and Amatikulu Rivers. This will be accomplished by addressing the physical and

chemical aspects of water quality, the sediment characteristics in terms of grain size

composition, moisture and organic content and, lastly, the state of the instream habitat of

these two systems at selected sites during a high and low flow period.

2.2 Materials and Methods 2.2.1 Water Quality

2.2.1.1 Sampling Protocol

Figure 2.1 A map of the Kwazulu Natal province indicating the location of the

Mvoti and Amatikulu Rivers and the sites assessed in this study.

Duplicate grab water samples were collected in polyethylene bottles from each site

(Figure 2.1) during the high and low flow periods in March and July 2006 respectively.

The bottles were rinsed with water from the site and submerged in the water so as to

avoid any air bubbles from entering the bottle. The bottles were then frozen and stored

21

until further analysis. The physical variables temperature, pH, oxygen concentration and

percentage saturation and electrical conductivity were also measured in situ using a

WTW Multi Meter 340i (WTW, Germany).

2.2.1.2 Laboratory Analysis

One set of the high and low flow samples were taken to Mhlathuze Water in Richards

Bay for analysis where the samples were analyzed for BOD and CODs and

microbiological variables (heterotrophic plate count, faecal and total coliforms). The

second set of samples were also analyzed for turbidity, nitrite, nitrate, sulphate,

ammonium, phosphorus and chlorides at the University of Johannesburg using a Merck

Photometer SQ 118 (Merck, West Germany) and relevant methods as set out in the

Merck SQ 118 manual.

2.2.2 Sediment


Sediment was sampled at the sites indicated in Figure 2.1 during high (March 2006) and

low (July 2006) flow periods occurred, respectively. The top 5cm of the sediment was

scooped into 240 ml polypropylene honey jars. These samples were then frozen and kept

on ice on the journey back to Johannesburg. The samples were transferred into a freezer

at the University of Johannesburg Zoology Department laboratories and were stored until

analysis. Prior to analysis the sediment was allowed to defrost overnight.

2.2.2.2 Laboratory Analysis

Sediment analysis was performed according to the techniques described by the United

States Environmental Protection Agency (2001) and involved the determination of the

moisture content, total organic carbon content (TOC) and particle size contribution of

22

each sediment sample. Each test was done in triplicate. Excess water was decanted from

the honey jar and the sediment was homogenised by mixing with a spatula and rolling the

jar from side to side, and shaken.

For moisture content determination, glass beakers were labelled and placed in a

desiccator for 1 hour and weighed on a scale (Zeiss, West Germany). Approximately 10g

of sediment was then placed in each bottle after which the mass of the wet sediment and

beaker were recorded. The beakers as well as the honey jars containing the rest of the

samples were placed in an oven (Gallenkamp, England) at 60°C for 24 and 96 hours

respectively. The glass beakers containing the now oven-dried sediment were reweighed

and the moisture content of the sediment was calculated by taking the difference between

the wet and dry sediment masses.

The TOC content was then determined. Porcelain crucibles were labelled with an HB

pencil, placed in a dry seal desiccator (Jencons, U.S) for 30 minutes and weighed.

Approximately 2 g of the oven-dried sediment was then placed in the crucibles after

which they were weighed once again. The crucibles were then placed in a Muffle

Furnace (Labcon, South Africa) at 600°C for 6 hours. Each crucible containing the

sediment was reweighed and the total organic carbon was calculated by taking the

difference between the oven-dried and the incinerator-dried sediment masses.

Classification was applied according to the USEPA (1991a) system shown in Table 2.1.

Table 2.1 Organic content classification system in sediment (USEPA, 1991a).

Classification Percentage

Very low < 0.05%

Low 0.05 – 1 %

Moderately

Low

1 – 2 %

Medium 2 – 4 %

High > 4%

23

The particle size was determined by shaking the sediment using a sieve system

(Endecotts, England) for 15 minutes, and then weighing off and recording the content of

each individual sieve. The sieve sizes ranged from > 4000 µm to 53 µm. Application of

grain size categories was carried out according to Cyrus et al. (2000), shown in Table 2.2.

Table 2.2 Grain size categories according to Cyrus et al. (2000)

Grain Size

(μm)

Categories

> 4000 Gravel

4000 - 2000 Very course sand

2000 - 500 Course sand

500 - 212 Medium sand

212 - 53 Very fine sand

< 53 Mud

2.2.2.3 Statistical analysis

The multivariate statistical program Canoco Version 4.5 was used to analyze the water

quality and sediment data. Ordination was done using Principle Component Analysis

(PCA) which assesses physico-chemical parameters on a spatial and temporal scale (Van

den Brink et al., 2003) and projects them, as well as the sampling sites onto a ‘best

fitting’ plane or other low-dimensional solution (Clarke and Warwick, 2001). This is

done in order deduce the (dis)similarities between the sampling sites (Van den Brink et

al., 2003).

24

2.2.3 Habitat


Habitat assessment was performed according to the IHAS and the HQI (McMillan, 1998).

IHAS scores were determined by completing both sections, the first being directly related

to the SASS5 sampling habitat and the second involving the physical features of the

system. The HQI scores were determined by taking note of instream habitat and

anthropogenic activities. The scores were then calculated as a percentage and an

ecological category (EC) was given according to Table 2.3.

Table 2.3 Rating system for the IHAS and HQI habitat quality indices.

Score Ecological Category

Description

> 70

A

Unmodified or almost natural conditions; natural biotic template will not be modified. Minimal risk of reduction in habitat availability.

60 – 70

B

Largely natural with few modifications; only a small risk of modifying the natural biotic template. Risk to the availability of habitat moderate, availability of specialist habitats at risk.

35 – 60

C

Modified state; moderate risk of modifying the biotic template occurs. Habitat unavailable to habitat specialists.

0 - 35

D

Largely modified / unnatural state; large risk of modifying the biotic template. Natural required habitat generally unavailable.

25

2.3 Results

2.3.1 Water Quality

2.3.1.1 Site GDR

During both high and low flows, the water quality variables were within the guidelines

for domestic use (DWAF, 1996b) excepting for turbidity, which was very high during the

high flow period, and the microbiological variables which also seemed to be higher

during the high flow period.

The historical reference data in Table 2.4 was provided by DWAF (1983) from the

Glendale Weir (Monitoring Station U4H800) from 1983 to 1996. Compared to the

median values, one can determine the Target Water Quality Ranges (TWQR) for aquatic

ecosystems (DWAF, 1996a) at each site. Compared to these values, the results showed

low conductivity and high nitrate and phosphate values at GDR during the high flow

period and low phosphate and ammonium values during the low flow period. From

Table 2.5 and 2.6, it can be seen that the sulfate content was 0.1 mg/ℓ during both high

and low flow periods. Chlorides were found to be 4 and 10 mg/ℓ respectively, which is

below target range. COD and BODs were found to be relatively low here compared to

the other sites giving no cause for concern.

Table 2.4 Results of the descriptive statistics of the historical water quality parameters

provided by DWAF from the Glendale Weir Monitoring Station U4H800

from 1983 to 1996. Conductivity

(μS/cm) Chloride

(mg/ℓ) pH Sulphate

(mg/ℓ) Ammonium

(mg/ℓ) Nitrate (mg/ℓ)

Phosphate(mg/ℓ)

Count 367 310 336 309 336 336 33695 Percentile 446.8 63.1 8.3325 19.6 0.05775 0.08 1.01550 Percentile 242 27 7.97 10 0.019 0.02 0.4825 Percentile 158.3 15 7.4175 5 0.008 0.02 0.09Mean 260.8 31.18 7.94 11.04 0.03 0.04 0.51

26

Table 2.5 Physical, chemical and biological water variables for all sites during the high

flow period. The Target Water Quality Ranges (TWQR) for domestic and

aquatic ecosystems are also given.

Variables GDR GDS USR USS AR AS TWQR

Domestic Use

TWQR Aquatic

Ecosystem

Dissolved Oxygen (mg/ℓ) 8.3 7.3 8.5 1.5 7.57 7.05 NA 80% to 120%

saturation Oxygen Saturation

(%) 111.2 91 102.5 18.3 92 88.2 NA 80% to 120% saturation

Conductivity (μS/cm) 140 146 153 1141 147 7.2 0 to 700 >15% of

Reference Temperature

(˚C) 30.4 25 25 25 25.9 26.2 NA >2ºC or 10% of reference

pH 8.2 7.44 7.6 7.13 7.34 7.2 6 to 9 >15% of reference

Nitrate (mg/ℓ) 1.3 4.1 3 7.8 1.4 6.5 0 to 6 >15% of

Reference Nitrite (mg/ℓ) 0.11 0.08 0.11 0.19 0.16 0.17 0 to 6 >15%

of reference Phosphate

(mg/ℓ) 0.04 0.05 0.08 0.51 0.02 0.02 NA >15% of reference

Ammonium (mg/ℓ) 0.02 0.03 0.02 0.9 0.05 0.07 NA >15%

of reference Turbidity

(NTU) 19 19 26 33 11 8 0 to 1 >15% of reference

Sulfate (mg/ℓ) 0.1 3 1 15 0.1 2 0 to 200 NA

Chlorides (mg/ℓ) 4 5 6 82 11 19 0 to 100 NA

Total Alkalinity (mg/ℓ CaCO3) 36 41 40 217 42 49 NA NA

BOD (mg/ℓ) <1 <1 <1 1 1 1 NA NA

COD (mg/ℓ) 20 22 24 41 26 26 NA NA

Chlorophyll a Talling (μg/ℓ)

2.19 0.88 0.61 1.35 0.56 0.53 NA NA

Confirmed E. Coli type1 Pos Pos Pos Pos Pos Pos 0 NA

Heterotrophic plate

(cells1mℓ ) 2240 3260 >10000 >10000 880 3800 0 to 100 NA

Faecal coliform (cells/100m) 1200 1650 1150 2800 900 350 0 NA

Total coliform (cells/100m) >10000 >10000 2000 >10000 1850 1500 0 to 5 NA

27

Table 2.6 Physical, chemical and biological water variables for all sites during the low flow

period. The Target Water Quality Ranges (TWQR) for domestic and aquatic

ecosystems are also given.

Variables GDRL GDSL USRL USSL ARL ASL TWQR

Domestic Use

TWQR Aquatic

Ecosystem

Dissolved Oxygen (mg/ℓ) 11.13 9.96 11.26 0.2 9.84 8.94 NA 80% to 120%

saturation Oxygen Saturation

(%) 105 99.3 108.5 2 97.4 87.7 NA 80% to 120% saturation

Conductivity (μS/cm) 183 203 225 18.45 300 377 0 to 700 >15% of

reference Temperature

(˚C) 12.3 14.6 14.3 12.3 15.4 14.1 NA >2% or 10% of reference

pH 7.83 7.73 7.66 7.64 7.5 7.44 6 to 9 >15% of reference

Nitrate (mg/ℓ) 0.4 0.9 1.4 0.125 2.4 0.125 0 to 6 >15% of

reference Nitrite (mg/ℓ) 0.01 0.01 0.01 0.09 0.01 0.01 0 to 6 >15%

of reference Phosphate

(mg/ℓ) 0.01 0.01 0.02 0.37 0.02 0.03 NA >15% of reference

Ammonium (mg/ℓ) 0.01 0.005 0.04 1.26 0.01 0.005 NA >15%

of reference Turbidity

(NTU) 2 3 8 26 3 1 0 to 1 >15% of reference

Sulfate (mg/ℓ) 0.1 2 4 15 2 0.1 0 to 200 NA

Chlorides (mg/ℓ) 10 12 6 42 20 22 0 to 100 NA

Total Alkalinity (mg/ℓ CaCO3) − − − − − − NA NA

BOD (mg/ℓ) 1 7 2 1 2 2 NA NA

COD (mg/ℓ) 12 22 8 42 <2.9 14 NA NA


− − − − − − NA NA

Confirmed E. Coli type 1 − − − − − − 0 NA

Heterotrophic plate

(cells1mℓ ) 2430 1525 1270 − 260 2160 0 to 100 NA

Faecal coliform (cells/100mℓ) 690 580 320 − 36 33 0 NA

Total coliform (cells/100mℓ) 950 750 420 − 230 49 0 to 5 NA

2.3.1.2 Site GDS

In Tables 2.5 and 2.6 it can be seen that results are similar to Site GDR in that the

turbidity and microbiological values exceed the target ranges for domestic use (DWAF,

1996b). The coliforms at Site GDS were higher during high flow than at Site GDR and

28

vice versa during low flow. In terms of turbidity the values increase during high flow

and decrease during low flow but the values during low flow are still not within the target

range.

In comparison to the historical reference data it Table 2.4, it is evident that there are high

phosphate and nitrate values as well as low conductivity at this site during high flow, as

was seen at Site GDR. During low flow, however, the amount of phosphate and nitrate

decreased, but were still not in the target range, with phosphate being below and nitrate

above the range. Compared to Site GDR, the BOD and COD values are higher, and DO

is lower at high and low flow periods. Sulfate levels at Site GDS increase to 3 and 2

mg/ℓ during high and low flow, respectively, compared to Site GDR where sulfate levels

were < 0.5 mg/ℓ. Chlorides also increase at Site GDS during both high and low flows but

only by one or two values. Similarly, the conductivity also increases slightly.

2.3.1.3 Site USR

Once again the only variables that exceed the target range in terms of domestic use

(DWAF, 1996b) at this site are turbidity and the microbiological variables. The turbidity

values are, however, higher than the previous sites indicating more sediment

transportation downstream of the Glendale sites.

This site as well as another site close to Site USS was monitored by Mackay et al. (2000).

These data were collected over six consecutive weeks and the mean values are shown in

Table 2.7. The variables that increased when compared to the Mackay et al. (2000) study

were oxygen saturation, nitrate, nitrite and phosphate during the high flow period and

nitrate during the low flow period. The conductivity, ammonium, sulfate, chloride,

alkalinity, phosphate (during low flow) and COD values decreased and the pH remained

the same.

When compared to another study at this site (Malherbe, 2006) it was found that nitrate

values increased during both high and low flows and the COD value also increased

during high flow from 10.58 to 24 mg/ℓ. The oxygen saturation, however, appeared to be

29

higher during the current study. The sulfate, chlorides and conductivity values, however,

showed a sharp decrease from the Malherbe (2006) study during high and low flow

periods indicating better conditions.

Table 2.7 The water quality data acquired by Mackay et al. (2000) for two sites on the

Mvoti River that was collected over a period of 6 weeks. The median values

are shown here. The TWQR for both domestic use and aquatic ecosystems

are also indicated.

Variable Reference data for

Site USR

Reference data for

Site USS

TWQR Domestic Use

TWQR Aquatic Ecosystem

Dissolved Oxygen (mg/ℓ)

6.12 5.95 NA 80% to 120% saturation

Oxygen Saturation (%)

65.48 64.73 NA 80% to 120% saturation

Conductivity (μS/cm)

350 360 0 to 700 >15% of Reference

Temperature (˚C)

18.89 19.37 NA >2ºC or 10% of reference

pH 7.68 7.62 6 to 9 >15% of reference

Nitrate (mg/ℓ)

0.24 0.24 0 to 6 >15% of Reference

Nitrite (mg/ℓ)

0.01 0.01 0 to 6 >15% of reference

Phosphate (mg/ℓ)

0.05 0.18 NA >15% of reference

Ammonium (mg/ℓ)

0.07 0.80 NA >15% of reference

Turbidity (NTU)

7.33 7.50 0 to 1 >15% of reference

Sulfate (mg/ℓ)

7.67 7.20 0 to 200 NA

Chlorides (mg/ℓ)

37.67 52.33 0 to 100 NA

Total Alkalinity (mg/ℓ CaCO3)

73.67 84.67 NA NA

COD (mg/ℓ)

214 207.33 NA NA


0.8 1.02 NA NA

Heterotrophic plate (cells1mℓ )

42000 50000 NA NA

Faecal coliform (cells/100m)

2204 603 0 NA

Total coliform (cells/100m)

54500 32250 0 to 100 NA

30

2.3.1.4 Site USS

The water quality at this site is poor due to high conductivity, nitrate content, turbidity

and coliform bacteria that exceed the ranges for domestic use (DWAF, 1996b). The

oxygen content is also very low at this site, especially during the low flow period (Tables

2.5 and 2.6). The results show COD values of 41 and 42 mg/l at high and low flow,

which are much higher than that recorded at any of the other sites. The high conductivity

value (1141 μS/cm) during high flow is also reflected in the high chloride content (82

mg/ℓ) and alkalinity. As was found in the Mackay et al. (2000) study, the variables of

concern include oxygen, conductivity, nitrites, nitrates, phosphates, ammonium and

turbidity.

2.3.1.5 Site AR

The TWQR for domestic use (DWAF, 1996b) are exceeded for turbidity and

microbiological variables during both high and low flow sampling periods. The turbidity

here is much lower than the Mvoti River sites. The microbiological variables are also

lower than the other sites.

Since there is no reference data available for the Amatikulu River, this data was

compared to the historical values taken from the Glendale Weir on the Mvoti River

(Table 2.4) as these rivers are in the same region. When comparing the water variables

with this data, the conductivity showed to be low during high flow and high during low

flow. Nitrate was high during both flows, especially during low flow and ammonium

was high during the high flow. The oxygen saturation was lower during high flow

whereas the COD was much higher during this time compared to low flow values (26

mg/ℓ). Sulfate levels were below detection limits during high flow and 2 mg/ℓ during

low flow. Chlorides increased from 11 mg/ℓ during high flow to 20 mg/ℓ during low

flow.

31

2.3.1.6 Site AS

In Tables 2.5 and 2.6, the turbidity and coliforms exceed the target range for domestic use

(DWAF, 1996b) during both flows at this site, with higher turbidity values when

compared to Site AR. During the high flow period, the conductivity of the system was

very low (7.2 μS/cm) and the nitrate and ammonium concentrations exceeded the target

values for aquatic ecosystems (DWAF, 1996a). At low flow, however, the exact opposite

occurs with conductivity being high (377 μS/cm) and nitrate and ammonium values

decreasing to below detection limits. Phosphates are in the acceptable range during the

high flow but not during the low flow period. Oxygen saturation does not deviate much

from high to low flow and is within range, but the COD is higher during high flow (26

mg/ℓ ; low flow =14 mg/ℓ). Sulfates seem to be present in low concentrations during

high flow but are not detectable during low flow. Chloride concentrations are higher

during the low flow period (22 mg/ℓ) but are within the TWQR for aquatic ecosystems

and domestic use (DWAF, 1996a & b).

When comparing Site AR and Site AS, there are significant increases in nitrates, sulfates,

chlorides and alkalinity at Site AS during high flow, and conductivity and COD during

low flow. Results also show that oxygen saturation is about 10% lower at Site AS, thus

supporting the high COD value.

2.3.1.7 Spatial and Temporal Analysis

The spatial and temporal analysis is indicated in the following PCA plot where high and

low flow periods are shown together (Figure 2.2). The high and low flow bi-plot

indicated that 68% of the variation in the data is explained, where 45.5% is explained on

the first axis, while 22.5% is explained on the second axis. The low oxygen content, as

well as the high chloride, sulfate, turbidity and COD values at the USS sites during both

high and low flow separate them from the rest of the sites. The reference sites, with the

exception of Sites ARL and USRL, are grouped together due to high oxygen content and

the sampling sites, with the exception of Sites GDS and AS, are in a group due to high

conductivity values. Sites ARL and USRL are different due to high conductivity and

32

BOD values, respectively, and GDS due to higher oxygen content. There is no specific

factor that separates Site AS from the other sites, but has a lower conductivity compared

to that during low flow.

-1.0 1.0

-1.0

1.0

Oxygen mg/l

Oxygen %

Conductivity

Temperature

pH

Nitrate

Nitrite

PhosphateAmmonium

Turbidity

SulfateChloride

BOD

COD

E.coli

GDR

GDRL GDS

GDSL

USR

USRL

USS

USSL

AR

ARL

AS

ASL

Figure 2.2 PCA bi-plot of water quality variables and sampling sites for both high and

low flow data. The L suffix indicates the low flow period and PC1 is represented by the

x-axis and PC2 by the y-axis.

33

2.3.2 Sediment

2.3.2.1 Moisture content, Organic content and Grain size determination

The moisture contained in the sediment during the high and low flow periods (Table 2.8)

ranged from 11% to 21% and 14% to 18% respectively. Similarly the organic content

ranged from 0.24% to 0.57% and 0.21% to 0.69%. The exception, however, was the

sediment at Site USS. The water content here was shown to be 38% during high flow

and 31% during low flow. This is supported by the organic content, being 11% and 6%

respectively. Second to Site USS, Site AS, had the highest moisture and organic content

during high flow and the highest organic content during low flow.

Table 2.8 Percentage moisture and organic content of all sites during high and low flow

periods.

Sites High Moisture Organic

Low Moisture Organic

GDR 18.99 0.27 14.51 0.22 GDS 15.23 0.32 18.92 0.21 USR 11.42 0.24 16.08 0.29 USS 38.80 11.90 31.40 6.93 AR 14.31 0.32 14.67 0.39 AS 21.97 0.57 15.67 0.69

During high flow, 90% of the sediment at Sites GDR and GDS is dominated by very fine

to medium sand (Table 2.9). 70% of Site USR is comprised of very course sand. Site

USS is not dominated by any particular grain size since the different sizes ranged from

6% to 29%. Site AR consists of sizes ranging from very fine sand to gravel, with the

latter courser particles dominating. Site AS is characterized by very fine to medium sand

particles.

34

Table 2.9 Percentage grain size distribution of sediment from the Mvoti and Amatikulu

sites during the high flow period (March 2006), with sieve sizes ranging from

>4000 μm to 53 μm.

Grain size (μm) GDR GDS USR USS AR AS < 4000 0.61 0.60 1.09 29.43 26.97 0.00

2000 – 4000 4.49 1.39 69.7 9.44 7.05 0.04 500 – 2000 66.92 50.65 5.87 21.61 39.22 1.69 212 – 500 24.86 41.94 23.0 13.98 22.18 52.20 53 – 212 3.05 5.26 0.24 18.78 4.36 45.02

0 – 53 0.05 0.13 0.03 6.72 0.19 1.03

During low flow conditions, all the sites are dominated by course sand with the exception

of Site USS that, once again, is not dominated by any particular grain size (Table 2.10).

Table 2.10 Percentage grain size distribution of sediment from the Mvoti and Amatikulu

sites during the low flow period (July 2006).

Grain size (μm) GDR GDS USR USS AR AS < 4000 8.03 0.19 1.41 25.56 8.25 11.11

2000 – 4000 20.15 3.38 10.99 20.47 15.12 9.15 500 – 2000 63.70 81.68 65.13 35.68 57.26 65.59 212 – 500 7.96 12.12 21.05 9.63 17.45 13.31 53 - 212 0.13 2.45 0.85 3.21 0.94 0.34 0 - 53 0.006 0.15 0.55 5.41 0.94 0.48

2.3.2.2 Spatial and Temporal Analysis

The high and low flow bi-plot in Figure 2.3 indicated that 83.8% of the variation in the

data is explained, where 55.9% is explained on the first axis, while 27.9% is explained on

the second axis. There is a spatial trend that is evident at the Mvoti sites and a temporal

trend at the Amatikulu sites. The organic content and gravel at Site USS, which is the

site below the Sappi Pulp and Paper Mill and the Ushukela Sugar Mill influence,

separates it from the others during both high and low flow. This is because of the organic

content brought in by the multiple stressors. The other sites have a low organic content

35

and are separated by course sand excepting Sites AR and AS during high flow which

have fine sand due to siltation. Course sand, however, occurs at Sites AR and AS during

low flow.

-1.0 1.0

-0.4

1.0

Gravel

Very coarse

CoarseMedium

FineMud

Moisture

Organic

GDRGDRL

GDSGDSL

USR

USRL

USS

USSL

AR

ARL AS

ASL

Figure 2.3 PCA bi-plot of grain size distribution and sampling sites for both high and

low flow data. The L suffix indicates the low flow period and PC1 is represented by the

x-axis and PC2 by the y-axis.

2.3.3 Habitat

According to Table 2.11 and Figure 2.4 A, the IHAS, and to an extent, the HQI results

show that the habitat integrity decreases downstream in the Mvoti River during high

flow. Sites GDR and GDS have the highest scores with Site GDR being in an almost

natural condition according to the HQI (High flow = 76, Table 2.11 and Fig. 2.4) and Site

GDS in an almost natural ecosystem according to the HQI (High flow = 79, Table 2.11).

Site GDR, however, is in a modified state according to IHAS at high flow because the

index was adjusted to only include the vegetation biotope in the assessment. Similarly,

36

Site GDS is slightly modified from the natural state according to the IHAS because of the

adjustment to include only the vegetation and stones biotopes (Table 2.11). Site USR is in

a modified state during high flow according to both IHAS (42.5%) and HQI (58%), but is

in an almost pristine state during low flow (Table 2.12, Fig. 2.4).

Site USS is in a modified state during high flow, having a relatively lower score on the

IHAS (36.2%) than when compared to the HQI (60%). According to HQI, Site AR is in

an A class with a score of 83% and Site AS (63%) is slightly more degraded than Site

AR. The IHAS indicates a score of 57% for Site AR and 58.7% for Site AS.


integrity of the Mvoti and Amatikulu sites during the high flow period. Site Total IHAS

(%) IHAS Class

Total HQI(%)

HQI Class

GDR 52.5 BC 76 A GDS 52 BC 79 A USR 42.5 C 58 C USS 36.2 CD 60 BC AR 57 BC 83 A AS 58.7 BC 63 B


integrity of the Mvoti and Amatikulu sites during the low flow period. Site Total IHAS

(%) IHAS Class Total HQI

(%) HQI Class

GDR 72 A 88 A GDS 67 B 78 A USR 72.5 A 79 A USS 42.5 C 35 CD AR1 70 A 69 B AS1 70 A 66 B

During low flow (Table 2.12 and Figure 2.4 B) most of the sites seem to be in a better

condition compared to high flow. The IHAS index points out that there is an impact at

Site GDS (67%), compared to Site GDR (72%). Site USS seems to still be in a modified

state, but Site USR is in an almost natural state according to IHAS and HQI. The

37

Amatikulu sites are almost natural (70%), according to the IHAS and largely natural with

few modifications according to the HQI.

A

0102030405060708090

GDR GDS USR USS AR AS

Sites

Perc

enta

ge

IHASHQI

B

0102030405060708090

100


Sites

Perc

enta

ge

IHASHQI

Figure 2.4 Graphical representation of IHAS and HQI scores during the (A) high flow

period and (B) the low flow period.

38

2.4 Discussion 2.4.1 Water Quality

The microbiological values exceed the target range for domestic use (DWAF, 1996b) at

all sites. The same is true for turbidity except at Site AS during low flow. The high

turbidity can be attributed to suspended soil particles from erosion (Boyd, 2000) that is

caused by sugar cane being planted close to the riparian zone, thus decreasing the

stability of the bank (Freeman and Rowntree, 2005). Turbidity of water greatly restricts

light penetration, decreasing photosynthesis and thus phytoplankton growth. This in turn

decreases the DO content (Boyd, 2000). The high microbe content is due to rural

settlement runoff and cattle farming in the area. The turbidity values at Site USR seem to

be higher than the previous sites indicating more sediment transportation downstream of

the Glendale sites. This is evident by the presence of extensive sandbanks in the channel.

In addition, indigenous riparian vegetation is removed, destabilizing the bank and adding

to the erosion causing the river bed to be composed of more than 90% sand. At Site USS,

bank erosion is prominent as both banks are undercut and near vertical. This, together

with the removal of riparian vegetation, contributes to the high turbidity of the stream.

The turbidity and microbiological variables at Site AR are much lower than the Mvoti

river sites and do not give cause for concern. However, the turbidity values at Site AR are

higher than at Site AS because the sediment size at Site AR is courser than the sediment

at Site AS, which consists mainly of very fine to medium sand. Thus sediment is

transported more efficiently at Site AR than at Site AS resulting in more sediment being

evident in the water at Site AR. The flow at Site AS has been greatly impacted by the

Amatikulu Sugar mill due to water abstraction upstream resulting in a decrease in the

volume of water and siltation. The microbiological variables appear to be higher at high

flow, and the high ammonium values at high flow supports this. Rural settlements do not

seem to be a major contributing factor here, but cattle farming is.

According to historical reference data supplied by DWAF or the Glendale Weir, the

variables that exceed the TWQR for aquatic ecosystems at all sites are nitrates and

phosphates. High nitrates and phosphates are attributed to fertilizers used for agricultural

39

purposes (Cheesman, 2005). There are sugar cultivated lands surrounding these sites that

may contribute to the high nitrate and phosphate values through runoff from these fields.

In addition, these variables have also been found to occur in sugar mill effluent (Pawar et

al., 1998). In comparing Sites GDR and GDS in terms of nitrates and phosphates, the

significant increases of nitrate concentrations at high flow from 1.3 mg/ℓ at GDR to 4.1

mg/ℓ at Site GDS and from 0.4 mg/ℓ at Site GDR to 0.9 mg/ℓ at Site GDS at low flow

indicate an impact by mill effluent. This is because both Sites GDR and GDS are

impacted by runoff from fertilizers used at the surrounding irrigated sugar-cultivated

lands but only Site GDS is impacted by effluent discharge. The amount of phosphate at

Site GDS at low flow, however, appeared to be below target range. This could be due to

the fact that phosphate is less mobile than nitrate and it is sorbed into the soil. Phosphate

is also removed from water by crops as it is one of the chief nutrients required by plants

(Starr and Gillham, 1993). Observation of high nitrate (low flow) and phosphate (high

flow) concentrations at Site USR, when compared to the Mackay et al. (2000) study, can

also be attributed to the highly irrigated sugar cultivated lands occurring in the area

surrounding this site. This gives way to the leaching of phosphates, nitrates, sulfates and

chlorides into the system through the use of fertilizers (Pawar and Shaikh,1995). Urban

runoff also contributes to this, resulting in eutrophication of the waterway (Campbell et

al., 2001). In addition, discharge from the upstream Glendale sites may also be having an

impact here. At Site USS, the nitrates and phosphates are much higher when compared

to the values at Site USR, demonstrating the impact caused by the mill effluent

discharged upstream. High nitrates were also found to occur at Site AS but when

comparing Site AR and Site AS, there were significant increases in nitrates, which is

once again, a mill effluent impact. It was also observed that the nitrate content at Site

USS was higher than that at Site AS during high flow, probably due to the dual effect of

the paper mill and sugar mill on Site USS. The reason why phosphates were not present

in elevated concentrations at Site AS during high flow was probably because of low

mobility and sorption by soil and crops as mentioned earlier.

The remaining variables that seem to have an influence on water quality are conductivity,

ammonium, sulfate, chloride, BOD, COD, DO, alkalinity and nitrites. The results at Site

40

GDR showed low conductivity during the high flow period and low ammonium values

during the low flow period. The sulfate and chloride values at this site are also within the

TWQR for domestic use and aquatic ecosystems (DWAF, 1996a & b). Compared to Site

GDR, the BOD and COD values at Site GDS are higher and the DO is lower during both

flows which indicate a possible impact by the discharge of the Glendale mill effluent into

the system from upstream. Distillery effluent (stillage) has been shown to have high

BOD and COD concentrations of about 40 000 to 100 000 mg/ℓ (Akbar and Khwaja,

2006). In addition, the increase in sulfate and chloride levels at Site GDS indicates that

the mill could have an influence here seeing that distillery effluent has been shown to

contain 1 310 mg/ℓ sulfate and 4 050 mg/ℓ chloride (Ramana et al., 2002).

The low oxygen content and high COD, conductivity, chloride and alkalinity at Site USS

is due to the fact that it is impacted by Ushukela Sugar Mill and Sappi Stanger Pulp and

Paper Mill effluent. This may eventually cause salinisation of this stream seeing that the

flow rate here is very slow. The sugar mill effluent has a high organic content

(Cheesman, 2005) (thus high BOD and CODs) and contains chlorides, sulfates,

phosphates and nitrates (Pawar et al., 1998). The pulp mill effluent is similar, with the

exception that phosphates and nitrates were not shown to be present (Malherbe, 2006).

Sulfate levels at this site are higher than any other site due to the combined influence of

the pulp and sugar mill. The oxygen, conductivity, nitrites, nitrates, phosphates,

ammonium and turbidity values that are considered to be variables of concern according

to the Mackay et al. (2000) study, are much higher when compared to the values at Site

USR, demonstrating the impact caused by the mill effluent. In comparison to the

Glendale Weir data, high ammonium and low oxygen saturation values were observed at

Site AR. The high ammonium concentration is due to cattle farming in the area seeing

that this site is visited by cattle herds from time to time. This is made evident by

livestock tracks and droppings on the river banks. Oxygen saturation was lower during

high flow due to the increased COD value, which could be due to the decomposition of

course alien woody debris present in the channel that has an extensive impact on the

channel. The presence of sulfates and chlorides during low flow is probably due to

fertilizer use since it has been shown that heavy use of fertilizers in a highly irrigated area

is responsible for high chloride and sulfate values (Pawar and Shaikh, 1995).

41

When comparing Site AR and Site AS, there are significant increases in nitrates, sulfates,

chlorides and alkalinity at Site AS during high flow and for conductivity and COD during

low flow. Results also show that oxygen saturation is about 10% lower at Site AS, thus

supporting the high COD value. The increase in the former values is typically an impact

of the discharge of sugar mill effluent into the system. In addition, it has been reported

by Cheesman, 2005 and Akbar & Khwaja, 2006 that sugar mills can also produce acidic

and basic wastes from cleaning of equipment (Cheesman, 2005). This explains the

increase in the alkalinity of the system.

According to the spatial and temporal analysis, the reference sites have a high oxygen

content and the sampling sites do not, indicating an effluent impact from the mills. The

sampling sites also have a high conductivity, which is an indication of nutrient loading.

Site USS seems to be the worst off with higher chloride, sulfate, turbidity and COD

values compared to the other sites. This is due to the impact from both the sugar and

paper mill.

2.4.2 Sediment

The results above indicate that the sites on the Mvoti River, GDR, GDS and USR have a

low organic content. This concurs with the findings of Mackay et al. (2000) and

Malherbe (2006). Site USS, however, has a high organic content (high flow = 11.9% and

low flow = 6.93%). This is probably due to the decomposition of the plant material

remaining after removal of natural riparian vegetation e.g. root wads and the aquatic

macrophytes in the system. In addition, the discharge of organic effluent from the Sappi

Pulp and Paper Mill and the Ushukela Sugar Mill also contributes to this. On the

Amatikulu River, Sites AR and AS also have a low organic content but Site AS had the

highest moisture and organic content during high flow and the highest organic content

during low flow (Low 0.57 and High 0.69), next to Site USS. This is possibly due to

flooding conditions, where many plants and trees were washed into the river from

upstream. Nevertheless, it is expected that these sites have low organic contents as they

42

are riverine sites at the head of the Amatikulu estuary (Wepener, 2008; Personal

Communication). Therefore, the energy available to organisms in terms of organic

content at all the sites on the Mvoti and Amatikulu rivers is low, with the exception of

Site USS.

The grain size analysis showed very fine to medium sand occurring at Sites GDR and

GDS during the high flow period. This is due to the flooding and fast-flowing water that

occurred during high flow in the Mvoti River, causing displacement of fine sediment and

transportation by the flow regime. Site USR, on the other hand, is comprised of very

course sand during this time. This is possibly due to flow modification that is caused by

bed and channel modification for water abstraction, as well as gravel extraction.

At Site USS there was a relatively homogenous distribution of grain size during both high

and low flow periods. Sites AR and AS were shown to be comprised of very fine to

medium sand (Figure 2.3) during high flow and course sand during low flow. This is

possibly due to the fact that very fast flows during high flow scour sediment from the

river banks, which are unstable due to the effects of erosion brought on by sugarcane

farming. This eroded silt is then deposited on the river bed. During low flow, coarse

sand was dominant at all sites except Site USS. This corresponds with the Mackay et al.

(2000) study and indicates that sediment transportation and aggregation is taking place in

this system.

2.4.3 Habitat

Some of the main impacts on habitat quality in aquatic ecosystems in general include

siltation of spawning and food production areas, unstable bed conditions, rapidly

changing flows, the absence of refuge areas for biota, the removal of riparian vegetation

and altered water temperature (Hoffman et al., 1995). The habitat assessment on the

Mvoti River indicated that the quality of conditions decrease as one moves downstream

from Sites GDR to USS. Tharme (1996) indicated that the habitat in this lower section of

the river was “modified completely, with an almost total loss of natural habitat” and the

study by Mackay et al. (2000) regarded it to be in a “poor” state. Tharme (1996) goes on

43

to say that eroded soils are deposited considerably, overgrazing is the cause of the loss of

indigenous vegetation alongside the river, and the effects of over-abstraction of water can

be seen. Similarly, Kemper (1996) stated that natural vegetation was removed, and

abstraction of water was at a critical level. Mackay et al. (2000) findings confirmed these

results and stated that there has been no improvement since 1996. In the latest study by

Malherbe (2006), it was noted that the habitat quality in the lower Mvoti River remained

in a modified to severely modified state and has been due to multiple impacts in the

surrounding area.

Sites GDR and GDS have the highest habitat scores, as expected, since they are upstream

of the dual effect of the sugar and paper mill. Site GDR is the uppermost site being

almost natural, but is impacted by rural settlements and sugarcane farming activities in

the vicinity, as well as flow modification and sediment deposition due to the bridge in the

channel. Continuous reeds and grasses, as well as patchy shrubs and trees occur on the

banks of the active channel. The river channel is dominated by alluvial sand deposits.

This site is in an almost natural condition according to the HQI (High flow = 76, Low

flow = 88, Table 2.11 and 2.12) and is in a modified state according to IHAS at high flow

because the index was adjusted to only include the vegetation biotope in the assessment.

The low flow results, however, support the HQI results with a score of 72 (Table 2.12).

This is because of the presence of vegetation, stones in current, and gravel, sand and mud

(GSM) biotopes at low flow.

Site GDS is the site below GDR, downstream of the Glendale mill. The habitat available

to organisms during high flow is vegetation and stones, providing fast and slow riffles

and deep pools. During low flow, however, there are more riffles present. Grass, trees,

shrubs and reeds cover and therefore stabilise the banks. Sandbanks were, however, also

visible downstream of the bridge during high flow. Limited agricultural activities occur,

but it is extensively impacted by residential areas, bridges and recreation. The IHAS

index points out that there is an impact at GDS, compared to GDR. This is probably due

to the sediment deposition resulting from water abstraction by the Glendale mill upstream

of GDS. Water abstraction decreases the volume and flow causing sediment to be

44

deposited in the system, ultimately resulting in the formation of sandbanks. This in turn

changes the morphology of the river channel and hence affects the type of habitats found

there (Freeman and Rowntree, 2005). Spawning and food production areas are also

covered by this sediment, affecting reproduction and survival of many organisms

(Hoffman et al., 1995).

Site USR is upstream of the Ushukela mill and paper mill impacts and acts as a reference

site for Site USS. It is in a modified state during high flow according to both IHAS and

HQI, but is in an almost pristine state during low flow. This is due to the limited habitat

availability during high flow. Indigenous vegetation removal does occur resulting in

moderate exotic vegetation encroachment and thus bank erosion. For this reason, the

river bed is composed of more than 90% sand. This is exacerbated by the construction

and bulldozing of a lagoon that has been artificially constructed to accommodate water

abstraction for sugar mill practices. This in turn has lead to flow modification. Biotopes

available are vegetation and GSM (little variation in flow) and deposition of sediment

results in the presence of extensive sandbanks in the channel. Runoff from rural

settlements and nearby sugarcane farming activities also impacts this site.

Site USS is downstream of sugar and paper mill impacts. It is a stream containing an

isolated back water pool with turbid stagnant water. It is in a modified state according to

IHAS and HQI. The substrate is composed of rocks (>80%) and the rest mud. Both

banks are near vertical and undercut, showing the effects of bank erosion. Root wads

were also visible indicating the removal of natural riparian vegetation and the

proliferation of aquatic macrophytes is also prominent. This is possibly due to the

discharge of sugar mill effluent which leads to oxygen deficiency, resulting in the

dominance of aquatic plant communities by macrophytes (Borhidi, et al., 1986).

Continuous reeds and patchy grasses, however, still occur on the banks of the river. The

natural flow regime is further disrupted by a small bridge across the river. Cultivated

sugarcane fields, residential areas and bridges occur in the vicinity of this site.

Site AR has an IHAS score of 57% and 70% during high and low flow respectively,

whereas HQI scores were 83% and 69% respectively. This site was taken as the

45

reference site on the Amatikulu River. Sand and mud dominate the river bed with more

than 50% boulders, forming riffles during high flow. Habitat availability is abundant

giving rise to a variety of flows. Impenetrable reeds and continuous grasses occur on the

banks of the active channel. There is also evidence of impact by the sugarcane fields

surrounding this site. .

Site AS is located downstream of Site AR and the potential impacts of the Amatikulu

mill. It is influenced by the deposition of sediment resulting from water abstraction

upstream and the local bridge in the channel. These have had an extensive impact in that

a very large sand bank has formed next to the bank of the river. This has changed the

morphology of the river to a great extent. The banks are covered by shrubs, trees, grass

and reeds resulting in less than 30% bank erosion. The site seems to have undergone a

massive flooding event such that coarse alien woody debris has moderately impacted the

bank vegetation from upstream. There is also evidence of runoff from informal

settlements. IHAS scores indicate a modified state during high flow (58.7%) and an

almost natural state during low flow (70%). The site is largely natural with a few

modifications according to the HQI (High = 63%, Low = 66%).

Most of the sites seem to be in a better condition during low flow (Table 2.12 and Figure

2.4 B) compared to high flow. This could be due to the fact that during low flow more

habitat is available.

2.5 Conclusion

The effects that the sugar milling activities have on the Mvoti and Amatikulu Rivers with

regard to sediment, habitat and water quality are numerous. The water quality at the sites

downstream of the mills are deteriorating due to the increase of nitrate, sulfate, chloride

and phosphate ions as well as organic material, which is a result of effluent discharge and

irrigation as well as fertilizer use. The ions in turn increase the conductivity of the water

body and the organic material decreases the oxygen content which is detrimental to the

46

47

survival of aquatic organisms. These effects are more prominent at Site USS mainly due

to the combined effect of the Ushukela sugar mill and the Sappi Stanger mill in terms of

effluent discharge. Sites GDS and AS are similarly effected by effluent from the

distillery and sugar mill, respectively, as well as by impacts from the surrounding sugar-

cultivated lands, cattle farming and urban and rural runoff.

In terms of sediment analysis, the energy available to organisms can be considered low at

all sites excepting Site USS. The low organic content is an area of concern since organic

particles make the sediment more cohesive (Hay et al., 2005) and thus decrease erosion.

In addition, there is less food available for organisms which is essential for survival. On

the other hand, a very high organic content as is seen at Site USS leads to decreased

levels of oxygen (cheesman, 2005). This is an impact caused by the discharge of effluent

from the Ushukela and Sappi Stanger mills. Siltation is occurring at Site GDS and AS

which are the sites downstream of the Glendale Distillery and Amatikulu sugar mill.

This is due to water abstraction and flow modification resulting in an increase in the

deposition of pollutants in this fine sediment (USEPA, 2001).

Similarly, the main driver affecting the habitat quality of the systems is water abstraction

and subsequent sediment deposition. Bank erosion is also prominent at all of the sites

because of the removal of natural riparian vegetation for sugarcane farming. Good soil

management practices need to be implemented here in order for rehabilitation to take

place.

Chapter 3 Macro-Invertebrates

and Fish

Chapter 3 Macro-Invertebrates and Fish

3.1 Introduction

Natural fluxes of water and materials in river catchments are disrupted by human

activities in the form of diffuse inputs and point-sources of toxicants (Admiraal et al.,

2000). In the past, the analysis of water samples was used as an indicator to monitor

and assess aquatic health in South Africa (Roux, 1999), but many deficiencies in the

management of aquatic ecosystems arose because of this (Cairns et al., 1993). This is

due to the fact that natural ecosystems are complex, being exposed to a multitude of

stressors at once, and causing cumulative effects. For this reason biota have been

incorporated into monitoring programmes as they have the ability to integrate the

responses to environmental change (Roux, 1999). Biological monitoring, or

biomonitoring, refers to the use of living organisms in monitoring procedures and is

carried out in order to measure and evaluate the consequences of human actions on

biological systems (Kleynhans, 2003).

Aquatic macro-invertebrates lack a backbone and are larger than half a millimeter. They

include crustaceans, mollusks, aquatic worms and immature forms of aquatic insects

(Miller, 2004). They are widespread in their distribution and can live on all bottom types

such as on rocks, logs, sediment, debris and aquatic plants. Unlike fish, invertebrates are

limited in terms of movement and are thus less able to escape the impacts of sediment

and other stressors that degrade the water quality (Miller, 2004). They are also small and

easy to collect (Dallas and Day, 1993) and more sensitive to lower concentrations of

toxicants than fish are (Miller, 2004). As most invertebrates are short-lived and remain in

one area during their aquatic life-phase, they are good indicators of localized conditions

over a period of a few months (WRC, 2002). They also represent an extremely diverse

group of aquatic animals, and the large number of species possesses a wide range of

responses to stressors such as organic pollutants, sediments, and toxicants. Invertebrates

are, however, difficult to identify to species level and very little is known about the life

history of most indigenous invertebrates (Malan and Day, 2002). While fish are better

48

indicators of long-term influences and general habitat conditions in a river, invertebrate

communities are good indicators of localized conditions over the short term (Uys et al.,

1996).

Invertebrate communities can either be used to monitor stream quality conditions over a

broad area or they can be used to determine the effects of point source discharges from

sources such as sewage treatment plants and factories. Taxa richness, pollution tolerance

and functional groups are characteristics that are used to determine the condition of the

river. Taxa richness is a measure of the different types of organisms and the greater the

richness the better the water quality (Miller, 2004). The tolerance of invertebrates to

pollution differs from species to species. Thus the abundance of relatively intolerant

species indicates good water quality, whereas the abundance of pollutant-tolerant species

indicates poor water quality. Lastly, the presence or absence of certain feeding groups

such as scrapers and filterers, may give an indication of a disturbance in the food supply

and thus possible effects of toxic chemicals (Miller, 2004).

Fish have been widely regarded as good biological indicators in that they can indicate

long-term effects in broad habitat conditions. This is because they are relatively long-

lived and mobile. They can also integrate the effect of detrimental environmental

changes because fish communities often include a range of species that represent a

variety of trophic levels (Kleynhans, 2003). As they occupy the top of the food chain in

most aquatic systems, their presence is an indication of other aquatic organisms.

However, it has been found that fish are often difficult to sample and sampling

approaches may be uneconomical and cumbersome. In addition, it is often difficult to

obtain quantitative data on fish, particularly in rivers, and this poses a problem when

recognizing that each river has a different fish community structure that can be

differently affected by environmental changes (Kleynhans, 2003).

In order to obtain the best possible information from biomonitoring procedures, indices

were developed. An index reduces a large quantity of data to its simplest form while

retaining the information essential to answering the questions it was designed for

(Kleynhans, 2003). A rapid assessment of the present ecological conditions of rivers

49

using invertebrates was developed especially for South African rivers by Chutter (1994).

This biomonitoring technique is called the South African Scoring System (SASS) and

assesses the chemical quality of stream and river water from the familial composition of

the aquatic invertebrates (Chutter, 1994). Invertebrates are collected, identified to family

level and pre-defined scores are assigned for each taxon (Chutter, 1995). The total SASS

score for the sample is determined by adding the individual scores. In addition, the

concept of Average Score Per Taxon, or ASPT, has been introduced in order to take the

availability and condition of suitable habitat (Parsons and Norris, 1996) as well as natural

variation in temporal and geographical distribution of species into account (Weatherley

and Ormerod, 1990). The particular biotopes sampled, the historical flow record and the

reference conditions of the area being assessed is information that is then needed in order

to interpret the SASS results (Palmer, 1997). For fish biomonitoring, the FAII was

developed by Kleynhans in 1999, specifically for South African rivers. It is based on the

comparison of expected and observed fish assemblage in order to determine the condition

of the system (Kleynhans, 2003). The numbers of species of fish that occur in a specific

reach, the tolerance of fish to certain physical and chemical variables, and the health of

fish are used as indicators of river health (DWAF, 2001). The fish populations are

categorized according to an intolerance rating that takes into account trophic preference

and specialization, requirement for flowing water during different life stages, and

association with habitats with unmodified water quality. The results are then expressed

as a ratio of observed conditions versus reference conditions and an ecological category

(EC) is given to the segment of the river in question (DWAF, 2001). A few shortcomings

of the FAII noted by Kleynhans (1999) include the exclusion of alien species and the

underestimation of the ecological integrity of the system.

The objective of this chapter is to assess the macro-invertebrate and fish communities at

all the study sites occurring in the lower Mvoti and Amatikulu Rivers (see Chapter 1).

This will be accomplished by addressing the macro-invertebrate and fish community

condition in terms of spatial and temporal trends and through the implementation of

SASS5 and FAII indices to determine the ecological class (EC) for each site

50

3.2 Materials and Methods 3.2.1 Macro-Invertebrates

Macro-invertebrates were sampled from all the sites (Chapter 1) in 2006 during the high

(March) and low (July) flow periods. This was done using a standard SASS net and by

following the sampling techniques indicated in the SASS5 protocol (Dickens and

Graham, 2002). The 3 biotopes specified by the SASS5 protocol, GSM (gravel, sand and

mud), stones and vegetation, were sampled where available. The GSM was sampled for

1 minute, stones for 3 to 5 minutes and marginal vegetation was sampled in a 2 m

section. The invertebrates were then carefully placed into an identification tray with

water and identified on site to family level for 15 minutes using the Gerber and Gabriel

(2002) invertebrate guide. This was done on a SASS score sheet. Following

identification, each sample was packed into a honey jar and preserved with a 10%

buffered formalin (Merck, SA) solution that was stained pink with Rose Bengal (Sigma-

Aldrich, Germany). The samples were placed on ice, taken to the laboratory and

enumerated using a dissection microscope (Zeiss, Germany). The SASS5 score and

Average Score per Taxon (ASPT) were calculated and used to determine the EC

according to Table 3.1.

Table 3.1 The Ecological classes and relevant conditions determined from the SASS5

and ASPT scores which are also shown (adapted from O’ Brien et al., 2005).

SASS5 Score ASPT Ecological Class Condition

>140 >7 A Excellent

100 – 140 5 – 7 B Good

60 – 100 3 – 5 C Fair

30 – 60 2 – 3 D Poor

< 30 < 2 E Very Poor

51

3.2.2 Fish

The sampling of fish was carried out during both high and low flow periods during the

months of March and July, respectively. The sites and habitats sampled, as well as

sampling technique and effort for high and low flows are presented in Tables 3.2 and 3.3.

The habitats sampled and the techniques used to capture the fish were dependant on the

habitat available at each site. Electroshocking was done using a standard 220V AC 50

Hz portable generator (Honda, Japan) and nets used in this survey were the gill, cast and

medium seine net. Once caught, the fish were identified using taxonomic keys (Skelton,

2001), counted and noted.

Table 3.2 Sampling habitat, technique and effort for fish at all sites during the high

flow period.

Site Habitat Sampling Method

Sampling Effort

(min)

GDR Slow deep Cast net 60

GDS Slow deep Shocking

Cast net

30

20

USR Slow deep Cast net 20

USS Slow deep Cast net 5

AR

Slow deep

Fast deep

Fast shallow

Shocking

Shocking

Shocking

5

2

4

AS

Fast deep

Fast shallow

Shocking

Cast net

Shocking

5

10

5

52

Table 3.3 Sampling habitat, technique and effort for fish at all sites during the low

flow period. Site Habitat Sampling Method Sampling Effort

(min)

GDR Slow deep

Slow shallow

Shocking

Shocking

10

10

GDS

Slow deep

Slow shallow

Fast deep

Shocking

Shocking

Shocking

10

10

10

USR Slow deep

Cast net

Gill net

med seine net

20

20

20

USS Slow deep Shocking 5

AR

Slow shallow

Slow deep

Shocking

Shocking

Cast net

30

10

15

AS Slow deep

Slow shallow

Shocking

Shocking

15

15

The fish assessment for all sites on the Mvoti and Amatikulu Rivers was performed

according to the FAII (Kleynhans, 1999). The fish species expected to be present in the

Mvoti River was added firstly from historical data. The program then automatically

entered the relative intolerance, frequency of occurrence and health expected for these

fish species, after which a FAII score was calculated. The same was done for the sites on

the Amatikulu River. The FAII scores for each site were determined by incorporating the

same aspects for the observed fish species. The relative FAII score was then calculated

as a percentage and an EC was given according to Table 3.4.

53

Table 3.4 The different FAII integrity classes listed from A to F accompanied by a

description and a score for each class (Kleynhans, 1999).

Integrity Class General expected conditions for each integrity class

FAII score (%)

A Unmodified or approximates natural conditions closely. 90-100

B Largely natural with few modifications. A change in community characteristics may have taken place but species richness and presence of intolerant species indicate little modification.

80-89

C Moderately modified. A lower than expected species richness and presence of most intolerant species. Some impairment of health may be evident at the lower end of this scale.

60-79

D Largely modified. A clearly lower than expected species richness and absence or much lowered presence of intolerant and moderately intolerant species. Impairment of health may become more evident at the lower end of this class.

40-59

E Seriously modified. A strikingly lower than expected species richness and general absence of intolerant and moderately intolerant species. Impairment of health may become very evident.

20-39

F Critically modified. An extremely lowered species richness and an absence of intolerant and moderately intolerant species. Only tolerant species may be present with a complete loss of species at the lower end of this class. Impairment of health generally.

0-19

3.2.3 Statistical Analysis

Primer Version 6 and Canoco Version 4.5 were used to analyze the invertebrate and fish

community structure. Univariate diversity indices, distributional K-dominance plots,

hierarchical clustering and non-metric dimensional scaling (NMDS) were performed

using PRIMER software and Redundancy Analysis (RDA) was performed with Canoco.

Univariate diversity indices, which indicate species diversity and component species

distribution, included the total species, total number of individuals, Margalef’s index,

Shannon-Wiener diversity index, Pielou’s eveness index and Simpson’s diversity index.

These distribution-free indices are commonly used to analyze data since they make no

assumption about the underlying species abundance distribution (Clark and Warwick,

1994). The Margalef’s index provides the number of individuals in a given number of

species by taking into account the total number of individuals and species. The Shannon-

Wiener Diversity index integrates species richness and equitability components. Pielou’s

54

eveness indicates the distribution of individuals over the species in the sample and lastly,

the Simpson Diversity Index takes into account the number of species present, as well as

the abundance of each species. It measures the probability that two individuals

randomly selected from a sample will belong to the same species (Clark and Warwick,

1994).

The data was also subjected to multivariate statistical analysis to identify spatial and

temporal patterns. The Bray-Curtis similarity coefficient establishes the difference

among the sites on a temporal scale and confirmation of these relationships is possible

through group-averaged clustering and NMDS. One-way Analysis of Similarities

(ANOSIM) was then performed on the data in order to determine the extent to which the

identified groupings differed, and the species responsible for the different groupings were

identified using SIMPER analysis. The ranking of species based on their order of

importance was done in a decreasing manner using k-Dominance plots. The rankings

were done in terms of abundance of each species and then these abundances were

expressed as a percentage of the total abundance. This rank was then plotted against the

relative species rank (Clarke and Warwick, 1994).

RDAs were used to determine the environmental variables that are responsible for the

grouping of sites. It is a weight summation method (Van den Brink et al., 2003) that is a

derivative of PCA. The difference between PCA and RDA is that RDA has an added

feature: best fit values are derived from multiple linear regressions between each variable

in turn and a second matrix of environmental data, instead of the original data. RDA is

interpreted via 2-dimensional maps of the samples that are referred to as tri-plots.

Interpretation is based on the placement of the samples where (dis)similarity is reflected

according to these placements (Shaw, 2003).

55

3.3 Results 3.3.1 Spatial and Temporal Analysis

3.3.1.1 Macro-Invertebrates

Figures 3.1 to 3.3 indicate the species diversity and abundances of the macro-invertebrate

communities for all sites in the study during both high and low flow periods. The

polynomial trend-lines in Figure 3.1 A indicate that the sampling sites on the Mvoti River

have a higher number of species compared to the reference sites during both flows. The

greatest impact seems to be at Site USR, which is lower than all the other sites. Site USS

has the highest number of species out of the sampling sites during high and low flow. On

the Amatikulu River, AR has a higher species number than at AS, and is especially

significant during low flow. It also has the highest number of species out of all the sites

in the study during both flows

The number of individuals (Figure 3.1 B) during the low flow period was higher than

those during the high flow period, excepting at Sites GDS and AS, which were only

slightly higher. During low flow, the reference sites show higher values than the

sampling sites, with the exception of Site USR, where the total number of individuals

increase drastically at Site USS. The Amatikulu sites show greater numbers during high

and low flow compared to the Mvoti sites and AR has a greater number of individuals

than AS during low flow.

56

A

0

5

10

15

20

25

30


Sites

Num

ber o

f Spe

cies

HighLow

B

0

500

1000

1500

2000

2500


Sites

Num

ber

of In

divi

dual

s

HighLow


individuals (B) during the high and low flow periods.

57

A

00.5

11.5

22.5

33.5

44.5


Sites

Mar

gale

f's S

peci

es R

ichn

ess

HighLow

B

00.10.20.30.40.50.60.70.80.9


Sites

Piel

ou's

Eve

ness

Inde

x

HighLow

Figure 3.2 Univariate diversity indices indicating Margalef’s Species Richness

(A) and Pielou’s Eveness Index (B) during the high and low flow periods.

58

Figure 3.2 A indicates that the reference sites on the Mvoti River have a lower diversity

and abundance than the sampling sites during both high and low flows. On the

Amatikulu River, however, AR has a higher diversity and abundance than AS. By

comparing the two reference sites on the Mvoti River, it is lower at Site USR than at Site

GDR during both flows and by comparing the two sampling sites it is lower at Site USS

compared to Site GDS at low flow. Low flow shows better diversity on the Mvoti River

compared to high flow, except at Site USS, and high flow shows better diversity at Site

AS on the Amatikulu River. When comparing the diversity in the two rivers, Site AR is

higher than Sites GDR and USR and AS is lower than Site GDS. Pielou’s index (Figure

3.2 B) indicates that there is a bigger change in the distribution of individuals during low

flow than during high flow and it also shows that there is a family that dominates at Site

USS and this is most prominent during low flow. This is also supported by the results

seen in Figures 3.1 and 3.2 A. Furthermore, there is a wide distribution of individuals at

Site GDS during both flows and there are no particular families that dominate at the

Amatikulu sites.

The same general trend is seen in the two diversity indices in Figure 3.3 that shows that

there is a decrease in diversity at Site USS, which is more prominent during low flow.

This correlates with the trend seen in the Eveness index. The diversity at Site GDS is

higher than at Site GDR during both flows, as is seen in all the other indices. Site GDS

also has the highest diversity score during both flows. During low flow, the diversity is

higher at Site USR than at Site USS but is lower at Site USR during high flow. The

diversity at Site AR is the same as Site AS during high flow but is lower at Site AS

during low flow.

59

A

0

0.5

1

1.5

2

2.5


Sites

Shan

non-

Wie

ner

Dive

rsity

Inde

xHighLow

B

00.10.20.30.40.50.60.70.80.9

1


Sites

Sim

pson

Div

ersi

ty In

dex

HighLow


Diversity Indices during the high and low flow periods.

60

A

USSLUSSUSRLGDRUSRARASARL GDRLASLGDSGDSL

40 60 80 100

Similarity

20

Stress: 0.14

GDR

GDRL

USRUSRL

ARARL

GDS

GDSL

USSUSSL

AS

ASL

54

3

21

B

Figure 3.4 Bray-Curtis similarity matrix-based cluster analysis (A) and NMDS

plot (B) of sites during high and low flow, indicating the different groups

according to presence/absence data.

61

Cluster analysis and the NMDS plot for high and low flow shown in Figure 3.4 indicates

spatial variability and some temporal trend. The NMDS plot shows a stress value of 0.14

which indicates an accurate representation of data on a two-dimensional scale (Clark and

Warwick, 1994). Five groups are recognized at a 50% similarity cut-off and their

significance (p<0.05) is confirmed with a global statistic value (r) of 0.951 using

ANOSIM. Reference sites have been grouped separately from sampling sites on the

Mvoti River whereas a combined grouping occurs on the Amatikulu River. There is no

seasonal variability in the invertebrate community at Sites GDS and USR as well as at the

Amatikulu sites. It is evident that Site GDR is grouped with Site USR during high flow

and with the Amatikulu sites during low flow. The invertebrate structure at Site USS

differs from the other sites as it is grouped separately. Furthermore, the community

structure at Site USS during high and low flow differs.

In order to determine the macro-invertebrate families that drive the order of the different

groupings, a SIMPER analysis was run. In Table 3.5, it can be seen that group 1 (GDS

and GDSL) is dominated by mostly sensitive species and contribute to 91.94% of the

community structure. The hardy Chironomidae, however, is dominant contributing to

38.71% of the community. Group 2, consisting of Site GDRL and all the Amatikulu

sites, also consist of sensitive species with the exception of Chironomidae and

Simuliidae. Chironomidae, Baetidae and Atyidae contribute to 97.37% of the community

structure at Group 3 (USR, USRL and GDR), where Chironomidae contribute to 78.79%

of the community. Site USS at high and low flow were grouped separately and therefore

determination of the contribution of species was not possible.

62

Table 3.5 The percentage contribution and cumulative percentage contribution for

Groups 1 to 5 during high and low flow determined by SIMPER analysis.

Species Average Abundance

Average Similarity

Percentage Contribution

Cumulative Percentage

Contribution

Group 1

Chironomidae Hydropsychidae Baetidae Simuliidae Elmidae Perlidae

40.00 13.50 15.50 8.50 5.00 4.00

18.05 7.52 7.52 4.51 3.01 2.26

38.71 16.13 16.13 9.68 6.45 4.84

38.71 54.84 70.97 80.65 87.10 91.94

Group 2

Chironomidae Baetidae Atyidae Simuliidae Hydropsychidae Leptoceridae

174.20 182.40 101.20 43.20 35.80 31.20

17.89 16.92 3.81 3.12 1.93 1.87

36.23 34.27 7.72 6.31 3.91 3.79

36.23 70.50 78.21 84.53 88.43 92.22

Group 3 Chironomidae Atyidae Baetidae

95.33 12.33 32.67

47.57 6.46 4.75

78.79 10.70 7.87

78.79 89.49 97.37

Group 4 Less than 2 samples in a group

Group 5 Less than 2 samples in a group

The k-dominance plot in Figure 3.5 indicates that a high degree of dominance of species

occurs at the Ushukela sites during both high and low flows and Site GDR during high

flow. Chironimidae are the dominant taxon at the all of the above mentioned sites.

63

1 10Species ran

100k

0

20

40

60

80

100

Cum

ulat

ive

Dom

inan

ce%

GDRGDRLUSRUSRLARARLGDSGDSLUSSUSSLASASL

Figure 3.5 k-Dominance plot of the high and low flow macro-invertebrate data

ranking species based on their order of importance.

Figure 3.6 indicates the water quality variables that significantly influenced the macro-

invertebrate community structure at each site in the study, as well as the spatial and

temporal differences between these sites. This RDA tri-plot explained 51.1% of the

variation in the data, with 33.5% explained on the first axis, while 17.6% is explained on

the second axis. It can be seen that Site USR is separated from Site USS during both

high and low flow due to the lower oxygen content occurring at Site USS and the higher

nitrite and nitrate content, turbidity and temperature as well as the presence of fine to

medium sand at Site USR. A temporal trend can also be seen at Sites AR and AS, due to

a higher content of course sand present at low flow. In addition, the water quality and

sediment variables mentioned earlier are higher at Site GDR during high flow and the

oxygen content is higher during low flow, separating them. Sites AS and AR have a high

oxygen content during high flow accounting for the presence of the taxa Atyidae,

Thiaridae, Perlidae and Philopotamidae.

64

-1.0 1.5

-0.8

0.8 Atyidae

Baetidae

Gomphidae

Hydropsychidae

Leptoceridae

Parecnomina

Perlidae

Philopotamidae

Tipulidae

Thiaridae

GDR

GDRL

USR

USRLAR

ARL

GDS

GDSL

USS

USSL

AS

ASL

Oxygen mg/l

Oxygen %

Temperature

NitrateNitrite

Turbidity

Very coarse

Medium

Fine

Figure 3.6 RDA tri-plot showing the position of the sampling sites and the water quality

and sediment variables responsible for the macro-invertebrate community structure

groupings present at each site. The L suffix indicates the low flow period and PC1 is

represented by the x-axis and PC2 by the y-axis.

Similarly during low flow, the high oxygen content accounts for the presence of

Gomphidae, Parecnomina, Baetidae, Leptoceridae and Tipulidae, but this is also due to

the very course sand found there. In addition, the same conditions occurring at Site

GDRL result in the presence of Hydropscychidae. The lower oxygen content at Sites

USS and GDS during both flows results in a lower abundance of these taxa at these sites.

Sites USR and USRL as well as Site GDR are influenced by turbidity, nitrite, nitrate,

temperature and fine to medium-sized sediment, once again resulting in a lower

abundance of the previously mentioned taxa, and a dominance of Chironomidae.

65

3.3.1.2 Fish

The fish diversity and abundances sampled during this study are presented in Tables 3.6

and 3.7 for high and low flow periods respectively. These data were used to calculate the

FAII and FRAI indices and to determine the ECs for each site. The Estuarine dependant

categories (EDCs) are also presented as some of the study sites are close to / or within the

freshwater – estuarine interface of the Mvoti and Amatikulu Estuaries.


Amatikulu Rivers during the high flow period. The common name, FAII


fish are presented. Scientific Name Common name Abbreviation EDC GDR GDS USR USS AR AS

Acanthopagrus berda River bream ABER IIa 2

Labeobarbus natalensis Scaly BNAT VI 7 1

Barbus trimaculatus Threespot barb BTRI VI 32

Barbus viviparus Bowstripe barb BVIV VI 7 3

Clarias gariepinus Sharptooth Catfish CGAR IV 1 3 1

Glossogobius giuris Tank Goby GGUI IV 1 1

Mugil cephalus Flathead mullet MCEP IIa 1

Monodactylus argenteus Natal Moony MARG IIa 1

Oreochromis mossambicus Mozambique tilapia OMOS IV 23 1 10 9

Pseudocrenilabrus philander Southern mouthbrooder PPHI IV 1 4 1 6

66


Amatikulu Rivers during the low flow period. The common name, FAII


fish are presented. Scientific Name Common name FAII EDC GDRL GDSL USRL USSL ARL ASL

Labeobarbus natalensis Scaly BNAT VI 5 7 2

Barbus trimaculatus Threespot barb BTRI VI 65 93 2

Barbus viviparus Bowstripe barb BVIV VI 16

Clarius gariepinus Sharptooth Catfish CGAR IV 4 2

Glossogobius giuris Tank Goby GGUI IV 4 5

Mugil cephalus Flathead mullet MCEP IIa 4

Oreochromis

mossambicus

Mozambique tilapia OMOS IV 29 17 31 16 2

Pseudocrenilabrus

philander

Southern mouthbrooder PPHI IV 3 1 1

Figures 3.7 to 3.9 indicate the species diversity and abundances for all sites in this study.

Figures 3.7 A and B show that there was an increase in the number of species and

individuals at Site GDS during the low flow period. The number of species at Site AS

was found to be the highest during high flow. The abundances at the Glendale sites were

significantly higher than those at the Ushukela sites during both flows and those at the

Ushukela sites are higher than those at the Amatikulu sites during low flow. No fish

were sampled at Site USS during the high flow period.

67

A

0123456789


Sites

Tota

l Spe

cies

HighLow

B

0

20

40

60

80

100

120

140

160


Sites

Tota

l Ind

ivid

uals

HighLow


abundances (B) during the high and low flow periods.

68

A

0

0.5

1

1.5

2

2.5


Sites

Mar

gale

f's S

peci

es R

ichn

ess

HighLow

B

00.10.20.30.40.50.60.70.80.9

1


Sites

Piel

ou's

Eve

ness

Inde

x

HighLow

Figure 3.8 Univariate diversity indices indicating Margalef’s Species Richness

(A) and Pielou’s Eveness Index (B) during the high and low flow periods.

The abundance and diversity of the fish community were the highest at Site AS during

the high flow period according to Margalef’s Species Richness in Figure 3.8 A. The

community structures at the Mvoti sites are not as abundant and diverse compared to the

69

Amatikulu sites during high flow but are similar during low flow. A decrease in diversity

is seen at AS during the low flow period as compared to the high flow period (Figure 3.7

A, B and 3.8 A). When comparing the Mvoti sites in Figure 3.8 A, there are limited

differences in terms of richness during both high and low flows with the exception of Site

GDR during low flow and Site USS during high flow. The trend indicated a continuous

decrease in evenness from Site GDR to USS during low flow (Figure 3.8 B). This

indicates an increase in dominance towards the downstream sites, with the most

noticeable change occurring at Site USS. The same change is noticeable for Site GDS

during the high flow period.

The diversity indices (Figures 3.9 A and B) follow the same trend as the number of

species plot and Margalef’s species richness (Figure 3.7 A; Figure 3.8 A), where the

diversity decreased at Site USS and showed the highest value at Site AS during high

flow. During low flow, the diversity was higher at the reference sites compared to the

downstream sites, with the exception of Site GDR.

70

A

00.20.40.60.8

11.21.41.61.8


Sites

Sha

nnon

Div

ersi

ty In

dex

HighLow

B

00.10.20.30.40.50.60.70.80.9


Sites

Sim

pson

Div

ersi

ty In

dex

HighLow


Diversity Indices during the high and low flow periods.

71

A

B

Figure 3.10 Bray-Curtis similarity matrix-based cluster analysis (A) and NMDS

plot indicating the different groups according to presence/absence data during

high and low flow.

72

Figure 3.10 indicates hierarchical cluster and NMDS plot which is based on the Bray-

Curtis similarity coefficient. The data were log transformed and are accurately

represented on a two-dimensional scale as is indicated by the stress value of 0.1 (Clarke

and Warwick, 1994). At 50% similarity cut-off, five groups were identified. Groups 1

and 2 consisted of the sites in the Amatikulu River, whereas Group 3 consisted of Sites

USR, USRL and USSL. Group 4 consisted of a mixture of Mvoti and Amatikulu River

sites, AS and GDR. The two surveys conducted downstream of Ushukela Mill and

upstream during the low flow period formed a distinct grouping (Group 5 - GDS, GDSL

and GDRL). The testing of the groupings’ significance was performed using ANOSIM

and were found to be significant (p<0.05) with a global statistic value (r) of 0.952. The

fact that there are limited groupings based on high and low flow sampling periods

indicated that there was no temporal trend, but there are spatial trends as the Amatikulu,

Ushukela and Glendale sites are grouped separately.

The water quality variables that were responsible for the (dis)similarity between sites are

graphically presented using RDA tri-plots. .Figure 3.11 displays the water quality

variables that had a significant effect on the fish community structure. The ordination

explained 82.4% of the variation in the data, with 64.1% explained on the first axis, while

18.3% is explained on the second axis. Ammonium and pH had the greatest influence on

the fish community structure at Site GDS during low flow and Site GDR during high

flow, respectively. The fish species most influenced by these variables were Barbus

trimaculatus and Oreochromis mossambicus. Habitat quality and flow regimes also had

an influence on the fish species present at Sites AR and AS and was characterized by the

presence of estuarine species during the high flow period. Labeobarbus natalensis and

Barbus viviparus were found at the reference sites but not at the sampling sites. The fish

community structure above and below the Ushukela Mill and below the Amatikulu Mill

were mostly influenced by high Escherichia coli counts and pH.

73

-1.0 1.0

-1.0

1.0

MCEP

BTRI

CGAR

PPHI

OMOS

BNAT

BVIV

GGUI MARGABER

pH

Ammonium

E.coli

High Flow

Low Flow

HQI

GDR

GDRL

USR

USRLAR

ARL

GDS

GDSL

USS

USSL

AS

ASL

Figure 3.11 RDA tri-plot performed with forward selection to indentify the significant

water quality variables responsible for the community structure groupings and the

dominant species present at the particular sites. The L suffix indicates the low flow

period and PC1 is represented by the x-axis and PC2 by the y-axis.

3.3.2 Biotic Indices


SASS5 results for high and low flow periods can be observed in Table 3.8. The IHAS

score was also included in the tables seeing that the distribution and abundance of macro-

invertebrates is not only affected by the presence or absence of pollutants, but also by the

availability and condition of suitable habitat (Parsons and Norris, 1996). This is because

fewer families of macro-invertebrates will be found where the habitat diversity is limited,

even if the water quality is not degraded. In this case, the SASSS5 score will be lower

than at a site with similar water quality and many different types of habitat. By

74

incorporating an ASPT score, the influence of habitat quality is minimized (Chutter,

1995). The EC and subsequent condition for each site was determined using the

classification shown in Table 3.1.

Table 3.8 SASS5 results indicating the SASS5 score, number of taxa and ASPT which

were used to determine the EC for each site during the high and low flow

periods. The IHAS score was also included

Site Flow SASS 5 Score

No. of Taxa ASPT Total IHAS (%)

EC

GDR High 52 8 6.5 52.5 B/C Low 77 12 6.4 72 B/C

GDS High 94 13 7.2 52 A/B Low 77 15 5.13 67 B/C

USR High 55 8 6.8 42.5 B/C Low 46 6 7.6 72.5 B/C

USS High 25 7 3.5 36.2 C/D Low 9 4 2.25 42.5 D/E

AR High 121 19 6.3 57 B Low 139 21 6.6 70 B

AS High 80 15 5.3 58.7 B/C Low 97 14 6.92 70 B/C

The high and low flow integrity of the macro-invertebrate community indicates similar

SASS5 scores at Sites GDR and GDS and lower scores at the Ushukela and Amatikulu

sampling sites compared to the reference sites. The similarity in scores at Sites GDR and

GDS is due to the good habitat quality at both sites and the lower scores at the Ushukela

and Amatikulu sampling sites are due to poor habitat and water quality. All the sites with

the exception of Site USS were in EC’s of B/Cs and above when relating the SASS5

scores to the classification system in Table 3.1. Site USS was in an EC of a C/D in high

flow and a D/C in low flow. This indicates that the macro-invertebrate community at Site

USS is poor, which is seen in the proliferation of the hardy Chironomidae and

Simuliidae.

3.3.2.2 Fish

75

The combined high and low flow integrity of the fish community indicates higher FAII

scores at the sites downstream of two of the mills when compared to the upstream

reference sites (Figure 3.12). The exception was at Site USS, downstream of the dual

impact of the Sappi Stanger Mill and Ushukela Mill, which had the lowest FAII score.

The two reference sites on the Mvoti River, i.e. Sites GDR and USR were in a better

condition than the reference site on the Amatikulu River (AR). When relating the FAII

scores to the classification system in Table 3.4 it is evident that all of the sites with the

exception of Site AS were in ECs of D and below. This indicates that the freshwater fish

species composition at all of the sites is modified in terms of species diversity and

abundances and that tolerant species predominate. Only Site AS was in a class C, which

is attributed to the presence of estuarine dependent species such as Glossogobius giurus,

Monodactylus argenteus Acanthopagrus berda and Ambassis sp.

0

10

20

30

40

50

60

70


Sites

FAII

Scor

e

Figure 3.12 Graphical representation of the FAII scores calculated for the

combined high and low flow periods.

76

3.4 Discussion

3.4.1 Spatial and Temporal Analysis


The reference sites on the Mvoti River had a lower number of species compared to the

sampling sites. The lower number at Site GDR can be attributed to the limited habitat

availability, as only the vegetation biotope was available during high flow and vegetation

and stones biotope during low flow. At Site GDS, however, there was more habitat

available for sampling during both high and low flows, resulting in a higher number of

species. The number of species at Sites GDS and AS does not seem to be affected by the

organic effluent discharged by the mills as the numbers are still quite high. Site USR is

impacted by flow and channel modification, as the Ushukela Sugar Mill abstracts its

water from this point. The river bed is mostly covered in sand, removing much of the

habitat essential for macro-invertebrate survival. Because of this, the number of species

here has declined. Site USS, on the other hand, has a higher number of species, which

are mostly of a very high tolerance. These include Chironomidae and Simuliidae and

proliferation of these and other hardy species are common where there is a high organic

content (Pearson and Penridge, 1987). The dominance of these individuals is shown in

the k-Dominance plot in Figure 3.5. This is a definite impact of organic pollution from

the Sappi Stanger and the Ushukela Sugar Mill. In contrast to Site USS, the Amatikulu

sites had a higher diversity due to the good habitat found at both AR and AS during high

and low flow. Domination by very hardy species did not occur here indicating minimal

impact by mill effluent. The numbers at Site AS are lower compared to Site AR during

low flow. This is probably due to the loss of the stones biotope at Site AS due to

sedimentation resulting from water abstraction by the Amatikulu Sugar Mill upstream

causing flow modification.

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The separation of reference and sampling sites on the Mvoti River indicated by cluster

analysis in Figure 3.4 shows that the macro-invertebrate community structures differ

above and below the mills. Unification of the Amatikulu sites, however, indicates that the

community structures above and below the Amatikulu Mill are similar. When combining

both flows, it can be seen that more hardy taxa occurred at Site GDR compared to Site

GDS. This is probably due to limited habitat availability at Site GDR, as well as the high

pH and temperature. Although the water quality at Site GDS is poorer than that at Site

GDR due to the influence of the Glendale Distillery, more sensitive species were found

here when combining high and low flows. These were in the form of Perlidae,

Hydropsychidae and Baetidae (Table 3.5). This can be attributed to the diverse habitat

that occurs giving an indication that, overall, water abstraction by the Glendale Distillery

upstream has a minimal effect downstream. Furthermore, the same can be said of the

discharge of organic effluent into the system. This, however, could change when looking

at high and low flow periods separately. Site USS differs from Site USR (as well as the

other sites seeing that its grouped on its own) in terms of macro-invertebrate structure in

that more hardy species were found at Site USS than Site USR (and the other sites) and

there is a dominance of Chironomidae at Site USS. The reason for this is the very poor

water quality, and more specifically, the organic pollution caused by the Sappi Stanger

and Ushukela Sugar Mill at Site USS. The habitat here is also very limited, having only

sampled vegetation during both flows. This is due to soil erosion and flow modification

resulting from sugarcane farming activities and the construction of a tunnel for the

provision of a road over the stream. The reason why the Amatikulu sites are grouped

together during both flows is that similar macroinvertebrate species were sampled here

and had the same EC. A few more sensitive species were, however, found at Site AR

which included the families Aeshnidae, Elmidae, Lestidae, Parecnomina and

Oligoneuridae. This is probably due to the loss of the stones biotope at Site AS due to

water abstraction by the Amatikulu Sugar Mill as mentioned earlier.

In terms of the mills’ impact, the RDA tri-plot in Figure 3.6 indicates that the lower

oxygen at Site USS, compared to the Amatikulu sites may be attributed to the organic

effluent discharged by the Sappi Stanger and Ushukela Sugar Mills as the high organic

78

content leads to a lowered oxygen concentration (Cheesman, 2005), and a thus a lower

species diversity. This also occurs at Site GDS but to a lesser extent, indicating a smaller

impact by the Glendale Distillery. The high nitrate, nitrite, turbidity and fine sediment as

well as low oxygen at Sites USR and GDR may also be due to the effects of sugarcane

farming in the area surrounding the site as runoff from effluent-irrigated, fertilized-soil

may occur. Poor soil management practices may contribute to the high turbidity and fine

sediment found here. The temporal trend seen at Sites AR and AS is shown to be

attributed to the presence of course sand during low flow and its absence during high

flow. Course sand is an indication that sediment transportation and thus erosion is taking

place in this system (Mackay et al, 2000). This again is due to poor soil management

practices on sugarcane fields and can result in a lower diversity of invertebrates since

their biotope is removed (Chapter 2). The high oxygen content, however, contributes to

the species richness here and indicates little impact by organic effluent.

3.4.1.2 Fish

From the results it is clear that spatial structuring in the fish community structure was far

more important than temporal differences due to e.g. changes in flow. The sites situated

in the Amatikulu River and those around the Ushukela Mill on the Mvoti River displayed

a more similar fish community structure than those around the Glendale Mill on the

Mvoti River. The greater degree of similarity between the former sites can be attributed

to distinctly different reasons. Just prior to the high flow sampling survey there were

localized flooding events in the catchment of the Amatikulu River. The extent of the

flooding resulted in removal of riparian vegetation, washing them downstream (Petts and

Calow, 1996). This could also have resulted in disruption of normal fish distribution

patterns and explain the differences in species abundances at Sites AR and AS during the

high flow period even though the general habitat quality was favorable to support a

greater diversity. The dominant species found at these sites included P. philander and C.

gariepinus, which are known to make use of refugia that can shield them from

disturbances like flooding events (Skelton, 2001). The high flow period is also

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characterized by the greater presence of estuarine dependent species making their way up

into the lower reaches of the Amatikulu River. All the species recorded are regarded as

euryhaline species that have some form of obligatory marine phase (Whitfield, 1998).

The water quality at the Ushukela sites was in a more deteriorated state when compared

to the two other areas and was characterized by high salt (sulfates, chlorides) and low

oxygen levels (see Figure 2.2). This is evident from the presence of the more tolerant

species, e.g. O. mossambicus and P. philander, dominating at these sites (Skelton, 2001).

The habitat quality at the Sites USR and USS was also found to be in a poorer condition

compared to the other two areas and could be attributed to flow modification and the

formation of sandbanks in the channel. The water quality and habitat modifications could

be attributed to the extensive agricultural, urban and industrial activities in the vicinity of

Stanger and have also been found by other authors (Mackay et al., 2000; Malherbe, 2006)

to be responsible for the critically modified fish community structures at these sites.

The Glendale sites were characterized by an altogether higher diversity of fish species

and therefore much more distinct community structure when compared to the other sites.

Once again there was no difference in the community structures based on temporal (flow)

parameters but there were distinct spatial differences between the structure above and

below the mill. The sites below the Glendale Mill (GDS and GDSL) were characterized

by higher ammonium levels when compared to the reference sites above the mill (GDR

and GDRL). This could be attributed to the use of ammonium nitrate as fertilizer for

sugarcane (Cheesman, 2005). This results in the more resistant species such as O.

mossambicus and B. trimaculatus being the dominant species present (Skelton, 2001).

The reference site is characterized by more sensitive species, i.e. L. natalensis and B.

viviparus. It is interesting to note that although there were differences between the

reference sites above the mill and the sites below the mill, the latter sites were

characterized by higher abundances of particularly the small barb species. One would

have expected lower diversity and abundances if the effluent from the sugar mill had a

detrimental effect on the fish fauna. It is however very likely that the effluent provided

80

more nutrients to these species making the site a preferred feeding area (Hartmann,

1977).

3.4.2 Biotic Indices


Poor water quality i.e. pH and temperature at Site GDR is responsible for the low SASS5

score at high flow and the higher score during low flow is due to better water quality.

The poor water quality at Site GDS shows its effects during low flow when the dilution

factor is low. More hardy taxa occur here during this time and this is due to the effluent

discharge from the Glendale Mill as well as the surrounding sugarcane farming activities.

Poor water quality is also the reason why low numbers of macro-invertebrates were

sampled at Site USR during high flow and an abundance of hardy species during low

flow, placing it in a B/C category. This can be attributed to the runoff from effluent used

for irrigation and the use of inorganic fertilizers. Results by previous studies done by

Mackay et al. (2000) and Malherbe (2006) indicate that the SASS4 EC during both flows

was a C during 2000 and the SASS5 score was a B during both flows in 2006. As with

this study, more than 2 species of Baetidae and Heptgeniidae were found in 2000. Only 2

biotopes were sampled during both studies, which is also the case here. It can therefore

be said that the macro-invertebrate community and the habitat at this site has remained

relatively unchanged since 2000.

The very poor ECs (C/D and D/E) at Site USS during both flows are due to a

combination of very poor water quality and habitat, as is indicated by SASS5 and ASPT

scores of 25 and 3.5, respectively, during high flow and 9 and 2.25 during low flow. The

poor water quality is indicated by the domination of the hardy taxa Chironomidae as well

as the presence of Simuliidae during low flow. This is mainly due to organic pollution by

the Sappi Stanger Mill and the Ushukela Sugar Mill and the poor habitat due to flow

modification by the construction of a tunnel. It was, however, surprising that

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Oligoneuridae, Philopotamidae with a sensitivity of 15 and 10 respectively were found

during high flow.

Good water quality and habitat characterize Site AR, giving a good balance of hardy and

sensitive taxa. These included Potamonautidae and Lymnaeidae as well as

Philopotanautidae and Heptageniidae, respectively, during both high and low flows. The

loss of the stones biotope due to sedimentation and poorer water quality at Site AS

resulted in the loss of certain hardy and sensitive taxa such as Tipulidae and Perlidae,

respectively, which mainly prefer this biotope (Dallas, 2007).

3.4.2.2 Fish

In terms of fish community, Site GDR has a lower FAII score than Site GDS. The

habitat at both sites is similar (Figure 2.12), having fast and slow riffles as well as deep

pools, and the water quality is in a better condition at Site GDR. Therefore, it is unlikely

that the lower abundance at Site GDR is related to habitat or water quality change. The

higher abundance at Site GDS is a possible indication that the Glendale Mill does not

have a big impact on the fish community.

Site USR is in a D category (FAII = 46.8) and Site USS is in an F category (FAII = 18.6),

indicating a disturbance between the two sites. This is probably due to the combined

effect of the effluent discharges by the pulp and paper mill at Stanger and the Ushukela

Sugar Mill. Mostly hardy species were found at these sites (O. mossambicus, P.

philander and C. gariepinus), which are expected due to the poor habitat quality at Site

USR and poor water and habitat quality at Site USS. M. cephalus, however, was also

found at Site USR during both high and low flows. This euryhaline fish has a general

intolerance of 2.6 indicating that it is not very tolerant to bad conditions. Mullet have,

however, been recorded to have occurred at this site by local fishermen (Mackay et al.,

2000). Previous studies have indicated that Site USR was in a D category with a score of

55.4 (Mackay et al., 2000) and a C category with a score of 66.69 (Malherbe, 2006). In

the Mackay et al. 2000 study, the expected species B. trimaculatus, L. natalensis and G.

giuris did not occur which contributed to the low EC, whereas the two former species

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were found by Malherbe (2006), thus increasing the score in that study. In this study,

however, only B. trimaculatus, the hardy species mentioned earlier and M. cephalus were

sampled, decreasing the score once more. It therefore seems that the site has deteriorated

in terms of fish since 2006 and is in a poorer condition than it was in 2000.

The low FAII score that was calculated at Site AR (E category) is possibly due to the

March-April 2006 flooding event that washed all the fish downstream. The habitat and

water quality at this site is good (Chapter 2) and thus it is unlikely that they play a role in

the low score. The lack of reference data for the Amatikulu River could also have an

influence on the results at this site. Site AS, on the other hand, was calculated to be in a

C category, with more species being found here than at Site AR during high flow. The

effects of the flood can be seen during low flow where only G. giurus (5) and P.

philander (1) were caught. The fact that this single fish was a juvenile and no adults

were found indicates that it probably came in from another system or from the estuary. It

can thus be said that Site AS had not fully recovered from the flood at this time.

3.5 Conclusion

Spatial analysis shows that the macro-invertebrate community structure at Site GDS is

good when combining both flows, but the biotic index indicates that more hardy taxa are

found during low flow when the dilution factor is low, thus showing the negative effect

of the Glendale Mill effluent on water quality. The habitat here was diverse resulting in a

high number of species, indicating minimal impact by water abstraction by the mill. The

very low number of macro-invertebrates at Site USR can be attributed to poor water

quality from effluent runoff and poor habitat quality due to flow and channel

modification by the Ushukela Sugar Mill for water abstraction purposes. This situation

has remained relatively unchanged since the Mackay et al. (2000) study. Site USS saw

the proliferation of the very hardy Chironomidae and Simuliidae taxa, typically due to

organic pollution, which is caused by the dual effect of effluent from Sappi Stanger and

83

84

Ushukela Sugar Mills, and poor habitat quality resulting from flow modification. This,

however, is not linked to the mills. The macro-invertebrate assemblages in the

Amatikulu River are in a much better state compared to that of the Mvoti River, with

both Sites AR and AS having a diverse number of taxa. Site AS, however, has not gone

unscathed as water abstraction by the Amatikulu Mill and resulting sedimentation has

lead to the loss of the stones biotope and many sensitive macro-invertebrate taxa.

Spatial differences in the fish community structure show that the Glendale sites have a

higher diversity of fish species compared to the Ushukela and Amatikulu sites. In terms

of mill activities, the site downstream of the Glendale Mill is impacted by high

ammonium levels, probably due to the runoff of mill effluent from sugarcane fields. This

site was also characterized by higher abundances of fish, which is possibly due to the

increased nutrient load resulting from the mill effluent, making this site a preferred

feeding area. Thus the mill’s effect is not, at this stage, potentially harmful unless the

system receives more organic effluent (Hartmann, 1977). The fish communities at the

Ushukela sites, however, are critically modified due to poor water and habitat quality,

and are in a poorer condition than what they were in 2006 (Malherbe, 2006).

Agricultural, urban and industrial activities including the Sappi Stanger Mill and the

Ushukela Sugar Mill contribute to this state. Lastly, the Amatikulu sites were found to be

more similar to the Ushukela sites than the Glendale sites due to a localized flooding

event that occurred in the catchment of the Amatikulu River, washing the community

downstream and resulting in a disruption of normal fish distribution patterns.

Chapter 4

DEEEP and Biomarkers

Chapter 4 DEEEP and Biomarkers

4.1 Introduction

As mentioned in chapter 1, the quality of the water in rivers is impacted by the cultivation

and processing of sugar crops. Runoff and leaching from sugarcane fields resulting from

sugarcane cultivation can lead to pollution of the surface waters. The major pollutants of

concern are nutrients and agrochemicals, particularly pesticides. Nitrate and phosphate

nutrients are mainly derived from fertilizers and can lead to the eutrophication of

waterways (Cheesman, 2005). The growing of sugarcane on the banks of rivers results in

the removal of natural riparian vegetation, which aggravates the situation in that the

filtering of agrochemicals running off from sugar cane fields does not occur. This then

increases their concentrations in surface waters (Arthington et al., 1997).

The main concern in terms of sugar processing is the pollution arising from the

wastewater in the waterways. This wastewater is often used to irrigate the sugarcane,

consequently running back into the river (Cheesman, 2005). Irrigation with wastewater is

common because sugarcane consume large amounts of water, estimated at 7.5 Ml/ha

water for 100t/ ha cane (Bakker, 1999) and rainfall to supply such needs is not sufficient.

In addition, large volumes of water are abstracted from rivers for irrigation and mill

processes, increasing the concentration of the wastewater in the system. These effluents

are often rich in organic matter having high biological (BOD) and chemical oxygen

demands (COD), which leads to lowered levels of dissolved oxygen in surface waters

(Cheesman, 2005).

In the past, water pollution was controlled by managing levels of single substances in

water. It has been shown, however, that the direct ecological and toxicity hazard in

complex industrial effluent discharges cannot be fully assessed by substance-specific

methods. In compliance with the National Water Act (Act 36 of 1998) that states that the

ecological integrity of aquatic ecosystems should be protected, a more comprehensive

approach to assessing potential toxicity hazard in whole effluent discharges has been

85

formulated. Substance-specific assessments can now be supplemented with this

integrated assessment. The Direct Estimation of Ecological Effect Potential, or DEEEP,

provides a reasonably direct ecological hazard assessment of known and unknown

mixtures of substances and is useful in the assessment of complex industrial discharges,

treated sewage point discharges and localized diffuse sources (DWAF, 2003). It is based

on a whole effluent approach developed in the USA called Whole Effluent Toxictiy

(WET) and the Totale Effluent Milieuhygiene (TEM), or Whole Effluent Environmental

Risk method developed in the Netherlands and involves the implementation of a battery

of tests (DWAF, 2003). These tests involve the exposure of test organisms to effluent to

assess its possible effect on similar organisms in aquatic ecosystems. These

methodologies are flexible in that the most appropriate combination of tests and test

organisms can be used with regard to each country’s own conditions and circumstances.

Internationally available methodologies are based on selecting a set of parameters derived

from the potential effects of discharges and subsequently using one or more tests to

measure each parameter, which results in a direct measurement of the effect (DWAF,

2003). The DEEEP methodology comprises a range of effect parameters and a battery of

tests that can provide direct information on the potential toxicity hazard of complex

discharges. Parameters include acute and chronic toxicity, mutagenicity and

bioaccumulation potential. Acute toxicity refers to the specific effects (usually lethal)

that occur within a short period of exposure to a substance or mixture and can be

determined using algae, bacteria, invertebrates and fish tests. Chronic toxicity refers to

specific effects (usually sub-lethal) that occur within a long period of exposure and can

be determined using invertebrate tests.

Mutagenicity is the introduction of hereditary changes in living organisms and can be

determined using the Ames Salmonella mutagenicity test (Slabbert, 2004). Mutagens in

effluent discharges and receiving waters can be detected using the Ames Salmonella

mutagenicity test. Two strains of the bacterium Salmonella typhimurium, TA98 and

TA100 are used to identify mutagens in these waters. These strains require the amino

acid hisitidine for growth, but they each contain a different type of mutation in the operon

coding for histidine biosynthesis. The bacteria are thus unable to produce histidine

86

independently. However, when exposed to certain mutagens, the capability of the

bacteria to synthesize histidine is restored (Maron and Ames, 1983). When placed on a

histidine-containing agar plate, the bacteria undergo several divisions until all the

histidine on the plate is exhausted. These bacterial colonies are known as the background

lawn. If no further division occurs, mutagens are absent in the sample, but if the bacteria

continue to form colonies (revertant colonies), mutagens are present. (Slabbert, 2004).

Mutagens such as 2-aminofluorine and benzo(a)pyrene restore histidine synthesis in

TA98 by shifting to the correct reading frame. These bacteria can therefore detect

frameshift mutagens. Sodium azide and ethyl methane sulphonate are examples of

mutagens that restore histidine synthesis in TA100 by substituting proline for leucine.

TA100 can thus detect mutagens that cause base-pair substitutions. In this test, 2-

aminofluorine and sodium azide are used as positive controls. A limitation is that this

test fails to detect certain classes of carcinogens for example, polychlorinated pesticides

(Maron and Ames, 1983).

The fish and water flea lethality tests measure the acute toxicity of effluent discharges

and receiving waters. Poecilia reticulata, commonly known as the Guppy are normally

the fish used in DEEEP methodology but Danio rerio, commonly known as Zebrafish,

were used in this study because of availability. Daphnia pulex were used in the waterflea

test. The fish and water flea tests involve an exposure period of 96 and 48 hours,

respectively, where they are exposed to increasing concentrations of effluent in a static

test. Lethality is recorded every 24 hours. Toxicity endpoints are determined after 96 or

48 hours by carrying out definitive tests using 10 to 21 day old fish or 24-hour-old water

flea (Slabbert, 2004).

In addition, biological markers (or biomarkers) can also be used as a regulatory adjunct to

or as a replacement of invertebrate tests. Biomarkers measure the responses of toxicants

in organisms that have been exposed to xenobiotics by responding to the mechanism of

toxicity rather than to the presence of a specific toxicant (Gokosyr and Frolin, 1992).

Because toxicants often have negative effects on enzyme activity, quantification of their

activity can be used to measure sub-lethal stress and to identify the affected organ, tissue,

87

cell, subcellular compartment, chemical group or even the specific toxicant. Toxicants

can either inhibit or induce enzymes, which is called direct enzyme inhibition and

biomolecule induction, respectively (Venter et al., 2004). Examples of enzymes that are

inhibited by toxicants are acetylcholinesterase (AChE), Butyrylcholinesterase (BuChe)

and Carboxyesterases (Fossi et al., 1995) and that are induced by them are

ethoxyresorufin-O-deethylase (EROD), ethoxcoumarine-O-deethylase (ECOD) and aryl

hydrocarbon hydroxylase (AHH) (Di Giulo et al., 1995). Toxicants can also affect the

cellular energy budget of an organism resulting in less energy available for growth and

maintenance. Hence, biomarkers of cellular energetics such as Cellular Energy

Allocation (CEA) can also be used to determine levels of sub-lethal stress (Verslycke et

al., 2004). By measuring sub-lethal stress in the long term, irreversible effects at higher

levels of biological organization can be prevented (Shugart, 1996).

A suite of biomarkers were measured in resident fish species from the study sites as it

provides a more significant evaluation of the effect of the pollutants on the individual

organisms (Lagadic et al., 2000). The biomarkers used in this study were AChE, which

was analyzed in brain tissue and EROD, in liver tissue. In addition CEA in muscle tissue

was also included in order to determine each organism’s energy level since the exposure

of organisms to pollutants have adverse effects on their energy budgets at a cellular level.

Exposure to organophosphates, carbamates or heavy metals inhibit AChE, causing an

accumulation of acetylcholine at the nerve synapse, ultimately blocking synaptic

transmission in the nervous system and affecting vital functions such as respiration

(Bocquené et al., 1995). For this reason, AChE activity has been proposed as a useful

molecular biomarker of direct neurotoxic effects (Payne et al., 1996). The use of EROD

activity in fish is a well-established in vivo biomarker of exposure to certain planar

halogenated and polycyclic aromatic hydrocarbons (PHHs and PAHs) and other

structurally similar compounds (Whyte and Tillit, 2000). It is induced by the

detoxification enzyme in fish, Cytochrome P450 1A (Cyp 1A) in response to PHH and

PAH exposure. Determination of EROD activity thus qualitatively and quantitatively

indicates exposure to toxicant stress, but it must be remembered that fish living in a

polluted water system can adapt and exhibit a persistent altered response to Cyp1A

88

inducers (Elskus et al., 1999). CYP 1A levels and EROD activity are both also highly

dependent on temperature (Sleiderink et al., 1995.) The CEA serves as a biomarker of

cellular energetics. It is based on the fact that exposure of organisms to pollutants has

adverse effects on their energy budgets at a cellular level (Smoulders et al., 2002). This is

because energy is required to maintain homeostasis in response to toxicant-induced stress

(Venter et al., 2004). As a result, less energy will be available for growth and

maintenance and will ultimately result in the impairment of reproductive and

developmental processes. In the assay, the energy reserves (glycogen, protein and lipid)

and consumption (electron transport activity / ETS) are quantified at a cellular level using

colorimetric measurements and are integrated into a general indicator of stress. The

difference between the energy reserves and consumption represents the net cellular

energy budget of the organism. The assay has been shown to be practical and

ecologically relevant, in that short-term changes in the global energy budget that are

induced by toxicants can be linked to effects at population level, which are evident only

after prolonged exposure periods (De Coen and Janssen, 1997).

The objectives of this chapter were to use DEEEP and biomarker protocols to assess the

toxicity effect of sugar mills alone and combined effect of sugar and pulp mill activities

on the ecological integrity of the Mvoti River and to compare the biological community

responses, DEEEP assessment and biomarker responses from the Mvoti River to

responses in the adjacent Amatikulu River that is only subjected to sugar mill activities.

4.2 Materials and Methods 4.2.1 Sampling Protocol

Effluents were collected from the Ushukela Sugar Mill, the Glendale Distillery and the

Amatikulu Sugar Mill in 25 ℓ plastic containers after which the effluent was transferred

to smaller glass containers and kept at 4°C until analysis. For the biomarker analyses, ten

Mosambique tilapia, O. mossambicus, were sampled from all the sites (Chapter 1) except

Site AS, as it was recovering from a massive flooding event that occurred during the

2006 high flow period. Sampling was done with cast nets and electroshocking. The

89

liver, muscle and brain tissue of each fish was dissected and placed in tubes into which

Hendrikson storage buffer was added. The tubes were then wrapped in parafilm and

frozen. In the laboratory, the tubes were frozen at -80°C until analysis.

4.2.2 DEEEP Analysis

4.2.2.1 The Fish and Invertebrate Lethality Tests

The fish and Daphnia lethality tests were performed according to the methods set out in

Slabbert (2004). The fish lethality test was performed using the Zebrafish, Danio rerio,

and met all the requirements for test organisms as set out in Slabbert (2004). The fish

were bred and maintained in the aquarium at the University of Johannesburg in

moderately hard water, and were restrained from consuming any food 24 hours before

testing. The effluent samples were tested as received and allowed to reach a temperature

of 23±2°C before use. The physical parameters, temperature, pH, DO, conductivity were

measured and the colour, odour and turbidity of each sample were noted. The physical

parameters were adjusted if they were not in the optimal range for Zebrafish. A range-

finding test was performed for all samples in order to determine the lowest dilution of

effluent that exhibited 100% lethality, but it was found that none of the samples caused

lethality. Thus, a definitive test was not necessary for any of the samples. Reconstituted

water was prepared according to methods stated in Truter (2004) and was used to dilute

the samples, which were shaken vigorously in order to achieve homogeneity. It was also

aerated overnight. Serial dilutions were made for these tests in duplicate, comprising of

12.5%, 25%, 50%, 75% and 100% concentrations as well as a control. The

concentrations were made up to a volume of 250 mℓ after which the physical parameters

mentioned earlier were measured. Five fish were then transferred to each duplicate set of

beakers as described by Slabbert (2004). The physical parameters were measured and the

number of dead fish was recorded in each container every 24 hours during the 96 hour

exposure period.

The water flea lethality test was performed using D. pulex that were less than 24 hours

old and were cultured according to methods stated by Truter (2004). Sample preparation

90

followed the same procedure as in fish, except that the sample was allowed to reach a

temperature of 20±2°C before use. The same range-finding test was performed for the

Daphnia test and a definitive test was once again not needed, as 100% lethality did not

occur. Reconstituted water used to dilute the samples was prepared according to methods

stated in Truter (2004) and aerated overnight. The samples were once again shaken

vigorously in order to achieve homogeneity. The samples were analyzed in duplicate and

serial dilutions were made comprising of 12.5%, 25%, 50%, 75% and 100%

concentrations as well as a control. The concentrations were made up to a volume of 25

mℓ after which the temperature, pH and OD were measured. Five water fleas were then

transferred to each duplicate set of beakers (Slabbert, 2004). The number of dead water

fleas was recorded and physical parameters were measured in each container every 24

hours during the 48 hour exposure period. The percentage lethality is then calculated for

each sample in relation to the total number of fish or water fleas used:

(Nto – Ntx)/Nto x 100%

where, Nto = Number of live fish/ water fleas at t = 0 h

Ntx = Number of live fish/ water fleas at t = 24, 48, 72 and 96 h / 24 and 48 h.

Toxicity of samples were regarded when lethality was ≥ 10%.

4.2.2.2 Ames Salmonella Mutagenicity Test

The Ames Salmonella Mutagenicity test was performed according to the methods set out

in Slabbert (2004). The sample was prepared by filtering it through Whatman No. 1 filter

paper (Whatman No. 1, England). A glass column was then packed with Amberlite

XAD-7 resin (Sigma-Aldrich, Germany) by pushing silanised glass wool (Merck, SA)

into the column to form a plug and pouring in XAD-7 slurry (XAD-7 in acetone). The

acetone was allowed to drain out slowly and the column was repeatedly topped up with

XAD-7 slurry until it was packed to approximately 50% of its length. The acetone was

drained to the top level of the XAD-7 bed and replaced by Milli-Q® water by continual

91

draining, filling and mixing. Another loose, glass wool plug was then inserted into the

column on the surface of the resin bed and the column was topped up with Milli-Q®

water. The column was then connected to an aspirator bottle containing 4 ℓ of sample.

This sample was then allowed to flow through the column so as to drip out at a rate of

approximately 2 drops/s (9 mℓ/min). Thereafter, the resin was washed with 3 x 10 mℓ

Milli-Q® water in order to re-slurry it, tilting as necessary to remove air bubbles. The

column was then connected to a compressed air devise and allowed to dry overnight.

Once dry, acetone (Associated Chemical Enterprises, SA) was added to the tilted column

to re-slurry it again. Approximately 7.5 g of anhydrous sodium sulfate was added to a

sintered glass funnel to dry the eluate. This funnel was placed onto a round bottomed

flask, which was in turn placed onto a cork ring. The acetone in the column was allowed

to run out into the flask after which it was eluted with 5 x 10 mℓ portions of acetone.

This acetone was also collected in the flask and then evaporated in a water bath at 30°C.

The residue was dissolved to a final volume of 2 mℓ.

Sterile protocols and practices were followed during culturing and the test procedure, and

samples were analyzed in triplicate. TA98 (1 mℓ) and TA100 (0.5 mℓ) cultures were

transferred into separate 50 mℓ nutrient broth medical flats, which were then incubated at

35 –37°C for 16 –18 hours on a shaker at 30 rpm. Top agar (TA) in a 100 mℓ medical

flat was melted in a water bath at 100°C and minimal agar plates were placed at room

temperature. The temperature in the water bath was then stabilized at 45°C, into which

the TA and sodium phosphate buffer (SPB) were placed. 10 mℓ Histo-bio solution was

added to the top agar and shaken. Agar plates and test tubes were provided for TA98 and

TA100 test and negative controls for each sample, as well as for the positive controls.

The test tubes were placed in a test tube rack in the water bath. 2.5 mℓ TA solution, 100

μℓ culture (TA98 or TA100), 100 μℓ sample and 500 μℓ SPB were added to test sample

test tubes and the same was done for the negative controls but 100 μℓ acetone was added

instead of sample. 2.5 μℓ TA solution, 100 μℓ culture (TA98 or TA100) and 600 μℓ SPB

was added to the positive control test tubes. Each test tube was heated with a Bunsen

burner, mixed with a vortex and evenly added to the relevant agar plate. The plates were

then placed on a level surface to solidify and inverted thereafter. A few crystals of

92

sodium azide (BDH AnalaR, England) were placed on one side of the TA100 positive

control plate and 2-aminoflourene (Sigma-Aldrich, Germany) in the center of the TA98

positive control plate. Sterility checks were performed by briefly exposing one minimal

agar plate to air, leaving one plate as is, adding TA to another and allowing it to solidify.

All plates were then placed in an incubator (Gallenkamp, England) at 37°C for 48-72

hours. The presence of mutagens is indicated by an increase in the number of revertant

colonies on the test plates when compared to the negative control plates:

Mutation Ratio(MR) = Mean number of colonies on test plate

Mean number of colonies on negative control plate

Mutagenicity was indicated by a MR of ≥ 2.0.

4.2.3 Biomarkers

4.2.3.1 Cellular Energy Allocation

Available Energy Reserves (Ea)

CEA was carried out according to the methods set out by De Coen and Janssen (1997).

All samples were analyzed in triplicate. Preparation of the sample was performed by

homogenizing the muscle tissue on ice in homogenizing buffer (3 parts buffer:1 part

sample) using a CAT homogenizer (Ingenieurburo, Germany) and centrifuging at 3 500

rpm for 10 minutes at 4˚C using a RMC 14 centrifuge (Sorvall Instruments, Germany).

The carbohydrate, protein and lipid content of the tissue were then determined using the

resulting supernatant. A glucose test kit (GOD-PAP, Roche Diagnostics, Germany) and

glucose standard (C-FAS, Roche Diagnostics, Germany) were used to determine the

whole body carbohydrate content by measuring the absorbance using an automated

microplate reader (Biotek Instruments, USA) at a wavelength of 490 nm. The protein

content was determined using Bradford’s reagent (Bradford, 1976) and the absorbance

was measured at 630 nm using Bovine Serum Albumin (BSA) (Sigma-Aldrich,

Germany) as a standard. Total lipids were extracted according to the methods set out by

93

Bligh and Dyer (1959) and the absorbance was measured at 405 nm using Tripalmitin

(Sigma-Aldrich, Germany) as a standard.

Energy Consumption (Ec)

The amount of energy consumed was determined by measuring the ETS. Preparation of

the sample was carried out by homogenizing the tissue on ice in homogenizing buffer (3

parts buffer:1 part sample) and centrifuged at 3 000 rpm for 10 minutes at 4˚C. 25 μℓ

supernatant of each sample was then added to a 96 well microplate containing 75 μℓ

Buffered Substrate Solution (BSS), 25 μℓ NAD(P)H solution (Sigma-Aldrich, Germany)

and 50 μl p-IodoNitroTetrazolium (Sigma-Aldrich, Germany), which started the reaction.

The absorbance was read kinetically at 490 nm for 10 minutes at room temperature. The

eqation Є = 15 900/M.cm was then used to determine the amount of formazan formed.

The CEA was determined by transforming Ea into energetic equivalents using the

enthalpy of combustion values which were 17 500 mJ/mg glycogen, 24 000 mJ/mg and

30 500 mJ/mg lipid (De Coen and Janssen, 1997). The theoretical stoichiometrical

relationship was used to determine the Ec where for every 2 μM of formazan formed, 1

μM oxygen was consumed in the ETS. An average oxyenthalpic equivalent of 484

kJ/mol oxygen was used to transform the amount of oxygen into energetic equivalents.

The equation CEA = Ea (E carbohydrate + E protein + E lipid) - Ec (E ETS) was then used to

determine the total energy budget.

4.2.3.2 Acetylcholinesterase

The protocol of Ellman et al. (1961) was followed. All samples were analyzed in

triplicate. Preparation of the sample was carried out by homogenizing the brain tissue on

ice in homogenizing buffer (3 parts buffer:1 part sample) with a CAT homogenizer

(Ingenieurburo, Germany) and obtaining the supernatant by centrifuging for 10 minutes

at 11 000 rpm and 4˚C. The AChE activity was determined by adding 5 μℓ supernatant

to 210 μℓ phosphate buffer, 10 μℓ S-acetylthiochonine iodide (Sigma-Aldrich, Germany)

and 10 μℓ Ellman’s reagent (Sigma-Aldrich, Germany) in a 96 well microplate and

94

incubating for 5 minutes at 37˚C. The absorbance was read kinetically at 405 nm for 10

minutes on a microplate reader (Biotek Instruments, USA). The protein content was

determined by adding 5 μℓ sample to 245 μℓ filtered Bradford’s reagent (Bradford, 1976)

in a 96 well microplate and incubating for 5 minutes at room temperature. The

absorbance was measured at 630 nm using BSA (Sigma-Aldrich, Germany) as a standard.

The enzyme activity for each sample was then calculated using the following equation:

AChE (Abs/min/mg protein) = (gradient/5 μℓ) / protein concentration

4.2.3.3 Ethoxyresorufin-O-Deethylase

EROD was performed according to the method of Burke and Mayer (1974). All samples

were analyzed in triplicate. The liver samples were homogenized on ice in homogenizing

buffer (3 parts buffer:1 part sample) with a CAT homogenizer (Ingenieurburo, Germany)

after which the supernatant was obtained by centrifuging at 9 500 rpm for 30 minutes at

4˚C. 3 μℓ sample was added in the dark to 1.5 μℓ ethoxyresorufin (Sigma-Aldrich,

Germany), 286 μℓ phosphate buffer and 1.5 μℓ NAD(P)H (Sigma-Aldrich, Germany)

solution, which started the reaction. The absorbance was read kinetically at an excitation

of 544 nm and an emission of 590 nm for 1 minute with a fluorimeter using resorufin

(Sigma-Aldrich, Germany) as a standard. The enzyme activity was determined using the

following equation:

EROD (nM/min/mg WW) = EROD nM/minsamp x TV

3 WW

where: 3 = Sample volume (μℓ) used in assay

TV = Total volume (μℓ) of sample homogenate

WW = Wet weight (mg) of sample

95

4.2.4 Statistical Analysis

SPSS 14.0 was used to analyze the biomarker data. All values are reported as mean +

standard error of the mean. The significant variations between the biomarkers from the

different sampling sites were tested by One-way ANOVA. Data were tested for

normality and homogeneity of variance using Kolmogorov-Smirnoff and Levene’s tests

(Zar, 1996), respectively, prior to applying post-hoc comparisons. Post-hoc comparisons

were made using the Scheffé test for homogeneous or Dunnett’s-T3 test for non-

homogenous data. The use of either one of the two tests resulted in the determination of

significant differences (p<0.05) between variables. For the multivariate analyses all data

were checked for skewness and log-transformed prior to normalisation (Clarke and

Gorley, 2006). Ordination bi-plots for the environmental and bioaccumulation data were

generated using principal component analysis (PCA), whilst both non-metric

multidimensional scaling (NMDS) and PCA were applied to the biomarker data. The

significance of the groupings formed was tested using the ANOSIM protocol in Primer

version 6. Probit analysis was used to calculate the lethal concentrations following

exposure to effluents, e.g. LC10 or LC50 values (USEPA, 1991b),

4.3 Results 4.3.1 DEEEP

Daphnia and fish lethality tests

The physico-chemical parameters of the effluents remained within acceptable limits

throughout the exposure periods, i.e. percentage oxygen saturation was above 40% and

mg/ℓ oxygen above 4.0 mg/ℓ; pH remained between 6 and 8.5 and temperature at 20±2°C

for the water flea and 23±2°C for fish tests. No D. pulex mortalities were recorded

following exposure to effluents from the Glendale Distillery and the Ushukela Sugar

Mill. Mortalities were only found following exposure to the Amatikulu Sugar Mill

effluent and a LC10 of between 50% and 75% was calculated for the effluent using Probit

analysis.

96

The only fish lethality was recorded following exposure to the Ushukela Sugar Mill

effluent and a LC10 value of 75% of the effluent was calculated.

Ames-Salmonella Mutagenicity Test

AMES test results are interpreted using a mutation ratio (MR) that is the mean number of

colonies on triplicate negative control and test plates. A MR ≥2 is indicative of

mutagenicity (Slabbert, 2004). Table 4.1 indicates that only the effluent from Ushukela

Sugar Mill showed mutagenic activity in the TA100 bacterial strain. All the other

effluents MR values were below 2.

Table 4.1 The mutation ratios (MRs) obtained with the two tester strains, the number of

colonies on the negative control plates and the results of the positive controls

and sterility checks for the mutagenicity test performed on the effluent from

the three mills. Site MR

TA98

MR

TA100

Negative

Control

TA98

Negative

Control

TA100

Positive

Control

Sterility

checks

Glendale

Distillery

1.27 0.92 12 245 Positive Negative

Ushukela Sugar

Mill


Amatikulu Sugar

Mill


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4.3.2 Biomarkers

The available energy reserves and the energy consumption of O. mossambicus from all

sites are shown in Figures 4.1 and 4.2. Carbohydrate reserves in fish from Sites USR and

USS were significantly lower compared to the other sites, while Site GDS was

significantly lower than Site GDR (Figure 4.1A). Similarly the lipid reserves at Sites

USR and USS were significantly lower when compared to the other sites (Figure 4.1B).

In contrast to this, Figure 4.1C indicates that the protein reserves at Sites USR and USS

was significantly higher than at the other sites and significantly higher protein energy

levels were recorded at Site GDS when compared to Site GDR.

The total energy reserves (Ea) decreased from Site GDR to Site USS on the Mvoti River.

It can also be seen that there is a significant difference between the two reference and the

two sampling sites, and that the energy reserves at the sampling sites were consistently

lower than at the reference sites (Figure 4.2A). The total Ec (Figure 4.2B) also decreased

downstream from Site GDR. However it was significantly higher at Site USS when

compared to the reference site at Site USR. This resulted in fish from Site USS

displaying significantly lower total energy budgets (Figure 4.2C). The highest CEA

values were found in fish from Site GDR and decreases as one moves downstream

(Figure 4.2C). The CEA of fish from Site AR was also lower when compared to Site

GDR.

98

A

GDR GDS USR USS AR0.0

250000.0

500000.0

750000.0

1000000.0

1250000.0

ba

eca

edb

dc

Sites

Car

bohy

drat

e R

eser

ves

(mJ/

g)

B

GDR GDS USR USS AR0

100000

200000

300000

cba

da

eb

fc

fed

Sites

Lipi

d R

eser

ves

(mJ/

g)

C

GDR GDS USR USS AR0

25000

50000

75000

100000a

Sites

Prot

ein

Res

erve

s (m

J/g)

ba

b

Figure 4.1 The carbohydrate (A), lipid (B) and protein (C) reserves of the resident fish,

O. mossambicus at the study sites. Error bars represent standard error of the mean and

bars with common letters differ significantly from each other (p<0.05).

99

A

B


250000.0

500000.0

750000.0

1000000.0

1250000.0

1500000.0

1750000.0ba

c

a cb

Site

Ea (m

J/g)

GDR GDS USR USS AR0

1000

2000

3000

4000

5000

ba

ca

Site

Ec(m

J/g)

bc

C


250000.0

500000.0

750000.0

1000000.0

1250000.0

1500000.0

1750000.0ba

a

c

cb

Sites

CEA

(mJ/

g)

Figure 4.2 The total energy available (A), the ETS activity (B) and the total energy

budget (C) of O. mossambicus. Error bars represent standard error of the mean and bars

with common letters differ significantly from each other (p<0.05).

100

The AChE activity (Figure 4.3A) differed significantly between Sites GDR and Site GDS

with greater inhibition at Site GDR. There is also a greater inhibition at Site USS

compared to Site USR. The lowest AChE concentrations were measured at Site AR. The

highest EROD levels (Figure 4.3B) were recorded in liver tissue of fish from Site AR.

There is also a higher induction at Site GDR compared to Site GDS, whereas EROD

activity at Site USS was higher than at Site USR, and Site GDS.

The integrated biomarker responses at the study sites are presented in a PCA plot in

Figure 4.4. In this plot, 69.4% of the variance can be explained on the first axis while the

second axis explains a further 13.4% of the variance. The plot indicates that the Glendale

Mill sites were distinctly different from the other two mill sites and that the former was

characterized by high protein energy reserve values. The sites in the vicinity of Ushukela

Mill are characterized by distinctively lower CEA and energy reserve values, whilst

EROD activity increases and AChE activity decreases at these sites. Site AR is

characterized by high EROD levels.

101

GDR GDS USR USS AR0

20

40

60

80

100

120

140

Site

B

ERO

D (n

M/m

in/m

g W

W)

GDR GDS USR USS AR0

1.0×10-5

2.0×10-5

3.0×10-5

bad

dca

cb

Site

AChE

(abs

/min

/mg

prot

ein)

A

Figure 4.3 The mean AChE concentrations in the brain tissue (A) and EROD levels in

liver (B) of resident O. mossambicus from the different study sites. Error bars represent

standard error of the mean. Post-hoc tests were not performed for EROD as at least one

group had fewer than 2 cases.

102

Figure 4.4 PCA bi-plot showing CEA, AChE and EROD results for the sites. The

numbers represent biomarker responses in individual fish from the following sites:

1=GDR, 2=GDS, 3=USR, 4=USS and 5=AR.

4.4 Discussion

The effluents from the three mills displayed limited acute (mortality-based) toxicity with

only the Ushukela Sugar Mill effluent causing some mortality in fish. This effluent also

had the lowest oxygen concentration that could be attributed to the high organic nature of

sugar mill effluent, resulting in a depletion of oxygen. In addition to this, the Ames test

indicated that this effluent was mutagenic as the TA100 strain revealed an MR of more

than 2 (MR = 3.08). This is possibly due to the mutagenic and carcinogenic PAHs

(Mason, 1991) that could be present in the effluent as a result of the burning of bagasse as

fuel in sugar mills (Cheesman et al., 2005) and the burning of sugarcane at harvesting

season to facilitate manual harvesting (Tfouni et al., 2006). The water flea test indicated

that there was some toxic properties in the Amatikulu Sugar Mill effluent. Daphnids are

known to be able to tolerate low ambient oxygen concentrations due to their ability to

synthesize haemoglobin (Clare, 2002). Thus the low oxygen concentration in the

103

Ushukela Sugar Mill effluent did not negatively affect them and some other toxic

properties in the Amatikulu Sugar Mill effluent was responsible for the observed

mortalities.

It was apparent that the carbohydrate and lipid reserves in fish from Sites USR and USS

were affected to a greater degree than the protein reserves. It has been shown that

toxicant stress causes the more readily available carbohydrate and lipid reserves to be

depleted before the protein reserves (Smolders et al., 2004). More of a protein reserve

(55%) was available at Site USR compared to the carbohydrate (2.74%) and lipid

reserves (42%), which supports this statement, but a lower protein reserve (45%) was

available compared to the lipid reserve (50%) at Site USS. De Coen and Janssen (2003)

also showed that there were increased protein levels at low pollutant concentrations, but

decreased protein levels at high concentrations. At low pollutant concentrations, protein

synthesis could be triggered for detoxification processes (Smolders et al., 2003), whereas

at high pollutant levels, these reserves could decrease, as they are now needed for

survival. Thus the higher carbohydrate and lipid reserves at the other sites could be

interpreted as better conditions prevailing than those at Sites USR and USS. The total

energy reserve and the total energy consumed showed the same decreasing trend as one

moves downstream in the Mvoti River. This was the case for all the sites except Site

USS, as at this site the cellular energy consumption of fish (measure as ETS activity) was

much greater than at Site USR. This would indicate the presence of a stressor, with

higher energy requiring processes. It could be attributed to the presence of multiple

stressors in the form of effluents from the Stanger sewage works, the Sappi Stanger Mill

and Ushukela Sugar Mill. This is supported by the total energy budget in Figure 4.2C

where the organisms at Site USS have a lower energy budget than those at Site USR.

Fish from Site GDR also had a higher CEA than those from Site GDS, indicating that the

Glendale Distillery also had an impact at Site GDS.

The lower AChE activity in fish from Site GDR when compared to Site GDS indicated

that there is a stressor impacting at Site GDR. This could be due to the runoff of

pesticides (Zinkl et al., 1991) from the sugar-cultivated fields surrounding Site GDR,

104

which is probably exacerbated by irrigation with wastewater. The higher enzyme activity

at Site GDS is an indication that it is unlikely that pesticides are present in the effluent

discharged into the river by the Glendale Distillery. Fish in the vicinity of the Ushukela

Mill sites displayed greater AChE inhibition indicating the presence of organic chemicals

in the multiple effluents below the mills.

Similarly, EROD activity in fish indicated that there were impacts at both Sites GDR and

USS when compared to Sites GDS and USR. Impacts at Site GDR are likely to be due to

PAHs produced due to the burning of sugarcane. Sugarcane is usually burnt at harvesting

season to facilitate manual harvest (Tfouni et al., 2006). The impact at Site USS could be

attributed effluents from both the pulp and paper as well as the sugar mills since PAHs

have been found to occur in pulp and paper mill effluent (Kirso et al., 2002) and bagasse

is burned as fuel in sugar mills (Cheesman et al., 2005), thereby possibly releasing PAHs

into the effluent.. It has also been found that dioxins, which are potentially produced in

paper bleaching (Fossi et al., 1995), also induce EROD activity (Payne et al., 1987). In

contrast to lower AChE activity however, there is a high induction of EROD at Site AR.

This is most likely due to other industrial impacts in the area or effluent runoff from

sugarcane fields.

4.5 Conclusion

In terms of the total energy budget, the decreasing trend, from the uppermost reference

site (GDR) to the last site on the Mvoti River (USS), indicates that pollution increases as

one moves downstream. The brunt of the pollution thus occurs at Site USS because it is

impacted by multiple stressors from a range of activities, e.g. Sappi Stanger Mill,

Ushukela Sugar Mill, Stanger sewage treatment works, as well as by runoff from the

surrounding sugarcane fields. This is supported by the fact that the carbohydrate reserves

at Sites GDR, GDS and AR were much higher than that at Sites USR and USS. The

AChE and EROD results, however, indicate that there is an impact at Site GDR, which is

not apparent in the energy allocation. At Site GDS, on the other hand, pesticide and PAH

105

106

exposure is less than that found at Site GDR and Site USS. This indicates that the

effluent discharged by the Glendale Distillery does not have a detrimental affect when

compared to the other sites in the study. The fish and water flea as well as the Ames

mutagenicity test also indicate that this effluent is not toxic or mutagenic. The fish

lethality test did indicate some toxicity at this site, which could be attributed to the low

oxygen concentration resulting from the high organic nature of the effluent, probably due

to the presence of PAHs. Site AR was found to have a high EROD induction, which is

probably due to effluent runoff from surrounding sugarcane fields or due to exposure to

other industrial effluent, as the Amatikulu Sugar Mill is situated further downstream.

The Ames mutagenicity test, however, did not indicate mutagenicity in this effluent. The

water flea test, however, indicated some toxicity that could not be attributed to a lack of

oxygen as Daphnia are not sensitive to low oxygen levels. They are, however, extremely

sensitive to metal ions (copper and zinc), pesticides, detergents, bleaches, disturbances of

the ionic composition of their environment (sodium, potassium, magnesium, and

calcium) and halides (e.g. chloride or fluoride) (Clare, 2002).

Chapter 5

Conclusion and Recommendations

Chapter 5 Conclusion and Recommendations 5.1 Conclusion Objective 1: Use invertebrate and fish community responses to determine the effect of sugar

mills alone and combined effect of sugar and pulp mill activities on the ecological

integrity of the Mvoti River.

When analyzing the macro-invertebrate community data, a spatial trend was identified.

This trend indicated that there was a separation of reference and sampling sites on the

Mvoti River. This implies that in terms of invertebrate structure, the sites above the mills

differ from sites below the mills on the Mvoti River. Unification of sites on the

Amatikulu River indicate that the reference and sampling sites are similar. Site GDS

seemed to be in a better condition than Site GDR as more sensitive species were found at

Site GDS. This can be attributed to the diverse habitat found here, which is an indication

that water abstraction by the Glendale Distillery has a minimal impact. Water

abstraction, however, does affect the water quality as it is poorer at lower dilutions when

compared to Site GDR, indicating an impact by the mill effluent. Site USS, which is in

the vicinity of the Ushukela Sugar Mill and the Sappi Stanger Mill, differs from Site USR

in that more hardy species were found at Site USS. In addition, dominance of these taxa

(Chironomidae and Simuliidae) occurred at Site USS. There were also lower oxygen and

higher conductivity levels, which could be attributed to organic pollution by the mills in

the area. The Amatikulu Sites, on the other hand, were found to be similar in community

structure in that diverse communities were found at these sites. The loss of the stones

biotope at Site AS, however, lead to the loss of some sensitive taxa. This is a result of

sedimentation due to water abstraction by the Amatikulu Sugar Mill. From this it can be

deduced that the effect of the sugar mills alone is minimal, whereas the combined effect

of the sugar and pulp mill activities is much worse.

107

The response in terms of the fish community has shown that the sugar mill activities

alone are not having a major impact during high and low flows when comparing the

downstream Glendale site (impacted by a distillery alone) to the downstream Ushukela

site (impacted by a sugar and paper mill). The downstream Amatikulu site was

disregarded in terms of fish response as it was impacted by a flooding event that

influenced the fish community structure to a large degree. Only hardy species were

found at Site USS due to poor water and habitat quality caused by the discharge of sugar

and paper mill effluent, runoff of sugar mill effluent from sugar-cultivated lands, water

abstraction by both mills, and removal of riparian vegetation by sugar farming practices

leading to siltation. At Site GDS, however, more intolerant species were found and the

abundance was greater. This site is also impacted by distillery effluent discharge and the

other activities mentioned above, but to a lesser extent compared to Site USS.

The spatial structuring in the fish community structure was similarly more important than

the temporal differences at all the sites. The Amatikulu and Ushukela sites were more

similar than the Glendale sites in terms of the fish community. This can be attributed to a

localized flooding event that occurred in the Amatikulu River catchment just prior to the

high flow period. This resulted in the washing down of fish communities and a

disruption of normal fish distribution, decreasing the abundance at Sites AR and AS. The

Ushukela sites were found to be characterized by high salt concentrations and the

presence of hardy species, indicating a more deteriorated state. This could be attributed

to poor habitat and water quality caused by the pulp and sugar mill as well as other

industrial, agricultural and urban impacts in the area. Distinct spatial differences

occurred at the Glendale sites, when compared to the other sites as the diversity was

higher here. Hardier species, however, were found at Site GDS compared to the reference

site, and high levels of ammonium were also prominent at Site GDS. Nevertheless, more

barb species occurred at this site. It is very likely that these species could be feeding off

the organic effluent from the Glendale Distillery, making this site a preferred feeding

area. The fish community structure thus supports the macro-invertebrate findings in that

the effect of sugar mills is minimal compared to the effect by both the pulp and sugar

mill.

108

Objective 2:

Use the Direct Estimation of Ecological Effect Potential (DEEEP) protocols to assess

the toxicity effect of sugar mills alone and combined effect of sugar and pulp mill

activities on the ecological integrity of the Mvoti River.

The results of the DEEEP methodology showed that the Glendale Distillery effluent was

neither toxic nor mutagenic and that the Amatikulu Mill effluent was toxic (at a 75%

concentration) to Daphnia but not mutagenic. The Ushukela Mill effluent was found

both toxic to fish and had mutagenic properties. The toxicity was ascribed to the high

oxygen demand of the organic material in the effluent, resulting in a depletion of oxygen

and death. The causative toxic properties in the Amatikulu Mill effluent could not be

related to oxygen depletion and could possibly be related to other factors such as metal

ions, pesticides, halides, etc. This only resulted in toxicity to D. pulex but not the fish.

This shows that the toxicity effect of pulp and sugar mill activities combined is more

significant than the effects of sugar mill activities alone.

Objective 3:

Use biomarker responses to determine the effect of sugar mills alone and combined

effect of sugar and pulp mill activities on the ecological integrity of the Mvoti River.

Spatial analysis once again indicates a difference between the Glendale sites and the

other sites. This is because the protein energy reserves were found to be much higher at

the Glendale sites compared to the other sites. The Ushukela sites also showed higher

EROD and lower AChE activity, indicating an impact at these sites. This indicates that

conditions are worse at the Ushukela sites compared to the Glendale sites and is most

likely due to the dual impact of the pulp and sugar mill. High levels of EROD were

found at Site AR, which is either due to runoff from the sugarcane fields or due to

another impact in the area. Hence, the combined effect is more detrimental to the

ecological integrity of the Mvoti River than the sole effect of the sugar mill.

109

Objective 4:

Compare the biological community responses, DEEEP assessment and biomarker

responses from the Mvoti River to responses in the adjacent Amatikulu River that is

only subjected to sugar mill activities.

Because of the effect of the flood that occurred in the Amatikulu River, the responses of

fish and biomarkers from the Mvoti River could not be compared to those from the

Amatikulu River. The fish and biomarker responses at Site USS were therefore evaluated

compared to Site GDS on the Mvoti River as Site GDS is not impacted by the pulp mill,

and both Site GDS and Site AS will be referred to as control sites. The fish and macro-

invertebrate community structure at Site USS were in a more degraded state compared to

the control sites as only hardy fish species were found at Site USS and proliferation of

hardy macro-invertebrate species occurred here. At Site GDS, more intolerant fish

species were found and the control sites showed greater macro-invertebrate species

diversity including sensitive species.

As for DEEEP and biomarker results, both indicated mutagenicity and toxicity occurring

at Site USS. The higher degree of toxcity at this site is due to multiple stressors from

multiple impacts in the Stanger region. Biomarker results also indicated higher energy

reserves and lower AChE inhibition and EROD induction at Site GDS compared to Site

USS. This indicates that impacts of sugar mill activities alone are not as severe as they

are due to impacts resulting from the combined effect of the pulp and sugar mill.

5.2 Recommendations

• Management of water abstraction, rehabilitation of the riparian zone and

implementation of improved soil management processes should be carried out in

order to minimize sedimentation and encourage natural filtration of runoff from

sugarcane fields.

110

111

• Control or elimination of burning of sugarcane at harvesting season as the

resultant PAHs end up in river systems causing mutagenicity and organic

pollution. In addition, management of the burning of bagasse as fuel as the

resultant PAHs end up in the effluent that is discharged into the system and are

used for irrigation purposes.

• Treatment of organic effluent should be carried out by the Sappi Stanger Mill as

well as the Ushukela Sugar Mill in order to increase the oxygen content and

prevent mutagenicity. This should also be followed by carrying out an Ames

Salmonella mutagenicity test on the effluent before it is discharged.

• The spraying of pesticides should be controlled as well as the use of ammonium

nitrate as fertilizer.

• The various land-use activities such as cattle farming and the use of detergent in

the river systems should be minimized in order to reduce the microbiological

count and decrease the alkalinity. This in turn will minimize disease and

conversion of ammonium to ammonia which is extremely toxic.

• A more detailed biomonitoring programme should be carried out seasonally and

should include macro-invertebrate and fish assessments. In addition, physical and

chemical water quality monitoring should be done monthly at all the sites used in

this study.

• Since the Ames Salmonella mutagenicity test indicated high mutagenicity in the

Ushukela Mill effluent, it is recommended that more detailed exposure studies

should be undertaken to determine whether mutagenic stress is expressed as

genotoxic responses.

Chapter 6

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