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WATER TEMPERATURE and FISH DISTRIBUTION in the Sable RIVER SYSTEM: TOWARDS the DEVELOPMENT of an ADAPTIVE MANAGEMENT TOOL

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WATER TEMPERATURE and FISH DISTRIBUTION in the Sable RIVER SYSTEM: TOWARDS the DEVELOPMENT of an ADAPTIVE MANAGEMENT TOOL WATER TEMPERATURE AND FISH DISTRIBUTION IN THE SABlE RIVER SYSTEM: TOWARDS THE DEVELOPMENT OF AN ADAPTIVE MANAGEMENT TOOL by NICHOLAS ANDREW RIVERS-MOORE Submitted in fulfilment ofthe academic requirements for the degree of Doctor ofPhilosophy in the School ofBioresources Engineering and Environmental Hydrology University ofNatal Pietermaritzburg 2003 11 ABSTRACT Water temperatures are a fundamental water quality component, and a key abiotic determinant of fish distribution patterns in rivers. A river 's thermal regime is the product of a multitude of thermal drivers and buffers interacting at different temporal and spatial scales, including, inter alia, air temperatures, flow volumes (including groundwater flows and lateral inputs from tributaries), channel geomorphology and riparian vegetation. "Healthy" river systems are self-sustaining, with adequate thermal variability to maintain biotic diversity. Temporal variability of flow volumes and water temperatures, and how these change along the longitudinal axis ofa river, contribute towards a rivers "signature". Rivers that have had their signatures altered through anthropogenic impacts may no longer be sustainable, and require varying levels of management. Successful river management should include a quantification ofthese signatures , a definition ofthe "desired" state which management aims to achieve, associated "thresholds" of change or concern, and monitoring programmes. Such an approach requires flexibility and adaptability, as well as appropriate tools being available to natural resource managers. Indices, the utility of which are enhanced when included in predicative modelling systems, are a common means of assessing system variability and change. The degree of confidence placed in such tools depends on the level of fundamental science, and the degree of system understanding, underpinning them. This research contributes to the understanding of the ecological significance. of water temperatures in variable semi-arid river systems, using the Sabie River (Mpumalanga, South Africa) as a case study, and indices derived from biological indicators (Chiloglanis , Pisces: Mochokidae) to quantify the effects ofcumulative changes in heat units against a hypothesised critical water temperature threshold. Hourly water temperatures for 2000­ 2002 collected at nine sites in the main rivers of the Sabie catchment, together with biannual surveys of relative abundances and community patterns of fish collected using standard electrofishing techniques, were used to provide the basis for a modelling system which aims to provide river managers with a tool for quantifying changes to the thermal regime of the Sabie River. This modelling system consisted of a suite of pragmatic models, including multiple linear regression models for simulating daily maximum water temperatures, and simple cause-and-effect relationships between biological indices (change III In condition factor and change in the ratio of relative abundances of two species of Chiloglanis) and annual metrics oftime-of-exposure to heat stress. It was concluded that changes in the thermal regimes of the rivers in the Sabie catchment are likely to lead to changes in fish distribution patterns, and a decline in river health. Inherent system variability suggests that management decisions will be made in the face considerable uncertainty. Indirect management of water temperatures may be possible through maintenance of flow volumes and flow variability. However, the most appropriate management approach for maintaining fish diversity within these rivers is to ensure that obstacles to fish migration are minimized, to maximise the ability of river biota to respond to thermal changes, by accessing suitable alternative habitats or refugia. Future research should focus on extending the time series of water temperatures from such river systems, and further understanding the drivers and buffers contributing to the thermal regimes of variable semi-arid river systems in South Africa. Additional testing of the validity of the hypothesized relationships between abiotic processes underpinning biotic patterns should be undertaken. IV PREFACE The research described in this thesis was carried out in the School of Bioresources Engineering and Environmental Hydrology, University of Natal , Pietermaritzburg, under the supervision of Doctor Graham P. W. Jewitt (School of Bioresources Engineering and Environmental Hydrology) and Professor Charles Breen (Institute ofNatural Resources). These studies represent original work by the author and have not otherwise been submitted in any form for any degree or diploma to any University. Where use has been made of the work ofothers it is duly acknowledged in the text. N.A. Rivers-Moore Date v LIST OF CONTENTS ABSTRACT 11 PREFACE IV LIST OF CONTENTS v LIST OF FIGURES ix LIST OF TABLES xv ACKNOWLEDGEMENTS xviii 1 INTRODUCTION 1 1.1 Background 1 1.2 Aims and objectives 6 1.3 Wider context to this research project 7 1.4 Thesis structure 9 2 INTRA-ANNUAL THERMAL PATTERNS IN THE MAIN RIVERS OF THE SABlE CATCHMENT 11 2.1 Introduction 11 2.1.1 Components ofwater temperature 11 2.1.2 Water temperature and aquatic biota 15 2.2 Methods to quantify the thermal regime ofthe Sabie River. 17 2.2.1 Data collection 21 2.2.2 Data analysis techniques 24 2.3 Results ofSabie River temperature monitoring 26 2.3.1 Thermal variation at the micro-scale 26 2.3.2 Thermal variation at the catchment scale 31 204 Discussion and conclusions 38 3 SIMULATING MAXIMUM DAILY WATER TEMPERATURES IN THE SABlE RIVER 42 3.1 Introduction 42 3.2 Review ofwater temperature modelling approaches .44 3.2.1 Statistical methods 44 3.2.1.1 Linear regression analysis 45 3.2.1.2 Time series analysis 47 3.2.1.3 Spectral analysis 49 3.2.2 Process-based approaches 51 3.2.3 Dynamic water temperature models 52 3.3 Methods for simulating water temperatures in the Sabie catchment 54 3.3.1 Tests for trend and stability ofmean and variance 55 3.3.2 Static water temperature models 55 3.3.3 Dynamic water temperature model.. 59 3.4 Results 62 304.1 Quality ofwater temperature data 62 3.4.2 Statistical approaches for simulating daily water temperatures 62 3.4.3 Synthesis 71 3.4.4 Dynamic water temperature model.. 73 35. DISCUSSlon" and coneI'usions 75 3.5.1 Physical versus statistical approaches 75 3.5.2 Choice ofmodelling approach and model scale 76 VI 4 WATER TEMPERATURE AND DISTRIBUTION PATTERNS OF FISH IN THE RIVERS OF THE SABIE CATCHMENT 80 4.1 Introduction 80 4.1.1 Water temperature and river species patterns 80 4.1.2 Quantification ofspecies patterns 81 4.2 Data collection and analysis methods 83 4.2.1 Diversity indices in explaining species patterns 86 4.2.2 Ordination techniques in explaining species patterns 89 4.3 Results 92 4.3.1 Species patterns in the Sabie River using diversity indices 92 4.3.2 Species patterns in the Sabie River using ordination techniques 97 4.4 Discussion and conclusions 104 4.4.1 Reliability of fish diversity data using relative abundances 104 4.4.2 Fish species patterns in the Sabie catchment 105 5 CHILOGLANIS ANOTERUS AS AN INDICATOR SPECIES OF CHANGING WATER TEMPERATURES IN THE SABIE RIVER 108 5.1 Introduction 108 5.1.1 Aims and objectives for choosing suitable indicator species for annual water temperature in the Sabie River. 108 5.1.2 Indicator species and river health 110 5.1.3 Indicator species and the niche hypervo1ume concept.. 113 5.1.4 Significance ofwater temperature to fish 115 5.2 Methods of quantifying niche dimensions of fish species in the Sabie River 117 5.2.1 Quantification ofniche dimensions using the niche hypervolume concept 119 5.2.2 Quantification ofniche dimensions using niche breadths and niche overlaps 120 5.3 Results 123 5.3.1 Quantification ofniche dimensions using the niche hypervolume concept 123 5.3.2 Quantification ofniche dimensions use niche breadths and niche overlaps 127 5.4 Discussion and conclusions 130 6 A TPC FOR CUMULATIVE ANNUAL WATER TEMPERATURE IN THE SABIE RIVER, AND ITS ASSESSMENT USING BIOLOGICAL INDICATORS (CHILOGLANIS SPP., MOCHOKIDAE) 134 6.1 Introduction 134 6.1.1 Natural systems and sustainability 134 6.1.2 Adaptive management and thresholds ofprobable concern (TPCs) 136 6.1.3 Adaptive management for water temperatures in the Sabie River 138 6.1.3.1 The Chiloglanis anoterus: C. paratus ratio ofrelative abundance index 140 6.1.3.2 The C. anoterus condition factor index 141 6.2 Methods for evaluating Chilog1anid biological indices 142 6.2.1 Chiloglanis ratio ofrelative abundances index 145 6.2.2 C. anoteruscondition factor index 147 6.3 Results 147 6.3.1 Chiloglanis ratio ofrelative abundances index 152 6.3.2 C. anoterus condition factor index : 157 V11 6.4 Discussion and conclusions 162 6.4.1 General considerations 162 6.4.2 Choice ofwater temperature TPC for the Sabie River 163 6.4.3 TPC definition 167 7 THE CONCEPTUAL DESIGN OF A CHILOGLANIS MODELLING SYSTEM AS AN ADAPTIVE MANAGEMENT TOOL FOR USE IN THE SABlE RIVER 171 7.1 Introduction 171 7.1.1 Aspects of natural resource management 171 7.1.2 Modelling approaches for natural systems 173 7.1.3 Modelling and adaptive management 175 7.2 The Chiloglanis modelling system 177 7.2.1 Objectives and conceptual approach 178 7.2.2 Conceptual Chiloglanis modelling system 179 7.2.3 Derivation ofdaily maximum water temperatures: The abiotic inputs 187 7.2.4 Linking abiotic drivers to biotic response 188 7.2.4.1 Change in condition factor of C. anoterus with change in water temperature 189 7.2.4.2 Change in relative abundance ofChiloglanids with change in annual water temperature 189 7.3 Potential for future development 192 8 APPLICATION OF THE CHILOGLANIS MODELLING SYSTEM 197 8.1 Introduction 197 8.1.1 Adaptive management and model complexity 197 8.1.2 Aims ofChiloglanis modelling system 198 8.2 Methods for assessing the "level of predictability" ofwater temperature data, and application ofthe Chiloglanis modelling system under different environmental scenarios 199 8.2.1 Colwell's indices ofpredictability 199 8.2.2 Application ofChiloglanis modelling system 20 1 8.3 Results 203 8.3.1 Predictability ofwater temperatures within the Sabie catchment.
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