A Diatom-Based Model to Monitor Trophic Status in Lowland Rivers

A D ia t o m -Ba se d M o d el t o M o n it o r T r o ph ic

S t a t u s in L o w l a n d R iv e r s U sin g

A r t ific ia l Su b st r a t a

Thesis submitted for the degree of Doctor of Philosophy in the University of London by Benjamin John Goldsmith

University College London 2002 (Submitted April 2000) ProQuest Number: U643959

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The aim of this thesis was to develop the methodologies required to sample diatoms from lowland rivers in southern England, and to use these techniques to establish a diatom-based model to assess trophic status. A number of different artificial and natural substrata were compared from four lowland river sites of contrasting nutrient chemistry to assess the most suitable means of obtaining representative, repeatable and diverse diatom assemblages. Natural substrata supported more variable diatom assemblages than the artificial substrata and could not be kept constant between all lowland river sites. Thus two artificial substrata were chosen for diatom sampling: rough ceramic tiles and frayed polyethylene rope. From a chemical survey of 115 lowland river sites, 61 were chosen which covered a wide phosphorus gradient (3-7530 pgL'^ FRP). From these sites the two artificial substrata were used to collect diatom samples, in order to develop two independent diatom training sets. Multivariate statistical techniques (PCA, DCA and CCA) were used to investigate the chemical relationships, species relationships and environmental-species interactions. From these analyses a strong, independent, relationship was identified between the diatom assemblages and the phosphorus concentration of the river sites. These data were demonstrated to be suitable for weighted average (WA) modelling. Weighted average regression and calibration techniques were used to generate diatom-based transfer functions, from which river phosphorus levels could be estimated with an acceptable level of error (r^ > 0.80, RMSEP < 0.30). When tested on independently derived diatom samples, the models performed well if the diatoms had been collected at the same time of year as the training set samples (autumn). Considerable seasonal variation was demonstrated within the diatom assemblages however, and the predictive power of the autumn-based models was greatly reduced when applied to samples collected at other times of the year. This thesis demonstrates that with carefully chosen sampling techniques, diatoms can be used as a reliable bio-monitoring tool for the assessment of phosphorus levels in lowland rivers. It is suggested that with the temporal and spatial extension of the training sets developed in this thesis, diatoms could provide a fast and effective means of assessing and monitoring the trophic status of lowland rivers. A cknowledgements

This work was funded jointly by the University College London Graduate School and ENSIS Limited. Fieldwork support and equipment was funded by UCL Graduate School and the Department of Geography, UCL.

I am indebted to many friends and colleagues who have contributed both personally and professionally to the completion of this research. In particular, I would like to thank Dr. Tim Allott, Prof. Rick Battarbee and Dr. Eileen Cox for their supervision, enthusiasm and encouragement, throughout this study. Their input and support has been invaluable.

I am grateful to Dr. Roger Flower for inspiration and help in the field in the formative stages of this study. The idea of using rope as an artificial substratum is acknowledged to Roger. I would also like to thank Anna McQueen, Eileen Cox, Tim Allott, Ewan Shilland and Janet Hope for their assistance with fieldwork. The encouragement and help of Dr. Martyn Kelly was also greatly appreciated.

Particular thanks go to Janet Hope. The laboratory analyses would not have been possible without Janet’s vast knowledge and experience. I am also grateful to Sarah James for assisting with cation analysis at Royal Holloway and Bedford New College, and to Tony Osborn for helping with anion analysis in the Department of Geological Sciences, UCL.

Finally, I would like to thank all those members of the ECRC and Department of Geography at UCL , who have provided me with help, encouragement and numerous distractions from the path of true science: you know who you are.

And to Felicity for companionship in the field, and constant support and patience during the writing of this thesis. Co n t e n t s

T it l e P a g e 1 A b s t r a c t 2 A cknowledgements 3 Co n t e n t s 4 L is t o f F ig u r e s 9 L is t o f T a b l e s 14 L ist o f P l a t e s 18

R One Introduction 20 1.1 Background 20 1.2 Eutrophication - Sources and Effects 21 1.2.1 River Eutrophication - The Legal Position 23 1.3 River Monitoring 26 1.3.1 The Biological Component 27 1.3.2 Diatoms as Indicators of Water Quality 28 1.3.3 The use of Diatoms to Monitor Rivers 30 1.3.3.1 Diversity Measurements 31 1.3.3.2 Diatom Ecology and Water Quality Assessment 32 1.3.3.3 Diatom Indices 32 1.4 Sampling River Diatoms 34 1.4.1 The Diatom Assemblages 35 1.4.1.1 The Epipelon 35 1.4.1.2 The Epiphyton 36 1.4.1.3 The Epilithon 37 1.4.2 Artificial Substrata 38 1.4.3 Sample Representivity 40 1.4.3.1 The Accumulation of Dead Cells 41 1.4.3.2 The Effects of Grazing 42 1.4.3.3 Seasonality 43 1.4.3.4 The Physical Environment 44 1.5 Thesis Aims 44 1.6 Definition of Trophic Status 45 1.7 Thesis Outline 45

RTWO M e t h o d s 47 2.1 Study Region 47 2.2 Water Chemistry 50 2.2.1 Sample Collection 50 2.2.2 Field Analysis 51 pH 51 Total Alkalinity 52 Conductivity 52 Current Velocity 52 Temperature 53 2.2.3 Laboratory Analysis 53 Ortho-Phosphate (FRP) 54 Nitrate 54 Total Phosphorus and Total Nitrogen 55 Silica 55 Cation Analysis - ICP 56 Anion Analysis - IC 57 Contamination 57 2.3 Diatom Sampling 57 2.3.1 Natural Substratum Sampling 58 Cobbles 58 Sediment Samples 58 Submerged Macrophytes 59 2.3.2 Artificial Substratum Sampling 59 Clay Tiles 60 Rope 60 Glass and Perspex 61 2.4 Diatom Slide Preparation and Counting 61 2.4.1 Preparatory Methods 61 Epilithon and Tile Samples 62 Epiphyton and Rope Samples 62 Epipelic Samples 62 2.4.2 Quantitative Assessment 63 2.43 Slide Preparation 63 2.4.4 Archiving 63 2.4.5 Diatom Counting 64 2.4.6 Counting LiveiDead Cell Ratios 64 2.4.7 Diatom Identification 65 2.5 Data Storage, Manipulation and Analyses 66

:r T h r e e T h e S e l e c t io n o f a S u it a b l e Su b s t r a t u m f r o m w h ic h TO S a m p l e D ia t o m s 68 3.1 Introduction 68 3.2 Aims 68 3.3 Methods 69 3.3.1 Site Selection 69 3.3.2 Water Sampling 72 3.3.3 Diatom Sampling 73 3.4 Results 75 3.4.1 Water Chemistry 75 3.4.2 Diatom Samples 75 3.4.3 Between-Site Species Variation 83 3.4.3.1 Variance Partitioning 86 3.4.4 Within-Site Variation 88 3.4.4.1 Alton 88 3.4.4.2 Hawbridge 90 3.4.4.3 The Elstead Sites 92 3.4.5 Within Substratum Variation 93 3.4.6 Diatom Diversity 95 3.4.7 Dead Cells as a Potential Source of Error 97 3.5 Discussion 98 3.5.1 The Epipelon 99 3.5.2 The Epiphyton 99 3.5.3 The Epilithon 100 3.5.4 Rope 101 3.5.5 Smooth Tile 101 3.5.6 Rough Tile 102 3.5.7 Dead Cells 102 3.6 Conclusions 103

:r F o u r T r a in in g S e t s - S it e s , S p e c ie s a n d E n v ir o n m e n t 104 4.1 Introduction 104 4.2 Aims 105 4.3 Methods 105 4.3.1 Site Selection 105 4.3.2 Diatom Sampling 107 4.3.3 Water Sampling 109 4.3.4 Data Analysis 109 4.4 Results 111 4.4.1 Site Selection 111 4.4.2 Sample Collection 114 Presence of Natural Epilithon 115 4.4.3 Chemistry 115 4.4.3.1 Rope Training Set Chemistry 118 Principal Components Analysis (PCA) 119 4.4.3.2 Tile Training Set Chemistry 123 Principal Components Analysis (PCA) 124 4.4.4 Diatom Assemblages 127 4.4.4.1 Rope Training Set 127 Diatom Species Occurrence and Abundance 127 Detrended Correspondence Analysis of the Rope Species Data 129 4.4.4.2 Tile Training Set 133 Diatom Species Occurrence and Abundance (Tile) 135 Detrended Correspondence Analysis of the Tile Species Data 136 4.4.5 Direct Species - Environment Relationships 139 4.4.5.1 Species Relationships with TP and Alkalinity 139 Rope Training Set 139 Tile Training set 142 4.4.5.2 Canonical Correspondence Analysis (CCA) 144 Rope Training Set 145 Forward selection of environmental variables 149 Tile Training Set 150 Forward selection of environmental variables 153 4.5 Discussion 155 4.5.1 Site Selection 155 4.5.2 Sample Collection 156 4.5.3 Environmental Relationships 156 Phosphorus 157 Alkalinity 158 pH 158 Nitrate 159 Silica 159 Conductivity and Ionic Composition 159 Flow 160 Overview 160 4.5.4 Diatom Assemblages 161 Species Diversity 161 Rope Training Set 162 Tile Training Set 164 4.5.5 Direct Species - Environment Relationships 165 Rope Training Set 166 Tile Training Set 168 4.5.6 The Suitability of River Diatoms for Modelling Phosphorus 169 4.6 Conclusions and Summary 172

Ch a p t e r F iv e The Development of Diatom-Based M odels for the Assessment of Trophic Status in Lowland Rivers 174 5.1 Introduction 174 5.2 Aims 174 5.3 The Theory of Weighted Averaging Methods 175 5.3.1 Weighted Averaging (WA) 175 5.3.2 Weighted Averaging - Partial Least Squares (WA-PLS) 177 5.4 Methods 178 5.5 Results 180 5.5.1 Rope Training Set Data 180 TP 181 FRP 185 5.5.2 Tile Training Set Data 192 TP 193 FRP 196 5.5.3 Comparison of the Models 201 5.5.4 Indicator Species: WA Optima and Species Distributions 205 Rope 206 Tile 220 5.6 Discussion 235 5.6.1 Model Selection 235 5.6.2 Outlier Samples 238 5.6.3 Indicator Species 241 5.6.4. Conclusions 243

Ch a p t e r S ix S e a s o n a l V a r ia t io n W it h in L o w l a n d R iv e r D ia t o m A s s e m b l a g e s : Implications f o r t h e D ia t o m -B a s e d M o d e l s f o r t h e A s s e s s m e n t o f T r o p h ic S t a t u s 245 6.1 Introduction 245 6.2 Aims 246 6.3 Methods 247 6.3.1 Diatom Collection 247 6.3.2 Data Analysis 247 6.4 Results 249 6.4.1 Seasonal Variation in Water Chemistry 249 6.4.2 Seasonal Variation within the Diatom Communities 252 6.4.3 Estimating FRP from the Diatom Assemblages 263 6.4.4 Estimating FRP from the Seasonal Diatom Data 274 The Rope Substratum 274 The Tile Substratum 277 6.4.5 Explaining the Seasonal Variation within the Diatom Assemblages 280 6.5 Discussion 286 6.5.1 Seasonal Variation 286 6.5.2 The Estimation of FRP from Autumn Sampled Diatoms 288 6.5.3 The Estimation of FRP from Seasonal Sampled Diatoms 291 The Rope Substratum 291 The Tile Substratum 294 6.5.4 Explaining the Seasonal Variation within the Diatom Assemblages 295 6.6 Conclusions 297

Ch a p t e r S e v e n Co n c l u s io n s 299 7.1 Introduction 299 7.2 The Use of Artificial Substrata 299 7.3 Diatom Phosphorus Relationships 301 7.4 The Diatom-Based Transfer Functions for Lowland Rivers 303 7.4.1 Evaluation of the Diatom-Based Models 304 7.5 Application of the Models 305 7.6 Future Development 307 7.6.1 Temporal Extension of the Training Sets 307 7.6.2 Spatial Extension of the Training Sets 308 7.6.3 Artificial Substrata 309 7.6.4 Model Validation 309 7.6.5 Applications of the Model 310 7.6.6 Modelling Other Environmental Pollutants 311

R e f e r e n c e s 313

A p p e n d ix I Descriptions of Unknown Diatom Taxa 330 A p p e n d ix II List of Diatom Taxa Identified in this Study 334 A p p e n d ix III Proposed Sampling Sites for the Initial Training Set Survey 341 A p p e n d ix IV Full Environmental Data-Set for the Rope Training Set 349 A p p e n d ix IV Full Environmental Data-Set for the Tile Training Set 353 L ist o f F ig u r e s

Figure 2.1 Map of the River Thames catchment and other lowland rivers sampled in this study 48

Figure 2.2 A simplified geological map of the River Thames catchment and other lowland rivers sampled in this study 49

Figure 2.3 River water sampling protocol for laboratory analysis 51

Figure 3.1 Map showing the four sampling sites on the River Wey 72

Figure 3.2 Light micrographs of the rough tile (a) and smooth tile (b) 73

Figure 3.3 DCA joint plot from the four River Wey sites showing the samples and major taxa 83

Figure 3.4 Box and whisker plot of species diversity (Hill’s N2) at the River Wey sites 85

Figure 3.5 DCA biplot of River Wey sites grouped by samples from the same substratum 86

Figure 3.6 Graphical representation of the possible combinations of variation from the variance partitioning 87

Figure 3.7 DCA plot showing substratum variation at Alton 90

Figure 3.8 DCA plot showing substratum variation at Hawbridge 91

Figure 3.9 DCA plot showing substratum variation at ELSTl and ELST2 92

Figure 3.10 DCA of the Elstead sites showing within-site variation between substratum type 94

Figure 3.11 Box and whisker plot comparing the species diversity (Hill’s N2) for the different substrata at the Elstead sites 96

Figure 3.12 Proportion of live to dead cells at the four study sites 97

Figure 4.1 Map of river sites sampled for the training sets 106

Figure 4.2 FRP - alkalinity matrix for the 115 survey sites, showing nitrate as a size variable 112

Figure 4.3 Stratified sampling matrix showing the 61 randomly selected sampling sites (solid circles). Nitrate as a size variable 112 Figure 4.4 Recovery of the tile and rope samples from a total of 84 samples taken in October and November 114

Figure 4.5 Availability of the natural epilithon at 57 lowland river sites 115

Figure 4.6 TP - alkalinity matrix for the 84 samples 116

Figure 4.7 Scatter plots showing the relationships between the major chemical variables 120

Figure 4.8 PCA plot of the rope training set environmental data 121

Figure 4.9 Scatter plots showing the relationships between the major chemical variables 124

Figure 4.10 PCA plot of the tile training set environmental data 125

Figure 4.11 Scatter plot of diatom occurrences and the maximum abundance achieved by each taxon from the rope training set 219

Figure 4.12 DCA plot of the rope training set sites based on their diatom flora 130

Figure 4.13 DCA plot of the rope training set species data showing the most commonly occurring taxa 131

Figure 4.14 Relationship between DCA axis 1 and species diversity for the rope training set sites 131

Figure 4.15 Scatter plot of diatom occurrences and the maximum abundance achieved by each taxon from the tile training set 135

Figure 4.16 DCA plot of the tile training set sites based on their diatom flora 136

Figure 4.17 DCA plot of the tile training set species data showing the most commonly occurring taxa 136

Figure 4.18 Species abundance along the TP gradient (rope) 140

Figure 4.19 Species abundance along the alkalinity gradient (rope) 141

Figure 4.20 Species abundance along the TP gradient (tile) 143

Figure 4.21 Species abundance along the alkalinity gradient (tile) 144

Figure 4.22 CCA - environment-species biplot of all environmental variables (18) and all sites (78), including all taxa >2% abundance and >5 occurrences (133) - Rope training set showing distribution of sites 146

Figure 4.23 CCA - environment-species biplot of all environmental variables (18) and all sites (78), including all taxa >2% abundance and >5 occurrences (133) - Rope training set showing distribution of the common species 148

10 Figure 4.24 CCA - environment-species biplot of all environmental variables (18) and all sites (69), including all taxa >2% abundance and >5 occurrences (120) - Tile training set showing distribution of sites 151

Figure 4.25 CCA - environment-species biplot of all environmental variables (18) and all sites (69), including all taxa >2% abundance and >5 occurrences (120) - Tile training set showing distribution of the common species 152

Figure 4.26 Comparative Hill’s N2 diversity between the two training sets 162

Figure 4.27 Distribution ofFragilaria spp. along the silica gradient (rope) 163

Figure 5.1 Comparative graphs showing the TP prediction values and model residuals for the three model types using the rope data, prior to sample deletion. Open circles show samples which exceed the deletion criteria 182

Figure 5.2 Comparative graphs showing the TP prediction values and model residuals for the three model types using the rope data, following the deletion of outliers 183

Figure 5.3 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the rope data, prior to sample deletion. Open circles show samples which exceed the deletion criteria 186

Figure 5.4 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the rope data, following the deletion of outliers 187

Figure 5.5 Comparative graphs showing the TP prediction values and model residuals for the three model types using the tile data, prior to sample deletion. Open circles show samples which exceed the deletion criteria 194

Figure 5.6 Comparative graphs showing the TP prediction values and model residuals for the three model types using the tile data, following the deletion of outliers 195

Figure 5.7 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the tile data, prior to sample deletion. Open circles show samples which exceed the deletion criteria 197

Figure 5.8 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the tile data, following the deletion of outliers 198

Figure 5.9 Group 1: Diatom taxa from the rope samples with WA FRP optima <100 pgL'* 208

11 Figure 5.10 Group 2: Diatom taxa from the rope samples with WA FRP optima between 101-200 pgL'^ showing logit regression curves 209

Figure 5.11 Group 3: Diatom taxa from the rope samples with WA FRP optima between 201-500 pgL'^ showing logit regression curves 210

Figure 5.12 Group 4: Diatom taxa from the rope samples with WA FRP optima between 501-1000 pgL'^ showing logit regression curves 212

Figure 5.13 Group 5: Diatom taxa from the rope samples with WA FRP optima >1000 pgL'^ showing logit regression curves 213

Figure 5.14 WA optima and tolerances for FRP from the rope training set, following the deletion of outliers 219

Figure 5.15 Group 1: Diatom taxa from the tile samples with WA FRP optima <100 pgL'^ showing logit regression curves 223

Figure 5.16 Group 2: Diatom taxa from the tile samples with WA FRP optima between 101-200 pgL'^ showing logit regression curves 224

Figure 5.17 Group 3: Diatom taxa from the tile samples with WA FRP optima between 201-500 pgL'' showing logit regression curves 225

Figure 5.18 Group 4: Diatom taxa from the tile samples with WA FRP optima between 501-1000 pgL'^ showing logit regression curves 227

Figure 5.19 Group 5: Diatom taxa from the tile samples with WA FRP optima >1000 pgL'^ showing logit regression curves 228

Figure 5.20 WA optima and tolerances for FRP from the tile training set, following the deletion of outliers 229

Figure 5.21 Distribution ofMeridian circulare with respect to the full temperature gradient in the rope and tile training sets 230

Figure 6.1 Seasonal variability in some of the major environment parameters at ALT 1, HAW 1 and ELST2 (River Wey) 250

Figure 6.2 Seasonal variation in the relative abundance of the diatom taxa from ALTl 254

Figure 6.3 Seasonal variation in the relative abundance of the diatom taxa from HAWl 255

Figure 6.4 Seasonal variation in the relative abundance of the diatom taxa from ELST2 257

Figure 6.5 DCA plots showing the seasonal variation within the diatom assemblages at ALTl from the three different substrata 260

12 Figure 6.6 DCA plots showing the seasonal variation within the diatom assemblages at HAW 1 from the three different substrata 261

Figure 6.7 DCA plots showing the seasonal variation within the diatom assemblages at ELST2 from the three different substrata 262

Figure 6.8 Diatom-based reconstructions of FRP at the three river sites using the rope substrata over a one year period 275

Figure 6.9 Diatom-based reconstructions of FRP at the three river sites using the tile substrata over a one year period 278

Figure III.l Map of river sites identified as possible training set sites 342

13 L ist o f Ta b l e s

Table 3.1 Summary of the sampling sites 72

Table 3.2 Summary of the chemistry for the November ’95 sampling at the four River Wey sites 75

Table 3.3 Summary of diatom samples 76

Table 3.4a Diatom summary of the epipelon from the River Wey - showing the ten most abundant taxa from each sample 77

Table 3.4b Diatom summary of the epiphyton from the River Wey - showing the ten most abundant taxa from each sample 78

Table 3.4c Diatom summary of the epilithon from the River Wey - showing the ten most abundant taxa from each sample 79

Table 3.4d Diatom summary of the smooth tiles from the River Wey - showing the ten most abundant (%) taxa from each sample 80

Table 3.4e Diatom summary of the rough tiles from the River Wey - showing the ten most abundant (%) taxa from each sample 81

Table 3.4f Diatom summary of the rope substrata from the River Wey - showing the ten most abundant (%) taxa from each sample 82

Table 3.5 Summary of the DCA output for all the River Wey sites 83

Table 3.6 Summary of the CCA output for all sites: substrata, sites and replicates as dummy variables 87

Table 3.7 Summary results from the CCA variance partitioning 88

Table 3.8 Summary of the DCA output for Alton 89

Table 3.9 Summary of the DCA output for Hawbridge 91

Table 3.10 Summary of the DCA output for combined Elstead sites 92

Table 4.1 Summary of the survey site chemistry 111

Table 4.2 Sample collection summary 113

Table 4.3 Translation of site codes and numbers into sample numbers 116

Table 4.4 Summary statistics for the rope training set data 119

14 Table 4.5 Summary statistics for the PCA on the rope training set environmental data 119

Table 4.6 Correlation matrix showing the relationship between the environmental variables from the rope training set 122

Table 4.7 Summary statistics for the tile training set data 123

Table 4.8 Summary statistics for the PCA on the tile training set environmental data 124

Table 4.9 Correlation matrix showing the relationship between the environmental variables from the tile training set 126

Table 4.10 Number of taxa per sample and Hill’s N2 diversity for the rope training set 127

Table 4.11 Summary of the DCA for the rope training set 127

Table 4.12 Significant correlations (rj between the measured environmental variables and the unconstrained DCA axes for the rope data 132

Table 4.13 Number of taxa per sample and Hill’s N2 diversity for the tile training set 134

Table 4.14 Summary of the DCA for the tile training set 137

Table 4.15 Significant correlations (r$) between the measured environmental variables and the unconstrained DCA axes for the tile data 138

Table 4.16 Summary statistics for the rope training set CCA 145

Table 4.17 Potential variance explained by each environmental variable and the added variance following forward selection for the rope training set 149

Table 4.18 Summary statistics for the tile training set CCA 150

Table 4.19 Potential variance explained by each environmental variable and the added variance following forward selection for the tile training set 153

Table 4.20 CCA summary statistics of the rope training set data, with TP as the only variable 170

Table 4.21 CCA summary statistics of the tile training set data, with TP as the only variable 171

Table 5.1 Weighted averaging regression results for the diatom-based modelling of TP using the rope training set data, prior to and following the deletion of outliers 180

15 Table 5.2 Weighted averaging regression results for the diatom-based modelling of FRP using the rope training set data, prior to and following the deletion of outliers 180

Table 5.3 Weighted average optima and tolerances of the diatom taxa for TP and FRP (rope training set). Values are back-transformed from the log data 189

Table 5.4 Weighted averaging regression results for the diatom-based modelling of TP using the tile training set data, prior to and following the deletion of outliers 192

Table 5.5 Weighted averaging regression results for the diatom-based modelling of FRP using the tile training set data, prior to and following the deletion of outliers 192

Table 5.6 Weighted average optima and tolerances of the diatom taxa for TP and FRP (tile training set). Values are back-transformed from the log data 199

Table 5.7 WA-PLS results show the improvement due to the inclusion of all diatom taxa 202

Table 5.8 Weighted averaging regression results for the diatom-based modelling of FRP using square root species data from the rope training set, prior to and following the deletion of outliers 203

Table 5.9 Weighted averaging regression results for the diatom-based modelling of FRP using square root species data from the tile training set, prior to and following the deletion of outliers 203

Table 5.10 Weighted average FRP optima and tolerances for the rope diatom data-set following the deletion of the outlier samples (68 sites), ranked by optima 206

Table 5.11 Weighted average FRP optima and tolerances for the tile diatom data-set following the deletion of the outlier samples (60 sites), ranked by optima 221

Table 6.1 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data and including all taxa 264

Table 6.2 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data and including all taxa 266

Table 6.3 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed 268

Table 6.4 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed 268

16 Table 6.5 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and Gomphonema parvulum made passive 269

Table 6.6 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and Gomphonema parvulum made passive 270

Table 6.7 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and Cocconeis placentula var. euglypta made passive 270

Table 6.8 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and Cocconeis placentula var.euglypta made passive 271

Table 6.9 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and Cocconeis placentula var. euglypta and Gomphonema parvulum made passive 272

Table 6.10 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and Cocconeis placentula var. euglypta and Gomphonema parvulum made passive 272

Table 6.11 Comparison of the diatom inferred FRP estimates with measured FRP using sum of differences squared. Rope samples 273

Table 6.12 Comparison of the diatom inferred FRP estimates with measured FRP using sum of differences squared. Tile samples 273

Table 6.13 CCA with forward selection using all sites and all substrata 281

Table 6.14 CCA with forward selection using the rope substratum at all three sites 282

Table 6.15 CCA with forward selection using the tile substratum at all three 282 sites

Table 6.16 CCA with forward selection at ALTl using only the rope 283 substratum

Table 6.17 CCA with forward selection at ALTl using only the tile substratum 283

Table 6.18 CCA with forward selection at HAWl using only the rope substratum 284

Table 6.19 CCA with forward selection at HAWl using only the tile substratum 284

Table 7.1 An example of reducing FRP estimates to a nominal trophic index 306

17 Table III.l Proposed sampling sites for the initial training set survey 341

Table IV.l Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the rope training set 349

Table IV.2 Chloride, sulphate, calcium, sodium, potassium, magnesium, iron, 351 manganese, and aluminium concentrations in the rope training set

Table V.l Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the tile training set 353

Table V.2 Chloride, sulphate, calcium, sodium, potassium, magnesium, iron, manganese, and aluminium concentrations in the tile training set 355

L ist o f P l a t e s

Plate 2.1 Rough and smooth tiles following sampling at Hawbridge 60

Plate 2.2 Polyethylene rope and smooth tile, in place at Elstead 61

Plate 3.1 The River Wey at Alton (ALTl), This site was overgrown with 70 Berula erecta during summer

Plate 3.2 The River Wey at Hawbridge (HAWl) 70

Plate 3.3 The River Wey, upstream of Elstead (ELSTl) 71

Plate 3.4 The River Wey, downstream of Elstead (ELST2) 71

Plate I I Light micrographs of Navicula [pseudogregaria] 330

Plate 1.2 Light micrographs of Navicula gregaria 331

Plate 1.3 Light micrographs of Navicula [small sp. 1] 332

Plate 1.4 Light micrographs of Navicula [species 2] 333

18 '''The Ouse flows on, unconcerned with ambition, whether local or national. It flows as every river must, to the sea. And, as we all know, the sun and the wind suck up the water from the sea and disperse it on the land, perpetually refeeding the rivers. So that while the Ouse flows to the sea, it flows, in reality, like all rivers, only back to itself, to its own source; and that impression that a river moves only one way is an illusion. And it is also an illusion that what you throw (or push) into a river will be carried away, swallowed for ever, and never return. Because it will return. And that remark first put about, two and a half thousand years ago, by Heraclitus of Ephesus, that we cannot step twice into the same river, is not to be trusted. Because we are always stepping into the same river.” (Swift 1983, pl27)

19 Ch a pter O ne

Introduction

1.1 Background

Streams and rivers have been used for centuries as a means of waste removal, conveniently carrying away downstream all manner of wastes. Prior to industrialisation in Britain, low population densities, coupled with the ability of rivers to break down organic matter, resulted in little reduction in water quality. However, with the onset of the industrial revolution and increased urbanisation, factories, mines, mills and sewers discharged directly into rivers, significantly increasing the pollution pressure on the receiving water bodies. In London, for example, the main sewers were diverted in 1843 into the already polluted River Thames causing gross organic pollution and giving rise to cholera epidemics and even disrupting the work of Parliament and the Law Courts due to “the great stink” of 1858 (Wood 1982).

The problems of organic pollution were largely solved by the introduction of sewage treatment works during the first half of the twentieth century and resulted in marked improvements in the water quality of British rivers (Mason 1991). With the reduction of gross organic pollution, however, another pollution issue was to emerge; that of eutrophication. The post-war trend towards agricultural improvements resulted in the widespread use of fertilisers (particularly forms of nitrogen and phosphorus). Also at this time, and perhaps of greater significance, was the introduction of phosphate-based detergents, which led to major increases in the phosphorus content of domestic sewage. A seven-fold increase in detergent use was recorded in the UK between 1950 and 1970 and it was estimated that 47-65 per cent of the phosphorus in domestic sewage was derived from detergents (Devey & Harkness 1973).

20 This thesis sets out to address what is a very specific area of river ecology and its associated methodology but the reasoning behind it comes from the great importance of our rivers as both a wildlife habitat and a valuable human resource. ''...they [river ecosystems] provide corridors through the landscape and interact with the catchments that surround them; they provide aesthetic enjoyment and recreational opportunities; they act as important navigational routes for people and goods; they provide an important source of potable water, and; they provide convenient sinks for some of our human wastes." (Calow & Petts 1996, p. 1) Thus the conservation of rivers is not simply a question of preserving the diversity of habitats, and species therein, but also ensuring that rivers will go on providing their vital role in human society well into the future. One of the major threats to lowland rivers in Britain is that of eutrophication (Mason 1991): this problem being most acute where the population is most highly concentrated, i.e. southern England.

Eutrophication in British rivers is now at the fore-front of conservation interest (Mainstone et al. 1992, Moss 1997) and under increasing legislative control (e.g. Urban Waste Water Treatment Directive 91/271/EC, CEC 1991). It is therefore necessary to apply research in this area to provide an increased understanding of the effects of eutrophication and to develop methods for the assessment and monitoring of the problem. The aims of this thesis are to investigate eutrophication in the lowland (<250 m) rivers of southern England and to develop the use of diatom-based models for assessing and monitoring trophic status.

1.2 Eutrophication - Sources and Effects

Eutrophication can most simply be defined as the "enrichment of waters by inorganic plant nutrients" (Mason 1991). Although this encompasses a wide range of elements, some of which may occasionally be limiting (e.g. silicon or manganese), it is usually only ever nitrogen (N) and/or phosphorus (?) which cause a marked increase in primary production and eutrophication. Within rivers the sources of eutrophication can be either natural or artificial (anthropogenic). Natural eutrophication, such as that caused by forest fires or wind throw events (Allen 1995), is of relatively little importance and will therefore

21 not be considered here. It is anthropogenic nutrient enrichment which accounts for by far the greatest component of the eutrophication of British rivers: the two major sources being domestic sewage and agriculture. Only a small fraction of the sewage treatment works (STW’s) in the UK are fitted with facilities for phosphorus removal (tertiary treatment), the majority being capable of only primary and secondary treatment (SCOPE 1999). Not surprisingly, therefore, nutrient levels in many British rivers are very high (Mason 1991, SCOPE 1999).

Nutrient pollution can be split into two forms on the basis of origin: discrete and diffuse sources. The most obvious is point source input (i.e. discrete) and is typified by the discharge from sewage treatment works. This form of pollution is relatively easily monitored, often with the added knowledge of the daily quantity, and quality of the discharge, providing valuable information. Far more difficult to trace and quantify is diffuse pollution, such as that caused by agricultural practices within the river catchment. Within the Thames catchment 11.5 million people occupy only 8% of the total land area of Britain; the majority of this land being intensively farmed. In the water passing over Teddington weir, however, 93 per cent of the phosphorus is estimated to be from sewage treatment works and the remaining 7 per cent from diffuse agricultural sources (R. Sweeting, pers. comm.). This illustrates the extent and potential severity of the problem of river eutrophication in highly populated areas.

Regardless of the source, the effects of nutrient enrichment on the aquatic ecosystem are the same. Eutrophication is characterised by an overall increase in primary productivity, which has a knock-on effect on the animal community. Coupled with this there is usually a decrease in overall species diversity with a resultant shift in the dominant taxa (Mason 1991). The drop in plant diversity is commonly due to the loss of rare species as more tolerant, generalist taxa, become dominant with considerable implications for conservation (Fox 1996). Physical changes can also be seen. The waters of slow flowing rivers often become turbid due to increased algal productivity and higher organic loadings, thus reducing light penetration and inhibiting the growth of submerged macrophytes (Allan 1995).

22 A classic example of these problems was documented by Mason and Bryant (1975) at Alderfen Broad in Norfolk. By the use of old photographs the dramatic loss of aquatic macrophytes can be clearly seen over a fifty year period. These losses were attributed to the effects of cultural eutrophication following the increased popularity of the area as a tourist resort. It is thought that phytoplankton levels increased sufficiently to shade out the majority of the higher plants (Mason 1991).

Within rivers, as well as having detrimental effects on the biota, increased plant production at the river margins may eventually restrict flow and impede navigation. Aesthetically the site may also lose value to many people. There can be commercial implications as potable water supplies become discoloured, with unpleasant odour and taste. Fish stocks also suffer as oxygen concentrations fall. A resultant shift from game fish to coarse fish can have a serious commercial effect. In extreme cases toxic algal blooms can render water bodies harmful to public health. It is imperative, therefore, that the trophic status of rivers is monitored and, if it can be demonstrated that a reduction in nutrients would be beneficial to the system, action is taken to reduce nutrient levels (Mainstone et al. 1992).

1.2.1 River Eutrophication - The Legal Position

The responsibility for regulating the water quality of British rivers has undergone considerable change in recent years. The ten regional Water Authorities of England and Wales were merged in 1989 to form the National Rivers Authority (NRA). The NRA was handed the responsibility for overseeing the newly privatised water supply and waste treatment companies. At this time the emphasis on water quality was predominantly focused on organic pollution with legislation concentrating on identifiable incidents of gross pollution. In 1991, however, the European Union (EU) published the Urban Waste Water Treatment Directive (UWWTD, 91/271/EC) (CEC 1991). Within this directive it was specified that there was a need to identify stretches of rivers, or river basins, that were “vulnerable to eutrophication” due to the direct effect of a “qualifying discharge”. A qualifying discharge being considered in the first instance as a STW of greater than 10,000 population equivalents (p.e.). Thus only river systems affected by larger STW’s fell under the legislative jurisdiction of the UWWT Directive.

23 The first step defined under the UWWTD was to identify “sensitive areas (eutrophic)” (SA(E)). An SA(E) is considered to be a stretch of river, affected by a “qualifying discharge”, that is either: “eutrophic or which in the near future may become eutrophic if protective action is not taken.” or: “if the water is used for drinking water abstraction.” or: “if the water requires a more stringent than a secondary treatment in order to meet other EU Directives.”

These somewhat ambiguous terms and definitions for the selection of SA(E)’s are carefully laid down in Article 5, Annex II A & B of the UWWTD and appear to have led to a great deal of confusion and independent interpretation among the majority of the European member states (SCOPE 1999). The directive (CEC 1991) states that all qualifying discharges (i.e. STW’s with >10,000 p.e.) in SA(E)’s should be fitted with phosphate stripping apparatus by December 1998. In the UK a total of 62 SA(E)’s were originally put forward by the then National Rivers Authority (NRA) in the first round of designations in 1993. Of these, 20 were rejected in-house and a further 9 by the Department of the Environment, leaving only 33 designated river stretches which resulted in 41 STW’s being ear-marked for the fitting of phosphate stripping apparatus by 1998 (EA, unpublished workshop report). By the end of 1999 a total of 50 STW’s should have been fitted with a phosphate stripping capability: there are over 2,000 STW’s in the UK (SCOPE 1999).

The UWWT directive is concerned entirely with the concentration of phosphorus in rivers rather than the effects of other plant nutrients (e.g. nitrogen). The rationale for this was that phosphorus is normally the limiting nutrient in surface waters (SCOPE 1999). Although this is not always the case, the availability of nitrogen in the majority lowland waters is rarely limited (Birch & Moss 1990), and thus this study focuses on phosphorus as the principal nutrient governing trophic status.

The timetable for the implementation of the UWWTD is currently well behind schedule in the UK with the second round of SA(E) designations only recently having been made (SCOPE 1999). Undoubtedly this is due in part to the implementation of new

24 methodologies for the determination of SA(E), the biological assessment of which is still under review (Harding & Kelly 1999). One of the original components of the UWWTD was that a SA(E) was not simply identified by chemical methods alone. Instead, strong emphasis was put on the importance of including components of the river biota. Furthermore, it is stated under the directive (CEC 1991) that it is insufficient to simply show that the biological community in a given stretch of river is damaged, it is necessary to demonstrate that nutrient enrichment is the cause of the damage. If this proof is not evident then the SA(E) is rejected and the costly installation of phosphate stripping is not required.

It is vital, therefore, that methods are established which can show beyond doubt if a river has suffered as a result of becoming more eutrophic and, furthermore, that if the source of nutrients was removed a visible and quantifiable improvement could be demonstrated. Between the years 2000 and 2005 a forecasted £8.5 billion is to be spent in the UK as a direct result of the UWWT Directive (SCOPE 1999). With such huge sums of money involved it is no wonder that the need arises to develop accurate methods for the assessment of eutrophication in rivers. Nor does the legislative pressure stop at the UWWTD. Currently under review in the EU is the Water Framework Directive (WFD) which takes a much more holistic approach to surface waters (including coastal waters) and ground waters and will not be restricted by STW size or to point sources of input (SCOPE 1999). The environmental objectives of the WFD are not only based on chemical criteria but also on ecological parameters, thus increasing the need to develop stringent, ecologically based, monitoring methods. The initial proposal for the WFD includes the wording:

‘‘‘‘Member states shall draw up and make operational within a comprehensive River Basin Management Plan the programmes of measures envisaged as necessary, in order to: a) prevent deterioration of ecological quality and pollution of surface waters and restore polluted surface waters, in order to achieve good surface water status in all surface waters by 31 December 2010....^ (SCOPE 1999, p. 32)

25 It would appear, therefore, that the major criterion for assessing the Water Framework Directive will be the achievement of good ecological status. This will require robust ecological monitoring methods.

1.3 River Monitoring

To assess the extent to which eutrophication, and other forms of pollution, are affecting our rivers it is essential to maintain long term monitoring programmes. In 1958 the water authorities of England and Wales implemented periodic surveys of water quality in both standing waters and rivers. This was followed in 1970 by the implementation of a more structured, quinquennial survey (NRA 1992). By 1985 a national list was drawn up dividing all inland waters into one of four water quality categories: 1. Good (divided into la & Ib), 2. Fair, 3. Poor and 4. Bad (B.E.S. 1990). These surveys were all largely based on simple chemical analysis with biochemical oxygen demand (BOD) being the major determinant. With the exception of some fish data in the 1985 survey, no account was taken of the biological communities as part of these surveys.

The reliance on the use of physico-chemical data for water quality assessment has a number of problems associated with it, in particular those recognised by the British Ecological Society (1990) are: 1. There is a strong reliance on the precision of chemical data. This is usually based on spot samples taken at too infrequent intervals. The implementation of more frequent sampling was not considered cost effective. 2. Due to the problem of cost it was often not possible to perform complete chemical analysis on water samples and thus potentially damaging pollutants could be missed. 3. The use of infrequent spot samples often fails to detect intermittent pollution events. 4. The levels of detection of some compounds may fall outside the limits of sensitivity used in the analytical procedures.

26 Due to the shortcomings of water quality surveys based on physico-chemical data alone there is a recognised need to include some form of biological component. Pratt and Coler (1976 p. 1020) stated that: '’'’Criteria restricted to chemical, physical and bacteriological parameters no longer suffice when the value of water extends beyond its utilization for agriculture, domestic and industrial ends to include aesthetic, recreational and ecological dimensions."' This is not to say that physico-chemical information is not of value, but instead, that any assessment of river water quality should include a reliable biological component rather than being based on physico-chemical data alone.

1.3.1 The Biological Component

The biological component of a water quality assessment offers many important advantages. Organisms integrate environmental conditions over time, thus reflecting some form of environmental history as opposed to instantaneous data acquired from a point water sample. It has also been found that the sensitivity of some taxa, or the community as a whole, is in many cases greater than that of chemical analysis (Metcalfe 1989). Furthermore if one wishes to assess a river for its intrinsic value to conservation, as well as for commercial and aesthetic reasons, it is necessary to measure the effect on the biota rather than trying to interpret physico-chemical data in an ecological context.

It would therefore seem that the most comprehensive assessment of river water quality should combine physico-chemical data with information on the entire aquatic community. This is clearly impractical and therefore usually only one sector of the aquatic community is chosen for study. Since work began on the use of biological indicators in surface waters in the late 19* century, well over fifty different methods have been employed, using a wide range of organisms (De Pauw & Vanhooren 1983). These studies have included fish, macroinvertebrates, macrophytes, zooplankton, phytoplankton and periphyton with varying degrees of success (Metcalfe 1989). For a particular group to act as a successful indicator it should fulfil a number of predetermined criteria, viz:

1. It should be cosmopolitan.

27 2. It should be possible to categorically identify individuals within the taxa. 3. Individuals should have precise ecological limits to it range, which do not vary unpredictably. 4. These ecological ranges should be definable. (Cox 1991)

The use of photosynthetic organisms has gained particular favour for the monitoring of eutrophication. Unlike invertebrates, macrophytes and algae have a more direct response to nutrient loadings. The final choice of organism is not however simple. Many submerged macrophytes gain their nutrients from the sediments rather than the water and thus show atypical results and long lag times following any change (Whitton & Kelly 1995). Less well rooted species (e.g. Ceratophyllum and Elodea), and floating species such as Lemna, are not sufficiently well distributed to be used in a universal monitoring programme; they have however been successfully used in ecotoxicological studies into metal pollution (Clarke 1981).

Due to some of the problems associated with using higher plants attention has been focused on algae for the monitoring of pollution, including eutrophication, in rivers. Algae offer the advantages of fast response times to pollution events and are widely distributed (Whitton et al. 1991). As a group the diatoms have featured heavily in much of this work (Descy 1979, Lange-Bertalot 1979, Kelly et at. 1995, Kelly & Whitton 1995, Kelly 1998, Harding & Kelly 1999).

1.3.2 Diatoms as Indicators of Water Quality

The sensitivity of diatoms to ambient water quality has long been recognised. In 1884 Cleve observed that different ocean currents could be characterised by their diatom flora. Since then, diatoms have been used extensively in the study of water quality in lakes (e.g. Huttunen & Merilainen 1983, Battarbee 1984, 1991, Guzkowska & Gasse 1990a, 1990b) and streams and rivers (e.g. Kolkwitz & Marsson 1908, Butcher 1940, 1947, Archibald 1972, Descy 1979, Lange-Bertalot 1979, Patrick 1986, Prygiel 1991, Round 1993, Kelly

28 1998, Harding & Kelly 1999). As a group the diatoms have many reported features which make them suitable for use as a biological indicator in water quality assessments:

1. Within lotie systems they are usually the most abundant autotrophic organisms, accounting in some cases for as much as 95% of the total biomass (Gale et al 1979). 2. Diatoms are found in all river habitats, from high mountain springs (Sabater & Roca 1990) to lowland and estuaiine reaches (Molloy 1992). 3. Diatoms have been shown to be very sensitive to changes in water quality, with many of the ecological ranges being well documented (e.g. Lowe 1974). Furthermore individual species can be grown under controlled laboratory conditions to test field observations (Cox 1994). 4. The rapid cell cycle of diatoms means that they react quickly to changes in environmental conditions, so enhancing their value as a monitoring tool (Stevenson & Lowe 1986). 5. Diatoms colonise almost all available surfaces in the photic zone, usually in large numbers, thus making the collection of representative samples both easy and fast. Douglas (1958) reported numbers of over 5 xlO^ cells cm^ on the bedrock of a fast flowing stream. 6. Unlike macroinvertebrates diatoms are not dependent on specific food requirements, gaining almost all that they need from the water column. Any diatom assemblage response is likely, therefore, to be due to changes in water quality. Similarly, they do not require specific habitat types; all submerged surfaces will potentially support diatoms (Round 1993). 7. Although physical effects, such as current velocity, are important within a site (Passy 2001) they have only a limited impact on diatom assemblages between sites of different water quality (Homer et al 1990). 8. Although the taxonomy of the diatoms can be complex the ease of preparing permanent slides allows for storage and cross referencing of material to be carried out where necessary.

29 1.3.3 The use of Diatoms to Monitor Rivers

The first recorded use of diatoms as a river monitoring tool was by Kolkwitz and Marsson (1908) in the development of the saprobien system in Germany. This system divided waters into one of five categories according to organic loading, which was predicted by a biological assessment of the water in which diatoms formed an important component. The saprobien system has since been employed in many European countries and is still used today e.g. in the former Czechoslovakia (Marvan 1991). Within Britain, however, the first studies to use diatoms were conducted by Butcher (1936, 1940, 1946 & 1947). Butcher recognised that the benthic algae of streams and rivers responded to changes in organic pollution and nutrient status (1940) with resultant changes in species composition and numbers of individuals. This work was extended in his 1946 paper with particular reference to the increase in algal numbers due to “..../? is suggested' (p. 282) increased eutrophication. An important distinction made by Butcher (1947) when looking at organic pollution was that species such as Nitzschia palea and Gomphonema parvulum were resistant to "polysaprobic" conditions rather than somehow being dependent on them. Thus such species may also be found in less polluted water ( c /. Evans & Marcan 1976). This is contrary to the work of Kolkwitz and Marsson (1908).

Despite a rising interest in the ecology of river diatoms very little of this work was concerned with water quality estimation. Instead the focus was on the effect of changing water chemistry on the ecology of the diatom assemblages. Although not related directly to water quality estimation, this work illustrated many of the complexities of diatom ecology which need to be understood. Patrick et a l (1968) examined the effects of a range of pH on the natural diatom communities growing in a circumneutral stream. A reduction in both diversity and biomass was observed below pH 5.26. What was more interesting however was the effect of light and temperature on these results. Under optimal temperature and light conditions in late spring the effect of a reduction in pH had less impact on the diatom community than that during the cool, dark autumn months. This demonstrates that a diatom assemblage is rarely governed by only one environmental variable but instead is the product of a complex array of factors. Similar effects of light and temperature were observed by Cox (1993) on the diatom Meridion circulare. In this case, however, M. circulare was seen to be more sensitive to higher light intensities at increased temperatures

30 (>10 °C). This species was therefore only found at high light intensities, during summer, in cool water (i.e. near to a spring). The combination of high light and temperatures greater than 10°C are intolerable to this species and explains why M. circulare is considered a vernal species (Cox 1993).

It can be seen, therefore, that a diatom assemblage reflects the overall ecological conditions of the water. Thus it is not always obvious exactly how a particular diatom association is reacting to measured environmental variables at a given time. In the estimation of water quality various methods have been used to try to overcome these complexities. The recognition that the diatom community as a whole is reacting to the environment led a number of workers to study diversity in relation to water quality.

1.3.3.1 Diversity Measurements

Much of the early work on diatom diversity in relation to water quality, was done by Patrick and co-workers (1954). Using glass slides Patrick et a l (1954) sampled a number of rivers and then plotted the resulting number of species against the number of individuals per species. This gave a series of truncated normal curves, which in general showed a smaller peak (i.e. lower diversity) in the more polluted waters. The major problem with this method, however, was that it involved counting large numbers of individuals (over 8000 in some cases). Archibald (1972) used a less intensive method in clean and polluted South African rivers. By applying sequential comparison indices (SCI) to the diatom associations he was able to distinguish between the clean and moderately polluted waters. The drawback in using diversity alone, however, was that the heavily polluted sites showed similar results to the very clean sites. This is perhaps not surprising considering the extreme conditions for autotrophs in pure, and therefore very clean, water. Archibald concluded that, although diversity was a good measure of water quality in systems with little natural variation, it could not in itself act as a reliable indicator on a wider scale.

The use of diatom diversity indices in other studies has led to the conclusion that the most important criteria in assessing water quality are the identity and autecology of all the species involved, and in particular those which are dominant (Van Dam 1982). By

31 understanding, and interpreting, the ecology of the diatom taxa, it should be possible to make valid assessments of the water quality.

1.3.3.2 Diatom Ecology and Water Quality Assessment

With an expanding amount of knowledge on diatom ecology it became easier to relate ecological preferences to water quality. Lange-Bertalot (1979) used his empirically observed estimations of diatom tolerance ranges (to organic pollution) to draw up four major classes of water quality in the polluted Rhine-Main river system in Germany. The basis for these groups depended on the relative abundance of diatom species for which water quality classes had been allocated. Thus a sample containing a high percentage of very tolerant species would have been given a pollution class of IV (i.e. polysaprobic). Similarly groups of species were identified which were indicative of increasingly less polluted waters. The least tolerant group was indicative of the cleanest water (in the Rhine-Main rivers).

This system seemed to be transferable world-wide when applied in other polluted rivers. Schoeman (1979) found it to work in the upper Hennops River; a system heavily polluted with secondary treated sewage. Both of these studies based the majority of their findings on just one chemical determinant, biochemical oxygen demand (BOD), without considering any other physico-chemical factors within the system. It would therefore appear that these studies provided little more information than could be obtained from regular chemical monitoring. In an effort to make diatom-based assessments more objective a number of workers have developed more ecologically robust diatom indices.

1.3.3.3 Diatom Indices

The use of diatom indices has become increasingly popular to express water quality. Not surprisingly the idea that one can reduce ecological data to a simple and meaningful numerical form is very attractive. The majority of indices are similar in form to that of Descy (1979), developed from the original work of Zelinka and Marvan (1961), whereby quantitative and qualitative species data are arithmetically combined into ecological information, e.g. species tolerance or sensitivity to pollution. Descy (1979) worked on the

32 Belgium basin of the River Meuse. By taking a wide range of physico-chemical measurements from rivers running over different geological substrata and comparing these to the diatom assemblages, Descy was able to produce two scores for each diatom species. The first was based on its sensitivity to pollution (1-5) and the second on its value as an indicator (1-3); the latter value being a measure of ecological amplitude (i.e. tolerance). These values were derived from two separate ordinations; one from the unpolluted sites alone and a second including the polluted sites. The ordinations demonstrated that the assemblages reacted independently and could therefore be distinguished from each other, i.e. from both natural and polluted waters. The pollution scores derived from the ordinations were then applied using a simple weighted averaging formula to give the Diatom Index (Id) (Descy 1979).

^ where: A, = relative abundance of species j Id= ^ ij = sensitivity index of species j (1-5) . 4 ;V V ( — Vj = indicating value of species j (1-3)

This gave a value between 1-5, thus enabling 5 classes of water quality to be defined, ranging from 1, clean, naturally acidic waters, to 5, i.e. waters with gross pollution and high pH. These are still very broad categories and Descy (1979) recognised that the complexities of pollution, as well as the heterogeneity of river systems, meant that it was very difficult to obtain consistently reliable results. Since the development of the above method, many refinements have been made and tested (Descy & Coste 1991) and further work is on-going (Descy & Ector 1999).

The use of similar diatom indices are numerous (e.g. Coste 1984, Watanabe et al. 1988, see Whitton et al. 1991). These methods were more thoroughly reviewed by Round (1993) but essentially they all express a small number (4-8) of water quality classes using mathematical formulae of varying complexities. It would appear that there are often gaps in the information from circumneutral, nutrient-enriched lower reaches of rivers where the majority of pollution occurs. To extend these method requires a more detailed study of the diatom ecology, and in particular the methods used to sample the assemblages and evaluate community structure (Round 1991 & 1993).

33 In the UK a similar diatom index (Kelly & Whitton 1995, Kelly 1998) has been extensively tested by the Environment Agency for the assessment of eutrophication. Under the Urban Waste Water Treatment Directive (UWWTD) (CEC 1991) such biological assessments were deemed necessary in order to recognise and monitor the areas considered sensitive to eutrophication. It was with this in mind that Kelly and Whitton (1995) developed the Trophic Diatom Index (TDl). This index uses a similar weighted average equation to that of Zelinka and Marvan (1961), but the species weightings are derived from the indicator value and tolerance range of individual diatom species to trophic status. Although this method is still undergoing fine-tuning it is now being routinely used by the Environment Agency. The original methods for the TDl relied on epilithic diatom samples. In lowland areas, however, one of the major problems has been the difficulty in collecting comparable samples from sites with little or no natural epilithic community (Kelly, pers. comm.).

There has also been a move away from ecological indices to more computer-intensive techniques such as multivariate statistical analysis. This has gained particular favour within lentic systems where diatoms from surface sediments (Battarbee 1991, Bennion 1994) and live communities (Guzkowska & Gasse 1990a & b) have been compared to ambient water chemistry. Guzkowska & Gasse (1990a) concluded that the use of techniques such as two-way species indicator analysis (TWINSPAN), detrended correspondence analysis (DCA) and principal components analysis (PGA) could be used to group diatom species within specific physico-chemical classes indicative of ranges of water quality. As well as being more objective, these methods were also able to identify seasonal patterns within the data and detect disruptive events such as storms (Guzkowska & Gasse 1990b).

1.4 Sampling River Diatoms

Having considered some of the applications for which diatoms have been used it is also important to identify some of the many problems associated with their use as water quality indicators in streams and rivers. Although it is clear that diatoms are a good bioindicator group, there remain some methodological problems in their use as a high resolution water quality monitoring tool. Many of the major problems come from the sampling of diatom

34 assemblages and the ecological information that is drawn from the samples. From the literature it is not always completely clear which assemblages has been sampled, despite the observation that the different substrata support quite different floras (Round 1993, Allan 1995). When trying to gain the maximum ecological information from the assemblages it is vital to pay close attention to the methods of sampling used.

1.4.1 The Diatom Assemblages

Before considering how the sampling of diatom assemblages can affect diatom-based water quality estimations, it is perhaps best first to identify the habitats in which they are found. Within most lotie systems the benthic algae - broadly defined as the periphyton - are the most important primary producers (Pryfogle & Lowe 1979), inhabiting almost all available surfaces. The majority of river diatoms, particularly in faster flowing systems, are benthic and can be divided into four main communities. These are: the epilithon - growing on stones or rock substrata, the epiphyton - growing on other algae and submerged aquatic macrophytes, the epipsammon - growing on sand grains and the epipelon - growing on or within fine silt (Round 1981). In addition there are also several less distinct associations which can be recognised e.g. epizoon, endolithon, endophyton and metaphyton. Although it is important to be aware of the existence of these lesser groups they are relatively rare and thus not considered here. There is inevitably some cross-over among the species found in each habitat (Cox 1990) but in general most taxa can be categorised into one of the major associations. A great deal of confusion can therefore arise if one is unaware exactly which habitat is being sampled (Round 1991). For example, the sampling of stones in slow-flowing rivers is often hindered by a covering of silt and/or other algae, thus giving a combined sample from three different habitats. Any ecological interpretation from such a sample should be treated with caution (Round 1991) particularly when comparisons are being made between different samples.

1.4.1.1 The Epipelon

The epipelon is the community living attached to, or moving within, the fine silt and mud of streams, rivers and lakes. Complex interactions within the silt could bias an ecological investigation where water quality is the focus. Phosphate ions are absorbed on to silt

35 particles and thus river sediments often have much higher P concentrations than the overlying water. Thus if epipelic taxa, which live within the surface of the sediments, can utilise this phosphorus source, they may be indicative of more nutrient-rich conditions than nearby species deriving their nutrients from the water column alone. Navicula gregaria ‘b’, a species normally associated with more eutrophic waters, was found in the epipelon of a relatively nutrient-poor stream by Cox (1990b). This species is presumably able to utilise the nutrients bound within the sediment and therefore would not give a true representation of the ambient water quality. Perhaps because of the complex relationships between silt nutrient binding and diatoms this community should be avoided for water quality estimations.

1.4.1.2 The Epiphyton

Within most streams and rivers there is an abundance of aquatic macrophytes and almost all of these can be found to have a covering of diatoms (Round 1981, Morin 1986). There is however some contention as to exactly how diatoms react with the host plant. Some studies have shown that little or no difference occurs between epiphytic communities and those growing on artificial, inert, substrata (Cattaneo & Kalff 1979). In contrast, a strong host specificity was reported by Eminson and Moss (1980) for four host species of macrophyte. This host specificity was however only found under very nutrient-poor conditions and almost disappeared in more nutrient-rich waters.

From experimental results aquatic macrophytes have been shown to excrete organic compounds (Wetzel 1969) and these must have some influence on the epiphytic algae. The results of Eminson and Moss (1980) suggest that in more nutrient-rich waters the external influences are far more important. They go on to conclude that, from an evolutionary viewpoint, it would be disadvantageous for aquatic plants to release nutrients as the resultant epiphyton would reduce incident light significantly. The shading effect of epiphytic algae has been shown to restrict macrophyte growth within lakes. Sand-Jenson and Borum (1984) calculated that plants found growing in water of less than Im depth could have penetrated to 3.5m in the absence of attached epiphytes.

36 Another problem in the study of the epiphyton is that of colonisation and succession. Aquatic macrophytes form a dynamic substratum which is continuously producing new surfaces as it grows. Thus any one plant can support a number of epiphytic assemblages at different stages of succession (Jones & Meyer 1983).

When sampling with water quality estimation in mind it is imperative that representative diatom samples, which reflect the water quality, are taken. These samples should not be influenced by other factors such as host specificity and irregular exposure times. The epiphyton, due mainly to a lack of understanding of these complexities, has not been used for the purpose of water quality assessment. The majority of studies to date have used either artificial substrata or the epilithon (Round 1993).

1.4.1.3 The Epilithon

Due mainly to its almost universal availability the epilithon is the most widely and most often sampled of all the natural communities (Cattaneo & Amireault 1992). This habitat includes the diatoms growing on bed-rock, boulders, stones, gravel and small pebbles. It is usually possible to find some sort of solid substratum to sample at stream and river sites, although problems can occur in very slow flowing, lowland rivers (Round 1993). The structure of the epilithon is not however simple and has been compared to that of a forest (Round 1993) with a number of canopy levels. The different growth forms of diatom taxa form complex layers; some in close contact with the rock, e.g. Achnanthes spp., some growing on mucilage stalks, e.g. Gomphonema spp., while other species form chains, e.g. Tabellaria flocculosa (Lock et a l 1984, Pringle 1990). Also associated with these different layers are motile species which move in-between trapped silt particles and mucilage stalks, e.g. Navicula spp. and Nitzschia spp. (Round 1981). The tendency for these layers to trap silt has led to some confusion in the literature as to exactly what comprises the epilithic diatom flora. Stevenson and Hashim (1989) suggested that the flora of most microhabitats was comparable. Round (1991) on the other hand considers the contamination of the epilithon by silt flora a serious source of error in sampling.

Even within the true epilithon considerable spatial and temporal variability has been reported (Jones 1978, Jones & Meyer 1983, Korte & Blinn 1983). The dynamic nature of

37 streams and rivers must in part be responsible for this but other factors too, such as rock type (Descy 1979, Allan 1995) and immigration and emigration of individuals (Stevenson & Peterson 1991) are also important. The geology of an area will inevitably influence the water chemistry and in turn the species composition. Within a river system, however, introduced foreign rock types have not been found to support significantly different floras to the local substratum (Gale et a l 1979). The reason for this is likely to be due to bacterial and fungal conditioning of the rock surface reducing the effects of the geology on the diatom community (Korte & Blinn 1983, Blinn 1986).

A more important effect of substratum is surface structure. Smooth substrata, such as glass microscope slides, have often been found to support a less diverse and less prolific community than rough substrata (Butcher 1940, Siver 1977, Antoine & Benson-Evans 1986). The reasons for this are given as: 1. a rough surface provides more opportunity for diatom colonisation (Antoine & Benson-Evans 1986), and 2. the availability of microhabitats enables the epilithon to overcome adverse conditions such as flood events or increased sediment load (Allan 1995). When sampling the epilithic diatom community it is therefore recommended that a number of stones be sampled, and the results pooled, to avoid the inherent heterogeneity in the epilithon (Round 1993).

1.4.2 Artificial Substrata

With the large variability both between and within the various habitats, many workers have tried to standardise sampling by the use of artificial substrata. These include: smooth glass slides (Butcher 1940, 1946, 1947, Patrick et a l 1954, Tippet 1970), sand blasted glass discs (Admiraal et a l 1999), clay bricks (Briggs 1983), clay tiles (Tuchman & Stevenson 1980), aluminium (Korte & Blinn 1983), plastic aquarium plants (Morin 1986), terrestrial twigs and leaves (Sladeckova 1962), perspex slides (Antoine & Benson-Evans 1986) and sterilised natural rock (Tuchman & Stevenson 1980).

The use of artificial substrata for diatom sampling is usually employed because of the ease of handling and reduction in sampling variability (Blinn 1986). As mentioned above however, some surfaces are better than others for supporting diverse diatom assemblages.

38 Thus the pros and cons of artificial substrata need to be weighed up against the natural communities. Some of the advantages are:

1. A reduction in variation between replicates. 2. Natural variation between sites can be overcome, e.g. where it is not possible to find natural substrata at the sampling site. 3. They can be placed exactly where the interest of the investigation is focused, e.g. above or below a point of pollution input. 4. They allow cross-comparison of assemblages from dissimilar systems, e.g. the headwaters and lowland reaches of a river where similar natural substrata are unlikely to occur. 5. They can provide a surface of known area for quantitative sampling.

6 . They allow for exposure times to be set for the study of succession or for more accurate comparisons.

Some of the disadvantages include:

1. The diatom assemblages growing on artificial surfaces may not be representative of the natural assemblages. 2. Short exposure times may not allow for an assemblage to reach equilibrium. 3. Artificial substrata are subject to loss due to flooding, fouling, vandalism and sedimentation. 4. Two visits are usually required to obtain one sample.

A number of studies have been conducted to compare the validity of using artificial substrata as an ecological tool for sampling the natural communities (Tippett 1970, Brown 1976, Siver 1977, Tuchman & Stevenson 1980, Morin 1986, Goldsmith 1997). The majority of these studies have concluded that, although many of the same species are common to both substrata, the artificial substrata are usually less diverse and rarely match the natural habitats in their biomass. Thus, from an ecological viewpoint, artificial substrata should be used with caution (Stevenson & Lowe 1986). The collection of

39 diatoms for ecological interpretation is usually therefore from natural habitats using methods similar to those described by Douglas (1958).

For the purposes of water quality assessment, however, it is the changes in an observed diatom flora which are important rather than a true representation of the natural community. For this reason the use of an artificial substratum, displaying good sample replication, may well be preferable (Stevenson & Lowe 1986). By removing much of the micro-environmental variation found within natural substrata, it should be possible to obtain more information on diatom responses to water quality rather than their response to other environmental and habitat factors.

The choice of artificial substratum can then be made on ease of use, good sample replication and finally, all else being equal, a good reflection of the natural community. Tuchman and Stevenson (1980) found that clay tiles yielded diatom assemblages with the least variation in replicate samples and the greatest similarity to the natural flora. The other substratum used in this study was sterilised rock, but this was deemed ineffective because topographical features altered the flow regime around the surface. This resulted in very high variability in sample replication. A great deal of artificial substratum work has been done using glass or perspex slides in variations of the diatometer (Patrick et al. 1954). The use of these very smooth surfaces has however been criticised because they tend to attract only those species which can attach firmly, e.g. Achnanthes minutissima, and they generally show low diversity (Siver 1977). The provision of some form of flat, roughened surface, e.g. ground glass, scratched perspex or clay tile would appear to be more satisfactory (Stevenson & Lowe 1986).

1.4.3 Sample Representivity

The problems of obtaining representative samples do not end with the choice of substratum. Many other factors can also affect the diatom assemblages. With the interest of water quality estimation in mind the observed diatom assemblages can be adversely affected by accumulation of dead cells, grazing, seasonal and temporal variation, flood events, current velocity and many other changes in the physical environment.

40 1.4.3.1 The Accumulation of Dead Cells

By their very nature diatoms are persistent within a system after their death. The siliceous valves of dead diatoms can remain entangled in the mucilage complex of the live community (Owen et al. 1979) or valves washed in from other, potentially very different, areas may become trapped (Pryfogle & Lowe 1979, Battarbee & Flower 1984). The inclusion of these dead cells in studies often goes unnoticed due to the use of oxidative preparatory techniques which remove all the organic matter (Battarbee 1986). The consequences of including the dead component could be very misleading. When using the diatom community to assess water quality, one is only interested in the living component at a given time. Thus dead cells from potentially different conditions, could bias a water quality estimation. Pryfogle and Lowe (1979) observed epilithic communities to have between 2-77% dead cells, with no apparent pattern in the variability. When compared to the live component alone the inclusion of dead cells led to an over-estimation of species diversity. Similarly the results of Owen et al (1979) showed approximately the same range of dead cells on glass slides. All the slides had equal exposure times and almost all of them had over 10% dead cells. This is contrary to the results of Patrick et al. (1954) who reported few dead cells on glass slides. It is unclear how these differences arise although a shorter exposure time was used in the latter study, therefore less dead cells would have accumulated.

The error is further increased if the dead cells are allochthonous, i.e. from an outside source. Lotie systems offer many secondary sources from which diatoms could originate, e.g. soil, impoundments, tributaries and even fossil diatoms from the erosion of bed-rock. Although in-washed diatoms have been investigated as a source of error in palaeolimnological studies (e.g. Battarbee & Flower 1984), there is very little information regarding their impact on the interpretation of modem diatom communities.

The assessment of live cells within a sample can be made quite easily by the use of staining techniques. The stain acts on the protoplast of living cells only, thus all dead cells will remain colourless (Owen et al. 1979). The stained samples can then be conventionally mounted in high index mountants for identification and counting. However, such methods can heavily stain some organelles and therefore compound the taxonomic problems. With

41 the preparation of conventionally cleaned slides for reference, however, this problem should be possible to overcome (Owen et a l 1979). The publication of live-diatom keys, which rely on features of the living cell, rather than the silica cell walls, also aid in identification of living material (e.g. Cox 1996).

1.4.3.2 The Effects of Grazing

Another impact on the diatom assemblages, which is less easily quantified, is the effect of grazing by invertebrates and other organisms. Diatoms, as one of the most abundant aquatic primary producers, form a major part of the diet of a wide range of grazers from amoebae to young fish (Round 1981). It would therefore be expected that an inverse relationship should exist between the numbers of diatoms and grazing intensity. This was demonstrated by Douglas (1958) with respect to the effect of the caddis larvae Agapetus fuscipes on A. minutissima populations. Similarly the total removal of grazers from a system has been show to facilitate algal blooms. Eichenberger and Schlatter (1978) added insecticides to outdoor channels and observed large blooms of filamentous algae followed by a cyanobacterial bloom. These blooms were ended with the réintroduction of grazers. It can therefore be assumed that algal numbers are, at least in part, controlled by grazing in most aquatic systems (Allan 1995).

It is not just simply loss from the diatom community that can cause problems in sampling. Not only do grazers reduce total standing crop of algae but they can also alter the floristic composition by selective grazing (Allan 1995). These effects are difficult to demonstrate but Hill and Knight (1988) found a seven-fold over-representation of one diatom species in the gut contents of the caddis Neophylax. Heavy grazing by this species could result in a biased sample being collected which could have serious implications for a diatom-based water quality assessment.

Even without selective grazing the removal of diatoms allows for recolonisation to take place thus disturbing community structure. With other microhabitat variations, e.g. light and nutrients, the response of different species will vary, further upsetting the community stability due to shifts in competitive forces (Burton et a l 1994). In contrast, the tube building activities of some chironomids have been observed to increase diatom biovolume

42 (Pringle 1985). The sand grains used to build the tubes were found to support large numbers of diatoms with an estimated 12-fold increase in diatom biovolume compared to adjacent substrata without tubes. There was also found to be an increase in species numbers due to greater habitat diversity.

When water quality is the primary issue under investigation, the interactions between grazers and diatoms become extremely complex. Grazing pressure is very variable due to the environmental preferences of the grazers. This is further complicated by the sensitivity of many invertebrates to water quality (Wright et al. 1989). Thus in areas of very poor water quality, grazing is likely to be negligible, whereas, in areas of high quality, where the water is well oxygenated, grazing could have a considerable affect on the observed diatom assemblage.

1.4.3.3 Seasonality

The variation in both diatom productivity and species composition over seasonal cycles has been well documented (Douglas 1958, Moore 1976, Marker & Casey 1982, 1983, Cox 1990a, 1990b). Marker and Casey (1982), working with artificial streams, reported diatom cell numbers of 4.2 x 10^ cells mm'^ during the month of May with numbers falling off to less than 20,000 cells mm'^ for the rest of the year. In natural systems this spring bloom is also often seen but with a less marked decline in the summer months (Moore 1976). There are of course many factors involved in the seasonality of a stream, including flow patterns, nutrient availability, water temperature, day length and light intensity. The observed seasonality can therefore be seen to be extremely variable between different systems {cf. Douglas 1958 and Marker & Casey 1982). It is usual to see shifts in species composition as well as productivity. Cox (1990a) showed seasonal shifts in small Navicula spp. from epilithic stream samples. Similarly some species have been reported as being typical of certain times of the year e.g. M. circulare is known as a vernal species; only occurring at other times of year under low temperature conditions (Cox 1993).

Seasonal changes in the diatom flora have been well studied from an ecological viewpoint and would appear to be very variable both within and between different river systems (Douglas 1958, Low 1972). Despite this there seems to be little available information on

43 the effect of such variation on a diatom-based monitoring programme. Using two diatom indices over the period of one year, Kelly et al (1995) found upland sites gave more consistent results than lowland sites, but the variation was not however considered to be significant. For future monitoring studies it would appear necessary to keep sampling dates and conditions consistent where possible and also to gain more information on the seasonal changes in the diatom community to establish how these seasonal shifts in species composition might affect a diatom-based assessment of trophic status.

1.4.3.4 The Physical Environment

The diatom assemblage at a site is also determined by the physical conditions. Only species which can firmly attach such as Achnanthes spp. and Cocconeis spp. are found at high current velocities, whereas motile and loosely attached taxa are more common at low current velocities, e.g. Melosira varians, Navicula spp., Gomphonema spp. and Cymbella spp. (Patrick 1977). Such morphological adaptations are a necessary response to the hydrological environment (Rott 1991). Within a site, changes in the current velocity have been found to explain the majority of observed species variation (Passy 2001). For the purposes of water quality assessment this means that two sites of identical water quality but very different current velocities may have very different diatom species present. Homer et at. (1990) observed different diatom taxa at the same current velocities when phosphoms concentrations were changed. It is possible, therefore, that providing biological information is gained from enough sites with different current velocities, over a range of phosphorus concentrations, the importance of velocity will be minimised. Effectively there can be different groups of indicator species to denote the same water quality range for both high and low current velocity sites.

1.5 Thesis Aims

The aim of this thesis is to explore the use of river diatoms as a means of enhancing the biological assessment of the trophic status of lowland rivers in southern England (with particular reference to phosphorus). The principal objectives are twofold. Initially it is considered necessary to establish a reliable method for sampling diatoms from lowland rivers. Having developed suitable sampling methodologies, the second objective is to

44 implement these sampling techniques at a wide range of different river sites that incorporate a long phosphorus gradient. These training set data will then be used to develop a weighted average model to appraise the use of this technique in the assessment of trophic status in lowland rivers.

1.6 Definition of Trophic Status

The term “trophic status” is used in this study to express the nutrient concentrations in the river water. The majority of this thesis, however, focuses only on the biological response of river diatoms to phosphorus concentrations and not nitrogen. The rationale for this is that despite nitrogen being a vital plant nutrient, nitrate concentrations have been widely reported as being rarely limited in the lowland rivers of the UK (Birch & Moss 1990, Mainstone 1997, SCOPE 1999). This observation is also demonstrated in this study where the majority of sites had nitrate-nitrogen concentrations in excess of 1000 pgL'\ Phosphorus is also the focus of the UWWT directive. The term “trophic status” is therefore used in this study on the assumption that phosphorus is the principal nutrient controlling the diatom assemblages.

1.7 Thesis Outline

The methods used throughout the thesis are presented in Chapter 2 and include the methods used to sample river diatoms from both natural and artificial substrata and the sampling and chemical analysis of river water. The analysis of environmental and biological data is also outlined in Chapter 2 but where specific numerical methods are applied in a chapter a detailed description of the technique is given in the relevant section.

Chapter 3 is a detailed study from four sites on the River Wey, a tributary of the Thames flowing from Alton (Hampshire) to where it meets the Thames at Weybiidge (Surrey). At each site diatoms were sampled from six different substrata (3 natural and 3 artificial) in order to find a suitable substratum from which to obtain representative and repeatable diatom samples. Two of the artificial substrata fulfilled the criteria and these were then used to collect diatom samples from a wide range of different river sites, in and around the River Thames basin. From the 115 sites that were sampled over 50 were used to develop a

45 diatom training set. These diatom and water chemistry data are presented in Chapter 4, where multivariate techniques were used to investigate the chemical and biological differences between the sites and the interactions of the diatom assemblages with the major chemical gradients.

Weighted averaging techniques (simple WA & WA-PLS) were then applied in Chapter 5, in order to assess the possibility of using river diatoms to predict the trophic status of lowland rivers. These are methods commonly used for the reconstruction of past environments (pH & phosphorus) from the fossil diatom record of lake sediments (Birks et al. 1990, Bennion 1994) but are not yet routinely applied to contemporary environmental monitoring. The internal model assessment error statistics presented are comparable with diatom training sets from lakes. A more stringent evaluation of these diatom-based modelling techniques, however, is to test them on independent samples, not used in the training set.

In Chapter 6 the diatom-based models are evaluated using the samples collected for Chapter Three. The effects of seasonal variation within the diatom assemblages are also investigated in this chapter. The implications of this study are discussed in Chapter 7. Recommendations for the future development of numerically-based diatom modelling of the trophic status of lowland rivers are suggested.

46 Ch a pter T w o

M eth o d s

2.1 Study Region

With the focus of this study being on lowland river systems in England, the obvious choice of river for a London-based study was the River Thames and its many tributaries. The Thames is the largest river system in the UK with a catchment of 9,950 km^ and a total length of approximately 320 km from its source to the North Sea. The Thames can also be classified as a lowland river, with no part of it exceeding 250 m in height. Within the River Thames basin there are many different river types, ranging from relatively fast flowing shallow streams with rocky substrata to large, very slow flowing deep river channels, with soft substrata. To gain an even greater variety of lowland river types a number of rivers were also chosen which were outside the Thames catchment. Some of the chalk streams of Hampshire were included because of their importance as wildlife habitats, as well as some of the less base-rich sites of the East Sussex and Kent Greensands to extend the range of river sites. Figure 2.1 shows the extent of the sampling area and Figure 2.2 gives a simplified overview of its geology.

A second reason for concentrating this study on the Thames catchment is that freshwaters in this area of Britain are under considerable human pressure. The Thames basin represents only 8 % of the total land area of the UK but over 20% of the country’s population lives within it. Furthermore the contribution to the eutrophication of the Thames made by this densely populated area is massive, with an estimated 93% of the total phosphorus loading entering the Thames estuary at Teddington weir identified as coming from sewage treatment works (Roger Sweeting, pers. comm.). Consequently many of the lower reaches of the River Thames are highly eutrophic with total phosphorus concentrations in excess of 1000 pgL'\

47 Extent o f the Thames Catchment

Principal Rivere

M inor Rivers

Canals

4- oo

Lana!

<3^ %

60 miles

Figure 2.1 Map o f the River Thames catchment and other rivers sampled in this study K e y Chalk m Reading & Thanet Beds m Norwich Clay London Clay

Upper & Lower Greensands m Weald Clay □ Hastings Beds Bagshot Beds m Oxford Clay ■ Hamstcad Beds & Marls □ Oolite ■ Lias m Triasic Mudstone è Devonian Sandstone

100 km

60 miles

Figure 2.2 A simplified geological map of the River Thames catchment and other lowland rivers sampled in this study The methods for selecting study sites are covered in detail in the following chapters, however, for the purposes of the training set sites the inclusion of a wide range of different rivers was considered important. Thus the Thames, plus other southern England rivers, provided a wide range of geological types and, from source to mouth, a large gradient of phosphorus and alkalinity was observed. The variation in geology and flow also had a considerable effect on the types of available natural substrata from which diatoms could be sampled. In the slow-flowing reaches of the River Thames near Windsor, for example, where it passes over London clay, no epilithic samples could be found. Conversely in the upper reaches of the River Windrush in the Cotswolds, small fast-flowing streams provided almost exclusively epilithic substrata; submerged macrophytes and epipelic environments did not occur. Between these extremes, however, many different forms of streams and rivers, with a variety of substrata were found and sampled.

2.2 Water Chemistry

2.2.1 Sample Collection

All the polyethylene sample bottles and the glassware used for laboratory analysis, were pre-washed by soaking for at least 24 hours in 2% hydrochloric acid, followed by thorough rinsing in distilled then deionised-distilled water (three times with each). Sample bottles were also rinsed in the field with the water sample being collected. Water samples were collected in 100 ml polyethylene bottles, pre-treated on site depending on the type of analysis they were to be used for (see below and Fig, 2.3). On each sampling trip a field blank was prepared by filtering 500 ml of deionised-distilled water in situ. These were then analysed in the laboratory together with the collected samples to ascertain whether any contamination was occurring in the field. On no occasion was any significant field contamination observed. Laboratory blanks were also prepared prior to analysis and analysed with the samples. Again no source of contamination was observed. To avoid bringing too many samples back to the laboratory, and to minimise sample deterioration, several of the physico-chemical parameters were measured in the field. These were pH, alkalinity, conductivity and water temperature.

50 Total Phosphorus FRP & Nitrate Silica Anions Cations

100 ml 100 ml 100 ml 100 ml 100 ml

Unfiltered Filtered Filtered Filtered Filtered (GF/C) (Cellulose (GF/C) (GF/C) Nitrate) V J L J L J

Acidify with 1 ml cone nitric acid

Refrigerate Refrigerate Refrigerate Freeze Freeze I Persulphate digestion

FRP Nitrate Silica Ion Inductively Coupled Analysis Analysis Analysis Cromatography Plasma Spectrometry

Figure 2.3 River water sampling protocol for laboratory analysis

2.2.2 Field Analyses

pH The measurement of pH was performed electrometrically using a Jenway pH meter with a BDH Gelplas general purpose combination electrode. The principal of the probe requires the glass electrode to adsorb a layer of the sample onto its surface; the resultant potential difference is a function of the hydrogen ion (H^) concentration in the sample and the electrolyte contained within the electrode (Wetzel & Likens 1991). Prior to measurement the pH meter was calibrated using freshly made pH 7 buffer solution and the slope of the electrode adjusted against pH 4 buffer. Temperature compensation was adjusted manually according to the ambient sample temperature. The electrode was thoroughly rinsed with distilled water before each measurement. Field measurements were made on a water sample collected in a clean glass beaker, well flushed with the sample; the electrode was allowed to stand for several minutes without agitation before the pH value was determined.

51 Total Alkalinity Total alkalinity of a water body refers to its ability to neutralise a strong acid, i.e. its buffering capacity. Although the alkalinity may in theory be caused by any weak acid anion it is usually only carbonate, or more strictly bicarbonate, alkalinity that is important in freshwaters (Wetzel & Likens 1991). The measurement of total alkalinity is achieved by titrating a known volume of a strong acid (e.g. 1.6N H 2 SO 4 ) against the water sample until all the carbonate has been used. This equivalence end point can be identified using a mixed bromocresol-green methyl-red indicator. The end point of pH 4.5 is recommended for waters containing elevated levels of phosphate. In the field a 100 ml sample of water was collected in a well rinsed measuring cylinder and transferred to a 250 ml conical flask.

6-8 drops of Bromocresol-green Methyl-red indicator were added to this: the sample turns blue-green. The titration was performed using a Hach digital titrator (model 16900-01) with 1.600N H 2 SO 4 as the titrant. The end point is determined by a colour change from blue to a light violet grey colour (pH 4.5): an excess of acid causes a bright pink colour. Total alkalinity was read directly from the digital display and expressed as mgL'^ CO]^. The titration was repeated three times to obtain a mean value.

Conductivity Conductivity (specific conductance) is a useful measure in bicarbonate dominated systems, where it has been shown to be directly proportional to the concentration of the major cations in solution (Ca^"^, Mg^"^, Na"^ and K^) (Wetzel & Likens 1991). Conductivity is measured using a standard conductance cell consisting of two 1 cm^ platinum black electrodes placed 1 cm apart. The conductivity of a water sample is the reciprocal of the specific resistance between the two electrodes and is expressed in micro-Siemens cm'^ (pS cm'^). In the field the conductivity was measured using a Phox 52E meter, the probe being placed directly in the stream flow. Calibration of the instrument was performed prior to use with a 0.00702N KCl solution, which gives a conductivity of 1000 pS cm'^ at 25°C. The meter was set to automatically compensate for temperature and thus all conductivity readings were expressed at a standard 25°C.

Current Velocity Current velocity was measured using a Valeport “Braystoke” BFM002 current flow meter fitted with a 50 mm impellor on a rod suspender and a simple counter box to determine the

52 number of revolutions per second. The current meter used a standard series 1178 impellor and a calibration based on standards set by the British Standards Institution, BS3680. Revolutions per second (Rev s'^) were converted to metres per second (m s'^) using one of two formulae, depending on the current velocity: between 0.01-1.5 Rev s'^ use Equ. 1 0.1001 n / 0.032 between 1.5-29.0 Rev s'^ use Equ. 2 0.1079 n / 0.030 Where n = number of Rev s'^ measured over a 50 second period. Equation 1 extends from 0.042-0.192 m s'^ and Equation 2 from 0.192-3.159 m s"\

In the field, the measurements were taken as close to the placement of the artificial substrata or natural substrata, as possible and set at a height of approximately 1 cm from the river bed, in order to avoid impediment of the impellor. Three 50 second readings were taken for each sample and an average value recorded. This measurement was not intended to be representative of the whole river site but rather to gain information at the sample point. Current velocity is measured in m s'^ and referred to as “flow” in this thesis.

Temperature On each visit to a site the water temperature was recorded using a mercury filled analogue thermometer and an electronic temperature probe attached to the pH meter. At no time did the two readings differ by more than 1°C.

2.2.3 Laboratory Analyses

All the water samples were collected in acid-washed 100 ml polyethylene bottles and, where necessary, filtered on site. Filtration was performed using a Nalgene filter apparatus and a Nalgene Mityvac hand operated vacuum pump. Whatman GF/C 4.7 cm diameter filters (1.2 pm pore size) were used in all cases except where the samples were for silica analysis, in which case cellulose nitrate filters were used (1.0 pm pore size). Samples were refrigerated directly after collection and analysed for soluble nutrients (N and P) and silica within 72 hours. Samples for anion and cation analysis were frozen the same day as collecting and analysed in bulk at a later date. Samples for total phosphorus analysis were kept refrigerated and analysed within 28 days of collection.

53 Ortho-Phosphate (FRP) Phosphorus can be operationally divided by filtration into dissolved ortho-phosphate

(P0 4 ^’) and total phosphorus (TP); the latter includes the organic and particulate fraction. The term filterable reactive phosphorus (FRP) is therefore used to describe the inorganic fraction more precisely. In this study the concentrations of FRP and TP are expressed as phosphorus (i.e. P 0 4 ^'-P) rather than phosphate (P 0 4 ^‘). The conversions between these two forms are: P04^-P = P04^x 0.326 P 04^= P 04^-P x 3.065

Determination of FRP was done spectrophotometrically. The principle of the analysis involves the phosphate reacting with molybdate, in a suitably acidified solution, to form molybdo-phosphoric acid. This product is then reduced by ascorbic acid to give a molybdenum blue complex. The intensity of the blue complex is directly proportional to the concentration of FRP in the sample and can be determined on a spectrophotometer at 885 nm against a set of standards of known concentration. The relationship between colour and FRP concentration is only linear between 0 to approximately 500 pgL'^ of FRP as P. Thus any samples which exceeded 500 pgL'^ were re-run following appropriate dilution with deionised-distilled water.

Nitrate-Nitrogen

Nitrogen can exist in four major forms in freshwaters: nitrate (NO 3 ), nitrite (NO2 ), ammonium (NH4 ') and organic nitrogen (amino acids, nucleic acids, urea and numerous synthetic organic compounds). Nitrite is generally considered to be scarce in well- oxygenated waters (APHA 1989) and is therefore included here with nitrate. The measurement of ammonia was not possible at UCL due to problems associated with its laboratory determination (primarily the use of phenol). The use of an ion-selective electrode was tried, but this proved both inaccurate and impractical because samples needed to be analysed within 3 hours of collection (Wetzel & Likens 1991).

Nitrate analysis was performed by a spectrophotometric method utilising the production of an azo dye. The principle of the method involves the reduction of nitrate to nitrite in the presence of a cadmium catalyst (spongy cadmium), which has been shown to give almost

54 complete conversion (APHA 1989). The resultant sample contains all the oxidised nitrogen as nitrite. The nitrite is then determined by diazotizing the sample with sulphanilamide and then coupling with N, 1 -naphthylethelene diamide to give an intense crimson azo dye. The absorbance is measured on a spectrophotometer at 543 nm and compared to a set of standards of known concentration.

As with FRP, all the nitrate concentrations in this study are expressed as nitrate-nitrogen (NOg'-N) rather than nitrate (NOg') the conversion between these being: NOg -N = NOg X 0.226 N0g=N0g-Nx 4.429

Total Phosphorus and Total Nitrogen The determination of both TP and TN requires organic P and N to be oxidised to an inorganic form so they can be analysed as above. This process can be achieved in one step by a carefully controlled persulphate digestion yielding a combined sample. The problem of performing simultaneous digestions, however, is that TN requires an alkaline environment whereas TP requires acid conditions. This is overcome by starting the digestion with just enough alkali to complete the nitrogen oxidation; the breakdown product of this reaction liberates hydrogen ions and leaves an acid solution for the TP digestion (Johnes & Heathwaite 1992). The digestion was performed using a microwave digester, applying controlled heat and pressure for the reaction. This method was used throughout the study to obtain TP concentrations and gave reliable results; recovery rates from ATP were 93-100 per cent. The results for TN, however, were not reliable. TN concentrations were often lower than the results for nitrate when this method was applied to water samples. Furthermore when the method was tested using known concentrations of organic nitrogen compounds (sulphanilamide and N, 1 -naphthylethelene diamide) at no time were recovery rates any higher than 60%. The inclusion of TN is this study was therefore not possible.

Silica

In solution silica exists as either silicic acid (H 4Si0 4 ) or silicate (SiOg^'). These react with an acidified molybdate solution to form a yellow silico-molybdate complex which on reduction with sodium sulphite forms a molybdenum blue complex. The absorbance of

55 this complex is measured on a spectrophotometer at 700 nm and compared to standard solutions of known concentration.

Water samples for silica analysis were filtered in the field using cellulose nitrate filters and refrigerated in the polyethylene collection bottles. Despite all the analyses being performed in boro-silicate glassware no contamination was observed.

Cation Analysis - ICP Inductively coupled plasma emission spectrometry (ICP) provides a very quick and accurate method for the determination of the major cations (Ca^^, Mg^^, Na'^ and K'^) as well as, in theory, any other cation required (Thompson & Walsh 1983). The samples in this study were analysed using the ICP at Royal Holloway and Bedford New College, University of London.

The technique of ICP is based on the measurement of the specific wavelengths of light that are emitted by atoms in an excited state. A small amount of the acidified sample is nebulised to form an aerosol which is carried in an argon stream to an inductively coupled argon plasma. The plasma is generated by radio frequencies and is maintained at a temperature of 6,000 to 10,000 °K. As the aerosol of sample enters the plasma the analytes within it are atomised, ionised and excited, resulting in them giving off light at characteristic wavelengths for each ionic species. The different wavelengths are dispersed by a grating spectrometer and the intensities of each line monitored by photomultiplier tubes, thus allowing for the measurement of multiple ions in the sample (Thompson & Walsh 1983). The ICP is calibrated using a standard solution containing a known concentration of all the major cations.

It is generally accepted that the accuracy and precision of ICP determination is very good (Thompson & Walsh 1983). In this study the precision over 2 hours was <0.5% (standard deviation, expressed as a percentage of the concentration of the standards) for all the samples; anything less than 1% is considered good for ICP techniques. The accuracy of the method is determined in part by the precision but also by interference effects. The major cations are not significantly affected by interferences (Thompson & Walsh 1983).

56 Anion Analysis - IC Ion chromatography is an ionic separation technique which depends on the affinity of an ion for a low capacity, strongly basic anion exchange site. Anions in the sample passing through the exchange column will have different rates of migration due to differing affinities for the exchange sites. Each ion can therefore be identified in the eluent by conductimetric detection. The time for each ion to pass through the column is known and the specific conductance of the eluent over time can be integrated to give the concentration of each specific ion (Wetzel & Likens 1991). This method is particularly good for the determination of chloride and sulphate. Phosphate, nitrate and nitrite may also be determined using 1C but these ions are liable to deteriorate when stored; hence spectrophotometric methods were used instead. The samples in this study were analysed using an ion chromatograph based in the Geology Department, UCL.

Contamination Assessment of contamination was performed at two levels: field contamination and laboratory contamination. Field blanks were prepared at each sampling site for the monthly sampling described in Chapter 3 and on a daily basis for the training set sites described in Chapter 4. The field blanks consisted of distilled-deionised water taken out into the field and treated in the same way as the water samples being collected, i.e. filtered and refrigerated. Laboratory blanks were simply distilled-deionised water run in the same way as the water samples being analysed. At no time was any contamination observed in the field blanks for any of the analytical techniques thus giving confidence in the values obtained. Similarly the laboratory blanks also showed that no further contamination was occurring in the majority of analytical procedures. The exception to this was one run of total phosphorus analysis. On this occasion the laboratory blank gave a TP measurement of 13 pgL'\ and the zero standard was visibly pale blue. The source of the contamination was unclear but did not re-occur when the samples were analysed a second time.

2.3 Diatom Sampling

In this study diatom samples were collected from many different substratum types including three natural and five artificial. These were: cobbles (epilithon), sediment (epipelon), submerged macrophytes (epiphyton), rough clay tile, smooth clay tile.

57 polyethylene rope, perspex and glass. All collections were made in triplicate for the first part of this study (Chapter 3) to assess sample variation. Later deployments of artificial substrata were restricted to one of each type (rough tile and rope only) per site. All the samples, with the exception of epipelon, were treated on site with acidified Lugol’s iodine to kill and preserve the diatoms and then refrigerated until preparation.

It was initially proposed that the artificial substrata would be held in 35 x 35 cm metal frames, securely anchored to the river bed. This was tried but found to be too susceptible to vandalism, with 8 out of the original 12 frames being removed in the first sampling month and 6 out of 11 in the second month. After this the tile substrata were left, unanchored, on the river bed and the rope either attached to a steel stake driven in to the river bed or attached to a heavy weight (normally a stone or brick).

2.3.1 Natural Substratum Sampling

Cobbles Strictly speaking the epilithon are those diatoms living attached to or on the surface of rock. It is recommended in the literature, therefore, that when selecting cobbles for diatom sampling, only those free from sediment and other algal or macrophyte growths should be chosen (Round 1993). In lowland rivers this was often not possible and therefore stones which were least contaminated with silt or other algae were chosen. If the cobble had an even surface, a 25 cm^ area was delimited using a plastic frame and the cobble carefully brushed with a toothbrush into a 30 ml sampling vial. For less even cobbles, or those smaller than 25 cm^, the area was calculated approximately using a ruler and the whole upper surface of the stone brushed. Care was taken to clean the sampling equipment thoroughly between each sample and particularly between sites.

Sediment Samples Diatoms living within the sediment (epipelon) move around in the uppermost surface layers. By simply taking a sediment sample not only the live fraction will be collected but also numerous dead cells. To avoid this a post-collection harvesting technique was used. The sediment was collected by taking a 1 cm deep core from a silty area of the river bed using a 5 cm diameter perspex tube. The sample was then transferred to a sample bottle

58 and placed in the dark. The method used for “harvesting” the live diatom fraction followed that of Eaton and Moss (1966). This method relies on the live diatoms moving up to the sediment surface as a result of the intrinsic diurnal rhythm. If a piece of lens tissue is placed on the surface the motile diatoms move from the sediment and into the lens tissue, which can then be removed.

In the evening of the day the sample was collected the sediment was thoroughly mixed and evenly spread in a petri dish. After allowing to settle, the surface water was carefully removed with a pipette. A double thickness of Whatman grade 105 lens tissue (5 x 7.5 cm) was placed directly onto the sediment surface and the sample left where it could receive direct daylight. The following morning the lens tissue was removed and treated with Lugol’s iodine. Eaton & Moss (1966) reported the best time of “harvesting” to be between 9.00 am and 10.00 am; prior to this many individuals had not moved into the tissue and by 12.00 noon a net downward movement was observed. Although untested here, Eaton & Moss reported 87.5% recovery of live epipelic diatoms using this method.

Submerged Macrophytes Samples of submerged macrophytes were taken at each site to assess the epiphytic assemblages. Whenever possible the species of plant selected was kept constant; Ranunculus sp. was the easiest to find at all sites in the summer. Later in the year, however, this was not found and it was therefore only possible to find species with similar growth forms (e.g. Myriophyllum sp.). At Alton only small, submerged leaves of Berula sp. were sampled in winter.

The sampling technique used was to select 3-4 fronds of the plant of approximately 10 cm in length. These were placed in sample bags and treated with Lugol’s iodine.

2.3.2 Artificial Substrata Sampling

All artificial substrata were left in the river sites for approximately one month and then removed for sampling. Replacements were put in on the same day for the following month’s samples. The sampled tiles were taken back to the laboratory and thoroughly cleaned by soaking in detergent and scrubbing with a wire brush. These were then reused.

59 The rope was replaced with a new 25 cm length of 15 mm diameter polyethylene rope. A sketch map was made after placement to aid relocation on the following trip.

Clay Tiles Both the rough and smooth clay tiles were sampled in the same way as the natural epilithon. A 25 cm^ area of the tile was delimited using a flat perspex frame with a 5 x 5 cm square cut in the centre (Plate 2.1). The inside area of the frame was brushed with a toothbrush and the sample washed into a collecting vial. Care was taken to wash the brush out into the vial and then clean it thoroughly before the next sample was taken.

Plate 2.1 Rough and smooth tiles following sampling at Hawbridge

Rope The rope used for diatom sampling was a 25 cm length of 15 mm diameter, spun polyethylene rope. Prior to deployment the end of the rope was frayed to maximise the surface area for colonisation. The rope was then anchored to the river bed with either a 50 cm long steel stake or a heavy weight (Plate 2.2). After one month in the water the rope looked similar to a frond of submerged macrophyte. This was sampled by removing the whole length of rope from the water and carefully cutting off approximately 5 cm from the loose end. This was placed in a sample bag and treated with Lugol’s iodine.

60 Glass and Perspex Both the glass and perspex substrata required the use of the metal frames to hold them in place. Due to their susceptibility to loss only a few samples were obtained. Of those , the majority were very species-poor, supporting mainly A. minutissima and Cocconeis placentula var. euglypta. These substrata have been criticised in other studies due to the lack of diversity and a bias towards species which can firmly attach (Siver 1977). Because of this and the difficulty in gaining samples, it was decided to abandon their use in this study and concentrate on the methods described above.

Plate 2.2 Polyethylene rope and smooth tile in place at Elstead

2.4 Diatom Slide Preparation and Counting

2.4.1 Preparatory Methods

All the diatom samples were taken from either a known area of substratum or, in the case of epiphytic and rope samples, from a known dry weight of material. This allowed for quantitative preparatory techniques to be used which, although not comparable in all cases, did allow for estimates of cell numbers to be made relative to a substratum type. All the

61 preparatory techniques followed those of Battarbee (1986) with some adaptations where necessary. Glassware was thoroughly washed with detergent and a soft abrasive cloth prior to use, followed by 30 minutes soaking in hot 10% sodium hydroxide. The latter treatment ensured that any diatom valves from previous preparations would dissolve. The beakers were thoroughly rinsed with distilled water before the samples were added.

Epilithon and Tile Samples The natural epilithic and tile samples were all prepared in the same way. The whole sample was transferred to a clean 100 ml glass beaker and 5 ml of hydrogen peroxide

(H2O2) added. This was allowed to stand until any violent effervescing had stopped and then placed on a hot plate at 60°C for 3-4 hours with additions of H 2O2 as required to stop the sample drying out. A few drops of 10% HCl were added to remove carbonates and then the contents of the beaker were transferred to a centrifuge tube. The sample was then centrifuged at 1200 rpm for 5 minutes and the supernatant removed. The pellet was then washed and re-suspended in distilled water and centrifuged again. This process was repeated a total of four times. At this stage microspheres were then added (see below).

Epiphyton and Rope Samples The first step in preparing the epiphytic and rope samples was to determine the dry weight of the sample. This was done by placing the sample in a pre-weighed 100 ml beaker (weighed to 0.0001 g) and then oven drying overnight at 105°C, followed by re-weighing the beaker plus sample. The preparation technique then followed that used for epilithon.

After about 2 hours in H2O2 the sample was sonicated for 5-10 seconds and any remaining plant material (or rope) removed with careful back-washing into the beaker with distilled water. The sample was then left on the hot plate for 1 hour before centrifuging and washing as above.

Epipelic Samples In the Eaton & Moss (1966) method of lens tissue extraction the tissue was dissolved using chromic acid. The use of chromic acid is no longer permissible and thus an alternative method was required. Hydrogen peroxide did not dissolve the tissue. Furnace ignition of the tissue was tried at 500°C but resulted in noticeable deterioration in some of the more delicate diatom species (e.g. valves of Nitzschia accicularis were seen to severely

62 deform). The final method adopted for the extraction of the diatoms from the lens tissue was to first dry the tissue in an oven at 105°C and then ignite it in a glass petri dish with a match. This resulted in a small ashy deposit which was transferred into a centrifuge tube and washed with distilled water.

2.4.2 Quantitative Assessment

The addition of external markers has been extensively used for the determination of diatom numbers, with commercially available microspheres being considered the most convenient of these (Battarbee 1986). The microspheres are supplied in a suspension of known concentration and thus a known number of microspheres can be added to each of the samples. The amount added was ideally enough to obtain a 1:1 ratio of microspheres to diatom valves. Slides were only re-made if the ratio dropped below 1:3. From this the number of diatom valves per unit area (or dry weight) can be calculated:

Microspheres introduced x Diatoms counted Microspheres counted

Battarbee (1986) 2.4.3 Slide Preparation

Slides were prepared by allowing a dilute suspension of the diatom sample to settle out and evaporate on a 19 mm, grade zero, circular coverslip until dry. This was then mounted on to a microscope slide using a high refractive index mountant (Naphrax), and heated on a hot plate at 130°C until set. Slides were made up with two cover slips on each with different concentrations to ensure one would be suitable for counting.

2.4.4 Archiving

Each diatom slide was given a unique ECRC slide number and archived in slide drawers. The remaining suspension was placed in a glass vial and the water replaced with methanol to prevent dissolution. This too was labelled with the same number as its corresponding slide and placed in the ECRC diatom suspension archive. Information on the sampling site, slide and suspension location and project to which it is related, were entered into

63 AMPHORA (Beare 1997), a purpose built computer database for storing and manipulating diatom and water quality data.

2.4.5 Diatom Counting

The techniques used when counting followed those described by Battarbee (1986). Counting was performed on a Leitz Laborlux S microscope at a magnification of xlOOO using predominantly phase contrast with an oil immersion lens. On each slide one or more transects were counted to a total in excess of 350 (±50) valves. Where one taxon occurred at greater than 50% abundance the count was doubled to ensure the inclusion of less common taxa. Care was taken to include equal proportions of central and edge areas of the cover slip to avoid any sorting that may have occurred during settling.

In general the preservation of the siliceous valves was very good, due to the sampling of live diatom communities. It is inevitable, however, that some valves are broken during sampling or preparation and thus it is important to adopt a counting strategy to avoid the over-representation of any species. Any valve fragments that included a distinctive central area were counted as a single valve, and thus any other fragments of the same taxon that could be identified but did not include the central area were not included in the count. Taxa identified in this way included: Navicula spp., Gomphonema spp., Cymbella spp., Sellaphora spp. and the raphe valves of Cocconeis spp. Where taxa did not have a distinctive central area the valve ends were counted as half-valves. Taxa counted by the identification of valve ends included: Fragilaria spp., Eunotia spp., Synedra spp., Diatoma spp. and the rapheless valves of Cocconeis spp.

2.4.6 Counting LiveiDead Cell Ratios

The epilithon and tile samples were assessed for the proportion of live material prior to preparation. The sample was well mixed by gentle agitation and one drop of the suspension was then placed on a glass slide and covered with a coverslip. 300 cells were then counted under bright field at x500 magnification. A live diatom was taken to be any cell which contained healthy chloroplasts, i.e. not shrivelled or grossly misshapen. Broken cells were judged to have been alive on collection if the cell contents were still visible and

64 not decayed. A dead diatom was counted as having only one valve or no cellular contents. Any cell with shrivelled and diminished cellular contents was also considered dead. No record of the diatom species was made during this count. Live:dead cell ratios were only recorded for the first stage of this study (Chapter 3).

2.4.7 Diatom Identification

Diatom identification to species level or below was primarily from the floras of Krammer and Lange-Bertalot (1986, 1988, 1991a, 1991b), Patrick and Reimer (1966, 1975), Hustedt (1930-1966) and Germain (1981). Valuable taxonomic advise was also obtained from numerous colleagues within the Environmental Change Research Centre at UCL and from Dr. Eileen Cox at the Natural History Museum, London.

The nomenclature used mainly follows that of Hartley (1986); the exceptions being the splitting of the genera of Sellaphora and Fallacia from Navicula (Round et al. 1990), the use of Ctenophora pulchella (Ralfs ex Kutz.) Williams & Round (1986) rather than its original inclusion in the genus Synedra and Reimeria sinuata (Greg.) Kociolek & Stoermer (1987) rather than Cymbella sinuata.

In the final analyses of species data only those taxa which exceeded 2% abundance in any one sample, or occurred in more than five samples, were included. Of these there were three species which could not be identified. Full descriptions of these taxa are given in Appendix I. In the text they are referred to as: Navicula [small species 1], Navicula [species 2] and Navicula [pseudogregaria]. Of the rare taxa that could not be identified from the available floras (i.e. those of < 2% abundance and <5 occurrences) a brief description was made, including the co-ordinates on the slide, and a temporary “Z” code assigned rather than the specific “diatcodes” given to the identified taxa. The Z codes consisted of a prefix of ZZZ followed by 999, the number descending for each successive unidentified taxon. A full list of the diatom species found in this study and their authorities is given in Appendix U.

Some difficulties were experienced with the exact identification of Gomphonema parvulum. This taxon is widely reported as being taxonomically indistinct because of

65 apparent polymorphism (Lowe 1972, Krammer & Lange-Bertalot 1986, Round 1991). An initial attempt was made to separate the ''Gomphonema parvulum complex” into morphs based on stria density, apical width and whether or not the valve ending was rostrate (pinched). This separation was not used for the majority of counts in this study because no ecological differences were observed between the morphs. All morphs were found at both low and high phosphorus concentrations. For the purposes of this study the name "Gomphonema parvulum^' is used for all the morphs as described in Krammer & Lange- Bertalot (1986).

2.5 Data Storage, Manipulation and Analyses

Physico-chemical data were stored in the computer spreadsheet program Microsoft EXCEL 5.0c (Microsoft Corporation 1994). The majority of analyses, however, required the data to be in Cornell Condensed format and thus the environmental data was imported from EXCEL into a PARADOX database (Release 3.0, Borland International 1988) and converted to Cornell using another program called CHEMOUT (Juggins, unpublished). This process has since been superseded by the program WINTRAN (Juggins 1998) which will convert most common computer data storage formats to Cornell Condensed.

The environmental analyses used in this study make the assumption that the data are normally distributed (Jongman et al. 1987). This was not the case for many of the variables and thus data transformations were used to approximate normality. This is discussed in more detail in Chapter 4. Transformations of Logio, Logio(x+1) or square root were performed in the computer program CALIBRATE (Juggins & ter Braak 1997), the assessment of “optimal” normality being judged by eye, using between 6-10 bins.

Diatom species data were entered into the Environmental Change Research Centre’s AMPHORA database (Beare 1997) and then downloaded in Cornell Condensed format. The methods of data analysis used in this study are discussed in detail within the following chapters. Ordinations (PCA, DCA and CCA) were performed using CANOCO 3.1 (ter Braak 1991) and ordination diagrams plotted using CALIBRATE 0.81 (Juggins & ter Braak 1997). Other graphical representations of the data have been plotted within EXCEL 5.0 and TILIAGRAPH 1.19 (Grimm 1991). Weighted averaging (WA) and weighted

6 6 averaging - partial least squares (WA-PLS) regressions and calibrations were performed on CALIBRATE 0.81 (Juggins & ter Braak 1997).

Detrended correspondence analysis (DCA) (Hill & Gauch 1980) is an indirect ordination method allowing for the representation of complex multivariate species data to be represented in two-dimensional space (bi-plots). An important feature of DCA is that the axes are scaled in units of average standard deviation of species turnover (Gauch 1982). A species can be seen to appear, rise to its mode and then disappear over a distance of just under 4 axis units (4 SD), thus complete species turnover is considered to have occurred over this range (Kent & Coker 1992). Similarly 50 per cent species turnover occurs in just over 1 SD unit, over which distance the data appear linear. DCA is only appropriate for use on data with unimodal structure (Hill & Gauch 1980), and thus it is important that the technique is not used on data with short gradient lengths. Exactly at what point the data can be considered as being unimodal rather than linear is difficult to determine but it is recommended not to use DCA if the axis one gradient is less than 1.5 SD units (Kent & Coker 1992). In this study linear methods (i.e. PCA) were used if the gradient was below 1.5 SD units in DCA.

Canonical correspondence analysis (CCA) was used to “forward select” environmental variables in Chapters Four and Six. The significance was tested using Monte Carlo permutation tests with 999 unrestricted permutations (ter Braak 1990), using an initial significance level of P < 0.05. This technique has the disadvantage that it tends to over select variables with each successive selection (ter Braak & Verdonschot 1995). Over selection was avoided with a Bonferoni-type adjustment, whereby the significance level of each successive test component was set at P < 0.05/n; where n is the rank number of the variable being tested (Miller 1990).

67 Ch a pter T h ree

T h e Se l e c t io n o f a Su it a b l e Su b st r a t u m fr o m WHICH TO Sa m pl e D ia t o m s

3.1 Introduction

The sampling of river diatoms for water quality assessment has focused almost entirely on the epilithic community on either cobbles (Cattaneo & Amireault 1992, Round 1993, Kelly 1998) or from fixed, solid substrata (Prygiel, pers. comm.). The use of artificial (introduced) substrata has also been employed, with glass slides and clear acrylic plastic (Perspex) being the most widely used (Patrick et a l 1954, McBride 1986, Korte & Blinn 1983, Eloranta 1995). In other studies the substratum type is either not specified or samples are taken from a range of substrata, despite it being well documented that these often support distinct and different diatom floras (Round 1993). For the purposes of diatom-based monitoring it is important to ensure that variations in community response, both within-site and between-site, are due to the ambient water quality, and not because of changes in substratum specificity. This has normally been achieved by sampling only the epilithon. In many slow flowing lowland rivers, however, the natural epilithon is not present, or where it does occur it is covered by silt, thus contaminating a sample with epipelic diatom taxa. The need therefore exists to develop a sampling technique that can be used at any site which minimises the chances of cross contamination between substrata.

3.2 Aims

The primary objective was to identify a suitable substratum for the development of a diatom-based model to monitor eutrophication in lowland rivers. Before identifying the substratum a number ofa priori criteria were considered as important:

• The substratum should be found at, or be possible to introduce to, all river sites.

68 • It must provide repeatable species assemblages from within a site. • The substratum should support a relatively high diversity.

• It should support a diatom flora that is representative of the river site. • It should be easy to sample.

Diversity was considered as an important criterion due to the intention to use the substratum for the collection of diatoms for the estimation of trophic status. It is therefore considered that by maximising the diversity of the assemblages the results of a diatom-based model will be more reliable. The aim, therefore, was to investigate the diatom communities on different substratum types, both natural and artificial, and to identify those which best fulfilled the above criteria. Furthermore, it was considered important to assess the variation between substrata at sites of different nutrient chemistry. This latter point was important for the future extension of the work where samples were to be taken from rivers covering a long TP gradient: it is important, therefore that the diatom assemblages behave in a similar manner under different nutrient conditions.

3.3 Methods

3.3.1 Site Selection

An initial survey of thirteen sites was conducted on the River Wey, a typical lowland river flowing from Alton (Hampshire) though Surrey where it meets the River Thames at Weybridge (Fig. 3.1). From this survey four river sites were selected to cover a wide range of phosphorus values (Tab. 3.1). It was not possible to select sites that were homogenous in all their physical characteristics, however care was taken to ensure flow was similar at sampling stations and that all sites were free of shading. Care was also taken to ensure all sites had easy access and permission to work on from the land owners. The four sites were Alton (ALTI), Hawbridge (HAWl), Elstead up-stream (ELSTl) and Elstead down-stream (ELST2). Table 3.1 summarises the selected site and shows their chemistry from the initial survey conducted in June 1995. Plates 3.1 - 3.4 show the four sites.

69 Plate 3.1 The River Wey at Alton (ALTI). This site was overgrown with Berula erecta during summer

Plate 3.2 The River Wey at Hawbridge (HAWl)

70 Plate 3.3 The River Wey, upstream o f Elstead (ELSTl)

Plate 3.4 The River Wey, downstream of Elstead (ELST2)

71 10 km Thames

5 miles • Weybridge

Woking # ST Key Stanford Brook R h er Wey A Sampling Site Sewage Treatment Works - > 10,000 STW Population Equ. Famham Pk. Trib. Compton Str. • Guildford

Farnham • ^ tw I Tillingbonme j I ELST2 Wey North ELSTI W STW \ e ' FI stead Godalming H A W l Alton • ^ ALTI Ock • Cranleigh STW Wey South Cranleigh Waters

• Hasiemere

Liphook •

Figure 3.1 Map showing the four sampling sites on the River Wey

Site NOR TPpgL' Alkalinity Underlying (as P) mgU* H C O f Geology

ALTI SU 726398 36 180 Chalk

HAW l SU 744412 2801 222 Chalk

ELSTl SU 905438 457 98 Lower Greensand

ELST2 SU 923438 1556 102 Lower Greensand

Table 3.1 Summary of the sampling sites

3.3.2 Water Sampling

All four sites were visited on October 29th 1995 in order to deploy the artificial substrata (see below). The diatom and water chemistry samples were collected one month later on November 27th 1995. Field analysis was canied out on site for pH, alkalinity and conductivity, and water samples were collected for nutrient chemistry and analysed in the laboratory as described in Chapter two. November 1995 was a month of

72 low rainfall and consequently the flow regime remained relatively constant over the one month sampling period.

3.3.3 Diatom Sampling

Three natural substrata were sampled: the epilithon, epiphyton (on Ranunculus sp.) and the epipelon. The choice of artificial substrata was based on those used successfully by other workers. The initial substrata selected were: glass slides (Patrick et al. 1954, Siver 1977, McBride 1986), Perspex (Grzenda & Brehmer 1960, Korte & Blinn 1983, Antoine & Benson-Evans 1985), clay tiles (Tuchman & Stevenson 1980, Briggs 1983,

Stevenson 1983) and polyethylene rope (Flower, pers. comm.). Two types of clay tile were used and are distinguished as rough tile and smooth tile. The smooth tile was a type of unglazed floor tile (“quarry tile”), with very little surface character (Fig. 3.2a). The rough tile was an unglazed tile of the type used for roofing, and had a highly textured surface (Fig. 3.2b). The full description of deployment and sampling of all the substrata is in Chapter 2. All samples were taken in triplicate to assess the internal variation at each site. Samples were preserved on collection with acidified Fugol’s iodine and stored in a fridge until slide preparation.

Figure 3.2 Light micrographs of the smooth tile (a) and rough tile (b) (xlOO)

Exposure time was kept constant at one month for all the artificial substrata. This has been widely reported as an optimal exposure time, particularly in waters of elevated trophic status (Sladeckova 1962, Brown 1976, Siver 1977, Stevenson & Fowe 1986),

73 and has been the most commonly used exposure period in previous studies (Cattaneo & Amireault 1992).

One of the major problems encountered when using artificial substrata was the loss of samples during the one month exposure period. Prior to this study, preliminary trials were conducted to establish the most reliable means of retrieving artificial substrata. Initially the tiles, glass slides. Perspex and rope were all mounted in a metal frame and secured to the river bed with a 60 cm steel stake. This method worked well where the frames could be placed away from public access and remained free of fouling. It was, however, highly susceptible to interference in areas with public access. This is one of the major problems reported with the use of artificial substrata (Blinn 1986). Eleven out of the original 16 frames were not recovered from the initial trial, nine of these due to vandalism. The remaining two frames could not be used due to fouling by twigs and decaying organic matter and a plastic carrier bag.

The frames were not used for further artificial substrata deployment. Instead the tiles were placed directly on the river bed and pushed down flush with the bed to avoid the tile being picked up in the current. The rope substratum was fastened with a 30 cm wire peg inserted into the river bed at an angle. These methods resulted in sufficiently high retrieval rates for the tile and rope substrata (82% & 93% respectively, see Chapter 4). The logistics of using the Perspex and glass substrata without the frames proved too difficult to solve in this study; only one Perspex sample was recovered and no glass. Due to the inherent disadvantages of these smooth substrata as a sampling medium (they have been reported as being less representative of the natural communities (Blinn 1986, Brown 1976)), their loss and impracticality of deployment, they were not considered important and thus were dropped from this study. Furthermore, where comparisons have been made between smooth versus rough artificial substrata, the rough, or roughened, substrata have been favoured, reportedly due to the increased microhabitat availability (Tippett 1970, Siver 1977, Neilson et al. 1984, Stevenson & Lowe 1986).

74 3.4 Results

3.4.1 Water Chemistry

The main determinants measured at the four sites are summarised in Table 3.2. All the sites were circumneutral but they differed in their alkalinity and ionic strength due to the underlying geology; Alton and Hawbridge (chalk) had higher alkalinities than the two Elstead sites (lower Greensand). Furthennore, the nutrient chemistry from the November sampling did not cover as wide a range of TP concentrations as the initial survey samples. This was most apparent at the Elstead sites where the phosphorus values were very similar. For this reason, and because of their geographical proximity, these two sites have been considered as one, later in the chapter. The chemistry at the Alton and Hawbridge sites remained similar to the initial survey.

Site pH Aik TP FRP NO3 Cond Silica Ca:+ Temp mgL^ p g L k p p g L k p pScm^ mgL'^ mgL’^ "C A LTl 6.77 245 45 29 3185 650 10.97 127 12.5 HAW l 7.70 297 2810 2721 2256 920 14.64 129 14 ELSTl 7.01 102 1074 943 2053 410 13.24 56 12.5 ELST2 7.13 104 1126 999 1705 395 13.56 56 12.6

Table 3.2 Summary of the chemistry for the November ’95 sampling at the four River Wey sites

3.4.2 Diatom Samples

A total of 66 diatom samples were collected at the four sites, from a possible 72 (Tab. 3.3). One Perspex sample was recovered from Alton and has been added for comparison. The samples were lost for a number of reasons. Three epipelic samples from Alton and one from Hawbridge contained no diatoms. Although the reason for this was unclear, the samples were possibly taken from freshly deposited sediment that had not had sufficient time to develop an observable diatom community. The lack of any epipelon was not observed again throughout a one year sampling programme. One of the Alton smooth tile samples could not be found on the collection date, and one rock scrape from Alton was spilt during slide preparation. A total of 130 diatom taxa were identified from the 67 samples (Appendix II), of these the dominant species are shown in Table 3.4.

75 Sample Site Substratum No. ECRC Sample Site Substratum No. ECRC Sample Site Substratum No. ECRC # Valves Slide # # Valves Slide # # Valves Slide # 182 HAW l Sediment 356 12625 206 HAW l Epiphyton 395 12649 232 HAWl Smooth Tile 365 12672 183 HAW l Sediment 323 12626 207 HAW l Epiphyton 384 12650 233 HAWl Smooth Tile 341 12673 185 ELSTl Sediment 327 12628 208 HAW l Epiphyton 456 12651 234 ELSTl Smooth Tile 344 12674 186 ELSTl Sediment 296 12629 209 ELSTl Epiphyton 342 12652 235 ELSTl Smooth Tile 389 12675 187 ELSTl Sediment 352 12630 210 ELSTl Epiphyton 378 12653 236 ELSTl Smooth Tile 360 12676 188 ELST2 Sediment 300 12631 211 ELSTl Epiphyton 360 12654 237 ELST2 Smooth Tile 351 12677 189 ELST2 Sediment 380 12632 212 ELST2 Epiphyton 356 12655 238 ELST2 Smooth Tile 319 12678 190 ELST2 Sediment 322 12633 213 ELST2 Epiphyton 371 12656 239 ELST2 Smooth Tile 342 12679 191 ALTl Rope 379 12634 214 ELST2 Epiphyton 341 12657 240 ALTl Rough Tile 398 12680 192 ALTl Rope 335 12635 217 ALTl Epilithon 330 12658 241 ALTl Rough Tile 349 12681 193 ALTl Rope 306 12636 218 ALTl Epilithon 325 12659 242 ALTl Rough Tile 331 12682 194 HAW l Rope 381 12637 219 HAW l Epilithon 320 12660 243 HAWl Rough Tile 369 12683 195 HAW l Rope 337 12638 220 HAW l Epilithon 332 12661 244 HAW l Rough Tile 363 12684 --40\ 196 HAW l Rope 353 12639 221 HAW l Epilithon 337 12662 245 HAWl Rough Tile 330 12685 197 ELSTl Rope 344 12640 222 ELSTl Epilithon 368 12663 246 ELSTl Rough Tile 372 12686 198 ELSTl Rope 321 12641 223 ELSTl Epilithon 305 12664 247 ELSTl Rough Tile 367 12687 199 ELSTl Rope 355 12642 224 ELSTl Epilithon 350 12665 248 ELSTl Rough Tile 334 12688 200 ELST2 Rope 343 12643 225 ELST2 Epilithon 392 12666 249 ELST2 Rough Tile 365 12689 201 ELST2 Rope 328 12644 226 ELST2 Epilithon 359 12667 250 ELST2 Rough Tile 360 12690 202 ELST2 Rope 414 12645 227 ELST2 Epilithon 324 12668 251 ELST2 Rough Tile 397 12691 203 ALTl Epiphyton 298 12646 229 ALTl Smooth Tile 348 12669 252 ALTl Perspex 433 12692 204 ALTl Epiphyton 317 12647 230 ALTl Smooth Tile 303 12670 205 ALTl Epiphyton 324 12648 231 HAWl Smooth Tile 369 12671

Table 3.3 Summary of diatom samples Species Code H A W la H A W lb E L ST la E L ST lb ELSTlc ELST2a E L ST lb E L S T lc ALTl Perspex Achnanthes conspicua AC023A 4.21 4.48 ÏÂ5 Achnanthes lanceolata ACOOIA 13.48 16.59 4.55 2.45 Achnanthes minutissima AC013A 3.14 69.28 Achnanthes lauenbersiana AC085A 1.62 Amphora ovalis ovalis AMOOl 2.70 2.27 5.67 4.74 Amphora ovalis pediculus AMOOl 22.64 2.84 2.00 3.16 9.01 Amphora pediculus AM 012 5.34 7.62 1.69 2.67 2.48 1.62 Cocconeis pediculus C 0005A 7.17 Cocconeis placentula euslvpta COOOIB 8.15 9.79 8.67 3.95 3.11 3.23 Cvmbella minuta CM031 3.00 Diatoma vulsare DT003A 3.93 13.92 3.06 2.84 3.16 Gomphonema olivaceum GOOOIA 1.15 Gomphonema parvulum GO013A 4.78 8.67 2 8 9 12.24 Melos ira varians ME015 10.96 6.28 25 35.17 18.33 21.32 5.28 Navicula capitata capitata N A 066A 20.61 2.45 7.67 17.11 34.16 Navicula dementis NA050A 1.69 Navicula crvptotenella N A751A 8.00 2.89 3.11 0.69 •-J Navicula decussis NA317A Z 36 Navicula eresaria N A023A 3.72 3.69 2.45 Navicula lanceolata NA009A 3.69 3.98 4.66 Navicula menisculus srunowii NA030D 3.11 Navicula menisculus menisculus NA030A 3.59 3.42 Navicula protracta N A 047A 2.03 Navicula tripunctata N A 095A 2.67 Navicula veneta NA054A 3.41 Navicula viridula linearis NA027E 34.46 4.47 6.83 Neidium productum NE002A 3.04 Nitischia amphibia NI014A 19.94 14.80 9.38 3.06 Rhoicosphenia curvata RCOOIA 6.46 5.38 4.89 3.68 0.92 Sellaphora minima SL003A 3.65 6.73 6.24 Sellaphora pupula SLOOIA 3.06

Table 3.4a Diatom summary of the epipelon from the River Wey - showing the ten most abundant taxa from each sample Species Code A L T la A L T lb A L T lc H A W la H A W lb HAWlc ELSTla ELSTlb ELSTlc ELST2a ELST2b ELST2C Achnanthes hunearica AC032A 2.19 Achnanthes lanceolata ACOOIA 4.03 2.84 4.94 1.77 2.08 8.99 2.05 1.39 2.35 Achnanthes lauenbersiana AC085A 3.69 Achnanthes minutissima AC013A 16.78 16.40 18.52 1.52 3.51 Amphora ovalis oediculus AMOOIB 1.58 Amphora pediculus AMO 12 A 6 J # 4.94 2.41 Cocconeis pediculus C0005A 19.49 11.98 9.43 Cocconeis placentula euslvpta COOOIB 4.42 2.47 42.28 53.13 36.18 21.35 29.89 9.72 19.66 39.89 29.33 Cvclotella meneshiniana CY003A 2.47 Cvclotella pseudostellisera CY002A 26.17 27.44 22.84 2.35 Cvmbella minuta. CM 031A 2.92 2 3 8 2J8 3.65 5.66 4.40 Diatoma vulsare vulsare DT003A 1.01 Frasilaria capucina sracilis FR009H 2.47 Frasilaria elliptica FR018A 2.35 Frasilaria vaucheriae FR007A 1.82 Gomphonema bohemicum G 0007A 1.04 Gomphonema olivaceum. GOOOIA 1.54 1.77 1.59 3.37 2.16 3 3 3 Gomphonema parvulum GO013A 5.03 8^ 3 2.78 13.92 15.63 11.84 11.40 5.56 7.78 14.61 12.40 7.33 00 Melosira varians M E015A 1.27 1.82 1.97 21.35 29.10 43.06 3.37 5.93 9 3 8 Navicula crvptotenella N A751A 9.65 5.56 4.72 19.10 9.97 7.04 Navicula lanceolata NA009A 4.682.91 5 3 8 1.89 Navicula menisculus menisculus NA 030A 1.58 Navicula tripunctata NA095A 4.39 3.44 4.44 7.58 3.50 5.87 Nitzschia amphibia NI014A 1.58 5.57 1.30 3.51 Nitzschia dissipata NI015A 3.65 Nitzschia palea palea NI009A 2.12 2.16 Rhoicosphenia curvata RCOOIA 3.04 1.82 3.29 1.75 2.38 6.46 6.47 6.16 Sellaphora minima SL003A 2.355.99 1.67 Stephanodiscus sp. ST9999 2.35 Svnedra acus acus SY003A 10.07 10.41 12.65 Navicula Isnecies 21 ZZZ999 2.08 3.51 3.33 2.53

Table 3.4b Diatom summary of the epiphyton from the River Wey - showing the ten most abundant taxa from each sample Species Code ALTla A L T lb H A W la H A W lb HAWlc ELSTla ELSTlb ELSTlc ELST2a ELST2b ELST2C Achnanthes clevei AC006A 2.99 Achnanthes consoicua AC023A 4.69 1.51 2.97 Achnanthes erana AC158A 2.72 2.16 Achnanthes lanceolata ACOOIA 3 3 3 1.54 11.88 19.88 7.42 4.89 2.62 5.71 3.58 3.62 2.78 Achnanthes lauenbersiana AC085A 9.09 1.85 11.88 5.72 24.63 8.15 5.37 32.41 Achnanthes minutissima AC013A 10.61 3.69 5.00 7.53 2.97 7.14 Achnanthes olonensis AC049A 1.19 4.89 3.71 2.78 Amphora pediculus AM 012A 39.09 46.15 17.81 11.45 40.06 25.82 4.92 5.71 13.81 6.13 14.20 Cocconeis placentula euslvpta COOOIB 3.26 3.53 7.21 7.14 Cvmbella minuta CM031A 2.81 6.96 Reimeria sinuata REOOIA 10.54 6 3 2 4.32 Fallacia subhamulata FA021A 0.91 Gomphonema olivaceum GOOOIA 9.47 Gomphonema parvulum GO013A 12.00 5.00 3.92 3.93 10.23 9.19 Melosira varians ME015A 3.80 14.10 8.00 o Navicula crvptotenella NA751A 6.97 7.69 5.57 3.43 3.07 VO Navicula isnota acceptata NA433D 6.17 Navicula subminuscula NA134A 3.61 Navicula tripunctata NA095A 4.92 Nitzschia amphibia NI014A 5.00 21.08 1.19 3.71 Nitzschia dissipata NI015A 1.85 2.62 15.71 11.51 8.91 Nitzschia inconspicua NI043A 2.81 2.16 Nitzschia paleacea NI033A 2.15 2.81 4.18 Rhoicosphenia curvata RCOOIA 9.70 2.15 5.29 Sellaphora minima SL003A 11.21 11.38 23.44 6.93 3.86 9.24 13.44 3.43 10.80 Sellaphora seminulum SL002A 0.91 2.50 Svnedra acus acus SY003A 0.91 Navicula Isnecies 21 ZZZ999 2.62 6.96 Navicula \pseudosresaria] ZZZ989 11.51 11.14 4.01 Navicula [small species 1] ZZZ994 4.35 6.14

Table 3.4c Diatom summary of the epilithon from the River Wey - showing the ten most abundant taxa from each sample Species Code ALTla ALTlb HAWla HAWlb H A W lc ELSTla ELSTlb ELSTlc ELSTZa ELST2b ELSTlc Achnanthes lanceolata ACOOIA 1.72 2.31 34.69 27.12 12.32 7.56 4.63 3.06 Achnanthes minutissima AC013A 4.60 10.23 5.69 15.89 14.37 6.69 9.00 6.94 1.14 0.63 Achnanthes plonensis AC049A 2.06 Amphora pediculus AMO 12 A 2.87 4.62 1.63 3.29 3.23 Cocconeis placentula euglypta COOOIB 20.98 9.24 4.34 3.01 1.47 2.31 3.33 Cyclotella pseudostelligera CY002A 1.15 2.31 Cymbella minuta CM031A 5.70 4.70 11.40 Gomphonema bohemicum G 0007A 17.24 41.58 1.14 Gomphonema olivaceum GOOOIA 12.93 3.96 1.36 17.38 7.21 5.56 Gomphonema parvulum GO013A 25.86 6.27 29.81 24.11 24.93 32.56 37.79 27.78 45.87 52.66 30.12 Melosira varians ME015A 2.91 3.60 2.51 2.63 Navicula atomus NA084A 2.64 2.33 10.00 Navicula capitata capitata NA066A 1.14 Navicula cryptotenella NA751A 00 8.06 0.94 4.39 o Navicula lanceolata N A009A 4.36 1.75 Navicula menisculus menisculus N A030A 1.92 Navicula subminuscula NA134A 1.17 Nitzschia amphibia NI014A 7.05 7.40 1.76 2.33 Nitzschia dissipata NI015A 8.14 4.63 12.50 2.56 3.45 16.08 Nitzschia fonticola NI002A 0.94 Nitzschia frustulum NI008A 2.62 1.14 1.57 2.05 Nitzschia paleacea NI033A 6.78 1.92 29.91 3.86 3.06 15.67 20.38 8.77 Rhoicosphenia curvata RCOOIA 6.32 2.64 3.25 4.17 Sellaphora minima SL003A 2.01 5.28 1.36 3.84 Navicula [species 2] 7 7 7 9 9 9 2.47 2.35 1.14 2.92 Navicula [ pseudogregaria] ZZZ989 4.94 2.83 3.33

Table 3.4d Diatom summary of the smooth tiles from the River Wey - showing the ten most abundant (%) taxa from each sample Species Code ALTla ALTlb ALTlc HAWla HAWlb HAWlc ELSTla ELSTlb ELSTlc ELST2a ELST2b ELST2C Achnanthes srana AC158A 3.49 3.81 4.49 3.02 Achnanthes lanceolata ACOOIA 20.35 7.16 8.46 51.22 33.88 42.42 9.14 9.81 6.29 2.52 Achnanthes lauenbersiana AC085A 0.91 Achnanthes minutissima AC013A 41.46 56.45 59.82 9.49 10.74 12.42 7.53 6.81 8.68 Amohora oediculus AM 012A 5.53 2 j 8 5.14 4.88 1.65 5.45 3 j # Cocconeis placentula euslvpta COOOIB 2.01 2.44 1.38 3.23 3.27 4.28 Cyclotella pseudostellisera CY002A 5.03 0.86 Cymbella minuta CMOS 1A 3.56 2.52 Reimeria sinuata REOOIA 1.82 Gomphonema bohemicum G 0007A 1.76 2 j 8 2.72 Gomphonema olivaceum GOOOIA 1.51 3.15 0.91 3.61 Gomphonema parvulum GO013A 9.55 12.89 11.18 4.88 18.18 3.03 10.48 13.90 12.28 21.10 19.44 19.40 Melosira varians M E015A 4.57 2.47 3.61 3.78 Navicula atomus NA084A 1.52 5.65 5.45 5.99 2.19 3.27 Navicula crvptotenella NA751A 3.81 6.30 3.06 4.28 00 Navicula lanceolata NA009A 3.76 Navicula menisculus menisculus NA030A 1.38 2.47 Navicula subminuscula NA134A 2.44 3.03 4.03 3.54 7.49 2.78 7.30 Nitzschia amphibia NI014A 1.15 6.23 15.15 10.00 Nitzschia dissipata NI015A 9.68 8.72 9.58 28.49 25 20.15 Nitzschia frustulum NI008A 3.56 2.50 Nitzschia palea NI009A 1.93 Nitzschia paleacea NI033A 2.78 Rhoicosphenia curvata RCOOIA 0.91 2.71 3.59 2.50 Sellaphora minima SL003A 3.02 7.16 5.74 5.42 3.86 7.58 3.59 Sellaphora seminulum SL002A 0.86 1.51 1.63 1.93 3.33 Svnedra acus acus SY003A 1.26 Navicula Isnecies 21 7.7.7999 3.81 2.19 Navicula \pseudosresaria} ZZZ989 5.485.56

Table 3.4e Diatom summary of the rough tiles from the River Wey - showing the ten most abundant (%) taxa from each sample Species Code ALTla ALTlb ALTlc HAWla HAWlb HAWlc ELSTla ELSTlb ELSTlc ELSTla ELST2b ELST2C Achnanthes lanceolata ACOOIA 6.60 5.37 15.69 33.07 28.78 23.51 3.20 3.12 3.10 6.41 3.05 Achnanthes lauenbersiana AC085A 2.08 3.68 Achnanthes minutissima AC013A 45.12 35.82 17.97 18.64 17.21 17.28 4.36 3.12 5.63 Amohora oediculus AM 012A 3jW 1.96 2.36 2.37 3.97 Cocconeis oediculus C 0005A 3.97 Cocconeis olacentula euslvota COOOIB 4.90 10.76 13.95 12.75 27.91 20.25 18.03 14.58 11.89 20.53 Cyclotella meneshiniana CY003A 1.63 Cyclotella oseudostellisera CY002A 13.98 10.45 34.97 Cymbella minuta. CM 031A 2.37 2.25 2 6 6 Frasilaria brevistriata FR006A 2.39 Gomohonema olivaceum GOOOIA 2.90 Gomohonema oarvulum 0 0 0 1 3 A 2.11 8.96 4.90 6 ^ 2 6 8 2 9j# 9.03 9.01 7.87 8.54 9.90 Melosira varians M E015A 3.12 3.20 6.54 6.76 5.18 10.63 Navicula atomus N A084A 3.12 2.62 Navicula cryotoceohala NA007A 5.01 4.25 oo w Navicula crvototenella NA751A 2.11 3.28 7.85 9 6 6 9 j # 4.96 3.96 7.97 Navicula sresaria NA023A 3.79 3 3 5 Navicula lanceolata. N A009A 6.10 4.05 3.66 3.14 Navicula menisculus menisculus NA 030A 2.10 4.45 Navicula triounctata NA 095A 6.98 4.98 5.07 4.08 3 3 6 Nitzschia amohibia N I0I4A 2.89 4.53 Nitzschia dissioata NI015A 3.10 7.29 4.88 3.14 Nitzschia oalea NI009A 2.90 2.09 Nitzschia oaleacea NI033A 2.64 1.63 3.41 3.78 Rhoicosohenia curvata RCOOIA 3.12 4.05 2.90 Sellaohora minima SL003A 5.54 3jW 2.62 2.37 3.50 5.79 Sellaohora seminulum SL002A 5.34 2 j 3 Steohanodiscus so. ST9999 2.94 Navicula Isnecies 21 7.7.7999 2.11 2.62 4.36 3.35

Table 3.4f Diatom summary of the rope substrata from the River Wey - showing the ten most abundant (%) taxa from each sample 3.4.3 Between-Site Species Variation

As would be expected from sites covering a wide environmental gradient, the diatom data show considerable floristic variation. Detrended correspondence analysis (DCA) (Hill & Gauch 1980) was used to assess how this variation was distributed though the diatom data. Detrending was by segments (26), species data were square-rooted and rare taxa downweighted. The DCA statistics are summarised in Table 3.5 and in Figure 3.3.

Axes 1 Total inertia

Eigenvalues: 0.257 0.138 0.072 0.048 1.489

Lengths of gradient: 2.949 1.717 1.735 1.396

Cumulative percentage variance of species data: 17.2% 26.5% 31.4% 34.6%

Sum of all unconstrained eigenvalues: 1.489

Table 3.5 Summary of the DCA output for all the River Wey sites

3.0 Key

• Alton A Achnanthes lauenbergiana o Hawbridge A Amphora pediculus □ Elstead up-stream 2 . 0 - ■ Elstead down-stream mz^chia amphibia ^ 219 ‘ Sellaphora minima A Achnanthes Achnanthes ploenensis a Navicula atomus ^ lanceolata

(/) Navicula species 2 ■ "x Nitzschia dissipata A 1. 0 - U A Navicula viridula O V. linearis Achnanthes A minutissima Navicula capitata A X Nitzschia sociabilis Nitzschia palea Navicula tripunctata a A 0. 0 - Gomphonema olivaceum Amphora ovalis v. pediculus Navicula cryptotenella ^ . A A Cocconeis placentula V. euglypta Gomphonema parvulum Amphora ovalis v, ovalis

- 1.0

- 1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1 Figure 3.3 DCA joint plot from the four River Wey sites showing the samples and major taxa

83 The DCA plot allows for the variation in sample similarity, due to species composition, to be visualised. Two or more points that are close together on the DCA plot will have a similar floristic composition, with many taxa in common, whereas those far apart will share very few species. Any two points more than three standard deviation (axis) units apart will share no common taxa (Kent & Coker 1992). The cumulative, explained variance on the first and second axis was 17.2% and 26.5% respectively. Although this appears low, it is high for these kind of diatom data, which are notoriously noisy due to the data having large numbers of taxa and zero values (Stevenson et al. 1991). From Figure 3.3 the sites can be clearly seen to differ in their species composition, with samples from Alton plotting to the right of the graph, Hawbridge towards the middle and the Elstead sites to the left. There is a high degree of overlap between the two Elstead sites, emphasising their floristic similarity.

It would appear, therefore, that the first DCA axis is associated with the between-site variation in diatom assemblages. This does not, however, reflect the phosphorus gradient but instead seems to follow either an alkalinity related signal or simply the distance from source. Despite being the most nutrient rich site, Hawbridge plots between the low and medium TP sites. The main taxa with high axis 1 scores, were Achnanthes minutissima, Achnanthes lanceolata, Achnanthes lauenbergiana, Sellaphora minima and Amphora pediculus. These species were found in higher abundance at Alton and Hawbridge. Low scoring taxa, associated with Elstead, included Navicula viridula v. linearis, N. capitata, Amphora ovalis v. ovalis. Amphora ovalis v. pediculus, Nitzschia sociabilis and Nitzschia dissipata. The Elstead sites also had higher species diversity (Fig. 3.4), possibly because they were further from the river source. The increase in diversity along stream longitudinal gradients has been observed in other studies (Molloy 1992). This would account for the higher number of unique species at Elstead, with low axis 1 scores, which separates it from the other two sites.

84 20 -

u 15 - > Q

% 10 -

ALTl HAWl ELSTl ELSTl

Figure 3.4 Box and whisker plot of species diversity (Hill’s N2) at the River Wey sites

The second axis explained less overall variation in the data (a further 9.3%) but this was clearly associated with substratum type (Fig. 3.5). For example, the epilithic samples from HAWl (219, 220 and 221) all plot very close together and have high axis 2 scores. The other epilithic samples all have high scores. The species with high axis 2 scores are generally small epilithic taxa, e.g. Achnanthes lauenbergiana, A. lanceolata, A. ploenensis, Sellaphora minima and Amphora pediculus. Similarly the epiphytic samples all have low axis 2 scores, e.g. 203, 204 and 205 (ALTl). Many of the species with low axis 2 scores are, based on the literature, those associated with the epiphyton, e.g. Cocconeis placentula v. euglypta, C. pediculus, Fragilaria elliptica and F. capucina v. gracilis. The artificial substrata fell between these extremes, with the rope plotting at lower scores (188, 189 and 190), the rough tile plotting near the epilithon and the smooth tile plotting centrally, reflecting its low diversity and more generalist taxa, e.g. Gomphonema panmlum and Achnanthes minutissima.

85 2.0 Key

o Epipelon 221. 219 ♦ Epiphyton ▲ Epilithon ■ Rope ▼ Smooth Tile X Rough Tile * Perspex .2 < < Ua

1890 190

* 2 0.1

0.0 1.0 2.0

D C A A xis 1

Figure 3.5 DCA biplot of River Wey sites, grouped by samples from the same substratum

3.4.3.1 Variance Partitioning

The above interpretations were made from indirect gradient analysis, without any information on the external variation due to substratum type, site or variability within the replicates. To quantify some of the observed variation these parameters must be included

within a direct gradient analysis. The length of the first DCA gradient is high (2.949 standard deviation units) and therefore unimodal gradient analysis can be used (ter Braak & Prentice 1988). To peifoiTn direct gradient analysis, information on substratum type, site and replicate were added as dummy variables and the data analysed using canonical conespondence analysis (CCA) within CANOCO (ter Braak 1991).

86 Axes 1 Total inertia

Eigenvalues: 0.235 0.144 0.116 0.081 1.489

Species-environment correlations: 0.968 0.936 0.953 0.815

Cumulative percentage variance of species data: 15.8% 25.5% 33.3% 38.7%

of species-environment relation: 33.4% 53.9% 70.3% 81.9%

Sum of all unconstrained eigenvalues 1.489

Sum of all canonical eigenvalues 0.704

Table 3.6 Summary of the CCA output for all sites: substrata, sites and replicates as dummy variables.

The total vaiiation is equal to the total inertia (1.489). The variation explained exclusively by the dummy variables is the sum of all canonical eigenvalues (0.704 or 47.28%). To obtain quantitative information on the relative importance of the explanatory variables as causes of variation in species assemblages, the technique of vaiiance partitioning can be used (Borcard et al. 1992). To do this the explanatory variables are placed into three groups: site, replicate and substratum (A, B & C), then by introducing one group (e.g. A) at a time as a co-variable and eliminating the other two groups (B & C) of external variables the exclusive variation due to that group (A) can be determined (Fig. 3.6 & Tab. 3.7)

Site Substrata

Replicate

Total Variation = 1.489 Explained Variation = 0.704

Figure 3.6 Graphical representation of the possible combinations of variation from the variance partitioning

87 By eliminating two explanatory groups of variables: I) A + D + F + G = 0.360 ii) B + D + E + G = 0.021 iii) C+E+F+G = 0.333 iv) A + B + D = 0.704 - 0.333 = 0.371 v) B + C + E = 0.704 - 0.360 = 0.344 vi) A + C + F = 0.704-0.021 = 0.683 Then by adding two groups as co-variables: vii) A = 0.350 viii) B = 0.021

ix) C - 0.323 x) D =0.371 - (A + B) = 0.000 xi) E = 0.344 - (B -k C) = 0.000 xii) F = 0.683 - (A + C) = 0.010 xiii) G = 0.704 -(A-kB + C-kD + E + F) = 0.000 As a percentage of the explained variation: A BCD E F G 49.7% 3.0% 45.9% 0.0% 0.0% 1.4% 0.0%

Table 3.7 Summary results from the CCA variance partitioning

By partitioning the variance within the CCA, it is clear that the majority of explained variation in the diatom assemblages was due to between-site (49.7%) and between- substratum differences (45.9%). Any variation due to within-substratum differences (replicates) was very low when all the sites were considered together (3.0%), suggesting that the replicates of the individual substrata supported a similar flora at any one site. The only other apparent variation was due to a small amount of site and substratum interaction (1.4%), possibly due to the higher diversity at the Elstead sites. To further assess these patterns of variation the sites must be considered separately.

3.4.4 Within-Site Variation

3.4.4.1 Alton

The aim of this chapter was to identify a suitable substratum for the development of a diatom-based model to monitor eutrophication in lowland rivers. To be able to do this it is necessary to consider each site separately and to assess the variation both between the different substrata and within each substratum type. Again the multivariate technique of DCA was used and the results are summarised below (Tab. 3.8). The Elstead sites were considered together because of their chemical and floristic similarity. In this section the DCA was performed using detrending by segments (26) and the down-weighting of rare taxa. The overall species variation was not as great as when all the sites were considered together and therefore these data were not square-root transformed.

ALTl

Axes 1 2 3 4 Total inertia

Eigenvalues: 0.419 0.102 0.038 0.015 1.415 Lengths o f gradient: 2.180 1.218 1.126 0.795 Cumulative percentage variance of species data: 29.6% 36.9% 39.6% 40.6%

Sum of all unconstrained eigenvalues 1.415

Table 3.8 Summary of the DCA output for Alton

When each site is considered separately the first and second DCA axes explain a high percentage of the species variation (Tabs. 3.8, 3.9 & 3.10). Although four samples were missing from Alton, it is clear from Figure 3.7 that the same-substratum samples all supported similar species assemblages and that the between-substratum variation was high in most cases. Without the influence of any epipelic samples at Alton the first axis separated the epiphytic and rope samples from the natural epilithon and smooth tile. The smooth tile samples were dominated by Gomphonema bohemicum, a species not found in high abundance in any other samples. This species had an axis 1 score of 3.13 and therefore a high influence on the position of these samples in the DCA analysis. The second axis was picking up more subtle differences in species between the rope and epiphyton samples (Fig. 3.7). These substrata are similar in form and have many species in common (e.g. A. minutissima, G. parvulum and Cyclotella pseudostelligera). However, the rope samples were more diverse and supported more epilithic taxa (e.g. A. lanceolata and Navicula [species 2]). (The presence of C. pseudostelligera, normally a planktonic species, at Alton was attributed to a small impoundment 250 m up-stream from of sample site. The majority of the cells on the rope and Ranunculus sp. were alive and appeared to be trapped within the living diatom community).

89 2.0 A Navicula species 2 Key

♦ Epiphyton Achnanthes lanceolata a A Epilithon ■ Rope

191 H A Achnanthes minutissima T Smooth Tile X Rough Tile 252 242 A Navicula cryptotenella 1.0 X Perspex 241 193 Cocconeis placentula a fS V. euglypta

Navicula atomus A Gomphonema I Sellaphora minima a olivaceum 218 23^ 229 A d 217 Q Gomphonema parvulum a 205 ♦— ^ 204 0.0 Rhoicosphenia Amphora pediculus a curvata 203

Navicula tripunctata a

A Achnanthes lauenbergiana

Nitzschia amphibia a

A Synedra acus v. aciis - 1.0

- 1.0 0.0 1.0 2.0 3.0 DCA Axis 1

Figure 3.7 DCA plot showing substratum variation at Alton

3.4.4.2 Hawbridge

The variation in substratum type could be seen more clearly at Hawbridge where the full range of substrata were represented. The first DCA axis explains 30.9% of the observed variation (Tab. 3.9) and can be seen to separate the epiphyton from the epilithon (Fig. 3.8). Taxa with low axis 1 scores are those often associated with the epiphytic diatom community: Cocconeis pediculus, C. placentula and Gomphonema species. High scoring species included many small, typically epilithic, taxa: Achnanthes conspicua, A. lauenbergiana. Amphora pediculus, S. minima and Navicula subminuscula. The other substrata had similar axis 1 scores but were separated on axis 2 , which explained a further 14.0% of the total variation. The second axis separated the epipelic communities from those growing on the smooth tile. The species driving this split were typical, motile, epipelic taxa with low scores: Nitzschia amphibia, N palea and Navicula menisculus v. menisculus. Taxa with high axis 2 scores are those more often thought of

90 as widely tolerant and fast growing (“weedy”) species, e.g. A. minutissima and G. parvulum. The smooth tile samples supported a much lower diversity than the epipelon (the mean Hill’s N2 were 10.85 and 5.17 respectively). The rope and rough tile samples plotted centrally and can be considered to represent a wider range of taxa from all the habitats. All the substrata showed relatively low internal variation (Fig. 3.8).

HAW l

Axes 1 2 Total inertia

1.399Eigenvalues: 0.433 0.195 0.045 0.014 1.399Eigenvalues: Lengths o f gradient: 2.959 2.092 1.277 0.859 Cumulative percentage variance of species data: 30.9% 44.9% 48.1% 49.1%

Sum of all unconstrained eigenvalues 1.399

Table 3.9 Summary of the DCA output for Hawbridge

3.0 Key

• Epipelon

♦ Epiphyton Gomphonema parvulum A A Navicula atomus ▲ Epilithon T 233 2.0 ■ Rope T Smooth Tile \ A Achnanthes A Navicula subminuscula minutissima X Rough Tile 231 \

Navicula species 2 a ^ 2 3 2

194 - A Achnanthes lanceolata 1.0 Gomphonema olivaceum 245 A 195 Amphora pediculus 207 A 208 219 A 206 221 Achnanthes a Cocconeis placentula 183 A lauenbergiana V. euglypta Nitzschia palea 0.0 A Cocconeis pediculus 182 / ^ Sellaphora seminulum Sellaphora minima Cymbella minuta a Nitzschia amphibia

Navicula menisculus a V. menisculus A Achnanthes conspicua

1.0 .0 0.0 1.0 2.0 3.0 4.0

DCA Axis 1 Figure 3.8 DCA plot showing substratum variation at Hawbridge

91 3 4.4.3 The Elstead Sites

3.0 Key 227 E L ST l ELST2 • Epipelon o Epipelon ♦ Epiphyton 0 Epiphyton ▲ Epilithon A Epilithon ■ Rope □ Rope 2.0 ▼ Smooth Tile V Smooth Tile

X Rough Tile + Rough Tile 225 I J 86 248 as

1.0 234^235 236V

210

0.0 213 0.0 1.0 2.0 4.0

DCA Axis 1 Figure 3.9 DCA plot showing substratum variation at ELSTl and ELST2

ELSTl & ELST2

Axes 1 2 3 4 Total inertia

Eigenvalues: 0.470 0.279 0.064 0.032 1.940 Lengths o f gradient: 3.714 2.810 1.316 1.348 Cumulative percentage variance of species data: 24.2% 38.6% 41.9% 43.6%

Sum of all unconstrained eigenvalues 1.940

Table 3.10 Summary of the DCA output for combined Elstead sites

The Elstead sites show a very similar pattern of variation to Hawbridge. The main difference occurred in the relative importance of the first two DCA axes due to the high internal variation within the epipelon at ELSTl. DCA axis 1 separates the epipelon from the smooth tile samples, and axis 2 separates the epilithon from the epiphyton (Fig. 3.9).

92 The diatom species driving the observed sample distribution within the DCA plot are illustrated in Figure 3.10. Although many of the main taxa are different from those at Hawbridge, the life forms strategies of the species associated with each substratum type are similar. Axis 1 was characterised by the typical “weedy” species with high scores (e.g. A. minutissima, G. parvulum and G. olivaceum) which were found commonly on the smooth tiles, as well as Nitzschia paleacea and N. dissipata. Low scoring taxa on axis 1 included the motile epipelic species, e.g. Navicula viridula v. linearis, N. capitata, A. ovalis v. ovalis and A. ovalis v. pediculus. The epipelon also contained taxa less commonly associated with this habitat (e.g. D. vulgare and M. varians) suggesting either that the sediments were relatively firm and provided a stable surface for diatom growth or that these taxa were simply settling in the sediments from elsewhere.

Axis 2 was characterised by small, often firmly attached, epilithic taxa (e.g. A. lauenbergiana, A. pediculus, Navicula [small species 1] and S. minima) with high axis 2 scores and epiphytic species (e.g. Cocconeis placentula v. euglypta) with low axis 2 scores. Two Navicula species, N. tripunctata and N. cryptotenella, and M. varians were also common in the epiphyton. The rough tile samples plotted between the smooth tile and natural epilithon and the rope samples were located centrally between the epiphyton and the epilithon. This was very similar to the pattern observed at Hawbridge and is due to these substrata supporting species found in all of the natural habitats.

3.4.5 Within Substratum Variation

Two further important criteria in the choice of a substratum for monitoring studies, were that it should provide repeatable species assemblages from within a site and that these assemblages should be representative of the natural diatom flora. These can easily be assessed from the DCA plots. If any two samples had identical species assemblages in the same proportions, then the points would plot in the same position. Thus by comparing the proximity of the replicates of each substratum it is possible to assess the similarity of two or more samples. Equally any two groups of different substrata that plot in close proximity can be assumed to be similar in their floristic composition. This makes it possible to compare the diatom assemblages of the artificial substrata with those of the natural substrata.

93 4 .0

Key A Achnanthes lauenbergiana ELSTl ELST2 • Epipelon o Epipelon Amphora pediculus ♦ Epiphyton 0 Epiphyton 3.0— ▲ Epilithon A Epilithon .Vflv/cM/a [small species 1] a ^ Sellaphora minima ■ Rope □ Rope ▼ Smooth Tile V Smooth Tile X Rough Tile + Rough Tile A Navicula [pseudogregaria]

2.0 — r4 Amphora ovalis a Nitzschia sociabilis Nitzschia amphibia V. pediculus Amphora ovalis a .2 X V. ovalis Navicula subminuscula Navicula menisculus a < A Navicula viridula < V. linearis A Nitzschia dissipata Navicula capitata ^ Nitzschia palea A Nitzschia paleacea t 1.0 Navicula gregaria ' ^ C^mphonema olivaceum Navicula lanceolata A Gomphonema parvulum Melosira varians a Cymbella minuta "^Achnanthes minutissima ''Navicula [species 2J ^Navicula atomus

0. 0 — Rhoicosphenia curx ata

Navicula tripunctata a A Navicula cryptotenella

Cocconeis placentula v. euglypta a

- 1.0

- 1.0 0.0 1.0 2.0 3.0 4.0 5.0 DCA Axis 1

Figure 3.10 DCA plot of the Elstead sites showing within-site variation between substratum type All the different substratum replicates sampled at both Alton and Hawbridge show a high degree of homogeneity (Figs. 3.7 & 3.8). At the Elstead sites, however, much greater differences occur within some of the substratum replicates (Figs. 3.9 & 3.10). The epipelic samples from BLST2 are highly variable on the first axis and the epilithon from both ELSTl and ELST2 shows relatively high variation on axis 2. All of the replicates from the artificial substrata and epiphyton at Elstead, however, plot very closely and can therefore be assumed to be giving repeatable diatom assemblages.

Similarly the position of the different artificial substrata on the DCA biplot can be used to determine how representative the diatom assemblages are, compared to the different natural communities. If there was little similarity, the artificial substrata would plot away from the natural communities. This is clearly not the case with the rope samples at HAWl (Fig. 3.8) and the Elstead sites (Fig. 3.9), which plot centrally and thus have species in common with all the natural substrata. This suggests that the frayed rope provides a wide range of different habitats suitable for diatom colonisation. Conversely the smooth tile samples at ELSTl and 2 have high DCA axis one scores and therefore share relatively few taxa in common with the epipelic samples. The species able to colonise this smooth surface (e.g. A. minutissima and G. parvulum) are typical of those reported from similar substrata (Butcher 1940, Patrick et al. 1954, Cattaneo & Amireault 1992). From the DCA plots it would appear that the rope samples generate the most representative diatom assemblages at Hawbridge and the Elstead sites, with the rough tiles being similar but more akin to the natural epilithon. The results from Alton are less conclusive due to the missing samples at this site (Fig. 3.7).

3.4.6 Diatom Diversity

Sample homogeneity and the provision of a representative diatom flora are not the only criteria used to assess the suitability of a substratum for monitoring purposes. If numerical methods of water quality assessment are to be applied to a diatom sample, it is of particular importance for the sample to support a diatom assemblage of high relative diversity for that site. Intuitively, an assessment based on only a few taxa is likely to be less reliable than one made on many.

95 25-

1 5 - CJu > 5 fS z (A = lOH

Epipelon Rope Epiphyton Epilithon Smooth Rough Tile Tile

Figure 3.11 Box and whisker plot comparing the species diversity (Hill’s N2) for the different substrata at the Elstead sites

Hill’s N2 diversity (Hill 1973) is a measure of the total number of species making an effective contribution to a sample and is considered to be a good overall relative measure of species diversity; particularly in large species data sets containing a high number of zero values (ter Braak 1990). The substratum with the highest mean Hill’s N2 diversity at the Elstead sites was the natural epilithon. This was closely followed by the rough tile and then rope samples (Fig. 3.11). All other substrata supported lower mean species diversities, with the smooth tile samples having the lowest.

Similar patterns were observed in the species diversity at ALTl and HAWl: both sites showing the rope and rough tiles to support higher relative Hill’s N2 diversity. At ALTl, however the epiphyton was more diverse and at HAWl the epipelic samples had the highest diversity. Despite the variation in the observed species diversities of the natural substrata, the results from the artificial substrata were the same at all sites, with the rope and rough tile supporting a more diverse community than the smooth tile.

96 3.4.7 Dead Cells as a Potential Source of Error

A primary concern of any biological monitoring study, and thus imperative to the methodology, is that the species assemblage reflects the ambient environmental conditions at the site. Due to the traditional diatom preparation techniques, where the cell contents are removed by oxidation to allow identification from the cleaned frustules, it cannot be determined whether the cell was actually living at the site. Dead cells may simply be due to natural turnover in the population and closely reflect the living community. However, the danger exists that dead cells may have been washed in from an area of different water quality, or are the remnants of a past flora which grew under different conditions. The complex micro-structure of the diatom community may also favour the post-mortem loss of less firmly attached or chain forming taxa over those which are firmly attached, resulting in a less representative sample.

Samples from the rough tile and natural epilithon at all sites were examined before preparation to quantify the proportion of live to dead cells on the different substrata. All cells with visible chloroplasts, in good condition, were counted as being live. In the observed samples the number of dead cells on the natural substrata exceeded those on the rough tiles. The results of this analysis are summarised below (Fig. 3.12).

Epilithon Live Cells Rough Tile Dead Cells

ALTl HAWl ELSTl ELST2 ALTl HAWl ELSTl ELST2

Figure 3.12 Proportion of live to dead cells at the four study sites

97 3.5 Discussion

The principal aim of this chapter was to compare the diatom assemblages from different natural and artificial substrata in order to identify a suitable substratum from which to obtain reliable diatom samples for the monitoring of trophic status at any lowland river site in the UK. The four sites that were chosen gave a good overall range of TP and, due to the similarity in the chemistry of the Elstead sites, also allowed comparisons to be made from separate sites of almost identical water quality. In this section the between and within-site variation in the diatom assemblages are considered, and the benefits of the different substrata are discussed with respect to their future use for the assessment of trophic status in lowland rivers.

From the analysis of all the samples together it is clear that sites of contrasting water chemistry support distinct and different diatom assemblages. Conversely, the two Elstead sites, which had very similar water chemistry, supported very similar diatom floras. The DCA of all samples together (Fig. 3.5) grouped the samples from each site close together on the first axis and showed separation between the sites. The Elstead sites, despite being geographically distinct, were chemically alike and supported very similar diatom communities on each of the different substrata (except for one epipelon sample). By using the technique of variance partitioning it was established that almost 50% of the total explained variation in the species data was due to site alone (Tab. 3.7). These data clearly demonstrate that diatoms are an excellent tool for water quality assessment: first, by splitting sites of different ambient water chemistry, and secondly by the floristic similarity observed between sites of almost identical water chemistry.

The focus of this chapter, however, was on the differences between the substratum types. The explained variation due to substratum type for all the sites was 46%, and can be attributed to the majority of the DCA axis 2 separation (Fig. 3.7). This would suggest that the substratum type is of considerable importance to the observed diatom assemblage and is responsible for the majority of within-site variation. This was clearly demonstrated when the sites were analysed separately.

98 The pattern of substratum distribution on the DCA plots was similar at all sites, with the between-substratum variation generally being higher than that observed within- substratum variation(Figs. 3.7, 3.8 & 3.9). The different substrata did, however, vary in the similarity of their replicates and the floristic diversity which they supported. At Alton and Hawbridge all the same-substratum replicates showed a high degree of species homogeneity but this was not the case at the Elstead sites. Each substratum is considered below for its suitability as a monitoring tool. The majority of this discussion is based on the results obtained from the Elstead sites where more samples were available for comparison.

3.5.1 The Epipelon

The epipelon displayed a high level of variability between replicates (Fig. 3.9) and was only of moderate species diversity at Elstead (Fig. 3.11). In addition, there are other well documented reasons why the epipelon is not a good habitat to use for monitoring trophic status. The sediment in rivers is extremely variable in structure, due to fluctuations in both flow and biological activity, making it a very dynamic habitat. Grain size, for example, will change with flow regimes and has been demonstrated to affect the species composition of the diatom community living within it (Cox 1990b). River sediments are also well known for their ability to bind phosphorus, as well as numerous other ions and organic compounds (Allen 1995), and this sediment-bound phosphorus has been shown to be utilised by algae (Grobbelaar 1983). If nutrients from the sediments are available to the epipelic diatoms, a sample would be influenced by the trophic status of the sediment and pore-water, rather than just the overlying water. Due to these findings, the use of the epipelic diatom community is not recommended where the goal is to monitor the trophic status of the water body.

3.5.2 The Epiphyton

The variability within the epiphyton was low at all sites, suggesting it may be a suitable substratum. However, the diversity at most sites was low, due to the dominance of C. placentula v. euglypta and G. parvulum. These species both have a very wide ecological range (Lowe 1974, Round 1993) and are therefore of little indicator value on their own.

99 This low diversity may have been partially due to the sampling technique, where only the tips of the plant {Ranunculus sp.) were taken. This represented the youngest portion of the plant that may only have been available for diatom colonisation for a short period of time. The highest mean Hill’s N2 diversity for this community was observed at Alton, where the growth of the Ranunculus sp. was noticeably slower than at the other sites. It is postulated that due to the poor performance of this substratum at Alton the exposure time was greater, and hence it supported a higher relative diversity. On subsequent visits to Alton, Ranunculus sp. was not found, thus not allowing plant type to be kept constant. The extent to which diatom specificity occurs on different species of submerged macrophytes is unclear from the literature. Siver (1977) found little influence of host type on the floristic composition of the attached algae. Other studies have shown high levels of host specificity (Prowse 1959) and particularly in nutrient poor waters (Eminson & Moss 1980). Due to the uncertainty of the dynamics and availability of this substratum it is considered unsuitable for monitoring purposes.

3.5.3 The Epilithon

The epilithic samples from the Elstead sites were quite variable, but generally supported a high species diversity. The epilithon has been well documented as a variable habitat (Round 1993) and therefore it has usually been recommended that multiple samples are taken to overcome this inherent variability (Kelly 1998). In slow flowing lowland rivers, however, the lack of availability of cobbles does not always allow for multiple sampling (Chapter 4). The observed variability in the diatom assemblages may be due to a range of different factors. For example, the exposure time cannot be determined and may vary from only a few days, to years. The surface character of a stone or rock will also vary from sample to sample, and from site to site, resulting in a wide array of different niche opportunities favouring different taxa. Finally there is some debate as to exactly what actually constitutes the true epilithon (Round 1993). Stones covered by macro-algae (e.g. Cladophora) or a fine layer of silt will yield a different flora to a bare stone surface. The true epilithon (i.e. that not “contaminated” by silt or other algae) is a rare habitat in the slow flowing reaches of a lowland river.

100 Despite the problems associated with sampling the epilithon and the possible build up of dead cells (see below), this habitat is recognised as a useful natural sampling medium under the right conditions. In upland areas the epilithon can provide regular and reliable diatom samples for stream water quality monitoring and assessment (Patrick et al. 1991, Patrick et al. 1995, Allott & Flower 1997,). Its use and availability in lowland rivers, however, is very limited.

3.5.4 Rope

The replicates from the rope samples were of moderate to low variability at all sites and displayed a high relative species diversity (Fig. 3.11). The original intention of using rope was that it would mimic the epiphytic diatom community. From the DCA plots (Figs. 3.8 & 3.9) it can be seen to fall centrally because it supports taxa from all habitats. The rope supported a wide range of species, from those normally considered epiphytic (C. placentula v. euglypta), epilithic (A. lanceolata and S. minima) and motile species normally thought of as epipelic (Navicula spp. and Nitzschia spp.). The ability of this substratum to attract a diverse flora, which is both repeatable and representative of the site, suggests that its use in monitoring studies warrants further investigation.

3.5.5 Smooth Tile

The smooth tile samples showed a high level of species homogeneity but were the least diverse of all the habitats at the Elstead sites, and had low Hill’s N2 diversity at both ALTl and HAWl. The regular, untextured, surface of the tile supported only a few taxa in high abundance. Most of these are considered as generalist taxa with wide ecological tolerance (A. minutissima, G. parvulum and N. paleacea). These samples tended to plot away from the natural substrata in the DCA analysis (Figs 3.7, 3.8 & 3.9) and are thus not considered representative of natural habitats. This agrees with the results of other studies on smooth substrata (Butcher 1947, Patrick et al. 1954, Siver 1977). Despite the good sample homogeneity the smooth tile is not considered suitable for future use.

101 3.5.6 Rough Tile

The rough tile samples displayed a high degree of similarity between replicates and high diversity at all four sampling sites. Furthermore they plotted towards the centre in the DCA analysis at Elstead and Hawbridge, in close proximity to the natural epilithon (Figs 3.8 & 3.9). The generalist taxa found on the smooth tile were often present on the rough tile but at lower abundances and associated with other taxa found in the natural habitats, e.g. Navicula atomus, A. pediculus, S. minima, N. dissipata and A. lanceolata. These results are similar to those obtained by Tuchman and Stevenson (1979) who concluded that an unglazed tile produced the most repeatable and representative results when compared to the natural epilithon. The results of this section suggest that the rough tile would be a suitable substratum to introduce for the purposes of monitoring trophic status in lowland rivers.

3.5.7 Dead Cells

The two most suitable artificial substrata, rope and rough tile, both supported a diverse, representative and repeatable diatom assemblage. It was also demonstrated that the rough tile samples had fewer dead cells than the natural epilithon. Whether this was also the case for the rope was untested but in other studies the proportion of dead cells have been found to be lower on artificial substrata (Patricket al 1954, Stevenson & Lowe 1986). In a study where the exposure time for artificial substrata was greater than one month Owen et al. (1979) observed no difference between the accumulation of dead cells on natural and artificial substrata. These results suggest that if the routine counting of diatom samples is not to include examination of the live material, then a carefully chosen artificial substratum, exposed for one month, can maximise the ecological information from the diatom assemblage.

102 3.6 Conclusions

The above discussion highlights some of the methodological problems encountered in sampling diatoms, from both natural and artificial substrata, to monitor the trophic status of lowland rivers. The main criteria considered as important for a substratum were:

• that it should be representative of the natural flora • it should support a high species diversity • the substratum should provide a repeatable diatom assemblage • it should be available at any river site

• it should be easy to sample. None of the natural substrata fulfilled all the criteria, and although they were all available at the four sampling sites, this was not the case at many of the sites visited later for this thesis (Chapter 4).

Of the artificial substrata, both the rope and the rough tile fulfilled the criteria. They are easily introduced at any river site and are quick to sample. Because no clear advantage was found for either the rope or rough tile, it was decided that both would be used for the future development of this work

103 Ch apter F o u r

T r a in in g Se t s - Sit e s, Spec ies a n d E n v ir o n m e n t

4.1 Introduction

The transfer function technique of diatom-based environmental reconstruction has been developed and applied in numerous studies, within lakes, to evaluate palaeolimnological changes in pH (Battarbee 1984, Stevenson et al. 1991, Kingston et al. 1992), salinity (Gasse 1987, Fritz 1990, Fritz et al. 1991) and trophic status (Hall & Smol 1992, Anderson et al. 1993, Bennion 1993, 1994, 1995, Bennion et al. 1996). Similar techniques have also been applied in estuaries to reconstruct salinity (Juggins 1992). These studies have demonstrated the capability of diatoms to accurately reflect changes in their aquatic environment. The use of such transfer function techniques have, however, been focused on the reconstruction of palaeoenvironments rather than for the biomonitoring of contemporary environmental change. If such procedures can be used to infer past environmental change accurately, then the possibility exists to develop similar methods for the assessment of trophic status in the contemporary aquatic environment.

The principal of the transfer function is first to generate a calibration data-set of diatom species from a wide range of specific environmental conditions (i.e. a training set). The assumption is made that the abundance of a particular diatom species will relate to an environmental gradient in a unimodal, Gaussian response, with each species occurring at its maximum abundance in the environmental conditions closest to its ecological optimum. Equally, assuming a long environmental gradient is covered, species tolerances can also be established. Using a training set the optima and tolerance values can be determined for all the diatom taxa and by applying numerical techniques, such as weighted average regression (Birks et al. 1993), a predictive diatom-based model can be derived {cf. Stevenson et al. 1991, Bennion 1994).

104 4.2 Aims

In this chapter the objectives were to develop two river-diatom training sets from the artificial substrata selected in the previous section (rope and rough tile). The aim was to identify 50-60 lowland river sites, from an initial survey of 150 within and around the Thames catchment. These sites were chosen to cover a wide phosphorus gradient, and it was also considered important that they should be distributed along an alkalinity gradient to account for the affects of water hardness on the diatom communities, and thus the resulting model.

Diatom samples would then be collected at the chosen subset of sites from both the rough tile and rope substrata, and at the same time comprehensive water chemistry determined at each site. These data would then be used to explore species-environment relationships from which the suitability of a transfer function approach could be assessed.

4.3 Methods

4.3.1 Site Selection

The original intention was to collect water samples from as many river sites, in and around the Thames catchment, as possible (150+) and to analyse them for filterable reactive phosphorus (FRP) and alkalinity. From these sites, a subset would be chosen at which the artificial substrata would be deployed. The importance of good experimental design and site selection has been well documented for ecological studies (Green 1978, Birks & Gordon 1985). It is recommended that to avoid systematic error and bias, a methodological procedure should be used which avoids subjectivity and, where possible, the random selection of sites should be adopted. Because of time constraints and problems of site accessibility, however, it was decided that the initial survey sites should be near bridges, and thus that some degree of subjectivity was inevitable. It was considered more important in this initial survey to cover the maximum number of sites and then use a system of stratified, random sampling to determine the final study sites.

105 Key Extent of the Thames Catchment

Principal Rivers Minor Rivers Canals

100 km

60 miles

Figure 4.1 Map of river sites sampled for the training sets The survey sites were chosen from a map to cover the entire River Thames catchment at regular bridging intervals. It was also decided a priori that because the underlying geology of the Thames basin does not include either very base-poor or very base-rich catchments, extra sites would be chosen to include the higher alkalinity chalk streams of Hampshire (R. Test, R. Meon, R. Avon, R. Ebble and R. Itchen) and the less alkaline rivers on the Lower Greensand of Kent and East Sussex (R. Medway, R. Ouse, R. Uck, and R. Rother). From a total of 273 sites identified from the map, time allowed for 187 to be visited and 115 to be sampled (App. in & Fig. 4.1). It was not possible to sample many of the sites due to access problems or, in the upper reaches, the stream beds were dry.

To select the subset of sites for further study a stratified matrix of FRP and alkalinity was used. FRP ranged from 3-7530 pgL'^ and alkalinity from 12-300 mgL'\ The matrix was therefore constructed with FRP on a logarithmic scale, divided into concentrations of 1- 10, 11-100, 101-1000 and 1001-10000 pgL'^ and the alkalinity into concentrations of 0-100, 101-200 and 201-300 mgL'\ Figure 4.2 shows the matrix with nitrate (as N) included as a size variable. From each grid square of the matrix a maximum of six sites were chosen at random by assigning each site a number and selecting the first six numbers to appear from random numbers tables (Hammond & McCullagh 1978). Not all the matrix squares had six sites in them and therefore, to achieve an even distribution of sites across the matrix, all sites from these squares were selected. This latter point was important for the development of the transfer function (Ch. 5): transfer functions perform best if the environmental gradient is long and without large gaps (ter Braak & Juggins 1993). A total of 61 sites were chosen from the original 115 (Fig. 4.3 & Tab. 4.2).

4.3.2 Diatom Sampling

The 61 sampling sites were visited between 6-13 September 1996 and at each site rough tile and rope substrata were deployed. The placement of the artificial substrata was, where possible, away from bridges and in areas without overhanging trees to avoid between-site variations in shading. The tiles were left flush with the river bed and the rope pegged to the river bed with a 30 cm wire pin. None of the substrata were placed in

107 a water depth of more than 40 cm. At sites with noticeable variation in flow, the substrata were placed in the riffle section of the river to prevent the build up of sediment. A sketch map was made at each site to aid relocation of the substrata.

The substrata were collected and sampled one month later (the sampling methods are described in Chapter 2). All the diatom samples were preserved, on-site, with 1 ml of Lugol’s iodine per 30 ml sample vial. To gain the maximum number of samples new substrata were left at each site in October and the sites re-visited in November. However, the November sampling trip was cut short by heavy rainfall, making retrieval of the substrata impossible. The samples collected after this period of river spate were discarded due to the increased exposure time ( 6-8 weeks) and the uncertainty of the effect of high flows on the resulting diatom assemblages.

One of the problems encountered with the use of artificial substrata was the loss of samples from a site. There were two main reasons for this. Some of the substrata could not be re-located and were presumed to have been lost or removed. This was most common where public access to the site was easiest. Other samples were re-located but found to be covered by sediment or fouled by plant material, these samples were not used. Furthermore, three tiles were found upside down and were therefore also discarded.

At each site the presence or absence of cobbles supporting a natural epilithic diatom community, was recorded. If present, the suitability of the cobbles for sampling was judged by assessing the amount of sediment build-up and growth of macro-algae on the stone surface.

The diatom samples were prepared and mounted onto microscope slides for counting using the techniques described in Chapter 2. Counting was performed at xlOOO magnification, using an oil immersion, phase objective. A total of 350 (±50) valves were counted from each slide with the exception of samples where a species occurred at greater than 50% relative abundance. In these cases the count was doubled to ensure the inclusion of less common taxa.

108 4.3.3 Water Sampling

Water samples were collected from all the sites at the same time as the substrata were retrieved. Samples were collected for TP, FRP, nitrate, silica, cation and anion analysis. Whilst in the field the determination of alkalinity, pH, conductivity, current velocity and water temperature were carried out, as described in Chapter 2.

4.3.4 Data Analysis

Diatom counts were entered into AMPHORA (Beare 1997), a database specifically designed for the handling of diatom and environmental data (Munro et al. 1990). Species data were then downloaded, as counts, in Cornell condensed format and converted into percentages using TRAN (Juggins 1994). Rare taxa provide very little extra ecological information for multivariate statistical analysis and thus any species not occurring in more than five samples, and not achieving greater than 2% relative abundance in any one sample, were excluded from further analysis (species not found in more than 5 sites but achieving over 2% abundance were not excluded).

Environmental data were stored in a database (PARADOX, Borland 1992), and converted into Cornell condensed format for further analysis using the computer script CHEMOUT (Juggins unpublished). The environmental determinants were transformed, where appropriate, to normalise the distributions prior to analysis using CALIBRATE (Juggins & ter Braak 1997). The following variables were log transformed: conductivity, TP, FRP, nitrate, silica, chloride, sulphate, calcium, sodium, potassium, magnesium and iron. The variables flow, manganese and aluminium were log(x+i) transformed and alkalinity, pH and temperature left untransformed.

Exploratory data analyses were performed in CALIBRATE (Juggins & ter Braak 1997), and all multivariate statistical analysis done using CANOCO (ter Braak, 1990 & 1991). CALIBRATE (Juggins & ter Braak 1997) was used to plot the statistical diagrams. Principal components analysis (PCA) was used to identify the major variation and gradients within the environmental data and allowed for sites to be screened for possible outliers. The PCA analysis was scaled for a correlation biplot and the variables centred

109 and standardised around the species norm. Detrended correspondence analysis (DCA) (Hill & Gauch 1980) was used to explore the main patterns of floristic variation in the diatom data. The DCA was performed using detrending by segments (26) on untransformed species data with the rare taxa down-weighted.

The direct gradient analysis technique of canonical correspondence analysis (CCA) (ter Braak 1986) was used to assess the species variation due to the measured environmental variables. CCA assumes that the species will be reacting to their environment in a unimodal pattern and the axes are constrained to represent linear combinations of the environmental variables (ter Braak 1990). The diatom taxa, sites and environmental influences can therefore be simultaneously represented in low dimensional space (ter Braak 1987a). Forward selection of the variables explaining the most statistically significant proportion of species variation was also performed using CCA within CANOCO. This technique allows for the minimum number of environmental variables to be selected, in order of their influence on the species data, by performing step-wise multiple regression (ter Braak 1990). The significance was tested using unrestricted Monte Carlo permutations with the significance level set at P < 0.05. The Monte Carlo test has the advantage that it allows for the data not being normally distributed but it can over-select non-significant variables with each successive test (ter Braak & Verdonschot 1995). To avoid this over-selection a rough Bonferoni-type adjustment was made, whereby the significance level of each successive test component was set at P < 0.05/»; where n is the rank number of the variable being tested {cf. Miller 1990). In practice this meant that the level of significance allowed for each successive environmental variable to be selected was: variable 1 P < 0.0500, variable 2 P < 0.0250, variable 3 P < 0.0166, variable 4 P < 0.0125, variable 5 P < 0.0100, variable 6 P < 0.0083, etc. To ensure this procedure was accurate a large number of permutations was needed to test each variable; 9999 unrestricted Monte Carlo permutations were used.

CCA was also used for data screening to identify environmental variables showing high collinearity and sites which had unusual species-environment relations. Collinearity of an environmental variable was assessed by the variance inflation factor being > 20 in a CCA (Pienitz et al. 1995). Samples were considered as possible outliers if the species- environment relations had extreme values of influence from the leverage diagnostics of

110 CCA within CANOCO (ter Braak 1990). Any sample which exceeded the mean leverage by a multiple > 8 , was judged to be extreme (Pienitz et a l 1995).

4.4 Results

4.4.1 Site Selection

From a possible total of 273 river sites identified from the map (see App. HI), water samples were collected from 115. These samples were analysed for FRP, alkalinity, nitrate, conductivity and pH, the results are summarised in Table 4.1. The measurement of phosphorus at this stage was based only on filterable reactive phosphate, although total phosphorus was used in all subsequent analyses. From previous observations (Chapter 3) the difference between FRP and TP was often relatively low and thus, given the time constraints, FRP could be assessed more quickly and was considered to give a good representation of the trophic status of a site. This was found to be the case from the training set chemistry where both TP and FRP were determined.

Variable Range M ean STDEV

FRP (pgL^ P) 3-7530 1517 1909 Alkalinity (mgL'^) 13-300 183 78 Nitrate (pgL^ N) 9-7951 2960 1879 Conductivity (pS cm'^) 130-1500 578 237 pH 6.93-8.87 7.74 -

Table 4.1 Summary of the survey site chemistry

111 N O ;-N (jigL ') O <1000 O up to 5000 Q up to 10000 10000 OQ

1000- r - a

&£

M 100------O-

è o

10-

1.0 0 50 100 150 200 250 300

Alkalinity (mgL )

Figure 4.2 FRP - alkalinity matrix for the 115 survey sites, showing nitrate as a size variable

NOj'-N (pgL ‘) O<1000 (3 up to 5000 up to 10000 10000 Ç02O7 147# 165 # o o Q|154 253 o # 180 Oo o 178 o 188IRK ^ ^_ 6 O O 183 g ) 199.

1000 ___ 181 ^ 261 . o O 196 249 262 • 1 6 3 # O I 1214 e 256 231 161 248 ^162------• 194------o M 100 127 • 170 23S0 105 O _ m # 2 5 1 # 192 177 O 252 # 247 260 2091 124 ^ • # 2, O 33

10 - # 94 259 2° 266 9 ( Ü Î 34 24

1.0 50 100 150 200 250 300

Alkalinity (mgL ')

Figure 4.3 Stratified sampling matrix showing the 61 randomly selected sampling sites (solid circles) Nitrate as a size variable

12 Collection Date Samples Collected Collection Date Samples Collected Site# Code October November Tile Rone Site# Code October November Tile Rone 2 C A SH V y _ y _ 178 RWEY2 y y y y y y 3 SORBl 180 RWEY3 y y y y y y 9 SW ERl y y _ y . 181 RWEY4 y y 'Z X y y 12 EVENl y y _ y . 182 RWEY5 y y y y 'Z X 13 EVEN2 y y . y . 183 ELST2 y y y y y y 14 EVEN3 y y _ y _ 184 RWEY6 y y y y y y 17 SHERI y y . y . 188 THAM2 y y y y y y 20 W INDl y y _ X - 190 G A D Bl 23 WIND2 y y _ y _ 192 D EA N l y y X 'Z y y 24 LEACl y y . y . 193 DEAN2 y y X 'Z y y 26 THAM l y y _ y _ 194 GATW l y y y y y y 27 COLNl y X - y . 196 REDHl y y _ y _ 28 C 0LN 2 y X - y . 199 MOLEl y y y X y y 31 CHURl y y _ y _ 207 BEVEl y y X X y y 33 CHUR3 y X - X - 209 HORTl y y . y . 34 SW ILl y y . y . 214 M ISBl y y y y y y 56 FARTl y y _ y . 238 EBBLl y y y y y y 68 H A R ll 247 ITCHl y y y y y X 94 PANG l y y _ y _ 248 ITCH2 y y ^ X y y 105 KENNl y y y y y y 249 M EONl y y y X y y 112 EN BO l y y y y y y 251 M EDW l y y y y 'Z X 124 BLACl y y y y X X 252 MEDW2 y y X 'Z y y 147 TCUTl 253 MEDW3 y y X 'Z y y 154 BO URl y y _ y . 256 O USEl y y . y . 161 STAN2 y X - X - 257 0U SE 2 y y . y . 162 TILLl y y 'Z X y y 259 CLAPl y X - y . 163 TILL2 y y y y y y 260 RUCKl y y _ y _ 165 CRANl y y y y y y 261 ROTHl y y . y _ 170 SLEAl y y y y y y 262 R 0TH 2 y y _ y _ 172 ALTl y y y y y y 266 SOCKl y X - y . 177 RW EYl y y y y y y

Table 4.2 Sample collection summary (- = site not visited, x = no sample recovered, = site visited / sampled recovered The site selection procedure for the training set was based on achieving an even spread of FRP and alkalinity, across the range of sites from the initial survey. The distribution of these variables was not, however, even across the survey sites. A higher proportion of sites had high FRP and high alkalinity and were thus over represented in the sampling matrix (Fig. 4.2). It was therefore necessary to select the majority of sites with less than 100 pgL'' FRP and alkalinity less than 100 mgU'. Figure 4.3 shows the stratified site selection matrix with the randomly chosen sites from each matrix square.

4.4.2 Sample Collection

From the 61 selected sites, 57 were visited in October and a further 29 again in November giving a total of 86 samples. At two of the sites, 33 and 161, neither substratum was retrieved. Four other sites from the original 61 (3, 68, 147 and 190) were not included in the training set due to their geographical isolation, and thus the time needed for their sample collection was considered impractical. The final total of sites, from which at least one sample was obtained, was 84 (Tab. 4.2). From these, 78 rope samples and 69 tile samples were retrieved (Fig. 4.4). The lower number of tile samples was due mainly to the build-up of sediment on the tiles rather than actual loss from the site.

Tile Rope

Recovered Recovered 82% 93% /

Lost or covered by sediment Lost 18% 7%

Figure 4.4 Recovery of the tile and rope samples from a total of 84 samples taken in October and November

114 Presence of Natural Epilithon The presence or absence of the natural epilithon was noted at each of the 57 sites sampled in October. In addition to cobbles being found at a site, their suitability for diatom sampling was also assessed. Cobbles were considered as unsuitable if they were covered by a layer of sediment or were host to obvious growths of macro-algae (e.g. Cladophora). If more than 3-5 cobbles could be found in a 10 m stretch of the river the site was considered to have a good epilithon. Figure 4.5 shows the availability of cobbles at the 57 sites sampled in October.

Good Epilithon Cobbles Present but 40% Contaminated 37%

No Epilithon 23%

Figure 4.5 Availability of the natural epilithon at 57 lowland river sites

4.4.3 Chemistry

There was a similar pattern of phosphorus and alkalinity across the sample sites, to those of the original survey (Fig. 4.6). The full chemistry is given in Appendix III. The one area which was lacking was at the low TP end of the gradient; only two sites were found below 10 pgL'^ TP, both of which had high alkalinity.

115 © Tile and rope recovered O Rope only • Tile only 10000

1000 -

2 100— e-

10 -

1.0 0 50 100 150 200 250 300

Alkalinity (mgL

Figure 4.6 TP - alkalinity matrix for the 84 samples

Due to some of the rope and tile samples being recovered from different sites it is necessary to consider each training set separately. Thus hereafter the chemical and biological data are divided into substratum type for further analyses. To avoid confusion and to provide a homogenous labelling strategy for the numerical analysis the chemistry samples were assigned the numbers that corresponded with the diatom samples from each site. Table 4.3, below, provides the translation between site codes, site numbers and sample numbers used for the analysis.

Rope Data-Set Tile Data-Sc ît Site Code Site No. Sample No. Site Code Site No. Sample No. CA STl 2 668 CA STl 2 595 SW ERl 9 669 SW ERl 9 596 EV EN l 12 670 EV EN l 12 597 EVEN2 13 671 EVEN2 13 598 EVEN3 14 672 EVEN3 14 599 SHERI 17 673 SHERI 17 600 W1ND2 23 674 W lN D l 20 601

Table 4.3 Translation of site codes and numbers into sample numbers

116 Rope Data-Set Tile Data-Se ;t Site Code Site No. Sample No. Site Code Site No. Sample No. LEACl 24 675 WIND2 23 602 TH AM l 26 676 LEACl 24 603 COLNl 27 677 TH AM l 26 604 C 0LN 2 28 678 CHURl 31 605 CHURl 31 679 SW ILl 34 606 SW ILl 34 680 FARTl 56 607 FARTl 56 681 PA NG l 94 608 PA NG l 94 682 KENNl 105 609 K ENNl 105 683 EN B O l 112 610 ENBOl 112 684 BLA Cl 124 611 BOURl 154 685 BO U R l 154 612 TILLl 162 686 TILLl 162 613 TILL2 163 687 TILL2 163 614 CRANl 165 688 CRANl 165 615 SLEAl 170 689 SLEAl 170 616 ALTl 172 690 ALTl 172 617 RWEYl 177 691 RW EYl 177 618 RWEY2 178 692 RWEY2 178 619 RWEY3 180 693 RWEY3 180 620 RWEY4 181 694 RWEY4 181 621 RWEY5 182 695 RWEY5 182 622 ELST2 183 696 ELST2 183 623 RWEY6 184 697 RWEY6 184 624 THAM2 188 698 THAM2 188 625 D EA N l 192 699 GATW l 194 626 DEAN2 193 700 REDHl 196 627 GATW l 194 701 MOLEl 199 628 REDHl 196 702 HORTl 209 629 MOLEl 199 703 M ISB l 214 630 BEV El 207 704 EBBLl 238 631 HORTl 209 705 ITCHl 247 632 M ISBl 214 706 ITCH2 248 633 EBBLl 238 707 M EONl 249 634 ITCHl 247 708 M EDW l 251 635 ITCH2 248 709 OUSEl 256 636 MEONl 249 710 0USE2 257 637 MEDWl 251 711 RUCK l 260 638 MEDW2 252 712 ROTHl 261 639 MEDW3 253 713 R 0TH 2 262 640 OUSEl 256 714 K ENN l 105 642 0USE2 257 715 E N B O l 112 643 CLAPl 259 716 BLA Cl 124 644 RUCKl 260 717 TILL2 163 645 ROTHl 261 718 CRANl 165 646 R 0TH 2 262 719 SLEAl 170 647 SOCKl 266 720 ALTl 172 648

Table 4.3 (cont.) Translation of site codes and numbers into sample numbers

117 Rope Data-Set Tile Data-S* ;t Site Code Site No. Sample No. Site Code Site No. Sample No. K ENNl 105 721 R W EY l 177 649 ENBOl 112 722 RWEY2 178 651 TILLl 162 723 RWEY3 180 652 TILL2 163 724 RWEY5 182 653 CRANl 165 725 ELST2 183 654 SLEAl 170 726 RWEY6 184 655 ALTl 172 727 THAM2 188 657 RWEYl 177 728 DEANl 192 658 RWEY2 178 729 DEAN2 193 659 RWEY3 180 730 G ATW l 194 660 RWEY4 181 731 MISBl 214 661 ELST2 183 732 EB BL l 238 662 RWEY6 184 733 ITCHl 247 663 THAM2 188 734 ITCH2 248 664 D EA N l 192 735 MEDWl 251 666 DEAN2 193 736 MEDW2 252 667 GATW l 194 737 MOLEl 199 738 BEV El 207 739 M ISBl 214 740 EBBLl 238 741 ITCH2 248 742 MEONl 249 743 MEDW2 252 744 MEDW3 253 745

Table 4.3 (cont.) Translation of site codes and numbers into sample numbers

4.4.3.1 Rope Training Set Chemistry

Although the main focus of this thesis is the response of diatoms to changes in trophic status, it is important in the development of a training set to assess the physico-chemical variables which may strongly influence the observed floristic composition. The environmental variables are summarised in Table 4.4.

The environmental data showed a high degree of variation from the 78 sites. These data are plotted against each other in Figure 4.7 to explore the relationships between the variables.

118 Variable Range Mean STDEV Alkalinity (mgL^) 12-280 136.50 75.40 Conductivity (pS cm^) 130-1520 496.90 251.70 pH 6.70-8.5 7.47 - Temp C O 4.2-15.5 9.55 2.47 Flow (msec'^) 0.036-0.986 0.24 0.21 TP (pgL ') 4-9458 1043.21 1756.06 FRP (pgL b 3-8665 903.85 1618.31 NO3 -N (pgL'b 9-10548 3042.67 2359.31 Silica (pgL'^) 3399-21097 10861.20 4214.70 Chloride (pgL^) 13146-263910 39368.31 36463.12 Sulphate (pgL'^> 5442-892448 51460.30 100898.70 Calcium (pgL^) 10485-324720 82667.34 57334.77 Sodium (pgL'^) 5512-111909 29617.56 23077.08 Potassium (pgL^) 947-29432 5986.22 5253.56 Magnesium (pgL^) 1803-28856 5202.50 3682.37 Manganese (pgL'^) 0-660 61.28 114.79 Iron (pgL ^ 1-9247 277.50 1161.43 Aluminium (pgL'^) 0-140 30.51 23.95

Table 4.4 Summary statistics for the rope training set data (n = 78)

Principal Components Analysis (PCA) Principal components analysis (PCA) was used to identify the environmental variables in the training set that best explain the overall variance. PCA allows the chemical gradients to be identified and also helps to screen the data for possible outliers. The summary statistics for the analysis are given in Table 4.5 and the ordination plotted in Figure 4.8.

Axes 1 2 3 4 Total variance

Eigenvalues: 0.305 0.239 0.130 0.088 1.000

Cumulative percentage variance of environmental data : 30.5% 54.5% 67.5% 76.3%

Sum of all unconstrained eigenvalues 1.000

Table 4.5 Summary statistics for the PCA on the rope training set environmental data

119 Cond

TP ■ff;

NO3-N

Cl 'A. ■4 so; ..4 .

Ca' j.’ #• ■•f . . ■ ».

Na" v - C - '

K M- ■4 /

Aik Cond pH TP NO;-N Cl so; Ca' Na"

Figure 4.7 Scatter plots showing the relationships between the major chemical variables (variables

transformed)

The first four axes of the PCA explain 76 percent of the total variation, suggesting that the 18 measured variables captured a high proportion of the total variance. The first two axes have high eigenvalues (0.305 and 0.239 respectively) compared to axes 3 and 4,

and thus the lower axes are considered as having low interpretative value. The major ions (Na^, K^, Mg^^, Cf & SO/'), TP and FRP are most strongly correlated with Axis 1 (TP: I's = 0.71). The low angles between the vector arrows also shows a high degree of correlation between these variables. Alkalinity, pH, calcium and nitrate are more strongly cornelated (negatively) with axis 2 (alkalinity: rs = -0.90). Conductivity is equally correlated to both axis 1 and 2 (r^ = 0.66 and -0.69 respectively) and shows no relation to any of the lower axes. TP and FRP also show a positive correlation with axis 3 but this is not as strong as the axis 1 relationship (r^ = 0.58 and 0.69 respectively) (Tab. 4.6)

120 3.0

Mn 712 'o o 744 2.0- 686 o 0 689 728 723 692 0 729 745 716 o 1.0- o 701 713 Silica Na* FRP TP 685 693 0 696 Flow Cl I 0.0-704 Mg' 726 Temp 0 739 g 680 o o 705 702 0 740 o 681 677 o o o 676 671 720 708° o o o 678 682 0 669 673 -2.0- Cond

p H Ca’ Alk -3.(1

- 2.0 - 1.0 0.0 1.0 2.0 3.0 PCA Axis 1 Figure 4.8 PCA plot of the rope training set environmental data

The sites show some degree of grouping on the PCA plot. For example towards the top left of the diagram there is a group of low conductivity, low alkalinity and low TP samples (e.g. 6 8 6 , 691, 711 and 712). Toward the bottom left are samples with slightly elevated conductivity and higher alkalinity, but similarly low TP (e.g. 673, 682 and 708). To the centre right of the plot are samples with high ionic concentration, high TP and average alkalinity (e.g. 681, 685 and 704). The other samples which plot more centrally on the diagram represent those with more average environmental conditions (e.g. 693, 696 and 726). The distribution of samples in the PCA plot, although not completely even, does not highlight any obvious outliers within the environmental data on the first two axes. Site 720, however, had a relatively high axis 3 score (3.82) and was noted as a possible problem site. The chemistry at SOCKl (720) was unusual in that it had very high conductivity (1520 pS cm'^ the highest recorded in this study) but low TP and FRP (16 & 6 pgL'’). All other sites with high conductivity in this data-set also had high phosphorus concentrations. At this stage all 78 training set sites were left in the data set for further analyses.

121 Cond 0.709 pH 0.663 0.410 Temp 0.213 «0.255 -0.059 Flow -0.050 -0.192 -0.034 -0.061 TP -0.141 0.274 -0.155 0.170 -0.034 FRP -0.147 0.238 -0.134 0.088 -0.002 0.923 N O f-N 0.349 0.357 0.281 -0.251 0.032 0.161 0.228 Silica -0.087 0.006 -0.202 -0.054 0.141 0.473 0.579 0.181 Cl -0.037 0.528 -0.200 0.228 -0.266 0.614 0.553 -0.014 0.202 s o / 0.095 0.604 -0.124 0.163 -0.271 0.282 0.214 0.034 -0.141 0.609 Ca'" 0.826 0.878 0.589 0.136 -0.055 0.011 -0.002 0.348 -0.120 0.165 0.418 Na+ -0.185 0.420 -0.326 0.286 -0.307 0.674 0.579 -0.118 0.116 0.882 0.665 0.037 K+ -0.064 0.470 -0.211 0.246 -0.227 0.724 0.609 -0.015 0.133 0.835 0.651 0.142 0.888 Mg'" -0.014 0.526, -0.140 *0.196 -0.337 0.224 0.141 -0.044 -0.336 0.668 0.855 0.311 0.694 0.673 Mn^" -0.639 -0.256 -0.721 0.055 -0.014 0.175 0.077 -0.397 0.067 0.311 0.298 -0.453 0.461 0.379 ^0.350 Fe'" . -0.468 -0.557 0.047 0.086 -0.072 -0.116 -0.324 0.011 -0.036 -0.184 -0.542 0.059 -0.001 -0.043 0.540 Al'" -0.291 -0.073 -0.128 -0.434 0.037 0.094 0.088 0.190 -0.011 -0.020 0.086 -0.044 0.011 0.104 0.097 0.117 ^ 0 .2 4 4 Variable Alk Cond pH Temp Flow TP FRP NO/-N Silica Cl s o / Ca'" Na" K" Mg'" Mn'" F e’"

Table 4.6 Correlation matrix showing the relationship between the environmental variables from the rope training set. Shaded cells denote a significant relationship (P < 0.05) 4.4.3.2 Tile Training Set Chemistry

A total of 69 samples were collected for the tile training set, and although only four of these samples were not covered by the rope data, it is important to consider this data-set as separate. The reason for this was due to observed differences in the chemistry of. the two data-sets but more importantly because of the differences in the species assemblages. The same analyses, performed on the rope samples, were used to investigate the environmental data from the tile training set. The environmental variables are summarised in Table 4.7, the data are plotted against each other in Figure 4.9 to explore the relationships between the variables, and PCA was used to identify the major gradients and to assess possible outliers.

Variable Range Mean STDEV Alkalinity (mgL^) 12-280 139.04 77.00 Conductivity (pS cm^) 130-1100 466.16 182.81 pH 6.75-8.5 7.50 - Temp (”C) 4.2-14.0 9.36 2.44 Flow (msec'^) 0.036-0.986 0.26 0.21 TP(MgL') 4-6882 848.09 1291.27 F R P (p g L ') 3-6091 730.99 116920 NO3 -N (pgL^) 9-8498 3135.55 2265.57 Silica (pgL‘^) 4999-21097 11046.19. 3813.32 Chloride (pgL'^) 13146-120671 33789.54 20401.29 Sulphate (pgL'^) 5442-139921 38387.93 29162.96 Calcium (pgL'^) 10485-247679 79334.81 47810.55 Sodium (pgL'^) 5512-111088 26067.68 19357.23 Potassium (pgL’^> 927-22464 5310.68 4140.06 Magnesium (pgL'^) 1803-13448 4593.54 2284.08 M anganese (pgL'^> 0-540 49.24 95.79 Iron (pgL b 1-9257 258.31 1153.86 Aluminium (pgL‘^) 0-140 30.43 23.97

Table 4.7 Summary statistics for the tile training set data (n = 69)

The variation in environmental data is high but it lacks the very high values for ionic concentration, TP and FRP seen in the rope training set. This was due to the lack of just three sites from the tile data-set: BOURl, SOCKl and BEVEl. The relationships between the environmental variables are shown in the multiple scatter plots below (Fig. 4.9).

123 Cond ■T

'ff: % ?;'■ TP ! - ri' ...'A ■1.

N O 3-N ■M .I-

Cl 4 . so; .4 .

Ca' ,#• #■ ¥ • ?

Na"

K .'■■'‘S' Mr ..ïfA"' /■

Alk Cond pH TP NO3-N Cl SO/ Ca' Na"

Figure 4.9 Scatter plots showing the relationships between the major chemical variables (variables

transformed)

Principal Components Analysis (PCA) The PCA summary statistics of environmental data, for the tile training set, are presented in Table 4.8 and the ordination is plotted in Figure 4.10.

Axes 1 2 3 4 Total variance

Eigenvalues: 0.306 0.259 0.105 0.086 1.000

Cumulative percentage variance of environmental data : 30.6% 56.5% 67.0% 75.6%

Sum of all unconstrained eigenvalues 1.000

Table 4.8 Summary statistics for the PCA on the tile training set environmental data

124 3.0

667 o Mn 618 o 666 2.0- o 635

649 o 616

651 1.0 626 o A|: Silica 636 0 637 FRP o 659 646 Flow 0 645 0 624 1 614 647 %C Mg' 612 0 625 o 606 f 617 Temp 0 629 631 o 627 o 661 632 0 \o634 0 607 o 604 608 o o o 0 596 597 630 642 600

NO;-N

Cond

Aik Ca -3.(1

- 2.0 - 1.0 0.01.0 2.0 3.0 PCA Axis 1

Figure 4.10 PCA plot of the tile training set environmental data

The distribution of environmental variables was very similar to that observed for the rope data. TP, FRP and the major ions are correlated with axis 1 of the PCA and alkalinity, pH and calcium with axis 2. The major difference being that conductivity was more strongly related to axis 2 in the tile data-set (rg = 0.81). The correlations between the environmental variables are presented in Table 4.9. There were no sites with extreme chemistry that could be identified as outliers from this analysis.

125 Cond 0.790 pH 0.670 0 4 4 3 Temp 0.214 0 J 5 9 0.007 Flow -0.060 -0.177 - 0.011 -0.108 TP -0.123 0.288 -0.132 0.247 -0.078 FRP -0.162 0 246 -0.157 0.155 -0.109 0.956 N O f-N 0.492 0.612 Œ352 -0.106 -0.075 0.132 0.118 Silica -0.114 0.002 -0.251 -0.003 -0.016 0.417 0.495 0.000 Cl -0.054 0.429 -0.174 0.181 -0.349 0.565 0^ 82 0.121 0.126 s o / 0.057 0.484 -0.183 0.158 -0.194 0.370 0348 0488 -0.038 0.669 C a^ &866 0.861 0.650 0.136 0T%2 0.018 -0.048 0.570 -0.144 0.040 0.271 K) ON Na^ -0.194 0.330 -0.295 % 0.239 -0.299 0.720 0.717 -0.006 0.179 0.910 0.631 -0.081 K+ -0.100 0.414 -0.192 0.221 -0.219 0.716 0.702 0.092 0.112 0 8 4 9 0.695 0.061 0.905 Mg'" -0.053 0 3 8 9 -0.130 0.147 -0.238 0.249 0 243 0.089 -0.301 0.707 0.819 0.149 % 6 5 0.701 Mn'" -0.672 -0.383 -0.702 -0.048 0.053 0.108 0.119 -0.474 0.142 0 3 3 8 0.298 -0.528 0363 0 3 0 2 0.312 Fe'" ^-0.452 -0 .4 4 8 6^-0.538 0.005 0.032 -0.066 -0.053 -0.451 0.047 0.016 -0.090 -0.514 0.103 0.027 0.026 0.536 a /" ^ 0 .3 15 -0.189 -0.164 -0.414 0.089 0.029 0.026 0.188 -0.072 -0.164 0.002 -0.099 -0.074 0.054 0.010 0.147 Variable Aik Cond pH Temp Flow TP FRP N O f-N Silica Cl s o / Ca'" Na" K" Mg'" Mn'" Fe'"

Table 4.9 Correlation matrix showing the relationship between the environmental variables from the tile training set. Shaded cells denote a significant relationship (P < 0.05) 4.4.4 Diatom Assemblages

A total of 291 diatom taxa were identified from the two training sets, of which 182 were common to both. The rope training set had a total of 235 species, of which 53 were found only in the rope samples. The tile training set had a total of 237 species of which 55 were unique to that data-set. The diatom taxa which were found exclusively in one or other of the two training sets were mainly rare species, not occurring at greater than 2 % abundance or in more than five samples. The one exception to this was Fragilaria capucina v. austriaca which, although it only occurred in one of the rope samples, accounted for 19.1% of the total count. The full species list, with authorities, is given in Appendix U. The floristic variation in the two training sets is considered separately below.

4.4.4.1 Rope Training Set

The diatom assemblages varied considerably between the different samples, both in their floristic composition and their species diversity. The minimum number of taxa recorded from a sample was 19 (sample 720, dominated by Cocconeis placentula v. euglypta and C. pediculus) and the maximum of 71 (sample 697, where no species occurred at greater than 10% relative abundance). The Hill’s N2 diversity ranged from 1.53, in sample 683 (dominated by Achnanthes minutissima: 80%), to 22.43, in sample 697. A summary of the total numbers of taxa per sample and species diversity is given in Table 4.10.

Diatom Species Occurrence and Abundance Figure 4.11 shows the relationship between the occurrence of each taxon and its maximum abundance. The samples tended to be dominated by only a small proportion of the total number of taxa; those plotting towards the top right of the graph (e.g. A. minutissima, C. placentula v. euglypta, G. parvulum and A. lanceolata). Only 26 species (11%) occurred in 50% or more of the samples. The majority of species can be seen to plot to the lower left of Figure 4.11 and thus occur infrequently and at low abundance. A total of 134 taxa (57%) occurred in less than 10% of the samples. A few taxa occurred infrequently but achieved relatively high abundances in at least one sample (e.g.

127 Fragilaria construens v. venter, Navicula capitatoradiata and Fragilaria capucina v. austriaca (ZZZ966)).

Sample No. No. of Taxa Hill’s N2 Sample No. No. of Taxa Hill’s N2 668 44 10.49 707 35 4.84 669 35 7.62 708 27 1.65 670 49 4.73 709 28 3.44 671 40 16.89 710 41 15.20 672 35 9.34 711 45 16.65 673 39 2^2 712 38 2.55 674 22 3.72 713 43 10.21 675 30 4.20 714 46 5.72 676 41 9.92 715 40 3.68 677 45 6.48 716 38 7.85 678 36 3.10 717 61 12.73 679 32 4.93 718 31 3.16 680 39 3.51 719 46 9.80 681 22 1.99 720 19 2.24 682 35 2.10 721 24 2.35 683 21 1.54 722 45 6^8 684 40 3.28 723 45 7.72 685 42 2.94 724 57 8.78 686 35 6.99 725 41 11.81 687 57 4.92 726 48 12.59 688 43 9.52 727 43 2.66 689 57 15.06 728 45 8.47 690 25 1.56 729 34 9.05 691 41 9.91 730 52 16.78 692 41 11.44 731 47 10.89 693 39 12.07 732 42 11.78 694 38 8.47 733 57 13.93 695 45 7.65 734 51 18.00 696 41 14.86 735 65 8.59 697 71 22.43 736 46 8.14 698 39 8.57 737 62 16.50 699 57 14.12 738 47 10.58 700 55 10.66 739 21 5.31 701 44 11.17 740 60 16.54 702 44 7.31 741 52 12.35 703 51 13.59 742 21 3.22 704 21 5.76 743 40 7.87 705 37 4.25 744 52 15.43 706 55 838 745 45 19.57

Table 4.10 Number of taxa per sample and Hill’s N2 diversity for the rope training set

128 90 Key F. c = F. capucina v. rumpens Achnanthes minutissima o F .v = F. vaucheriae 75- N. c = Nav. cryptotenella N. I = Nav. lanceolata Cocconeis placentula v. euglypta o N.m = Nav. menisculus N. 2 = Nav. [species 2] 60 N .d = Ni. dissipata 8 N . f = Ni. jrustulum Gomphonema parvulum o S S. s = Sel. seminulum

I 45 F. construens v. venter a a Navicula capitoradiata Melosira varians q A. lanceolata 30 o o 9t C. pediculus o ° N. gregaria

N. cryptocephala o Nitzschia amphibia o S. minima F. brevistriata o ZZZ966 o Am. pediculus G. pumilum o N.io o Ni. paleacea 15 S- N. 2 G. angustatumo N. co V o tripunctata F .c o N. d o N. atomus ° curvata o°°o ° O O O g ° o Ni. palea oo oo o o ^ ° o ° ^ C y m b e lla minuta J8i! Oo@0 o ° Svnedra ulna ______10 20 30 40 50 60 70 80 Number of Occurrences

Figure 4.11 Scatter plot of diatom occurrences and the maximum abundance achieved by each taxon from the rope training set

Detrended Correspondence Analysis of the Rope Species Data

Axes 1 2 3 4 Total variance

Eigenvalues: 0.427 0.267 0.184 0.123 3.651

Lengths of gradient: 2 8 8 9 2 8 5 7 2.729 2.076

Cumulative percentage variance of species data : 11.7% 19.0% 24.0% 274%

Sum of all unconstrained eigenvalues 3.651

Table 4.11 Summary of the DCA for the rope training set

The DCA only captured 19.0% of the floristic variation on the first two axes (Tab. 4.11). This is typical for diatom data, which is generally noisy and contains many zero values (Stevenson et al. 1991). DCA is a data reduction technique and thus the floristic patterns revealed by the first few axes of the ordination are more important than the

129 overall explained variation (Hill & Gauch 1980, Pienitz et al. 1995). Such analysis can, therefore, still provide a useful tool for the interpretation of diatom species data.

Axis 1 was the dominant axis of variation in the DCA, accounting for 11.7% of the total variation. The DCA plot of sites (Fig. 4.12) show the samples to be spread evenly along the first axis. Sites with low axis 1 scores included 673, 682, 683, 690, 708 and 721. All these samples were dominated by A. minutissima (>50%), which can also be seen to plot to the left of the DCA plot (Fig. 4.13). Samples plotting to the right of the diagram included 712, 713, 716, 723, 725 and 745. These samples had high relative abundances of S. minima, A. lanceolata, G. parvulum and M. varians, all of which plotted to the right of Figure 4.13. The samples which plot towards the centre of the DCA were either of high diversity, and thus included many of the species (e.g. 671, 697, 730 and 740), or were of low diversity but dominated by taxa which were common to many samples (e.g. 670, 680, 684 and 685; dominated by C. placentula v. euglypta, >40%). Figure 4.14 shows the Hill’s N2 diversity of each sample in relation to the axis 1 scores.

3.0 O 728

o 691

o 698

704 o 692 712 0 7390 o 729 2.0— 744 0 ’ 711

685 o 688 673 675 O 740 683 o 707 0 681 676 .o ° o o o 723 o o 7 1 3 0 724 690 0 o 742 o° o680 709 o o 695 o 725 682 g 7 0 5 % 674 0 679 ° 730 o 745 0 7 1 6 670 o o 719 o 689 669 671 o 722 668

o 703

o 699 738 o o 735

700 o 736 0.0 0.0 1.0 2.0 3.0

D CA A xis 1

Figure 4.12 DCA plot of the rope training set sites based on their diatom flora

130 4.0 Key Fragilaria brevistriata M. V = Melosira varians N. a = Navicula atomus Fragilaria elliptica N. c = Navicula cryptotenella 3.0 - N. t = Navicula tripunctata A Nitzschia amphibia Fragilaria vaucheriae a Fragilaria pinnata 6 A Sellaphora seminulum

A Gomphomena parvulum Cocconeis pediculus Achnanthes laruteolata «s 2.0 A Sellaphora minima Cymbella minuta ^ Fragilaria capucina V. rumpens I Achnanthes minutissima A Cocconeis placentula v. euglypta d y Navicula subminuscula Nitzschia /rustulum a N. c G 1.0- / N. a A M V A Gomphonema Nitzschia dissipcHa / A angustatum Navicula [species 2] a „ ^ ^ Nitzschia paleacea Ni. palea ^ Gomphonema olivaceum a ^ Navicula menisculus A Navicula capitata Amphora pediculus a ^ A Navicula [pseudogregaria] 0.0 — Nitzschia sociabilis lanceolata a

Navicula cryptocephala

Navicula gregaria a -1.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1

Figure 4.13 DCA plot of the rope training set species data showing the most commonly occurring taxa

697 20- o 745

qo 730 1 5 -

I 700 3 725 % 10- oo o 0 7 1 3

I 728 716 oo 670,

712 70sio^^o^*^ 681 „ 683 690 0.0 2.01.0 3.0

DCA Axis 1 Score

Figure 4.14 Relationship between DCA axis 1 and species diversity for the rope training set sites

The second DCA axis explained a further 7.3% of the species variation, the majority of this being among the samples with high axis 1 scores (Fig. 4.12). The main sites accounting for this variation were, with high axis 2 scores, 691, 698, 704, and 720 and

131 separate out from the other samples due to high abundances of G. parvulum, N. amphibia, S. seminulum, Fragilaria brevistriata, and F. construens v. venter (Fig. 4.13). Samples 699, 700, 735, 736 and 738 had low axis 2 scores and were dominated by Navicula gregaria, N. cryptocephala and N. lanceolata. Although these taxa were common to many of the samples, they only occurred as dominants at the listed sites. The DCA did not identify any samples which could be regarded as outliers due to their floristic composition. Axes 3 and 4 accounted for 5.0% and 3.4% of the variation, respectively, and provided little further information on the floristic patterns.

Detrended correspondence analysis can also be used to identify the correlations between the unconstrained floristic distribution and the measured environment. This was achieved by introducing the physico-chemical data into CANOCO (ter Braak 1991) and performing DCA on the species data as above. With the environmental data present CANOCO then calculates the Spearman’s rank correlation (rg) coefficients for all of the environmental variables with each of the first four DCA axes. The significance of these data were then tested using a standard r-test (Tab. 4.12).

Environmental Ts vyith DCA Ts with DCA rs with DCA r* with DCA Variable Axis 1 Axis 2 Axis 3 Axis 4 Alkalinity -0.748*** - 0.221** - Conductivity -0.410*** - -0.196** -0.241** pH -0.644*** -0.219** -- Temperature - 0.179* - -0.207** Flo’w ---- TP 0.325*** - -0.354*** -0.317*** FRP 0.249** 0.170* -0.327*** -0.310*** NO f-N -0.313*** - -- Silica - 0.458*** - - Cl 0.346*** - -0.415*** -0.270** S04^ - -0.276** -0.320*** -0.265** Ca^+ -0.603*** -0.175* - -0.156* Na+ 0.442*** - -0.405*** -0.382*** K+ 0.420** -0.234** -0.319*** -0.336*** Mg'" 0.184* -0.325*** -0.330*** -0.316*** Mn'" 0.695*** -0.162* -- Fe^" 0.411*** - 0.158* - Al'" 0.291** -0.250** --

Table 4.12 Significant correlations (rj between the measured environmental variables and the unconstrained DCA axes for the rope data (.♦* P < 0.005, *♦ P < 0.05, * P < 0.1)

132 From this analysis it would appear that DCA axis 1 was most strongly correlated (negatively) with alkalinity (rs = -0.748), thus sites with low axis 1 scores tend to have high alkalinity (e.g. 683, 690, 708 and 721), and sites with high axis 1 scores have low alkalinity (e.g. 712, 713, 728 and 736). There were however exceptions to this, for example, sites 698 and 700 had above average alkalinity but plot to the right in the DCA (Fig. 4.13). Other important variables correlated to axis 1 include manganese, pH, calcium and conductivity. These determinants are correlated with alkalinity (Tab. 4.6). TP was also correlated to axis 1, but less strongly than alkalinity.

The floristic variation on the remaining axes showed less direct relationship with the environmental variables. Axis 2 was most significantly correlated with silica and magnesium. The species variation on axes 3 and 4 was correlated with TP and the major ions (particularly sodium, potassium, magnesium, chloride and sulphate). These results suggest that the floristic composition is being driven most strongly by the acid-base status of the water, followed by ionic components and phosphorus concentration.

4.4.4.2 Tile Training Set

Like the rope samples, the diatom assemblages from the tile training set showed a high degree of variation between the samples, both in taxonomic composition and in their species diversity. The minimum number of taxa recorded from a sample was 17 (sample 642, dominated by three taxa: A. minutissima, Fragilaria pinnata and A. pediculus) and a maximum was 69 (sample 614, where no species occurred at greater than 8 % relative abundance). The Hill’s N2 diversity ranged from 1.46, in sample 601 (dominated by: A. minutissima, 82%), to 26.96, in sample 614. A summary of the total numbers of taxa per sample and species diversity is given in Table 4.13.

133 Sample No. No. of Taxa Hill’s N2 Sample No. No. of Taxa Hill’s N2 595 30 7.12 630 54 7.72 596 22 8.33 631 59 11.80 597 42 5.94 632 37 1.77 598 40 16.15 633 20 3.21 599 42 10.35 634 36 10.31 600 40 6.29 635 40 9.35 601 42 1.46 636 56 15.44 602 22 3.52 637 35 5.40 603 25 1.94 638 52 10.41 604 50 10.05 639 18 4.37 605 51 12.24 640 40 8.72 606 35 8.96 642 17 4.14 607 35 5.33 643 59 8.66 608 42 13.58 644 35 12.91 609 27 4.17 645 70 18.07 610 33 5.67 646 29 4.28 611 27 6.05 647 33 2.19 612 37 6.76 648 45 2.60 613 42 3.73 649 47 11.64 614 69 26.96 651 35 8.12 615 22 4.44 652 48 12.04 616 37 7.65 653 37 9.93 617 37 2.59 654 36 12.79 618 53 7.32 655 51 12.00 619 37 7.63 657 47 13.96 620 32 9.16 658 56 14.68 621 37 7.25 659 51 15.05 622 55 12.42 660 35 7.54 623 50 21.02 661 46 9.91 624 66 26.23 662 43 4.98 625 46 12.17 663 26 3.11 626 31 9.30 664 33 5.67 627 42 16.19 666 54 6.42 628 41 2.62 667 53 7.20 629 29 4.86

Table 4.13 Number of taxa per sample and Hill’s N2 diversity for the tile training set

134 Diatom Species Occurrence and Abundance (Tile) A similar pattern of species occurrence and abundance was observed in the tile training set to that of the rope samples. Figure 4.15 shows the relationship between the number of occurrences of each taxon and its maximum abundance. The samples tended to be dominated by only a small proportion of the total number of taxa; those plotting towards the right of the graph (e.g. A. minutissima, A. lanceolata, A. pediculus, C. placentula v. euglypta, G. parvulum and S. minima). Only 20 species (8.4%) occurred in 50% or more of the samples. A total of 142 taxa (60%) occurred in less than 10% of the samples. A few taxa occurred infrequently but achieved relatively high abundances in at least one sample (e.g. Achnanthes biasolettiana and Gomphonema anoenum (ZZZ983)).

90 Key A. s = Achnanthes saxonica Achnanthes minutissima o C. m = Cymbella minuta 7 5 - G. a = Gomphonema angustatum G. o = G. olivaceum

G.p= G. parvulum Navicula lanceolata N. 2 = Nav. [species 2] 60 N. s = Nav. [small sp. 1] o Navicula gregaria & N. t = Nav. tripunctata 8 N . f = Nitzschia jrustulum

Achnanthes lanceolata o Ach. biasolettiana 45 I Sellaphora seminulum o N. atomus o o C. placentula Fragilaria pinnata q . , , . , V. euglypta o Navicula subminuscula I M elosira varians ° 30 Sellaphora minima Fragilaria brevistriata o o o Ni. amphibia Gomphonema o G.po anoenum o Ni. paleacea o Am. pediculus Surirella brebisonii , o N. [pseudogregaria] o Nitzschia dissipata 15 Ni. sociabilis o o °N.2 o Nav. capitatoradiata o Nitzschia palea o Ach. clevei °0 V. 5 N .t C m ° Rhoicosphenia curvata OO h ^ o ° ° G . o i 0 0 0 0 o ° o ° ° o ° " Nav. menisculus o Ach. lauenbergiana

15 30 45 60 75 Number of Occurrences

Figure 4.15 Scatter plot of diatom occurrences and the maximum abundance achieved by each taxon from the tile training set

135 Detrended Correspondence Analysis of the Tile Species Data

3.0

0 605 655

654(P 636

602 0 624 o 664 634 0 627 5960 0 663 o638 0 658 2.0 601 o o 606 o 616 ^33 o 662 , o 632 0 597 595 0^23 0 643 645 666 644 Oq O o 628 o 647 o 611 o 660 o 653 o 617 642 0 g 629 o 661 o 637 o 626 1.0- o 609 o 613 649 o

651 625 0 618 612 o 646

615 0.0 0.0 1.0 2.0 3.0 4.0 D C A A xis 1

Figure 4.16 DCA plot of the tile training set sites based on their diatom flora

4.0

Navicula tripunctata Navicula cryptotenella a a Navicula [species 2] Nitzschia dissipata a 3.0 - Navicula menisculus a Fallacia subhamulata Ni. paleacea é Ci a [pseudogregaria] Nitzschia frustulum a Cymbella minuta Nitzschia palea^ Melosira varians

Gomphonema olivaceum a Navicula atomus Cocconeis pediculus a

2.0 Achnanthes a A Cocconeis placentula \. euglypta Navicula lanceolata minutissima Amphora pediculus Navicula gregaria a a Fr vaucheriae Gomphonema pumilum a Fragilaria capucina a V. rumpens 1.0 Fr. elliptica Synedra acus a Reimeria sinuata A Rhoicosphenia curvata

A Achnanthes lanceolata

A Sellaphora minima Fragilaria pinnata a 0.0 — A Nitzschia amphibia Fragilaria brevistriata a

Sellaphora seminulum -1.0 -1.0 0.0 2.0 3.0 4.01.0 D CA A xis 1

Figure 4.17 DCA plot of the tile training set species data showing the most commonly occurring taxa

136 Axes 1 2 3 4 Total variance

Eigenvalues: 0.419 0.293 0.198 0.141 4.232

Lengths of gradient: 3.551 2.835 2.363 1.832

Cumulative percentage variance of species data : 9.9% 16.8% 21.5% 24.8%

Sum of all unconstrained eigenvalues 4.232

Table 4.14 Summary of the DCA for the tile training set

The floristic variation captured by the first two DCA axes for the tile samples, was slightly lower than that for the rope: 16.8% versus 19.0% (Tab.4.14), and the total variation over the first four axes was only 24.8%. The pattern of variation, however, was similar, particularly on axis 1, which showed an even distribution of sites. Axis 2 was characterised by a more even spread of samples along the second axis (Fig. 4.16).

Axis 1 accounted for 9.9% of the total variation. Sites with low axis 1 scores included 600, 601, 603, 632, and 642 and were dominated by A. minutissima, (or A. biasolettiana) and, to a lesser extent, A. pediculus (Fig. 4.17). Samples plotting to the right of the diagram included 616, 628, 643, 647, and 648. These samples all had high relative abundances of Navicula species {N. lanceolata, N. gregaria, N. cryptocephala) and, in the case of sample 643, A. lanceolata, all of which can be seen to plot to the right of Figure 4.17. As with the rope samples, those sites plotting centrally within the DCA were either of high diversity (e.g. 623, 624, and 637), or of low diversity but dominated by taxa which are common to many samples (e.g. 610, 613, and 637; dominated by A. lanceolata and C. placentula v. euglypta).

The second DCA axis explains a further 6.9% of the species variation. The main sites associated with this variation were, with high axis 2 scores, 598, 605, 636, 654 and 655. These separated out from the other samples due mainly to high abundances of N. dissipata (Fig. 4.17). Samples 612, 615, 619, 651 and 657 had low axis 2 scores and were dominated by N. amphibia, S. seminulum and S. minima. The DCA did not identify any samples which could be regarded as outliers.

137 The significant relationships between the unconstrained ordination of species data and the measured environmental variables are given in Table 4.15.

Environmental Cs with DCA rs with DCA rs with DCA rs with DCA Variable Axis 1 Axis 2 Axis 3 Axis 4 Alkalinity -0.563*** 0.224** -- Conductivity -0.243** - -0.164* - pH -0.287** 0.467*** -0.296** 0.169* Temperature - -0.198* - - Flow - 0.340*** 0.206** - TP 0.401*** -0.230** -- FRP 0.403*** -0.277** -- NOy-N -0.282** - -0.226** - Silica 0.176* -0.490*** - 0.238** Cl 0.324*** -0.260** -- s o / 0.291** --- Ca^+ -0.303*** 0.303** -- Na+ 0.424*** -0.298** - -0.169* K+ 0.437*** -0.169* - -0.217** Mg"+ 0.296** - - -0.204** Mn^+ 0.491*** -0.196* 0.216** - Fe^" --- -0.250** A /+ 0.226** - - -

Table 4.15 Significant correlations (r^) between the measured environmental variables and the unconstrained DCA axes for the tile data (.♦* P < 0.005, ♦♦ P < 0.05, * P < 0.1)

DCA axis 1 was most strongly, negatively, correlated with alkalinity and positively with phosphorus and the major ions. Axis 2 was related to pH and silica, and, to a lesser extent, flow. The main differences between the tile and rope analyses were the closer relationship of phosphorus with species axis 1, and an increased importance of flow in the tile training set; this latter observation was possibly due to a greater scouring effect on the tile substratum. In addition to this, the tile samples showed less significant correlations between the floristic patterns and the environmental variables on axes 3 and 4. These results suggest that the floristic composition was being driven most strongly by a combination of alkalinity, phosphorus and the major ionic components. Silica, pH and flow could also be considered as important in determining the distribution of taxa.

138 4.4.5 Direct Species - Environment Relationships

The purpose of developing a training set is to gain ecological information on the species distribution along the measured environmental gradients. Although in this thesis the main focus was to establish the relationship between phosphorus and the floristic composition of the diatom communities, it is also important to determine the influence of the other environmental variables. The results presented above consider the variation in the environment and diatom assemblages of the training sets separately. The aim of the next section is to evaluate the species variation which can be explained by the measured environmental variables. Before examining the whole data set with the use of multivariate statistics, the two gradients used to select the sites, TP and alkalinity, are considered separately, to examine any direct patterns in the floristic variation.

4.4.5.1 Species Relationships with TP and Alkalinity

Rope Training Set Figures 4.18 shows the abundance of some of the diatom taxa along the measured TP gradient. From these graphs it is clear that some species appear to have little, or no, relationship to the phosphorus gradient. C. placentula v. euglypta and A. lanceolata were well represented within the data-set and occurred across the range of TP, without any noticeable preference for high or low concentrations, i.e. these species are tolerant of low TP and are not out-competed at high TP concentrations. Other taxa, also well represented in the data-set, occurred at higher abundances over a distinct range of TP concentrations. A. minutissima and, to a lesser extent, G. parvulum showed a preference for the lower end of the gradient, although they were also present at high TP in lower abundance. Similarly N. gregaria only occurred at high abundance in the mid TP range and N. amphibia at the high end of the TP range. Other taxa were less well represented and only occurred over a narrow range of TP concentrations, e.g. N. rhynchocephala at low TP and A. veneta, at high TP (Fig. 4.18).

139 100 ^gL ‘ 1000 ^gL ' 25 Amphora veneta 25 Nitzschia amphibia -.-ill-.— - llliill -ill 25 Sellaphora sem inulum I. i-i. Ill

Navicula subminuscula

Navicula cryptotenella

Melosira varians III_

lanceZHta" ! JJl... Ijj.jL .L . i^.illlll. ilii JL.IiIiiIiiILJIi.Ji

50 w

Cocconeis placentula V. euglypta

Navicula lanceolata _m_ . i__ m I-— 1 J-■ _mâi.*i-M-llM.###^Ëmm_|#mlmirn»l—

Navicula gregaria . . I..1 I li I _>jij I • 1 l..iJllii->ii jfji

Achnanthes minutissima ilL J j_ l

Gomphonema parvulum ■ Il ■«■mlalm—1 m_Ëmm_.mmmlamrnlÊ#lmlm_mmmElmlmÊml.-mm—-■■111 _

Gomphonema angustatum

Navicula rhynchocephala 1 10 20 30 40 50 60 70 78 Sites - Ranked by TP

Figure 4.18 Species abundance along the TP gradient (rope)

140 100 mgL ' 200 mgL 100

Achnanthes minutissima Jl- IbIj 1.1

Navicula cryptotenella 1,- 25 Amphora pediculus A*Ji— - l»iB—■B-. ..JuuL 25 Nitzschia amphibia ■I 1.1 I I 75

Cocconeis 25 piacentula V. euglypta iLii__ III. IhJll III I.L i 25 Sellaphora sem inulum m .. iL L 50 ÿ «c

A chnanthes lanceolata

Navicula gregaria

Melosira varians U .mlak-—- —»_i —

Navicula lanceolata

Gomphonema parvulum

Sellaphora m inim a 25 Navicula atomus

Eunotia curvata

Navicula rhynchocephala 1 10 20 30 40 50 60 70 78 Sites - Ranked by Alkalinity

Figure 4.19 Species abundance along the alkalinity gradient (rope)

141 Figures 4.19 shows the abundance of a selection of the diatom taxa along the measured gradient for alkalinity. C. placentula v. euglypta and A, lanceolata showed a similar pattern to that observed for the TP data (Fig. 4.18), with no distinct alkalinity preference. G. parvulum, N. atomus and S. minima achieved highest abundances at low alkalinity, and A. minutissima at high alkalinity. The samples with low abundances of A. minutissima at the high end of the gradient are usually sites with high TP (e.g. 704, 706, 710 and 740). Conversely, N. amphibia occurred only at sites of high TP, irrespective of alkalinity {cf. Figs. 4.18 and 4.19).

Tile Training Set Figures 4.20 shows a selection of the tile training set species along the TP gradient. The floristic patterns are similar to those observed for the rope data-set. Some species appeared to show no response to TP (e.g. A. lanceolata & N. lanceolata). A. biasolettiana, A. minutissima and, to a lesser extent, G. parvulum showed a preference for lower TP, while N. amphibia, S. seminulum and A. veneta all performed better at the high TP end of the gradient. Other species (e.g. N. atomus and N. [pseudogregaria]) were more abundant towards the middle of the TP range. Similar patterns can be seen in the species responses to alkalinity (Fig. 4.21).

These data show that many of the species in both training sets are responding to the TP and alkalinity gradients in a uni modal fashion. Where taxa do not conform in a uni modal pattern (e.g. A. lanceolata) it is likely that some other environmental variable is having a greater influence on their distribution, or that such species have very wide tolerances to trophic status or alkalinity. To further assess the impact of the measured environment on the species distributions the technique of canonical correspondence analysis (CCA) was used.

142 100 ^gL ' 1000 ugL '

Am phora veneta

Sellaphora sem inulum _lu .1— I ■■■ I... Nitzschia amphibia Navicula (small sp. 1]

Navicula subminuscula

Navicula atomus Navicula \pseudogregaria] i T3c 3 < Navicula gregaria I« 3

Navicula lanceolata L ._^I1lJ_I .lil-i-lil.

Achnanthes lanceolata 100

I'' j50

' 25 Achnanthes minutissima | . I U I ■ I.IIILlI I II .JJJI 25 Gomphonema parvulum ' -Il■ a-lll. ii.«>11 _ | . t m #mKm m__ 50

25 Achnanthes biasolettiana 1 10 20 30 40 50 60 69 Sites - Ranked by TP

Figure 4.20 Species abundance along the TP gradient (tile)

143 100 mgL ‘ 200 mgL ‘ 50

25 Fragilaria pinnata II . I I I 100

25 Achnanthes , minutissima :i:lll. mil 25 Fragilaria elliptica 25 Navicula cryptotenella II I I ■- - Il 50

Cocconeis placentula V. euglypta J-l—_ aX ..III ll III II -, .ll.lil.il-il i.l.lililil I ll .. Nitzschia amphibia J ___ I l ^ j _ l III 1 . I Navicula [pseudogregaria |

Navicula lanceolata

[

i 1 Navicula gregaria i .ll 1 - . 1 Navicula cryptocephala -.■■■1 . . . .-1 Eunotia curvata Navicula rhynchocephala 1 10 20 30 40 50 60 69 Sites - Ranked by Alkalinity

Figure 4.21 Species abundance along the alkalinity gradient (tile)

4.4.S.2 Canonical Correspondence Analysis (CCA)

Canonical coirespondence analysis (CCA) is a direct ordination technique in which linear combinations of the environmental variables can be used to explain optimally the species distributions (ter Braak & Verdonschot 1995). An exploratory CCA was performed on each of the training sets with all the sites included, using the full

144 environmental data-set. Forward selection was then used within CCA to find the minimum number of environmental variables which significantly explained the variation in the diatom species data. These analyses were also used to identify any environmental variables that showed high collinearity (inflation factor > 2 0 ) and sites which exerted high leverage due to unusual combinations of species and environment

(>8 x influence).

Rope Training Set

Axes 1 2 3 4 Total inertia

Eigenvalues: 0.371 0.239 0.198 0.125 3.651

Species-environment correlations: 0.942 0.891 0.869 0.803

Cumulative percentage variance of species data: 10.2% 16.7% 22. 1% 25.5%

of species-environment relation: 25.3% 41.6% 55.0% 63.5%

Sum of all unconstrained eigenvalues 3.651

Sum of all canonical eigenvalues 1.468

Table 4.16 Summary statistics for the rope training set CCA

The cumulative species variation explained by the first two axes of the CCA was slightly lower than that observed for the DCA (16.7% and 19.0% respectively) (cf. Tab 4.11 and Tab. 4.16). The variation due to species-environment relationships, however, was high with 41.6% explained on the first two axes and a further 21.9% (63.5% total) on axes 3 and 4 (Tab. 4.16). The first four axes were all significantly (P <0.005) correlated with the diatom-environment relationships (rs = 0.94, 0.89, 0.87 and 0.80 respectively). These data suggest a strong relationship between the diatom taxa and the measured environmental variables.

The simplest way in which to interpret the output from a CCA is by means of an environment-site biplot (Fig. 4.22) and an environment-species biplot (Fig. 4.23). Some care is needed when extracting information from these biplots because they only display two dimensions of what is in fact a multi-dimensional output (ter Braak & Verdonschot 1995).

145 3.0 744 0 712

716

2.0 - 700 0 o 711 705 690 0 691 735 683 o 675 Mn 723 707 1.0 686 0 728 P701 o 708 725 ^89 682 699 0 721 0.0 — 742 o 673 713 0 678 688 liiica o Temtf Alk

671 709 -1.0 - ,676 TP o 706 Ca' FRP 687 Cond 0 739 685 702 -2.0 - 681 720

-3.0

- 2.0 - 1.0 0.01.0 2.0 3.0 CCA Axis 1

In Figures 4.22 and 4.23, only the first two axes are shown and are valid for interpretation because they explain the highest proportion of the total variance in the combined species-environment data. It is, however, important to remember that the lower axes are also important in the distribution of the points (sites and species) and thus the following rules were observed for the interpretation of the biplots:

Environmental vectors {arrows) • Each environmental vector points in the direction of maximum change of the standardised value for the variable. • The vector length is proportional the rate of maximum change (i.e. variables with long vectors tend to be more influential in determining species distributions). • In the perpendicular direction a variable does not change in value; perpendicular distance is due to the combined influence of other variables.

146 • The angle between any two vectors gives a crude impression of their correlation. If the angle is acute the correlation is positive and if obtuse the correlation is negative.

Environment-site biplot • Two sites that are close together are likelv to have similar species assemblages but this may not be observed in all cases, due to the influence of the lower axes (not plotted). • Any two sites that are far apart however will always have dissimilar species assemblages.

Environment-species biplot • Each species point on the biplot is at the weighted average (centroid) of the site points in which it occurs, and is thus positioned at its optimum (based on all variables) on each axis. (ter Braak & Verdonschot 1995)

From the exploratory CCA (Fig. 4.22) of the rope training set, alkalinity was most strongly correlated with (species) axis 1 (rs = 0.76). Consequently, sites which plot to the right of Figure 4.22, have higher alkalinity (e.g. 673, 675, 683, 709 and 721) and sites plotting to the left, have lower alkalinity (e.g. 6 8 8 , 713, 716, 723 and 744). The role of TP in determining the species distribution is less clear. TP was significantly (P < 0.005) correlated (negatively) with both CCA species axis 1 and 2 (rs = -0.49 and -0.41 respectively). Sites with high TP, therefore, plot towards the bottom left of the CCA (e.g. 681, 685 and 739) and sites with low TP plot towards the top right (e.g. 675, 683 and 690) (Fig. 4.22). The analysis was further complicated by the significant correlation of TP with CCA species axis 4 (rs = -0.35), hence explaining the unlikely position of site 720, a low TP site, on the CCA axis 1 and 2 plot. This result suggests that site 720 had an unusual species-environment combination and was a possible outlier (see below).

147 3.0 Key M V = Melosira varians N. m= Nav. menisculus A Gomphonema angustatum N [p]= Nav. [pseudogregaria] A Navicula cryptocephala N. s = Nav. subminuscula 2.0 N. 2 = Nav. [species 2] N. t = Nav. tripunctata Gomphonema parvulum N. d = Nit. dissipata A Fe"" Fragilaria capucina N. p == Nit. palea V. rumpens ^ Mn'

~~- 1.0 Nov. cryptotenella Nitzschia amphibia~~

~~Cocconeis placentula V. euglypta~~

~~- 2.0~~

~~- 2.0 - 1.0 0.0 1.0 2.0 3.0 CCA Axis 1~~

From the CCA biplot of environment-species relationships (Fig. 4.23) it is possible to relate the diatom taxa responses to the important environmental gradients. Alkalinity was the most strongly correlated variable with axis 1 and thus species with low axis 1 scores show a preference for low alkalinity sites. This was confirmed from the weighted average optima for the diatom taxa (see Chapter 5). It should be stressed at this stage that the species points on Figure 4.23 are plotted at three times their actual axis scores for ease of interpretation and thus taxa not plotted on the diagram, i.e. those with low optimum for alkalinity, would plot even further to the left (e.g. N. rhynchocephala, WA opt. = 38 mgL'^ and Eunotia curvata, WA opt. = 52 mgL'^). Common taxa, which are plotted on the CCA diagram include, with low alkalinity optima: G. parvulum, N. gregaria, N. lanceolata and S. seminulum, and with high alkalinity optima: A. minutissima, Nitzschia frustulum, A. pediculus and F. pinnata. Similar results were observed for species lying perpendicular to the TP vector. N. amphibia, A. veneta and S.

148 seminulum were found in higher abundance in sites of high TP, and have high weighted average optima (see Ch. 5). A. minutissima, F. capucina v. rumpens and F. pinnata plot towards the top right of the CCA and have lower TP optima.

Forward selection of environmental variables From the CCA, conductivity showed high collinearity (inflation factor = 28.7) due to its correlation with alkalinity and calcium. No other environmental variable had inflation factors greater than 20. CCA for the rope training set data was re-run, excluding conductivity, with forward selection. In forward selection CANOCO (ter Braak 1991) first ranks the environmental variables by performing a separate CCA for each variable on its own. The variable with the highest explained variance is then selected and the additional fit of the next best variable is then tested in conjunction with the selected variable. This process is then repeated for each variable, as the only addition, until the Monte Carlo significance is exceeded (p < 0.05, 999 permutations) (ter Braak & Verdonschot 1995).

Variable Variance Explained by Added After Individual Variable Forward Selection Alkalinity 0.29 0.29 TP 0.20 0.18 K+ 0.20 0.13 PH 0.21 0.12 Na" 0.19 0.07 Mn^" 0.22 0.07 Silica 0.13 0.07 Ca^" 0.23 0.07 Temperature 0.07 0.06 FRP 0.19 -ns- Cl 0.16 -ns- Fe^" 0.13 -ns- Mg"" 0.13 -ns- s o / 0.10 -ns- N O f 0.09 -ns- Flow 0.07 -ns- Al"" 0.07 -ns- Sum of variance = 1.06

~~Table 4.17 Potential variance explained by each environmental variable and the added variance following forward selection for the rope training set (ns = not significant P < 0.05)~~

~~With forward selection nine environmental variables were independently significant in explaining the floristic variation for the rope training set samples, with alkalinity and TP~~

149 having the greatest influence on species distribution (Tab. 4.17). Other important explanatory variables were pH and potassium. The subset of nine forward selected variables explained 74.6% (1.06) of the total environmental-species variation (1.42) (conductivity excluded).

The CCA was re-run, using only the forward selected variables, for the detection of possible outliers. No samples exceeded the outlier criteria (Section 4.3.4) but site 720 did exert a relatively high species-environment influence (x4.4). This site also had a very high axis 4 score (4.8). Site 720 was atypical in its chemistry due to it having very high ionic content (conductivity = 1520) but very low TP (16 pgL'^). It was left in the data-set but noted as an unusual site for further analyses.

It is worth noting here that the variation explained by TP was only marginally less than FRP, prior to forward selection. Following the forward selection process it added no significant relationship due to its high correlation with TP. The species response to FRP is discussed further in Section 4.5.5 and in Chapter 5.

~~Tile Training Set~~

~~Axes 1 2 3 4 Total inertia~~

~~Eigenvalues; 0.306 0.226 0.175 0.123 4.232~~

~~Species-environment correlations: 0.899 0.834 0.886 0.796~~

~~Cumulative percentage variance of species data: 7.2% 12.6% 16.7% 19.6%~~

~~o f species-environment relation: 20.0% 34.8% 46.2% 54.2%~~

~~Sum of all unconstrained eigenvalues 4.232~~

~~Sum of all canonical eigenvalues 1.530~~

~~Table 4.18 Summary statistics for the tile training set CCA~~

The cumulative species variation explained by the first two axes of the CCA was slightly lower than that observed for the DCA (12.6% and 16.8% respectively) (cf. Tab 4.14 and Tab. 4.18). This was also lower than the observed, explained variation for the rope training set data (Tab. 4.16) The variation due to species-environment

150 relationships, however, was relatively high with 34.8% explained on the first two axes and a further 19.4% (54.2% total) on axes 3 and 4 (Tab. 4.18). The first four species axes were all significantly (P <0.005) correlated with the diatom-environment relationships (rg = 0.90, 0.83, 0.89 and 0.80 respectively), suggesting a strong relationship between the diatom taxa and the measured environmental variables.

~~3.0~~

~~O 642 2. 0 - 0 630~~

~~o 608 612o~~

6150 o 661 1.0 - 6510 o 622\^P Temp 629 o o 603 o 632 619 0 635 0 n O,--N o 617 I 646 0 o620 64^ Aik 657 ) 0 621 597 d i607 0 645 Cond o 648 Cl 664 u 0. 0 — o 663 o 631 652" 623 o 662 653 5 9 9 0 C a ’

~~Ar 0 666 o 633 o 600 Mn' 637^ o 601 o Mg' 660 o 634 - 1. 0 - 628 655 654 Flow o 596 616 o 636 627 0 o602 o 644 - 2.0~~

~~- 2.0 - 1.0 0.0 1.0 2.0 3.0 CCA Axis 1~~

Figure 4.24 CCA - environment-species biplot of all environmental variables (18) and all sites (69), including all taxa >2% abundance and >5 occurrences (120) - Tile training set showing distribution of sites

From the exploratory CCA (Fig. 4.24) of the tile training set, TP and FRP showed strongly significant (P < 0.005), negative, correlations with species axis 1 (rg = 0.64 and 0.65 respectively). (This was contrary to the rope training set CCA in which alkalinity was more highly correlated with axis 1). Alkalinity was, however, also significantly correlated (positively) to axis 1 (rg = 0.60). Thus, on the CCA biplot (Fig. 4.24), sites which plot to the left tend to be high in phosphorus and lower in alkalinity (e.g. 612, 615, 619, 651 and 652), and sites plotting to the right are those with low phosphorus and

~~151 high alkalinity (e.g. 600, 603, 609, 631 and 632). The major ions (Na"^, K^, Cl ) and, to a lesser extent, Mn^"^ were also correlated with axis 1.~~

The second CCA axis was significantly (P < 0.05) correlated with flow (r^ = -0.40) and silica (rs = 0.44). Axis 3 was negatively correlated with both TP (rs = -0.39) (FRP, rg = - 0.32) and alkalinity (r^ = -0.45), with much of the variation being due to the separation of sites with low TP and low alkalinity from sites with high TP and high alkalinity. This was the reverse of the axis 1 variation. Axis 3 was also strongly correlated with pH (rg = -0.54). Axis 4 was correlated only with pH (rg = 0.38).

2.0 Key Sellaphora seminulum N. a = Navicula atomus ^ ^Amphora veneta N. 2 = Nov. [species 2] A Nitzschia amphibia Fragilaria elliptica N. t = Nav. tripunctata Silica A A N. p = Nitzschia palea Achnanthes lauenbergiana N. pa = Nit. paleacea

~~1.0 Sellaphora minima à~~

A Achnanthes lanceolata NO3-N Fragilaria Aik A vaucheriae ^ Fragilaria capucina Cocconeis placentula V. rumpens V. euglypta a \ \ /C ond Amphora pediculus ^ Achnanthes 0.0 minutissima Gomphonema parvulums' Â KT Melosira varians a S O / . /A 1 Mn N a v ^ la cryptocephala\ Cymbella minuta Navicula a [pseudogregaria]

~~- 1.0 Navicula A Nitzschia dissipata subminuscula a N. 2 a Flow~~

~~Navicula gregaria a Navicula menisculus~~

~~Navicula lanceolata a~~

~~A Navicula cryptotenella~~

~~- 2.0~~

~~- 2.0 - 1.0 0.0 1.0 2.0 3.0 CCA Axis 1~~

With phosphorus and alkalinity as the strongest, but opposing, gradients along axis 1 the common taxa, which are plotted on the CCA diagram (Fig. 4.25), show a clear response to these variables. Those species found commonly at high phosphorus plot to the left of the diagram and include: N. amphibia, A. veneta, Navicula subminuscula and S.

152 seminulum. Likewise, species plotting to the right of Figure 4.25 were found in greater abundance at sites of high alkalinity, e.g. A. minutissima, A. pediculus and Cymbella minuta. F. capucina v. rumpens is an exception, and plots on the right of the biplot, being found only at low TP sites.

On the basis of the CCA, flow appeared to be of much greater importance in the tile training set than in the rope data. The vector length (Fig. 4.25) and significant correlation with CCA axis 2 indicate a strong influence on species distribution. Consequently taxa with low axis 2 scores were more abundant at sites with greater flow, e.g. N. cryptotenella, N. lanceolata and N. menisculus.

Forward selection of environmental variables From the initial CCA, with all variables included, conductivity showed high collinearity (inflation factor = 32.1) and was therefore removed from further analyses. No other environmental variables were excluded. The CCA was re-run with forward selection .

Variable Variance Explained by Added After Individual Variable Forward Selection TP 0.21 0.21 Alkalinity 0.20 0.19 Silica 0.16 0.13 Flow 0.11 0.11 pH 0.20 0.11 0.19 0.10 FRP 0.21 -ns- Na+ 0.20 -ns- K+ 0.18 -ns- Ca^+ 0.14 -ns- Cl 0.14 -ns- 0.11 -ns- NO3 0.10 -ns- Fe^^ 0.09 -ns- S04^ 0.08 -ns- Temperature 0.08 -ns- AP+ 0.06 -ns- Sum of variance = 0.85

~~Table 4.19 Potential variance explained by each environmental variable and the added variance following forward selection for the tile training set (ns = not significant P < 0.05)~~

~~After forward selection, a total of six environmental variables were independently significant in explaining the floristic variation (Tab. 4.19). Without forward selection~~

153 TP and FRP each explained an equal amount of variance (0.21). TP was, therefore, selected first to be consistent with the rope training set. The six significantly selected variables explained 57.8% (0.85) of the total environmental-species variation (1.47), with TP and alkalinity as the major determinants. Other important explanatory variables were silica, flow, pH and manganese.

The CCA was re-run, using only the forward selected variables, for the detection of possible outliers. No samples exceeded the outlier criteria (Section 4.3.4) and the environmental variables showed no further collinearity.

~~154 4.5 Discussion~~

The primary aim of this chapter was to develop two lowland river-diatom training sets from the artificial substrata selected in Chapter 3. From the initial survey of 115 sites it was clear that trophic status and alkalinity of lowland rivers varied considerably and thus it was possible to collect diatom samples across wide ecological gradients. This section will therefore concentrate on the environmental characteristics and species assemblages from the training set sites, and assess the suitability of these data for the development of a diatom-based model to monitor trophic status.

~~4.5.1 Site Selection~~

With phosphorus as the main focus of this thesis it was important that the selected sites should cover a wide range of trophic levels. From the initial survey it was clear that the majority of lowland rivers in Southern England were relatively high in phosphorus concentration. Of the 115 sites sampled for the initial survey, 44% had FRP concentrations of over 1000 pgL'\ and only 24% of sites were below 100 pgL'\ The final choice of sites, selected from the stratified matrix of FRP and alkalinity (Section 4.4.1) was satisfactory in covering the phosphorus gradient, although there were only two training set sites with TP values below 10 pgL'\ This is typical of lowland rivers, where the most pristine sites are often reported as having over 25 pgL'^ TP (Allan 1995). The even distribution of sites with respect to alkalinity was more easily attained; alkalinity is primarily determined by the underlying geology of the rivers (Allan 1995). The highest alkalinity sites were found in the chalk streams and on the Oolitic limestone of the Cotswolds (e.g. River Kennet, R. Meon, R. Ray & R. Chum) and low alkalinity sites from the base-poor greensands (e.g. River Medway, Tilling Bourne & South Wey). The majority of these lower alkalinity sites were, however, close to the river source, with considerable increases occurring further down-stream. These observations suggest that alkalinity (as well as phosphoms) is increased with anthropogenic activity.

~~155 4.5.2 Sample Collection~~

The decision to use artificial, introduced substrata for sampling the diatom communities at the river sites was clearly justified by the poor availability of natural epilithon. At many of the sites, and particularly those with low flow, there were either no cobbles to be found (23%), or where cobbles could be found they were covered by sediment or macroalgae (37%). Round (1991 & 1993) strongly recommended that the sampling of rocks covered by sediment or macroalgae should be avoided. Only 40 percent of the sample sites had cobbles which were considered to be suitable for sampling epilithic diatom communities. The use of an introduced substratum therefore allowed for diatom samples to be collected from a wide range of sites, without compromising the integrity of the sampled habitat. The use of a constant substratum is of particular importance when comparing a large number of sites, avoiding the influence of within-site variation.

The use of artificial substrata was not without its problems. Loss from a site or fouling of a substratum by sediment or plant material resulted in the loss of data. The total number of diatom samples collected for each training set was, however, far greater than could have been reliably retrieved by sampling the natural epilithon. The rope substrata resulted in the highest return of samples, with only 7 percent lost during the one month sampling periods. The loss of tile samples was higher (18%), due mainly to fouling of the tile surface rather than loss from a site. The tile substratum was also susceptible to “flip” over in the current; three tiles were found upside down and had to be discarded. It is worth noting that only one of each substratum was left at the sites. Retrieval rates would probably have been higher had two, or more, been left. This latter point is recommended for any future work involving artificial substrata.

~~4.5.3 Environmental Relationships~~

Both the rope and tile training sets were similar in their environmental parameters and are, therefore considered together in this section. Where differences do occur, comparisons are made. The principal purpose of analysing the environmental data-sets was to screen the training set sites for unusual or extreme values and to ensure they covered the major gradients under investigation. In addition to phosphorus and

156 alkalinity it is important to assess the influence of the other parameters which may exert an influence on the observed species assemblages. The distribution of environmental determinants are considered separately below.

Phosphorus Total phosphorus and filterable reactive phosphorus were highly correlated. From the exploratory analysis it was clear that several of the values obtained for TP were too low (i.e. they were lower than the FRP values). Despite repeated analysis of these samples the source of this error could not be determined. In the majority of cases the error was very low and fell within the experimental errors of the methods. For sample 647 (SLEAl, November sampling), however, the TP value was 29 pgL'^ and FRP was 115 pgL'\ This error was possibly due to adsorption of phosphorus onto the walls of the polyethylene sample bottles prior to analysis, which has been reported in some circumstances (APHS 1989). Alternatively it could have been due to contamination of the FRP sample during analysis. This latter point was thought unlikely because none of the field or laboratory blanks from the same batch of samples showed any trace of contamination. Despite the error this site did not show up as an outlier in the canonical correspondence analysis. It was, however, regarded as a possible problem for further analysis in the development of the transfer function technique.

The sources of phosphorus were almost certainly due to inputs from sewage treatment works and, in more rural areas, leaching from septic tanks. It has been estimated that 93% of the phosphorus exported from the Thames catchment comes directly from domestic STW’s, with the majority of the remaining 7% coming from diffuse agricultural sources (Roger Sweeting, Environment Agency, pers. comm.). Consequently most of the larger lowland rivers in southern England have high phosphorus concentrations and only the more remote river sites, close to their sources, were found to have TP levels less than 100 pgL'\ The range of phosphorus values in this study is comparable to lowland rivers in other heavily populated regions of Europe (Prygiel et al. 1999).

~~The principal components analyses identified both TP and FRP as major gradients, highly correlated to the first PGA axes. Phosphorus was therefore considered to be well~~

157 represented in the two data-sets. The major difference between the two training sets was that no diatom samples were collected for the tile substratum at the very high TP site of Beverly Brook (site 207). The TP range was, therefore, slightly lower in the tile training set.

Alkalinity Alkalinity was evenly distributed through the data-sets and was strongly correlated to the second PC A axes. Alkalinity was highly correlated with calcium and was predominantly governed by the underlying geology of the river sites. The highest alkalinity sites were on rivers running though the Hampshire and Wiltshire chalk (e.g. sites 105, 172, 238 & 248) and on the Oolitic and Liassic limestone of the Cotswolds (e.g. sites 9, 12, 13, 14 & 28). The lowest alkalinity sites were on the base-poor Lower Greensands (e.g. sites 162, 177 & 178) and Hasting Beds (e.g. 251, 252, 253 & 262) of Kent and Sussex. The larger river sites and those flowing through the London clays tended to have more average alkalinity (e.g. sites 188, 207, 209 & 214). The values for alkalinity found in this study are typical of agriculturally impacted lowland rivers (Allan 1995) pH The majority of sites were circum-neutral to alkaline, which is typical of lowland waters in southern England (Bennion 1993). pH was highly correlated to alkalinity and, like alkalinity, was mainly governed by the underlying geology of the sites. Some of the sites on the very base-poor Lower Greensands may have been expected to have lower pH values, for example Old Lodge stream in the Ashdown Forest (E. Sussex) has a pH range between 4.0-5.0 (Monteith et al. 1997). The sites sampled in this region were, however, all on intensively farmed land and, although representing the lower pH end of the gradient in this study, were between pH 6.7 and 7.1. Agricultural practices have been demonstrated to cause an increase in surface water pH in Wales (Homung et al. 1990) and thus the observed (high) values in this study are less surprising. Despite the relatively low range of values in the data-sets (6.7 - 8.5), the PCA showed pH to be an important variable, correlated with the second axes.

~~158 Nitrate The level of nitrate-nitrogen was relatively high at almost all sites (91 - 10,548 pgL-1,~~

~~X = 3,043); this is typical of rivers running though intensively farmed, agricultural~~

areas (Omemik et al. 1980). In the training sets, nitrate concentrations showed no obvious geographical distribution nor did they correlate highly with any other variables in the data-set. This suggests that the major sources of nitrates, at these sites, were due to land use as well as sewage effluent; a situation which is typical of many of lowland England’s freshwaters (Birch & Moss 1990).

Silica Silica is an important nutrient for diatoms and has been demonstrated to influence the dynamics of diatom populations in lakes (Wetzel 1983). In rivers, however, silica has rarely been found to be in short supply (Lack 1971, Allan 1995), with solubility being higher in circum-neutral to alkaline waters (Marshall 1964). This was found to be the case at all the sample sites, where silica concentrations never dropped below 3.4 mgL'\ From work conducted in lakes, diatom competition with other algal groups has not been found to affect species assemblages until the concentration drops below 0.5 mgL'^ (Wetzel & Likens 1991). Silica was most highly correlated to phosphorus in the training sets, which is likely to be due to STW’s contributing extra silica, and thus adding to the already high background levels. The European average for silica concentrations in rivers

~~is 6.8 mgL'\ in this study the mean silica concentration was 10.86 mgL'\ This higher value perhaps reflected the high sewage input into the rivers of southern England.~~

~~Conductivity and Ionic Composition~~

The conductivity and concentration of the major ions (Ca^"^, Na"^, K^, Mg^"^, Cl' & S 0 4 ^’) was relatively high at all sites. The lowest value for conductivity was 130 pS cm'^ and only potassium dropped slightly below 1000 pgL'^ at one site. This is in contrast to many upland waters in the UK, where conductivity and ionic concentrations are generally much lower (Stevenson et al. 1991). The relationship between conductivity and ionic composition was not, however, straightforward. There was a strong correlation between conductivity and calcium (and alkalinity) but calcium was poorly, or negatively, correlated with the other major ions (Tabs. 4.6 & 4.9 and Figs. 4.8 & 4.10), suggesting that a large proportion of these ions (unlike calcium) were not geologically

159 derived. Potassium, sodium and chloride were most strongly correlated with phosphorus and their source is probably sewage treatment works. The source of magnesium and sulphate was less clear but given significant correlations with both calcium and the other major ions, it is probably of both geological and anthropogenic origin (atmospheric and STW ’s).

Due to the complex ionic structure of the lowland rivers sampled, conductivity could only provide a single measure of ionic strength, rather than information on composition. This was also the reason for the high collinearity between conductivity and the major ions and its eventual removal from the data-sets as a nuisance variable. In effect, by measuring conductivity as well as the major ions, some measurements were effectively being made twice; hence the collinearity.

Flow As would be expected, many of the lowland river sites were relatively slow flowing, despite active selection for faster flowing reaches. Only 11% of the sites had current velocities greater than 50 cm see"’ and a further 20% had flow rates below the detection limit of the flow meter used (approx. 4.0 cm sec’’). There was little correlation between flow and any other variables. The effects of flow on the diatom communities are discussed below.

Overview The results from the principal components analyses, for both training sets, suggested that the site selection procedure adequately covered a wide range of lowland river types. With respect to phosphorus and alkalinity the PCA identified these variables as major gradients within the selected sample sites and thus provided a suitable range of river sites from which the collection of two diatom training sets could be made, using artificial substrata. With the possible exception of the high end of the TP gradient in the tile training set, there was even coverage of phosphorus and alkalinity in both training sets. The lack of extremely high TP sites in the tile data was not considered as a problem, because an even range of TP from 4-6882 pgL’’ was achieved. Phosphorus concentrations rarely exceed this upper limit in rivers (Allan 1995).

160 The variability within the other environmental data was high but there were very few sites with extreme values. One site had an unusually high PCA score on axis 3 (site 266, rope training set sample 720) due to very high conductivity (1520 pS cm'^) and concentrations of the major ions, but very low phosphorus concentrations (total P = 16 pgL'^ & FRP = 6 pgL'^). It was noted as a possible outlier but left in the data-set for further analyses. No other sites were identified as outliers with respect to the measured environment.

~~4.5.4 Diatom Assemblages~~

The overall diatom species assemblages differed in their composition and relative abundance of taxa between the two substrata. This was expected from the results obtained in Chapter 3 and reflects the findings from other comparative studies between different artificial substrata (Siver 1977, Tuchman & Stevenson 1979, Stevenson & Lowe 1986, and Cattaneo & Amireault 1992). A. minutissima was the most common species in the tile samples and achieved the highest relative abundance in both training sets. The most common taxon in the rope samples was A. lanceolata, followed by G. parvulum and C. placentula v. euglypta, the latter taxa being more typical of the natural epiphytic communities. Small species, normally associated with the natural epilithon, were more common on the tile samples (e.g. N. atomus and A. pediculus).

Species Diversity The Hill’s N2 diversity of the samples from both training sets was very variable between the sites but, in the majority of cases, was similar between the two different substrata at any one site. There were notable exceptions to this: at sites PANGl and TILL2 the tile samples were very diverse but the rope samples were dominated by A. minutissima and C. placentula v. euglypta, respectively. Conversely, at MOLEl and SLEAl (Nov. sample) the rope samples were very diverse and the tile samples dominated by only one taxon: N. gregaria and N. lanceolata, respectively. Overall the mean and range of species diversity for the rope and tile samples was very similar: rope samples had a very slightly higher mean Hill’s N2 value but a tile sample achieved the highest diversity at any one site (Fig. 4.26).

~~161 25 -~~

~~î 20 - s 15 - s~~

~~Tile Rope~~

~~Figure 4.26 Comparative Hill’s N2 diversity between the two training sets~~

Rope Training Set The detrended correspondence analysis of the rope species data revealed the major gradient in the diatom assemblages to be associated with alkalinity. Sites with high alkalinity tended to be dominated by A. minutissima. The exception to this was high alkalinity sites which also had high phosphorus where A. minutissima was normally replaced by C. placentula v. euglypta. The dominance of A. minutissima at sites of high alkalinity was instrumental in the distribution of samples along DCA axis 1. Samples with low alkalinity had high DCA axis 1 scores and were characterised by taxa including: G. pannilum, S. minima, M. varians, Eunotia spp., N. cryptocephala and N. rhynchocephala. The Hill’s N2 diversity of the lower alkalinity sites was generally higher than the A. minutissima dominated sites with high alkalinity.

The remaining variation within the species data was less clearly related to the measured environmental variables. The strongest correlation on the second DCA axis, using the environmental data passively, was with silica. Silica was high at all sites and thus it is unlikely that it would have a direct controlling influence on the diatom assemblages due to silica limitation. On the contrary, it would appear that this relationship is most likely due to some Fragilaria species showing a strong preference for high silica concentrations (Fig. 4.27). The reason for these Fragilaria species performing better at very high silica concentrations is not clear, and does not seem to have been recorded in other diatom studies. Silica was correlated with phosphorus but the response of the Fragilaria species to TP and FRP was less clearly defined.

~~162 10.0 7.5 1.5 Fragilaria pinnata o Fragilaria elliptica Ctenophora pulchella~~

~~° ° % % %~~

~~=0 0.0 O o 0 O 0.0 0.0~~

~~20.0 50.0 7.5 Fragilaria brevistriata Fragilaria construens ° Fragilaria oldenburgiana V. venter~~

~~% % %~~

~~o°dS> ° ° 0 0.0 8n B ° ° 0.0 " 0O®ntl? 0.0 2.8 22.4 2.8 22.4 2.8 22.8 Silica mgL ' (log)~~

~~Figure 4.27 Distribution of C. pulchella and Fragilaria spp. along the silica gradient (rope)~~

Phosphorus was important in the observed distribution of diatom samples in the DCA, with TP being significantly correlated to species axes 1, 3 and 4. The fact that TP was correlated with three DCA axes suggests that its importance in determining species assemblages was dependant on the other environmental conditions at any one site. Under such conditions, where species may be reacting to numerous environmental gradients, detrended correspondence analysis becomes very difficult to interpret and hence the use of constrained ordination techniques (CCA), see below.

The correlations of the remaining environmental variables with the DCA axes, were generally low. Interestingly, only current velocity appeared to have no significant relationship with the diatom assemblages despite the wide range of flow conditions between the sample sites (3.6-98.6 cm sec'^). This is contrary to the findings of other studies, where current velocity was seen to have a marked effect on the diatom communities (Douglas 1958, Stevenson 1983, Homer et al. 1990 and Biggs 1996). This may be due to the structure of the rope substratum causing the effects of flow rates to be limited. Reynolds (1996) reported on the reduction of velocity within the dense fronds of river macrophytes, due to the creation of local micro-environments, thus allowing unattached and less robust forms of algae to develop at relatively high flow conditions.

~~163 The structure of the rope can be considered analogous to submerged macrophytes and will, therefore, be likely to create similar micro-environmental conditions.~~

Tile Training Set The distribution of tile samples, within the DCA, was somewhat different from that observed for the rope data-set. The gradient length on the first axis was greater in the tile data, and the total variation in the species data was also larger. Unlike the rope samples, however, the sites were more evenly distributed along the first two axes, without the dispersion of samples at the high end of axis 1 seen in the rope DCA (cf. Figs. 4.12 & 4.17). The main pattern of species variation was correlated with alkalinity but not as strongly as the rope samples. This was mainly because of the response of Achnanthes minutissima to the alkalinity gradient, but unlike the rope samples there was also a stronger correlation (negative) between DCA species axis 1 and phosphoms. These results suggest that the diatom taxa growing on the tile substratum reflect the trophic conditions slightly better than the rope samples.

The second DCA axis was similarly correlated with silica, but negatively in the tile samples. Again this was due to the greater occurrence of some Fragilaria species at higher silica concentrations. Unlike the rope data, however, DCA axis 2 was also noticeably correlated to pH and flow. The relative importance of flow in determining species distributions in the tile training set is doubtless due to the increased physical stresses caused by current velocity across the flat surface of the tile. Under such conditions some motile taxa and delicate chain forming diatoms are unable to maintain stable conditions for growth (Reynolds 1996). The distribution of some taxa in the tile training set was found to be restricted to sites of low flow rates; e.g. M. varians, F. pinnata and Navicula veneta. Other, non-attached taxa, appeared to be able to exploit the microphytic boundary layer, where laminar flow occurs, and maintain relatively high abundances; e.g. N. dissipata, N. lanceolata, N. gregaria, N. tripunctata and N. cryptotenella. Similar relationships, between flow and species distributions, were not observed in the rope training set.

~~On the remaining DCA axes, there were no strong correlations (P < 0.005) between the species assemblages and the environmental variables. The even distribution of sites~~

164 across the first two DCA axes, coupled with the strong passive relationships with the environmental variables, suggests that the tile samples reflected their overall environment more closely than the assemblages growing on the rope substratum. The response to phosphorus alone, however, is more difficult to assess from these data. In the tile training set TP was more strongly correlated to DCA axis 1, and to a lesser extent to axis 2. In the rope data TP was strongly correlated with three axes, and thus the use of constrained ordination techniques (CCA) were needed to identify the direct response of the species assemblages to phosphorus.

~~4.5.5 Direct Species - Environment Relations~~

The primary environmental variable under investigation in this thesis is phosphorus and thus before considering the relationships between the species assemblages and the entire environmental data-set, it is helpful to establish any simple patterns between the diatom taxa and phosphorus concentrations. By ranking the sites in order of their increasing phosphorus concentration and comparing their relative species abundances, it is clear that some taxa show a preference for different levels of TP (Figs. 4.18 & 4.20). For example, A. minutissima occurred throughout both training sets at almost all sites, but only achieved high abundances at the low to mid range of TP concentrations and would, therefore, appear to have a low TP optima, but also be widely tolerant. Similarly, A. biasolettiana, in the tile training set, only occurred at high abundance at the very lowest TP sites. Other taxa performed better in the mid range of TP concentrations and appeared to exhibit a classical Gaussian response; e.g. M. varians, in the rope samples, and N. atomus, in the tile samples. At the high end of the TP gradient, in both data-sets, N. amphibia, S. seminulum and A. veneta all appeared to perform better. Conversely other taxa showed little or no response to the TP concentration. A. lanceolata and C. placentula v. euglypta were evenly distributed throughout both training sets, but showed no preference for a delimited range of TP concentrations.

Phosphorus levels are not, of course, the only important criterion for diatom growth, there will be many other environmental and biotic factors influencing the distribution of the diatom taxa. Indeed, if the sites are arranged in order of their alkalinity, which was the other main gradient across which the sites were selected, similar patterns in species

165 response can be observed (Figs. 4.19 & 4.21). Notably, A. minutissima shows a strong preference for higher alkalinity sites, as well as lower TP concentrations. Other species only occurred at low alkalinity; e.g. E. curvata and N. rhynchocephala. These types of analyses could be performed with all the environmental variables and doubtless similar patterns would emerge in the species data. Thus it is inconclusive to look simply at the effect of one variable, in this case phosphorus, but instead all variables need to be considered together. Hence the use of canonical correspondence analysis, in which all the variables are considered together, and the assumption is made that the species will be distributed unimodally across these variables.

Rope Training Set Unlike the DCA, where sample points on the biplot are derived purely from their species assemblages, CCA uses the environmental variables to constrain the samples within linear combinations of the variables (ter Braak 1990). The CCA confirmed that alkalinity was having a strong influence on the species assemblages. The alkalinity vector was the longest and was highly correlated to the first CCA axis. Manganese also appeared as a major determinant. This was due in part to its high, negative correlation with alkalinity in the data-set, but it did not show excessive collinearity in the analysis and thus manganese is likely to be exerting its own, independent influence on the species composition. Similar results have been observed with manganese in the study of upland stream systems in Wales (Allott & Flower 1997). The reason for this strong species response to manganese is unclear but its limitation at a number of sites may be an important factor. Manganese is a vital component in the oxidation of water via the photosynthetic pathway (Taiz & Zeiger 1991) and its limitation could have a detrimental effect on some taxa, or conversely if some species are able to store manganese or use alternative metabolic pathways, a competitive advantage would arise. These theories are untested but the similar importance of iron, also a vital photosynthetic component, in the analysis suggests that these trace elements may be influential in determining species distributions.

Phosphorus, both as the filterable reactive form and total P, was an important variable and was correlated to CCA axes 1 and 2. The correlation of P with two axes is due to the analysis separating sites with high alkalinity and high phosphorus from those with

1 6 6 high alkalinity and low P and vice versa. Variation in the phosphorus concentrations which could not be accounted for on the first two axes was expressed on axis 4 and was generally due to sites with unusual combinations of diatom taxa and environmental variables, e.g. sample 720 which had very high concentrations of the major ions but low TP. This sample was almost entirely dominated by C. placentula v. euglypta and C. pediculus. Calcium and the major ions were also important variables in the CCA. Calcium being correlated with alkalinity and the other major ions with phosphorus, suggesting STW’s as a major source of ionic loading.

With forward selection a subset of nine environmental variables was significant (P = < 0.05) in explaining the distribution of species data with conductivity removed from the data-set due to its high collinearity. Alkalinity explained the highest amount of variation, followed by TP. The selection of TP over FRP was only marginal and from the results there is only a very slight statistical advantage in the use of total phosphorus. The selection of potassium was representative of the overall ionic component, excluding calcium. The selection of one major ion (K"^) resulted in the other highly correlated ions being demoted in their importance. Manganese, pH and calcium were selected despite their correlation with alkalinity, and were thus responsible for some of the floristic variation, independent of alkalinity. Silica was also selected, but was less important in the constrained ordination than it appeared to be in the DCA with the variables introduced passively. This is likely to be due to the taxa which were responding to high silica concentrations (mainly Fragilaria spp.) occurring at low abundances or being relatively rare. It is also likely that these species were not only responding to silica and, therefore, the importance of other variables reduced its apparent influence in the CCA. Temperature was only just significant in the forward selection. The role of temperature in determining diatom assemblages in this study is unclear. The range of temperature across the sites was wide (4.2 - 15.5°C), but the time of sampling varied from 7.00 am. to 7.00 pm. and thus does not account for diurnal fluctuation, which can be as great as 10°C in small temperate rivers (Allan 1995). A factor that may be important in this study is that the larger rivers are more stable in temperature and thus the low and high temperature readings reflected sites with greater diurnal fluctuation. This may have had some influence on diatom assemblages but is not tested in this study.

167 Tile Training Set Unlike the rope training set, phosphorus appeared to be the major explanatory variable in the CCA, with both a stronger correlation to the first axis and a slightly longer vector than alkalinity. Principally the first CCA axis was separating sites with high P and low alkalinity from those with low P and high alkalinity. Because not all sites could be arranged in this manner, the third CCA axis was negatively correlated with phosphorus and alkalinity, thus including the high alkalinity sites with high phosphorus. The second CCA axis also differed from the rope analysis, with silica and flow being the most important variables. As mentioned above, flow appeared to be of greater importance in the distribution of taxa in the tile samples. The variation in the remaining variables was very similar to that seen in the tile analysis.

With forward selection of the environmental variables, a subset of only six was statistically significant (P = < 0.05). TP and FRP jointly explained the greatest independent variation in the species data and, therefore, TP was selected to be consistent with the rope analysis. If FRP had been selected the results, with respect to the other variables, would have remained the same. The selection of TP as the primary explanatory variable suggests that the diatom taxa were responding to trophic status above all other variables, including alkalinity. The most common species found at high TP plotted to the left of the CCA biplot and included N. amphibia, S. seminulum and A. veneta. This differs considerably from the rope training set, in which alkalinity was by far the most important variable. In fact, the amount of variation due to TP was very similar in both data-sets but the influence of alkalinity, on the diatom assemblages, was lower from the tile substratum. These results appear to suggest the tile substratum is a more suitable surface to use for the diatom-based monitoring of phosphorus in lowland rivers.

Silica, flow, pH and manganese were also significant in the forward selection of the tile training set data. Sites with high silica had higher abundances of F. pinnata, F. elliptica, F. construens, v. venter and F. brevistriata as discussed in section 4.5.4. Similarly the results from the CCA confirm the greater influence of flow rates on the diatom assemblages seen in the distribution of samples in the DCA. The importance of pH in determining diatom distributions has been well demonstrated in aquatic systems over

168 long pH gradients (Stevenson et al. 1991). The range of pH values in this data-set, however, was relatively small (6.75-8.50). Despite this a number of taxa were observed to show a clear preference for either the low pH sites (e.g. N. cryptocephala, N. rhynchocephala and all the Eunotia species) or the high pH sites (e.g. G. olivaceum, F. pinnata and C. bacillum). The majority of taxa showing a clear response to pH were not common in the data-set and are, therefore, not likely to have an important influence on a predictive model for trophic status. The response of species to manganese was very similar to the rope training set results and thus strengthens the view presented here that it is an important element in governing species distributions, although the mechanisms remain unclear.

~~4.5.6 The Suitability of River Diatoms for Modelling Phosphorus~~

With the use of multivariate numerical techniques it is possible to ascertain the suitability of the two different artificial substrata for the development of predictive, diatom-based, models for the assessment of trophic status. The methods used in this thesis allow for an understanding of ecological patterns to be determined but they are not, however, infallible. They rely entirely on the data provided and thus any taxonomic errors or incorrect environmental measurements will increase the observed errors of the analysis. Likewise it would be naïve to assume that all the influential environmental variables are included in the data-set. The diatom taxa react to the total environment in which they live, which is bound to involve many subtle changes which were either not measured or are impossible to measure accurately. The choice of environmental variables measured for this study was chosen a priori as the most likely to be having a major controlling influence on the diatom assemblages.

Needless to say there are other factors involved; the importance of which were not accounted for in this thesis. For example, grazing by snails, caddisfly larvae and, to a lesser extent, mayfly larvae has been shown to have a marked effect on algal biomass in artificial streams (Lamberti et al. 1987). The extent to which this may affect algal accumulation in rivers is very difficult to assess, but Lamberti and Resh (1983) reported a 5-20 fold increase in algal biomass (not only diatoms) when the caddisfly larvae, Helicopsyche was excluded. Perhaps of greater concern in this thesis is the evidence that

169 invertebrate herbivory may result in changes in the species composition due to selective grazing (Sumner & Mclintire 1982, Hill & Knight 1987, 1988, Steinman et al. 1987). Hill & Knight (1988) demonstrated up to a sevenfold over-representation of some diatom taxa in the gut contents of caddisfly larvae {Neophylax). Similarly, the numbers of adnate diatoms were under-represented in the gut contents of mayfly larvae (Ameletus). The physical disturbance of the micro-sediment layer on the substratum by invertebrates, may also reduce the occurrence of some motile diatom taxa (Hill & Knight 1987). The extent to which these biotic processes were important in determining the training set diatom assemblages can not be tested here. Regardless of these factors, and other unquantified variability, clear signals can be established from the measurements that were made. Phosphorus and alkalinity were the two main environmental variables that accounted for the majority of observed diatom distributions and as such both training sets should be suitable for the development of a predictive model for trophic status.

One further method can be used to ascertain the suitability of a variable for the purposes of environmental prediction. This is to perform CCA, as above, but using only the variable which is to be modelled and exclude all other variables from the analysis. The result is that only the first axis is constrained to the environment; all remaining axes are effectively the same as in DCA (i.e. unconstrained). If the axis 1 (Xi), to axis 2 (X2) ratio is high, it can be assumed that the variable (TP) explains a sufficiently high proportion of the total variance to be used for prediction (Pienitz et al. 1995). The results for a TP constrained, axis 1 CCA of the two river diatom training sets are summarised in Table 4.20 and 4.21.

~~Axes 1 2 3 4~~

~~Eigenvalues: 0.199 0.388 0.295 0.268~~

~~Species-environment correlation: 0.794 0.0 0.0 0.0~~

~~Cumulative percentage variance o f species data: 5.5% 16.1% 24.2% 31.5%~~

~~Xi'.Xi ratio: 0.51~~

~~Table 4.20 CCA summary statistics of the rope training set data, with TP as the only variable~~

~~170 Axes 1 2 3 4~~

~~Eigenvalues: 0.214 0.403 0.297 0.275~~

~~Species-environment correlations: 0.824 0.0 0.0 0.0~~

~~Cumulative percentage variance o f species data: 5.1% 14.6% 21.6% 28.1%~~

~~Xi'.X2 ratio: 0.53~~

~~Table 4.21 CCA summary statistics of the tile training set data, with TP as the only variable~~

Where pH has been the focus of environmental reconstruction the ÀiiXz ratio can be as high as 0.93 (Kingston et al. 1992) or 0.84 (Dixit et at. 1991) compared to 0.51 and 0.53 in the data-sets presented here. The response of diatoms to pH, however, is due to direct physiological processes, associated with the pH modification of other water chemistry parameters, e.g. aluminium solubility (Round 1990). This is contrary to the more complex relationships controlling circum-neutral, nutrient rich lowland river diatom assemblages. In other studies on phosphorus the ratio has been found to be much lower, e.g. 0.40 in a British Columbian lake data-set (Hall & Smol 1992) and 0.50 in a South East England pond data-set (Bennion 1993). Dixit et at. (1991) quoted a ratio of > 0.50 for the variable to be suitable for weighted averaging methods of calibration. The Xi:X2 ratios, for the rope and tile training sets, just fall above this criterion and thus these data sets are both considered suitable for the development of a diatom-based model for the assessment of trophic status in lowland rivers.

~~171 4.6 Conclusions and Summary~~

There was considerable variability in both the diatom assemblages and environment in both training sets. The major environmental gradients determined by principal components analysis were alkalinity and phosphorus and the sites from both training sets conformed well to the a priori selection of these variables. In the rope data, there was a clear species response to alkalinity, identified from detrended correspondence analysis, and despite other variables having a clear influence (i.e. silica, ionic content and manganese), phosphorus showed highly significant correlations with the patterns in species distribution. The tile training set data also showed alkalinity as a dominant factor in shaping the species assemblages, but the role of phosphorus appeared to be greater than in the rope samples, with a higher correlation on the first DCA axis. The importance of flow was also more significant in the tile data, and was attributed to the greater exposure to laminar flow patterns over the flat tile surface.

These patterns were confirmed with the use of direct multivariate gradient analysis (CCA) and with forward selection a subset of variables was identified that independently explained the floristic patterns. Alkalinity was the strongest gradient in the rope data, followed by total phosphorus. The tile samples showed a similar response to phosphorus but the influence of alkalinity was reduced to slightly below that of TP. The choice of using TP as an explanatory variable rather than filterable reactive phosphorus (FRP) had only a very minor statistical advantage and thus either variable is considered suitable for the purposes of this study. With the inclusion of good cation and anion chemistry, conductivity was identified as having excessive collinearity within CCA. This was due to conductivity being dependant on both calcium and the concentration of the other major ion, which were not correlated at all sites. Conductivity was therefore removed from the analysis in both data-sets.

CCA was also used to screen for outlying samples, with unusual environmental-species relations. Only one sample from the rope training set was prominent as a possible outlier (720), but it did not completely satisfy the predetermined criteria for outliers, and was thus left in for further analysis. There were no outliers from the tile sample.

172 Despite the complexities of diatom responses to their environment, much of which cannot be measured reliably, both the rope and tile substrata yielded diatom assemblages that showed a clear response to phosphorus in lowland rivers. Their suitability was further evaluated by the use of CCA, constrained only to the TP gradient. Both training sets showed X i’Xi ratios of > 0.5 and can therefore be considered as appropriate for weighted averaging methods to model phosphorus in lowland rivers. The advantage of one substratum over the other remains unclear. Both substrata yielded different, but equally valid diatom assemblages suitable for modelling. The tiles did have the disadvantage of lower recovery rates but by the placement of duplicates, or an efficient method of securing them to the river bed, it is postulated that this problem could be overcome.

~~173 Ch apter F ive~~

~~T h e De v e l o p m e n t o f Dia t o m -B a se d M o d e l s f o r THE A sse ssm e n t o f T ro ph ic St a t u s in L o w l a n d R ivers~~

~~5.1 Introduction~~

In the previous chapter, ordination methods were used to express the diatom species response to their environment. In this chapter, the reverse procedure is investigated to assess to what extent the environment (phosphorus) can be expressed as a function of the diatom assemblages. This is achieved by the development of a “transfer function” or “biotic index” and uses the technique of calibration (ter Braak 1987b).

The use of diatoms to assess river water quality is by no means a new phenomenon {cf. Whitton et al. 1991, Whitton & Rott 1996 and Chapter one (this study) for an outline of the historical development). What has changed, however, is the availability of new, computationally intensive, numerical techniques, that allow ecological and palaeoecological environmental reconstructions to be achieved with an ever increasing degree of accuracy and statistical reliability {cf. Imbrie & Kipp 1971, ter Braak 1987b, ter Braak & van Dam 1989, Birks et al. 1990, ter Braak et al. 1993). In this study weighted averaging regression (WA) (ter Braak & van Dam 1989) and the extension of this technique, weighted averaging - partial least squares regression (WA-PLS) (ter Braak et al. 1993), are used to develop diatom-based, predictive models for the assessment of phosphorus in lowland rivers.

~~5.2 Aims~~

The primary objective of this chapter is to use the rope and tile training set data to develop predictive models for the assessment of phosphorus in lowland rivers. Having established that both the rope and the tile substrata yielded training sets suitable for

174 these types of environmental reconstruction, WA methods will be used on both data sets. The relative predictive performance can then be assessed by comparison of the regression statistics (r^) and error statistic (i.e. the root mean squared error of prediction (RMSEP)).

~~5.3 The Theory of Weighted Averaging Methods~~

~~5.3.1 Weighted Averaging (WA)~~

The principal of WA is that at a river site with a defined environmental variable, in this case phosphorus (P), diatoms with their P optima close to the P concentration will tend to be the most abundant species present. The estimation of an optimum for species k can therefore be obtained by the average of P concentrations at which k occurs, weighted by the relative abundance (y) of k at each site; this is WA regression (Equ. 5.1). Hence, if a value for P is to be obtained from the diatom assemblages, the estimated P will be the weighted average of P optima for all the taxa present; this is WA calibration (Equ. 5.2) (Birks et al. 1990).

~~Equation 5.1 WA regression Uk = ____~~

~~Equation 5.2 WA calibration X = k=J____~~

The notation used follows that of Birks et al. (1990): ûk is the estimated WA optimum for species k. X is the environmental variable (P). Xi is the value of x at site i. yik is the abundance of species k at site /, where >0,i=l...n sites and k= I...m diatom taxa. X is the estimated or inferred value for x at site i. The ecological value of WA can be further enhanced by the inclusion of an expression for species tolerances. Intuitively, a taxon with a narrow P tolerance will provide a better estimate of P than taxa which are widely tolerant, and thus the former can be given

~~175 greater weight in WA regression and calibration. A taxon’s tolerance (n)is determined by Equation 5.3 and the resultant WA calibration by Equation 5.4 (Birks et al. 1990)~~

~~0.5~~

~~^ yik{^Xi — Mi) Equation 5.3 Species tolerance f* =~~

~~1 = 1~~

i=l Equation 5.4 Tolerance downweighted calibration X = ÊW?*

A problem associated with these methods is that the average is taken twice, once at the regression stage and again in calibration, and thus results in a reduction or “shrinking” in the range of inferred values. To compensate for this a simple linear regression step is incorporated into the model to “deshrink”. The initial inferred values (x ) are regressed onto the observed values (x ) using the so called “classical regression” (Equ. 5.5 & 5.6, where a is the intercept, b is the slope and £; is the residual error) (ter Braak 1988, Birks et al. 1990).

~~Equation 5.5 First deshrinking regression step Initial Xi = a + bxi + E \~~

~~Equation 5.5 Second deshrinking regression step Final X = (initial x — a)!b~~

An important component in the development of a transfer function is the rigorous internal statistical testing (self validation) to determine the goodness of fit (r^), between the observed and predicted results and, more importantly, the prediction error (RMSEP). The r^ statistic, on its own has no predictive importance and can be very misleading. For

example it is possible to achieve an r^ = 1 in a data-set with n sites and n-1 species, even if there is no relation between the species and environment (ter Braak & Juggins 1993). The prediction error is therefore paramount in the assessment of the model and can most easily be determined by cross-validation. The technique used for obtaining the root mean squared error of prediction in this study is a process known as jack-knifing. In a training set with, for example, 78 samples the WA is performed 78 times on a subset of 77 samples and the resultant transfer function is applied to the one site that is left out. This is performed for each sample to give a predicted x, which is then subtracted from

176 the actual x. The RMSEP is the mean of all the prediction errors and gives the most accurate and robust statistical error term, without the use of a second data set on which to test the model (ter Braak & Juggins 1993).

The method of WA performs very well on noisy, species rich data with many zero values, and particularly when the species are distributed across a wide ecological gradient of >3 standard deviations (ter Braak & Juggins 1993). The method does however have its weaknesses, ter Braak & Looman (1986) highlighted the susceptibility of WA to the distribution of the environmental variables in the training set. Each environmental variable is considered separately, and any residual correlations between species and environment is ignored. Such correlation are often due to variables that have not been taken into account. Hence the method of WA-PLS (ter Braak & Juggins 1993) was devised to overcome this problem and endeavoured to add the residual correlations, to further improve on the weighted averaging technique.

~~5.3.2 Weighted Averaging - Partial Least Squares (WA-PLS)~~

Weighted averaging - partial least squares was developed from the technique of partial least squares (PLS: Wold et al. 1984), that has been used extensively in the field of chemometrics (Geladi 1988, Martens & Naes, 1989). PLS can be likened to principal components analysis, in that it assumes a linear response within the data but, unlike PCA, it can be used as a multi-component, predictive tool. The assumption that the response is linear, however, renders it inappropriate for the modelling of species- environment data, which are almost always non-linear (unimodal). WA-PLS can, therefore, be likened to correspondence analysis, in that it is simple PLS with a weighted averaging component that allows for a Gausian species response; hence WA- PLS (ter Braak & Juggins 1993). The PLS and WA-PLS algorithms are complex, and thus not presented here. A full account of the mathematical formulae can be found in ter Braak et al. (1993).

Where WA-PLS normally has an advantage over simple WA is that it makes use of the significant patterns in the residual species data after fitting the first component. In fact, although the method of WA and WA-PLS are different the first component of WA-PLS

177 has been proved to be analogous to WA (ter Braak & Juggins 1993). Following the fitting of the first component of WA-PLS, which is a two-way weighted average of the original environmental variable, it then goes on to perform further two-way weighted averaging on the residuals of the species-environment data, to extract extra ecological information. The number of components used in WA-PLS is effectively limitless but for the purposes of prediction, more than seven are rarely required to achieve the minimal RMSEP, and usually less (ter Braak & Juggins 1993). The prediction error is achieved in the same way as for WA (jack-knifing) and the choice of which WA-PLS component to use can be determined by whichever component produces the lowest RMSEP. Because the results of the first component of WA-PLS are the same as WA, if the lowest RMSEP is from the first component, then no advantage is gained and weighted averaging is thus the better choice of method due to its relative simplicity. The final choice of method is, therefore, a balance between the improvement gained by using a more complex technique, and the use of the “minimum adequate model”. The principle of parsimony in statistics should always be applied to gain the best possible model, with the fewest number of redundant parameters or components (Birks 1998).

~~5.4 Methods~~

The data used in this section are the same as those presented in the previous chapter for both the rope and tile training sets. The phosphorus data are expressed as logio to ensure more even unimodal diatom species responses, under which circumstances weighed averaging methods perform optimally (ter Braak & Looman 1986). The diatom species data are, in the first instance, based only on taxa which occurred in more than five samples or above 2% relative abundance at any one site. For WA-PLS, however, the inclusion of all taxa has been found to increase the precision of the resultant model (Birks 1998) and thus the entire species data-sets are presented, to assess any extra value gained by the addition of rare taxa.

The relative importance of the two forms of phosphorus, TP and FRP, was discussed briefly in Chapter 4. From the analyses it was clear that the relationship between the two forms of P was very close and thus the observed diatom response was also seen to be

178 very similar. A further intention of the model development, therefore, is to determine to what extent the different forms of phosphorus have on the resultant transfer functions. The data analysis in this chapter was performed using the computer program CALIBRATE (Juggins & ter Braak 1997). CALIBRATE performs both WA and WA- PLS regression and calibration on species and environmental data, and also provides the r^ and error statistics (RMSEP) by the process of jack-knifing. CALIBRATE was also used to plot the results.

The first step in the data analysis was to screen the data for possible outliers. In the previous chapter no samples were identified as outliers under the a priori criteria set for DCA and CCA, although several samples did show unusual species-environmental relations. A further check for outliers was therefore used within the WA methods. If any sample had model residuals (i.e. measured P concentration - estimated P) of > ±0.75 (logio phosphorus concentration) in both simple two-way weighted averaging (WA) and tolerance down-weighted weighted averaging (WA(toi)), it was deleted (e.g. Birks et al. 1990, Larsen et al. 1996).

The choice of which model to use was made by running the three different types of analysis (WA, WA(toi) & WA-PLS) on both the rope and tile training sets for TP and FRP to establish which method provided the lowest root mean squared error of prediction and highest correlation (r^) between the measured P and predicted P. This was done following the deletion of any samples that fell outside the outlier criteria. With WA-PLS the choice of which component to use is determined by the same process; the component with the lowest RMSEP and highest r^ is chosen. If the improvement is only very slight, however, then the minimum number of WA-PLS components is chosen to maintain the principle of the minimum adequate model (Birks 1998). A further measure of the model’s suitability comes from the mean and maximum bias statistics, which can be considered as the systematic differences in the prediction values of the model (Juggins & ter Braak 1993). The mean bias was calculated by a simple average of the model residuals. The value for maximum bias was estimated by subdividing the sampling interval of the environmental variable into 10 equal groups. The mean bias was calculated for each group using a jack-knifed cross-validation, and the maximum bias taken as the largest mean value for any one interval (Juggins & ter Braak 1993).

~~179 From the weighted averaging results, species optima and tolerances were estimated for both TP and FRP. The role of some diatom species as indicators of trophic status can therefore be evaluated~~

~~5.5 Results~~

~~5.5.1 Rope Training Set Data~~

The first step in the weighted averaging regression was to identify any outlier sites which exceeded a model residual of ±0.75 in both simple weighted averaging (WA) and in tolerance downweighted WA (WA(toi)}. This was performed for both total phosphoms and filterable reactive phosphorus, to give two reliable training sets for the development of the two respective predictive models. The deletion of atypical sites from WA analysis has been found to be necessary in almost all cases of its use, to provide an optimal model (Birks et al. 1990, Bennion 1993, Jones & Juggins 1995, Larson et al. 1996). From the initial 78 samples in the rope training set, nine exceeded a ±0.75 model residual for TP (samples: 669, 679, 680, 68 6 , 690, 710, 720, 726 & 743) and ten for FRP (samples: 669, 679, 680, 691, 699, 700, 710, 717, 720, & 728). Tables 5.1 and 5.2 show the weighted averaging results prior to, and following sample deletion.

All samples included (n=78) Outlier samples deleted (n=69) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component 2 Component 2 r' 0.510 0.548 0.529 0.724 0.734 0.762 RMSEP 0.637 0.542 0.555 0.425 0.424 0.384 Mean Bias 0.028 0.020 0.016 0.028 0.034 0.012 Max. Bias 0.610 0.638 0.831 0.315 0.620 0.529

~~Table 5.1 Weighted averaging regression results for the diatom-based modelling of TP using the rope training set data, prior to and following the deletion o f outliers~~

All samples included (n=78) Outlier samples deleted (n= 68 ) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component Component r: 0.478 0.497 0.484 0.742 0.723 0.850 RMSEP 0.740 0.637 0.647 0.425 0.457 0.316 Mean Bias 0.032 0.037 0.009 0.019 0.034 0.001 Max. Bias 0.844 1.145 1.243 0.758 1.098 0.448

~~Table 5.2 Weighted averaging regression results for the diatom-based modelling of FRP using the rope training set data, prior to and following the deletion o f outliers~~

~~180 A number of clear deductions could be made from the summary statistics presented in Tables 5.1 and 5.2:~~

• Prior to sample deletion, the WA(toi) model performed marginally better than simple WA and WA-PLS for both TP and FRP. • For both TP and FRP, the deletion of outliers significantly improved the performance of the models with respect to the correlation between observed and predicted P (r^) and gave a considerable reduction in the RMSEP. • Following the deletion of outliers, WA-PLS gave the optimal model statistics for both TP and FRP.

~~• Following the deletion of outliers the diatom-based model for FRP gave better results than the TP model with respect to the r^ and RMSEP statistics.~~

Figures 5.1 - 5.4 show the TP and FRP model results graphically, both prior to and following the deletion of outlier samples. For each model the observed phosphorus is plotted against the predicted P (left hand side of the figures) and the model residuals are also plotted to show the extent to which the diatom-based, jack-knifed predictions for each sample differ from the observed phosphorus (right hand side of the figures). The graphs showing the model residuals are fitted with Cleveland Loess scatter plot smoother curves (Cleveland 1979) to demonstrate the mean over- or under-estimation of each model for the full range of observed phosphorus concentrations.

TP Prior to the deletion of the outlier samples the tolerance down-weighted, WA model gave the highest r^ (0.55) and lowest RMSEP value (0.542). These values are, however, rather poor in terms of their use for estimating the TP of a site of unknown chemistry, hence the decision to delete samples which gave high prediction errors in both the WA and WA(toi) models. In other studies, the WA models have achieved RMSEP values of less than 0.35 (log units) and r^ well in excess of 0.75 (Birks et al. 1990, Bennion 1993 Jones & Juggins 1995), following the deletion of any outlier samples. The deletion of statistical outliers in this study gives a clear improvement in the error statistics.

~~181 a) Simple Weighted Average Model 4.0 2.0 720 0~~

~~726 0^^o 680 686 0 o 686~~

~~3.0 680 1.0- 743 0 o g: 2.0 1 0.0- o~~

~~t -1.0- 7100 1^ = 0.510 RMSEP = 0.637 6900 0.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~b) Tolerance Down-Weighted Weighted Average Model 2.0 S 720 0~~

~~686 ■ • 726 03 680 686 y 3.0 - 1.0- 679 o o 669 o . » ° 743 ™°726cPfi«'>° * Î 0 669 * • / & & 2.0 i 0.0- o~~

~~1 Ï 1.0 -1.0- r" = 0.548 RMSEP = 0.542 0.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~c) WA-PLS - Two Component Model 4.0 2.0 720 o~~

679 o 680 '20 0 • /C". 1.0- 686 669 0 Î” T • •/ 0 743 6790 0726 . V"* «. • • • O 6690 • » $i 2.0 ^ 0.0- • 7100 0 o & 1 6900 *2 1.0 - 1. 0 - r = 0.529 7100 RMSEP = 0.555 0.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)

~~182 a) Simple Weighted Average Model 4.0 1.0~~

~~3.0 H • t . 0.5- $ I o I & H 2.0 è 0.0- o~~

~~t ■ 1.0 -0.5 - % f = 0.724 RMSEP = 0.425~~

~~0.0-f- “ 1 I r -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 1.0 . s>".^~~

~~y 3.0 - X- 0.5- r & I ^ 2.0 H É 0.0 - ■o o I I 2.0 H -0.5- •••! r' = 0.734 RMSEP = 0.424 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.01.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~c) WA-PLS Two Component Model 4.0 1.0~~

~~» . 0.5- I “ •V o H i$ 2.0 É 0.0- \ 0~~

~~1 ■ 1.0- -0.5- I = 0.762 RMSEP = 0.384 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~Figure 5.2 Comparative graphs showing the TP prediction values and model residuals for the three model types using the rope data, following the deletion of outliers. See text for full explanation~~

183 For TP the 2 component WA-PLS model gave the optimal results following deletion, with an r^ of 0.762 and a RMSEP of 0.384 (Tab. 5.1 & Fig. 5.2c). The statistical justification for outlier deletion is therefore clearly apparent, but it has serious consequences if such a model was to be applied in the monitoring of river sites of

~~unknown chemistry, due to over 10% of samples not conforming to the model results. The need therefore exists to understand why some of the sites do not conform to the model.~~

Two of the outlier samples, 726 and 743, can easily be justified for deletion due to errors in the chemistry. Both samples had total phosphorus values considerably lower than their FRP concentrations and thus should perhaps not have been included in the initial TP data-set. Neither of these samples were outliers for the FRP models which further suggests an unreliable TP measurement rather than non-representative diatom assemblages.

~~The other seven sites fell into two groups based on their diatom assemblages and~~

chemistry. Samples 669, 679, 680, 686 and 720 all had TP concentrations of <100 pgL'^ but were dominated by diatom taxa which were widely tolerant, with TP optima >100 pgL'^ (Tab. 5.3). All the above samples had Cocconeis placentula var. euglypta as a

dominant or co-dominant species, with the exception of sample 686 which was dominated by 26% Achnanthes lanceolata but had 15% C. placentula var. euglypta. C. placentula var. euglypta has a weighted average TP optimum of 395 pgL'^ (tolerance: -333, +2117 pgL'\ back-transformed from the log data) and A. lanceolata an optimum of 555 pgL'' (toi.: -464, +2837 pgL'^). Because WA methods give the most dominant taxa the greatest weight, and given that these samples are dominated by widely tolerant species, the model estimates their TP to be considerably higher than the observed measurement.

The two remaining samples, 690 and 710, both have higher TP but are dominated by taxa with lower optima. Sample 690 has a TP concentration of 163 pgL'^ but is dominated by 80% A. minutissima with a TP optimum of 77 pgL'^ (toi.: -61, +294 (Tab. 5.3)), the model, therefore, will calculate the estimated TP to be very close to the optimum of the dominant taxon, based on the assumption that a taxon will occur at a

184 maximum under optimal conditions. Sample 710 had an observed TP of 1633 pgL'^ but was dominated by Navicula capitatoradiata with a WA TP optimum of 534 pgL'^ (toi.: -447, +2754). Both of these last two samples were also very high alkalinity sites and thus it is likely that alkalinity was having an overriding influence on the floristic composition.

Although there is a clear reason to delete the two samples with erroneous chemistry the other seven samples can only be deleted due to them being dominated by widely tolerant taxa or taxa which are less representative of TP than the other chemical determinants; especially alkalinity. At this stage in the model development the statistical outlier criterion will be maintained, to obtain the optimal predictive model. The consequences of removing such samples are discussed further below.

From Table 5.1 and Figure 5.2c it can be seen that 2 component WA-PLS gave the best predictive model for TP following the deletion of the outlier samples. There is, however, a tendency for the model to slightly over-estimate TP concentrations below approximately 600 pgL'^ and then under-estimate the TP above 600 pgL'\ This is clearly shown by the Cleveland Loess smoother curve on the model residual graphs.

FRP Like the TP models the tolerance down-weighted, WA model gave the highest r^ (0.497) and lowest RMSEP (0.637) for FRP, prior to the deletion of the outlier samples (Tab.5.2 & Fig. 5.3). Following the deletion of the ten statistical outliers the 4 component WA- PLS model gave a greatly improved fit between the observed FRP and the diatom-based predicted values (Tab. 5.3 & Fig. 5.4c), with an r^ of 0.850 and a RMSEP of 0.316. The WA and WA(toi) gave r^ of 0.742 and 0.723, and RMSEP of 0.425 and 0.457 respectively. Not only were these results an improvement on the full data-set but the FRP model also showed considerably improved error statistics over the optimal TP- based diatom model, suggesting a closer and more reliable diatom response to the available phosphorus in the water column as opposed to total phosphorus.

~~185 a) Simple Weighted Average Model 0 720 4.0 2.0 700 669 o °o o 680~~

~~691 o 3 .0 - 1.0- 728 699 0 ° 728^ 700 00 679 ° • • 2.0 o . / • « 0.0- o~~

~~t 1.0- -1.0- r = 0.478 RMSEP = 0.740 710 o -2.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 2.0 669 0 O' o 700 679 0 o 680~~

~~I 3 .0 - 691 o 71^^ 7170 • 699 0 720 0 . % . O 728 ‘”"’° 728° . " X ' . • 0^679 2.0 - 2 0.0- • /• ' « * 710 o , / i t & 1.0 H -1.0- 7100 = 0.497 RMSEP = 0.637~~

~~0.0 T -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~c) WA-PLS Two Component Model • O 720 4.0 2.0~~

~~700 0 6800 a 3.0 1.0- X": 691 o • • O 717 679 0 “«° • . 691 o • • / # *• 2.0- « 0.0- 7 0 0 ° 699 W 0 710 0 1 •a 1.0 — • 728 o -1.0- f = 0.484~~

~~RMSEP = 0.647 7100 0.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~186 a) Simple Weighted Average Model 4.0 1.0~~

~~3.0 0.5- f~~

~~2.0- « 0.0- o f' • t 1.0 -0.5- = 0.742 RMSEP = 0.425 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 1.0~~

~~Î 3.0 0.5- I I 2.0- « 0.0 - o~~

~~I i ft- 1.0 -0.5- r' = 0.723 RMSEP = 0.457 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~c) WA-PLS Four Component Model 4.0 1.0 s>-' I. B 3.0 0.5- 'ui U o ... •~~

~~I 2.0 •V; 'ui I i 1.0 - -0.5- 1^ = 0.850 RMSEP = 0.316 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~Figure 5.4 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the rope data, following the deletion of outliers. See text for full explanation~~

187 As with the TP model, however, the question still arises as to why the statistical outlier samples do not conform to the model. One sample (700) had a possible erroneous FRP measurement: the FRP was 4 pgL'^ whereas the TP value was 743 pgL'\ Five of the outlier samples (669, 679, 680, 710 & 720) were also deleted from the TP data-set due to the dominance of widely tolerant species (see above). The four remaining samples, which exceeded the outlier criteria, also had atypical diatom assemblages, although they were not dominated by C. placentula var. euglypta. Samples 691 and 728 both had high silica concentrations and were dominated by Fragilaria brevistriata and F. construens var. venter. The response of these species to high silica concentrations was highlighted in Chapter 4 and, although the reason for this was unclear, the dominance of Fragilaria species at these sites appeared to override the diatom response to phosphorus. The FRP concentration at these sites was low (39 pgL'^ & 41 pgL'^ respectively) and thus the dominance of F. brevistriata (optimum 299 pgL'^) and F. construens var. venter (optimum 72 pgL'^) resulted in over-estimation by the models. Samples 699 and 717 were both diverse in terms of their diatom assemblages, but were again dominated by one taxon. Sample 699 had an FRP concentration of 28 pgL'^ and was dominated by 20.5% N. gregaria (optimum 247 pgL'^). Sample 717 had a slightly higher FRP (71 pgL'^) but was dominated by 22% M. varians (optimum 676 pgL'^). It is therefore clear why these two samples fell out of the models as statistical outliers, but the reason for the atypical diatom assemblages remains unaccounted for.

Again the problem exists with the FRP model that nine of the ten outlier samples cannot justifiably be deleted on anything more than their statistical non-conformity. As with the TP results however, the deletion of the outliers leads to a greatly improved predictive model for FRP, with 4 component WA-PLS giving better error statistics than simple WA and WA(toi). From Table 5.2 and Figure 5.4c it can be seen that although the 4 component WA-PLS provides the best fitting model, the tendency is to over-estimate FRP concentrations below approximately 600 pgL'^ and then under-estimate the FRP above 600 pgL"\ At this stage in the model development the statistical outlier criterion will be maintained, to obtain the optimal predictive model.

188 Total Phosphorus Filterable Reactive (n g L ‘) Phosphorus (pgL'^) No. of Diatom Taxa Opt. Tolerance Opt. Tolerance Occ. Achnanthes lanceolata 76 555 -464 +2837 442 -371 +2301 Gomphonema parvulum 75 188 -161 +1112 122 -107 +906 Cocconeis placentula v. euglypta 74 395 -333 +2117 296 -262 +2257 Achnanthes minutissima 73 77 -61 +294 54 -45 +255 Sellaphora minima 67 544 -442 +2348 415 -343 +1964 Nitzschia palea 65 381 -314 +1792 242 -212 +1708 Nitzschia paleacea 65 385 -307 +1506 266 -222 +1343 Rhoicosphenia curvata 64 498 -408 +2252 340 -298 +2429 Amphora pediculus 63 240 -191 +933 161 -137 +896 Melosira varians 57 676 -494 +1840 524 -394 +1592 Navicula lanceolata 57 350 -262 +1039 237 -181 +770 Navicula tripunctata 57 327 -254 +1140 280 -225 +1137 Navicula [species 2] 57 202 -167 +981 145 -128 +1076 Navicula gregaria 55 247 -183 +711 137 -112 +624 Navicula atomus 54 424 -321 +1317 312 -228 +851 Cymbella minuta 53 275 -235 +1600 229 -198 +1437 Nitzschia frustulum 53 297 -245 +1406 254 -206 +1097 Fragilaria vaucheriae 50 265 -234 +2017 221 -196 +1764 Sellaphora seminulum 50 1026 -909 +7971 903 -802 +7140 Navicula cryptotenella 49 533 -410 +1771 398 -322 +1697 Nitzschia amphibia 47 2259 -1490 +4378 2001 -1394 +4597 Nitzschia dissipata 47 426 -338 + 1646 294 -252 +1787 Navicula cryptocephala 44 206 -160 +714 49 -42 +308 Navicula menisculus 41 638 -492 +2163 254 -228 +2243 Synedra ulna v. ulna 41 206 -179 +1395 148 -132 +1198 Navicula subminuscula 39 717 -515 +1832 596 -458 +1988 Fragilaria capucina v. rumpens 38 180 -149 +864 129 -109 +707 Navicula [pseudogregaria] 37 323 -245 +1017 227 -190 +1156 Nitzschia linearis 36 240 -188 +861 178 -144 +744 Surirella brebissonii 35 554 -420 +1737 416 -350 +2212 Nitzschia recta 34 310 -262 +1699 222 -194 +1521 Cocconeis pediculus 33 96 -87 +967 58 -55 +925 Fragilaria pinnata 33 195 -169 +1285 186 -158 +1035 Gomphonema minutum 33 234 -187 +935 156 -133 +914 Navicula molestiformis 33 941 -802 +5437 686 -605 +5142 Fragilaria brevistriata 31 299 -265 +2342 274 -242 +2045 Gomphonema olivaceum 31 218 -178 +968 136 -120 +1065 Navicula capitata v. capitata 31 544 -393 +1418 194 -171 +1441 Nitzschia pusilla 29 372 -283 +1173 186 -164 +1396 Nitzschia supralitorea 29 394 -311 +1473 247 -194 +902 Achnanthes lauenbergiana 27 445 -362 +1939 406 -311 + 1337 Cyclotella pseudostelligera 27 141 -113 +584 120 -96 +478 Fragilaria elliptica 27 514 -435 +2844 463 -392 +2560 Nitzschia constricta 26 355 -261 +983 122 -110 +1062 Sellaphora pupula 26 151 -123 +660 108 -88 +466

~~Table 5.3 Weighted average optima and tolerances of the diatom taxa for TP and FRP (rope training set). Values are back-transformed from the log data~~

189 Total Phosphorus Filterable Reactive (pgL'b Phosphorus (pgL'^) No. of Diatom Taxa Opt. Tolerance Opt. Tolerance Occ. Gomphonema angustatum 25 68 -52 +227 34 -29 +196 Navicula veneta 25 283 -226 +1128 158 -137 +1042 Amphora ovalis v. pediculus 24 649 -535 +3052 465 -410 +3465 Gomphonema pumilum 24 175 -143 +799 113 -99 +770 Cyclotella meneghiniana 22 438 -370 +2370 194 -178 +2210 Nitzschia capitellata 22 385 -323 +1998 155 -130 +789 Nitzschia sociabilis 22 208 -164 +766 325 -275 +1802 Diatoma vulgare 21 452 -372 +2093 376 -319 +2106 Eunotia pectinalis v. minor 21 68 -54 +276 37 -30 +163 Navicula subrotundata 21 585 -513 +4143 463 -405 +3216 Nitzschia perminuta 21 311 -262 +1671 254 -209 +1169 Navicula tenelloides 20 127 -93 +350 67 -52 +225 Synedra tabulata 20 1184 -895 +3664 640 -577 +5872 Gomphonema truncatum 19 292 -253 +1872 225 -200 +1777 Achnanthes plonensis 18 630 -466 + 1794 541 -391 +1417 Amphora veneta 18 2622 -1674 +4627 2386 -1600 +4852 Navicula capitatoradiata 18 534 -447 +2754 270 -250 +3401 Nitzschia fonticola 18 833 -590 +2022 747 -531 +1837 Nitzschia inconspicua 18 475 -423 +3884 381 -343 +3438 Stephanodiscus parvus 18 349 -274 + 1268 344 -261 +1086 Navicula rhynchocephala 17 49 -35 +121 37 -26 +93 Navicula agrestis 17 625 -537 +3811 425 -368 +2726 Reimeria sinuata 16 241 -194 +985 176 -147 +877 Fragilaria construens v. venter 15 72 -61 +388 83 -67 +346 Navicula trivialis 15 298 -223 +886 103 -91 +748 Achnanthes lanceolata v. rostrata 14 650 -508 +2314 529 -426 +2181 Achnanthes clevei 14 993 -728 +2726 894 -664 +2584 Fallacia subhamulata 14 406 -307 +1252 211 -185 +1475 Surirella ovalis 14 265 -202 +853 206 -162 +755 Fragilaria bidens 14 722 -614 +4090 577 -506 +4120 Achnanthes grana 13 452 -321 +1099 264 -194 +727 Craticula accomoda 13 530 -460 +3455 394 -351 +3194 Fragilaria capucina v. gracilis 13 23 -20 +131 16 -14 +89 Nitzschia heufleriana 13 340 -263 +1152 124 -113 +1234 Amphora ovalis v. ovalis 12 546 -459 +2879 485 -409 +2611 Diploneis oblongella 12 419 -298 +1031 48 -44 +592 Surirella minuta 12 93 -81 +614 76 -65 +466 Achnanthes conspicua 11 441 -318 +1140 314 -254 +1325 Achnanthes hungarica 11 1545 -1171 +4841 988 -885 +8536 Eunotia curvata 11 42 -32 + 128 26 -20 +89 Fallacia monoculata 11 167 -135 +718 87 -67 +298 Fragilaria leptostauron 11 89 -77 +569 62 -54 +400 Fragilaria parasitica subconstricta 11 315 -248 +1168 300 -233 +1049 Frustulia vulgaris 11 62 -53 +384 42 -34 +179

~~Table 5.3 (cont.) Weighted average optima and tolerances of the diatom taxa for TP and FRP (rope training set). Values are back-transformed from the log data~~

190 Total Phosphorus Filterable Reactive (pgL’^) Phosphorus (pgL^) No. of Diatom Taxa Opt. Tolerance Opt. Tolerance Occ. Gomphonema acuminatum 11 114 -100 +792 36 -3 +366 Gyrosigma attenuatum 11 138 -120 +907 51 -48 +709 Meridion circulare 11 97 -77 +375 112 -87 +395 Navicula radiosa 11 60 -53 +484 25 -23 +246 Navicula viridula v. linearis 11 256 -207 +1082 210 -177 +1106 Cyclostephanos dubius 10 325 -266 +1452 241 -202 +1257 Navicula protracta 10 487 -301 +789 872 -727 +4353 Navicula goeppertiana 10 951 -797 +4936 309 -173 +394 Nitzschia acicularis 10 259 -205 +976 120 -106 +897 Eunotia formica 9 387 -349 +3514 289 -267 +3563 Achnanthes biasolettiana 8 12 -11 +91 8 -7 +69 Caloneis bacillum 8 107 -84 +387 49 -44 +399 Synedra acus 8 390 -316 +1664 237 -210 +1846 Ctenophora pulchella 8 2256 -1908 +12376 2191 -1817 +7665 Navicula decussis 7 719 -406 +933 385 -168 +296 Navicula ignota v. acceptata 7 538 -466 +3467 367 -313 +2118 Nitzschia tubicola 7 326 -289 +2521 113 -108 +2444 Gomphonema angustum 7 67 -59 +458 50 -45 +445 Achnanthes subatomoides 6 249 -189 +793 141 -96 +301 Cymatopleura solea 6 519 -329 +896 127 -113 +1045 Gyrosigma acuminatum 6 392 -276 +925 177 -155 +1251 Navicula cincta 6 167 -133 +652 114 -98 +696 Nitzschia sigma 6 266 -199 +790 234 -179 +751 Nitzschia wuellerstorffii 6 480 -346 +1249 367 -235 +652 Eunotia exigua 5 22 -12 +29 35 -13 +21 Fragilaria parasitica v. parasitica 5 632 -319 +643 599 -322 +696 Navicula capitata v. hungarica 5 336 -255 +1061 302 -230 +969 Nitzschia littoralis 5 93 -80 +597 44 -40 +418 Simonsenia delognei 5 124 -101 +547 68 -48 +158 Achnanthes delicatula 4 872 -378 +669 618 -370 +920 Achnanthes saxonica 4 2018 -483 +635 1788 -470 +637 Aulacoseira sp. 4 431 -353 +1949 394 -322 +1772 Cymbella ajfinis 4 14 -9 +27 8 -5 +15 Fragilaria oldenburgiana 4 1096 -961 +7796 1049 -892 +5954 Asterionella formosa 3 311 -200 +563 278 -179 +501 Cymbella silesiaca 2 19 -6 +9 29 -14 +25 Denticula tenuis 2 5 -2 +2 3 -0.5 +2 Nitzschia acicularioides 2 122 -97 +484 18 -14 +59 Tabellaria flocculosa 2 27 -8 +12 16 -7 +14 Fragilaria delicatissima 2 30 -6 +6 15 -3 +3 Gomphonema pseudoagur 2 8764 -1096 +1253 8098 -907 +1022 Navicula subrhynchocephala 1 1162 -- 1071 -- Fragilaria nitzschiodes 1 213 -- 200 -- Fragilaria capucina v. austriaca 1 7 - - 4 --

~~Table 5.3 (cont.) Weighted average optima and tolerances of the diatom taxa for TP and FRP (rope training set). Values are back-transformed from the log data~~

~~191 5.5.2 Tile Training Set Data~~

The same steps were used for the tile data that were outlined above for the rope training set. The three WA regression models were run on the entire data-set to identify statistical outliers which exceeded a model residual of ±0.75 in both simple weighted averaging (WA) and in tolerance downweighted WA (WA(toi)}. This was performed for both total phosphorus and filterable reactive phosphorus. From the initial 69 samples in the tile training set, nine exceeded a ±0.75 model residual for TP (samples: 596, 605, 606, 608, 613, 618, 628, 638 & 647) and also nine for FRP (samples: 596, 605, 606, 608, 613, 618, 626, 628, & 638). With the exception of 647 in the TP model and 626 in the FRP model the same samples were detected as outliers for both TP and FRP. Tables 5.4 and 5.5 show the weighted averaging results prior to, and following sample deletion.

All samples included (n=69) Outlier samples deleted (n=60) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component 2 Component 2 r' 0.512 0.535 0.522 0.749 0.695 0.836 RMSEP 0.630 0.590 0.564 0.405 0.445 0.320 M ean Bias 0.017 0.076 0.027 0.000 0.076 -0.004 M ax. Bias 1.269 0.496 0.576 0.704 0.581 0.520

~~Table 5.4 Weighted averaging regression results for the diatom-based modelling of TP using the tile training set data, prior to and following the deletion of outliers~~

All samples included (n=69) Outlier samples deleted (n=60) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component 2 Component 2 0.481 0.496 0.471 0.804 0.796 0.877 RMSEP 0.709 0.673 0.624 0.358 0.377 0.283 M ean Bias 0.015 0.090 0.013 0.003 0.082 0.000 M ax. Bias 0.449 0.745 0.797 0.780 0.745 0.581

~~Table 5.5 Weighted averaging regression results for the diatom-based modelling of FRP using the tile training set data, prior to and following the deletion o f outliers~~

The summary statistics for the tile models are very similar to those obtained for the rope training set. Prior to the deletion of outlier samples, tolerance down-weighted WA gave the highest r^ and the lowest RMSEP for both TP and FRP (Fig. 5.5 & 5.7). This provides further evidence that the statistical outliers did not conform to the model due to the dominance of widely tolerant taxa in these samples. The downweighting is not, however, sufficient to improve the model to the same extent as deleting the samples.

192 Following the deletion of outliers the model results are significantly improved, with 2 component WA-PLS providing the optimal model for both TP and FRP (Fig. 5.6 & 5.8). A further parallel with the rope data was that following deletion, the FRP model gave the highest r^ and lowest RMSEP for the tile samples. Overall the best diatom-based model results came from the tile training set with the FRP model (r^ = 0.877 & RMSEP 0.283, after deletion of outliers).

TP A similar pattern emerges from the tile TP results to that seen in the rope models. With all the samples included, eight of the nine which exceed the outlier criterion, have an observed TP of less than 109 pgL"^ and are over-estimated by all the models. One outlier sample (647) had an erroneous TP measurement (TP = 29 pgL'\ FRP =115 pgL'^) and was from the same site as sample 726 in the rope data. Sample 647 did not occur as an outlier in the FRP model.

Four of the outliers (596, 605, 606 & 613) were dominated by either C. placentula var. euglypta or A. lanceolata which had WA optima of 394 pgL'^ (toi. -330, +2045) and 432 pgL'^ (toi. -358, +2095) respectively. These samples were also deleted from the rope training set for the same reasons (669, 679, 680 & 6 8 6 ), suggesting a site specific problem rather than a substratum specific error.

Samples 608 & 618 were both from very high silica sites and were dominated by Fragilaria species, as well as having relatively high abundances of C. placentula var. euglypta and A. lanceolata. Sample 638 was a low TP site (109 pgL'^) but was dominated by M. varians which has a TP optimum of 362 pgL'^ (toi. -287, +1289). M. varians was found to be significantly related to lower flow rates in the tile training set (Ch. 4, 4.5.5), and thus the very low flow recorded for sample 638 is likely to have favoured this taxon. The WA TP optimum of M. varians is therefore more likely to be a function of the majority of low flow sites having higher TP than a true reflection of the species phosphorus requirements.

~~193 a) Simple Weighted Average Model 4.0 2.0~~

~~647 0 647 638 606 Co 618 605 o 3.0 606cP '^613 638 5960 1.0 - 613 6050 o • *608 * 596 I t 'So « 6 0 8 » o 0 2.0 628 & É 0. 0 - "3 0 •4 t3 1.0 1o. 1 628 0 - 1.0 - 0.0 r^ = 0.512 RMSEP = 0.630~~

~~- 1.0 T - 2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 2.0~~

~~647 0 638 6050 o 606 . 596 1. 0 - % o#. o 613 & 618 596 o o 628 & É 0.0 - t — — — — — — —---- o~~

~~t 628 0 1.0 - 0.535 RMSEP = 0.590~~

~~- 2.0 1.0 2.0 3.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~c) WA-PLS Two Component Model 4.0 2.0 s>: o 606 647 605 o o g gjg~~

~~606 o o 638 596 0 638 3.0 1.0 - 6080 6470 ° 618 °6 1 3 613 0 è 605 o o 596 Î 608 0 # I 2.0 Î I 73 1.0 - 1.0 -~~

~~f = 0.522 RMSEP = 0.564~~

~~0.0 - 2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~194 a) Simple Weighted Average Model 4.0 ------71 1.0-1------~~

~~3 .0 - 0.5- $ •0 o 0.0 - I ». 1.0 - -0.5- r' = 0.749 RMSEP = 0.405~~

~~0.0 - 1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 ------71 1.0-1------—~~

~~3 .0 - y v 0.5- Î • • I • • • I 0.0 - I 1.0 - -0.5- r' = 0.695 RMSEP = 0.445~~

~~0.0 - 1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~c) WA-PLS Two Component Model 4.0 1.0 i Q. 3 .0 - 0.5- § u Î~~

~~2 .0 - i 0.0 - Î 0 o? 1 ■ 1 .0 - -0.5- r = 0.836 £ RMSEP = 0.320~~

~~0.0-p - 1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed TP (log) Observed TP (log)~~

~~Figure 5.6 Comparative graphs showing the TP prediction values and model residuals for the three model types using the tile data, following the deletion of outliers. See text for full explanation~~

195 The final outlier sample (628) had a higher observed TP (1217 pg'^) but was under estimated by the WA models. This sample was dominated by 60% N. gregaria which has a WA TP optimum of 297 pgL'^ (toi. -235, 4-1125). The reason for this dominance is unclear but may have been due to the very slow flow at this site, allowing fine sediments to accumulate. N. gregaria is often found in the epipelon and favours fine sediments (E. Cox, pers. comm.). The rope sample from the same site (703) had only 12% N. gregaria and fitted the model well. Sediment build up was less evident in the rope samples, even at slow flowing sites. Following the deletion of the outliers the predictive model for TP was greatly improved, with 2 component WA-PLS giving the highest r^ (0.836) and lowest RMSEP (0.320) (Tab. 5.4 & Fig. 5.6c). The model is very similar to that derived from the rope TP data, although the error statistics are slightly improved the WA-PLS tile model also over-estimates at low TP sites and under estimates at the higher end of the gradient. This is clearly shown by the Cleveland Loess smoother curve on the model residual graphs.

FRP Like all the other model results the FRP models for the tile samples showed a poor correlation (WA(toi): r^ = 0.496 & RMSEP = 0.673) between the observed FRP and the predicted values prior to the deletion of the statistical outliers (Tab. 5.5 & Fig.5.7). Eight of the outliers in the FRP models were the same as those described above for the TP. Only sample 626 exceeded the outlier criterion for FRP but not TP. This sample was taken from a low FRP site (44 pgL'^) and was dominated by 20.5% S. minima (FRP WA opt. = 293 pgL'\ toi. = -252, 4-1777 (Tab. 5.6)) and 17% N. gregaria (FRP WA opt. = 250 pgL'\ toi. = -189, 4-771). The chemistry at this site showed no unusual parameters other than having a relatively low alkalinity (56 mgL'^). Both the dominant species and N. lanceolata, which occurred at 12% in sample 626, showed a preference for lower alkalinity sites, which may have had a greater influence on the species assemblage than FRP. Following the deletion of the outliers the 2 component WA-PLS model gave the highest r^ (0.877) and lowest RMSEP (0.283) (Tab. 5.5 & Fig. 5.8c). The model is similar to the others in that it still over-estimates at low FRP sites and under-estimates at the higher end of the gradient. The results from the tile WA-PLS model for FRP gave the best error statistics overall, showing a slight improvement over the FRP models from the rope samples and both the TP models.

~~196 a) Simple Weighted Average Model 4.0 2.0 0 605 0 606 6180 638 6080 6180 Oog]3 3.0 H 626 o o 638 626 • . 4 ' 1. 0 - 613 V... $ Î i Î-0~~

~~I 1.0~~

~~1 628 0 0.0 r’ = 0.481 RMSEP = 0.709 -1.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 2.0 0 605 5960 638 0~~

~~606 o 608 0 0618 J 3.0 1. 0 - 626 o 0 613 ^ • 0 618 605 o 626°^ 596 0 % 2.0 i 0.0- - \ / v 0~~

~~1 1 628 0 & 1.0 H - 1.0 - r" = 0.496 RMSEP = 0.673 0.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~c) WA-PLS One Component Model 4.0 2.0 0 605~~

608 0 0 606 618 0 6260 q638 3.0 638 1.0 - 618 0 00 613 0613 606 0 4 :' o 0 605 T. ~ " " • # 7 628 « 0.0- 0 1 ■o 1.0 - -1.0- r =0.471 RMSEP = 0.624 0.0 -2.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)

~~197 a) Simple Weighted Average Model 4.0 1.0~~

~~3.0 0.5- Î # 2.0-~~

~~t 1.0- -0.5- r = 0.804 RMSEP = 0.358 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.01.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~b) Tolerance Down Weighted - Weighted Average Model 4.0 1.0~~

~~I 3.0 % » « » V- • • V. .*• o 2.0 /• È 0.0- O~~

~~K t ■ a, 1.0 -0.5- 1^ = 0.796 RMSEP = 0.377~~

~~0.0 T T -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~c) WA-PLS Two Component Model 4.0 1.0 s>-~~

~~B 3.0 0.5- i f * ' o~~

~~« 0.0- o~~

~~t ' •o 1.0 -0.5 ^ = 0.877 RMSEP = 0.283 0.0 -1.0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 Observed FRP (log) Observed FRP (log)~~

~~Figure 5.8 Comparative graphs showing the FRP prediction values and model residuals for the three model types using the tile data, following the deletion of outliers. See text for full explanation~~

198 Total Phosphorus Filtei able Reactive (PgL'b Phosi chorus (pgL'^) No. of Diatom Taxa Opt. Tolerance Opt. Tolerance Occ. Achnanthes minutissima 68 78 -60 +270 53 -43 +239 Achnanthes lanceolata 67 432 -358 +2095 368 -306 +1815 Sellaphora minima 64 388 -326 +2049 293 -252 +1777 Amphora pediculus 63 153 -129 +814 131 -110 +691 Gomphonema parvulum 63 205 -179 +1429 156 -139 +1281 Cocconeis placentula v. euglypta 62 394 -330 +2045 284 -254 +2437 Navicula atomus 57 442 -318 +1129 375 -276 + 1047 Nitzschia dissipata 56 201 -163 +859 147 -126 +860 Nitzschia palea 54 218 -179 +1012 151 -129 +883 Navicula gregaria 52 297 -235 +1125 250 -189 +771 Rhoicosphenia curvata 50 454 -385 +2538 340 -303 +2818 Sellaphora seminulum 49 1134 -994 +8060 993 -877 +7473 Navicula [species 2] 49 448 -365 +1970 270 -234 +1757 Navicula menisculus 46 256 -215 +1331 198 -166 +1023 Nitzschia paleacea 44 441 -353 +1764 291 -242 +1424 Navicula lanceolata 43 214 -175 +945 204 -145 +496 Navicula subminuscula 42 918 -599 +1724 771 -531 +1703 Cymbella minuta 40 376 -320 +2160 299 -257 +1837 Nitzschia amphibia 40 1721 -1093 +2995 1558 -1013 +2899 Gomphonema olivaceum 38 151 -128 +831 77 -70 +810 Navicula tripunctata 37 239 -191 +946 179 -154 + 1080 Navicula [pseudogregaria] 36 542 -379 + 1258 389 -288 +1117 Navicula cryptotenella 35 337 -266 +1270 271 -217 + 1073 Achnanthes lanceolata v. rostrata 34 589 -500 +3301 517 -446 +3267 Reimeria sinuata 32 209 -177 +1155 163 -140 +997 Achnanthes lauenbergiana 31 355 -307 +2267 287 -246 +1704 Cyclotella pseudostelligera 30 142 -118 +687 132 -108 +596 Nitzschia frustulum 30 402 -306 +1285 359 -278 +1238 Nitzschia recta 30 300 -220 +818 231 -181 +829 Fragilaria pinnata 29 86 -75 +578 92 -80 +600 Gomphonema pumilum 29 119 -99 +601 84 -73 +556 Navicula cryptocephala 29 139 -111 +552 117 -90 +395 Gomphonema minutum 28 124 -102 +569 84 -73 +556 Melosira varians 28 369 -287 +1289 282 -232 +1297 Fragilaria brevistriata 27 244 -216 +1875 231 -203 +1684 Surirella brebissonii 27 534 -415 +1869 409 -349 +2357 Fallacia subhamulata 26 188 -128 +401 151 -109 +393 Fragilaria vaucheriae 26 153 -134 +1056 136 -119 +991 Navicula subrotundata 25 320 -286 +2654 247 -227 +2740 Nitzschia pusilla 25 201 -148 +571 147 -109 +423 Nitzschia supralitorea 25 381 -282 +1078 269 -199 +772 Amphora ovalis v. pediculus 24 349 -259 +1014 253 -194 +828 Navicula veneta 24 112 -89 +427 74 -61 +360 Nitzschia sociabilis 24 198 -165 +981 116 -106 +1200

~~Table 5.6 Weighted average optima and tolerances of the diatom taxa for TP and FRP (tile training set). Values are back-transformed from the log data~~

199 Total Phosphorus Fiterahle Reactive (n g L ‘) Phosphorus (pgL *) No. of Diatom Taxa O p t Tolerance Opt. Tolerance Occ. Navicula molestiformis 23 319 -274 +1945 268 -231 +1672 Navicula agrestis 23 290 -223 +971 195 -139 +485 Cocconeis pediculus 22 228 -201 +1732 163 -149 +1785 Nitzschia linearis 22 187 -161 +1174 137 -123 +1243 Sellaphora pupula 22 72 -62 +429 34 -31 +348 Synedra ulna 22 175 -152 +1179 124 -111 +1029 Fragilaria capucina v. rumpens 21 45 -39 +265 34 -29 +222 Fragilaria elliptica 21 313 -265 +1738 271 -233 +1682 Amphora veneta 20 1608 -1031 +2875 1343 -1002 +3955 Nitzschia constricta 20 270 -181 +549 215 -157 +588 Cyclotella meneghiniana 19 337 -272 +1416 243 -209 + 1466 Stephanodiscus parvus 19 242 -198 +1084 241 -188 +854 Achnanthes conspicua 18 228 -186 +1009 166 -142 +970 Caloneis bacillum 18 104 -85 +471 82 -71 +524 Navicula capitata 18 315 -218 +709 256 -185 +666 Nitzschia fonticola 17 600 -484 +2499 303 -255 +1609 Achnanthes clevei 16 1372 -1061 +4687 1295 -984 +4100 Diatoma vulgare 15 846 -714 +4590 646 -577 +5392 Nitzschia capitellata 15 311 -218 +732 249 -161 +453 Surirella minuta 15 119 -102 +725 104 -91 +732 Achnanthes plonensis 14 796 -566 +1962 700 -536 +2287 Nitzschia inconspicua 14 783 -588 +2361 599 -418 +1375 Achnanthes biasolettiana 12 7 -6 +32 5 -4 +27 Eunotia pectinalis v. minor 12 189 -167 + 1460 157 -141 + 1356 Gomphonema angustatum 12 61 -52 +364 15 -14 +296 Navicula goeppertiana 12 1383 -1249 +12860 1165 -1065 +12437 Gomphonema anoenum 12 34 -26 +105 43 -32 +116 Amphora ovalis 11 466 -396 +2657 352 -322 +3731 Fragilaria leptostauron 11 67 -61 +628 49 -45 +542 Fragilaria parasitica subconstricta 11 180 -160 +1469 113 -104 +1368 Navicula menisculus v. grunowii 11 448 -342 +1443 316 -253 +1263 Navicula ignota v. acceptata 11 513 -383 +1513 418 -304 +1114 Nitzschia perminuta 11 203 -164 +848 164 -131 +635 Synedra acus 11 286 -254 +2281 230 -210 +2453 Achnanthes grana 10 747 -552 +2113 656 -482 +1816 Diploneis oblongella 10 140 -84 +210 90 -62 +193 Fallacia monoculata 10 180 -149 +866 143 -121 +774 Meridian circulare 10 64 -42 +123 56 -32 +79 Navicula rhynchocephala 10 34 -22 +65 44 -26 +65 Navicula decussis 10 888 -584 +1704 781 -484 +1274 Navicula capitatoradiata 10 495 -393 +1897 405 -346 +2350 Fragilaria capucina v. gracilis 9 167 -143 +973 164 -137 +825 Gomphonema truncatum 9 142 -113 +564 101 -83 +473 Navicula tenelloides 9 84 -64 +264 77 -58 +241

~~Table 5.6 (cont.) Weighted average optima and tolerances of the diatom taxa for TP and FRP (tile training set). Values are back-transformed from the log data~~

200 Total Phosphorus Fiter able Reactive (MgL') Phos [)horus (pgL’^) No. of Diatom Taxa Opt. Tolerance Opt. Tolerance Occ. Navicula [small species 1] 9 1342 -711 +1511 741 -292 +482 Achnanthes saxonica 8 1788 -1189 +3544 1617 -982 +2500 Gomphonema acuminatum 8 856 -690 +3554 716 -593 +3457 Navicula protracta 8 363 -308 +2048 340 -261 +1122 Nitzschia acicularis 8 273 -232 +1523 145 -123 +824 Nitzschia heufleriana 8 79 -68 +487 63 -57 +579 Simonsenia delognii 8 166 -132 +636 145 -112 +498 Cyclostephanos dubius 7 67 -61 +734 70 -63 +672 Cymbella affinis 7 39 -28 +103 25 -19 +89 Cocconeis disculus 7 232 -200 +1418 178 -160 +1533 Eunotia curvata 7 43 -33 +151 34 -27 +143 Navicula trivialis 7 141 -100 +351 88 -68 +301 Synedra tabulata 7 1768 -1166 +3427 1582 -1101 +3618 Craticula accomoda 6 129 -90 +298 116 -75 +211 Fragilaria construens v. venter 6 372 -304 +1658 345 -278 +1428 Fragilaria parasitica 6 436 -261 +647 414 -202 +394 Nitzschia dubia 6 395 -207 +438 324 -188 +448 Nitzschia sublinearis 6 118 -104 +868 90 -82 +918 Navicula viridula v. linearis 5 427 -351 +1973 414 -341 +1939 Navicula capitata v. hungarica 5 352 -242 +781 321 -225 +753 Achnanthes hungarica 4 1154 -1087 +18634 1026 -987 +26063 Achnanthes subatomoides 4 75 -62 +351 47 -41 +304 Asterionella formosa 4 340 -155 +284 304 -139 +253 Cymbella microcephala 4 11 -10 +82 6 -5 +33 Denticula tenuis 4 8 -6 +20 5 -3 +11 Fragilaria construens 4 268 -160 +395 253 -149 +363 Nitzschia angustata 4 29 -5 +6 21 -11 +22 Cocconeis diminuta 3 1063 -937 +7936 960 -855 +7790 Navicula oblonga 3 1363 -332 +438 1275 -267 +337 Fragilaria bidens 3 701 -518 +1984 639 -508 +2474 Eunotia exigua 2 21 -7 + 11 39 -6 +7 Navicula sub rhynchocephala 2 27 -8 +13 16 -7 +14

~~Table 5.6 (cont.) Weighted average optima and tolerances of the diatom taxa for TP and FRP (tile training set). Values are back-transformed from the log data~~

~~5.5.3 Comparison of the Models~~

All the models from the rope and tile samples were greatly improved by the deletion of the statistical outlier samples. Furthermore, following deletion it was the WA-PLS models which gave the best error statistics. All these results were based on the diatom taxa which occurred in more than five samples or at more than 2 % abundance in any

201 one sample. One of the features of WA-PLS is that the model results are almost always improved by the inclusion of all taxa in the data set, regardless of the number of occurrences (Birks 1998). Table 5.7 shows the added advantage of including all taxa for the four WA-PLS models described above.

~~R ope TP WA-PLS Mo del~~

~~Taxa >5 occurrences or >2% 0.762 All taxa included 0.764 RMSEP 0.384 RMSEP 0.384~~

~~Ro pe FRP W A-PLS M o del~~

~~Taxa >5 occurrences or >2% 0.850 All taxa included 0.853 RMSEP 0.316 RMSEP 0.314~~

~~Tile TP W A-PLS PdODEL~~

~~Taxa >5 occurrences or >2% 0.836 All taxa included 0.841 RMSEP 0.320 RMSEP 0.317~~

~~Tile FR P W A-PLS M o del~~

~~Taxa >5 occurrences or >2% 0.877 All taxa included 0.879 RMSEP 0.283 RMSEP 0.283~~

~~Table 5.7 WA-PLS results show the improvement due to the inclusion of all diatom taxa~~

The inclusion of the full diatom data-sets in the WA-PLS models makes a very slight improvement in the error statistics for all the models. This improvement did not, however, change the order of the model results and was not sufficient to warrant the use of the full data-sets in future diatom-based modelling.

Overall the diatom models produced the best predictive results for FRP on both substrata. It would appear that the diatoms are responding more strongly to the FRP concentration than TP, particularly with respect to the diatom assemblages from the rope substrata. The difference between the FRP models from the two substrata was relatively small, suggesting that either substratum would be suitable for the collection of diatom samples for the monitoring of FRP in lowland rivers.

202 Furthermore, it should be pointed out here that all the model results have been extracted from the untransformed relative abundances of species data. Diatom data are notoriously “noisy” and it has been found that the transformation of the species data can enhance the model performance, and resultant calibration, by reducing the inherent variability (e.g. log(x+i) (ter Braak & Juggins 1993) and square root (Birks 1998)). The use of log transformations greatly increases the importance of rare taxa and is thus not considered suitable in this study. The use of square root transformation, however, has less effect on the promotion of rare taxa but reduces the influence of the dominant species. This is an attractive proposition in this study due to the number of “atypical” samples, which are most likely outliers due to the dominance of one widely tolerant taxon. The models for FRP from both substrata were therefore re-run using the square root transformed species abundances (Tab. 5.8 & 5.9), using the same criteria as above.

All samples included (n=78) Outlier samples deleted (n=70) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component 2 Component 6 r' 0.511 0.432 0.516 0.704 0.575 0.845 RMSEP 0.682 0.683 0.632 0.449 0.558 0.323 M ean Bias 0.037 0.075 0.006 0.028 0.068 0.013 M ax. Bias 0.774 1.372 1.036 0.787 1.293 0.673

~~Table 5.8 Weighted averaging regression results for the diatom-based modelling of FRP using square root species data from the rope training set, prior to and following the deletion of outliers~~

All samples included (n=69) Outlier samples deleted (n=61) Jack knifed WA WA(toi) WA-PLS WA WA(toi) WA-PLS Statistic Component 2 Component 5 r: 0.482 0.224 0.512 0.698 0.350 0.879 RMSEP 0.666 1.061 0.601 0.440 0.708 0.280 Mean Bias 0.024 0.165 0.036 0.016 0.030 0.005 Max. Bias 0.682 0.961 1.133 0.887 0.981 0.412

~~Table 5.9 Weighted averaging regression results for the diatom-based modelling of FRP using square root species data from the tile training set, prior to and following the deletion o f outliers~~

The model results based on the square root species data are very similar to those from the untransformed data, with WA-PLS giving the best results following the deletion of outliers. There are, however, several points of interest in the use of the square root data.

~~First, the number of outlier samples was slightly reduced with only 8 outliers rather than~~

~~10 in the rope data and 8 rather than 9 in the tile data. The reason for this is most likely to be due to the reduction in the importance of the dominant taxa following square root~~

203 transformation. Samples 691, 717 and 728 from the rope data, did not fall out of the analysis, although one extra sample, 690, did exceed the outlier criteria. Within the tile data samples 613 and 626 did not exceed the outlier criteria in the square rooted species data analysis but sample 639 did. Interestingly the samples which were not excluded in this analysis were all from low FRP sites (> 75 pgL'^). Of the two additional outliers 690 had an FRP concentration of 67 pgL'^and 639 an FRP of 609 pgL'\

A second point of interest in the use of square rooted species data was the lack of performance of the tolerance down-weighted, weighted averaging methods (Tab. 5.8 & 5.9). This method is unsuited for square rooted data due to its reliance on some taxa having narrow tolerance ranges. In transforming the species data the apparent tolerance ranges are greatly increased for many rarer taxa and thus their value as indicator species in the model is reduced. This is not a problem in standard WA or WA-PLS.

Third, although the use of square rooted species data provided almost identical WA-PLS model results for FRP, the number of components required to achieve these was higher. In the case of the rope samples, 4 components were needed prior to the transformation

~~of the species data but 6 required following transformation and in the tile data only 2 components were needed when using untransformed data but 5 were required following transformation.~~

The use of square root transformations on the species data in the WA-PLS model development results in no overall effect on the results but it would appear to provide both advantages and disadvantages. The principal advantage being the slight reduction in the number of outliers and thus the inclusion of more low FRP sites in both models. The disadvantage, however, is that both models require more WA-PLS components to achieve the same results as those based on untransformed data. This contravenes the principal of parsimony that should ideally be applied to such statistical models (H. J. B. Birks 1998; unpublished WA-PLS workshop notes). Nevertheless the use of square rooted species data can be considered as an alternative method for WA-PLS modelling.

~~204 5.5.4 Indicator Species: WA Optima and Species Distributions~~

A conflict arises when quoting the diatom species weighted average optima and tolerances for phosphorus. The values given in Tables 5.3 and 5.6 are for the full diatom data sets before the deletion of the outlier samples, and thus include those sites which have been identified as atypical in some way. Irrespective of these sites being statistical outliers, however, particular diatom assemblages were seen to be growing there and intuitively should be included when calculating the species optima. The conflict, therefore, is whether, because the predictive models have been based on the statistically reliable sub-set of samples, the species optima should also be based on the same data? The decision made in this study was to quote the diatom species optima from the sub-set of samples in order to be consistent with the model results. Although the ecological importance of the full data-set optima is recognised, the primary justification for not using these optima is that the reason many of the outlier samples were atypical was not immediately obvious. In addition some of the sites were suspected of having incorrect values for phosphorus and should not therefore be included when calculating the species optima. In the majority of cases the calculated species optima do not differ greatly between the full data-set and the sub-set of samples used for the predictive models. Tables 5.10 and 5.11 show the FRP optima calculated for the common and the more indicative taxa from the rope and tile samples, following the deletion of outliers. Only FRP optima and tolerances are considered below because both the rope and tile models gave better WA-PLS estimates for FRP.

Many of the diatom taxa can be seen to have marked distribution patterns along the FRP gradient. Figures 5.9 -13 and 5.15-5.19 show the distribution of some of the taxa from the rope and tile samples with narrower weighted average tolerances as well as the most commonly occurring species. In these diagrams each taxon is fitted with a Gaussian logit regression curve which, although not identical to the WA results, can be considered representative of the species distribution across a gradient (ter Braak & Looman 1986).

205 Rope To aid in the description of the observed ecological preferences and diatom distribution patterns, five subjectively defined groups have been used, based on the species WA optima for FRP. These are: group 1; species with optima of <100 pgL'^ FRP (Fig. 5.9), group 2, species with optima between 101-200 pgL'^ (Fig. 5.10), group 3, species with optima between 201-500 pgL'^ (Fig. 5.11), group 4, 501-1000 pgL'^ (Fig. 5.12) and group 5, species with optima >1000 pgL'^ (Fig. 5.13). These groups are in no way intended to form the basis of a river classification but have simply been chosen as a convenient means of describing the observed species distributions. Table 5.10 shows the WA optima and tolerances for the common taxa and those considered as better indicators due to narrower tolerance ranges.

Filterable Reactive Phosphorus (MgL*) No. of Diatom Taxa Opt. Tolerances Occ. Achnanthes biasolettiana 7 8 -7 +76 Fragilaria capucina v. gracilis 12 15 -13 4-87 Eunotia curvata 9 25 -20 4-94 Eunotia exigua 5 35 -13 4-21 Navicula rhynchocephala 14 35 -26 +112 Eunotia pectinalis v. minor 17 37 -31 +1S4 Gomphonema angustatum 22 41 -35 4-226 Frustulia vulgaris 11 42 -34 4-179 Achnanthes minutissima 64 60 -49 4-263 Navicula tenelloides 17 77 -60 4-272 Navicula cryptocephala 39 99 -81 4-433 Fragilaria capucina v. rumpens 32 110 -92 -k551 Sellaphora pupula 22 120 -100 -H578 Gomphonema pumilum 20 129 -112 4-832 Navicula capitatoradiata 14 134 -89 4-271 Gomphonema parvulum 66 136 -120 4-1031 Synedra ulna v. ulna 37 177 -157 4-1388 Navicula trivialis 14 180 -144 4-721 Nitzschia linearis 32 201 -160 4-782 Navicula gregaria 48 217 -163 4-664 Cymbella minuta 49 219 -187 -H1283 Amphora pediculus 54 235 -188 4-945 Nitzschia sociabilis 19 241 -179 -H700 Fragilaria vaucheriae 44 258 -229 4-2072 Gomphonema minutum 28 267 -208 4-935 Navicula lanceolata 49 283 -200 4-678 Achnanthes grana 11 292 -210 -H751

~~Table 5.10 Weighted average FRP optima and tolerances for the rope diatom data-set following the deletion of the outlier samples (68 sites), ranked by optima~~

206 Filterable Reactive Phosphorus (m s l ’) Diatom Taxa No. of Opt. Tolerances Occ. Navicula [species 2] 49 293 -228 +1022 Nitzschia frustulum 46 297 -232 +1067 Gomphonema olivaceum 27 306 -241 + 1125 Navicula protracta 10 309 -173 +394 Surirella ovalis 11 318 -210 +621 Navicula tripunctata 49 328 -244 +951 Navicula atomus 46 340 -240 +814 Navicula decussis 7 385 -168 +296 Navicula [pseudogregaria] 33 390 -266 +844 Cocconeis pediculus 30 400 -351 +2834 Achnanthes lauenbergiana 27 406 -311 +1337 Nitzschia palea 56 410 -334 +1794 Nitzschia constricta 22 416 -296 +1031 Nitzschia paleacea 56 435 -314 +1126 Nitzschia capitellata 18 435 -361 +2122 Diatoma vulgare 18 455 -379 +2265 Fallacia subhamulata 10 459 -333 +1213 Navicula capitata v. capitata 27 460 -335 +1231 Sellaphora minima 60 470 -384 +2082 Navicula cryptotenella 43 470 -357 +1484 Achnanthes lanceolata 66 498 -412 +2387 Nitzschia dissipata 41 519 -368 +1263 Navicula menisculus 37 531 -423 +2084 Achnanthes plonensis 18 541 -391 +1417 Cocconeis placentula v. euglypta 65 558 -445 +2193 Fragilaria parasitica v. parasitica 5 599 -322 +696 Rhoicosphenia curvata 55 600 -478 +2347 Fragilaria parasitica subconstricta 7 661 -371 +845 Melosira varians 51 668 -473 +1621 Navicula subminuscula 36 674 -477 +1633 Nitzsch ia fonticola 17 689 -501 +1842 Navicula viridula v. linearis 7 697 -311 +563 Surirella brebissonii 30 699 -485 +1580 Nitzschia tubicola 6 752 -480 +1321 Fragilaria brevistriata 28 765 -646 +4137 Achnanthes clevei 14 894 -664 +2584 Navicula molestiformis 28 1016 -856 +5423 Sellaphora seminulum 45 1288 -1112 +8148 Synedra tabulata 17 1296 -995 +4284 Achnanthes hungarica 8 1693 -1298 +5563 Nitzschia amphibia 44 2072 -1387 +4193 Amphora veneta 18 2386 -1600 +4852 Ctenophora pulchella 6 3588 -2178 +5538

~~Table 5.10 (cont.) Weighted average FRP optima and tolerances for the rope diatom data-set following the deletion of the outlier samples (68 sites), ranked by optima~~

~~207 8 12~~

~~WA Opt. = 8.0 pgL ‘ WA Opt. = 25 pgL~~

~~6 9~~

~~4 6~~

~~2 3~~

~~0 0 1.010 100 1000 10000 1.0 10 100 1000 10000 FRP(pgL‘) FR P(pgL') 5 7.5~~

~~WA Opt. = 35 pgL WA Opt. = 35 pgL~~

~~4 6.0 -~~

~~3 I 4.5- f I I r - 1 I 1.5-~~

~~o o9> 0 0.0 1.0 10 100 1000 10000 1.010 100 1000 10000 FRP(pgL') FRP CigL ‘) 4~~

WA Opt. = 42 pgL' WA Opt. = 60 pgL *

~~2 I 8 ■S § 30- 1 ^ 15-~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP(pgL') FRP (pgL ') 3~~

~~WA Opt. = 77 pgL" WA Opt. = 99 pgL~~

~~0 1.0 10 100 1000 10000 100 1000 10000 FRP(pgL-) FR P(pgL')~~

Figure 5.9 Group 1: Diatom taxa from the rope samples with WA FRP optima <100 pgL' showing logit regression curves. (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top o f each plot)

~~208 9.0 2.0~~

~~WAOpt. = 110 pgL ' WA Opt. = 120 PgL 7.5- 1.5- 6.0~~

~~4.5 3.0 H I 0.5- 1.5~~

~~0.0 0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP(pgL‘) FRP (pgL') 20 4~~

~~WA Opt = 129 PgL ' WA Opt. = 134 pgL~~

~~15 3~~

~~10 2 It 1~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP(pgL') FRP (pgL ■) 7.5~~

~~WA Opt. = 136 PgL WA Opt. = 180 PgL * 50- 6. 0 -~~

~~■I 4.5- ?t a I 3.0- 1.5-~~

~~0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL')~~

Figure 5.10 Group 2: Diatom taxa from the rope samples with WA FRP optima between 101-200 pgL' showing logit regression curves. (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~209 WA Opt. = 217 pgL' WA Opt. = 235 PgL' 25- 15-~~

~~2 0 - •g I1 0 -~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL') 2.0 6~~

~~WA Opt. = 241 PgL WA Opt. = 267 PgL 5 1.5- g 4~~

~~§ 3~~

~~2 I I 1~~

~~0.0 0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL )~~

~~WA Opt. = 283 pgL' WA Opt. = 293 pgL'~~

~~1 2 - 15- g I 1 Î I~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ) 3~~

~~WA Opt. = 297 PgL' WA Opt. = 318 pgL' g 9-~~

~~I 1~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ■) FRP (pgL ')~~

Figure 5.11 Group 3: Diatom taxa from the rope samples with WA FRP optima between 201-500 pgL' showing logit regression curves. (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~2 1 0 10~~

~~WA Opt = 328 PgL WA Opt. = 340 pgL~~

~~1 2 - 8 I 6 4~~

~~I 3- 2~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL ) 6 7.5~~

~~WA Opt. = 390 PgL WAOpt. = 410 pgL 5 6.0 -~~

~~4 d a 4.5- 3 t I I I 2 I 1.5- 1~~

~~0 0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL') 5~~

~~WA Opt. = 435 pgL' WA Opt. = 459 PgL'~~

~~15- g I 3 S 1 0 -~~

~~2 I 1~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ■) FRP (pgL )~~

~~WA Opt. = 470 PgL WA Opt. = 498 PgL 25- 1 2 - g S 4 2 0 - 1 I I 15- I I 10-~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL')~~

Figure 5.11 (cont.) Group 3: Diatom taxa from the rope samples with WA FRP optima between 201-500 pgL'^ showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( |) at the top of each plot)

~~21 1 5~~

~~WA Opt. = 519 pgL' WAOpt. = 531 PgL 4 d I 3 I 2~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

~~d WA Opt. = 558 pgL' WA Opt. = 668 PgL' 60- I d "~~

~~Ig 20- 5 I 30- I~~

~~1 0 - 1§ 15- a~~

~~1.010 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ' ) FRP (pgL')~~

~~WA Opt. = 674 PgL WA Opt. = 689 pgL' 4 -~~

~~1 !- I I I~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ') 4~~

~~WA Opt. = 699 PgL WA Opt. = 894 pgL '~~

~~0 100 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL')~~

Figure 5.12 Group 4: Diatom taxa from the rope samples with WA FRP optima between 501-1000 pgL' showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~2 1 2 7.5~~

~~WA Opt. = 1016 ugL" WA Opt. = 1288 pgL' 6.0- g 12-~~

~~I I « 3.0- I I,,. I~~

~~0.0 1.010 100 1000 10000 10 10010001.0 10000 FRP (pgL') FRP (pgL ") 5 5~~

~~WA Opt. = 1296 pgL' WA Opt. = 1693 pgL ' 4 4~~

~~3 a.~~

~~2 I~~

~~1 1~~

~~0 0 1.0 100 1000 1000010 1.0 100 100010 10000 FRP (pgL") FRP (pgL ) 4~~

~~WA Opt. = 2072 PgL WA Opt. = 2386 pgL' 20- 3~~

~~I 1~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ) FRP (pgL ') 1.5~~

~~WA Opt. = 3588 PgL' E ^ 1.0- I~~

~~|« .s-~~

~~1.0 10 100 1000 10000 FRP (pgL ")~~

Figure 5.13 Group 5: Diatom taxa from the rope samples with WA FRP optima >1000 pgL'* showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

213 The distribution of diatom taxa with respect to phosphorus has not been well documented within rivers. The majority of studies have instead concentrated on diatom responses to organic pollution (Descy 1979, Lange-Bertalot, 1979, Watanabe et al. 1986, Descy & Coste 1990, Round 1993). Within British rivers the trophic diatom index (TDI) (Kelly & Whitton 1996, Kelly 1998) has provided the first major study into the response of river diatoms to trophic status. Many of the WA optima quoted for the species in this study are consistent with the results of Kelly (1998) and also show similar results to the distribution of taxa observed across organic pollution gradients.

Group 1 taxa, with FRP optima of <100 pgL'\ include A. biasolettiana, A. minutissima, E. curvata, E. exigua, Erustulia vulgaris, N. cryptocephala, N. rhynchocephala and N. tenelloides (Fig. 5.9). The two Achnanthes species have been reported as being difficult to separate due to their similarity (Kelly 1997). It is clear however that A. biasolettiana is restricted to the lowest P levels, whereas A. minutissima is more widely tolerant. This example highlights the need for taxonomic accuracy when using diatoms as biological indicators. A. biasolettiana has been identified in other studies as being indicative of very high water quality with an apparent intolerance for any elevation in pollution (Descy & Coste 1990, Coste et al. 1991). Unlike A. biasolettiana, A. minutissima was a very common taxon, observed in all but four samples, but was found in highest relative abundance in the lower FRP sites. This taxon has been widely recorded in circumneutral waters as one of the most abundant diatom species (Round 1993) but the decline of A. minutissima at elevated levels of nutrients and organic loadings has been well documented (Lowe 1974, Evans & Marcan 1976, Anderson 1989, Bennion 1993), making it a valuable indicator species for the assessment of trophic status.

Eunotia exigua and E. curvata are typical of low alkalinity waters and have also been considered as intolerant to pollution by Descy & Coste (1990) and Kelly (1998). The available ecological preferences of the other group 1 taxa are less well defined in the literature but would appear to be indicative of the least eutrophic rivers covered in this study with FRP <100 pgL

214 Group 2 taxa, with FRP optima between 101-200 pgL'\ included F. capucina var. rumpens, Gomphonema parvulum, G. pumilum, N. capitatoradiata, N. trivialis and S. pupula (Fig. 5.10). This group, with the exception of N. capitatoradiata, consisted of widely tolerant species. G. parvulum was found in all but two of the samples and occurred at up to 10% abundance throughout the gradient. The relatively low weighted average optimum of 134 pgL'^ was due to two occurrences of 27% and 58% at low FRP sites. This taxon is generally accepted as being widely tolerant of trophic status and organic pollutants (Descy & Coste 1990, Round 1993). There is however a problem in the exact identification of the "'Gomphonema parvulum complex” due to the apparent polymorphism within the species (Lowe 1972, Round 1991). It is therefore possible that the observed range is not due to just one species but several different taxa of similar morphology or ecologically distinct varieties. Alternatively this taxon may simply be able to compete successfully over very wide ecological gradients. Considerable taxonomic problems were encountered with this species (see Ch. 2).

Navicula capitatoradiata appeared to be the best indicator species within group 2 from the rope samples. This species gave a much lower FRP optimum in the rope samples (WA opt. 134 pgL'^) compared to that from the tile substratum (WA opt. 604 pgL'\ see below). The reason for this is unclear but a higher optimum would be consistent with the findings of Descy & Coste (1990), who identified this species as indicative of higher ionic and pollution levels. F. capucina var. rumpens was also widely distributed across the FRP gradient but occurred at highest abundances around 100 pgL'\ Round (1993) reported this taxon as appearing just below what he considered the "Eunotia exigua zone” in British rivers which is consistent with the findings of this study. Kelly (1998) and Descy (1979) also identified F. capucina as being indicative of slightly elevated nutrient status, but recognised it as having a broad tolerance. Overall this group is poorly represented by good indicator species.

Group 3, with taxa in the 201-500 pgL'^ optima range, formed the largest subset of species including; A. lanceolata var. lanceolata, A. pediculus, Fallacia subhamulata, G. minutum, N. atomus, N. cryptotenella, N. gregaria, N. lanceolata, N. [pseudogregaria], N. [species 2], N tripunctata, Nitzschia frustulum, N. palea, N. paleacea, N. sociabilis and Surirella ovalis (Fig. 511). These taxa varied greatly in their WA tolerance ranges

215 (Fig. 5.14). A. lanceolata, which fell at the top end of group 3 with a WA optimum of 498 pgL'\ was found in all but two of the samples. This taxon is widely reported to be tolerant to moderate levels of pollution (Descy & Coste 1990) and can be found in almost all circumneutral, aerated waters (Patrick & Reimer 1966, Round 1993). Patrick and Reimer (1966) suggested that A. lanceolata is not tolerant of high levels of pollution. This is not consistent with this study, where A. lanceolata achieved the highest relative abundance at the most nutrient rich site, and neither does it reflect the observations of Kelly (1988), who considered large abundances of this taxon to be indicative of the highest nutrient conditions. The wide tolerance of A. lanceolata to FRP does not make it a good indicator species.

Many of the group 3 species, particularly the Navicula spp., did show a marked response within the 201-500 pgL"^ range. N. gregaria, N. [pseudogregaria], N. lanceolata, N. atomus and N. tripunctata all had relatively narrow WA tolerances and achieved maximum abundances within the 201-500 pgL'^ range displaying a “typical” Gaussian response (Fig 5.11). The published ecological information on these taxa is confusing with respect to trophic status, although it is generally agreed that they favour circumneutral to alkaline waters with elevated mineral content. (Patrick & Reimer 1966, Descy & Coste 1990, Round 1993). Of these species only N. atomus is consistently referred to as having a preference for more eutrophic and organically enriched waters (Patrick & Reimer 1966, Descy & Coste 1990, Round 1993, Kelly 1998). Evans and Marcan (1976) observed an increase in N. atomus with the addition of sewage effluent to artificial streams. N. tripunctata was reported as being indicative of the cleanest waters by Descy (1979), whereas Kelly (1998) and Round (1993) place this taxon in moderate to highly enriched rivers; the results of this study agree with Kelly. N. lanceolata showed a relatively narrow WA tolerance and can thus be considered as a good indicator of moderate trophic status. This does not agree with the results of Kelly (1998) who placed this taxon in a group indicating high levels of phosphorus. N. gregaria and N. [pseudogregaria] both appear to be good indicators within group 3. N. gregaria is quoted as being typical of high ionic and brackish waters (Patrick & Reimer 1966) which is not wholly consistent with the findings of this study. Again these results do not agree with Kelly (1998), who considers this taxon as indicative of the highest trophic status and Round (1993) who eluded to it being most common in eutrophic

2 1 6 waters. The ecology of N. [pseudogregaria], which is superficially similar to N. gregaria in valve morphology (see Appendix I), appears to be similar to that of N. gregaria with respect to phosphorus.

Nitzschia palea is normally considered as being typical of highly polluted waters (Lowe 1974, Evans & Marcan 1976, Descy & Coste 1990, Round 1993). The majority of high FRP sites in this study did contain N. palea but not at high abundances. This species is obviously tolerant of high trophic status but seems to perform optimally under moderate nutrient conditions. From the results of this study it is not considered to be a good indicator species for FRP. The reason for it being used as one of the major indicator species in other studies is probably due to its response to organic loading rather than phosphorus. N. paleacea was less widely tolerant and showed a distinct drop-off in abundance at the high end of the FRP gradient. This result is consistent with the findings of Descy and Coste (1990) who place N. paleacea in the mid range of indicator species from French rivers. Similar patterns were observed for N. frustulum, N. sociabilis and S. ovalis.

At the top end of group 3 N. cryptotenella showed a distinct increase in relative abundance followed by a sharp fall in abundance at sites over 1000 pgL'\ Although this taxon is common at low FRP concentrations, it only occurs in small numbers and thus at high relative abundances it is considered a good indicator of moderately high FRP.

Group 4 taxa, with FRP optima between 501-1000 pgL'\ included A. clevei, C. placentula var. euglypta, M. varians, N. menisculus, N. subminuscula, Nitzschia dissipata, N fonticola and S. brebissonii. The distribution of C. placentula var. euglypta has already been identified above as being poorly related to the FRP gradient. Pringle and Bowes (1984) reported increases in this taxon following the inoculation of artificial substrata with nitrate but similar experiments with phosphate did not yield the same increase. In this study C. placentula var. euglypta did not show any marked response to nitrate concentrations. Its distribution is generally considered to favour alkaline waters (Patrick & Reimer 1966, Round 1993) and it has been reported as being tolerant of organic pollution (Butcher 1940). The value of this taxon as an indicator of trophic status appears to be negligible and its high occurrence over the entire FRP gradient is

~~217 probably a function of its reported success as a quick coloniser; particularly of artificial substrata (Round 1993).~~

The occurrence of M. varians in more eutrophic waters is consistent with the reported ecology of this species from a number of studies (Hustedt 1937, Cholnoky 1968, Lowe 1972, Kelly 1998). From the observed distribution in this study it would appear to be a good indicator of moderate to high trophic status. N. subminuscula, N. dissipata, N. fonticola and S. brebissonii also conform to most of the published data by occurring at maximum abundances in more eutrophic waters and are considered in this study to be good indicator taxa.

Group 5, with taxa having WA optima >1000pgL'\ consisted of only a few species (Fig. 5.13) of which N. amphibia, S. seminulum, A. veneta and S. tabulata showed the clearest response to high FRP concentrations. N. amphibia is reported to favour alkaline waters and to be capable of heterotrophy (Cholnoky 1968), being found in highest abundance at (and possibly stimulated by) heavily enriched sites (Lowe 1972). Although this taxon was found throughout the study sites it showed a dramatic increase in

~~abundance at sites over 1000 pgL'^ and is thus considered a very good indicator of highly eutrophic water when comprising >5% of the total assemblage.~~

Sellaphora seminulum was also a common taxon showing a very marked increase at the highest FRP concentrations. Similar findings have been reported by Lange-Bertalot (1979), Watanabe et al. (1988) and Kelly (1998). A. veneta was much less common and never exceeded 4% abundance but, with the exception of one sample (possible contamination?), only occurred at very eutrophic sites. This is also consistent with the literature. Lange-Bertalot (1979), Descy and Coste (1990), Round (1993) and Kelly (1998) all considered this taxon indicative of the most polluted river zones. Despite this taxon occurring in low numbers it would still appear to be a good indicator species for the most eutrophic rivers.

~~218 G ro u p 1~~

Achnanthes hiasoleaiana • ------—• ------Fraffllaria capucina v. gracilis Eunotia curvata Eunotia exigua Navicula rhynchocephala Eunotia pectinalis v. m inor Gomphonema angustatum Frustulia vulgaris Achnanthes minutissima Navicula tenelloides Navicula cryptocephala Fragilaria capucina v. rum pens Sellaphora pupula Gomphonema pumilum Navicula capitatoradiata Gomphonema parvulum Synedra ulna v. ulna Navicula trivialis Nitzschia linearis N avicula gregaria Cymbella minuta Amphora pediculus Nitzschia sociabilis Fragilaria vaucheriae Gomphonema minutum Navicula lanceolata Achnanthes grana N avicula [species 2] Nitzschia frustulum Gomphonema olivaceum Navicula protracta Surirella ovalis Navicida tripunctata Navicula atomus Navicida decussis Navicula \pseudogregarid\ Cocconeis pediculus Achnanthes lauenbergiana Nitzschia palea Nitzschia constricta Nitzschia paleacea Nitzschia capitellata Diatoma vulgare Fallacia subhamulata Navicula capitata v. capitata Sellaphora minima Navicula cryptotenella Achnanthes lanceolata Nitzschia dissipata Navicula menisculus Achnanthes plonensis Cocconeis placentula v. euglypta Fragilaria parasitica v. parasitica Rhoicosphenia curvata Fragilaria parasitica subconstricta Melosira varians Navicula subminuscula Nitzschia fonticola Navicula viridula v. linearis Surirella brebissonii Nitzschia tubicola Fragilaria brevistriata Achnanthes clevei Navicula molestiformis Sellaphora seminulum Synedra tabulata Achnanthes hungarica Nitzschia amphibia Amphora veneta Ctenophora pulchella

~~1 10 32 100 316 1000 3160 10000 FRP (PgL ')~~

~~Figure 5.14 WA optima and tolerances for FRP from the rope training set, following the deletion of outliers~~

219 Achnanthes hungarica, C. pulchella and S. tabulata were also found in relatively low numbers but showed a distinct preference for the high end of the FRP gradient. The latter two species have been associated with waters of high conductivity (Patrick 1966). The observed distribution in this study may therefore reflect the high ionic concentration at the highest FRP sites rather than the trophic status per se.

The above groups were subjectively defined, simply to aid in the description of the species distributions. Overall, with the possible exception of group 2, each group contained a number of taxa which showed distinct distribution patterns, relating to the observed FRP gradient (Fig. 5.14). This helps to demonstrate how the WA-PLS model uses these distributions to derive a diatom-based representation of the trophic status at a river site.

Tile By using the same five subjectively defined groups used to describe the distributions of the rope species, the WA optima and tolerances of the tile training set taxa can be similarly considered. The species assemblages observed on the tile substrata differed slightly to those from the rope, and thus some of the taxa that have been selected below (Tab. 5.11 & Figs. 5.15-5.20) differ in their WA optima and tolerances from those described above. This difference in the assemblages can be explained by the habitat characteristics of the two artificial substrata {cf. Ch. 3, 3.3.4). Rather than reiterate the information given for the rope species distributions, this section will concentrate on species which were not covered in the rope data and those which differ greatly from the observed WA results from the rope substrata. Table 5.11 shows the WA optima and tolerances for FRP calculated for the common and the more indicative taxa from the tile samples, following the deletion of outliers. The distribution of some of these taxa are illustrated in Figures 5.15-5.19, in order of the WA optima groups.

2 2 0 Filterable Reactive Phosphorus (UgL') No. of Diatom Taxa Opt. Tolerances Occ. Achnanthes biasolettiana 11 5 -4 +26 Denticula tenuis 4 5 -3 +11 Cymbella microcephala 4 6 -5 +33 Eunotia curvata 6 34 -28 +162 Fragilaria capucina v. rumpens 18 35 -30 +241 Cymbella affinis 6 38 -29 +122 Gomphonema anoenum 9 42 -32 +128 Navicula rhynchocephala 8 44 -27 +69 Meridian circulare 10 56 -33 +78 Achnanthes minutissima 59 58 -47 +252 Fragilaria leptostauron 10 60 -55 +689 Navicula trivialis 7 88 -68 +301 Diploneis oblongella 7 95 -59 +152 Navicula cryptocephala 25 103 -78 +328 Navicula tenelloides 7 104 -70 +216 Caloneis bacillum 16 142 -115 +603 Navicula veneta 19 146 -119 +626 Fragilaria vaucheriae 23 155 -137 +1165 Gomphonema pumilum 25 157 -132 +831 Amphora pediculus 56 159 -131 +737 Fallacia subhamulata 22 174 -109 +290 Sellaphora pupula 18 176 -151 +1070 Nitzschia pusilla 20 183 -120 +346 Navicula agrestis 20 210 -152 +544 Gomphonema parvulum 54 211 -185 +1528 Navicula gregaria 44 212 -150 +509 Reimeria sinuata 28 222 -196 +1621 Nitzschia dissipata 47 229 -180 +836 Navicula lanceolata 36 241 -155 +436 Eunotia pectinalis v. minor 11 250 -228 +2586 Navicula menisculus 40 254 -206 +1075 Nitzschia capitellata 14 262 -168 +470 Gomphonema olivaceum 30 264 -213 +1105 Nitzschia constricta 17 272 -171 +464 Nitzschia supralitorea 22 290 -207 +729 Navicula cryptotenella 32 290 -227 +1038 Amphora ovalis v. pediculus 21 290 -213 +795 Navicula capitata 15 300 -216 +774 Navicula tripunctata 32 302 -225 +880 Nitzschia palea 47 313 -241 +1043 Cymbella minuta 37 323 -276 +1905 Nitzschia dubia 6 324 -188 +448 Nitzschia recta 25 344 -220 +612 Nitzschia frustulum 28 369 -285 +1259

~~Table 5.11 Weighted average FRP optima and tolerances for the tile diatom data-set following the deletion of the outlier samples (60 sites), ranked by optima~~

221 Filterable Reactive Phosphorus (PgL'b Diatom Taxa No. of Opt. Tolerances Occ. Achnanthes lauenbergiana 29 371 -302 -kl621 Fragilaria parasitica v. parasitica 6 414 -202 +394 Navicula atomus 49 442 -305 +987 Sellaphora minima 56 450 -374 +2225 Nitzschia paleacea 35 464 -342 +1302 Navicula [species 2] 43 464 -351 +1432 Nitzschia linearis 18 470 -365 +1624 Nitzschia fonticola 15 485 -332 + 1049 Nitzschia sociabilis 21 486 -285 +690 Navicula [pseudogregaria] 30 501 -310 +812 Achnanthes lanceolata 59 523 -424 +2237 Navicula capitoradiata 9 604 -423 +1417 Nitzschia inconspicua 12 608 -420 +1363 Cocconeis placentula v. euglypta 53 679 -524 +2293 Achnanthes lanceolata v. rostrata 30 703 -582 +3370 Surirella brebissonii 24 709 -428 +1081 Rhoicosphenia curvata 42 756 -600 +2923 Navicula [small species I] 8 768 -239 +347 Navicula decussis 10 781 -484 + 1274 Achnanthes ploenensis 13 794 -546 + 1751 Navicula subminuscula 38 806 -535 + 1589 Melosira varians 25 990 -533 +1156 Achnanthes grana 8 1098 -448 +756 Sellaphora seminulum 43 1407 -1214 +8871 Achnanthes clevei 15 1429 -1035 +3752 Amphora veneta 18 1517 -994 +2878 Synedra tabulata 7 1582 -1101 +3618 Achnanthes saxonica 8 1617 -982 +2500 Nitzschia amphibia 37 1627 -997 +2577 Navicula goeppertiana 10 1790 -1541 +11108

~~Table 5.11 (cont.) Weighted average FRP optima and tolerances for the tile diatom data-set following the deletion of the outlier samples (60 sites), ranked by optima~~

Within group 1, A. biasolettiana, A. minutissima and N. cryptocephala showed very similar distributions to those seen in the rope samples (Fig. 5.15). F. capucina var. rumpens was less common in the tile samples and the WA optima was lower (35 pgL'^ versus 110 pgL'^). Although this species falls into different groups, as defined in this study, the results remain consistent with the findings of Round (1993) and Kelly (1998) who consider F. capucina as indicative of the first signs of nutrient enrichment.

~~2 2 2 7.5~~

~~WA Opt. = 5.0 pgL ' WA Opt. = 5.0 PgL'~~

~~6. 0 - I 30- I 4.5- I I I 2 0 - I 3.0- I I X 10- 1.5-~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

~~& WA Opt. = 35 PgL WA Opt. = 42 pgL 2 0 - i 6-~~

~~15- S s~~

~~K 1 0 - .sI I I.~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL') 5 0.9~~

~~WA Opt. = 44 PgL ' WA Opt. = 56 PgL' 4~~

~~^ 0.6 - 3~~

~~IV) 2 I I 0.3- 1~~

~~0 0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ') 7.5~~

~~WA Opt. = 58 PgL WA Opt. = 95 PgL 75- 6 . 0 - â I g I 4.5- ■a ■§ I 3.0-~~

~~1 1.5- 15-~~

~~0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

Figure 5.15 Group 1: Diatom taxa from the tile samples with WA FRP optima <100 pgL' (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~223 1.2~~

~~WA Opt. = 103 PgL ' WA Opt. = 104 pgL~~

~~& 0.9- I I~~

~~0.6 - I- I I I ‘ 0.3-~~

~~0.0 1.010 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL') 4~~

~~WA Opt = 142 PgL WA Opt. = 146 pgL'~~

~~3 d 2 - I I 2~~

~~! 1~~

~~0 1.0 10 100 1000 10000 10 100 10001.0 10000 FRP (pgL') FRP (pgL')~~

~~WA Opt. = 157 PgL WA Opt. = 159 PgL~~

~~2 0 - d~~

~~I 1 0 -~~

~~100 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

~~3 6~~

~~WA Opt. = 174 PgL WA Opt. = 183 PgL 5~~

~~2 4~~

~~1 2~~

~~0 0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

Figure 5.16 Group 2: Diatom taxa from the tile samples with WA FRP optima between 101-200 pgL' (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~224 3~~

~~WA Opt. = 210 pgL' WA Opt. = 211 pgL I 2 i 1~~

~~0 1.0 10 100 1000 10000 100 10000 FRP(pgL') FR P(pgL')~~

~~WA Opt. = 212 pgL WA Opt. = 229 pgL~~

~~30- 15 & I~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP(pgL') FRPGigL') 8~~

~~WA Opt. = 241 pgL ' WA Opt. = 262 pgL * 60 6~~

~~■§ 45- i 4~~

~~2 15-~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ‘) FR P(pgL')~~

~~WAOpt. = 272pgL- WA Opt. = 290 pgL ' 5 - 1 2 - g I I 2 -~~

~~1.010 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ‘) FRP (pgL')~~

Figure 5.17 Group 3: Diatom taxa from the tile samples with WA FRP optima between 201-500 pgL' (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~225 WA Opt. = 290 PgL' WA Opt. = 302 PgL' & 4- g 1 - I f i . !.. I.~~

~~1.010 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL ') 6 5~~

~~WA Opt. = 313 PgL' WA Opt. = 344 PgL 5 4~~

~~4 3 t 3 2 Î2 1 1~~

~~0 0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ■) FRP (pgL')~~

~~WA Opt. = 442 PgL WA Opt. = 464 PgL' 30- g 3 % I I I 1 0 - 2 -~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ) FRP (pgL')~~

~~16 10~~

~~WA Opt. = 464 PgL'' WA Opt. = 486 pgL 8 12 -~~

~~4- 2~~

~~O % Oo“ o o o 0 ™0 ° o 0 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL") FRP (pgL’)~~

Figure 5.17 (cont.) Group 3: Diatom taxa from the tile samples with WA FRP optima between 201-500 pgL'' showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~226 15~~

~~WA Opt. = 501 pgL WA Opt. = 523 PgL 40- g~~

~~9 30- I 3 a « 2 0 - I 3~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ) 7.5~~

~~WA Opt. = 608 PgL' WA Opt. = 679 PgL' 6.0 - 30~~

~~4.5-~~

~~i 2 0 - ■s Si i I 1.5- I 10 I 0.0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ) FRP (pgL ')~~

~~4 10~~

~~WA Opt. = 709 PgL 17% WA Opt. = 768 PgL' 8~~

~~6 2 4~~

~~1 2~~

~~0 0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL ')~~

~~6 12~~

~~WA Opt. = 806 PgL' WA Opt. = 990 pgL ' 5 9 4~~

~~3 6~~

~~2 3 1~~

~~0 0 1.0 10 100 1000 10000 1.0 10 1001000 10000 FRP (pgL ') FRP (pgL ')~~

Figure 5.18 Group 4: Diatom taxa from the tile samples with WA FRP optima between 501-1000 pgL’ showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

~~227 WA Opt. = 1098 pgL WA opt. = 1407 PgL’~~

~~30- I I 20- « I I " !■~~

~~1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL') FRP (pgL ')~~

~~12 3~~

~~WA Opt. = 1429 pgL’ WAOpt. = 1517 PgL '~~

~~1 3 I~~

~~0 0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ) FRP (pgL')~~

~~1.5 12~~

~~WA Opt. = 1582 PgL WA Opt. = 1617 pgL~~

~~9 1.0 - ! 6~~

~~I 3~~

~~0.0 0 1.0 10 100 1000 10000 10 100 1000 100001.0 FRP (pgL') FRP (pgL')~~

~~WA Opt. = 1627 PgL’ WA Opt. = 1790 PgL~~

~~2 0 - 4 & I 3 t " e« I 1»- 2 I I 1~~

~~0 1.0 10 100 1000 10000 1.0 10 100 1000 10000 FRP (pgL ') FRP (pgL')~~

Figure 5.19 Group 5: Diatom taxa from the tile samples with WA FRP optima >1000 pgL' showing logit regression curves (The vertical axes are scaled according to the maximum relative abundance of each taxon and the WA optima denoted by a tick-mark ( | ) at the top of each plot)

228 G roup 1 Achnanthes hiasolettiana ' ------Denticula tenuis Cymhella micmcephala •------Eunotia curvata Fragilaria capucina v. rumpens Cymhella affinis Gomphonema anoenum Navicula rhynchocephala Meridian circulate Achnanthes minutissima Fragilaria leptostauron Navicula trivialis Diploneis ohlongella Navicula cryptocephala Navicula tenelloides Caloneis bacillum Navicula veneta Fragilaria vaucheriae Gomphonema pumilum Amphora pediculus Fallacia suhhamulata Sellaphora pupula Nitzschia pusilla Navicula agrestis Gomphotiema parvulum Navicula gregaria Reimeria sinuata Nitzschia dissipata Navicula lanceolata Funotia pectinalis v. minor Navicula menisculus Nitzschia capitellata Gomphonema olivaceum Nitzschia constricta Nitzschia supralitorea Navicula cryptotenella .imphora ovalis v. pediculus Navicula capitata Navicula tripunctata Nitzschia palea Cymhella minuta Nitzschia duhia Nitzschia recta Nitzschia frustulum Achnanthes lauenhergiana Fragilaria parasitica v. parasitica Navicula atomus Sellaphora minima Nitzschia paleacea Navicula [species 2] Nitzschia linearis Nitzschia fonticola Nitzschia sociahilis Navicula [pseudogregaria] Achnanthes lanceolata Navicula capitatoradiata Nitzschia inconspicua Cocconeis placentula v. euglypta Achnanthes lanceolata v. rostrata Surirella hrehisonii Rhoicosphenia curvata Navicula [small species I] Navicula decussis Achnanthes ploenensis Navicula suhminuscula Melosira varians Achnanthes grana Sellaphora seminulum Achnanthes clevei Amphora veneta Synedra tahulata Achnanthes saxonica Nitzschia amphihia Navicula goeppertiana 1 3 10 32 100 316 1000 3160 10000

~~FRP (pgL ')~~

~~Figure 5.20 WA optima and tolerances for FRP from the tile training set, following the deletion of outliers~~

229 Other less common taxa within group 1 were Denticula tenuis, G. amoenum, M. circulare and D. oblongella. D tenuis was considered by Descy (1979) and Lange- Bertalot (1979) as intolerant to organic pollution and by Kelly (1998) as indicative of very slight eutrophication. In this study D. tenuis was found only at sites >100 pgL'^ FRP, but the low number of occurrences leads to caution in claiming that it is a good indicator of this group. Similarly M. circulare is reported as typical of nutrient poor waters (Round 1993) and particularly close to the source of spring-fed streams (Olive & Price 1977). This taxon never reached high abundances in this study, which is consistent with it being considered a vernal species (Cox 1993). Cox observed M. circulare to have a preference for waters of below 10 °C, and this was also the case in this study. In both the tile and rope samples this species was found in waters of high FRP concentrations, but only where the water temperature was low (Fig. 5.21). The indicator value of this taxon is thus brought into question with respect to trophic status.

~~4.0 1.0 n~~

~~Rope training set samples o Tile training set samples ° 3.0 --0 .7 5 - & % 1 •b 2.0 0.5- O c a 5 1.0 o ° ^ 0.25 - o o 0 o~~

~~° O 0 0 o~~

~~0.0 1 1 1 ■ 1 ■ r 0.0- 8 10 12 14 16 8 10 12 14 16 Water Temp. (”C) Water Temp. (“C)~~

~~Figure 5.21 Distribution of Meridion circulare with respect to the full temperature gradient in the rope and tile training sets~~

Diploneis oblongella was also a relatively uncommon species but was observed to have a distinct preference for waters of low nutrient status. This is consistent with the findings of Descy and Coste (1990) and Kelly (1998), the latter author placing all species of this genus in the lowest phosphorus indicator group.

Within the group 2 taxa (Fig. 5.16), two species {N. cryptocephala and N. tenelloides) were in group 1 in the rope samples. N. cryptocephala showed a very similar WA optimum in both training sets (99 & 103 pgL'^) and N. tenelloides only differed by 27

230 |igL‘\ Two other taxa in the tile group 2 were in the third group of the rope samples. A. pediculus showed a similarly wide distribution in both data-sets and only differed in their optima by 76 pgL'\ The distribution of F. subhamulata was less similar between the two data-sets. In the rope data the WA optimum was 459 pgL'\ while only 174 pgL'^ in the tile training set. The reason for the higher optima calculated from the rope training set is due to the deletion of the outlier samples, this taxon was represented in four of the low FRP sites that were deleted; when the outliers were included in the rope samples the optimum was 211 pgL'\ This suggests that the optimum observed in the tile data is more reliable and the narrow tolerance of F. subhamulata makes it a good indicator of low to moderate trophic status in lowland rivers. This is consistent with the findings of Descy (1979).

Other taxa in the tile group 2 included C. bacillum, G. pumilum, N. veneta and N. pusilla. G. pumilum showed a very similar distribution to the that observed in the rope data. C. bacillum and N. veneta were both poorly represented in the rope data-set but, where they did occur, showed very similar patterns to the distributions in the tile data. The distribution of C. bacillum is consistent with Descy and Coste (1990) who considered this taxon as a good indicator of the first signs of river pollution. The ecology of N. veneta is less clear. Descy and Coste (1990) considered this taxon as widely tolerant but more indicative of high pollution levels. In this study it was also observed to be widely tolerant but the highest relative abundances were at lower FRP concentrations. N. pusilla appeared to be a good indicator within group 2. Watanabe (1988) and Descy and Coste (1990), however, attributed this taxon as being tolerant to, and indicative of, high levels of organic pollution. Similarly Kelly (1998) places N. pusilla in a group of high phosphate indicator species. These results are not consistent with the findings of this study.

Many of the taxa within group 3 (Fig. 5.17) were also found in the group 3 of the rope samples (Fig. 5.11), although the WA optima were seen to differ slightly between the training sets. Like the rope training set many taxa fell into the 201-500 pgL'^ FRP range. Species with very similar WA optima to those in the rope training set included: N. atomus, N. gregaria, N. lanceolata, N. tripunctata, Nitzschia palea and N. paleacea. Other taxa in this group differed in their optima by over 100 pgL'\ including: N.

~~231 cryptotenella, N. capitellata and N. constricta with lower optima and N. [species 2] and N. sociabilis with higher optima.~~

The wide distribution of G. parvulum was similar to that seen in the rope samples, although the optimum was slightly higher in the tile samples (WA opt. = 211 pgL'^ versus 136 pgL'^) placing it into group 3 rather than 2. The reason for the higher optimum in the tile samples was a small increase in the abundance of this taxon at the upper-most end of the FRP gradient {cf. Figs. 5.17 & 5.10). The taxonomic problems, and possible adaptive strategies of this taxon, resulted in it having little or no value as an indicator species.

Nitzschia dissipata was found in more low FRP samples in the tile data than in the rope, resulting in it having a relatively low group 3 optimum compared with being in group 4 in the rope training set (WA opt. = 229 pgL'^ & 519 pgL'^ respectively). The reason for this wider distribution in the tile samples is unclear. The literature tends to agree that this taxon is indicative of low to moderate organic pollution (Descy 1979, Watanabe 1988, Descy & Coste 1990). The relationship with phosphorus is less clear; this study shows a distinct fall-off in abundance at the very highest end of the FRP gradient, whereas Kelly (1998) considered it to be indicative of the most eutrophic sites.

Three Nitzschia species which showed relatively narrow distributions in group 3 of the tile samples were N. capitellata, N. constricta and N. recta. These taxa were more common or occurred at higher relative abundances than in the rope training set. The documented ecology of these Nitzschia species is rather confused. Descy (1979) originally placed N. recta in the least tolerant group to organic pollution, although this was updated by Descy and Coste (1990) into a group tolerant of moderate pollution. Kelly (1998) considers the genus Nitzschia, with some exceptions, as indicative of moderately high trophic status. From the results of this study these three species are considered to be good indicators of lower FRP levels (approx. 300 pgL'^) than those reported by Kelly.

232 Two common species in group 4 of the tile samples were placed in group 3 of the rope data. A. lanceolata var. lanceolata showed a very similar WA optimum (523 pgL'^ versus 498 pgL'^) and a wide distribution across the FRP gradient, with a tendency for higher abundances at the top end. A. lanceolata is not considered to be a good indicator. N. [pseudogregaria] had a higher optimum than in the rope samples (501 pgL'^ versus 390 pgL'^) but showed a very similar distribution pattern. Although the ecology of this taxon has not been documented, it would appear to be a good indicator of moderate FRP concentrations in lowland rivers.

Cocconeis placentula var. euglypta, M. varians, N. subminuscula, N. inconspicua and S. brebissonii all had optima in the group 4 range of both training sets. C. placentula var. euglypta was very widely distributed and only after the deletion of outlier samples did it show the illustrated distribution with an apparent preference for more eutrophic waters (Fig. 5.18). This taxon has little or no indicator value. M. varians was less common and occurred at lower abundances in the tile samples, suggesting that the rope substratum offered a better growing surface than the tile. This taxon was found to be partially dependent on current speed in the tile data (Ch. 4, 4.5.4) and thus it may only be a good indicator of FRP in low flow sites. Conversely its occurrence at higher phosphorus sites could simply be a function of the lack high FRP samples with high current velocities. The value of M. varians as an indicator of trophic status is therefore questionable.

Navicula subminuscula and S. brebissonii showed very similar distribution patterns to those observed in the rope training set. N. subminuscula had a slightly higher WA optimum in the tile samples (806 pgL'^ versus 674 pgL'^), but is considered a good indicator of moderately high trophic status. S. brebissonii was also considered a good indicator of this group which is consistent with the findings of Descy and Coste (1990) and Kelly (1998). This taxon was not illustrated for the rope data but the WA optima only differed by 10 pgL ^

Navicula [small species 1] was only observed in the tile samples and appeared to be restricted to a very narrow range of FRP concentrations (WA opt. = 768 pgL'^). This taxon has not previously been described and the only other record of its occurrence was in samples taken from the River Thames between Putney and Canning Town (Juggins

233 1992, recorded as unknown Navicula sp. 1). It was also observed in epilithic and rough tile samples from site ELST2 on the River Wey in this study (Ch. 3), where the FRP concentration was 999 pgL'\ The limited data on this taxon suggests that it is a good indicator of trophic status although more information is needed to confirm this.

The group 5 tile samples had three widespread species in common with the rope substratum: A.veneta, N. amphibia and S. seminulum. These taxa showed similar WA optima and are considered good indicators of the highest trophic status observed in this study. Of the rarer taxa S. tabulata, A. clevei and N. goeppertiana were found at high FRP concentrations, although the latter two species had optima just below 1000 pgL'^ in the rope data (WA opt. = 893 and 871 pgL'^ respectively). N. goeppertiana is widely reported as being tolerant of pollution (Watanabe 1988, Descy & Coste 1990, Round 1993) and is considered alongside many Navicula species by Kelly (1998) as indicative of high trophic status. The observed distribution of A. clevei is not, however, consistent with the literature. Descy & Coste (1990) place this taxon in the same indicator group as A. biasolettiana. In this study A. clevei was only found in higher abundances at the top end of the FRP gradient.

Achnanthes grana, although uncommon, gave very different WA optima from the two training sets. In the rope samples its optimum was 292 pgL'\ versus 1098 pgL'^ in the tile samples. The reason for this discrepancy is unclear but it highlights the possible problems associated with placing too much reliance on taxa with few occurrences.

The species distributions across the FRP gradient show a wide range of responses in both the WA optima and tolerances of the different taxa. The indicator values of many of these species are limited when considered alone. It is the combined effect of the whole species assemblage used in weighted averaging methods that allows for statistically reliable FRP estimations to be made, emphasising the need to encourage maximum diversity when sampling river diatoms for monitoring purposes.

~~234 5.6 Discussion~~

The use of WA, and more recently WA-PLS, has provided a valuable tool for the reconstruction of past water quality changes from the fossil diatom records of lake cores (ter Braak & van Dam 1989, Birks et al. 1990, Bennion 1994, Jones & Juggins 1995, Bennion et al. 1996). The intention in this study was to apply these, traditionally palaeoecological, techniques for the assessment of modem phosphorus concentrations by using the two lowland river diatom training sets from the rope and tile substrata.

~~5.6.1 Model Selection~~

Of the three regression models used (simple two-way weighted averaging, tolerance down-weighted WA and weighted averaging - partial least squares) the tolerance down- weighted WA method gave the highest correlation (r^) between the observed and predicted phosphorus concentrations, based on the leave-one-out jack-knifed statistic, for both the rope and tile training sets. This was the ease for both TP and FRP. The low r^ (approx. 0.5) and high RMSEP (> 0.55) values for these results, however, suggests that these models had limited predictive power; hence the decision to delete any samples which exceeded a prediction residual of > ±0.75 (logio P concentration) in both the WA and WA(toi) models (e.g. Birks et al. 1990, Larson et al. 1996).

Following the deletion of the statistical outliers a marked improvement was seen in all the models, with WA-PLS giving the best error statistics in both training sets. Furthermore, better model results were obtained for the prediction of FRP than for TP, suggesting the diatom assemblages responded more strongly to the phosphorus available to them in the water column than to total phosphorus. Hence the decision was made to use only FRP concentrations in any further evaluation of the models.

The reason for the WA-PLS models outperforming WA and WA(toi) is likely to be two fold. Firstly the WA methods are more susceptible to the “edge effect” which is well known to be a problem at either end of a gradient (Hill & Gauch 1980). The edge effect is due largely to the truncation of the gradient and thus the model does not have the required information to accurately calculate species optima at the gradient ends, thus

235 resulting in the model pulling the predicted values towards the mean. This typically results in an over-estimation at the low end of the gradient and an under-estimation at the high end. WA-PLS exploits these patterns in the errors to reduce the edge effect (ter Braak & Juggins 1993). Although not solving the edge effect error completely it is clearly demonstrated in Figures 5.4 and 5.8 that WA-PLS reduces the bias at the gradient ends. The edge effect is more often a serious problem in the development of transfer functions where the gradient lengths are short and species turnover is low (Bennion et al. 1996). In this study the gradient lengths were high and thus help to reduce the impact of the edge effect. The second reason for WA-PLS performing better than WA is that it uses the residual structure in the data that is ignored by the other methods. WA-PLS does not assume that P will be the only variable governing the species distribution and thus uses any extra patterns in the data that are related to the P concentrations to improve the overall fit of the model (ter Braak & Juggins 1993).

As well as assessing the performance of the models on the RMSEP and F alone, it is also important to consider the mean and maximum bias statistics. These measurements give an indication of the systematic error across the entire gradient and allow for the identification of the particular section of the gradient which results in the highest errors. In both the WA-PLS models for FRP the mean bias was almost zero (0.001) and maximum bias was lower than the WA models. The maximum bias for both models occurred at the lowest end of the gradient suggesting that sites with low FRP are poorly represented in the training sets and consequently the gradient is being truncated. This problem is exacerbated by the removal of the outlier samples, the majority of which were from low FRP sites. A logical extension to this work would therefore be to add more low FRP sites to the training sets to gain more reliable ecological information on the species found in rivers where eutrophication has had little or no impact.

The WA-PLS models for FRP are therefore considered the optimal models for the diatom-based estimation of trophic status in lowland rivers. The inclusion of rare taxa gives negligible improvements and is thus not deemed necessary. The use of square rooted species data gave very similar results and should therefore be considered as a viable alternative method, particularly due to the reduction in outlier samples at the low end of the FRP gradient. Furthermore, because both the rope and tile substrata produced

236 very similar model results the opportunity arises to use either substratum. This is important from a practical point of view where, for example, the tile substratum is very difficult to use in deep rivers and canals, whereas the rope can easily be placed in the river bank or tied to a lock gate. Conversely in shallow rivers with hard bed rock the tile substratum is more easily deployed.

It should be stated here that the selection of the WA-PLS models is based purely on internal validation. The apparent strengths of the correlations between the diatom inferred FRP levels and observed concentrations may be inflated by the circularity inherent in self validation; i.e. the prediction of FRP levels for the same river sites as those used to develop the model (ter Braak & van Dam 1989, Pienitz et al. 1995). The only true test of the ecological and statistical accuracy of a model is to apply it to an independent river data-set from similar river sites but not those used in the model

~~development. This chapter is only concerned with the model development; Chapter 6 covers the application of the model to assess the trophic status of independent sites.~~

The model results in this study are similar to those calculated for the phosphorus training sets constructed by other workers in lakes. Bennion (1993) produced a WA transfer function for lowland ponds in south-east England and achieved an r^ of 0.793 and cross-validated RMSEP of 0.279. This model was updated and amalgamated with five other regional data-sets from the UK (England, Wales and N. Ireland), Denmark and Sweden (Bennion et al. 1996). Following taxonomic harmonisation and data screening this combined transfer function produced an F of 0.91 and a RMSEP of 0.21 using a two-component WA-PLS model. One of the reasons that the combined data-set of Bennion et al. (1996) produced better model results was due to the extension of the phosphorus gradient by the addition of many more lower P sites as well as some higher P sites thus reducing the effect of truncated species distributions.

It is perhaps surprising that the diatom assemblages can be used to model FRP to such a high degree in an environment that has been demonstrated to have a multivariate influence on species distributions (see Ch. 4). Alkalinity, for example, was seen in both training sets to be exerting a similar influence on the observed species assemblages and can in fact be modelled with a similar degree of statistical accuracy as FRP. Likewise a

237 number of other measured environmental variables were seen to have an influence on the patterns of species distribution. Furthermore it is unlikely that FRP has a direct physiological effect on the performance of many of the diatom taxa, particularly at the higher nutrient sites where P is certainly not limited. This does not appear to matter for the purposes of WA regression models. Birks et al. (1990) stated that the basic assumption for these techniques was that “...the environmental variable to be reconstructed is, or is linearly related to, an ecologically important variable in the system of interest”. It is therefore likely that although the interest of this study is to model FRP in lowland rivers, what is actually being modelled are undetermined river variables that are directly related to trophic status and have a direct eco-physiological influence on the diatom species distributions. This may be one environmental variable or more likely a complex interaction of variables.

~~5.6.2 Outlier Samples~~

Where the validity of these predictive models must be brought into question is in the need to delete over 10% of the original samples to produce models with high predictive power. The deletion of sites where the observed chemistry is suspect is a necessity but the removal of sites which simply do not fit the model, is less easily justified. The reason these outlier samples were deleted was the considerable increase it had on the resultant r^ value for the rope and tile transfer functions (58% & 57% respectively) and the reduction in RMSEP (50% & 58%).

This data screening procedure followed by the deletion of “rogue” samples has been deemed necessary in the development of many transfer functions, where the goal of the study was to reconstruct past environmental conditions from the fossil diatom record (Birks et al. 1990, Bennion 1993, Jones & Juggins 1995, Bennion et al. 1996, Korsman & Birks 1996). The justification behind the removal of samples is that within the natural environment there will always be some atypical sites where, for example, diatom assemblages are weakly related to the environmental variable to be reconstructed or that an environmental variable other than the one of interest is having a major influence on the diatom composition (Birks et al. 1990). This makes sound ecological sense for palaeoecological reconstructions where the modem assemblage is often the only way in

238 which the past environment can be inferred. When the purpose is for the assessment or monitoring of the contemporary environment however, the fact that over 10% of the samples do not conform to the model, suggests that an equal number of sites run the risk of being wrongly classified. The implications for a river management scheme based on such a model are therefore serious, particularly when, as in this study, it is the less polluted sites that are being overestimated by the model (9 out of the ten outliers from the rope samples and 8 out of the 9 from the tile samples were sites of <100 pgL"^ FRP). The overestimation of trophic status at a river site could prompt the water regulators to implement costly management regimes that were not necessary. A conflict therefore arises between whether or not to delete these samples. If they are deleted, the above scenario could develop and if not then the possibility exists for poor water quality predictions across the entire gradient which may result in similar management problems, or indeed the lack of action causing potentially valuable river sites to decrease in conservation value.

The above examples are of course only hypothetical, but they illustrate that the risk of wrongly classifying a site will remain, regardless of whether or not the least representative samples are deleted. The best way to tackle this problem is perhaps by the development of the most statistically reliable model (as above), with the recognition that its future application must be viewed with some caution. Once a statistically robust model has been derived it should not be considered as the end point. Having shown that trophic status can be modelled at the majority of river sites using the diatom assemblages, further work is now required to increase the reliability by adding more sites in the FRP range of the training set that performs least well; i.e. sites with <100 pgL'^ FRP. This is outside the scope of this study but could easily be achieved by further collections from the two artificial substrata at low FRP sites.

One of the key problems associated with the outlier samples was the dominance of widely tolerant diatom taxa, e.g. C. placentula var. euglypta and A. lanceolata. This causes a problem which cannot be overcome by the model alone. Deletion of these taxa causes no improvement in the model because they are very common and although they detract from the model at the low ends of the gradient they also add value in the mid to top end of the FRP range. Also their deletion from samples where they occur at high

239 abundance adds greater weight to other taxa which may not be representative of the observed FRP concentrations. The use of square rooted species data resulted in a slight reduction in the number of outliers by calming the influence of the abundant taxa with wide tolerance ranges. Despite the square root models giving comparable results, the predictive power of the alternative methods can only be properly appraised by the testing of independent samples rather than a reliance on internal validation.

Rather than looking to ways in which the model can be enhanced the focus should instead be on the methods used to collect the samples. The selection of the two artificial substrata was made partially on the observation that they supported a higher diversity (Hill’s N2) than the other surfaces used (see Ch. 3). The importance of sampling a more diverse community for WA methods to work efficiently is well illustrated by the fact that it was some of the less diverse samples that were outliers in the model. This is simply because the model has less ecological information to use for estimating a value for FRP and therefore more chance of the estimation being inaccurate.

The question that has to be asked therefore is why were so many of the outliers of lower diversity? Is this simply a function of lower species diversity in less productive waters? This has certainly been reported in some studies (Archibald 1972, Molloy 1992), although the low diversity seen in clean water areas in these studies was accompanied by taxa indicative of the conditions rather than generalist taxa like C. placentula var. euglypta and A. lanceolata. The problem may therefore be one of exposure time for the artificial substrata. Although a four week exposure time has been widely reported as optimal (Cattaneo & Amireault 1992), this may not be the case in waters of lower nutrient status. C.placentula var. euglypta and A. lanceolata are both very common throughout lowland rivers and are fast colonisers of new surfaces (Round 1993). Furthermore, C. placentula was also considered by Round (1993) as performing particularly well on artificial substrata. It may be, therefore, that the four-week exposure time allowed in this study was not sufficient for the diatom community to equilibrate at low FRP concentrations. Thus, rather than a species assemblage typical of the water quality being observed, a species assemblage comprising of good initial colonisers was obtained instead. Further tests are required to investigate the effects of varied exposure time in waters of different trophic status.

240 Regardless of the possible reasons for the models overestimating FRP at some of the less eutrophic sites, the deletion of the outlier samples is considered the most statistically robust approach. It is recognised, however, that the inclusion of more low FRP sites and the possible extension of substratum exposure time could enhance the predictive power of the models and allow for a more rigorous evaluation of the results.

~~5.6.3 Indicator Species~~

It is important to consider the value of different species with respect to their distributions along a phosphorus gradient. It should, however, be reiterated here that the optima and tolerances quoted in this study are not intended to represent the “true” optimum of a taxon, and neither do the tolerances quoted reflect the actual limits of growth for a species. Instead these values represent training set dependant values for the point at which a species reaches its maximum relative abundance and the effective range over which it occurs with respect to the measured phosphorus gradient. These differences between field observations and carefully controlled laboratory growth experiments have been debated in the literature {cf. Cox 1994). For the purposes of this study however the terms “optima” and “tolerance” are intended only to represent the field observations (calculated by weighted averaging) on species distributions, which in the context of this study are more relevant than laboratory values.

It should also be reiterated that the calculated optimum and tolerance values presented in this study for FRP, may not in any way reflect the actual physiological response of a species to phosphorus (see above). The species-environment relationships are based on simple measurements which are assumed to influence performance, growth and survival. The actual relationships between the diatom taxa and their environment, is likely to be far more complex. Therefore it is the use of techniques like WA that enable us to break down the complexities of the species-environment relations into simple patterns that can be effective for bio-monitoring.

It is clear from the plots of diatom species abundance versus FRP (Figs. 5.9-13 & 5.15- 20) that many of the taxa show a distinctive response across the gradient. These range from taxa which only occur at high abundance at very low FRP concentrations (e.g. A.

241 biasolettiana & D. tenuis) through those with relatively narrow tolerances in the mid range (e.g. N. capitatoradiata, N. lanceolata, N. [pseudogregaria] & Nitzschia recta), to species which favoured waters of very high trophic status (A. veneta, N. amphibia & S. seminulum). Similarly, some taxa showed a preference for low, mid or high levels of FRP but were also very widely distributed (e.g. A. minutissima, N. palea & A. lanceolata respectively). Other taxa showed little or no relationship to the gradient and are thus able to grow without any apparent preference for FRP concentration (e.g. C. placentula var. euglypta & G. parvulum). For these taxa it is likely that other factors are more important for successful growth; possibly habitat related or the ability to establish quickly on newly exposed surfaces.

The observed WA optima were not seen to differ greatly between the two training sets, particularly where species were very common. Some minor differences are to be expected due to the contrasts in habitats provided by the two artificial substrata (see Ch. 3). Among the rarer taxa, however, some very large differences were observed. Achnanthes grana, for example, had a WA FRP optimum of 292 pgL'^ in the rope samples and 1098 pgL'^ in the tile samples. This may have been due to factors arising from substratum specific requirements or simply an inherent problem of including rare taxa in the training set. Pienitz et al. (1995) warned against the over-reliance on WA optima from taxa with only a few occurrences.

What is clear from the plots of diatom species abundance versus FRP is that almost all the taxa show erratic relative abundances around their calculated optima. In many cases a taxon may not even be present at sites of similar FRP concentration to its WA optimum. This illustrates the complex array of environmental and biotic controls influencing the diatom community at individual sites. It also highlights the importance of collecting samples of high diversity: in a sample where many different species are present the WA model has more ecological information from which to produce a diatom-based prediction for FRP concentration.

~~242 5.6.4 Conclusions~~

With careful data screening followed by the removal of unreliable or atypical sites, both the rope and tile training sets can be used to produce predictive WA-PLS models for FRP based on their diatom species assemblages. Although TP can also be reliably modelled the predictive power of the models for TP was lower than for FRP and thus the decision was made to concentrate on FRP. For the rope samples four components of WA-PLS were required to produce the best fitting model, whereas only two components were needed for the tile data. Both the rope and tile models, however, are considered to be suitable for the development of a lowland-river phosphorus assessment scheme, although it is recommended that the models are kept separate due to the slight differences in species distributions that were observed. These are likely to be brought about by the contrasting habitats provided by the different artificial substrata. The final model choice cannot confidently be made by internal validation alone. Instead, independent diatom samples from sites not included in the model development are required for externally validation of the models. Further work is also needed to compare the use of untransformed species data with square rooted data.

One major area of concern encountered in the development of these models was the need to remove over 10% of the sites to achieve statistically reliable prediction errors. Had these “erroneous” sites been evenly distributed across the FRP gradient then little could be done to counter the problem. However, the majority of outliers occurred at sites of low FRP (<100 pgL'^). It is therefore recommended that the low end of the FRP gradient is added to, in order to provide more ecological information on taxa with low FRP requirements. It is also postulated that the sampling procedures for the artificial substrata may warrant greater attention. The primary aim of any further investigation should focus on the effect of exposure time on the distribution of apparently widely tolerant species that are known to be fast colonisers of clean surfaces. It is possible that such taxa have a competitive advantage at low nutrient levels and only after an increased time period (i.e. longer than one month) would a diatom assemblage develop which might be considered as more “typical” of the ambient water quality.

243 Despite these problems many of the diatom species showed distinct preferences for lowland river sites of particular FRP concentrations, allowing for reliable predictive models to be constructed. It is recognised that this response is unlikely to be due to a direct physiological relationship between FRP and the diatom species. However, it would appear that the diatoms are reacting to ecological parameters that are related to the trophic status in these lowland river sites. Regardless of the absolute species- environment relations, diatom-based WA-PLS can be used to provide valuable information on the trophic status of lowland rivers in the study area.

~~244 Ch a pter Six~~

Sea so n a l V a r ia t io n W it h in L o w l a n d R iv e r Dia t o m A ss e m b l a g e s : Implications f o r th e D ia t o m -Ba se d M o d e l s f o r t h e A sse ssm e n t o f T r o ph ic St a t u s

~~6.1 Introduction~~

The previous chapter demonstrated that lowland river diatoms can be used reliably to model phosphorus concentrations when sampled from selected artificial substrata. The diatom assemblages used to develop these models, however, were sampled during October and November, and thus it is important to consider how these assemblages differ during the year and to assess the implications of seasonal change on the resultant diatom-based assessment of trophic status. These considerations are of particular practical importance to water regulators who cannot realistically be expected to restrict their biological water quality assessments to a limited time period.

The diatom assemblages of river sites have been identified in numerous studies as showing considerable annual variation, both in taxonomic composition and, although not considered in this study, standing crop (e.g. Butcher 1940, Douglas 1958, Lowe 1972, Moore 1976, Gale et al. 1979, Marker & Casey 1982, Esho & Benson-Evans 1984, Cox 1990a, 1990b). The controlling factors for seasonal variations are complex, but within the relatively stable lowland rivers of the UK, where nutrients are rarely limiting, include changes in light regimes (day length and shading), water temperature, grazer activity and physical disturbance due to high or low flow conditions (Biggs 1996). The majority of seasonal studies have shown standing crop (cell numbers) to be highest in spring with a secondary peak in the autumn, the summer months being dominated by cyanobacteria and green algae (Biggs 1996). Total diatom numbers do not, however, influence the models presented in Chapter 5; these were based entirely on

~~245 the relative abundances of the taxa present. It is annual changes in relative abundance of the diatom species that are investigated in this chapter.~~

In this chapter the seasonal variations within the diatom assemblages of three river sites are investigated. These data are then used to assess the ability of the predictive models to estimate the phosphorus concentrations. The a priori assumption is that, regardless of the other ecological and physical impacts which have an influence on the diatom flora throughout the year, there will remain an overriding species-dependent response to FRP, or variables related to trophic status, and thus seasonality will be unimportant. This assumption was one of the major ecological requirements for environmental reconstruction laid down by Imbrie and Webb (1981). The ability of the models to estimate FRP accurately is tested by first using the diatom models on data collected during October and November, and then by applying the same models to monthly data from a single year.

It should be stated that, although the calibration methods used in this chapter give an absolute value for estimated FRP concentration, it is not the final intention to describe the results in such terms. Neither is it, however, within the scope of this study to devise a trophic classification for lowland rivers. Thus the results obtained below are only used to assess and compare the model results. Methods for reducing the calibration results to a workable system for the biological assessment of the trophic status of rivers are discussed in more detail in Chapter 7.

~~6.2 Aims~~

There were three main objectives in this part of the study. First, to investigate to what extent the diatom assemblages from the two artificial substrata (rope and tile) changed during a single year. This was conducted at three lowland river sites of contrasting nutrient status. Comparisons were also made between the seasonal shifts in the diatom flora sampled from artificial substrata and those from the natural epilithon. Second, the ability of the predictive models developed in Chapter 5 to estimate FRP concentrations was assessed using the October and November assemblages from the three test sites (i.e. samples from the same time of year as the training set). Third, the models were applied

~~246 to monthly diatom samples collected from the three test sites to assess the implications of seasonal variations within the diatom assemblages on resultant FRP estimations.~~

~~6.3 Methods~~

~~6.3.1 Diatom Collection~~

Diatom samples and water chemistry were collected from three of the sites on the River Wey described in Chapter 3: Alton (ALTl), Elstead downstream (ELST2) and Hawbridge (HAWl). These sites were chosen to represent contrasting trophic conditions and had annual mean FRP concentrations of 46 pgL'\ 944 pgL'^ and 1655 pgL'^ respectively. Diatom samples were taken monthly from rope and tile substrata, as well as the natural epilithon, over the period of one year from September 1995 to August 1996. At the same time as the diatom samples were taken, water samples were collected for nutrient chemistry (see Ch. 2 for methods). Due to the loss of some of the artificial substrata diatom data are not available for some months. No losses occurred at ALTl but one tile sample (June ’96) was lost at HAWl and four tile samples from ELST2 (Sept. & Oct. ’95 and July & Aug. ’96). Two rope samples were also lost from ELST2 (Sept. & Oct. ’95). Diatom samples were prepared and counted as described in Chapter 2 and counts entered into AMPHORA, a data-base specifically designed for handling diatom data (Beare 1997).

~~6.3.2 Data Analysis~~

Seasonal patterns in the diatom data were investigated using detrended correspondence analysis (DCA) or, where the DCA axis 1 gradient length fell below 1.5 standard deviation units, principal component analysis (PCA). Below approximately 1.5 SD units the data can be considered as linear and thus PCA is the more appropriate method (Kent & Coker 1992). Canonical correspondence analysis (CCA) with forward selection was used to determine the principal environmental parameters influencing diatom assemblages in the seasonal data. Significance was tested using Monte Carlo permutation tests with 999 unrestricted permutations (ter Braak 1990), using an initial significance level of P < 0.05. Over-selection was avoided with a Bonferoni-type

247 adjustment, whereby the significance level of each successive test component was set at P < 0.05/«; where n is the rank number of the variable being tested (Miller 1990). These analyses were performed on the computer program CANOCO (ter Braak 1990, 1991). The use of DCA and PCA allows for the complex shifts in species assemblages to be represented in simple two dimensional space. The ordination diagrams were plotted using the computer program CALIBRATE (Juggins & ter Braak 1997).

Following the assessment of seasonal shifts in the diatom assemblages, the same data from the three river sites were used in a calibration exercise to establish the performance of the diatom-based models in estimating FRP from independent samples. Initially this was for the October and November samples using different species data treatments (square root and untransformed data, and the deletion of widely tolerant taxa) to assess the optimal model. The optimal models were then applied to the full season’s data. As already stated in Chapter 5 the only true validation of a predictive model is to test it against an independent set of samples from sites not used in the model development. Both ALTl and FLST2 were used in the initial training set and thus these samples were removed from the model before its application in this chapter.

The calibration step used to estimate FRP involves the reverse process of the regression described in Chapter 5. When estimating the FRP of a river site the assumption is made that the weighted average of the FRP optima for all the taxa present will provide a reasonable estimate of the FRP at the given site (Birks et al. 1990). The equation for simple WA calibration is given in Chapter 5 (5.3.1). The principal of WA-PLS calibration is the same as simple WA calibration although the algorithm used to achieve it is considerably more complex (see ter Braak et al. (1993) for a full description of the technique). In simple terms WA-PLS adds components which utilise the residual structure in the species data to improve the species ‘optima’ in the final weighted averaging predictor (ter Braak & Juggins 1993). All calibrations were performed using the computer program CALIBRATE (Juggins & ter Braak 1997).

~~248 6.4 Results~~

~~6.4.1 Seasonal Variation in Water Chemistry~~

Prior to assessing the changes in the diatom assemblages at ALTl, HAWl and ELST2 data are presented below (Fig. 6.1) for some of the physico-chemical parameters at these sites to demonstrate the system variability that is likely to be contributing to changes in the observed diatom assemblages. The phosphorus (FRP) concentrations at the three sites on the River Wey provided contrasting trophic conditions (Fig. 6.1a). FRP concentrations at ALTl remained consistently low (<50 pgL’*) throughout the sampling period, with the exception of the last sample (Aug. 1996) which was measured at 176 pgL’*. HAWl showed the most variation in FRP, ranging from 931-2721 pgL’*, with the highest concentrations recorded in the winter months. This site was only 3 km

~~downstream from a large sewage treatment works (approximately 11,000 population equivalents). The ELST2 FRP concentrations were generally lower than those measured at HAWl (670-1332 ugL'').~~

Nitrate concentrations were similar at all three sites. The annual means for ALTl, HAWl and ELST2 were 2600, 2685 and 2207 pgL * respectively (Fig. 6.1c). Alkalinity reflected the underlying geology of the sites, with ALTl and HAWl having

approximately twice the values of ELST2 (Fig. 6 .Id). The former sites are in a chalk dominated catchment whereas the majority of the ELST2 catchment is in a less base- rich Lower Greensand region. All three sites were circumneutral to alkaline in pH (Fig.

~~6 .le). Conductivity showed a similar pattern to alkalinity with ELST2 having lower~~

values (Fig. 6 .If). Silica, an important nutrient for diatoms, was variable at all sites (Fig. 6.1g), but was always an order of magnitude above the critical value of 0.5 mgL * considered limiting to diatom productivity in lakes (Wetzel & Likens 1991). The water temperature followed a characteristic seasonal pattern at all three sites, although the range was slightly lower at ALTl due to its proximity to its ground water source (Fig. 6.1h). In summary, the main physico-chemical differences between the sites were the FRP concentrations and alkalinity. These sites are therefore considered to be ideal for testing the WA-PLS predictive model developed in Chapter 5.

~~249 a) FRP concentrations at the three river sites over a one year period~~

~~3000~~

~~2500 --~~

~~2000~~

~~1500 +~~

~~1000~~

~~500 +~~

~~0 Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 1995 1996 ♦------♦ ALTl ■------• HAWl A A ELST2~~

~~b) TP concentrations at the three river sites over a one year period~~

~~3000~~

~~2500~~

~~2000 - I 1500 -~~

~~1000 - .--A~~

~~500 No data 0 Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug~~

~~1995 1996 ♦ A ALTl ■ A HAWl A A ELST2~~

~~c) Nitrate concentrations at the three river sites over a one year period 5000~~

~~4000~~

~~3000~~

~~2000 g 1000~~

~~Sept OctNov Dec Jan Feb Mar Apr May Jun Jul Aug~~

~~1995 1996 -A ALTl HAWl ELST2~~

~~Figure 6.1 Seasonal variability in some of the major environment parameters at ALTl, HAWl and ELST2 (River Wey)~~

~~250 d) Alkalinity at the three river sites over a one year period 300~~

~~250~~

~~§ 200~~

~~■ | 150 Î 100 50~~

~~0 Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 1995 1996 *- -♦ ALTl HAWl A- ELST2~~

~~e) pR at the three river sites over a one year period~~

~~8.5~~

~~8.0~~

~~7.5~~

~~7.0~~

~~6.5 Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug~~

~~1995 1996 ♦ ------♦ ALTl ■------* HAWl A...... A ELST2~~

~~f) Conductivity at the three river sites over a one year period 1000~~

~~800 -~~

~~I 600~~

~~a 400 - U 200 -~~

~~Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug~~

~~1995 1996~~

*- ALTl HAWl ■A ELST2

~~Figure 6.1 (cent.) Seasonal variability in some of the major environment parameters at ALTl, HAWl and ELST2 (River Wey)~~

~~251 g) Silica concentrations at the three river sites over a one year period~~

~~14 ----- 12 j;' i I~~

~~Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 1995 1996 ♦------♦ ALTl ■------« HAWl A...... A ELST2~~

~~h) Water temperature at the three river sites over a one year period~~

~~A 14 -- I 12 - 10 - I 6 --~~

~~Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug~~

~~1995 1996~~

~~ALTl HAWl A- A ELST2~~

~~Figure 6.1 (cent.) Seasonal variability in some of the major environment parameters at ALTl, HAWl and ELST2 (River Wey)~~

~~6.4.2 Seasonal Variation within the Diatom Communities~~

With the observed variation in chemistry at the three study sites it is not surprising that the diatom species assemblages also differed considerably. Within each site the relative abundances of the species were seen to differ over the one year sampling period and considerable difference was noted between the different substrata. At ALTl the dominant taxa on both artificial substrata were Achnanthes minutissima, Cyclotella pseudostelligera, A. lanceolata, Gomphonema parvulum and Sellaphora minima (Fig. 6.2a & b). This was not the case with the epilithic samples where (with the exception of the dominance of A. minutissima) Amphora pediculus, S. minima and Achnanthes

252 lauenbergiana were found at higher relative abundance (Fig. 6.2c). Another marked difference between the artificial substrata and the epilithon at ALTl was that the common taxa did not appear in such high relative abundance in the epilithon. A. minutissima, for example, was the most abundant species from all three substrata but in the epilithon only reached a maximum of 57%, whereas maxima of 71% and 83% were observed in the rope and tile samples respectively. Also of interest in the ALTl samples is the high abundance of C. pseudostelligera during the late summer and autumn months. This centric species is usually planktonic (Krammer & Lange-Bertalot 1991a) and thus it is unlikely that it was actually growing at the sampling site which was shallow and relatively fast flowing. Its presence was probably explained by an upstream impoundment, the outflow of which drains into the River Wey approximately 200 m above ALTl. If this is the case (samples were not taken from the impoundment), the occurrence of such planktonic taxa are of little value to this study, and may indeed complicate any diatom-based reconstruction where they occur. Due to the mobility of planktonic taxa in a riverine environment they are of little indicative value in site- specific studies. The only relevant indicator value that large numbers of planktonic taxa give in moderate to fast flowing water is that there is probably an impoundment upstream: a fact that is more easily determined from a map or local knowledge of the site. The problems associated with planktonic taxa are discussed below.

Achnanthes minutissima was found in highest abundance during the summer and autumn on both the rope and tile substrata although it was present throughout the year. During the winter months and early spring A. lanceolata and G. parvulum achieved higher abundance. Within the natural epilithon there appeared to be less seasonal variation in these taxa and two other species, A. pediculus and A. lauenbergiana, were found in much higher abundance. Both of these taxa are small and attach closely to the rock surface (Round 1993, Krammer & Lange-Bertalot 1986, 1991b). Due to their adnate nature the loss of such taxa from the rock surface after death may be less likely and the large numbers observed probably represented both live individuals and dead valves which have accumulated over time. These samples were not analysed prior to cleaning and therefore this hypothesis could not be verified.

~~253 a) Relative abundances of the diatom taxa found in the rope samples at ALTl over a one year period~~

~~S e p t'95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M ar '96 A pr '96~~

~~M ay '96~~

~~Ju n '96~~

~~Jul '96 Aug '96 20 40 60 80 20 40 60 20 20 Relative Abundance~~

~~b) Relative abundances of the diatom taxa found in the tile samples at ALTl over a one year period~~

~~S e p t'95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Jan '96~~

~~Feb '96~~

~~M ar '96 A pr '96~~

~~May '96~~

~~J u n '96~~

~~Ju l '96 Aug '96 20 40 60 80 20 40 60 20 20 20 20 Relative Abundance~~

~~c) Relative abundances of the diatom taxa found in the epilithon samples at ALTl over a one year period~~

~~si- Sept '95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M ar '96 A pr '96~~

~~M ay '96~~

~~Ju n '96~~

~~Ju l '96 Aug '96 20 40 60 20 40 20 20 40 60 20 20 20 40 60 Relative Abundance Figure 6.2 Seasonal variation in the relative abundance of the common diatom taxa from ALTl~~

~~254 a) Relative abundances of the diatom taxa found in the rope samples at HAWl over a one year period~~

~~Sept '95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M a r '96 A p r '96~~

~~M ay '96~~

~~Ju n '96~~

~~Ju l '96 A ug '96 20 20 20 20 20 Relative Abundance~~

~~b) Relative abundances of the diatom taxa found in the tile samples at HAWl over a one year period y y~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M ar 96 A p r '96~~

~~May '96 ; \~~

~~Jun '96 No Data No Data No Data No Data No Data~~

~~Ju l '96 A ug '96 Ê 1 F r y 20 40 20 40 20 40 20 40 20 20 20 20 20 20 20 20 Relative Abundance~~

~~c) Relative abundances o f the diatom taxa found in the epilithon samples at HAWl over a one year period~~

~~"4* V Sept '95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M ar '96 A p r '96~~

~~M ay '96~~

~~Ju n '96~~

~~Ju l '96 Aug '96 20 40 20 20 20 40 20 40 60 20 20 20 20 20 20 20 20 Relative Abundance Figure 6.3 Seasonal variation in the relative abundance of the diatom taxa from HAWl~~

255 The species assemblages observed at HAW 1, the high nutrient site, were very different to those seen at ALTl. Figure 6.3 shows the seasonal variation observed from the three substrata at HAW 1. The rope samples were dominated by A. lanceolata, A. minutissima, N. amphibia (summer & autumn), G.parvulum and C. placentula var. euglypta. Other taxa also appeared for short periods during the year in relatively high abundance. For example, M. circulare, G. angustatum, G. olivaceum and N. lanceolata showed peaks in spring, and C. meneghiniana appeared in the warm summer months. Again the origin of the planktonic Cyclotella was thought to be from slow flowing reaches of the river upstream.

During the summer months the water level in the upper reaches of the River Wey was low and some deeper upstream reaches were seen to be noticeably slower flowing. It is possible that during this time localised conditions existed where the growth of planktonic taxa was favoured, thus live and/or dead planktonic cells were being washed down to HAWl. The rope appeared to collect more planktonic diatoms than the other substrata, possibly due to its position in the main flow rather than flush with the river bed like the other substrata.

Within the tile samples the dominant taxa were similar to the rope samples, with the addition of higher abundances of Navicula [species 2] and Nitzschia paleacea and a reduction in A. minutissima and C. placentula var. euglypta. N. amphibia showed a similar season pattern to the rope samples but was found in higher abundance on the tiles. In spring the appearance of similar species to the rope samples was observed with the addition of Surirella ovalis and S. brebissonii. S. minima was also seen to be most abundant in September with numbers dropping off through the autumn and winter months, with no occurrences recorded in March and April.

The diatom assemblages observed on the natural epilithon were considerably different to those seen in the artificial substrata. A. lanceolata, A. minutissima, N. amphibia and G. parvulum all occurred as subdominants but the majority of samples were dominated by A. pediculus and also higher numbers of A. lauenbergiana. These samples were more similar to the epilithon from ALTl than the artificial substrata from the same site. Considering the difference in FRP between these two sites this is rather surprising.

~~256 a) Relative abundances of the diatom taxa found in the rope samples at ELST2 over a one year period~~

~~S e p t'95 tnr-L jrz-: i -■-L-Lt-:-inn: No Data N o Data No Data No Data O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Jan '96~~

~~Feb '96~~

~~M ar '96 A pr '96~~

~~M ay 96~~

~~Jun '96~~

~~Ju l '96 Aug '96 \ 20 20 20 Relative Abundance~~

~~b) Relative abundances of the diatom taxa found in the tile samples at ELST2 over a one year period~~

~~No Data No Data No Data No Data No Data O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M a r '96 A pr '96~~

~~M ay '96~~

~~Ju n '96~~

~~Ju l '96 No Data No Data No Data No Data No Data Aug '96 ■f^T= ’F^'F 20 20 20 20 20 20 20 40 20 Relative Abundance~~

~~c) Relative abundances of the diatom taxa found in the epilithon samples at ELST2 over a one year period~~

~~y y y~~

~~Sept '95~~

~~O ct '95~~

~~Nov '95~~

~~Dec '95~~

~~Ja n '96~~

~~Feb '96~~

~~M ar '96~~

~~A p r '96~~

~~M ay '96~~

~~Ju n '96~~

~~Ju l '96 Aug '96 20 40 20 20 20 20 20 20 20 20 20 40 20 20 Relative Abundance~~

~~Figure 6.4 Seasonal variation in the relative abundance of the diatom taxa from ELST2~~

257 The results suggest that some taxa, particularly small adnate forms, are more indicative of their substratum (i.e. substratum specific) than water quality or, as discussed above, they may simply be more persistent within the epilithon after death, rather than representing the living community.

At ELST2 there was less similarity between the two artificial substrata than at the other sites (Fig. 6.4). The rope samples had a constant presence of Melosira varians over the sampling period (Sept. & Oct. samples were not retrieved), but were otherwise dominated by C. placentula var. euglypta, Navicula cryptotenella and G. parvulum during the summer, autumn and winter and by N. lanceolata, Diatoma vulgare and the planktonic species Stephana dis eus parvus in the spring. Again it is thought that the source of S. parvus was not from the site but instead came from a mill pond 300 m upstream. While there was a marked seasonal switch in the dominant taxa several other species maintained lower abundances (<10%) throughout the year; e.g. A. lanceolata, A. pediculus, N. gregaria, N. [pseudogregaria], N. tripunctata, Nitzschia dissipata, and S. minima.

The main similarity between the rope and tile samples was the spring dominance of N. lanceolata. Other comparisons are more difficult to make due to the loss of the summer data. From the data that were collected it was seen that Navicula subminuscula, N. dissipata and G. parvulum were dominant until the winter, and following the spring peak of N. lanceolata these taxa were replaced by N. [pseudogregaria] towards the end of spring.

The seasonal distribution of taxa on the natural epilithon was more erratic than on the artificial substrata. A. pediculus and A. lauenbergiana were again present in the epilithic samples, with the latter species being co-dominant with S. minima in autumn. During winter A. pediculus maintained high abundance and peaks in N. subminuscula, N. gregaria, Nitzschia dissipata and Rhoicosphenia curvata were also observed. Spring and early summer were not characterised by the large peak of N. lanceolata seen in the artificial substrata, although a small rise was seen. The major spring taxa were A. pediculus and N. [pseudogregaria] followed by a large peak of N. atomus in the

~~258 summer. Other taxa maintained low abundance throughout the year; e.g. A. lanceolata, C. placentula var. euglypta, G. parvulum, N. cryptotenella and N. [species 2].~~

To compare and contrast the seasonal variation in the diatom species assemblages observed at the three river sites further, detrended correspondence analysis (DCA) was employed. All the analyses produced gradients greater than 1.5 so PCA was not used. The technique of DCA allows a sample plot to be constructed so that samples which are similar in their species composition are close together and the least similar plot at the greatest distance. If the assumption is made that seasonal change occurs in a uniform,

cyclical manner it would be expected that species data taken over a 12 month period would show the greatest dissimilarity at six month intervals and the highest similarity between assemblages from consecutive months. Thus the DCA plot should form a circle. This is purely hypothetical but some “circularity” in the species data would be expected. Figures 6.5 - 6.7 show the DCA plots of the diatom assemblages from the three different substrata at the three river sites. Samples are joined in chronological order. Species plots are also displayed (note different axes scales) to show the common taxa which are influencing the analyses.

In reality the DCA plots do not show uniform circularity in the seasonal species data, but there are some interesting patterns. The ALTl DCA analyses from the artificial substrata show some circularity (Fig. 6.5a & b). The rope samples (Fig. 6.5a) show a DCA axis 1 split between the late summer and autumn samples with low scores, and the spring samples with high axis 1 scores. On the second axis there is a split between the winter samples with low scores, and summer samples with high scores. This does not result in perfect circularity but a definite seasonal pattern can be seen. This pattern is similar, although less obvious, in the tile samples (Fig. 6.5b). The biggest contrast, however, comes with the comparison of the artificial substrata results and the distribution of samples from the natural epilithon, where the samples show no coherent seasonal trend. The greatest split along the first axis is between the May, April and June samples, with low scores, and the July samples. On the second axis the greatest difference is between the August and September species.

~~259 a) ALTl - DCA of the diatom species found on the rope samples over a one year period~~

1.50 3.0 A Qym. minuta ALTl Rope samples ALTl Rope species Ni. acicularis May '96 1.25 2.0 A G. olivaceum A. minutissima r/Jun '96 \ Oct '95 Na. subminuscula \ ) Apr '96 A Ni. paleacea 1.0 - | Na. tripunctata A No. [species 2] A G. parvulum 0.75- A Am. pediculus Se. m i n i ^ A u g ' ^ ^ D ec'95 00 A A. lanceolata . - I A Co. placentula 0.50 V. euglypta A No. cryptotenella Sept ’95 Cyc. pseudostelligera J u i '9 y G. angustatum -1.0- Sy. ulrut V. acusA N a trivialis a 0.25 Am. avails v.pediculs a F. construens V. venter '-^ o v '95 Feb '96 0.00- Jan " 9 6 V / ^ o M ar'96 -2.0 Na cryptocephala A 0.0 0.5 1.0 1.5 2.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1

~~b) ALTl - DCA of the diatom species found on the tile samples over a one year period~~

~~1.25 3.0 ALTl Tile samples AMay '96 ALTl Tile species Aug '96 A N l acicularis ^A3Jun '96 \ 1.00 ^ _ ^ O c t '95 \ 2.0 Na minusculoides A~~

~~0.75 A. minutissima J u l'96 / Apr ' 9 ^ Sept '95 1.0 A Na. atomus Nov '95^1 y / A Cyc. pseudostelligera 0.50~~

F eb'96 y / A Co. placentula Sy.ulnav.acus 0.0 H ^ A Se. minima 0.25 Dec '95 / y G. olivaceum (%ar '96 Am. pediculus A A Ni. amphibia Na subminuscula A . A. lanceolata ^ d. lauenbergiana ^ 0.00 Jan ' 9 b J / -1.0 0.0 0.5 1.0 1.5 2.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1

~~c) ALTl - DCA of the diatom species found in the natural epilithon samples over a one year period~~

~~3.0 Aug 96 ALTl Epilithon ALTl Epilithon species Co. placentula a samples V. euglypta~~

~~2.0 N a atomus Mar 96 ^ Sy. idna v. acus NL dissipata ^Na cryptotenella A~~

~~Oct '95 Ni. paleacea ^ A A. lauenbergiana May '96 1.0 A A. minutissima Cyc. pseudostelligera^ A pr'96 Feb '96 Cym. minuta A curvata Pe. sinuata A a. Jun 96 0.0 Jan '96 A. lanceolata~~

~~Nov 95 Ju l'96 Am. pediculus A -1.0 - A. conspicua~~

~~Dec '95 -2.0 3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 DCA Axis 1~~

~~Figure 6.5 DCA plots showing the seasonal variation within the diatom assemblages at ALTl from the three different substrata~~

~~260 a) HAWl - DCA of the diatom species found on the rope samples over a one year period~~

~~1.25 Jul '96 2.0 n HAWl Rope samples HAWl Rope species a Cym. minuta~~

~~M. varians *■ Su. brebissonii \)Ju n '96 Ni paleacea d. vulgare a Aug '96 o 1.00 ^ ^ , * Ntt- [species 2] Nifrustulum menisculus ^ A F r vaucheriae~~

~~A A 1.0 ‘ Ni amphibia ^ minutissima Na lanceolata ^ a y '96 0.75 Se. minima ^ Am. pediculus |sep t'9 5 Apr '96 N i palea ^ ‘ G. angustatum a~~

G. parvulum a A. lauenbergiana ^ ^ olivaceum Q 0.50 \ k i a r '96 0.0- A Na subminuscula Nl. circulare A \ o c t '95 0.25- \ Jan '96 Co. placentula V. euglypta a \ D e c "9 ^ \ ^ A A. lanceolata Se. seminulum Nov ' 9 5 V ^ ^ VT e b '96 0.00 1 ° I ■ -1.0 1 1 1 1 1 0.0 1.0 2.0 3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Aiis 1

b) HAWl - DCA of the diatom species found on the tile samples over a one year period June sample not recovered 1.50 3.0 Jul '96 C HAWl Tile samples HAWl Tile species Ni. paleacea ▲ 1.25 A Ni.frustulum 2.0 N a atomus. Cym. minuta A. minutissima A \ A A^ Aug '96 G. angustatum 1.00 Na menisculus Ni. amphibia M. circulare Apr '96 1.0 Na subminuscula I \ N a [species 2] a a a | à. Se. minima May '96 Ni. dissipata A SuSbrebissonii iSept '95 G. olivaceum \ 0.0 Am. pediculus , O ct'95 Feb '96 4 N a lanceolata -c /M a r’96 ^A. lanceolata \ Jan '96p^ Am. veneta A a a F r vaucheriae G. parvulum A M. varians -1.0 0.25 rDec '95

~~A Se. seminulum 0.00 Nov '95 ) -2.0 -| 1---- 0.0 1.0 2.0 3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1~~

~~c) HAW l - DCA o f the diatom species found in the natural epilithon samples over a one year period~~

~~1.50-1------3.0 HAWl Epilithon samples HAWl Epilithon species Na. gregaria Feb '96 N t dissipata a Apr '96 A. lauenbergiana 2.0 ^ A Ni. palea Na atomus q angustatum A Sept '95 Am. pediculus A~~

1.0 Ni. inconspicua N a J ^ a u d i i a G. olivaceum A Re. s i n u a t a \ . lanceolata 0.75- A. conspicua. \ A ,A. minutissima Na menisculus Jun'96 Se. minima A Na subminuscula a , 0.0 N i paleacea à „ , , Aug '96 Se. seminulum a ^ N a lanceolata Ni amphibia ^ A

~~Dec '95 w) Jan 96 G. parvulum | -1.0 N a tripunctata 0.25- Jul '96~~

~~A A. ploenensis Nov 95 -2.0 Co. p la cei^la v. euglj^ta i 3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1~~

~~Figure 6.6 DCA plots showing the seasonal variation within the diatom assemblages at HAWl from the three different substrata~~

261 a) ELST2 - DCA of the diatom species found on the rope samples over a one year period Sept. & Oct. samples not recovered 3.0 ELST2 Rope samples t^Na. atomus ELST2 Rope species Jul *96 A No. cryptotenella No. tripunctata Jan 96 2.0- Co. placentula V. euglypta Aug'96 A Ni. sociabilis

Nov 95 A G. parvulum minuta A Na. [pseudogregaria] 1.0 A. ploenensis KjiR k curvata aA A Na gregaria Jun 96 ^ Na. [species 2] a D. vulgare May 96 Se. minima Ni. dissipata a ^ lanceolata 0.0- M ar'96 A pr'96 Na lanceolata 0.50- M. varians Dec'95 Am. pediculus a A A. grana -1.0 A .clevei^ A A. lauenbergiana

~~N a ignota V. acceptata Feb '96 -2.0 2.0 -1.0 0.0 1.0 2.0 3.0 DCA Aiis 1~~

b) ELST2 - DCA of the diatom species found on the tile samples over a one year period Sept.,., Oct., JuJul. & Aug. samples not recovered 1.25 3.0 Jun '96 c r^ M a y '96 ELST2 TUe ELST2 Tile species samples D. vulgare A N a ignota V. acceptata M. varians A A A. grana 1.00 2.0 Na subminuscula > Nov '95 / pediculus A ^ % u ^ p t ^ ^ ‘^ A N a [pseudogregaria] A N a atomus 0.75 A ti A. lanceolata 1.0- Na tripunctata * Hk curvata G. parvulum ^ Na cryptotenella Na minisculus ^ A A. minutissima !)Dec'95 A à. NL paleacea A G. olivaceum 0.50 Apr '96 0.0 Ni. dissipata Na lanceolata A Na gregaria Mar '96 y A Cym. minuta 0.25 -1.0 N a [species 2] ^ Jan '96 o ------A. lauenbergiana A A N a [small sp. 1] 0.00 ~------_ ypeb '96 -2.0 0.0 1.0 2.0 3.0 -1.0 ^!o^ 1.0 2.0 3.0 DCA Axis 1

~~c) ELST2 - DCA of the diatom species found in the natural epilithon samples over a one year period~~

2.0 4.0 ELST2 Epilithon sampies ELST2 Epilithon species A Na. gregaria P D ec'95 Ni. palea A 3.0 N a [small sp. 1] a 1.5 Na subminuscula A. minutissima Mar '96 Q --~ y 1 Ni. paleacea L / ^m . pediculus / 2.0- A A A Ni. dissipata qA pr '96 G. olivaceum A A N a atomus , N a lanceolata.^ J ) ^ 6 / G. parvulum Cym. minuta X ^ 1.0 1.0 N a [speciesles 2]21A • ^ N a [pseudogregaria] __~_Jim'96 \ F e T w A. lauenbergiana A | Na minisculus ^ vulgare a ^ A. lanceolata May '96 0.0 Se. minima JVg. cryptotenella A a M varions 0.5 N a ignota v. acceptata A ^ A M. sociabilis Nov ' 9 5 \ S ® P ‘ Aug '96 -1.0 CaplLntula Na tripunctata V. euglypta ^ A X. ploenensis \ Jan '96 A. grana 0.0 O ct'95 -2.0 0.0 1.0 2.0 3.0 -1.0 0.0 1.0 2.0 3.0 4.0 DCA Axis 1

~~Figure 6.7 DCA plots showing the seasonal variation within the diatom assemblages at ELST2 from the three different substrata~~

262 These patterns are repeated at HAW 1 and ELST2 with an obvious degree of circularity in the artificial substrata and a lack of any obvious seasonal pattern within the natural epilithon (Fig. 6.5 & 6.6). These results are perhaps less surprising when considered in context with the findings of Chapter 3, where the variability in the epilithic samples was found to be much greater than those of the artificial substrata. These results reinforce the view that a carefully selected artificial substratum gives a more accurate reflection of the recent environmental conditions (i.e. the previous month) than the natural substrata which appear to support very variable, substratum specific assemblages and are thus less indicative of the changing environmental conditions.

~~6.4.3 Estimating FRP from the Diatom Assemblages~~

Having established that both FRP and the diatom species assemblages vary over the course of a year, the opportunity exists to test the WA-PLS models developed in Chapter 5 and establish the extent to which they can be used to predict FRP concentration at the three river sites. It should be reiterated here that, due to the inherent short term variability in FRP which is known to occur in lowland river systems, particularly when looking at phosphorus sources from sewage treatment works (Allan 1995), the aim was not to determine an absolute value for FRP, but rather to establish whether the diatom communities can be used to detect gross differences and directional changes in trophic status. The methods of WA-PLS calibration used in this study, however, do provide absolute values of the reconstructed variable (FRP) and these will therefore be used to evaluate the performance of the models. The implications of this and other methods for reducing the calibration results to a meaningful, workable classification system are discussed in Chapter 7.

The use of WA-PLS for monitoring purposes, as opposed to its more traditional application in palaeoecological studies, allows for more stringent testing of the methods due to the availability of current water chemistry data for comparison. In the majority of lake studies where historical water chemistry data are missing or unreliable, this is not possible and the calibration results form the only means of environmental reconstruction. In this study the calibration results can be compared directly with measured FRP concentrations thus allowing for a more critical analysis of the results.

263 Due to the considerable seasonal variation observed in the diatom assemblages the models were initially only evaluated on the data collected at the same time of year (i.e. October and November). The rationale for this, rather than attempting to estimate FRP concentrations for all the seasonal samples, was that the autumn samples were most likely to provide the most reliable results and allow the optimal models to be chosen. The results presented below (Tabs. 6.1 - 6.10) show the diatom-based WA-PLS calibration results for FRP at the three test sites for the October and November collected samples, and compares these results to the measured FRP. Different species data treatments were used in an attempt to find the model which best estimated FRP for the three test sites. Species data were used untransformed and with square root transformations for each model assessment. The deletion of species groups and of individual taxa is discussed below.

The performance of the models was evaluated by whether or not the estimated FRP value fell within the prediction errors of the WA-PLS model when compared to the measured FRP. These error limits were determined from the back transformation of the root mean squared error of prediction (RMSEP). In the tables below (Tabs. 6.1 - 6.10) FRP estimations which fall within the error limits of the model are denoted by a shaded cell. Those falling outside the model errors are not shaded.

~~Sampling Measured Estimated FRP (pgL^) Site date FRP (ugL ') Rope samples Tile samples A LTl Oct. ’95 26~~

~~Nov. ’95 30 951 (*'«% ,/* H AW l Oct. ’95 1068~~

~~Nov. ’95 2721 880(«%„) 440(-oj/^„)~~

~~ELST2 Oct. ’95 1332 No data No data~~

~~Nov. ’95 1000 1195 (««-KsvJ~~

~~Table 6.1 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data and including all taxa (shaded cells fall within the error limits of the model)~~

Of the ten diatom-based WA-PLS reconstructions, six values for FRP fell within the error limits of the model (Tab. 6.1). At ALTl both the rope and the tile samples reflected the low trophic status of the site in October although the tile sample calibration was too low and fell just outside the error limit for the model. The opposite was true for

264 November where the tile calibration was accurate but the value obtained from the rope sample was extremely high. The rope sample was dominated by the planktonic species C. pseudostelligera in November which, as discussed above, is unlikely to be representative of the river site. This taxon was not found in high numbers on the tile, nor was it found in such high numbers in October, hence the disparity between the resultant calibrations.

At HAWl, during October, both the rope and tile samples gave calibration results within the model error limits. This was not the case in November where both sample calibrations considerably underestimated the high measured FRP, this was particularly apparent in the tile sample. This may have been partly due to the higher abundance (32%) of G. parvulum in this sample, the WA optima of which was only 211 pgL'^ in the tile training set. This emphasises the problem caused by the occurrence of a widely tolerant taxon at high relative abundance when using weighted averaging methods for reconstruction. Alternatively the measured FRP may not have been representative for the site during the month of November: this problem is inherent when analyses are based only on one water chemistry sample. Given that the average FRP concentration for HAWl was 1655 pgL'^ for the year, however, the values obtained from the WA-PLS calibration are still considered very low for this site.

At ELST2 the November calibrations for both the tile and rope samples gave relatively good estimates when compared to the measured FRP. This is perhaps not surprising if the taxonomic composition of these samples is considered (Fig. 6.4). The ELST2 samples were not dominated by any one taxon in November. The more even diatom assemblages of taxa with relatively high WA optima resulted in good estimations of the FRP concentration.

The problem of dominance by only one, or perhaps two, diatom species is inherent in many diatom communities and this is particularly apparent when artificial substrata are used for sampling (Cattaneo & Amireault 1992). Despite care having been taken in this study to maximise the species diversity by careful substratum selection, the problem of dominance appears to be unavoidable. It is, however, still possible to reduce the effects of single species dominance at the modelling stage by the square root transformation of the

265 species data (Ch. 5, 5.5.3). This results in a down-weighting of the dominant taxa and an effective increase in the weight of the less abundant taxa. The effect this had on the model development was to reduce the number of outlier sites without any noticeable deterioration of the model results. Table 6.2 shows the calibration results for the same samples as above but this time using square root transformation of the species data at both the model regression and calibration steps.

~~Sampling Measured Estimated FRP (pgL *) Site Date FRP (msL ') Rope samples Tile samples A L T l Oct. '95 26 9 (% ) Nov. '95 30~~

~~H A W l Oct. '95 1068 2784 (.27%,ml Nov. '95 2721 810(*«y-4«) ELST2 Oct. '95 1332 No data No data Nov. '95 1000~~

~~Table 6.2 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data and including all taxa (shaded cells fall within the error limits of the model)~~

The effects of using square root transformed species data are clearly apparent on some samples but less so on others. At ALTl it reduced the calibration value for FRP on both the October samples but did not change the overall accuracy of the results. The accuracy of the November rope sample showed considerable improvement following square root transformation, resulting in an FRP estimate which fell within the error limits of the model. This was at the cost of increasing the tile calibration value slightly for November, putting it just outside the error limits of the model. At HAW 1 all the FRP calibration values were higher when using square root species data. These results gave a better indication of the high trophic status at this site, but only the rope samples were within the error limits. The October tile sample resulted in a very high FRP estimate and although the November value was higher than for the untransformed results it remained below the measured FRP value: both samples were outside the error limits of the model. This situation was similar for ELST2 where the rope sample gave a very good estimation of FRP but the tile result was too high. In summary, the use of square root transformed species data produced accurate FRP estimations for all the rope samples, whereas none of the tile sample results fell within the error limits of the models.

266 If the results are considered outside the bounds of absolute values and instead only the relative ranks in trophic status are taken from the model results, the square root transformed rope model accurately ranks all the samples into the order of their measured FRP (Tab. 6.2). This is an improvement on all the models without species data transformation and on the tile samples using square root data. However, the latter model does identify the gross difference in trophic status between the sites, i.e. both the ALTl samples show low FRP estimates and HAWl and ELST2 would be considered as having high trophic status from the calibration results. The value of the square root tile model as a tool for monitoring trophic status cannot therefore be discounted.

The above results are based entirely on the full species data-sets. Some species, however, are known to be of little indicative value and in some cases are actually detrimental to the model results (e.g. planktonic taxa, C. placentula var. euglypta and G. parvulum). These taxa either do not grow at the site, and are thus unrepresentative, or are so widely tolerant that they are of little ecological value in a WA-PLS model. In an attempt to improve the model calibration results further, data from which “problem” taxa have been removed are presented below. The planktonic species, which were assumed not to be growing in situ, have been deleted from the training set and the test sites; the resultant data were re-calculated as percentages prior to the WA-PLS regression and calibration steps. The other two species which were considered possible “problem” taxa were C. placentula var. euglypta and G. parvulum. These were both very common and widely tolerant. The removal of these taxa, which were growing in situ, was treated differently from the plankton in an attempt to maintain the structure of the resultant data. The species were deleted but the resultant data were not re-calculated as percentages. This effectively rendered them passive in the analysis, but did not change the relative abundance of the other taxa.

Table 6.3 and 6.4 show the calibration results for the three test sites following removal of the planktonic taxa without species transformations (Tab. 6.3) and using square root transformed species data (Tab. 6.4). It should be noted that removal of the planktonic taxa will only influence the results where these species were abundant. Plankton did not tend to accumulate on the tile samples and thus it is only the rope samples which showed any changes. The major difference was seen at ALTl where C. pseudostelligera

267 was abundant during October and November. Using untransformed species data, the most noticeable effect was on the November rope sample at ALTl, where the calibration value dropped from 951 pgL"' FRP to 125 pgL'V Although this latter value remained outside the error limits of the model it was a considerable improvement over the full species data result. The other rope values were not greatly affected because of the low planktonic abundances.

~~Sampling Measured Estimated FRP (pgL'^) Site date FRP (pgL ') Rope samples Tile samples A L T l Oct. '95 26 16(+%)~~

~~Nov. '95 30~~

~~H A W l Oct. '95 1068 813~~

~~Nov. '95 2721 867 4 4 0 |.% „ )~~

~~ELST2 Oct. '95 1332 No data No data Nov. '95 1000 83 2(*% „)~~

~~Table 6.3 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed (shaded cells fall within the error limits of the model)~~

~~Sampling Measured Estimated FRP (pgL *) Site date FRP (pgL *) Rope samples Tile samples A LT l Oct. '95 26 18(*%) 7 (% ) Nov. '95 30 69 (% 5 )~~

~~H A W l Oct. '95 1068 1552(»"%.4) 2784|.277^„„) Nov. '95 2721 2048(«26j/,„,J 8 1 0(" Y « .) ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 1132(4'%,.) 2705 (+27,X ,3,j~~

Table 6.4 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed (shaded cells fall within the error limits of the model)

The effect of using square root transformed species data, following the deletion of plankton, had slightly more influence on the other test sites (for rope), due to the relative increase in rare taxa weighting which is inherent when using square root data (Tab. 6.4). Accurate calibration results were obtained for all the rope samples, with the exception of the ALTl November sample which, although falling just outside the upper confidence limit of the model, still reflected the low trophic status of the site. The one area where the square root model does show improvement is at HAWl, where the November result

268 was elevated by 25% compared to the similar calibration using all taxa (Tab. 6.2). The rank order of the rope sample FRP estimates was the same as the measured FRP. The tile sample calibrations showed very little difference following the deletion of planktonic taxa due to the lack of plankton accumulating on the tile substratum(cf. Tab. 6.2 & 6.4).

The removal of planktonic taxa would therefore appear to be justified for the rope samples and the use of square root transformed data gave the best overall results. The tile samples, however, were not improved by using square root transformations. The main problem with the tile samples was at HAWl where the November sample gave a very low FRP calibration. One possible reason for this under-estimate was the high abundance of G. panniliim during November. G. panailum had a WA optima of 211 pgL‘‘ in the tile training set but was observed to be widely tolerant, occurring at >5% abundance at many river sites with FRP concentrations in excess of 1000 pgL * (Fig. 5.17). In fact G. panniliim was found to occur abundantly at all three test sites (Figs. 6.2 - 6.4) which would further suggest that it is of little use as an indicator of trophic status. Tables 6.5 and 6.6 below show the calibration results with the planktonic taxa removed and G. panniliim made passive in the analysis

~~Sampling Measured Estimated FRP (pgL *) Sitedate FRP (pgL Rope samples Tile samples A LT l Oct. '95 26 18(.% ) 12 (*'%.) Nov. ’95 30~~

~~H A W l Oct. '95 1068 2015 2766 Nov. '95 2721 1022 1055 (•■>%„,)~~

~~ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 736 («%,„) 1417~~

Table 6.5 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and G. p a n ’ulum made passive (shaded cells fall within the error limits of the model)

With G. p an’ulum rendered passive in the analyses there appears to be little improvement in the calibration results using the untransformed species data (Tab. 6.5). The only sample to show any marked improvement was the November tile sample at HAWl which, although remaining below the lower error limit of the model, was

269 nonetheless nearer to the measured FRP value than any of the previous calibrations. The net effect of making G. parvulum passive, when using the untransformed species data, was an overall deterioration in the accuracy of the results.

~~Sampling Measured Estimated FRP (ugL'^)~~

~~Site date f r p (m s l ‘) Rope samples Tile samples A LT l Oct. '95 26 5(% ) Nov. '95 30~~

~~H A W l Oct. '95 1068 19341+2,%^) 2685(*«8^„„)~~

~~Nov. '95 2721 2687 (+30'%,,,j 732 (•%,,) ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 927 2608|+247^„„)~~

Table 6.6 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and G. parvulum made passive (shaded cells fall within the error limits of the model)

When square root transformations were applied to the same species data some additional improvements in the WA-PLS reconstructions could be seen (Tab. 6.6). This was particularly apparent for the rope samples at HAWl where both estimated values were within the model eiTor limits: the November rope sample estimate compared very closely to the measured value. Although some improvements were seen in the rope sample calibrations, the effects on the tile samples was generally negative, with less accurate results for all but the November tile sample at ALTl. Tables 6.7 and 6.8 show similar calibration results with C. placentula var. euglypta made passive in the analyses. Planktonic taxa were again removed but G. parvulum has been included in the species data-sets.

~~Sampling Measured Estimated FRP (ugL^) Site date FRP (pgL ') Rope samples Tile samples A LT l Oct. '95 26 23 («?/„) 18(*% )~~

~~Nov. '95 30 4 3 ( % ,| H A W l Oct. '95 1068 9 5 9 M '% ,j 14421*13%^) Nov. '95 2721 986 (•'%„) 382(*3~~

~~ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 1093(*o%,J 1207 (*..%„)~~

Table 6.7 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and C placentula var. euglypta made passive (shaded cells fall within the error limits of the model)

270 Sampling Measured Estimated FRP (pgL^) Sitedate FRP (pgL ‘) Rope samples Tile samples A L T l Oct. ’95 26 24(*r,3) 10 (-'K J Nov. ’95 30 79 (•'%,) 71 (% » ) H A W l Oct. ’95 1068 2547(-3,y_„„) 3445 («77^, J

~~Nov. ’95 2721 3538(*«3X,96,) ELST2 Oct. ’95 1332 No data No data~~

~~Nov. ’95 1000 1747(7'%,,)~~

Table 6.8 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and C placentula var. euglypta made passive (shaded cells fall within the error limits of the model)

The effects of making C. placentula var. euglypta passive in the WA-PLS reconstructions reveals some interesting changes, but the main improvements are within the tile samples rather than the rope. The rope sample results showed better FRP estimates with all taxa present (excluding plankton) and will therefore not be considered here. Prior to any species transformation, marked improvements were seen in the estimated FRP values from the tile samples at ALTl and ELST2 and for the October sample at HAWL This was at the cost, however, of a very low value for the November tile sample at HAWl (Tab. 6.7). C. placentula var. euglypta was not found at >1.0% abundance on the tile samples at HAWl and thus the WA-PLS results for this site are not greatly influenced by its removal. The results are similar to Table 6.3.

The use of square root species transformations on the same data provided no noticeable improvements to either the tile or rope sample reconstructions (Tab. 6.8). The tendency was to cause excessive elevations in the estimated FRP values where C. placentula var. euglypta was common (e.g. HAWl rope samples). Tables 6.9 and 6.10 below show the effects of making both G. parvulum and C. placentula var. euglypta passive in the analysis.

With the two species made passive (and plankton deleted) no overall improvement was identified using untransformed species data (Tab. 6.9), or following square root species transformation (Tab. 6.10). In fact the reverse is true with the model prediction enors increasing and excessively high estimations of FRP being obtained for some samples.

~~271 Sampling Measured Estimated FRP (pgL^) Sitedate FRP (pgL ') Rope samples Tile samples A L T l Oct. '95 26 23(«K.2) Nov. '95 30 155 (•'% ,)~~

~~H A W l Oct. '95 1068 2091 (*2'y.,062) 3268(*3»x,„o) Nov. '95 2721 905 (*■>%„) 1177 (•"% „)~~

~~ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 1299 1573 (+15,~~

Table 6.9 Diatom-based WA-PLS reconstructions at the three test sites using untransformed species data with the planktonic taxa removed and C placentula var. euglypta and G. parvulum made passive (shaded cells fall within the error limits of the model)

~~Sampling Measured Estimated FRP (pgL'^) Sitedate FRP (pgL ') Rope samples Tile samples A LT l Oct. '95 26 23 («K ,3) 7(*X3)~~

~~Nov. '95 30 73(*% J 56(*% ,) H A W l Oct. '95 1068 2651(*«'^,3,J 3993 (.3"^ Nov. '95 2721 3294(«'y_.,3,) 962(*>y.g~~

~~ELST2 Oct. '95 1332 No data No data~~

~~Nov. '95 1000 1212(«%„) 2655 g~~

Table 6.10 Diatom-based WA-PLS reconstructions at the three test sites using square root transformed species data with the planktonic taxa removed and C. placentula var. euglypta and G. parvulum made passive (shaded cells fall within the error limits of the model)

The models which provided the best FRP estimates were determined by comparing the calibration results from each data treatment with the measured FRP concentrations by taking the square root of the sum of squared differences, i.e.

~~p._ v)- , where % = measured FRP and y = predicted FRP (Tabs. 6.11 & 6.12).~~

From the above results the best calibration performance was obtained using the rope samples following the deletion of planktonic taxa and using square root species data (Tab. 6.4). The best predictive model using the tile data was that which included all species (with the exception of plankton) without any data transformations (Tab. 6.3). Planktonic taxa were removed from the tile data-set despite little difference being seen. Plankton did not accumulate to such a great extent on this substratum but it was considered necessary to delete any planktonic taxa due to the likelihood of them being from an external source.

272 ALTl H A W l ELST2 Data Treatment Oct. Nov. Oct. Nov. Nov. J'Z All taxa - no species transformation 37 951 815 880 816 2082 All taxa - SQRT transformed 17 51 1416 1510 919 1263 Plankton removed - no species transformation 16 125 813 867 832 1881 Plankton removed - SQRT transformed 18 80 1552 2048 1132 841 G. parvulum passive - no transformation 18 130 2015 1022 736 1965 G. parvulum passive - SQRT transformed 20 71 1934 2687 927 871 C. placentula passive - no transformation 23 159 959 986 1093 1746 C. placentula passive - SQRT transformed 24 79 2547 3538 1348 1726 G. par. & C. plac. passive - no transformation 23 155 2091 905 1299 2109 G. par. & C. plac. passive - SQRT transformed 23 73 2651 3294 1212 1697 Measured FRP 26 30 1068 2721 1000

~~Table 6.11 Comparison of the diatom inferred FRP estimates with measured FRP using sum of differences squared. Rope samples (shaded cell indicates the optimal value)~~

The square root of the sum of differences squared gives the lowest value for the rope model using square root species data, following the deletion of planktonic taxa (Tab. 6.11). The tile data gave the same optimal results both prior to and following plankton deletion with no other data transformations (Tab. 6.12). These results were, however, considerably higher than those obtained using the rope model, suggesting that the diatom assemblages sampled from the rope substratum provide the most reliable means of estimating lowland river phosphorus with a WA-PLS model.

ALTl H A W l ELST2 Nov. Nov. Data Treatment Oct. Nov. Oct. / £ ('-o ' All taxa - no species transformation 13 35 1448 440 1195 2321 All taxa - SQRT transformed 9 69 2784 810 2705 3083 Plankton removed - no species transformation 13 35 1448 440 1195 23211^ Plankton removed - SQRT transformed 7 69 2784 810 2705 3083 G. parvulum passive - no transformation 12 34 2766 1055 1417 2415 G. parvulum passive - SQRT transformed 5 49 2685 732 2608 3026 C. placentula passive - no transformation 18 43 1442 382 1207 2378 C. placentula passive - SQRT transformed 10 71 3445 540 1747 3312 G. par. & C. plac. passive - no transformation 18 46 3268 1177 1573 2748 G. par. & C. plac. passive - SQRT transformed 7 56 3993 962 2655 3793 Measured FRP 26 30 1068 2721 1000

~~Table 6.12 Comparison of the diatom inferred FRP estimates with measured FRP using sum of differences squared. Tile samples (shaded cell indicates the optimal value)~~

~~273 6.4.4 Estimating FRP from the Seasonal Diatom Data~~

Having established that a carefully chosen, diatom-based WA-PLS model can be used to estimate FRP from river sites of contrasting trophic status, it remains to investigate if such a model can be applied throughout the year or whether, due to the natural seasonal variability in the diatom assemblages, an autumn-based model is restricted to use only on diatom samples collected at that time of year. From the DCA plots of the diatom assemblages collected monthly over one year, seasonal variation was seen to be considerable on both the artificial substrata (Figs. 6.5 - 6.7). The optimal WA-PLS models for reconstructing FRP for the autumn samples were therefore used to estimate FRP at the same three river sites over a one year period.

The Rope Substratum At ALTl there was, with some exceptions, a good relationship between the measured FRP and the WA-PLS calibration values with seven out of the twelve samples giving estimated FRP results within the prediction errors of the model (Fig. 6 .8 a). Of the months that did fall outside the model errors, only September 1995 and August 1996 were in >10% outside the error margin. Thus the annual mean for measured FRP was 46 pgL'^ and the mean estimated FRP was 71 pgL'\ Nine of the twelve FRP estimates were greater than the measured FRP but the general trend in the estimated values followed the measured concentrations. This was not the case for September 1995 and August 1996, which were seen to greatly over- and under-estimate the FRP respectively

(Fig. 6 .8 ). The high estimated value for September appears to be due to the presence of a number of taxa with higher WA optima at greater than 5% abundance (e.g. A. lanceolata, Opt. = 498 pg L '\ A. pediculus, Opt. = 235 p g L '\ N.. atomus. Opt. = 340 pgL'^ and S. minima. Opt. = 470 pgL'^). This had the effect of elevating the estimated FRP value despite the high abundance (37%) of A. minutissima (Opt. = 60 pgL'^). The reason for the very low estimate for the August sample is less clear but can possibly be attributed to the occurrence of two species, D. tenuis and C. affinis, which although only occurring at low abundance, both have very low WA optima for (FRP 3 pgL'^ and 8 pgL'^ respectively). The use of square root transformation would have elevated the relative weight of these rare taxa in the analysis.

~~274 a) AI J1 ; WA-PLS dialom-based reconstructions using square root spp. data (plankton removed)~~

~~250 Estimated FR P Measured FRF 200~~

~~150 «~~

~~too~~

~~0 baa H-H Sept Oct Nov Dec Jan Feb M ar Apr May Jun Jul 1995 1996~~

~~b) HAW I: WA-PLS diatom-based reconstructions using square root spp. data (plankton removed) 3500 Estimated FRP 3000 Measured FRP~~

~~2500~~

~~J 2000 +~~

~~Qi 1500 - - u,~~

~~1000~~

~~500~~

~~0 Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 1995 1996~~

~~c) ELST2: WA-PLS diatom-based reconstructions using square root spp. data (plankton removed)~~

~~2000 2003 [. Estimated FRP Measured FRP 1500 u 5 1000 0. oc u.~~

~~500~~

~~km Sept Oct Nov Dec Jun Feb M ar Apr Mav Jun Jul Aug 1995 1996~~

Figure 6.8 Diatom-based reconstructions o f FRP at the three river sites using the rope substrata over a one year period. Error bars represent the limits o f confidence for the model predictions based on back- transfoimations o f the RMSEP

275 Overall the estimated FRP values for ALTl closely reflect the measured FRP, suggesting that a training set collected in autumn could be used to assess trophic status throughout the year. Due to the large errors in the FRP estimates for September and August, however, the reliability of the results is perhaps questionable. These errors cannot be entirely accounted for and thus warrant further attention. Are they in fact errors? Perhaps the diatoms are giving a better indication of the preceding months FRP concentrations than the spot water chemistry sample. This problem is discussed below.

At HAWl more FRP estimates fell outside the error limits of the model (Fig. 6 .8 b). The September diatom assemblage resulted in a very high FRP estimate and, similarly, all the samples taken from January to May resulted in an under-estimation of the FRP. The remaining samples, with the exception of October 1995, also produced estimates below the measured values, but they were within the error limits of the model. The mean annual FRP at HAWl was 1655 pgL'^ and the mean estimated FRP for the same period 1178 pgL'\ The observation that the high FRP site (HAWl) calibrations tended to underestimated phosphorus concentration, whereas the low FRP site (ALTl) tended to overestimate FRP is consistent with the findings during model development. In Chapter 5 the optimum predictive model was seen to overestimate at the low end of the FRP gradient and underestimate at the high end (Fig. 5.8c).

The main diatom species contributing to the higher FRP estimates during the summer and autumn was N. amphibia with a WA optimum of 2072 pgL'\ This taxon showed a marked reduction in relative abundance during the winter and spring and was not replaced by any species with a similarly high optimum, hence the lower subsequent FRP estimates. A. lanceolata was the dominant taxon during winter (opt. = 498 pgL'^) with a more diverse array of species during the spring and early summer (Fig. 6.3b). A. minutissima was also abundant thus lowering the estimates.

At ELST2 seven out of the ten WA-PLS results were within the error limits of the model. This reflects the higher reliability of WA modelling within the mid-range of the measured variable where many more of the diatom taxa are well represented. Nevertheless the April and May samples produced very poor estimates and March only just fell within the model error. The most likely explanation for this was the higher

276 abundance of N. lanceolata (WA opt. = 283 pgL'^) during the spring (Fig. 6.4) resulting in a reduction in the proportion of C. placentula var. euglypta (WA opt. = 558 pgL'^). Although overall the FRP estimates were slightly better than those from HAWl, the tendency was for the model to underestimate. The mean measured FRP at ELST2 was 944 pgL'^ and the mean estimated FRP 709 pgL"\ The results from the rope data, therefore, suggest that there are potential problems associated with the use of seasonal diatom data to estimate FRP concentrations, based on an autumn sampled training set. The extent to which these problems are due to faults in the model, seasonality or the possibility of errors caused by the use of spot-sampled water chemistry, are discussed in further detail below.

The Tile Substratum Similar results were obtained from the tile substratum when the autumn-based model was applied to the seasonal diatom data (Fig. 6.9). At ALTl A. minutissima (WA opt. = 58 pgL'^) was the dominant species throughout most of the year resulting in a reasonable estimation of FRP concentrations. The most noticeable exception to this was during January when the abundance of A. minutissima dropped below 20% resulting in an estimated FRP value of 333 pgL'\ Although six out of the twelve estimates fell outside the model error limits the overall pattern of results at ALTl was good. The WA- PLS estimates reflect the low FRP concentrations of the site throughout most of the sampling period. Interestingly, the August 1996 sample did not accurately infer the apparent five-fold increase in FRP concentration. This was also the case with the rope samples. This raises one of the inherent problems of this type of study, where biological data are compared to spot-sampled water chemistry measurements. The lack of biological response to the increased August FRP concentration from either of the substrata, combined with a high measured FRP value, would suggest that this measurement may be inaccurate or unrepresentative of FRP concentrations over the preceding month.

~~277 a) ALTl : WA-PLS diatom-based reconstructions using untransformed spp. data (plankton removed)~~

~~250 333 Estimated FRP Measured FRP 200~~

~~150 I 100 u,~~

~~0 U ; Sept O ct Nov Dec Jan Feb M ar A pr May Jun Ju l Aug 1995 1996~~

~~b) HAWl : WA-PLS diatom-based reconstructions using untransformed spp. data (plankton removed)~~

~~3500 3653 Estimated FRP 3000 Measured FRP~~

~~2500~~

~~2000 eu £ 1500 +~~

~~1000~~

~~500~~

~~Sept O ct Nov Dec Jan Feb M ar A pr May Jun Jul Aug 1995 1996~~

~~c) ELST2: WA-PLS diatom-based reconstructions using untransformed spp. data (plankton removed)~~

~~2000 Estimated FRP Measured FRP 1500~~

~~I 1000 oteu bu~~

~~500~~

~~0 - a e r Ar a u u Sp Oct Nov Dec Jan Feb M ar Apr May Jun JulSept 1995 1996~~

Figure 6.9 Diatom-based reconstructions o f FRP at the three river sites using the tile substrata over a one year period. Error bars represent the limits of confidence for the model predictions based on back- transformations o f the RMSEP

278 At HAW 1 the estimates for FRP from the tile data were very poor, with only four of the twelve values falling within the error limits of the model; two of which were at the upper limit (Fig. 6.9b). September and October estimates were accurate but the majority of other samples resulted in gross under-estimation of FRP by the model, the one exception being August 1996 when FRP was considerably over-estimated. Like the rope samples this can be attributed to the lack of good indicator species at the high end of the FRP gradient. Where N. amphibia is abundant (20%), predominantly August, September and October, the FRP estimates are higher. The lower abundance of N. amphibia during the rest of the year, and the absence of other taxa of similarly high FRP optima, resulted in the observed under-estimation of FRP. The taxa with the highest abundance during the remainder of the year were G. parvulum in winter, A. lanceolata in the early spring, and a range of co-dominant species during the summer (Fig. 6.3b). These were mainly widely tolerant taxa with mid-range FRP optima, resulting in the lower than expected estimates. It would, therefore, seem that the application of the autumn-based diatom model cannot be reliably used to estimate the trophic status of river sites over a seasonal cycle where nutrient pollution is very high.

Although data were missing for four of the months at ELST2, there was a marginal improvement over the results from HAWl. Five of the eight samples produced FRP estimates which were within the error limits of the WA-PLS model. As with the rope data at ELST2, the spring samples produced the least reliable estimates because of the high abundance of N. lanceolata (WA opt. 241 pgL'^) at this time. The FRP estimates obtained for the remaining months are higher, which can be attributed to more diverse diatom assemblages containing taxa with higher FRP optima, e.g. N. subminuscula (WA opt. = 806 pgL'^), N. [species 2] (WA opt. = 464 pgL'^) and N. [pseudogregaria] (WA opt. = 501 pgL'^).

The results from the two substrata highlight the problems associated with using an autumn-sampled diatom training set to predict the FRP concentrations of river sites over the course of a whole year. The seasonal shifts in species composition within the diatom assemblages from the artificial substrata appear to have a detrimental influence on the WA-PLS modelling of FRP. This was particularly apparent at the high end of the FRP gradient where very few good indicator species were found, resulting in the under-

279 estimation of FRP at the high phosphorus site. The question therefore remains, that if the diatoms are not responding to FRP over an annual period, what factors are important in determining the species assemblages? Data are presented below which attempt to explain some of the observed seasonal irregularities in the diatom assemblages.

~~6.4.5 Explaining the Seasonal Variation within the Diatom Assemblages~~

It is apparent from the above results that an autumn-sampled diatom model has reduced predictive power when applied to species assemblages collected outside the autumn period. If the diatoms at any one river site are responding to seasonal changes more strongly than to changes in the FRP concentration, then application of the autumn-based models does not comply with one of the principal ecological assumptions of weighted average modelling; i.e. that “environmental variables other than the one of interest have negligible influence ” (Birks et al. 1990). To assess how the diatoms were reacting to their environment over the course of a year forward selection was used within canonical correspondence analysis (CCA). Forward selection allows for the quantification of species/environment relationships, thus establishing which of the measured variables is most important in determining the observed species composition (ter Braak & Verdonschot 1995). When forward selection is performed using CANOCO (ter Braak 1991) the significance of each variable can be tested using a Monte Carlo permutation test (999 unrestricted permutations were used, P < 0.05 with Bonferoni adjustment).

The data used in the CCA were those presented at the beginning of this Chapter (section 6.4.1), with the addition of information on day length, time of year and substratum type. Day length was calculated from sun-rise/sun-set tables (Philip 1994) for each of the monthly samples, time of year was expressed in the data-set as six, two-monthly dummy variables; i.e. Jan. & Feb., Mar. & Apr., May & Jun., etc. and substratum type was also added as a dummy variable. A number of different CCA’s were performed, these were: • all samples together, to assess the overall importance of the different variables. • samples from one specific substratum but including all three sites. • samples from one site and only one substratum.

~~280 When all the samples are analysed together the variables which appear most important in driving the species assemblages are alkalinity and FRP (Tab. 6.13).~~

% of Measured Required Signifîcance Forward Selected Explained Significance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Alkalinity 21.8 P = 0.001* P < 0.0500 FRP 18.1 P = 0.001* P < 0.0250 Epilithon 12.8 P = 0.001* P < 0.0167 Mar./Apr. 10.5 P = 0.001* P < 0.0125 May/Jun. 6.8 P = 0.001* P < 0.0100 Day length 6.0 P = 0.001* P < 0.0083 Rope 5.3 P = 0.001* P < 0.0071 Silica 3.0 P = 0.009"' P < 0.0062

~~Table 6.13 CCA with forward selection using all sites and all substrata. Total inertia = 3.31, explained variation = 1.33. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)~~

These results are similar to those obtained when forward selection was applied to the training sets (Ch. 4), and are not surprising when the between-site differences in alkalinity and FRP are considered. In effect the three test sites provide very long gradients for these variables. It is also interesting that the epilithon is singled out as supporting significantly different species assemblages from the artificial substrata, despite the varied conditions between the three sites. This is likely to be due to the occurrence of A. lauenbergiana and A. pediculus in the epilithon at all three sites. These taxa appear to be more dependant on habitat than the ambient water quality at the test sites.

Chemistry and substratum are therefore the most important measured variables when all samples are considered together. There is also, however, a significant amount of variation accounted for by the spring samples. Mar./Apr. and May/Jun. account for 17.3% of the explained variation. This suggests that, despite the strong effects that chemistry and substrata are having on the diatom communities, there also exists an important seasonal response. This is most extreme in the spring months. Day length would appear to be an important factor in the observed seasonal response, accounting for a small but nevertheless significant proportion of the variation. These results show that, even across very wide chemical gradients, seasonality is an important factor

~~281 determining species assemblages and thus may influence the predictive power of a diatom-based model which does not incorporate a seasonal component.~~

Chapter 3 demonstrated the importance of substratum in accounting for different diatom assemblages, the next CCA’s therefore were performed using the data from all three sites, but with only one substratum at a time.

% of Measured Required Significance Forward Selected Explained Significance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Alkalinity 23.7 P = 0.001* P < 0.0500 FRP 19.7 P = 0.001* P < 0.0250 Mar./Apr. 11.0 P = 0.001* P < 0.0167 May/Jun. 9.5 P = 0.002* P < 0.0125 Temperature 7.1 P = 0.013"' P < 0.0100

Table 6.14 CCA with forward selection using the rope substratum at all three sites. Total inertia = 2.09, explained variation = 1.27. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)

When only the rope samples are included alkalinity and FRP are again the most important explanatory variables (Tab. 6.14). Without the other substrata, however, the spring months (Mar./Apr. and May/Jun.) are the next most important explanatory variables, with temperature falling just outside the adjusted significance level of the forward selection. Again this demonstrates the importance of seasonal variation in the diatom communities and may explain why the WA-PLS estimates for FRP were

~~inaccurate outside the autumn period (Fig. 6 .8 ).~~

~~With the tile samples as the only substratum and all three sites included, the forward selection results are very similar to the rope samples (Tab. 6.15).~~

% of Measured Required Signifîcance Forward Selected Explained Signifîcance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Alkalinity 2 2 6 P = 0.001* P < 0.0500 FRP 20.7 P = 0.001* P < 0.0250 Mar./Apr. 14.4 P = 0.001* P < 0.0167 May/Jun. 8.2 P = 0.007* P < 0.0125 Day Length 7.5 P = 0.010* P < 0.0100 Temperature 5.6 P = 0.106"' P < 0.0083

Table 6.15 CCA with forward selection using the tile substratum at all three sites. Total inertia = 2.68, explained variation = 1.59. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)

282 The months Mar./Apr. explain a higher relative proportion of the variation with the tile samples (Tab. 6.15) than in the rope data. The other difference is that day length is selected as significant. When these results are considered in the context of the WA-PLS estimates for the seasonal data, three main points require consideration. Firstly, alkalinity is a strong explanatory variable and thus, when attempting to estimate FRP, the assumption that other variables are of negligible influence (Birks et al. 1990) is not fulfilled. However, phosphorus was shown in Chapter 4 (Section 4.5.6), to explain sufficient overall variation to provide adequate predictive power in a WA-PLS model. This was demonstrated with the autumn samples in Section 6.4.3. Secondly, FRP remains a strong explanatory variable within the seasonal data and thus it is not surprising that the seasonal estimates indicate the gross differences in FRP between the three test sites. Thirdly, the influence of seasonality, particularly the spring months, is very strong and thus the inability of the model to estimate FRP accuratelv in spring, using an autumn-based training set, is less surprising.

It remains, therefore, to establish the effect of seasonality within a single site, from a single substratum, where the strong influence of alkalinity is removed (alkalinity showing little within-site variation) and changes in the FRP concentrations are more subtle. Tables 6.16 and 6.17 show the results of forward selection using only the ALTl samples from rope and tile respectively.

% of Measured Required Significance Forward Selected Explained Significance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Temperature 28.7 P = 0.003* P < 0.0500 May/Jun. 15.9 P = 0.038"' P < 0.0250

~~Table 6.16 CCA with forward selection at ALTl using only the rope substratum. Total inertia = 1.03, explained variation = 0.94. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)~~

% of Measured Required Significance Forward Selected Explained Significance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Temperature 20.4 P = 0.024* P < 0.0500 Day Length 11.7 P = 0.186"' P < 0.0250

~~Table 6.17 CCA with forward selection at ALTl using only the tile substratum. Total inertia = 1.09, explained variation = 1.02. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)~~

283 Both the rope and tile data show similar results. Water temperature appears to be the major variable determining species composition over the course of a single year. The diatom assemblages on the rope were not significantly influenced by any other variable, although the May/Jun. sampling time was the second highest selected variable. Similarly the tile data showed day length as the secondary influential variable. From these results it is clear that seasonally governed changes have a major effect on the diatom assemblages at ALTl.

At HAWl the seasonal variables also appeared to be driving the observed diatom assemblages (Tab. 6.18 & 6.19). Rather than temperature being the main explanatory variable at HAWl, however, the months of March and April explained the greatest variation for both the rope and tile samples. Exactly what environmental signal this represents is unclear but these results, and the WA-PLS estimates obtained for the spring samples, demonstrate that a diatom-based model developed from a November sampled training set cannot be reliably used to estimate trophic status at other times of the year.

% of Measured Required Significance Forward Selected Explained Significance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Mar ./Apr. 26.0 P = 0.009* P < 0.0500 Sept./Oct. 21.4 P = 0.001* P < 0.0250 May/Jun. 13.5 P = 0.002* P < 0.0166 Day Length 9.0 P = 0.119"' P < 0.0125

~~Table 6.18 CCA with forward selection at HAWl using only the rope substratum. Total inertia = 0.99, explained variation = 0.89. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)~~

% of Measured Required Significance Forward Selected Explained Signifîcance using Bonferoni Variable Variation (999 permut.) Adjustment (P/n) Mar ./Apr. 29.5 P = 0.001* P < 0.0500 Temperature 16.4 P = 0.001* P < 0.0250 May/Jun. 14.7 P = 0.003* P < 0.0166 Day Length 9.8 P = 0.184"* P < 0.0125

~~Table 6.19 CCA with forward selection at HAWl using only the tile substratum. Total inertia = 1.33, explained variation = 1.22. (* = significant, = not significant, P < 0.05 with Bonferoni adjustment)~~

284 The use of CCA on the ELST2 samples was not possible because of missing samples, and thus the number of environmental variables exceeded the number of samples. This results in excessive collinearity occurring in the data, rendering the technique unsuitable. The seasonal patterns in the diatom data at ELST2 (Fig. 6.4), however, suggest that the results would be similar to those obtained for ALTl and HAWl.

~~285 6.5 Discussion~~

~~6.5.1 Seasonal Variation~~

In this study it was considered vital to assess to what extent the diatom assemblages varied throughout the course of the year. Unlike the palaeoecological studies from lakes where diatoms from a surface sediment sample are used to represent the integration of relatively stable environmental conditions over time (e.g. Stevenson et al. 1991, Jones & Juggins 1995, Bennion et al. 1996) this study attempts to do the opposite, i.e. to use the diatoms to monitor water quality over short time periods, from rivers where seasonal variation in the environment is often considerable. In rivers the seasonal changes in, for example, light, temperature and biotic factors (e.g. grazing) are often much greater than in lakes (Allan 1995), and such changes have been demonstrated in numerous studies to have a considerable effect on diatom species composition (Butcher 1940, Douglas 1958, Moore 1976, Casey et al. 1981, Marker & Casey 1982, Esho & Benson-Evans 1984, Cox 1990a, Reynolds 1996). In this study, therefore, the seasonal patterns in both water chemistry and diatom assemblages were investigated prior to using the autumn-based predictive models to assess trophic status. It was demonstrated in the previous chapter that diatom assemblages could be related to FRP concentrations in lowland rivers. In this chapter the predictive power of the autumn-based diatom models are assessed, first against independent autumn samples and then using seasonal diatom data from the same sites to establish if the phosphorus signal can be reliably determined in spite of seasonal changes in the assemblages.

With the observed differences in FRP concentration between the three test sites, it is not surprising that they support different species assemblages. The within-site variation in FRP, however, was less apparent and thus the impact of the physical environment on the diatoms during the year might be expected to be more influential than small changes in chemistry. Temperature, for example, has been shown to influence algal composition in rivers (Reynolds 1996) and in this study was seen to display a typical annual pattern (Fig. 6.1h). Other factors which were not included or measured, such as day length, shading, grazing pressure and disturbance, have all been reported as having a strong influence on diatom assemblages over the course of a year (Cox 1990a, Reynolds 1996).

286 Not surprisingly, therefore, the diatom assemblages sampled at the three sites showed considerable variation over the 12 month sampling period. The most obvious changes in the relative abundance of diatom taxa were observed on the artificial substrata. The natural epilithon also showed some seasonal patterns in species composition, but these were not as marked as in the rope and tile samples. At ELST2, for example, a clear peak in the abundance of N. lanceolata was seen on the artificial substrata during March and April, but not in the epilithic samples. Similarly G. parvulum was common throughout the 6 month period from December to May on both artificial substrata at ALTl, but showed no obvious seasonal response in the epilithic samples. The DCA plots very clearly showed the lack of seasonal response in the epilithon (Figs. 6.5 - 6.7), which was possibly due to the large inherent variation in epilithic samples, demonstrated in Chapter 3. It may also have been due to a build-up of dead cells, which would mask the composition of the living community. If either of these reasons apply, this adds weight to the argument that the natural epilithon is not a suitable substratum when the focus of the study is to monitor changes in water quality over relatively short time periods.

Although the seasonal shift in species composition was most clearly marked in the two artificial substrata, their diatom assemblages were quite different at all three sites. Generally the most common abundant taxa on both the rope and tile were A. minutissima at ALTl, A. lanceolata at HAWl and, to a lesser extent N. lanceolata at ELST2. Other taxa, however, showed a distinct preference for either the rope or the tile, with more typically epiphytic species occurring on the rope, and epilithic species on the tile. For example, C. placentula was common on the rope at ELST2 but only found at low abundance on the tile. The rope also yielded more planktonic taxa, probably because it moved within the water column, whereas the tiles were flush with the river bed. Rather than being an important part of the rope sample assemblage, the planktonic species were simply trapped by the strands of the frayed rope.

The implications of the seasonal variation in the diatom assemblages could be to reduce the effectiveness of modelling FRP with an autumn-sampled training set. The somewhat naive assumption at the beginning of this study was that ecological preferences could be “defined” for each diatom species for FRP by the construction of a training set. Thus, if each diatom species had a direct and independent ecological response to FRP, then the

287 time of year would be unimportant, providing the same species were found throughout the year, regardless of when the training set diatoms were sampled. Clearly this is not the case in lowland rivers where nutrient levels are almost never limiting. The ecological “response” to FRP that the models appears to represent is in fact no more than a surrogate response to what is a very complicated array of ecological factors. Part of this array almost certainly includes the environmental factors controlled by the time of year, i.e. seasonal variation.

The following discussion therefore focuses on three areas. First, to assess to what level of accuracy the autumn-based models can be used to estimate FRP using diatom samples taken at the same time of year. Following this, the implications of using an autumn-based model to estimate FRP from a sample collected at another time, are discussed. Finally, the extent to which seasonal factors influence the diatom assemblages is considered.

~~6.5.2 The Estimation of FRP from Autumn Sampled Diatoms~~

Although the internal validations of the two WA-PLS diatom models presented in Chapter 5 showed comparable results to similarly constructed models from lakes (e.g. Bennion 1995), a more thorough validation exercise was to test the model on independent samples that were not used to construct the original training set but where the chemistry was known. Furthermore, because the model results were not adversely affected by the transformation of species data, nor deletion of widely tolerant taxa, the opportunity arose to select the optimal model following different data treatments. The decision was also made to delete all planktonic species from the data-set. Plankton is often present in slow flowing rivers, but for the purposes of this study, it was considered unrepresentative of the chosen sites, the most likely source of planktonic taxa being impoundments draining into the rivers. The source of “truly” planktonic algal taxa in rivers has long been attributed to standing waters rather than from within the river itself (Zacharias 1898, cited in Reynolds 1996), a view that has been strengthened by more recent studies (Reynolds 1988).

~~Initially, including all diatom taxa and without the use of any data transformations, the FRP estimates for the three river sites were very varied. An extreme overestimate was~~

288 observed for the ALTl November rope sample, and underestimates for HAWl November samples from both substrata. The high abundance of C. pseudostelligera, a planktonic species, was the most likely reason for the over-estimate at ALTl. Following deletion of the planktonic taxa, the rope sample estimates showed considerable improvement. This was further enhanced by the square root transformation of the species data. The tile samples were not contaminated with planktonic taxa and thus only the transformation of the species data had any effect on the calibration results. Unlike the rope samples, species data transformation appeared to reduce the reliability of the FRP estimates for the tile samples.

Intuitively, it might be thought that the removal of species which are known to be very widely tolerant, and thus poor indicators of water quality, would have improved the calibration results by increasing the weight of the less commonly encountered taxa (i.e. more indicative species). This was not found to be the case. The independent removal of G. parvulum or C. placentula var. euglypta, or both taxa together, produced an overall deterioration in the calibration results. Although this may seem counter-intuitive it is consistent with the workings of a WA-PLS model. The inclusion of all taxa in a WA- PLS model almost always results in the best error statistics; even removal of rare species will result in a slight increase in the root mean squared error of prediction (Birks 1998).

The optimal predictive model, therefore, appeared to be that using square root transformed species data for the rope samples (plankton removed) and untransformed species data for the tile samples. By taking the square root of the sum of squared differences of all the results it seemed that the rope samples gave the better apparent overall estimation of FRP. There remained, however, one particular error in the tile samples that could not be accounted for by the species composition. The November diatom assemblage at HAWl did not differ greatly from the October sample and yet the measured chemistry for November was almost three times as high. The resultant calibration was very good for the October sample but the November estimate was only 440 pgL'\ when using the selected model, compared to the measured FRP concentration of 2721 pgL'\ This may be due to a problem with water chemistry sampling in this study and highlights the unavoidable paradox of the “spot sample”.

289 All the water chemistry sampling in this study was limited by time and resources to a single sample per site per monthly visit (i.e. a spot sample). Hence the risk of obtaining an unrepresentative sample must be considered. This is particularly critical when the focus of the sampling is phosphorus concentrations in lowland rivers, where sewage treatment works account for the majority of phosphorus entering the system. STW’s do not release effluent continuously, and thus at a site like HAWl, which is relatively close to its source (approx. 5 km) but down-stream of a STW, it is likely that FRP will vary greatly depending on the quality and quantity of effluent being discharged. Sites further down-stream are generally less affected by this problem because more STW’s are feeding into the system and phosphorus spiralling processes, via the sediments, have the effect of averaging the phosphorus concentration over distance and time (Allan 1995).

The paradox encountered in the above model validation exercise, therefore, is this: does one accept that the measured FRP value is correct for a given site and assess the model on this result or does one have faith in the ability of the diatom model to estimate FRP, and thus question the representivity of the measured FRP value for the site? This problem is further complicated by two other factors. Firstly, the whole model was constructed using spot monthly water chemistry samples and thus the same issue arises with every site in the training set. Secondly, the rope sample for the same site at the same time gave a very reasonable estimate of FRP. The first issue cannot be resolved without intensive water chemistry sampling which is beyond the scope of this study. Furthermore, the initial data screening process, which removed outlier samples, may have removed samples with unrepresentative water chemistry. The second point is perhaps of greater consequence because, regardless of whether the measured chemistry at HAWl was correct or not, one of the models, either tile or rope, is giving a spurious result. These problems, however, are unavoidable and, although the validation exercise should be treated with a degree of caution, it does provide the only means of testing the models at this stage. The results are therefore presented and discussed within this context. With an opportunity of future work a larger number of validation sites would be selected to gain more confidence in the model results. It is also suggested that the chemistry of the validation sites should be studied closely prior to the validation exercise.

290 Despite the problems encountered in the validation of the two models they appear to give good estimates of FRP for the majority of the autumn samples. This is certainly the case if the results for the three sites are considered as a general guide to trophic status rather than as a prediction of absolute FRP concentration. Both models imply that ALTl is a site of low trophic status. Similarly, with the exception of the one tile sample discussed above, the rope and tile models both estimate ELST2 and HAW 1 to be of high trophic status, with the latter being the most eutrophic site. When the inherent variability of lowland river chemistry is taken into consideration, coupled with the problems of the spot sampling methods used to determine it, the results obtained for the two diatom- based models are in fact surprisingly good. This strengthens the view that diatoms are excellent indicators of their environment and can, therefore, be used to model and monitor trophic status in lowland rivers.

~~6.5.3 The Estimation of FRP from Seasonal Sampled Diatoms~~

Having established that with careful model selection the rope and tile models can be used to estimate phosphorus concentrations in lowland rivers when using autumn sampled diatoms, it remains to assess the extent to which these models can be applied to data collected at other times of the year. The assumption that an autumn-based model will be suitable for all year round FRP estimations may be too general when the observed seasonal variation of lowland river diatom assemblages is taken into consideration.

The Rope Substratum The site ALTl on the River Wey is close to its ground-fed source and, with the exception of one water sample taken in August 1996, maintained FRP concentrations below 50 pgL'^ (annual mean = 46 pgL'^). When the rope model was applied to the diatom data sampled over the same one year period, reasonable estimations for FRP were obtained. The mean estimated FRP concentration was 71 pgL'^ suggesting that the autumn model performed well on these data. The possible problem of spot water chemistry sampling was also revealed at this site. One sample (Aug. 1996) had a relatively high measured FRP concentration (176 pgL'^). This high value was atypical of the site and did not appear to be reflected in any way by the diatom assemblage which

~~291 was similar to that seen in the previous month. Hence it is thought likely that the estimated FRP value of 19 pgL'^ was more representative of that month than the measured spot sample.~~

Conversely the estimated FRP for September 1995 was over 200 pgL'^ at ALTl but no elevation was seen in the measured chemistry for the same month (23 pgL'^). Although unlikely, it is possible that the measured FRP was unrepresentative of the month and that the diatom model was giving a more accurate snap-shot of the September water quality. A more likely reason for the elevated estimate was the data treatment. The September sample comprised over 50% of the planktonic taxon C. pseudostelligera, which was deleted. The calibration at this site was therefore based on a total diatom count of less than 150 individual diatom valves; a number that was considered too low for obtaining representative counts in this study. Thus the few occurrences of taxa such as N. atomus and S. minima, which have high WA optima for FRP, were over represented in the analysis. This effect was further exaggerated by the use of square root transformation on the species data, adding weight to already over-represented taxa. In future analyses it is recommended that a total of at least 300 valves of non-planktonic taxa are counted.

In an attempt to test the above hypothesis two separate recounts were made of the September 1995 rope sample from ALTl. The first was a count of 300 non-planktonic diatom valves, and the second a count of 600 non-planktonic valves. It was hoped that by including a larger number of benthic diatoms a more representative sample would be obtained and thus a better calibration result. In fact the reverse was true. The original calibration result was 214 pgL'\ the count of 300 returned a calibration of 224 pgL'^ and the count of 600 non-planktonic valves a calibration of 302 pgL'\ The proportions of taxa remained similar in all three counts and thus little difference was seen in the calibrations. The reason for the high WA-PLS estimate in September remains unclear.

Overall, however, the annual FRP estimates, based on the autumn-sampled rope training set data, are good. Despite an over-estimation of FRP for most of the year they do show ALTl to be a site of relatively low trophic status. Furthermore the general trend in the estimates was for a slight increase in winter FRP, a pattern also seen in the measured chemistry.

292 Although only 3 km downstream of ALTl, HAWl was a very high phosphorus site. This was due primarily to a discharge from a sewage treatment works of approximately 11,000 population equivalents directly below ALTl. Unlike the estimated FRP values for ALTl, which were mainly greater than the measured FRP, the majority of the annual samples at HAWl resulted in under-estimations. The under-estimates were greatest during winter and early spring. This under-estimation of high values and over estimation of low values is inherent in WA techniques because of the truncation of species response curves at either end of the FRP gradient. This has the effect of compressing species optima towards the centre of the gradient and results in the observed calibration results (Birks et al. 1990); i.e. under-estimates of FRP at HAWl and over-estimates at ALTl. WA-PLS is less affected by this problem than simple weighted averaging, but is does still occur (Birks 1998).

A degree of under-estimation of FRP at HAWl should therefore have been expected. The extent to which it occurred, however, resulted in very inaccurate results. The under estimation in this case is unlikely to be due only to inherent problems in the WA-PLS modelling, but rather the change in species composition during the year (i.e. the effect of seasonality) must be implicated. The principal taxon which appeared to be responsible for returning the high FRP estimates in the autumn samples was N. amphibia, which had a WA optimum of 2072 pgL'\ The high abundance of this taxon during summer and autumn, and low abundance in winter and spring, was also observed by Lowe (1972) suggesting it is strongly influenced by seasonal factors. This highlights the problem of the reliance of a WA-PLS model on only a very few taxa at the ends of the measured gradients. If such taxa are not represented in a sample because, for example, of seasonal shifts in species assemblages, the model is less likely to return a reliable estimate of FRP. This problem is less acute at mid-range FRP sites because far more species have optima within this range. Overall the seasonal estimations of FRP at HAWl were too low, suggesting an autumn-based model is inappropriate for estimating FRP during the rest of the year. The monitoring of FRP based on these results would have placed HAWl in the mid-range of nutrient status for lowland rivers. The WA-PLS model results did, however, display the same general pattern as the recorded concentrations. For example, the spring reduction in measured FRP concentrations followed by a gradual increase to the summer was reflected by the model results. This implies that the

~~293 model is sensitive to changes in FRP at the high end of the phosphorus gradient although it cannot accurately reconstruct it using an autumn-based diatom training set.~~

The results obtained for FRP estimates at ELST2, a site which fell in the mid to high phosphorus range, were slightly better than those from HAW 1. This reflects the greater species diversity at this site and hence the presence of a larger number of taxa with optima in this FRP range. Like HAWl the species assemblages changed considerably throughout the year, but the loss of one taxon was replaced with a greater abundance of another, which was well represented in the training set and had a similar FRP optimum. For example, N. lanceolata partially replaced C. placentula var. euglypta during early spring. This resulted in a lowering of the FRP estimates in spring but this was not as extreme as that seen for the HAWl samples.

The Tile Substratum The results obtained from the tile data were very similar to those for the rope data although in the majority of cases the FRP estimates were slightly lower. For this reason they will not be discussed separately here.

What these data show is that, although carefully selected autumn-based diatom models from both the rope and tile substrata can be used to infer gross changes in FRP concentration during the course of a year, the level of accuracy is not sufficient to make reliable decisions on the nutrient status of the river sites at times other than the autumn. This is particularly true of the spring sampled diatoms from HAWl and ELST2 which considerably under-estimated FRP concentrations. It should be emphasised here that the original intention of this study was not to produce absolute values of FRP from a given site, but rather to use the value obtained from the WA-PLS results to infer a level of phosphorus pollution. Nevertheless, even with this in mind the results shown above would rank HAW 1 as having lower phosphorus concentrations than ELST2 for four out of twelve months from the rope data and three out of the seven months for the tile data. This was not the case according to the measured FRP and is unlikely to be a true reflection of the trophic status of HAWl which is only 3 km downstream of a major (>10,000 population equivalents) sewage treatment works.

~~294 6.5.4 Explaining the Seasonal Variation within the Diatom Assemblages~~

Seasonality (i.e. the changes seen in the species assemblages during the year), therefore, appears to reduce the ability of the autumn-based diatom models to estimate FRP concentrations. With the use of forward selection in CCA it was demonstrated that FRP (and alkalinity) remained the strongest environmental variables controlling the species assemblages when all three sites were considered together. It is perhaps not surprising therefore that the diatom models reflected this gross difference in the nutrient status of the test sites. Even with this large range in water chemistry however, the next most important variable in explaining the species assemblages on both the rope and tile substrata was the time of year, with the spring months (Mar./Apr.) having the strongest influence. Within a single site the observed annual range of FRP values was much lower and thus the importance of seasonality was much greater.

At ALTl the species changes appeared to be temperature driven, with both the rope and tile samples having water temperature as the only significant forward selected variable in CCA (Tabs. 6.16 & 6.17). These results are consistent with many other studies on stream diatoms which examined relative changes in species composition during the spring months (e.g. Butcher 1940, Douglas 1958, Lowe 1974, Moore 1976, Gale et al. 1979, Cox 1990a). Similarly, an observed increase in diatom numbers has been attributed to the increase in water temperature following winter (Butcher 1940, Marker 1976, Marker & Casey 1982) and thus shifts in relative abundance during this period are likely to occur if this favours an increased division rate of some taxa more than others.

The results from HAWl, however, suggest that the relationship is more complicated than just changes in water temperature. The forward selection using the rope data selected Mar./Apr. as the most influential variable, indicating that temperature alone is unlikely to be responsible for the changes in species composition. Sept./Oct. and May/Jun were also significant. Day length was the first non-significant variable and it is likely that a combination of factors, including both water temperature and day length (and light intensity), are having an effect on the species assemblages. The ecology of individual species in relation to light and temperature was discussed by Cox (1990a) as

~~295 a principal reason for the observed difference in species assemblages at sites along a stream where water quality remained similar.~~

The tile data from HAWl also showed Mar./Apr. to be the most significant explanatory variable on species composition. Water temperature and May/Jun. were also significant, with day length as the first non-significant variable. The results from this study, and many other seasonal diatom studies, show that species composition varies considerably during the spring months. The results of the WA-PLS FRP estimates were generally poor for the spring months and it is therefore suggested that this reflects the strong biological response to vernal processes, thus masking any effect that subtle changes in FRP concentration might have. The initial a priori assumption was that the environmental variables other than the one of interest would have negligible influence (Birks et al. 1990). This assumption could not be upheld with respect to alkalinity, but with careful sampling design the effect of alkalinity was spread over the whole data-set and a good predictive model for FRP was developed. From the seasonal data, however, it would appear that the effect of seasonal changes in temperature and light have such a strong influence on the diatom assemblages that these mask any subtle changes in FRP concentration.

These results show that a significant amount of the observed species variation is due to seasonality, and is concentrated during the spring months, probably because of temperature changes and light regimes (day length and light intensity). The apparent inability of the autumn-based models to provide reliable estimations of trophic status for the rest of the year is not therefore a weakness in the WA-PLS model itself, but rather it shows that the initial assumption made in this study, that the diatom species response to FRP would be independent of seasonality cannot be upheld. This has been demonstrated not to be the case, i.e. that the response of lowland river diatoms is not independent of season.

The data presented above do show that the diatom-based models developed in this study can provide a reliable estimation for FRP if the sampling is restricted to October and November. Furthermore, the strong vernal influence on the diatom assemblages would

~~296 suggest that spring is also an inappropriate time of year to obtain samples for diatom- based monitoring of trophic status in lowland rivers.~~

~~6.5.4 Conclusions~~

In this chapter it was clearly demonstrated that lowland river diatom assemblages differ considerably, both between sites of different water quality, and within a single site over the course of one year. These findings are consistent with many other studies on river diatoms (Butcher 1940, Douglas 1958, Moore 1976, Casey et al. 1981, Marker & Casey 1982, Esho & Benson-Evans 1984, Cox 1990a, Reynolds 1996, Kelly 1998). The main purpose of this chapter was to establish whether a diatom model, based on autumn- collected samples, could be used to assess the between-site differences in water quality over a similar time period. A secondary aim was to assess the capability of such a model to estimate FRP on samples collected at other times of the year, despite the observed within-site seasonal changes.

The results for the autumn-based FRP estimates were encouraging from both artificial substrata. With the exception of the HAWl November tile sample, all the WA-PLS calibration estimates for FRP were representative of the test sites. Although this has obvious limitations for immediate use in river monitoring studies it does show that diatom-based WA-PLS modelling techniques can be used to assess accurately the trophic status of lowland rivers. The limitation is that the current models can only be used reliably for autumn-sampled diatom assemblages. Although outside the bounds of this study, it is suggested that many more samples should be collected from artificial substrata in autumn to assess the capability of the diatom models more thoroughly for the evaluation of trophic status.

The results obtained from the application of the models to seasonal data were indeed found to be less reliable than when applied to the autumn samples only. Typically, over estimates were obtained from the low FRP site and under-estimates from the high FRP sites. The original assumption was that, regardless of the ecological and physical impacts which have a controlling influence on the diatom flora throughout the year, there would remain a strong species-dependent response to FRP, or variables related to

297 trophic status, at a given river site and that seasonality would be unimportant. This was, it is safe to say, a naive assumption and proved to be incorrect. For example, N. amphibia was only common at HAWl during summer and autumn. This species had a very high FRP optima and was responsible for the high estimates where it occurred. During the spring, however, this taxon was not present at HAW 1 and the species which replaced it had much lower model-derived FRP optima, despite the FRP concentrations remaining high. The quoted optima in this study can therefore only be considered valid for the autumn period. Rather than being “true optima” they are simply a reflection of the overall conditions for the given time of year, which includes nutrient levels but also other important physical factors; e.g. day length, light intensity, temperature and physical disturbance. This allows for a good estimate of river trophy to be assessed in autumn but renders any sample taken at a different time of year less reliable.

From the forward selection analysis of the seasonal data it is clear that a high degree of species change was occurring during the spring months. The major variables responsible for these changes remains uncertain but the analyses suggest that increasing temperature and day length between March and June may have an important role in determining the species composition. It is recommended therefore, that the spring months are not a good time to sample diatoms for biological monitoring. The summer and autumn periods are relatively more stable in terms of diatom growth and thus likely to be more representative of the ambient water quality.

These results show that a carefully constructed WA-PLS model can be used to determine phosphorus levels in lowland rivers when the sampling time is kept constant. Without doubt, there is a response in the diatom communities which is related to FRP concentrations. This is almost certainly not a direct physiological response, but it can nonetheless be effectively modelled to provide good estimates of levels in autumn. Any extension of such a model for use at other times of the year requires further study. The two areas which are considered as in need of investigation, are the construction of separate training sets for different seasons, and the construction of an annual training set. Due to the considerable variation introduced by seasonality it is suggested that separate training sets would be the most logical means of extending the temporal range of a diatom-based model for monitoring trophic status in lowland rivers.

~~298 Ch a pter Sev en~~

~~Co n c lu sio n s~~

~~7.1 Introduction~~

This thesis has explored the methodologies involved in the use of diatoms to assess and monitor the trophic status of lowland rivers in southern England. The need to develop reliable river diatom sampling strategies has been demonstrated, and the use of two different artificial substrata presented as the most suitable methods of diatom collection. These sampling methods have been used to develop two lowland-river diatom training sets from which predictive models for phosphorus were derived.

In this chapter the use of artificial substrata for sampling river diatoms is evaluated in the context of a transfer function approach to modelling trophic status in rivers. This section also considers the possible use of the predictive models for river management and suggests how this research may be developed and enhanced in the future.

~~7.2 The Use of Artificial Substrata~~

The huge variation in the form and function of lowland rivers leads to considerable variability in the naturally available substrata on which diatoms can grow. In the majority of studies, where water quality has been the focus, attempts have been made to sample diatoms from hard substrata (Whitton & Rott 1991, Round 1993, Kelly 1998). Round (1993) identifies the problems encountered in finding such substrata in lowland rivers and discusses the implications of mixing different habitats. The findings of this study were similar. Of the 57 river sites used to develop the training sets in this study, only 40% had good epilithic diatom assemblages present. A further 37% had cobbles and boulders, but these were covered by a layer of fine sediment and thus cannot be considered as true epilithon (Round 1993). The remaining 23% of river sites had no epilithic diatom community. The implications of using different habitats were

~~299 demonstrated in Chapter 3. The three natural diatom communities not only differed in their species composition between substrata, but also showed considerable within- substratum variation.~~

The use of artificial substrata for the sampling of diatoms has been reported as reducing within-site variation, but often with an associated reduction in the species diversity compared to the natural assemblages (Stevenson & Lowe 1986, Cattaneo & Amireault 1992). With the careful selection of an artificial substratum, however, these problems can be overcome. In the past, studies on artificial substrata have favoured the use of glass slides (Butcher 1940, Patrick et al. 1954, Tippet 1970) which provide very little variation in surface structure, and thus favour those algal species which can firmly attach. The introduction of rough surfaces, and material with increased surface structure to water bodies resulted in greater species richness and increased the similarity between their assemblages and the natural ones (e.g. Tuchman & Stevenson 1980, Cattaneo & Amireault 1992).

In this study the use of a rough tile substratum and a frayed polyethylene rope gave comparable Hill’s N2 diversities to the natural epilithon and also resulted in low within- site variation. The high relative diversity is considered an advantage when weighted averaging techniques are to be used for the prediction of water quality because it provides a greater number of taxa with higher weights in the analysis, from which a prediction can be made. The reliance on only one or two taxa at high relative abundance may result in other factors (apart from water quality) being of greater importance (e.g. the ability of a species to colonise a smooth surface), and thus the water quality is not accurately represented. Low within-site variation is also important because when using artificial substrata it is less practical to take replicate samples. If a single sample can provide a reliable “snap-shot” of the ambient water quality the logistics of sampling are greatly simplified.

The practicalities of using an artificial substratum do need to be taken into account. The most widely reported problems associated with artificial substrata are the requirement for two visits to a site and the possible loss of an introduced substratum, resulting in the loss of data (Tuchman & Stevenson 1980, Cattaneo & Amireault 1992). The first

300 problem is greatly reduced if regular monitoring is the goal of the study. In such a case only one extra trip is required at the start of the study, all subsequent visits can be for sample collection and the simultaneous introduction of a new substratum. The consequences of losing a substratum are obviously more serious, with the potential loss of important data. In this study only one of each substratum was left at each of the 84 training set sampling sites. One month later, 93% of the rope samples were recovered and 82% of the rough tiles. The higher loss of tiles was not because of any inability to relocate them, but rather the build-up of fine sediment in the slow-flowing rivers resulted in them being excluded from the analysis. As this is unavoidable, the rope substratum is perhaps a better method for diatom collection in the slower flowing rivers. From those sites where the substrata were lost completely, the minimal extra time and cost of placing two of each substratum could perhaps have yielded even better returns.

The separate use of two different artificial substrata in this study highlighted the need to keep the sampling medium constant. The two training sets and their resultant predictive models, gave very similar results overall. However, many of the diatom species showed different weighted average optima between the two substratum types, and neither model could be used successfully to model phosphorus using the species assemblage from the other substratum. This implies that an individual substratum type has an important role in determining the species assemblage. The technique of variance partitioning used in Chapter 3 supported this, with 46 percent of the observed (explained) species variation from four sites on the River Wey being due to substratum type alone, sampling medium is almost as important as between-site variation (Section 3.4.3). This confirms the need to keep the sampling medium constant between sites when using diatoms for the assessment of water quality. Artificial substrata have now been recommended for routine diatom monitoring in Europe where the presence of natural, solid substrata are absent (Kelly et al. 1998).

~~7.3 Diatom Phosphorus Relationships~~

This study focused on the responses of river diatoms for changes in phosphorus concentrations. It was necessary to demonstrate that diatoms actually respond to FRF concentrations before trying to model these processes with weighted averaging

301 techniques. The choice of sites for the development of the training sets deliberately covered a long phosphorus gradient and also encompassed a wide range of alkalinity values. These variables were therefore important in the resultant analysis of the environmental data (PCA). Similarly the indirect analysis of the species data (DCA) from both training sets reflected these major gradients. Furthermore, direct species/environment analysis (CCA & CCA with forward selection) identified phosphorus and alkalinity as the most important variables influencing the diatom assemblages.

These steps are vital in the development of a transfer function type model, allowing for any unusual patterns in the data to be identified. Although a strong diatom-response to FRF was demonstrated, there were samples which had unusual assemblages compared to others from similar trophic conditions. Criteria were therefore set at each stage of the data analysis for the identification of samples which were unusual in some way, either in their chemistry, species assemblage or a combination of the two. At this stage of the analysis only one sample (720 in the rope training set) exceeded any of the criteria and thus, although noted as a possible problem sample, it was left in the analysis.

Despite considerable variability in the measured environment and the presence of other factors that could not be accounted for (e.g. grazing, disturbance & shading), both training sets were considered suitable for the development of a diatom-based model for the monitoring of trophic status in rivers. Canonical correspondence analysis, constrained only to phosphorus, showed Xi :^2 ratios of > 0.5 in both training sets and can therefore be considered as appropriate for weighted averaging methods to model phosphorus in lowland rivers (Pienitz et al. 1995). This analysis demonstrated that despite other environmental variables influencing the diatom assemblages, there remained a strong and statistically significant relationship between the diatoms and FRP. Thus, if the trophic status of a river influences the biota in a measurable and predicable manner, the opposite should also be possible, i.e. to assess the trophic status of the river using its biota.

~~302 7.4 The Diatom-Based Transfer Functions for Lowland Rivers~~

This study demonstrates that the technique of weighted averaging - partial least squares (WA-PLS, ter Braak et al. 1993) which has primarily been established as a palaeoecological tool for use in lakes (ter Braak & Juggins 1993, Bennion et al. 1996, Korsman & Birks 1996), can also be used successfully to estimate lowland river phosphorus concentrations from the diatom communities. With internal validation (jack- knifing) the two models gave comparable f and error statistics to similar lake-based studies (Hall & Smol 1992, Bennion 1993). For the rope model a 4 component WA-PLS model was used, whereas only 2 components were required in the tile training set.

However, it remains of major concern that, in order to achieve acceptable error statistics a number of “outlier” samples had to be removed from the models. This is often the case with transfer functions, but nevertheless, the removal of any sample without a clear reason gives cause for concern. Some samples had erroneous chemistry where, for example, the TP was lower than FRP. These samples could be removed from the training sets with confidence. Others however, simply did not fit the model because they contained unusual diatom assemblages that were not adequately explained by the measured chemistry of the sites. The justification for deletion, therefore, was purely statistical. This problem highlights the difficulties of attempting to condense the highly variable environment of lowland rivers into a simple value for phosphorus using the river biota. The large number of unquantified environmental and biotic factors may simply be masking the phosphorus signal in these samples. This is perhaps inevitable in some lowland river systems where phosphorus is not a limiting nutrient. Thus, at some sites, the model does not work, and without further investigations at these particular sites, it is safer to remove them from the analysis rather than include what may be an erroneous sample.

In total 13% of the both the rope samples and the tile samples were deleted in order to obtain the best model results. These samples were deleted without a clear reason and thus the errors quoted for the models are perhaps being under-estimated. There exists an unavoidable “trade-off’ between attempting to reduce the model errors, to gain the best statistically viable model, against the inclusion of all samples, despite some of them

303 having unusual species-environmental relationships, in an attempt to maintain the integrity of the data-set. The decision to delete the samples which did not conform was made in this study, in order to assess the model capabilities under the most statistically stringent conditions. Given larger training set sites, and more independent test sites, it would be interesting to compare the performance of models with and without their outliers removed.

~~7.4.1 Evaluation of the Diatom-Based Models~~

The internal validation of a diatom-based WA-PLS model allows for statistical errors to be quoted and provides a degree of confidence in the methodologies. The only true test of the models, however, is via an external validation exercise, using diatom samples that were not collected as part of the training sets. The external validation exercises in this study demonstrated that the diatom-based models could be used to estimate FRP concentrations accurately, in the majority of cases, using diatom samples collected at the same time of year as the training set assemblages. Weighted averaging techniques can therefore provide a valuable contribution to the biological assessment and monitoring of trophic status in lowland rivers.

However, the results presented in this study also highlight the necessity to keep the time of sampling constant if these methods are to be reliable. The seasonal variation observed between the species assemblages was considerable, and there were large errors in the diatom-based FRP estimates for non-autumn samples. Therefore, the models developed in this study can only be considered reliable if they are applied to autumn-collected samples. The aim of this thesis was to develop a diatom-based weighted average model and to appraise the use of this technique in the assessment of trophic status in lowland rivers. The degree to which these methods can be extended and applied is discussed below, but it is not within the scope of this study to produce a final model which could be implemented by the water regulators. It has been clearly demonstrated that the diatom assemblages from carefully chosen artificial substrata can be used to estimate phosphorus concentrations reliably, but currently this can only be applied to samples collected in the autumn.

304 This study illustrates the physico-chemical and biological complexities of lowland rivers and demonstrates that diatom species respond to multivariate environmental conditions. With the assumption that the diatoms do not have a direct physiological response to FRP concentrations at many of the sites, it is perhaps surprising that the models perform as well as they do, even when the sampling period is kept constant. The use of CCA with forward selection showed that the time of year had an over-riding effect on the diatom assemblages which could not be accounted for by these models. It was not, however, that the diatoms were not reacting to FRP, but rather that the effect of time of year was stronger than the FRP signal seen in the autumn data-sets. Diatoms do respond to trophic status; whether it is possible to show this outside the autumn can only be tested by the temporal extension of this work (see below).

~~7.5 Application of the Models~~

As discussed in Chapter 1, biological methods are instrumental in the legislative control of eutrophication in European rivers. The diatom-based models presented in this study show that accurate estimates can be obtained for FRP concentrations. The actual phosphorus concentration, however, is perhaps not the ideal way of expressing a biologically-derived measure of trophic status. Of more use to the water regulators and to conform with other biological assessments of water quality (e.g. RIVPACS (Wright et al. 1989) & TDI (Kelly 1998)), the reduction of the results to a number of “trophic classes” or the use of a finite scale (e.g. 0-100) would be more appropriate. This can most simply be achieved by dividing the estimated FRP value by a theoretical maximum value.

For example, in this study no river site was found to exceed 10,000 pgL"^ FRP and thus 10,000 could be taken as a maximum value. If, therefore, the model returned an estimated value of 563 this could be standardised against the theoretical maximum to give a value which fell between 0-100, i.e. 563 4- 10,000 x 100 = 5.63. Similarly a value of 12 from the model would give a scaled value of 0.12. Any standardised value of over 100 (i.e. where the model has given a value in excess of 10,000 pgL'^) would indicate an extreme case of hyper-eutrophication. This example is purely hypothetical, but illustrates that a more readily understandable method of expressing the model results

305 can be derived, allowing for quick and meaningful comparisons to be made for monitoring or assessing trophic status. Table 7.1 summarises the values for trophic status obtained when this method was applied to the test sites used in this study.

~~Estimated FRP Trophic Index~~

~~Substratum Site October November October November~~

~~ALTl 18 ; 80 0.2 ; 0.8~~

~~Rope HAWl 1552 I 2048 15.5 : 20.5 ELST2 no data ; 1132 no data ; 11.3~~

~~ALTl 13 ; 35 0.1 ; 0.3 Tile H AW l 1448 I 440 14.5 ; 4.4 ELST2 no data 1 1195 no data ! 12.0~~

~~Table 7.1 An example of reducing FRP estimates to a nominal trophic index~~

The use of a finite scale (e.g. Tab. 7.1) allows the data to be viewed more objectively, and by reducing the size of the numbers involved, makes comparisons easier. The disadvantage of this particular example is that sites with high trophic status, e.g. HAWl, come nowhere near the top of the scale. The resolution could be enhanced by setting an acceptable maximum value (e.g. 2,500) rather than a theoretical maximum. Any site which exceeds a value of 100 could simply be expressed as >100 with the actual value also recorded to enable future comparisons to be made.

Alternatively the model results could simply be expressed on a logarithmic scale. This would give a value between 0.1 and approximately 4.0. The advantage of using this system is that it increases the resolution of the model results at the low end of the trophic gradient. Regardless of the type of system used to reduce the model estimates to a trophic classification, the numerical complexities of the models can easily be reduced to a user-friendly computer based system. Once a WA-PLS model has been devised the operator need only put in the diatom counts and the computer will generate the required information on the inferred trophic status.

The statistical complexities of the models have the advantage of allowing the error to be assessed and quoted. Thus, if using a diatom-based model to make an up-stream, down stream comparison either side of a STW outflow, the two values obtained would have

306 an error component. If the error of the two samples does not over-lap then it could be stated, with a set level of confidence, that the STW outflow was having an effect on the biology. One of the required components of the UWWT directive (CEC 1991) is that the qualifying discharges not only are affecting the biota but that this affect can be categorically demonstrated to be due to the STW. It is therefore necessary to be able to quote an error term rather than simply comparing what may be two quite similar values.

~~7.6 Future Development~~

Having demonstrated that diatom-based weighted averaging method can be applied to model the trophic status of lowland rivers, there are a number of areas which this study has highlighted as requiring further work to enhance and extend these methods.

~~7.6.1 Temporal Extension of the Training Sets~~

One major area of improvement required in this work is the extension of the diatom training sets to cover a wider time scale. As discussed above, the models developed in this study can only be reliably used to assess the trophic status of rivers during the autumn. More river-diatom training sets need to be collected throughout the year and analysed to establish whether similar models to those presented in this study can be derived. Ideally, training sets would be collected each month over a one year period from the two artificial substrata. With these data it would be possible to establish if weighted averaging methods can be used at any time of the year or if there are times when the relationship between river phosphorus and the diatom communities is too weak to model.

A full seasonal data-set could provide two alternative approaches in the model development. The first would be to amalgamate all the diatom and water chemistry data from one year in an attempt to incorporate the seasonal variation. In this study, however, the seasonal variation in the species assemblages was seen to be considerable and thus the amalgamation of annual data may reduce the overall predictive power of a resultant model. The alternative method would be to use the data in seasonal blocks. For example, if the test sample was taken in May, a training set from April, May and June

307 samples could be used to make the WA estimation of trophic status. By “sandwiching” the training sets in this way the seasonal effects should be minimised as much as possible. Conversely, the use of only one month’s data (i.e. assessing February samples with only a February sampled training set) may result in inter-annual variations in the climate having a strong influence (e.g. samples from a colder than average February may not conform to a February training set collected in a warmer year).

The above hypotheses remain untested but if assessments are to be made outside the autumn period, the results from this study suggest that the collection of monthly training sets would be the next logical step.

~~7.6.2 Spatial Extension of the Training Sets~~

The sites used to develop the training sets in this study were very varied, both in terms of their physical characteristics and their chemistry. Nevertheless the geographical restriction of this study to Southern England meant that many lowland river types were not sampled. If a similar model to those developed in this study were to be applied on a national (or European) scale it would be necessary to extend the range of the training set to encompass the entire area of interest. There is also no reason why such a model should be restricted to lowland rivers. The rationale for sampling lowland rivers was that these systems are the most heavily impacted by nutrient pollution. The extension of the training set to cover sites above 250 m would allow for many more nutrient poor sites to be added. This would have two advantages over the current training sets. Firstly, low phosphorus sites are under-represented in this study due to a lack of suitable sites, and several of those that were found were deleted as outliers. Secondly, the addition of more low phosphorus sites is likely to enhance the performance of a predictive model at the low end of the gradient. This is important from a monitoring viewpoint because it is the “pristine”, low nutrient sites which require the greatest protection. If the model can give accurate and reliable estimates of trophic status at these sites, their conservation value can be more adequately monitored and maintained.

~~As well as filling in the gaps in the current training sets the spatial extension of a diatom-phosphorus training set would also result in an increase in the predictive power~~

308 of the model. Bennion et al. (1996) combined six regional lake data-sets from north west Europe to give a 152 lake training set from which epilimnetic phosphorus was modelled. The combined training set had smaller prediction errors that any of the individual data-sets, and showed an improved relationship between the measured and inferred phosphorus concentrations. The increased confidence in the resultant predictions enabled Bennion et al. (1996) to estimate baseline TP conditions and advise on management strategies for unimpacted lakes. Thus by increasing the number of sites in a river training set the predictive power of the model is likely to be increased adding to the value of the model as a management tool. The spatial extension of the river training sets is therefore recommended.

~~7.6.3 Artificial Substrata~~

The use of introduced, artificial substrata was found to be very successful in most cases. There was, however, some question over the exposure time required at the low phosphorus sites. Several of these sites were identified as outliers and had very low diversity (Hill’s N2). It is possible, therefore, that the exposure time of one month was not long enough in these low nutrient sites, where diatom growth and community development was likely to be slower than in the more productive sites. This hypothesis remains untested but it is recommended that further field trials are conducted to establish whether this is the case and, if so, how much longer the substrata need to be left to provide reliable diatom samples. Alternatively, low diversity may simply be a natural response in the lower nutrient sites. Archibald (1972) observed low diatom diversity in South African river sites with high water quality; as a result the best and worst water quality sites could not be separated by diversity measurements alone.

~~7.6.4 Model Validation~~

With the development of transfer function techniques to estimate water quality, there is a need for thorough external validation to confirm that the models are reliable. In this study the external validation exercise was limited to three sites. Ideally an independent validation data-set of the same size as the training set should be used to ensure all the error components are determined (ter Braak & van Dam 1989). If this were done.

309 information on the likelihood of mis-classification could be determined, thus aiding any interpretation of the model results for management purposes. The data presented in this study demonstrate that the diatom-based models can be used to estimate river phosphorus but that these data are insufficient to provide thorough model validation. The ease of sampling and generally good accessibility to lowland river sites means that test data-sets could easily be created for the validation of future models.

~~7.6.5 Applications of the Model~~

The introduction of the Urban Wastewater Treatment Directive provided an impetus for the development of new methods to monitor nutrients in rivers in the UK. This study and the trophic diatom index (TDI) (Kelly 1998) were two such methods, the latter having now been adopted by the Environment Agency for the routine monitoring of trophic status in England and Wales.

The TDI has been used to demonstrate the effects of STW’s on the trophic status of rivers and has proved particularly successful in up-stream, down-stream comparisons of the effects of STW’s (Kelly 1998). It was less accurate however, where the focus of the study was on stretches of river where the background concentrations of nutrients were already high. One reason for this is the reliance of the TDI on a finite number of taxa (approximately 100), which in many cases are to genera only. This was deemed necessary for the TDI in order to achieve a system that required only a limited level of taxonomic skill and could thus be implemented widely throughout the EA regions with minimal training. This lumping of species results in an inevitable reduction in ecological information.

In situations where the TDI is inconclusive it is possible that the models developed in this study might provide a more detailed assessment. There is considerable scope for the extension of this work to draw comparisons between the TDI and a WA-PLS model to assess if the extra taxonomic input yields a more robust, ecologically-based result for trophic status. Currently however the models suffer two major weaknesses which require addressing before they can be more widely applied. The first is the seasonal aspect discussed in Chapter six, restricting the models for use only in autumn. The

310 second limitation is the geographical range. Due to the focus of this study on Southern England, many river types have not been sampled and therefore ecological information is missing (Section 7.6.1). The geographical range would need to be extended country wide prior to more general application. Conversely the development of catchment specific training sets may prove to be a valuable monitoring tool within the context of the new water framework directive. These could be used to monitor change over time at different catchment levels and also to focus on specific changes in catchment management. Like many biological models therefore, those developed in this study can only realise their full potential with their wider application and further testing.

~~7.6.6 Modelling Other Environmental Pollutants~~

The focus of this study has been on the use of diatoms to assess trophic status but it has also been demonstrated that the diatoms react to their environment in a multivariate manner. The opportunity exists, therefore, to extend this work to focus on the bio monitoring of other pollutants in rivers (e.g. heavy metals, herbicides). Providing that permanent slides are made of diatom samples and that these are archived, the collection of diatoms can enable long term biological records to be built up. These data can be used for future work where the interest is no longer trophic status alone.

There are many diatom archives in existence, some of which hold slides dating back over 100 years (e.g. The Natural History Museum, London). An interesting extension of this study would be to attempt to establish management base-lines by predicting past trophic status from historical diatom data.

~~311 Blank Page~~

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~~329 A ppendix I~~

~~D escriptions o f U n k n o w n D ia t o m t a x a~~

~~Navicula [pseudogregaria]~~

~~a mmiàmm~~

~~Plate I.l Light micrographs of Navicula [pseudogregaria]. Scale bar = 10 pm (a &c xlOOO DIC, b&d xlOOO phase contrast)~~

Navicula [pseudogregaria] is lanceolate in shape with slightly rostrate valve apices. Length ranges from approximately 8-21 pm and breadth from approximately 4.5-6.0 pm. Striae are uni seriate and have apically elongate poroids (18-20 str. per 10 pm), radiate at the centre, becoming convergent at the apices. This taxon is almost certainly a true Navicula (Bory de St. Vincent, 1822). The valve form is similar to Navicula gregaria Donkin (hence the name used in this study), but it lacks the drawn out rostrate to capitate valve apices of N. gregaria {cf. Plate 1.2). Although there is an overlap in size ranges, N. gregaria is larger, Krammer and Lange-Bertalot (1986) give a length range of 13-42 pm.

N. [pseudogregaria] was common in the lowland river sites. It was found in 43% of all the samples analysed for this study. In the training sets it was found on 52% of the tiles and 47% of the rope samples. It showed a preference for elevated levels of conductivity, and had weighted average FRF optima of 390 and 501 pgL"' in the rope and tile training sets, respectively (see Figs. 5.11 & 5.18). This is higher than the optima for V. gregaria, which were 217 and 212 pgL '\ respectively.

~~330 Appendix I~~

~~Plate 1.2 Light micrographs of Navicula gregaria. Scale bar = 10 pm (xIOOO phase contrast)~~

~~331 Appendix I Navicula [small sp. 1]~~

~~Plate 1.3 Light micrographs of Navicula [small sp. 1]. Scale bar = 10 pm (a, b, c & e xIOOO phase contrast, d XlOOO DIC)~~

Navicula [small sp. 1] is narrowly elliptical in shape with course striae (approx. 15 per 10 pm), a clear shortening of the central pair of striae is universal, and the valves are heavily silicified at the apices and in the central region. Length ranges from approximately 5-12 (15) pm and breadth from approximately 3.5-4.5 pm. Striae are mainly uni seriate but have both apically and trans-apically elongate poroids, which can be seen in SEM (not illustrated) to be rather untidily arranged. It is unlikely that this taxon is a Navicula (Bory de St. Vincent, 1822) sensu stricto, although no other genus is recommended here.

N. [small sp. 1] was not common throughout this study but was found to be locally abundant in parts of the River Thames and River Wey. It occurred in 13% of the tile training set samples, but only 4% of the rope samples. The size, structure and occurrence of this taxon would suggest it to be mainly epilithic. The only other record of this taxon, found in the literature, was from the sediments of the tidal Thames (Juggins 1991a), where it was also unidentified {Navicula spl. Fig. A.l, p. 216).

The WA FRP optima for Navicula [small sp. 1] was 768 pgL'' in the tile training set (see Fig. 5.18). It also appeared to occur at higher abundance in waters of <120 mgL'^ alkalinity, the maximum abundance was found on the southern arm (site 182) of the River

~~Wey which had an alkalinity of 86 mgL‘' and an FRP of 921 pgL'\~~

~~332 Appendix I~~

~~Navicula [species 2]~~

a a);*

~~Plate I. Light micrographs of Navicula [species 2]. Scale bar = 10 pm (a, c, d & e xIOOO phase contrast, c XlOOO DIC)~~

Navicula [species 2] is lanceolate and slightly rostrate. Length ranges from approximately 13-22 pm and breadth from approximately 5.0-6.0 pm. Striae are uni seriate and have apically elongate poroids (15-17 str. per 10 pm). The striae are radiate at the centre, becoming convergent at the apices. The central area is small and typified by 2 short, and 1 long striae on either side. This taxon is almost certainly a true Navicula (Bory de St. Vincent, 1822).

N. [species 2] was very common in the lowland rivers sampled for this study, occurring in 79% of the samples analysed. In the training sets it was found on 71% of the tiles and 73% of the rope samples. In was also common in epiphytic samples. This taxon is similar in form to many other Navicula species (e.g. N. veneta, N. phyllepta, N. cryptocephala & N. notha) but was considered distinct enough to warrant separating it as an unknown species.

The WA FRP optima for N. [species 2] were 293pgL ' in the rope data and 464 pgL'* in the tile training set (Figs. 5.11 & 5.17). The disparity between these values reflected the wide tolerance of this taxon to trophic status.

~~333 A ppen d ix II~~

~~L is t o f D ia t o m T a x a Id e n t if ie d in t h is S t u d y~~

Code Diatom Species and Authority AC014C Achnanthes austriaca var. helvetica Hust. 1933 AC037A Achnanthes biasolettiana Grun. in Cleve & Gmn. 1880 AC006A Achnanthes clevei Grun. in Cleve & Grun. 1880 AC023A Achnanthes conspicua A. Mayer 1919 AC016A Achnanthes delicatula (Kutz.) Grunow 1880 AC008A Achnanthes exigua Grun. in Cleve & Grun. 1880 AC158A Achnanthes grana Hohn & Hellerman 1963 AC134A Achnanthes helvetica (Hustedt) Lange-Bertalot 1989 AC032A Achnanthes hungarica (Grun.) Grun. in Cleve & Grun. 1880 ACOOIA Achnanthes lanceolata var. lanceolata(Bréb.) Grun. 1880 ACOOIB Achnanthes lanceolata var. rostrata (Ostr.) Hust. 1911 AC018A Achnanthes laterostrata Hust. 1933 AC085A Achnanthes lauenbergiana Hust. 1950 AC002A Achnanthes linearis (W. Sm.) Grun. in Cleve & Grun. 1880 AC022A Achnanthes marginulata Grun. in Cleve & Grun. 1880 AGO 13A Achnanthes minutissima Kutz. 1833 ACOllA Achnanthes peragalli Brun & Herib. in Herib. 1893 AC049A Achnanthes ploenensis Hust. 1930 AC035A Achnanthes pusilla Grun. in Cleve & Grun. 1880 AC116A Achnanthes rossii Yiwsi. 1954 AC028A Achnanthes saxonicaY^diSskt'mYixiSi. 1933 AC136A Achnanthes subatomoides (Hust.) Lange-Bertalot & Archibald 1985 AC029A Achnanthes sublaevis Hust. 1936 APOOIA Amphipleura pellucida (Kutz.) Kutz. 1844 AP002A Amphipleura rutilans (Trentepohl ex Roth) Cleve 1894 AM013A Amphora inariensis Krammer 1980 AMOOIA Amphora ovalis var. ovalis (Kutz.) Kutz. 1844 AMOOIB Amphora ovalis var. pediculus (Kutz.) Van Heurck 1885 AM012A Amphora pediculus (Kutz.) Grunow 1880 AM004A Amphora veneta Kutz. 1844 AGOOIA Aneumastus tuscula (Ehrenb.) Mann & Stickle 1990 AN009A Anomoeoneis sphaerophora {EhrQnh.)'Pfitz. 1871 ASOOIA Asterionella formosa Hassall 1850 AU031A Aulacoseira alpigena (Grunow) Krammer 1990 AU003A Aulacoseira granulata (Ehrenberg) Simonson 1979 BAOOIA Bacillariaparadoxa Gmelin in Linnaeus 1788

~~334 Appendix II~~

Code Diatom Species and Authority CA006A Caloneis amphisbaena (Bory) Cleve 1894 CA002A Caloneis bacillum (Grun.) Cleve 1894 CA048A Caloneis molaris (Grunow) Krammer 1985 CA003A Caloneis silicula (Ehrenb.) Cleve 1894 C0006A Cocconeis diminuta Pant. 1902 COOlOA Cocconeis disculus (Schum.) Cleve 1896 C0005A Cocconeis pediculus 1838 COCO IB Cocconeis placentula var. euglypta (Ehrenb.) Grun. 1884 CI002A Craticula accomoda (Hust) Mann 1990 CI004A Craticula cuspidata (Kutz.) Mann 1990 CI005A Craticula halophila (Grun. ex Heurck) Mann 1990 YHOOIA Ctenophora pulchella (Ralfs ex Kutz.) Williams & Round 1986 CCOOIA Cyclostephanos dubius (Fricke in A. Schmidt) Round 1982 CY003A Cyclotella meneghiniana Kutz. 1844 CY009A Cyclotella ocellata Pant. 1902 CY002A Cyclotella pseudostelligera Hust. 1939 CY019A Cyclotella radiosa (Grunow) Lemmerman 1900 CY004A Cyclotella stelligera (Cleve) Van Heurck 1882 CLOOIA Cymatopleura solea (Breb. & Godey) W. Sm. 1851 CM022A Cymbella ajfinis Kutz. 1844 CM016A Cymbella amphicephala'^diQ.gtlÏQxKutz. 1849 CM005A Cymbella aspera (Ehrenb.) H. Perag. in Pell. 1889 CM070A Cymbella caespitosa (Kutz.) Brun 1880 CM006A Cymbella cistula (Ehrenb) Kirchner 1878 CM029A Cymbella ehrenbergii Kutz. 1844 CM041A Cymbella lanceolata (Ag.) Ag. 1830 CM004A Cymbella microcephala Grun. in Van Heurck 1880 CM031A CymZ?e //(2 Hilse ex Rabenh. 1862 CM009A Cymbella naviculiformis Auersw. ex Heib. 1863 CM045A Cymbella prostrata (Berkeley) Brun 1880 CM113A Cymbella reichardtii Krammer 1985 CM 103 A Cymbella silesiaca^loisch. QxKdibouh. 1864 CM050A Cymbella subaequalis Grun. in Van Heurck 1880 DEOOIA Denticula tenuis Kutz. 1844 DT004B Diatoma tenue var. elongatum Lyngb. 1819 DT004A Diatoma tenue var. tenue Ag. 1812 DT003A Diatoma vulgare Bory DP007A Diploneis oblongella (Naegeli ex Kutz.) R. Ross 1947 EP007A Epithemia adnata (K\xtz.)KQbo,vih. 1853 EU049A Eunotia curvata (Kutz.) Lagerst. 1884

~~335 Appendix II~~

Code Diatom Species and Authority EU009A Ewnof/a ex/gwa (Breb. ex Kutz.) Rabenh. 1864 EU018A Eunotia formica Wartvib. 1843 EU047A Eunotia incisa W. Sm. ex Greg. 1854 EU008A Eunotia monodon Ehrenb. 1843 EU040A Eunotiapaludosa Grun. IS62 EU002B Eunotia pectinalis var. minor (Kutz.) Rabenh. 1864 FA009A Fallacia helensis (Schulz) Mann 1990 FA013A Fallacia insociabilis (Krasske) Mann 1990 FA014A Fallacia lucinensis (Hust.) Mann 1990 FA016A Fallacia monoculata (Hust.) Mann 1990 FAOOIA Fallacia pygmaea (Kutz.) Stickle & Mann 1990 FA021A Fallacia subhamulata (Grun. in Van Heurck) Mann 1990 FA023A Fallacia tenera (Hust.) Mann 1990 FR003A Fragilaria bicapitata A. Mayer 1917 FR026A Fragilaria bidens^itih. 1863 FR006A Fragilaria brevistriata Grun. in Van Heurck 1885 FR009H Fragilaria capucina var. gracilis (Oestrup) Hustedt 1950 FR009A Fragilaria capucina var. capucina Desm. 1825 FR009G Fragilaria capucina var. rumpens (Kutz.) Lange-Bertalot 1991 FR002B Fragilaria construens var. binodis (Ehrenb.) Grun. 1862 FR002A Fragilaria construens var. construens (Ehrenb.) Grun. 1862 FR002E Fragilaria construens var. subsalina Hust. 1925 FR002C Fragilaria construens var. venter (Ehrenb.) Grun. in Van Heurck 1881 FR008A Fragilaria crotonensis Kitten 1869 FR018A Fragilaria elliptica Schum. 1867 FR019A Fragilaria intermedia Grun. in Van Heurck 1881 FR014A Fragilaria le p to sta u ro n Ç E \\rtx \b . 1931 FR013A Fragilaria oldenburgiana Hust. 1959 FR045E Fragilaria parasitica var. subconstricta Grun. in Van Heurck 1881 FR045A Fragilaria parasitica var. parasiticaÇW. Sm.) Grun. in Van Heurck 1881 FROOIA Fragilaria pinnata Ehrenb. 1843 FR059A Fragilaria radians (Kutz.) Williams & Round 1987 FR007A Fragilaria vaucheriae (Kutz.) J.B. Petersen 1938 FR005A Fragilaria virescens var. virescens Ralfs 1843 FR005D Fragilaria virescens var. exigua Grun. in Van Heurck 1881 FU002F Frustulia rhomboides var. viridula (Breb. ex Kutz.) Cleve 1894 FUOOIA Frustulia vulgaris (Thwaites) De Toni 1891 G0006A Gomphonema acuminatum Ehrenb. 1832 G0020A Gomphonema affine Kutz. IS44 GO075A Gomphonema amoenum Lange-Bertalot 1985 G0003A Gomphonema angustatum (Kutz.) Rabenh. 1864

~~336 Appendix II~~

~~Code Diatom Species and Authority~~

GO019A Gomphonema augur H&t. IS40 G0007A Gomphonema bohemicum Reichelt & Fricke in A. Schmidt 1902 GO029A Gomphonema clavatum Her, 1832 GO036A Gomphonema dichotomum Kutz. 1833 G0004A Gomphonema gracile Ehrenb. 1838 GO014B Gomphonema intricatum var. pumilum Grun. In Van Heurck 1880 G0050A Gomphonema minutum (Ag.) Ag. 1831 GOOOIA Gomphonema olivaceum (Homcmaim) Brob. 1838 GO013A Gomphonema parvulum (Kutz.) Kutz. 1849 GO023A Gomphonema truncatum^biQïib. 1832 GY005A Gyrosigma acuminatum (Kutz.) RâbQnh. 1853 GYOOIA Gyrosigma attenuatum (Kutz.) Rabenh. 1853 HAOOIB Hantzschia amphioxys var. capitata O. Mull. 1909 HAOOIA Hantzschia amphioxys var. amphioxys (Ehrenb.) Grun. 1877 ME015A Melosira \arians Ag. 1S27 MROOIA Meridion circulare (Gxc\.) Ag. 1831 NA190A Navicula agrestisYiviSX. 1937 NA084A Navicula atomus (Kutz.) Grun. 1860 NA071A Navicula bacillum'Ebitnb. 1840 NA066A Navicula capitata vdx. capitata ^biQVLb. 1838 NA066B Navicula capitata var. hungarica (Grun.) R. Ross 1947 NA745A Navicula capitoradiata G&nmin 1981 NA051A Navicula cari Ehrenb. IS36 NA021A Navicula cincta (Ehrenb.) Ralfs in Eritch. 1861 NA050A Navicula dementis Gmn. 1882 NA046A Navicula contenta Grun. In Van Heurck 1885 NA007A Navicula cryptocephala Kniz. 1844 NA751A Navicula cryptotenella Lange-Bertalot 1985 NA317A Navicula decussis Osir. 1910 NA115A Navicula dijficillimaYhxsX. 1950 NA057A Navicula elginensis (Greg.) Ralfs in Pritch. 1861 NA747A Navicula goeppertiana (Bleish) H.L. Smith 1974 NA023A Navicula gregaria E>onh. 1861 NA433D Navicula ignota var. acceptata (Hustedt) Lange-Bertalot 1985 NA462A Navicula joubaudii Germain 1982 NA044A Navicula krasskei Hust. 1930 NA102A Navicula laevissimaYmXz. 1844 NA009A Navicula lanceolata (Agardh) Kutz. NA030D Navicula menisculus var. grunowii Lange-Bertalot 1991 NA030A Navicula menisculus var. menisculus Schum. 1867

~~337 Appendix II~~

Code Diatom Species and Authority NAl 12A Navicula minuscula var, minuscula Grun, In Van Heurck 1880 NA112D Navicula minuscula var, muralis (Grun,) Lange-Beralot 1981 NA512A Navicula minusculoidesYimi. 1942 NAl23A Navicula modica Hust, 1945 NAl24A Navicula molestiformis'Hxxsi. 1949 NA025A Navicula mutica var, mutica Kutz, 1844 NA025J Navicula mutica var, ventricosa (Kutz,) Cleve & Grun, 1880 NA024A Navicula oblonga(Kuiz.)YL[xiz. 1844 NA562E Navicula peregrina (Ehrenb,) Kutz, 1844 NA565A Navicula perminuta Grun, In Van Heurck 1880 NAl 19A Navicula placentula (Ehrenb,) Kutz, 1844 NA578A Navicula praeterita Hust, 1945 NA047A Navicula protracta Grun, In Cleve & Grun, 1880 ZZZ989 Navicula [pseudogregaria] NA079A Navicula pseudolanceolata Lange-Bertalot 1980 NA590A Navicula pseudoventralis Hust, 1953 NA003A Navicula radio sa K\iiz. 1844 NA026A Navicula reinhardtii Grun, In Van Heurck 1880 NA008A Navicula rhynchocephala Kuiz. 1844 NA752A Navicula riparia Hustedt 1942 NA090A Navicula rotunda Hust, 1945 NA035A Navicula salinarum Grun, In Cleve & Grun, 1880 NA080A Navicula slesvicensis Grun, In Van Heurck 1880 ZZZ994 Navicula [small sp, 1] ZZZ999 Navicula [species 2] NA134A Navicula subminuscula Manguin 1941 NAl66A Navicula submuralis Hust, 1945 NA743A Navicula subrhynchocephala Hustedt 1935 NAl 14A Navicula subrotundata Hust, 1945 NA675A Navicula tenelloides Hust. 1937 NA095A Navicula tripunctata (O F, Mull,) Bory 1822 NA063A Navicula trivialis Lange-Bertalot 1980 NAl44A Navicula utermoehlii Hust. 1943 NA076A Navicula wariostriata Krasske 1923 NA054A Navicula veneta Kutz, 1844 NA027E Navicula viridula var, linearis Hust, 1937 NA027A Navicula viridula var, viridula (Kutz,) Ehrenb, 1836 NAl68 A Navicula vitabunda Hust, 1930 NE008A Neidium binodis (Ehrenb,) Hust, 1945 NE007A Neidium dubium (Ehrenb.) Cleve 1894

~~338 Appendix II~~

Code Diatom Species and Authority NE002A Neidium productum (W, Sm.) Cleve 1894 NI057A Nitzschia acicularioides Archibald 1966 NI042A Nitzschia acicularis (Kutz.) W. Sm. 1853 NI021A Nitzschia acula Hantzsch ex Cleve & Grun. 1880 NI063A Nitzschia agnita Hust. 1957 NI014A Nitzschia amphibia Grun. 1862 NI020A Nitzschia angustata (W. Sm.) Grun. in Cleve & Grun. 1880 NI076A Nitzschia calida Grun. in Cleve & Grun. 1880 NI028A Nitzschia capitellata Hust. 1930 NIOlOA Nitzschia communis Rabh. 1860 NIOl lA Nitzschia commutata Grun. in Cleve & Grun. 1880 NI083A Nitzschia constricta (Kutz.) Ralfs in Pritch. 1861 NI088A Nitzschia debilis Grun. in Cleve & Grun. 1880 N1015A Nitzschia dissipata (Kutz.) Grun. 1862 NI018A Nitzschia dubiaW. Sm. 1853 NI098A Nitzschia fHiformis (W. Sm.) Van Heurck 1896 NI002A Nitzschia fonticola Grun. in Van Heurck 1881 NI008A Nitzschia frustulum (Kutz.) Grun. in Cleve & Grun. 1880 NI017A Nitzschia gracilis Hantzsch 1860 NI052A Nitzschia heufleriana Grun. 1862 NI043A Nitzschia inconspicua Grun. 1862 NI044A Nitzschia intermedia Hantzsch ex Cleve & Grun. 1880 NI 198A Nitzschia lacuum Lange-Bertalot 1980 N il27A Nitzschia levidensis (W. Sm.) Grun. in Van Heurck 1881 NI031C Nitzschia linearis var. subtilis (Grun) Hustedt 1923 NI031A Nitzschia linearis var. linearis W. Sm. 1853 NI 129A Nitzschia littoralis Grun. in Cleve & Grun. 1880 NI027A Nitzschia microcephala Grun. in Cleve & Grun. 1880 NI009A Nitzschia palea (Kutz.) W. Sm. 1856 NI033A Nitzschia paleacea (Grun.) Grun. in Van Heurck 1881 NI 194A Nitzschia parvula W. Sm. 1853 NI005A Nitzschia perminuta (Grun. in Van Heurck) M. Perag. 1903 NI191A Nitzschia perspicua Cholnoky 1960 NI054A NitzschiaplanaNJ. Sm. \^52) NI152A Nitzschia pusilla Grun. 1862 NI025A Nitzschia recta Hantzsch ex Rabenh. 1861 NI006A Nitzschia sigma (Kutz.) W. Sm. 1853 NI046A Nitzschia sigmoidea (Nitzsch) W. Sm. 1853 NI 164A Nitzschia sinuata (Thwaites ex W. Sm.) Grun. 1880 NI166A Nitzschia sociabilis Hust. 1957

~~339 Appendix II~~

Code Diatom Species and Authority NI171A Nitzschia subacicularisYixisX. 1937 NI024A Nitzschia sublinearis Hust, 1930 NI195A Nitzschia supralitorea Lange-Bertalot 1979 NI048A Nitzschia tubicola Grun. In Cleve & Grun. 1880 NI 184A Nitzschia umbonata (Ehrenb.) Lange-Bertalot 1978 NI049A Nitzschia vermicularis (Kutz.) Hantzsch. In Rabenh. 1859 PI031A Pinnularia acrosphaeria (Breb.) Rabenh. 1853 PI014A Pinnularia appendiculata (Ag.) Cleve 1896 PI012A Pinnularia borealis P.hr&r\b. 1843 PIOOIA Pinnularia gibba (Ehrenb.) Ehrenb. 1843 PI004A Pinnularia interrupta W. Smith 1853 PI005A Pinnularia major (Kutz.) W. Sm. 1853 PIOl lA Pinnularia microstauron (Ehrenb.) Cleve 1891 PI056A PmnM/arm Hantzsch in Rabenh. 1861 PI022A Pinnularia subcapitata Greg. 1856 PI007A Pmnw/ana v/nWw (Nitzsch) Ehrenb. 1843 REOOIA Reimeria sinuata (Greg.) Kociolek & Stoermer 1987 RCOOIA Rhoicosphenia curvata (Kutz.) Grun. 1860 SL003A Sellaphora minima (Grun. In Van Heurck) Mann 1990 SLOOIA Sellaphora pupula (Kutz.) Mereschkowsky 1902 SL002A Sellaphora seminulum (Grun.) Mann 1990 SAOOIA Stauroneis ancepsPhrQwh. 1843 SA012A Stauroneis kriegeri Patr. 1945 SA013A Stauroneis palustris Hust. 1945 SA003A Stauroneis smithii Grun. 1860 STOOIA Stephanodiscus hantzschii Grun. In Cleve & Grun. 1880 STOlOA Stephanodiscus parvus Stoermer & Hakansson 1984 SUOOIA Surirella angusta Kutz. 1844 SU014A Surirella birostrata Hust. Ex A. Mayer 1917 SU073A Surirella brebisonii Krammer & Lange-Bertalot 1987 SU016A Surirella minuta Breb. Ex Kutz. 1849 SU003A Surirella ovalis Breb. 1838 SY003A Synedra acus Kutz. 1844 SY007A Synedra amphicephala Kutz. 1844 SY015A Synedra tabulata (Ag.) Kutz. 1844 SYOOIA Synedra ulna (Nitzsch) Ehrenb. 1836 TAOOIA Tabellaria flocculosa (Roth) Kutz. 1844

~~340 A ppen d ix III~~

~~P r o p o s e d S a m p l in g S it e s f o r t h e In it ia l T r a in in g S e t S u r v e y~~

Sites with FRP and alkalinity measurements were included in the initial site survey, those without, were either not visited due to time constraints, or were deemed inappropriate for sampling because of access problems. Other sites, identified from the map, were found to be dry when visited.

Site River FRP Aik NGR OS Sheet Altitude No. (mgL-') (m) 1 Sor Brook 399 175 SP 423438 151 119 2 Castle brook 335 176 SP 399399 151 115 3 Sor Brook 275 210 SP 459372 151 95 4 Bloxham Brook SP 463356 151 90 5 Hanwell Brook 151 95 6 River Cherwell SP 471466 151 95 7 Highfurlong Brook SP 489515 151 109 8 River Cherwell 1482 252 SP 519479 151 109 9 River Swere 7 175 SP 386323 151 115 10 River Swere 340 225 SP 462333 151 90 11 Lt. Compton Stream SP 225305 151 125 12 River Evenload 853 182 SP 203313 151 127 13 River Evenload 1135 215 SP 274197 163 100 14 River Evenload 803 220 SP 420148 164 75 15 River Glyme 137 255 SP 444186 164 85 16 River Dom SP 454225 164 97 17 Sherborne Brook 3 204 SP 177147 163 115 18 River Windrush 161 174 SP 178177 163 120 19 River Eye SP 181197 163 125 20 River Windrush 7 128 SP 100253 163 167 21 River Windrush 1027 195 SP 403015 164 64 22 River Thames SP 403014 164 64 23 River Windrush 162 117 SP 289115 163 95 24 River Leach 4 225 SU 225991 163 75 25 River Leach SP 193069 163 99 26 River Thames 1757 242 SU 223991 163 75 27 River Coin 87 157 SP 051150 163 135 28 River Coin 20 215 SP 151052 163 98 29 Marston Maysey Br. SU 127964 163 77 30 Ampney Brook SU 094968 163 83

~~Table III.l Proposed sampling sites for the initial training set survey~~

~~341 Key Extent of the Thames Catchment~~

~~Principal Rivers Minor Rivers Canals 163 Training Set Sites Sites not used in the Training Set~~

~~100 km~~

~~60 mile#~~

iR- Figure lll.l Map of river sites identified as possible training set sites a Appendix III Site River FRP Aik NGR OS Sheet Altitude No. (pgL'^) (mgL'^) (m) 31 River Chum 20 142 SP 019079 163 130 32 River Chum SP 038000 163 100 33 River Chum 14 300 SU 100945 163/173 75 34 Swill Brook 4 194 SU 060933 163/173 82 35 River Thames SU 004973 163 96 36 Swill Brook SU 017933 163/173 85 37 Derry Brook SU 042912 163/173 86 38 River Key SU 094917 163/173 79 39 River Ray SU 122873 173 87 40 River Ray 4541 280 SU 111897 173 85 41 River Thames 3832 260 SU 175961 163 74 42 Blunsden Brook SU 125932 163/173 78 43 Share Ditch SU 155946 163/173 75 44 Bydemill Brook SU 192936 163/173 80 45 Tuckmill Brook SU 239903 174 98 46 River Cole SU 223906 174 84 47 River Cole SU 235935 163 80 48 River Cole SU 211981 163 75 49 Radcot Cut 1714 236 SP 333008 164 66 50 River Thames SP 334004 164 66 51 River Ock SU 344926 164 69 52 River Ock SU 400956 164 65 53 River Ock SU 456953 164 55 54 Childery Brook SU 430949 164 58 55 Standford Brook SU 467969 164 56 56 Farthinghoe Stream 3261 166 SP 496393 151 91 57 Ockley Brook 156 245 SP511318 151 88 58 Gallos Brook SP 536145 164 60 59 Langford Brook SP 566169 164 57 60 Gubbins Hole Ditch SP 664225 164/165 66 61 Tetchwick Brook SP 669196 164/165 65 62 River Ray 3846 204 SP 678213 164/165 67 63 River Ray 3239 300 SP 624189 164/165 65 64 River Ray SP 570163 164 61 65 River Thame SP 885168 165 81 66 Thistle Brook SP 879175 165 83 67 Hardwick Brook 3123 220 SP 845237 165 112 68 Hardwick Brook 1134 283 SP 806187 165 82 69 River Thame SP 846176 165 78 70 River Thame SP 796152 165 73 71 River Thame SP 728113 165 67 72 River Thame 3991 254 SP 668065 164/165 64 73 Scotsgrove Brook SP 816094 165 85

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

343 Appendix III Site River FRP Aik NGR OS Sheet Altitude No. (pgL-') (mgL') (m) 74 Scotsgrove Brook SP 752077 165 66 75 Kingsey Brook 3239 237 SP 735065 165 68 76 Worminghall Brook SP 646084 164/165 61 77 Danes Brook 4613 280 SP 605119 164/165 71 78 Holton Brook SP 617063 164/165 58 79 River Thame 3745 264 SP 613053 164/165 57 80 River Thame 3225 270 SU 602977 164/165 50 81 Baldon Brook SU 576987 164 59 82 Haseley Brook 4454 280 SU 624993 164/165 58 83 Haseley Brook SP 686005 164/165 75 84 Chalgrove Brook SU 640965 164/165 67 85 River Thames SP 443086 164 65 86 River Thames SU 536985 164 55 87 River Thames SU 546954 164 52 88 River Thames SU 602855 175 45 89 Ginge Brook SU 455893 174 78 90 Moor Ditch SU 530927 174 48 91 Mill Brook SU 536875 174 56 92 Mill Brook SU 592888 174 48 93 River Pang SU 532774 174 88 94 River Pang 10 245 SU 544710 174 68 95 River Pang SU 635748 175 44 96 River Bourne SU 583694 174 85 97 River Bourne SU 623732 175 49 98 Winterboume Stream 48 260 SU 453695 174 85 99 River Lamboume 622 220 SU 335779 174 123 100 River Lamboume 159 233 SU 454692 174 84 101 River Dun 23 245 SU 305677 174 105 102 Aldboume Brook 174 232 SU 293713 174 105 103 River Og SU 196696 173 125 104 River Kennet SU 099696 173 155 105 River Kennet 95 250 SU 159688 173 135 106 River Kennet SU 343690 174 98 107 River Kennet 104 257 SU 407679 174 85 108 River Kennet 405 252 SU 526663 174 65 109 Kingsclere Brook SU 528627 174 65 110 Baughurst Brook SU 557623 174 66 111 River Enboume SU 418637 174 109 112 River Enboume 1513 135 SU 501635 174 77 113 River Enboume 925 200 SU 569649 174 61 114 Holy Brook SU 683714 175 41 115 Burghfield Brook SU 687686 175 38 116 West End Brook SU 658633 175 58

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

~~344 A p p e n d ix III~~

Site River FRP Aik NGR OS Sheet Altitude No. (pgL') (mgL^) (m) 117 Foudry Brook SU 654625 175 58 118 Foudry Brook SU 702688 175 38 119 River Kennet 376 250 SU 612671 175 53 120 River Thames SU 775815 175 35 121 Emm Brook SU 786727 175 36 122 Emm Brook SU 824671 175 55 123 Barkham Brook SU 766680 175 44 124 River Blackwater 23 108 SU 868491 186 73 125 River Blackwater SU 835609 186/175 68 126 River Blackwater SU 743635 186/175 48 127 River Blackwater SU 732647 186/175 47 128 River Whitewater SU 741634 186/175 48 129 River Whitewater SU 738582 186/175 55 130 River Whitewater SU 727520 186 75 131 River Hart SU 786529 186 68 132 River Hart SU 748599 186/175 50 133 Fleet Brook SU 786568 186/175 59 134 River Lyde SU 696543 186 68 135 Bow Brook SU 676584 186 55 136 River Loddon SU 661528 186 72 137 River Loddon SU 683584 186 54 138 River Loddon SU 706628 186/175 47 139 River Loddon SU 744677 175 42 140 River Loddon SU 781729 175 37 141 River Thames SU 898856 175 26 142 River Wye SU 915884 175 35 143 White Brook SU 898842 175 26 144 Hambledon Brook SU 787855 175 35 145 Bull Brook 6616 203 SU 884711 175 59 146 The Cut SU 884719 175 55 147 The Cut 6936 270 SU 855733 175 43 148 The Cut SU 874782 175 25 149 Salt Hill Stream 3918 300 SU 943787 175 20 150 Chalvey Ditch SU 935781 175 21 151 River Thames SU 979775 175 15 152 River Thames TQ 055666 176 12 153 The Bourne (N) TQ 007672 176 20 154 The Bourne (N) 6558 156 TQ 044664 176 13 155 Windle Brook SU 924636 186 50 156 Windle Brook 2380 111 SU 966619 186/176 30 157 The Bourne (S) SU 946603 186 34 158 The Bourne (S) 3685 142 SU 992606 186/176 23 159 The Bourne (S) 1625 110 TQ 035628 186/176 16

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

~~345 A p p e n d ix I I I~~

Site River FRP Aik NGR OS Sheet Altitude No. (MgL-^) (mgL^) (m) 160 Stanford Brook SU 945544 186 34 161 Stanford Brook 113 135 TQ 021575 186 21 162 Tilling Bourne 104 13 TQ 108472 187 94 163 Tilling Bourne 281 75 TQ 012474 186 38 164 Cranleigh Waters TQ 056370 187 50 165 Cranleigh Waters 6515 86 TQ 036419 186 40 166 Cranleigh Waters TQ 005457 186 37 167 River Ock SU 951422 186 49 168 Oakhanger Stream 1553 235 SU 768355 186 78 169 River Slea SU 779376 186 71 170 River Slea 78 74 SU 802383 186 67 171 Caker Stream SU 725375 186 103 172 River Wey (N) 52 296 SU 724397 186 100 173 River Wey (N) 2546 298 SU 744412 186 90 174 River Wey (N) 2510 290 SU 786432 186 78 175 River Wey (N) 2002 296 SU 844470 186 65 176 River Wey (N) SU 871456 186 56 177 River Wey (S) 41 17 SU 896317 186 135 178 River Wey (S) 2322 45 SU 874326 186 110 179 River Wey (S) SU 816347 186 78 180 River Wey (S) 2554 88 SU 818386 186 65 181 River Wey (S) 853 68 SU 872435 186 49 182 River Wey 1110 81 SU 874434 186 49 183 River Wey 1444 116 SU 922439 186 45 184 River Wey 1045 125 SU 993455 186 38 185 River Wey TQ 005531 186 33 186 River Wey 1705 137 TQ 061595 187 17 187 River Wey TQ 069647 176/187 14 188 River Thames 2684 202 TQ 174714 176 4 189 Tanners Brook 210 123 TQ 195489 187 46 190 Gad Brook 17 152 TQ 217477 187 49 191 Leigh Brook 7342 148 TQ 226466 187 50 192 Deanoak Brook 46 76 TQ 216423 187 65 193 Deanoak Brook 147 162 TQ 244457 187 53 194 Gatwick Stream 102 66 TQ 292397 187 61 195 Burston Stream 5731 119 TQ 275451 187 51 196 Redhill Brook 447 250 TQ 298488 187 65 197 Salfords Stream TQ 309476 187 60 198 Salfords Stream 981 245 TQ 266466 187 50 199 River Mole 1814 169 TQ 214366 187 75 200 River Mole TQ 260398 187 58 201 River Mole TQ 263462 187 49 202 River Mole 5543 130 TQ 196496 187 47

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

~~346 A p p e n d ix I I I~~

Site River FRP Aik NGR OS Sheet Altitude No. (mgL') (m) 203 River Mole 3511 160 TQ 133580 187 25 204 River Mole TQ 104624 176/187 15 205 River Mole TQ 145676 176 7 206 Hogsmill River TQ 201679 176 15 207 Beverley Brook 7530 210 TQ 214738 176 8 208 River Wandle TQ 265707 176 12 209 Horton Brook 28 196 TQ 028792 176 22 210 Colne Brook TQ 047795 176 23 211 River Finn 458 120 TQ 063820 176 30 212 River Alderboume TQ 008854 176 43 213 River Misboume SU 983954 176 78 214 River Misboume 200 280 TQ 028876 176 40 215 River Chess SU 980996 176 90 216 River Chess TQ 054962 176 45 217 River Gade TL 053083 166 88 218 River Ver TL 119108 166 89 219 River Ver TL 151023 166 68 220 Tykes Water TL 155015 166 65 221 Mimmshall Brook TL 231017 166 80 222 River Colne TL 205058 166 72 223 River Colne TQ 120981 166/167 55 224 River Colne TQ 041935 176 44 225 River Colne TQ 053789 176 25 226 River Lee TL 121181 166 94 227 River Lee TL 204138 166 75 228 River Lee TL 309109 166 43 229 River Mimram TL 198185 166 75 230 River Mimram TL 261141 166 58 231 River Beane TL 292263 166 81 232 River Beane TL 313169 166 46 233 River Rib TL 364285 166 85 234 River Rib TL 338164 166 43 235 River Ash TL 433210 167 62 236 River Stort TL 494260 167 67 237 Pincey Brook TL 490126 167 45 238 River Ebble 70 229 SU 053254 184 78 239 River Nadder 218 226 SU 035310 184 65 240 River Wylye 329 236 SU 036371 184 66 241 River Avon SU 134572 173 96 242 River Avon 295 266 SU 129330 184 50 243 River Avon 406 230 SU 177216 184 35 244 River Bourne SU 163329 184 52 245 River Test SU 524505 185 88

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

~~347 A p p e n d ix I I I~~

Site River FRP Aik NGR OS Sheet Altitude No. (UgL-^) (mgL-^) (m) 246 River Test SU 353232 185 18 247 River Itchen 32 262 SU 574317 185 57 248 River Itchen 131 258 SU 473250 185 24 249 River Meon 798 293 SU 664236 185 96 250 River Meon 70 267 SU 618213 185 61 251 River Medway (trib.) 48 42 TQ 460332 188 65 252 River Medway (trib.) 45 32 TQ 492357 188 48 253 River Medway 3120 60 TQ 516402 188 35 254 River Medway 2322 94 TQ 629473 188 15 255 River Medway 1146 88 TQ 724539 188 5 256 River Ouse 171 75 TQ 341273 198 38 257 River Ouse 1627 77 TQ 385245 198 23 258 River Ouse TQ 428214 198 13 259 Clapwater Stream 9 60 TQ 439247 198 36 260 River Uck 36 66 TQ 463207 198 15 261 River Rother 678 68 TQ 630259 199 35 262 River Rother 363 155 TQ 654268 199 28 263 River Rother TQ 738240 199 8 264 River Rother TQ 835270 199 3 265 River Dudwell TQ 677239 199 30 266 Socknersh Stream 7 130 TQ 714241 199 20 267 River Line TQ 775176 199 13 268 River Brede TQ 826175 199 5 269 Cuckmere River TQ 537176 199 43 270 Cuckmere River TQ 556136 199 25 271 Waldrons Gill 3485 157 TQ 600149 199 27 272 Nunningham Stream TQ 662128 199 7 273 River Ouse 2082 80 TQ 282274 198 50

~~Table III.l (cont.) Proposed sampling sites for the initial training set survey~~

~~348 A ppe n d ix IV~~

~~F u l l E nvironmental D a t a -S e t f o r t h e R o p e T r a in in g S e t~~

Site Site Sample Aik Cond pH Temp Flow TP FRP NO y-N Silica Code No. No. mgL^ pS cm^ "C mSec^ PgL^ PgL^ PgL^ MgL^ C A STl 2 668 178 560 7.84 10.5 0.107 291 290 4116 10401 SWERI 9 669 220 550 8.01 10.0 0.986 26 3 4485 6139 EV EN l 12 670 202 560 7.98 11.0 0.036 583 551 6860 8118 EVEN2 13 671 218 560 8.11 10.0 0.169 1131 1123 3166 7458 EVENS 14 672 214 600 7.84 10.0 0.321 939 906 5541 6951 SHERI 17 673 205 510 8.13 9.0 0.244 4 3 7440 5429 WIND2 23 674 180 450 7.86 8.5 0.802 161 149 7018 5277 LEAC1 24 675 228 620 7.48 9.0 0.036 7 4 5330 5327 TH AM l 26 676 242 860 8.08 10.5 0.036 1148 1080 3430 9792 COLNl 27 677 164 450 8.13 8.5 0.204 128 96 6089 5249 C 0L N 2 28 678 204 520 7.90 9.5 0.036 52 36 3008 5277 CHURl 31 679 160 390 8.20 9.0 0.054 20 4 3545 4999 SW ILl 34 680 148 360 8.13 9.0 0.093 33 15 9 6049 FARTl 56 681 178 830 7.80 10.5 0.105 3494 3318 6121 11010 PANG1 94 682 246 480 8.50 12.0 0.038 18 6 3454 13048 K ENN l 105 683 245 550 7.85 10.0 0.089 30 45 4635 17147 EN B O l 112 684 104 400 7.59 9.0 0.095 1112 1015 1291 17897 BO UR l 154 685 130 1100 7.39 14.0 0.264 6882 6091 8498 21097 TILLl 162 686 12 130 7.15 11.0 0.452 99 87 464 10848 TILL2 163 687 77 310 7.57 10.0 0.326 309 276 3090 12348 CRANl 165 688 87 540 7.17 12.0 0.178 6387 5917 4181 17097 SLEA1 170 689 78 320 7.14 10.5 0.787 1517 281 1091 11898 ALTl 172 690 280 750 7.02 11.5 0.259 163 67 2590 13598 RWEYl 177 691 18 150 6.75 9.5 0.198 38 39 573 17697 RWEY2 178 692 52 280 7.26 11.0 0.390 1853 1639 1000 15397 RWEY3 180 693 77 320 7.58 11.0 0.324 935 827 1509 14298 RWEY4 181 694 71 300 7.22 10.0 0.396 847 679 2309 12998 RWEY5 182 695 86 360 7.37 10.5 0.279 1112 921 2090 13348 ELST2 183 696 107 410 7.60 11.0 0.259 1482 1399 1491 13598 RWEY6 184 697 112 420 7.72 11.5 0.308 1006 964 1863 12798 THAM2 188 698 168 690 7.34 13.5 0.036 3176 2884 3641 13395 DEAN1 192 699 112 480 7.53 11.0 0.046 77 28 91 4199 DEAN2 193 700 174 590 7.55 11.5 0.036 743 4 545 4899 GATW l 194 701 56 240 7.34 12.5 0.237 99 44 782 6749 REDHl 196 702 198 680 8.10 11.5 0.401 591 557 636 13398 MOLEl 199 703 137 590 7.57 12.0 0.054 1217 1102 754 8799 BEVEl 207 704 198 1050 7.54 15.5 0.319 9458 8665 106 20853 HORTl 209 705 242 750 7.34 13.5 0.036 49 33 2427 8828

~~Table IV .l Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the rope training set~~

~~349 Appendix IV~~

Site Site Sample Aik Cond PH Temp Flow TPFRP NOa'-N Silica Code No. No. m gL’^ pS cm'^ "C mSec^ MgL'^ PgL'^ MISBl 214 706 225 750 7.86 13.0 0.054 2153 1934 4908 12685 EBBLl 238 707 222 480 8.10 8.5 0.119 95 70 3317 8649 ITCHl 247 708 240 510 7.89 8.0 0.259 31 25 2999 9748 ITCH2 248 709 250 580 8.09 9.5 0.572 213 200 2318 10898 MEONl 249 710 256 630 7.88 7.0 0.337 1633 1479 1818 10948 MEDWl 251 711 40 190 7.10 12.0 0.117 29 15 91 7799 MEDW2 252 712 43 190 6.70 12.0 0.527 38 19 245 7899 MEDW3 253 713 77 400 7.32 12.5 0.036 2485 2349 2272 8999 O USEl 256 714 78 320 7.52 12.5 0.412 200 183 707 5378 0U SE 2 257 715 79 380 7.48 12.0 0.282 1296 1268 1784 7661 CLAPl 259 716 54 250 7.25 11.5 0.080 31 10 718 3399 RUCKl 260 717 75 320 7.46 10.5 0.036 109 71 1024 8169 ROTHl 261 718 76 380 7.61 11.0 0.282 524 609 559 8372 R0TH2 262 719 127 450 7.69 11.0 0.115 630 517 718 8930 SOCKl 266 720 145 1520 7.64 11.5 0.036 16 6 264 3907 K ENN l 105a 721 258 560 7.94 9.0 0.056 19 37 6151 17789 EN BO l 112a 722 75 330 7.85 6.8 0.200 426 348 2225 14128 TILLl 162a 723 18 185 7.55 6.0 0.244 302 76 1079 11363 TILL2 163a 724 69 310 7.64 6.8 0.288 293 262 4365 12185 CRANl 165a 725 83 440 7.29 6.2 0.119 1846 1817 7335 10951 SEE Ai 170a 726 112 390 7.64 5.8 0.694 29 115 3141 16603 ALTl 172a 727 261 710 7.64 10.0 0.381 84 17 2945 10622 RW EYl 177a 728 17 150 7.26 5.3 0.147 27 41 759 17016 RWEY2 178a 729 49 310 7.37 7.7 0.036 2113 1918 1387 16294 RWEY3 180a 730 77 370 7.65 6.7 0.279 1456 732 2016 14283 RWEY4 181a 731 65 320 7.50 7.1 0.266 1607 582 3403 13510 ELST2 183a 732 116 410 7.62 7.0 0.461 1802 817 3141 14025 RWEY6 184a 733 108 420 7.68 6.2 0.485 556 582 2945 13664 THAM2 188a 734 120 600 7.65 8.3 0.036 1897 1789 7032 12031 D EA N l 192a 735 74 460 7.36 4.4 0.036 107 65 5213 6787 DEAN2 193a 736 82 500 7.54 5.0 0.036 150 106 6438 6890 GATW l 194a 737 66 365 7.43 7.0 0.282 41 56 1770 7918 MOLEl 199a 738 88 540 7.51 5.4 0.036 624 521 3467 7866 BEV El 207a 739 130 1500 7.45 9.5 0.341 7829 7325 10548 18612 M ISB l 214a 740 218 720 7.68 5.9 0.074 1949 1923 7274 13111 EBBLl 238a 741 218 480 8.14 9.5 0.295 120 78 3468 8353 1TCH2 248a 742 232 530 8.15 8.7 0.443 143 159 4777 11963 M EONl 249a 743 228 540 8.18 8.8 0.722 73 128 5693 8095 MEDW2 252a 744 21 200 6.95 4.2 0.547 17 35 655 12956 MEDW3 253a 745 66 360 7.38 4.2 0.036 1162 1071 1091 13008

~~Table IV 1 (cont.) Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the rope training set~~

~~350 Appendix IV~~

Site Site Sample Cl S04^ Ca 2+ Na+ K+ Mg:+ M n^ Fe3+ Code No. No. PgL^ MgL^ MgL'^ PgL^ PgL^ PgL^ MgL'^ C A STl 2 668 33660 50870 104448 20292 3816 7197 10 2.80 40 SW ERl 9 669 24493 35829 136170 12134 2862 4714 0 6.30 30 EV ENl 12 670 30827 47725 103326 20920 9116 5371 0 5.60 30 EVEN2 13 671 46444 56222 94554 37133 9752 6425 0 5.60 20 EVEN3 14 672 40188 57424 83946 31171 7632 5434 0 7.00 20 SHERI 17 673 13146 28296 122808 6067 1272 6748 0 7.00 30 WIND2 23 674 18954 30898 113424 10146 2014 5246 0 7.00 30 LEACl 24 675 16755 37792 130968 8263 1272 5976 0 7.70 20 TH AM l 26 676 64514 68434 87740 63648 9951 7014 0 8.00 30 COLNl 27 677 19329 23780 63149 10192 1819 3471 0 9.00 30 C 0LN 2 28 678 16279 32805 166373 8008 1605 6796 0 9.00 20 CHURl 31 679 17414 23929 113344 7280 1498 3978 0 9.00 30 SW ILl 34 680 27992 19363 95533 13416 3424 3284 0 3.00 20 PARTI 56 681 62433 121338 120630 63960 19153 9283 0 7.00 30 PANGl 94 682 14400 5442 75495 8008 1926 1844 0 11.00 20 KENNl 105 683 17983 18771 79341 5512 1177 1803 0 11.00 30 EN BO l 112 684 37494 30737 67905 30056 6206 5677 30 330.00 30 BO UR l 154 685 110941 96351 101902 11108 22464 8521 120 344.10 30 TILLl 162 686 18575 12489 10485 9956 1456 2128 10 399.60 30 TILL2 163 687 20281 19249 41942 11318 2184 2722 0 7.70 20 CRANl 165 688 40579 38253 54972 62461 13104 4714 80 2.20 30 SLEAl 170 689 25490 26870 56295 18864 5616 3911 250 9257.4 110 ALTl 172 690 20677 127463 247679 16558 3848 4714 40 99.90 30 RW EYl 177 691 19428 16498 15474 11109 1144 2263 20 333.00 30 RWEY2 178 692 29384 23848 22498 27772 4680 2889 30 88.80 20 RWEY3 180 693 23362 23468 37055 18340 3640 2754 10 6.60 20 RWEY4 181 694 24491 23909 35255 19853 4472 3136 20 33.00 30 RWEY5 182 695 25918 27525 36678 23782 4992 3312 10 1.00 20 ELST2 183 696 29755 32374 55575 28849 6032 3591 20 11.00 20 RWEY6 184 697 27626 31889 52934 24506 4992 3498 20 11.00 20 THAM2 188 698 60882 61865 97028 59145 12480 5755 10 8.00 10 D E A N l 192 699 41852 89206 74168 51080 9880 8518 240 4.00 10 DEAN2 193 700 59487 75543 68072 57904 29432 9812 660 187.00 30 GATW l 194 701 20015 26594 30480 18715 3640 3188 90 33.00 30 REDHl 196 702 36333 139921 190195 20680 5304 6251 60 9.00 10 MOLEl 199 703 48308 94616 85329 57482 14523 9082 180 121.00 40 BEVEl 207 704 180921 65459 138323 84314 18334 8497 30 6.00 20 HORTl 209 705 120671 121206 67565 45718 11536 13448 70 8.00 0 M ISBl 214 706 77130 24729 115568 59237 6798 3290 0 9.00 10 EBBLl 238 707 14870 12682 108083 7018 927 2245 10 11.00 10 ITCHl 247 708 15933 6453 111876 8050 1133 2183 0 11.00 20 ITCH2 248 709 16277 7590 174550 10114 1751 2163 0 10.00 10 M EONl 249 710 28766 23128 153892 20434 9991 2932 0 9.00 20

~~Table IV 2 Chloride, sulphate, calcium, sodium, potassium, magnesium, manganese, iron and aluminium concentrations in the rope training set~~

~~351 Appendix IV~~

Site Site Sample Cl SO,: Ca 2+ Na+ K+ Mg:+ Mn=+ Fe3+ Code No. No. PgL^ PgL^ PgL^ PgL* PgL^ PgL^ PgL'^ M EDW l 251 711 22230 10093 12275 19402 2472 3157 120 1716.0 10 MEDW2 252 712 21753 10432 17166 17750 2266 3495 410 3960.0 20 MEDW3 253 713 44981 46400 46332 54268 8736 6161 100 296.80 30 O USEl 256 714 30224 30944 46926 24630 4680 6039 110 116.60 30 0U SE 2 257 715 36467 36000 43857 34952 7072 6395 50 63.60 30 CLAPl 259 716 25432 17431 25839 19418 2288 4568 190 95.40 20 RUCKl 260 717 35276 30688 41481 30149 4472 6110 150 265.00 20 ROTHl 261 718 53982 26091 35838 43946 6864 5968 10 137.80 10 R0TH2 262 719 40310 31882 39402 63057 5512 6009 30 286.20 10 SOCKl 266 720 53402 892448 324720 111909 4992 28856 60 2.40 30 K ENN l 105 721 20388 21226 154836 8299 1470 1956 0 11.00 20 EN B O l 112 722 31731 33114 50195 19152 3675 5345 20 11.00 20 TILLl 162 723 38737 16007 12549 23302 2100 2437 60 242.00 40 TILL2 163 724 29357 21404 40379 17556 2625 2835 10 2.00 20 CRANl 165 725 47082 41932 46451 39474 7770 5596 20 2.00 50 SLEAl 170 726 23806 34822 78936 12662 3570 3807 50 4.00 30 ALTl 172 727 22329 55836 150889 20642 2520 2897 20 8.00 10 RWEYl 177 728 19392 18470 15382 10853 1470 2238 20 220.00 20 RWEY2 178 729 29241 26530 22871 28302 5040 3232 30 66.00 30 RWEY3 180 730 40097 26798 42189 26900 3432 2991 30 7.20 20 RWEY4 181 731 25097 26214 41165 17646 3848 3359 40 6.00 10 ELST2 183 732 23068 31747 48742 23134 4888 3664 40 7.20 20 RWEY6 184 733 29110 35452 62566 21412 4368 3538 40 8.40 20 THAM2 188 734 52242 57842 81920 44008 9880 6160 30 22.40 80 D EA N l 192 735 41411 70870 66150 26792 7384 8350 60 78.40 140 DEAN2 193 736 42510 75056 76083 29267 8528 9245 30 67.20 120 GATW l 194 737 36721 46162 43520 28837 4264 5465 270 190.40 40 MOLEl 199 738 49158 93229 70554 37445 8736 9593 40 22.40 90 BEV El 207 739 263910 73433 219632 79032 17640 11577 50 6.00 50 M ISBl 214 740 80891 29775 137063 59052 6825 3610 0 12.00 30 EBBLl 238 741 15687 14814 120280 7215 1995 2179 0 16.50 30 ITCH2 248 742 18215 10025 109712 9879 2520 2135 0 16.50 30 MEONl 249 743 19203 19475 128671 7770 2310 1886 0 15.00 30 MEDW2 252 744 26935 17675 13468 17094 4200 3447 540 2127.5 60 MEDW3 253 745 40096 44753 36985 34410 7665 5713 130 69.00 70

~~Table IV 2 (cont.) Chloride, sulphate, calcium, sodium, potassium, magnesium, manganese, iron and aluminium concentrations in the rope training set~~

~~352 A ppe n d ix V~~

~~F u l l E nvironmental D a t a -S e t f o r t h e T il e T r a in in g S e t~~

Site Site Sample Aik Cond pH Temp Flow TP FRP NOa'-N Silica Code No. No. mgL^ pS cm'^ °C m Sec * PgL^ PgL'^ UgL* PgL^ C ASTl 2 595 178 560 7.84 10.5 0.107 291 290 4116 10401 SW ERl 9 596 220 550 8.01 10.0 0.986 26 3 4485 6139 EV ENl 12 597 202 560 7.98 11.0 0.036 583 551 6860 8118 EVEN2 13 598 218 560 8.11 10.0 0.169 1131 1123 3166 7458 EVEN3 14 599 214 600 7.84 10.0 0.321 939 906 5541 6951 SHERI 17 600 205 510 8.13 9.0 0.244 4 3 7440 5429 W IN D l 20 601 160 420 7.90 9.0 0.434 24 6 7757 5429 W IND2 23 602 180 450 7.86 8.5 0.802 161 149 7018 5277 LEACl 24 603 228 620 7.48 9.0 0.036 7 4 5330 5327 THAM l 26 604 242 860 8.08 10.5 0.036 1148 1080 3430 9792 CHURl 31 605 160 390 8.20 9.0 0.054 20 4 3545 4999 SWILl 34 606 148 360 8.13 9.0 0.093 33 15 9 6049 PARTI 56 607 178 830 7.80 10.5 0.105 3494 3318 6121 11010 PA NG l 94 608 246 480 8.50 12.0 0.038 18 6 3454 13048 K ENN l 105 609 245 550 7.85 10.0 0.089 30 45 4635 17147 EN BO l 112 610 104 400 7.59 9.0 0.095 1112 1015 1291 17897 BLACl 124 611 92 360 7.40 12.0 0.271 31 13 282 7449 BOURl 154 612 130 1100 7.39 14.0 0.264 6882 6091 8498 21097 TILLl 162 613 12 130 7.15 11.0 0.452 99 87 464 10848 TILL2 163 614 77 310 7.57 10.0 0.326 309 276 3090 12348 C RANl 165 615 87 540 7.17 12.0 0.178 6387 5917 4181 17097 SLEAl 170 616 78 320 7.14 10.5 0.787 1517 281 1091 11898 ALTl 172 617 280 750 7.02 11.5 0.259 163 67 2590 13598 RW EYl 177 618 18 150 6.75 9.5 0.198 38 39 573 17697 RWEY2 178 619 52 280 7.26 11.0 0.390 1853 1639 1000 15397 RWEY3 180 620 77 320 7.58 11.0 0.324 935 827 1509 14298 RWEY4 181 621 71 300 7.22 10.0 0.396 847 679 2309 12998 RWEY5 182 622 86 360 7.37 10.5 0.279 1112 921 2090 13348 ELST2 183 623 107 410 7.60 11.0 0.259 1482 1399 1491 13598 RWEY6 184 624 112 420 7.72 11.5 0.308 1006 964 1863 12798 THAM2 188 625 168 690 7.34 13.5 0.036 3176 2884 3641 13395 GATW l 194 626 56 240 7.34 12.5 0.237 99 44 782 6749 REDHl 196 627 198 680 8.10 11.5 0.401 591 557 636 13398 M OLEl 199 628 137 590 7.57 12.0 0.054 1217 1102 754 8799 HORTl 209 629 242 750 7.34 13.5 0.036 49 33 2427 8828 M ISBl 214 630 225 750 7.86 13.0 0.054 2153 1934 4908 12685

~~Table V I Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the tile training set~~

~~353 Appendix V~~

Site Site Sample Aik Cond pH Temp Flow TPFRP NOa'-N Silica Code No. No. mgL^ pS cm^ "C m Sec * PgL'^ PgL^ PgL^ EBBLl 238 631 222 480 8.10 8.5 0.119 95 70 3317 8649 ITCHl 247 632 240 510 7.89 8.0 0.259 31 25 2999 9748 ITCH2 248 633 250 580 8.09 9.5 0.572 213 200 2318 10898 M EONl 249 634 256 630 7.88 7.0 0.337 1633 1479 1818 10948 M EDW l 251 635 40 190 7.10 12.0 0.117 29 15 91 7799 O USEl 256 636 78 320 7.52 12.5 0.412 200 183 707 5378 0USE2 257 637 79 380 7.48 12.0 0.282 1296 1268 1784 7661 RUCKl 260 638 75 320 7.46 10.5 0.036 109 71 1024 8169 ROTHl 261 639 76 380 7.61 11.0 0.282 524 609 559 8372 R0TH2 262 640 127 450 7.69 11.0 0.115 630 517 718 8930 K ENNl 105 642 258 560 7.94 9.1 0.056 19 37 6151 17789 EN BO l 112 643 75 330 7.85 6.8 0.200 426 348 2225 14128 BLACl 124 644 111 430 7.74 4.5 0.410 17 31 772 9075 TILL2 163 645 69 310 7.64 6.8 0.288 293 262 4365 12185 CRANl 165 646 83 440 7.29 6.2 0.119 1846 1817 7335 10951 SLEAl 170 647 112 390 7.64 5.8 0.694 29 115 3141 16603 ALTl 172 648 261 710 7.64 10.0 0.381 84 17 2945 10622 RW EYl 177 649 17 150 7.26 5.3 0.147 27 41 759 17016 RWEY2 178 651 49 310 7.37 7.7 0.036 2113 1918 1387 16294 RWEY3 180 652 77 370 7.65 6.7 0.279 1456 732 2016 14283 RWEY5 182 653 88 360 7.52 7.1 0.293 1607 703 2945 13664 ELST2 183 654 116 410 7.62 7.0 0.461 1802 817 3141 14025 RWEY6 184 655 108 420 7.68 6.2 0.485 556 582 2945 13664 THAM2 188 657 120 600 7.65 8.3 0.036 1897 1789 7032 12031 D EA N l 192 658 74 460 7.36 4.4 0.036 107 65 5213 6787 DEAN2 193 659 82 500 7.54 5.0 0.036 150 106 6438 6890 GATW l 194 660 66 365 7.43 7.0 0.282 41 56 1770 7918 M ISBl 214 661 218 720 7.68 5.9 0.074 1949 1923 7274 13111 EBBLl 238 662 218 480 8.14 9.5 0.295 120 78 3468 8353 ITCHl 247 663 243 540 8.02 9.0 0.562 62 53 5366 9849 ITCH2 248 664 232 530 8.15 8.7 0.443 143 159 4777 11963 M EDW l 251 666 20 190 7.17 4.4 0.088 30 45 521 6684 MEDW2 252 667 21 200 6.95 4.2 0.547 17 35 655 12956

~~Table V I (cont.) Alkalinity, conductivity, pH, water temperature, flow, TP, FRP, nitrate and silica in the tile training set~~

~~354 Appendix V~~

Site Site Sample Cl SO 4" Ca 2+ Na+ K+ M g 2+ Mn^+ Fe3+ Al3+ Code No. No. MgL'^ MgL^ PgL^ UgL^ PgL^ PgL^ PgL^ PgL^ CASTl 2 595 33660 50870 104448 20292 3816 7197 10 2.80 40 SW ERl 9 596 24493 35829 136170 12134 2862 4714 0 6.30 30 EV EN l 12 597 30827 47725 103326 20920 9116 5371 0 5.60 30 EVEN2 13 598 46444 56222 94554 37133 9752 6425 0 5.60 20 EVEN3 14 599 40188 57424 83946 31171 7632 5434 0 7.00 20 SHERI 17 600 13146 28296 122808 6067 1272 6748 0 7.00 30 W IN D l 20 601 20000 27078 124950 8054 2332 2660 10 4.20 30 WIND2 23 602 18954 30898 113424 10146 2014 5246 0 7.00 30 LEACl 24 603 16755 37792 130968 8263 1272 5976 0 7.70 20 THAM l 26 604 64514 68434 87740 63648 9951 7014 0 8.00 30 CHURl 31 605 17414 23929 113344 7280 1498 3978 0 9.00 30 SW ILl 34 606 27992 19363 95533 13416 3424 3284 0 3.00 20 PARTI 56 607 62433 121338 120630 63960 19153 9283 0 7.00 30 PA NG l 94 608 14400 5442 75495 8008 1926 1844 0 11.00 20 K ENN l 105 609 17983 18771 79341 5512 1177 1803 0 11.00 30 EN BO l 112 610 37494 30737 67905 30056 6206 5677 30 330.00 30 BLACl 124 611 29515 31309 64363 19656 4173 6195 90 4.00 30 BO UR l 154 612 110941 96351 101902 111088 22464 8521 120 344.10 30 TILLl 162 613 18575 12489 10485 9956 1456 2128 10 399.60 30 TILL2 163 614 20281 19249 41942 11318 2184 2722 0 7.70 20 CRANl 165 615 40579 38253 54972 62461 13104 4714 80 2.20 30 SLEAl 170 616 25490 26870 56295 18864 5616 3911 250 9257.4 110 ALTl 172 617 20677 127463 247679 16558 3848 4714 40 99.90 30 RWEYl 177 618 19428 16498 15474 11109 1144 2263 20 333.00 30 RWEY2 178 619 29384 23848 22498 27772 4680 2889 30 88.80 20 RWEY3 180 620 23362 23468 37055 18340 3640 2754 10 6.60 20 RWEY4 181 621 24491 23909 35255 19853 4472 3136 20 33.00 30 RWEY5 182 622 25918 27525 36678 23782 4992 3312 10 1.00 20 ELST2 183 623 29755 32374 55575 28849 6032 3591 20 11.00 20 RWEY6 184 624 27626 31889 52934 24506 4992 3498 20 11.00 20 THAM2 188 625 60882 61865 97028 59145 12480 5755 10 8.00 10 GATW l 194 626 20015 26594 30480 18715 3640 3188 90 33.00 30 REDHl 196 627 36333 139921 190195 20680 5304 6251 60 9.00 10 MOLEl 199 628 48308 94616 85329 57482 14523 9082 180 121.00 40 HORTl 209 629 120671 121206 67565 45718 11536 13448 70 8.00 0 MISBl 214 630 77130 24729 115568 59237 6798 3290 0 9.00 10 EBBLl 238 631 14870 12682 108083 7018 927 2245 10 11.00 10 ITCHl 247 632 15933 6453 111876 8050 1133 2183 0 11.00 20 ITCH2 248 633 16277 7590 174550 10114 1751 2163 0 10.00 10 M EONl 249 634 28766 23128 153892 .20434 9991 2932 0 9.00 20 M EDW l 251 635 22230 10093 12275 19402 2472 3157 120 1716.0 10

~~Table V.2 Chloride, sulphate, calcium, sodium, potassium, magnesium, manganese, iron and aluminium concentrations in the tile training set~~

~~355 Appendix V~~

Site Site Sample Cl so/ Ca^+ Na+ K+ Fe^+ Al3+ Code No. No. MgL^ MgL'^ PgL^ PgL/ PgL^ PgL/ PgL* PgL^ O U SEl 256 636 30224 30944 46926 24630 4680 6039 110 116.60 30 0U SE 2 257 637 36467 36000 43857 34952 7072 6395 50 63.60 30 RUCK l 260 638 35276 30688 41481 30149 4472 6110 150 265.00 20 ROTHl 261 639 53982 26091 35838 43946 6864 5968 10 137.80 10 R 0TH 2 262 640 40310 31882 39402 63057 5512 6009 30 286.20 10 K EN N l 105 642 20388 21226 154836 8299 1470 1956 0 11.00 20 EN BO l 112 643 31731 33114 50195 19152 3675 5345 20 11.00 20 BLACl 124 644 41127 45319 59607 29366 4200 8033 40 7.00 20 TILL2 163 645 29357 21404 40379 17556 2625 2835 10 2.00 20 CRANl 165 646 47082 41932 46451 39474 7770 5596 20 2.00 50 SLEAl 170 647 23806 34822 78936 12662 3570 3807 50 4.00 30 ALTl 172 648 22329 55836 150889 20642 2520 2897 20 8.00 10 RW EYl 177 649 19392 18470 15382 10853 1470 2238 20 220.00 20 RWEY2 178 651 29241 26530 22871 28302 5040 3232 30 66.00 30 RWEY3 180 652 40097 26798 42189 26900 3432 2991 30 7.20 20 RWEY5 182 653 27234 29574 48742 20121 4368 3496 40 7.20 30 ELST2 183 654 23068 31747 61645 23134 4888 3664 40 7.20 20 RWEY6 184 655 29110 35452 62566 21412 4368 3538 40 8.40 20 THAM2 188 657 52242 57842 81920 44008 9880 6160 30 22.40 80 DEANl 192 658 41411 70870 66150 26792 7384 8350 60 78.40 140 DEAN2 193 659 42510 75056 76083 29267 8528 9245 30 67.20 120 GATW l 194 660 36721 46162 43520 28837 4264 5465 270 190.40 40 MISBl 214 661 80891 29775 137063 59052 6825 3610 0 12.00 30 EBBLl 238 662 15687 14814 120280 7215 1995 2179 0 16.50 30 ITCHl 247 663 16957 9371 137063 8658 2205 2255 0 16.50 40 1TCH2 248 664 18215 10025 109712 9879 2520 2135 0 16.50 30 M EDW l 251 666 25554 18828 12121 17094 3885 3263 460 1069.5 70 MEDW2 252 667 26935 17675 13468 17094 4200 3447 540 2127.5 60

~~Table V.2 (cont.) Chloride, sulphate, calcium, sodium, potassium, magnesium, manganese, iron and aluminium concentrations in the tile training set~~

~~356~~